Ebook Competing on Analytics: The New Science of Winning
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Competing on Analytics: The New Science of Winning
Ebook Competing on Analytics: The New Science of Winning
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Review
“[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
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From the Back Cover
In a world where traditional bases of competitive advantage have largely evaporated, how do you separate your company's performance from the pack? Use analytics to make better decisions and extract maximum value from your business process.InCompeting on Analytics: the New Science of Winning,Thomas H. Davenport and Jeanne G. Harris argue that the frontier of using data has shifted dramatically. Leading companies are doing more than just collecting and storing information in large quantities. They’re now building their competitive strategies around data-driven insights that are, in turn, generating impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling supported by data-savvy senior leaders and powerful information technology.Why compete on analytics? At a time when companies in many industries offer similar products and use similar technology, distinctive business processes count among the last remaining points of differentiation. Many previous bases for competition—such as geographical advantage or protective regulation—have been eroded by globalization. Proprietary technologies are rapidly copied, and breakthrough innovations in products or services are increasingly difficult to achieve.That leaves three things as the basis for competition: efficient and effective execution, smart decision making, and the ability to wring every last drop of value from business processes—all of which can be gained through sophisticated use of analytics.Davenport and Harris show how exemplars—organizations as diverse as the Boston Red Sox, Netflix, Amazon.com, CEMEX, Capital One, Harrah’s Entertainment, Procter & Gamble, and Best Buy—are using new tools to trump rivals. Through analytics, these companies identify their most profitable customers, accelerate product innovation, optimize supply chains and pricing, and leverage the true drivers of financial performance.A timely, much needed resource,Competing on Analyticspromises to rewrite the rules of competition.
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Product details
Hardcover: 240 pages
Publisher: Harvard Business Review Press; 1 edition (March 6, 2007)
Language: English
ISBN-10: 9781422103326
ISBN-13: 978-1422103326
ASIN: 1422103323
Product Dimensions:
6.5 x 1 x 9.5 inches
Shipping Weight: 14.4 ounces (View shipping rates and policies)
Average Customer Review:
3.7 out of 5 stars
135 customer reviews
Amazon Best Sellers Rank:
#34,934 in Books (See Top 100 in Books)
This book shows how several companies are effectively "competing on analytics" - defined as having the following four characteristics:1. Analytics supports a strategic, distinctive capability2. Approach to and management of analytics is enterprise-wide3. Senior management is committed to the use of analytics4. The company makes a significant, strategic bet on AnalyticsIn 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.
Competing on Analytics will explain the different forces you have to overcome in every organization if you want to compete on analytics.The book explains the key drivers behind a successful BI-endeavor and what paths you should avoid if possible.I really liked the methodology where the author treats the subject as a stair in five steps you have to climb. From knowing nothing about analytics, to be an analytical competitor, and what actions you need to take to climb each step.Competing on Analytics is full of hands-on actions you can take and gives you plenty of material to go further.
Writing a book takes a lot of commitment and deliberation, so I while I would attest to speaking so derisively against anyone’s hard work, this book is of particularly poor and quality and insulting to the intelligence. The fact of the matter is that the authors are consultants, and in the world of consultancy, perception is everything. It looks very good on a resume to have a book published by Harvard Press, with the subject of predictive analytics, before it became a much more commonly understood phenomenon. So one has to question if the intentions of the authors were to make a book to 'educate' the masses on predictive analytics, or to further their own career. So for sake of brevity, I will restrict my review to the actual contents of and merit the book, and not speculate on the intentions, merits, or morals of the authors; This book reads like an infomercial, proselytizing it's precepts as the panacea to all business functions with no regards for anything that came before. Everything is a nail, and predictive analytics is the hammer. This book essentially takes 6 fundamental concepts, and repeats them incessantly throughout the book under the guise of new chapters. You can read the first chapter and maybe the 7th chapter, and walk away from this book just as informed as the poor souls who endeavored to consume this tome of drivel in it's entirety. This book is rife with anecdotal stories of companies who did use predictive analytics however, the authors neglected to actually show any real examples of how the analytics were formed, used, or even gathered. There are no supporting statements or logic supporting their assertions of stating how unbelievably awesome these companies are because they used predictive analytics. A prime example is the authors blatant assertion “The New England Patriots began using predictive analytics and then they won 3 out of 4 super bowls, so you can see the power of predictive analytics.†This violates the prime statistics fallacy ‘correlation is not causation’. There is no explanation of what they used to improve, or even a supporting argument that proves their statement. Their assertions can be logically reduced to, group A was successful because reasons and magic. Occasionally, to help lure the unsuspecting reader into a sense of awe of the authors mastery of statistical knowledge, the authors would throw around advanced statistical phrases like ‘linear reduction algorithms’ and have large section explaining statistics concepts. Of course, many of their advanced phrases were not explained, and the ones that were explained, were never correlated to anything said in the rest of the book. I found the book to be replete with grammatical errors that often made it difficult to understand, as well as formatting issues with tables, spliced right into a non-related paragraph, that is referenced 2 pages later to go back and look at. This low attention to common formatting was quite disappointing from ‘renown business consultants’. One exception I will take note into criticizing the authors specifically, are some of the elitist comments I found very distasteful in the book; the first was the authors calling anyone who didn’t pay their cell bill ‘deadbeats’. Yes, they actually used the word ‘deadbeat’ in a statistical analytics book. The second instance, was their apparent astonishment that a bank charged a hedge fund person an overdraft fee because they bounced a check. Because apparently, if you have 100 million in a hedge fund, but are too incompetent to manage your checking account, you shouldn’t be penalized like the rest of us mortals who weren’t lucky enough to win the genetic lottery. To show credit where credit is due, I am actually giving this book 2 stars and not just a hateful 1. 2 stars is because I did find many of the anecdotal stories to be informative, and sometimes insightful and helpful, despite lack of clarifying or supporting logic. In short, don’t read this book. Whether you care about the authors agenda or not, having spent 15 years in the industry, I can personally attest to the fact that there is nearly nothing of material value in this book. In fact, if you don’t believe me, I challenge you to read the last paragraph of the final chapter, and tell me this does not sound like a television add for the next weight loss product.
This book is great for those who wish to grasp a better understanding of why analytics are such an important tool in today's business world. Although this book is not very long, Davenport delivers some of the most dry information into a relevant, easy-to-read format for the average student, professional, or those just seeking analytical education. This book provides an in-depth look at how the growing amount of company information can be utilized as a competitive edge. Much of the content consists of analytical theory, however Davenport backs it with real-world brand examples such as Netflix, Amazon.com, Progressive, and Capital One. You will see that not all industries are equally susceptible to using these techniques to improve business processes. Davenport shows the reader that there are many companies on the verge of competing with their analytical process and some are already at that level. You will begin to understand why so many companies perform poorly even though they collect mountains of consumer data. Some of the information is a little repetitive, mainly previews of further chapters that go into to much detail. This would not be a book for those who are familiar with current business intelligence models and trends as the information is presented to a general audience. Overall if you are looking to expand you knowledge about business intelligence and don't want a "dry" book, I would recommend Competing on Analytics!
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