Competing on Analytics: The New Science of Winning [Hardcover]

This book is a brilliant development of core concepts in an article co-authored by Thomas H. Davenport and Jeanne G. Harris that originally appeared in the Harvard Business Review. In it and now in this book, they explain how to become an analytical competitor: "an organization that uses analytics extensively and systematically to outthink and outexecute the competition" through support of a strategic, distinctive capability (e.g. Netflix and Wal-Mart), taking an enterprise-level approach to and management of analytics (e.g. Harrah's Entertainment and RBC Financial Group), sustaining a commitment to analytics by senior management (e.g. Jeff Bezos, founder and CEO of Amazon, and Rich Fairbank, founder and CEO of Capital One), and having large-scale ambition (i.e. the aforementioned companies as well as others "bet their future success on analytics-based strategies"), with senior executive commitment "perhaps the most important because it can make the others possible." Davenport and Harris classify companies within five stages of analytical competition:

Stage 1: analytically impaired ("flying blind")

Stage 2: Localized analytics (isolated, fragmented, disconnected, inconsistent, etc.)

Stage 3: Analytical aspirations (sees need, begins to explore options)

Stage 4: Analytical companies (enterprise-wide perspective, eager to innovate and differentiate)

Stage 5: Analytical competitors (analytics are the primary driver of performance and value)

Obviously, the challenge is to become a Stage 5 organization but an even greater challenge is to remain one. According to Davenport and Harris, companies that successfully compete on analytics have analytical capabilities that are difficult to duplicate, unique, adaptable to many situations, better than the competition, and renewable. By design and when utilized, those capabilities must also be able to accommodate all manner of changes within the given competitive marketplace. In some circumstances, in heavily regulated industries or when the analytics support an obsolete business model (e.g. large U.S. airlines such as American and United), analytics are not enough. Still another challenge is to identify those internal applications of business analytics that are clearly strategic and involve competitive advantage.

For me, some of the most valuable material is provided in Chapter 8 as Davenport and Harris explain how to align a robust technical environment with business strategies when incorporating analytics and other business intelligence (BI) technologies into their overall IT architecture. That is, a Stage 5 organization has "a full-fledged analytical architecture that is enterprise-wide, fully automated and integrated into processes, and highly sophisticated." Effective management of data requires correct answers to questions such as these:

1. Which data are needed to compete on analytics?

2. Where can these data be obtained?

3. How much are needed?

4. How can the data be made more accurate and valuable for analysis?

5. What rules and processes are needed to manage data from their creation through their retirement?

Here's another question which at least a few of those who read this review may be asking: Why make such a substantial investment in what it takes to become - and then remain -- a Stage 5 organization? Davenport and Harris provide an answer in the book's final paragraph: "analytical competitors will continue to find ways to outperform their competitors. They'll get the best customers and charge them exactly the price that the customer is willing to pay for their product and service. They'll have the most efficient and effective marketing campaigns and promotions. Their customer service will excel, and their customers will be loyal in return. Their supply chains will be ultraefficient, and they'll have neither excess inventory nor stock-outs. They'll have the best people or the best players in the industry, and the employees will be evaluated and compensated based on their specific contributions. They'll understand what nonfinancial processes and factors drive their financial performance, and they'll be able to predict and diagnose problems before they become too problematic. They will make a lot of money, win a lot of games, or solve the world's most pressing problems. They will continue to lead us into the future."

I saw my first application of advanced mathematics to a strategic business problem in 1970. Since then, I've seen hundreds of such applications. In over 95 percent of the cases, those charged with making decisions didn't want to rely on the math, didn't understand the math, and stopped using the math within a few years. Ten years later, no one even knows that the math was ever used.

There's a second problem: A lot of the advanced math looked better than it was. Nice graphs suggested certainty where the numbers and assumptions shouldn't have permitted such impressions to be formed.

Beyond that, a lot of the data being used had no predictive value . . . a particular problem with correlation-based conclusions and time series.

