Big Data @ Work: Dispelling the Myths, Uncovering the Opportunities
Thomas H. Davenport
Harvard Business Review Press (2014)

How to make much better decisions about and then with big data systems and capabilities

Over the years, I have read and reviewed all of Thomas Davenport’s previously published books as well as most of his articles and again acknowledge my substantial debt to him for all that he has helped me to understand — or to understand much better — in terms of the major issues, perils, and opportunities that business leaders have had to address during the last 12-15 years. His work in recent years has been especially valuable to me and countless others. More specifically, the information, insights, and counsel he has provided in Competing on Analytics (2007), co-authored with Jeanne Harris; Analytics at Work (2010), co-authored with Harris and Robert Morrison; Judgment Calls (2012), co-authored with Brooke Manville; Keeping Up with the Quants (2013), co-authored with Jinho Kim; and now Big Data @ Work (2014).

I agree with him: “Big data is here to stay and of substantial importance to many organizations. [Therefore] organizations and managers ignore it at their peril.” It is also true that a number of myths about big data have developed, in part because of confusion about the term. As Davenport explains, “First, there is the issue that big is only one aspect of what’s distinctive about new forms of data, and for many organizations, it’s not the most important characteristic…The term big is obviously relative — what’s big today won’t be so large tomorrow [and] what’s big to one organization is small to another…but the only real way in which ‘size matters’ with data is in the amount of hardware you will have to buy to store and process it…One other problem is that too many people- — and vendors in particular — are already using big data to mean any analytics or in extreme cases even reporting and conventional business intelligence.”

What to do…and what NOT to do? Davenport responds to these and other impirtant questions in the book. In his discussions with countless senior-level executives about what is often referred to as “Big Data, he explains,”I sense that most (if not all) of them are convinced of its potential importance but defer almost entirely to others (quants) to answer the two questions. Davenport is quite correct when stressing that there are business decisions — not big data decisions — to be made at the highest level, in collaboration with those who will be primarily responsible for the installation and maintenance of the systems needed. Moreover, those systems will be most effective and most efficient only if those who generate the data (whatever the data’s nature and extent may be) understand the needs to be served and the strategic objectives to be achieved. There must be an action plan for managers. Davenport includes a set of questions at the conclusion of each chapter. The answers to those questions will guide and inform the design and implementation of that plan.

In this context, I am again reminded of a passage in Judgment Calls, when Davenport and Manville offer “an antidote for the Great Man theory of decision making and organizational performance”: [begin italics] organizational judgment [end italics]. That is, “the collective capacity to make good calls and wise moves when the need for them exceeds the scope of any single leader’s direct control.” Those involved with the action plan for managers need to keep that passage in mind.

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

o Beyond the Big Data Hype (Pages 2-5)
o Deconstructing the Term Big Data (6-9)
o What’s New from a Management Perspective? (15-18)
o The New Opportunities from Big Data (22-26)
o Big Data and Key Business Functions (50-56)
o What’s Your Big Data Objective? (60-70)
o How Rapidly to Move Out? (79-84)
o The Team Approach (99-101)
o Retention of Data Scientists (104-106)
o What’s Really New About Big Data Technology? (115-118)
o The Big Data Stack (119-126)
o Other Factors to Consider in Big Data Success (146-151)
o Lessons from Big Data Start-Ups and Online Firms (154-167)
o Lessons Not Learned by Start-Ups and Online Firms (167-172)
o Integrating Organizational Structures and Skills (182-185)
o The Rise of Analytics 3.0 (194-203)

No brief commentary such as mine can do full justice to the wealth of material that Thomas Davenport provides. However, I hope I have at least indicated why I think so highly of him and of his contributions knowledge leadership. Here is how he concludes his latest and, in my opinion, his most important book thus far: The primary value from big data “comes not from the data in its raw form (no matter how big it is) but from the processing and analysis of it and the insights, products, and services that emerge from analysis. The sweeping changes in big data technologies and management approaches need to be accompanied by similarly dramatic shifts in how data supports decisions and product/service innovation. There is little doubt that analytics can transform organizations and the firms that lead the 3.0 charge will seize the most value.”

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How prepared is your organization to embark on big data projects?

Check out the “Big Data Readiness Assessment Survey” in the Appendix (Pages 205-209). It is based on the DELTA model described in Chapter 6 (Pages 135-136). The acronym refers to data, enterprise, leadership, targets, and analysts.