Preface

This book was twenty-five years in the writing. It started in 1984, with the publication of my How to Measure Human Resources Management; it was augmented with Human Value Management six years later; and then the concept was updated ten years ago in The ROI of Human Capital. Those books chronicle the development of metrics in human resources from its inception in the 1970s to today. They have passed the test of time with second and third editions, and two were honored with Book of the Year Awards from the Society for Human Resource Management.

Now, The New HR Analytics is both the product of these endeavors and the look into the future. Although this book talks to human resources managers, it deals with the broader issue of human capital management processes. Hence, it is as applicable to the work of line managers as to that of the human resources department. Anyone who manages people can find value in the model we present here and the case studies that are offered in support of that model.

HR as an Expense

Having come into HR in 1969 from ten years in line jobs, I could not understand why any company would create a function that was only an expense. But then, too, at that time line management itself was not so sophisticated. Management models of the day were a patchwork quilt of fads that came and went, sometimes to reappear later. Others flashed across the sky like a meteor and burned out when they hit the atmosphere of managerial impatience. During that period, HR was simply a place where you put people “who couldn’t do any harm,” as a manager in my company said at the time.

I quickly discovered the problem behind the perception. It had two parts. One part was that HR people actually believed and accepted the idea that they were an expense center and nothing more. To be sure, there were a few who fought that perception, but they were overwhelmed by the accounting-driven belief system of the time. The second part of the problem was that HR didn’t know, and never talked about, the value they were generating because they couldn’t—they had no language for it. All their terms were qualitative, subjective, and equivocal. Anecdotes were their only way of responding when management asked for evidence of the value added by HR’s services.

“How is employee morale?”

“It’s good!”

“How good?”

“Very good.”

Could you run any other function with such performance indicators? It is enough to make one despair.

The Introduction of Metrics

The solution was obvious. We in HR needed to learn to speak in quantitative, objective terms, using numbers to express our activity and value added. Business uses numbers to explain itself. Sales, operating expenses, time cycles, and production volumes are principal indices that express business activity. In the 1970s, productivity was the key issue. In the 1980s, the quality movement emphasized process quality as a competitive advantage. Both relied on numbers to express degrees of change.

At the time, I asked the HR director of a major corporation if he was involved in these initiatives. He answered that they were not human resources management issues. Here were the major initiatives of the day, and he could not see what they had to do with people. Is it any wonder that people write about nuking the HR function?

During the 1970s, we in HR began to experiment with simple cost, time, and quantity metrics to show that HR was at least managing expense and generating something of value. In the beginning it was largely a defensive maneuver. But by the 1980s, we were able to show that we were indeed adding measureable value. In 1984, I wrote the first book mentioned earlier. In 1985, at my consulting company, the Saratoga Institute, we published the first national benchmarks, and this led to publication of Human Value Management, which was a marketing model applied to the HR function. By 2000, we had advanced the methodology to a point where we were talking about return on investment. Basically, we shifted the paradigm from that of running the HR department to that of managing human capital in the organization. At that point we were still using primarily standard arithmetic functions. Later in the decade we began to apply simple statistical tools, and this opened up the era of human capital analytics—which brings us to today.

The Era of Analytics

We are on the threshold of the most exciting and promising phase of the evolution of human resources and human capital management. We’ve gone from the horse and buggy to the automobile to the airplane. Now it’s time to mount the rocket and head for the stratosphere.

Like arithmetic, statistics are bias free and are applicable over a vast range of opportunities. They can be used in studies of single, localized problems or for supporting organization-wide makeovers. The secret sauce of statistics is just like the source code of computer programs—a buried logic that can go step-by-step or leap ahead, using macros to speed to the solution.

Today, we shift our attention to predictability. This book is about predictive management. We think of it as “managing today, tomorrow.” Predictive management, or HCM:21®, is the outcome of our eighteen-month study called the Predictive Initiative. It is the first holistic, predictive management model and operating system for the human resources function. We launched it in the last quarter of 2008 and it has been successfully applied in industry and government, in the United States and overseas.

HCM:21 is a four-phase process that starts with scanning the marketplace and ends with an integrated measurement system. In the middle, it addresses workforce and succession planning in a new way and shows how to optimize and synchronize the delivery of HR services. It is detailed in the chapters that follow.

The Organization of This Book

This book has been divided into four parts. Part I is an introduction to predictive analytics; Part II presents the HCM:21 model; Part III provides case studies; and Part IV offers a look at future applications. Part I lays the foundation. Chapter One explains the reasoning behind predictive analytics. It points out that major advances and sustainable performance typically disrupt the status quo, and it argues that human resources badly needs a model change if it is to catch up in the marketplace. Chapter Two extends that reasoning to show the benefits of and need for predictive analytics. It describes the various levels of analytics and their uses and benefits, and shows the evolution of metrics into predictive analytics. Accompanying essays by experts in the field reinforce this point.

In Part II, each chapter breaks down into two sections. The first section is a discussion of one step in the predictive management model, with its underlying premise, a description of the process, and some examples. The second section includes how-to-do-it research essays by practitioners and thought leaders in the field of human resources and human capital measurement.

