Competing on Analytics: The New Science of Winning

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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.

Customer Reviews:

  • "Going by the Gut" may be going by the wayside
    This book shows how several companies are effectively "competing on analytics" - defined as having the following four characteristics:

    1. Analytics supports a strategic, distinctive capability
    2. Approach to and management of analytics is enterprise-wide
    3. Senior management is committed to the use of analytics
    4. The company makes a significant, strategic bet on Analytics

    In my opinion, the fourth item is really redundant, as the investment in obtaining and implementing the first criteria would satisfy the fourth. But the other three are critical. Included in both #2 and #3 is the requirement of an evidence-based decision making criteria. This is what I expect most companies will have an issue with. While the book mentions the difficulty in changing the culture and the potential for a virtual war between the "quants" and the "old guard", there are not really any good suggestions for overcoming it. The one suggestion given, proving it on a smaller scale won't work, because "proof" is not relevant for those who believe the gut is better than evidence.

    That said, for those who can appreciate the value of evidence, this book provides much of it. Perhaps the best is the fact how the book describes Capital One carefully targeting sub-prime risks using analytics. Since the book was published, that market in general has collapsed, but Capital one is doing fairly well. The chapters on how to build the technical and people aspects raise good points, but are far from thorough.

    In short, this book is a great overview, and provides many good ideas to consider, but it should not be confused as a how-to book.
    ...more info
  • Great Concept - Too Long
    This book could be about half as long and just as effective. After you get through the first few chapters you're pretty much rehashing the same things, but overally the book makes good points....more info
  • Well worth the time
    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....more info
  • Helpful checklist of your BI capability
    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.
    ...more info
  • Solid Book on Analytics in business
    Great introduction into the application of Analytics in business. Primarily focused on usage and benefits, with a small component on underlying technologies used to implement an analytics as a primer.

    Highly recomend for anyone looking to better understand analytics as a whole, and how they can impact your business. ...more info
  • Using data analysis to eliminate guesswork
    Spawned from an article in Harvard Business Review, Competing on Analytics is about gaining competitive advantage by using data analysis, as opposed to making decisions based on intuition. While the concept applies to smaller businesses, the orientation of the book is on large enterprises. The authors drop a lot of corporate names, such as Marriott, Capital One, Progressive Insurance, and Royal Bank of Canada. However, they don't get into any depth with case studies.

    Davenport and Harris define analytics as "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions... Analytics are a subset of what has come to be called business intelligence." They emphasize that analytics is not simply an IT issue; the book also addresses the human and organizational aspects of becoming an analytical competitor.

    A few examples of how analytics are used include identifying the most profitable customer prospects, fraud detection, and optimization of logistics, supply chain, and pricing.

    A single discovery can increase profits by tens of millions of dollars. As explained in the foreward by Gary Loveman, CEO of Harrah's, "we found that a ten-basis point move of slot pricing toward the estimated demand curve for a given game could enhance our profitability by an eight figure amount and be unobservable to the guest."

    However, analytics won't help everyone. Some business models are based on hard to measure factors, such as fashion. The authors also point to American and United airlines, neither of which has thrived. "Their analytics support an obsolete business model. They pioneered analytics for yield management, but other airlines with lower costs can still offer lower prices."

    Likewise, analytics can't save Kmart or General Motors. "Without a distinctive capability, you can't be an analytical competitor, because there is no clear process or activity for analytics to support."

    The authors advocate a centralized analytical function. One reason is to make efficient use of specialized talent, such as a Ph.D. in statistics. Another reason is to maintain consistency and avoid "multiple versions of the truth."

    The book covers the process of implementing an analytical IT infrastructure. Data from various sources are consolidated into a data warehouse for use by analytical application software. However, before this can happen, data quality issues must be resolved. "Detecting and removing data that is out of date, incorrect incomplete, or redundant is one of the most important costly and time-consuming activities in any business intelligence technology initiative... A common metadata repository used by all analytical applications is critical to ensure data consistency."
    ...more info
  • Too high level, not useful as practicioner's guide
    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....more info
  • "Analytics" with flawed logic (2.5 stars)
    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....more info
  • The way every company will compete over the next 5 years
    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.
    ...more info
  • Excelent
    It one of the most interesting book that I have read, it show you the future tendent...more info
  • An impactful book to the industry
    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! ...more info
  • Good introduction, but further reading will be necessary
    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....more info
  • Awesome book
    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....more info
  • For very high level managers who have no idea of CRM nor analytics
    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. ...more info
  • If you are already in analytics, then you don't need this book.
    I thought this book provided a very general overview of using analytics in organizations. There was way too much covering the obvious that you have to get management buy-in for it to be successful. Duh, that's what you have to do with any initiative, whether analytics or something else. Anyway, you don't need this book if your are already in analytics, only if you want a 'broad general overview' of analytics....more info
  • Flawed but pretty useful
    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)....more info
  • simplistic
    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....more info
  • Excellent book on business strategy
    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....more info
  • A refreshing change
    I enjoyed this book because it's about the upside of IT, rather than the boring, risk-reduction topics we've had since the end of the dot com days. The authors focus on the use of analytics for growth, competitive advantage, and better performance. That's a much more positive and interesting set of issues than Sarbanes-Oxley compliance, preventing security problems, Y2K, etc. Nick Carr, eat your heart out!...more info
  • Targeted towards executives and mid managers
    The summary of this book is that using data analytics as a competitive tool has become essential. Not only it serves to make better business decisions, but it can increase effectiveness and efficiency at every phase of business such as marketing, HR, Customer Relations, and Supply Chain Management. Proper execution and usage of Business Analytics can make and save company money. More importantly, it can allow an organization to stay relevant in the marketplace by enabling an organization to understand the needs of the marketplace and how its products and services are fulfilling them.

