Cover Page

Contents

Cover

Praise

Series

Title Page

Coptright

Dedication

Preface

NOTES

Part One: “Why”

Chapter 1: Why Analytics Will Be the Next Competitive Edge

ANALYTICS: JUST A SKILL, OR A PROFESSION?

BUSINESS INTELLIGENCE VERSUS ANALYTICS VERSUS DECISIONS

HOW DO EXECUTIVES AND MANAGERS MATURE IN APPLYING ACCEPTED METHODS?

FILL IN THE BLANKS: WHICH X IS MOST LIKELY TO Y?

PREDICTIVE BUSINESS ANALYTICS AND DECISION MANAGEMENT

PREDICTIVE BUSINESS ANALYTICS: THE NEXT “NEW” WAVE

GAME-CHANGER WAVE: AUTOMATED DECISION-BASED MANAGEMENT

PRECONCEPTION BIAS

ANALYSTS' IMAGINATION SPARKS CREATIVITY AND PRODUCES CONFIDENCE

BEING WRONG VERSUS BEING CONFUSED

AMBIGUITY AND UNCERTAINTY ARE YOUR FRIENDS

DO THE IMPORTANT STUFF FIRST—PREDICTIVE BUSINESS ANALYTICS

WHAT IF . . . YOU CAN

NOTES

Chapter 2: The Predictive Business Analytics Model

BUILDING THE BUSINESS CASE FOR PREDICTIVE BUSINESS ANALYTICS

BUSINESS PARTNER ROLE AND CONTRIBUTIONS

SUMMARY

NOTES

Part Two: Principles and Practices

Chapter 3: Guiding Principles in Developing Predictive Business Analytics

DEFINING A RELEVANT SET OF PRINCIPLES

PRINCIPLE 1: DEMONSTRATE A STRONG CAUSE-AND-EFFECT RELATIONSHIP

PRINCIPLE 2: INCORPORATE A BALANCED SET OF FINANCIAL AND NONFINANCIAL, INTERNAL AND EXTERNAL MEASURES

PRINCIPLE 3: BE RELEVANT, RELIABLE, AND TIMELY FOR DECISION MAKERS

PRINCIPLE 4: ENSURE DATA INTEGRITY

PRINCIPLE 5: BE ACCESSIBLE, UNDERSTANDABLE, AND WELL ORGANIZED

PRINCIPLE 6: INTEGRATE INTO THE MANAGEMENT PROCESS

PRINCIPLE 7: DRIVE BEHAVIORS AND RESULTS

SUMMARY

Chapter 4: Developing a Predictive Business Analytics Function

GETTING STARTED

SELECTING A DESIRED TARGET STATE

ADOPTING A PBA FRAMEWORK

DEVELOPING THE FRAMEWORK

SUMMARY

NOTES

Chapter 5: Deploying the Predictive Business Analytics Function

INTEGRATING PERFORMANCE MANAGEMENT WITH ANALYTICS

PERFORMANCE MANAGEMENT SYSTEM

IMPLEMENTING A PERFORMANCE SCORECARD

MANAGEMENT REVIEW PROCESS

IMPLEMENTATION APPROACHES

CHANGE MANAGEMENT

SUMMARY

NOTES

Part Three: Case Studies

Chapter 6: MetLife Case Study in Predictive Business Analytics

THE PERFORMANCE MANAGEMENT PROGRAM

IMPLEMENTING THE MOR PROGRAM

BENEFITS AND LESSONS LEARNED

SUMMARY

NOTES

Chapter 7: Predictive Performance Analytics in the Biopharmaceutical Industry

CASE STUDIES

SUMMARY

NOTE

Part Four: Integrating Business Methods and Techniques

Chapter 8: Why Do Companies Fail (Because of Irrational Decisions)?

IRRATIONAL DECISION MAKING

WHY DO LARGE, SUCCESSFUL COMPANIES FAIL?

FROM DATA TO INSIGHTS

INCREASING THE RETURN ON INVESTMENT FROM INFORMATION ASSETS

EMERGING NEED FOR ANALYTICS

SUMMARY

NOTES

Chapter 9: Integration of Business Intelligence, Business Analytics, and Enterprise Performance Management

RELATIONSHIP AMONG BUSINESS INTELLIGENCE, BUSINESS ANALYTICS, AND ENTERPRISE PERFORMANCE MANAGEMENT

OVERCOMING BARRIERS

SUMMARY

NOTES

Chapter 10: Predictive Accounting and Marginal Expense Analytics

LOGIC DIAGRAMS DISTINGUISH BUSINESS FROM COST DRIVERS

CONFUSION ABOUT ACCOUNTING METHODS

HISTORICAL EVOLUTION OF MANAGERIAL ACCOUNTING

AN ACCOUNTING FRAMEWORK AND TAXONOMY

WHAT? SO WHAT? THEN WHAT?

