Power of People, The Learn How Successful Organizations Use Work [PDF]

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Praise for The Power of People “The Power of People provides an exceptional primer for doing workforce analytics. It includes wonderful insights from thought leaders, and specific and usable tools for performing analytics.” —Dave Ulrich, Rensis Likert Professor, Ross School of Business, University of Michigan, and Partner of The RBL Group “Data analytics is a crucial and fast evolving organisational capability. This intriguing and fascinating book demonstrates not only the power of people analytics, but also creates a clear blueprint for building action-taking capability. A must read for any manager determined to add this valuable skill to their portfolio.” —Lynda Gratton, Professor of Management Practice, London Business School “Trusting your gut on people issues turns out to be a bad idea. Analytics on your workforce is the most rapidly growing field of analytics. The Power of People is an excellent guide to this important and burgeoning topic.” —Thomas H. Davenport, Distinguished Professor, Babson College, and Research Fellow, MIT Initiative on the Digital Economy “I believe you will find, like I did, that the frameworks and insights in The Power of People offer valuable steps toward realizing the potential of your workforce to create sustainable strategic success.” —Dr. John Boudreau, Professor, Marshall School of Business; and Research Director, Center for Effective Organizations, University of Southern California. “This is quite an exceptional book. Extremely well-researched, it constitutes essential reading for those involved in the burgeoning field of Big Data, giving first-rate advice on good practices for all those involved in Workforce Analytics.” —Professor Peter Saville, Chairman 10X Psychology and Founder SHL and Saville Consulting ******ebook converter DEMO Watermarks*******



“To build an extraordinary workplace, you need to harness the power of analytics. The Power of People provides a comprehensive look at latest research, offering best practices for leveraging the wealth of data now within our reach. If you want to master HR, you need to read this book.” —Ron Friedman, Author of “The Best Place to Work: The Art and Science of Creating an Extraordinary Workplace” “Today’s business executives are applying pressure to all aspects of their business (including HR and workforce areas) to use analytics to improve their bottom line. Despite this pressure there remain few resources for those looking to begin. The Power of People is an excellent primer providing definition and guidance for identifying, framing, and successfully deploying analytics solutions to solve workforce challenges.” —Greta Roberts, CEO Talent Analytics, Corp. “We are barreling along toward the collision between Big Data, Analytics, and the successful acquisition, development, and retention of people in our organizations. The Power of People gives data-led comfort and practical guidance to business leaders that shows we not only can survive the collision, we can harness its potential and emerge with a stronger workforce that is motivated for business and personal success.” —China Gorman, Board Chair, Universum Americas “Finally! An authoritative, thoroughly researched, clearly written book to help HR professionals be more data-driven. This volume discusses everything you always wanted to know about workforce analytics but were afraid to ask, with answers from top practitioners in the field.” —Dr. Tomas Chamorro-Premuzic, Professor of Business Psychology (UCL and Columbia University), CEO of Hogan Assessments, and author of The Talent Delusion. “Today Workforce Analytics is an emerging discipline which, in a few years time, will become mainstream. The Power of People is exceptionally practical and inspiring—essential reading for those executives willing to take on the challenge of transforming their organisations. By leveraging the authors’ as well as other leaders’ extensive experience, this book is a true compendium for those wishing to navigate their transformation.” ******ebook converter DEMO Watermarks*******



—Manish Goel, CEO TrustSphere “The Power of People is a great book for those who want to build, refine, or fundamentally improve their HR Analytics offering. The authors have clearly undertaken some extensive research and are drawing on the experience of a wide range of people analytics experts. As a result, their book is full of great advice and can be considered a really good guide for those wanting to realise the full potential of workforce analytics in their organisation.” —Dr. Martin Edwards, Kings College London Business School “Listening to what employees tell us and acting on it distinguishes ‘average HR’ from ‘HR excellence.’ New analytical capabilities mean we can discern what people are telling us by their actions rather than what they say they would do. The Power of People is an excellent book describing how to harness organizational capabilities using workforce analytics to predict what workers are most likely to do in the future and therefore how to impact business outcomes.” —Alan Wild, Vice President Human Resources; Employee Relations and Engagement, IBM



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THE POWER OF PEOPLE Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance



NIGEL GUENOLE JONATHAN FERRAR SHERI FEINZIG



Cisco Press 800 East 96th Street Indianapolis, Indiana 46240 USA



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© Copyright 2017 by Nigel Guenole, Jonathan Ferrar, and Sheri Feinzig Pearson Education, Inc. For information about buying this title in bulk quantities, or for special sales opportunities (which may include electronic versions; custom cover designs; and content particular to your business, training goals, marketing focus, or branding interests), please contact our corporate sales department at [email protected] or (800) 382-3419. For government sales inquiries, please contact [email protected]. For questions about sales outside the U.S., please contact [email protected]. Company and product names mentioned herein are the trademarks or registered trademarks of their respective owners. The following terminology is a copyright of the authors of this book: Nigel Guenole, Jonathan Ferrar, and Sheri Feinzig: Eight Step Model for Purposeful Analytics (Chapter 4) Seven Forces of Demand (Chapter 7) Complexity-Impact Matrix (Chapter 9) Six Skills for Success (Chapter 12) All rights reserved. Printed in the United States of America. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permissions, request forms, and the appropriate contacts within the Pearson Education Global Rights & Permissions Department, please visit www.pearsoned.com/permissions/. ISBN-10: 0-13-454600-8 ******ebook converter DEMO Watermarks*******



ISBN-13: 978-0-13-454600-1 Pearson Education LTD. Pearson Education Australia PTY, Limited Pearson Education Singapore, Pte. Ltd. Pearson Education Asia, Ltd. Pearson Education Canada, Ltd. Pearson Educacion de Mexico, S.A. de C.V. Pearson Education—Japan Pearson Education Malaysia, Pte. Ltd. Library of Congress Control Number: 2017933825 1 17 Editor-in-Chief Greg Wiegand Editor Kim Boedigheimer Senior Marketing Manager Stephane Nakib Editorial Assistant Cindy Teeters Managing Editor Sandra Schroder Senior Project Editor Lori Lyons Cover Designer Chuti Prasertsith Production Manager Dhayanidhi ******ebook converter DEMO Watermarks*******



Copy Editor Krista Hansing Editorial Proofreader Sathish Kumar Indexer Erika Millen Compositor codeMantra



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CONTENTS AT A GLANCE Foreword by John Boudreau Acknowledgments About the Authors Interviewees Vignettes Preface I Understanding the Fundamentals 1 Why Workforce Analytics? 2 What’s in a Name? 3 The Workforce Analytics Leader 4 Purposeful Analytics 5 Basics of Data Analysis 6 Case Studies II Getting Started 7 Set Your Direction 8 Engage with Stakeholders 9 Get a Quick Win III Building Your Capability 10 Know Your Data 11 Know Your Technology 12 Build the Analytics Team 13 Partner for Skills 14 Establish an Operating Model IV Establishing an Analytics Culture 15 Enable Analytical Thinking ******ebook converter DEMO Watermarks*******



16 Overcome Resistance 17 Communicate with Storytelling and Visualization 18 The Road Ahead Glossary References Index



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CONTENTS Foreword by John Boudreau Acknowledgments About the Authors Interviewees Vignettes Preface I Understanding the Fundamentals 1 Why Workforce Analytics? Adoption of Analytics HR’s Contribution to Business Value The Changing Nature of HR The Future of Work Summary 2 What’s in a Name? Focus of the Function Activities of the Function A Name Fit for the Future Summary 3 The Workforce Analytics Leader Reporting to the Chief Human Resources Officer Responsibilities of the Workforce Analytics Leader Business Acumen Leadership Attributes Summary 4 Purposeful Analytics ******ebook converter DEMO Watermarks*******



A Model for Purposeful Analytics Project Sponsors Why Do Analytics Projects Fail? Summary 5 Basics of Data Analysis Research Design Objectives of Analysis Unstructured Data Traditional Statistics versus Machine Learning Social Consequences of Algorithms More on Design and Analysis Summary 6 Case Studies Eight-Step Methodology Case Study Improving Careers Through Retention Analytics at Nielsen Case Study From Employee Engagement to Profitability at ISS Group Case Study Growing Sales Using Workforce Analytics at Rentokil Initial Case Study Increasing Value to the Taxpayer at the Metropolitan Police Case Study Predictive Analytics Improves Employee Well-Being at Westpac Summary II Getting Started 7 Set Your Direction You Have the Job! Now What? Listening to Prospective Project Sponsors The Seven Forces of Demand Agreeing on the Scope of Analytics ******ebook converter DEMO Watermarks*******



Developing a Vision and Mission Statement Summary 8 Engage with Stakeholders Who Are Stakeholders? Stakeholders Served Stakeholders Depended Upon Stakeholders Impacted Working Effectively with Stakeholders Summary 9 Get a Quick Win Identifying Potential Projects Complexity-Impact Matrix Assessing Complexity and Impact Summary III Building Your Capability 10 Know Your Data A Pragmatic View of Data Solving Data Quality Challenges Data Types and Sources Data Governance Remember the Basics Summary 11 Know Your Technology Starting with Vision and Mission Components of Workforce Analytics Technology On-Premise Versus Cloud Technology Vendor Relationships ******ebook converter DEMO Watermarks*******



Summary 12 Build the Analytics Team Six Skills for Success Configuring Team Roles Remember the Fundamentals! Summary 13 Partner for Skills Why Consider Partners? Options for Building the Team Choosing Among the Options Summary 14 Establish an Operating Model Defining Your Operating Model Strategy Governance Implementation Accountability Summary IV Establishing an Analytics Culture 15 Enable Analytical Thinking Perspectives of Analytics in HR The Translator Role The Importance of Leadership Summary 16 Overcome Resistance Resistance to Workforce Analytics Stakeholder Skepticism ******ebook converter DEMO Watermarks*******



Financial Frugality HR Hesitancy Summary 17 Communicate with Storytelling and Visualization What Is Storytelling? Effective Visualization Knowing Your Audience Keeping It Simple Summary 18 The Road Ahead Analytics Provides New Opportunities for HR Emerging Data Sources Considering New Data Sources Evolving Technology The Workforce Analytics Function Summary Glossary References Index



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Foreword by John Boudreau The availability and power of workforce analytics have never been greater. Leaders, workers, HR professionals, policy makers, and investors increasingly recognize the pivotal role that their people play in strategic success. Organizations face unprecedented change and unpredictability, requiring new organizational forms and processes that work even when you cannot predict the future. Volatility, unpredictability, complexity, and ambiguity lead to a world of greater strategic opportunities but also greater threats and pitfalls. At the same time, organizations still face the perennial paradox of HR analytics—the substantial opportunity it offers versus the stubborn challenges of making a real impact on decisions, actions, strategy, and organizational outcomes. That paradox is magnified by emerging ethical issues that require organizations to establish limits on what should and should not be measured and reported about employees. This promise and paradox of workforce analytics explains why so many leading organizations have built workforce analytics functions. Those analytics functions bring together an amazing array of skills and disciplines. To be sure, they include leaders from HR and psychology. However, they often reach out to new disciplines such as marketing, storytelling, engineering, and anthropology. I worked with one organization that had enlisted quantum physicists to apply their frameworks to the complex interactions in the workforce. This book captures the practical insights from those leaders. It provides a compendium of frameworks and guides for building and realizing the value of workforce analytics expertise, whether it resides in a dedicated function or is dispersed across the organization. With this book, you sit at the shoulder of seasoned and experienced workforce analytics leaders, and hear their “voice” to guide your workforce analytics journey. It aims to show how organizations can tap the power of people through analytics. Workforce analytics has existed since the first craftsmen hired apprentices and assistants, observed the quality and quantity of their work, and formulated pre-hire apprenticeships and other tests to detect their ability. The two World Wars motivated the development of sophisticated aptitude tests ******ebook converter DEMO Watermarks*******



that revolutionized the assignment and training of troops. The 1960s and 1970s saw the emergence of systems designed to measure the cost and value of the workforce, and “put people on the balance sheet.” In the 1980s and 1990s, the Saratoga Institute and others developed benchmark indices for the entire employment lifecycle, from recruitment, to development, to rewards, to engagement, to retention. Yet, today the issue of workforce analytics has reached a tipping point. The information formerly contained in a six-inch report from the Saratoga Institute in the 1990s is now available at the click of a button in many of today’s human resources systems. Reports and statistical analyses that once took months to compile using paper and spreadsheets now appear instantly. Data mining tools can now unearth relationships that were previously invisible. Predictive analytics hold the promise of calculating a “risk-of-leaving” index for every employee, which changes with their work and life situation, and alerts managers to take preventive action. Today, the limits on workforce measurement are seldom due to a lack of data or computing power. The priority has shifted from gathering and reporting data to making sense of the data, finding the pivotal stories, and getting the insights to those who can make the critical decisions—whether they be leaders, managers, employees, boards, or investors. As mentioned in the first two chapters of this book, when I recently worked with a unique volunteer gathering of more than 50 chief human resources officers and other thought-leaders (see www.CHREATE.net), we identified five forces that will change the nature of organizational success. These include social and organizational reconfiguration, an all-inclusive global talent market, and a truly connected world. These forces will change the very nature of work, which will be more democratic with shorter-duration and varied work relationships that are more balanced between the worker and the workplace, and more agile and responsive work arrangements through purpose-built networks, supported by social norms and policies. Work via platforms, projects, gigs, freelancing, contests, contracts, and tours of duty will evolve and be increasingly empowered by automation and technology. Leaders instinctively know that to answer these demands will rely on people, and that great decisions about their workforce should rest on evidence and analysis. They can clearly see the coming deluge of Big Data about the workforce, enabled by personal devices, cognitive computing, cloud-based storage and applications, and the innovative reconstruction of work to blend humans and automation. They can see powerful real-time analytics applied to ******ebook converter DEMO Watermarks*******



global supply chains, consumer insights, and financial investment optimization, and they yearn for workforce analytics that offers similar insights and decision power. For example, when interviewed about the future of their organization, one chief executive officer said, “I know that culture is vital to our future, and what I need is a chief operating officer of culture, who can measure, analyze and support my decisions about culture with the same rigor that my chief operations officer can measure, analyze and support decisions about our operations.” A pillar of such a role will be workforce analytics. So, workforce analytics must become a standard capability in the field of human resources, and every organization should expect HR to have that competency. Yet, workforce analytics must also become a capability outside the HR profession. Just as leaders, investors, and workers are expected to have facility with analytics applied to money, customers, and technology, they should also be more adept at using and understanding workforce metrics. Leaders must demand workforce analytics functions that not only respond to their requests, but that proactively guide them toward insights. In The Power of People, you will find frameworks to help answer these demands, and the descriptions of analytics leaders who are doing it. This makes a substantial contribution to the existing materials and builds on frameworks like LAMP (logic, analytics, measure, and process) that I currently use. I believe you will find, like I did, that the frameworks and insights in The Power of People offer valuable steps toward realizing the potential of your workforce to create sustainable strategic success. John Boudreau Professor of Management and Organization, and Research Director of the Center for Effective Organizations, University of Southern California February 2017



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Acknowledgments We wish to thank all the experts who agreed to be interviewed for this book. Their insights have been invaluable, and without them, this book would not have been possible. A few people deserve special recognition. The first is Louise Raisbeck, Managing Director of Raisbeck PR. Her outstanding editing skills and unlimited patience are beyond compare. Our publishers and editors, Jeanne Levine, Kim Boedigheimer, Michael Thurston, and Lori Lyons believed in us, brought clarity and purpose, and provided much-needed editorial, sales, and marketing support. We are grateful to several people at IBM who helped with particular aspects: Xiaoyuan (Susan) Zhu for assistance with literature reviews for Chapter 17, Jackie Ryan for her help on Chapter 10, Sadat Shami for his help on Chapter 5, Dave Millner for reviewing Chapters 1 and 18, and Emily Plachy and David Green for general guidance. Finally, we thank Steven Stansel for guidance and coaching in turning our idea into a comprehensive proposal. —Nigel Guenole, Jonathan Ferrar, and Sheri Feinzig I would like to thank my wife, Magdalena, my daughters, Mia and Olivia, and my parents, Geoff and Aivi. —Nigel Guenole I would like to thank my late Great Uncle Bill (William Ferrar), who sent me two of his own books on calculus as a gift when I was 12 years old and spurred me on in my own life to write and share my experiences with others. I thank my son, Arthur, who has brought much joy to my life and reminded me throughout the writing of this book of the importance of balance in life. I also wish to thank my parents, James and Janet, and sister, Melloney, who have supported me through the peaks and troughs of life and for keeping me grounded. —Jonathan Ferrar I would like to thank my mother, Marilyn, my lifelong source of inspiration, and my late father, Stanley, my role model for having a dream and making it ******ebook converter DEMO Watermarks*******



a reality. Thanks to my brother, Roy, and sister, Bonnie, for always being there to celebrate my successes. And my most heartfelt thanks to my husband, Steven, and our children, Ileana and Zachary, who allowed me the hours during countless nights and weekends to “work on the book.” You mean everything to me, and this could not have happened without you. —Sheri Feinzig



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About the Authors



Nigel Guenole is an executive consultant with IBM, where he consults with many of the world’s most successful organizations about improving organizational performance with psychological science. He is also Director of Research at the Institute of Management at Goldsmiths, University of London. Nigel’s consulting, research, and teaching focus on topics in industrial-organizational psychology and statistical modeling. He is an associate fellow of the British Psychological Society (BPS), a member of the Academy of Management (AOM), and a member of the Society for Industrial and Organizational Psychology (SIOP). His work on topics related to workforce analytics has been featured in the media and popular press, as well as in numerous scientific journals, including Frontiers in Quantitative Psychology & Measurement and Industrial and Organizational Psychology: Perspectives on Science and Practice.



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Jonathan Ferrar is a respected consultant, speaker, and influencer in HR strategy, workforce analytics, and the future of work. He advises clients on how to establish human resources strategies that will improve business performance and make HR more relevant. He was listed as one of the global Top 50 HR Analytics Influencers on LinkedIn in 2014 and as one of the 15 HR and People Analytics Experts to Follow for 2017 by Jibe. Before he started his own consultancy business, Jonathan worked for more than 25 years in corporate business in IBM, Andersen Consulting (now Accenture), and Lloyds Bank, for many of those years in senior executive management roles in both the United Kingdom and the United States. Jonathan has worked with C-suite clients and business leaders across the globe on human resources management and workforce analytics. He holds a bachelor of arts degree and a master of arts degree from the University of Cambridge and a postgraduate diploma in human resources management from Kingston Business School. He is a Chartered Fellow of the Chartered Institute of Personnel and Development (Chartered FCIPD).



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Sheri Feinzig is a director at IBM, where she leads a global team of consultants, content development experts, and the Smarter Workforce Institute. Sheri has more than 20 years of experience in human resources research, organizational change management, and business transformation. She has applied her analytical and methodological expertise to numerous research-based projects on topics such as employee retention, employee engagement, performance feedback, social network analysis, and organizational culture. Sheri received her Ph.D. in Industrial-Organizational Psychology from the University at Albany, State University of New York. She has presented on numerous occasions at national conferences and has coauthored a number of publications and white papers. She has served as an adjunct professor in the psychology departments of Rensselaer Polytechnic Institute in Troy, New York, and the Illinois Institute of Technology in Chicago, Illinois, where she taught doctoral, masters, and undergraduate courses on performance appraisal, tests, and measures. Sheri is a member of the Society for Industrial and Organizational Psychology (SIOP).



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Interviewees The material in this book is derived in part from interviews the authors collectively undertook with dozens of analytics practitioners, human resources leaders, business executives, academics, and consultants. Together they represent a global perspective on the state of the art regarding workforce analytics. Conversations occurred between January and October 2016. • Al Adamsen. Founder and Executive Director, Talent Strategy Institute. San Francisco, CA, United States. • Peter Allen. Managing Director, Agoda Outside. Singapore, Republic of Singapore. • Morten Kamp Andersen. Partner, proacteur. Copenhagen, Denmark. • Ian Bailie. Global Head, Talent Acquisition and People Planning Operations, Cisco. London, United Kingdom. • Laurie Bassi. CEO, McBassi & Company. New York, NY, United States. • Michael Bazigos. Managing Director, Global Head of Organizational Analytics & Change Tracking, Accenture Strategy. New York, NY, United States; and Professor, Organization and Leadership Development, Columbia University. New York, NY, United States. • Mark Berry. Vice President and Chief Human Resources Officer, CGB Enterprises, Inc. New Orleans, LA, United States. • Josh Bersin. Principal and Founder, Bersin by Deloitte. Oakland, CA, United States. • Mats Beskow. Director of Human Resources, Landstinget Västmanland. Stockholm, Sweden. • Max Blumberg. Founder, Blumberg Partnership, Ltd. London, United Kingdom; and Visiting Researcher, Goldsmiths, University of London. London, United Kingdom. • John Boudreau. Professor of Management and Organization, and Research Director of the Center for Effective Organizations, University of Southern California. Los Angeles, CA, United States. • Ralf Buechsenschuss. Global HR Manager, People Analytics & Transformation, Nestlé. Vevey, Switzerland. • John Callery. Managing Director, Global Head of Workforce Strategy, ******ebook converter DEMO Watermarks*******



BNY Mellon. New York, NY, United States. • Marcus Champ. Senior Manager, HR Analytics, Standard Chartered Bank. Singapore, Republic of Singapore. • Arun Chidambaram. Head of Global Talent Analytics, Pfizer. New York, NY, United States. • Patrick Coolen. Manager HR Metrics and Analytics, ABN AMRO Bank N.V. Amsterdam, Netherlands. • Christian Cormack. Head of HR Analytics, AstraZeneca. Cambridge, United Kingdom. • Damien Dellala. Head of People Data & Analytics Enablement, Westpac Group. Sydney, Australia. • Sally Dillon. Head of Business Intelligence at UK Life, Aviva. York, United Kingdom. • Antony Ebelle-ebanda. Global Director HCM Insights, Analytics & Planning, S&P Global (formerly McGraw Hill Financial). New York, NY, United States. • Giovanni Everduin. Head of Strategic HR, Communications & Change, Tanfeeth. Dubai, United Arab Emirates. • Alexis Fink. General Manager, Talent Intelligence & Analytics, Intel Corporation. Seattle, WA, United States. • Jonathon Frampton. Director, People Analytics, Baylor Scott & White Health. Houston, TX, United States. • David Green. Global Director, People Analytics Solutions, IBM. London, United Kingdom. • Peter Hartmann. Director, Performance, Analytics and HRIS, Getinge Group. Malmö, Sweden. • Mark Huselid. Distinguished Professor of Workforce Analytics and Director, Center for Workforce Analytics, Northeastern University. Boston, MA, United States. • Placid Jover. Vice President of HR–Organisation & Analytics, Unilever. London, United Kingdom. • Dawn Klinghoffer. General Manager, HR Business Insights, Microsoft. Redmond, WA, United States. ******ebook converter DEMO Watermarks*******



• Terry Lashyn. Director, People Intelligence, ATB Financial. Edmonton, Alberta, Canada. • Tracy Layney. Senior Vice President & Chief Human Resources Officer, Shutterfly, Inc. Redwood City, CA, United States. • Alec Levenson. Economist and Senior Research Scientist, Center for Effective Organizations, University of Southern California. Los Angeles, CA, United States. • Stela Lupushor. Head of People Analytics, Fidelity. Boston, MA, United States. • Eric Mackaluso. Senior Director, People Analytics, Global HR Strategy & Planning, ADP. Roseland, NJ, United States. • Salvador Malo. Head of Global Workforce Analytics, Ericsson. Mexico City, Mexico. • Andrew Marritt. Founder, OrganizationView. Zürich, Switzerland. • Piyush Mathur. Senior Vice President, Global People Analytics, Nielsen. Wilton, CT, USA. • Dave Millner. Executive Consulting Partner, IBM. London, United Kingdom. • Mihaly Nagy. CEO, The HR Congress and Managing Director, Stamford Global. Budapest, Hungary. • Ben Nicholas. Director of Global HR Data & Analytics, GlaxoSmithKline. London, United Kingdom. • Adam Chini Nielsen. Workforce Planning Manager, Nordea. Copenhagen, Denmark. • Andre Obereigner. Senior Manager, Global Workforce Analytics. Groupon. Zürich, Switzerland. • Martin Oest. Director and Partner, True Picture Europe Limited. Manchester, United Kingdom. • Peter O’Hanlon. Founder and Managing Director, Lever Analytics. Sydney, Australia. • Ian O’Keefe. Managing Director, Head of Global Workforce Analytics, JPMorgan Chase & Co. New York, NY, United States. • Sofia Parveen. Wealth Management Remuneration & Development ******ebook converter DEMO Watermarks*******



Specialist, Nordea. Copenhagen, Denmark. • Tanuj Poddar. HR Analytics Consultant, Citibank. Mumbai, India. • Thomas Rasmussen. Vice President, HR Data & Analytics, Shell. Amsterdam, Netherlands. • Jackie Ryan. Director, Watson Talent Analytics, IBM. San Jose, CA, United States. • Kanella Salapatas. HR Data Manager and Reporting Service Owner, ANZ Bank. Melbourne, Australia. • Sadat Shami. Director, Center for Engagement & Social Analytics, IBM. New York, NY, United States. • Jeremy Shapiro. Global Head of Talent Analytics, Morgan Stanley. New York, NY, United States. • Luk Smeyers. Co-founder iNostix by Deloitte. Antwerp, Belgium. • Mariëlle Sonnenberg. Global Director, HR Strategy & Analytics, Wolters Kluwer. Amsterdam, Netherlands. • Simon Svegaard. Business Analytics Manager, ISS Facilities Services A/S. Copenhagen, Denmark. • Eric van Duin. Manager HRIS & Analytics, PostNL N.V. The Hague, Netherlands. • Bart Voorn. Lead HR Analytics, Ahold Delhaize. Zaandam, Netherlands. • Rebecca White. Talent Analytics Senior Manager, LinkedIn. San Francisco, CA, United States. • Patrick Wright. Thomas C. Vandiver Bicentennial Chair in Business, Darla Moore School of Business, University of South Carolina. Columbia, SC, United States; and Director, Center for Executive Succession, Darla Moore School of Business, University of South Carolina. Columbia, SC, United States. • Paul Yost. Associate Professor, Seattle Pacific University. Seattle, WA, United States. • Susan Youngblood. Global Senior Director of Human Resources, BNY Mellon. New York, NY, United States.



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VIGNETTES Several vignettes have been incorporated into chapters throughout the book. Each is meant to highlight specific practical tips. We are particularly thankful to the interviewed experts who agreed to include their ideas and stories. Chapter 1, “Why Workforce Analytics?” • “Run Your Business with Analytics,” by Tracy Layney • “The Future of HR Is Analytics,” by Mark Huselid Chapter 3, “The Workforce Analytics Leader” • “Be Ego-less,” by Alexis Fink Chapter 4, “Purposeful Analytics” • “Relationship Power,” by Morten Kamp Andersen Chapter 7, “Set Your Direction” • “Preparing for Success: The First Few Months,” by John Callery • “Understand Your Culture, Understand Your Demand,” by Peter Allen Chapter 8, “Engage with Stakeholders” • “The Case for Workforce Analytics: CEO Succession,” by Patrick Wright • “Bring Finance Along with You,” by Martin Oest • “Gaining Credibility with Executives,” by Adam Nielsen and Sofia Parveen Chapter 9, “Get a Quick Win” • “An Inspired First Project,” by Eric van Duin • “Simple Changes, Big Impact,” by Marcus Champ Chapter 10, “Know Your Data” • “Don’t Let the Lack Of One Integrated HRIS Stop You,” by Mariëlle ******ebook converter DEMO Watermarks*******



Sonnenberg • “A Data Dictionary Brings You Credibility,” by Giovanni Everduin Chapter 11, “Know Your Technology” • “A Mind-Set for Technology,” by Kanella Salapatas • “Drillability Is Key,” by Sally Dillon Chapter 12, “Build the Analytics Team” • “A Blend Of Skills Is Best,” by Rebecca White • “Invest in Data Privacy Skills,” by Dawn Klinghoffer • “Anticipating Business Needs,” by Ian Bailie Chapter 13, “Partner for Skills” • “Start Small, Keep It Focused,” by Thomas Rasmussen • “Accelerating Time to Value with an External Partner,” by Patrick Coolen Chapter 14, “Establish an Operating Model” • “Set Yourself Up for Success,” by Damien Dellala • “Tips for Successful Analytics Operations,” by Placid Jover Chapter 15, “Enable Analytical Thinking” • “Clarifying What Analytics Is and What It Is Not,” by Salvador Malo • “Building a Culture of Analytics Through Training,” by Bart Voorn Chapter 16, “Overcome Resistance” • “The Accountability Hazard,” by Luk Smeyers • “Don’t Take ‘We Can’t’ for An Answer,” by Andre Obereigner Chapter 17, “Communicate with Storytelling and Visualization” • “An Analytics Project Summarized in One Sentence,” by Mark Berry • “Simplify Your Story,” by Paul Yost.



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Preface Workforce analytics is the discovery, interpretation, and communication of meaningful patterns in workforce-related data to inform decision making and improve performance. The human race’s quest for information is never ending. Businesses and organizations are no exceptions. Business leaders continually seek out knowledge about their organizations to gain insights from all the data that exist so that they can make evidence-based decisions to improve the organization’s performance and gain competitive advantage in the marketplace. The discipline called analytics exists to meet this need. Analytics concerning human resources, people, and the workforce is known as workforce analytics. The Power of People explores how to establish, operate, and lead workforce analytics to better serve organizational ambitions.



Background to The Power of People In researching the world of analytics, we came across the book Competing on Analytics (Harvard Business Review Press, 2007) by Thomas Davenport and Jeanne Harris. That book remains a well-referenced resource on the topic of analytics and reminds us just what a difference a good book can make in exploring new disciplines. Davenport and Harris’s book was recommended to us as a starting point for all analytics, so we pass on that recommendation to you. In addition, we recommend an article that evolved from that book, called “Competing on Talent Analytics” (Harvard Business Review, October 2010). Laszlo Bock, former Senior Vice President of People Operations at Google, has more recently authored a book Work Rules! Insights from Inside Google That Will Transform How You Live and Lead (Twelve Books, 2015) that has ignited the interest of businesses globally with its scientific and analytical approaches to the world of work. All of these resources provide an excellent start for the topic of analytics ******ebook converter DEMO Watermarks*******



applied to work. However, we still felt there was a gap in the market for more detailed guidance on how a wide variety of organizations can successfully implement workforce analytics. This topic is the focus of the book you are now reading. So how did our book come about? We three authors first met in 2013. We come from different cultural, national, and professional backgrounds, but between 2013 and 2015, we collectively and individually wrote several articles and undertook research on topics related to people, work, and analytics. Something important happened in spring 2015. Together with a fourth colleague, we wrote and published a paper called “Starting the Workforce Analytics Journey: The First 100 Days.” The paper was launched at an analytics conference in New York, with 50 copies available for a free takeaway. Early on the first day of the conference, we discovered that all 50 copies of the paper had been taken. We printed another batch, and all of those also disappeared during the conference. The overwhelming feedback from conference attendees was that it was the first document people had read that gave a structured approach and practical tips on how to undertake workforce analytics. Within a few days we published an infographic and other material. Then we took a step back to discuss the success of the paper. Clearly, we had only scratched the surface with our paper; a book would deliver much more practical guidance to our thirsty audience. And so the book began. Over the next several months, we met many people and interviewed scores of experts in the analytics space—academics, consultants, practitioners, HR leaders, and data scientists. We cannot thank those people enough for the insights they provided, which helped shape this book into what it has become.



Who Is the Audience for This Book? This book is for anyone who is interested in improving business performance through the use of workforce data and analytics. In particular, we researched and wrote this book with the following audiences in mind: • Business executives who want more from HR • HR executives or leaders who want to understand how to set up analytics for success • HR professionals who are charged with establishing, leading, or managing an analytics function ******ebook converter DEMO Watermarks*******



• HR professionals who want to enhance their knowledge and skills in workforce analytics



Our Approach Building and running a workforce analytics function and delivering meaningful projects that improve business performance can be complicated, but learning from the experiences of others can help in successfully navigating the journey. As we collected ideas from others, we amalgamated those into four parts in this book.



Part I: Understanding the Fundamentals Part I focuses on how workforce analytics got its name, why it is important, and its potential business impact. It also articulates a recommended approach to undertaking any analytics project, to ensure that it has purpose and clarity and also uses robust research design and analysis. In addition, this part offers case studies to help the reader understand potential benefits. Finally, we discuss the important role of the workforce analytics leader and why that person is essential for success. This first part is important for everyone to read because it covers the fundamental elements you need before you get started. Business and HR leaders will be particularly interested in Chapter 1, “Why Analytics?” and Chapter 6, “Case Studies,” to understand potential value from a workforce analytics team or function.



Part II: Getting Started Part II focuses on important concepts when starting out in workforce analytics, such as establishing the purpose of a workforce analytics program, determining why your organization wants analytics, and identifying where that demand is coming from. It also focuses on the stakeholders who will enable success and how to get started with “quick win” projects. This part helps chief human resources officers (CHROs), aspiring workforce analytics leaders, and HR professionals tackle the first few steps and it details what they might spend the first few weeks and months doing. This part is also helpful for anyone who wants to collect ideas about workforce analytics before recruiting someone to lead the function. ******ebook converter DEMO Watermarks*******



Part III: Building Your Capability Part III enables workforce analytics leaders and other HR executives to really understand how to ensure success. It has detailed sections on managing data, technology, and partners, plus suggestions for necessary skills. Finally, this part recommends an operating model to ensure continued and integrated success of workforce analytics as it becomes operationalized in the organization. This part gives the reader practical tips and recommendations for ensuring continued and long-lasting success. It is particularly aimed at analytics leaders and HR executives who are accountable for workforce analytics.



Part IV: Establishing an Analytics Culture Sometimes simply undertaking analytics projects is not enough. Instead, time and energy are needed to change the culture of the organization. Part IV focuses specifically on how to change your organization’s HR function from largely administrative to one that embraces an analytical mindset. In addition, this part focuses on two skills that HR usually lacks: storytelling and visualization. Finally, Part IV envisions what might happen in the field of workforce analytics in the next few years. This part is useful for HR and other business professionals who need to tell stories around an analytics topic and who need to change the mindset of the people with whom they interact.



For Reference At the end of the book, we provide a glossary that gives standardized terms and definitions for important elements of workforce analytics. Many analytics leaders requested this list to aid them in meaningful discussions with business leaders without getting lost in confused terms and misunderstandings. Using standardized terms, we can build a professional common understanding of workforce analytics.



Practical Tips A growing number of people around the world are involved in the field of workforce analytics. We were in touch with many of these people while writing this book. We talked to speakers and attendees at conferences, we ******ebook converter DEMO Watermarks*******



spoke with global business leaders and practitioners, and we formally interviewed many of the world’s leading practitioners of workforce analytics. Almost everyone we spoke with in researching this book asked us to provide practical tips. Most people recommended that we focus the book not on the why, but more on the how and what. In response, we added practical pointers from the people who practice this every day. In addition, at the end of each chapter, we summarize the main points that we believe will prepare leaders and practitioners as they set up or expand their analytics practice. Furthermore, this book contains vignettes that describe experiences from real professionals and offer great insights into their successes. As in the pursuit of data perfection, it is not our expectation that a book such as this can ever be 100 percent complete. We do not claim to supply every answer or cover every possible situation. But we do have a rich collection of practical advice to share, based on our own experience and the advice of other analytics practitioners, academics and leaders. Whatever your role, and whatever your reason for reading this book, our goal is to add insight and practical knowledge to the world of workforce analytics. We hope you find it helpful in developing your function and improving your organization’s performance. Visit the authors’ website at www.thepowerofpeople.org for more details about the book.



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I Understanding the Fundamentals 1 Why Workforce Analytics? 2 What’s in a Name? 3 The Workforce Analytics Leader 4 Purposeful Analytics 5 Basics of Data Analysis 6 Case Studies



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1 Why Workforce Analytics? “Industries are being disrupted. Talent is more mobile. All organizations need to understand the workforce better, and how it is executing the business strategy. And workforce analytics is at the heart of how to do this.” —Mark Huselid Distinguished Professor of Workforce Analytics, Northeastern University In a globally connected world of Big Data, complexity, and disruption, the business landscape is evolving faster than ever. Growing competition for talent coupled with shifting worker expectations and opportunities are changing the very nature of work. More data exist about people than ever before, along with more advanced technology for analysis. These developments are requiring changes of the human resources (HR) function, which needs to adopt an analytical mind-set and become more quantitative. Workforce analytics allows organizations to gain insight about people at a level never before witnessed, offering competitive advantage to improve business performance. The reasons driving HR’s adoption of workforce analytics can be summarized in the following categories covered in this chapter: • The need for HR to contribute to business value • The democratization and consumerization of HR • The evolution of work



Adoption of Analytics Many organizations are already realizing the benefits of analytics. A 2014 PWC report, written by The Economist Intelligence Unit, found that 89 percent of large company executives surveyed either already were using Big Data to make decisions or planned to start doing so in the next three years. In ******ebook converter DEMO Watermarks*******



HR specifically, in its 2016 CHRO report, IBM found that the number of chief human resources officers (CHROs) using predictive analytics to make more informed workforce decisions across HR activities had increased by approximately 40 percent over two years. The evidence of the trend is clear, as Deloitte’s Global Human Capital Trends 2017 reports: “People Analytics, a discipline that started as a small technical group that analyzed engagement and retention, has now gone mainstream.”



HR’s Contribution to Business Value In the commercial world, businesses need to stay ahead of their competitors to sell more products and services and to increase revenue and profit. The general aim of businesses is to increase market share and value for their owners. In the public or voluntary sector, organizations need to increase value through efficiency and effectiveness in delivering products and services to their constituents, whether they are service recipients, taxpayers, or donors. Whether public, voluntary, or private, all organizations need to deliver value. This requires using financial metrics and key performance indicators to monitor and improve operations. Evidence already shows the importance of workforce analytics to profitability, one key financial metric. In a 2015 KPMG report, written by the Economist Intelligence Unit, a large majority of executives (91 percent in IT and technology, 81 percent in biotechnology, and 70 percent in financial services and healthcare) indicated that an increase in the use of data-driven insights in their HR function would affect profitability over the subsequent three-year period. As organizations seek to improve performance, the onus is on HR to build value. The best way to do this is through an analytical approach. This is not necessarily where HR has seen itself in the past. Patrick Wright, a professor at the University of South Carolina, states: “When you hear about the work of Finance, Marketing, or Information Technology, it’s about numbers, numbers, numbers. When you hear about the work of HR, it’s about words, words, words.” We also believe, however, HR should not aim to transform itself into a purely analytical function and lose touch with the human behaviors and characteristics that also help people and businesses succeed. For many organizations, the analytical transformation has already begun in areas familiar to HR, such as attrition and retention analytics, recruitment analytics, workforce planning, compensation optimization, and employee ******ebook converter DEMO Watermarks*******



engagement. John Boudreau, a professor at the University of Southern California, has witnessed this: “There are some very prominent examples of analytics being used to answer important questions in HR—for example, which people will leave and how successful a candidate will be if hired. These are important questions that have been answered with analytics.” Workforce planning is another area of HR that is ripe for analytics attention because analytically driven techniques make it more strategic and sophisticated. Salvador Malo, Head of Global Workforce Analytics, explains how this is playing out at Ericsson: “Optimizing the workforce requires a two-pronged approach: First, understand the business requirements and translate these into needs for the future; second, get to know your workforce in some depth. Together these insights about the business and the people who work in it will lead to recommendations that improve workforce planning.” Prediction of attrition and candidate success, and workforce planning are all important topics for workforce analytics attention. Even greater value is realized when workforce analytics contributes to business outcomes. An impressive body of scholarly literature1 shows that a firm’s HR practices affect performance outcomes at all levels, from individual employees, teams and units, all the way to organizations as a whole. These HR practices can improve the breadth and depth of employee knowledge and skills in organizations—for example, through learning and development or the attraction, selection, and retention processes. “Our job as analytics experts is to ask the tough questions to enable executives to better manage their organization and perform their fiduciary duties.” —Alec Levenson, Economist and Senior Research Scientist, Center for Effective Organizations, University of Southern California A multiyear project undertaken in ISS, a global facilities services organization, offers an example of how workforce analytics contributes to business outcomes. The project brought together employee engagement and customer advocacy data to link to financial outcomes. ISS concluded that when both employee engagement and customer advocacy are high, profitability is highest. The average profitability in units scoring highest on both dimensions was 7.75 percent, versus 4.52 percent in the lowest-scoring groups. ******ebook converter DEMO Watermarks*******



RUN YOUR BUSINESS WITH ANALYTICS “My perspective on analytics in HR is that every other business leader I know runs their business with analytics, but there’s a black hole when it comes to HR.” This is the view of Tracy Layney, Senior Vice President and Chief Human Resources Officer (CHRO) at Shutterfly, Inc.2 She goes on to explain two general types of analytics, which she tries to keep separate: • True workforce analytics. The approach of measuring behaviors in organizations and knowing how to knit them together to improve business performance. The approach is similar to that taken with customer behavior, but this one concerns employee behaviors. • HR analytics. The functioning of the HR team itself—for example, analyzing key performance indicators (KPIs) such as time to hire. Such analytics are about holding the HR team accountable. Tracy says that every CHRO should be focusing on the first point. “We should be giving more levels of data showing leading and lagging indicators about our people. We have to make it an ‘insights’ exercise, not a reporting exercise—for example, using the insights to achieve business outcomes, or to expand into new markets.” She says it is essential to both increase the skills in HR and reset the mind-set of business executives. “We talk about increasing the skills of HR, but we have to recognize that we [HR] have also trained our leaders how to think about the people part of their business, for example—to run it in a very program-driven way (such as the annual salary cycle or talent reviews). We have to push out of those expectations, to do things quite differently.” Tracy concludes, “This is a huge area of opportunity for the HR profession. Workforce analytics and strategy together is a really powerful combination.” A second project, this time at global pest control firm Rentokil Initial, focused on the predictability of sales success. The project isolated the key ******ebook converter DEMO Watermarks*******



behaviors of high-performing sales professionals and used automated assessment techniques to select future candidates based on those behaviors. Global sales rose more than 40 percent and the project had a return on investment of more than 300 percent. These examples, described in more detail in Chapter 6, “Case Studies,” demonstrate that workforce analytics can contribute not just to improving the effectiveness of HR processes, but also to improving and predicting business outcomes such as profitability and sales.



The Changing Nature of HR Workers, managers, and executives are demanding more from their HR function. • The need for more information to “run the business.” The required response to this is the democratization of HR. • The desire for personalized services. The required response to this is the consumerization of HR. These demands strengthen the argument for workforce analytics because analytics can help deliver insights directly to managers and also provide intelligence that enables the personalization of services to employees.



Democratization of HR At a time when data are more readily available than ever, HR is being asked for more information, better insights, and more precise recommendations to help executives and managers run their businesses. This puts a strain on the traditional HR function that primarily dealt with the process side of recruitment, resourcing, development, and employee relations. For the last 40 years or so, HR has delivered structured programs, developed policies, and implemented best practices to allow executives and managers to manage people in a cyclical pattern—for example, through annual performance reviews, specific salary increase programs, and succession planning cycles. However, the demand has changed and new requests for information are emerging, as Table 1.1 illustrates. Table 1.1 Examples of Traditional, Current, and Future HR Requests from Managers ******ebook converter DEMO Watermarks*******



For executives and managers to get timely answers to questions and make ******ebook converter DEMO Watermarks*******



informed decisions about their people, they need information, insights, and recommendations. HR needs to respond to these requests in real time, providing information and insights to managers and executives as they need it. The workforce analytics function is at the heart of this change because HR is sharing more than just data with managers and executives—it is also giving them business insights and recommendations generated by sophisticated algorithms.



Consumerization of HR Bringing the type of customization experienced by consumers to the world of work can yield great benefits. In 2012, Amazon reported a 29 percent increase in second quarter fiscal results. A Fortune article at the time discussed how Amazon’s recommendation engine contributed much to that success by using algorithms to heavily customize the browsing experience for returning customers. HR can learn a lot from examples like this and begin to use its data to create predictive models for the “workforce of one” (a term referring to personalized employee experiences in Accenture’s report “The Future of HR: A Radically Different Proposition”). But more than this, workers are starting to expect similar customization from their employers. Many workers would appreciate recommendations to improve their working experience. This change is referred to as the consumerization of HR, further discussed by Mark Feffer in a 2015 Society for Human Resource Management article focused on recruitment: “Today, job seekers are thought of as customers.” Examples of workforce personalization include the following: • Recommendation of modular courses to enhance employees’ skills • Information on benefits relevant as a worker enters new life stages (for example, a new baby, marriage, or house purchase) • Internal job and career moves that best meet a worker’s skills and expertise • Opportunities to contribute to projects across the business based on an individual’s expertise and knowledge • Provision of performance feedback in real time through manager- toemployee and peer-to-peer social feedback ******ebook converter DEMO Watermarks*******



Ian Bailie, Global Head, Talent Acquisition and People Planning Operations at Cisco, explains how the consumerization of HR begins with the Cisco Talent Cloud, a huge database of all workforce-related data: “The primary catalyst of the Talent Cloud was for employees to manage their own development. For example, it helps them find training that matches directly to skills, as well as potential new jobs and new assignments. It also gives them visibility of opportunities across Cisco, breaking down silos. All that is in the employees’ hands. And that gives us an overview of the entire workforce that is also really helpful in running the business.”



THE FUTURE OF HR IS ANALYTICS Mark Huselid, Distinguished Professor at Boston’s Northeastern University, is one of the world’s experts in workforce analytics.3 He sees this area as the future for the HR profession. “In my experience, the outside world is changing more quickly than the organization is changing on the inside. So there is an increased demand for talent related information. “The arc of the analytics story is that it is both very new and very old. We’ve been playing at this for a long time. So what’s new? A confluence of factors: access to data and better, easier, faster analytics tools.” The workplace is also changing with the Internet, social media, smartphones, and work marketplaces for jobs virtually everywhere and for any skill. “Executing strategy through the workforce, and helping managers do a better job of that has gotten much more complex,” Mark says. And so we come to analytics. “I was at Rutgers University for two decades at the School of Labor Relations,” Mark says. “I focused on HR in that program. I spent a lot of time working with executives and trying to understand from them the relative returns of HR. Analytics is just the next evolution and there’s a lot more interest now in building analytical skills.” He continues: “There’s enormous pressure to do things faster, better, quicker, cheaper. In today’s world, there’s much more information available to employees about the quality of experience in other businesses, making talent exponentially more mobile. People won’t ******ebook converter DEMO Watermarks*******



put up with crummy jobs—they’ll just leave.” Mark’s message is simple: Businesses must understand their workforce better. And to do that, they must use analytics.



The Future of Work Workforce analytics is most relevant when we consider the way the world of work will change in the future. Although firm predictions are difficult, some trends have their foundations in today’s reality. According to the Global Consortium to Reimagine HR, Employment Alternatives, Talent, and the Enterprise (CHREATE), five fundamental forces are driving change for the future world of work: • Social and organizational reconfiguration • An all-inclusive global talent market • A truly connected world • Exponential technology change • Human–automation collaboration “Over the next 10 years, we will see work liberated from the idea of a job. Work will be disaggregated and re-combined in ways that better suit employers and employees.” —John Boudreau, Professor of Management and Organization, and Research Director of the Center for Effective Organizations, University of Southern California In a 2014 CHRO study undertaken by IBM’s Institute for Business Value, 66 percent of C-suite executives said their organizations rely on third-party providers for contingent workers, 57 percent rely on alternative workforce arrangements, and 36 percent rely on crowdsourcing. The report argues that predictive analytics will be needed to make more accurate workforce decisions as the nature of the workforce shifts. CHREATE and the IBM CHRO study underline the importance of analytics in helping organizations succeed in this changing world, specifically in these areas: ******ebook converter DEMO Watermarks*******



• The speed of change will alter the nature of work: • Work will be deconstructed and analytics will be helpful in determining the parts of the work that will remain strategic and the parts that will remain peripheral to an organization’s core mission. • Some work will become automated by robots and other machines. Analytics will help define the best workforce that becomes a mix of robots and humans. • Workers themselves will continue to redefine work: • The number of independent workers will continue to increase, thanks to technological advances connecting people anywhere, anytime. Organizations will tap into this “gig economy” for experts to add value to work at the right place, right price, and right time. Workforce analytics should be used to understand what work is suitable for independent workers and for permanent employees. • Workers’ expectations will continue to drive the need for personalized services—for example, in learning, healthcare, benefits, and so on. But in addition, new services will be needed as the gig economy intensifies —for example, the need for legal advice for intellectual property (IP) for freelancers who use their IP in multiple firms concurrently. • The volume of data will change the nature of workforce insights: • Wearables, sensors, nanotechnology, and other devices will provide incredible amounts of data for analysis. Some devices will become so small due to technological advances that they will almost disappear from view, a term described as “disappearables” in a Reuters article by Jeremy Wagstaff in April 2015. The decrease in size is expected to increase the personal desire for and usefulness of devices in the workplace. • As the gig economy builds, the growing associated data will help redefine a worker’s reputation based on information about the gigs undertaken and related endorsements about the value of the work. It’s a new world of work. Ian Bailie of Cisco summarizes this well: “It’s about understanding the skills and capabilities of the internal workforce; dealing with the new gig economy, contractors, and freelancers, and understanding their skill set; getting better at moving people around the ******ebook converter DEMO Watermarks*******



organization; and enabling them to build careers and their own personal brands. This will become a dataset that we don’t have today.” This is an important time for the HR profession to adapt and create momentum in the field of workforce analytics to capitalize on the changes shaping the future world of work. As Max Blumberg, founder of Blumberg Partnerships, Ltd., stresses: “You’d have to be a very brave human resources director to say you’re not taking analytics seriously.”



Summary Workforce analytics is a discipline that is increasingly needed in organizations. This growing demand can be attributed to the following: • The continued need for increased business value and market competitiveness • The requirement for information and data in real time from managers and executives, to help them run their operations more efficiently and effectively • The move toward a consumerized working environment and the provision of personalized services using workforce-related recommendation engines • The deconstruction of traditional business models and the proliferation of the gig economy and independent workers • The ongoing explosion of Big Data from devices such as wearables and sensors that will expand the amount of available workforce-related data



1 For pioneering work in this field, see the contributions of Mark Huselid, Distinguished Professor of Workforce Analytics, at Northeastern University. 2 In 1999, Shutterfly, Inc., began as a company that helped people print 4by-6-inch photographs from their digital cameras. Today it is an industry leader for photo and video storage, award-winning photo books, gifts, home decor, premium cards, invitations, stationery, and much more. Shutterfly is headquartered in Redwood City, California (www.shutterflyinc.com). 3 Mark Huselid spent more than 20 years at Rutgers University and the last ******ebook converter DEMO Watermarks*******



2 years at Northeastern University. In addition, he has been a visiting faculty member and has taught at schools and universities around the world to help in developing the next generation of HR leaders.



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2 What’s in a Name? “The beginning of wisdom is to call things by their proper name.” —Confucius There are almost as many names for the workforce-focused analytics function as there are functions that perform workforce analytics. This itself is indicative of a discipline in its formative years. Getting the name right is important; without a common language, practitioners risk confusion about exactly what the function does. This chapter explains why we recommend using the term workforce analytics to accurately describe the function. The focus of analysis is the workforce, and the activities of the function involve applying an analytical approach.



Focus of the Function Numerous articles cover the topic of analytics in the “people space.” These articles use the terms talent, human capital, human resources, people, and workforce interchangeably. In 2015 alone, notable authorities in this field wrote articles in key publications using different terms—for example, people analytics (Josh Bersin in Forbes), HR analytics (Patrick Coolen on LinkedIn), workforce analytics (Rebecca Atamian and Travis Klavohn in Workforce), and talent analytics (Ed Lawler in Forbes). Clearly, no standard name for the function has yet taken hold. The definition of talent varies widely across organizations. According to the Chartered Institute for Personnel and Development 2016 fact sheet, “Talent consists of those individuals who can make a difference to organisational performance either through their immediate contribution or, in the longerterm, by demonstrating the highest levels of potential.” This definition does not represent the entire spectrum of workers in an organization and, used in the title of an analytics function, does not describe analytics relating to the ******ebook converter DEMO Watermarks*******



whole organization. In support of this, many analytics experts believe the word talent is too narrow here, so it is no longer commonly used. The term human capital seems to be more fashionable among consulting firms and other institutions that see people as financial assets. However, few organizations refer to the analytics function as human capital analytics; perhaps not surprisingly, the ones that do are usually financial institutions. The descriptor human resources (HR) is commonly used in the analytics business, although different schools of thought have arisen. After all, the term HR analytics is often accurate because it describes the analytics department in the HR function. Furthermore, HR might be broader than people because it covers the management of human resources and the interfaces with all other business functions (finance, marketing, sales, and so on). However, other experts argue that HR is a limiting term because managers and executives often link HR only to employees and the policies and processes for their management. This perspective excludes other categories of the workforce, including temporary, nonemployed contract staff; freelancers; and managed services. In short, the term HR analytics implies that HR focuses only on analytics for the HR function (that is, using analytics to affect and inform the policies, practices, and processes that HR as a function manages—or “HR for HR,” as it is sometimes described). Since early 2015, the term people analytics has been gaining traction. However, this term can be misleading because it can imply analyzing factors about people beyond the workforce—for example, citizen or consumer behaviors. In addition, the word people does not cover gaps in the workforce (for example, numbers acting as placeholders for people yet to be recruited) and the growing presence of robots in the workplace. Therefore, although the term people analytics is fashionable, it lacks clarity as a functional descriptor in an organization and does not represent the entire workforce. Considering all the limitations of these terms, it is our view that the word workforce is more descriptive of all workers (not just employees) and includes contract staff, managed services, freelancers, and other people. The term also allows for the future inclusion of machines that will replace current jobs performed by humans, a topic discussed later in this chapter. Therefore, because this book focuses on analytics that relate to the entire group of workers for an organization, we recommend the functional descriptor workforce. ******ebook converter DEMO Watermarks*******



Activities of the Function Analytics, reporting and analytics, reporting and insights, metrics and analytics, planning and insights, and planning and analytics are all names that have been used to describe the activities of the function. Most teams use the word analytics in the name of their function. Some, however, explicitly call out reporting and analytics, to make a clear distinction between the two disciplines. Some business professionals (including HR) might think that reporting is analytics, so using both terms enables us to distinguish between them. In other cases, the function is called planning and analytics, to articulate that planning is separate and distinct from analytics. Functions labeled as planning and analytics tend to have an analytics leader with additional, specific responsibility for elements of strategic workforce planning. Occasionally, the function is called insights and analytics, to expressly state that analytics is about insights, not data or reporting. However, because insights are part of the entire analytics methodology, as Chapter 4, “Purposeful Analytics,” demonstrates, there seems little need to highlight this one element. Therefore, we can conclude that analytics is the most accurate word to describe the work of the function. This includes all aspects of analytics within the end-to-end methodology, as Chapter 4 describes.



A Name Fit for the Future Finding the right name for the function is key, but that name needs to continue to have relevance in the future. Three key changes will impact workforce analytics in the future: • Artificial intelligence, robotics, and other technologies will transform work that humans currently perform. • The gig economy, an environment in which temporary positions are common and organizations contract with independent workers for shortterm engagements, will expand. • The amount of workforce-related data will exponentially increase with the “Internet of Things” as workforce-applicable sensors, wearables, and other devices become ubiquitous. ******ebook converter DEMO Watermarks*******



These changes will create a workplace that is more extensive, more democratized, and more fluid, as the Global Consortium to Reimagine HR, Employment Alternatives, Talent, and the Enterprise (CHREATE) describes (www.CHREATE.net; see Chapter 1, “Why Workforce Analytics?”). As a result, the workforce will continue to evolve, expand, and change; some employees will morph into freelancers, and machines will do jobs that people once handled.



Summary Taking into account all of these points, the most descriptive and accurate name for this function is workforce analytics. This term best describes the broadest set of workers that contribute to organizational success and the fullest responsibilities of the function both now and in the future. Other experts concur with the use of the term workforce analytics as the best description of the function. Most notably, the SHRM Foundation, the research arm of the Society for Human Resource Management (the professional body for the HR profession in the United States), uses the term workforce analytics in its report “Use of Workforce Analytics for Competitive Advantage,” undertaken in partnership with the Economist Intelligence Unit. With the name of the function in mind, we can define the work of the function as follows: Workforce analytics is the discovery, interpretation, and communication of meaningful patterns in workforce-related data to inform decision making and improve performance.



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3 The Workforce Analytics Leader “The analytics leader must frame things in language the business leaders understand and articulate the opportunity and impact from the work that can be done. That’s a critical capability.” —Mark Berry Vice President and Chief Human Resources Officer, CGB Enterprises, Inc. Clearly, workforce analytics leaders know why they have the role, what the expectations are, and broadly what they are required to deliver. A little less clear is the precise job description and the detailed requirements for successfully performing the role. This chapter brings clarity to this topic. Of course, the exact skills needed for the leader of any specific workforce analytics function vary depending on the size, industry, and geographical complexity of the organization. Still, most cases share some common requirements. This chapter provides details on the essential elements that enable a workforce analytics leader to succeed: • Internal reporting structure • Key job responsibilities • The importance of business acumen and influencing • Core leadership attributes



Reporting to the Chief Human Resources Officer To whom should the workforce analytics leader report? The internal positioning of the function’s leader is of critical importance in terms of both the kinds of analytics projects the team will undertake and how the rest of the organization will view its output. The workforce analytics leader needs strong human resources (HR) connections, as well as ready access to other parts of ******ebook converter DEMO Watermarks*******



the organization. Reporting to the chief human resources officer (CHRO) addresses these requirements and also sends another message: The CHRO is putting analytical decision making at the heart of the HR function. Luk Smeyers, Cofounder of iNostix by Deloitte, recommends that the workforce analytics leader be a part of the HR leadership team itself: “Don’t hide a key role like the workforce analytics leader away in the reporting or HR systems department. After all, if you ‘hide’ your analytics leader away there, the analytical reflections of your company will inevitably stay limited to everyday HR matters.” Josh Bersin, Principal and Founder of Bersin by Deloitte, also advocates clearly positioning the workforce analytics leader within HR: “Don’t have this job report to the head of HR technology; that’s a different role and focus, and that person will not have time for analytics.” With this advice in mind, forward-thinking CHROs will want to have the workforce analytics leader report to them and be accountable directly to them. With that reporting structure, the CHRO has the best opportunity to create an analytically driven function. If direct reporting is not feasible, the workforce analytics leader must have very strong access to the CHRO (see also Chapter 14, “Establish an Operating Model”).



Responsibilities of the Workforce Analytics Leader The workforce analytics leader’s primary role is to deliver analytically driven recommendations that, when implemented, improve business performance. Some important associated responsibilities also include: • Manage. Workforce analytics leaders usually have a large number of projects and operations to handle at one time. They need to manage not only this workload, but also the associated relationships. Senior stakeholders, including business executives, the CHRO, and other senior HR leaders, commission projects to address important business issues. The leader of the workforce analytics function must manage all of these stakeholders appropriately so that projects get delivered. • Challenge. Workforce analytics leaders must be able to challenge the thinking of the specialists on their team, to ensure that their work is accurate and complete. For example, data scientists should account for possible explanations for business outcomes that executives might not ******ebook converter DEMO Watermarks*******



have considered already. By asking the technical specialists how they reached conclusions, the analytics leader can challenge their thinking before the business challenges the results of that thinking. Workforce analytics leaders should also be able to challenge the thinking among business executives and other leaders to ensure that projects deliver new insights to improve business outcomes. Some business managers shut down ideas about workforce analytics projects that do not reflect their understanding and experiences of the way the business is operating. Analytics processes and outcomes thus must be challenged and validated internally before they are shared outside the function. Mark Berry, Vice President and Chief Human Resources Officer of CGB Enterprises, Inc., shares this view: “Effective analytics leaders are always asking ‘why?’ They never accept an answer until they are at the actual cause. They collect data and analyze it to find the solutions. Then they frame opportunities with business leaders so results can be derived from analyses.” • Integrate. Workforce analytics teams consist of people from a variety of different backgrounds. This includes experts from technical disciplines such as statistics and computer sciences, experts in human resources policies and practices, and people well versed in the psychology of human behavior. Strong leadership skills are key in galvanizing this diverse team around a common vision and mission. Salvador Malo, Head of Global Workforce Analytics at Ericsson, explains: “Expect to have a team made up of people from different backgrounds: statisticians, programmers, HR, and people who understand business. The role is to bridge the gap that separates the disciplines so they learn from each other and work together effectively.” • Represent. The leader must be able to represent the position of technical specialists in the workforce analytics team to stakeholders. To do this, the workforce analytics leader should be able to talk credibly about the business and how and why analytics will help improve business performance. At the same time, the workforce analytics leader needs to talk eloquently about the details of analytics projects to business leaders. Storytelling and visualization techniques can aid the leader’s representation of the team’s work. Chapter 17, “Communicate with Storytelling and Visualization,” describes some of these techniques. ******ebook converter DEMO Watermarks*******



Business Acumen Chapter 12, “Build the Analytics Team,” outlines the Six Skills for Success required across the analytics team. Of those skills, business acumen is the most important one for successful leaders to have. This skill includes financial literacy, political astuteness, and awareness of both the internal organization and the external marketplace. Other members of the workforce analytics team will undoubtedly have business acumen as well. However, the leader of the workforce analytics team is primarily responsible for managing the political landscape and articulating projects and analytics in synchronicity with business objectives within the competitive landscape of the company. The leader must help the team navigate the business environment successfully; otherwise, workforce analytics projects will be, at best, suboptimally implemented or, at worst, ignored. Successfully navigating the business environment also brings credibility to the workforce analytics leader. Terry Lashyn, Director of People Intelligence at ATB Financial, explains how this element of business acumen helps ensure the leader is heard: “What the leader really needs is credibility so that when he or she goes to a business leader to talk about workforce analytics, the business leader knows it is going to be worth taking the time to listen and that the workforce analytics leader is there to help.” Successful internal navigation is only one part of successful leadership, though. Sally Dillon, Head of Business Intelligence at UK Life, Aviva, also encourages the importance of closeness to the business: “While our analyses might be general, our recommendations for implementation are specific, taking into account the context in which they will play out at the unit level, at the team level, and sometimes at the level of the individual. We check the idea before implementation to avoid having unintended consequences in the business.” Workforce analytics can fail when the team sits in an analytical ivory tower, isolated from an understanding of how its recommendations actually impact the business and its workers. Workforce analytics leaders need to understand and even work in the business. Alexis Fink, General Manager of Talent Intelligence & Analytics at Intel, suggests another element of business acumen for the successful workforce analytics leader: sophisticated influencing strategies. She says influencing the major strategic initiatives in the business is essential: “Build partnerships ******ebook converter DEMO Watermarks*******



with your strategy office, get to know your business leaders, and find the greatest opportunities in your organization. Then you can find the moment to add talent and workforce agendas to the most significant strategic projects. That way, you are influencing what is most important in your organization.” Clearly, the one skill workforce analytics leaders must have and should continuously improve is business acumen. Spend time with other leaders, read voraciously about your organization, understand the marketplace, be intimately aware of the metrics and key performance indicators of your organization, ensure that you are financially literate, and network extensively with your leaders.



