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

Book Description

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

  1. Cover
  2. Contents
  3. Title
  4. Copyright
  5. Dedication
  6. Preface
  7. Acknowledgments
  8. Introduction
  9. Chapter 1: Human Capital Analytics
    1. Human Capital Analytics Continuum
    2. Summary
    3. Notes
  10. Chapter 2: Alignment
    1. The Stakeholder Workshop: Creating the Right Climate for Alignment
    2. Aligning Stakeholders
    3. Who are Your Stakeholders?
    4. What Should You Accomplish in a Stakeholder Meeting?
    5. Deciding What to Measure with Your Stakeholders
    6. Leading Indicators
    7. Business Impact
    8. Assigning Financial Values to “Intangibles”
    9. Defining Your Participants
    10. Summary
    11. Notes
  11. Chapter 3: The Measurement Plan
    1. Defining the Intervention(s)
    2. Measurement Map
    3. Hypotheses or Business Questions
    4. Defining the Metrics
    5. Demographics
    6. Data Sources and Requirements
    7. Summary
    8. Note
  12. Chapter 4: It’s all about the Data
    1. Types of Data
    2. Tying Your Data Sets Together
    3. Difficulties in Obtaining Data
    4. Ethics of Measurement and Evaluation
    5. Telling the Truth
    6. Summary
    7. Notes
  13. Chapter 5: What Dashboards are Telling You: Descriptive Statistics and Correlations
    1. Descriptive Statistics
    2. Going Graphic with the Data
    3. Data over Time
    4. Descriptive Statistics on Steroids
    5. Correlation Does Not Imply Causation
    6. Summary
    7. Notes
  14. Chapter 6: Causation: What Really Drives Performance
    1. Can You Create Separate Test and Control Groups?
    2. Are There Observable Differences?
    3. Did You Consider Prior Performance?
    4. Did You Consider Time-Related Changes?
    5. Did You Look at the Descriptive Statistics?
    6. Have You Considered the Relationship between the Metrics?
    7. A Gentle Introduction to Statistics
    8. Basic Ideas behind Regression
    9. Model Fit and Statistical Significance
    10. Summary
    11. Notes
  15. Chapter 7: Beyond ROI to Optimization
    1. Optimization
    2. Summary
    3. Notes
  16. Chapter 8: Share the Story
    1. Presenting the Financials
    2. Telling the Story and Adding Up the Numbers
    3. Preparing for the Meetings
    4. Summary
    5. Notes
  17. Chapter 9: Conclusion
    1. Human Capital Analytics
    2. Alignment
    3. The Measurement Plan
    4. It’s All about the Data
    5. What Dashboards are Telling You: Descriptive Statistics and Correlations
    6. Causation: What Really Drives Performance
    7. Beyond ROI to Optimization
    8. The Ultimate Goal
    9. What Others Think about the Future of Analytics
    10. Final Thoughts
    11. Notes
  18. Appendix A: Different Levels to Describe Measurement
  19. Appendix B: Getting Your Feet Wet in Data: Preparing and Cleaning the Data Set
  20. Appendix C: Details of Basic Descriptive Statistics
  21. Appendix D: Regression Modeling
  22. Appendix E: Generating Soft Data from Employees
  23. Glossary
  24. About the Authors
  25. Index