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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 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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