Leadership Attributes Four key leadership attributes are considered most important for the workforce analytics leader to bring people together as an integrated and cohesive workforce analytics team: • Capacity to think • Willingness to develop others • Ability to inspire • Drive to achieve These attributes ensure that the team achieves more together as a result of the leader’s actions. They also drive the team members’ engagement with the business. Let’s look at each of these four key attributes for success: • Capacity to think. Many factors will compete for the attention of the workforce analytics leader: multiple projects, demand for projects (which can outstrip supply of resources), varied backgrounds of team members, the need to form a cohesive team, the culture and mind-set of HR colleagues, and resistance from others. All these leadership demands are the result of a relatively new team (workforce analytics) in a function (HR) that is often not considered analytically astute. Such demands require the leader to be able to think carefully about prioritizing actions. Furthermore, the projects themselves can be highly complex. They might involve disparate data sources and complicated analyses to answer business questions that are, by nature, not easy to answer (if they were ******ebook converter DEMO Watermarks*******



straightforward, they would not likely require the skills of a specialist analytics team). So in addition to the need to sort through competing priorities, team leaders must be able to think about very complex projects. • Willingness to develop others. Team members who are perfectly equipped to perform workforce analytics roles are in short supply. Back in 2011, McKinsey & Company reported that the United States would experience a shortage of at least 140,000 people with deep analytical skills by 2018. Certain skill sets likely will need to be hired and developed; this is particularly true for data science, the most demanded job in the United States in 2017, according to CareerCast. Although hiring and developing skills for any team is a typical role of all leaders, it is particularly important in workforce analytics for the following reasons: • The profession of workforce analytics is very young. As such, more commitment is needed initially to develop people, especially given the short supply of certain skills. • The team will come from varied backgrounds, so more cross-training in a variety of skills such as statistics or financial literacy will be needed. • The team will need to learn new and unfamiliar skills, such as consulting and data science. • All workforce analytics professionals who deal with internal clients must be proficient in stakeholder management, another skill that can be difficult to teach and learn. Leaders need to integrate the work of people from different backgrounds and disciplines into a cohesive team. They also need to nurture and grow talent, take a developmental outlook toward employees, and show a passion and drive for collective action and teamwork. • Ability to inspire. Workforce analytics leaders need to instill a belief in the team to succeed, often in the face of limited resources and external pressures. Effective leaders do this by influencing key people and using others to exert influence when necessary. They build confidence in their team members’ ability to succeed and inspire them to work as a coherent team around a common vision and mission. This ability is important because the team might encounter skepticism and resistance from other HR professionals and stakeholders (see Chapter 16, “Overcome Resistance”). ******ebook converter DEMO Watermarks*******



• Drive to achieve. Workforce analytics leaders need tenacity and resilience. Analytics projects can be complex in both the business questions they seek to answer and the analytical methodology needed to derive insights and recommendations. And projects don’t end there (see Chapter 4, “Purposeful Analytics”): Workforce analytics projects also require implementation to drive change in the business. All this means that projects can take weeks or months to undertake; in some cases, delivering a return on investment (ROI) can take years. To ensure that the workforce analytics work gets done, team leaders must have a proactive nature, a mentality for continuous improvement, and a strong customer focus mindset.



BE EGO-LESS Alexis Fink is the General Manager of Talent Intelligence & Analytics at Intel1 and has enjoyed success in business analytics across several organizations. She has one main piece of advice for workforce analytics leaders: “Don’t make it about yourself.” She explains that some people forget that analytics is about the business and become consumed with their own self-importance. “Their ego gets in the way and analytics projects begin to succeed or fail due to the person behind the analytics. This is wrong.” Alexis suggests three strategies to stay grounded: • Don’t try to be a “know-it-all.” As an analytics leader, you have access to all the data, but you don’t necessarily know best. Help your leaders and colleagues understand the workforce-related data and lead them on a journey. As Alexis recommends, “Be nonthreatening.” • Keep pushing forward. Get to know your leaders. Deliver what leaders ask for, and then introduce additional insights so they get more than they asked for. This way, you will build your credibility. As Alexis points out, “You have to serve the appetizer to get to the main course.” • Don’t get yourself excluded from the big projects. Learn the business and understand who the decision makers are. Find a way to bring analytical insights into the fold. Then impress them with your team’s ******ebook converter DEMO Watermarks*******



work. Alexis summarizes, “I learned to be ego-less. I had to get really good at getting my ideas to come out of someone else’s mouth.” Bringing the team together around a common goal and leading them to achieve great things is as important for workforce analytics leaders as it is for other leaders in the business. Encouragingly, scientific research suggests that the vast majority of leadership capabilities are learned from experiences, and the best experiences are often on the job. Morgan McCall, a professor at the University of Southern California, sees the following formula for acquiring leadership skills: 70 percent of the learning should be acquired on the job, 20 percent of the learning should come from other people (for example, experiences with good and bad bosses), and the remaining 10 percent should come from classroom-based learning such as formal leadership training programs. Beyond the leadership attributes described here, workforce analytics leaders also need a credible level of familiarity with each of the team’s specializations. Although leaders are not likely to have the same specialization depth as individual team members, they should have, for example, a working knowledge of statistics and a good feel for numerical information. For more on the core skill areas for the workforce analytics team, see Chapter 12, specifically the section “Six Skills for Success.”



Summary The leader of the workforce analytics team need not have followed a specific career path before taking on the leadership role, but certain approaches, attributes, skills, and experiences will provide a better platform for success: • Have the workforce analytics leader report directly to the CHRO. • Clarify the role of the workforce analytics leader and the specific responsibilities for success. • Develop business acumen by continuously learning about the internal operations, metrics, and key performance indicators for the business. • Build external awareness by learning about the marketplace, competitors, and other external factors. ******ebook converter DEMO Watermarks*******



• Improve financial and numerical literacy. • Develop leadership attributes to challenge the team, develop a cohesive team, inspire team members for success, and drive to achieve. • Create a workforce analytics team that has a blend of the Six Skills for Success (see Chapter 12) and ensure familiarity with the team’s areas of specialization.



1 Intel Corp., headquartered in Santa Clara, California, is the world’s largest semiconductor business. The organization is focused on supplying computer chips for the next wave of technology, the Internet of Things, by making connected chips for everything from selfdriving cars to jet engines (www.intel.com).



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4 Purposeful Analytics “After a recent speech, an attendee came up to me and said, ‘I can predict attrition for my firm to 92 percent accuracy.’ I said, ‘Wow! That’s great. Is attrition a problem for your firm?’ And she said, ‘No, not really.’” —Josh Bersin Principal and Founder, Bersin by Deloitte As this quote indicates, some people are undertaking analytics in the field of HR to solve irrelevant problems. This lack of a practical purpose is the result of a more fundamental issue: No standard methodology exists for undertaking workforce analytics projects to ensure that they deliver meaningful results. Another underlying issue is the lack of analytics project sponsors. For workforce analytics to impact the business, senior executives need to care about a project, what it reveals, and how it can change the business as a result. This chapter aims to increase the robustness of workforce analytics projects by focusing on three key topics: • A model for purposeful analytics • The important role of the project sponsor • Typical reasons analytics projects fail



A Model for Purposeful Analytics Given the nascent state of the field, it is not surprising that workforce analytics has no standard methodology. However, it is also clear that many people struggle with achieving impact from their workforce analytics activity because they do not set about the project in a way that will lead to success. For these reasons, it is sensible, if not essential, for analytics practitioners to have a methodology for structuring analytics work. The methodology proposed here has eight steps that can be grouped into three parts, as Figure ******ebook converter DEMO Watermarks*******



4.1 shows. Chapter 6, “Case Studies,” illustrates this methodology through real examples that demonstrate how a clear approach can lead to organizational change and improved business performance.



Figure 4.1 The Eight Step Model for Purposeful Analytics.1



Why Undertake the Project? Unless you know why you are undertaking an analytics project, you will find it almost impossible to bring any meaningful value to the business. When it comes to workforce analytics, people often start with the data, but starting with the end in mind is a better approach. In other words, first define what you are trying to change and why. Alec Levenson, an economist and senior research scientist at the University of Southern California and the author of Strategic Analytics, states: “To complete an analytics project well takes time and energy. This means you need to be a systems thinker and consider all the links from the HR process to the business issues. For every project, there should be a business problem to solve.” Step 1. Frame Business Questions ******ebook converter DEMO Watermarks*******



Framing the business question is another way of clarifying the business problem. This step must come first to avoid undertaking the wrong analysis and also to give the project the best chance of success. A clearly framed and well-defined business question ensures that the project or analytics work is actually necessary. Without such clarity, the project is unlikely to gain investment and sponsorship. Appropriately framing the business question provides unambiguous direction. Defining effective business questions involves several aspects: • Focus on understanding the business. Christian Cormack, Head of HR Analytics at AstraZeneca, believes business understanding is the basis of framing questions: “You need to spend time with senior people understanding how the business works so that when the business requests something, you understand the context and can give a higher-quality answer. I’m lucky that there’s never a shortage of questions from our business.” • Use appropriate consulting techniques. Questioning, listening, and paraphrasing skills help get to the heart of the problems. Issues such as internal business dynamics, external market forces, financial impact, and linkage to organizational values must be considered. Investigating these areas and using effective questioning techniques ensure that the business problem is properly understood and defined. Chapter 12, “Build the Analytics Team,” details the skills needed for this step. • Summarize the business question back to the project sponsor. Be sure to get agreement on the business question your analytics project will address. Write down the question and ensure that the project sponsor signs off on it, to signal agreement that the project should begin. • Be thorough. This step can be as simple as one conversation or it can be much more complex, with multiple conversations among several stakeholders to ensure clarity. Substantial resources (money, people, time) might be needed to deliver the project, so it is important to get clarity on the problem to be investigated. To proceed successfully, it is best not to move too quickly; although speed is important, diligence is critical. Step 2. Build Hypotheses Building and clarifying a hypothesis is important for “testing” beliefs about the causes of business issues. Strong hypotheses should guide the data ******ebook converter DEMO Watermarks*******



gathering and analysis phases in a way that links to business questions. Formulating hypotheses in advance helps guard against reaching conclusions based on observed relationships in your data that result from chance instead of genuine underlying relationships. Appropriate hypotheses also make it easier to select the most appropriate analysis for the project in question. These steps are key to writing a good hypothesis: • Write a hypothesis as a statement, not as a question. Hypotheses are informed, testable explanations or predictions. The statement might look something like: If [we do this], then [this] will happen. For example, if people are leaving the company at a higher rate than expected, the hypothesis might be articulated as: If we increase salaries, then the turnover rate will reduce. • Use relevant literature to inform the hypothesis. Industrial and organizational psychology and management scholars have studied many common people-related problems for years, and the industry has accumulated a wealth of knowledge on the causes and consequences of different problems. As you develop your hypotheses, make use of the latest scientific thinking in key academic journals in the field. Google Scholar is a good first port of call for a literature review. • Discuss the hypothesis with the project sponsor. Share your hypothesis with the project sponsor to ensure that it accurately reflects the situation as the sponsor believes it to be. • Ensure clarity. A good hypothesis is written in clear and simple language. Reading the hypothesis should clarify whether each possible analysis result will support or reject the hypothesis. • Make sure the hypothesis is testable. A scientific approach tries to disprove hypotheses because a single refutation of a hypothesis shifts the attention to a different line of thinking. Analysts usually proceed when their hypotheses are not rejected. The number of times a hypothesis should be tested depends on, for example, the rigor of the research design, the quality of the data, and the magnitude of the work’s impact. However, keep in mind that numerous results supporting your hypotheses could be overturned by one additional test that disproves it. • Don’t make the hypotheses too ambitious. Answering some business questions will likely involve more than one hypothesis. However, multiple ******ebook converter DEMO Watermarks*******



hypotheses complicate the analysis, so be prepared to rationalize the number of hypotheses where possible to reduce complexity.



How Should the Project be Carried Out? The choices you make here influence the validity of your project’s outcomes. This part of the methodology will likely be the most complex because of the deep technical knowledge and skills required. Step 3. Gather Data The data gathering step requires identifying the most relevant data for testing the hypotheses and determining whether data quality is sufficient to proceed. Decisions need to be made about whether to gather existing data, collect new data, or do both. Note that this step can easily become unwieldy. Projects might begin with good, clear data intentions, but when analysts start data cleansing and then run some initial analyses, new and tempting areas of exploration could emerge. Reminding yourself of the business question you set out to answer and the related hypothesis you sought to test should help to avoid these distractions and ensure a better data focus. This step also includes managing any legal and ethical aspects concerning data privacy challenges; see Chapter 10, “Know Your Data,” for more details. There are several tips for the data collection step of any project: • Keep sight of your objective. Don’t get lost in data too quickly and, as a consequence, lose sight of your end goal. Naturally, many analysts are curious; this curiosity is an important element of succeeding in their role, but it can also distract from the desired focus of your project. • Build a picture of your data before you gather it. Projects commonly progress too slowly. Resources are initially devoted to integrating existing data that people believe will permit hypothesis testing, but they find that the data are not as comprehensive as initially expected. To overcome this, take the time to map out data and undertake some checks before gathering it—for example, to ensure that it contains unique identifiers to link the datasets that you plan to analyze. • Focus on data that you already have. This might sound straightforward, but many analytics practitioners complicate matters by thinking that they always need new data. Start with the data you have and evaluate its ******ebook converter DEMO Watermarks*******



quality for testing your hypotheses before you collect new data. • Think carefully about new data. Existing research (from inside or outside the organization) might help to answer at least part of the question driving your need for new data. If a new data set is required, think carefully about how to collect it. A small amount of new, high-quality data is better than a lot of semiuseful data. Also think comprehensively about data you might need in the future, to avoid having to repeat data collection. • Remember existing scientific research. If existing data are poor and collecting new data is too difficult, you might be better off relying on research evidence in the scientific literature instead of collecting your own new data. Step 4. Conduct Analyses This step is what many people consider the real part of analytics. This is where the methodology and statistics are applied to data to test the hypotheses and provide the basis for insights. Without this step, the fundamental building blocks of any analytics project simply do not exist; without performing analysis, patterns in data will never be discovered. At this juncture, choosing the right method for analysis is critical because choosing the right—or wrong—method will determine the validity of the results. Many different analytical methods and associated technologies can make analysis more focused and successful. Choosing the right technology and the right method for analysis requires at least a basic knowledge of what the various methods and technologies can deliver. To help with your selection see Chapter 5, “Basics of Data Analysis,” and Chapter 11, “Know Your Technology.” Step 5. Reveal Insights One of the most frequent requests in workforce analytics is, “Bring me insights, not data.” Data are useful and analytical results are interesting, but understanding the context and implications of the results is what leads to insights. Workforce analysts must uncover insights for two main reasons. First, analysts cannot assume that project sponsors and stakeholders are able to derive the most pertinent insights themselves. Project sponsors might not be experts at this, so workforce analytics practitioners should make it one of ******ebook converter DEMO Watermarks*******



their main tasks. The second reason is more subtle: If analysts present only data and analysis without insights, executives and project sponsors might draw their own conclusions to best fit their preconceptions. Bart Voorn, HR Analytics Leader at Ahold Delhaize, endorsed this: “We want to improve the quality of decision making by using insights. I prefer not to build dashboards or to create reports. We should focus on generating insights.” Although this step has no magic formula, some helpful hints apply: • Write insights clearly in single sentences. If you cannot write an insight clearly in a single sentence, ask yourself whether it really is an insight. Describing insights in single sentences is also helpful so that you can create effective visualizations and draw clear recommendations. • Express each insight in one visualization, where possible. As part of the process of clarifying insights, try to express each insight in a visual format. Not all insights can be easily visualized, but whenever possible, visualization significantly aids effective communication. Chapter 17, “Communicate with Storytelling and Visualization,” discusses this in more detail. • Avoid displaying raw data or analysis without interpretation. Leading practitioners recommend not using raw data as insights because this can be distracting and cumbersome. Similarly, avoid showing analytical output, such as lengthy tables of correlations and technical details. Instead, present the interpretation of the data. • Ask yourself what each insight means. As you derive the insight, you should be able to articulate why it is important. Test the importance of the insight by asking questions such as the following: • What does this insight tell me? • Does this insight relate to the business question? • Is this insight unique or just another twist on a familiar topic? • Is the insight clear? • What actions might result from this insight? • Focus on revealing key insights. Leading practitioners in workforce analytics emphasize that staying focused on the most important insights is vital. You might generate a large number of insights that you feel compelled to share, but it’s better not to overwhelm your audiences with ******ebook converter DEMO Watermarks*******



too much information. Refer back to the business question as a guide for where to focus the attention. Retaining this focus will contribute to the project’s success. Chapter 17 offers more guidance in this area. Step 6. Determine Recommendations Just as you need data for insights, you need insights for recommendations. Ask yourself the following: If my insight is important enough to highlight, then what should the business do about it? Analytics projects are all about helping the business improve its performance, so although insights are interesting, only recommendations will help improve the business. Recommendations are what business leaders and, in this case, project sponsors need. A well-articulated recommendation makes a great impetus for change. Some analytics projects fail at this stage simply because recommendations are not expressed clearly. To ensure that recommendations are determined from insights, consider these tips: • Provide one recommendation for each insight. Starting in this way focuses the analytics professional on clarifying why the insight is important and what the business will look like if something is done about it. • Group individual recommendations into main themes. After all recommendations have been derived from insights, review them and draw out the main themes and major recommendations. • Be bold. Analytics professionals have a unique perspective on the business question. Having considered empirical data and drawn insights, they are in a strong position to make recommendations. Go ahead and make those recommendations. Be bold. • Write each recommendation clearly as a statement. Each recommendation should stand alone as a clear, simple statement. It should not need explanation. It might require discussion, but the recommendation itself should be very clear—for example: This insight shows … and, as such, I recommend ….



What Will Result from the Project? Successful analytics projects conclude with decisions. Those decisions might ******ebook converter DEMO Watermarks*******



drive change in the organization or might reinforce the status quo—either way, a clear decision is made. Driving change, if necessary, requires effectively communicating the analytics findings and a clear process. Decision making, communication, and action should then prompt a period of evaluation for both the project itself and its associated outcomes. Step 7. Get Your Point Across All analytics projects have a moment of truth. This often happens as you communicate the outcomes of the project to the sponsor or other stakeholders, to get your point across. This is the moment when you are able to inform their decision making. Experienced practitioners and leaders know when they have been impactful and can tell whether their chosen method of communication has been effective. Ralf Buechsenschuss, Global HR Manager of People Analytics & Transformation at Nestlé, agrees: “Storytelling is the connection between the analyses, the data, and the senior leader. Analytics really adds value when you have that connection.” We recommend the following approaches to get your point across: • Translate insights into stories. Stories have to be inspired by insights and link, through visualizations, to recommendations. Figure 4.2 illustrates this.



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Figure 4.2 Linkage of insights, visualizations, and recommendations to the story. • Carefully consider your visualizations. Use interesting visualizations that capture attention. Before you add any visualizations, make sure that the message you expect from each one is clear. One tip is to write down the message you intend before you start creating the visualization. Don’t just copy and paste screenshots of data or statistical results, thinking that these will suffice. Terry Lashyn, Director of People Insights at ATB Financial, explains: “People look away from technical spreadsheets, so show the numbers in a way that others can understand. We tell a story using pictures rather than using columns and rows of data.” • Start with a blank sheet of paper. Before you use any presentation software, such as Microsoft PowerPoint, sketch out your storyboard. You will get a better flow for your story and your presentation will no doubt be ******ebook converter DEMO Watermarks*******



shorter: You are unlikely to sketch out 45 pages on paper, yet you’ve probably seen many presentations with that number of slides—or more! Step 8. Implement and Evaluate The implementation and evaluation step has three discrete aims. First, it ensures that decisions are made as a result of your project. Second, it formulates actions for implementation based on those decisions. Finally, it facilitates evaluating the project against whether it returned value to the organization. This is where the hard work really starts. As Ben Nicholas, Director of Global HR Data and Analytics at GlaxoSmithKline, articulates, “I’d describe the analytics function as welcome dinner party guests, but not someone the other guests want to bump into the next day.” In other words, they want to hear what you have to say, but they don’t necessarily want to have to do the hard work of using your analytical insights to change the business. Despite the risk of this perception, it is vital that analytics projects continue to their conclusion and return value back to the organization’s stakeholders (shareholders, workers, communities, and so on). In this part of the project, several tips can help: • Push for decisions. At this stage, sponsors and stakeholders need to accept (or reject) recommendations and agree on clear decisions. To help with this, ask the following questions as part of your communication and discussions: Do you agree with my recommendation? Do you agree something should be done about this recommendation? Perhaps you can take the discussion further by asking, What are the next steps for implementation? Who will be responsible for implementing the actions? Push for clarity to get real commitment. • Engage a change management or implementation expert. Analytics projects can be small and can impact a few workers, or they can be extremely complex, extensive projects that impact thousands of workers in an organization. Depending on the scope of the recommendations, you or your sponsor might want to engage change management or implementation consultants to ensure that the results are delivered. This step might be unnecessary for something as straightforward as a one-off training course. However, if your recommendation is to implement, for example, a new technology to automate a process that affects every ******ebook converter DEMO Watermarks*******



employee, you will undoubtedly need additional support. • Work with your HR business partner (HRBP). Work closely with your HRBP, particularly as actions from your project are being implemented. Jonathon Frampton, Director of People Analytics at Baylor Scott & White Health articulates this: “Our impact is in empowering our HRBPs to act on the outcomes of the projects. We don’t measure ourselves on the number of reports we complete, but rather on the impact we have.” • Evaluate your project at appropriate time points. Analyzing the effectiveness of the implemented recommendations by calculating a return on investment (ROI) is a key responsibility of every analytics professional. Consider the timing of the evaluation, taking into account both the short- and long-term impacts of the project. In addition to evaluating ROI, it is good practice to reflect on how well the project itself was delivered and identify what could enhance the success of future projects. Setting milestone dates for this reflection and evaluation during and at the end of the project will help determine the success of the entire project. Alexis Fink, General Manager of Talent Intelligence & Analytics at Intel, supports these points: “Workforce analytics projects should be evaluated over both the short and long term. As an example, at a prior company, we built an executive assessment that was very difficult to pass. We developed coaching and learning programs based on analysis of the differences between successful and unsuccessful candidates and, in the space of a few years, we had more than doubled the pass rate.” Alexis indicated that, over time, they had measurably improved the overall quality of their leadership pipeline. However, the assessment was no longer differentiating. “By using analytical techniques, we could monitor results over time, short and long term, and identify when we needed to refresh the research. This ensured that the assessment delivered exactly in the way we wanted.” For further discussion on evaluating the outcome of projects, see Chapter 14, “Establish an Operating Model.”



Project Sponsors Throughout this chapter, you have seen project sponsors identified as a critical party. In this section, we discuss the relationship between the workforce analytics practitioner and the sponsor and explore why ******ebook converter DEMO Watermarks*******



sponsorship is important for success. We also map the analytics practitioner’s requests of the project sponsor to each step in the purposeful analytics methodology. Project sponsors are individuals who have an active interest in one or more specific workforce analytics project. They are usually responsible for approving the project, providing resources needed for project execution, advocating for the project, and working with stakeholders to ensure that actions are implemented. In short, the sponsor has a vested interest in the project from start to finish. How do you know whether you have the right sponsor to help you deliver a successful analytics project? Strong sponsors are highly respected leaders in their organizations, with a deep understanding of the organization and the challenges it is facing. They are well connected and able to rally support, and are willing and able to secure the resources needed. Finally, the best sponsors are highly motivated and available to see the project through to completion and benefit realization. Damien Dellala, Head of People Data & Analytics Enablement at Westpac Group, emphasized the importance of a good sponsor: “Experience has taught me that, when starting a workforce analytics project, having an engaged sponsor is at the top of the list of success factors.” When securing sponsorship, setting expectations on outcomes before undertaking an analytics project is important. This means thinking through the possible scenarios and posing them to the sponsor. For example, if the issue is high turnover among a certain group of employees, and the analysis reveals compensation is a strong contributing factor, is the sponsor willing to allocate budget to solve the problem? What if lack of career growth is a root cause—is the organization willing to design new career guidance and promote people at a more rapid pace than it has traditionally done? Thinking through these possible scenarios and building commitment and willingness to act increases the probability that the organization will actually implement the necessary actions. Figure 4.3 summarizes the many actions required of the project sponsor to ensure analytics success.



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Figure 4.3 Project sponsor involvement in workforce analytics. In general, project sponsors need to prioritize project reviews and invest sufficient time. They also need to secure funding where needed, remove roadblocks, and leverage their relationships and status to help the analytics team succeed. Sponsors are expected to see the project through all the steps to completion and to be consistently available when needed. They must act as role models in embracing and acting on the analytics insights and recommendations, and they must hold others accountable for doing the same. Sponsors need to challenge the naysayers and be highly visible champions of workforce analytics.



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RELATIONSHIP POWER Some people in organizations have power because it comes with the role—for example, the chief financial officer. This power is institutional power. Other people have power that comes from the relationships and influence they hold. This is relationship power. Morten Kamp Andersen,2 an experienced consultant, puts workforce analytics leaders in the latter category. “They don’t have power because of the role; they create power because of the way they bring value through relationships.” He continues to identify their key skills in this role: “Workforce analytics leaders need to be able to sell their projects into the organization. They need to have strong business acumen, be good at storytelling, and be experts in stakeholder management. These are all relationship and communication skills.” Without such relationship power, projects do not always run smoothly. Morten believes that a successful analytics leader in human resources builds relationship power and uses that with key stakeholders and sponsors: “Don’t build the project in the HR environment alone. If you leave your sponsorship too late, you will probably end up with no sponsorship. Create relationships with senior business leaders and make sure you get a non-HR sponsor for every project.” Wise words indeed!



Why Do Analytics Projects Fail? Issues with a project sponsor are only one of the reasons analytics projects can falter. Being aware of all the potential pitfalls helps you take steps to avoid them and increase your chances of success. This section looks at reasons an individual analytics project might not succeed and examines the three parts of the model for purposeful analytics.



Undertaking the Project A project with no clear purpose is in high danger of failure. Some reasons for a lack of clarity at this stage follow: • Lack of a project sponsor ******ebook converter DEMO Watermarks*******



• Poor communication with the project sponsor • No clear purpose for the project or request • Poorly defined reason for the project Fundamentally, the best analysis in the world is of limited value unless you know why you are doing it and have clear project sponsorship.



Carrying Out the Project Projects often fail because of poor execution and delivery. This can result from one or more of the following factors: • Incomplete, inaccurate, or irrelevant data • Poor choice of statistical technique or method • Lack of investment in the right systems and technology • Lack of technically competent people • Poor extraction of insights and recommendations • Not enough time to undertake the project properly Even the most expertly defined business problem and well-crafted hypotheses will not yield benefit if the project is poorly executed and an inadequate investment is made.



Project Outcomes Projects can fail when results are poorly communicated, no action results, or action occurs but with no evaluation to determine the business return. These factors can hinder success: • Poor visualization with unclear insights • Ineffective storytelling that loses the simplicity of the message or fails to articulate it • No buy-in for the outcomes of the analytics project, with no accepted or implemented recommendations • No clear plan or owner to implement the organizational change recommended by the project • Lack of evaluation to determine the project’s impact ******ebook converter DEMO Watermarks*******



Even a good analysis that highlights well-founded recommendations to a known business problem can fail if it is poorly understood. And if nothing happens as a result of the analytics, the entire project was not worth undertaking anyway. As Terry Lashyn explains, “You need to believe that when you undertake analytics, you are going to change something.”



Summary Discovering, interpreting and communicating meaningful patterns in workforce-related data demands a robust methodology. Whether for a short project of a few weeks or a longer one that spans many months, methods need to be straightforward and complete. Successful workforce analytics projects follow these main methodology recommendations: • Consistently apply all eight steps of the purposeful analytics model to your analytics projects. • Start projects by defining the business question and building strong hypotheses. • Use the most appropriate technology and methods for your analyses to ensure robust results. • Get the point of your analytics projects across to your sponsors and stakeholders through clear visualization and storytelling. • Help your business translate project recommendations and decisions into action and evaluate the results. • Choose your sponsors wisely, with an eye toward strong, consistent support throughout the entire lifecycle of your projects, from request to implementation. • Familiarize yourself with the common reasons analytics projects fail, and throughout the eight steps, ensure that potential problems are avoided or addressed.



1 The Eight Step Model for Purposeful Analytics is a copyright of the authors of this book: Nigel Guenole, Jonathan Ferrar and Sheri Feinzig. 2 Morten Kamp Andersen is a partner and analytics expert at proacteur, a consulting firm based in Copenhagen, Denmark. It is an agile ******ebook converter DEMO Watermarks*******



consultancy company that focuses on change management, manager development, project and program management, and process-driven support for IT implementation (www.proacteur.com).



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5 Basics of Data Analysis “You can rely on the data scientist for the details of the statistics. However, it’s important that everyone in workforce analytics has a core understanding, as it is easier to manage your project if you know the basics of analysis.” —Peter Hartmann Director, Performance, Analytics, and HRIS, Getinge Group If you are not a data scientist or statistician yourself—and perhaps even if you are—refreshing or familiarizing yourself with the basic analytical methods used in workforce analytics is helpful. The array of statistical methods for exploring data or testing a given hypothesis is not limitless, but the number of methods available can certainly make it seem that way. Despite the potentially dizzying complexity, many methods aim to achieve conceptually similar results. Focusing on the objectives of the methods instead of the methods themselves is a sensible starting point for learning about the goals and becoming conversant in statistical analysis in workforce analytics. This chapter discusses the following topics: • The importance of strong research design • The objectives of quantitative analysis • The objectives of qualitative analysis • Traditional statistics versus machine learning • Bias and fairness in analyses



Research Design Before quantitative or qualitative analyses can occur, decisions must be made regarding what data will be collected, when it will be collected, how it will be ******ebook converter DEMO Watermarks*******



collected, and from whom it will be collected. These questions fall under the topic of research design. The research design you apply determines how rigorously you can reach conclusions about causes and effects following your analysis. Identifying cause and effect is not the only goal in workforce analytics, but it is a common one. You are unlikely to unequivocally show causal effects from single research studies. However, designs that allow more confidence in causality increase the probability of creating successful interventions. Research designs can be categorized by an effectiveness hierarchy, summarized in Table 5.1. The strongest research designs are randomized experiments, followed by quasi-experimental designs, observational or correlational designs, and, finally, qualitative designs. Without a thoughtful research design, even the most sophisticated analyses will likely prove fruitless. Work with your data scientists to select a strong research design before you begin your analyses. Table 5.1 Research Design Hierarchy for Causal Inference



Experimental Designs In a randomized experiment, you generally have two groups: one that receives an intervention (the experimental group) and one that does not (the control group). Employees are randomly allocated to either group. The randomization means that the groups are, on average, equivalent in every way at the start of the experiment. After an intervention with the experimental group, the groups are measured ******ebook converter DEMO Watermarks*******



on the outcome under study. Consider, for example, a project that examines the effect on engagement after giving employees more autonomy. In an experimental approach, any difference in engagement between the groups must have been caused by the intervention, assuming there are no confounding factors (such as groups becoming aware of the goals of the experiment). This is because before the experiment, the groups had equivalent engagement due to randomization. In addition, the control group did not receive the intervention that increased autonomy. A key point to note is that variables under study are manipulated. Randomized experiments, therefore, meet the three criteria for showing a causal effect: • The cause happens before the effect. • The experiments show a relationship between the cause and the effect (when a change occurs). • Other possible causes are ruled out due to randomization. The design can be further strengthened by statistically controlling for a range of other plausible causal variables. This can help rule out alternative explanations of findings, in case the randomization was not perfect. For example, continuing with the autonomy and engagement example, if the randomization did not lead to equivalent personality types across groups, the results could be jeopardized because different personality profiles might cause different preferences for autonomy across groups. In these cases, worker personality could be measured and the results of the analyses adjusted for any differences between the groups.



Quasi-Experimental Designs Conducting randomized experiments in everyday working life is often not possible. We cannot always isolate the variables we want to study, and randomly allocating workers to different conditions is often impossible. As a result, other designs are chosen that still allow some confidence in identifying a causal relationship, even though they are not as strong as in a randomized experiment. The quasi-experimental design is similar to the randomized experiment, aside from one key feature: The employees are not randomly allocated to the experimental and control conditions. This limits the strength of inferences ******ebook converter DEMO Watermarks*******



that can be drawn from analyses when compared to randomized experimental designs because the lack of randomization makes it impossible to confidently declare that the two groups were equal, on average, before the experiment. Despite these limitations, robust conclusions about effective interventions can still be drawn from quasi-experimental designs in workforce analytics, particularly if results replicate across multiple studies. A quasi-experimental approach to the autonomy and engagement topic might involve delivering the intervention to two different business units because randomization is often unfeasible; treating two people differently is difficult if they are working in the same team or unit. With these designs, it is not possible to account for other factors, such as different managers giving different levels of recognition; those factors might indeed have caused differences in engagement levels. Nevertheless, this design often provides enough confidence to decide whether to continue the intervention.



Correlational Designs Designs that do not involve randomization and manipulation, or a control group, are referred to as correlational or observational. In these studies, analytics professionals observe the way variables relate to one another without inferring a causal connection between variables. Strong dependable correlations (for example, sizable correlations based on large random samples) can still be useful in workforce analytics, but it is important to understand when a causal association cannot be confirmed. Whereas the experimental and quasi-experimental approaches to the autonomy and engagement example included a control group, correlational designs typically have no control group. Instead, the existing levels of autonomy and engagement are assessed for all individuals, and the strength of the association is studied. Inferring causal associations from these designs is difficult, but they may be useful in identifying variables for further study using experimental or quasi-experimental designs. These designs lead to stronger conclusions than when relying on intuition. Advanced techniques can help identify causal relationships by analyzing data from correlational designs (for example, propensity scores and instrumental variable techniques), although their effectiveness varies depending on the situation.



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In contrast to the designs discussed so far, which all involve numerical assessments of the relationships between variables, the hallmark of qualitative studies is that they try to understand organizational phenomena from the perspective of workers without using quantitative methods. Examples include ethnographic studies and focus groups. A qualitative study that examines the association between autonomy and engagement might involve interviews or focus groups with workers to discuss their experiences of autonomy and engagement. An ethnographic approach might have the researcher work for a period of time in the job, side by side with actual workers, to understand how autonomy relates to engagement through the eyes of the workers. It is possible to use qualitative and quantitative methods to complement one another, a topic we discuss in the section, “Qualitative Analysis,” later in this chapter. On their own, qualitative methodologies do not provide the level of confidence regarding correlational or causal effects that many business leaders and analysts want to see before they act on recommendations from workforce analytics. For this reason, we place qualitative studies at the lowest level of the hierarchy of research designs for causal inference (see Table 5.1).



A Note on Longitudinal Designs All of the designs discussed in the research hierarchy can be strengthened when the variables being studied are measured repeatedly at multiple points in time. This is referred to as a longitudinal study. To understand how repeated measurement of variables such as autonomy and engagement leads to a clearer understanding of the effects of interventions, consider the following: An experimental intervention shows an effect on an outcome variable that might show a causal effect, but because the outcome of interest has been measured only once, we cannot determine how long lasting the effect is. Furthermore, the study does not show whether the effect becomes weaker or stronger over time, or whether the effect is reversible. To understand these issues, it is important to measure repeatedly over a period of time—that is, to undertake a longitudinal study.



Objectives of Analysis The overview of analysis objectives (quantitative and qualitative) presented in the following sections and in Figure 5.1 represents the level of detail that ******ebook converter DEMO Watermarks*******



the workforce analytics team should be comfortable discussing when it comes to types of analyses.



Figure 5.1 Overview of analysis objectives.



Quantitative Analysis In workforce analytics, most quantitative analysis (that is, statistical analysis of numerical data) aims to do one of the following: explore, associate, predict, classify, reduce, or segment information about employees and organizations. • Explore. Exploratory analysis helps understand your variables. Analysis might involve summarizing the data with a statistic such as the average, or looking at how spread out the values of the variable are, using a statistic known as the variance (that is, the variability) of the variable. Exploring might involve identifying extreme cases or determining the extent of missing data. Analysis focused on exploration often uses simple graphing ******ebook converter DEMO Watermarks*******



techniques to reveal the distribution of the data (for more on these topics, see Chapter 10, “Know Your Data”). More advanced exploratory analysis might check differences in scores on variables for known groups, such as men versus women, or ethnic minority versus majority groups. Exploratory analysis is particularly useful for monitoring and reporting demographic trends, as well as for preparing data for more advanced analyses. Approaches include techniques such as t-tests or analysis of variance, referred to as ANOVA. Exploring data involves understanding your data, preparing your data for more advanced analysis, and testing simple hypotheses. • Associate. One goal of analytics is to look at the relationship, or association, between variables that can take on any value between their minimum and maximum possible values (for example, age or tenure), with a higher score indicating more of the variable. An example might be the relationship between extroversion and sales performance. Sales performance can likely take on any value within a plausible minimum and maximum, so it is considered a continuous variable. Rating scales common in surveys (for example, strongly disagree to strongly agree) are often analyzed as continuous. Relationships between these types of variables are usually studied using methods that estimate correlations. The most common correlation, Pearson’s product-moment correlation, has a possible range from –1 to +1. A value of –1 means that two variables are perfectly negatively related (that is, as one increases, the other decreases by an equivalent amount). A value of 0 indicates no relationship, and a value of +1 means the variables are perfectly positively related (that is, as one increases, the other increases by an equivalent amount). • Predict. When analyses are used to make forecasts about the future, such as estimating the value of a continuous variable of interest (referred to as an outcome variable or dependent variable), the analyses are referred to as predictions. Given what we currently know about employees, the general pattern in predictive analysis is to estimate their future behavior at work. For example, if we know that the relationship between levels of extroversion and sales performance is strong, sales performance should improve if we hire people who score high on an extroversion assessment. Methods used ******ebook converter DEMO Watermarks*******



for making predictions include linear and multiple regression, regression trees, neural nets, support vector machines, and time series analysis. • Classify. Classification analyses can be thought of as analogous to association, but this analysis focuses on associations when the outcome variable is discrete. A discrete variable has a limited number of possible categories and does not have an intrinsic ordering. It is sometimes called a nominal variable. An example of a discrete variable might be whether a worker receives a promotion or whether an employee leaves the business. In such cases, the outcome is referred to as a discrete or categorical outcome (not a continuous outcome). Methods such as correlation and regression have been adapted to deal with these types of variables. Instead of finding correlations, chi-squared tests examine associations for categorical variables; instead of using regressions to make predictions, techniques such as logistic regression are used to make classifications. For example, the risk of an employee leaving (which has only two possible values, stay or leave) can be expressed as a probability; employees with a risk of leaving can be looked at more closely for some sort of intervention. • Reduce. Workforce analytics teams analyze data sets that often contain hundreds or thousands of variables. This is too much information to make sense of without combining some variables. The primary goal of some statistical techniques, such as principal components analysis (PCA) and factor analysis (FA), is to summarize (reduce) the information in many different variables to create a smaller number of variables. Having fewer variables makes analysis and interpretation more manageable. The basic principle in reduction analyses is to aggregate the similar variables into fewer variables. Say, for instance, that you have three variables that measure employee performance: a manager performance rating, data on whether the employee met critical milestones, and the success that the employee demonstrated in training. Which of these variables should you focus on predicting? Statistical analyses such as PCA and FA can create, if appropriate, a single performance variable out of other similar performance variables for prediction. This is usually appropriate only if you can show that all three together are strong measures of performance, which PCA and FA check. The new variable(s) can then be explored or predicted without losing too much information. ******ebook converter DEMO Watermarks*******



• Segment. Although the focus of reduction analyses is to group a large number of variables into a smaller number for exploration or further analysis, the goal with segmentation is to group the number of cases (for example, workers) in your data set into a smaller, more manageable number that provides a meaningful representation of groups in your data. Techniques in the segment category fall under the general label of clustering. Cluster analysis puts similar cases into groups, for either exploration or further statistical analysis. An example application might involve using cluster analysis to identify subgroups of employees who have similar responses to a variety of survey questions indicating high stress so that some form of intervention can be applied to improve their ability to manage stress.



Combining Quantitative Objectives in a Single Analysis When you understand that any statistical analysis has only a few basic goals, you can see possibilities for combining objectives in a single analysis. For instance, certain techniques enable you to perform analyses that reduce and segment data sets in a single analysis, segment and associate in a single analysis, and so on. Some variations examine relationships between many different variables simultaneously, and even on many levels (for example, the individual worker level and the team level). These analyses have names such as mixture modeling, structural equation modeling, and multilevel modeling. Consider decisions about how to combine the different analytic methods to achieve analysis objectives in relation to specific analysis problems. This is best carried out with input from your team’s data scientists. Still, the six analytics objectives provide you with the basic building blocks to understand conceptually what the analyses are doing. This makes you more knowledgeable and conversant on the topic of workforce analytics and enables you to both appropriately challenge and accurately represent the workforce analytics team’s work.



Qualitative Analysis The goals of qualitative research are to understand organizational phenomena without using quantitative data. Techniques include ethnography, focus groups, and detailed case studies. Objectives of qualitative research in workforce analytics include hypothesis formulation, interpretation, and ******ebook converter DEMO Watermarks*******



contextualization. • Hypothesize. Qualitative analyses can be particularly valuable in generating hypotheses when no theories or quantitative data exist. The resulting hypotheses can then be tested using quantitative methods. For example, interpretation and discussion about the reasons people leave a firm can help in creating a hypothesis that can be tested with quantitative data. In this example, a qualitative analysis of exit interview data might suggest that the physical office environment was a factor in decisions of employees who quit. A quantitative study might test the hypothesis that employees are more likely to stay if they work in buildings that have been recently refurbished. • Interpret. Another objective of qualitative research is to help interpret quantitative results. Qualitative studies explain why events occur instead of simply describing the strength of relationships in quantitative terms. For example, if quantitative data reveal that new hires are taking too long to get up to speed, qualitative research might explain why that is the case. In this example, focus groups might reveal that managers are not meeting with new hires in the first week. • Contextualize. Michael Pratt, a professor at Boston College, notes that qualitative approaches are good for contextualizing quantitative results. Contextualizing aims to explain technical quantitative findings to HR and business leaders by adding color and depth to illustrate a point. For example, including verbatim quotes from interviewees can bring to life the results of quantitative analyses. The explanations can then help turn the analytics project into a story, emphasizing the perspective of those impacted by the recommended actions. For an excellent introduction to contextualization in qualitative research, see Chapter 3, “Qualitative Research Strategies in Industrial and Organizational Psychology,” by Tomas Lee and colleagues in the Handbook of Industrial and Organizational Psychology (American Psychological Association, 2010).



Unstructured Data Increasingly, data that workforce analytics professionals encounter will be unstructured, or difficult to store in rows and columns of a “flat” data file. Examples include video, audio, and the vast amount of text and language that is available via the Internet. ******ebook converter DEMO Watermarks*******



In the past, much of this information was used qualitatively, to help form hypotheses, interpret findings, and contextualize results for audiences. Today these data sets are commonly analyzed quantitatively. Perhaps most advanced is text-mining of data sets to assess the sentiment of text passages, such as comments in open-ended surveys. Sadat Shami, Director of the Center for Engagement & Social Analytics at IBM, describes the potential of text analytics. “Developments in managing and analyzing unstructured data, such as text from social media, are really showing the value of social media as a data source for signals that we can use to infer, for example, the level of an employee’s engagement.” Methods for quantitatively analyzing qualitative and unstructured data generally focus on converting data that were captured as language or images into a numerical representation. After that point, the objectives of analytical techniques are the same as those already mentioned for quantitative analysis. Examples of these techniques are rare event detection, sentiment analysis, and trending topics.



Traditional Statistics versus Machine Learning A noteworthy area of innovation in quantitative analyses is machine learning methods, which are increasingly common in organizations today. Workforce analytics techniques can often be distinguished by whether the methods emerged from a tradition of statistical modeling or computer science (from which machine learning emerged). In statistical modeling, the focus is often on accurately describing associations between variables in order to describe the world as it is and to identify causal relationships. In machine learning, the focus is often on making the most accurate possible classifications or predictions, regardless of whether causal mechanisms are identified. For example, a traditional statistician might use logistic regression for classification. Machine learning professionals (that is, highly trained data scientists from a computer science background) might also try logistic regression, but they will likely adopt a wide array of other techniques as well, to try to improve the accuracy of the model. For example, their approach might involve comparing the results of different techniques, such as a logistic regression, classification trees, and support vector machines. Despite the different origins of the traditions, the objectives of analysis in machine learning and statistical ******ebook converter DEMO Watermarks*******



modeling are similar and fit into the six objectives presented earlier. Machine learning techniques tend to perform better when building models to predict outcomes from many variables and when examining complex relationships. Machine learning experts also tend to apply more rigorous approaches to evaluating whether models will be valid when applied to new samples. These models are often deployed in an iterative manner, with the models updated in real time and with results deployed into operational business intelligence systems. Statistical methods, on the other hand, tend to perform better when data meet the assumptions required for the statistical method. In many cases, however, the methods can produce very similar results. For this reason, and also because your team should ideally have access to both types of skills, this is a distinction with more relevance for the data scientists in a workforce analytics team. Today’s data scientists are usually skilled in both approaches. The decision of whether to use machine learning or traditional statistical approaches for any given situation is best left for data scientists. They should have a clear understanding of what the analysis must achieve.



Social Consequences of Algorithms Data-based decision making in workforce analytics comes with a responsibility to ensure that decisions made on the basis of algorithms are statistically, legally, and socially appropriate. This is clearly illustrated in Cathy O’Neill’s book Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown, 2016). It is a particularly important consideration in workforce analytics because work is increasingly becoming more automated and the nature of decision making is evolving. In high-volume recruitment, for example, algorithms are often used to filter candidates; in the past, humans made those decisions. Our role in evaluating the consequences of the decision-making algorithms in workforce analytics is critical to ensure that the outcomes they produce are consistent with the organization’s values and expectations. Another reason to give attention to the appropriateness of outcomes is that workforce analytics is becoming populated with professionals who have strong mathematical, statistical, and computer science skills, but less knowledge and experience in managing the legal and social consequences of analytics in employment contexts. The computer science field refers to this ******ebook converter DEMO Watermarks*******



area of study as fairness-aware data mining. The topic of fairness is still garnering attention in computer science, but it is important to note that the realm of industrial psychology already has directly portable concepts in place that can be implemented to ensure equitable outcomes for individuals from different groups. Here we introduce three of these concepts: impact, bias, and fairness. • Impact. In the context of workforce analytics, impact refers to differences in the rates of job selection, promotion, or other employment decisions that disadvantage members of a particular group, such as women or ethnic minorities. For example, if selection decisions are made on the basis of a pre-hire employment test of integrity, impact against women can result if males score higher than females on average. Impact can occur due to bias, discussed in the next point, but it can also result from genuine differences between groups. The issue might not necessarily result from measurement accuracy; it might stem from the choice of the variable itself. To consider another example, if a candidate’s postcode were adopted as a selection measure, the measurement of postcode itself would be equally accurate for every demographic group (that is, postcode is a highly accurate indicator of where a person lives, regardless of group). However, postcode can be a proxy for socioeconomic status, which could result in adverse impact. The situation is more complex when a variable is measured imprecisely, which is often the case for psychological concepts. In this case, impact can be the result of genuine differences between groups or bias on the selection test. Simply observing impact does not allow someone to differentiate between true differences and bias on the test. • Bias. The notion of bias can be explained in the context of the previous integrity example: the effect of a pre-hire personality test to measure integrity on male and female job selection rates. The test is said to be free of measurement bias if a randomly chosen man and woman of equal integrity would get the same integrity score. If this wouldn’t happen, the test produces measurement bias and should not be used. Analyses can be undertaken to check whether selection scores relate to performance scores in the same way for all groups. In the integrity example, a test could examine whether integrity scores are equally ******ebook converter DEMO Watermarks*******



predictive of performance for men and women. If they are not equally predictive, the test produces predictive bias, so alternative selection variables might be preferred. • Fairness. Bias and impact are statistical phenomena. Whether the process and outcomes are “fair” is a social judgment. One interpretation of fairness relevant to workforce analytics is that all workers should receive consistent treatment. In the context of employment testing, for example, this means candidates should experience the same testing conditions, have access to the same practice materials, get the same chances to retake questionnaires, and, where appropriate, experience accommodation for disabilities. Although much of this discussion has focused on detecting instances in which algorithmic decision making can create unfairness, it is also possible for algorithms to create outcomes that are considered “more fair.” For example, algorithms can eliminate factors such as favoritism and other biases that often enter into decisions about people, especially when individual managers are making decisions. It is important to recognize that perspectives on fairness vary among organizations and people, as well as across time and cultures. Impact, bias, and fairness are closely intertwined concepts that are nevertheless important to differentiate. Doing so helps clarify the appropriate course of action when bias, impact, or unfairness might exist.



More on Design and Analysis The glossary in this book provides definitions of technical terms used in this chapter and is a useful resource for readers who are not familiar with statistical terms. For those interested in more depth on statistical analysis, Predictive HR Analytics, by Martin Edwards from King’s College, London and Kirsten Edwards from Pearn Kandola (Kogan Page, 2016), is an excellent entry point. For advanced discussion, The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (Springer, 2011), is a key reference.



Summary Analytically curious HR professionals, and certainly all members of the ******ebook converter DEMO Watermarks*******



workforce analytics team, should understand the basics of analysis so they can have sensible and informed conversations. • Select research designs that are appropriate for the business questions you are trying to answer and know how to interpret the results. • Learn the objectives that quantitative analyses aim to achieve: explore, associate, predict, classify, reduce, and segment. • Use qualitative analysis to generate a hypothesis, interpret results, and contextualize findings by adding color to quantitative analysis. • Consider the use of unstructured data for both qualitative and quantitative analysis. • Familiarize yourself with conditions under which statistical modeling or machine learning is more appropriate. • Be clear about the tools and skills you have at your disposal for advanced analyses such as analyzing unstructured data. • Scrutinize your analyses for their potential to produce adverse impact, bias, or unfairness, and take corrective action as needed.



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6 Case Studies “One way to help HR on its way is to highlight examples of great work that the pioneers in this space are doing. This is not merely to imitate their peers, but rather to learn from and be inspired by the successes others have had.” —David Green Global Director, People Analytics Solutions, IBM This chapter illustrates the methodology by examining five case studies through the lens of the eight steps. Although the eight-step methodology was not necessarily explicitly considered as these projects were unfolding, each case nonetheless implemented each step. These successful cases were chosen to cover a range of business challenges, types of organizations, and geographic locations. The intent is to illustrate different organizational challenges and the value that can be achieved by addressing them methodically with workforce analytics. In addition to following the methodology, success requires strong project sponsorship. Each of these cases describes how the right sponsorship contributed to workforce analytics success. Following are the case studies discussed in this chapter: • Improving Careers Through Retention Analytics at Nielsen • From Employee Engagement to Profitability at ISS Group • Growing Sales Using Workforce Analytics at Rentokil Initial • Increasing Value to the Taxpayer at the Metropolitan Police • Predictive Analytics Improves Employee Well-Being at Westpac



Eight-Step Methodology ******ebook converter DEMO Watermarks*******



Our eight-step methodology for workforce analytics is described in detail in Chapter 4, “Purposeful Analytics.” The first steps focus on understanding why an analytics project has been initiated: Step 1: Frame Business Questions Step 2: Build Hypotheses The next steps describe how the project will be conducted: Step 3: Gather Data Step 4: Conduct Analyses Step 5: Reveal Insights Step 6: Determine Recommendations The final steps ensure that action will be taken as a result of the project: Step 7: Get Your Point Across Step 8: Implement and Evaluate



Case Study: Improving Careers Through Retention Analytics at Nielsen Nielsen Holdings PLC is a global information and measurement company headquartered in the United States. It has a presence in more than 100 countries, approximately 44,000 employees, and revenues of $6.2 billion in 2015. Nielsen measures what consumers buy (categories, brands, products) and what consumers watch (programming, advertising) on a global and local basis. Nielsen was just beginning its people analytics journey in mid-2015 when Piyush Mathur was appointed Senior Vice President of People Analytics. He was tasked with building, developing, and growing the people analytics function and creating business value. Given the company’s heritage for collecting and studying data, Piyush was not short of passionate people interested in joining the team; he internally sourced a technologist, a compensation analyst, an HR partner, and a data scientist during his first few weeks. Later in 2015, Piyush and his team started a significant project focused on attrition.



The Impact of Increasing Attrition ******ebook converter DEMO Watermarks*******



Piyush explains, “Very quickly after I took the role, we realized a trend that had gone virtually unaddressed: the rate of attrition was rising, year over year.” Even without a standardized definition of attrition, the analytics team could see that the organization was constantly looking to hire externally while continuing to lose valuable associates in key business areas and roles. The problem was difficult to focus on, for two main reasons. First, the team needed a standard definition of attrition to examine the problem. “We had counted over 16 different ways people were measuring voluntary attrition,” Piyush says. “We needed operational definitions, data governance, and robust analytical methods.” Second, Piyush’s team did not have a sponsor for the project. To address this challenge, Piyush identified large businesses in the organization that had attrition higher than the company average. He approached the president of one such business (with approximately $1 billion revenue) and asked her about people issues. During the discussion, the leader recognized that retention was a problem and indicated that she wanted to do something about it. She became the sponsor of the analytics project. Piyush explains, “Sometimes we feel we need to find the solution and then approach the business leader. But it is also important to get the business leader involved early on, to recognize the problem before starting on the search for answers.” With the sponsorship settled, Piyush initiated the analytics project. He clarified the business questions as follows: • What factors make associates more likely or less likely to leave Nielsen? • What could we do about it? • What is the financial impact of people leaving?



Understanding the extent and size of the problem and identifying a business leader to sponsor and benefit from the project allowed the people analytics team to clarify the intent of the project and frame the business questions (Step 1). Piyush was confident that his team could define, measure, and understand the factors causing attrition in a clear, predictable, and sustainable way. He was ******ebook converter DEMO Watermarks*******



also convinced that his team could find insights and make actionable recommendations to address the problem. When reviewing the attrition problem, the team identified two specific groups of people they suspected were a high retention risk, and they tested this suspicion in the following hypotheses: Hypothesis 1: Women and diverse employees have higher attrition risk than men. Hypothesis 2: Employees who work remotely (for example, at a client’s location) have higher attrition risk than employees who work from a Nielsen office.



By isolating one sizeable yet discrete business unit, Piyush and his team could focus on specific hypotheses to address the business questions (Step 2).



An Analytical Approach to Analyzing Attrition After clarifying the business questions and hypotheses and having the sponsor sign off on them, the people analytics team under Piyush’s leadership focused on four activities: • Isolating the geographical areas to be studied • Defining the time period to be studied • Collecting the correct data • Selecting the analytical model (see Figure 6.1)



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Figure 6.1 Data and model used for the study of attrition in Nielsen. The study was limited to people in the U.S. business, to avoid specific works council and data privacy challenges. Piyush explains: “We involved the general counsel and the chief privacy officer and their teams from the start. In doing so, they helped us by endorsing our project and recommending that we begin our project in the U.S. only to simplify the gathering of data.” The team focused on a time period of five and a half years; this was the longest time period of consistent data, and it gave them sufficient data to complete a strong analysis. The two predominant technology systems that the team used to collect the required data were the SAP Human Resources Information System (HRIS) and the Oracle Taleo recruitment applicant tracking system. Choosing and gathering the right data is important for any analytics project, but Piyush outlines another caution: “Sometimes we wait for data to become perfect. I believe in ‘design thinking,’ where we imagine the future state but start building with what we have and keep improving as we go along.”



By identifying a relatively small number of data elements from two key ******ebook converter DEMO Watermarks*******



sources over a finite time period and a defined geographical area (Step 3), the team was able to quickly undertake a specific longitudinal analysis. The next step was analysis. As Figure 6.1 shows, the team decided to use a Cox regression analysis, an example of the quantitative analysis objective of classification (see Chapter 5, “Basics of Data Analysis”). Although the team considered other techniques, such as logistic regression, it chose Cox regression because that method allowed them to study attrition over time as a function of the various predictor variables.



The methodology chosen is the preferred approach for modeling and predicting employee turnover (Step 4), leading to a confident outcome. In its analysis, the team found no support for the first hypothesis: Women and diverse employees have no higher attrition risk. But the team did discover three core factors contributing to attrition: lack of lateral moves,1 being located at a client site, and recent hire date (tenure of less than one year). The second factor supported the second hypothesis: Attrition risk was indeed higher for associates working remotely from a Nielsen location. These insights were clarified with a high degree of confidence. For example, someone given a lateral move was proven to be 48 percent less likely to leave than someone who was not given a lateral move. Interestingly, Nielsen had a very low percentage of managers offering lateral moves or associates asking for them; less than two percent of people received a lateral move globally. Although lateral moves might bring a small compensation change, the new role essentially has a similar responsibility level as before, but in a different environment (such as for a different business, manager, or function).



The insights were revealed (Step 5) with confidence due to the strong quantitative analysis used. One of Piyush’s guiding principles is that business leaders will get more excited if financial benefit for analytics can be proven. His next step was to ******ebook converter DEMO Watermarks*******



demonstrate exactly that. Piyush and his team, together with compensation, talent acquisition, and other HR experts, plus people from the financial planning and analysis group, built a cost of attrition model based on actual voluntary attrition data. This model included factors such as lost productivity and time (and associated cost) to recruit. Working with members of the finance department, they ensured that any model created would be taken seriously and be financially validated. The financial impact analyses revealed that, for every 1 percentage point decrease in attrition, Nielsen avoided approximately $5 million in business costs. This analysis got the attention of the senior leaders, as Piyush elaborates: “We shared the model with our CEO and CHRO—they loved it. They really liked how we were using analytics empirically and financially.” With the insights (the factors shown to contribute to attrition) gathered and the cost of attrition model developed and validated by finance, the team could focus on building very specific recommendations focused on talent reviews, lateral moves, and onboarding. The first major recommendation was to focus on lateral moves as part of talent reviews. As part of discussions about talent and succession, leaders were expected to spotlight key individuals who would benefit from lateral movement to another part of Nielsen. This was embedded in the talent review process so successfully that it became part of discussions with the CEO beginning in late 2015. The second recommendation concerned a program called Ready to Rotate that already existed but was poorly used. It was designed for associates to self-identify when they would like to be considered for a lateral move. It was originally created but not implemented because it lacked a committed sponsor. With the level of support Piyush had created, he was confident that a program like this could be reignited and implemented. Finally, beyond programs supporting lateral moves, recommendations were implemented around onboarding. These were designed to address one of the other insights derived regarding the relatively high attrition among employees with less than one year of tenure. The onboarding actions included a buddy program and an “information all in one place” system for new recruits to allow them to more quickly feel connected and integrated.



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The insights on lateral moves and onboarding led to recommendations with associated financial benefits for Nielsen (Step 6).



Turning Recommendations into Action Piyush remembers the president of the U.S. business and the sponsor of this project saying, “Insight without action is overhead.” As such, he wanted to make sure that the recommendations were implemented and communicated properly. The analytics team partnered internally with Nielsen’s communications team to make the recommendations a reality. The communications team members had the expertise to make the Ready to Rotate program a success. They created a variety of materials, including a video2 about factors that contribute to higher levels of retention, an innovative approach to articulating the outcome of a people analytics study. Together with the continued strong sponsorship of the business leader and the buy-in of the CEO and CHRO, Nielsen decided to implement the Ready to Rotate program globally, to shine a spotlight on talent that is asking or willing to make a move within Nielsen.



