Managerial Analytics: An Applied Guide to Principles, Methods, Tools, and Best Practices - Couverture rigide

“A manager who wants to learn the underlying techniques and their applicability is bound to tutorials from a data scientist with good communication skills. This book gives managers the opportunity to learn the concepts by themselves, and thus, it should be on a bookshelf of everyone who leads and manages analytics efforts.”
–Diego Klabjan, Professor of Industrial Engineering and Management Sciences, Director of Master of Science in Analytics Program, Northwestern University

“Watson and Nelson help to bridge the gap between the marketing hype and the technical details, making it easier to evaluate analytics-based solutions and better understand their potential.”
–Irv Lustig, Ph.D., Manager, Optimization and Mathematical Software, Business Analytics and Math Science, IBM Research at IBM

• Everything you need to know to get results!
• Concepts, applications, tools, techniques, and pitfalls to avoid
• How to derive more value from tools and data you already own

Want to start leveraging analytics and Big Data for profit? Managerial Analytics is your ideal first resource. Whatever your industry or management role, this up-to-date guide will help you get started fast, get started right, and quickly start driving value.

Right now, analytics is helping organizations do everything from saving money to saving lives. Chances are, your organization has enormous opportunities to derive value through analytics—if you apply it properly.

Managerial Analytics will show you how. This book is for every manager who knows they can benefit from analytics, but doesn’t know where to start…every consultant and software provider who wants to deliver better results to clients…every professional tasked with evaluating analytics products or vendors.

Using practical examples, the authors demystify each form of analytics: descriptive analytics for presenting and visualizing data; predictive analytics for recognizing trends and relationships; and prescriptive analytics for optimizing decisions based on what you know and expect.

For each, they introduce today’s best tools and techniques, offering just enough technical detail to help you make the best possible choices. You’ll learn how modern analytics differs from older techniques; what kind of questions you can now answer for the first time; and how to extend the power of analytics throughout your entire organization.

• What analytics is, what it isn’t…
…and what it really means to “compete on analytics”

• Overcoming pitfalls that lead to the wrong answers
Getting the right samples, avoiding “confirmation bias,” and more

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”