Predictive Business Analytics: Forward-LookingCapabilities to Improve Business Performance
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More About This Title Predictive Business Analytics: Forward-LookingCapabilities to Improve Business Performance


Discover the breakthrough tool your company can use to make winning decisions

This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting.

  • Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making
  • Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling
  • Written for senior financial professionals, as well as general and divisional senior management

Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions.


LAWRENCE S. Maisel, President of DecisionVu, specializes in corporate performance management, financial management, and IT value management. He has extensive industry experiences with numerous Global 1000 companies including MetLife, TIAA-CREF, Citigroup, GE, Bristol-Myers, Pfizer, and News Corp/Fox Entertainment. Larry co-created with Drs. Kaplan and Norton the Balanced Scorecard Approach, and co-authored with Drs. Kaplan and Cooper Implementing Activity-Based Cost Management. He is a CPA, holds a BA from NYU and an MBA from Pace University, and was an adjunct professor at Columbia University's Graduate Business School. Contact him at [email protected]

GARY COKINS is the founder of Analytics-Based Performance Management, LLC. He is an internationally recognized expert, speaker, and author in advanced cost management and performance improvement systems. He served fifteen years as a consultant with Deloitte Consulting, KPMG, and Electronic Data Systems (EDS, now part of HP). From 1997 until recently, Gary was in business development with SAS, a leading provider of enterprise performance management and business analytics and intelligence software. He has a degree in operations research from Cornell University and an MBA from Northwestern University Kellogg School of Management. Contact him at [email protected]


