I bet if the term "analytics" is replaced by "CRM" throughout this book, it will remain intact as it is. It gives the high level management the basics of CRM/analytics, and the need to commit seriously company wide, especially their own time and career. However, little is offered on the execution, that the employment of external consultants like the authors is the legitimate way out. In short, if you know not CRM/analytics, this is marginally readable and helpful. If you already have one or more book else on CRM/analytics, please give this a pass.

It one of the most interesting book that I have read, it show you the future tendent

As data becomes more available across the enterprise, the challenge becomes how to leverage data for a competitive advantage. This well-written book defines the benchmarks from which organizations should measure their ability to use data to establish a competitive advantage. Highly recommended reading.

This was an excellent book on analytics, with great examples of how companies have been able to leverage data to make better business decisions with software tools like CUBE IT. It is well written and very timely.

I find this book very interesting for my academic program. I'm using it for my dissertation. It is very specialized on increasing business performance through IT, above all analytical process.

Overall though these writers are correct that organizations now days are sitting on huge amounts of data. Yet currently we are only know little on how to get information out of this data.

This book does little to explain how to do it. What it does do is waffle on again and again on the same point. Often reads more like an advertisement then an explanation on the subject. If you want to know what analytics are all about you will not find it here. Also many of the facts quoted here are dubious e.g. I know many mathematicians that do this and they do not earn more then people with MBAs. It also needs summarizing.

The book was a disappointment. It has interesting info but I couldn't figure out who their target audience would be. The book is structured as a how-to guide for companies who would like to become analytics oriented. It is divided into two parts, the first of which offers examples of different analyses used by various companies and the second focuses on the skills and tools needed to implement an analytics-based decision making strategy. Unfortunately each part seems to be geared to a different audience. The first gives detailed information on different kinds of analyses, down to the methods recommended. This is very interesting for those of us already using analytics as a decision making tool but it would be hard going for people who are not analytically oriented to start with. And the second part goes into great detail on what tools and skills are needed to develop analytical muscle, information. Anyone who is knowleadgeable enough to enjoy the first half does not need the second half (there is nothing new there, trust me). And those who could use the info from the second half, will not find the first half interesting enough to make it all the way to the second part. A book like Supercrunchers, that focuses on successful projects in all areas, presented in very general terms, would be more appropriate to people who are neophytes in the wonderful world of analytics as a business tool.

This is a great overview on analytics and how to start implementing them in a corporate environment. Would be a good start to have a management team read and get on board with analytics.

This was a great book about the current and future direction of reporting and analytics. I would recommend it for anyone interested in pursuing this field. I am proposing some of these initiatives to my company.

Analytics has been a hot topics since late 1990s. Lots of focus has been given in the investment of technolgoy but little companies has been successful in getting the full benefits or return on their investment. This book is the first book that provide a holistic view of how to integrate analytics into business and make it a competative advantage. For business that are still looking for ways to set up the analytics, it is an excellant book to give an high level roadmap (Chapter 6). It also highlights the key issues of implementations eg internal business process and the political involved, which is very real. However, it doesn't provide a solution or any suggestions on how to handle these issues. Chapter 7, Managing Analytics People is the most interesting one, but I think it is arguable. Overall, this book is a high impact book as it bring up the real issues/challenges. But if you can make it a success, it involved the whole organizational change!

Davenport and Harris give an overview of the field of business intelligence (BI), which concerns systems for improving decisions and workflow. The ideal is the "analytical competitor", a company that has eliminated guesswork from business, and is driven by strategic decisions based on factual reality. We get numerous examples, including Google, Amazon and Netflix, but BI is not limited to technology companies.

BI is the infrastructure of systems and people that process objective facts into decisions, which is often automatic. Any company can use analytics to augment its strategy, and the first step is to assess your current analytical capability, and then to progress through one or more steps to the right level of analytics.

The book is best read is a checklist for your BI capability. It is a complete overview of what is possible, with enough technical detail to suggest your next move. The most exciting part is that only a few companies make sufficient use of analytics now. If you can get BI to support your company's distinctive strategic capability, then your reliance on the facts of reality will out-execute the competition.

I highly recommend this book as an introduction to analytics. Sure it won't make you an expert overnight, but it will succinctly describe the field, along with current efforts of various companies in applying information to gain a competitive advantage.

However, it is really just an introduction to a broad range of ideas. If you want to know more about customer analytics, definitely check out Managing Customer Relationships: A Strategic Framework. It's a lot longer than this book, but well worth the time.
And there are a lot of more technical books available for the internal processes described in this book, such as Pricing and Revenue Optimization to name just one, but also books on spreadsheet modeling and decision analysis, along with supply chain management, and a host of other topics.

Let this book be your road map to finding out where your organization currently stands in terms of competing on analytics, and what you'll need to learn more of in order to move to the next level.

So I can't give "Competing on Analytics" 5 stars, but I can say that you'll probably be glad that you read it.

I highly recommend this book as an introduction to analytics. Sure it won't make you an expert overnight, but it will succinctly describe the field, along with current efforts of various companies in applying information to gain a competitive advantage.

However, it is really just an introduction to a broad range of ideas. If you want to know more about customer analytics, definitely check out Managing Customer Relationships: A Strategic Framework. It's a lot longer than this book, but well worth the time.
And there are a lot of more technical books available for the internal processes described in this book, such as Pricing and Revenue Optimization to name just one, but also books on spreadsheet modeling and decision analysis, along with supply chain management, and a host of other topics.

Let this book be your road map to finding out where your organization currently stands in terms of competing on analytics, and what you'll need to learn more of in order to move to the next level.

So I can't give "Competing on Analytics" 5 stars, but I can say that you'll probably be glad that you read it.

I too was disappointed by this book. Maybe my expectations were too high, but I thought a book about analytical competition would have been a little less superficial about the specific analytical techniques being implemented to gain an edge in business. It seems like this book was written for the analytical impaired (to use the authors' verbiage) as a means to convince them that quantitative analysis is useful. I would encourage the authors to follow-up with a volume more focused on the creative application of analytical methods to supporting business decisions (based upon the research they accomplished to produce this book).