The team communicated the various recommendations (Step 7) to the individual associates and managers in Nielsen by partnering with and using the skills of the internal communications team. Piyush’s team proved the financial benefits of implementing retention programs through a systematic and methodical analytical approach with strong sponsorship throughout. By mid-2016, some impressive results had emerged: • Nielsen identified 120 key individuals and, through lateral moves for 40 percent of this group, reduced the attrition rate to zero for the first six months after implementation. • Through increased participation in Ready to Rotate and one-on-one ******ebook converter DEMO Watermarks*******



engagement, the voluntary attrition rate in the U.S. business for the first quarter of 2016 decreased to half the rate it was during the same period in 2015. For the global enterprise, attrition was 2 percentage points lower in the first eight months of 2016. This translated to a benefit of more than $10 million. • Following successful implementation in the United States, Nielsen rolled out the analytics project to another seven countries.



The team continues to work with business leaders to measure attrition and advocate the Ready to Rotate program (Step 8). A methodical approach enabled the people analytics team in Nielsen to contribute to the company’s business success. However, for Piyush, this project was not just about improving the business. “At the end of the day, we are improving people’s lives by helping them stay in Nielsen through these programs. Moving companies is very stressful for people and it often affects their families, too. So we are not only helping Nielsen financially—we are helping people by providing them with rewarding jobs in other parts of our business.”



Case Study: From Employee Engagement to Profitability at ISS Group ISS Group was founded in Copenhagen in 1901 and has grown to become one of the world’s largest facility services providers. ISS offers a wide range of services, such as cleaning, catering, security, property and support services, and facility management. It has approximately 505,000 employees and local operations in 77 countries across Europe, Asia, North America, Latin America, and the Pacific, serving thousands of public- and privatesector customers.



The Business Impact of Engagement ISS drives profitability through the productivity of its employees as they work to deliver services in client organizations. Given the central role of its employees to its success, a key component of the ISS business strategy is to ******ebook converter DEMO Watermarks*******



ensure that all its employees are highly engaged. ISS takes the challenge of employee engagement seriously. This is illustrated by Group Head of Marketing Peter Ankerstjerne’s sponsorship of a workforce analytics project to explore the relationships among employee engagement, customer experience, and profitability at ISS. Simon Svegaard, Group Business Analytics Manager, gives this background on the project: “While there is substantial evidence of a positive association between engagement and performance in the scientific and business literature, before making considerable investment in increasing engagement at ISS, we wanted to see if we could identify that association in our own organization.” In other words, ISS wanted to know whether it would see a return on investment from interventions aimed at increasing employee engagement.



The analytics team ensured that ISS had clarity on the business priority of employee engagement to determine a return on the investment (Step 1). To ensure collective agreement on the aims of the analytics work and strong management, a project team was established. The team included representatives from Group HR, Group Marketing, and an external consultancy. Of particular note was the external partner selected to assist the team: Morten Kamp Andersen is an experienced business consultant in the field of analytics and had already worked with ISS and several of its senior leaders on earlier projects. Simon explains, “It was critical that the right people, including the main stakeholders, were involved in this project from the outset. In many projects, collaboration across the business only begins when the results are presented, but we wanted to establish that collaborative approach from the start.” With such an approach the team could more easily get buy-in, support, and the resources it needed to deliver. One of the project team’s first tasks was to clarify its hypotheses. Two clearly stated hypotheses captured the expectations about the project and guided future data collection and analyses: Hypothesis 1: Employee engagement is positively related to both employee and customer experience. ******ebook converter DEMO Watermarks*******



Hypothesis 2: Customer experience is positively associated with contract profitability.3



The organization took an exploratory approach regarding which aspects of engagement determined loyalty and profitability to clarify the hypotheses (Step 2).



From Analysis to Recommendations Before data collection could begin, ISS had to identify the most appropriate measures of the variables relevant to the hypotheses. Specifically, the team needed to identify reliable and valid measures of employee engagement, employee and customer experience, and profitability. The best measure of employee engagement was the ISS global employee engagement survey. Workers take part in this annual survey covering all of the organization’s countries and operating units. The survey is administered in 52 languages using a combination of paper, email, and web-based questionnaires. The response rate in 2015, the year of this study, was 72 percent of the total population. Employee and customer experience variables were assessed using the Employee Net Promoter Scores (eNPS) and Customer Net Promoter Scores (cNPS), which ask employees and customers whether they would recommend ISS to a friend or colleague.4 Contract profitability data also were available for analysis. Given the sensitivity associated with this information, the analytics team undertook careful communications regarding how results would be used and provided guarantees of individual worker anonymity to ensure that country managers were comfortable sharing the profitability data for the project. By building a clear picture of the data required before gathering it, the team ensured that it was not distracted by other interesting but less relevant analysis opportunities along the way. The team’s confidence in the potential of this project was boosted by the global nature of the data it had. However, the team was careful to retain its focus on the two outcomes that the organization pays a lot of attention to: customer loyalty and contract ******ebook converter DEMO Watermarks*******



profitability. The project team conducted its analyses using three primary analytical strategies. First, team members used a data reduction technique (see Chapter 5) to limit the number of survey items in their final analyses. Next, they used a number of exploratory graphical techniques to examine the association between the variables identified in their hypotheses. They coupled these graphical techniques with regression-based methods such as partial least squares (this is an example of the prediction objective of quantitative analysis that Chapter 5 describes) to examine how well engagement could predict customer experience and profitability.



ISS identified data sources that were strong indicators of each variable relevant for the hypotheses (Step 3) and used a variety of statistical procedures to simplify the dataset and test these hypotheses (Step 4). The analyses provided support for the first hypothesis at ISS: Employee engagement was indeed positively related to customer satisfaction. However, as Simon says: “While this finding reflected the existing external research suggesting that engagement is associated with performance outcomes such as customer satisfaction and profitability, the project team wanted to know more.” The real insight was revealed when the analytics team considered exactly what aspects of employee engagement were related to customer satisfaction as measured by the cNPS. Three aspects of engagement turned out to be particularly strongly linked with customer satisfaction: motivation, capability, and purpose. In other words, cNPS scores were higher for business units in which employees were more motivated to do a good job, were well trained, and understood customer expectations. Moreover, as the second hypothesis predicted, eNPS and cNPS were positively related with contract profitability. Figure 6.2 illustrates the contract profitability (shown as a percentage) as a function of eNPS and cNPS. It shows that, as both eNPS and cNPS increase, so does contract profitability.



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Figure 6.2 Links between eNPS, cNPS, and contract profitability. The analytics team identified motivation, capability, and purpose as the key drivers of employee engagement, and these had the highest influence on the cNPS. The next step was deciding what to do about it. The team concluded that the findings about employee engagement had important implications for HR processes and strategic initiatives. As a result, the team had the following recommendations: • Functional training programs to address the capability factor (for instance, skills training for facilities cleaners). • A behavioral training program called Service with a Human Touch, to ******ebook converter DEMO Watermarks*******



focus on understanding the emotional connection between workers and clients and delivering superior user service. The training was implemented for first-line managers responsible for contract delivery initially in Denmark before it was implemented globally. • A manager education program to address the purpose factor. This would focus on ensuring that staff knew what both ISS and customers expected of them. • A motivation toolkit to address the motivation factor. This would be part of an existing manager development program. In addition, ISS was advised to conduct a managerial training needs assessment to determine where additional training was needed before delivering the training.



ISS analyses revealed the drivers of engagement, customer loyalty, and contract profitability (Step 5). Actions based on these insights were recommended (Step 6). Not content to stop there, ISS wanted an even stronger research design to increase confidence in its conclusions. As a result, ISS implemented the recommendations intended to increase engagement among its staff at one of its customer locations, a financial services firm. This would enable the team to study the effects of the changes using a pre-/post-research design. In the study, customer satisfaction was measured with a user survey both before and after interventions intended to increase the three drivers of engagement: motivation, capability, and purpose. The interventions included extensive training for all ISS managers, supervisors, and front-liners on how to manage and deliver a service against predefined behavioral standards. In addition, customers were asked to clearly communicate with front-line staff what they expected in terms of service quality and standards. Evaluation of this follow-up study revealed a significant increase in employee engagement following the training, as well as a significant increase in customer satisfaction.



Turning Recommendations to Action ******ebook converter DEMO Watermarks*******



Communication to stakeholders was critical throughout this project, as Simon stresses: “If you conduct analytics too far away from what the organization is doing, then nothing will be adopted. You need to link what you are doing to something that has relevance to the organization. Then when you present to your sponsors, you need to have a plan for what should happen next: Look at processes already in the company and build on and complement those. If you just come with a new HR process, people will not do anything.” Simon communicated with the stakeholders before having conversations with C-suite executives, and he managed communications in the following way. First, he identified an existing manager communications platform that he could use for his communications. He then linked his communications to a goal that was also the focus for managers—in this case, contract profitability. Second, he communicated with the core stakeholders individually. These ten people comprised the executive group management, including the ISS Group chief executive officer (CEO), chief financial officer (CFO), and chief operating officer (COO), plus the various regional CEOs. Simon had to make sure they each saw the benefit of the project individually, and he tailored his conversations to the different personalities and management styles of his audiences. Finally, Simon, along with the external consultant, Morten, presented to the executive group management. Because each of the executives had already been involved throughout the project, the C-suite conversations were well received and the recommendations were readily accepted.



Stakeholder communications throughout the project were essential to gain commitment (Step 7). ISS implemented actions to address the employee engagement drivers that its analytics project had identified (Step 8). Encouraged by the findings from this analytics project and their ability to link engagement to business outcomes, ISS team members are now exploring links between engagement and sickness rates, as well as customer churn. ISS says that what made this project successful was the team’s ability to link HR practices (notably, efforts to increase engagement) with business outcomes outside of HR (customer satisfaction and, ultimately, contract profitability). ******ebook converter DEMO Watermarks*******



Case Study: Growing Sales Using Workforce Analytics at Rentokil Initial Founded in 1925 and listed on the London Stock Exchange, Rentokil Initial provides pest control and hygiene services across 60 countries with more than 30,000 employees. The company has three global brands (Rentokil, Initial, and Ambius) and several local brands, including Dexinfa in Lithuania, Calmic in Asia, and JC Ehrlich in the United States. Rentokil has revenues of approximately £1.8 billion, according to its 2015 annual report. In the late 2000s, the company came under the leadership of Chief Executive Officer (CEO) Alan Brown, who started a period of examining operations, particularly sales expertise and performance. Workforce analytics came to the fore amid this atmosphere of scrutiny.



Variability in Sales Performance Sales results and turnover at Rentokil Initial were highly variable among the 700 global salespeople. Some regions were overachieving their targets easily, whereas others were consistently underachieving. Alan was keen to explore this using an analytical approach, in contrast to the anecdotal information he was receiving from the various managers and directors in the business. Alan had no assumptions regarding the reasons for the varying sales performance. However, because everyone was highlighting people-related topics, he focused on the sales workforce itself instead of territory alignment, market opportunity, or competition. Because of his scientific background, Alan wanted a more methodical approach to assessing and improving sales performance, so he hired external business consultant Max Blumberg and his team to undertake an analysis. Internally, Max worked closely with Steve Langhorn, Director of Rentokil Global Academy. As a first step in this project, Max interviewed sales leaders in different regions around the world in a bid to isolate a hypothesis that might explain the issue with sales performance. However, following these initial interviews, it was clear that different potential hypotheses existed, depending on who was interviewed: • Effective sales training delivered at the right time will develop the technical confidence needed for successful sales performance. • Better recognition tools will increase seller motivation to deliver higher ******ebook converter DEMO Watermarks*******



performance. • A globally consistent recruitment process for sales staff will deliver higher-performing sales people.



This analytics project had strong sponsorship from the CEO and a clear business problem (Step 1). Multiple hypotheses existed and needed to be validated and verified (Step 2) as part of the analytical methodology.



An Iterative Approach to Solve the Problem Max Blumberg set about gathering data relevant to his hypotheses in a multipronged approach. First, he conducted a detailed literature review of all the work that had been undertaken on sales performance in various industries. The intent was to understand whether examples of similar sales challenges existed elsewhere. Second, Max undertook an analysis of the HR practices at Rentokil Initial. Given the range of hypotheses and ideas presented to him, Max wanted to clarify the various processes and policies that existed for each of the main HR functions, including recruitment, compensation, management training, and leadership development. The final part of this initial phase was to collect new data from the workforce. A survey investigated employees’ perspectives of the HR processes in the company. People were asked to score the efficiency and importance of each HR process to sales performance. When the survey results were analyzed, it was clear that recruitment was viewed as the most inefficient yet most important HR process (see Figure 6.3).



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Figure 6.3 HR processes plotted by inefficiency and importance. This analysis allowed Max to focus on a single hypothesis: Hypothesis: A globally efficient and consistent recruitment process with clear selection criteria will improve sales performance. Max continued his investigations and next looked at whether the most commonly used selection tests correlated with sales performance. The analytics team found only small correlations between the two most frequently used tests and sales performance. As such, the team recommended discontinuing these tests. This recommendation was implemented. Next, the team collected and analyzed another new set of data, gathered from surveying the sales force, to identify the specific attributes that most highly correlated with sales performance. These attributes were grouped into categories such as conscientiousness, interests, interpersonal skills, and cognitive ability. The next step of the analysis involved looking for a selection test to accurately assess these attributes. The team undertook a literature review to source validated and relevant tests, and it also conducted an extensive review of selection tests that were available in the marketplace. In the end, Max’s ******ebook converter DEMO Watermarks*******



team chose six externally sourced tests that appeared to meet the criteria needed to improve sales performance. Using these six tests, Max and his analysts undertook a study among 270 sales people in the United Kingdom and the United States. Each person took all six tests, and their results were analyzed against their sales performance. This became a complex and sensitive exercise, partly because of the need to work with six vendors across the many global assessment platforms in Rentokil at that time, but also because of a significant level of concern among both the sales professionals taking the tests and their sales leaders about how the company would use the results. Using this seller assessment dataset, the team undertook various statistical analyses to identify which traits could be linked to high sales performers. These analyses included logistic regression, an example of the quantitative analysis objective of classification (see Chapter 5). The analysis revealed that one test in particular, a personality assessment, had a strong relationship to sales performance. Max predicted that this assessment would identify an above-average salesperson with a high degree of accuracy. Using the U.K. sales population, Max then converted that into financial value. If the assessment were adopted and implemented, he estimated that the United Kingdom business alone would see a potential increase in sales of £1.5 million per year. From these and further analyses, Max was able to make a very specific recommendation to implement one external test for the selection of all future salespeople worldwide. He also made detailed recommendations for the redevelopment and global standardization of the recruitment process, as well as the implementation of new induction programs and associated recruitment and induction training for managers.



The iterative process of collecting data (Step 3), analyzing it (Step 4), and discovering insights (Step 5) led to the recommendation to implement a single selection test (Step 6). Strong statistical evidence backed up the recommendation, and the financial impact was quantified.



From Recommendations to Business Impact ******ebook converter DEMO Watermarks*******



Max and Steve jointly set up strong governance throughout the project. As part of that governance, regular checkpoints were established with three core groups of stakeholders: • Global executive team and sales directors. This group required ongoing one-to-one meetings, as well as some team meetings at every stage of the project to ensure that they not only understood the insights from the analyses and associated recommendations, but also were clear about the decisions required. • Works councils, especially in Germany and France. This audience was briefed and managed carefully. In particular, the works councils were made aware of the benefits of the project to those countries’ businesses and to the individual workers within them. • The entire sales force. Communications with the entire sales force were handled through regular newsletters and emails. When needed, personalized communications were sent to salespeople asking for their participation in the surveys outlined earlier. These emails were privately addressed, to strengthen the message that the collection of personal data would be treated with a high degree of confidentiality. Communication was only part of the story for the analytics team; implementing the recommendations required extensive planning within both the recruitment function and the associated HR functions, such as induction training and sales enablement. Specific elements of the plan included these actions: • Procuring the selected assessment • Implementing the technology needed to manage one global assessment test, which resulted in implementing a standard global recruitment process • Training in interviewing techniques to align all hiring managers with the new recruitment process and selection criteria Furthermore, the plans needed to be implemented worldwide. To achieve this, the team used a phased approach starting with the United States and the United Kingdom, moving on to Europe, and concluding with the rest of the world. The implementation plan took one year to roll out fully to all countries. The entire project took more than two years to complete. The first year ******ebook converter DEMO Watermarks*******



clarified the business problem and enabled data collection and analysis. The second year focused on implementing the recommendations. With the CEO sponsoring the project and effective stakeholder management and involvement of sales professionals throughout, the project had a high chance of adoption and success. And it did succeed. In the year following the project, sales improved by more than 40 percent and the return on investment from the project was more than 300 percent.



Messages were communicated extensively, with much thought given to each stakeholder group (Step 7). The business objective to improve sales was measured and achieved (Step 8). Overall, this project demonstrated a clear and direct business impact in terms of increased sales. It succeeded because of its high-level sponsorship, effective stakeholder management, and strong methodical approach to analytics. As Steve summarizes: “This project demonstrated how analytics can shape the future through helping people secure the right jobs that will make them successful and bring benefit to business leaders and owners through increasing sales. It’s a win–win for everyone.”



Case Study: Increasing Value to the Taxpayer at the Metropolitan Police London’s Metropolitan Police Service (the Met) is the United Kingdom’s largest police force, employing approximately 31,000 officers, 9,000 police staff, 1,500 Police Community Support Officers (PCSOs), and 2,800 volunteer police officers.5 It covers an area of 620 square miles and a population of approximately 7.2 million, and it is funded entirely from taxpayers’ money. Robin Wilkinson, a board member and Director of People & Change, and Clare Davies, Director of Human Resources (HR), were responsible for implementing strategic workforce planning as part of a large transformation program. Although they had already set up a small project team, they contracted specialist analytics practitioner Martin Oest in late 2013 to expand ******ebook converter DEMO Watermarks*******



their analytics and workforce planning expertise in HR. Collectively, they set about helping to deliver on the promise of this strategic transformation to reduce the overall cost of the organization while at the same time recruiting a more diverse workforce. Sometimes undertaking an analytics project is extremely complicated not because the analysis is difficult or it lacks sponsors, but simply because there is no infrastructure for such work. This case study is an example of such a situation. It shows how to start from a low base and deliver successful workforce analytics.



A Police Force to Reflect the Community The HR team faced a significant list of challenges and defined several priorities under the Met’s people strategy. The overall aim was to have a police force representative of the diverse population of London. The Met believed that a more diverse workforce would be better able to serve London by speaking the languages and understanding the various cultures that make up the city’s neighborhoods. The team had to overcome other specific challenges, such as recruiting more police officers and reducing the overall workforce cost. It had to deliver this over a period of two years as part of the Met’s broader strategic transformation. After identifying the challenges, Martin and his team set about clarifying the underpinning hypotheses to support this: Hypothesis 1: By modeling scenarios for the future workforce, actions will be identified to enable new approaches to recruitment. Hypothesis 2: Providing accurate real-time information to hiring managers will result in the hiring of more diverse candidates.



Martin and his team found that spending time framing the business question (Step 1) and clarifying the hypotheses (Step 2) was important in providing focus to the analytics team.



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Workforce analytics capability in the Met was at a fledgling level at the start of this project. When Martin joined the project, he increased the focus on data governance that had already started with the team: “In the first phase, we delivered some simple quick wins, such as visualizing current headcount to achieve control of the data and to create one version of the truth for headcount.” In addition to data governance, Martin surveyed stakeholders about their requirements for analytics across various HR processes to provide the workforce analytics team with the right stakeholder input. Also in the first phase of the analytics project, the HR analytics team established a steering group. This consisted of major stakeholders, including the finance department and a group called Portfolio and Planning. This latter group was responsible to the board for change management and the overall transformation program in the Met, so it had to ensure that any analytics activities were aligned to the overall transformation agenda. Martin’s participation added focus and clarity to the objectives of the steering group to enable effective decision making, governance, and alignment with the Met’s strategic direction. As Martin explains, “This steering group was needed to ensure buy-in and commitment across the Met.” With the steering group, basic headcount metrics, and governance in place, Martin turned to the recruitment data in the second phase of the project. This was a crucial dataset for understanding ethnic diversity at the hiring stage. Lending visibility to the recruitment data was a critical step in increasing awareness of the diversity challenge among HR leaders, as Martin explains: “We delivered a recruitment dashboard that provided new insights to hiring managers. For example, we provided metrics for each stage of the recruitment process. This was the first time HR leaders had seen regular information about recruitment, so this was very well received. For example, it enabled them to spot the drop-out rate at each stage of the recruitment cycle for different ethnicities.” The third phase of Martin’s work was to create a full-scale HR dashboard. If progress were to be made against the targets the Met had set, it was essential for HR and departmental leaders to have access to consistent and accurate information. This information included metrics beyond just headcount and recruitment, such as diversity and succession. Martin consulted carefully on the creation of the dashboard: “Scope and objectives were set and stakeholders interviewed. They were then involved in an ‘objectives and ******ebook converter DEMO Watermarks*******



requirements’ workshop to ensure that the outcome met the business needs.”



The phased approach helped the HR team gain credibility, establish metrics, and gather the data required to undertake more complicated analyses (Step 3). With consistency and clarity around the Met’s current data established, Martin and his team set out to visualize what the future workforce could look like. They analyzed potential scenarios using “what if” models. Using Excel, they built an analytical model embedded with forecasting calculations to provide insights on minorities, gender, recruitment, and several more aspects of the entire workforce. Martin confirms, “This visual model was greatly appreciated and game changing for the Met. It was updated and distributed monthly to all stakeholders.” Focusing back on the core topic of diversity and workforce costs, Martin was able to apply more analytical models to the work. For example, the team forecast attrition for police officers using historical data, predicted recruitment targets, and forecast year-end headcount. The analytics methods gave the HR team at the Met significant credibility. Clare Davies had previously described a “we can’t rely on HR data” sentiment that had prevailed across the organization before this project. After just a few months, and with the implementation of the analytical models and methods, the mood changed. Clare explains: “We created a relentless focus on accurate data and insight, regularly using it to improve aspects of the operations. We worked hard to ensure that finance and HR data reconciled so that we had one version of the truth. We involved stakeholders along the way and gained credibility.”



Various models were used to manage diversity, hiring, and workforce costs (Step 4). These allowed the workforce analytics team within HR to gain credibility. Conducting accurate analyses and sharing the resulting data and insights was ******ebook converter DEMO Watermarks*******



only part of the story. The analytics team had to reduce four recruitment databases down to two, to simplify data management and reduce costs. In addition, hiring had to be reengineered with input from many stakeholders to simplify the process for both candidates and hiring managers. These changes revealed yet more insights, Martin says: “We identified a 20 percentage point drop in ethnic minority candidates between being ‘interested in applying to the Met’ and being ‘hired to the Met.’ This led to the creation of a dashboard to allow hiring managers to have insights of the data at every stage of the new hiring process.” The continued focus on metrics, governance, and analytical improvement led the wider HR team to highlight several recommendations for action. One of these concerned the entry criteria for applicants to the Met. Martin explains: “We introduced a London residency6 criterion to encourage applications from London residents. That way, we would be more aligned to the London we serve.” Another result of the analytical approach was to challenge the selection tests administered by an external provider. These tests were changed after the Met’s analysis showed different selection rates across different ethnic groups for certain assessments. This finding was possible only because of the Met’s deep and methodical approach to analytics, the confidence team members had established in understanding the data, and their relationship with the external provider. The provider made changes to the selection tests as a result.



Many insights were uncovered (Step 5) and recommendations were defined (Step 6) to allow for discrete change to happen in the Met.



Creating Business Impact Successfully implementing these changes required strong messaging to both the workforce and the population the Met serves. The Met marketing and communications team were involved to provide expert advice on this. Communications tactics included a press release to the London population about a recruitment drive; the resulting media coverage brought about a sharp increase in ethnic applications to the Met. ******ebook converter DEMO Watermarks*******



The result of two years of implementing analytically driven changes and business recommendations was impressive. Diversity representation from new hires improved by more than 10 percentage points, headcount targets were achieved, and workforce costs for police officers in the fiscal year 2014–15 were under budget. The legacy of this analytics project is noteworthy not just because it achieved those objectives: The Met now has both a strong platform for workforce analytics data governance and reporting and visualization technology that offers a single version of the truth, giving stakeholders access to the information they need to manage their operations.



A professional level of communications was implemented (Step 7) and the business objective to increase ethnic diversity in the police force and reduce overall cost of the workforce was achieved (Step 8). Robin Wilkinson summarizes the achievement: “Our journey of workforce analytics has totally changed how HR operates at the Met. It is more trusted, delivers better-quality insights, and saves money. Its contribution to the Met and to the taxpayer is significant.”



Case Study: Predictive Analytics Improves Employee WellBeing at Westpac Westpac Group is Australia's first bank, originally established in 1817 as the Bank of New South Wales. The organization has a portfolio of financial services brands and businesses, with a vision to become one of the world’s great service companies, helping customers, communities, and people to prosper and grow. Damien Dellala, Westpac’s Head of People Data and Analytics Enablement, came to the role from a digital strategy and analytics background. Damien brought the perspective of treating employees similarly to the way customers are treated, that is, segmenting, analyzing, and acting on insights to create positive experiences at Westpac. With a love for data, an analytical mindset and digital strategy experiences to guide him, Damien successfully delivered new analytical capability to the HR function. ******ebook converter DEMO Watermarks*******



This example shows how Westpac laid the foundation for workforce analytics to support employee well-being.



Societal Impact of Stress Stress is a costly societal problem for individuals and the organizations that employ them. The Australian Psychology Society found in its 2015 Annual Stress and Wellbeing Survey that 45 percent of Australians experienced work-related stress. Depression and anxiety symptoms also have been on the rise since the organization began conducting the survey five years earlier. According to a 2016 Australian Psychological Society article, stress takes a notable toll on individuals’ physical and psychological health. Additionally, U.S. companies spend about $300 billion per year on stress-related healthcare and missed days of work as noted in a 2016 Business Insider article. Concerned for their employees’ well-being, Westpac was ready to take on the challenge of better understanding the impact of these trends on its workforce. To start, the HR Analytics team worked in partnership with the Health, Safety, and Wellbeing team to pose this question: Could we use the data we have and apply advanced analytics techniques to better understand the spectrum of well-being? Importantly, the intention was to support Westpac’s credo of “people always helping people” by developing an environment that equips people with skills to cope with stress or to maintain a positive balance. The project team suspected that certain events were leading to employees experiencing distress or, conversely, preventing them from thriving. With a renewed analytical capability to quantify or predict, the team tested hypotheses that challenged anecdotes, myths, and beliefs associated with the well-being of employees: Hypothesis 1: Work flexibility is associated with well-being. Hypothesis 2: Employees with higher team volatility are more likely to experience stress.



Understanding the nature and context of the problem allowed the HR Analytics and Insights team, with the help of its sponsor, to frame the business question (Step 1). Translating the business question into testable statements yielded strong hypotheses (Step 2) to guide the selection of data ******ebook converter DEMO Watermarks*******



sources and analytical techniques.



Tackling Employee Stress with Analytics More than 10 traditional and nontraditional data sources were combined within an advanced analytics platform, which enabled sophisticated analysis and modeling of the datasets. Types of data spanned platforms, and more than 170 variables were assessed, including demographics, career history, leave history, work location, work patterns, business performance, collaboration patterns, technology usage, and employee opinion survey data. The analysis assessed multiple predictors to identify both the propensity or likelihood of distress for employees and the specific drivers of stress, including reasons for leaving and team dynamics. This is an example of the classification objective of quantitative analytics, which Chapter 5 covers. This approach seeks to predict a discrete future event—in this case, a distress incident—by calculating the probability of experiencing the event for each employee, based on a variety of potential predictor variables. The statistical models demonstrated that distress events were, in fact, predictable based on observed behaviors. The analyses revealed the factors associated with distress for specific groups of employees, setting the stage for a range of possible active or passive interventions that could be taken to reduce or prevent employee stress. Given the variety of data and abundance of variables available, the data discovery phase became crucial to perform segmentation and understand early key drivers of the outcome variables. Damien explains, “This enabled us to start enriching the relevant data elements so they speak well to the outcome—in this case, did the employee raise a stress incident (yes or no)?”



Identifying the right data sources (Step 3) and their choice of analyses (Step 4) allowed the team to appropriately test the hypotheses, lending confidence to the outcome. Interpreting the results of the statistical models allowed the team to derive insights about the specific drivers of stress (Step 5).



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Equipped with richer insights, the challenge was to translate the data into specific actions and interventions. The opportunity presented was to be targeted in intervention and to challenge traditional approaches within safety and employment. For example, one idea was to use the model to curate specific learning content for employees. Tableau dashboards were prototyped to allow the specialist teams to filter the model results by different segments of the workforce. Armed with this information, the analytics team spent time communicating its recommendations to its key stakeholder, the health and safety leader. Together they communicated actions, including changes to policies, to the wider business management team and to individual managers, where needed. As a result, Westpac was able to rally support for these interventions and reinforce leadership fundamentals for well-being.



The team made recommendations to put findings into action (Step 6), and got its point across (Step 7) to key stakeholders, energizing and encouraging them to act. Team members worked with the business to operationalize the recommendations by delivering insights to managers where and when needed; they also established a plan to follow through, to ensure that the expected value would be delivered (Step 8). This methodical approach allowed the HR Analytics team, together with the Health, Safety, and Wellbeing team, to effectively address Westpac’s challenge and create an environment that helps its people flourish and grow. Damien says he and his team enjoyed being part of this feel-good project because they “helped solve a business problem to help employees be their best selves at work.”



Summary The expert analytics professionals in these case studies undertook their projects with focus and intent. The organizational challenges and the techniques used to address them varied across the cases, but the following factors were common to all: • The problem to be addressed was clearly articulated and linked to the ******ebook converter DEMO Watermarks*******



overall strategy of the organization. • Sponsorship of the project by an influential person positively impacted all stages, from initiation through implementation. • Hypotheses were clearly understood and testable (and, in some cases, developed iteratively). • Data gathered and analyses undertaken were appropriate for the project. • Insights and recommendations were clear and precise. • Communications to various stakeholders were well planned and developed with the help of expert communications professionals, when needed. • Projects were designed to provide business impact and were evaluated for success and learning. • Internal or external partners contributed expertise to the team to ensure success.



1 In a lateral move, an associate is given an opportunity to work in a different business but does not change either job band or corporate title. This typically occurs with associates who have been in the same role for 24 to 36 months with good or excellent performance ratings. 2 Retrieved at www.youtube.com/watch?v=h3S1bUhK3Fo. 3 The profitability of the aggregated contracts for each ISS customer was used as the measure for contract profitability. 4 Net Promoter Score (NPS) is a customer loyalty metric developed by (and a registered trademark of) Fred Reichheld, Bain & Company, and Satmetrix Systems, Inc. Reichheld introduced NPS in his 2003 Harvard Business Review article “One Number You Need to Grow.” At ISS, the eNPS (employee NPS) question was “How likely would you be to recommend ISS to others as a good place to work?” and the cNPS (customer NPS) question was “How likely is it that you would recommend ISS to a friend, colleague, or customer?” 5 As of January 2017 (https://beta.met.police.uk/about-the-met/structure/) 6 The London residency criterion was designed to promote applications from residents of London. ******ebook converter DEMO Watermarks*******



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II Getting Started 7 Set Your Direction 8 Engage with Stakeholders 9 Get a Quick Win



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7 Set Your Direction “Understand the sort of people who are successful in the company and what sort of returns a business is getting from its workforce. These two points translate into fertile ground for analytics work.” —Salvador Malo Head of Global Workforce Analytics, Ericsson When you are given responsibility for establishing workforce analytics in your organization, knowing where to begin can be difficult. This chapter presents a structured approach for getting started and covers the following: • What to do in your first few weeks as the leader • Understanding the demand your organization has for workforce analytics • Creating a vision and mission statement



You Have the Job! Now What? You are the executive or manager who has the role to lead, build, and grow the workforce analytics function in your company. Your objectives are to build the team, take it to the next level, deliver more projects and improve business performance. It may even be to build this function from scratch. Anyone who has ever built, grown, and led successful analytics practices has heard these expectations, as have executives who have recruited people to undertake these missions. Here’s what you should do next: • Pause. Before you do anything else, stop and look at what you already have. What data do you have, what technology do you have, and under what processes are you operating? If you have a team, what are the team members doing, what tools do they have, and what is their reputation? ******ebook converter DEMO Watermarks*******



And what are your goals? In particular, consider that last point. Ask yourself, “Why have I been given this job?” If you don’t know explicitly, talk to the most senior person who gave you the role. Establishing why human resources (HR) needs an analytics function is your first objective. Without this clarity, projects you undertake might solve immediate needs but struggle to address longer-term needs in an integrated and systematic way. In other words, without knowing why you exist, you are, at best, trying to stumble onto success and, at worst, moving in entirely the wrong direction. • Listen. Don’t dive into the first project you encounter. Although getting some projects under your belt early on is important to establish credibility, don’t dive into the first project just because someone requests it. You need to listen to the people who gave you the job, as well as to potential project sponsors, to understand the demand for your work. • Think. Take a few days or weeks to think through your long-term goal(s). Try to look into the future—the next six months, one year, or three years —and then discuss your proposed vision and mission for the function with influential people. This doesn’t need to be perfect in the early days, but knowing your ultimate destination helps you take the right steps today. “You need to know why analytics is required in the first place, whether that’s because there’s ‘smoke in the buildings’ or because the scale of the enterprise means managing the workforce numerically is the only option.” —Ian O’Keefe, Managing Director, Head of Global Workforce Analytics, JPMorgan Chase & Co.



PREPARING FOR SUCCESS: THE FIRST FEW MONTHS John Callery joined BNY Mellon1 in 2015 as part of a broader integrated Talent and Development organization. He was brought in to set up a workforce strategy function overseeing the global workforce analytics and planning groups as part of this team. John had three main objectives in his first three months: 1. Creating a basic infrastructure: “Before I joined, there were cottage industries in finance, technology, and business groups doing ******ebook converter DEMO Watermarks*******



workforce analytics and planning work, and we identified a better approach to enable us to be successful. We focused on partnering with these teams to consolidate the work and build appropriate infrastructure, governance, and processes to drive a solid basis for reliable, accurate, and timely data and analytics.” 2. Delivering “quick win” projects to gain credibility: “One project was focused on our military veteran population. Our analysis helped us understand where our military veteran employees had better longterm outcomes when initially hired into certain roles. The executive sponsor of BNY Mellon’s Veterans Network (VETNET) was delighted, as we were able to better support our existing veteran population, quantify the successful business and people outcomes, and make a case for strategic hiring initiatives. This was important: a high visibility project with a very senior sponsor showing business benefits all delivered in a couple of weeks.” 3. Selecting long-term strategic projects: “We did a review of the demand for workforce analytics and, following that, selected a couple of big, strategically important projects. It was key to validate that some workforce analytics projects solve complex issues with longterm impact. We were also able to demonstrate that analytics is a serious discipline and can often take months to undertake impactful and scientifically valid analysis.” Overall, John has built a solid foundation to support the continued transformation of HR’s approach to analytics. He credits a supportive and analytically driven CHRO. “I joined because of a shared vision of aligning workforce strategy with business strategy, and a shared belief that HR initiatives should be focused on and accountable to these strategies in a data-driven way.”



Listening to Prospective Project Sponsors In the first few days, and certainly before the end of the first month, you should seek out prospective project sponsors. A project sponsor is a person or group who provides support (through financial means or personal endorsements) for a workforce analytics project or activity. Try using a snowball strategy to identify the project sponsors: Ask people ******ebook converter DEMO Watermarks*******



what they think, listen carefully, and ask who else they recommend you talk to. By interviewing a broad set of influential leaders, you will be able to identify the source of your main base of support and possible projects for the workforce analytics function. This entire information-gathering process is also useful because it provides the “raw material” for a vision and mission, which are covered later in this chapter. Your first interviews should be with people who have the most influence over your work and could sponsor your future analytics projects. Start with the most influential people to whom you are accountable and work your way back toward those with less influence over your work. Recognizing that individuals have different levels of influence is important in working out where to cultivate relationships. Wide canvassing of views is important because a frequent question about analytics projects from practitioners who have achieved success is “Who is asking for this?” (to put it another way, “Who is the project sponsor?”). You need a well-developed and immediate response to this question. The more conversations you have with prospective project sponsors early on, the more likely you will be able to separate good projects from really exceptional projects. These conversations will help ensure that you have the information needed to prioritize projects that have a clear line of sight from the business question to the impact on the business. “I choose my projects carefully and make sure I understand them. You have to think it all the way through. Strong analysis is an accepted prerequisite, but you need to know what are you going to do with the results and how you will implement the recommendations. You need to think through the project all the way to the individual workers who will be impacted. If you don't do this, you will find it difficult to demonstrate why your project will make a difference to the business and, therefore, it will be difficult to get funding.” —Simon Svegaard Business Analytics Manager, ISS Facilities Services



The Seven Forces of Demand In interviewing people, including your prospective project sponsors, you will identify the drivers of demand in your own organization. Although the details will likely be unique, some common themes stand out. We refer to these ******ebook converter DEMO Watermarks*******



themes as the Seven Forces of Demand (see Figure 7.1).2



Figure 7.1 The Seven Forces of Demand. As organizations grapple with the challenge of data proliferation within dynamic and complex business environments, they are increasingly turning to analytical approaches to help inform decisions. In solving these challenges, organizations experience growing demand for and expectation from workforce analytics, summarized as follows: • Desire for a competitive edge. For some organizations, the demand for workforce analytics comes from widespread acceptance that if workforce decisions are made empirically (that is, based on data), better results will follow. Antony Ebelle-ebanda, Global Director of HCM Insights, Analytics & Planning at S&P Global (formerly McGraw Hill Financial), says: “We see workforce analytics as a cutting-edge approach that enables the firm to select the individuals with the best intellect and shape the environments in which they work to maximize worker productivity. Workforce analytics is viewed as one element of an overall analytical mind-set characterizing the firm. Every other function is driven by ******ebook converter DEMO Watermarks*******



analytics, and the job of HR is to catch up to the sophistication of these other areas.” • Requests coming top down. Some demands for workforce analytics projects come directly from the top of the organization. Both Thomas Rasmussen, Vice President, HR Data & Analytics at Shell, and Peter Allen, Managing Director of Agoda Outside, shared experiences of topdown demand. Thomas explains: “I was asked by the CHRO to create an end-to-end value chain, from data to reporting to analytics. I was asked to build capability in the HR function (up-skill our HR leaders to work with fact-based decision making), understand the pros and cons of letting this ‘animal’ loose in the organization.” Additionally, Peter says, “I have seen the constant tension between HR and the business where the issues HR focuses on and the linkages to business results are very difficult to quantify, since often there are no quick, easy-to-prove cause-and-effect relationships. Nevertheless, CEOs always want to see the impact of their investment in money spent on talent and HR programs. This may not be in standard ROI terms, but since every headcount represents a strategic choice, CEOs need to see the benefits to their organization.” • Regulatory requirements. Another trend observed is the increasing interest that board members, especially those who work across multiple organizations, have in understanding talent in organizations. This interest is spurred by developments such as discussions with regulatory bodies about the value of certain human capital metrics during a company’s annual reporting (for example, attrition, employee engagement, and salary ratios). This interest from the board creates a demand from a firm’s top executives for robust analytics from the HR function. Other examples of compliance-driven analytics include the need to conduct analytics on diversity issues to adhere to affirmative action legislation and the need to ensure adherence to business controls. • Need for operational efficiency. The sheer size of the business sometimes drives the need for analytics. Ian O’Keefe, Managing Director, Head of Global Workforce Analytics at JPMorgan Chase & Co. says that the nature of business leaders’ understanding of the scope of a workforce analytics function is often based on the scale of an enterprise. He said of some organizations in which he has worked, “There can be hundreds of thousands of employees to keep track of, along with the added complexity ******ebook converter DEMO Watermarks*******



of seasonal swelling in employment numbers.” In these situations, a workforce analytics function is necessary because understanding the workforce numerically and influencing the business with analytics is the only effective option for managing it. • Pressure to reduce cost. For some companies the nature of the mandate for a workforce analytics function can depend on whether or not there is “smoke in the building.” That is to say, sometimes when companies are not as profitable as they need to be there is a greater appetite for change. Leaders of the business will then ask the CHRO how HR could contribute to the profitability of the business or reduce the level of workforce cost. When this happens, those in the HR function, as well as the wider business environment, quickly see that analytical approaches to HR decision-making need to be included alongside more traditional HR practices. • Concerns of a humanistic nature. Analytics in HR is not solely revenue or profit driven. Indeed, the development of analytics in HR is sometimes borne of the desire to create a better workplace for employees or a more socially responsible organization. For instance, in a 2004 article entitled “Beyond Money,” psychologists Ed Diener and Martin Seligman argued that organizational over-reliance on economic productivity indicators while excluding well-being indicators leads to a focus away from what society values. A leading practitioner in analytics who shares this perspective is Laurie Bassi, an economist by training and CEO at McBassi & Company. Laurie describes her role as steering firms into the sweet spot at the intersection of profit and employee well-being. Laurie captured the spirit of this perspective with the vision of her workforce analytics firm: to create sustainably profitable, enlightened, and progressive workplaces. Bart Voorn, Lead for HR Analytics at Ahold Delhaize, supports this idea: “In some situations, we don’t want to quantify people in terms of euros. Sometimes it’s just not the right thing to do; we are talking about people. For example, the case on diversity is not a financial one. It’s morally a human one.” • HR analytics for the HR function. An organization might decide to focus on improving the efficiency of HR as a function by implementing a workforce analytics program. Analytics projects in this situation are likely ******ebook converter DEMO Watermarks*******



to be about the efficiency of HR processes or a need to change certain HR policies to better align with the organization’s overall mission. Embryonic workforce analytics teams are full of examples of this, especially those teams that are just “dipping their toe in the water.” In this case, you are trying to create demand for workforce analytics from inside HR and selling the benefits to the broader organization. Patrick Coolen, Manager of HR Metrics and Analytics at ABN AMRO, describes his success in selling the benefits of HR analytics from the inside out by asking internal customers whether they want to know how HR is impacting their key performance metrics: “My internal customers were very interested to understand in a more formal way how HR policies and practices impacted people-related outcomes, which, in turn, related to business outcomes.” In many cases, Patrick created the demand for workforce analytics from inside HR and sold the benefits to the wider business.



UNDERSTAND YOUR CULTURE, UNDERSTAND YOUR DEMAND Peter Allen has helped Agoda.com3 become one of the most valued online firms in the travel industry. From 2012 to 2016, as Vice President of People Organization and Development, he helped to solidify the people culture and its focus on analytics in Agoda. Agoda’s organizational culture highly values analytical approaches to decision making, which Peter says supported this strong analytics orientation: “Our management team has an engineering approach to our work. They like data and information and take a methodical, problem-solving approach to the way the business is operated. “They want to run a company that focuses on continuous improvement, and they want to see key performance indicators that show how well we’re doing in achieving this objective.” To ensure that management similarly embraced workforce analytics, Peter used this existing strongly analytical organizational culture to make his department more effective. “We already have a strong focus on metrics from web traffic, for example, so it was not such a huge leap to bring that sort of mind-set to HR problems.” ******ebook converter DEMO Watermarks*******



Working for an analytically appreciative CEO means Peter knows what is expected of his team: “The CEO certainly wants to see that he’s getting a good return; otherwise, why wouldn’t he just invest his headcount in hiring more engineers?” Working within such a culture gave Peter and his department the opportunity to bring analytics to HR and deliver on the high expectations for his team.



Agreeing on the Scope of Analytics Toward the end of the information-gathering process, evaluate whether the perceived need for analytics matches the type of analytics function you initially envisioned. It is important for the scope of your analytics function to match the expectations of your sponsors. Otherwise, you might find that all of your team’s time is devoted to tasks that are only tangentially related to workforce analytics and that are completely unrelated to your specific objectives. And in this situation, you will not have time to focus on the strategic projects that can really affect the organization. Negotiating this scope is vital if the focus of the workforce analytics function’s original mandate is not considered wide enough. Arun Chidambaram, Head of Global Talent Analytics at Pfizer, says, “Rather than considering a function mature because it does a particular type of analytics, a function is mature if it can serve the business in the way the business requires. Make sure you know what your consumers of analytics need. Then if you can meet their demand, prioritize effectively, build your team, and select the right technologies, you are on the way to being mature.” Understanding the match between your analytics objectives and your capabilities to support those objectives is the key to understanding maturity. Just as important, capability should be assessed against current as well as expected future objectives.



Developing a Vision and Mission Statement You’ll hear the same message from nearly all the experts: You need a vision and you need a mission. But in the early days of a project, the vision and mission statements don’t have to be perfectly refined and polished. You can adapt the statements as your understanding of the requirements crystallizes. ******ebook converter DEMO Watermarks*******



People who have established a workforce analytics function describe having a general sense of their end state (vision) and how to get there (mission) instead of having “perfect” vision and mission statements that can be recounted word for word. Although many advanced analytics functions do have precisely phrased vision and mission statements, this degree of specificity tends to come later after agreeing on the direction and approach of the workforce analytics function. For clarity, the following grounded and practical definitions of vision and mission statements come from the management consultancy Bain and Company (adapted to apply to an internal workforce analytics function): • A vision statement describes the desired future impact of your function on the organization. • A mission statement defines the function’s business, objectives, and approach to reach those objectives. If you experience any resistance to these foundational planning steps, consider this: A view will certainly be formed within the wider business about the core competencies of your function, how your function should look in the future, and the work you should do. Define your own vision and mission statements and offer them for discussion and debate; don’t settle for having management hand them down to your team. Setting your team’s future direction, in conjunction with sponsors, is important. Another reason to undertake these preliminary steps is that you can use the vision and mission statements to guide your team, project selection, and the way you interact with people outside your function. Following are some examples of vision and mission statements from analytics experts. Remember, don’t get concerned with fine-tuning them yet; that can come later. Also avoid using “corporate speak,” to ensure common understanding. • Vision examples: • Better business through better people decisions • HR makes evidence-based people decisions using data and analytics • Market leadership through human capital management analytics and planning • Mission examples: • Building a human capital analytics organization to enable deep and ******ebook converter DEMO Watermarks*******



innovative data mining to support business decisions and maximize shareholder value • Building the infrastructure to sustain and provide workforce analytics, with the aim of creating a data-driven culture that allows our business leaders to make superior decisions • Working with the business as partners providing analytics solutions to business problems, therefore making sure the company has the right skills at the right time to bring competitive advantage • Providing workforce intelligence to HR and business executives so that, in turn, they know their people: who they are, what they do, how they do it, and what they need to succeed No magic formula covers writing the ideal vision and mission statements. However, the statements should be well informed by the early views of your key sponsors about why they believe a workforce analytics function is needed and what the demand is for workforce analytics projects. If you have to dig out documents every time you need to refer to them, your vision and mission statements are not serving their purpose.



Summary These key steps set the direction for workforce analytics in your organization: • Pause, listen, and think before you dive into your first analytics project. • Talk to the person who appointed you, or the most senior person in your functional line (for example, the CHRO), to clarify the scope of your role. • Identify prospective project sponsors for interviewing and ask them about business challenges. • Identify which of the Seven Forces of Demand are driving the primary need for analytics in your organization. • In a memorable vision statement, describe the desired future impact of your function on the organization. • In a memorable mission statement, describe your objectives and how you will address them. • Use the vision and mission statements to communicate your function’s identity to your team and the wider organization, and to prioritize your ******ebook converter DEMO Watermarks*******



projects.



1 BNY Mellon is the corporate brand of the Bank of New York Mellon Corporation. Its heritage dates back to 1784. As of December 31, 2016, BNY Mellon had $29.9 trillion in assets under custody and/or administration, and $1.6 trillion in assets under management (www.bnymellon.com). 2 The Seven Forces of Demand is a copyright of the authors of this book: Nigel Guenole, Jonathan Ferrar and Sheri Feinzig. 3 Agoda.com is one of the world’s fastest-growing online hotel platforms. Established in 2005 as a start-up, Agoda.com expanded quickly in Asia and was soon acquired in 2007 by the world’s largest seller of rooms online, the Priceline Group. Agoda.com is now a truly global enterprise offering accommodations around the world, with offices in 50 locations in 31 countries and more than 3,000 employees of 65 nationalities (www.agoda.com).



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8 Engage with Stakeholders “The analytics is relatively ‘easy’ (of course, we know it’s hard, but with the right skills you can get it done). It’s the change management and positioning that’s the tricky part. Spend time on that in the beginning; start with the stakeholders.” —Thomas Rasmussen Vice President, HR Data & Analytics, Shell The workforce analytics team cannot operate in a vacuum. When the team has an initial vision and mission defined, it is essential to build on the relationships you have already formed and identify additional stakeholder relationships that you need. Stakeholders are people who have a specific interest in a project or activity, and the workforce analytics team should consider many potential stakeholders. The analytics team is accountable to these stakeholders, so understanding how various stakeholders perceive the work of your team is important. You also need to monitor those perceptions over time. This information allows you to tailor your communications and address any reservations or concerns. This chapter discusses the following: • Types of workforce analytics stakeholders • Conversation topics for each stakeholder type • Tips for working successfully with stakeholders



Who Are Stakeholders? In the simplest terms, stakeholders can be defined as people who have an interest or concern in something. In the organizational context, this translates to members of the organization who can affect, or are affected by, the topic of interest. Workforce analytics stakeholders are people in the organization who ******ebook converter DEMO Watermarks*******



have a vested interest in the work of the workforce analytics function; they may be leaders whose problems your projects are intended to solve, they could be the keepers of the data needed for the analyses, or they might be those affected by the actions that result. All these individuals need to be explicitly acknowledged: Their perspectives must be taken into account and your relationships with them managed so that you set the analytics team on a path to success. Stakeholders reside at various levels in the organization, even outside organizational boundaries. Although there is a tendency to focus on those higher up in the hierarchy, particularly those who control or influence funding decisions, it is equally important to nurture relationships with individuals and teams on whom you depend for data, expertise, implementation of actions, and other needs, as well as those whose work experiences will change as a result of your projects. We also caution against the HIPPO principle, that the highest-paid person’s opinion is the most important. The different types of stakeholders you are likely to work with fall into three broad categories (see Figure 8.1): • Those you are serving: human resources (HR) leaders, business leaders, and the board of directors • Those you are dependent upon: data owners, technology owners, the legal function, the finance function, subject matter experts, and unions and works councils • Those whose work experience will change as a result of your analytics recommendations: workers, managers, and executives Note that these groupings are not mutually exclusive; stakeholders can fall into two or more of these broad categories. For example, subject matter experts might provide the knowledge needed to interpret findings, which then inform actions that impact the subject matter experts as members of the organization. The three stakeholder groupings and each stakeholder type are discussed next.



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Figure 8.1 Summary of stakeholder types.



Stakeholders Served Stakeholders served are typically leaders, often at the top of the organization. The most successful workforce analytics teams have the support of these leaders. For some organizations, such as financial services and technology firms, the core business is often built on a foundation of analytics. In some cases, the chief executive officer (CEO) or the chief human resources officer (CHRO) has a proclivity toward analytics. For these organizations, support for workforce analytics comes easily. For others, securing support could be more of a challenge. An important stakeholder management task is determining your leaders’ views on workforce analytics and planning your communications accordingly. “It was less that we did an analysis and showcased it; it was more proactively answering questions with data from our senior leaders. The CEO and CHRO are very data driven.” —Mariëlle Sonnenberg Global Director, HR Strategy & Analytics, Wolters Kluwer ******ebook converter DEMO Watermarks*******



Understanding the views of your CHRO is particularly important, and the best way to do so is through direct conversations. Al Adamsen, Founder and Executive Director of Talent Strategy Institute, describes an experience early in his career: “I was three levels down from the CHRO and four levels down from the CEO. It was like playing a game of telephone tag—very frustrating from an analyst point of view. You never know what’s really needed when you’re just relying on messages being passed down.” An organization’s board of directors can also play a decisive role in bringing an analytics focus to HR. As discussed in the later section, “Board of Directors,” human capital is growing as an area worthy of board attention. Of course, you need buy-in and commitment from your organization’s HR leadership and HR business partners; in many cases, they will be the liaisons to the business leaders. With the HR stakeholder group, strive to create and nurture an analytical mind-set. This does not require a detailed tutorial on statistical techniques; instead, it involves building an appreciation for the value that analytics can bring to the HR function. And make it personal: Convey how analytics can improve stakeholders’ own contributions and effectiveness.



HR Leaders Because workforce data reflect information about people, and the work of HR focuses on people, HR leaders should be integrally involved in workforce analytics efforts. They might be on the receiving end of workforce analytics output, they might sponsor a project, or they might be the ones expected to act on the results. Andre Obereigner, Senior Manager of Global Workforce Analytics at Groupon, says the role of HR in his work included addressing a retention problem analytically: “Once I had a valuable working solution, we showed it to the regional HR directors and they were quite excited. The people using it now are the worldwide, regional, and country HR managers.” HR leaders need to be part of the analytics process, beginning with issue identification and hypothesis formation, all the way through to data collection, analysis, recommendations, and action planning. Buy-in and commitment to act are best achieved by working together throughout the project lifecycle. “Most HR people didn’t join HR because of an interest in analytics. To help them to think analytically, we get them involved in a project that shows the ******ebook converter DEMO Watermarks*******



value of analytics—first-hand involvement helping them solve a problem that’s relevant for what they do.” —Thomas Rasmussen Vice President, HR Data & Analytics, Shell Collaboration is key to successfully bringing analytics to HR, but you might need to push or gently prod your HR professionals to move outside the boundaries of their comfort zone and think differently about HR challenges and why they matter to the organization. If they are not accustomed to thinking this way, HR can learn how to work in a fact-based manner. That doesn’t mean running a master course on analytical techniques, but it does necessitate encouraging them to think about the organization’s success metrics instead of focusing exclusively on familiar HR metrics. Some basic education on analytics can be helpful as well. See Chapter 15, “Enable Analytical Thinking,” for additional suggestions. For many workforce analytics teams, the only connection to the organization’s leadership is through HR leaders. Although the analytics team should work with business leaders and HR to drive change through analytics, in some organizations, the culture might not support this approach. Success is more likely to be achieved when the HR leaders you are working with are well connected to the organization’s leaders and have a keen understanding and appreciation of how workforce issues influence organizational outcomes. HR leaders can also be a good resource for identifying additional stakeholders, facilitating introductions, and sharing insights on individuals’ perspectives and working styles. Suggestions for Engaging HR Leaders • Identify a broad set of analytics stakeholders, and agree on how best to manage the relationships. • Discuss the organizational leaders’ top priorities and concerns. • Identify which HR practices and metrics are associated with organizational issues and have a direct impact on performance. • Share examples of successful analytics projects from within or outside the organization. • Gauge attitudes and perceptions toward evidence-based decision making. ******ebook converter DEMO Watermarks*******



Business Leaders As explained in Chapter 7, “Set Your Direction,” HR for HR is one of the Seven Forces of Demand for workforce analytics. However, HR analytics serving only the HR function is not sufficient to effect meaningful change in organizations. Insights and actions that result from workforce analytics projects must move beyond the boundaries of HR. “The reason analytics has stalled in HR in the past is because there weren’t any business stakeholders. If there aren’t business stakeholders, HR analytics is interesting but not essential.” —Josh Bersin Principal and Founder, Bersin by Deloitte Whether the analytics team works directly with organizational leadership or indirectly through HR business partners and leaders, discussions are needed to understand and prioritize the organization’s issues, determine how workforce issues relate to organizational issues, share insights that the analytics reveal, and agree on actions to both address and resolve the issues. Leaders can then provide the influence needed to drive actions for implementation. Andrew Marritt, Founder of OrganizationView, describes this well: “You need to have a senior business stakeholder. Without this, it becomes an HR problem. If you go to a general manager and talk about revenue, they sit up and listen, but if you talk to them about HR, they won’t. The projects that will change the business will have a senior business stakeholder.” Bear in mind that not all leaders have a favorable predisposition to analytics. In addition to all the benefits of evidence-based decision making that analytics can provide, Laurie Bassi, CEO of McBassi & Company, has observed, “Analytics also brings transparency and accountability, which isn’t always welcomed.” An important aspect of stakeholder management involves understanding your stakeholders’ views, particularly when they are inconsistent with an analytical approach. This understanding is a necessary first step to managing concerns and differences of opinion, as well as building a constructive working relationship. Finally, prepare your leaders for the potential outcomes of the analytics projects. In some cases, the analyses will merely confirm what was already ******ebook converter DEMO Watermarks*******



suspected (and there is definitely value in supporting beliefs with data). In other cases, unexpected findings will emerge. In yet other instances, the analyses will be inconclusive. Be sure to discuss all these potential outcomes in advance and manage expectations appropriately. Thomas Rasmussen, Vice President of HR Data & Analytics of Shell, offers this advice: “For senior leaders, we tell them to be prepared to be surprised. Be prepared to accept the findings that come back, to have your beliefs challenged.” Suggestions for Engaging Business Leaders • Discuss what keeps leaders up at night and which of these issues and challenges are good candidates for workforce analytics projects. • Discuss hypotheses and beliefs regarding causes of organizational issues, including people-related causes. • Agree on data needed to address top issues, identify data owners, and request introductions to facilitate access. • Establish realistic timelines, including the time needed to identify and access data sources. • Gauge attitudes toward analytics, both favorable and unfavorable, and adjust your communications accordingly.



Board of Directors Some organizations are experiencing an increased interest in workforce analytics from their board of directors. Given the influence boards tend to have on organizational leadership, understanding this perspective is critical. Laurie Bassi of McBassi & Company says, “Boards are asking more piercing questions about HR issues that go deeper into the organization, beyond the executive layer that has previously been the focus. Generally, boards are paying much greater attention to their fiduciary obligations, and they are realizing that the people in their businesses represent an asset at risk that needs to be actively managed. They see human capital as a topic worthy of board attention.” Proactively investigate the board’s views and gain an understanding of the board’s responsibilities regarding people-related topics (for example, succession planning, executive compensation, and employee engagement). The CHRO (or other senior HR leaders) can be a good source of the board’s ******ebook converter DEMO Watermarks*******



views of workforce-related topics. Your organization’s annual report, as well as the annual reports of other relevant companies, can also provide useful information. This insight can help shape the workforce analytics agenda and will certainly strengthen your understanding of your leadership’s perspective and expectations. Also talk to your CHRO about his or her specific accountability to the board and, if you have the opportunity, work with the board directly. Suggestions for Understanding the Board’s Perspective • Understand board members’ views on compliance and risk management as it relates to people. • Understand the board’s obligations and perspectives on specific topics such as leadership, succession planning, and executive compensation. • Determine whether board members are interested in other workforcerelated topics, such as employee engagement.



THE CASE FOR WORKFORCE ANALYTICS: CEO SUCCESSION In answer to the question “What’s the biggest challenge CHROs face today?” eminent academic Pat Wright1 responded without hesitation: CEO succession. He went on to explain how a conversation with a board director influenced his thinking: “We were interviewing many board directors, and one of them said, ‘I can pretty well make my decision about a CEO within the first one and a half minutes of a conversation.’ This didn’t seem right. Don’t we need more evidence to select the right person for the role? After all, the CEO is arguably the most important hiring decision for the company. You are, in effect, betting the whole company on this person.” This is where evidence-based decision making comes into its own. Instead of basing such appointments on gut feel, leaders can use analytics to provide validated insights. Data sources can include the performance history of the candidates, behavioral assessments, and psychometric tests. The resulting insights give the board members the ******ebook converter DEMO Watermarks*******



opportunity for more informed, holistic decision making. Instead of merely hoping they’ve made the right decision, they can answer a simple question with evidence: How can we be sure that we are hiring the right person—and then, after six months, know that we made the right decision? You’ll be hard pressed to find a stronger case for workforce analytics.



Stakeholders Depended Upon Throughout your analytics work, you will depend on various groups that will enable you to progress. These include people who will provide access to the data you need and people who will help you understand and interpret the data. You might also need to work (and negotiate) with technology owners, finance people, unions, and works councils. Positive, productive, and mutually beneficial working relationships are the goals with all of these stakeholder groups.



Data Owners Identifying the people who can grant you access to the different types of data you need for your analyses can be challenging. You might be able to work out with senior leaders the types of data available, but actually finding the person who can get you the data and enable you to work with it can be difficult. Nonetheless, this is a necessary step. When you identify the right people, you will need their cooperation to get what you need, when you need it. “People who work with business data are key. They know how to interpret data, find possible bias in the data, and explain the results together with HR. It’s very tempting to think you know the data when you get the first data dump, but you need to know it well to understand it. The subject matter experts in the business can help you with this.” —Patrick Coolen Manager, HR Metrics and Analytics, ABN AMRO Keep in mind that the people managing these data sources are probably completely occupied with their full-time job responsibilities. You want to ******ebook converter DEMO Watermarks*******



build a trusting, collaborative working relationship with them and convey a sense of your project’s importance to the organization. Don’t hold back on expressing appreciation and gratitude for the assistance you receive, and be sure to publicly recognize their contributions. You might need to seek assistance from project sponsors or other influential stakeholders if you are unable to get the access you need, but try to keep the relationships with data owners as positive and constructive as possible. Suggestions for Engaging Data Owners • Convey the importance of the role their data will play in bringing value to the business through workforce analytics; convey how visibility and scrutiny of data will grow along with increased analytics expectations. • Discuss the need for common data definitions (see Chapter 10, “Know Your Data”) if these do not exist. • Discuss the implications of analyzing outdated, incomplete, or otherwise inaccurate data. • Jointly determine how to best work together in a way that is least intrusive and most efficient for the data owner. • Jointly determine how the analytics team can positively contribute something back to the data owner (for example, assistance with data accuracy and currency). • Understand the data governance process, including who has access and how the datasets are maintained, and verify that you are working with the single trusted source.



