Predictive Analytics for Human Resources

Jac Fitz-enz, John Mattox, II

• Publisher: Wiley
• Year: 2014
• Print ISBN: 9781118893678
• eBook ISBN: 9781118940693
• Edition: 1
• Topic: Business
• Subtopic: Decision Making
• Index: Business

Create and run a human resource analytics project with confidence

For any human resource professional that wants to harness the power of analytics, this essential resource answers the questions: "Where do I start?" and "What tools are available?" Predictive Analytics for Human Resources is designed to answer these and other vital questions. The book explains the basics of every business—the vision, the brand, and the culture, and shows how predictive analytics supports them. The authors put the focus on the fundamentals of predictability and include a framework of logical questions to help set up an analytic program or project, then follow up by offering a clear explanation of statistical applications.

Predictive Analytics for Human Resources is a how-to guide filled with practical and targeted advice. The book starts with the basic idea of engaging in predictive analytics and walks through case simulations showing statistical examples. In addition, this important resource addresses the topics of internal coaching, mentoring, and sponsoring and includes information on how to recruit a sponsor. In the book, you'll find:

• A comprehensive guide to developing and implementing a human resource analytics project
• Illustrative examples that show how to go to market, develop a leadership model, and link it to financial targets through causal modeling
• Explanations of the ten steps required in building an analytics function
• How to add value through analysis of systems such as staffing, training, and retention

For anyone who wants to launch an analytics project or program for HR, this complete guide provides the information and instruction to get started the right way.

Chapter 1
Where’s the Value?

Our only security is our ability to change.

—John Lilly

In a famous 1984 TV commercial, 82-year-old actress Clara Peller looks at a huge hamburger bun overwhelming a tiny meat patty and mutters the now-iconic phrase in her raspy voice, “Where’s the beef?” It is the same question asked today by nonstatisticians. In our new world of Big Data and outrageously fast computers, many of us feel overwhelmed. When the numerati speak effusively about the power of analytics, laypeople roll their eyes. Without a graduate degree in statistical analysis, and especially in predictive analytics, the average person feels woefully ignorant, powerless, blind, and lost. Paradoxically, analytics is logical and understandable. It is simply a method for letting computers apply their power of manipulation to expose valuable insights. This book will take you step by step from the desire to analyze data to a comprehensible, actionable result and on to a view of the future of human resource analytics. In the end, you will find the beef.

SOME BASICS

There are at least two ways of solving problems. The most common one is simply to attack it head on and hope for the best. This is similar to dealing with a pesky mosquito. You feel it on your arm and you swat it. This is a simple, highly effective reaction. The only apparent consequence is perhaps a little blood spot. But if there is more than one mosquito, you have to do it over and over. More important, mosquitoes often carry diseases, such as West Nile virus and malaria. More than 600,000 people die every year from malaria. If you were going to be in an area where mosquitoes are a menace, you would want to do something more than swat them when they land on you, right? This brings us to the second method for solving problems: analysis and prevention. If mosquitoes are more than a nuisance, you would prepare for them by protecting yourself with repellent and covering most exposed skin, wouldn’t you?

So it is with organizational management. If your modus operandi is to continually swat problems as they appear, you waste time and resources by repeatedly dealing with the same problem. You seldom make progress. There is another alternative that will avoid costly, redundant investment of scarce resources. The better way is to invest a little time in analyzing the problem before you act. If you gather data on what has happened (descriptive analysis), analyze it in terms of why it happened and what will likely continue if untreated (predictive analysis), and then design a treatment for fixing it, most likely you will eliminate a recurrence of the problem (prescriptive analysis). This is the efficient way to manage. It also frees you to concentrate on being effective—that is, doing something that advances the organization. With this approach, you will have time to focus on building a better future rather than endlessly repeating the past.

WHAT IS ANALYTICS?

Arguably the most practical tool and greatest potential for organizational management is the emergence of predictive analytics. Analytics is a meeting of art and science. The arts teach us how to look at the world. The sciences teach us how to do something. When you say “analytics,” people immediately think of statistics. That is incorrect. Statistics play a major role, but only after we understand something about the interactions, the relationships, of the problem’s elements. Analytics is first a mental framework, a logistical progression, and second a set of statistical operations.