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.

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.

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.

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.

Tom Davenport's book provides the reader with excellent insight into what your company should be doing to gain and maintain competitive advantage. All companies have access to data - the challenge is to manage that data to turn it into fact-driven insights and decisions that drive higher performance. That data can be from internal resources (process performance, product performance) external data (from customer interaction, supply chain info, market info) or other. The key is to have strategy aligned metrics that drive performance and analytics to monitor and manage performance --> using analytics to drive performance.

The best thing about this book is the inspiration it can provide business leaders who are still skeptical about the value of analytics and business intelligence. In working with customers trying to implement these solutions, far too many cite lack of executive level interest and understanding as a cause for failure. Competing on Analytics gives case after case of how businesses have differentiated themselves on their use of data, delivering better customer service and improved financial performance. The cases range from the innovators like Netflix to traditional such as BankCo and airlines (here, the authors highlight both the successes and the analytic failures). This book should be on the must read list for business people looking for ways to perform better and for analytic and BI experts charged with supporting the business in leveraging these capabilities.

This book is incredibly useful, and some of the hard-core propellerheads who have criticized it for not being rigorous enough are missing the point. It is not intended to be a highly analytical proof of the value of any particular analytical technique. It's a clear, persuasive discussion of how to establish a broad analytical capability within a company, and some of the benefits that might accrue from having that capability. It shows that there is a correlation (no causation is argued) between being analytical, and being successful from a financial standpoint. I have used it to convince other managers in my company that we need to take a more analytical perspective on our business and industry. They have found it just as compelling as I did. The stories about the Red Sox (who seem to be doing pretty well again this year--surely in part because of their analytical orientation) and other sports teams only add to its appeal.

This book is a good introduction to the applications and benefits of business analytics. It focuses on benefits and has minimal coverage of analysis methods.

I am somewhat put off by the subtitle "The New Science of Winning" which probably helps sell the book, and which reflects the extreme culture of winners and losers that dominates business culture these days. History shows that any culture without a better balance of competition with cooperation and ethics will be eclipsed.

Mark Twain once said something to the effect that it isn't what you don't know that gets you into trouble, it's what you know for certain that isn't so that will get you. Too many businesses are run on assumptions, guesses, and inertia. What we are doing now worked in the past so lets keep doing it. Shareholders lose a lot of money when their businesses are run with that kind of thinking.

This book is about fact-based decision making. It is really more of an introduction to the subject than a detailed text, but it is still quite useful for those wanting to learn the basics of the subject. The first five chapters discuss what analytics are, how you compete using them, and the growth path from wondering what an analytic competitor is through the fives steps to becoming one. They also discuss what it means when using internal data that you completely control, and what it means when you do it using data you control and supplier or customer data that you do not control.

The last four chapters take on the practical side of implementing a road map to becoming an analytic competitor. I particularly enjoyed the chapter emphasizing that all your plans will fail if you don't have the right people. Systems alone won't do it. The next chapter discusses the kinds of systems you need. The last chapter discusses the future of analytics.

For the right audience, this is a fascinating book. The stories about businesses succeeding by using analytics or getting themselves into serious trouble by ignoring them are all good and entertaining. Be careful, though. Some of the stories talk about instances (such as the Red Sox losing the World Series by letting the pitcher go beyond his statistical maximum pitching range) rather than trends and large numbers of events. Statistics don't work on instances. That is, at any given moment a coin might come up heads or tails. Just because there have been ten heads flips in a row does not mean you should take less than 50-50 odds on the next flip. It is still 50-50. That pitcher might have won, might have lost that game and it would have become part of the statistical information. However, for the stats to become powerful, you would have to be able to make a strong prediction over a series of games that he pitched. That is, if he goes beyond X pitches in 10 games he will lose about 8 of them. That means he still wins two (or one or three) and you don't know when in the series the wins will come.

The idea that very small observations can be exploited for big advantage is very important in today's ever more competitive business climate. For example Harrah's learned that moving the odds on slot machines one-tenth of one percent in their favor did not affect customer play at all, but netted them at extra $80 million (company wide). Marriott's hotel management system improves hotel performance by a couple percent. Remember that these improvements incur little cost, so most of the improvement flows quickly to the bottom line.

I thought that might get your attention. Read it so you can learn and profit from it.

Reviewed by Craig Matteson, Ann Arbor, MI

Mark Twain once said something to the effect that it isn't what you don't know that gets you into trouble, it's what you know for certain that isn't so that will get you. Too many businesses are run on assumptions, guesses, and inertia. What we are doing now worked in the past so lets keep doing it. Shareholders lose a lot of money when their businesses are run with that kind of thinking.

This book is about fact-based decision making. The first five chapters discuss what analytics are, how you compete using them, and the growth path from wondering what an analytic competitor is through the fives steps to becoming one. They also discuss what it means when using internal data that you completely control, and what it means when you do it using data you control and supplier or customer data that you do not control.

The last four chapters take on the practical side of implementing a road map to becoming an analytic competitor. I particularly enjoyed the chapter emphasizing that all your plans will fail if you don't have the right people. Systems alone won't do it. The next chapter discusses the kinds of systems you need. The last chapter discusses the future of analytics.

For the right audience, this is a fascinating book. The stories about businesses succeeding by using analytics or getting themselves into serious trouble by ignoring them are all good and entertaining. The idea that very small observations can be exploited for big advantage is very important in today's ever more competitive business climate. For example Harrah's learned that moving the odds on slot machines one-tenth of one percent in their favor did not affect customer play at all, but netted them at extra $80 million (company wide). Marriott's hotel management system improves hotel performance by a couple percent. Remember that these improvements incur little cost, so most of the improvement flows quickly to the bottom line.

I thought that might get your attention. Read it so you can learn and profit from it.

