The field of analytics is rapidly evolving, making it difficult for professionals and students to keep up the most current and effective applications. Managerial Analytics will help readers sort through all these new options and identify the appropriate solution. In this reference, authors Watson, Nelson and Cacioppi accurately define and identify the components of analytics and big data, giving readers the knowledge needed to effectively assess new aspects and applications. Building on this foundation, they review tools and solutions, identify the offerings best aligned to one’s requirements, and show how to tailor analytics applications to an organization’s specific needs. Drawing on extensive experience implementing, planning, and researching advanced analytics for business, the authors clearly explain all this, and more:
- What analytics is and isn’t: great examples of successful usage – and other examples where the term is being degraded into meaninglessness
- The difference between using analytics and “competing on analytics”
- How to get started with big data, by analyzing the most relevant data
- Components of analytics systems, from databases and Excel to BI systems and beyond
- Anticipating and overcoming “confirmation bias” and other pitfalls
- Understanding predictive analytics and getting the high-quality random samples necessary
- Applying game theory, Efficient Frontier, benchmarking, and revenue management models
- Implementing optimization at the small and large scale, and using it to make “automatic decisions”
Table of Contents
Part I Overview 1
Chapter 1 What Is Managerial Analytics? 3
Chapter 2 What Is Driving the Analytics Movement? 23
Chapter 3 The Analytics Mindset 35
Part II Analytics Toolset 63
Chapter 4 Machine Learning 65
Chapter 5 Descriptive Analytics 93
Chapter 6 Predictive Analytics 139
Chapter 7 Case Study: Moneyball and Optimization 155
Chapter 8 Prescriptive Analytics (aka Optimization) 163
Part III Conclusion 199
Chapter 9 Revenue Management 201
Chapter 10 Final Tips for Implementing Analytics 211
Nontraditional Bibliography and Further Reading 215
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