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

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

    ...more info
  • Who Is The Audience
    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....more info
  • Waffle
    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.
    ...more info
  • Competing, perhaps... but not competitive edge
    This book is well written, structured and thought out. All combined, this book is easy to follow. There are some good examples of companies leveraging analytics to compete in the market. Furthermore, the prescient reader will find some interesting areas for analytic investigation to pursue in his own organization.

    On the negative side, the notions of data warehouses and data marts are only superficially explored. No practical architectural analysis is provided. After reading this book, Im not convinced that data warehouses or data marts are anything more than glorified databases.
    It is true that customer/supplier/market data is becoming widely available, the challenge being in making sense of this data. However, I dont believe a company can find its competitive edge solely by crunching data and making intelligent forecasts. The better (read accurate) the forecast, the more bridle the model. How can it ever respond to non-linear events? What will happen to companies which invested heavily in analytics - like the authors suggest - when a paradigm shift inevitably occurs in the market? What good will all those forecast models be then? But thats a decision the reader should make for himself.

    Overall, the book relatively short and worth the read.
    ...more info
  • Great Subject/Weak Effort
    Not a lot of meat to this topic other than the obvious. Not very exciting stuff....more info
  • Do not buy!
    This book is superficial and anecdotal at best. It lacks any real, useful content: if you were to cut out all the fat out of this book, its 186 pages could easily be summarized into a 2-page article. It uses very few examples of actual organizations competing on analytics (variations of which are repeated over and over throughout the book) and offers very little or no insight or details on each of these. For example, the authors might say that company X began competing on analytics a few years back, and has managed to reduce costs by Y percent thanks to its efforts. The rest of the book attempts to "organize" the implementation of analytical capabilities into five "stages"... hardly worth your time or money.

    My recommendation is do not buy this book....more info
  • Covers the basics of both the what-is and the how-to of fact-based decision making
    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
    ...more info
  • Harnessing the Hidden Power of Data with Strategic Analytics

    Competing on Analytics provides a well documented and proven road map to assist businesses achieve profitable and sustainable market growth leveraging data-driven decisions. In a methodical style, it introduces a framework to guide the transition from Tactical Analytics (description) to Strategic Analytics (prediction) in harnessing the hidden power of enterprise data. The book through its numerous real world examples shows convincely that whereas Tactical Analytics yield good results, the adoption of Strategic Analytics yield even higher returns.

    The major contribution of this book is its clarity in outlining a clear roadmap to manage process and organizational challenges to ensure the deployment of a successful Analytic Strategy:

    * Guidance on how to harness data to improve business performance through the implementation of Information-Based strategies
    * Identification and management of the key operational issues (Decision Making, Business Processes, Manager/Employee Behavior and Customer expectations) in introducing and incorporating Analytics as part of a company corporate strategy.

    The book provides ample of examples on how different companies have leveraged Information-Based strategies to establish market leadership across several industries: Financial Services, Retail, Travel & Entertainment, Industrial Production and Sports.

    Although highlighting Analytics on its title, this is not a book on "how to do analytics", analyze data, CRM analytics, build and validate statistical models or how to apply data mining algorithms; rather, it is comprehensive, well written and structured book written to assist Decision Makers, Managers, Influencers and company Leaders to understand and harness the power hidden in the data to compete and win in the marketplace.
    ...more info
  • Lists Applications, but Provides Little Else
    Davenport argues that leading companies are now building their competitive strategies around sophisticated analyses and predictive modeling. Many previous bases for competition, such as geographic advantage or protective regulation, have been eroded by globalization.

    Some industries are more amenable to analysis than others. Examples include those with lots of transaction data, such as in financial services, travel and transportation, and Internet sales. More specifically - hotel and airline yield management, health care (where to focus improvement efforts), insurance pricing, selecting sports talent, general hiring (using FICO scores), tax compliance focus, etc.