In this part of the book, Chapter Three presents the first phase of the HCM:21 process. It makes the point that we need to shed light on and understand the market forces and internal factors that affect human, structural, and relational capital. And it introduces risk assessment as a fundamental part of modern human capital management. Chapter Four presents a model for workforce planning that replaces the industrial-era, gap-analysis, structure-focused practice of filling positions as needed with the concept of generating human capability. It details how this concept is different and better, and it concludes with a surprising example of how succession planning can be designed to drive top-line revenue growth.

Chapter Five shows how to change HR service delivery into a value-generating process. Examples are provided on how to analyze HR processes such as staffing and development, as well as turnover. In applying an input-throughput-output model, you discover how to find the most cost-effective combination of inputs and throughputs to produce the best output at your organization. Chapter Six completes the presentation of the HCM:21 model by offering a comprehensive approach to performance measurement and reporting. It posits an integrated three-point system that links strategic, operational, and leading indicators. Then it recaps the topic of analytics with an overview of the evolution of metrics that ends with business intelligence and predictability.

Part III consists of five chapters that constitute a series of detailed case studies from government and private companies. These are real-world examples of how problems were solved using predictive analysis. For instance, the Chapter Seven example is a supply-chain case at Ingram Content Group, which applied analytics to attack a long-term turnover and productivity problem. The results clearly demonstrate the practical gains that can be achieved through the application of measurement and analysis.

Chapter Eight shows how Enterprise Rent-A-Car and Monster partnered in selecting a site for an Enterprise call center. Monster’s market and demographic database helped Enterprise select the most cost-effective location. In Chapter Nine, we have a case from Asia. Descon Engineering, headquartered in Lahore, Pakistan, used the predictive management model to improve operations. The case study describes the rationale, the process, and the results.

Chapter Ten’s case study is of a government agency that applied predictive analytics to the problem of a suboptimized mission-critical position. I describe the unique circumstances and the barriers to analytics that we were able to overcome. Chapter Eleven is a case from the health-care industry that illustrates how analytics and technology were combined at UnitedHealth Group to improve staffing and retention, two of HR’s major challenges.

Part IV of the book is but one chapter, but a critical one for your organization’s success in the future. Chapter Twelve points out what we know and what we need to know to keep going forward. It makes the case again for a disruptive strike, and it concludes with short statements from many leading practitioners and thought leaders, including Tim Mack, president of World Future Society, on the future of human capital analytics.

The appendix contains a series of sample worksheets, which you can use to translate the model described in these chapters into spreadsheets. There are instructions and examples of how to operationalize these HCM:21 concepts for your particular situation.

Acknowledgments

Thank you, all. Over the past three decades, many people have supported the development of metrics. Starting three years ago, a small group of people saw the potential for analytics and invited me to speak on the topic in over a dozen countries in North America, Europe, and Asia. I thank you for helping me broadcast the message to the world.

People who have specifically encouraged me and helped me think through the process include Kent Barnett and Jeffrey Berk, at KnowledgeAdvisors; Karen Beaman, Erik Berggren, Deb Besemer, Carol DiPaolo, Nick Bontis, Ray Burch, Mary Kay Byers, Kevin Campbell, Jim Benton, and John Hindle at Accenture; Luis Maria Cravino and Cecilia Bastide at AO Consulting in Buenos Aires; Joni Doolin, Sal Faletta, Charlie Grantham, and Jim Ware at The Future of Work; Humair Ghauri, Kirk Hallowell, Nancy Hanna, Jesse Harriott, Row Henson, Annette Homan, Doug Hubbard, Steve Hunt, Paul Jamieson, Michael Kelly, Pat Leonard, Hugo Malan, Tahir Malik, Raul Navarro, and Rugenia Pomi of Sextante Brasil in Sao Paolo; Sara Palmer, Ron Pilenzo, and Mike Losey past presidents of SHRM; Lori Riley, David Scarborough, Ken Scarlett, Denise Sinuk, Kirk Smith, Florence Stone, Tony Tasca, Dave Ulrich, Eleo Ventocillo … and with my sincere apologies to anyone I missed.

Christina Parisi was the editor who helped me corral this rambling account of analytics. I also was helped by the services of Carole Berglie, an exceptional copyeditor.

When you step off the deep end, it is nice to know that there are folks ready to throw you a life ring if you need it. Many years ago, when I was first going public with this crazy idea that we could show the business value of human resources services, a good friend told me to ignore the horde of naysayers because what I was doing was the right thing. I can’t tell you how much that helped my shaky confidence. I’m here to help you beat back the rabble.

My special gratitude goes to Robert (Bob) Coon, my best friend and compatriot for nearly thirty years, up and down the human capital analysis trail and on the golf course. He keeps me focused whenever I want to ramble and reins me in when my euphoria gets the better of my common sense. Last, but certainly not least, and most important of all, I thank my wonderful wife, Laura Esperanza Sanchez (isn’t that a beautiful name?) Fitz-enz for her undying support. She takes care of everything so that I can focus on having fun writing.