The New HR Analytics: Predicting the Economic Value of Your Company’s Human Capital Investments
Jac Fitz-enz
AMACOM (2010)

A brilliant explanation of “the first holistic, predictive management model and operating system for the HR function”

Those who have read one or more of Jac Fitz-enz’s previously published books know that he possesses superior reasoning and writing skills as well as an insatiable curiosity to understand what works, what doesn’t, and why at all levels and in all areas of organizational management. I read this, his latest book, in combination with the late Jeremy Hope’s last book, Beyond Performance Management, co-authored with Steve Player. However different the two books are in most respects, the authors of both insist (and I wholly agree) that superior business performance requires effective management of human resources that is guided and informed by effective measurement of what is most important today and tomorrow as well as in months and years to come.

According to Fitz-enz, “This book is twenty-five years in the writing.” He has been centrally involved in the development of metrics in human resources from its inception in the 1970’s. I first became familiar with his work when I read How to Measure Human Resources Management (1984) and, I think, have read and reviewed every other of his books since then. What we have in his latest “is both the product of these endeavors and the look into the future.” He is joined by 29 highly esteemed contributors, each of whom is the author or co-author of an article that increases our understanding of one or more dimensions of predictive analytics. Fitz-enz also introduces and examines what he calls the “HCM:21®” model, the outcome of an 18-month study called the Predictive Initiative.

The material is carefully organized within four Parts: First, an introduction to predictive analytics; next, the HCM:21® model; then a series of immensely informative case studies (Ingram Content Group, Monster, Enterprise Rent-a-Car, Descon Engineering, a federal agency identified as “Research Economic Services,” UnitedHealth Group); and then a concluding chapter that consists “Look What’s Coming Tomorrow” (i.e. “What We Know About Tomorrow–What Analytics Can Deliver for Your Organization–Thought Drives Action–Still Evolving” and then “Views of the Future: Human Capital Analytics” in the form of brief commentaries provided by 34 additional contributors who include Dave Ulrich, Kevin Wilde, John Boudreau, and Libby Sartain…thought leaders with whose work I am especially familiar.

Fitz-enz and his associates respond brilliantly to especially important business subjects such as why predictive analytics is important…indeed essential; how to improve HR processes; how to put capability planning into practice; and how human capital research data are being applied to manufacturing, supply chain management, and time and labor analysis. Note the references to “how.” This is a research-driven book in which the information, insights, and recommendations provided are based on real-world companies in real-world situations, hence the incalculable value of the case studies.

I wholly agree with Fitz-enz asserts that following the four stage process of the HCM:21® model (i.e. scanning, planning, producing, and predicting) can help business leaders manage tomorrow today. “I’ve shown how this model of predictive management is built around the tools of human capital analytics…By applying these principles and tools, you will be helping build the competitive position of your company or, in the case of nonprofits, contribute to its organizational effectiveness.” Obviously, it remains for each reader to determine which of the material provided is relevant to the needs, interests, resources, and strategic objectives of her or his company.

Congratulations to Jac Fitz-enz and his associates on a brilliant achievement. Bravo!

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Those who read the book are invited to check out bonus material – including “The Role of Assessment in Driving Operating Results” by John Malone – by visiting the AMACOM website.