COEXISTING COST ACCOUNTING METHODS

PREDICTIVE ACCOUNTING WITH MARGINAL EXPENSE ANALYSIS1

WHAT IS THE PURPOSE OF MANAGEMENT ACCOUNTING?

WHAT TYPES OF DECISIONS ARE MADE WITH MANAGERIAL ACCOUNTING INFORMATION?

ACTIVITY-BASED COST/MANAGEMENT AS A FOUNDATION FOR PREDICTIVE BUSINESS ACCOUNTING

MAJOR CLUE: CAPACITY EXISTS ONLY AS A RESOURCE

PREDICTIVE ACCOUNTING INVOLVES MARGINAL EXPENSE CALCULATIONS

DECOMPOSING THE INFORMATION FLOWS FIGURE

FRAMEWORK TO COMPARE AND CONTRAST EXPENSE ESTIMATING METHODS

PREDICTIVE COSTING IS MODELING

DEBATES ABOUT COSTING METHODS

SUMMARY

NOTES

Chapter 11: Driver-Based Budget and Rolling Forecasts

EVOLUTIONARY HISTORY OF BUDGETS

A SEA CHANGE IN ACCOUNTING AND FINANCE

FINANCIAL MANAGEMENT INTEGRATED INFORMATION DELIVERY PORTAL

PUT YOUR MONEY WHERE YOUR STRATEGY IS

PROBLEM WITH BUDGETING

VALUE IS CREATED FROM PROJECTS AND INITIATIVES, NOT THE STRATEGIC OBJECTIVES

DRIVER-BASED RESOURCE CAPACITY AND SPENDING PLANNING

INCLUDING RISK MITIGATION WITH A RISK ASSESSMENT GRID

FOUR TYPES OF BUDGET SPENDING: OPERATIONAL, CAPITAL, STRATEGIC, AND RISK

FROM A STATIC ANNUAL BUDGET TO ROLLING FINANCIAL FORECASTS

MANAGING STRATEGY IS LEARNABLE

SUMMARY

NOTES

Part Five: Trends and Organizational Challenges

Chapter 12: CFO Trends

RESISTANCE TO CHANGE AND PRESUMPTIONS OF EXISTING CAPABILITIES

EVIDENCE OF DEFICIENT USE OF BUSINESS ANALYTICS IN FINANCE AND ACCOUNTING

SOBERING INDICATION OF THE ADVANCES YET NEEDED BY THE CFO FUNCTION

MOVING FROM ASPIRATIONS TO PRACTICE WITH ANALYTICS

APPROACHING NIRVANA

CFO FUNCTION NEEDS TO PUSH THE ENVELOPE

SUMMARY

NOTES

Chapter 13: Organizational Challenges

WHAT IS THE PRIMARY BARRIER SLOWING THE ADOPTION RATE OF ANALYTICS?

A BLISSFUL ROMANCE WITH ANALYTICS

WHY DOES SHAKEN CONFIDENCE REINFORCE ONE'S ADVOCACY?

EARLY ADOPTERS AND LAGGARDS

HOW CAN ONE OVERCOME RESISTANCE TO CHANGE?

THE TIME TO CREATE A CULTURE FOR ANALYTICS IS NOW

PREDICTIVE BUSINESS ANALYTICS: NONSENSE OR PRUDENCE?

TWO TYPES OF EMPLOYEES

INEQUALITY OF DECISION RIGHTS

WHAT FACTORS CONTRIBUTE TO ORGANIZATIONAL IMPROVEMENT?

ANALYTICS: THE SKEPTICS VERSUS THE ENTHUSIASTS

MAXIMIZING PREDICTIVE BUSINESS ANALYTICS: TOP-DOWN OR BOTTOM-UP LEADERSHIP?