Technology Owners The tools available to analytics teams are usually chosen, implemented, and managed by a Human Resources Information Technology (HRIT) function or an enterprise-wide technology function. When this is the case, you want to form positive working relationships with the IT decision makers. This could give you an opportunity to influence future technology purchases, as well as alert you to any system-related scheduling and timing constraints. For example, if a system that you need for extracting data is scheduled for a maintenance window and will be unavailable for a period of time, work that into your timeline so you don’t find yourself without a dataset at exactly the ******ebook converter DEMO Watermarks*******



time you need it. Suggestions for Engaging Technology Owners • Gain an understanding of technology owners’ existing commitments and competing priorities. • Discuss technology decisions that would best support the analytics mission and the increased visibility these decisions will have as analytics expectations continue to grow. • Jointly search for ways to meet (seemingly) competing demands. • Understand IT schedules and associated constraints, such as testing cycles and maintenance windows when systems will be unavailable.



Subject Matter Experts In addition to data owners, you will likely need help from people in other parts of the business who understand the context behind the data; this will be indispensable in guiding your use of the data and interpreting the analytics results. Identify subject matter experts early and involve them as quickly as possible in the analytics project. Patrick Coolen of ABN AMRO reflects, “We learned quickly that subject matter experts are key, that we needed to involve them in projects sooner.” Subject matter experts might reside in the sales, marketing, or finance departments; other parts of HR; or elsewhere in the organization, depending on the type of data. Consider, for example, data on customer turnover rates, or “churn” rates (a measure of the percentage of customers who leave a business or fail to renew a license or service subscription within a specified period of time). Of course, high churn rates are undesirable, and you might want to do an analysis to determine what, if any, workforce factors are contributing to the churn rate (positively or negatively). To begin your analysis, you gather data on customer renewals and calculate the churn rate. But how do you know what time period is appropriate before considering that a nonrenewal contributes to the churn rate? It could be 1 year for certain offerings, or 18 months or 2 years for others. Furthermore, how do you know whether all customer turnover is bad? Maybe some offerings were taken out of the market, so the turnover was intentional. Subject matter experts can provide invaluable insights such as these, to help avoid the wrong judgment call when working ******ebook converter DEMO Watermarks*******



with unfamiliar data. Suggestions for Engaging Subject Matter Experts • Share exploratory data analyses and get insights into patterns and characteristics of the data. • Discuss preliminary findings to ensure accurate interpretation. • Brainstorm recommended actions and implications, and model scenarios for different courses of action. • Publicly acknowledge the invaluable assistance that the subject matter experts provide.



Legal HR datasets generally contain sensitive information, including personal details about employees’ work history, performance evaluations, compensation, and national identification codes such as Social Security or National Insurance numbers. Some of these data elements are considered sensitive personal information (SPI) in the United States and have similar designations in other countries. This is information that, if compromised or disclosed, could result in substantial harm such as identity theft. “On every single project, we encounter legal issues—for example, getting the right access to data. Engage your legal team as early as possible.” —Andrew Marritt Founder, OrganizationView Given the personal nature and potential risk associated with handling HR data, extra care and precautions are strongly advised. Work with your legal department for guidance, advice, and counsel. Ensure that you understand and are adhering to your organization’s policies and practices, and stay mindful of country-specific regulations regarding employee data. Also be sure to recognize that data sensitivities do not represent an insurmountable obstacle. Understanding and adhering to guidelines will not hold you back, but will instead help you to operate properly and securely. If your organization has a chief privacy officer, this is an important person to rely on for successfully navigating data security and privacy requirements. ******ebook converter DEMO Watermarks*******



Suggestions for Engaging Legal • Discuss your analytics goals and the types of data you need to achieve those goals. • Understand your organization’s policies and practices in working with employee data. • Agree on procedures your team will adopt as safeguards in protecting data. • Gather advice on how best to handle data discussions with unions or works councils, as necessary.



Finance Aligning with the finance department on the value of workforce analytics is an important success factor. If you are able to anchor your data definitions to those of finance, you will increase your credibility and be able to provide insights that finance and business leaders find valuable. For example, after you’ve identified the metrics that the business and HR focus on, you can work with finance leaders to ensure that these metrics and their definitions are sound. Mariëlle Sonnenberg, Global Director of HR Strategy & Analytics at Wolters Kluwer, did this and also obtained the actual data from the finance group. This approach allowed Mariëlle to focus on a targeted set of key HR metrics that were most valuable to the business. “If finance is not on your side, the workforce analytics function is not going anywhere.” —Placid Jover Vice President of HR, Organisation & Analytics, Unilever Another reason to gain finance support is funding. Some level of funding is necessary for any workforce analytics team, and the finance function needs to sign off on that funding. Although the organizational leadership has ultimate decision-making authority when it comes to budget allocation, the finance team is responsible for managing budgets within overall spending guidelines and regularly discusses tradeoffs with leaders. You certainly don’t want your workforce analytics project (or, worse, your team) to be on the proverbial chopping block in such tradeoff discussions. ******ebook converter DEMO Watermarks*******



Spend time with finance leaders to help them understand the value your workforce analytics projects will bring. Share examples of workforce analytics projects in other businesses that have yielded measurable benefits, as well as your own business case projections. In most organizations, peoplerelated expenditures are among the largest, and managing that expenditure analytically makes sense. The idea of using a fact-based approach to optimize the organization’s investment in people should resonate with finance professionals.



BRING FINANCE ALONG WITH YOU Martin Oest2 has helped transform workforce analytics at different organizations, including the Metropolitan Police3 in London. One of his top tips is to bring your finance colleagues along with you. When asked how he did this, he revealed: “At first, I lost them!” But he then went on to explain how he won them over. “I organized regular meetings with my finance colleagues,” Martin says. “At first it’s just about making friends. Sometimes it feels really hard because it’s so much about personalities and getting on with someone. It is demonstrating to them that we have something in common; that I understand the numbers.” This might sound straightforward, but the reality can be very different. According to Martin, the focus is on being persistent, learning about the business, and understanding where the revenue comes from and where money is spent. At the Metropolitan Police, Martin spent a lot of time talking with his finance colleagues about common goals: “At the time, we had big goals to reduce costs, yet improve and increase the number of police officers. So I talked at length about how it’s important to get down to the detail of costs (workforce-related costs) per level of officer. “I tried to bring process rigor to organize the data and perform the analyses. At first when I asked for detailed costs, I got voted down. But later on, as my analysis matured, the finance team came back to me and said that I should really have this data. I tried to make it easier for them by showing them that it will actually save them time if they just let me have the data.” ******ebook converter DEMO Watermarks*******



The support of colleagues in finance gets easier, Martin says, when you can demonstrate these five points: 1. You are an expert at analytics and understand numbers. 2. You have a process and methodology. 3. You will save people time if they give you the raw data. 4. You are working toward a common business goal. 5. You are friendly. Martin says it’s impossible to overstate the importance of the involvement and support of finance: “You need to bring finance in. There was a lack of belief we could deliver; they had almost given up. In the end, I delivered a lot of value and now we all work well together.” Suggestions for Engaging Finance • Work with your finance team to share data and agree on data definitions, especially those related to headcount and attrition. • Learn how your organization views the workforce from a financial perspective (for example, types of workforce-related costs). • Share cost-benefit analyses of workforce analytics projects, conveyed in a way that is consistent with the finance team’s method of working. • Discuss the return on investment (ROI) that is expected from analytics projects (referencing actual experiences, where possible). • Be prepared to discuss the investment needed to execute workforce analytics projects.



Unions and Works Councils Unions and works councils advocate for workers and protect their rights and interests. Naturally, they are protective of the people they represent, and they may be concerned that their members are the subject of analytics projects. Work in concert with these groups to find common ground and demonstrate how analytics will improve employees’ work experiences. Andrew Marritt of OrganizationView shares his experience and advice: “Works councils are a constant challenge. Our recommendation is to highlight reasons the ******ebook converter DEMO Watermarks*******



employees will benefit from the project. Be open and transparent, and engage the stakeholders early on.” When involved early in a project, unions and works councils can bring a real benefit to workforce analytics projects. They can help anticipate a range of implications and ensure that projects are handled sensitively and effectively. Sadat Shami, Director of the Center for Engagement & Social Analytics at IBM, emphasizes this for projects that use social data and mini-polls: “We began in those countries where regulations allowed so that we could get started with the project, then spent a lot of time with works councils in those countries where we needed their agreement. It took a long time. In one country, it took almost two years, but it was worth it. The project is still going strong, and the amount of insight we are getting is valuable to executives and employees across the world.” Suggestions for Engaging Unions and Works Councils • Engage early and often, and listen to concerns and suggestions. • Convey how the analytics project(s) will benefit the workforce, with specific examples to make it real (such as better career development or more equitable HR practices). • Provide feedback on positive outcomes, including employee testimonials, where possible.



Stakeholders Impacted By their very nature, workforce analytics projects will have an impact on people. The individuals most likely to be affected are the very people whose data are being analyzed (the workforce), the people who will be asked to administer those changes (their managers), and the people who will model and support the changes (their executives). Always keep these stakeholders in mind when analyzing data and formulating recommendations based on the results.



The Workforce Workers are arguably the most directly affected by workforce analytics actions. The workforce analytics team should never lose sight that each and every worker is a unique individual, not merely a collection of data points. ******ebook converter DEMO Watermarks*******



Workforce analytics deals with people, and when it comes to implementing actions that result from the analytics, you need to remain aware of the impact on people. “I do this work not only because it can drive organizational performance, but also because it can improve people’s lives.” —Al Adamsen Founder and Executive Director, Talent Strategy Institute The analytics might point to a very clear finding and recommended action, but implementing that action might not be so straightforward. For example, singling out a group of critically important employees and treating them to special benefits might stem a specific attrition problem, but can you afford to potentially alienate the 90 percent of the workforce that doesn’t receive the benefit and is needed to keep the business operating? Implementing actions for some while managing the effect on others is certainly possible, but ample time and careful planning are needed to get it right. Openness with employees about workforce analytics is recommended. Organizations should disclose that they are managing HR with a systematic, fact-based, equitable approach. Communicating the benefits should help allay any concerns employees might have, says Mihaly Nagy, CEO of The HR Congress and Managing Director of Stamford Global: “Probably there are questions among employees about what HR analytics can do for them, what benefits it can provide. With the right communication and education, this resistance can be managed.” Suggestions for Engaging the Workforce • Gather feedback from representative samples of workers to identify issues (such as inhibitors to productivity) that can be addressed through workforce analytics. In other words, listen to the voice of the employees. • Encourage employees to maintain and share their data, and demonstrate the benefits (as well as the downsides of using old or inaccurate data). • Share actions with employees that result from analytics projects and gather feedback on those actions; gather suggestions for additional areas where workforce analytics can be beneficial. ******ebook converter DEMO Watermarks*******



Managers The actions resulting from workforce analytics projects often fall to managers. For example, if an analysis is undertaken to identify predictors of turnover and the findings reveal that compensation is an issue, managers of employees at risk of leaving might be asked to issue pay raises in a manner that the analytical model indicates. If those managers were not informed of the analytics project and simply received directives to distribute their compensation budget in a prescribed way, they would likely dismiss the directive and go about their management responsibilities as usual. It is unreasonable to expect managers to follow such a prescribed path without informing them of the rationale or viewing them as participating partners in the analytics journey. Statistical models can often achieve relatively high levels of predictive accuracy, but they are still merely models. At the individual level, some degree of error and unpredictability will always exist. Managers deal with individuals. They should not be expected to relinquish their decision-making responsibilities to a statistical model. Instead, the goal should be to educate them on the value of the model and obtain their buy-in for using it to better inform their decisions. Peter Allen, Managing Director of Agoda Outside, shares his perspective on how to get this right: “After the last compensation cycle, we discovered that in some teams there was no correlation between performance and salary increases. Sometimes the same raises were being given to high performers and average performers, which was demotivating and an attrition catalyst. Our director of operations and compensation, Jeff Lee, used our analyses of the compensation figures to illustrate to managers how they should be making their compensation decisions more effectively. The impact of this intervention: Managers adjusted compensation in a number of cases, reducing the risk of attrition for high performers on their teams.” Suggestions for Engaging Managers • Understand managers’ issues and pain points. • Share examples of successful analytics projects and illustrate how managers and their teams have benefited. • Explain the rationale for actions you are asking them to take; demonstrate ******ebook converter DEMO Watermarks*******



the positive impact the actions are expected to yield and the negative impact expected if no action is taken. • Gather feedback post-implementation to determine the effectiveness of the actions and any obstacles encountered in executing them.



Executives Given their positional power and authority, executives play a particularly important role in workforce analytics projects. In addition to being recipients of workforce analytics outcomes, they are often relied upon to support the recommendations in words and actions, to carry the message forward and lead the way. Yet some executives might perceive an analytics project as threatening to their authority. Laurie Bassi of McBassi & Company observes, “Senior vice presidents sometimes have their own hypotheses and aren’t always interested to have them debunked. After all, they’re doing pretty well without analytics.” Executives who have built successful careers with their current style of managing and way of thinking might not be inclined to approach problems from the same perspective as the analytics team, and understandably so. This is an important possibility to be aware of because some executives might then withhold needed support or block actions. If your team experiences this situation, seek assistance from your project sponsors. Suggestions for Engaging Executives • Discuss current and past workforce analytics projects and how they have helped the organization become more successful. • Discuss how peer executives have benefited from workforce analytics projects. • Understand executives’ issues and pain points, and identify their views on what’s causing the issues. • Discuss how executives can help successfully implement projects. • If the executive offers alternative hypotheses or recommendations, model the expected outcomes of these alternatives and compare that with the outcomes expected from the analytics recommendations. • Gather feedback post-implementation and gauge the perspective at that time. ******ebook converter DEMO Watermarks*******



GAINING CREDIBILITY WITH EXECUTIVES Bringing evidence to business leaders is imperative to gain credibility in today’s world. Without this, you will not succeed. Executives expect it, and in the absence of good analytics (both the analysis and the communication of it), the leaders will make their own decisions. This almost happened once in Nordea.4 An executive intended to give salary increases to a certain segment of employees after the unexpected departure of some key individuals. Without any HR data analysis, it was suggested that attrition could be stemmed by giving the segmented group of people a pay increase. In stepped the head of HR and his team to challenge the hypothesis. They recognized that the executive in question was number oriented. Sofia Parveen, Wealth Management Remuneration and Development Specialist at Nordea Bank, was approached for the analytics task and started working with the relevant HR data. The company also engaged an external firm to do an analysis. “It was the executive’s first experience with this type of HR data, and we presented a story using facts, figures, and visuals,” Sofia recounts. “The data-based analysis led him to revise his original decision. In the end, we isolated the problem to a selected and smaller group of individuals.” The executive supported the analytics-based recommendations and helped implement the solutions through his leadership. The recommendations were less costly for the business and resulted in a reduced attrition rate when measured one year later. Adam Chini Nielsen, a workforce planning expert in Nordea, worked with Sofia on the salary benchmark analysis. He explains: “It’s important to partner with someone in the executive’s management team and find out about their particular business. We mapped the biggest competitors and did the market analysis. We got to know the business environment before we did the HR analysis.” Nordea’s experience shows that engaging people with the right analysis and the right story makes executives listen. And that builds credibility for the future, too. In fact, Adam and Sofia’s experience prompted the executive to come back for more. “Before doing the salary benchmark study, there was a sense of reluctance to listen to ******ebook converter DEMO Watermarks*******



HR, but after this episode, the executive wanted to know what more we could provide,” Adam explains. Nordea’s experience also highlights the value of workforce analytics answering a business question that really matters to both an executive and the wider organization. Focusing on the executive’s area of interest secures both the leader’s support and his or her appreciation for the value of workforce analytics in solving a pressing problem. Figure 8.2 summarizes the various stakeholder types. The figure shows recommended topics of conversation for engaging each group and the topics relevant to specific stakeholder types.



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Figure 8.2 Summary of Stakeholder Messaging



Working Effectively with Stakeholders As the previous sections showed, your team has many stakeholder groups with which to collaborate. The next section provides guidance on how to make the most of these relationships. Although the onus is on the analytics team to identify stakeholders and nurture the relationships, all parties have a role to play. Following is a summary of the roles and responsibilities of the analytics team and stakeholders.



Analytics Team Responsibilities The analytics team, particularly the analytics leader, must proactively identify the various affected stakeholders in the course of executing the team’s mission. The success of these relationships hinges on two-way communication, demonstrating the connection between HR and business outcomes, driving toward action, and helping stakeholders succeed. Listening and Communicating An important responsibility of the analytics team is to understand each stakeholder’s perspective on specific projects and on analytics in general. Knowing where they stand allows you to tailor your communications and discussion points to the stakeholders’ needs and preferences. It also gives you the opportunity to help shift mind-sets, if needed, by asking a progression of thoughtful, logical questions that challenge conventional wisdom. The analytics team is also responsible for all necessary stakeholder communications. A systematic approach is recommended: Develop a communications plan, segmented by audience, and be proactive in executing the plan (and updating it periodically, as needed). Keep track of where each stakeholder stands with regard to analytics, and note any changes over time. Figure 8.3 shows a sample portion of a communications plan. An important component of communicating is listening, and the importance of listening to your stakeholders and learning from them cannot be overstated. Understand their challenges and concerns, their goals and priorities, and the reasons for any hesitancy or doubt in what your project can accomplish. Thomas Rasmussen at Shell recommends, “Put yourself in the ******ebook converter DEMO Watermarks*******



place of the decision maker. Make sure that the analysis informs the decisions and that the input is relevant.” You certainly do not want to discover at the end of your project that all your work is deemed irrelevant.



Figure 8.3 Sample portion of a communications plan. Translating HR Matters to Business Matters The analytics team needs to view the problems it can address through a business lens. For example, if you are addressing an attrition problem, ask why it’s a problem for the organization: How does it negatively impact the business? What does it prevent the organization from achieving? Convey this way of thinking to your HR stakeholders. Similarly, the analytics team should help leaders understand how people issues impact business challenges. HR partners can help identify which HRrelated actions influence key business metrics. Then translate the challenges into analytics projects and ensure that your stakeholders see that connection. Getting to Action You can also influence the likelihood that actions recommended from your analytics projects will be implemented. Reflecting on experiences early in his career with previous employers, Ian O’Keefe, Managing Director and Head of Global Workforce Analytics at JPMorgan Chase & Co. took certain steps to encourage action. Ian computed statistical models to predict the outcomes if actions were not taken and showed those scenarios to stakeholders: “We showed trends, told them what would happen next week or next month if we ******ebook converter DEMO Watermarks*******



did nothing and what would happen over those time periods if we made some changes. For example, we showed the overall cost of not managing poor performance, in dollar terms. We showed them that this was pain that the business didn’t need to live with.” Helping Stakeholders Succeed Finally, you can greatly increase the likelihood of a successful stakeholder relationship by making it your goal to help your stakeholders succeed. Arm them with insights to help them create value for the business.



Stakeholder Responsibilities Healthy relationships are two-way, and stakeholder relationships are no exception. Stakeholders have responsibilities, too. Ideally, they will share their knowledge and expertise, challenge the analytics team’s thinking, suggest hypotheses to test, and be prepared to be surprised. Most important, stakeholders need to take ownership of conclusions and actions, and they need to acknowledge the full set of findings, not just those that confirm their beliefs. “We wanted them to commit to take action on the insight—we are strict about that at the beginning. We also say you cannot choose the insights you work on, you have to do all of them. Otherwise, they take only what’s convenient or what they like.” —Patrick Coolen Manager, HR Metrics and Analytics, ABN AMRO



Planning the End, from the Beginning It’s always useful to think your analytics project through to the end before you even begin the analysis. What if you don’t find any meaningful relationships in the data? How will stakeholders react if you merely confirm the obvious? What if you find counterintuitive results? What if the insights support actions that aren’t feasible to implement? All these scenarios are possible legitimate outcomes, and it is best to be prepared so you can prepare your stakeholders, set expectations accordingly, and discuss implications of such outcomes in advance. You should also think about the possible actions that will result if you do find ******ebook converter DEMO Watermarks*******



meaningful results in the data analysis. Getting support for various potential actions before you undertake an analysis is helpful. If the organization is unwilling to implement actions that the analyses recommend, your team has little reason to take on the project. Finally, assuming that the analysis yields meaningful insights and actions that can be implemented, you should also plan early for evaluating the effectiveness of those actions. Sometimes actions yield precisely the results needed; other times, projects do not work out as planned. A proper evaluation of action effectiveness will let you know whether you achieved what was expected or whether you need to course-correct. An evaluation study also helps you quantify the benefits of the actions, which will prove instrumental in securing support for additional analytics projects.



Summary Working effectively with stakeholders is essential for workforce analytics success. Relationships are needed with a wide variety of people throughout your organization. Approach the task of stakeholder management thoughtfully and systematically, with the following actions: • Identify the various stakeholder groups, taking into account those served by the analytics team, those you are dependent on, and those whose working lives might be affected. • Determine your stakeholders’ perspectives on analytics and adjust your communications accordingly. • Be cognizant of data privacy and security requirements, and seek legal expertise to successfully navigate the requirements. • Create, execute, and maintain a communications plan to structure your interactions with stakeholders, including key messages and discussion topics for each. • Help stakeholders understand and fulfill their roles and responsibilities. • Bring value to your stakeholders; make them more successful, acknowledge their contributions, and listen to and address their concerns. • Plan for the end from the beginning: Think through and discuss potential analytics results and recommended actions, as well as how you will evaluate actions that you implement. ******ebook converter DEMO Watermarks*******



1 Patrick Wright, Ph.D., is the Thomas C. Vandiver Bicentennial Chair in Business and Director at the Center for Executive Succession, Darla Moore School of Business, University of South Carolina. He was previously a professor of strategic HR at Cornell University. 2 Martin Oest is an award-winning workforce planning and people analytics expert based in the United Kingdom. Educated at the University of Gothenburg and Copenhagen Business School, he has helped organizations such as the Metropolitan Police, Barclays, and Homeserve improve their business through analytics. In April 2016, he was the winner of the Individual Achievement—People Analytics Award presented by Tucana. 3 The Metropolitan Police Service is the police force for London, covering a population of 7.2 million. It employs approximately 31,000 officers, together with about 9,000 police staff and 1,500 Police Community Support Officers and 2,800 volunteer police officers as of January 2017 (www.met.police.uk). 4 Nordea Bank AB is the largest financial services group in the Nordic and Baltic region. Nordea is headquartered in Stockholm and has more than 10 million customers. The bank’s roots run deep; its family tree includes approximately 300 banks in the Nordic countries, founded from the 1820s onwards (www.nordea.com).



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9 Get a Quick Win “You need to calculate the financial impact of Human Resources practices on workforce outcomes. That’s really what people are interested in, so pick your first project carefully.” —Patrick Coolen Manager of HR Analytics and Metrics, ABN AMRO Successfully executed analytics projects are always important, particularly when establishing your credibility. The way your first project is chosen, how it is completed, and the results it delivers all send clear signals to your stakeholders about you and how the workforce analytics function will fare in the future. Because your first project should deliver business impact and be relatively easy to complete, we refer to it as a Quick Win project. This chapter covers the factors to consider when choosing your first workforce analytics project, including the following topics: Identifying Quick Win projects Using a complexity-impact matrix to enable project assessment Understanding what makes projects complex Gauging the likely impact of a project



AN INSPIRED FIRST PROJECT Eric van Duin leads the HR Information Systems and Analytics function at PostNL N.V.1 He shared the details of the project that first got him started: “I was reading the newspaper one weekend and saw a story about how a Ph.D. researcher had investigated the relationship between the age of a manager and the engagement of the team. It got me thinking, ******ebook converter DEMO Watermarks*******



so I started doing a similar analysis at PostNL. While insights like this would not be used for decision making on individual employees,2 they could be valuable for informing HR policies relating to training and communication awareness programs.” Eric found that, at PostNL, the tenure of a manager strongly correlated to the engagement of the team. The shorter the tenure of the manager, the higher the team’s engagement. The study presented useful information that enabled Eric to help longer-tenured managers better engage with and manage their teams. The data also allowed the HR team to look at processes and policies for managers (for example, revising the training for managers with longer tenure). “In this first project, I opened the eyes of HR leaders and more senior executives,” Eric says. “I shone a light on an important topic, engagement, with some hard evidence. It was the project that helped me get started and gave the function credibility.” Since this project’s completion, Eric has had senior business leaders coming directly to his team wanting to understand the human factors that influence business outcomes, such as delivery quality. Clearly, Eric established credibility for his team. When you show that you can complete important projects and provide evidence, the business leaders will come directly to you.



Identifying Potential Projects Before you can prioritize projects, you need a list of projects to consider. Adopting a consultancy approach to identifying your projects can be effective. A consultancy approach includes interacting with prospective sponsors about projects you could complete on their behalf. The point to remember when talking to business leaders is that your projects should relate to your organization’s key performance indicators (KPIs) and the way people influence these indicators. While you’re doing this, to get inspired, you can review the examples in Chapter 6, “Case Studies,” and talk to peers in other organizations. Then, using this chapter’s structured approach to thinking about complexity and impact, you can prioritize your list of projects to decide where to start. Importantly, closely following our prioritization process enables you to plan ******ebook converter DEMO Watermarks*******



for most events that you will experience in delivering projects. Forecasting every eventuality is impossible, but careful planning helps you overcome most of the challenges you will likely encounter.



Complexity-Impact Matrix To help decide on your first project, a good approach is to plot the potential project opportunities on a two-by-two matrix according to the level of expected impact and the amount of complexity involved. Delivering a project of moderate-to-high impact makes the most sense; a project that does not have at least a moderate impact will likely go unnoticed. A project with lowto-medium complexity is also a good candidate; high-complexity projects take more time, and senior stakeholders might end up asking why your project is taking so long. Figure 9.1 shows the Complexity-Impact Matrix3 that results from this exercise. Notice that it includes four types of projects: Quick Win, Big Bet, Trivial Endeavor, and Pet Project. Readers of this book are likely approaching workforce analytics tasks from different perspectives. Some have a new role in a new function, others are new to the role but are joining an existing function, and still other readers are in the same role but with an expanded focus. Both experienced and inexperienced practitioners can encounter each scenario. For these reasons, it is important to consider these concepts relative to your level of personal experience and your function’s history. For example, one team’s Big Bet might be another team’s Quick Win. Similarly, a project that establishes an analytics functions’ reputation in one organization might be a standard project for a more experienced function.



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Figure 9.1 The Complexity-Impact Matrix for workforce analytics projects.



Quick Win A Quick Win is a project of low-to-medium complexity with moderate-tohigh impact. In short, this project is one that you feel confident you will be able to deliver in a reasonable period of time and with tangible results. Projects that involve uncertainty about the team’s capability and projects that rely on dependencies over which you have little or no control are not Quick Wins; they generally involve too much complexity to ensure a successful result so early in the function’s existence. Projects that involve only a small or moderate impact (for example, to the efficiency of an HR metric) also do not constitute Quick Wins because the results are unlikely to get noticed beyond HR.



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SIMPLE CHANGES, BIG IMPACT Senior managers at a public sector agency were interested in understanding the distribution and prevalence of sick leave across the organization. Marcus Champ,4 an analytics professional on the human resources team was tasked with reviewing the data and helping to develop an action plan to address the issue. A review of the initial data highlighted a distinct spike in sick leave during a few weeks in the year. After some investigation, Marcus noticed that the leave usage coincided with an annual festival that attracted thousands of local and nonlocal visitors over a short time period in a confined area. The festival brought obvious risks of exposure to and spread of illnesses such as the common cold and flu. The agency already had a flu vaccination program in place, but it did not seem to be having much impact. Management could not identify a reason why. Marcus noticed that the annual vaccinations were administered around the time of the festival. However, doctors indicated that the vaccinations required four to six weeks to take effect. This prompted a recommendation to reschedule the vaccination program to occur at least six weeks earlier the following year. Not only was such a move simple to administer, but it also came at no extra cost to the organization. The following year, the outcome was significant: Absenteeism was reduced by 5 percent. Marcus explains, “Now, 5 percent might not sound like a high reduction on the face of things, but when you cost this out, it equates to over one million Australian dollars, and it cost the company nothing to change.” Sometimes the least complex projects can have the most profound impact.



Big Bet A Big Bet project is a high-complexity project that is expected to deliver high impact. Numerous factors can make projects complex. For example, you ******ebook converter DEMO Watermarks*******



might still need to develop key internal or external relationships to execute the project. You might not have the data you need for your intended analyses, and even if you do, the data could be held in vendor systems or even governed by privacy considerations that prevent analysis. You might experience challenges from other functions that have overlapping responsibility for the area you are focusing on, and you might need to work with people in these areas at similar levels of seniority without having formal authority over them.



Trivial Endeavor A Trivial Endeavor project has low-to-medium complexity and low-tomedium impact. If possible, avoid projects with lower expected impact in the early phases of an analytics function’s development. The critical objective of your first project is to deliver results that make a material difference to the business and build confidence in your ability to undertake more projects. Trivial Endeavors are unlikely to fulfill this requirement. Even though they are not as desirable as other project types, it is useful to know what kinds of projects fall into this category. For example, it can help to reframe your project so that it resembles a more desirable project type, such as a Quick Win. The most common types of projects in the Trivial Endeavor quadrant are those that lead to decisions that management can make just as effectively without the use of advanced analytics. For instance, perhaps the company can benefit from cost savings or additional features by switching providers of an engagement survey or by shopping for a new supplier at the end of the term. These problems are generally manageable using standard administrative approaches.



Pet Project A Pet Project is overly complex for the impact it delivers. These projects are more aligned with personal interests than what the business requires. Avoid Pet Projects: These projects (and Trivial Endeavors as well) won’t likely get you the positive recognition you require to build the support you need for your workforce analytics function. In fact, they could end up attracting negative attention. To make matters worse, projects in this quadrant are complex to execute because of factors such as the political environment, data ******ebook converter DEMO Watermarks*******



requirements, skill gaps, data complexity, or project scale. You might find it difficult to imagine real-world workforce analytics projects that fall into this space, but they do arise. Consider one anecdote that perfectly describes the idea of a low-impact, highcomplexity Pet Project: A business analytics manager decided to crowdsource machine learning capabilities to address an analytics problem for the business. He offered a prize for its solution. A contractor who was crowdsourced solved the problem, but by that time the business could not implement the solution because it had changed the way it operated. The analytics manager was not concerned, though; he was happy that the challenge had been won, even though it did not ultimately have any business impact. Nothing is wrong with being passionate about analytics in HR, but it should always be pursued with the goal of making an impact on business effectiveness or worker well-being.



Assessing Complexity and Impact The two important criteria discussed above when prioritizing projects are complexity and impact. In terms of complexity, consider five broad factors when rating potential projects as low or high: • Politics • Skills • Data • Technology • Implementation When it comes to impact, three broad factors play into rating projects as low or high: • Return on investment (ROI) • Timing • Opportunity cost



Assessing Complexity Project complexity refers to the scope of the challenge you face in delivering the project. Complexity is a relative concept. An advanced workforce analytics function might consider a project low complexity, yet a nascent ******ebook converter DEMO Watermarks*******



workforce analytics function that lacks access to more advanced technologies and resources might consider it high complexity. Therefore, your team must always evaluate the project’s complexity in terms of its own capabilities and the context in which you are operating. This section covers the five factors to consider when assessing the complexity of a project for your team. “Make sure to evaluate the complexity of the problem before embarking on long and sophisticated approaches to solving problems.” —Michael Bazigos Managing Director, Global Head of Organizational Analytics & Change Tracking, Accenture Strategy • Political complexity. Gerald Ferris and Michele Kacmar developed an influential model of political perception at work in their 1992 Journal of Management paper. The model shows that the way workers perceive the political landscape in organizations is affected by both personal factors about themselves and organizational factors (such as how hierarchical and formalized the organization’s structure is and how much interaction colleagues have with each other). Perceptions of the political context then impact productivity at work. To succeed with workforce analytics, you need a finely tuned sense of political judgment to identify where you have backing for your ideas and where you don’t, and which of your ideas are worth pressing when you encounter resistance. You will certainly encounter people who support your goals, and reserving your energy for only these people is tempting. But be sure to pay attention to areas of the business where you do not have support. Ensure that you have accurate answers to the following questions: Do senior executives agree that the project is worthwhile? And do they agree that your team is the best one to tackle the project? The less consensus you have, the more politically complex the project might be. • Skill complexity. Analytics projects require a level of expertise in several areas. In particular, projects that require any of the Six Skills for Success (see Chapter 12, “Build the Analytics Team”) that your team lacks (or lacks in depth) are more complex. Ask yourself whether the people resources you have for the project are sufficient to deliver the services required for the project. A lack of technical or statistical skills means a ******ebook converter DEMO Watermarks*******



higher degree of skill complexity. If you do not currently have the skills, you will need to hire in, develop, or partner to fill the gaps. • Data complexity. The process of data collection and integration rarely proceeds uneventfully. Higher data complexity exists when creating the dataset required for analysis is difficult or impossible because of data access or characteristics. For instance, you might not be able to match cases across datasets if they lack a common unique identifying variable. For example, compensation data and turnover data will be difficult to link if compensation data are stored against employee identification numbers but turnover data are stored alphabetically according to surnames. Another situation that leads to data complexity arises when privacy concerns prevent reanalysis of data that were originally collected for a different purpose. For example, linking selection data to individual development records might not be possible if employees were told that development data were to be used strictly for personal development. Nearly all data challenges can be overcome (see Chapter 10, “Know Your Data”), but often this requires specialist knowledge and expertise. When choosing your first project, consider your present ability to address the specific data challenges for the project. • Technology complexity. Many straightforward projects do not require substantial investment in resources. Some projects can be undertaken with basic spreadsheet software. Still, it is good to remember that recent developments in data management technology using cloud-based computing make most of the technology and techniques available to organizations of all sizes for a reasonable cost. Regardless of the technology required to deliver your first project, you need to clearly understand how well you are currently equipped to meet the demands of the project. Does the project require the use of specialized data management or data analysis technologies that are beyond the technologies you currently possess? For example, a project that requires integrating a data capture approach for streaming data or using special technologies for handling very large datasets is more complex than a project that requires analysis of a single snapshot of data. • Implementation complexity. Organizational change theories make it clear that the best way to get a group of people to behave differently is to ******ebook converter DEMO Watermarks*******



let the people who will be affected by any changes help you decide what to do. You often hear this captured in the wise maxim that change should be implemented “with people, not on people.” Change processes take hold over time as people consider and, hopefully, become accustomed to new ways of doing things. The more change that is required and the more people that are affected by the change, the greater the implementation complexity. Carefully consider how easy it would be to implement recommendations that result from your analyses. The lower the level of change and the fewer people being asked to do things differently, the easier it will be to implement the recommendations. This is because these recommendations will require less training, communication, and stakeholder management to bring about change. If analytics leads to recommendations that require many people to behave in different ways, the project has a higher level of implementation complexity. A balance must be struck, of course: If change affects too few people, it will have lower impact. You can use these five complexity factors to help classify your initial project. In the early days, pick a project that is low-to-moderate complexity so that you will be able to successfully complete it in a reasonable time frame, clearly establishing your value to the organization. Keep in mind that what a well-established, highly experienced analytics function considers complex might not be the same for a recently established function.



Assessing Impact Impact is the level of benefit the business receives from undertaking the analytics and implementing the follow-up recommendations. When considering the expected impact of an analytics project, practitioners should have three issues in mind: return on investment, the timing of the project returns, and the opportunity costs of not undertaking other projects. “Learn the logic of the business. What makes the business more successful? How do people contribute to achieving this success? This is how you identify projects that will make an impact.” —Marcus Champ Senior Manager, HR Analytics, Standard Chartered Bank ******ebook converter DEMO Watermarks*******



• Return on investment. The central aim of workforce analytics is to realize business efficiencies and take advantage of opportunities. Therefore, it is difficult to discuss the idea of impact in workforce analytics without some discussion of cost. At its simplest, the issue comes down to whether the expected cost of the project is less than the expected return to the business from completing the project. This concept might seem straightforward, but the cost decision is not quite so simple. Managers must consider the cost of the project relative to the returns it will deliver in relation to the next two factors (when precisely the benefits will be realized and the opportunity cost of not investing elsewhere). • Timing. The project’s timing issues can often be addressed by asking yourself whether the project is focused on reducing costs or improving productivity. For the most part, cost reductions are quicker to realize than productivity gains. Therefore, focusing initial Quick Wins on cost reduction might make sense. The impact of a project, like its complexity, is relative to the business and the situation. In general, select a project that will have a short-term impact; otherwise, it cannot really be considered a Quick Win. Although few people ever intentionally undertake an analytics project that will not have an impact, this situation does happen. For this reason, business executives must have clear insight into the project to make sure that the improvements the workforce analytics team is predicting are clearly tied to business expectations. • Opportunity cost. When considering the impact of a project, it is important to realize that the evaluation must occur in the context of other possible workforce analytics projects—and also in the context of other possible business projects. Even highly appealing projects that are seemingly low complexity and high impact might be ranked behind other projects when all options are considered. For this reason, it is important to simultaneously consider several projects for impact, in case this process reveals that another project is even more attractive than one you are ready to initiate.



Summary Selecting your first workforce analytics project can be a difficult challenge, but taking a systematic approach to considering both complexity and impact ******ebook converter DEMO Watermarks*******



ensures that you make the most appropriate choices. In particular, remember the following guidelines: • Spend enough time planning your project to address all the hurdles you expect to encounter, but be prepared for the unexpected hurdles that will invariably arise. • Identify potential projects that relate to the organization’s key performance indicators. • Classify your projects according to their complexity and their expected impact, and go for a Quick Win that is low-to-moderate complexity and moderate-to-high impact. • When rating the complexity of the project, consider the following factors: politics, skills, data, technology, and ease of implementation. • When rating the expected impact of your first project, remember that the project should deliver a sufficient return. The benefits should also be realized in the short to medium term and should offer a greater net return than investing in any other workforce analytics project.



1 PostNL is the premier provider of postal and parcel services in the Netherlands. Each day, PostNL delivers more than 1.1 million items to 200 countries. In addition PostNL operates the largest mail and parcel distribution network in the Benelux (Belgium, Netherlands, Luxembourg) region (www.postnl.com). 2 In some countries, including the Netherlands, using employee age in employment-related decision making is considered discriminatory. 3 The Complexity-Impact Matrix is a copyright of the authors of this book: Nigel Guenole, Jonathan Ferrar, and Sheri Feinzig. 4 At the time of discussion, Marcus Champ was a senior manager in HR Analytics at Standard Chartered Bank, based in Singapore.



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III Building Your Capability 10 Know Your Data 11 Know Your Technology 12 Build the Analytics Team 13 Partner for Skills 14 Establish an Operating Model



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10 Know Your Data “There’s a tendency to think all the HR data need to be together in one place to get started, but this is not the case. Don't fall into the trap of unnecessarily postponing analytics." —Peter O’Hanlon Founder and Managing Director, Lever Analytics Data are the very foundation of analytics. Without data, there are no analytics. As we emphasized in Chapter 7, “Set Your Direction,” the starting point for analytics should always reflect your vision and include a clear articulation of what you are striving to accomplish. However, achieving that vision and mission is not possible without data. This chapter covers the following: • A practical approach to data • Data challenges and solutions • Types and sources of data • Data governance



A Pragmatic View of Data All analytics projects require data, but they do not require data perfection. High data quality should always be a goal, but the pursuit of complete, perfectly clean data shouldn’t be an impediment to progress or a reason not to undertake an analytics project. In many cases, data are incomplete, inconsistently defined, outdated, missing, “dirty” (containing errors of some sort), or stored in multiple disconnected systems. The challenges are real and numerous, but they are not insurmountable. Techniques do exist for dealing with all these issues and more. Those who ******ebook converter DEMO Watermarks*******



have faced these problems successfully agree that the goal is to do your best with the resources you have. You will make progress. You will bring valuable insights. And you will improve as you go. Laurie Bassi, CEO of McBassi & Company, emphasizes, “Do what you can with what you’ve got. You can still move forward.” The good news is that most organizations have plenty of data for workforce analytics and ample opportunities to address business questions with existing data. It’s important to note that many organizations have restrictions on who can access certain data and for what purpose, and sound justification for data requests is needed. Be sure to allot sufficient time to address data-related issues, but by all means, forge ahead.



Solving Data Quality Challenges Data analysts routinely take several steps to assess data quality and determine the best path forward. This section discusses common approaches. In addition to understanding these fundamentals, it can be helpful to remain current on the latest views of data challenges and solutions by participating in relevant online forums and user groups. “The biggest challenge? Data. Not many organizations have a global data warehouse. Data aggregation, data cleansing, having a single trusted source —these are the things we spend most of our time on with clients, not analysis. The latter turns out to be straightforward once the data are in shape.” —Michael Bazigos Managing Director and Global Head of Organizational Analytics & Change Tracking, Accenture Strategy



What Is Good Enough? The usefulness of analytics hinges on the quality of the data being analyzed and the relevance of the data to the business problem. The well-worn phrase “garbage in, garbage out” is wholly appropriate in the context of workforce analytics. That said, all hope is not lost when faced with imperfect datasets— expecting data nirvana is unrealistic. Don’t become so consumed with trying to fill all data gaps and fix all problems that you lose sight of the overall analytics objectives. Data issues will always arise. When you have confirmed the relevance of the data, the question becomes, how do you know whether ******ebook converter DEMO Watermarks*******



the data quality is good enough for the project you are undertaking? To answer this question, you must get familiar with the data. In many cases, this means learning from others’ expertise. You need to know what to look for when examining the data so that you can proceed with analysis. As a very simple example, suppose you have a data element that represents people’s ages, and you see negative values (for example, –11, –29, –4). You know something is wrong. Most people can be considered subject matter experts in such a common domain (that is, we all understand the age variable and what values are associated with it; an age of –11 does not make sense). Now suppose you have another data element representing sales. What if you see negative values in this field (such as –$151,783 or –$22.99)? Does this indicate an error, too? Perhaps, but if you check with experts in sales operations, you might learn that negative sales values are, in fact, valid and represent a cancelled order or a renegotiated price from a previous transaction. Negative sales numbers might seem wrong at first (surely the organization didn’t pay people to take their products), but investing time to understand the data before analyzing it can clarify the situation and confirm the validity of the data. Working with subject matter experts helps you educate yourself about what to look for and how best to address any problems or unusual values in the dataset (and also avoids rework and the need for post-analysis troubleshooting). Automated data profiling can also help in overcoming data challenges. Data profiling refers to checking datasets for allowable values, logic, and consistency. Data profiling tools (available as open source software or from vendors) analyze data for consistency with business rules and provide recommendations on areas to investigate further in a dataset. After profiling your data, how do you determine whether the data are “good enough” to proceed with analysis? Again, you turn to the data owners. They are best positioned to know when the data quality is sufficient to produce useful results. If you can expose people to their own data for validation, even better. People will often spot errors in the information about them, and providing this type of visibility can serve as a useful crosscheck of data validity.



Common Data Challenges and Solutions What if you determine that the data are not good enough to proceed with ******ebook converter DEMO Watermarks*******



analysis? The first step is to understand the challenges. Sometimes the data element you want to analyze has missing values for some cases. Sometimes the data haven’t been refreshed and, therefore, are not reflecting the most recent values. In some cases, the data you want to analyze do not even exist. Each of these scenarios might seem frustrating and even daunting, but there is almost always a way forward. Following are some examples to illustrate both the challenges and the corresponding solutions for addressing them. Missing Data Suppose you want to determine whether experiences at work differ for men and women. You might choose to conduct a survey to answer this question. Several participants in the survey might choose not to answer the question asking them to indicate their gender, resulting in output like Figure 10.1. With missing data such as this, you must determine how to proceed with the analysis.



Figure 10.1 Example survey data with missing values. The missing data challenge is common. Depending on the specifics (how ******ebook converter DEMO Watermarks*******



many data points are missing, the nature of what’s missing), you can apply methods to account for the missing data. The first step is determining the cause of missing values. Understanding the reasons data are missing is very important and can serve as a guide to determining whether the dataset can be used as is. One consideration is whether the data are missing for random reasons or reasons that actually relate to the topic of study. If some data fields are blank because of intermittent data entry errors, for example, these are likely random. In contrast, suppose that you are studying attitudes about privacy. The people most concerned about data privacy are less likely to respond to certain questions on a survey because they do not trust what will happen with their data. In that case, the very topic being studied is directly related to the cause of missing data—that is, respondents who are sensitive to privacy issues intentionally skip questions they believe could be used to identify them. Ignoring the missing data and proceeding with the analysis would likely lead to incorrect conclusions in this case. In this type of scenario, the best solution might be to use a proxy variable (that is, a different variable) for the desired measure or to identify a different method to study the topic of interest. For this specific example, observing people’s actual online behaviors (what privacy settings they choose, for example) might be a better course than asking for opinions via a survey. Although an observational study could be more difficult to conduct, the increase in validity will yield far superior insights. If you have verified that you can proceed with the analysis, you must determine the best approach to address the missing values. One option is simply to eliminate cases with missing values from the analysis, although this can reduce the representativeness of the sample (causing biased results) and the overall sample size (which is undesirable—in general, the more data, the better). Alternatively, missing values can be “filled in” by estimating them, using appropriate assumptions or modeling techniques (such as regression equations to estimate the missing values). Dealing with missing data requires a degree of expertise, and data profiling tools (discussed earlier) can help. If your team is inexperienced in dealing with missing data, you might also want to seek guidance and counsel from data experts elsewhere in your organization. Marketing and finance functions often have people with these skills. Another option that has served many ******ebook converter DEMO Watermarks*******



practitioners well is to hire a data scientist directly onto the workforce analytics team. If direct hiring is not feasible, working with an intern offers a cost-effective way for some analytics functions to acquire the needed expertise and keep projects on track. Interns bring the added benefit of exposure to the latest analytical thinking and techniques. As another solution, companies can contract external partners to help with data issues. Outdated Data You might have access to the data you need for your analysis, but some of the values might not be up-to-date. As an example, suppose you want to determine whether compensation is related to productivity (to find out whether more highly paid people produce more). To do this analysis, you get a data extract from the organization’s core Human Resources Information System (HRIS). You learn that the dataset does not reflect recent off-cycle salary increases because the compensation system (which records salaries) has not yet synchronized with the core system. If you are able to source this information only from the HRIS, the current salary will be incorrect for the people who were part of this off-cycle salary program. You then need to determine the implications for your analysis. Judgment is needed to determine how much of an issue this is for your analysis and conclusions. If the number of outdated values is large enough to appreciably influence the outcome of the analysis, you will want to make every effort to obtain updated information. A sensitivity analysis can be helpful in determining whether updated values will significantly change the findings. If updates are required, work with data owners to identify the best option (some of which are described here). If data refresh cycles are frequent (or imminent), your best course of action might be simply to wait for the next refresh. For example, if the compensation system feeds the core HRIS monthly and you need the very latest compensation data, find the specific update schedules and determine whether it makes sense to wait. If waiting for a refresh does not fit with your timeline, you might be able to obtain access to the data from a different, more updated source for specific variables (for example, the source compensation system itself); then you can merge this data extraction with your master dataset. Another option is to manually update values, if necessary. If you opt for this ******ebook converter DEMO Watermarks*******



approach, make sure your updates match the subsequent source system updates. You always want to be consistent with a single trusted source of data. Technology known as change data capture (CDC) can automate the data update process. The goal of CDC is to ensure that data are synchronized across the organization; it achieves this by replicating data changes from a source system to other systems. Updates can be scheduled for specific points in time or even real time, and CDC mitigates risks associated with manual updates. If none of these options is feasible, you must collect new data for precisely the information you need. Depending on the importance of the analysis and the timing, this might be a worthwhile endeavor. No Data Available Sometimes no systems or databases have the data you need for an analysis. Imagine that employees in a specific part of the business are quitting their jobs at a high rate, and you need to determine the cause. You have several factors to consider, but perhaps the sponsor of the project is particularly interested in looking at promotion history (that is, when and how often people have been promoted to the next job level). However, you learn that no system has recorded promotion information. The data you need for the analysis simply do not exist. Does a lack of data mean that you cannot consider this variable in your analysis? As with other data challenges, one solution is to initiate a new data collection effort. However, you might be able find a better option with a bit of creativity and ingenuity: You might be able to approximate the data you need by using a combination of variables that do exist. For example, if you need data on people’s promotion history, you could look for instances in which people had a title change and a corresponding salary change. If someone got a new job title and a raise at exactly the same time, this combination of events is a strong indicator of a promotion and can be used to create what you need without any incremental data collection. Another creative approach is to consider external publicly available data as a proxy. For example, this could be represented by job title changes posted on LinkedIn.



Additional Data Challenges ******ebook converter DEMO Watermarks*******



Although missing, outdated, or unavailable data are obvious challenges to overcome, it’s important to be aware of less obvious data challenges as well. Specifically, characteristics of the data themselves can potentially lead an analyst astray if they are overlooked. The following sections discuss these types of challenges. Non-normal Data Distributions Many commonly used statistics (for example, mean-difference tests and regression) are based on assumptions about the data being analyzed. One important assumption is that the data are normally distributed: If you took many samples, calculated the mean (average) score from each sample, and plotted all the means in a frequency graph, they would look like a bell-shaped curve. Not all variables are normally distributed, though. Consider net worth as an example: Very few people have extremely high net worth values, relative to the overall population. Two common indicators of non-normal distributions are skewness and kurtosis, which are measures of a distribution’s shape. Skewness measures lack of symmetry in a data distribution (as in the net worth example); zero skewness indicates perfect symmetry, as would be expected in a normal distribution. Kurtosis reflects whether the lengths of the tails in a data distribution are extreme; zero kurtosis indicates tails that are neither longer nor shorter than would be expected in a normal distribution. See Figure 10.2 for examples of skewness and kurtosis.



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Figure 10.2 Examples of skewness and kurtosis. Why does this matter? If you use statistics that require normality, but this assumption is not met, the statistical tests could yield misleading results. The whole point of statistics is to build a fact base on which to inform decisions; if the fact base is inaccurate because analysis tools were misused, it runs counter to that goal. As described earlier, tests are available to determine whether your data meet the normality assumption. If you suspect they do not (see Figure 10.3 for another example of a non-normal distribution), you can either apply corrections to approximate normality in the data or use alternative statistical techniques for analysis. Experts such as data scientists, analysts, and industrial-organizational psychologists (within your team, from other functions in the organization, or outside partners) can offer guidance on how best to proceed.



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Figure 10.3 Example of a non-normal distribution of data. Data Outliers Outliers are values that are abnormally higher or lower than most other values in a sample of data. Identifying outliers in your data is important because a few extreme values can alter the results considerably. Sometimes outliers are legitimate values; other times, they are the result of a data error. Either way, they can lead to misleading conclusions. Tests are available to measure outliers. At a minimum, always examine the distributions of your data as a check before you run statistical tests (see the examples in Figure 10.4 to get a sense of what to look for when plotting data graphically). If you identify outliers, you need to make an informed decision on whether to include or exclude them in the analysis. Including extreme values might mask an important relationship or insight. Excluding them might hide a meaningful variation. Consult the data owners to help with this decision.



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Figure 10.4 Examples of data outliers: Single Variable Histogram (top) and Bivariate Scatter-plot (bottom). Inconsistent Data Definitions Another frequently encountered challenge is inconsistent definitions of the same data elements. As an example, you might want to join two or more different datasets, linking them with a common identifier. In one dataset, the identifier might be a six-digit, alphanumeric employee identification number. In the second dataset, the identifier might be the same employee identification number with a three-digit alphanumeric country indicator appended, making it a nine-digit character. And in yet a third dataset, the identifier might be a government-issued number such as a Social Security ******ebook converter DEMO Watermarks*******



number. How can you connect these datasets in this scenario? The first and second datasets are relatively straightforward but require a data calculation to create a new field in the second dataset. Let’s assume you are analyzing data from one country only, so the country indicator is unnecessary. Using computer programming or data modeling software, you can automate the removal of the three-digit country indicator from each case, resulting in an exact match of employee identifier in the first two datasets. (Alternatively, if you need the country indicator, you can automate the addition of that indicator to the first dataset, although that requires a few more steps.) For the third dataset, you can use a lookup function from data in the core HRIS that equates Social Security number with employee identification number. Apply a matching algorithm, and you then have three datasets with identical employee identifiers, allowing you to focus on the analyses of interest. Data inconsistency can also arise when information is not entered in a standardized way across the organization. Andre Obereigner, Manager for Workforce Analytics at Groupon, explains: “It’s very important to get highlevel agreement of HR metrics—which are most relevant for the organization and how we define them. For example, consider headcount: It seems easy, but does it include contingent workers? What about interns? Or are you only considering regular fixed-term workers? What about people on leave—are they headcount or not? Define the metrics you are using and write that definition down. It can be very challenging if different parts of the organization use different definitions.” Andre managed these metric challenges by educating the local HR teams through guidelines and videos showing how to record data in a standard way, and he advised on steps to ensure data quality. Andre also noted the importance of senior management support: When the head of HR is very focused on data quality, this also becomes a priority for the HR community. Ultimately, establishing robust data governance processes is good practice, as discussed later in this chapter. This helps in improving data quality and gaining efficiencies for the long term.



Data Types and Sources To get the most benefit from workforce analytics, it’s important to think broadly about the types of data to incorporate into analyses. Traditional ******ebook converter DEMO Watermarks*******



sources such as employee information stored in the core HRIS and other HR systems should certainly be considered in scope, along with non-HR data such as financial performance and customer satisfaction. Also think beyond your organization’s walls—for example, relevant social media data. New technologies such as sensors and “smart” devices are continually creating additional data sources to consider as well.



DON’T LET THE LACK OF ONE INTEGRATED HRIS STOP YOU Mariëlle Sonnenberg has been leading the Global HR Strategy and Analytics team at Wolters Kluwer1 since 2013. In the last few years, she has achieved a strong reputation both internally and externally. Although many workforce analytics practitioners begin their work using the data in the core HRIS for their analyses, Mariëlle did not have a single enterprise-wide source of HR data, and she was able to use that to her advantage. Instrumental to Mariëlle’s early success was the fact that she was not beholden to the enormous amount of reporting, metrics, and data that consume some analytics professionals when they have, or are implementing, an integrated HRIS. “When I started, we had many different systems that were not all integrated. We got our headcount information from our finance reporting systems. There were no specific analytical capabilities within the HR function. I therefore started by looking at questions asked of HR leaders, mostly customer-related questions—for example, how much revenue-generating capabilities do we have? I went to our finance system, which is accurate, and I started with their definitions. I began reporting to the CHRO and the CEO based on finance information.” While Mariëlle brought together the data she needed, she didn’t spend time implementing a single HRIS. Instead, she concentrated on the most important metrics to the business and used the most appropriate system for that metric when she needed it. “I remember a question our CEO had around varying personnel costs across countries and businesses,” Mariëlle says. “The analysis we ******ebook converter DEMO Watermarks*******



conducted had a great impact, and it prompted discussions about which workforce-related metrics would enhance our operational focus to drive cost and margin improvements.” Mariëlle could answer the personnel costs question without the need for an integrated HRIS. Instead of holding her back, the lack of such a system enabled her to focus only on core metrics and business questions like the one from the CEO. “This is where I got lucky,” she says. “We had nothing in terms of systems (so no complexity), and rather than consuming time with endless amounts of reports and data, I focused on a few important topics.”



Data from Inside the HR Function An organization’s core HRIS provides a ready source of relevant and analyzable data, with information such as tenure, promotion history, compensation information, job category, educational background, and various demographic variables. Additional data typically available in HR systems (often outside the HRIS) include learning history, performance ratings, aptitude scores, personality scores, skills, competencies, and employee engagement scores. As Chapter 11, “Know Your Technology,” discusses, you can extract these data elements directly from the HRIS and other systems, or you can access them through data feeds to reporting systems or data warehouses. These elements will likely form the core of many workforce analyses.



Data from Outside the HR Function Workforce analytics should strive to show a link between HR data and key business metrics. To do so, you need to bring together data that are often housed in different systems within the organization, such as financial or customer databases. This can be tricky because different systems have different owners. Patrick Coolen, Head of People Analytics at ABN AMRO, describes this challenge: “Sometimes we had trouble getting the data from the business. They said that they wanted to do it and they had the data. But it can be a struggle to get the right data in the right format on time. Management can say it is okay, but you also need a specialist to deliver the data to your team. And only then can you start connecting and cleaning the data.” ******ebook converter DEMO Watermarks*******



Advice from practitioners on accessing needed data is to build strong, trusting relationships early on with the owners of the different data sources. You will need to partner with the data owners throughout the analytics process, so these relationships are essential. In addition to obtaining access to the data, you will likely need help interpreting the data (as illustrated by the negative sales numbers referred to earlier). Expect this to be an iterative process. As you build positive relationships, set expectations that you will likely have periodic questions about the data over the course of the project, and secure permission for follow-up discussions and queries. Ultimately, it is best to establish roles (such as a data steward) and practices (such as building a business glossary or data dictionary, as discussed in the data governance section later in this chapter) to achieve efficiencies through repeatable data management processes. Initial contact with data owners typically comes from senior sponsors of the project. The sponsors identify the people in the organization who can provide data access. They can also remove any roadblocks encountered along the way. As Michael Bazigos, Managing Director and Global Head of Organizational Analytics & Change Tracking at Accenture Strategy, describes: “The stakeholders defined the problem and provided the political juice to access the data, and when we had trouble, we were able to get help from the sponsor. The road to change is lined with a thousand guardians of the past.”



Nontraditional Data Sources The early twenty-first century has been a time of data proliferation. It stretches the imagination to think about the volume and types of data the future will hold, especially with the emergence and growth of the Internet of Things (such as wearables, sensors, and tracking devices) and social media. This, of course, presents a tremendous opportunity for workforce analytics. In addition to the types of data that might typically be considered for analysis (employee engagement scores, years of service, performance ratings, revenue and market share, to name a few), new insights can come from tapping into less traditional sources of data. Consider social network postings. Not only was this information virtually nonexistent (or outside the mainstream) until the early 2000s, but the technology to analyze such large amounts of unstructured data was also out ******ebook converter DEMO Watermarks*******



of reach for most organizations. Subsequently, entire businesses have been built around the ability to capture and react to real-time insights (for example, the job review website Glassdoor can reveal to potential employees what it’s like to work for any one of thousands of companies). With this evolution, new sources of insights have opened up to organizations from publicly available external social media sites, internal intranets, and collaboration software. An example from IBM illustrates the potential power of this data source. In 2015, IBM had a policy of not reimbursing expenses associated with rideshare services (due to employee safety concerns in this newly emerging mode of transportation). Using an internal social media platform, an employee expressed frustration with this policy. The posting quickly went viral internally, with many employees responding and expressing support for ridesharing. An online petition arose. Within hours the company’s leadership became aware of this trending topic, thanks to the use of an analytics tool that detects rare events. And within 24 hours, the issue was discussed and a resolution agreed: The policy was changed to allow for rideshare service expense reimbursement (for more details, see Business Insider, 3 June 2015). This is an example of the need for evolving types of analytics for emerging types of data. Organizations are advised to keep pace with developments such as these so they can remain well informed and respond to the internal and external environment in a timely manner. Another nontraditional source is employee benefits call center data. Recorded information can be analyzed to determine which aspects of their benefits package employees find confusing or need assistance in navigating. Information such as this represents a ready source of “employee voice” data that has already been collected and stored. Metadata of website activity (also known as click stream or click path data) is another nontraditional data source to consider. This refers to tracking web page viewing behavior (for example, where and when people click on a page, time spent on a page, and viewing patterns). Companies can gain insight into the type of online content that is more or less valuable to employees, and not always in the ways expected. As an example, a company’s IT support function might be happy to know that people are spending a great deal of time on the information and support pages of the website. This could indicate that the content on those pages is helpful and worth visiting. However, an ******ebook converter DEMO Watermarks*******



alternative explanation is that people are spending time there because they are having difficulty finding the information they need on the main website pages. A traditional data collection method (for example, focus groups) can complement the valuable web tracking data in this situation and help explain the online behavior being observed. Recruiting is another area that can apply this technology to good effect. Evaluating a candidate’s click path provides a view of the candidate’s experience, shows where candidates drop out of the application process, and sheds light on the technology’s effectiveness in attracting candidates to jobs. Thanks to machine-learning technology, all this insight can be “learned” by cognitive systems, resulting in work experiences customized to an individual employee’s needs. Technology is potentially transforming the very nature of work and the way it gets done.