Preface xv

Part One “Why” 1

Chapter 1 Why Analytics Will Be the Next Competitive Edge 3

Analytics: Just a Skill, or a Profession? 4

Business Intelligence versus Analytics versus Decisions 5

How Do Executives and Managers Mature in Applying Accepted Methods? 6

Fill in the Blanks: Which X Is Most Likely to Y? 6

Predictive Business Analytics and Decision Management 7

Predictive Business Analytics: The Next “New” Wave 9

Game-Changer Wave: Automated Decision-Based Management 10

Preconception Bias 11

Analysts’ Imagination Sparks Creativity and Produces Confidence 12

Being Wrong versus Being Confused 12

Ambiguity and Uncertainty Are Your Friends 14

Do the Important Stuff First—Predictive Business Analytics 16

What If . . . You Can 17

Notes 19

Chapter 2 The Predictive Business Analytics Model 21

Building the Business Case for Predictive Business Analytics 27

Business Partner Role and Contributions 28

Summary 29

Notes 29

Part Two Principles and Practices  31

Chapter 3 Guiding Principles in Developing Predictive Business Analytics 33

Defining a Relevant Set of Principles 34

PRINCIPLE 1: Demonstrate a Strong Cause-and-Effect Relationship 34

PRINCIPLE 2: Incorporate a Balanced Set of Financial and

Nonfinancial, Internal and External Measures 36

PRINCIPLE 3: Be Relevant, Reliable, and Timely for Decision Makers 37

PRINCIPLE 4: Ensure Data Integrity 38

PRINCIPLE 5: Be Accessible, Understandable, and Well Organized 39

PRINCIPLE 6: Integrate into the Management Process 39

PRINCIPLE 7: Drive Behaviors and Results 40

Summary 41

CHAPTER 4 Developing a Predictive Business Analytics Function 43

Getting Started 44

Selecting a Desired Target State 46

Adopting a PBA Framework 49

Developing the Framework 49

Summary 60

Notes 60

CHAPTER 5 Deploying the Predictive Business Analytics Function 61

Integrating Performance Management with Analytics 63

Performance Management System 64

Implementing a Performance Scorecard 67

Management Review Process 76

Implementation Approaches 78

Change Management 80

Summary 81

Notes 82

Part Three Case Studies  83

CHAPTER 6 MetLife Case Study in Predictive Business Analytics 85

The Performance Management Program 88

Implementing the MOR Program 93

Benefi ts and Lessons Learned 108

Summary 108

Notes 108

CHAPTER 7 Predictive Performance Analytics in the Biopharmaceutical Industry 109

Case Studies 113

Summary 127

Note 127

Part Four Integrating Business Methods and Techniques  129

CHAPTER 8 Why Do Companies Fail (Because of Irrational Decisions)? 131

Irrational Decision Making 131

Why Do Large, Successful Companies Fail? 132

From Data to Insights 134

Increasing the Return on Investment from Information Assets 135

Emerging Need for Analytics 136

Summary 137

Notes 138

CHAPTER 9 Integration of Business Intelligence, Business Analytics, and Enterprise Performance Management 139

Relationship among Business Intelligence, Business Analytics, and Enterprise Performance Management 140

Overcoming Barriers 143

Summary 144

Notes 145

CHAPTER 10 Predictive Accounting and Marginal Expense Analytics 147

Logic Diagrams Distinguish Business from Cost Drivers 148

Confusion about Accounting Methods 150

Historical Evolution of Managerial Accounting 152

An Accounting Framework and Taxonomy 153

What? So What? Then What? 156

Coexisting Cost Accounting Methods 159

Predictive Accounting with Marginal Expense Analysis 160

What Is the Purpose of Management Accounting? 160

What Types of Decisions Are Made with Managerial Accounting Information? 161

Activity-Based Cost/Management as a Foundation for Predictive Business Accounting 164

Major Clue: Capacity Exists Only as a Resource 165

Predictive Accounting Involves Marginal Expense Calculations 166

Decomposing the Information Flows Figure 169

Framework to Compare and Contrast Expense Estimating Methods 172

Predictive Costing Is Modeling 173

Debates about Costing Methods 174

Summary 175

Notes 175

CHAPTER 11 Driver-Based Budget and Rolling Forecasts 177

Evolutionary History of Budgets 180

A Sea Change in Accounting and Finance 182

Financial Management Integrated Information Delivery Portal 183

Put Your Money Where Your Strategy Is 185

Problem with Budgeting 185

Value Is Created from Projects and Initiatives, Not the Strategic Objectives 187

Driver-Based Resource Capacity and Spending Planning 189

Including Risk Mitigation with a Risk Assessment Grid 190

Four Types of Budget Spending: Operational, Capital, Strategic, and Risk 192

From a Static Annual Budget to Rolling Financial Forecasts 194

Managing Strategy Is Learnable 195

Summary 195

Notes 196

Part Five Trends and Organizational Challenges  197

CHAPTER 12 CFO Trends 199

Resistance to Change and Presumptions of Existing Capabilities 199

Evidence of Defi cient Use of Business Analytics in Finance and Accounting 201

Sobering Indication of the Advances Yet Needed by the CFO Function 202

Moving from Aspirations to Practice with Analytics 203

Approaching Nirvana 210

CFO Function Needs to Push the Envelope 210

Summary 215

Notes 216

CHAPTER 13 Organizational Challenges 217

What Is the Primary Barrier Slowing the Adoption Rate of Analytics? 219

A Blissful Romance with Analytics 220

Why Does Shaken Confidence Reinforce One’s Advocacy? 221

Early Adopters and Laggards 222

How Can One Overcome Resistance to Change? 224

The Time to Create a Culture for Analytics Is Now 226

Predictive Business Analytics: Nonsense or Prudence? 227

Two Types of Employees 227

Inequality of Decision Rights 228

What Factors Contribute to Organizational Improvement? 229

Analytics: The Skeptics versus the Enthusiasts 229

Maximizing Predictive Business Analytics: Top-Down or Bottom-Up Leadership? 234

Analysts Pursue Perceived Unachievable Accomplishments 235

Analysts Can Be Leaders 236

Summary 237

Notes 237

About the Authors 239

Index 243