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Gone are the days of solely relying on intuition to make decisions. Today, number crunching affects your life in ways you might never imagine. Economist Ian Ayres shows how today's organizations are analyzing massive databases at lightning speed to provide greater insights into human behavior. From Web sites like Google and Amazon that know your tastes, to a physician's diagnosis or your child's education, to boardrooms and government agencies, this new breed of decisionmakers are calling the shots. And they are delivering staggeringly accurate results. How can a football coach evaluate a player without ever seeing him play? How can a formula outpredict wine experts in determining the best vintages? Super crunchers have the answers. In this brave new world of equation versus expertise, Ayres shows us the benefits and risks, who loses and who wins, and how super crunching can be used to help, not manipulate us.--From publisher description.… (more)
User reviews
From the opening pages, Ayers pits the "super crunchers" (read: people applying statistics to large data sets) against experts
Although I am more or less convinced by Ayers' arguments I found myself questioning his credibility in several places during the book. I think the main reason for this was due to the tone of the book occasionally crossing the fine line separating "enthusiastic, popular account" and "overly simplistic, gushing rave". The constant use of "super crunching" throughout the book got on my nerves after a while. It began to overemphasise the newness of what could as easily be called "statistical analysis". After a while I mentally replaced "super crunching" with the less sensational "statistical analysis" wherever I encountered it.
Conversely, Ayers constantly refers to "regression" when talking about the techniques analysts use to make predictions. At first, I thought this was a convenient short-hand for a range of techniques that he didn't want to spend time distinguishing between. It was only when neural networks are described as "a newfangled competitor to the tried-and-true regression formula" and "an important contributor to the Super Crunching revolution" that I realised that Ayers may not know as much about the nuts and bolts of computational statistics as I first thought. This impression was confirmed when Ayers later confuses "summary statistics" for "sufficient statistics" and talks tautologically of "binary bytes".
Stylistically, there is too much foreshadowing and repetition of topics throughout the book for my liking. This feels a little condescending at times, as does him directly asking the reader to stop and think about a concept or problem at various points.
Overall, I wanted to like this book more than I did. It was a light, enjoyable read and I wholeheartedly agree with Ayers' belief in the continuing importance of statistics in decision-making and his call to improve the average person's intuition of statistics. Unfortunately, I found much of "Super Crunchers" substituting enthusiasm for coherence, as well as impressions and anecdote for any kind of meaningful argument.
I'm well aware that Amazon has a pretty good idea of what I like and of what I've been interested in recently.
But, after reading all those excellent books about bad statistics, I find that this book comes off as far too credulous. Its basic thesis seems to be that any statistical or machine learning analysis will ultimately do better than the experts. That opinion is much better explained by the unfortunate existence of fake expertise in many areas rather than anything else. The author does back-pedal a bit toward the end of the book, pointing out that mistakes can be made with numbers and data, and that there is room for smart people to intuit relationships that may exist, but it is too little, too late.
Ultimately, this book is so mathematics free as to seem more like a confidence trick than an exposition.
Some particular examples have been discussed in other books with a much different message. One of Joel Best's books claims to show that most of these deaths in hospital due to mistakes by staff result in a person dying about six hours earlier than they would if the mistake hadn't been made. Viewed in that light these mistakes seem less significant, and maybe in some cases a mercy.
1. The insight into his use of the "2SD" rule to calibrate the range of one's confidence
2. The reference to Michael Lewis book Moneyball.
I don't think the author gave enough focus on the issue of measuring all that's important (if you can't do that then you'll
Whilst interesting & easy to read,
What I liked: The many examples of applications in current day situations (both for & against the consumer), the current psychological barrier adopting this methodology in certain industries, highlighting how absolute judgement by human perception is a myth & encouraging verification of data crunchers.
What could have been better: More tips on how to apply it in daily life (perhaps the very reason why I find it inadequate is because it promises to do this), clearer segmentation, better summaries, & I also found the author's way of describing his fellow associates was slightly excessive...
Overall quite a good read, but I was slightly let down because of the promises at the start of the book.
Nearly overlooked in the book is the manner in which the data is obtained and the validation of the same. A regression model is only as good as the quality of the data and the manner in which the raw data is understood by the statistician. Anyone who is involved with model development will tell you that the real work is in the data prep phase.
It's interesting to contrast this book with "The Black Swan", by N. Taleb. In my opinion, this author (Ayers) is a "true believer" in the value of statistics, regression models, and the like. Taleb, on the other hand, rails at those who put their faith in statistical models. I think there is some middle ground, in that Taleb may agree that statistical models are useful, but users need to be wary - of unlikely events and the *impact* of those events, and knowing where the models apply and where they don't.
3.0 out of 5 stars An easy read on data-driven decision making, October 17, 2007
Ian Ayres book is another book extolling the virtues of data-driven decision making. In that regard it is very similar to Competing on Analytics: The New Science of
While Ian is a little in love with the subject, and while he has created an unnecessary and irritating label (Super Crunchers) when he could have called these people Data Miners like everyone else, the book is well written and an easy read.
He has some fun examples - everything from the mathematical prediction of wine vintages to established stories like Harrahs and CapOne.
I liked the way in which he talks about the changing role of experts in this world. Not interpreting results but providing the subjective or face-to-face input that algorithms need to make better decisions. I think many organizations will go through a similar progression. First they might adopt a purely rules-driven or expert-centric approach. Gradually as their data, and their understanding of it, improves they might tune these rules with analytic models. Ultimately they may well find that the rules are definitively subordinate to the models with most or even all of the decision making power coming from the models. Unlike the experts in Ian's stories, one hopes the rules will not be upset by this!
One section also made a great point, highlighting in passing a potential advantage of adopting decision automation over more traditional forms of decision support. While people using decision support systems do better than people alone, they still don't do as well as the analytic model would on its own. Decision automation, with its reliance on the model, would obviate this problem.
He does not spend enough time discussing the difference between causation and correlation nor does he talk much about the constraints that can be imposed through regulation or explicit company policy. His focus is often on one-off insight that changes how organizations do something rather than on the use of this kind of decision making in high-volume, transactional systems.
Finally I agree with him that the rise of automation in decision making will force consumers to retaliate by getting access to data, and the implications of that data, to resist the ability of companies to use data to their advantage.
Overall a good book, though not perhaps as good as Competing on Analytics: The New Science of Winning.
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519.5 |