Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics
Bill Franks
John Wiley & Sons (2014)

How Big Data can help any organization improve productivity, create value, stay competitive, and recognize new trends

What we have in Taming the Big Data Tidal Wave is a wealth of information, insights, and counsel provided by Bill Franks that will help business leaders in almost any organization — whatever its size and nature may be — to locate and then take full advantage business opportunities in huge data streams with advanced analytics. These are among dozens of his key points, listed in the order in which he discusses them:

1. Big data will continue to evolve. What we think is big and intimidating today won’t raise an eyebrow in a decade, but another new data source certainly will.

2. The opportunity to be an early adopter and get ahead of the competition is almost closed. Get started taming this big data source now.

3. Social network data can lead to new ways of valuing customers. In the telecommunications industry, social network analysis has shifted from account profitability to network profitability.

4. Relational databases, clouds, and MapReduce (see Pages 110-117) all add value in taming big data. The three technologies can integrate and work together to make each better and more effective than it would be on its own.

5. Legacy processes for deploying analytical processes and models aren’t designed to take advantage of the current state of the world. To tame big data, it is crucial that the processes are updated to take full advantage of the scalability available.

6. Data visualization is not about fancy looking graphics. It is about displaying data in a way that allows greater comprehension of the point(s) being made.

7. The most important part of any analysis happens before it begins. The way the problem is framed up-front can determine the success or failure of the analysis.

8. Great analytic professionals tie the level of concern about data’s accuracy to the level of granularity of the decision required. Imperfect data can still have enough power to answer a lot of questions effectively.

9. Be very choosy when hiring or assigning people to an analytics team. Success is far more dependent on the individuals who make up that team than it is on the organizational structure in which that team is working.

10. A lot can be learned from failures, including analytic training center failures. They aren’t all bad. Some failures can be quite valuable if (HUGE “if”) what is learned in the process of failing is applied broadly to improve either past or future processes.

These are among the dozens of business subjects and issues of special interest and value to me, also listed to indicate the scope of Franks’ coverage.

o How Is Big Data Different? (Pages 7-9)
o Risks of Big Data (10-12)
o The Structure of Big Data (14-16)
o Today’s Big Data Is Not Tomorrow’s Big Data (24-25)
o Web Data Overview (30-36)
o Customer Segmentation (47-48)
o Multiple Industries: The Value of Big Data (57-60)
o Retail Manufacturing: The Value RFID and Gaming: The Value of Casino Chip Tracking Data (64-68 and 71-73)
o A history of Scalability (88-89)
o The Convergence of Analytic and Data Environments (90-93)
o Cloud Computing (102-109)
o Analytic Sandbox Essentials (122-130)
o Analytic Data Set Essentials (133-141)
o Embedded Scoring (145-147)
o Analysis: Make It Guided, Explainable, Actionable, and Timely! (184-186)
o Core Analytics versus Advanced Analytics (186-188)
o The Often Underrated Traits of a Great Analytic Professional (208-222)
o The Guiding Principles of an Analytic Innovation Center (263-269)

Bill Franks provides an eloquent as well as thorough explanation of how to find business opportunities in huge data streams with advanced analytics. He urges his reader to keep in mind that big data is real and here to stay, that scalability is more important than ever before (and will become even more important), that new processes as well as a new mindset are required, and there is an urgent need to formulate and then implement new analysis methodologies such as text analysis, ensemble models, and commodity models.

Those who share my high regard for this book are urged to check one or more of Tom Davenport’s books (notably Big Data @ Work and Keeping Up with the Quants), Christopher Surdak’s Data Crush, and Big Data co-authored by Viktor Mayer-Schönberger and Kenneth Cukier.