Reviewed by Craig Matteson, Ann Arbor, MI

MY RATING SYSTEM:

* - if you have to chose between torture and reading this book, then you might want to consider reading the book - although it depends on just how severe the torture would be.

** - if you've lost your job and have quite a bit of free time on your hands, and don't have anything else better to do, then you might want to consider reading this book; don't expect to learn much or really be entertained. It will however, help you pass the time until your death.

*** - meh...I'm indifferent. Reading this book will not alter your life in any significant way, yet it is not so horrendously dreadful that your taking the time to read it will be a complete waste of time.

**** - Good book to great book zone here. You should probably read this book if you have some spare time. This book could be interesting, entertaining, or informative.

***** - Outstanding book! Make time to read this book - you'll learn or be entertained or intrigued. The book might even be good enough to provide original or helpful insights into the world that we live in.

REVIEW:

Competing on Analytics serves as an interesting, albeit limited, introduction to the concept of using complex data collection, management, and analysis techniques to gain a competitive edge in business.

For me, the book served as a useful introduction, but fell far short of satisfying the objectives I had in mind when I first came across it. What I was expecting was a book that provide a detailed guide to developing and implementing an analytical approach to business decision making. While early on the authors acknowledge the limitations of the book, I found what followed to be less than satisfying.

The book contained a variety of examples of companies that were using analytical techniques to improve the quality of business decision making, and discussed a variety of business areas in which companies might want to adopt such analytical techniques but failed to present comprehensive case studies that would provide real guidance to readers. I would have liked to have been led through a few cases, from a diverse set of industries, where the authors describe what information was collected and why, how the information was manipulated, analyzed and presented, and how the entire analytics process was influenced by and/or influenced the company's strategy and performance. Instead, the book left me with the impression that I need to go out an hire a consulting firm to lead me through the development of an analytics program.

One of the most ironic components of the book was that while it touted the use of analytical techniques and objective analysis to motivate business decision making, it's argument was largely based on anecdotal evidence of a handful of companies that have adopted analytical approaches.

Technology & the easy with which information spreads has rendered many products and services easily replicable. Companies need to compete on the basis of something their competitors can't recreate. What companies don't have ready access to is each other's data, i.e., on customers, suppliers, & processes. What companies do with this data is what can set them apart from competitors.

Davenport & Harris describe how data is transformed into competitive advantage by discussing the types of information used in analytics, the stages of becoming a more analytic corporation, and many examples of companies who have applied analytics to successful operations. Problems encountered down the road to becoming more analytical were similar to those described in another recent book on the criticality of enterprise data, Information Revolution by Davis, Miller, & Russell.

This book contains no numeric formulas or specific procedures for using analytics, but it is an excellent as an overall survey of business analytics as used today.

This book is meant for those who make things happen and need to gain a fresh perspective. It is not meant for those who know a lot but can't make things happen yet keep looking for more information, while criticizing a good effort, which without doubt could have been better.

Not a lot of meat to this topic other than the obvious. Not very exciting stuff.

I was eager to read this book due to my experience in this subject.

While everyone wants to be a stage 5 organization this book never develops a path between an organization that is stage 4 versus one that is stage 5. The apparent difference (left to the reader's imagination) is that a stage 5 organization - one that is a analytic competitor - has a great story to tell. They were a stage 4 organization, but someone figured it out something useful and voila, they're now stage 5.

On the other hand, they do a decent job of defining the three lower stages (anti-analytics = 1, open-to-analytics-but-not-doing-it = 2, have-people-on-it = 3, and organizational-acceptance-of-analytics=4).

I also took exception to the their assertion that optimization is the highest value analytic method in figure 1-2 they present on page 8. The point of competing on analytics is to determine what the central business problem is and to apply the appropriate technique. In some business scenarios predictive modeling may be much more valuable than optimization. There are areas of thinking like this that they simply don't develop to a level that is useful. The editor really let the authors down by not making them develop these kind of ideas or forced them to pull the idea.

Much of this book's material in later chapters seems to have been pulled out of PowerPoint presentations - for example their treatise on Data Quality. It consists of these questions:
Is it correct?
Is it complete?
Is it current?
Is it consistent?
Is it redundant?
Is it in context?
Is it controlled?
* are you kidding me?!? That's it? Why they included this sort of death by PowerPoint junk really surprised me. Where was the editor?

By the time I reached that last third of the book I had to make myself finish it due my disappointment on a great subject. How this became a Harvard Press book is really amazing - it's far below their other books.

As a business professional who is very passionate about the value of analytics, I found this book an excellent overview on how companies can benefit from embracing analytics. The content is very accessible and it is a fairly quick read. I have shared this book with my department managers and encouraged my colleagues to read it as well.

I read Competing on Analytics because my boss began swearing by it, and my conversations with her were starting to get seriously confusing. You should read this book if you don't have a ready and clear answer to the question: "what are the differences among the concepts of business intelligence, data mining, analytics and six sigma?" That's actually also a pretty good interview question for the hordes of job-seekers who are undoubtedly going to repackage themselves as analytics professionals following this book. There are two good reasons to read this book. First, you are going to hear a lot about it wherever you work, and it is likely going to figure in your company's next effort at introspection and change, so you might as well get ahead of the crowd. Second, there is actually a lot of good stuff in this book, whether or not you are part of the "data-driven" choir (I am not; though I work closely, kicking and screaming, with many people who are).

This book presents a fairly simple overview of using quantitative methods in business. If you have an engineering or other quantitative orientation, it should take no more than 2 - 3 hours to read this; and you won't have gathered much more than you started with.

This book is, for the most part, a disappointing mix of fallacy, circularity, inconsistency, banality and utopian promises. If you've read books such as N. Taleb's "Fooled by Randomness", P. Rosenzweig's "The Halo Effect", or, for the classically educated, D. Fischer's comprehensive "Historians' Fallacies" (1970), you can easily while away a few lazy hours spotting the bad reasoning throughout this book. I'll give a few examples in a minute or two.