Bringing Together Different Data Sources To realize the potential impact of all these data sources, it is helpful to connect them. The reality is, data are typically stored in multiple places in any given organization, and systems are likely even more disparate if a company has had one or more acquisitions in its past. Knitting together data sources should not be the first order of business (as Chapter 9, “Get a Quick Win,” underscores, you are better served demonstrating the value of workforce analytics by first successfully executing a lower complexity project), but tremendous benefit can come from creating a mechanism for connecting data sources. Cloud technology help address this challenge, as discussed in Chapter 11. Data service providers can also be of great help, particularly in knowing what questions to ask, advising on best practices, facilitating the process, and defining reference architectures (descriptions of system structures) that account for various data types. The goal is to develop a manageable, ongoing approach that avoids having to re-create datasets time and again.



Data Governance After the first successful analytics project has been implemented and recognized, it’s important to take the time to invest in data governance. Data governance refers to comprehensive strategies, policies, standards, and rules for managing data in your organization. This includes decisions and ******ebook converter DEMO Watermarks*******



agreements on all things data—what data elements to measure and store, how to define each element, who is responsible for integrity and maintenance, who can access the data, and more. “Data governance has become a hot topic in HR. Ten years ago, it wasn’t a consideration. Data quality is recognized as important today, and it’s essential in getting to a level of analytics maturity with defined and repeatable systems.” —Jeremy Shapiro Head of Talent Analytics, Morgan Stanley One important aspect of data governance is ensuring accountability for data quality. Establishing roles such as data stewards can accomplish this. Another recommended practice is to establish a data dictionary or business glossary for each data source used. A data dictionary includes the business and technical definitions of the elements within a dataset, reducing the need to repeatedly define datasets. The data steward is typically responsible for establishing the business glossary. Data governance can be established incrementally, starting inside the HR function. It helps build a deeper understanding of the data, guides decisions on data management and access, and creates end-to-end data lifecycle management across various systems. Data governance is part of the larger workforce analytics governance discussed in Chapter 14, “Establish an Operating Model.” Taking the time to get this right (and agreed upon) will serve you well for subsequent data analytics projects.



Remember the Basics The world of Big Data and nontraditional data sources opens up a new realm of possibilities for workforce analytics, but it is important not to lose sight of the basics. In some cases, collecting a “small” new dataset might be better than analyzing a “big” existing dataset that does not contain the necessary variables or cases to answer your question accurately. Max Blumberg, Founder of Blumberg Partnership Limited, advocates: “Big Data is very fashionable at the moment but sometimes not very practical. Fishing in Big Data for relationships that may or may not exist is not the best use of time if there are business problems to be solved. Instead, if the analytics resources are put to work on specific problem solving, analytics teams are likely to see ******ebook converter DEMO Watermarks*******



a much better ROI.”



A DATA DICTIONARY BRINGS YOU CREDIBILITY Giovanni Everduin, Head of Strategic HR, Communications, and Change, used his management consulting experience to help bring credibility to the HR function at Tanfeeth,2 a relatively new business services company based in Dubai with approximately 2,300 employees. “When I arrived in 2011, I noticed there was no discipline for uploading HR data at the company. We couldn’t even answer a simple question like, how many people do we have today?” The company was growing incredibly quickly and needed to get a good grip on its basic data to be able to forecast and predict workforce costs. In addition, the CEO at Tanfeeth wanted to make the company a data-driven organization. Giovanni explains that HR couldn’t initially operate like that: “We were in a meeting and the CEO asked what the attrition rate was. Three people came up with three different answers. All were correct, but each used a different definition; it was embarrassing. We went away and worked with the finance function to get a clearly agreed definition. Getting that level of clarity and agreement is so important. Without that, it is just confusing.” Instead of jumping straight into the analytics, Giovanni focused on building a data dictionary. Giovanni advises using best-practice definitions for every metric and data element: “There are best practices I had from my work as a consultant, but you don’t need that background. Just do a Google search and you’ll find good definitions. Or go to SHRM3 or CIPD.4” Having a clearly agreed-upon set of definitions for all data elements and HR metrics allows the analytics function to build credibility and avoid the sort of situation Giovanni found himself in. The best approach might be to conduct an experiment to answer your specific question. For example, suppose a company is no longer satisfied with its ******ebook converter DEMO Watermarks*******



performance management system, and the HR team designs what it believes is a better approach. How will they know whether the new approach works better than the old? The best way to answer this question is to conduct an experiment (or a quasi-experiment, if a true experiment is not feasible). See Chapter 5, “Basics of Data Analysis,” for more information on research designs. In the performance management example, the HR team can implement the new approach in one business unit while other business units continue with the old approach. The team needs to define the desired outcome (for example, employees will be more motivated to improve their performance in the new system versus the old system). Next, the team needs to measure employees’ motivation before and after experiencing the new approach, as well as measure similar attitudes, at a similar time, for the control group of employees experiencing the old approach. Finally, the team needs to compare the measurements. If statistical analysis shows that employees in the new approach are more motivated relative to both their baseline levels and the control group, strong evidence exists to indicate that the new approach is better (at least, in terms of employee motivation). The term A/B testing, often used in marketing and web development, refers to a randomized experiment with two groups: a control group that experiences the current design and an experimental group that receives some variation of the design (the A group and B group, thus the name A/B testing). In the context of web design, the objective is to introduce a change to a web page and determine whether that specific change results in corresponding changes in an outcome (such as click-through rates for advertisements). By randomly assigning users to the A and B groups, and ensuring that the only difference in the web page is the one thing you are varying (for example, advertisement placement), you can confidently attribute any differences observed in clickthrough rates to the change. Similar approaches can be applied in the workplace. As an example, during benefits enrollment cycles, a company could randomly assign people to enrollment activities using either a traditional approach (the A group) or a digital assistant (the B group) and then track differences in benefit choices between the groups. If employees in Group B are choosing best-fit plans at a higher rate than employees in Group A, evidence shows that the digital assistant is beneficial. ******ebook converter DEMO Watermarks*******



Deciding Between Big and Small Data Previous sections discussed examples of Big Data (for example, social media postings) and more traditional small data (such as survey responses), as well as various data challenges that you will likely encounter along the way. How do you decide the best way forward? A pragmatic approach is recommended. Start with a clear idea of the questions you are trying to answer and find the best available data sources at your disposal to answer those questions. Then dive in with eyes wide open. Apply analytics to the best of your ability, being aware of potential pitfalls (highlighted in this chapter) and heeding the advice of experts as you go. But don’t get paralyzed into inaction; have rigorous debates, challenge yourself, and triangulate where you can (that is, use multiple data sources and techniques to point to the same conclusion). Know your audience and overdeliver on data quality, if necessary for stakeholder buy-in. But don’t lose sight of the purpose of the project, and don’t let the data become the project. In sum, strike the right balance. You will be much better off than if you rely purely on intuition and assumptions.



Summary Data are essential building blocks for workforce analytics, and relevant, highquality data are needed for quality results. The following guidance helps you strike the right balance between ensuring data quality and progressing the workforce analytics agenda: • Build relationships with data owners to facilitate data access and learn the details of the datasets (such as allowable data values, methods for interpreting the data, and ways to spot errors); utilize data profiling technology to assist with data checking. • Check data for missing values, determine reasons for the missing data, and take appropriate corrective action (drawing on expertise as needed). • Verify that you have the most current and complete version of the data needed for the analysis. • Determine whether you can create the data you need from the data you have, or find a proxy for the data you need. • Consider a full spectrum of data sources and choose those that best answer your questions. ******ebook converter DEMO Watermarks*******



• Take the time to establish data governance processes, including data steward roles, to ensure ongoing data quality. • Participate in online forums and user groups to stay current with the latest views on data challenges and solutions. • Hire or partner with a data scientist to assist with data decisions; an intern can be a cost-effective approach. • Recognize that data collection and analysis will be iterative and that you will refine and improve as you go.



1 Wolter Kluwers is a global leader in information services and solutions for professionals in the health, tax and accounting, risk and compliance, finance, and legal sectors. The company serves customers in more than 180 countries, maintains operations in more than 40 countries, and employs 19,000 employees worldwide. It is headquartered in the Netherlands and is listed on the Euronext Amsterdam (www.wolterskluwer.com). 2 Tanfeeth is a large-scale business service partner based in Dubai that handles back-office operations for the Emirates NBD Group. Tanfeeth was established on September 19, 2011 (www.emiratesnbd.com). 3 Society for Human Resource Management (headquartered in Virginia, United States). 4 Chartered Institute of Personnel and Development (headquartered in London, United Kingdom).



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11 Know Your Technology “When choosing technology, we suggest people start with, ‘What is it the business is asking you to do?’ Then ask yourself ‘Where would you like to go?’ This way, people can map out what role they want technology to play in the business.” —Jackie Ryan Director, Watson Talent Analytics, IBM Technology in workforce analytics refers to the combination of hardware and software services (that is, tools, applications, platforms, and so on) that facilitate the activities of the workforce analytics team by increasing speed, accuracy, and the level of automation of work. Technology helps workforce analytics scale because it makes tasks more easily repeatable and also makes addressing compliance requirements more straightforward. On one hand, discussions about workforce analytics technology can entail incredible complexity. This potential complexity is compounded by the rate at which new technologies emerge and existing technologies are rendered obsolete. On the other hand, the core elements of workforce analytics technology are straightforward because certain common requirements across organizations are unlikely to change for the foreseeable future. This chapter covers the following points: • Vision and mission as the starting points for technology discussions • Components of workforce analytics technology • Decisions about sharing, subscribing, or owning technology



Starting with Vision and Mission An overarching principle is to understand your starting point with respect to ******ebook converter DEMO Watermarks*******



technology and where you need to be to accomplish your workforce analytics goals. This starts with your workforce analytics vision and mission, described in Chapter 7, “Set Your Direction.” The potential range of technology options is wide, from spreadsheet software delivered through desktop computing to cognitive systems delivered in the cloud (see Figure 11.1).



Figure 11.1 Technology options to support workforce analytics. For example, if your analytics mission is in its early stage and you have both a limited team and limited technology, you might be working mostly with spreadsheets to report workforce data. For relatively simple analyses, this could be fine, but spreadsheets struggle to cope as the volume of data and the demand for analytics across the business increase. Eventually, you should switch from basic spreadsheet software and consider what other technology you need to profile and interpret the data. Consider what tools and services enable better understanding and aggregation of the data than standard desktop spreadsheet software such as Microsoft Excel. Other organizations that have been working with analytics for a long time have a very different starting point than those that rely mostly on Microsoft Excel. They might have already built a data warehouse and could be asking questions such as “How can we get more value from our data?” Considerations for these organizations depend on the business problems inspiring their analytics efforts. For example, they might be interested in sharing their data with the rest of the HR function through some kind of selfservice provision. These organizations also need to understand their current challenges and the technology that will help them meet those challenges. Working with a partner on your technology strategy is worth considering; we discuss this later in this chapter in the section, “Technology Vendor Relationships.” ******ebook converter DEMO Watermarks*******



A MIND-SET FOR TECHNOLOGY At ANZ Bank,1 Kanella Salapatas has had a great deal of success winning over people to the possibilities of data and analytics technology. Kanella is the HR Data Manager and Reporting Service Owner, and her trick has been to get people involved with the technology throughout the journey. According to Kanella, the value in involving HR people with technology comes from the confidence they gain when they know what’s going on with the data and the analyses. “If a business leader asks why a certain number is 18 and not 20, for example, the HR Business Partner (HRPB) must have an answer and must be confident about it.” Kanella has found that some HRBPs are reluctant to use technology to really understand those answers. To overcome such technological reluctance, Kanella has taken a twofold approach: • First, she helps train people in basic skills of using technology such as Microsoft Excel or business intelligence toolsets. Learning how to query and explore information for themselves builds confidence in the technology solution and people’s knowledge of where they can get an answer when put on the spot by a business leader. • Second, Kanella gets people involved in creating the reports they receive. “Getting HRBPs involved in the construction of their reports means that they will know the formula for the metrics and the individual data elements used. In turn, this will allow them to more confidently have conversations with their business leaders, and this gives them additional credibility.” Kanella’s advice is simple: Involve your HR leaders and HRBPs in your technology and take away the mystery.



Components of Workforce Analytics Technology In this section, we introduce the main elements of the technology required for undertaking purposeful analytics (see Chapter 4, “Purposeful Analytics,” for more detail on this area). Here we focus on the minimum level of detail ******ebook converter DEMO Watermarks*******



everyone in the workforce analytics team should be aware of when it comes to technology. Data scientists with computer science skills will undoubtedly have deeper knowledge on the topic, but the level of detail here will enable all team members to have sensible conversations about technology with each other and with people outside the workforce analytics team. If you want more detail, Jackie Ryan and Hailey Herleman provide a comprehensive discussion of the elements of a modern HR analytics platform, including data integration and governance, in Chapter 2, “A Big Data Platform for Workforce Analytics” of the edited book Big Data at Work: The Data Science Revolution and Organizational Psychology (Routledge, 2015). The elements discussed in the following sections are Human Resources Information Systems, HR data warehouses, reporting, statistical analysis and machine learning, visualization, and cognitive technologies.



Human Resources Information Systems Human Resources Information System (HRIS) technology platforms store information about employees. Operational versions for the different HR subfunctions often exist (for example, payroll, recruitment, and learning), and some providers offer an integrated HRIS with modules that cover multiple HR subfunctions. These systems serve two main purposes. First, they formalize many of the organization’s HR policies and practices into a workflow that reflects the organization’s view of best practice in HR. Second, they capture all data generated about employees (and, in some cases, by employees) in the course of performing HR function responsibilities.



HR Data Warehouse Most HRIS platforms from major providers are built around a vendor’s data warehouse technology. However, in many cases, organizations using these HRIS platforms find that they do not provide the flexibility to handle the variety of analytics they want to undertake. Organizations with multiple HRISs are also interested in linking their data across systems. For these reasons, many organizations take regular data exports from proprietary vendor systems and store the data in a specially constructed HR data warehouse. These periodic data exports form the system of record used to populate all other downstream systems that need accurate data for either advanced analytics or HR reporting purposes. The data from the operational ******ebook converter DEMO Watermarks*******



systems (for example, payroll) are prepared in staging areas where the data are cleansed and validated. Next, the data are transferred to an enterprise data warehouse. The data warehouse then becomes the master record and provides a longitudinal view of the workforce data of the organization. Business intelligence tools can then be overlaid on top of this internal HR data warehouse to enable data exploration and reporting. Data marts, or views customized to specific business needs, can also be created. Figure 11.2 depicts the components in a standard data warehouse and associated systems.



Figure 11.2 Structure of typical data systems. “On a monthly basis, we export selected parts of our HRIS data and store it in our wider data warehouse. We can overlay the definitions that we require for particular metrics and the specific hierarchies we need for data mapping rather than having to rely on how the HRIS defines things. We combine these data exports with such things as customer research data and can do all sorts of useful analytics on it.” —Sally Dillon Head of Business Intelligence, UK Life, Aviva



Reporting Technology Reporting technology in workforce analytics refers to business intelligence (BI) software applications that sit over the top of the HRIS, the HR data warehouses, or both. The HRIS provider can supply this functionality in an ******ebook converter DEMO Watermarks*******



integrated single system, but many third-party vendors also offer reporting technology to complement the large HRIS provider systems. Reporting applications allow users to undertake tasks such as querying the HRIS for information about each of the HR subfunctions. Types of reporting that can be carried out include generating payroll reports for tax reporting requirements, examining the average length of time taken to fill open job roles, and checking the level of engagement with online learning management systems. BI technology can also be used to regularly examine how well the HR function is performing relative to a set of metrics or key performance indicators (KPIs). These tasks often involve using reporting technology to populate dashboards or generate scorecards with information on topics such as HR compliance violations, turnover rates in different parts of the business, and numerous other HR metrics.



DRILLABILITY IS KEY Sally Dillon is Head of Business Intelligence at UK Life at Aviva,2 a large insurance company based in the United Kingdom. She has been the data lead on large systems implementation projects, such as Workday, to help HR get the most from their people systems. One of her key approaches is to make sure the technology allows for drillability, or the ability for users to thoroughly explore their data at increasingly fine levels of detail. For most HR professionals who lack highly technical skills, drillability is possible only with a strong user interface to the technology. “We encourage people to understand their data, and that means having a very usable technology,” explains Sally. “The interface must be user friendly. The technology should also have strong mobile capabilities, since many HR professionals rely on their mobile device to access information. Finally, it must have connectors into the enterprise people systems and the data warehouse.” Keeping her user’s hat on at all times has helped Sally ensure that Aviva’s technology choices are delivered for even the least technologically minded: “It’s not only about the user interface,” Sally reveals,“You also need to put the data in the language of the business, ******ebook converter DEMO Watermarks*******



and that means knowing the specific audience. I talk to our finance team with numbers. With our business leaders, I start with a story about customer impact.” Sally realizes that the technology is an enabler for business. She looks for great technology that helps people view their data and reports in a mobile-friendly and visually compelling way so that they want to drill down into their data. As she says, “Drillability is key.”



Statistical Analysis and Machine Learning Technology Advanced analytics technology takes the form of standalone software for carrying out many of the analytical techniques Chapter 5 describes. This is the technology that many machine learning and statistical modeling experts use to test hypotheses. It also includes advanced data management systems such as distributed computing used in Big Data platforms to share the computational burden of time-consuming data analysis across many different computers. For these types of analyses, your data scientists will rely on a range of software programs. They are likely to take a best-of-breed approach, selecting a software package for the type of analysis they want to perform or else writing their own algorithms using open source or commercial software packages. Be aware that many packages might appear comprehensive in their functionality but do not incorporate the latest analytical techniques. Data scientists are the best people to guide the team in adopting the best technology.



Cognitive Technology Cognitive computing is a new area of technology based on artificial intelligence intended to help advise HR. This technology learns, reasons, and advises the user based on models that the system builds and inferences that it makes. The user can interact with the system through natural language and apply the models that the system builds. Cognitive computing technology is playing an increasing role in analytically enabling HR practitioners.



Visualization Technology Visualization technology refers to software used to generate graphs and other ******ebook converter DEMO Watermarks*******



visual representations of data. Visualization in analytics serves several purposes. First, visualization can help analysts gain a clearer picture of the data they are considering. By their very nature, summary statistics such as means and standard deviations convey limited information about the data they summarize. For example, the mean does not tell you how spread out the data are for a particular variable. Even summary statistics developed to describe variation, such as the variance, will not readily spot outliers. Graphs can easily highlight the average and the variability in data. See Chapter 10, “Know Your Data,” for a discussion of why issues such as data variability and outliers matter in analytics. Second, visualizations help those who have a hard time interpreting data understand the results of analyses. People who are less familiar with interpreting statistical information often find graphical representations more intuitively understandable. For this reason, visualization is an important part of the insight generation and communication processes in analytics. Finally, visualization can reveal complex results. Important developments in statistical modeling and machine learning allow complex statistical relationships to be developed. These cannot easily be represented graphically using basic presentation software. Visualization software can bring these complex relationships to life.



On-Premise Versus Cloud In the past, HRISs comprised technology purchased and installed on the organization’s physical premises (this is known as on-premise). Occasionally, an alternative was to purchase technology but have a technology provider host it on its own premises (referred to as hosted). The expense involved in installing, maintaining, and updating technology in both these instances meant that only large organizations were able to invest the substantial financial resources needed. Since the early 2000s, HRIS platforms started to be developed “in the cloud” using a Software as a Service (SaaS) approach. The cloud refers to the delivery of computing on demand over the Internet on a pay-for-use basis (see Figure 11.3). Cloud-based services enable widespread access to workforce analytics information and insights for organizations of all sizes. These systems will become even more prevalent except in areas where factors such as data ******ebook converter DEMO Watermarks*******



privacy legislation or requirements for local data storage restrict their use. Hybrid cloud technologies are often used in such scenarios where data must be managed within the business but interact with cloud-based applications.



Figure 11.3 Cloud computing technology.



Technology Vendor Relationships Technology vendor relationships encompass all forms of supplier relationships surrounding workforce analytics technology, whether your analytics function uses shared technology systems, purchases its hardware and software outright, or decides to lease it. The types of technology you might purchase or lease span the entire spectrum of technology components discussed earlier in this chapter—namely, the HRIS for essential HR administrative functions and reporting on the performance of those functions, an HR data warehouse that functions as the system of record for the organization’s workforce data, and reporting, analysis, and visualization ******ebook converter DEMO Watermarks*******



technology. In general, the fundamental aspects of your workforce analytics technology architecture, such as the HRIS and data warehouse, are likely to be owned or accessed via subscription. Beyond these fundamental elements of workforce analytics architecture, you must decide whether you will share, subscribe to, or own your workforce analytics technology.



Share Before you decide whether to lease or own technology for workforce analytics, it is important to assess whether having exclusive access to the technology is necessary for your workforce analytics goals. If you need access to the technology on a more limited basis, you might be able to leverage existing technology elsewhere within the organization. An example might be access to high-powered computers for complex analyses or access to technologies that distribute processing tasks across multiple idle computers throughout the organization. If you do not need immediate access to such technology, you might be able to share technology—that is, access existing technology that other functions, such as marketing, use. However, if you require access to technology on demand, you will likely want to lease or own your own systems.



Subscribe Many technology vendors now offer the option to subscribe to their software through Software as a Service (SaaS) licenses. Other service subscriptions include Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). These cloud technologies have important implications for workforce analytics. First, cloud technology removes the need for significant upfront capital expenditure (CAPEX) by turning it into a monthly subscription (which is appealing for cash flow reasons as well as reduction in CAPEX). In addition, with cloud technology, HR typically does not need to involve the IT department in either the purchase decision or ongoing support. Subscription approaches have the advantage of distributing the cost of the software over time. They also disperse some of the responsibility for maintaining the capability and performance of the hardware and software to the technology vendor. Technology vendors that lease technology often give service agreements, which means that while the lease is current, your organization has right of access to any updates to the technology that the ******ebook converter DEMO Watermarks*******



vendor develops. This can include both bug fixes and capability enhancements. Typical technologies that it makes sense to lease include HRIS and data warehouse technology systems. Leasing offers two important benefits. First, as long as the lease is not overly long, the organization has some freedom to exit a relationship if it is not sufficiently productive or if the organization finds a vendor that can better service its needs. Second, organizations are quickly developing their technology to ensure that their offerings are perceived as market leading, so the regular updates leasing arrangements provide can be highly advantageous. If you decide to lease, it is important to understand the nature of your lease—for instance, at the end of some leases, the leasing organization owns the technology.



Own If the function requires exclusive access and you are not concerned with the rate of renewal and refreshing of service capabilities, buying the technology outright might be a sound option. In general, buying technology makes sense if important new functionality is unlikely to emerge and if the technology will be needed longer than it takes the asset to depreciate financially. Technology that typically falls into this category includes hardware for analytics, such as powerful desktop computers and servers. Although new and faster technology will certainly come out, the desktop machines and servers you purchase for your function will remain capable of performing all the tasks you require of them for the foreseeable future (albeit not as fast as newer, more powerful machines). Of course, ownership entails upfront capital expenditure, although the value depreciates over the life of the asset.



Vendor Selection If your workforce analytics function determines that leasing or buying equipment is the best approach, you need to select the best vendor. Most organizations have technology policies dictating whether an open competitive tender is required. The key point is that workforce analytics professionals need to influence the direction of tender processes so that decisions support the goals of workforce analytics. Involvement in the tender process will include writing the tender brief, answering requests for information, and selecting the vendor. At a minimum, ******ebook converter DEMO Watermarks*******



workforce analytics professionals need a good working relationship with procurement teams because their expertise is needed to guide the tendering process for workforce analytics.



Summary Decisions about technology are among the most important ones you will make in workforce analytics. To make the right decisions, you need good advice from Information Technology (IT) and HR Information Technology personnel and from your data scientists. To prepare for these discussions, take the following steps: • Decide on the appropriate mix of technology by understanding what you have now and what you need to perform the required types of analyses in support of your workforce analytics vision and mission. • Get familiar at a high level with the basic components of workforce analytics technology, including the benefits of cloud technology. • Consider whether and when cloud-based services will work in your organization. • Understand the breadth of technology required for a workforce analytics technology system: the HRIS, the HR data warehouse, reporting and business intelligence technology, analysis and data integration software for advanced analytics, cognitive solutions, and visualization software. • Decide whether to share, subscribe to, or own your technology, based on ease of access, financial cost, and your degree of concern over how the technology ages.



1 ANZ Bank traces its origins to the Bank of Australasia in 1835. Today it is one of the five largest companies listed in Australia and is the biggest bank in New Zealand. As of April 3, 2017, it had over nine million customers and more than 50,000 employees. ANZ Bank is headquartered in Melbourne, Australia (www.anz.com). 2 Headquartered in London, U.K., Aviva is an insurance company with 33 million customers and approximately 28,000 employees in 16 markets in the United Kingdom and throughout Europe, Asia, and Canada. It has a rich heritage dating back to 1696; one of its most famous customers was ******ebook converter DEMO Watermarks*******



Winston Churchill, who took out a policy in 1896. In its 2015 annual report, Aviva reported revenues in excess of £23 billion (www.aviva.com).



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12 Build the Analytics Team “There are new skills that are fundamental. You must see the world from the outside in, use storytelling, be numerate, and have a consulting mind-set. The world of Human Resources is changing.” —John Boudreau Professor of Management and Organization, and Research Director of the Center for Effective Organizations, University of Southern California The human resources (HR) profession is undergoing a transition that will shift HR’s approach from primarily intuition based to primarily evidence based. To make this transition, HR professionals need new skills. These developments have partly spilled over from the benefits that analytical approaches have delivered in other areas, such as finance and marketing. The best way for HR to realize these same benefits is to develop a properly skilled workforce analytics function or team. This chapter covers the following topics: • Leveraging the Six Skills for Success in workforce analytics • Configuring team roles based on anticipated workload • Hiring versus developing the workforce analytics team • Remembering the fundamentals of personnel selection



Six Skills for Success Figure 12.1 illustrates the Six Skills for Success1 in workforce analytics, a comprehensive and simple guide to help HR leaders earn credibility and achieve success.



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Figure 12.1 The Six Skills for Success. “There are so many different skill sets needed, from data infrastructure through to reading literature, forming hypotheses, collecting data, doing the analysis, and implementing recommendations.” —Mark Huselid Distinguished Professor of Workforce Analytics, Northeastern University



Skill 1. Business Acumen Business acumen refers to a keen and agile ability to understand, interpret, and deal with business situations. It requires gaining expertise across multiple disciplines, integrating lessons learned from diverse experiences, and then applying that knowledge to make decisions that lead to better organizational outcomes. Business acumen is one of the Six Skills for Success because it ensures that you understand the finances that drive your business and that you are politically astute enough to complete projects in the face of complex internal and external operating environments. Without business acumen, analytics professionals might not fully tune their analytics projects to the specific ******ebook converter DEMO Watermarks*******



business problem being analyzed. Business acumen involves the following components: • Financial literacy. Financial literacy involves an understanding of key accounting concepts such as profit, revenue, and sales, and an awareness of the factors that impact important business metrics, such as seasonal business cycles. It also includes the ability to read financial statements and an awareness of the commercial operating environment. Financial literacy is important in workforce analytics to give you credibility with other business leaders, to help you build investment cases for analytics projects, and to provide insight into how to align analytics with the important metrics in your business. Although formal classes can help you learn accountancy skills, much of this knowledge is acquired through what you do at work, such as getting involved in projects with financially literate people and working on tasks that require financial understanding. • Political astuteness. Political astuteness means being able to understand organizational relationships, influence others, and resist influence as required. It also means being adept at navigating both the organization’s formal decision making and its often important informal structures. Political astuteness is critical in workforce analytics because the success of your work not only depends on its quality, but also how it is perceived in the organization. Gerald Ferris, a professor at Florida State University and an expert on politics at work, shows in his book Politics in Organizations (Routledge 2012), co-authored with Darren Treadway, that the willingness and ability to successfully navigate political relationships depend on your perceptions of the political environment and your ability to control politics. Political astuteness requires abilities such as emotional intelligence, self-awareness, and general courtesy. However, the most important skill is to be able to identify which of your many relationships and conversations are critical to success so that you can rigorously prioritize work activities. The best way to develop political astuteness is to work closely with people who seem to get things done despite challenges they face in the organization and then learn how they do it. • Internal awareness. Whereas political astuteness refers to an ability to understand relationships and use this understanding to influence others, internal awareness refers to an understanding of your organization’s work environment. This means understanding the macro perspective on what ******ebook converter DEMO Watermarks*******



your organization is there to do, as well as how your team’s work contributes to this overall objective. Even projects with substantial promise for cost savings or revenue generation might face obstacles if they conflict with or are not aligned with other organizational initiatives. You can develop internal awareness in many ways. For example, you can build relationships with people in your organization from a broad range of backgrounds and with diverse responsibilities. You can also monitor both media coverage of your organization and internal communications. The best way is likely to be a blend of multiple approaches.



A BLEND OF SKILLS IS BEST In analytics, business acumen and the ability to define the business problem are key. “When we get a request, we think through what the problem really is, what’s driving the request, and then we figure out which skills on our team will best answer that question.” This is the philosophy of Rebecca White, Senior Manager of People Analytics at LinkedIn.2 Rebecca explains that building a team with complementary skills is important. “We have diversity of skills, and it serves us well,” she says. “I come from a consulting background, we have a couple of people with a traditional industrial–organizational psychology background, some people are more data science and analytics focused, and some have data visualization skills.” When Rebecca and her colleagues seek to drive change, they draw on this breadth of skills to expertly channel the analytics projects in the right direction. “This range of skills has really helped our team to achieve success. Because we have so many skills on the team, it gives us flexibility to answer the business questions.” She continues: “We focus every project on the real business need and what the impact will be. You can go very far in analytics down a rabbit hole, so we figure out how to collaborate across the team, using the right skills at the right time, to drive impact instead of giving executives a bunch of data for their back pockets.” That philosophy and approach have served her well. ******ebook converter DEMO Watermarks*******



• External awareness. Having external awareness means taking account of external environments when running the workforce analytics function. This requires awareness of factors such as economic conditions, industry sustainability, competitors, and supply and demand. External awareness is important in workforce analytics to enable you to forecast the effects of decisions you make. You can learn much from the past, but given the rapid pace of change in most industries, you need to be aware of the past without being overly constrained by it. A strong external awareness means knowing the historical context of your market and having an informed perspective on the likely future conditions in which your business will need to operate. You can acquire external awareness about your industry by reading business news and industry magazines and by networking with leaders in your field.



Skill 2. Consulting Consulting skills include the ability to provide specialist expertise to organizations to improve some aspect of their business. Core components of consulting skills in the context of workforce analytics include the ability to define business problems, generate hypotheses about the causes of business problems, manage projects, develop solutions, manage stakeholders, and manage change. • Problem definition. Problem definition refers to accurately understanding the nature of a problem. Doing this well requires skills with language, such as listening, questioning, and paraphrasing, as well as the ability to exercise good judgment. These skills are required in the first step of the analytics model that Chapter 4, “Purposeful Analytics,” outlines. Problem definition is an essential skill for workforce analytics success because it allows you to not only clarify the business problems, but also identify the most important ones. A good way to acquire skills in defining problems is to consider case study materials about business problems (for example, in business journals such as Harvard Business Review). You can aim to apply the same approaches and thinking to your own situations. • Hypothesis building. Hypothesis building involves identifying plausible causes of the problems you have defined and, more important, generating potential courses of action that might solve the problem. Being able to ******ebook converter DEMO Watermarks*******



articulate a hypothesis about the causes of the business problem allows analysts to determine whether the hypothesis itself is plausible. Hypothesis building also involves identifying the different data sources that allow you to test hypotheses. Hypothesis building is important in workforce analytics because hypotheses that focus on the causes of business problems and have data to support them represent potential intervention points to affect change in the organization. Some people have good intuition on formulating hypotheses. The most skilled people have received formal training in a discipline that prioritizes logic and rationality, such as economics or work psychology. “When you are clarifying and building hypotheses, I have come to realize that one of the most useful skills is to have what you could call a beginner’s mind-set so that you can tackle every problem in an open-minded way.” —Salvador Malo Head of Global Workforce Analytics, Ericsson • Project management. Project management skills enable the workforce analytics team to hold people accountable for what they agree to deliver: They agree on what is feasible, develop plans, list dependencies, identify risks, allocate owners, and monitor milestone achievement. Project management skills are required in workforce analytics to integrate and align numerous different streams and types of work. Such skills help ensure that projects are delivered to specification and on time. These skills are often acquired through project management training courses, some of which include an exam to ensure that participants understand the key concepts. If you work in an organization with a strong project management approach, you may be able to acquire these skills in the course of your work. • Solution development. In addition to being able to identify the problem, successful management consultants can structure possible solutions to the problem. They generally propose multiple solutions and examine the feasibility of successful implementation of different solutions under various conditions before they determine the most plausible and effective solution. These experts, often known as organizational development specialists, tend to focus on solutions to problems that address changing the mind-sets of workers and/or the environment in which they work. ******ebook converter DEMO Watermarks*******



Many people learn skills for solution development on the job, but often experts have some formal training in the field of organizational development. Alternatively, to acquire solution development skills without studying, you might consider taking a role in a consultancy that focuses on organizational development. This can give you experience in proposing solutions to business problems and evaluating their likely effectiveness. • Change management. These skills involve understanding the stages workers go through as they experience change and, most important, helping workers understand the reasons for change and become comfortable in the new world. Essential skills involve communicating, educating, influencing, and managing people affected by change. This is important because analytics ultimately need to drive change. Terry Lashyn, Director of People Intelligence at ATB, captures this point: “If your insights or recommendations do not confirm or drive new strategies or change something in your business, then you have failed.” Implementing follow-up actions suggested by analyses provides the greatest source of possible value. For successful follow-up interventions, workers must be persuaded of the benefits that the new ways of working will deliver. • Stakeholder management. Workforce analytics involves linking information about people to outcomes in nearly all areas of the business. This means you will come into contact with a diverse range of stakeholders who have different interests and perspectives (Chapter 8, “Engage with Stakeholders,” discusses this further). Identifying who these stakeholders are, understanding their perspectives, and managing their expectations are critical tasks. Because many workforce analytics teams are small, one potential strategy is to align the interests of as many stakeholders as possible and address the common needs of stakeholders simultaneously instead of trying to manage different needs on a stakeholder-by-stakeholder basis. Recognize the different types of stakeholders and take a systematic approach to managing your relationships with each.



Skill 3. Human Resources Workforce analytics is about executing strategy through people, so you want ******ebook converter DEMO Watermarks*******



to have human resources (HR) skills on your team to successfully implement analytics projects. This is because interventions in workforce analytics often require changes to HR policies and practices. The key areas are HR subfunction skills, HR interdependencies, international HR, and an HR “sixth sense”: • HR subfunctions. These skills refer to knowledge of the HR subfunctions, including compensation, recruitment, and learning. Practices within HR subfunctions are often highly specialized. Specialist awareness in each area is important to ensure that your interventions take into account best practices and any company-specific policies. You might also need legal expertise. If a team member wants to develop skills in this area, some formal qualification will likely be needed. Work-shadowing an expert while undertaking a project can also help team members learn about HR subfunctions. • HR interdependencies. This refers to knowledge of how the different functions in HR relate to one another—specifically, how changes in one function interact with others to impact the effectiveness of other functions and the workplace overall. This is important so that analytics projects are discussed alongside the impact they might have on other areas of HR and the business. • International HR. If any of your workforce analytics projects span multiple countries, you might need people with a knowledge of the international HR environment on your team. International HR experts know the legal landscape and whether policies are likely to need tailoring for different operating environments. For projects with implications in numerous countries, it is wise to involve HR experts from the countries your projects affect. Alternatively, you might consider contracting with an HR consultancy firm that specializes in international HR or industrial relations. • Privacy and ethics. The use of and interpretation of workforce-related data require sensitivity and, in some cases, legal involvement. Although building effective relationships with the chief privacy officer or other legal experts in your organization is important (see Chapter 8), having team members with knowledge of privacy regulations and ethics is helpful. Examples of particular relevance here are projects that involve several countries, the potential use of sensitive personal information, and ******ebook converter DEMO Watermarks*******



algorithms from machine learning that might not be sensitive to the ethical values of your organization (see Chapter 5, “Basics of Data Analysis”). • HR “sixth sense.” Someone who has a sixth sense for HR has strong HR knowledge, experience, and judgment. This skill involves an approach to thinking about HR issues that identifies important variables for analyses and follow-up actions. Not surprisingly, experienced HR professionals often naturally adopt such an approach. Such experience is also useful in ensuring that the team avoids pitfalls such as using data inappropriately or unethically. Because a sixth sense for HR usually comes from years of experience as an HR professional, the best way to bring this skill into a project is to make sure someone with HR expertise is involved in analytics from the outset. Christian Cormack, Head of HR Analytics at AstraZeneca, emphasizes this: “Our CEO wanted details of all the recently recruited senior leaders. A list had been extracted from our HR system and was about to be sent before someone asked me to doublecheck it. I knew instinctively that the list was too long, so I took a closer look. The list also included people who had recently moved from one country to another on assignment. Sometimes as an HR leader you have a sixth sense, which is important to use in analytics. The first test is always the common sense test: Does this look right?”



INVEST IN DATA PRIVACY SKILLS Dawn Klinghoffer is the General Manager of HR Business Insights at Microsoft.3 She has worked in analytics for much of her career and has a unique perspective about one particular skill on her team: “About 8 years ago, I made a case for having the employee data privacy manager on my team. She is a lawyer by background and understands the business very well.” Dawn believes this skill is important for ensuring that the fundamental building blocks of the analytics function are based on the right principles and ethics:“I want someone on the team who understands what we really do with data and who can advise us on using them in the correct ways. This is not a one-off project, but ongoing advice to ensure our projects are always undertaken respectfully.” ******ebook converter DEMO Watermarks*******



Dawn explains that although the privacy expert works closely with the legal department as needed, often her role is to advise the analytics team on ethical (instead of legal) matters. In a recent analytics project, for example, Dawn describes how the data privacy expert was intimately involved throughout to advise on the correct use of the data. “We needed advice throughout the project, clear guidelines on how to use the data, how it is stored, how it is contained, what elements were allowable in the algorithms, and how to ensure that the outcomes were used appropriately. “It helps to have this person on my team to know exactly where our guard rails are.”



Skill 4. Work Psychology The Society for Industrial and Organizational Psychology (SIOP)4 defines IO psychology as “the strategic decision science behind human resources” and identifies I-O psychologists as “versatile behavioral scientists specializing in human behavior in the workplace.” The skills of I-O psychologists can be broadly grouped into industrial psychology skills and organizational psychology skills, which SIOP describes as follows: • Industrial psychology. Industrial psychology is generally concerned with maximizing individual potential and related topics, including psychometric testing, selection and promotion, training and development, employee attitudes and motivation, and reduction of burnout, conflict, and stress. Industrial psychology skills in these areas are important so that you do not spend a lot of time trying to solve problems that scientists have already explored in depth (for example, identifying the best predictors of performance or attrition). Industrial psychology skills are best acquired through formal postgraduate study. • Organizational psychology. Organizational psychology is generally concerned with maximizing organizational potential. It includes topics such as change management, strategic planning, surveys, job design and evaluation, organizational restructuring and workforce planning, and cross-cultural understanding. Organizational psychology skills are important for similar reasons as industrial psychology skills—that is, organizational psychologists already know a lot about the determinants of ******ebook converter DEMO Watermarks*******



important outcomes such as work engagement. Study the existing literature first before starting your own investigations. Formal postgraduate study is the best way to acquire organizational psychology skills. • Research design and analysis. In addition to having content expertise in the areas of industrial and organizational psychology, work psychologists often have technical experience in both designing studies to test hypotheses and running analyses to reach conclusions. These skills include experimental designs (for example, formal experiments) that allow you to infer causal relationships between variables, and designs that allow you to draw reasonably strong conclusions about causal relationships even when you cannot run an actual experiment (see Chapter 5). These skills are best learned formally rather than on the job. However, you can take data analysis courses to learn these skills; a number of prestigious universities often offer them for free.



Skill 5. Data Science Data scientists play a pivotal role in decisions regarding the causes of and likely solutions to problems in the realm of workforce analytics. Data science skills are often very specialized, and your team will benefit from including members who possess three broad types of skills: • Quantitative skills (mathematics and statistics). Quantitative skills in workforce analytics refer to an ability to build statistical models. These types of analyses often involve methods known as traditional statistical modeling, which aims to understand causal relationships. On the other hand, quantitative analysis sometimes focuses exclusively on prediction without regard for causation, using machine learning methods that maximize prediction accuracy. Without strong quantitative skills, you risk applying the wrong methods and reaching unfounded conclusions. These skills are best learned through formal study, either in a classroom or via the increasing number of online courses available. • Computer science (databases and programming). Computer science skills are necessary to interrogate databases and data storage systems and to integrate data from different sources in an auditable way. This skill is important because it allows computer scientists to think through problems in a logical way with associated implications for how different datasets relate to one another. When datasets become very large and cannot be ******ebook converter DEMO Watermarks*******



managed with spreadsheets and personal computers, computer scientists use techniques such as parallelization, which distributes computing tasks across multiple computers and often involves cloud technology. To find out more about the requirements for this skill area in your team, speak with the people responsible for maintaining your HRIS and related technology. Computer science can involve high levels of specialization, so you need to know the precise technology used and the types of programming languages most suitable for workforce analytics in your organization. Then you can hire people with either the precise computer science skills you require or strong formal qualifications in computer science and a willingness to learn new technology and programing languages. • Data awareness. The rate at which data are being produced and captured is increasing rapidly. A special skill set required for workforce analytics is to ascertain what new forms of data are becoming available and, more important, to assess their relevance to workforce analytics. One example is social media data. Some forms of social media have relevance to the workplace (for example, LinkedIn profiles), whereas the relevance of others is less clear. Knowing what these new forms of data are, how they can be captured, and their likely utility for workforce analytics is an important skill.



Skill 6. Communication As your team begins to build momentum, your team members will need to develop or gain access to communication skills. These skills are critical to effectively create and tailor messages, and they involve the following key areas: • Storytelling. Storytelling is a method of explaining a series of events through narrative. Telling an effective story to capture the essence of the problem, analysis, insights, recommendations, and change helps you gain buy-in for your ideas and projects. As analytics have become more important in the business environment, so have the skills required to explain data and insights using a compelling narrative. Chapter 17, “Communicate with Storytelling and Visualization,” covers this topic. • Visualization. Skills in data visualization include the use of technology, graphics, and artistry to clearly show insights. These skills often require a ******ebook converter DEMO Watermarks*******



combination of artistic creativity and technical ability with visualization methods and tools. These skills are relatively new in the world of workforce analytics; Chapter 17 discusses this topic, and Cole Nussbaumer Knaflic’s book Storytelling with Data (Wiley, 2015) is a comprehensive resource. • Presenting. Creating and communicating your story through a structured presentation is an important and much-needed skill. The presentation might be informal and one-to-one, or it might be formal with many people in attendance. It might be in person, or it might be delivered remotely. Being able to adjust your presentation to the medium and audience is a critical skill if you are to communicate your ideas in a compelling way. For example, a slide deck should not be distributed without an accompanying explanation. Presentation skills can be acquired though experience and often via internal corporate training programs. All analytics professionals should receive formal training in presentation skills and basic presentation software such as Microsoft PowerPoint. • Written communication. Clearly articulating written messages is essential in workforce analytics. This is because you are often dealing with complex ideas, diverse audiences, and different communication media (for example, email versus extended documents). Succinct written expression of ideas contributes to a clarity of understanding between your team and others. For highly important documents, you might choose to use writing and media specialists to improve the clarity of your messages. • Marketing. Marketing is about communications that persuade an audience that an organization’s brand, products, or services have value. As you seek to persuade others of the benefits of your analytics team’s work, either internally or externally, you will want to market your team well. This includes managing your team’s brand and its reputation. Effective marketing often requires the services of a marketing professional, whether within your organization or from an outside specialist.



ANTICIPATING BUSINESS NEEDS Ian Bailie leads Global Talent Acquisition and People Planning Operations at Cisco.5 It’s an important role that requires him to think ******ebook converter DEMO Watermarks*******



differently. He views recruitment as one of the most measurable parts of HR. “Recruitment is a measurable process, basically a supply chain; you can measure every step of the process, and that provides good data.” Ian uses the data to take a quantitative approach. He complements that with his experience in the business to create a strong connection with business executives. “During my nine years at Cisco, I have run analytics teams and been an operations manager in recruitment,” he says. “That enables me to really understand what the company is trying to achieve and what recruiters are trying to do. I think about the data, get to understand it, and use it to drive change.” In his role, Ian has expanded his core mission to use analytics to look at the external world and undertake market mapping for talent. He started with the recruitment process and used his internal data to quantify the “talent supply chain.” He then introduced external market data (including LinkedIn data) into his analyses. The result was a business-critical market-mapping insight tool that the entire organization can use to understand talent. Essentially, Ian has used new levels of analytics to tell stories about where the best people are in the world and how to hire them.



Configuring Team Roles The number of people you need on your team and how their skills should be organized into specific roles depends on the amount of work your team has and how long you have to complete the work. This, in turn, depends on the scope of your analytics function. Answering the following questions helps you structure the required skills into roles and determine how many and what types of workers you need (whether on your team or available to your team).



What Projects Should Be Delivered? Before you can accurately estimate the number of workers you need on your team, carefully consider what you will commit to delivering in the foreseeable future. Planning and budget periods are typically annual processes, so a 12-month window is a good time horizon to use. In deciding what projects you will work on, be sure to revisit the list of project ******ebook converter DEMO Watermarks*******



opportunities (see Chapter 7, “Set Your Direction”) in light of your function’s vision and mission. How many of these opportunities do you need to deliver so that you are seen to be meeting your mission? For example, you might have three major new projects to be delivered in the coming months, along with day-to-day requirements related to any ongoing projects from the previous year.



How Many People Do You Need? A common approach for large firms is to have workforce analytics teams with just a handful of workers. However, this tends to be for workforce analytics functions that keep reporting separate from analytics. Even then, workforce analytics functions could well have dozens of team members. In some cases, organizations of more than 100,000 employees have workforce analytics teams of more than 50 people. Therefore, the best recommendation is to match the size of the team with the scale of the work you need to deliver. A general rule of thumb is that an organization of 100,000 employees with an approximate ratio of 1:100 HR practitioners to employees will have a team of roughly 1,000 HR people (the Bloomberg BNA HR Department Benchmarks recommends this ratio). In an HR team of this size, it is not uncommon to see a ratio of 1 workforce analytics practitioner to every 100 HR professionals. In fact we found organizations that had a ratio of almost 1:50, such that in organizations of 300,000 employees, there were approximately 60 workforce analytics practitioners (excluding reporting). This ratio is a guide, however we do see and predict a trend toward larger teams. The exact number varies depending on whether the organization is public, private, or voluntary, and also depends on its geographical breadth, industry complexity and the scale of workforce issues you encounter. With a list of projects in mind, you can estimate the number of people who have some combination of the Six Skills for Success that you need to deliver the projects. As an illustration, a complex project that involves integrating datasets held by both internal departments and external vendors might require a full-time project manager and data scientist. On the other hand, a less complex project with easily accessible data in a central data management system might only need a shared project manager and a shared data scientist, both working across several projects. ******ebook converter DEMO Watermarks*******



What Existing Skills Do You Have? Now that you know what projects you are likely to deliver and the skills you need to access if you are to successfully complete the projects, you should conduct a gap analysis. This involves determining the resources you currently have and then comparing those resources to what you discovered you need from the previous two questions. Resources you currently have should include your own personal skills, as well as the skills of any existing team members who will be working with you in the coming year. The difference between what you need and what you have represents the number of people and combination of skills that you need to acquire, whether from within or outside your organization, and whether on a temporary or permanent basis. Of course, you also need to build the business case for acquiring these skills (Chapter 14, “Establish an Operating Model,” covers this).



Hiring Skilled Workers Hiring workers for your team signals commitment from the business that your work is important. Whether you get people who already have the skills you need or you hire people who can develop them depends on several factors. Hiring a person who has the skills you need now is certainly preferable to hiring someone you hope will develop the skills. First, having more qualified people helps you accelerate the rate at which you can achieve desired results. Second, your team will have enough challenges to focus on without adding concern about whether members need to develop additional skills to complete projects. However, hiring qualified people might not always be possible, and the workforce analytics function itself also needs to develop the ability to grow talent. “I have an economics background and I was a management consultant in the former Soviet Union with EY. I left to run a water business and then came to workforce analytics. It was a great learning platform. You don’t necessarily follow a linear track. There's not a formal education track needed to be effective in workforce analytics.” —Al Adamsen Founder and Executive Director, Talent Strategy Institute Tom Davenport, a professor at Babson College, wrote about the internal ******ebook converter DEMO Watermarks*******



versus external hiring point in the online article “What Data Scientist Shortage? Get Serious and Get Talent.” He states: “I am convinced that many companies—particularly those that already employ a lot of technical people —could retrain substantial numbers of people to become data scientists. In some instances, it may be possible for workers in a closely related field to make a successful transition to workforce analytics, particularly if there is strong support for retraining.” An example might be moving from a data science role in marketing to taking on a similar role in workforce analytics. Davenport points to Cisco as an example of a firm that developed a distancelearning retraining program for staff. Learning how to develop workforce analytics skills is also an important capability that can help establish an analytics culture in your organization. However, if timely delivery of projects is a concern and you have the choice between experienced and inexperienced potential workers, hire people who have formally trained in the skill sets you require and who can do the job from the outset.



Filling Crucial Roles At a minimum, your function needs to have a team leader. (Chapter 3, “The Workforce Analytics Leader,” covers this position in detail.) The next most important role to fill is someone with a deep understanding of the systems that store your HR data. This usually means a data scientist that has computer science skills for programming and database management. In addition, your team should include someone with enough HR expertise to be able to translate workforce analytics projects into the business environment. Beyond these requirements, you have enormous flexibility in how you configure the team. You can have the skills permanently on your team (in-house), elsewhere in the organization (in-source), or supplied through vendor relationships (outsource). We discuss these three approaches in Chapter 13, “Partner for Skills.”



Remember the Fundamentals! So far, we have discussed the technical skills a workforce analytics function requires. Also be sure to remember the fundamentals of personnel selection that we know from I-O psychology. The basic building blocks of successful performance are cognitive ability, personality, and work interests. Cognitive ability and related concepts, such as intelligence quotient (IQ), predict task ******ebook converter DEMO Watermarks*******



performance (or the quality and quantity of work) across nearly all occupations, and cognitive ability becomes more important as job complexity increases. Workforce analytics is recognized as a complex discipline, so we advocate cognitive ability testing for all roles within workforce analytics. Personality tends to predict how people go about their work. Industrial psychologists stress desirable factors such as an ability and willingness to perform tasks that are not officially documented in job descriptions but are required for effective organizational functioning. This includes continuing to work hard when under pressure or stressed. The two personality factors most relevant here are conscientiousness and emotional stability. Each can be well assessed using standardized psychometric questionnaires. Finally, whether a worker’s career interests are aligned with the skills required for the role predicts whether the workers will stay in their role. Candidates should express an interest in technical work, particularly for team members in highly specialized roles. However, when it comes to the leader of the workforce analytics function, assess the candidate’s interest in being a general manager instead of his or her willingness to get involved in highly technical work.



Summary Building the analytics team or function is a critical task in the early phases of workforce analytics because the capability of your team is an important determinant of your success. This can seem like a challenging task, but taking a structured approach and following the principles outlined in this chapter sets you on the path to success. Be sure to cover these tasks: • Ensure that you understand and have access to the Six Skills for Success, whether they are on your team or are easily accessible elsewhere. Configure the skills in your team and its size based on the expected workload and nature of projects. • When deciding whether to hire new staff or develop existing staff, be aware of the pros and cons of each approach, such as whether skills developed in another area transfer to a workforce analytics context. • Remember the fundamentals of personnel selection. Cognitive ability and certain personality traits, particularly conscientiousness, are important in all roles and can be tested with standardized psychometric testing. ******ebook converter DEMO Watermarks*******



1 The Six Skills for Success is a copyright of the authors of this book: Nigel Guenole, Jonathan Ferrar, and Sheri Feinzig. 2 LinkedIn is a business-oriented social networking service. Founded in December 2002 and launched in May 2003, it reported 433 million users in more than 200 countries and territories worldwide as of May 2016 (www.linkedin.com/about-us). 3 Microsoft, founded in 1975, is one of the world’s largest technology companies and most recognized brands. Based in Redmond, Washington, it employs more than 114,000 workers (www.microsoft.com). 4 The Society for Industrial and Organizational Psychology is the world’s leading professional association for industrial and organizational psychology practitioners. 5 Cisco, founded in 1984, is an American multinational technology company headquartered in San Jose, California, that designs, manufactures, and sells networking equipment. At the end of 2015, it had annual revenues of $49.2 billion and more than 71,000 employees globally (www.cisco.com).



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13 Partner for Skills “Synergy comes from knowing who you are, and finding the strength in others to complement your strengths.” —Jeremy Shapiro Global Head of Talent Analytics, Morgan Stanley Team up or go it alone? When setting up a workforce analytics function, you have various options for bringing together the needed skills, including partnering with experts from inside or outside your organization. All workforce analytics functions need a core set of foundational skills, but beyond that, team composition and use of partners can vary depending on the circumstances. Although having all the required skills within the function has benefits, partners can provide a unique advantage in overcoming the challenges an analytics team faces. It is also worth noting that the partner options appropriate for your initial team formulation might not necessarily match your ultimate operating model. As circumstances change, team composition and partner relationships will likely evolve as well. This chapter discusses the following topics: • Deciding when to partner for skills • Options for acquiring skills: in-house, in-source, and outsource • Factors to consider when choosing among the options



Why Consider Partners? Partnering involves some form of contracting or agreement, either formal or informal, that defines the relationship and obligations between the workforce analytics team and a third party. The third party can be internal (from another business unit or function) or external (a vendor or consultancy). Partners can assist with data, technology, and skills. Chapter 18, “The Road Ahead,” ******ebook converter DEMO Watermarks*******



discusses the emerging field of partnering for data, and Chapter 11, “Know Your Technology,” covers partnering for technology. When contracting for skills, the topic of this chapter, your partner can be an individual expert, a large consultancy, or some variation in between. The relationship can range from short-term assistance on a single project to long-term retained relationships. A workforce analytics team might choose to partner for skills for many reasons, such as budget limitations, specific expertise gaps, or an accelerated timeline for producing results. Partnering might also be needed when the analysis is of a sensitive nature or there are potential conflicts of interest, which require third-party objectivity or separation of duties. In all these situations, partners can be an indispensable resource. Partnering can offer a great deal of flexibility, with the nature of the relationship varying depending on the team’s needs. Some teams might only require an initial consultation for strategic guidance and advice before they can proceed successfully on their own. Other teams opt for short-term support for specific projects on an interim or as-needed basis. In other situations, teams establish effective long-term relationships with a partner that provides ongoing support. The partner might even serve as an extended member of the workforce analytics team. The next section discusses partner and non-partner options to consider when assembling the skills needed to fulfill your workforce analytics vision and mission.



Options for Building the Team Earlier chapters (Chapter 3, “The Workforce Analytics Leader,” and Chapter 12, “Build the Analytics Team”) describe the skills needed for success, including the minimum skills needed internally within the workforce analytics team. This includes a workforce analytics leader with a breadth of knowledge who is well versed in the art of business acumen; a team member with a deep understanding of human resources (HR) data systems and the ability to extract and work with the data; and sufficient levels of HR expertise among team members to translate workforce analytics projects into the workings of the organization. Beyond this recommended minimum, teams have flexibility in achieving the full end-to-end skill set needed for workforce analytics success. Three broad options exist: ******ebook converter DEMO Watermarks*******



• In-house (no partners). All required skills (the Six Skills for Success that Chapter 12 describes) reside directly within the internal workforce analytics team. • In-source (internal partners). The analytics team supplements its skills with experts elsewhere in the organization. • Outsource (external partners). Third-party vendors or consultants are formally contracted to support the workforce analytics team and complement their skills. The best approach might be a combination of options—for example, insourcing specific skills that are readily available internally and then contracting externally for others. The following sections discuss the options in detail.



The In-house Option Building a fully staffed in-house workforce analytics team means having complete, end-to-end capability (the Six Skills for Success) contained within the team, with all resources reporting to the workforce analytics leader. This requires deep specialist skills that go beyond the minimum—for example, advanced statistical skills such as structural equation modeling and unstructured text analytics, or advanced communication skills such as storytelling and data visualization. People with these specialized skills need to be sourced, staffed, and continually developed into a high-performing collaborative team capable of delivering the projects needed to fulfill the workforce analytics vision and mission. Chapter 12 provides guidance on determining the specific skills needed and deciding how many people to hire. Advantages of In-house The in-house approach for skills offers several advantages, such as control over the nature and timing of projects. Having resources all contained within a single team makes it relatively easy to reassign people to projects as needed, adjust project timelines as business demands evolve, and facilitate collaboration and knowledge sharing among team members. Another advantage of the in-house approach is the opportunity to invest time (for example, when in-between projects) to innovate and explore new statistical methods and novel data sources. ******ebook converter DEMO Watermarks*******



Having HR domain expertise and statistical skills on the same team also mitigates the risk of potentially misusing or misinterpreting HR data. This can happen, for example, when someone is adept at finding statistical relationships but is unfamiliar with the content and context of the data being analyzed (and, therefore, is unaware of the meaning and implications of the observed relationships). In-house teams also have opportunities to expand their scope of responsibilities beyond specific project work, to support the overall workforce analytics mission. For example, they can engage in transformational activities such as training and coaching to increase the analytical acumen of the larger HR organization and help to advance an HR analytical mind-set. Disadvantages of In-house Potential disadvantages of the in-house approach include needing to make an upfront investment in people and the tools they need to do their jobs. This requires in-depth planning for decisions with long-term implications; after committing to a team of people and purchasing a set of tools, backtracking is difficult. Staffing an in-house team also involves a lengthier initial startup time as you source candidates, bring new hires on board, train them and get them up to speed, and put tools and methodologies in place. The leader of a fully staffed in-house team can expect to spend a large proportion of time managing the team, with less time for managing projects and stakeholder relationships. Sizable investments are often accompanied by sizable (if not outsized) expectations, and that can be the case when building an in-house analytics team. Expectations of what the team can deliver according to certain timelines could be unrealistic, leading to disappointment. Some practitioners operate with a low profile early on to avoid this issue. Their preference is to surprise stakeholders with valuable insights and results, as a way to garner further support. Another factor to consider is the political environment in the organization, specifically in the context of challenging long-held assumptions and being able to effect change. In some organizations, an internal team might lack the credibility that comes from (perceived) third-party objectivity. Also relevant is whether HR is seen as having analytical prowess instead of being thought ******ebook converter DEMO Watermarks*******



of primarily as a compliance-oriented and reporting function. Such perceptions can be challenging to overcome (although not impossible, as Chapter 16, “Overcome Resistance,” shows). Bringing in external expertise can help allay these types of concerns.



START SMALL, KEEP IT FOCUSED Thomas Rasmussen, Vice President of HR Data and Analytics, joined Shell1 with the mission of setting up a workforce analytics function. He quickly observed that Shell was already mature in two key areas: data management (including data definitions and data quality) and reporting. With Shell’s goal of building an end-to-end value chain, Thomas was asked to “take it to the next level with a focus on analytics.” Thomas achieved this by building an analytics capability within the HR function, yet keeping it separate and distinct from data and reporting, and by up-skilling HR leaders, enabling them to facilitate fact-based decision making. Thomas recommends maintaining a balance in the team between technical expertise and business consulting skills: “It’s important to have people who are technically proficient in working with data and running statistics, as well as people who are adept at change management, who can work with stakeholders and position the results of analytics projects for action.” Thomas also advises keeping the team relatively small (in his case, about three to five people). This forces a focus on the most important and impactful projects and keeps from overwhelming the organization with more findings than it can reasonably act on in a given period of time. With this model in place, Thomas’s team is able to focus on the highest priorities for Shell, including projects related to productivity, talent, and risk. Given the breadth and depth of the work, the team is clearly in strong demand and is serving the business well.



