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This book is a brilliant development of core concepts in an article co-authored by Thomas H. Davenport and Jeanne G. Harris that originally appeared in the Harvard Business Review. In it and now in this book, they explain how to become an analytical competitor: "an organization that uses analytics extensively and systematically to outthink and outexecute the competition" through support of a strategic, distinctive capability (e.g. Netflix and Wal-Mart), taking an enterprise-level approach to and management of analytics (e.g. Harrah's Entertainment and RBC Financial Group), sustaining a commitment to analytics by senior management (e.g. Jeff Bezos, founder and CEO of Amazon, and Rich Fairbank, founder and CEO of Capital One), and having large-scale ambition (i.e. the aforementioned companies as well as others "bet their future success on analytics-based strategies"), with senior executive commitment "perhaps the most important because it can make the others possible." Davenport and Harris classify companies within five stages of analytical competition:

Stage 1: analytically impaired ("flying blind")

Stage 2: Localized analytics (isolated, fragmented, disconnected, inconsistent, etc.)

Stage 3: Analytical aspirations (sees need, begins to explore options)

Stage 4: Analytical companies (enterprise-wide perspective, eager to innovate and differentiate)

Stage 5: Analytical competitors (analytics are the primary driver of performance and value)

Obviously, the challenge is to become a Stage 5 organization but an even greater challenge is to remain one. According to Davenport and Harris, companies that successfully compete on analytics have analytical capabilities that are difficult to duplicate, unique, adaptable to many situations, better than the competition, and renewable. By design and when utilized, those capabilities must also be able to accommodate all manner of changes within the given competitive marketplace. In some circumstances, in heavily regulated industries or when the analytics support an obsolete business model (e.g. large U.S. airlines such as American and United), analytics are not enough. Still another challenge is to identify those internal applications of business analytics that are clearly strategic and involve competitive advantage.

For me, some of the most valuable material is provided in Chapter 8 as Davenport and Harris explain how to align a robust technical environment with business strategies when incorporating analytics and other business intelligence (BI) technologies into their overall IT architecture. That is, a Stage 5 organization has "a full-fledged analytical architecture that is enterprise-wide, fully automated and integrated into processes, and highly sophisticated." Effective management of data requires correct answers to questions such as these:

1. Which data are needed to compete on analytics?

2. Where can these data be obtained?

3. How much are needed?

4. How can the data be made more accurate and valuable for analysis?

5. What rules and processes are needed to manage data from their creation through their retirement?

Here's another question which at least a few of those who read this review may be asking: Why make such a substantial investment in what it takes to become - and then remain -- a Stage 5 organization? Davenport and Harris provide an answer in the book's final paragraph: "analytical competitors will continue to find ways to outperform their competitors. They'll get the best customers and charge them exactly the price that the customer is willing to pay for their product and service. They'll have the most efficient and effective marketing campaigns and promotions. Their customer service will excel, and their customers will be loyal in return. Their supply chains will be ultraefficient, and they'll have neither excess inventory nor stock-outs. They'll have the best people or the best players in the industry, and the employees will be evaluated and compensated based on their specific contributions. They'll understand what nonfinancial processes and factors drive their financial performance, and they'll be able to predict and diagnose problems before they become too problematic. They will make a lot of money, win a lot of games, or solve the world's most pressing problems. They will continue to lead us into the future."
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on 6 July 2007
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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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.
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on 8 November 2007
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.
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on 1 August 2011
There are some very interesting ideas in this book, notably the ideas of analytics and the competency model. However, as with many new management insights, especially as promulgated by the Harvard Business Review, one is always left pondering whether there is really anything new here for managers to ingest and take to their businesses, or whether what we are looking at is either a description or building of a theory around events that have happened in a much more natural or even chaotic way or simply a reframing of other older business models or insights. After all, this is not a forward looking piece from the past predicting the revolution that technology would unleash and nor is it saying that much beyond better information creates the potential for better business decisions.

I understand that that the book is blown up from an original piece in the HBR, and one wonders whether it holds enough to be turned into 100 plus pages in this way. As with many HBS articles it offers evidence as proof, without any real rigour behind it, the success of Tesco is as much to do with logistics and gaming of the UK planning rules, as it is to building a very large data base on customer preferences. We are in the world of stories and the power of the storyteller to create the myth of truth in this way.

Overall, there are some very simple but seduction over arching narratives in this book, about the harnessing of information and what are the foundation blocks and stepping stones to achieving this, but there is also the broad sweep of the magazine article, rather than the detailed analysis of facts and the pain, effort and struggle that companies must overcome in order to achieve the reality of competing in this way.
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on 6 July 2012
If you've already figured out that you should be interested in how to use data and analytics in your business then this book will help make you feel good about making the right decision. Unfortunately it stops short of doing a lot more.
The problem is the case study references are too high level to be of practical use and the models and stages of development stuff are great theory but again don't add much value in practice.
For people already using data, there simply isn't enough added value here, the book simply restates well trod paths e.g. we already know senior level sponsorship and backing is important.
For new starters on the journey, congratulations, you've already passed the stage where this book adds much value. There are better reads out there that will help more e.g. try Sean Kelly, Avinash Kaushik or Jim Sterne
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on 13 October 2009
OK - cards on the table - I want this future to be the truth. It suits me as a business person and as a mathematician. The examples are great but they only show what can be done. They don't prove that it should be done for every business.

For me - books like this always miss out on "meta" analysis - showing trends across the entire FTSE100 / NASDAQ500. This is for good reason (the data is impossible to get) - however that's what is really needed.

Why 4 stars - well it is well written, approachable and a great primer. On the flip side, it didn't give a compelling reason to tell others about it.
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on 9 April 2010
An informative read which I can recommend to anyone working in the field of data management or analytics. Although the first part of the book sets the scene, which may be of lesser interest to readers, it progressively gets more interesting, particularly in respect of how to build and integrate an analytical capacity across your organisation and how to lead analytical teams.
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on 21 May 2016
Good introduction, free of technobabble, that describes the gist of analytics. It is also becoming a bit dated though and could probably do with an update (still refers to data warehouses and BICCs in a significant way - whereas Gartner now sees them as not so important or central to analytics success). There is also not much on analytics as applied to public sector organisations. Otherwise this is an intelligent, if dated, overview of the principles of analytics in competitive domains. I think the authors offer some good models to help thinking on analytics (The 4 Pillars of Analytical Competition, The 5 Stages of Analytical Competition, etc). Definitely worth reading!
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on 17 December 2012
The book I received was in great condition and the delivery service was great value for money!
I would recommend this product to anybody else with a passion for improving performance through the use of data
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