PREDICTIVE BUSINESS ANALYTICS

1 PREDICTIVEBUSINESSANALYTICSLAWRENCE MAISELGARY COKINSF orward Looking Measures to Improve BUSINESS PerformanceixContentsPreface xvPart 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 Confi dence 12 Being Wrong versus Being Confused 12 Ambiguity and Uncertainty Are Your Friends 14Do the Important Stuff First PREDICTIVE BUSINESS ANALYTICS 16 What If.

2 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 ix9/11/13 8:38 PM9/11/13 8:38 PMFrom PREDICTIVE BUSINESS ANALYTICS . Full book available for purchase PREDICTIVE Modeling with SAS Enterprise MinerTM: Practical Solutions for BUSINESS Applications, Second Editionby Kattamuri S. Sarma, PhD. Copyright 2013, SAS Institute Inc., Cary, North Carolina, USA. ALLRIGHTS Two Principles and Practices .. 31 Chapter 3 Guiding Principles in Developing PREDICTIVE BUSINESS ANALYTICS 33 Defi ning 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 Nonfi nancial, 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.

3 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 78x x9/11/13 8:38 PM9/11/13 8:38 PMCONTENTS xiChange 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.

4 129 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 xi9/11/13 8:38 PM9/11/13 8:38 PMCHAPTER 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 152An Accounting Framework and Taxonomy 153 What?

5 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 175xii xii9/11/13 8:38 PM9/11/13 8:38 PMCHAPTER 11 Driver-Based Budget and Rolling Forecasts 177 Evolutionary History of Budgets 180A 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.

6 197 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 CONTENTS xiii9/11/13 8:38 PM9/11/13 8:38 PMA Blissful Romance with ANALYTICS 220 Why Does Shaken Confi dence 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?

7 227 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 243xiv xiv9/11/13 8:38 PM9/11/13 8:38 PMFrom PREDICTIVE BUSINESS ANALYTICS : Forward-Looking Capabilities to Improve BUSINESS Performance by Lawrence Maisel and Gary Cokins. Copyright 2013, SAS Institute Inc., Cary, North Carolina, USA. ALL RIGHTS ANALYTICS Will Be the Next Competitive EdgeThe farther backward you can look, the farther forward you are likely to see. Winston ChurchillAnalytics is becoming a competitive edge for organizations.

8 Once a nice to have, applying ANALYTICS , especially PREDICTIVE BUSINESS ANALYTICS , is now becoming August 6, 2009, New York Times article titled For Today s Graduate, Just One Word: Statistics 1 refers to the famous advice to Dustin Hoffman s character in his career-breakthrough movie The Graduate. The quote occurs when a self-righteous Los Angeles busi-nessman takes aside the baby-faced Benjamin Braddock, played by Hoffman, and declares, I just want to say one word to you just one word plastics. Perhaps a remake of this movie will be made and updated with the word ANALYTICS substituted for plastics. This spotlight on statistics is apparently relevant, because the article ranked in that week s top three e-mailed articles as tracked 39/11/13 8:36 PM9/11/13 8:36 PMFrom PREDICTIVE BUSINESS ANALYTICS .

9 Full book available for purchase WHY by the New York Times. The article cites an example of a Google em-ployee who uses statistical analysis of mounds of data to come up with ways to improve [Google s] search engine. It describes the employee as an Internet-age statistician, one of many who are changing the image of the profession as a place for dronish number nerds. They are fi nding themselves increasingly in demand and even cool. ANALYTICS : JUST A SKILL, OR A PROFESSION?The use of ANALYTICS that includes statistics is a skill that is gaining mainstream value due to the increasingly thinner margin for decision error. There is a requirement to gain insights, foresight, and inferences from the treasure chest of raw transactional data (both internal and external) that many organizations now store (and will continue to store) in a digital format.

10 Organizations are drowning in data but starving for information. The New York Times article states:In fi eld after fi eld, computing and the Web are creating new realms of data to explore sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fi vefold by 2012, according to a projection by IDC, an IT research fi rm.. Yet data is merely the raw material of knowledge. We re rapidly entering a world where everything can be monitored and measured, but the big problem is going to be the ability of humans to use, analyze and make sense of the data.. [Analysts] use powerful computers and sophisticated mathematical models to hunt for meaningful patterns and insights in vast troves of data. The applications are as diverse as improving Internet search and online advertising, culling gene sequencing information for cancer research and analyzing sensor and location data to optimize the handling of food experienced analyst is like a caddy for a professional golfer.