The In-source Option The workforce analytics team can supplement its skills by bringing in people ******ebook converter DEMO Watermarks*******



from other parts of the organization to assist with specific workforce projects. This can be a desirable option if building an end-to-end in-house workforce analytics team does not seem feasible, yet the preference is to keep workforce analytics within the organization. One variation of this option is to temporarily assign an expert to a workforce analytics project. Alternatively, a practitioner can formally transfer to the HR team from a function such as marketing or finance and apply his or her analytics know-how to the domain of HR. “The risk modeling division inside the bank are very good partners. They have made fraud calculation predictions to very advanced levels. We use their data scientists when we need them. They are very good, they already predict customer attrition, so we just ask them to apply their models to different variables.” —Kanella Salapatas HR Data Manager and Reporting Service Owner, ANZ Bank If a temporary, project-based assignment seems like a good option, it might be a challenge to borrow the time and expertise of people focused on fulfilling their own function’s mission. A possible solution is to arrange for rotational assignments that formalize participation on a temporary basis. This can be mutually beneficial for the functions and individuals involved. Crosspollination of skills and ideas is almost always healthy for an organization, and the HR function is best positioned to structure and implement such a program. Some practitioners have mentioned that the most valuable analytics conversations happen when people from different functions meet informally and share stories and perspectives, such as during occasional lunch meetings. Rotational assignments can make such conversations more intentional and less subject to chance encounters. Teams might also be able to leverage expertise in centralized functions such as communications on an as-needed basis. Peter Hartmann, Director of Performance, Analytics and HRIS at Getinge Group, used this approach in a previous role to in-source storytelling skills: “The ability to translate findings into a nice story is not my core competence. I translate the numbers into a conclusion and a recommendation, but when I have to take it into a nice rounded story, frankly, I am not a very good storyteller. But I know it’s important, so I’ve worked with the communications department, where they ******ebook converter DEMO Watermarks*******



write using a journalistic approach, and this was a match made in heaven.” Advantages of In-sourcing In-sourcing skills offer several advantages, such as the depth of knowledge that internal experts have about the organization, in addition to their subject matter expertise. Internal experts bring a level of understanding about goals, challenges, operations, and systems that will be unmatched by an external provider or a newly hired team member. And somewhat similar to an external party, internal experts are less likely than an HR insider to have preconceived notions about HR-related projects and can bring a fresh perspective (and credibility) to the analysis. In-sourcing is a cost-effective option that requires no substantial incremental investment, as long as all parties involved are able to fulfill their collective scope of responsibility. In-sourcing also extends the reach of workforce analytics to a broader audience within the organization, helping to build a coalition of supporters. Disadvantages of In-sourcing Among the disadvantages of relying on other people in the organization who have their own commitments and responsibilities is the uncertainty associated with team members’ availability, time, and focus to work on your projects. Other projects might take priority for them at any given time, and resources could be redirected, impacting your timelines and ability to fulfill your own commitments (although this is not an issue when resources are formally assigned to the HR team). Another challenge is that, if the analysts are too far removed from the HR function, a lack of domain expertise could potentially lead to inefficiencies and rework. Without the HR “sixth sense” (see Chapter 12), the analysts might identify relationships in the data and recommend actions, without taking into account issues such as unfairness and discrimination. Finally, borrowing expertise from elsewhere in the organization could give the appearance that HR is not capable of delivering analytically based insights. The in-sourcing option could potentially and inadvertently reinforce this type of preconceived notion that exists in some organizations.



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Outsourcing allows you to quickly and flexibly acquire skills needed beyond the minimum capabilities. An external partner can be contracted on an asneeded basis and, if used judiciously, can provide a cost-effective option. External partners can be called upon to assist with a variety of tasks, such as advanced data management (for example, normalizing data or using predictive techniques to fill gaps in a dataset) or sophisticated analytical techniques that are difficult or unaffordable to source internally. Partners typically provide their own technology solutions to assist with these tasks, minimizing the need to invest in technology. “Businesses don’t have two years to wait for you to build sophisticated analytical capability. By tapping into external partners, you get the skill you need immediately.” —Mark Berry Vice President and Chief Human Resources Officer, CGB Enterprises, Inc. When chosen wisely, external providers will work with you as a true partner in realizing your workforce analytics goals. As Andrew Marritt, founder of OrganizationView, states, “The best consultants put themselves in the shoes of their clients.” Advantages of Outsourcing In addition to providing a ready source of deep expertise, external partners have the distinct advantage of providing a third-party perspective. Strong partners are skilled at asking provocative, constructive questions that might be difficult for an internal person in a politically complex environment to ask. Laurie Bassi, CEO of McBassi & Company, expands: “Our job really is shifting mind-sets by focusing on the power of asking good questions. As an outsider, this can be easier to do because we are not lost in the daily grind.” Partners can also offer experience in overcoming obstacles and points of resistance. Peter O’Hanlon, Founder and Managing Director of Lever Analytics, says, “Partners can say the hard things and make things happen quickly because they’ve seen it before and they know how other organizations work.” Partners often bring repeatable approaches that have worked well in other organizations. As a result, you can benefit from their learning and experiences and thus accelerate the time to realizing value from analytics. Michael Bazigos, Managing Director and Global Head of ******ebook converter DEMO Watermarks*******



Organizational Analytics & Change Tracking at Accenture Strategy, describes his approach at a previous organization: “We built a team with domain expertise as well as quantitative expertise, and we developed replicable solutions to take to clients to solve problems fast.” Skilled external partners can customize their support to meet the evolving needs of their clients. They might be able to assume all responsibilities for statistical modeling, for example, or they might be able to coach their more advanced clients to continually improve their analytical skills. For those in between, partners can do as Andrew Marritt of OrganizationView has done and coach them to be a consumer of analytics—for example, teaching clients what to look for, what questions to ask, and how to interpret results. In addition, not every organization has the need or desire to staff a complete analytics function in HR. Mark Berry of CGB Enterprises, Inc., asks, “Why build something internally when you can ‘buy’ deeper expertise on the outside? As an organization, you don’t have to build internal capabilities for every area of HR analytics. You can let expert service providers do it for you.” Disadvantages of Outsourcing Although utilizing external providers allows a degree of flexibility in spending, the cost can exceed that of having one or more full-time team members if the team uses external providers extensively. Another potential challenge is lack of continuity: Specific individuals at partner organizations might not always be available for your projects if they are in high demand by other clients. External providers also need time to learn about your particular organizational culture and environment, the specifics of your data, and the details of your organization’s operations. Data security might also be a consideration because you will need to release your sensitive people data for analysis. In addition, you will likely have less flexibility to adjust your efforts to accommodate changing priorities. Organizational demands can and do shift, sometimes in the midst of a project. Keep in mind that you will still get billed for time spent on paused or halted projects, with limited, if any, value to show for it.



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EXTERNAL PARTNER For Patrick Coolen, Manager of HR Metrics and Analytics at ABN AMRO Bank2, initially partnering with an external analytics firm was the way to go. “We started with an external analytics company. We didn’t want to worry about the quality of our models, so we did it to scale up quickly.” They began as a small internal team of three and, in the early days, spent their time serving as “translators” or liaisons among the business, HR, and the analytics team. The focus was on demonstrating to the business leaders how HR is impacting their key performance indicators (KPIs), the main metrics used to monitor business performance. This was time well spent. Following early successes, the team doubled in size and increased its analytical capability. In this more mature state, it functions as “more of an internal consultancy with HR strategy-type people who have an affinity for and knowledge about statistics and machine learning,” Patrick says. Outsourcing served the team well in the beginning, and as they built internal capability over time, they were able to bring more of the data-related projects in-house. Although the analytics provider continues to play an important role in the team and handles the bigger projects, Patrick’s own team has been continually learning, becoming better informed, asking the right questions, and ultimately taking on the smaller projects themselves. External expertise allowed Patrick’s team to quickly bring the value of workforce analytics to the business while building their own capability for the future. This proved a worthwhile investment: The team established credibility quickly, resulting in more requests from the business than the team can handle. Mission accomplished! Table 13.1 summarizes the advantages and disadvantages of the three broad options for building workforce analytics skills: in-house, in-source, and outsource. Table 13.1 Advantages and Disadvantages of Partner Options



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Choosing Among the Options We have described the Six Skills for Success that are needed for a workforce analytics team, and the options available for obtaining those skills. Now consider the factors that are important when building the team. First, follow the guidance in Chapter 12 to determine how many people with the required skills are needed, relative to the skills you are starting with and the work that needs to get done. Then consider the following factors in choosing among an in-house, in-source, or outsource approach (or some combination): budgets, skill availability, time, organizational expertise, organization size, perceptions of HR, and need for third-party objectivity.



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Some organizations have the good fortune of generous budgets for building workforce analytics capabilities, but most face budgetary constraints. The amount of funding available defines the boundaries of what can initially be built. If you are in the fortunate position of having sufficient funds available, you might opt for fully staffing an internal team and supporting it with stateof-the-art tools and technology. As noted in the following sections, however, budget is not the only consideration. Teams with more limited budgets need to estimate the cost of each best-fit option and choose the path that conforms to their budget. As you execute your first few projects and demonstrate success, you will likely have further opportunities to build a business case for a larger investment.



Availability of Required Skills Funding is necessary but not sufficient for staffing a team. You also need to find skilled resources in the local labor market, and certain skills such as data science might be in high demand but short supply (as a 2011 McKinsey & Company report highlighted). If you are unable to find all the skills you need locally, you might need to start with an external provider or find a way to leverage skills elsewhere in the organization. Other strategies to consider include recruiting outside your local labor market and hiring remote workers. If you ultimately expect to build more capability in-house, you might want to start building a pipeline of skilled people you can eventually call upon when needed. For deep analytical skills, developing relationships with universities and expanding your personal network of analytics experts can help with sourcing skills in the future. Attending analytics conferences, for example, provides exposure to both ideas and the experts themselves. Associating with a network of analytics practitioners also gives you a source of potential qualified candidates when you need it.



Time to Deliver Value The ability to respond to requests within the timeframe needed is an important consideration when deciding whether to hire or partner. If a great deal of urgency surrounds a particular business problem, or if the team needs to quickly demonstrate the benefits workforce analytics can bring, the fastest path might involve partnering with an external provider. Even if your longterm strategy is to build capability in-house, working with a partner initially ******ebook converter DEMO Watermarks*******



can be an effective way to get started and accelerate time to value.



Depth of Expertise in the Organization Some organizations are more analytically oriented by nature, given the type of business they are in; others might not have needed a strong analytics focus in the past. Josh Bersin, founder and Principal of Bersin by Deloitte, explains: “Insurance companies have actuaries—they understand statistics, it’s their core business. Retail companies have sophisticated analytics in customer and market segmentation. It’s starting to hit manufacturing, where there’s been a lot of focus on quality. In these industries, analytics is already embedded in the company, so it’s easier for them to get their arms around it.” If your organization has strong analytics capabilities in functions such as marketing, research and development, finance, or supply chain, you might want to arrange access to these skills for your analytics function. This can come in the form of bringing a workforce analytics mission to an existing enterprise analytics function or bringing resources from other functions into the HR team to build analytics skills within.



Organization Size The size of an organization partly dictates the approach for building analytical capability. Small organizations are likely to benefit most from outsourcing anything beyond the minimum capabilities. This is because, for a small organization, benefits are unlikely to scale to the same extent as in larger organizations. In addition, partnering allows small companies to connect with highly skilled experts when they need them, without the burden of large salary expenditures and lengthy ramp-up times. Mark Berry explains, “I don’t have to maintain those sunk costs, and I can manage the cyclical nature of the business. The outsourcing model is the right model for us.”



Perception of HR Another consideration is the internal perception of the HR function. In some instances, lines of business develop their own HR analytics capabilities separate from the HR function. This can occur because of a perception that HR is not meeting their business demand. For example, a business needing to hire a large number of staff in a short period of time might have prompted those business areas to develop their own analytical techniques for ******ebook converter DEMO Watermarks*******



forecasting workforce requirements and filling vacancies. If you encounter such a situation, a collaborative approach is recommended. Listen carefully to your colleagues’ needs, and demonstrate your team’s analytics know-how and willingness to experiment and take risks. If this situation arises an in-source approach, partnering with the line of business, is recommended. In most instances, the best outcomes result when the lines of business and HR work together to address business challenges.



Third-Party Objectivity Some organizations use external parties because of their impartiality and perceived credibility. In addition, some projects are sensitive in nature and preclude an internal team from working on them. For example, leadership analytics for selecting potential CEO successors would require restricted visibility, as would analytics related to a merger, acquisition, or divestiture. In these instances, partnering with an external provider will likely be necessary. Partnering allows flexibility in acquiring the skills needed to fulfill the workforce analytics vision and mission. As your team evolves and your positive contributions to the business become clear, you will have opportunities to secure additional budget, source more skills, and, ultimately, build the function that best serves your needs. Mihaly Nagy, CEO of the HR Congress and Managing Director of Stamford Global, observes, “When organizations don’t have the resources in-house, they turn to consultants to help them on the journey. Once they see early wins, more funds become available. Then dedicated functions get built.”



Summary Establishing a workforce analytics function does not necessarily mean staffing a fully skilled end-to-end in-house team. Although that is certainly one option, collaborating with partners (either internal to the organization or external providers) could be the best path to success. Consider the following when choosing among your options: • Understand the options for bringing together the needed workforce analytics skills (in-house, in-source, outsource) and the advantages and disadvantages of each. ******ebook converter DEMO Watermarks*******



• Determine the availability of needed skills in the local labor market. • Assess the time required for sourcing, hiring, and training new team members relative to timing expectations for project delivery. • Estimate the cost of hiring versus outsourcing, given the skills needed. • Assess the organization’s perception of HR, to determine whether the credibility typically associated with external experts will be advantageous. • Determine whether the required skills reside elsewhere in the organization, and assess the feasibility of partnering with those functions to execute your initial projects. • Investigate whether any people in other parts of your organization have undertaken their own workforce analytics efforts; if so, seek opportunities to collaborate. • Consider all the factors (including budgets, skill availability, time, organizational expertise, organization size, perceptions of HR, and the need for third-party objectivity) to select the best option for your circumstances.



1 Royal Dutch Shell is a global group of energy and petrochemical companies that, at the end of 2015, had an average of 93,000 employees in more than 70 countries. It was formed in 1907 and is headquartered in The Hague, the Netherlands. Shell is helping to meet the world’s growing demand for energy in economically, environmentally, and socially responsible ways (www.shell.com). 2 ABN AMRO serves retail, private, and corporate banking clients with a primary focus on the Netherlands and with selective operations internationally. In the Netherlands, clients are offered a comprehensive and full range of products and services through omni-channel distribution, including advanced mobile application and Internet banking (www.abnamro.nl).



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14 Establish an Operating Model “We are being very proactive in defining the operating model and who catches what—which person to go to for which types of questions.” —Rebecca White Talent Analytics Senior Manager, LinkedIn The goal of workforce analytics is to inform decision making and improve business performance. To fully realize this objective, the organization must embrace the workforce analytics function and integrate it into its strategy and operations. In this chapter, we outline an operating model that will set you on the path to this desired state. The intent of this guidance is to create efficiencies that help you maximize the time you spend conducting analyses, communicating insights, developing recommendations, and implementing and assessing actions that solve your business challenges. Instead of reacting in an ad hoc manner to issues and challenges, your operating model should allow you to focus on high-priority work with minimal unnecessary diversions. This chapter discusses the following: • The components of a workforce analytics operating model • Ways to link your operations to your strategy • Governance to guide your operations • Clarity and operational discipline for your team and projects • How to hold yourself accountable



Defining Your Operating Model An operating model describes how a group will conduct its business within the larger organization and external environment in which it resides. ******ebook converter DEMO Watermarks*******



Operating models are useful for identifying important working relationships, helping resolve issues and conflicts, and guiding decision making. When designed carefully and thoughtfully, an operating model helps you achieve your workforce analytics mission by aligning daily execution with what you ultimately set out to achieve. Figure 14.1 illustrates the recommended workforce analytics operating model elements.



Figure 14.1 The workforce analytics operating model. As you can see at the top of Figure 14.1, an operating model keeps your dayto-day operations consistent with your workforce analytics strategy, as defined by your vision and mission. A governance framework helps you balance the needs of various stakeholders, from specifying the proper handling of data, to operating within the designated reporting structure, to defining decision-making procedures. Within that framework, you need to structure the implementation of your ongoing work through team structure, clarity of roles and responsibilities, and a disciplined approach to project ******ebook converter DEMO Watermarks*******



management. And as with any business investment, accountability for results is required, through articulating a business case and tracking metrics that demonstrate your impact relative to those investments.



Strategy To ensure ongoing relevance, it is important that the workforce analytics team’s work aligns with and supports the organization’s overall strategy. As business conditions change and the organization adapts, the workforce analytics team’s focus should similarly evolve to meet new challenges and requirements. Periodically verifying your team’s vision and mission will help maintain the alignment needed.



Confirming Your Vision and Mission As you read in Chapter 7, “Set Your Direction,” validating your vision and mission early on is important. As you gain experience, business priorities shift, and you demonstrate what is possible, your vision and mission should continue to reflect these dynamics. Re-engage stakeholders and project sponsors with a renewed perspective of what your team can accomplish. Reformulate your team’s desired future (your revised vision statement) and perhaps articulate a bolder statement of what your team can accomplish and how (your revised mission statement). Validate these revised statements of purpose with your key stakeholders and project sponsors, and communicate methodically to reach all necessary audiences. As you revisit your vision and mission, ensure that they reflect the right guiding policies to overcome the challenges you strive to address with workforce analytics.



Governance Governance provides a framework for how a group operates within its larger organization and environment. It is extremely useful for clarifying rules of engagement when working with workforce data, as well as specifying effective reporting relationships and guiding decision making.



Working with HR Data Ethically and Responsibly Working with HR data requires special care because of the often personal and sensitive nature of the information. HR datasets generally contain personal ******ebook converter DEMO Watermarks*******



information about employees. This is referred to in the United States as sensitive personal information (SPI) and in the United Kingdom as sensitive personal data; other countries have similar designations. This is information that, if compromised or disclosed, could result in such damaging situations and legislative violations as identity theft, discrimination, or unwanted negative publicity. Proper data handling procedures must be established for the long-term operations of the workforce analytics function. Working with organizational data requires a clear understanding of and adherence to all relevant data legislation, regulations, and guidelines. Some policies come from the organization itself, and the team must also follow country-specificlegislation and regulations. Additionally, requirements exist for transferring data between nations. For example, the Privacy Shield Frameworks1 were developed to help U.S. companies transfer personal data from European Union countries and Switzerland. When working with people data in organizations, the sensitivity is further heightened. Extraordinary care must be taken to respect and adhere to all requirements regarding data collection, storage, access, and use. Organizations often have functions such as corporate information councils and roles such as the chief privacy officer that are charged with overseeing data-related matters. The workforce analytics team should work with such functions and people to ensure appropriate use of the organization’s workforce data and avoid any misuse. “There’s a risk of discrimination if the domain knowledge is not there. For example, a statistical model could detect something about people who take six to nine months off and then return to work. HR people would know this was probably due to mothers on maternity leave, whereas a computer scientist might not.” —Andrew Marritt Founder, OrganizationView Data policies and regulations exist for good reason: to keep everyone safe from data misuse, fraud, and unethical and illegal activity. The increasing availability of data and the increasing ease of analysis should not be used to justify unconstrained and ill-conceived analyses. For example, organizations might have the ability to collect information on marital status or the number of children in an employee’s family, but using that information to make decisions about employees would be inappropriate in some jurisdictions or a ******ebook converter DEMO Watermarks*******



violation of employment law. Furthermore, no organization should take action on the basis of the marital or family status of its workers. Eric Mackaluso, Senior Director of People Analytics, Global HR Strategy & Planning, ADP, further points out, “It’s an ethical question; just because you can do it doesn’t mean it’s the right thing to do.” HR domain expertise is extremely useful in ensuring proper use of data. Having this expertise on your team makes you better prepared to avoid the pitfalls and dead ends that result from spending time on statistical relationships that are inappropriate, potentially unethical, and possibly irrelevant for consideration. As a cautionary tale, consider the case of a multinational organization we encountered. In this example, an entire analytics project was derailed because it pointed to recommendations that were potentially discriminatory. The analysis was sound and the business question was highly relevant to the organization, however, the team failed to spot the warning signs until the storytelling phase with the executive sponsors. At this point the project was halted. Upon reflection, the analytics professional realized this outcome could have been avoided with proper knowledge of HR practices surrounding the use of people-related data. You must also be mindful that data collected for some purposes cannot always be analyzed for other purposes. Even if you have what appears to be the perfect dataset, you must respect legislation, regulations, and policies that indicate it is inappropriate for further analysis. In cases such as this, alternative data might need to be identified or new data collected. When collecting new data, do so with an eye to the future analyses you may wish to conduct and obtain suitable permissions accordingly. Note that often it is perfectly acceptable to analyze existing data in aggregate at the group level. For example, a retail company might want to understand differences in performance across its different store outlets. Analyzing variables such as employee survey scores at the outlet level should be perfectly acceptable. The sensitivity tends to arise when data are used for making decisions about individuals—specifically, decisions that were not intended when the data were collected. For example, employee survey scores designed to measure department-level engagement should not be used to isolate particular employees with low scores for some sort of action (such as managing them out of the business). Finally, when collecting new data, you must be open and transparent about ******ebook converter DEMO Watermarks*******



analytical intentions. Teams can take steps to increase employees’ comfort level with sharing their data for analytics purposes. We recommend a fourfactor framework developed by Nigel Guenole and Jonathan Ferrar in 2015, identified by the acronym FORT: • Listen to feedback on the analytics goals from people who will be affected. • Where possible, make sharing of data optional. • Give some recognition to those who agree to share their data for workforce analytics. • Be transparent about everything that is done. To summarize, the following actions are essential to ensure responsible and ethical handling of HR data: • Understand and adhere to all legal and regulatory requirements regarding collection, usage, storage, and sharing of data. • Understand and adhere to all company-specific data guidelines. • Keep your knowledge of guidelines and regulations up-to-date through relationships with information councils in your organization. • Understand the boundaries of the appropriate use of people data. • Strive for broad approvals for future data usage when undertaking new data collection efforts. • Establish an environment (based on gathering feedback, opting in, recognizing contributions, and ensuring transparency) that encourages employees to share their data for workforce analytics purposes.



Optimizing Your Reporting Structure An important structural consideration is where the workforce analytics function reports organizationally. Two broad options exist: The workforce analytics team either reports into HR or is part of an enterprise-wide analytics function. Making it part of the HR function is the most common approach. In some cases, teams report directly to the chief human resources officer (CHRO). Others report into a subfunction of HR, such as organization development, talent, HR planning and strategy, or HR information systems. Still others are part of a shared services model within HR. Chapter 3, “The ******ebook converter DEMO Watermarks*******



Workforce Analytics Leader,” covers this. Despite the prevalence of workforce analytics functions reporting into HR, some practitioners favor the enterprise analytics approach to organizational structure. Rebecca White, the Talent Analytics Senior Manager at LinkedIn, is part of a team that reports into the head of business operations in the finance function. The team has had great success addressing business issues through workforce analytics within this reporting structure. For example, the team has improved the efficiency of the recruiting process in support of LinkedIn’s aggressive growth strategy, and it has increased the diversity of hires, a business imperative. Although the team does not report into the HR function, HR expertise resides directly in the team. Some experts view this approach as a desirable progression as workforce analytics becomes more established in the organization. For example, Mariëlle Sonnenberg is the Global Director of HR Strategy and Analytics at Wolters Kluwer, and her team currently reports into HR. She aspires, however, to move her team into an enterprise-wide analytics function. She would like to see workforce analytics ultimately integrated fully into the business. “Workforce analytics does not need to be separate because the analytical capabilities are an integrated part of our organization,” she says. “It does not need to reside in HR.” In our view, reporting directly to the CHRO (or at least having direct access to the CHRO) yields the best outcomes for the analytics team and the organization. This C-level reporting structure keeps the focus of the team closely aligned with strategic business priorities and has the greatest chance of informing decision making about important business topics. If the analytics team reports further down in the HR function, it is important to have a strong relationship with and direct access to the CHRO. If the workforce analytics team is part of a broader enterprise-wide analytics function, access to knowledgeable HR professionals, particularly the CHRO, is even more important. This provides the guidance that HR expertise brings in understanding people-related issues and working with people data. A dotted-line reporting relationship to the CHRO can formalize this connection.



SET YOURSELF UP FOR SUCCESS Damien Dellala leads the People Data and Analytics Enablement ******ebook converter DEMO Watermarks*******



team at Westpac.2 He comes from a digital banking background: “I started in digital, doing business analysis. I always loved data and am analytically minded. I wrote the digital strategy, operating model, and governance. I delivered a new enterprise data warehouse, forced all the data in there, created it from scratch.” Damien’s experience in digital banking meant he understood the importance of setting things up the right way in an analytics function. He took on this challenge when he started leading the analytics team in HR. “When I joined the HR team, I noticed the analytics team was very immature compared to other organizations in the bank, and this was an opportunity to take Westpac to the next level. The concept was, we need to treat our employee data like we do our customer data.” Damien decided on some key principles: • Make sure everyone understands what you are doing. • Define your capability. • Set clear goals for your team and measure supply and demand. • Get a really good senior stakeholder, a sponsor for the team. Damien focused on setting things up properly. Skills, capability, data governance, mission, business governance, and even the name of the group took time to decide. This is where his senior stakeholder helped to supplement his earlier experience in digital banking and build a strong team. Damien concludes, “All of this led us to undertake some important projects with strong sponsorship to support the business success.”



Establishing a Decision-Making Approach Many decisions will need to be made in the course of your workforce analytics activities. For example, you need to decide whether the data you have obtained have adequate quality and completeness to proceed with analysis. After you have analyzed the data, you need to decide whether the results make sense and are actionable. You also need to decide on the right set of recommendations and how best to communicate them to your various audiences. And before all of this, you must decide which projects to select ******ebook converter DEMO Watermarks*******



and how to prioritize if the number of potential projects exceeds the team’s capacity to manage them all. To maximize your decision-making effectiveness, establish a decisionmaking process that will work well for your team. Decision making can be defined as selecting a course of action (or inaction) from a set of alternatives to achieve the best possible outcome. In many ways, the decision-making process mirrors the analytics process: defining a problem, gathering information, identifying options and analyzing the pros and cons, choosing an option based on evidence, implementing, and following up (see Figure 14.2 for our recommended workforce analytics decision-making process). Approaching decisions in such a structured manner should result in better outcomes than relying on intuition and “gut feel,” which is the very reason for embracing workforce analytics in the first place.



Figure 14.2 Workforce analytics decision-making process. ******ebook converter DEMO Watermarks*******



Seeking a broad perspective on key decisions, including perspectives that differ from your own, is also beneficial. In a 2014 Scientific American article, Katherine Phillips of Columbia Business School cites research showing that diverse groups challenge and process information more deeply, resulting in better decisions. This means the decision-making process will likely be less comfortable and more time consuming, but getting better outcomes is worth navigating the additional challenges. Diversity of opinion also provides useful checks and balances that help you avoid overlooking important facts and considerations. Engaging a broad set of stakeholders in key decisions helps secure commitment to the decisions as well, improving the chances of follow-through. Finally, determine what you will do in case of an impasse. At some point, differing perspectives will likely impede getting to a decision. You will want a defined and agreed approach for moving forward. This could take the form of a designated final arbiter for different types of decisions or an advisory panel that will weigh in. You might even insist on consensus among a specific group of stakeholders for certain decisions. For particularly challenging decisions, agreeing on a set of decision criteria might be a good first step, followed by evaluating the alternatives relative to the criteria. For example, in deciding which project to undertake, criteria could include the following: • Project complexity • Business value (quantitative and qualitative) • Cost of implementation • Ease of implementation • Willingness to implement Agreeing on the criteria beforehand makes the actual decision-making process easier and more objective. Table 14.1 shows examples of typical workforce analytics decisions and potential methods for resolving an impasse. Thinking through the options and choosing the most effective one for your organization’s culture will improve the time to resolution. The best way to address the full spectrum of decisions you are likely to encounter—both routine and unanticipated—is to establish an advisory panel ******ebook converter DEMO Watermarks*******



to provide guidance and assistance as needed. This can prove invaluable in deciding which projects are strategic, what data will be needed, how to handle privacy issues, and how to prioritize multiple project demands. Advisory panel members will likely vary, depending on factors such as the reporting structure for the analytics team; the panel might include representatives from finance, legal, HR, and other functions. The additional perspective the advisory panel provides will enable the team’s success, ensure the right sponsorship, and hold the team accountable to the organization and the workforce (in addition to HR). Table 14.1 Examples of Typical Workforce Analytics Decisions and How to Resolve an Impasse



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Implementation Well-defined structures and operating procedures help optimize your team’s efficiency and effectiveness. A properly designed team structure, welldefined roles and responsibilities, and a systematic approach to conducting projects will ensure that your team focuses on high-value work.



Structuring Your Team An important decision is how best to structure your team, given its mission and scope. If your scope includes both HR metrics reporting and statistical analyses, you need to decide whether to separate analytics and reporting organizationally or to keep both responsibilities within a single team. Teams can achieve success in both scenarios; the option you choose should be consistent with and enable your team’s mission. Importantly, if reporting is part of the mission, you should have clear and distinct definitions of reporting versus analytics, to avoid the common situation of reporting tasks consuming the time and capacity of the team’s analytics experts. An example of separating reporting from analytics comes from Thomas Rasmussen, Vice President of HR Data and Analytics at Shell Oil and Energy. As you learned in Chapter 13, “Partner for Skills,” when tasked with building an end-to-end value chain from data through reporting to analytics, Thomas did so by separating the components into distinct teams. This allowed each team to focus on its core competencies, with reporting expertise residing within a shared-services center of excellence (CoE), and helped the analytics team focus on applying advanced statistical techniques to solve business problems. Without this clear separation, you risk having business managers turn to anyone on the analytics team to support their reporting needs. When highly skilled analysts spend time on routine data requests, the team is not optimizing its capabilities. Another dynamic that can consume the analytics team’s time with reporting tasks is the differing skill levels—specifically, the business knowledge and acumen common among analytics professionals. Christian Cormack, Head of HR Analytics at AstraZeneca, describes this: “Despite having a separate reporting team, we found that the analytics team was being used as a high-quality reporting service. As the analytics team was closer to the business, we were often able to provide a more relevant answer. To be successful, whether in analytics or reporting, you need to really ******ebook converter DEMO Watermarks*******



understand in detail what people in the business do; otherwise, it’s very hard to put the business questions into context.” A different perspective comes from Damien Dellala at Westpac Group. Damien found synergies in keeping reporting and analytics together in a single team. Damien explains, “The data warehouse was the underlying infrastructure, and it makes sense to have analytics off the same common platform as reporting, therefore leveraging an aligned dataset. There’s a lot of prototyping that needs to be done first with a nice synergy at this stage of development. You hope your reporting generates the right questions to be asked of the analytics, and the actions from the analytics should inform the reporting.” Placid Jover, Vice President of HR–Organisation & Analytics at Unilever, goes even further, arguing that reporting versus analytics is a meaningless distinction. “Not everyone needs reporting, just like not everyone needs some of the flashy predictive modeling. The business just knows that it has a problem that needs to be solved, and if you solve it, the business does not care that you used a reporting solution or an advanced analytics solution.” Clearly, differing perspectives result from varying experiences and philosophies. Our view is that analytics goes beyond reporting. If reporting is part of your function’s scope, be very clear about the distinction between analytics and reporting and who is responsible for each. Ultimately, you will choose the setup that best supports your mission, best matches your culture, and is the most feasible to implement based on your organization’s existing capabilities (within HR as well as across the broader enterprise).



Clarifying Roles and Responsibilities To enable productivity and avoid confusion, clarity of roles and responsibilities in organizations is essential. Tammy Erickson, in a 2012 Harvard Business Review article, wrote that teams perform and collaborate best when each member’s roles and responsibilities are clearly defined. This clarity allows team members to execute well even when the task at hand is ambiguous; they stay focused on the work, as opposed to negotiating who does what. The leader must ensure that roles are clearly defined and understood. A responsibility matrix can be a useful framework for defining and communicating roles and responsibilities. One such matrix is commonly ******ebook converter DEMO Watermarks*******



known by the acronym RACI, for responsible, accountable, consulted, and informed. The framework is used by creating a matrix of tasks and roles and then, for each cell in the matrix, indicating the following: • Responsible. People who perform the work needed to execute a task • Accountable. The person who is ultimately looked to for task completion (only one person should be designated accountable for any given task) • Consulted. People whose opinions and input are solicited for their subject matter expertise (typically, two-way communications) • Informed. People who need to be aware of the task and are kept updated on progress (typically, one-way communication) This is one approach for achieving the clarity needed for success. Several variations of this approach have been developed as well; you can find them on publicly available sources (for example, www.racitraining.com). Select the approach that works best for your team and ensures the following: • The buck stops here. For each major task, agree on the one person ultimately accountable for its successful completion. • Nothing slips through the cracks. Agree on who will actually perform each task. • No work takes place in a vacuum. Identify who to consult for input and who to update on progress, decisions, and outcomes. Figure 14.3 illustrates how a partial RACI matrix might look for a workforce analytics project; this is an example and not intended as guidance for all projects. In this hypothetical example, a three-person workforce analytics team consists of the leader, the data scientist, and the industrialorganizational psychologist. The team must work with four stakeholders: a project sponsor, a data owner, a subject matter expert, and the organization’s data privacy officer. This example demonstrates that, for each task, a single person is accountable to ensure completion and specific individuals are designated as responsible for carrying out the work. In other words, the framework creates clarity about who is doing what.



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Figure 14.3 Sample RACI matrix.



A Consulting Approach to Project Management Another operating procedure to consider is how the team will conduct its work. When that work involves advanced analytics, it’s best to think in terms of projects with a defined beginning (project initiation), middle (project execution), and end (project conclusion). Our recommended approach to managing projects is to adopt a consulting model, with the attendant discipline that ensures clarity of objectives and deliverables, well-planned execution, and proper completion. See Figure 14.4 for an illustration.



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Figure 14.4 Example consulting approach to project management. Project Initiation Well-managed projects are initiated with clearly defined objectives, designated project teams with specified roles and responsibilities, defined deliverables, and an initial high-level timeline. One of the most important roles is the project manager, who has accountability for fulfilling commitments, managing the team, communicating with stakeholders, negotiating changes when needed (for example, extending timelines and responding to changing priorities), and ensuring high-quality work. During the initiation phase, the project manager assembles the team, identifies stakeholders and sponsors, gathers preliminary input and perspectives, creates a high-level project plan with overall timelines and milestones (see Figure 14.5), and conducts a project kickoff meeting to discuss and establish the plans with the project sponsor and team.



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Figure 14.5 Sample high-level project plan. At this planning stage, it is helpful to build in work activities for potential actions to be implemented, along with methods for assessing the impact of actions. Although you cannot know at this early stage what the results will indicate or what actions will be recommended (or who will be responsible for implementing them), it is important to make explicit in your planning that implementation and follow-up evaluations are expected. In other words, the project does not end when results are presented. Implementation and assessment activities should be part of the project plan. Project Execution When the project is formally kicked off, a detailed project plan might be necessary. The rigor needed depends on the size and scale of the project. For more extensive projects (for example, 6 to 12 months or longer in duration), more detailed project management is needed. This includes specifying tasks, due dates, owners, dependencies, key meetings, and milestones. The project manager will track task status on a regular basis, highlight risks, identify and implement mitigating actions, and escalate issues to senior project leadership when needed. For shorter projects, a simpler high-level project plan will suffice. See Figure 14.6 for a sample portion of a detailed project plan.



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Figure 14.6 Sample portion of detailed project plan. With project management processes firmly in place, the team can proceed with the analytics work. This includes further defining the key issues and hypotheses, identifying data and analysis requirements, collecting and analyzing data, developing insights and recommended actions, and developing the story to be communicated.



TIPS FOR SUCCESSFUL ANALYTICS OPERATIONS In establishing his analytics operating model, Placid Jover, Vice President of HR–Organisation & Analytics at Unilever,3 has some clear advice. He believes that the following three points are essential in delivering successful analytics projects: • Make better decisions about which projects to undertake: “Understand the way you are making money and losing money, where you are competing well and not competing well, and so on,” Placid says. He further explains that if you focus on the business, deciding which analytics projects to choose becomes much easier. • Get the right skills for the right analytics: “Once you know the problems and hypotheses, make sure you then bring in the right people. Don’t just use Excel; this will not make you a good analytics person.” Placid strongly believes that you need to match the skills to ******ebook converter DEMO Watermarks*******



the desired level of analytics. This can include bringing in a third party or other experts from elsewhere in the company. Whatever you do, bring in credible people and be clear about everyone’s roles and responsibilities. • Be accountable with numeric targets: “If you can’t marry your agenda with the numbers agenda, then you will not succeed,” Placid says. He is clear that you need to be numerate not just in your analysis, but also in the way you tell your story and take accountability. Demonstrating success in a numerical way is important for a team’s credibility in the company. When creating an analytics team, Placid advises taking on the right projects with the right skills to maximize your chances for success. Project Conclusion When the project execution phase is complete, closeout actions begin. Design an implementation plan for the recommended actions. Include key milestones, implementation responsibilities, change management actions, and success metrics. Formal feedback should be given to team members (in addition to ongoing informal feedback throughout the project duration). Feedback should be gathered from project sponsors and stakeholders, to get their perspective on what went well and what future projects can improve. Finally, the team should discuss and document the lessons learned and the insights gained from this project. For example, if you learned that you underestimated the amount of time it would take to obtain data access, build additional time into your next project plan for that activity.



Accountability If an organization makes an investment in workforce analytics, it is only reasonable to expect a positive return on that investment. The analytics team must demonstrate the value of workforce analytics through a well-structured business case and the capability to track the team’s performance through a set of relevant metrics.



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A business case is a formal, structured, and logical justification used to obtain approval for an investment. It might be needed to justify setting up a team or function, or for obtaining initial funding for exploratory projects that will pave the way for more substantial projects in the future. As Mariëlle Sonnenberg of Wolters Kluwer puts it,“It takes investment to show what you can do. Because it is behavioral science, you always need a bit of experimenting with the data. This requires time, resources, and an investment.” In addition, a business case can be used to justify investment in specific technologies to support the team, or for changing the skills or direction of the team to “take it to the next level.” This was the approach for Arun Chidambaram, Head of Global Talent Analytics at Pfizer: “It is a good idea to plan for growth and get investment early on. Why would you plan to keep trying to compete with the same people in your team if you could do better with different people?” Arun also regularly revises his plan against the demand from the business:“Once you have a solid analytics base, then be flexible and grow where your demand is, including growing skills in new countries and experimenting with new technologies when needed.” Finally, a business case should include a clear statement of the problem, the impact of the problem (such as unnecessary costs, inefficiencies, or negative effect on a company’s brand), the recommended solution, the investment required for the solution, and benefits expected from the solution. Impact and benefits should be comprehensive and can include quantifiable as well as qualitative information. The business case should compare the benefits of the recommended action with the cost of doing nothing about the problem. For workforce analytics, your focus should stay on linking people-related issues to organizational outcomes. The benefits of workforce analytics typically far exceed the investment needed. If you consider that peoplerelated costs are among the largest expenditures for an organization, you have ample opportunity to bring value to your organization by improving the effectiveness of people-related processes and practices. “HR has opportunities to think of their employees more like customers and leverage data that is generated from user interactions. Between 50 percent and 60 percent of operating expenses are people related. By looking at employees as customers, there are many business case examples for analytics ******ebook converter DEMO Watermarks*******



across the employee lifecycle, from talent acquisition, to engagement, productivity to retention.” —Damien Dellala Head of People Data & Analytics Enablement, Westpac Group In building the business case, you should work with stakeholders and prospective project sponsors. When consulting with them about what is possible, you might find it helpful to present case studies of similar work that has been well executed and clearly shows the return on investment (for example, quantified value of improved performance, faster time to productivity, or reduced cost). This helps set expectations and, if necessary, stimulates conversation about the expected deliverables of the analytics work. In addition to comprehensively quantifying the benefits of workforce analytics, you must fully understand the cost of the investment. Specify what is needed in terms of people (internal staff and external partners), workspace, tools and technology, and any other expenditure needed. Be sure to obtain the organization’s commitment to the resources and funding required in exchange for your commitment to deliver value.



Defining Your Success Metrics You’ve made projections and commitments in the business case about benefits that will result from your analytics work. To build momentum and keep the team funded and functioning, you need to demonstrate those benefits as they are realized. To do this, it is important to agree on a set of metrics for your team. Two broad categories of metrics exist: outcome metrics and proximal metrics. • Outcome metrics. These metrics are your ultimate indicators of success. For example, what cost savings did you achieve? By how much did you improve productivity? How did customer satisfaction ratings change as a result of better enabling the customer support staff? How much did contract extensions and renewals increase by keeping customer facing staff in their roles for a longer period of time? Of course, you do not want to wait until the end of a project to learn that you did not get the result you were intending. This is why proximal metrics are so important. • Proximal metrics. These metrics serve as interim measures of how you are doing relative to your ultimate outcome, and they allow for timely ******ebook converter DEMO Watermarks*******



course correction as needed. Suppose your organization has a problem with contract extensions and renewals—specifically, compared to the competition, your company’s rates are subpar. Suppose, then, that your analyses reveal, as hypothesized, that customers are dissatisfied when the people working on their account change frequently. You recommend extending client assignment length from an average of four months to eight months, thus providing more continuity for clients. The recommendation is agreed upon, and after 12 months, you measure yearover-year renewals and extensions. Unfortunately, the rates are the same as last year at this time, before the intervention. Was the recommendation ineffective? Perhaps, but what if staff members weren’t actually staying the full eight months as recommended? Measuring the staff assignment duration would be a proximal metric that could shed light on an unexpected outcome—better yet, it could indicate an implementation issue that the company could fix before measuring the outcome metric and drawing a conclusion. For each project your team undertakes, agree on a concise set of outcome and proximal metrics with the sponsors. Ensure that you are actually able to measure all your planned metrics, and be prepared to act on the results. Finally, keep in mind that not every outcome metric will have a monetary value associated with it. Some people-related objectives have a nonfinancial goal for individuals, the organization, and society. You will still want to measure your outcomes relative to that purpose, but you will do so in a nonfinancial way.



Summary A well-planned operating model drives efficiencies and allows the workforce analytics team to focus on the work at hand: successfully implementing analytics projects that improve business results. Following are the key actions for establishing your operating model: • Confirm your vision and mission, incorporating the renewed perspective that comes with experience. • Understand data privacy legislation, regulations, and policies for all countries in which your organization operates, and adhere to them at all times. • Understand the sensitivities of HR data; rely on local HR expertise to ******ebook converter DEMO Watermarks*******



guide your analysis and ensure that you operate within policies and guidelines. • Request broad permissions for data use when embarking on a new data collection effort, and encourage employee participation by applying the FORT framework (gathering feedback, opting in, recognizing contributions, and ensuring transparency). • Ensure that the reporting structure provides access to the CHRO. • Manage your team’s scope and configuration to ensure that analytics does not get displaced with metrics reporting activities. • Clearly define the roles and responsibilities of your team and the people you interact with to execute your mission. • Agree on a decision-making process that will serve your team well, and use an advisory panel as part of your process. • Adopt a consultancy approach to project management, with clearly defined activities and checkpoints during project initiation, execution, and conclusion. • Build a business case at least annually that contains a comprehensive view of investments and benefits, to demonstrate return on investment and obtain commitment for funding. • Agree on and implement your own outcome and proximal metrics to hold your team accountable for delivering value to the organization.



1 Implemented in 2016 and 2017, the EU-U.S. and Swiss-U.S. Privacy Shield Frameworks are mechanisms to comply with EU and Swiss data protection requirements when transferring personal data from the European Union and Switzerland to the United States in support of transatlantic commerce (www.privacyshield.gov). 2 Westpac was Australia’s first bank, established in 1817 as the Bank of New South Wales; it became Westpac Banking Corporation in 1982. It has operations mainly across the Asia Pacific region, with nearly 13 million customers (www.westpac.com.au). 3 Unilever is an Anglo-Dutch multinational consumer goods company coheadquartered in Rotterdam and London. Its products include food, beverages, cleaning agents, and personal care products. It had a turnover ******ebook converter DEMO Watermarks*******



of almost €53 billion in 2016, and its products are sold in approximately 190 countries globally (www.unilever.com).



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IV Establishing an Analytics Culture 15 Enable Analytical Thinking 16 Overcome Resistance 17 Communicate with Storytelling and Visualization 18 The Road Ahead



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15 Enable Analytical Thinking “For most HR professionals, this is not how they were trained, it’s not why they went into HR. We need HR people to accept that it’s a good thing to use workforce analytics to make decisions.” —Tracy Layney Senior Vice President & Chief Human Resources Officer, Shutterfly Inc. A common refrain among analytics practitioners is that human resources (HR) professionals like to work with people, not data. One goal of workforce analytics teams therefore should be to enable the wider HR community within their organizations to embrace an analytical mind-set and approach. Broad acceptance of an analytical approach to HR will maximize the reach and level of impact of the analytics teams’ work. he best approach for helping HR professionals embrace analytics is to understand their starting point and build from there. In other words, determine their comfort and skill levels for an analytical approach to HR, and plan your enablement activities accordingly. This chapter discusses the following: Types of analytics perspectives in HR • How to enable an HR analytics mind-set • The analytics “translator” role • The role of leadership in creating a culture of analytics



Perspectives of Analytics in HR HR professionals have varying degrees of analytics comfort and expertise. A logical first step in enabling a broad analytics mind-set in HR, therefore, is to identify where HR professionals fall along the spectrum of analytical ******ebook converter DEMO Watermarks*******



perspectives. Segmenting the HR community within your organization into categories allows you to customize your approach to analytics enablement. “There are data-savvy individuals and then those who are less numerically literate. I think that is normal in HR.” —Andre Obereigner Senior Manager, Global Workforce Analytics, Groupon Most HR functions encompass three types of analytics perspectives: analytically savvy, analytically willing, and analytically resistant (see Figure 15.1). After you have identified where people stand regarding analytics, you can take the appropriate actions to encourage, educate, and nurture an analytical approach to HR.



Figure 15.1 Perspectives of analytics in HR.



Analytically Savvy Examples of quantitatively experienced HR professionals abound. These are often people who joined HR from other functions or were trained in analytical techniques as part of their formal education. HR professionals in the analytically savvy category view HR through a business lens, one that uses analytics to inform decision making and improve performance. They can be helpful in embedding that mind-set into the culture of HR. Some HR specialties are more likely than others to have analytically oriented practitioners; examples include compensation and benefits, employee engagement, and health and wellness, where numerical literacy is needed for performing the job. This quantitative focus has helped move the HR function toward increased financial literacy and has potentially set the stage for improving analytical literacy. ******ebook converter DEMO Watermarks*******



Finding people in HR who already have analytics capability gives you an opportunity to leverage the hidden analysts in your organization. These people, particularly if they are well-respected and influential members of the HR community, can help their less analytically oriented colleagues understand and appreciate the value an analytics lens brings to HR. Salvador Malo, Head of Global Workforce Analytics at Ericsson, says, “Some people are self-starters and primed for workforce analytics; you should find that latent skill that exists in organizations. Once you have a few people here and there, they serve as examples. There are a lot of internal champions.” Enabling the Analytically Savvy Your enablement approach for this group should begin with determining current baseline knowledge. You want to ensure that your specific philosophy and approach to workforce analytics is well understood, and you can determine where additional education would be helpful. For example, you might want to supplement any gaps in knowledge with education on advanced analytical techniques. Your team can directly offer enablement or you can point to online courses and discussion groups for the latest thinking. Thomas Rasmussen, Vice President of HR Data & Analytics at Shell, says of the analytically savvy workers in HR, “For the university people already trained in analytics, we just need to keep it up for them.” If you can’t identify analytically savvy people within your organization’s HR function, you need to bring in people with those skills. Mark Berry, Vice President and Chief Human Resources Officer at CGB Enterprises, Inc., asserts, “HR has to begin at the source, hiring people who have the orientation and acumen to drive fact-based decision making in the organizations they serve. Development programs need to be focused on honing these skills.” Keeping skills current is particularly important for the analytically savvy. In addition to pointing employees to internal and external courses, encourage participation in online user groups and discussion forums. Professional affiliations with analytics societies can be advantageous, and conference attendance should be encouraged and supported. Presenting at professional conferences helps maintain a presence in the larger analytics community, and attendance provides exposure to new thinking and techniques. Providing access to new technologies can also keep skills fresh and stoke interest levels, and early involvement in projects can build skills and provide specific ******ebook converter DEMO Watermarks*******



opportunities to champion the cause. In summary, the following actions are recommended for enabling the analytically savvy: • Identify current analytics strengths and any gaps related to your team’s methods and approach. • Identify specific development needs to close any gaps, and offer learning opportunities (internally developed and administered, or externally sourced) to address the needs. • Recommend online resources (such as discussion groups) and reference books for ongoing skill enhancement. • Support membership in professional societies and attendance at analytics conferences for visibility in the larger community and opportunities to learn new approaches. • Provide access to new technologies to explore and expand areas of interest. • Involve the analytically savvy in projects early on so they can help preach the benefits of analytics to others.



Analytically Willing The analytically willing are open-minded about analytics and prepared to learn, but they have not had the formal training or exposure needed to develop analytical skills. Mark Berry says most HR professionals fall within this category, which he describes as those who “don’t necessarily get it, but want to understand and use it.” HR training programs and educational curricula have not traditionally focused on building analytics capability, and many people who have been attracted to the profession in the past would be unlikely to name statistical analysis as a particular interest or strength. That said, members of this group are not opposed to applying analytics to HR; they simply lack the know-how to do so. This willingness and openmindedness presents an encouraging opportunity to build the desired analytical culture and way of thinking. Enabling the Analytically Willing The goal in enabling this group is to identify any areas of concern or confusion, and work together to address them. An important starting point is ******ebook converter DEMO Watermarks*******



clarifying what analytics is and what it is not. As defined in the Preface, workforce analytics is the discovery, interpretation, and communication of meaningful patterns in workforce-related data to inform decision making and improve performance. This is not the same as reporting, nor is it solely about the data itself, the management of data, or data analysis tools. Analytics is the application of analytical approaches to data that allows professionals to discern, share, and act upon meaningful patterns and insights. With this clarity in place, a good next step is to adopt a hands-on approach. Work with actual datasets to educate this group on fact-based approaches. Thomas Rasmussen describes, “We get them involved in a project that shows the value of analytics—first-hand involvement helping them solve a problem that’s relevant for what they do.” Ian O’Keefe, Managing Director and Head of Global Workforce Analytics at JPMorgan Chase & Co. has taken this approach further by modeling scenarios to demonstrate the likely outcomes associated with inaction versus action: “When someone got hesitant, we showed them data. We showed trends, we told them what would happen next week or next month if we did nothing, and contrasted that with what would happen over those same time periods if we made some changes.” Analytics workshops or learning modules can help this group build expertise in analytical techniques and basic statistics. Andre Obereigner of Groupon finds value in enhancing HR’s knowledge of data they are already familiar with and tools they are already using. “A big focus for us is to further educate the HR community on the value that workforce data holds and to show them how to read the information and what actions they could take,” says Obereigner. “One of the first things I did was organize Excel workshops. If they are using Excel as a main tool, they should know how to use it. I guided the local HR team on the different features, and this made a big difference.” Through conversations and observations, you can ascertain people’s current skill levels and determine their further learning needs. “Capability is growing. We’ve had some training efforts to build that, but it’s really about changing behavior, helping people realize that using data will empower them and build credibility.” —Ian Bailie Global Head of Talent Acquisition and People Planning Operations, Cisco In summary, the following actions are recommended for enabling the ******ebook converter DEMO Watermarks*******



analytically willing: • Identify baseline analytics knowledge, concerns, and sources of confusion. • Provide clarity on what analytics is and what it is not. • Address concerns and confusion through examples with actual data relevant to their jobs. • Offer analytics-focused training on tools they already use (such as spreadsheet software). • Develop and deliver analytics workshops or learning modules to help them build expertise in analytics techniques and basic statistics.



CLARIFYING WHAT ANALYTICS IS AND WHAT IT IS NOT Salvador Malo is the Head of Global Workforce Analytics at Ericsson.1 He has a Ph.D. in mathematics and consulting skills honed from half a dozen years at McKinsey & Company and almost twice that time at Egon Zehnder. Salvador takes a sophisticated view of three fundamental aspects of workforce analytics: analysis, consulting, and people. For Salvador, analytics is a way of thinking: “You have to have a mind-set for analytics. It is important not to get confused about what it is. It is not the data. It is not the business questions. It is the activity that people do with the data to answer the questions.” According to Salvador, such clarity about workforce analytics is essential to avoid confusion, particularly when working with leaders and HR business partners all over the world: “For example, it is not just about headcount reports. This is not analytics.” That’s not to say that technology and systems are unimportant. “It pays to have a good IT plan,” explains Salvador. “But analytics is time consuming, so you don’t want to get distracted by other HR operations and systems integration projects. Yes, it’s important to have good process, systems, and data, but what’s important is what you do with these things to answer the business questions. Make sure people understand this. Always.” ******ebook converter DEMO Watermarks*******



This point of view serves Salvador well as he leads the analytics function in a company that operates across 120 countries in an organization with more than 116,000 people. Mathematics Ph.D. or not, it’s the mind-set that counts.



Analytically Resistant The analytically resistant group represents the biggest challenge. For a variety of reasons, some people simply reject the notion that applying an analytical approach to HR is a valuable and worthwhile endeavor. And if the analytically resistant are particularly influential members of the organization, their mind-set, as conveyed through words and actions, can create obstacles and limit the potential impact of workforce analytics. The goal in changing this mind-set is not to transform the resistant into analytics experts, but rather to have them accept and even embrace the concept that an analytical approach can enhance the value of their business. Peter Allen, Managing Director, Agoda Outside, stresses the importance of analytics to HR’s credibility: “HR should be an advisory service that helps managers to do their jobs well, and to be a credible adviser, you need to be seen to know what you’re talking about. One of the best ways to do this, of course, is with good data and meaningful analytics.” Enabling the Analytically Resistant After you have identified the analytically resistant, understanding the reasons for their resistance is helpful. Is it a lack of confidence in their own analytical and numerical abilities? Is it a true disbelief that a data-based approach is superior to gut feel, instinct, and intuition? Or is it something else? Understanding the sources of resistance best positions you to address it. Consulting skills are helpful in uncovering the sources, and change management skills are invaluable in overcoming the resistance. Given the power of resistance to limit the impact of workforce analytics and the breadth of potential reasons underlying resistance, we dedicate the next chapter to this topic. In it, we discuss the various types of resistance, including everything from “It’s too difficult,” to “We’ve tried it before and it didn’t work,” to “The data challenges are insurmountable.” And for each of these objections, the chapter offers guidance for moving past them. ******ebook converter DEMO Watermarks*******



The key to winning over the skeptics is demonstrating how analytics can enhance their own personal effectiveness and success. As Ian Bailie notes, “You will always find the least resistance in helping someone solve a problem that they have. And if you succeed in winning them over, they may become your biggest advocates.” In summary, the following actions are recommended for enabling the analytically resistant: • Determine the reasons for their resistance. • Use analytics to help them solve a problem they are facing. • Help make them successful through analytics. It should be noted that some practitioners avoid the problem of analytical resistance by screening for an analytical mindset when hiring HR professionals. Over time, and depending on the size of your HR function, this can be a feasible strategy for establishing an analytical mindset in your HR function. Mark Berry endorses this approach: “What we need to be doing is minimizing this issue by ensuring that our selection and promotion processes, including querying candidates about analytics experience. We need to drive the culture within our HR organizations that elevate evidence-based HR leadership as foundational to what we do.” Ultimately, as analytical skills are incorporated into professional HR qualification programs, we should see fewer analytically resistant practitioners entering the profession. Figure 15.2 summarizes the key actions for enabling each type of analytical perspective in the HR function.



Figure 15.2 Key actions for enablement.



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The Translator Role With the right training and focus, HR professionals with an analytical mindset are candidates for an essential role: the analytics translator. This is a role that McKinsey & Company described in 2014 as “crucial for unlocking the full value of advanced analytics.”Luk Smeyers, cofounder of iNostix by Deloitte, says the analytics translator serves as the “coordinator in an analytics project.” In a 2015 article, Tom Davenport, Professor of Information Technology and Management at Babson College, described translators as “extremely skilled at communicating the results of quantitative analyses.”He argues, “Almost every organization would be more successful with analytics and Big Data if it employed some of these folks.” Translators perform the important work of bridging different functions in an organization and enabling effective communications between them. McKinsey & Company says, “Translators form the links that bind the chain of an effective advanced-analytics capability.” Put simply, translators turn the technical outcomes from analytics projects into insights that business leaders can understand and act upon. To perform this role effectively, translators must know the business, understand analytics, and have strong communication and relationship management skills. Good candidates for this role are HR business partners, with their understanding of the HR domain and the business they are supporting, combined with a working knowledge and appreciation of analytics. The best candidates are likely to be both analytically savvy and analytically willing.



BUILDING A CULTURE OF ANALYTICS THROUGH TRAINING Bart Voorn has led the HR Analytics team at Ahold Delhaize2 since 2014. During that time, he has built a culture of analytics across the company’s HR function through a deep and extensive training program. “The field of analytics is so new to HR that people have difficulty in formulating their questions and demands,” Bart says. “It’s like the iPad question: Do you need something between a computer and smart phone? No, but Apple introduced it and now it’s everywhere.” ******ebook converter DEMO Watermarks*******



Bart goes on to explain what this meant in Ahold Delhaize with respect to analytics and the HR function.“HR analytics does not end with an HR analytics team—it only starts there. We have created a capability-building program for HR. We trained in total a few hundred people worldwide—vice presidents, directors, but mostly HR business partners.” Bart says that some of the very senior HR leaders had more trouble grasping these ideas than newer people to the function. In the program, he first conducted a capability scan to explore the maturity level of data-driven decision making both for HR and individually. Next, he delivered e-learning courses and master classes on statistics, and the leaders practiced analytics cases. “We essentially trained HR business partners how to see and formulate research questions. The population now acts as ‘spotters of opportunities’ for us. If they come to us with the questions, then they should also actually act upon the insights. Without them, we, in workforce analytics, are useless. They are key in driving action. We continue to run regular training for the HR community.” The training program at Ahold Delhaize helped to improve the overall level of analytical competency in HR. As Bart delivered the training, he found that the demand for analytics projects increased, project selection became more effective, and the resulting recommendations became easier to implement. The HR business partners became both the spotters and the translators of the business analytics topics. “When you do this, it gives the feeling that HR is right on track at the decision-making table and is business relevant.”



The Importance of Leadership Leadership values, beliefs, and actions are highly influential in establishing and maintaining an analytics mind-set. When the top leaders in an organization are analytically oriented, it creates a data-centric culture that permeates throughout. This was the experience of Mariëlle Sonnenberg, Global Director of HR Strategy & Analytics at Wolters Kluwer: “Although other companies report difficulties in getting the sponsorship needed, our ******ebook converter DEMO Watermarks*******



CEO and CHRO are very data-driven people, so that was easy.” The same is true for HR leadership: When the leader is a “true believer,” not just someone with a more traditional HR mind-set, that person will want to understand and manage the workforce analytically. This leadership messaging—that an analytical approach is important and it is the way things get done in HR—is a catalyst for gaining traction in an organization. Empathy and relationships have traditionally been important in HR and will likely remain so, but data-driven, fact-based insights are needed as well. Andre Obereigner has experienced this leadership impact: “You need to have senior management support. In one organization I worked in, I noticed that when we had a new global head of HR who was very focused on data, suddenly people were talking about analytics.” For some organizations, the impetus for establishing a workforce analytics function comes from the organization’s leadership. When this is the case, the leadership support should be ample. In other organizations, a savvy practitioner successfully makes the case for bringing an analytical approach to HR. Under those circumstances, senior leaders might or might not be fully committed to workforce analytics. When the leadership support for analytics exists, take full advantage of it. Incorporate the leaders’ words into your communications and stories, align your projects with the leadership priorities, and secure direct sponsorship for projects and the analytics function overall. Without that leadership, you must build a coalition of supporters among the most influential analytically oriented members of the organization: leaders of other functions, lines of business leaders, and even the board of directors. You will likely encounter more resistance to workforce analytics if your organization lacks key leadership support. To encourage an analytics mind-set in leaders, offer oneon-one coaching on analytics fundamentals, involve them directly in analytics projects, and perhaps conduct a project that will likely be personally and directly beneficial to them.



Summary To reap the most benefit from workforce analytics and achieve the widest impact, the broader HR function within an organization must embrace and demonstrate an analytics mind-set through their words and actions. The following recommendations will help you enable an analytics mind-set in ******ebook converter DEMO Watermarks*******



your HR function: • Understand the variations of analytics literacy among your organization’s HR professionals and categorize them accordingly: analytically savvy, analytically willing, or analytically resistant. • Customize communications and enablement based on the analytics literacy categorizations: • For the analytically savvy, provide training on the latest analytical techniques, tools, and methods; encourage them to connect with community forums. • For the analytically willing, demonstrate and coach them on familiar data and tools, and have them attend basic statistics courses. • For the analytically resistant, win them over by using analytics to provide insights that will make them more successful. • Identify HR business partners with the right analytical mind-set and skills to perform the important translator role in workforce analytics. • Understand your leaders’ views of analytics, leverage their support fully, and form a coalition of additional supporters as needed. • Encourage an analytical mind-set in leaders through one-on-one coaching and involvement in projects that are directly beneficial to them.



1 Ericsson is a world leader in communications technology that is publicly listed on both NASDAQ OMX Stockholm and NASDAQ New York. It is headquartered in Stockholm, Sweden (www.ericsson.com). 2 Ahold Delhaize is an international retailing group based in the Netherlands that serves customers in the United States, Indonesia, and Europe. It operates 21 local brands and serves more than 50 million shoppers each week in 11 countries. It has been retailing for more than 125 years and has more than 370,000 associates. Its brands include Stop & Shop (United States), Foodlion (United States), Albert Heijn (Western Europe), Delhaize (Western Europe), Alfa-Beta (Greece), and Albert (Czech Republic) (www.aholddelhaize.com).



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16 Overcome Resistance “Some people will say it will never work. That’s when the risk–reward analysis would be helpful: Determine the investment needed to get an early win, take on a smaller problem, demonstrate a win, then move on to more projects.” —Mats Beskow Director of Human Resources, Landstinget Västmanland In their quest to bring analytics to the HR function, practitioners have faced many forms of resistance. These range from stakeholder concerns, to unwillingness to invest financially, to doubts from the HR function itself. All of these forms of resistance can be overcome. This chapter discusses the following: • Types of resistance to workforce analytics: Stakeholder skepticism, financial frugality, HR hesitancy. • Suggestions for overcoming each type of resistance.



