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

It’s not just about the data

The phrase “data is the new oil” accurately captures just how important data has become; the increasing availability of big data has only helped to stoke people’s excitement and enthusiasm. Back in 2001, industry analyst Doug Laney wrote the now-famous blog post that concentrated on the three “V”s of big data: volume, velocity, and variety.5

The ensuing debate over what exactly big data was (versus what it wasn’t) was soon taking up virtually as much bandwidth as the technology that supported it. Joining that debate, many vendors tried to add their own proprietary “V”s to the equation, but this only confused the market and stalled the adoption of the technology.

For their part, many businesses have remained mystified by this “magical technology” that some claimed was a cure-all for anything data-related. The simple truth, however, is this: data by itself might be interesting...