ANALYSTS PURSUE PERCEIVED UNACHIEVABLE ACCOMPLISHMENTS

ANALYSTS CAN BE LEADERS

SUMMARY

NOTES

About the Authors

Index

Additional praise for
Predictive Business Analytics:
Forward-Looking Capabilities to Improve Business Performance

“In the words of Harvard Professor MENG Xiao-Li (quoted by Thomas Davenport), ‘you don't need to become a winemaker to become a wine connoisseur.' This book constitutes an excellent introduction to anyone wishing to grow into a data connoisseur. Skipping all the technical aspects of predictive analytics, it focusses on how to better appreciate quantitative analysis, allowing readers to become more sophisticated consumers of data. A first-class and extremely enlightening read about fact-based decision making.”

—Dr. Olivier Maugain, CEO, AsiaAnalytics (formerly SPSS China)

“The authors make a compelling case: to win in tomorrow's marketplace, a company must know—not just guess at—the ways in which non-financial factors will impact financial results. But many managers will fail to adjust to this new decision-making paradigm. Reading this book is your first step in avoiding that fate. The authors use an engaging writing style and tons of practical examples to provide a clear picture of the competencies and skills sets you need to succeed.”

—Mary Driscoll, Senior Research Fellow, APQC

“Simply put, Larry and Gary have nailed the ‘why' and the ‘how' of Predictive Business Analytics in this publication. To be an economically viable company in today's transparent, global and competitive world, business leaders must champion the predictive analytics journey and embed this powerful management practice as an operational core competency. The companies that thrive integrate predictive business analytics into their DNA to out-smart their competitors in strategic and tactical decision making that yields sustainable success.”

—Chris D. Fraga, Chief Strategy Officer and President, Acorn International

                                                   
Wiley & SAS Business Series

The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

Titles in the Wiley and SAS Business Series include:

Activity-Based Management for Financial Institutions: Driving Bottom-Line Results by Brent Bahnub
Big Data Analytics: Turning Big Data into Big Money by Frank Ohlhorst
Branded! How Retailers Engage Consumers with Social Media and Mobility by Bernie Brennan and Lori Schafer
Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation by Lora M. Cecere and Charles W. Chase
Business Analytics for Customer Intelligence by Gert Laursen
Business Analytics for Managers: Taking Business Intelligence beyond Reporting by Gert Laursen and Jesper Thorlund
The Business Forecasting Deal: Exposing Bad Practices and Providing Practical Solutions by Michael Gilliland
Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure by Michael S. Gendron
Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy by Olivia Parr Rud
CIO Best Practices: Enabling Strategic Value with Information Technology, Second Edition by Joe Stenzel
Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner
Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors by Clark Abrahams and Mingyuan Zhang
Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring by Naeem Siddiqi
The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher
Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs
Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition by Charles Chase
Demand-Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain by Robert A. Davis
The Executive's Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow
Executive's Guide to Solvency II by David Buckham, Jason Wahl, and Stuart Rose
Fair Lending Compliance: Intelligence and Implications for Credit Risk Management by Clark R. Abrahams and Mingyuan Zhang
Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications by Robert Rowan
Health Analytics: Gaining the Insights to Transform Health Care by Jason Burke
Human Capital Analytics: How to Harness the Potential of Your Organization's Greatest Asset by Gene Pease, Boyce Byerly, and Jac Fitz-enz
Information Revolution: Using the Information Evolution Model to Grow Your Business by Jim Davis, Gloria J. Miller, and Allan Russell
Killer Analytics: Top 20 Metrics Missing from Your Balance Sheet by Mark G. Brown
Manufacturing Best Practices: Optimizing Productivity and Product Quality by Bobby Hull
Marketing Automation: Practical Steps to More Effective Direct Marketing by Jeff LeSueur
Mastering Organizational Knowledge Flow: How to Make Knowledge Sharing Work by Frank Leistner
The New Know: Innovation Powered by Analytics by Thornton May
Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics by Gary Cokins
Retail Analytics: The Secret Weapon by Emmett Cox
Social Network Analysis in Telecommunications by Carlos Andre Reis Pinheiro
Statistical Thinking: Improving Business Performance, Second Edition by Roger W. Hoerl and Ronald D. Snee
Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics by Bill Franks
Too Big to Ignore: The Business Case for Big Data by Phil Simon
The Value of Business Analytics: Identifying the Path to Profitability by Evan Stubbs
Visual Six Sigma: Making Data Analysis Lean by Ian Cox, Marie A. Gaudard, Philip J. Ramsey, Mia L. Stephens, and Leo Wright
Win with Advanced Business Analytics: Creating Business Value from Your Data by Jean Paul Isson and Jesse Harriott

For more information on any of the above titles, please visit www.wiley.com.