Resistance to Workforce Analytics In their quest to bring analytics to the HR function, practitioners have faced many counterarguments. Types of resistance can be grouped into three general categories (see Figure 16.1): • Stakeholder skepticism • Financial frugality • HR hesitancy The first step when faced with resistance is understanding the underlying causes. Armed with that knowledge, you will be able to address the resistance and succeed in bringing analytics to HR. ******ebook converter DEMO Watermarks*******



Figure 16.1 Categories of resistance to workforce analytics.



Stakeholder Skepticism Stakeholder skepticism can range from doubts about the value of analytics to doubts about the value of HR. Following are frequently heard expressions of doubt and suggestions for addressing them.



I Don’t Need Analytics to Tell Me What to Do A stakeholder might resist analytics with a declaration of “I already know the answer” or “I know what the problem is and I know how to fix it.” This can be a particularly pervasive mind-set: All business leaders have had responsibility for managing people, and many consider themselves experts in people issues, given their achievements as a manager or executive. Therefore, the notion of people-related analytics might seem entirely unnecessary. However, if they think they already know the answers to their challenges, it is fair to ask, why haven’t they addressed them already? You might think it’s clear that foregoing an analytical approach can lead to ******ebook converter DEMO Watermarks*******



misguided and costly actions (or inaction), but your stakeholders might be less convinced. Our guidance is to first determine the underlying cause of the skepticism. Do you suspect that your stakeholder is worried about being proven wrong? If yes, the best course of action might be to enlist the services of a third-party partner who is better positioned to ask challenging questions and deflect any negative ramifications if the findings don’t confirm what the stakeholder expected. In other cases, the underlying issue might be a reluctance to address a particular problem, a failure to acknowledge the problem, or an unwillingness to change the way things have always been done. In these circumstances, the solution might be to find a different, influential sponsor in the organization. Antony Ebelle-ebanda, Global Director of HCM Insights, Analytics & Planning at S&P Global (formerly McGraw-Hill Financial) advises: “Ensure you have a strong senior champion to move things forward. It is not enough to be doing great work. This doesn’t need to be a specific person or role, but it does need to be someone from the business who has influence.”



Our Organization Is Unique, That Won’t Work Here When leadership’s objection is that the company is unique and analytics won’t work, the response should be: That’s all the more reason to invest in workforce analytics (or, for that matter, any analytics). If the environment is truly unique, you need to understand what works and what doesn’t for the organization’s specific context. Being unique is not at odds with being analytical. Furthermore, analytics customized for your organization will enable the specificity needed for market differentiation and increased competitiveness. Although local customizing is important, so is taking advantage of any existing relevant knowledge base before undertaking your project. Decades of scientific research have produced valuable insights on a wide range of work-related topics, and overlooking that knowledge would be counterproductive. Mark Huselid, Distinguished Professor of Workforce Analytics and Director of the Center for Workforce Analytics at Northeastern University, reflects: “We’ve been playing at workforce analytics, at various levels, for a long time. I hope to see greater integration of what we already know—domain-specific knowledge—into the questions and answers we’re looking at. I see a lot of analytics work that could be much better if the folks ******ebook converter DEMO Watermarks*******



involved only knew there’s a body of literature on it.”



I Don’t Trust Your Analysis Avoid getting defensive if someone doesn’t view your work as credible. A lack of trust in the analysis, or a failure to appreciate the impact of people factors on the business, is best addressed by building positive relationships with the skeptics over time. Listen carefully to try to determine what specifically they don’t trust (for example, concerns about the data or unexpected findings that they don’t view as credible), but don’t feel that you always have to convince people you are correct. Make sure to listen and determine how you can be helpful in subsequent opportunities to address their business challenges. “Start by understanding where you can be helpful. Delivering something useful for decision making, regardless of how simple or fancy it is, may help relationship building.” —Jeremy Shapiro Global Head of Talent Analytics, Morgan Stanley



HR Doesn’t Have the Skills In some organizations, the HR function has a reputation for focusing on the “soft” people side of things or lacking an analytical mind-set and quantitative skills. HR practitioners might even see themselves this way. If you face this type of reputational resistance, the best way to overcome it is with evidence to the contrary: Demonstrate your analytics prowess with your first project (see Chapter 9, “Get a Quick Win”). If other functions in your organization, such as marketing or finance, are known to have strong analytical skills, you can benefit from their reputation by aligning your work with theirs and borrowing some skilled resources or ideas to get you started. Make those resources highly visible to fully benefit from their reputation. If this is not feasible, you need to hire or contract analytical people for your team. Ideally, they should be reputable analytics powerhouses (based on their previous experience or qualifications). With these resources, demonstrate your team’s own analytical contributions, and enable others in the HR function to think analytically and embrace an analytical approach. You will then be well on your way to altering the ******ebook converter DEMO Watermarks*******



nonanalytical reputation of HR.



HR Is Not Strategic to the Business Some organizational leaders do not view HR as a strategic business partner and instead see the function as mainly administrative in nature. When faced with this perspective, your credibility might be in doubt simply through association with the HR function. The way to remedy this situation is to prove the skeptics wrong: Forge ahead with an analytics project and return with a solid story demonstrating the value to the business. Ian Bailie, Global Head of Talent Acquisition and People Planning Operations at Cisco, illustrates this point: “You find the least resistance in helping someone solve a problem they have. As a function, HR can sometimes be seen to be doing something because HR wants to do it rather than thinking of the business and what is of value to it.” Focusing on what is of value to the business often leads to incorporating financial data into workforce-related analyses. This has the added benefit of boosting HR’s credibility by working with finance colleagues to demonstrate the quantitative nature of workforce analytics projects. Another way to help business leaders appreciate the strategic value of workforce analytics is to raise awareness that leading organizations are increasingly embracing an analytical approach to HR. Your competitors are likely already applying analytics to the HR function and gaining an advantage as a result. Furthermore, the amount of data available for such analyses will only continue to grow. Ignore it at your peril; you will fall further behind if you do. Finally, pay attention to how you communicate your analytical findings. Demonstrate your understanding of the business in the language you use, and draw a clear link between business challenges and your work. Make good use of storytelling and data visualization, to have the greatest influence. Eric Mackaluso, Senior Director of People Analytics, Global HR Strategy & Planning, ADP, shares: “We started focusing on storytelling, laying out a storyboard. The data tell you what’s happening; stories tell you why it matters. You have to frame it in a way people understand.” For more on this, see Chapter 17, “Communicate with Storytelling and Visualization.”



We Have Other Priorities Besides People ******ebook converter DEMO Watermarks*******



Some leaders might not see the links among people-related decisions, actions, and business outcomes. If people are viewed as easily replaceable and interchangeable, the logic of investing time and resources into workforce analytics might not be immediately apparent. Although the wisdom of viewing the workforce in that manner can be debated, the role that analytics can play should be clear.



THE ACCOUNTABILITY HAZARD Luk Smeyers is Cofounder and Principal of iNostix by Deloitte.1 He describes a frequent challenge that he calls the accountability hazard: “As soon as managers from highly political, nontransparent, or even negative organizational cultures learn what HR analytics could mean for their organization, the business benefits become overshadowed by the risk of having areas of weakness, dysfunction, and incompetence exposed.” This hazard recently affected Luk’s team: “Not long ago, we had to call a halt to a new HR analytics project after very animated internal discussions about who was responsible for the less pleasant potential analytical outcomes.” This presents a challenge for the workforce analytics leader who is investing time and resource into analytics projects, only to have them derailed at the action stage. Based on his experiences, Luk offers some tips to help the workforce analytics leader and other HR professionals overcome this accountability hazard: • Work in close collaboration with other departments to establish a cocreation atmosphere, with early engagement and buy-in from other functions. • Develop strong support from business leaders early on specific analytics projects. • Ensure that the privacy of employees is never violated, and stay away from “the hunt for the bad performer.” • Strive for new insights so that managers willingly take accountability for actions because they support new ways of improving their businesses. ******ebook converter DEMO Watermarks*******



With these approaches, Luk has found that workforce analytics leaders can avoid the problems of accountability and ensure that actions can be implemented successfully. At the end of the day, analytics is impactful only if action is taken. Even if people are viewed narrowly as merely a cost of doing business, people acquisition and lifecycle management processes can certainly be optimized using an analytical approach. And people costs, for which HR is usually responsible, are often the greatest operational costs in an organization (capital-intensive industries are an exception). Does any other expenditure of this magnitude escape analysis, tracking, and management with scrutiny and an eye toward optimization? Perhaps more important is the recognition that, in most cases, without people, there is no organization. The viability and effectiveness of public and commercial institutions alike depend on the attributes of the workforce. Understanding the relationships of those attributes to outcomes requires an analytical approach.



Financial Frugality A strong emphasis on managing costs can sometimes result in missed opportunities to create future value. This is true for many organizational decisions, including whether to invest in workforce analytics. Following are typical financial reasons for holding back and suggestions for addressing them.



We Can’t Afford to Do This When faced with this situation, we recommend asking this question: Can you afford not to do it? This is where the business case becomes crucial. The message you need to convey is how costly it can be, and how growth opportunities can go unrealized, when the people side of the business is run solely on intuition or the way things have always been done. Workforce analytics projects often result in substantial savings or improved productivity and effectiveness (see Chapter 6, “Case Studies”). Considering that people costs are often among the highest in an organization, how can organizational leaders even think about not managing their biggest ******ebook converter DEMO Watermarks*******



expenditure analytically? Patrick Coolen, Manager of HR Metrics and Analytics at ABN AMRO, agrees: “If you ask a business leader ‘Do you want to know how you make money on insights from people data?’ no one will say no.” And remember, the initial investment does not need to be big (especially when considered in proportion to total people costs). You can certainly start small and build as you go.



The Budget Is Already Decided Perhaps leadership is interested in workforce analytics, but HR expenditures have already been allocated for the budgeting cycle. Under these circumstances, a little creativity might be in order. Identify where money is being spent and determine whether the workforce analytics team can take on some of the work and the associated budget. If you can do the work for the same cost (or less) while delivering incremental insights and value, you will garner support for investment in your team. Ian Bailie of Cisco was able to do just this: “I’ve gone after money that was being spent on external work and used it to fund our analysts instead. We were able to deliver what external research was providing plus so much more. And that supports continued investment.”



We Can’t Afford to Implement the Recommended Changes Analytics projects should result in actions. Depending on the nature of the recommended actions, funding might be required to implement them. Planning for large-scale or costly changes should be anticipated in advance and built into your analytics project plan; where possible, choose less costly actions for your initial projects. When funding is, in fact, needed for the recommended actions, the business case again becomes an important tool. In addition to estimating the short- and long-term anticipated benefits of the recommended actions, opportunity costs associated with inaction should be also modeled. These ideas can be conveyed with great effect by skillfully weaving scenarios and projections into a story linking the business problem to the analyses and to the recommended actions.



HR Hesitancy Sometimes the reluctance to embrace workforce analytics comes from the HR function itself. Potential causes of resistance are many, from not knowing ******ebook converter DEMO Watermarks*******



where to begin, to having concerns about data, tooling, and the ability to follow through with actions. Following are common concerns from HR and suggestions for addressing them.



We Don’t Know Where to Start The thought of applying analytics to a workforce challenge can be overwhelming for someone who has never done it. If that describes you, the fact that you are reading this book is a good start! You might also want to bring in someone who has relevant experience to help you. Hire someone with data analytical skills (such as a data scientist or an industrialorganizational psychologist). Identify your business leaders’ key performance indicators (as an HR leader, you should be well positioned to do this—a useful line of questioning is “What keeps you up at night?” and “Which of your business metrics are you most concerned about?”) and think through how people-related issues influence those metrics. A skilled analyst can then help design and implement appropriate analytical approaches to address the business questions with people data.



We’ve Tried Analytics in HR Before and It Didn’t Work Recent advances in technology and data management, along with analytics maturity and sophistication achieved by other functions such as marketing and finance, have opened up opportunities for HR that were not as readily available to practitioners in the past. Times have changed. The advent of cloud technology has brought tremendous computing power to people’s fingertips, and new data sources have become available along with methods and tools for analyzing them. Approaches used in other functions can be applied successfully to HR data (for example, customer churn analytics can serve as a model for analyzing employee attrition). If you’ve tried before, it’s time to try again. Yesterday’s challenges and roadblocks are likely a thing of the past. “The advent of cloud technology has brought all this computing power to the fingertips of people in HR. Now everyone on the planet has access, when nobody had access before.” —Josh Bersin Principal and Founder, Bersin by Deloitte ******ebook converter DEMO Watermarks*******



It’s Too Difficult to Work with Our Data Nobody’s data are perfect, but that should not prevent you from moving forward. If your data are particularly difficult to work with (incomplete, outdated, scattered across systems, and even buried in spreadsheets), scale down and start small. Begin your work with a business question that uses a well-contained, well-defined dataset. The key is to get started and build as you go. Starting anew with workforce data can actually be a liberating experience. When Mariëlle Sonnenberg started as Global Director of HR Strategy & Analytics at Wolters Kluwer, no enterprise-wide HR information systems were in place. Although this might have been concerning for some team leaders, Mariëlle saw it as an opportunity: “Because of this, I was able to define a small number of metrics, and the HR teams became actively focused on collecting good-quality data and maintaining it. It is more difficult to go the other way, to start with one big system of data.”



DON’T TAKE “WE CAN’T” FOR AN ANSWER Andre Obereigner, the Senior Manager of Global Workforce Analytics at Groupon,2 heard many reasons for procrastination as he was starting out: “We don’t know where to start. We don’t have the data we need. We don’t have the right tools. We don’t have the right skills.” However, when it comes to starting, he has a few tricks up his sleeve. Andre started a project by himself to prove the value of analytics. The business problem concerned retention, but no one had data or skills or the right systems to quickly find out why retention was a problem. So Andre went out and gathered the data himself: “In 2013, I started by doing an EMEA (Europe, Middle East, and Africa) exit survey in which we asked those who had resigned from Groupon to provide general feedback and recommendations for the organization.” Andre’s skills alone were not enough. He needed tools, but he didn’t buy complex technology at that stage: “I used Tableau and imported the English-language data into it. I then cleaned and prepared it for the text prediction model. Initially, I had 100 to 150 responders and I went through each manually to come up with six different topics of ******ebook converter DEMO Watermarks*******



feedback. I found features in the feedback that predicted the topics; then I could apply that to new feedback from more exit surveys.” Andre’s success was mostly due to starting small, being persistent, and ensuring that he adapted as he went along. “The stakeholder was me at the beginning. I started this project on my own—I spoke with my manager to see if she would support me. It was part of my studies, but no one else really knew about this. I also worked on it outside of my regular work hours.” In the end, the project was a success: “At the beginning of 2015, our new global head of HR wanted to have a global exit survey. So we made adjustments to the EMEA one and got going.” If Andre had been put off back in 2013 by all the reasons why not, it would have been almost impossible to do the global project two years later. Andre’s motto? Don’t take “We can’t” for an answer.



We Don’t Have the Data We Need All hope might seem lost when you really need a particular piece of data and it’s nowhere to be found in the organization. But chances are, there’s a way forward. Get creative. Look at what you do have available, and try to think of ways to approximate the data you need by combing existing data elements or tapping into less traditional data sources. You always have the option of initiating new data collection. As Mariëlle Sonnenberg’s experience shows, not having to deal with legacy systems can be beneficial in its own right. Also keep in mind that many more sources of readily available data exist today than in the past, along with tools to analyze different data types. For example, internal and external social media data might provide useful information for testing some of your hypotheses, and modern tools provide the means of analyzing topics, trends, and sentiment.



We Don’t Have the Right Skills A base level of knowledge is needed to successfully bring analytics to the HR function. However, you might be able to acquire those skills in a costeffective way. Explore opportunities to borrow skilled people from another function in the organization, or consider hiring an intern: Interns are often exposed to the latest and greatest in data analytic techniques, and many ******ebook converter DEMO Watermarks*******



highly skilled students are eager to gain some real-world experience. Contacting local colleges and universities can be a good starting point for connecting with talented analytics students.



We Don’t Have the Right Tools Sophisticated software can be extraordinarily powerful and valuable, but it’s perfectly fine to start simple. Many practitioners will tell you that their most impactful projects started with rather simple, rudimentary analyses. In fact, some highly skilled practitioners even lament the dearth of opportunities to apply sophisticated analytics techniques to their organization’s most pressing challenges. Complexity is not a requirement for analytics success. Ubiquitous spreadsheet software might even suffice in providing the basics you need for an initial analysis—for example, plotting distributions of your data, calculating average differences across groups, and computing correlations.



We Won’t Be Able to Implement the Actions Although it’s true that analytics are of limited value if follow-up actions aren’t implemented, concerns about the ability to implement actions should not prevent you from bringing this potential source of value to your organization. Instead, think through the possible outcomes of the analytics, as well as the associated actions. For example, if you are analyzing which recruiting sources yield the best-fit candidates, you might prepare to abandon some recruiting sources and further invest in others. Discussing these potential actions with your stakeholders before you begin the analyses might be helpful so that you prepare them for the possible outcomes and actions (for example, their favorite recruiting source, their alma mater, might be deprioritized). Having these conversations up front helps you substantially mitigate the implementation risks. Ian O’Keefe, Managing Director and Head of Global Workforce Analytics at JPMorgan Chase & Co. offers this advice: “Always ask the question ‘What would you do if you knew the answer to this?’ Ask it at the start of the analyses and again when you finish, and then you still won’t have asked it enough.” In addition to preparing for potential outcomes in advance, we recommend focusing on Quick Win projects to get started because these projects are characterized by less implementation complexity. In summary, you might encounter several types of resistance in your efforts ******ebook converter DEMO Watermarks*******



to bring an analytical approach to HR. Each is best addressed with logic and assistance from experienced colleagues and practitioners (see Table 16.1). Table 16.1 Reasons for Analytics Resistance and Tips for Addressing Them



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Summary Efforts to bring workforce analytics to an organization are sometimes met with resistance. For almost any reason offered for why workforce analytics “won’t work here,” an appropriate counterargument can be made. The following tips can help successfully overcome resistance: • Understand how your organization views the value of analytics. • Identify the sources and the nature of resistance by spending time listening to others’ perspectives. • Determine the underlying cause of resistance, such as skepticism, perceived threat, lack of trust, lack of understanding, perception of insufficient skills, or unwillingness to invest. • Choose how to overcome the resistance; possible actions include educating, demonstrating value, partnering for skills, starting small, keeping it simple, making your case, and helping the resistor succeed.



1 iNostix by Deloitte is a team of market-leading predictive HR analytics experts based in Belgium. It offers clients a unique combination of senior expertise in HR management and data science. iNostix was founded by Luk Smeyers and Dr. Jeroen Delmotte in 2008 and was acquired by Deloitte in 2016 (www.inostix.com). 2 Groupon (NASDAQ: GRPN) is a global e-commerce marketplace that connects millions of subscribers with local merchants by offering activities, travel, goods, and services in more than 28 countries. According to its website, it is building the daily habit in local commerce, offering a vast mobile and online marketplace where people discover and save on amazing things to do, see, eat, and buy. It was founded in 2008 and is headquartered in Chicago (www.groupon.com).



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17 Communicate with Storytelling and Visualization “The most important thing about communicating with data is the communication, not the data. And storytelling is the way to do it.” —Andrew Marritt Founder, Organization View Warming his hands on a hot drink in a noisy cafe just off 42nd Street in Manhattan, Jeremy Shapiro, Global Head of Talent Analytics at Morgan Stanley, talked about what’s new in analytics. The conversation was not about new methodologies, new technologies, or even statistical skills. The topic was a critical aspect of analytics, which also happens to be one of the oldest traditions in human history: storytelling. Here’s what Jeremy said during that conversation: “It’s a great feeling when, after your team has worked so hard to develop strong insights, the story comes through loud and clear to someone else, particularly to a leader. People can’t make decisions based on data they don’t understand.” Given the importance of storytelling in workforce analytics, this chapter discusses the following: • Principles and techniques for constructing fact-based stories • A three-step model and other essentials to build effective visualizations • Understanding your audience and tailoring communications • The need to keep things simple



What Is Storytelling? People have been telling stories for millennia. The U.S. National Storytelling Network (www.storynet.org) defines storytelling as “an ancient art form and a valuable form of human expression.” In her 2002 Harvard Graduate School ******ebook converter DEMO Watermarks*******



of Education article, Deborah Sole describes storytelling as “an ancient and traditional way of passing on complex, multi-dimensional information and ideas through narrative.” Nancy Duarte, a TED Talk storyteller and author of award-winning books on presentation skills, explains that an idea is powerless if it stays inside you: “Maybe some of you have tried to convey your idea and it wasn’t adopted; it was rejected and some other mediocre or average idea was adopted. And the only difference between those two [ideas] is in the way it was communicated.” Storytelling is not only about passing on wisdom or sharing ideas; in the business environment, it is an agent for change. David Boje, a professor at New Mexico State University, stated in his 2006 Academy of Management Review article, “Storytelling is widely acknowledged as instrumental to organization change, training, strategy, and leadership.” Paul Yost, associate professor at Seattle Pacific University, supported this view in a 2015 paper when he and his co-authors wrote, “storytelling is becoming an increasingly utilized art for leading and managing change.”



Storytelling in Workforce Analytics Data and insights from workforce analytics can be complex and plentiful. Storytelling, with emotional content and characters, is a great technique for ensuring that the data and insights from workforce analytics projects are memorable. In fact, studies have shown that listeners have better understanding and recall of a speaker’s key points when emotional content and character-driven stories are used. Paul Zak’s 2014 Harvard Business Review article “Why Your Brain Loves Storytelling” is a good first article to read on this topic. With better understanding, we increase the chances of action—and that is, after all, why we undertake workforce analytics projects. That action could be to change something or to actively decide to continue doing things as they are. Either way, storytelling can ensure that recommendations are discussed, understood, endorsed, and implemented. Christian Cormack, Head of HR Analytics at AstraZeneca, illustrated this point when he summarized one of his analytics projects: “We looked historically at the turnover in one business. It was very low and the management thought that was a good thing, until we showed them what the potential impact was, such as an inability to refresh talent and bring in new ideas. If we had just shown the turnover numbers, it ******ebook converter DEMO Watermarks*******



would have looked like there was no problem. But we didn’t—we told a story, and that meant we created impact.” Furthermore, the very process of developing the story for workforce analytics communication can clarify the business problem. Josh Bersin, Principal and Founder of Bersin by Deloitte, goes even further: “If you are an analyst and can’t turn your project into a story, then you really don’t know the problem yet.”



Guiding Principles for Fact-Based Storytelling People often associate stories with fiction, whether a book, a film or some other media. However, storytelling with data must not be fictitious; it is, and should be, fact based. The following principles (see Figure 17.1 for illustration) are designed to guide how the analytics practitioner communicates with data through storytelling.



Figure 17.1 Principles for fact-based storytelling. • Principle 1: Educate, don’t fabricate. Storytelling with data in workforce analytics is about presenting facts using words and images that convey a message to the audience. It is not about embellishing the facts, concocting information that does not exist, or selectively omitting information. In summary, don’t make things up and don’t leave out relevant information. • Principle 2: Enlighten, don’t overwhelm. Storytelling in workforce analytics should highlight key insights and associated recommendations. Your audience does not need to know about all the excruciating details and multiple iterations of your analysis; present just the most relevant. In summary, don’t show up with a 40-page presentation when 3 pages will ******ebook converter DEMO Watermarks*******



suffice. • Principle 3: Convince, don’t confuse. Storytelling in workforce analytics is about informing decision making and guiding your audience convincingly by clearly stating the actions needed. It is not about confusing the audience by leaving them overwhelmed and directionless. In summary, don’t leave your audience wondering what to do. These principles are a guide to help ensure that professionalism is maintained and the story stays true to the data.



Constructing a Workforce Analytics Story Good stories told well leave the audience with the impression that storytelling is easy. It is not. A well-told story is based on logical, sound construction, which takes time and good planning. Table 17.1 summarizes four techniques for constructing a story. These are not necessarily sequential. They provide simple and effective guidance for the fact-based analytics storyteller. For more ideas, Akash Karia’s book TED Talks Storytelling: 23 Storytelling Techniques from the Best TED Talks (CreateSpace Independent Publishing Platform, 2015) is a helpful reference. Table 17.1 Techniques for Storytelling with Data



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Set the Scene Workforce analytics projects are conducted for a reason, so an essential element of a story is to convey that rationale. This does not necessarily need to be done at the start of the story, but outlining the business situation at some point helps the audience understand why the project was needed. In addition, providing context prevents the audience from being distracted while trying to discern what the project is about. Context can include elements such as the geographical scope of the project, the business situation, the marketplace, the number of employees affected, the sponsor, or the business units impacted. This is not an exhaustive list, but these examples all help to frame the business situation and set the scene. Figure 17.2 provides an example of this technique in the context of a workforce analytics project on leadership retention.



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Figure 17.2 An example of setting the scene. Create an Emotional Attachment Andrew Stanton, the scriptwriter for Pixar films such as Toy Story, said in his 2012 TED Talk, “Make me care—aesthetically, emotionally, intellectually.” Effective storytellers of analytics projects create an emotional connection between their audience and the project. Two factors are key to achieving thismaking it relatable and revealing detail and intrigue: • Make it relatable. By describing a worker as the character of your story, you personalize it to your business. This makes the story relevant and real to the audience. Where possible, tell the story from the perspective of a specific (but, ideally, fictitious) worker, whether that is a manager, an employee, a scientist, an administrator, or a call center operator. One way to do this is to create a persona, a technique that marketing and design professionals use to great effect. Give the person a fictitious name and characteristics that bring them to life without revealing the name and details of a real person. A persona could also be an amalgamation of several people in the organization. Figure 17.3 demonstrates this approach in the description of an executive in an analytics project focused on leadership retention.



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Figure 17.3 An example of making a story relatable by using a persona. • Reveal detail and intrigue. Effective stories provide detail that is both interesting and striking. In George Orwell’s dystopian novel Nineteen Eighty-Four, the opening paragraph helps draw the reader in with detail and intrigue: “It was a bright cold day in April, and the clocks were striking thirteen.” Scriptwriter Andrew Stanton also suggests, in his “Clues to a Great Story” TED Talk, revealing information progressively. He describes it as his Unifying Theory of 2 + 2: “Don’t give the audience four, give them 2 + 2.” The theory is that the story needs to be told sequentially with emotional elements that unfold to lead the audience along a journey. In other words, don’t just give your audience the destination; take them on the journey, too. Figure 17.4 shows how to add detail to a personalized story.



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Figure 17.4 An example of adding detail and intrigue. Although personalization, intrigue, and detail make a story come alive, too much technical detail can reduce the impact of your story, especially if you are communicating with nontechnical people. Michael Bazigos, Managing Director and Global Head of Organizational Analytics & Change Tracking, Accenture Strategy, explains: “Avoid using the language of statistics, even though that’s what underlies the analysis.” Patrick Coolen, Manager of HR Metrics and Analytics at ABN AMRO, agrees: “We always think about how we’re going to present our insights to each different audience. We leave out all of the technical information. We work with strong visuals and add personal details to create the emotional bond.” Reveal the Conflict All great stories have conflict, whether that is the hero fighting against a villain to save the world, someone overcoming an evil force or natural disaster, or just an individual overcoming hardship with a view to a better life. In workforce analytics, describing a conflict in the business can similarly enhance your story. Use these kinds of questions to identify your own business conflicts: • What created the situation that compelled you, as the analytics professional, to undertake the project? ******ebook converter DEMO Watermarks*******



• What is contributing to a substandard business scenario? • What people factors are holding back the business from growth or increased profitability? • What is negatively impacting customer satisfaction? And what are people doing to improve the customer experience? • Why is market share declining? Why are competitors seemingly doing better? Christian Cormack from AstraZeneca provided a good example of conflict earlier in this chapter when he talked about challenging the long-held notion that low attrition is always desirable. In contrast, he proposed that insufficient turnover could actually suppress innovation. Figure 17.5 shows another example of conflict in the ongoing example of the leadership retention project. This example highlights conflicting business objectives.



Figure 17.5 An example of creating conflict in storytelling while still retaining emotional attachment. Paul Yost, an associate professor at Seattle Pacific University, summarizes the purpose of conflict well: “You need context, your character, a villain, and a main message. Create emotion in how you are going to defeat the villain. Present an inflection point for a compelling argument—for example, if we ******ebook converter DEMO Watermarks*******



don’t do it now, the industry will pass us by.” Call to Action with a Memorable Message All good stories have a clear point: a short and memorable message for the audience to retain. In the TED Talks Storytelling book mentioned earlier, this is called the “takeaway,” and it is preceded by the “spark” and the “change.” The spark is what happens when characters realize they can overcome the conflict. The change is what the characters must do to resolve the conflict— they must change their circumstance or their actions. And the takeaway is the point of the story. Thinking about the spark, change, and takeaway for workforce analytics stories can help you construct your message. Consider the spark as the main insight and the change as the recommendation. Then leave the audience with your main message, the takeaway—and the compelling reason to act on the outcomes of your project. In Figure 17.6, our leadership retention example shows how to outline a call to action using a memorable message.



Figure 17.6 An example of creating a call to action with a memorable message. ******ebook converter DEMO Watermarks*******



When creating your call to action, consider the following questions to help clarify your takeaway message: • What was the most illuminating insight from the analysis? • What is the business question, and was it answered? • Were your hypotheses proven or disproven? • Overall, if you had to summarize your findings in 20–30 words, what would those words be? • What one recommendation overall would you make to your chief executive officer if she or he asked you about this project? Finally, if you need to, get help defining your key message from other people inside or outside the analytics organization, such as internal communications professionals or marketing experts, or from external consultants. Peter Hartmann, Director of Performance, Analytics and HRIS at Getinge Group, takes exactly this approach: “When I need to prepare analytics stories and presentations, I work closely with the corporate communications team, who have the expertise to shape the messages effectively.” You might also want to follow the advice of Simon Svegaard, Business Analytics Manager at ISS: “I connect with people uninvolved with the project, often over lunch, to test out the story and to see whether the messages are clearly understood.”



AN ANALYTICS PROJECT SUMMARIZED IN ONE SENTENCE “As a company, we have distinguished ourselves as the most effective organization at developing technical talent for our competitors.” A gasp filled the room. Mark Berry, Chief Human Resources Officer at CGB Enterprises,1 paused. He had opened his discussion on retention to the executive committee with this statement. He went on to explain, “We are a training ground—we are the best at bringing people in, training them … and then they go to competitors. We are an exporter of talent to our competitors.” About three months earlier, Mark had commissioned a project to look at why talent was leaving the company. When he arrived at CGB ******ebook converter DEMO Watermarks*******



Enterprises, Inc., he had asked why people were leaving, who was leaving, and how many people were leaving. The executive committee knew that there was a problem, but nobody had any facts or insights from which to make decisions. Mark’s project focused on a well-defined business problem, had a clear hypothesis, and was completed with effective analysis using both existing and new data gathered from employees leaving the business. When he was ready, Mark took a steady and thoughtful approach to telling the story. And he effectively articulated his entire project into that one sentence. The insights were clear: “Technical talent was leaving the company to go to competitors.” The recommendations were also clear, and although the details of the recommendations were articulated later in his presentation, the implication was well communicated: “We need to invest in technical talent.” Finally, Mark’s communication approach of using storytelling emotionally captured the attention of the executive committee. Mark explained why he chose this approach: “Taking provocative positions when I know I’m right (based on the data) gets the audience emotionally connected and engaged with what I’m talking about. It gets them thinking about the issues the way I want them to think about them.” One sentence was all it took, Mark says. “After I said that statement and people gasped, I knew the project had been a success.”



Effective Visualization Having pictures, or visualization, as part of a workforce analytics story is fundamental to effective communication. Our brains are hardwired to interpret visual information. As James Zull says in his book, The Art of Changing The Brain (Stylus Publishing, 2002), we can cognitively process graphs and images faster than text, which is why visualization is such an efficient means of communication. However, visualization should not replace the story; it should be used to enhance the story, draw attention to data, and more deeply influence some kind of behavior. Many books and articles delve into visualization with data. Recommended are any of Edward Tufte’s books, although his first book, The Visual Display ******ebook converter DEMO Watermarks*******



of Quantitative Information (Graphics Press, 1984), is an excellent and oftenquoted text. In addition, the outstanding book Storytelling with Data, by Cole Nussbaumer Knaflic (Wiley, 2015), is a helpful contemporary reference with dozens of tips for effective visual aids. This section does not repeat what those books articulate. Instead, we summarize Edward Tufte’s main elements for graphical excellence and provide a three-step model to help you create visual aids in the context of workforce analytics.



Graphical Excellence Edward Tufte is known for his pioneering thinking and writing on visual communication of information. His guiding focus is that all visual communication of quantitative information should be characterized by three elements: restraint, simplicity, and impartiality. • Restraint. Data visualization design should not be dictated by how the visualization can be produced. The design should be conceptualized first, before creating it with technology. This is especially important today, as visualization technology claims to automatically present data in new and exciting ways without the need for any input from analytics practitioners. Be careful not to rely on visualization technology alone. Take time to conceptualize and design the visual on paper first, to keep your message honest. • Simplicity. Avoid unnecessary visual elements in graphics—what Edward Tufte calls “chartjunk.” An example of chartjunk is gimmicky icons or ornate backgrounds on presentation or report pages. The point is that clean design reflects clear thinking. Do away with unnecessary aspects that distract the audience. • Impartiality. In addition to simplicity, visual aids should accurately represent the data. Avoid distorting the visual chart or graphic with elements that fail to represent the data properly (for example, misrepresenting data by skewing the axis of a chart unnecessarily). Such elements are not only distracting, but they also misguide the viewer. Eric Mackaluso, Senior Director of People Analytics, Global HR Strategy & Planning at ADP, provides additional simplicity to Edward Tufte’s advice with his Four C’s technique for preparing communications materials (including graphics) for workforce analytics: ******ebook converter DEMO Watermarks*******



• Make it concise. • Ensure that it looks clean. • Be sure it is clear. • Produce it in a captivating way. As a final point on graphical excellence, Edward Tufte warned in a 2003 article in Wired that PowerPoint is overused as a tool to represent data: “PowerPoint is a competent slide manager and projector. But rather than supplementing a presentation, it has become a substitute for it. Such misuse ignores the most important rule of speaking: Respect your audience.”



Three-Step Model for Creating Visualization Effective visualizations can bring your story to life. Ineffective visualizations can be distracting and get in the way of clear communication of your message. Producing effective visualizations takes time and effort. The simple three-step model in Figure 17.7 enables a more considered and effective route to building relevant and impactful visual aids.



Figure 17.7 Three-step model for constructing visualizations. Step 1: Clarify Your Visualization Message The first step in constructing effective visual aids is to decide on your key message. What key point do you want your audience to take away from this visualization? Make sure the message is clearly reflected in your visualization ******ebook converter DEMO Watermarks*******



for the intended audience. The visual aid should accelerate the understanding of your intended message. Step 2: Design Your Visualization The second step focuses on constructing the best visual chart, graph, or diagram for communicating your message. Consider three elements in this design step: selecting the presentation medium, selecting the visualization style, and emphasizing contrasts and similarities. Select the Presentation Medium You need to decide what medium to use to display the visualization and what form the visualization will take. By medium, we are referring to whether the visualization will be viewed on paper or presented on a screen. Furthermore, if it is screen based, consider whether it will allow a viewer to actively explore by scrolling, zooming in and zooming out, or drilling in and drilling out to give greater numerical detail at different levels of analysis (for example, from organizational-level insights down to divisions and teams). You might even have multiple visualizations presented in a dashboard, as in Figure 17.8, which shows levels of employee sentiments and trending topics amalgamated from social sentiment analysis (adapted from Shami, N.S. et al, 2014).



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Figure 17.8 Multiple visualizations presented as a dashboard. The data on which the visualization is based might not be static, but instead gathered from a live data feed. In this situation, you have less opportunity to control the message you want to convey. If exploration of the data is the top priority, then dynamic visualizations make sense. However, if the idea is to convey a specific point, you might prefer static visualizations. Even with the prevalence of screen-based visualizations, do not discount the paper option. Admittedly, you have fewer opportunities to explore the data, and you might even be limited to a black-and-white presentation (as we are in the print version of this book), but sometimes a simple black-and-white chart is all you need to make your point. Select an Appropriate Visualization Style ******ebook converter DEMO Watermarks*******



The visual style you select should be informed by your variables. If you have geographical data, some form of map visualization might be appropriate, such as the one in Figure 17.9. This particular figure shows the geographical representation of employee privacy preferences in 24 surveyed countries (light = low privacy preference, medium = moderate privacy preference, dark = high privacy preference).



Figure 17.9 Visualization using a geographical map. On the other hand, if you have time series data, you might show changes in sales performance, for example, plotted as a function of time. Such a representation would see the time points plotted on the horizontal (x) axis of a graph and sales performance plotted on the vertical (y) axis of the graph. Figure 17.10 provides an example of a visualization showing a time series model of sales performance for five teams. This reveals variability in performance across the teams but an overall increase in average sales performance over time.



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Figure 17.10 Example of a time series visualization. Note that, with two variables, such as in Figure 17.10, relationships are relatively easy to visualize. The relationships among three variables can be visualized with 3D representations, but more than three variables can be difficult to represent. Analysts often revert to summarizing multiple variables into just two or three variables using the techniques in Chapter 5, “Basics of Data Analysis,” or alternatively, presenting the relationships in tables or correlation matrices. Emphasize Contrasts and Similarities Many of the most striking visualizations emphasize contrasts or similarities. Continuing with the example of employee privacy preferences, one observation from the original data was that considerable variation existed among countries, in addition to considerable variation within countries. Figure 17.11 shows a visualization that illustrates this, highlighting the difference in worker privacy preferences between India, where workers report being willing to share their personal data for workforce analytics, and Germany, where workers report being less open to sharing their personal data for workforce analytics. This visualization shows the contrast between the ******ebook converter DEMO Watermarks*******



typical levels of willingness to share information between people in India and Germany. The graph also clearly highlights the different levels of variation within each country and the area of commonality. Germany has considerably greater variation in willingness to share personal information than India. Graphing the extremes to show ranges in this manner often highlights the point you want to convey in an impactful way.



Figure 17.11 Visualization emphasizing contrasts and the area of commonality. Step 3: Test Your Visualizations Finally, after you’ve created your visualization, you need to test it to see whether your intended message is being received accurately. The best way to do this is to solicit feedback about the visualization from people with similar backgrounds to your ultimate audience. At this stage, open questions such as “What message do you take away from this visual?” are a good way to proceed. Answers that match your desired key messages will confirm that you have achieved your objective. Developing a visualization that successfully conveys your intended message ******ebook converter DEMO Watermarks*******



is often an iterative process. If you receive feedback that the visualization is not conveying the message you intended, you need to make amendments and seek feedback again.



Knowing Your Audience Fundamental to the success of your workforce analytics story and the effective use of visualization is understanding your audience. All good communications should be tailored to the audience, and workforce analytics is certainly no exception. Your communications, whether delivered as a story, as a presentation, or in written format, need to be aligned with the motivations and preferences of the audience receiving it. To align your message to your audience, you need to understand their perspectives and expectations. To do this, think about your audiences in two dimensions: people who have experience with analytical methods and techniques, and people who have experience with the specific project or business issue being analyzed (see Figure 17.12).



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Figure 17.12 Types of audience for workforce analytics communications. • Master. This type of audience has knowledge of both analytics methodologies and techniques, plus a good understanding of the specific analytics project in question. With this group, it is worth sharing more details of the project, as well as insights and recommendations. Furthermore, you can arm them with visuals and stories so they can advocate for the project. • Enthusiast. This type of audience is knowledgeable about the project but has limited experience with analytics. In communicating with these people, limit the details and use storytelling to create impact. Give an overview of the insights, recommendations, and actions. • Scientist. This type of audience involves you in a deeper discussion of the analytics methods and techniques used. Limit storytelling and expect statistical discussion on techniques and methods instead of the outcomes. Focus on visuals instead of the story. ******ebook converter DEMO Watermarks*******



• Newcomer. This type of audience knows little about the specific project or about analytical methodologies. Use storytelling to provide emotional attachment. Focus on actions and the business impact by providing context and highlights.



Customizing Content to Your Audience When you have identified the different types of audiences, you can customize the story accordingly. Eric van Duin, Manager of HRIS & Analytics at PostNL N.V., explains: “Look for the insights that will help business or HR executives themselves. It’s really important to keep in mind who you are doing it for and why you are doing it.” Giovani Everduin, Head of Strategic HR, Communications & Change at Tanfeeth, is one of many others to support this point of view and recommends the following approach: “You should pitch the same analytics case differently to different people. It is about tailoring the message effectively.” Not only the content of the story should be tailored to the interests and motivations of different audiences; the communication style can also be adapted. Consider visual, auditory, and kinesthetic communication preferences and adjust your presentation accordingly. Auditory communication includes the tone and volume of your voice, plus the use of video and music to demonstrate messages. Kinesthetic communications can include using 3D objects created as visualizations for the audience to physically handle something. You can even ask your audience members for their preferred communication style. Kanella Salapatas, HR Data Manager and Reporting Service Owner at ANZ Bank, offers her audience a choice: “At one point, the CEO asked for some monthly workforce information. Rather than just supplying a report, we gave him three reports of the same information: a traditional PowerPoint presentation, screenshots of data and metrics, and an infographic.” The CEO chose the latter and has since become more engaged on content.



Being Effective with Your Time Many workforce analytics practitioners have stressed the importance of tailoring content to the time available. For example, if you only have a few moments in the metaphorical elevator, you had better give your audience the main message quickly. ******ebook converter DEMO Watermarks*******



Antony Ebelle-ebanda has been an analytics professional at several organizations. His mantra for presenting to executives and leaders is, “Make it minimal, make it meaningful.” Regardless of the time allocated, getting your audience interested very early in the allotted time is always a good idea: “The person has maybe 2 minutes to get interested—if you don’t have them hooked in those 2 minutes, you’ll lose them.”



Choosing Your Presenters Tailoring your communication to your audience also includes considering who can best present and tell the story. Ralf Buechsenschuss, Global HR Manager of People Analytics & Transformation at Nestlé, says that, in certain meetings with executive stakeholders, he prefers not to have presenters who focus on too much detail. In Ralf’s experience, if those individuals join those meetings, the conversation tends to move away from the insights, recommendations, and actions, and instead stalls on the statistics and analysis. This can disrupt meetings and draw attention to the input instead of the output. Conversely, in some meetings with certain audiences (for example, the master audience in Figure 17.12), having a statistician join is highly recommended. The skill that these experts can bring adds credibility to the depth of analysis that leads to the conclusions. Fundamentally, it is important to get the right presenters for your audience. Al Adamsen, Founder and Executive Director of the Talent Strategy Institute, advises: “Understand the needs of your customer, how they consume information, and when they consume it.”



Thinking Like an Executive One way to ensure that an executive presentation goes well is to “think like an executive.” Considering what keeps executives awake at night, what business pressures they face, and why this project is relevant to them can build the interest needed for success. Thinking about your executive in the following ways should help: • Treat your executives like your best customers—understand what they need from you. • Understand the business from their perspective; workforce issues might ******ebook converter DEMO Watermarks*******



not be at the forefront of their minds. • Try to find out the most important item on their agenda this week, this month, and so on. • Check their schedule to see what meetings they will have both before and after yours; this can tell you a lot about what might be on their minds. • Find out about them—learn what makes them tick so that you can personalize your story. Jonathon Frampton, Director of People Analytics at Baylor Scott & White Health, knows his executives are big Harvard Business Review (HBR) readers, so he developed his HBR theory of communication. He emulates the clear, well-structured, and highly professional content of that journal: “Executives read HBR—they like the style. Good communication is not only about the ability to make it clear; it is also about the listener’s ability to understand. So if we make it HBR-like for executives, then they will understand it.”



Keeping It Simple This chapter has covered a great deal of material about crafting stories, providing effective visualization, and tailoring your communication to different audiences, but the final word has to be about the importance of keeping things simple. When you have dedicated weeks or even months to detailed data gathering and analyses, it is tempting to share all that effort with your audience. Don’t. Laurie Bassi, CEO of McBassi & Company, says: “Do not share everything you’ve learned—executives don’t care. Less is more.” She always tries to condense her analytics work to one slide: “No matter how complex the study, I condense insights down to one slide, with one graph that people can immediately understand.” Thomas Rasmussen, Vice President of HR Data & Analytics at Shell, has had a lot of experience presenting to a variety of audiences. His method for simplification is based on the practice of writing abstracts: “Think of an abstract in a journal. Condense your analytics work down to an abstract. When my team does this, I say, ‘Great, now give me the abstract of the abstract,’ and keep doing that until the message is succinct and clear.” Making your presentation simple and getting to the point might take some ******ebook converter DEMO Watermarks*******



time. Many analytics practitioners say that communicating and preparing for presentations often takes much more time than expected. Whatever time you allow, it is worth doubling the time you think it will take. As the French philosopher and mathematician, Blaise Pascal, wrote in the 17th century, “I have made this letter longer than usual, because I lack the time to make it short.” Salvador Malo, Head of Global Workforce Analytics at Ericsson, appreciates the time it can take to create great communications: “I tell my own team members to spend as much time on the communication as on the analysis itself. I can spend time making ten versions or more of the communication before I deliver it.”



SIMPLIFY YOUR STORY Practitioners and scholars have been researching the art and science of storytelling for many years. One such scholar is Paul Yost, Associate Professor at Seattle Pacific University. However, he came to understand the value of storytelling not just from his research, but from his own practical experience. Before his days in academia, Paul was the manager of research and was responsible for, among other things, the employee engagement survey at The Boeing Company.2 There he had two very different experiences with the chief executive officer (CEO) that strongly influenced his approach to storytelling. “One year I went to see the CEO to discuss the employee engagement results. I took a presentation with 35 pages full of charts.” Paul remembers the CEO selecting two charts. He looked at only those two and omitted the rest of the information. “He saw the data and selected those pages that supported his opinion. The rest of the story was lost. I had failed to communicate the results clearly enough for him to pick up the story I was trying to tell.” The following year, Paul did not want to repeat the same mistake: “The next time, I took just one slide up front.” He said to the same CEO, “I have a limited perspective on the organization; I do not know all about the operations, the finances, etc. But I do know about employee engagement. I am going to show you three things from the ******ebook converter DEMO Watermarks*******



data that are important for Boeing.” Paul learned how to tell the story that he wanted to communicate clearly and simply. The key messages from his presentation had no ambiguity because he signposted them clearly at the beginning: “I am going to show you three things… .” He did not rely on his audience to work out the story behind the 35 slides he was presenting. Paul presented the story, not just the data.



Summary The art of storytelling and visualization with data requires technique and practice. Effective storytelling increases the retention rate of the messages and the likelihood that action will be taken as a result. Visualization helps to create an understanding of the insights and recommendations, and a good understanding of your audience helps you tailor communications to maximize impact. For these reasons, storytelling and visualization are skills that workforce analytics practitioners should practice. Following are the main points this chapter emphasizes: • Ensure that your stories follow these principles: Educate, don’t fabricate; enlighten, don’t overwhelm; and convince, don’t confuse. • Create effective workforce analytics stories by providing context, creating emotional impact, revealing the conflict, and conveying a memorable message. • Construct visualizations that show the intent of your analytics by clarifying the message of your visualization, designing it appropriately, and testing it before you use it with your selected audience. • Understand the different types of audiences (newcomer, enthusiast, scientist, or master) and tailor your communications accordingly. • Get help from others in preparing your communication materials if you need it, and strive to keep things simple.



1 CGB Enterprises, Inc., is headquartered in Louisiana and is a privately owned company. It provides an array of services for grain farmers, from buying, storing, selling, and shipping of the crop, to financing and risk ******ebook converter DEMO Watermarks*******



management. It has global operations and more than 2,000 employees, and was founded in the 1970s (www.cgb.com). 2 The Boeing Company, headquartered in Chicago, is the world’s largest aerospace company and the leading manufacturer of commercial jetliners and defense, space, and security systems. A top U.S. exporter, the company supports airlines and U.S. and allied government customers in 150 countries (www.boeing.com).



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18 The Road Ahead “With all the technology like wearables, it would be nice to get these ideas into our company. We thought about using wearable analytics on our factory staff, with the goal of preventing health issues. That would be a long-term aspiration.” —Ralf Buechsenschuss Global HR Manager, People Analytics & Transformation, Nestlé Throughout this book, we have presented the recommendations and experiences of practitioners, academics, and thought leaders as advice for organizations to develop their workforce analytics capability. With the input of analytics experts, we have painted a picture of the discipline of workforce analytics as it is today and how it can be used to create business value. In doing so, we have explained, to a large degree, why it is important to human resources (HR) and to businesses and organizations as a whole. Although notable exceptions exist, the overall discipline is in its infancy, and practitioners are focused on building their organizations’ foundations. This chapter forecasts what we believe will happen next in workforce analytics. Predictions about the future are not likely to be 100 percent accurate; however, the trajectories of existing trends should continue, unless unimaginable events disrupt those trends. In this chapter, we cover the following: • Ways to meet business challenges analytically • Emerging data sources • Evolving technology • The evolution of the workforce analytics function



Analytics Provides New Opportunities for HR ******ebook converter DEMO Watermarks*******



Chapter 1, “Why Workforce Analytics?”, describes a pervasive demand for competitive advantage that transcends industries and geographies. The demand is driven by global competition for business in a labor market that is internationally mobile and globally connected. Faced with these pressures, organizations have a stronger need than ever to acquire, develop, and retain the best talent. Employees also have much more choice about who they will work for, when they will do the work, and where they will work. “HR can no longer rely on an old road map to meet the ever-changing demands it faces. The world of analytics opens up new opportunities that will enable HR to have different conversations and implement new solutions that drive better results for the business.” —Dave Millner Executive Consulting Partner, IBM Resolving these challenges satisfactorily requires addressing a variety of complex psychological and logistical issues. This is because the factors that make a worker a good fit for a role (a strong performer, someone who is unlikely to leave, and so on) include a mix of psychological attributes and technical capabilities. These features of workers need to be aligned with the demands of job roles in international markets that span geographies and cultures as well as time zones. Anecdotal evidence suggests that highly capable professionals who were once attracted to roles in disciplines such as finance and economics are now being drawn to HR because of its turn toward analytics. Until this point, workforce analytics projects have commonly been ad hoc efforts and have been deployed on a case-by-case basis to solve localized problems. Increased levels of capability, coupled with the ability to leverage technology, mean that in the future we should see more integrated solutions to solve problems for greater business impact. These solutions will be able to match workers to opportunities, develop worker capabilities, and optimize work environments more quickly and effectively than ever. Taken together, these factors make it an exciting time to be working in the area of workforce analytics and in the profession of HR.



Emerging Data Sources Data sources that we could not have imagined five years ago are becoming ******ebook converter DEMO Watermarks*******



common (for instance, text-based analysis of a worker’s digital footprint). New data sources will continue to emerge in the future, and those that scientists successfully demonstrate utility for are likely to make their way into applications of workforce analytics. Sources of emerging data include sensors, wearables, disappearables, and the Internet of Things. Whereas much traditional data in workforce analytics is structured (easy to store and analyze using traditional databases and spreadsheet software), data from emerging sources are often unstructured. Examples of unstructured data include data used to create reputational assessments from digital footprints (such as text, images, and video). Data-management technology for largescale storage and analysis of unstructured data has only recently evolved to the point that analysis of such data is feasible in workforce analytics.



Connected Devices One major anticipated development is that the data sources that once told us something about workers at a single point in time (for example, via an employment survey) will increasingly be replaced by systems that stream data in real time from technology that is perceptive (sensors), mobile (wearables), and small (disappearables). Many of these devices are relevant to workforce analytics and are part of the Internet of Things, an interconnected world linking electronic devices to others—clocks, refrigerators, cameras and so on. This means that devices useful for workforce analytics will be linked to devices used in other areas of the business, providing greater opportunity for workforce analytics practitioners. Recent developments in sensor technology will begin to be deployed live in organizations. These sensors will permit digital badges to monitor all social interactions (virtual and in person), including how, where, and when the interactions occur. The effectiveness of worker interactions can even be assessed with facial monitoring while people are having conversations. The information from these devices will see physical office architectures optimized for collaboration and productivity. The communication patterns of workers via electronic mobile devices will be examined for ways in which the digital work environments of employees can be altered to facilitate productivity, in the same way that sensor data can be used to optimize the physical environment. For example, the heart rates of workers in highly stressful occupations can be monitored to identify when work becomes too ******ebook converter DEMO Watermarks*******



stressful, and the delivery routes of couriers can be optimized with global positioning data.



Digital Footprints Standardized testing of ability or personality scores is common. Our ability to measure these attributes on job candidates was previously limited to the people we could encourage to complete a test, either in person or remotely. Now, however, nontraditional data sources on candidates abound that do not require the applicants to expend any effort or even know they are being assessed. Biographical data, or a candidate’s personal history (for example, educational attainment), can be found, and psychological profiles of candidates can be derived from digital footprints they leave via their use of the World Wide Web. Based on this information, businesses can proactively approach candidates instead of having to wait for the candidates to seek out opportunities. Digital footprints can be analyzed to help identify change management influencers. The data can come from sources such as emails, instant messaging, or social networks. Text-based analysis of candidates, using information from online digital footprints, will become an established approach to assessing worker suitability for jobs in the context of high-volume candidate screening. This will require overcoming numerous barriers related to employee privacy. Whether workers have a right to privacy when on the Internet depends on setting and circumstance; the question is not easily answered with a clear yes or no. Text-based analysis of digital footprints also presents challenges related to inclusivity. Although digital information likely exists for most individuals living in developed countries, those without Internet access will not have as complete a digital profile, nor will they be able to influence it. For these reasons (and others we turn to later in this chapter, such as validity), standardized testing should remain the primary approach in high-stakes testing until we understand more about the appropriate use of text mining for personnel selection. In other words, if today you have the chance to assess a candidate with a good personality questionnaire or a text profile, go for the standardized personality test.



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Another new development involves molecular genetic analysis of suitability for jobs. Recent developments have seen researchers establish links between genetic profiles and work outcomes. For instance, a Journal of Applied Psychology article by Chi Wei and colleagues from the National University of Singapore showed a link between genes associated with extraversion and job-hopping tendencies. Importantly, the practical significance of these relationships has not yet been determined, and the sizes of the relationships so far appear small. Many other aspects of work behavior are caused by one’s environment, not one’s genetic profile. Moreover, genetic influences can be switched on or off by one’s environment. The implications of using these methods need to be better understood before they are adopted in workforce analytics.



Considering New Data Sources The availability of new data sources does not mean that their use is scientifically justified, legally defensible, or socially appropriate. As this section emphasizes, careful consideration of each of these issues is recommended before using new data sources in workforce analytics.



Validity New data sources come with as many questions as answers about people at work. The biggest question of all relates to exactly what constitutes appropriate use. An important factor to consider is whether the methods can be scientifically demonstrated as effective. For example, although scientific journals have related social media and text profiling of personality to standardized personality questionnaires, these relationships are typically modest: Social media and text-based profiles of personality measure something similar to standardized questionnaires, but they do not measure the same concepts. Scientifically speaking, these approaches are likely adequate for high-volume pre-employment screening, but right now there are better approaches to use when selecting among the final few candidates.



Legal Appropriateness Along with considering the scientific merit of utilizing new data sources, organizations must closely consider the legal appropriateness of doing so. Legislation generally reacts and follows developments in technology. This is ******ebook converter DEMO Watermarks*******



because the technology that yields the data workforce analytics professionals might use is evolving quickly and in ways that legislators cannot predict or imagine. New applications of emerging data sources can be scientifically validated before they are even considered legal. In other words, science and technology are often ahead of the law. Deloitte’s 2017 Human Capital Trends Report explores the rate of change of technology and public policy, including legislation, in more detail. Different countries also have different laws regarding the use of technology in workforce analytics. Employment lawyers should be consulted before new data sources are used in workforce analytics for employee-related decision making.



Social Impact Industrial psychologists have developed a thorough understanding of the social consequences of using different forms of employee-related information. Using some characteristics in personnel-related decision making will lead to adverse impact (across socially, legally, or politically salient groups). However, this understanding is either nonexistent or in its infancy when it comes to many of the new data sources we have discussed. Organizations therefore need to carefully consider the social consequences of the workforce analytics decisions they make based on new data sources. Just because the science says an application of a new data source is effective and the law doesn’t say it is inappropriate does not mean that it won’t have undesirable social consequences.



Evolving Technology Chapter 11, “Know Your Technology,” outlines the requirements for establishing an analytics function today. But what technological developments are on the horizon? This section discusses open standards, cognitive technology, real-time analytics, and self-service technologies.



Open Standards The approach of completing time-consuming large technology implementations before undertaking analytics will fall out of favor as technology based on open standards becomes the norm. Open standards mean that HR analytics can begin with the technology on hand today, together with skilled computer scientists who can integrate the data. We will continue to ******ebook converter DEMO Watermarks*******



see the adoption of cloud technology except where legislation restricts its use.



Artificial Intelligence and Cognitive Technology Recent developments in artificial intelligence and cognitive computing, which have incorporated sophisticated analytical techniques that were once available only to highly trained data scientists, will continue. Because the technology permits it, HR practitioners will be expected to have a stronger analytical perspective. Insights will be at the fingertips of those who know how to access them. The acceleration toward evidence-based HR is most likely to happen with ready access to cognitive computing—that is, systems that understand, learn, and reason as they interact with humans using natural language. Cognitive technology does this by taking advantage of some of the machine learning technologies referenced in Chapter 5, “Basics of Data Analysis,” and combining these with other technologies such as natural language processing. Cognitive technology promises large benefits for business because it puts all forms of data (structured and unstructured) to work in analytics, whether numerical, text based, or rich media such as audio and video files. Artificial intelligence also holds great promise for business and workforce analytics. Its applications are numerous, for example with bots and digital agents, where their introduction will change the nature of the workforce, and hence workforce analytics. This and other emerging technologies bring great opportunity as discussed in PwC’s 2017 Global Digital IQ® Survey.



Self-service Technology Expect to see a much wider distribution of analytics capability to workers, managers, and executives through the use of self-service technology in HR. This analytical empowerment of workers will become increasingly app driven and enabled via mobile devices. Numerous benefits follow a shift to self-service HR environments. First, workers can obtain the information they need immediately instead of having to wait for a response from HR business partners. Second, managers will have access to information that allows them to make insight-driven decisions in real time. Finally, advances in self-service technology will put information at the fingertips of HR business partners, enabling them to become more insight driven and strategically influential. ******ebook converter DEMO Watermarks*******



Real-Time Model Updating and Reporting Practitioners can expect to see much wider adoption in the future of real-time reporting and model updating. This technology means that when recurring events happen (for example, employees resign and exit the business at the end of any particular day), predictive models and reports are updated automatically and immediately. The latest information can then be passed to managers using intranet technology, a mobile app, or a text alert. Managers will be able to take advantage of the outcomes of real-time analytics immediately instead of having to wait for the results of new models to be disseminated. These real-time analytics will deliver the information leaders in organizations need to manage change in increasingly dynamic operating environments.



The Workforce Analytics Function The continued pressure for workforce efficiency, coupled with new data sources and the technology to manage them, will require workforce analytics functions to look different in the future. This difference will be evident in terms of the function’s structure and reporting, the types of skills required, and an enhanced need for collaboration.



Functional Reporting Line Today the most common structural approach for the workforce analytics function is to locate the team within HR, often several levels down from the chief human resources officer (CHRO). More team leaders are beginning to report either directly to the CHRO (see Chapter 3, “The Workforce Analytics Leader,” and Chapter 14, “Establish an Operating Model”) or to a direct report of that person, such as the head of organizational development or talent. This provides the advantage of a sponsor who is hierarchically close to the CEO and is focused on workforce-related business issues. Nevertheless, for many organizations, reporting to the CHRO might be an interim structure before workforce analytics is integrated into an enterprisewide analytics function. The benefit of this approach is that the business develops a quantitative understanding of how all functions interact with one another to impact business performance. A centralized model could have a chief analytics officer or chief insights officer, with the function of workforce analytics becoming part of that team. Such a structure makes access to the ******ebook converter DEMO Watermarks*******



information required to accurately link workforce measurements to business performance more readily available, and projects will likely become more complex to tackle much larger business issues. For other organizations, such as those that are highly federated, a better workforce analytics model might be one in which responsibility for workforce analytics is dispersed into the lines of business, with an HR analytics partner role established to complement the now common HR business partner role. Either way, having the workforce analytics function remain solely within HR will become less common in the future.



Skills of the Future Chapter 12, “Build the Analytics Team,” detailed the Six Skills for Success, the knowledge, skills, and abilities that a workforce analytics function should have access to in order to succeed. The demand for these skills will increase and the mix and importance of each skill will evolve. First, the workforce analytics leader will be required to manage greater complexity and prioritize the ever-growing volume of information. This ability will become critical as projects become more integrated into the broader analytics work of businesses. Analytics leaders will also need to become adept at leading highly complex projects in unfamiliar areas and for a broader range of stakeholders. Second, availability of new and increasingly personalized data sources, together with machine learning technology and the algorithms it generates, will make managing privacy more complicated. Workforce analytics projects and activities will require more sophisticated privacy and ethical skills to manage multiple considerations: behavioral (what people like and how they will behave), legal (what is permitted), and ethical (what is the right thing to do with people’s data). Dawn Klinghoffer, General Manager of HR Business Insights at Microsoft, already has someone on her team fulfilling this behavioral, compliance, and ethical role. Dawn believes such a role will become more common in the world of workforce analytics: “With all the new data emerging, such as metadata, network behavior, and email traffic data, we have to get a handle on what this means and how we will use this for decision making.” Third, as workforce analytics technology evolves and new data formats become available, data scientists will need to keep up with the latest ******ebook converter DEMO Watermarks*******



techniques and technologies. Workforce analytics team members will need to continuously learn about new data sources and analytical techniques to ensure that they can contribute effectively to solving business challenges. Finally, as people data sources become more varied, larger, and more complex, there will be greater demand for people trained in the nuances of workforce-related data, such as data scientists and industrial-organizational psychologists.



