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Published: December 2, 2013
 / Winter 2013 / Issue 73

 
Business Literature: Best Business Books 2013
 

Best Business Books 2013: Digitization

Three Harbingers of Change


Viktor Mayer-Schönberger and Kenneth Cukier
Big Data: A Revolution That Will Transform How We Live, Work, and Think
(Houghton Mifflin Harcourt, 2013)

Marina Gorbis
The Nature of the Future: Dispatches from the Socialstructed World
(Free Press, 2013)

Henry Jenkins, Sam Ford, and Joshua Green
Spreadable Media: Creating Value and Meaning in a Networked Culture
(New York University Press, 2013)


Whether you invest, build, teach, research, regulate, investigate, heal, entertain, or sell, major changes in how you do what you do are looming. “Big data,” much in the media spotlight recently—particularly for the revelations of the National Security Agency’s (NSA’s) surveillance of “metadata”—is probably already changing how you do your work. But socialstructing and spreadable media, two new terms that signal similarly momentous shifts, may still be unfamiliar. This year’s best business books on digitization can equip you to better understand all three phenomena and the changes that they will enable and engender.

Tsunamis of Data

We humans and our machines are generating towering tsunamis of data. The Sloan Digital Sky Survey collected more data in its first few weeks than had been collected throughout the history of astronomy. A single lab can now sequence more DNA in a day than was sequenced in the decade-long, multinational effort required to decode the human genome. Google processes thousands of times the quantity of text in the Library of Congress every day. Its executive chairman, Eric Schmidt, claims we are generating more information every two days than in all of human history up to 2003.

Storing, processing, and making sense of these trillions of bits of data used to be impossible. But today it’s stupendously inexpensive to store data, it’s far easier to process it, and there is a library of sophisticated algorithms for making sense of it. Most tellingly, businesses (and other organizations and individuals as well) are recognizing a growing number of novel ways to apply it.

Thus, Target can deduce when specific women have just become pregnant—or are likely to become pregnant—from the patterns in their purchases. Google Flu Trends competes with the Centers for Disease Control and Prevention in predicting influenza outbreaks by tracking billions of Web searches for flu symptoms and related subjects with a half billion different algorithms. The SecDev Group can identify the location of probable cease-fire violations in geopolitical conflicts within 15 minutes. High-frequency traders can buy and sell stocks in microseconds, based on ultrafast analysis of all the stocks traded a microsecond in the past, a practice said to account for more than half of all stock trades and flash crashes. Scientists at HP Labs can successfully forecast the box-office success of films by looking at the rate at which relevant tweets are posted. The list of profitable applications of big data is far longer than this—and growing fast.

Viktor Mayer-Schönberger, a professor at Oxford University, and Kenneth Cukier, an editor of the Economist, plumb this phenomenon in their book, Big Data: A Revolution That Will Transform How We Live, Work, and Think. They base their bold subtitle on three assertions.

First, big data is qualitatively different from sampled data, yielding insights that are possible only when the size of your sample is close to the totality of the observed population. The really, really big picture can reveal details that were invisible with less than near-total sample sizes. Look through the right algorithmic lens and you can see things in big data that you can see only with big data.

Second, big data enables valuable forecasting of a wide swath of phenomena through the use of correlation, even though causation may be unknown. “Society will need to shed some of its obsession for causality in exchange for simple correlation,” suggest the authors, “not knowing why but only what.” Correlations that can be acted upon profitably are good enough to justify the use of big data.

 
 
 
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