The effect is more disappointing than infuriating because, unlike many other business authors, the authors aren't claiming to have some unique insights or to discovered some new principle of strategy; their aims are refreshingly modest. About the best I can say for it is (a) if you never read the January 23, 2006 Business Week cover story "Math Will Rock Your World" (which, as of this writing, was available for free online) you can learn that sophisticated mathematical tools are being used in business, and that the market value of math Ph.D.s is increasing, and (b) if you did read that article and don't know much else about these tools, you can learn a little bit of terminology/jargon from the text boxes scattered throughout the book, and maybe a little bit about the political problems of implementing them (@145-146). As other reviewers have pointed out, the book won't teach you how to use or implement such tools. (The authors are forthright about this, e.g. @22.) Unfortunately, the authors also don't give any concrete illustration, with formulas or pictures or even an extended analogy, of how any such tool is used; they merely assert the tools' efficacy.

Or rather, -- and this is where the trouble begins -- they don't merely assert, they *emphatically* assert, as in the book's rhapsodic concluding paragraph about what the future looks like for analytic competitors (@186): "They'll get the best customers and charge them exactly the price that the customer is willing to pay ... They'll have the most efficient and effective marketing campaigns and promotions. Their customer service will excel ... Their supply chains will be ultraefficient, and they'll have neither excess inventory nor stock-outs," etc., a prophetic vision of near-Biblical proportions (cf. Dvorim a/k/a Deuteronomy, Chapter 11). (However, I was stumped by one item in this catalogue of blessings for the faithful: "They'll have the best people or [sic] the best players in the industry" -- what's the difference?)

Having treated of utopian promises, here are a few examples of the other flaws I mentioned:

A. FALLACY (and related sins): The most obvious ones in the book are: (i) confusing causation with correlation, (ii) attempting to lead the reader into such confusion, and (iii) "post hoc, propter hoc" (if Y comes after X, Y must have been caused by X).

(i): At page 178, the authors discuss "direct discovery technologies" that mine data and would "let managers go directly to the cause of variances in results or performance. This would be a form of predictive analytics, since it would employ a model of how the business is supposed to perform, and would pinpoint factors that are out of range in the causal model of business performance."

First we need to deal with a textual ambiguity: the meaning of "supposed" in this context. If "supposed to" is normative -- i.e. meaning "is desired to" -- then to call technology "predictive" when it uses such a model is quite a stretch. So does "supposed to" have a more neutral meaning, like "is anticipated to"? I'll assume that this fits the context better.

Now let's get to the real problem: The model is looking at results and performance -- i.e., the past. As statistical programs are wont to do, the model can identify correlations; and let's assume that it will make predictions based on the observed correlations (there are some commercial software packages that promise this). That is quite different from divining causes, which nonetheless is what the authors have twice asserted in this passage. I leave aside the question of predictive value based on past results; read Taleb or your mutual fund prospectus ("Past results are no guarantee of future performance").

(ii) At pp. 46-47, the authors describe correlations between "low performance" in using analytics and financial underperformance, and "high performance" in using analytics and financial overperformance. The ratings of analytics and financial performance are based on self-evaluations, not objective measures. This is the "halo effect" in spades, as most recently described in Rosenzweig's book -- happy (profitable) companies are happy about everything, and unhappy (less profitable) companies blame themselves about everything. More to the point, though: the companies in these two groups make up an aggregate of only 29% of their sample. They say nothing about the middle 71%. For all we know, "high performance" in analytics also correlates well with mediocre financial performance.

(iii) At pp. 18-19, the authors tell a cautionary tale about the Red Sox manager who defied the quants in the 2003 American League Championship Series against the Yankees: Red Sox analysts "had demonstrated conclusively" that pitcher Pedro Martinez became much easier to hit against after about 7 innings or 105 pitches, and warned the manager that "by no means should Martinez be left in the game after that point." However, "in the fifth [sic] and deciding game of the series," the manager allowed Martinez to continue pitching into the 8th inning. The result? "[T]he Yankees shelled Martinez. The Yanks won the ALCS, but [the manager] lost his job. It's a powerful story of what can go happen if frontline managers and employees don't go along with the analytical program." Sounds like a sportscaster channeling the Borg.

Even if we take this story at face value, one has to wonder, was that all there was to it? Does the Red Sox' losing the series after Martinez pitched into the 8th inning mean that his pitching was the cause? Was there bad fielding involved, for example? Or did the Yankees' adrenalin have anything to do with it? And what was the score when Martinez was removed?

Thoughts like these moved me to look up the box score of the game. First of all, Martinez didn't pitch in the fifth game -- probably what the authors were referring to was the 7th game. In that game, it's true, Martinez gave up 3 runs in the 8th inning. But what was the result? The Yankees only TIED the game, 5-5, to that point. They didn't win until the bottom of the 11th inning, when they scored one more run (off the third Red Sox pitcher brought in after Martinez). By the way, the game was in New York, so do you think the home crowd's energy might have been a factor? "Post hoc, propter hoc": it don't come any better than this.

B. CIRCULARITY: E.g.: At pp. 48-49, one of the 5 characteristics of analytic capabilities possessed by companies "that compete successfully on analytics" is that such capabilities are "better than the competition [sic]." I guess that's why they "compete successfully." BTW, two others in the list of five are that such capabilities are "hard to duplicate" and "unique" (@48). Same cannot be said of items in this list.

The discussion about the ideal characteristics of executives in "analytic competitors" (@135-136) hints at a more substantive circularity. One such characteristic an exec should possess is he or she should be a "passionate believer in analytical and fact-based decision making". However, when describing how "analytical leadership emerge[s]" (@136-137), the authors can only adduce cases in which the leaders (i) found a company on the principle of using analytics from the get-go, (ii) come in as a new senior exec bringing with them the idea of using analytics, or (iii) are a younger generation in a family-owned business. The authors don't mention anyone who "saw the light" and became a convert. So companies whose leaders are passionate about analytics will use analytics.