    The frustrating and highly limiting part about "Competing on Analytics" is that it doesn't reveal the mechanics of these improvement efforts....more info
  • Excellent book
    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....more info
  • Bland management blah blah
    Davenport's book was a trite overview replete with success stories. The ground reality is that analytics runs into problems; math phobia, technical issues and labor shortage stymie progress in this field....more info
  • Best business book on analytics
    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....more info
  • Good Overview of Business Analytics
    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.
    ...more info
  • Five stars but... for the right audience!
    I was excited by the title, some of the reviews and rushed to buy this. Read it quite fast and got little disappointed. Probably the correct title could be ''Advocacy for Competing on Analytics''. To be clear, the book is very good if you are: a student, a junior project manager, a junior consultant, a manager looking for Business Intelligence ideas, an expert looking for tools to sell analytics, or Business Intelligence, to your top managers.
    If you are experienced in using analytics, design and use data collection tools, or using Business Intelligence, the book might bring you little value. I was constantly reading it and looking forward for the real meat, but it didn't really appeared. I certainly will keep it to use as a reference in the future, but still looking for books to provide deeper insights on the subject....more info
  • Place Your Business At The Optimization Level of Performance
    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. ...more info
  • A disappointment
    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. ...more info
  • Disappointing
    Maybe, since I work in analytics, my review is different from the others. If you are a new incomer in the field or if you don't have any idea of the issue, this book is maybe a good beginning. But if you know something (even if this is very low) about analytics, CRM or Customer Strategy, this book is really worth nothing. It is written from a tom manager point of view, and it would not give you any new insight about how analytics is changing the way the corporations do business.

    Only acceptable for those who know nothing about the stuff. ...more info
  • Competing on Analytics: The New Science of Winning
    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. ...more info
  • Very disappointing
    I was intrigued by the book's description and I've found other HBS Press books very useful. However, after the foreward by Gary Loveman, the CEO of Harrah's, the book deteriorates into a 186-page argument for the use of analytics in business. The problem with that is I don't need convincing. I'm already interested in using analytics to improve my business - that's why I bought this book! There was very little actionable information presented. If I hadn't been reading this book for a grad school class, I never would have finished it. ...more info
  • Becoming an analytic competitor
    Tom and Jeanne have written an excellent new book (building on a paper they wrote some time ago) about what they call "analytic competitors", that is to say companies that use their analytic prowess not just to enhance their operations but as their lead competitive differentiator. The book discusses a number of these analytic competitors and gives an overview of how analytics can be used in different areas of the business and how you can move up the analytic sophistication scale.

    The book has two parts - one on the nature of analytical competition and one on building an analytic competency. The first describes an analytical competitor and how this approach can be used in both internal and external processes. The second lays out a roadmap for becoming an analytical competitor, how to manage analytical people, a quick overview of a business intelligence architecture and some predictions for the future.

    They define an analytical competitor as an organization that uses analytics extensively and systematically to outthink and outexecute the competition. The analytics are in support of a strategic distinctive competency and they argue, persuasively, that without a distinctive capability you cannot be an analytic competitor.

    The book outlines what they call four pillars of analytical competition- a distinctiive capability, enterprise-wide analytics, senior management commitment and large scale ambition. They lay out 5 stages of analytic competition from "analytically impaired" to "analytic competitor". The importance of experimentation is made clear and the book repeatedly emphasizes the need for companies and executives to be willing to run the business "by the numbers".

    The book is full of stories about how companies compete analytically and this is one of the book's strengths. It also has a great list of questions to ask about a new initiative and outlines a number of ways to get a competitive advantage from your data. Regardless of the competitive approach, the need for analytical executives to be willing to act on the results of analyses is made clear. The book ends with a great list of changes coming.

    This is a very interesting book both for those interested in competing on analytics and those interested simply in making more use of their data....more info
  • A limited introduction to business analytics

    * - 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.


    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....more info
  • The Future of Business
    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....more info
  • Not-so-new consulting speak
    In spite of its alluring title, this book is mediocre at best. The authors rehash a bunch of consulting speak that has been around the data warehousing and business intelligence space for a decade or more. After finding Davenport's Thinking for a Living: How to Get Better Performances And Results from Knowledge Workers to be pretty thought provoking, I was disappointed with this work. Unless you are brand new to data warehousing and business intelligence, don't waste your time.

    The authors promote analytics as the sound way to make decisions that ultimately make a company more competitive. There is some obvious truth in that concept, I guess. However, they fail to acknowledge that first movers (those companies that usually have competitive advantage) often have to make decisions without the benefit of clean, historical data. In fact, the authors go so far as to say that clean data is a prerequisite to good analysis which is in turn a prerequisite to good decision making which, only then, leads to competitiveness.

