Social Physics 101: How Data Defines Us


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In a country where all men and women are created equal, data may prove to be the great differentiator.

This is what is implied with the concept of “Data-Driven Societies,” as recently explained in a compelling story in The New York Times. The report details a gathering of academics, business executives and journalists who assembled in mid-February to mull over this concept, and what it could mean for public policy.

Leading the group was Alex Pentland, a computational social scientist at the Media Lab. Pentland authored a presentation called “Reinventing Society in the Wake of Big Data.” His theory: That the most valuable data available today is that which analyzes people’s behavior.

The reason, in part, is because such information is vast and highly detailed. Consumers use their credit cards to buy items as small as a cup of coffee, while all day long location-tracking technology in their smart phones follows them from location to location.

It has, according to the NYT story, become possible to track social phenomena down to the individual level and to gauge the social and economic connections among individuals. Pentland said the ability to follow these micro-patterns indicates our culture is entering an era of “social physics.”

Using the term, “social physics,” to describe the use of transaction data really resonates. The idea that different customers could be treated in varying ways based on their consumption is something social policy may need to accept. In the context of the NYT story, the thought was applied to government incentive and benefits programs, such as tax credits for solar panels or Social Security. But “social physics” is already taking place among the more advanced merchants and financial services companies, which have figured out how to encourage and reward individuals based on what they purchase, where and how often.

Policy makers should look to these experts for some pointers.

Another statement from the story that caught my eye up involved the fact that algorithms, which determine the patterns and insights that guide decision-making, are in fact created by people and therefore susceptible to human assumptions. “At some point, you are in the hands of the algorithm,” said John Henry Clippinger, chief executive of the Institute for Data Driven Design, a nonprofit research and educational organization.

This thought should not be dismissed. It should, instead, introduce an open-minded debate on the role of human intervention in the entire decision-making process. Is the practice of data analytics really just a question of design vs. applied science?

I’d love to hear your thoughts.

Republished with author's permission from original post.

Bryan Pearson
Retail and Loyalty-Marketing Executive, Best-Selling Author
With more than two decades experience developing meaningful customer relationships for some of the world’s leading companies, Bryan Pearson is an internationally recognized expert, author and speaker on customer loyalty and marketing. As former President and CEO of LoyaltyOne, a pioneer in loyalty strategies and measured marketing, he leverages the knowledge of 120 million customer relationships over 20 years to create relevant communications and enhanced shopper experiences. Bryan is author of the bestselling book The Loyalty Leap: Turning Customer Information into Customer Intimacy


  1. Bryan: I enjoyed reading your blog very much. I’ve been intrigued by the social implications of data profiling, and have been scratching my head in befuddlement when considering the data and analytical power that some companies have compared to the popular idea that “customers today have more information power than ever.”

    Now, I have taken that to mean “customers give others more information power than ever.” It seems more accurate. Of course, customers have been visually profiled ever since the first face-to-face transaction, and continue to be profiled that way today. (see my blog “Age Verification Bypassed by Cashier” on this site). When telephony was adopted by customer service, there was a fear that calls would be routed according to the location of the caller. The concern was that for some businesses, calls from lower-income neighborhoods would be handled differently – longer waiting queues, etc.

    Now the data analysis issues you’ve described bring “digital profiling” to a new level. Whether algorithms and statistical analysis will make experiences and outcomes better for all consumers remains to be seen.


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