Human Capital Analytics: How to Harness the Potential of Your Organization's Greatest Asset(67 Wiley and SAS Business Series)

An insightful look at the implementation of advanced analytics on human capital Human capital analytics, also known as human resources analytics or talent analytics, is the application of sophisticated data mining and business analytics techniques to human resources data. Human Capital Analytics provides an in-depth look at the science of human capital analytics, giving practical examples from case studies of companies applying analytics to their people decisions and providing a framework for using predictive analytics to optimize human capital investments. Written by Gene Pease, Boyce Byerly, and Jac Fitz-enz, widely regarded as the father of human capital Offers practical examples from case studies of companies applying analytics to their people decisions An in-depth discussion of tools needed to do the work, particularly focusing on multivariate analysis The challenge of human resources analytics is to identify what data should be captured and how to use the data to model and predict capabilities so the organization gets an optimal return on investment on its human capital. The goal of human capital analytics is to provide an organization with insights for effectively managing employees so that business goals can be reached quickly and efficiently. Written by human capital analytics specialists Gene Pease, Boyce Byerly, and Jac Fitz-enz, Human Capital Analytics provides essential action steps for implementation of advanced analytics on human capital.

Table of Contents:
Preface xi Acknowledgments xiii Introduction Realizing the Dream: From Nuisance to Necessity 1 Chapter 1 Human Capital Analytics 13 Human Capital Analytics Continuum 16 Summary 28 Notes 28 Chapter 2 Alignment 31 The Stakeholder Workshop: Creating the Right Climate for Alignment 33 Aligning Stakeholders 33 Who Are Your Stakeholders? 35 What Should You Accomplish in a Stakeholder Meeting? 37 Deciding What to Measure with Your Stakeholders 41 Leading Indicators 42 Business Impact 44 Assigning Financial Values to “Intangibles” 44 Defining Your Participants 45 Summary 59 Notes 60 Chapter 3 The Measurement Plan 61 Defining the Intervention(s) 62 Measurement Map 63 Hypotheses or Business Questions 66 Defining the Metrics 67 Demographics 68 Data Sources and Requirements 70 Summary 77 Note 77 Chapter 4 It’s All about the Data 79 Types of Data 80 Tying Your Data Sets Together 86 Difficulties in Obtaining Data 89 Ethics of Measurement and Evaluation 90 Telling the Truth 92 Summary 97 Notes 98 Chapter 5 What Dashboards Are Telling You: Descriptive Statistics and Correlations 101 Descriptive Statistics 102 Going Graphic with the Data 103 Data over Time 104 Descriptive Statistics on Steroids 106 Correlation Does Not Imply Causation 108 Summary 115 Notes 116 Chapter 6 Causation: What Really Drives Performance 117 Can You Create Separate Test and Control Groups? 120 Are There Observable Differences? 121 Did You Consider Prior Performance? 121 Did You Consider Time-Related Changes? 122 Did You Look at the Descriptive Statistics? 123 Have You Considered the Relationship between the Metrics? 123 A Gentle Introduction to Statistics 123 Basic Ideas behind Regression 125 Model Fit and Statistical Significance 126 Summary 130 Notes 131 Chapter 7 Beyond ROI to Optimization 133 Optimization 134 Summary 143 Notes 144 Chapter 8 Share the Story 145 Presenting the Financials 147 Telling the Story and Adding Up the Numbers 148 Preparing for the Meetings 152 Summary 152 Notes 153 Chapter 9 Conclusion 155 Human Capital Analytics 156 Alignment 156 The Measurement Plan 157 It’s All about the Data 159 What Dashboards Are Telling You: Descriptive Statistics and Correlations 159 Causation: What Really Drives Performance 161 Beyond ROI to Optimization 162 The Ultimate Goal 164 What Others Think about the Future of Analytics 164 Final Thoughts 169 Notes 169 Appendix A: Different Levels to Describe Measurement 171 Appendix B: Getting Your Feet Wet in Data: Preparing and Cleaning the Data Set 181 Appendix C: Details of Basic Descriptive Statistics 193 Appendix D: Regression Modeling 199 Appendix E: Generating Soft Data from Employees 205 Glossary 209 About the Authors 225 Index 227

About the Author :
GENE PEASE is cofounder and CEO of Capital Analytics, a consultancy revolutionizing the way companies evaluate their investments in people. He has over 25 years' experience as a CEO managing mid-cap and early stage companies. Under his leadership, Capital Analytics has been recognized by Bersin and Associates, CLO Magazine, Gartner, and the ROI Institute. BOYCE BYERLY, PHD, is cofounder and chief scientist of Capital Analytics. He has more than fifteen years of experience designing and managing pure and applied research projects with high technology firms in the Research Triangle Area of North Carolina. He directed the Capital Analytics team that developed the methodology and the analytical tools that are the core intellectual assets of Capital Analytics. JAC FITZ-ENZ, PHD, is widely regarded as the father of human capital strategic analysis and measurement. He founded the famous Saratoga Institute and published the first HR metrics in 1978 and the first international HR benchmarks in 1985. HR World cited him as one of the top five "HR Management Gurus," IHRIM gave him its Chairman's Award for innovation, and SHRM chose him as one of the persons in the twentieth century who "significantly changed what HR does and how it does it." He has authored twelve books and trained 90,000 managers in forty-six countries on strategic management and measurement. His book, The New HR Analytics, introduced predictive analytics to HR.