Title Page





I would like to dedicate this book to my wife, Claudia, whose patience and intelligence have always been a source of inspiration. I also want to acknowledge my parents and brother, who provided gentle guidance, and my children, Nicole, Dana, and Jonathan, who always bring out the best in me.

Lawrence S. Maisel


I express my thanks in remembrance to Bob Bonsack, my true mentor at Deloitte and EDS, for educating and training me in business methods and bringing value to people. I also thank my wife, Pam Tower, for her endless patience when I am distracted with projects such as writing this book.

Gary Cokins

                                                   
Preface

An organization's ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage.

—Jack Welch

“Apple's Steve Jobs was known to explicitly discount the value of surveys and focus groups for designing new products. How do you explain this apparent anti-empiricism? One explanation is that, much like a creative scientist, people like Jobs recognize when there is not enough data or the right kind of data to form a theory. They recognize that, for completely new lines of products that will change a user's experience or behavior, the only useful data is experiential data, not commentary and reactions from those who have never used the product.

This approach to decision making using empiricism and analytics might seem like a death knell for such vaunted business traits as intuition, gut feel, killer instinct, and so forth, right? Not so fast! Business decision making can be purely empirical and dispassionate, but decision makers are not. Sound decision making favors those who are creative, are intuitive, and can take a leap of faith.

The enterprise of the future, based on empiricism and analytical decision making, will indeed be considerably different from today's enterprise.”1 In the future, even more than today, businesses will be expected to possess the talent, tools, processes, and capabilities to enable their organizations to implement and utilize continuous analysis of past business performance and events to gain forward-looking insight to drive business decisions and actions.

Over the years, we have been working with companies like yours to gain deeper insights and understand the dynamics related to ­managing operations, controlling cost, increasing profit margins, and leveraging data-driven analytics. We've helped companies enhance employees' skills and competencies, and managers and staff to improve their organization's performance and the effectiveness of their decision making. Along with contributing author Eileen Morrissey, we have been at the forefront of important contributions to management practices, including activity-based costing and enterprise performance management, including balanced scorecards.

Now we have embarked on an additional path along this career journey by writing this book on predictive business analytics (PBA). Although in today's parlance the term analytics can be associated with any number of business methods and practices as well as software tools, we have sought to distinguish PBA from other related business practices such as enterprise performance management, driver-based forecasting, business intelligence, predictive analytics, and so on (see Part Four for a fuller discussion on those topics) because its effectiveness as a recognized business practice will be sustainable only if it demonstrates how it contributes to value and growth.

In fact, many recent surveys are quantifying just how valuable PBA has become as a contributor to the success of a business. In one survey, 90 percent of respondents attained a positive ROI from their most successful deployment of predictive analytics, and more than half from their least successful deployment.2 In another survey, “Among respondents who have implemented predictive business analytics, 66% say it provides ‘very high' or ‘high' business value.”3 And alarmingly, in another survey, “respondents that have not yet adopted predictive technologies experienced a 2% decline in profit margins, and a 1% drop in their customer retention rate.”4

In fact, case examples after case examples are demonstrating that for a company to use PBA effectively it must commit to a sustained and rigorous process in order to achieve meaningful results. This includes the ability to establish a team of individuals with complementary skills and competencies, a repeatable set of practices, functional data and tools, and (importantly) a management process to review its results and forge its decision making by leveraging these results and insights (see Part Three: Case Studies). Together, these are used to analyze continuously the right business and cost drivers and measures that have a strong cause-and-effect relationship to gain insight to better manage the business and to improve decision making.

A widely accepted best practice is to embed predictive business analytics models in operational systems for use in decision management. Key business decisions need to be made with their likely expectation of outcomes or results—from possibilities to probabilities. PBA is a backbone to enable more effective analysis and decision making that recognize how the future might play out. PBA should (1) reflect the needs of business users, (2) be the result of a consistent and trusted process, and (3) represent the appropriate time frame for the decisions being made. Users need meaningful data at the right time and in a form they can rely on. For PBA information to be meaningful, it should be tailored to the designated consumers of that information in a form and context that describe the outcomes, causes, and consequences of decisions and actions associated with alternative future drivers (amounts or quantities) and business conditions. Information should be presented in a manner that conveys the key messages and portrays the alternative actions in an unambiguous and straightforward manner, using formats that are graphic and ­intuitively ­understood.

