Product Description

You have more information at hand about your business environment than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool.

In Competing on Analytics: The New Science of Winning , Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling.

Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics.

Product Details

• Amazon Sales Rank: #70887 in Books
• Published on: 2007-03-01
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 240 pages

Information Age, April 2007
Thought-provoking and inspiring

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.

From the Back Cover

In a world where traditional bases of competitive advantage have largely evaporated, how do you separate your company's performance from the pack? Use analytics to make better decisions and extract maximum value from your business process.

In Competing on Analytics: the New Science of Winning,Thomas H. Davenport and Jeanne G. Harris argue that the frontier of using data has shifted dramatically. Leading companies are doing more than just collecting and storing information in large quantities. They’re now building their competitive strategies around data-driven insights that are, in turn, generating impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling supported by data-savvy senior leaders and powerful information technology.

Why compete on analytics? At a time when companies in many industries offer similar products and use similar technology, distinctive business processes count among the last remaining points of differentiation. Many previous bases for competition—such as geographical advantage or protective regulation—have been eroded by globalization. Proprietary technologies are rapidly copied, and breakthrough innovations in products or services are increasingly difficult to achieve.

That leaves three things as the basis for competition: efficient and effective execution, smart decision making, and the ability to wring every last drop of value from business processes—all of which can be gained through sophisticated use of analytics.

Davenport and Harris show how exemplars—organizations as diverse as the Boston Red Sox, Netflix, Amazon.com, CEMEX, Capital One, Harrah’s Entertainment, Procter & Gamble, and Best Buy—are using new tools to trump rivals. Through analytics, these companies identify their most profitable customers, accelerate product innovation, optimize supply chains and pricing, and leverage the true drivers of financial performance.

A timely, much needed resource, Competing on Analytics promises to rewrite the rules of competition.

Customer Reviews

How to become an "analytical competitor"

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."

Becoming an analytic competitor
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.

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.