Lawrence Maisel, Gary Cokins

Predictive Business Analytics (eBook, ePUB)

Forward Looking Capabilities to Improve Business Performance

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 272
• Erscheinungstermin: 26. September 2013
• Englisch
• ISBN-13: 9781118240151
• Artikelnr.: 39584943

Autorenporträt

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 [e-mail]. 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 [e-mail].

Inhaltsangabe

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 First--Predictive 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 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 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 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 152 An Accounting Framework and Taxonomy
153 What? 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
175 CHAPTER 11 Driver-Based Budget and Rolling Forecasts 177 Evolutionary
History of Budgets 180 A 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 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 A Blissful Romance with
Analytics 220 Why Does Shaken Confidence 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? 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 243