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  • Gebundene Ausgabe: 240 Seiten
  • Verlag: Harvard Business Review Press (5. Februar 2007)
  • Sprache: Englisch
  • ISBN-10: 1422103323
  • ISBN-13: 978-1422103326
  • Größe und/oder Gewicht: 2,5 x 16,5 x 24,1 cm
  • Durchschnittliche Kundenbewertung: 3.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 71.863 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)

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“[the] seminal work, Competing on Analytics, helped shape the evolution of the discipline of business analytics.” — Health Data Management

“Harvard Business School Press, Davenport in particular, has produced some excellent books on competitive analytics and the like, with good case studies…” - ZD Net


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.

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23 von 25 Kunden fanden die folgende Rezension hilfreich Von Donald Mitchell TOP 500 REZENSENT am 13. November 2007
Format: Gebundene Ausgabe
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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Analytics for beginners 9. Juni 2007
Von Peter Lorenzi - Veröffentlicht auf
Format: Gebundene Ausgabe
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.
415 von 483 Kunden fanden die folgende Rezension hilfreich
"Analytics" with flawed logic (2.5 stars) 12. Juli 2007
Von A. J. Sutter - Veröffentlicht auf
Format: Gebundene Ausgabe
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 have 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.
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The way every company will compete over the next 5 years 25. Februar 2007
Von Mark P. McDonald - Veröffentlicht auf
Format: Gebundene Ausgabe Verifizierter Kauf
Davenport and Harris have followed up their influential HBR article with a well thought out, clearly communicated and detailed analysis of how companies will really compete in the future -- by using what they know to take the right actions throughout their companies. Davenport and Harris call these types of companies analytical competitors and they look at the world differently and produce significantly different results.

Analytics is becoming a requirement in every industry as customers have choice and companies face increased competition. They define analytics as "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact based management to drive decision and actions" This may sound like an academic book. But Davenport and Harris go well beyond hyping a new idea to provide dozens of practical examples from companies we all know. This blend of explaining a new way of competition using practical examples from proven companies makes this book a must read for business people.

The book breaks down into chapters that discuss each aspect of becoming an analytical competitor.

Chpt 1: The Nature of Analytical Competition describes how companies can consistently beat the market by knowing more and doing more with what they know. This chapter ties analytics with competitive strategy in a way that goes well beyond traditional market-ese.

Chpt 2: What makes an Analytic Competitor provides a detailed description and checklist of attributes that these leading companies share. The interesting point is that the examples range across industries demonstrating that

Chpt 3: Analytics and Business Performance looks at how this technique drives top and bottom line growth. This chapter demonstrates that analytics is more than just a good idea it's a good idea that business professionals should get their heads around.

Chpt 4: Competing on Analytics with Internal Processes connects information with the capabilities that form the basis for competitive advantage. This chapter dispels the myth that analytics is purely a marketing tool for customer segmentation and messaging.

Chpt 5: Competing on Analytics with External Processes focused on how companies use information for partnering and collaboration with suppliers. This is particularly critical to companies as many outsource and create relatively `uninformed' partners.

Chpt 6: A Road Map to Enhanced Analytic Capabilities connects these benefits with specific stages and actions required to become an analytic competitor

Chpt 7: Managing Analytical People proves that Davenport and Harris have investigated, thought through and are providing practical advice as they address key leadership and management issues that arise when information becomes an integral part of operations.

Chpt 8: The Architecture of Business Intelligence clarifies a stumbling block for many who think of analytics as just something they can buy as part of their BI solution. Its not and understanding the architecture and difference is something that separates those who buy tools and those who compete with their capabilities.

Chpt 9: The future of Analytical Competition highlights future issues and how analytics will shape markets as people, devices and activities become smarter.

There are few books that you want to read from start to finish and fewer that you recommend to peers. This book is both. So read this to get ahead of the competition and stay there.
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Competing on Analytics: The New Science of Winning 12. Mai 2007
Von Prasenjit Chaudhuri - Veröffentlicht auf
Format: Gebundene Ausgabe Verifizierter Kauf
DECISI0N ANALYTICS is a fast-rising competency in many large companies that deal with retail customers. Being a hot-subject also makes the term vulnerable to abuse by lesser-skilled folks looking for a short-cut.

The book was a disappointment partly on account of the expectations the book-title raised. One would have expected to see how major corporations used data-based analytics to "out-think" the competition and gain market share profitably.

Sadly, the book ended up as a random assortment of shallow descriptions of companies that use analytics as a competitive advantage, presumably drawing on newspaper and internet articles (perhaps a few interviews) as the key source. I saw very little analysis of what steps the companies took to achieve this capability and the practical problems faced. There is a lot of information available out there in the public-domain for free that provide better insights in to how Harrah's Casino or Netflix or Amazon succeeded. Presenting these as case-studies is a more common practice used by management consultants that are too lazy to carry out independent research to support new learnings.

One would have expected Tom Davenport, a well-respected management consultant and teacher to rise above this pratice and inspire new generations of MBA-grads., those who might certainly be tempted to go out and read this book.

Personally I would have loved to see a coherent approach to developing a conceptual framework around use of analytics as a core-competency, distilled from all the gathered information. Instead I found a cliched-use of standard chevrons and boxes without a coordinated flow-of-insightful thoughts.

An interesting afternoon read but nothing worth retaining or referring back to.
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Becoming an analytic competitor 16. Februar 2007
Von James Taylor - Veröffentlicht auf
Format: Gebundene Ausgabe
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.
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