Too Big to Ignore: The Business Case for Big Data (Wiley and SAS Business Series)
Phil Simon
John Wiley & Sons (2015)

“Before we demand more of our data, we need to demand more of ourselves.” Nate Silver

First of all, I commend Phil Simon on his skillful use of reader-friendly devices that include 4 Tables, 16 Figures, and dozens of relevant quotations strategically located throughout his lively and eloquent narrative as well as “Summary” and “Notes” sections at the conclusion of Chapters 1-8. These devices will facilitate, indeed expedite frequent review of key material later.

As Simon explains, “This book is about an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip or fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate.” I wholly agree that “Big Data is becoming too big to ignore. And that sentence, in a nutshell, summarizes the book.”

Personal digression: I have read several dozen books about Big Data in recent years and these are among what I consider to be the key points, listed in no particular order of importance:

1. There are no Big Data or IT issues, only business issues.
2. The value of “Big” should not be measured in terms of quantity; rather, in terms of relevance and sufficiency as well as effective use thereof.
3. Moreover, data should be frequently evaluated in terms of relative relevance to priorities of the given strategic objectives.
4. Data needs must be in proper alignment with the Technology Adoption Life Cycle (TALC).
5. I agree with Nate Silver, in The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t: “Data-driven predictions [and data-driven decisions] can succeed — and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves.”

These are among the dozens of passages of greatest interest and value to me (in Chapters 1-5), also listed to suggest the scope of Simon’s coverage:

o How Big Is Big? The Size of Big Data, and, Why Now? Explaining the Big Data Revolution (Pages 10-13)
o Central Thesis of This Book, and, Plan of Attack (22-25)
o The Beginnings: Structured Data, and Structure This! Web 2.0 and the Arrival of Big Data (30-39)
o The Composition of Data: Then and Now, and, The Current State of the Data Union (39-43)
o Characteristics of Big Data (50-71)
o Statistical Techniques and Methods (80-84)
o Predictive Analytics (100-105)
o Projects, Applications, and Platforms (114-121)
o Hardware Considerations (133-136)

Note: Simon next provides three mini-case studies in which he explores “how they have successfully deployed Big Data tools and seen amazing results.” In fact, the material for each is organized within this format: Approach or Background, Steps, Results, and Lessons

o Quantcast: A Small Big Data Company (141-146)
o Explorys: The Human Case for Big Data (147-152)
o NASA: How Contests, Amplification, and Open Innovation Enable Big Data (152-158)

When sharing his Final Thoughts, Phil Simon observes, “Organizations should be asking new and penetrating questions and letting those answers inform new ways of thinking. The uninitiated, the skeptics, and the laggards who refuse to integrate data into their decision-making — and Big Data in particular — will only be left further and further behind.” I agree and with Simon: It is very, very hard work to establish and then sustain data-driven success because it is even more difficult to get the right people incorporating the right data in their predictions and decisions…at all levels and in all areas of the given enterprise. The negative consequences of being unwilling and/or unable to do so are also “too big to ignore.”