C. INCONSISTENCY: E.g.: The "most analytically sophisticated and successful" companies use analytics, inter alia, to support "a distinctive strategic capability" (@23). "Having a distinctive capability means that *the organization* views this aspect of its business as what sets it apart from competitors" (@24; emphasis added). However, "not all businesses have a distinctive capability" -- e.g., Kmart, USAirways and GM don't, because "to *an outside observer* they don't do anything substantially better than their competitors" (id., next paragraph; emphasis added.)

D. BANALITY: Parts of the book (esp. Chapter 6, a five-step "road map to enhanced analytical capabilities"), sound like a MadLibs that could just have easily been filled in with strategic planning, Six Sigma, or dozens of other management fads through the decades. E.g., a "Stage 4" company is defined as "analytics are respected and widely practiced but are not driving the company's strategy" (@ 125); "It is important to specify the financial outcomes desired from an analytical initiative to help measure its success," @ 127; "Assuming that an organization already has sufficient management support and an understanding of its desired outcomes, analytical orientation, and decision-making processes, its next step is to begin defining priorities," @id.

Finally, the whole enterprise of "analytics" has a certain banality too, through no fault of the authors of this book: it's one more in a string of dreary revivals of Taylorism on steroids, albeit this time with 21st-Century pharmaceutical know-how -- and with far greater potential to invade personal privacy. Some of its practitioners think it would be a good idea to, say, deny jobs to people simply on the basis of low credit scores, since people with low credit scores can be assumed to have lots of other problems too (reported without any explicit endorsement or disapproval by the authors @ 26). That such an "analytical" criterion might compound those folks' problems and low credit scores is not worth a mention. Here is the point at which the authors' omissions and gaffes stop being silly, and where banality stops being benign. It is more than a disappointment that you won't find ethics discussed in this book.

This book is, for the most part, a disappointing mix of fallacy, circularity, inconsistency, banality and utopian promises. If you've read books such as N. Taleb's "Fooled by Randomness", P. Rosenzweig's "The Halo Effect", or, for the classically educated, D. Fischer's comprehensive "Historians' Fallacies" (1970), you can easily while away a few lazy hours spotting the bad reasoning throughout this book. I'll give a few examples in a minute or two.

Otherwise, about the best I can say for it is (a) if you never read the January 23, 2006 Business Week cover story "Math Will Rock Your World" (which, as of this writing, was available for free online) you can learn that sophisticated mathematical tools are being used in business, and that the market value of math Ph.D.s is increasing, and (b) if you did read that article and don't know much else about these tools, you can learn a little bit of terminology/jargon from the text boxes scattered throughout the book, and maybe a little bit about the political problems of implementing them (@145-146).

As other reviewers have pointed out, the book won't teach you how to use or implement such tools. In fact the authors never provide a concrete example of how any such tool is used or describe any of them with formulas or pictures; they merely assert the tools' efficacy. Or rather, they don't merely assert, they *emphatically* assert, as in the book's rhapsodic concluding paragraph about what the future looks like for analytic competitors (@186): "They'll get the best customers and charge them exactly the price that the customer is willing to pay ... They'll have the most efficient and effective marketing campaigns and promotions. Their customer service will excel ... Their supply chains will be ultraefficient, and they'll have neither excess inventory nor stock-outs," etc., a prophetic vision of near-Biblical proportions (cf. Deuteronomy, Chapter 11). (However, I was stumped by one item in this catalogue of blessings for the faithful: "They'll have the best people or [sic] the best players in the industry" -- what's the difference?)

Having treated of utopian promises, here are a few examples of the other flaws I mentioned:

A. FALLACY (and related sins): The most obvious ones in the book are: (i) confusing causation with correlation, (ii) attempting to lead the reader into such confusion, and (iii) "post hoc, propter hoc" (if Y comes after X, Y must have been caused by X).

(i): At page 178, the authors discuss "direct discovery technologies" that mine data and would "let managers go directly to the cause of variances in results or performance. This would be a form of predictive analytics, since it would employ a model of how the business is supposed to perform, and would pinpoint factors that are out of range in the causal model of business performance."

First we need to deal with a textual ambiguity: the meaning of "supposed" in this context. If "supposed to" is normative -- i.e. meaning "is desired to" -- then to call technology "predictive" when it uses such a model is quite a stretch. So does "supposed to" have a more neutral meaning, like "is anticipated to"? I'll assume that this fits the context better.

Now let's get to the real problem: The model is looking at results and performance -- i.e., the past. As statistical programs are wont to do, the model can identify correlations; and let's assume that it will make predictions based on the observed correlations (there are some commercial software packages that promise this). That is quite different from divining causes, which nonetheless is what the authors have twice asserted in this passage. I leave aside the question of predictive value based on past results; read Taleb or your mutual fund prospectus ("Past results are no guarantee of future performance").

(ii) At pp. 46-47, the authors describe correlations between "low performance" in using analytics and financial underperformance, and "high performance" in using analytics and financial overperformance. The ratings of analytics and financial performance are based on self-evaluations, not objective measures. This is the "halo effect" in spades, as most recently described in Rosenzweig's book -- happy (profitable) companies are happy about everything, and unhappy (less profitable) companies blame themselves about everything. More to the point, though: the companies in these two groups make up an aggregate of only 29% of their sample. They say nothing about the middle 71%. For all we know, "high performance" in analytics also correlates well with mediocre performance.

(iii) At pp. 18-19, the authors tell a cautionary tale about the Red Sox manager who defied the quants in the 2003 American League Championship Series against the Yankees: Red Sox analysts "had demonstrated conclusively" that pitcher Pedro Martinez became much easier to hit against after about 7 innings or 105 pitches, and warned the manager that "by no means should Martinez be left in the game after that point." However, "in the fifth [sic] and deciding game of the series," the manager allowed Martinez to continue pitching into the 8th inning. The result? "[T]he Yankees shelled Martinez. The Yanks won the ALCS, but [the manager] lost his job. It's a powerful story of what can go happen if frontline managers and employees don't go along with the analytical program." Sounds like a sportscaster channeling the Borg.

