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Competing on Analytics: The New Science of Winning Kindle Edition

4.2 out of 5 stars 12 customer reviews

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Length: 244 pages Word Wise: Enabled

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Product Description


Thought-provoking and inspiring
-- Information Age, April 2007

From the Author

We've been hearing a lot about analytics these days. Explain what the term means.

By analytics we mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Analytics are a subset of what is now known as business intelligence.

How can the use of data to make business decisions lead to competitive advantage?

In today's global and highly interconnected business environment, traditional competitive differentiators--like geography, protective regulation, even proprietary technology--are no longer enough. What's left is the opportunity to execute a business with more efficiency and effectiveness than your competitors, and to make the smartest business decisions possible. Analytics can help do this.

Analytical competitors are organizations that select a few distinctive capabilities on which to base their strategies, and then apply extensive data, analysis and decision-making to support these capabilities. Whatever the capabilities, analytics can propel them to a higher level. We also want to point out that it is the human and organizational aspects of analytical competition that are truly differentiating.

Will readers find tools in your book to help them navigate this "new science," as you call it?

Competing on Analytics offers information on the topic, including key attributes of analytical competition. We also give examples of firms--companies like Netflix, Amazon.com, Google, E&J Gallo, and Procter & Gamble, and sports teams including the Boston Red Sox and New England Patriots--that are using analytics extensively within their organizations today. As well, the second half of the book is somewhat of a how-to guide that includes a roadmap for organizations wanting to compete on their analytical capabilities. We also devote time to discussing the two key resources--human and technological--needed to make this form of competition a reality.

You mention a few professional sports teams above, and in the book you discuss how analytics cuts across both industries--business and sports. What's the connection?

Think of what business and professional sport organizations have in common: both have large amounts of data; talented but expensive human resources; the need to optimize critical resources; and of course, the need to win. Many baseball teams--the Red Sox, the Oakland A's, the St. Louis Cardinals--and U.S. professional football teams are taking a more analytical approach and winning. In addition to the Patriots, we highlight the Tennessee Titans and Green Bay Packers football teams, both increasing their reliance on analysis and statistics to stay competitive. And it's not just a U.S. phenomenon: European soccer team AC Milan uses predictive modeling to prevent player injuries; and has even created the "Milan Lab" to identify risk factors. In fact, several members of the World-Cup winning Italy national team trained at the lab.

In the foreword to the book, Gary Loveman of Harrah's lists several reasons why common organizational thinking actually impedes "analytic management." Can you talk about this?

As you know, Gary is one of the pioneers in this industry and Harrah's successes have been widely documented. Gary cites four common factors that hinder analytical competition: deeply embedded conventional wisdom that has been around for so long, it's hard to reverse; decision making--especially at high levels--that fails to demand rigor and analysis; employees themselves who are not willing or equipped to do analytic work; and the power of ideas over data. It's our hope that this book will upend these barriers and help organizations start thinking of analytics as a framework for success.

Product details

  • Format: Kindle Edition
  • File Size: 528 KB
  • Print Length: 244 pages
  • Page Numbers Source ISBN: 1422103323
  • Publisher: Harvard Business Review Press; 1 edition (6 Mar. 2007)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ASIN: B004OC072Q
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Enabled
  • Enhanced Typesetting: Not Enabled
  • Average Customer Review: 4.2 out of 5 stars 12 customer reviews
  • Amazon Bestsellers Rank: #314,706 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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4.2 out of 5 stars
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Top Customer Reviews

Format: 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.
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Comment 19 of 19 people found this helpful. Was this review helpful to you? Yes No Sending feedback...
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Format: Hardcover
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".
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Format: Hardcover
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.
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