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Review of Tom Davenport’s "Competing on Analytics"

Competing on Analytics: The New Science of Winning, Davenport, Thomas H. and Jeanne G. Harris

 

Tom Davenport has turned his attention of late to the prospects for business intelligence and information analytics. Competing on Analytics offers a managerial introduction to the topic. It emphasizes why organizations ought to be interested in the topic, what kinds of payoffs they might expect, and how organizations will need to adapt to take advantage of robust analytics. Davenport and co-author Jeanne Harris of Accenture split the book into two major sections. The first deals with describing how analytics can be used as a competitive tool; the second with the organizational challenges of building analytical capabilities. Overall, it’s a relatively short book and is well-suited to its target audience. On the other hand, if you’re on the receiving end of a mandate to build an analytical capability after someone higher in the food chain has gotten excited about the topic, don’t expect quite as much in the way of detailed implementation advice.

Davenport and Harris set out a stage-model of analytical capabilities starting with "analytically impaired" and ending with "analytical competitors." Partly, this is to support an argument they make that there’s an advantage to managing analytics at an enterprise level. My cynical side suspects that this advantage lies primarily in providing a clear target for the likes of Accenture or SAS to sell to.

Given that every new capability benefits from senior executive attention and that everyone wants to get on the CEO’s calendar, are there, in fact, compelling reasons that analytics deserves to be on this short list? Two come to mind. One is that the expertise called for in effective analytics is scarce. Better to have that expertise directed at the targets of greatest opportunity by those best positioned to judge. Two, the competitive business opportunities that might yield to analytics are more likely to be found from the perspective of those with an integrative view of the enterprise.

The authors walk through major functions of the enterprise identifying opportunities and examples of how analytics have been successfully applied. There are clearly an abundance of opportunities to apply analytical tools and techniques to improving internal processes, optimizing supply chains, and leveraging marketing.

One problem with the focus on describing the business opportunities for analytics is that the variety of potentially applicable tools gets short shrift. All books have to make decisions about what to put in and what to leave out. Given the intended audience, I can understand the decision to focus on the business side of the equation rather than on the tools side. On the other hand, glossing over the complexities of the statistical tools and algorithms has its own risks. Organizations risk creating a new class of wizards whose dark arts must be taken on faith or they risk putting dangerous tools in the hands of amateurs who will be blind to both the limits and the dangers of the tools.

This brings us to the second part of the book and the challenges of building an analytical capability. I’ve already alluded to my fundamental concern, which is that the stage model that Davenport and Harris develop doesn’t lead to the level of actionable advice you will need to navigate from stage to stage. I’m actually not terribly concerned about the obligatory call for senior executive support. Everybody does it, it does make a difference, and you had better invest some time in making a business case that will draw and hold the attention of someone in the food chain who controls the necessary resources.

What I wanted to see (and have yet to see in any of the discussions of BI and information analytics I’ve seen so far) is a systematic way to connect analytic capabilities to decision makers. Davenport and Harris touch on this in their useful distinction between analytical professionals and analytical amateurs, but they don’t dig deeply enough.

Analytical professionals will be skilled in extracting insights from masses of data. Given an hypothesis, they are capable of testing and validating it. Whether they will have sufficient sense for the business to formulate productive hypotheses is more debatable. On the converse, analytical amateurs, more deeply knowledgeable of the business, will possess a better sense for where to look in the data and less skill in teasing out the insights buried within. Bridging this gap requires educating each side about the other. It’s a two cultures problem that I haven’t seen effectively addressed and is at the heart of getting analytics to take root organizationally beyond the tenure of the current managerial advocates.

There are two questions I am left with at the end of this book. The first is how do you make evidence and facts the ultimate source of power for decision making if they aren’t already? There will always be an existing distribution of power in any organization. In Daniel Patrick Moynihan’s famous dictum “everyone is entitled to their own opinions, but not their own facts.” Are we in an organization where the best facts win or where the strongest opinions win? How do we get from the latter to the former?

The second question I am left with is how does the role of management evolve as we move toward greater reliance on data and algorithms to make the best decisions? We survived when inventory management, for example, shifted from being a serious managerial responsibility to a programmatic system output. Although I suspect it wasn’t a great time if you were an experienced inventory manager. This is an ongoing evolutionary process, of course. But, like other technologically driven processes, the cycles of change are now faster than the cycles of managerial careers. That’s raising the anxiety levels of many people currently occupying positions of power and influence. Anxiety and power in the same mix make me anxious. Although it’s unfair and unrealistic to expect it, I would have preferred that Davenport and Harris had more advice and counsel to offer in how to reduce that anxiety.

{ 2 } Comments

  1. Tim van Gelder | July 29, 2008 at 9:52 pm | Permalink

    Interesting post… particularly (to me) your point about the need for “a systematic way to connect analytic capabilities to decision makers”. BI suites always seem to stop short of the actual decision process itself. They deliver information, possibly insight, but even insight has to be reconfigured as argument or evidence if it is going to make a positive contribution to decision. So how do we go beyond displaying information and start organising it in the form of the case for (or against) some action?

  2. Jim | July 30, 2008 at 2:01 pm | Permalink

    I think the first step is to help make the process more visible. Especially for decisions that sort of meander along via disorganized email interaction. But even face-to-face decision meetings are hard to parse in terms of the connections between options, evidence, and arguments.

    What’s been your experience with tools like bCisive in helping making these decision cases more visible?

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