Even if we take this story at face value, one has to wonder, was that all there was to it? Does the Red Sox' losing the series after Martinez pitched into the 8th inning mean that his pitching was the cause? Was there bad fielding involved, for example? Or did the Yankees' adrenalin have anything to do with it? And what was the score when Martinez was removed?

Thoughts like these moved me to look up the box score of the game. First of all, Martinez didn't pitch in the fifth game -- probably what the authors were referring to was the 7th game. In that game, it's true, Martinez gave up 3 runs in the 8th inning. But what was the result? The Yankees only TIED the game, 5-5, to that point. They didn't win until the bottom of the 11th inning, when they scored one more run (off the third Red Sox pitcher brought in after Martinez). By the way, the game was in New York, so do you think the home crowd's energy might have been a factor? "Post hoc, propter hoc": it don't come any better than this.

B. CIRCULARITY: E.g.: At pp. 48-49, one of the 5 characteristics of analytic capabilities possessed by companies "that compete successfully on analytics" is that such capabilities are "better than the competition [sic]." Two others in the list of five are that such capabilities are "hard to duplicate" and "unique" (@48).

The discussion about the ideal characteristics of executives in "analytic competitors" (@135-136) hints at a more substantive circularity. One such characteristic an exec should possess is he or she should be a "passionate believer in analytical and fact-based decision making". No doubt this was true at the most avidly analytic (a/k/a "Stage 5") companies the authors studied -- it's no surprise that if top execs are passionate about this stuff they will use it. However, when describing how "analytical leadership emerge[s]" (@136-137), the authors can only adduce cases in which the leaders (i) found a company on the principle of using analytics from the get-go, (ii) come in as a new senior exec bringing with them the idea of using analytics, or (iii) are a younger generation in a family-owned business. The authors don't mention anyone who "saw the light" and became a convert. So sounds like if you don't already have a passion for analytics, you are a dinosaur.

C. INCONSISTENCY: E.g.: The "most analytically sophisticated and successful" companies use analytics, inter alia, to support "a distinctive strategic capability" (@23). "Having a distinctive capability means that *the organization* views this aspect of its business as what sets it apart from competitors" (@24; emphasis added). However, "not all businesses have a distinctive capability" -- e.g., Kmart, USAirways and GM don't, because "to *an outside observer* they don't do anything substantially better than their competitors" (id., next paragraph; emphasis added.)

D. BANALITY: Parts of the book (esp. Chapter 6, a five-step "road map to enhanced analytical capabilities"), sound like a MadLibs that could just have easily been filled in with strategic planning, Six Sigma, or dozens of other management fads through the decades. E.g., a "Stage 4" company is defined as "analytics are respected and widely practiced but are not driving the company's strategy" (@ 125); "It is important to specify the financial outcomes desired from an analytical initiative to help measure its success," @ 127; "Assuming that an organization already has sufficient management support and an understanding of its desired outcomes, analytical orientation, and decision-making processes, its next step is to begin defining priorities," @id.

Finally, the whole enterprise of "analytics" has a certain banality too, through no fault of the authors of this book: it's one more in a string of dreary revivals of Taylorism on steroids, albeit this time with 21st-Century pharmaceutical know-how. That some of its practitioners think it would be a good idea to, say, deny jobs to people simply on the basis of low credit scores (reported without any explicit endorsement or disapproval by the authors @ 26), thereby compounding those folks' problems and low credit scores, is a chilling prospect -- especially because "analytics" allows much deeper invasions of privacy than did Taylorism's earlier avatars. I won't stretch the metaphor of "banality" to the type of which Hannah Arendt famously wrote, but suffice it to say that you won't find ethics discussed anywhere in this book.

This book is, for the most part, a disappointing mix of fallacy, circularity, inconsistency, banality and utopian promises. If you've read books such as N. Taleb's "Fooled by Randomness", P. Rosenzweig's "The Halo Effect", or, for the classically educated, D. Fischer's comprehensive "Historians' Fallacies" (1970), you can easily while away a few lazy hours spotting the bad reasoning throughout this book. I'll give a few examples in a minute or two.

The effect is more disappointing than infuriating because, unlike many other business authors, the authors aren't claiming to have some unique insights or to discovered some new principle of strategy; their aims are refreshingly modest. About the best I can say for it is (a) if you never read the January 23, 2006 Business Week cover story "Math Will Rock Your World" (which, as of this writing, was available for free online) you can learn that sophisticated mathematical tools are being used in business, and that the market value of math Ph.D.s is increasing, and (b) if you did read that article and don't know much else about these tools, you can learn a little bit of terminology/jargon from the text boxes scattered throughout the book, and maybe a little bit about the political problems of implementing them (@145-146). As other reviewers have pointed out, the book won't teach you how to use or implement such tools. (The authors are forthright about this, e.g. @22.) Unfortunately, the authors also don't give any concrete illustration, with formulas or pictures or even an extended analogy, of how any such tool is used; they merely assert the tools' efficacy.

Or rather, -- and this is where the trouble begins -- they don't merely assert, they *emphatically* assert, as in the book's rhapsodic concluding paragraph about what the future looks like for analytic competitors (@186): "They'll get the best customers and charge them exactly the price that the customer is willing to pay ... They'll have the most efficient and effective marketing campaigns and promotions. Their customer service will excel ... Their supply chains will be ultraefficient, and they'll have neither excess inventory nor stock-outs," etc., a prophetic vision of near-Biblical proportions (cf. Dvorim a/k/a Deuteronomy, Chapter 11). (However, I was stumped by one item in this catalogue of blessings for the faithful: "They'll have the best people or [sic] the best players in the industry" -- what's the difference?)

