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