Human Capital Analytics
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HR Management

Human Capital Analytics How to Harness the Potential of Your Organization's Greatest Asset

Edited by Gene Pease · Boyce Byerly · Jac Fitz-enz
John Wiley & Sons October 2012

Specifications

ISBN-13
9781118466766
Publisher
John Wiley & Sons
Publication
October 2012
Format
Hardback , 236 pages
Jurisdiction
International ? Countri(es) for reference only

Details

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.

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