Predictive Business Analytics Forward Looking Capabilities to Improve Business Performance

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 LMaisel@DecisionVu.com.

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 gcokins@garycokins.com.

Preface

Acknowledgments

Part One “Why”

Chapter 1 Why Analytics 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 with 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 Partners 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 Non-Financial, 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: Integrated 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 Measurement 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 Mor Program

Benefits And Lessons Learned

Summary

Notes

Chapter 7 Predictive Performance Analytics in Bio/Pharmaceutical Industry (Eileen Morrissey)

Case Studies

Challenges to Implementation

Summary

Note

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

Part Four Integrating Business Methods and Techniques

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

Relationship between 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

An Historical Evolution of Managerial Accounting

An Accounting Framework and Taxonomy

What? So What? Then What?

Coexisting cost accounting methods

Predictive Accounting with Marginal Expense Analysis

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 Accounting

Major Clue: Capacity Only Exists 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

A Quick Quiz

Where Are the Skeptics Coming From?

Skeptics’ Reliance on Buffers to Protect Against Errors

Where Are the Enthusiasts of Analytics Coming From?

Enthusiasts Use Analytics to Replace Buffers

Enthusiasts Can Win Buy-In from the Skeptics

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

Carlson’s Law: Bottom-Up versus Top-Down Ideas

Analysts Pursue Perceived Unachievable Accomplishments

Analysts Can Be Leaders

Summary

Notes

About the Authors

Index

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