    As a two-decade veteran of the business intelligence space, I do agree with much of what the authors have suggested. The formula they propose works well for established companies with large, historical data stores to draw upon. The trouble is, they imply that analysis-driven decision making is the secret to competitiveness. Making good decisions, especially when you don't have all (or very much) of the data -- a very typical scenario in first-mover environments -- is the real secret to competitiveness....more info
  • Competitive Insights through analysis
    Davenport does an excellent job presenting the case for using data to complete as well as provide practicial guidance on just what and how to use it. Very well written and readable...more info
  • A "Gee Whiz" Overstatement of the Impact of Analytics and the Potential of ERP Analytics
    I saw my first application of advanced mathematics to a strategic business problem in 1970. Since then, I've seen hundreds of such applications. In over 95 percent of the cases, those charged with making decisions didn't want to rely on the math, didn't understand the math, and stopped using the math within a few years. Ten years later, no one even knows that the math was ever used.

    There's a second problem: A lot of the advanced math looked better than it was. Nice graphs suggested certainty where the numbers and assumptions shouldn't have permitted such impressions to be formed.

    Beyond that, a lot of the data being used had no predictive value . . . a particular problem with correlation-based conclusions and time series.

    Finally, the mathematicians often solved the wrong problem.

    Have there been a few places where advanced math has made a lot of difference? Sure, especially where real time decision making would overload an organization. Load management in airlines, logistical optimization in supply chains, and in providing alerts that service is needed.

    The most valuable applications that I've seen came in places where proprietary data added new perspectives that no one else could imagine. These advantages came from new ways of gathering data . . . not just compiling all transactions into large data bases. In fact, the best math solutions I've seen for strategy wouldn't strain any body's calculator to solve. Typically, these are done on personal computers anyway because the graphical choices are better for presenting what's been learned.

    Can more advanced math be employed for strategy and operations? Sure. But the failure rate will be high, the cost will be enormous, and many managements won't engage.

    People like Gary Loveman are unusual: Most executives don't appreciate and pay attention to analytics while running a large company. They prefer accounting reports instead. That's not going to change very fast except among start-ups by mathematically literate leaders.

    What's really going to happen is that the off-the-shelf business intelligence software companies are going to make progress in selling their offerings to those who want and can use better data and analysis. But I suspect it will take another generation before you'll see much company-wide use of analytics.

    You'll notice that I didn't discuss this book very much so far. Why? It doesn't reveal much of anything other than what you read in business periodicals and press releases by various vendors who want to sell offerings related to analytics. I recommend you skip the book. It won't tell you what you need to know. You would do better to spend a few hours with someone who understands analytics discussing what might be done to improve your performance.

    I've read and appreciated a number of excellent books by Thomas H. Davenport in the past, so I'm surprised this book turned out to be so over optimistic based on so little evidence . . . and stated awareness of the problems. I can only conclude that this book is intended to sell services related to analytics rather than to give people an objective sense of what they are up against.

    Ultimately, there's another problem with this book: If you use analytics to fine tune the current business model, you'll steal time, money, and effort from the more important task of creating an improved business model. The authors fail to make a distinction between business-model-optimizing analytics and analytics for business-model improvement. The former runs the risk of making companies less flexible and less able to compete.

    The Balanced Scorecard approach, by comparison, is a healthier way to go by encouraging quantification of what needs to be done and tracking of how you are doing. From that discipline, you define the areas where innovation is needed . . . including analytics. Hiving off analytics as a separate subject simply creates the potential for misuse of a potentially valuable discipline.

    ...more info
  • Not quite enough
    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). ...more info
  • Numbers Made More Mind-Numbing
    I confess that despite being a bit of a numbers freak I was disappointed in "Competing on Analytics." The authors provide a good overview of how statistical analysis can be designed to help businesses make good decisions and create a competitive advantage. They give many examples from companies involved in analytical competition.

    "Competing on Analytics" would have been more enjoyable if time was spent concentrating on a single company or a single industry. As it is, the authors flit from one company to another and don't allow the reader to see how analytics started in a company, became part of the company culture, how it benefited the company, and how the company plans to use analytics in the future. Perhaps showing that progression would be giving away too much inside information from a single entity.

    The beginning chapters were the most interesting. The authors related specific examples showing how analytics have been used at corporations such as Harrah's Entertainment, Capital One, and the Boston Red Sox. Portions of the final chapter that discussed the future of analytics were also of some interest.

    Perhaps my expectations were just too high based on reading the cover blurbs, but I was just barely able to slog through "Competing on Analytics." If you have been exposed to this subject before, you probably won't find much new in this book.

    I would love to read a book on this subject that did not feel like it was written for a stodgy academic journal.
    ...more info
  • Is this really new thinking?
    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....more info
  • A pick for any serious business library.
    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....more info


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