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”

MICHAEL WATSON (Evanston, IL) is a recognized leader in the supply chain network design community. He has been involved with network design projects since 1998 through LogicTools, ILOG, IBM, and now The Optimization and Analytics Group. He is also an adjunct professor at Northwestern University teaching Master’s level operations and supply chain courses in the McCormick School of Engineering’s MEM) and Analytics programs and Kellogg’s MMM program.

DEREK NELSON (Evanston, IL) is Adjunct Professor at Northwestern University in the McCormick School of Engineering, and worldwide client technical lead for IBM’s ILOG Supply Chain Applications. He has been involved in the design and implementation of optimization based solutions at many companies across a wide range of industries since 2001; and has been a guest speaker at universities including MIT and Ohio State.

PETE CACIOPPI (Eugene, OR) is lead scientist for IBM’s network design product, He has been working on this product since 1997, and is responsible for translating business problems into mathematical optimization problems, and finding innovative ways to solve these complex problems. He holds an M.S. in Operations Research and Computer Science from the University of Chicago.

Part 1: State of the Field

1. Definition of Analytics

2. Descriptive Analytics

3. Predictive Analytics

Part 2: Tools and Applications

4. Prisoners Dilemma and Game Theory

5. Efficient Frontier and benchmarking

The first book that completely demystifies modern “big data” analytics, explains the tools and applications, guides you around the pitfalls, and helps you apply analytics for profit.