Extreme Collaboration In the future, managing workers will require applying analytics to see the way HR policies and practices interact with the approaches being applied in other functions, such as finance, marketing, and legal departments. The new approach will view HR practices and processes as just one factor that impacts the way organizations turn inputs (for example, materials and labor) into outputs (that is, products and services) for customers. The workforce analytics function therefore needs to embrace the approaches and language of other functions to fully understand the likely complex ways that HR policies and practices impact business performance. These demands will see the workforce analytics function work more collaboratively, and with a broader range of colleagues and stakeholders, than it has to date. Expect to see a greater number of partnerships as projects become more complex and require abilities beyond the immediate capabilities of the team. This integration will also see the data that once flowed only within silos in businesses become available in real time for authorized users across the organization.



Maturity Models Perhaps notable due to its absence in this book is the notion of analytics maturity models. Maturity models in analytics essentially specify a roadmap that organizations can choose to follow to develop their workforce analytics capability. In the early days of workforce analytics, maturity models were useful because they highlighted the possibilities for workforce analytics at a time when the discipline was finding its feet. They spotlighted the usefulness of data warehouses (the reporting level in many frameworks) and the potential to predict employee events with a useful degree of accuracy (the predictive level in many maturity frameworks). ******ebook converter DEMO Watermarks*******



In the future, the implied need to follow a linear progression through seemingly more sophisticated levels of analytics capability is a misrepresentation of what is needed and what is possible for most organizations. In fact, all forms of workforce analytics are likely to be needed concurrently. Organizations do not need to surmount the hurdles associated with earlier levels of an analytics maturity framework before reaching the desired stage of workforce analytics capability. Instead, organizations should simply focus on the analytics capability they require to solve their most pressing business challenges. In other words, don’t focus on the maturity model which is an inside-out view of workforce analytics. Instead focus on the business issues and the stakeholders that are served, which gives an outside-in emphasis.



Summary Workforce analytics is an essential organizational capability and experts believe its impact on business performance will only increase. By taking the following steps, you can position your function well to capitalize on the opportunities: • Familiarize yourself with emerging data sources in workforce analytics, such as worker digital footprints, sensors, and wearable data. • Ensure that your function is ready to utilize new and emerging technology that blends reporting and predictive analytics using open source technology, all deployed with artificial intelligence and cognitive technology. • Decide on the appropriateness of data sources for particular purposes based on the scientific evidence, legal context, and social implications of using the data. • Prepare your team to evolve its skills, particularly the ability to lead in a complex environment, with privacy as a specialty and using data scientists who can learn new methods and techniques quickly. • Learn why workforce analytics is important for tomorrow’s business and prepare to embrace the road ahead.



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Glossary A/B testing. A randomized experiment with two groups: a control group that experiences the current or standard treatment, and an experimental group that receives some variation of the standard treatment. Any observed differences between the groups can be attributed to the treatment. Algorithm. A step-by-step set of rules to follow in calculations to meet analytical objectives such as prediction or classification. Analysis of variance (ANOVA). A statistical method for examining quantitative differences between two or more groups. Analytics. The discovery, interpretation, and communication of meaningful patterns in data to inform decision making and improve performance. Application programming interface (API). A set of definitions, protocols, and tools for building software applications, and for allowing software components from different sources to communicate with each other. Bias. Statistical differences in scores for majority and minority groups that are unrelated to the underlying concept you are trying to measure. This occurs either in measuring a variable (measurement bias) or applying the measure to predict outcomes (predictive bias). Big Data. Datasets of structured and unstructured information that are so large and complex that they cannot be adequately processed and analyzed with traditional data tools and applications. Business acumen. A keenness and agility in understanding, interpreting, and dealing with business situations. Business glossary. See data dictionary. Business intelligence tools. Software for generating insights and reports from data stored in a data warehouse. Causality. The effect of one variable on another (cause and effect). Two variables have a causal relationship if changes in one variable produces changes in the other. Center of Excellence (CoE) or Center of Competence (CoC). A team or entity that provides leadership, best practices, research, support, and ******ebook converter DEMO Watermarks*******



training for a focus area. Change data capture (CDC). An automated approach for ensuring that data changes are synchronized across an enterprise by replicating data changes from a source system to other systems. Change management. The process, tools, and techniques to manage the people side of change to achieve a required business outcome. Chartered Institute of Personnel and Development (CIPD). A professional body for human resources and people development that has a worldwide community of members committed to championing better work and working lives. It is headquartered in London, United Kingdom. Chief human resources officer (CHRO). The most senior person in an organization responsible for overseeing all aspects of the strategies, policies, practices, and operations of human resource management. Classification tree. A machine learning approach that uses training data to create a model that can then be used for assigning cases (for example, workers) in a dataset to different possible groupings (for example, leavers or stayers). Click-path data. The tracking of web page viewing behavior, such as where and when people click on a web page, the time they spend on a web page, and their viewing patterns. Also known as click-stream data. Cloud computing. A type of Internet-based technology in which different services (such as servers, storage, and applications) are delivered to an organization’s or an individual’s computers and devices through the Internet. Cluster analysis. A statistical technique for finding natural groupings in data; it can also be used to assign new cases to groupings or categories. Cognitive assistant. A technology application that interacts with users through natural language, providing levels of confidence in its answers. A cognitive assistant continuously learns and improves. Cognitive computing. Systems that understand, learn, and reason as they interact with humans using natural language to mimic the way the human brain works and enhance human performance. Confounding factor. A variable other than the one you’re interested in that might affect the outcome variable and lead to incorrect conclusions. ******ebook converter DEMO Watermarks*******



Consumerization of HR. A term referring to employees’ expectations that technology experiences at work will be similar to technology experiences as consumers. Continuous variable. A variable that can take on any value between a minimum and maximum (for example, age or tenure), where a higher score indicates more of the variable. Control group. A group in a research study that does not experience treatment, but instead acts as a baseline against which change in the experimental group is compared. Correlation (Pearson product–moment correlation). A statistical measure that indicates the extent to which two variables are related. A positive correlation indicates that, as one variable increases, the other increases as well. For a negative correlation, as one variable increases, the other decreases. Correlational design. A research design that does not involve randomization or manipulation of who receives treatments. Dashboard. A data display tool that provides at-a-glance views of key performance indicators relevant to a particular objective or business process. Data (plural); datum (singular). Facts, information, and statistics collected together for reference or analysis. Data analysis. A process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful insights, suggesting conclusions, and supporting decision making. Data analyst. A person whose job is to collect and study data to reveal meaningful patterns and insights. Data architecture. Models, policies, and guidelines that structure how data are collected, stored, used, managed, and integrated within an organization. Data dictionary. A comprehensive record of business and technical definitions of the elements within a dataset. Also referred to as a business glossary. Data ethics. The fundamental legal and moral principles of right and wrong that govern the collection, storage, use, and dissemination of data in analytics. ******ebook converter DEMO Watermarks*******



Data governance. The overall management of the availability, usability, integrity, and security of the data employed in an organization. Data mart. A subset of a data warehouse that allows data to be accessed and customized by specific business functions. Data mining. The process of collecting, searching through, and analyzing a large amount of data in a database to discover patterns or relationships. Data privacy. The legal, political, and ethical issues surrounding the collection and dissemination of data, the technology used, and the expectations of what information is shared with whom. Data profiling. Checking datasets for allowable values, logic, and consistency. Data scientist. A person whose job is to perform statistical analysis, data mining, and retrieval processes on a large amount of data to identify trends and other relevant information. Data steward. A person responsible for managing data content, quality, standards, and controls within an organization or function. Data visualization. The representation of quantitative information in a pictorial or graphic format so that an audience can easily grasp difficult concepts or patterns. Data warehouse. A repository for storing business-relevant data. Database. A collection of information that is organized so that it can be easily accessed, managed, and updated. Dataset. A collection of variables or information that is composed of separate elements but can be managed as a single entity for analysis. Democratization of HR. A term given to HR so that the information known about employees or policies and programs is more readily available; for example, information about a manager’s team is provided by HR applications without the need to request it. Digital footprint. The personal electronic trace or trail left from the use of Internet-connected devices. Disappearables. Wearable devices that will become so small due to technological advances that they will almost disappear from view. Discrete variable. A variable with a limited number of possible categories and no intrinsic ordering. ******ebook converter DEMO Watermarks*******



Dynamic visualization. A display of an analytics message that is animated, interactive, and contains live data so that the image changes as the information refreshes. Ethnographic study. A qualitative, small-group research design that attempts to understand organizational events from the perspective of those experiencing the events. Experiment. A scientific procedure undertaken to make a discovery, test a hypothesis, or demonstrate a known fact, with participants randomly assigned to groups so that every participant has an equal chance of being in the experimental group or the control group. Experimental group. A group in a research design that experiences a new treatment or intervention intended to improve an individual or organizational outcome. The effects on this group are often contrasted with a control group that does not receive treatment. Factor analysis. A statistical technique for summarizing many variables with fewer variables, with particular applications to measuring psychological attributes. Fairness. The social evaluation of whether decisions are free from discrimination. Fairness-aware data mining. The science of applying statistical techniques while managing the social consequences of analytics. Future of work. A term referring to how work will develop and be delivered in a globally interconnected world in which almost all work can be done anywhere and the processing power of computers will enable machines to outperform humans for an ever-increasing scope of work. Gig economy. The freelance economy, in which workers support themselves with a variety of part-time jobs, or gigs, that do not provide traditional employment-style benefits such as healthcare. Governance. A broad term referring to the establishment of policies and guidelines, along with continuous monitoring of their proper implementation, by the members of the governing body of an organization. Human resources business partner (HRBP). HR professionals who work closely with an organization’s senior leaders to develop an agenda for managing people that supports the overall aims of the organization. These ******ebook converter DEMO Watermarks*******



are generalists and do not normally specialize in any subfunction of HR. Human resources information system (HRIS). Software that provides a single, centralized view of data that a human resources management group requires for executing HR processes. Human resources information technology (HRIT). A subfunction within the human resources function that is responsible for selecting, implementing, and maintaining the HR technology systems for an organization. Hypothesis. A proposed explanation, in the form of a testable and falsifiable statement, often informed by observations and previous research. Impact. Differences in the rates of job selection, promotion, or other employment decisions that disadvantage members of a particular group, such as women or ethnic minorities. Infographic. A short-form, visual representation of information, data, or knowledge presented through simple images that highlight patterns, trends, or insights. Simplified from the term information graphic. Insight. A deep and clear understanding derived from analysis. Instrumental variable approach. A statistical technique common in economics that attempts to assess causal effects from correlational data when randomized experiments are not possible. Internet of Things (IoT). An interconnected network of physical devices, vehicles, buildings, and other items embedded with sensors that gather and share data. Intervention. An action taken with the intent of producing a specific outcome or result. Key performance indicator (KPI). A variable or metric against which the success of a function or business is judged. Kurtosis. A numerical indicator of whether the heights of the tails (the low and high values of a variable) in a data distribution are extreme; zero kurtosis indicates tails that are neither heavier nor lighter than would be expected in a normal distribution. Machine learning. A subdiscipline of computer science that addresses similar challenges to traditional statistical modeling, but with different ******ebook converter DEMO Watermarks*******



techniques and a stronger focus on predictive accuracy. Metrics. Facts and figures representing the effectiveness of business processes that organizations track and monitor to assess the state of the company. Mission statement. A description of an organization or function’s business, its objectives, and its approach to reach those objectives. Nanotechnology. The application of technology at such a small scale that devices and sensors can be implanted into articles such as clothing. Neural net model. A machine learning technique for making forecasts and classifications from many predictors, suitable when the relationships between predictors and outcomes are too complex to be modeled with traditional statistical methods such as regression. Normal distribution. Also known as a bell-shaped curve or Gaussian curve, this is a distribution of data that is symmetrical around the mean: The mean, median, and mode are all equal, with more density in the center and less in the tails. Observational study. A study that examines how variables relate to one another in their natural environment, without randomizing subjects to conditions and manipulating who receives an intervention. On-premise technology. Software installed and run on computers physically located on-site (on the premises) at an organization. Open standard. A public technology standard that allows interoperability and communication between technology systems. Operating model. Describes how a group will conduct its business within the larger organization and external environment in which it resides. It is useful for defining working relationships, resolving issues and conflicts, and guiding decision making. Outcome metric. The measurable result (financial or nonfinancial) of an action, program, or project—an indicator of the extent to which objectives have been met. Outlier. An observed value that falls outside the overall pattern of an expected data distribution. Predictive analytics. A branch of advanced analytics that is used to make forecasts about future events. ******ebook converter DEMO Watermarks*******



Principal component analysis. A statistical technique used to reduce the number of variables in a dataset to a smaller number while preserving the information in the larger dataset. It is a type of factor analysis and is often used as a first step to make further analyses more manageable. Proximal metric. An indicator of progress toward a desired outcome, reflecting observable results closer in time to when an action is taken. Proxy variable. An alternative measure of a variable of interest, when the desired variable is unavailable or of insufficient quality for analysis. Qualitative analysis. An approach to studying phenomena when the data collection, analysis, and interpretation do not involve statistics. Quantitative analysis. An approach to studying phenomena when the data collection, analysis, and interpretation are based on statistics. Quasi-experiment. A research design in which the effects of an experimental intervention are compared to the effects of no intervention on a control group, without the benefit of randomizing participants to conditions. Randomization. The process of allocating research participants across conditions of an experiment in a way that ensures no differences between the groups. Regression analysis. A statistical process for estimating the relationships between variables, often used to forecast the change in a variable based on changes in other variables. Linear regression is used to analyze continuous variables, and logistic regression is used for discrete variables. Regression tree. A machine learning method for making predictions about a continuous outcome variable (such as job performance) from one predictor or a series of predictors. Reporting. The function or activity for generating documents that contain information organized in a narrative, graphic, or tabular form, often in a repeatable and regular fashion. Research design. A research plan regarding what data will be collected, when it will be collected, how it will be collected, and from what or whom it will be collected. Return on investment (ROI). The measure of benefit of an investment divided by the cost of the investment, usually expressed as a percentage and often converted to a monetary value. ******ebook converter DEMO Watermarks*******



Sensitivity analysis. A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. It allows an analyst to determine whether a statistical finding will remain consistent under a variety of conditions. Sensor. An object designed to detect and record data, and provide the information back to a central database. Skewness. A numerical indicator of lack of symmetry in a data distribution. Zero skewness indicates perfect symmetry, as would be expected in a normal distribution. Snowball strategy. An approach for identifying potential stakeholders by interviewing select individuals and requesting recommendations of additional people to interview. Society for Human Resource Management (SHRM). The world’s largest HR professional society and leading provider of resources serving the needs of HR professionals and advancing the practice of human resource management. It is headquartered in Alexandria, Virginia, United States. Software as a Service (SaaS). An approach to software licensing and delivery in which software is hosted remotely in the cloud and accessed via an Internet browser. Sponsor. A person or group providing support for a project or activity through financial means or personal endorsements. Stakeholder. A person in the organization who has a vested interest in a project or activity and the outcomes. Static visualization. An image to communicate an analytics message that is based on a snapshot of data at a point in time (that is, the image is still). Statistical modeling. The use of mathematical equations, based on a set of assumptions, intended to predict or explain relationships among variables. Statistics. The organization, analysis, interpretation, and presentation of quantifiable data. Storytelling. A method of explaining a series of events through narrative. Strategic workforce planning. A process used to align the needs and priorities of the organization with those of its workforce, through understanding labor supply and demand and the long-term objectives of both the organization and its competitive environment. ******ebook converter DEMO Watermarks*******



Support vector machine. Machine learning techniques that are used to make predictions of continuous variables and classifications of categorical variables based on patterns and relationships in a set of training data for which the values of predictors and outcomes for all cases are known. t-test. A statistical method for estimating the magnitude of quantitative differences between two groups. Text analytics. The process of deriving insights from large volumes of text, typically through the use of specialized software to identify patterns, trends, and sentiment. Time series analysis. A class of statistical methods used for studying how values of a variable or a group of variables change over time. Triangulation. A method for establishing the validity of research findings by using multiple approaches and techniques and looking for convergence. Variance. A statistical measure of how spread (or varying) the values of a variable are around a central value such as the mean. Vision statement. A description of the desired future impact of your function on the organization. Wearables. Devices worn on the body to gather and provide information to the user through advanced technology, such as smart watches, activity trackers, and smart glasses. Workforce analytics. The discovery, interpretation, and communication of meaningful patterns in workforce-related data to inform decision making and improve performance.



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References Preface Bock, Laszlo. Work Rules!: Insights from Inside Google That Will Transform How You Live and Lead. Twelve Books, 2015. Davenport, Thomas, and Jeanne Harris. Competing on Analytics: The New Science of Winning. Boston, MA: Harvard Business School Press, 2007. Davenport, Thomas, Jeanne Harris, and Jeremy Shapiro. “Competing on Talent Analytics.” Harvard Business Review, Vol. 88, Issue 10 (2010): 52–58. Guenole, Nigel, Sheri Feinzig, Jonathan Ferrar, and Joanne Allden. “Starting the Workforce Analytics Journey—The First 100 Days.” IBM Smarter Workforce Institute, 2015. Retrieved at: http://www01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=LOL14045USEN.



Chapter 1, “Why Workforce Analytics?” Accenture Strategy. “The Future of HR: A Radically Different Proposition.” Accenture Strategy, 2015. Retrieved at: https://www.accenture.com/t20150523T024235__w__/been/_acnmedia/Accenture/ConversionAssets/DotCom/Documents/Global/PDF/Dualpub_14/Accenture-Futureof-HR-Overview.pdf. Boudreau, John. “HR Expertise: Facing the Future of Work.” Society for Human Resource Management, January 2016. Retrieved at: https://www.shrm.org/hr-today/news/hr-magazine/pages/0116competencies-hr-expertise-boudreau.aspx. Deloitte. “2017 Deloitte Global Human Capital Trends.” Deloitte University Press, 2017. Retrieved at: https://www2.deloitte.com/uk/en/pages/humancapital/articles/introduction-human-capital-trends.html. PwC, in association with the Economist Intelligence Unit. “Gut and Gigabytes: Capitalising on the Art and Science of Decision-making.” PwC, 2014. Retrieved at: http://www.pwc.com/mx/es/serviciostecnologias-de-la-informacion/archivo/2014-10-big-decisions.pdf. ******ebook converter DEMO Watermarks*******



Feffer, Mark. “The Democratization of Talent Management.” Society for Human Resource Management, April 2015. Retrieved at: https://www.shrm.org/resourcesandtools/hr-topics/talentacquisition/pages/democratization-talent-management.aspx. IBM. “New Expectations for a New Era: CHRO Insights from the Global C-suite Study.” IBM Institute for Business Value, March 2014. Retrieved at: https://www-01.ibm.com/common/ssi/cgi-bin/ssialias? subtype=XB&infotype=PM&appname=GBSE_GB_TI_USEN&htmlfid=GBE03592US IBM. “Redefining Talent: The CHRO Point of View.” IBM Institute for Business Value, March 2016. Retrieved at: https://www01.ibm.com/common/ssi/cgi-bin/ssialias? subtype=XB&infotype=PM&htmlfid=GBE03739USEN&attachment=GBE03739USE KPMG International. “Evidence-based HR: The Difference Between Your People and Delivering Business Strategy.” KPMG International, 2015. Retrieved at: https://assets.kpmg.com/content/dam/kpmg/pdf/2015/04/evidence-basedhr.pdf. Mangalindan, J. P. “Amazon’s Recommendation Secret.” Fortune, July 2012. Retrieved at: http://fortune.com/2012/07/30/amazonsrecommendation-secret/. Wagstaff, Jeremy “As Sensors Shrink, Watch As ‘Wearables’ Disappear.” Reuters, 30 April 2015. Retrieved at: http://www.reuters.com/article/ustech-wearables-idUSKBN0NK2KV20150430.



Chapter 2, “What’s in a Name?” Atamian, Rebecca, and Travis Klavohn. “The Race is On: Winning Analytics Fitness.” Workforce, 2015. Retrieved at http://www.workforce.com/2015/08/17/the-race-is-on-winning-analyticsfitness/. Bersin, Josh. “The Geeks Arrive in HR: People Analytics Is Here.” Forbes, 2015. Retrieved at http://www.forbes.com/sites/joshbersin/2015/02/01/geeks-arrive-in-hrpeople-analytics-is-here/. Chartered Institute of Personnel and Development. August 2016 factsheet. Retrieved at http://www.cipd.co.uk/hr-resources/factsheets/talent******ebook converter DEMO Watermarks*******



management-overview.aspx. Coolen, Patrick. “The 10 golden rules of HR analytics (revisited).” LinkedIn, 2015. Retrieved at https://www.linkedin.com/pulse/10-goldenrules-hr-analytics-revisited-patrick-coolen. Lawler III, Edward E. “Talent Analytics: Old Wine in New Bottles?” Forbes, 2015. Retrieved at http://www.forbes.com/sites/edwardlawler/2015/05/20/talent-analyticsold-wine-in-new-bottles/#3cff7a622613. SHRM Foundation, in association with the Economist Intelligence Unit. “Use of Workforce Analytics for Competitive Advantage.” SHRM Foundation, 2015. Retrieved at http://whitepaperadmin.eiu.com/futurehrtrends/wp-content/uploads/sites/2/2016/06/Use-ofWorkforce-Analytics-for-Competitive-Advantage.pdf.



Chapter 3, “The Workforce Analytics Leader” CareerCast, “Toughest Jobs to Fill in 2017.” Retrieved at http://www.careercast.com/jobs-rated/toughest-jobs-fill-2017. McCall, Morgan W. Jr. “Recasting leadership development.” Industrial and Organizational Psychology Vol. 3 (2010): 3–19. McKinsey Global Institute. “Big Data: The Next Frontier for Innovation, Competition and Productivity.” McKinsey & Company, May 2011. Retrieved at: http://www.mckinsey.com/business-functions/digitalmckinsey/our-insights/big-data-the-next-frontier-for-innovation. Smeyers, Luk. “HR Analytics, You Report to the CHRO. Period!” HRN Blog, 16 March 2016. Retrieved at: https://blog.hrn.io/hr-analytics-youreport-to-the-chro-period/.



Chapter 4, “Purposeful Analytics” Levenson, Alec. Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness. Oakland, CA: Berrett-Koehler Publishers, 2015.



Chapter 5, “Basics of Data Analysis” Edwards, Martin, and Kirsten Edwards. Predictive HR Analytics: Mastering the HR Metric. London: Kogan Page, 2016. ******ebook converter DEMO Watermarks*******



Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York, NY: Springer,2009. Kelly III, John, and Steve Hamm. Smart Machines: IBM’s Watson and the Era of Cognitive Computing. New York, NY: Columbia University Press, 2013. Lee, Thomas W., Terence R. Mitchell, and Wendy Harman. “Qualitative Research Strategies in Industrial and Organizational Psychology.” In APA Handbook of Industrial and Organizational Psychology, edited by Sheldon Zedeck. Vol. 1, 73–83. Washington, DC: American Psychological Association, 2011. O’Neill, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York, NY: Crown Publishing Group, 2016. Pratt, Michael, and Sylvia Bonaccio. “Qualitative Research in I-O Psychology: Maps, Myths, and Moving Forward.” Industrial and Organizational Psychology: Perspectives on Science and Practice. Vol. 9, Issue 4, (December 2016): 693–715.



Chapter 6, “Case Studies” Australian Psychological Society. “Stress and Wellbeing in Australia Survey.” 2016. Retrieved at: https://www.psychology.org.au/psychologyweek/survey/. Nielsen Workforce Analytics Excellence Awards, June 2016. Retrieved at www.youtube.com/watch?v=h3S1bUhK3Fo. Reichheld, Fred, “One Number You Need to Grow.” Harvard Business Review, Vol. 81, Issue 12, (2003): 46–54. Smith, Jacquelyn. Stress at work is costing employers $300 billion a year— here's why. Business Insider, June 2016. Retrieved at: http://www.businessinsider.com.au/how-stress-at-work-is-costingemployers-300-billion-a-year-2016-6.



Chapter 7, “Set Your Direction” Bain and Company. “Mission and Vision Statements.” 2016. Retrieved at http://www.bain.com/publications/articles/management-tools-mission******ebook converter DEMO Watermarks*******



and-vision-statements.aspx. Diener, Ed, and Martin E. P. Seligman. “Beyond Money: Toward an Economy of Well-being.” Psychological Science in the Public Interest Vol. 5, Issue 1, (2004): 1–31.



Chapter 9, “Get a Quick Win” Ferris, Gerald R., and K. Michele Kacmar. “Perceptions of Organizational Politics.” Journal of Management, Vol. 18 (1992): 93–116.



Chapter 10, “Know Your Data” Bort, Julie. “How a 26-year-old caused IBM to abolish its ban on Uber.” Business Insider, June 2015. Retrieved at: http://uk.businessinsider.com/why-ibm-abolished-its-ban-on-uber-2015-6.



Chapter 11, “Know Your Technology” Ryan, Jacqueline, and Hailey Herleman. “A Big Data Platform for Workforce Analytics.” In Big Data at Work: The Data Science Revolution and Organizational Psychology, edited by Scott Tonidandel, Eden B. King, and Jose M. Cortina, New York, NY: Routledge, 2015.



Chapter 12, “Build the Analytics Team” Bloomberg BNA. “HR Department Benchmarks and Analysis.” The Bureau of National Affairs. 2015–2016. Knaflic, Cole N. Storytelling with Data: A Data Visualization Guide for Business Professionals. Hoboken, NJ: Wiley, 2015. Davenport, Tom. “What Data Scientist Shortage? Get Serious and Get Talent.” Data Informed: Big Data and Analytics in the Enterprise. 28 July 2016. Retrieved at http://data-informed.com/what-data-scientist-shortageget-serious-and-get-talent/. Ferris, Gerald. R., and Darren C. Treadway. Politics in Organizations: Theory and Research Considerations. New York, NY: Routledge, 2012.



Chapter 13, “Partner for Skills” McKinsey Global Institute. “Big Data: The Next Frontier for Innovation, Competition and Productivity.” McKinsey & Company, May 2011. ******ebook converter DEMO Watermarks*******



Retrieved at: http://www.mckinsey.com/business-functions/digitalmckinsey/our-insights/big-data-the-next-frontier-for-innovation.



Chapter 14, “Establish an Operating Model” Erickson, Tammy. “The Biggest Mistake You (Probably) Make with Teams.” Harvard Business Review. 5 April 2012. Retrieved at: https://hbr.org/2012/04/the-biggest-mistake-you-probab. Guenole, Nigel and Jonathan Ferrar. “Active Employee Participation in Workforce Analytics: A Critical Ingredient for Success.” IBM Smarter Workforce, 2015. Retrieved at http://www-01.ibm.com/common/ssi/cgibin/ssialias?infotype=SA&subtype=WH&htmlfid=LOW14280USEN. Phillips, Katherine W. “How Diversity Makes Us Work Smarter.” Scientific American, October 2014. Retreived at: https://www.scientificamerican.com/article/how-diversity-makes-ussmarter/



Chapter 15, “Enable Analytical Thinking” Ariker, Matt, Peter Breuer, and Tim McGuire. “How to Get the Most from Big Data.” December 2014. Retrieved at: http://www.mckinsey.com/business-functions/business-technology/ourinsights/how-to-get-the-most-from-big-data. Davenport, Tom. “In Praise of ‘Light Quants’ and ‘Analytical Translators.’ ” Deloitte University Press, May 2015. Retrieved at http://dupress.com/articles/new-big-data-analytics-skills/.



Chapter 17, “Communicate with Storytelling and Visualization” Boje, David M. “Book Review Essay: Pitfalls in Storytelling, Advice and Praxis.” Academy of Management Review, Vol. 31, Issue 1 (2006): 218– 230. Duarte, Nancy. “The Secret Structure of Great Talks.” Filmed November 2011. Retrieved from https://www.ted.com/talks/nancy_duarte_the_secret_structure_of_great_talks Karia, Akash. 23 Storytelling Techniques from the Best TED Talks. CreateSpace Independent Publishing Platform, 2015. ******ebook converter DEMO Watermarks*******



Knaflic, Cole N. “Storytelling with Data: A Data Visualization Guide for Business Professionals.” Hoboken, NJ: Wiley, 2015. Shami, N. Sadat, Jiang Yang, Laura Panc, Casey Dugan, Tristan Ratchford, Jamie Rasmussen, Yanick Assogba, Tal Steier, Todd Soule, Stela Lupushor, Werner Geyer, Ido Guy, and Jonathan Ferrar. 2014. “Understanding employee social media chatter with enterprise social pulse.” In Proceedings of the 17th ACM conference on Computer Supported Cooperative Work & Social Computing (pp. 379–392). ACM. Sole, Deborah. “Sharing Knowledge Through Storytelling.” Harvard Graduate School of Education, 2002. Retrieved at: http://www.providersedge.com/docs/km_articles/Sharing_Knowledge_Through_Storyt Stanton, Andrew. “The Clues to a Great Story.” Filmed Feb 2012. Retrieved at: https://www.ted.com/talks/andrew_stanton_the_clues_to_a_great_story? language=en. Tufte, Edward R. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press, 2001. Tufte, Edward R. “PowerPoint is Evil.” Wired, September 2003. Retrieved at: https://www.wired.com/2003/09/ppt2/. Yost, Paul R., Michael P. Yoder, Helen H. Chung, and Kristen R. Voetmann. “Narratives at Work: Story Arcs, Themes, Voice, and Lessons that Shape Organizational Life.” Consulting Psychology Journal: Practice and Research, Vol. 67, Issue 3 (2015): 163–188. Zak, Paul J. “Why Your Brain Loves Good Storytelling.” Harvard Business Review (October 2014). Retrieved at: https://hbr.org/2014/10/why-yourbrain-loves-good-storytelling Zull, James E. The Art of Changing the Brain. Sterling, VA: Stylus Publishing, 2002.



Chapter 18, “The Road Ahead” Chi, Wei., Wen-Dong Li, Nan Wang, and Zhaoli Song. “Can Genes Play a Role in Explaining Frequent Job Changes? An Examination of Gene– Environment Interaction from Human Capital Theory.” Journal of Applied Psychology, Vol. 101, Issue 7 (2016): 1030–44. Deloitte. “2017 Deloitte Global Human Capital Trends.” Deloitte ******ebook converter DEMO Watermarks*******



University Press, 2017. Retrieved at: https://www2.deloitte.com/uk/en/pages/humancapital/articles/introduction-human-capital-trends.html. PwC. “2017 Global Digital IQ® Survey: 10th anniversary edition.” Retrieved at: https://www.pwc.com/us/en/advisory-services/digitaliq/assets/pwc-digital-iq-report.pdf



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Index A ABN AMRO, 94, 107, 108, 120, 123, 149, 195, 245, 259 Accenture Strategy, 129, 138, 149, 194, 259 accountability accountability hazard, 244 business cases, 221–222 success metrics, 222–223 Adamsen, Al, 102, 113, 183, 272 adoption of analytics, motivations for, 3 consumerization of HR, 9 contribution to business value, 4–6 democratization of HR, 7–9 future of work, 11–12 ADP, 206, 243, 264 Agoda.com, 92, 94–95, 114 Ahold Delhaize, 32–33, 93, 235 algorithms, social consequences of, 54–56 Allen, Peter, 92, 94, 114 analysis, conducting, 32 analytically resistant professionals, 232–234 analytically savvy professionals, 228–230 analytically willing professionals, 230–232 analytics, workforce. See workforce analytics analytics perspectives analytically resistant professionals, 232–234 analytically savvy professionals, 228–230 analytically willing professionals, 230–232 analytics teams Six Skills for Success business acumen, 170–172 ******ebook converter DEMO Watermarks*******



communication, 179–181 consulting, 173–175 data science, 178–179 human resources, 175–177 overview of, 170 work psychology, 177–178 size of, 182 skilled workers, 183–184 team roles, 181–185 Andersen, Morten Kamp, 40, 65 anticipating business needs, 181 ANZ Bank, 159, 192, 271 assessment complexity, 129–131 impact, 132–133 association analysis, 49 AstraZeneca, 29, 176, 213, 254 Atamian, Rebecca, 16 ATB Financial, 22, 36, 174–175 attributes of workforce analytics leaders, 23–26 attrition impact of, 59–60 implementation of recommendations and, 60–63 audience, targeting, 269–271 Aviva, 22, 161, 162 awareness data awareness, 179 internal/external, 171–172



B A/B testing, 154 Bailie, Ian, 9, 12, 181, 231, 233, 243, 246 Bassi, Laurie, 93, 104, 105, 115, 138, 194, 273 ******ebook converter DEMO Watermarks*******



Baylor Scott & White Health, 37, 273 Bazigos, Michael, 129, 138, 149, 194, 259 Berry, Mark, 19, 21, 193, 194, 199, 229, 230, 262 Bersin, Josh, 16, 20, 27, 104, 199, 247, 255 Bersin by Deloitte, 20, 27, 104, 199, 247, 255 Beskow, Mats, 239 bias, 55 Big Bet projects, 127 Big Data, 152–155 Blumberg, Max, 12, 71–72 Blumberg Partnerships, Ltd., 12 BNY Mellon, 89 board of directors, engaging, 105 The Boeing Company, 274 Boje, David, 254 Boudreau, John, 5, 11, 169 Brown, Alan, 71 budgets financial frugality, 245–246 partners and, 198 Buechsenschuss, Ralf, 35, 272, 277 business acumen, 170–172 business cases, 221–222 business leaders, engaging, 103–105 business needs, anticipating, 181 business question framing, 29–30 business value, HR (human resources) contribution to, 4–6



C Callery, John, 89–90 capability, as driver of employee engagement, 65–66 CAPEX (capital expenditure), 165 case studies ******ebook converter DEMO Watermarks*******



ISS Group, 65–70 Metropolitan Police, 76–79, 111 Nielsen Holdings PLC, 59–63 overview of, 57 Rentokil Initial, 71–75 Westpac Group, 80–82 causal inference, research design for, 44 CEO succession, 106 CGB Enterprises, Inc., 19, 21, 193–194, 229, 262 chain of command, 20 Champ, Marcus, 127, 132 change data capture (CDC), 142 change management, 174–175 Chi Wei, 280 Chidambaram, Arun, 95 chief human resources officers. See CHROs (chief human resources officers) CHREATE (Global Consortium to Reimagine HR, Employment Alternatives, Talent, and the Enterprise), 11, 18 CHROs (chief human resources officers), 283 roles reporting to, 20–21, 208–209 understanding views of, 102 Cisco, 9–12, 181, 231, 243, 246 clarifying roles/responsibilities, 214–216 visualization message, 265 classification analysis, 50 click stream data, 151 cloud technology, 151, 164 cluster analysis, 51 cNPS (Customer Net Promoter Scores), 65 cognitive technology, 163, 282 collaboration, 285 communication, 35–36 ******ebook converter DEMO Watermarks*******



analytics team responsibilities, 118–120, 179–181 Rentokil Initial case study, 71 competitive edge, desire for, 91 complexity assessment, 129–131 complexity–impact matrix Big Bet projects, 127 Pet Projects, 128 Quick Win projects, 126–127 Trivial Endeavor projects, 128 computer science skills, 179 concluding projects, 221 conducting analysis, 32 conflict, revealing, 259–260 consulting, 173–175, 217–221 consumerization of HR (human resources), 9 content, customizing to audience, 271 contextualization, 52 contrast, emphasizing, 268–269 Coolen, Patrick, 16, 94, 107, 108, 120, 123, 149, 195, 245, 259 Cormack, Christian, 29, 176, 213, 254 correlation analysis, 49 correlational designs, 46 costs cost reduction, pressure for, 93 financial frugality, overcoming, 245–246 opportunity cost, 133 credibility, gaining with executives, 116 culture, changing analytically resistant professionals, 232–234 analytically savvy professionals, 228–230 analytically willing professionals, 230–232 leadership, 236 overview of, 227–228 ******ebook converter DEMO Watermarks*******



training, 235 translator role, 234 Customer Net Promoter Scores (cNPS), 66 customizing content to audience, 271



D data analysis. See also data quality correlational designs, 46 data governance, 152–153 experimental design, 45 experimental designs, 45 longitudinal designs, 47 objectives of, 47–48 overview of, 43–44 qualitative analysis, 51–52 qualitative studies, 47 quantitative analysis, 48–51 quasi-experimental designs, 45–46 research design, 44 social consequences of, 54–56 traditional statistics versus machine learning, 53–54 unstructured data, 52–53 data awareness, 179 data complexity, 130 data dictionaries, 153 data gathering, 31–32 data governance, 152–153 data outliers, 145–146 data owners, 107 data privacy, 176–177 data quality A/B testing, 154 Big Data, 152–155 ******ebook converter DEMO Watermarks*******



challenges data outliers, 145–146 inconsistent data definitions, 146–147 lack of data, 142–143 missing data, 140–141 non–normal data distributions, 143–145 outdated data, 141–142 data dictionaries, 153 data types and sources data from inside HR, 148–149 data from outside HR, 149 nontraditional data sources, 150–151 emerging data sources, 277–280 evolving technology, 282–284 issues surrounding, 280–281 lack of data, 249 pragmatic view of, 138 sufficient data quality, determining, 138–139 data science, 178–179 data types and sources bringing together, 151 data from inside HR, 148–149 data from outside HR, 149 emerging data sources, 277–280 nontraditional data sources, 150–151 data warehouses, 160–161 Davenport, Tom, xxviii, 183, 234 Davies, Clare, 76–78 decision-making approach, 210–212 defining operating model, 204 Dellala, Damien, 38, 80, 209, 213, 222 demand, seven forces of, 91–95 democratization of HR (human resources), 7–9 ******ebook converter DEMO Watermarks*******



design correlational designs, 46 experimental designs, 45 longitudinal designs, 47 quasi-experimental designs, 45–46 research design, 44 visualization, 265–269 development mission statements, 95–97 requests from managers, 8 team development, 23 devices, connected, 279 digital footprints, 279–280 Dillon, Sally, 22, 162 directors, board of, 105 distributions, non-normal, 143–145 diversity of opinion, 211 drive to achieve, 34 Duin, Eric van, 124, 271



E Ebelle-ebanda, Antony, 91, 241, 271 Edwards, Martin, 56 effective visualization, 263 efficiency, operational, 93 Eight Step Model for Purposeful Analytics, 28, 58 emotional attachment, creating, 257–259 employee engagement. See engagement Employee Net Promoter Scores (eNPS), 66 enabling analytical thinking. See analytical thinking, enabling engagement business impact of, 65 ISS Group case study ******ebook converter DEMO Watermarks*******



analysis, conducting, 66–68 business impact of engagement, 65 implementation of recommendations, 69–70 overview of, 65 stakeholders analytics team responsibilities, 118–120 board of directors, 105 business leaders, 103–105 data owners, 107 executives, 115–116 finance department, 110–111 HR leaders, 102–103 legal department, 109–110 managers, 114–115 overview of, 100 planning, 120 securing support from, 101–102 SMEs (subject matter experts), 108–109 stakeholder messaging, 117 stakeholder responsibilities, 120 technology owners, 108 unions and works councils, 112 workers, 113–114 eNPC (Employee Net Promoter Scores), 66 enthusiast audiences, 270 Erickson, Tammy, 214 Ericsson, 5, 21, 87, 229, 232, 273 ethics, 176 evaluation, 36–38 Everduin, Giovanni, 153, 271 evolving technology, 282–284 execution of projects, 218–220 executives ******ebook converter DEMO Watermarks*******



engaging, 115–116 executive presentations, 272–273 experimental designs, 45 exploratory analysis, 48 external awareness, 171–172 extreme collaboration, 285



F FA (factor analysis), 50 failure of analytics, reasons for, 40–42 fairness–aware data mining, 54–55 feedback, 207 Feffer, Mark, 9 Ferris, Gerald, 129–130, 171 finance department, engaging, 110–111 financial frugality, overcoming, 245–246, 251 financial literacy, 171 Fink, Alexis, 22, 25, 37 focus of workforce analytics, 16–17 FORT framework, 207 framing business questions, 29–30 Frampton, Jonathon, 37, 273 Friedman, Jerome, 56 frugality, overcoming, 245–246, 251 funding, 110



G gathering data, 31–32 genetic testing, 280 Getinge Group, 43, 192, 261 gig economy, 12, 18 GlaxoSmithKline, 36 Global Consortium to Reimagine HR, Employment Alternatives, Talent, and the Enterprise (CHREATE), 11, 18 ******ebook converter DEMO Watermarks*******



“good enough” data, 138–139 governance, 152–153 operating model decision-making approach, 210–212 HR data, working with, 205–208 reporting structure optimization, 208–209 Rentokil Initial case study, 71 graphics, 263–264 Green, David, 57 Groupon, 102, 147, 228, 231, 248



H Handbook of Industrial and Organizational Psychology (Lee), 52 Hartmann, Peter, 43, 192, 261 Hastie, Trevor, 56 health and wellness, requests from managers, 8 Herleman, Hailey, 160 hesitancy, overcoming, 246–251 HIPPO principle, 100 in–house teams, 189–191, 196 HR (human resources) changing nature of, 7 consumerization of, 9 contribution to business value, 4–6 data warehouses, 160–161 democratization of, 7–9 future of, 10 hesitancy, overcoming, 246–251 HR leaders, engaging, 102–103 lack of skills in, 242 perception of, 199–200 requests from managers, 8–9 skills of, 175–177 ******ebook converter DEMO Watermarks*******



as strategic business partner, 243 HR analytics. See workforce analytics HR business partners (HRBPs), 37 HR data, working with, 205–208 HRBPs (HR business partners), 37 HRIS (Human Resources Information System), 141–142, 160 HRIT (Human Resources Information Technology), 108 human capital, 16 human capital analytics. See workforce analytics Human Resources Information System (HRIS), 141–142, 160 Human Resources Information Technology (HRIT), 108 humanistic concerns, 93 Huselid, Mark, 3, 10, 170, 241–242 hypothesis building, 30–31, 52, 173



I IaaS (Infrastructure as a Service), 165 IBM, 11, 52, 57, 112, 150, 157, 278 impact, 55 impact assessment, 132–133 impartiality, 264 impasse, resolving, 211 implementation, 36–38 implementation complexity, 131 operating model consulting approach to project management, 217–221 role/responsibility clarification, 214–216 team structure, 213–214 of recommendations, 119 risks, 249–250 inconsistent data definitions, 146–147 industrial psychology, 177–178 Infrastructure as a Service (IaaS), 165 ******ebook converter DEMO Watermarks*******



initiating projects, 217–218 iNostix by Deloitte, 20, 234, 244–245 insights, 17, 32–33 inspiration, 24 integration, responsibility for, 21 Intel, 22, 25, 37 internal awareness, 171–172 international HR, 176 Internet of Things, 18, 150 interpretation of results, 52 interviewing project sponsors, 90–91 ISS Group case study, 65–70 analysis, conducting, 66–68 business impact of engagement, 65 implementation of recommendations, 69 overview of, 65 iterative approach, 72–74



J Jover, Placid, 110, 214, 220 JPMorgan Chase & Co. 89, 92, 119, 231, 250



K Karia, Akash, 256 key performance indicators (KPIs), 6, 125 Klavohn, Travis, 16 Klinghoffer, Dawn, 177, 284 Knaflic, Cole Nussbaumer, 263 KPIs (key performance indicators), 6, 125 kurtosis, 143–145



L lack of data, 142–143, 249 Landstinget Västmanland, 239 ******ebook converter DEMO Watermarks*******



Lashyn, Terry, 22, 36, 42, 175 Lawler, Ed, 16 Layney, Tracy, 6, 227 leadership attributes of, 23–26 board of directors, 105 building analytics culture with, 236 business acumen of, 22–23 business leaders, 103–105 chain of command, 20 data owners, 107 HR leaders, 102–103 requests from managers, 8 responsibilities of, 20–21 SMEs (subject matter experts), 108–109 technology owners, 108 leasing technology, 165–166 Lee, Tomas, 52 legal appropriateness of data sources, 281 legal department, engaging, 109–110 Levenson, Alec, 5, 28–29 Lever Analytics, 137, 194 linguistic analysis, 280 LinkedIn, 172, 203, 208 listening analytics team responsibilities, 118–120 to project sponsors, 90–91 London Metropolitan Police. See Metropolitan Police case study longitudinal designs, 47



M machine learning, 53–54, 162–163 Mackaluso, Eric, 206, 243, 264 ******ebook converter DEMO Watermarks*******



Malo, Salvador, 5, 21, 87, 174, 229, 232, 273 management, responsibility for, 21 managers engaging, 114–115 manager requests, 8–9 marketing, 180 Marritt, Andrew, 104, 109, 112, 194, 206, 253 master audiences, 270 Mathur, Piyush, 59–63 maturity models, 285–286 McBassi & Company, 93, 104, 105, 115, 138, 194, 273 McCall, Morgan, 26 McGraw Hill Financial, 91 messages call to action, 260–262 stakeholder messaging, 117 metadata of website activity, 151 methodology, 28. See also case studies analysis, conducting, 32 business question framing, 29–30 communication, 35–36 data gathering, 31–32 hypothesis building, 30–31 implementation/evaluation, 36–38 insights, revealing, 33–34 recommendations, 33–34 metrics, 222–223 Metropolitan Police case study, 76–79, 111 analytical insights, 77–79 challenges, 76 implementation of recommendations, 79 overview of, 76 Microsoft, 177, 284 ******ebook converter DEMO Watermarks*******



Millner, Dave, 278 missing data, 140–141 mission statement developing, 95–97 operational model and, 205 technology and, 158–159 model (analytics), 28 failure and, 40–42 methodology analysis, conducting, 32 business question framing, 29–30 communication, 35–36 data gathering, 31–32 hypothesis building, 30–31 implementation/evaluation, 36–38 insights, revealing, 33–34 recommendations, 33–34 Morgan Stanley, 152, 187, 242, 253 motivation, as driver of employee engagement, 65–66



N Nagy, Mihaly, 113, 200 National Storytelling Network, 254 negotiating scope of analytics, 95 Nestlé, 35, 272, 277 new data sources, 277–280 evolving technology, 282–284 issues surrounding, 280–281 newcomer audiences, 271 Nicholas, Ben, 36 Nielsen, Adam Chini, 116 Nielsen Holdings PLC case study, 59–63 analysis of attrition, 60–62 ******ebook converter DEMO Watermarks*******



impact of increasing attrition, 59–60 implementation of recommendations, 63 overview of, 59 non–normal data distributions, 143–145 nontraditional data sources, 150–151 Nordea Bank, 116



O Obereigner, Andre, 102, 147, 228, 231, 236, 248 Oest, Martin, 76, 111 O’Hanlon, Peter, 137, 194 O’Keefe, Ian, 88, 92, 119, 231, 250 O’Neill, Cathy, 54 on–premise technology, 164 open standards, 282 operating model accountability business cases, 221–222 success metrics, 222–223 chain of command, 20 defining, 204 governance decision-making approach, 210–212 HR data, working with, 205–208 reporting structure optimization, 208–209 implementation consulting approach to project management, 217–221 role/responsibility clarification, 214–216 team structure, 213–214 linking to strategy, 205 operational efficiency, 92 opportunity cost, 133 optimizing reporting structure, 208–209 ******ebook converter DEMO Watermarks*******



organizational psychology, 178 OrganizationView, 104, 109, 112, 206, 253 Orwell, George, 258 outcome metrics, 223 outdated data, 141–142 outliers, 145–146 outsourcing, 193–197 overcoming resistance. See resistance, overcoming owners data owners, 107 technology owners, 108 owning technology, 166



P PaaS (Platform as a Service), 165 partners advantages of, 188 budget considerations, 198 factors in selecting, 197–200 outsourcing, 193–197 in–sourcing, 191–193, 196 Parveen, Sofia, 116 Pascal, Blaise, 273 PCA (principal components analysis), 50 Pearson’s product–moment correlation, 49 people analytics. See workforce analytics perception of HR, 199–200 personalization, workforce, 9–10 perspectives analytically resistant professionals, 232–234 analytically savvy professionals, 228–230 analytically willing professionals, 230–232 Pet Projects, 128 ******ebook converter DEMO Watermarks*******



Pfizer, 95 Phillips, Katherine, 211 Pixar, 257 planning, 17, 120 Platform as a Service (PaaS), 165 politics political astuteness, 171 political complexity, 129–130 Politics in Organizations (Ferris), 171 PostNL, 124, 271 Pratt, Michael, 52 prediction, 49 Predictive HR Analytics (Edwards), 56 preparation BNY Mellon case study, 89–90 overview of, 88–90 project sponsors, interviewing, 90–91 scope of analytics, 95 Seven Forces of Demand, 91–95 vision and mission statement, 95–97 presentations, 180 executive presentations, 272–273 presentation medium, 265–266 presenters, selecting, 272 simplicity, 273–274 presenters, selecting, 272 principal components analysis (PCA), 50 priorities, 243–245 privacy, 176–177 Privacy Shield Frameworks, 206 problem definition, 173 project management, 174 consulting approach to p, 217–221 ******ebook converter DEMO Watermarks*******



project conclusion, 221 project execution, 218–220 project initiation, 217–218 project sponsors, 38–40 complexity–impact matrix, 129–131 interviewing, 90–91 projects complexity–impact matrix impact assessment, 132–133 project management, 174 consulting approach to p, 217–221 project conclusion, 221 project execution, 218–220 project initiation, 217–218 project sponsors, 38–40 complexity-impact matrix, 129–131 interviewing, 90–91 Quick Win projects definition of, 123–124, 126–127 identifying, 124–125 timing, 132 proximal metrics, 223 psychology, 177–178 purpose, as driver of employee engagement, 65–66 purposeful analytics case studies ISS Group, 65–70 Metropolitan Police, 76–79 Nielsen Holdings PLC, 59–63 overview of, 59 Rentokil Initial, 71–75 Westpac Group, 80–82 failure of, 40–42 ******ebook converter DEMO Watermarks*******



methodology for, 28, 58 analysis, conducting, 32 business question framing, 29–30 communication, 35–36 data gathering, 31–32 hypothesis building, 30–31 implementation/evaluation, 36–38 insights, revealing, 33–34 recommendations, 33–34 project sponsors, 38–40



Q qualitative analysis, 51–52 qualitative studies, 46–47 quality. See data quality quantitative analysis, 48–51 quantitative skills, 178–179 quasi–experimental designs, 45–46 Quick Win projects complexity–impact matrix, 125–126 definition of, 123–124, 126–127 identifying, 124–125



R RACI responsibility matrix, 214–216 Rasmussen, Thomas, 92, 99, 102, 104, 118, 191, 213, 229, 231, 273 real-time model updating, 283 recognition, 207 recommendations determining, 33–34 implementation of, 119 recruitment, 8 reduction analysis, 50 reduction of costs, pressure for, 93 ******ebook converter DEMO Watermarks*******



regression analysis, 62 regulatory requirements, 92 relationship power, 40 Rentokil Initial case study, 71–75 implementation of recommendations, 74–75 iterative approach, 72–73 overview of, 71 variability in sales performance, 71 reporting, 17 real–time reporting, 283 reporting structure, optimizing, 208–209 technology, 161–162 representation, responsibility for, 21 requests from managers, 8–9, 92–93 research design, 44, 178 resistance, overcoming, 239–240 financial frugality, 245–246, 251 HR hesitancy, 246–251 stakeholder skepticism, 240–245, 250–251 responsibility matrix, 214–216 responsibility/role clarification, 214–216 restraint, 263 retention analysis, Nielsen Holdings PLC case study analysis of attrition, 60–62 impact of increasing attrition, 59–60 implementation of recommendations, 63 overview of, 59 return on investment (ROI), 37, 132 revealing conflict, 259–260 revealing insights, 33–34 robotics, 18 ROI (return on investment), 37, 132 roles (team), 181–185 ******ebook converter DEMO Watermarks*******



leadership, 236 role/responsibility clarification, 214–216 training, 235 translator, 234 Ryan, Jackie, 157, 160



S S&P Global, 91, 241 SaaS (Software as a Service), 164–165 Salapatas, Kanella, 159, 192, 271 sales growth, Rentokil Initial case study implementation of recommendations, 74–75 iterative approach, 72–73 overview of, 71 variability in sales performance, 71 scene, setting, 256–257 scientist audiences, 270 scope, agreeing on, 95 segmentation analysis, 50 self-service technology, 282–283 Seligman, Martin, 93 sensitive personal information (SPI), 205 setting the scene, 256–257 Seven Forces of Demand, 91–95 Shami, Sadat, 52, 112 Shapiro, Jeremy, 152, 187, 242, 253 sharing technology, 165 Shell, 92, 99, 102, 104, 118, 191, 213, 229, 273 Shutterfly, Inc., 6, 227 similarities, emphasizing, 268–269 simplicity, 263, 273–274 Six Skills for Success business acumen, 22–23, 170–172 ******ebook converter DEMO Watermarks*******



communication, 179–181 consulting, 173–175 data science, 178–179 future of, 284–285 human resources, 175–177 overview of, 170 work psychology, 177–178 “sixth sense”, 176 size of teams, 182 skepticism, overcoming, 240–245, 250–251 skewness, 143–145 skills Six Skills for Success business acumen, 22–23, 170–172 communication, 179–181 consulting, 173–175 data science, 178–179 future of, 284–285 human resources, 175–177 overview of, 170 work psychology, 177–178 skill complexity, 130 skilled workers, 183–184, 249 SMEs (subject matter experts), engaging, 108–109 Smeyers, Luk, 20, 234, 244 social consequences of algorithms, 54–56 social impact of data sources, 281 social network data, 150 societal impact of stress, 80–81 Software as a Service (SaaS), 164–165 Sole, Deborah, 254 solution development, 174 Sonnenberg, Mariëlle, 101, 110, 148, 208, 221, 236, 247 ******ebook converter DEMO Watermarks*******



in–sourcing, 191–193, 196 SPI (sensitive personal information), 205 sponsors, 38–40 stakeholder engagement analytics team responsibilities, 118–120 board of directors, 105 business leaders, 103–105 data owners, 107 executives, 115–116 finance department, 110–111 HR leaders, 102–103 legal department, 109–110 managers, 114–115 overview of, 100 planning, 120 securing support from, 101–102 SMEs (subject matter experts), 108–109 stakeholder management, 175 financial frugality, 245–246, 251 HR hesitancy, 246–251 stakeholder skepticism, 240–245, 250–251 stakeholder messaging, 117 stakeholder responsibilities, 120 technology owners, 108 unions and works councils, 112 workers, 113–114 stakeholder management financial frugality, 245–246, 251 HR hesitancy, 246–251 stakeholder skepticism, 240–245, 250–251 stakeholder skepticism, overcoming, 240–245, 250–251 Stamford Global, 113, 200 Standard Chartered Bank, 132 ******ebook converter DEMO Watermarks*******



standards, open, 282 Stanton, Andrew, 257–258 storytelling, 179–180 as agent for change, 254 audience, targeting, 269–271 definition of, 254 executive presentations, 272–273 overview of, 253 presenters, selecting, 272 principles for, 255 role in workforce analytics, 254–255 simplicity, 273–274 techniques, 256 call to action, 260–262 conflict, revealing, 259–260 emotional attachment, creating, 257–259 scene, setting, 256–257 time management, 271 visualization creating, 264–269 effective visualization, 263 graphics, 263–264 testing, 269 Storytelling with Data (Knaflic), 263 Strategic Analytics (Levenson), 29 strategic business partner, HR (human resources) as, 243 strategy, linking operating model to, 205 stress, societal impact of, 80–81 style (visualization), 267 subject matter experts (SMEs), engaging, 108–109 subscribing to technology, 165–166 success, skills for, 284–285 business acumen, 170–172 ******ebook converter DEMO Watermarks*******



communication, 179–181 consulting, 173–175 data science, 178–179 human resources, 175–177 overview of, 170 Westpac Group case study, 209 work psychology, 177–178 success metrics, 222–223 tips for, 220–221 succession (CEO), 106 sufficient data quality, determining, 138–139 Svegaard, Simon, 65–66, 90, 261



T talent, definition of, 16 talent analytics. See workforce analytics Talent Strategy Institute, 102, 113, 183, 272 talent supply chain, 181 Tanfeeth, 153, 271 teams, 188–189 development, 24 in-house, 189–191, 196 outsourcing, 193–197 partners advantages of, 188 budget considerations, 198 factors in selecting, 197–200 outsourcing, 193–197 in–sourcing, 191–193, 196 role/responsibility clarification, 214–216 Six Skills for Success business acumen, 170–172 communication, 179–181 ******ebook converter DEMO Watermarks*******



consulting, 173–175 data science, 178–179 human resources, 175–177 overview of, 170 work psychology, 177–178 size of, 182 skilled workers, 183–184, 249 in-sourcing, 191–193, 196 stakeholder engagement responsibilities, 118–120 structure of, 213–221 team roles, 181–185 technology cognitive technology, 163 complexity, 131 evolving technology, 282–284 HR data warehouses, 160–161 HRIS (Human Resources Information System), 160 machine learning, 162–163 overview of, 157 owners, 108 owning, 166 on-premise versus cloud, 164 reporting technology, 161–162 sharing, 165 subscribing to, 165–166 vendor relationships, 165–167 vision and mission, 158–159 visualization technology, 163 TED Talks Storytelling: 23 Storytelling Techniques from the Best TED Talks (Karia), 256 testing A/B testing, 154 visualizations, 269 ******ebook converter DEMO Watermarks*******



thinking. See analytical thinking, enabling third-party objectivity, 200 thought, capacity for, 23 Tibshirani, Robert, 56 time management, 271 time to value, 195 timing, 132 tools, 249 top-down requests, 92–93 traditional statistics, 53–54 training, building analytics culture with, 235 translator role, 234 transparency, 207 Trivial Endeavor projects, 128 Tufte, Edward, 263–264



U Unilever, 110, 214, 220 unions, engaging, 112 unstructured data, 52–53 U.S. National Storytelling Network, 254



V validity of data sources, 280–281 vendor relationships, 165–167 vignettes Allen, Peter, Agoda.com, 94 Andersen, Morten Kamp, proacteur, 40 Bailie, Ian, Cisco, 181 Berry, Mark, CGB Enterprises, 262 Callery, John, BNY Mellon, 89 Champ, Marcus, Standard Chartered Bank, 127 Coolen, Patrick, ABN AMRO, 195 Dellala, Damien, Westpac Group, 209 ******ebook converter DEMO Watermarks*******



Dillon, Sally, ANZ Bank, 162 Duin, Eric van, PostNL, 124 Everduin, Giovanni, Tanfeeth, 153 Fink, Alexis, Intel, 25 Huselid, Mark, Northeastern University, 10 Jover, Placid, Unilever, 220 Klinghoffer, Dawn, Microsoft, 177 Layney, Tracy, Shutterfly, Inc., 6 Malo, Salvador, Ericsson, 232 Nielsen, Adam Chini, Nordea, 116 Obereigner, Andre, Groupon, 248 Oest, Martin, True Picture Europe Ltd., 111 Parveen, Sofia, Nordea, 116 Rasmussen, Thomas, Royal Dutch Shell, 191 Salapatas, Kanella, ANZ Bank, 159 Smeyers, Luk, iNostix by Deloitte, 244 Sonnenberg, Mariëlle, Wolters Kluwer, 148 Voorn, Bart, Ahold Delhaize, 235 White, Rebecca, LinkedIn, 172 Wright, Patrick, University of South Carolina, 106 Yost, Paul, Seattle Pacific University, 274 vision developing, 95–97 operational model and, 205 technology and, 158–159 The Visual Display of Quantitative Information (Tufte), 263 visualization creating, 264–269 effective visualization, 263 graphics, 263–264 technology, 163, 180 testing, 269 Voorn, Bart, 32–33, 93, 235 ******ebook converter DEMO Watermarks*******



W Westpac Group case study, 80–82 analysis, conducting, 81 implementation of recommendations, 82 overview of, 80 societal impact of stress, 80 success, principles of, 209 White, Rebecca, 172, 203, 208 willingness to develop others, 24 Wolters Kluwer, 101, 110, 148, 208, 221, 236, 247 work future of, 11–12 work psychology, 177–178 workers engagement, 113–114 workforce analytics leaders attributes of, 23–26 business acumen of, 22–23 chain of command, 20 responsibilities of, 20–21 “workforce of one”, 9 workforce personalization, 9–10 workforce planning, 5 works councils, engaging, 112 Wright, Patrick, 4, 106 writing mission statement, 30–31, 95–97 written communication, 180



X-Y-Z Yost, Paul, 254, 260, 274 Zak, Paul, 254 Zull, James, 263



******ebook converter DEMO Watermarks*******



******ebook converter DEMO Watermarks*******



******ebook converter DEMO Watermarks*******



******ebook converter DEMO Watermarks*******