Author: Ian Ayres

Publication Date: 2007

Bottom Line:  An easy to follow insight into the behind the scenes data and data crunchers driving today's biggest companies and decisions.

What It Covers: Have you ever wondered how Netflix or Amazon always seems to suggest a relevant book or movie, how eHarmony can find your future spouse, or how Google seems to always find you what you're looking for so quickly?  If so, you've been wondering about the world of super crunchers - individuals who compile and process huge quantities of data.  These super crunchers are able to make highly accurate predictions today by examining variables influencing outcomes, reviewing data trends, and then creating an algorithm or formula that may be applied to situations in the future in order to make predictions.

The premise of the book is this: numerical predictions are replacing human predictions because they are more accurate, more dependable, and more testable.

The book begins with the story of a mathematician who decided to apply his trade to baseball.  With one equation, he was able to predict who the leagues best hitters would be, not by simply reviewing RBI or home run stats, but through an equation that predicted the total bases in a season a player obtained through walks or hits.  The formula is now used by baseball scouts, often in lieu of relying on the "scout's eye" for talent alone.

While the author does not directly state how the trend of super crunching might be applied to government, it's easy to figure out.  Imagine relying on a simple formula based on previously recorded data to predict enemy troop movement, instead of a team of analysts pouring over stagnant satellite images.   Perhaps super crunchers could use their predictive skills to tackle public transit problems by more accurately predicting times of heavy ridership, so transit officials can allocate the correct number of trains to those times and fewer trains during quieter times.  The possibilities are endless and we are undoubtedly heading in that direction.

Recommended For:  Anyone interested in how large organizations are making better decisions and offering more personalized services.  Scientists & engineers looking for hard core numbers are going to be disappointed - this book was written for Joe Public, not you. 

About the Author:  Ian Ayers is an econometrician and lawyer who teaches at Yale's School of Management.   He's a regular columnist for Forbes magazine and editor of the Journal of Law, Economics, and Organization.

Excerpts: 

"Wal-Mart learns three different kinds of things from its employment test regression.  First, it learns how long a particular applicant is likely to stay on the job.  Second, it learns how precisely it made this prediction..."

"When you call Capital One, a recoding immediately prompts you to enter your card number.  even before the service representative's phone rings, a computer algorythm kicks in and analyzes dozens of characteristics about the account and about you, the account holder...Capital One found that some customers call each month just to find out their balance or to see whether their payment arrived.  The computer keeps tracks of who makes these calls, and routes them to an automated system that answers the phone this way: 'The amount now due to your account is $164.27.' 

Suggested Backdrop:  Coffee shops, airplanes, trains, and any time you need to stimulate an "ah-hah!" moment of clarity.

OhMyGov Rating: 3 (out of 4) Stars.  A well simplified look at a complicated process.  Good for the average reader, but leaves you wanting some snapshots of these math geniuses in motion.