Book Image

Analytics: How to Win with Intelligence

By : John Thompson, Shawn P. Rogers
Book Image

Analytics: How to Win with Intelligence

By: John Thompson, Shawn P. Rogers

Overview of this book

Today, business is moving into an era where information is more valuable than services. Organizations that connect information with their products will have a huge advantage. This book helps people understand the power of data analytics and explains how some of the tools available can be applied to a wide range of applications. It begins with a brief history of analytics and explains how it all began. You'll learn about several common analytical approaches and the tools that data scientists use to analyze data. You'll gain insight into some staffing models, technologies, organizational structures, and analytical approaches used in the previous two eras of analytics. As you progress through the chapters, you'll also get a glimpse into the future of the analytical marketplace. After reading this book, you will be able to help your team deploy analytical elements into your operations and become competitive in your business.
Table of Contents (11 chapters)
Free Chapter
1
Foreword by Tom Davenport

Context, permission, and accuracy

We would be the first to admit that we are not experts on ethics or the legal issues involved with respect to data analytics. Based on our extensive business experience, though, we suggest that companies deploying the technology consider three important factors: context, permission, and accuracy. By taking into account all three factors, executives will be better able to gauge whether a particular application will be perceived as reasonable to the many stakeholders involved.

Context. Many people are perfectly fine with expert predictive analytics that improve customer service. When they visit a leading e-commerce site that’s able to suggest “what else” they might like or predict the color of a garment that they might like best, they aren’t put off by these types of insights into their buying habits, because the suggestions are contextually in line with the buying experience.

But consider the following example. Alice...