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

Competing on Analytics: The New Science of Winning | Thomas H. Davenport, Jeanne G. Harris | Helps Prepare us for the Business Future

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Competing on Analy...

	Competing on Analytics: The New Science of Winning Thomas H. Davenport, Jeanne G. Harris Harvard Business School Press, 2007 - 240 pages average customer review:based on 54 reviews view larger image
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highly recommended

Competitive analytics is a winning company culture.

This excellent book explains exactly what competitive analytics are and what you need to know to implement them. Thomas H. Davenport and Jeanne G. Harris divide it into two sections. The first five chapters constitute a handy guide to analytics: how high performance companies use them (and why underperforming companies do not), how to become a true analytic competitor, and how to use analytics to assess external and internal company processes. The second section gives you a roadmap to analytical competition: Why analysts are crucial to your success, the ins and outs of technology, and some thoughts about the future. The authors use many examples of true analytic competitors, such as Harrah's Entertainment, Google, Progressive Insurance and Amazon, to illustrate their message. We find that this interesting book is written in clear language for the general reader, but is sophisticated enough to engage those with more expertise.

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Helps Prepare us for the Business Future

I work in the Business Intelligence space and am quite impressed by how the authors show that proper and pervasive use of Business Intelligence differentiates companies from their competitors. This book also talks about how a company can determine where it is on a BI continuum, and discussed how to move from a low Level 1 to a high performing Level 5 "analytic competitor".

This book shows that BI has helped companies like Wal-Mart, Capital One, Harrah's and others, to drive themselves, and through their actions, growth in the US and global economies. As BI becomes widespread, companies will focus their strengths and, when they use BI correctly, grow themselves.

It is clear to me in reading this book that developing and maturing a BI capability is a must-do for all companies hoping to truly compete.

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Targeted towards executives and mid managers

The summary of this book is that using data analytics as a competitive tool has become essential. Not only it serves to make better business decisions, but it can increase effectiveness and efficiency at every phase of business such as marketing, HR, Customer Relations, and Supply Chain Management. Proper execution and usage of Business Analytics can make and save company money. More importantly, it can allow an organization to stay relevant in the marketplace by enabling an organization to understand the needs of the marketplace and how its products and services are fulfilling them.

The book is not technical. It is geared towards the executives and mid managers who want to get a better understanding of the growing Business Analytics (Business Intelligence) as a competitive tool.

The downside of book, strangely enough, is lack of concrete data analytics to support authors' claims. Although most of their conclusions are backed up by solid case studies and facts, there are almost no data analysis.

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A "Gee Whiz" Overstatement of the Impact of Analytics and the Potential of ERP Analytics

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.

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