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<p>Foreword xiii</p> <p>Preface xv</p> <p><b>Chapter 1 Where’s the Value? 1</b></p> <p>Some Basics 1</p> <p>What Is Analytics? 2</p> <p>Two Values 4</p> <p>Analytic Capabilities 4</p> <p>Analytic Value Chain 6</p> <p>Analytic Model 8</p> <p>Typical Application 14</p> <p>Training Value Measurement Model 15</p> <p>Inside the Data 16</p> <p>Notes 19</p> <p><b>Chapter 2 Getting Started 21</b></p> <p>Go-to-Market Models 22</p> <p>Assessment 23</p> <p>Developmental Experiences 23</p> <p>Financial Connections 24</p> <p>Sample Case 26</p> <p>Focusing on the Purpose 26</p> <p>Present-Day Needs 28</p> <p>How Human Capital Analytics Is Being Used 29</p> <p>Turning Data into Information 30</p> <p>Three Value Paths 30</p> <p>Solving a Problem 31</p> <p>Essential Step 31</p> <p>Prime Question 32</p> <p>Case in Point 32</p> <p>Preparing for an Analytics Unit 33</p> <p>Ten Steps for an Analytics Unit 35</p> <p>Structure and Team Building 36</p> <p>Developing an Analytics Culture 37</p> <p>Notes 37</p> <p><b>Chapter 3 What You Will Need 39</b></p> <p>Dealing with the C Level 40</p> <p>Breaking Through 41</p> <p>Research 41</p> <p>Recruiting a Sponsor or Champion 42</p> <p>Making the Sale 43</p> <p>Selling Example 44</p> <p>Working with Consultants and Coaches 46</p> <p>Designing and Delivering Reports 48</p> <p>Making an Impact 50</p> <p>Process Management 50</p> <p>Preparation 52</p> <p>Notes 54</p> <p><b>Chapter 4 Data Issues 55</b></p> <p>Efficiency Measures 56</p> <p>Effectiveness Measures 61</p> <p>Business Outcome Measures 67</p> <p>Note 69</p> <p><b>Chapter 5 Predictive Statistics Examples 71</b></p> <p>Begin with the End in Mind 71</p> <p>Go Back to the Beginning 74</p> <p>Who Owns Data, and Will They Share It? 75</p> <p>What Will You Do with the Data? 77</p> <p>What Form Is the Data In? 79</p> <p>Is the Data Quality Sufficient? 80</p> <p>Note 82</p> <p><b>Chapter 6 Predictive Analytics in Action 83</b></p> <p>First Step: Determine the Key Performance Indicators 83</p> <p>Second Step: Analyze and Report the Data 89</p> <p>Relationships, Optimization, and Predictive Analytics 96</p> <p>Predictive Analytics 97</p> <p>Interpreting the Results 102</p> <p>Predicting the Future 111</p> <p>Structural Equation Modeling 113</p> <p>Notes 114</p> <p><b>Chapter 7 Predicting the Future of Human Capital Analytics 115</b></p> <p>What Does the Future Look Like? 116</p> <p>Bringing It All Together 126</p> <p>Predictive Analytics for HR in Action 126</p> <p>Notes 128</p> <p>Epilogue 129</p> <p>Appendix: Example Measures of Efficiency,</p> <p>Effectiveness, and Outcomes 133</p> <p>About the Authors 135</p> <p>Index 137</p> <p> </p>

<p><b>JAC FITZ-ENZ, P<small>H</small>D,</b> is founder and Chief Executive Officer of Human Capital Source, Inc. He founded the Saratoga Institute in Santa Clara, California after holding human resource positions at several major technology and financial services companies. He is widely regarded as the father of human capital strategic analysis, having published the first HR metrics in 1978 and the first international HR benchmarks in 1985. <p><b>JOHN R. MATTOX II, P<small>H</small>D,</b> is Director of Research at KnowledgeAdvisors. He is a former Associate Director of Performance Management at KPMG, Manager of Learning Effectiveness at PricewaterhouseCoopers, and a Manager at Arthur Andersen.

<p><i>Predictive Analytics for Human Resources</i> offers human resource professionals the information and tools they need to harness the power of analytics and determine future outcomes. Written by Jac Fitz-enz (one of the founders of human resources measurement) and John Mattox II, this important resource answers the questions: "Where do I start?" and "What tools are available?" The book explains the fundamentals of every business—the vision, the brand, and the culture, and shows how predictive analytics supports these basic elements. Fitz-enz and Mattox put the focus on the essentials of predictability and include a framework of logical questions to help set up an analytic program or project. The authors include a clear explanation of statistical applications that will help a human resource project run smoothly. <p>The book discusses the technology needed to implement and sustain a successful program. And since most programs need support, the authors include suggestions for whom and what can offer support along with rationales provided for each example. Next, Fitz-enz and Mattox describe a typical case of building an analytics project. As this example clearly demonstrates, rather than starting off by asking what to measure, analytics begins with logical questions???statistics come later. <p>When building a project, often overlooked is the need for persuasive skills and effective change management. <i>Predictive Analytics for Human Resources</i> gets down to the nature of data and examines the critical issues of sources, ownership, and quality. The authors explore how to deal with where it is, who owns it, whether it is valid and reliable, and what forms it comes in. The book then digs deeper into analytics with illustrative examples of regression, correlation, and structural equation modeling. <p>This vital resource is written for anyone who wants to launch an effective analytics project or program for human resources management. <p><i>Predictive Analytics for Human Resources</i> is a complete guide filled with the vital information and instructions needed to get started on a critical program or project right way. <ul> <b><li>Illustrative examples that clearly show how to go to market</b></li> <b><li>Develop an approach to analytics that will link HR activities to HR outcomes</b></li> <b><li>Critical steps required in building an effective analytics function</b></li> </ul>

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