Finally, the mathematicians often solved the wrong problem.

Have there been a few places where advanced math has made a lot of difference? Sure, especially where real time decision making would overload an organization. Load management in airlines, logistical optimization in supply chains, and in providing alerts that service is needed.

The most valuable applications that I've seen came in places where proprietary data added new perspectives that no one else could imagine. These advantages came from new ways of gathering data . . . not just compiling all transactions into large data bases. In fact, the best math solutions I've seen for strategy wouldn't strain any body's calculator to solve. Typically, these are done on personal computers anyway because the graphical choices are better for presenting what's been learned.

Can more advanced math be employed for strategy and operations? Sure. But the failure rate will be high, the cost will be enormous, and many managements won't engage.

People like Gary Loveman are unusual: Most executives don't appreciate and pay attention to analytics while running a large company. They prefer accounting reports instead. That's not going to change very fast except among start-ups by mathematically literate leaders.

What's really going to happen is that the off-the-shelf business intelligence software companies are going to make progress in selling their offerings to those who want and can use better data and analysis. But I suspect it will take another generation before you'll see much company-wide use of analytics.

You'll notice that I didn't discuss this book very much so far. Why? It doesn't reveal much of anything other than what you read in business periodicals and press releases by various vendors who want to sell offerings related to analytics. I recommend you skip the book. It won't tell you what you need to know. You would do better to spend a few hours with someone who understands analytics discussing what might be done to improve your performance.

I've read and appreciated a number of excellent books by Thomas H. Davenport in the past, so I'm surprised this book turned out to be so over optimistic based on so little evidence . . . and stated awareness of the problems. I can only conclude that this book is intended to sell services related to analytics rather than to give people an objective sense of what they are up against.

Ultimately, there's another problem with this book: If you use analytics to fine tune the current business model, you'll steal time, money, and effort from the more important task of creating an improved business model. The authors fail to make a distinction between business-model-optimizing analytics and analytics for business-model improvement. The former runs the risk of making companies less flexible and less able to compete.

The Balanced Scorecard approach, by comparison, is a healthier way to go by encouraging quantification of what needs to be done and tracking of how you are doing. From that discipline, you define the areas where innovation is needed . . . including analytics. Hiving off analytics as a separate subject simply creates the potential for misuse of a potentially valuable discipline.

Tom and Jeanne have written an excellent new book (building on a paper they wrote some time ago) about what they call "analytic competitors", that is to say companies that use their analytic prowess not just to enhance their operations but as their lead competitive differentiator. The book discusses a number of these analytic competitors and gives an overview of how analytics can be used in different areas of the business and how you can move up the analytic sophistication scale.

The book has two parts - one on the nature of analytical competition and one on building an analytic competency. The first describes an analytical competitor and how this approach can be used in both internal and external processes. The second lays out a roadmap for becoming an analytical competitor, how to manage analytical people, a quick overview of a business intelligence architecture and some predictions for the future.

They define an analytical competitor as an organization that uses analytics extensively and systematically to outthink and outexecute the competition. The analytics are in support of a strategic distinctive competency and they argue, persuasively, that without a distinctive capability you cannot be an analytic competitor.

The book outlines what they call four pillars of analytical competition- a distinctiive capability, enterprise-wide analytics, senior management commitment and large scale ambition. They lay out 5 stages of analytic competition from "analytically impaired" to "analytic competitor". The importance of experimentation is made clear and the book repeatedly emphasizes the need for companies and executives to be willing to run the business "by the numbers".

The book is full of stories about how companies compete analytically and this is one of the book's strengths. It also has a great list of questions to ask about a new initiative and outlines a number of ways to get a competitive advantage from your data. Regardless of the competitive approach, the need for analytical executives to be willing to act on the results of analyses is made clear. The book ends with a great list of changes coming.

This is a very interesting book both for those interested in competing on analytics and those interested simply in making more use of their data.