The Answer Isn't Less Data, It's More Data Science

August 29, 2012 Paul M. Davis

Photo by Sarah Joy via Flickr. (CC BY-SA 2.0)

How much data is too much? Depending on who’s answering, the answer may be “there’s never enough.” Many don’t share that perspective, however, and are instead overwhelmed by the amount of data available at their fingertips. It’s a growing concern for consumers of online media, engorging themselves on the endless buffet of information served through social media, smartphones, and news aggregators. Researchers have coined the term FOMO, or “fear of missing out” to describe the obsessive consumption of information, to the detriment of individuals’ relationships, personal well-being, and even rest. A number of prominent bloggers have even advocated for a “Slow Web” movement, urging a much more measured and restrained approach to web consumption.

Fast Company’s Ron Friedman argues that it’s not merely FOMO that’s causing the sense of information overload, and argues that having more information does not lead to better decision making. He cites a 1998 study conducted by psychologists at Princeton and Stanford University, “On the Pursuit and Misuse of Useless Information”, which found that better-informed test participants made poorer decisions during a number of controlled experiments.

Friedman extrapolates from the study’s results that “We’re fascinated with filling information gaps and that obsession can lead us astray.” He extends his argument from social and news media consumption to the enterprise, obliquely referencing the influx of Big Data, writing “…It’s data that drives major business decisions. There’s always one more report, one more analysis, and one more perspective that’s a click or two away.”

In light of data science as a space and a discipline, this is a large leap for Friedman to make. It’s hard to disagree that it may be healthy to take regular breaks away from email, Twitter, and perhaps stop sleeping with our smartphones beside our heads. And it’s true that data-deluged enterprises which lack the tools to manage and reveal insight from the available information may make poor decisions, despite operating under the illusion of expertise. Making sense of it all — separating the signals from the noise, knowing what questions to ask, and applying predictive analytics — is both an art and a science. The information glut doesn’t undermine the value of dedicated analysis; rather, it illuminates the value of dedicated data science teams for organizations producing and collecting far more information than they can usefully leverage.

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