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Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart
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Audible Audiobook
Listening Length: 7 hours and 34 minutes
Program Type: Audiobook
Version: Unabridged
Publisher: Books on Tape
Audible.com Release Date: August 20, 2007
Whispersync for Voice: Ready
Language: English, English
ASIN: B000VKCIV8
Amazon Best Sellers Rank:
Decision making permeates our everyday life. It ranges from the personal to the industrial and commercial where the process becomes costly and critical. Traditionally businesses resorted to intuition and the advice of experts to make a major decision. Lately, however, more and more decision making is relying on data analysis and the use of computer.In this groundbreaking new book, “Super Crunchersâ€, the author, Ian Ayre, believes that the days of making decisions by relying on intuitions are gone. Today’s best and brightest organizations are analyzing massive databases at lightening speed and gaining greater insights into our human behavior. They are the super crunchers. Companies like Google and Amazon have amassed huge data about our behavior. They know our tastes better than we do, they understand our children’s needs, they know all about the shoes and clothes we buy and wear. They even know how much we’ll pay for our flights and whether the fares will go up or down. They are a new breed of decision makers who are now calling the shots.But how do these companies arrive at such massive information. Obviously, they need a huge amount of data to get comprehensive conclusions, and the data should be as randomized as possible to avoid bias. Interestingly, such data is now available through specialized companies whose business is to collect figures from computer charts, store receipts, company reports and the like, and sell them to researchers. They have data on the size of our shoes, the foods we eat, the medicines we buy, the games we play and even the colors we favor. Once the data is collected, researchers use regression and statistical analysis to evaluate the effect of different variables on a commodity or a behavioral trend, e.g. does music in factories increase productivity? Would a salary raise improve employee loyalty…..? And so on.Putting This new data-analysis approach to many fields has made remarkable improvements. Consider the effect on the medical field. The rise of data-based decision making has reversed conventional wisdom. For example it showed that beta blockers can actually help cardiac patients and that estrogen therapy does not help aging women. Now there is a diagnostic program called “Isabel†which allows physicians to enter a patient’s symptoms into a computer and receive the most likely diagnosis. It will also tell the doctor the possible drug that caused the symptoms. Soon it will even specify the most likely therapy. No wonder many doctors are getting concerned about the possible loss of their control over diagnoses once considered a most important factor in their profession.How are we then to look at this groundbreaking approach to decision making? One cannot dispute its speed and efficiency. It is simply remarkable! Business has put it to beneficial use, but mostly to maximize its profits from the unaware consumers. Medicine stands to gain from it technically while also helping the patient financially and health-wise. But buyer be ware! There are no guarantees and consumers have to view it critically on a case-by-case basis in order to evaluate its risks and benefits.F.R. QUBEINNov. 2017
Great book on the importance of data-driven decision making. While I have always been someone that has let the data do the talking, I haven't found an easy way to explain why. Super Crunchers is that easy way! Below I have summarized some of the important points of the book....Super Crunching is crucially about the impact of statistical analysis on real-world decisions. Two core techniques for Super Crunching are the regression and randomization.1. Regression will make your predictions more accurate (Historical approach):It all starts with the use of regressions, and although this method is a basic statistical test of causal relationship it's still a very powerful tool that I need to re-introduce in my analytical life.Regressions make predictions and tell you how precise the prediction is. It tries to hone in on the causal impact of a variable on a dependent. It can tell us the weights to place upon various factors and simultaneously tell us how precisely it was able to estimate these weights.2. Randomization and large sample sizes (Present/Real-Time approach):Reliance on historical data increases the difficulty in discerning causation. Large randomized tests work because the distribution amongst the sample are increasingly identical. Think A/B testing on steroids that allows you to quickly test different combinations! Boils down to the averages of the "treated and untreated" groups.Government has embraced randomization as the best way to test what works. Statistical profiling led to smarter targeting of government supportWith finite amounts of data, we can only estimate a finite number of causal effects3. Neural networkUnlike the regression approach, which estimates the weights to apply to a single equation, the neural approach uses a system of equations represented by a series of interconnected switches.Computers use historical data to train the equation switches to come up with optimal weights. But while the neural technique can yield powerful predictions, it does a poorer job of telling you why it is working or how much confidence it has in its prediction.Super Crunching requires analysis of the results of repeated decisions. If you can't measure what you're trying to maximize, you're not going to be able to rely on data-driven decisions.We humans just overestimate our ability to make good decisions and we're skeptical that a formula that necessarily ignores innumerable pieces of information could do a better job than we could.
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