“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 profi t 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.
Part One “Why”
Chapter 1 Why Analytics Will Be the Next Competitive Edge
Chapter 2 The Predictive Business Analytics Model
Part Two Principles and Practices
Chapter 3 Guiding Principles in Developing Predictive Business Analytics
CHAPTER 4 Developing a Predictive Business Analytics Function
CHAPTER 5 Deploying the Predictive Business Analytics Function
Part Three Case Studies
CHAPTER 6 MetLife Case Study in Predictive Business Analytics
CHAPTER 7 Predictive Performance Analytics in the Biopharmaceutical Industry
Part Four Integrating Business Methods and Techniques
CHAPTER 8 Why Do Companies Fail (Because of Irrational Decisions)?
CHAPTER 9 Integration of Business Intelligence, Business Analytics, and Enterprise Performance Management
CHAPTER 10 Predictive Accounting and Marginal Expense Analytics
CHAPTER 11 Driver-Based Budget and Rolling Forecasts 177 Evolutionary History of Budgets
Part Five Trends and Organizational Challenges
CHAPTER 12 CFO Trends
CHAPTER 13 Organizational Challenges