Human Capital Analytics

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.

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