Having treated of utopian promises, here are a few examples of the other flaws I mentioned:

A. FALLACY (and related sins): The most obvious ones in the book are: (i) confusing causation with correlation, (ii) attempting to lead the reader into such confusion, and (iii) "post hoc, propter hoc" (if Y comes after X, Y must have been caused by X).

(i): At page 178, the authors discuss "direct discovery technologies" that mine data and would "let managers go directly to the cause of variances in results or performance. This would be a form of predictive analytics, since it would employ a model of how the business is supposed to perform, and would pinpoint factors that are out of range in the causal model of business performance."

First we need to deal with a textual ambiguity: the meaning of "supposed" in this context. If "supposed to" is normative -- i.e. meaning "is desired to" -- then to call technology "predictive" when it uses such a model is quite a stretch. So does "supposed to" have a more neutral meaning, like "is anticipated to"? I'll assume that this fits the context better.

Now let's get to the real problem: The model is looking at results and performance -- i.e., the past. As statistical programs are wont to do, the model can identify correlations; and let's assume that it will make predictions based on the observed correlations (there are some commercial software packages that promise this). That is quite different from divining causes, which nonetheless is what the authors have twice asserted in this passage. I leave aside the question of predictive value based on past results; read Taleb or your mutual fund prospectus ("Past results are no guarantee of future performance").

(ii) At pp. 46-47, the authors describe correlations between "low performance" in using analytics and financial underperformance, and "high performance" in using analytics and financial overperformance. The ratings of analytics and financial performance are based on self-evaluations, not objective measures. This is the "halo effect" in spades, as most recently described in Rosenzweig's book -- happy (profitable) companies are happy about everything, and unhappy (less profitable) companies blame themselves about everything. More to the point, though: the companies in these two groups make up an aggregate of only 29% of their sample. They say nothing about the middle 71%. For all we know, "high performance" in analytics also correlates well with mediocre performance.

(iii) At pp. 18-19, the authors tell a cautionary tale about the Red Sox manager who defied the quants in the 2003 American League Championship Series against the Yankees: Red Sox analysts "had demonstrated conclusively" that pitcher Pedro Martinez became much easier to hit against after about 7 innings or 105 pitches, and warned the manager that "by no means should Martinez be left in the game after that point." However, "in the fifth [sic] and deciding game of the series," the manager allowed Martinez to continue pitching into the 8th inning. The result? "[T]he Yankees shelled Martinez. The Yanks won the ALCS, but [the manager] lost his job. It's a powerful story of what can go happen if frontline managers and employees don't go along with the analytical program." Sounds like a sportscaster channeling the Borg.

Even if we take this story at face value, one has to wonder, was that all there was to it? Does the Red Sox' losing the series after Martinez pitched into the 8th inning mean that his pitching was the cause? Was there bad fielding involved, for example? Or did the Yankees' adrenalin have anything to do with it? And what was the score when Martinez was removed?

Thoughts like these moved me to look up the box score of the game. First of all, Martinez didn't pitch in the fifth game -- probably what the authors were referring to was the 7th game. In that game, it's true, Martinez gave up 3 runs in the 8th inning. But what was the result? The Yankees only TIED the game, 5-5, to that point. They didn't win until the bottom of the 11th inning, when they scored one more run (off the third Red Sox pitcher brought in after Martinez). By the way, the game was in New York, so do you think the home crowd's energy might have been a factor? "Post hoc, propter hoc": it don't come any better than this.

B. CIRCULARITY: E.g.: At pp. 48-49, one of the 5 characteristics of analytic capabilities possessed by companies "that compete successfully on analytics" is that such capabilities are "better than the competition [sic]." I guess that's why they "compete successfully." BTW, two others in the list of five are that such capabilities are "hard to duplicate" and "unique" (@48). Same cannot be said of items in this list.

The discussion about the ideal characteristics of executives in "analytic competitors" (@135-136) hints at a more substantive circularity. One such characteristic an exec should possess is he or she should be a "passionate believer in analytical and fact-based decision making". However, when describing how "analytical leadership emerge[s]" (@136-137), the authors can only adduce cases in which the leaders (i) found a company on the principle of using analytics from the get-go, (ii) come in as a new senior exec bringing with them the idea of using analytics, or (iii) are a younger generation in a family-owned business. The authors don't mention anyone who "saw the light" and became a convert. So companies whose leaders are passionate about analytics will use analytics. And if you don't already have a passion for analytics, you're a dinosaur.

C. INCONSISTENCY: E.g.: The "most analytically sophisticated and successful" companies use analytics, inter alia, to support "a distinctive strategic capability" (@23). "Having a distinctive capability means that *the organization* views this aspect of its business as what sets it apart from competitors" (@24; emphasis added). However, "not all businesses have a distinctive capability" -- e.g., Kmart, USAirways and GM don't, because "to *an outside observer* they don't do anything substantially better than their competitors" (id., next paragraph; emphasis added.)

D. BANALITY: Parts of the book (esp. Chapter 6, a five-step "road map to enhanced analytical capabilities"), sound like a MadLibs that could just have easily been filled in with strategic planning, Six Sigma, or dozens of other management fads through the decades. E.g., a "Stage 4" company is defined as "analytics are respected and widely practiced but are not driving the company's strategy" (@ 125); "It is important to specify the financial outcomes desired from an analytical initiative to help measure its success," @ 127; "Assuming that an organization already has sufficient management support and an understanding of its desired outcomes, analytical orientation, and decision-making processes, its next step is to begin defining priorities," @id.

Finally, the whole enterprise of "analytics" has a certain banality too, through no fault of the authors of this book: it's one more in a string of dreary revivals of Taylorism on steroids, albeit this time with 21st-Century pharmaceutical know-how -- and with far greater potential to invade personal privacy. Some of its practitioners think it would be a good idea to, say, deny jobs to people simply on the basis of low credit scores, since people with low credit scores can be assumed to have lots of other problems too (reported without any explicit endorsement or disapproval by the authors @ 26). That such an "analytical" criterion might compound those folks' problems and low credit scores is not worth a mention. Here is the point at which the authors' omissions and gaffes stop being silly, and where banality stops being benign. It is more than a disappointment that you won't find ethics discussed in this book.