Human resources (HR) or human capital analytics is primarily a communications device. It brings together data from disparate sources, such as surveys, records, and operations, to paint a cohesive, actionable picture of current conditions and likely futures. This is an evidence-based approach to making better decisions. This popular term is simply the gathering of primarily objective facts and secondarily related subjective data. Analytics is divided into three levels:

1. Descriptive. Traditional HR metrics are largely efficiency metrics (turnover rate, time to fill, cost of hire, number hired and trained, etc.). The primary focus here is on cost reduction and process improvement. Descriptive HR analytics reveal and describe relationships and current and historical data patterns. This is the foundation of your analytics effort. It includes, for example, dashboards and scorecards; workforce segmentation; data mining for basic patterns; and periodic reports.
2. Predictive. Predictive analysis covers a variety of techniques (statistics, modeling, data mining) that use current and historical facts to make predictions about the future. It’s about probabilities and potential impact. It involves, for example, models used for increasing the probability of selecting the right people to hire, train, and promote.
3. Prescriptive. Prescriptive analytics goes beyond predictions and outlines decision options and workforce optimization. It is used to analyze complex data to predict outcomes, provide decision options, and show alternative business impacts. It involves, for example, models used for understanding how alternative learning investments impact the bottom line (rare in HR).

The process starts with the simple reporting of HR metrics and goes all the way up to prescriptive modeling of business practices. Although financial capital (cash) and economic capital (intangible assets) are the lifeblood of a business, it is human capital (people) that apply cash and leverage intangible assets to drive business performance. As you move from descriptive to prescriptive, the value add grows exponentially.

The fundamental management question is: How do we manage talent more effectively? Human behavior is much more complex and less predictable than tangible assets. This volatility and capriciousness has diverted many managers to focus on more stable assets. Yet physical assets like equipment are inert and inherently incapable of adding value. Only when a human being works with the tools does value flow. In the market of the twenty-first century, people simply cannot be relegated to anecdotal methodologies. Thomas Edison borrowed a phrase from Sir Joshua Reynolds that put the problem rather directly:

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There is no expedient to which a man will not go to avoid the labor of thinking.

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TWO VALUES

The purpose of analytics is to find the best path through a mass of data to uncover hidden value. Value comes in two forms: financial and economic. There is a distinct difference between these forms initially, yet they eventually coalesce. Economic data includes practical, noncash significant items or processes affecting material resources. Examples include market reputation, customer satisfaction, best companies to work for, and community relations. These are often referred to as off–balance sheet assets. Each of them eventually should turn into financial value as stockholders invest in company stock, customers purchase products or services, high-performing personnel seek employment with the organization, and favorable community support ensues. Financial value examples are cash and other liquid resources such as stock and bonds. These are recorded on the income statement and balance sheet, the building blocks of accounting.

ANALYTIC CAPABILITIES

Data can be viewed two ways: structured and unstructured. Structured data is similar to financial data, and unstructured data typically is economic or less tangible data. Analytics and data intersect, as seen in Exhibit 1.1. Since the arrival of the industrial revolution 200 years ago, we have focused on structured data: costs, process time cycles, and quantities. Yet, according to IBM, at least 80% of the data currently being produced is unstructured, nonnumeric images, text, and audio. As social networking continues its explosive growth, the percentage of unstructured data will necessarily expand. In practice, structured and unstructured data can be merged into a mixture, amalgam, or fusion. In short, it will be what some now call hybrid data. While hybrid data will be essential for future analysis, it will also make the process much more complicated.

Exhibit 1.1 Analytic Paths

This is precisely why analysis is essential. When a situation is a complex mixture of objective facts and subjective beliefs, there is no way...

Table of contents

• Cover
• Contents
• Title
• Copyright
• Dedication
• Foreword
• Preface
• Chapter 1: Where’s the Value?
• Chapter 2: Getting Started
• Chapter 3: What You Will Need
• Chapter 4: Data Issues
• Chapter 5: Predictive Statistics Examples
• Chapter 6: Predictive Analytics in Action
• Chapter 7: Predicting the Future of Human Capital Analytics
• Epilogue
• Appendix: Example Measures of Efficiency, Effectiveness, and Outcomes
• About the Authors
• Index
• End User License Agreement