For example, in traveling to a business meeting, the driver sees a series of data points on an automobile dashboard (e.g., gauges for speed, engine temperature, oil pressure). These may be complete, but unless they inform the user of the range of acceptable tolerances and the implications related to the situation (e.g., highway versus bumpy country road), they will usually not be sufficient for meaningful decision making and actions about safety and timely arrival. Building on this example, PBA can be expanded to provide alerts and suggested alternative decisions and actions that might be considered. Another example might be a health care organization analyzing its staffing needs; it will likely gather data about its (1) service area population (e.g., age, ethnicity, gender) and (2) present and future health care reimbursement contracts and conditions. These attributes (and others) will enable the organization to better select the range of options regarding its longer-term staffing levels, competencies and skills requirements, and specialties, as well as service-level capacities (e.g., number of beds) in each of these specialty areas.

The data from the analysis should be useful to the user or it will not be used. The tolerance of the ranges needs to be “fit for purpose.” For example, predicting required production volumes by location for next week's operating plans and scheduling is different from predicting revenues six months forward.

In contrast, James Taylor, coauthor of Smart (Enough) Systems,5 categorizes business intelligence in a more limited light and concludes that “insights delivered by standard business intelligence and reporting are not readily actionable; they must be translated to action by way of human judgment. Metrics, reports, dashboards, and other retrospective analyses are important components of enterprise business intelligence, but their execution is ad hoc in that it is not clear a priori what kind of actions or decisions will be recommended, if any.”6

Many years ago, we learned that for a theory to be applied in business, it must be practical and implementable with a reasonable allocation of resources. It is no different with PBA, which is most impactful when it supports business decisions that can be acted upon (e.g., open a new market, hire additional sales personnel, invest in new products, close down a factory, and so on). As a result, PBA's true value is in its practical and implementable application, which will be discussed in the book.

The PBA theory likely has numerous originators and proponents. However, for us, our origination started more formally with a request from the Financial and Performance Management Task Force of the International Federation of Accountants (IFAC), chaired by Eileen Morrissey and directed by IFAC's Stathis Gould, to author an International Good Practice Guidance entitled “Predictive Business Analytics,”7 published in October 2011. This was an 18-month process to determine guiding principles (see Chapter 3) and summarize important frameworks and practices for these principles with Morrissey, Gould, and their other task force members providing ongoing support and contributions to refine the guidance. In Chapters 4 and 5, we expand on these principles and approaches for deploying PBA.

What followed was the opportunity for us to coauthor a book that leverages these principles with real-world experiences and illustrates, through case studies and exhibits, materials that can be used as ­adaptable templates. We address how PBA integrates with several important business management and improvement methods and ­techniques in Part Four, and conclude in Part Five with chapters that anticipate trends and recognize organizational challenges.

Our intent is to:

However, our most important commitment is to motivate and challenge our readers to agree, disagree, and improve or refine the ­principles and practices we present. Each step in this process helps to further that body of knowledge to foster more competitive and stronger organizations. We hope that you find the discussions and case studies rewarding and that they enable you to participate in the furtherance of this game-changing body of knowledge.

We are indebted to many people for helping us understand how to create and deploy an effective predictive business analytics capability. We have learned from and been inspired by clients and colleagues and to each of you we express our gratitude for your insights and contributions.

We want to gratefully acknowledge the editorial support from Sheck Cho, Stacey Rivera, and Helen Cho, whose patience and guidance helped us create this book.

Lawrence S. Maisel
Gary Cokins
October 2013

NOTES

1. Kishore S. Swaminathan, “What the C-Suite Should Know about Analytics,” Accenture Outlook 1, February 2011.

2. Predictive Analytics World survey, www.predictiveanalyticsworld.com/Predictive-Analytics-World-Survey-Report-Feb-2009.pdf.

3. Wayne Eckerson, “Predictive Analytics: Extending the Value of Your Data Warehousing Investment,” TDWI Report.

4. David White, “Predictive Analytics: The Right Tool for Tough Times,” an Aberdeen Group white paper, February 2010.

5. James Taylor and James Raden, Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating the Decisions Hidden in Your Business (Upper Saddle River, NJ: Prentice Hall, 2007).

6. James Taylor, CEO and Principal Consultant, Decision Management Solutions, www.decisionmanagementsolutions.com.

7. The International Federation of Accountants (IFAC) and Lawrence S. Maisel have published an International Good Practice Guidance titled “Predictive Business Analytics: Forward-Looking Measures to Improve Business Performance,” October 2011.

PART
ONE
                  

“Why”