Business information can and should be used to outthink rivals, and there's no better way to outthink them than by using analytics to make decisions. COMPETING ON ANALYTICS: THE NEW SCIENCE OF WINNING argues that leading companies do more than just gather data - they are building company strategies around it by using analytics - sophisticated statistical analysis and predictive modeling supported by information technology. This book covers all the basics of using analytics to foster business and is a pick for any serious business library.

This book brings us the opportunity to place our business at the optimization level of performance. Reading it we perceive the importance of the effective use of data. Reports, queries and alerts are just the begining. The important data use level is the statistical analysis, forecasting/extrapolation, predictive modeling and finally optimization. At the optimization level you can make decisions about what is the best that can happen to your business.

This is a civilian structured version of the Military Decision Making Process. It is a reason why military officers usually become CEOs. They are constantly applying analytics in their jobs.

I recommend this easily read book to economists, managers and military officers.

The topic is kept at a very high level, mainly mentioning examples of companies or executives who have successfuly implemented business analytics as their "raison d'etre". I didn't find any useful tool that could possibly be applied.

This is probably the second best business book I have ever read (best is Jim Collins's 'Good to Great'). Davenport walks through an increasingly important topic of how to use data for taking a company's competitiveness to a new level. Having practised many of the topics for 10+ years - I believe I can say that the book doesn't offer anything novel. The ideas are pretty much known by certain circles for many many years - however adoption of them has been diminutive especially at large enterprises.

I was walking through the airport and saw a book prominently displayed in the bookstore, Competing on Analytics. I just about had whiplash with the speed with which I turned to buy the book. And once I sat down and took a look at it, I noticed it was written by you.

Why does that matter? Your book Process Innovation had a great deal to do with forging the direction of a company I helped start, IMG (Insource Management Group) which was later renamed to Healthlink and purchased by IBM two years ago. You may recall this company while you were at UT in Austin.

Great book. And Great timing. There's no place where these concepts will have more impact than Healthcare. Ivo.

This is the glib, anecdotal book built around a basic, almost stereotypic Harvard Business Review five-level model, this one focusing on various levels of use of analytical methods, systems and processes. At the lowest level, there is almost nothing going on in terms of analytics and, at the highest level, analytics are systematic, widespread and strategic. You can figure the middle three levels. In my experience, there would be some use in providing a zero-level or even negative-level use of analytics, those firms operating in the "data free" zone. They would provide some humor and color, not just useful references.

As to the subtitle, "The new science of winning," to be clear, "competing" and "winning" are not synonymous or even necessarily linked. Competing is not necessarily about winning and winning isn't as important as remaining competitive in the long run. Winning isn't everything and it is not the only thing.

The anecdotes tend towards Harrah's, the Boston Red Sox and several less-than-mainstream firms, along with a few data-crazed firms, e.g., Google. More and more detailed examples of the first-rate use of analytics by top competitors in the corporate world would have been welcome. Personally, Harrah's use of analytics to maximize gambling revenues strikes me as exploiting people's addictions. As to the Red Sox, at least they finally won a Series. As to data, the authors seem to think that 'data' is a singular noun, which leaves me somewhat perplexed as to the analytics applied to editing the text.

The book is shorter than the listed 240 pages. The anecdotes tend to be repetitive, the analytics more descriptive than analytic, and the five-level model gets driven home right away and then driven in repeatedly. We can probably all agree that the information age provides the capacity to mine data, to analyze it thoroughly, to disseminate it approporiately and widely, to use it strategically, and to provide the essential leadership to hire the people, structure the organization, and put the entire system in place in the first place.

"Competing" was not as boring as I expected it to be and not as informative as a I wanted it to be.

There is *so* much value and information locked up in the data that a company maintains on their business. But how can a company turn that into a competitive edge? That question is explored in the book Competing on Analytics: The New Science of Winning by Thomas H. Davenport and Jeanne G. Harris. While not a detailed "how to" book on the subject, it makes a strong case for what needs to be done to survive and compete in today's marketplace.

Content:
Part 1 - The Nature of Analytical Competition: The Nature of Analytical Competition; What Makes an Analytical Competitor?; Analytics and Business Performance; Competing on Analytics with Internal Processes; Competing on Analytics with External Processes
Part 2 - Building an Analytical Capability: A Road Map to Enhanced Analytical Capabilities; Managing Analytical People; The Architecture of Business Intelligence; The Future of Analytical Competition
Notes; Index; About the Authors

I'll be the first to admit that a book on analytics doesn't necessarily sound like "edge-of-your-seat" reading. But surprisingly, this book is much more readable than I expected. Davenport and Harris avoid getting bogged down in academic posturing and theorizing, and the examples of real companies and scenarios are numerous. You'll find everything from financial services (like Capital One) to sports teams (such as the New England Patriots). Through these actual companies and case studies, the foundation is set for why this is critical to business success, as well as the mind-set changes that are needed to make it all happen. They also do a great job in explaining the difference between reporting and true analytics, as well as presenting a continuum of stages of analytical competition. You may be anywhere from analytically impaired (not good) to being an analytical competitor (very good). While you may not like where you are, at least you'll understand what you need to do to move up the pyramid.

Even if you're not directly responsible for analytics, it's worth understanding what it's all about. This is a good intro to the topic, and it may be what spurs you on to take the next steps from "what" to "why"...

My overall impression of the book is that it does provide a good survey of what is going on in the business world as it relates to analytics, whether they relate to the Internet or just to good business metrics. I find, however, that there is a complete lack of a bibliography of publications that might be the next step in pursuing more knowledge of this subject. I realize that there are extensive notes which do make reference to specific publications (articles, etc) but they are not organized into a usable bibliography. I suspect that I need to do a "second read" of this book to ensure that I have its intent well in hand.