Discover the breakthrough tool your company can use to make
winning decisions
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.
Autorentext
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.
Zusammenfassung
Discover the breakthrough tool your company can use to make winning decisions
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.
Inhalt
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 FirstPredictive 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
Benefits 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 Predicti…