THE society of the future will be a society of communicative capital. Anyone with a high degree of influence in forming opinions on social media will enjoy checking in as if they had booked business class; their rooms will be upgraded on arrival at the hotel, and they will be rewarded with perks in many other situations. “Perks” is the name given by the San Francisco startup Klout, founded in 2008, for the small extras that one receives, here and there, for a high “Klout score.” The Klout score indicates a person’s communicative importance, their “influence across several social networks,” on a scale of 1 to 100. The score is calculated from more than four hundred signals, including the number of followers, retweets, comments, likes, friends on Facebook, the number of citations on the Internet, and backlinks to a personal website. In the end we arrive at a score that can be compared with all the others, something that Klout calls for explicitly on its Facebook site: “Invite your friends to compare your scores.”1
A society that creates differentiations based on rankings and provides financial advantages based on communicative capital—which is close to social capital in Pierre Bourdieu’s terms—appears as a better alternative relative to a society that operates chiefly on the basis of economic power. However, this understanding will change when, given viral marketing, communicative capital becomes firmly integrated in the business models of hotels and airlines and as communicative capital itself becomes commodified—through the purchase of Facebook friends and Twitter followers, for example. Whatever happens, it is an important sociological question demanding a wide-ranging critical discussion at least as soon as schoolchildren begin to select their friends, or companies their employees, based on Klout scores.
For the media theorist, the phenomenon of the Klout score directly addresses a fundamental question of the discipline: Does technology determine society, or vice-versa? Do computation and the Internet impose Klout scores on people, or do human practices make them inevitable? If society’s guiding medium already carries the drive to calculate in its name, and if, given the digitization of almost all communication on the Internet, this medium has an ever-increasing amount of data on the behavior of individuals at its disposal, then everything seems to point in the direction of technological dominance. On the other hand, people’s interest in statistics and ranking did not originate with the rise of the personal computer. A paradigm change from qualitative to quantitative understanding can already be found in the late Middle Ages.2 In the contemporary public-management society that increasingly pursues its own rationalization—and even, for example, calculates the quality of scientific publications on the basis of citation indexes—the invention of an influence index in social networks is a logical consequence facilitated but not caused by computers and the Internet.
Regardless of how one answers this crucial question of media studies, one can hardly deny the prognosis: The society of the future will be one of data mining and number crunchers. Business analysts are now promoted as “data scientists”; statistics—once the domain of nitpickers—has become a profession with sex appeal. The super crunchers are the superheroes of the twenty-first century, their names consistently popping up on the latest lists of up-and-coming millionaires.3 The generation of Klout scores is not their most profitable activity, but it symbolizes perfectly the obsession with measurement within the “statistical turn” that accompanies the digitization of society.
The business of big-data mining is distinguished by three distinct modes of operative agency: Data owners who possess the data but do not analyze what they collect (Twitter), data specialists who help the data owners use their data most effectively through complex methods of analysis (Teradata, for Walmart), and businesses and individuals who gather information with original and unconventional perspectives or methods for which no one had yet thought of an application (FlightCaster.com predicting flight delays based on the analysis of ten years of data on flights and weather). The modes may overlap—for example, when MasterCard evaluates the accumulated data on the buying patterns of its customers and then sells its findings to advertisers—and business models can evolve: Credit-card issuers may in the future allow transfers of money free of charge in return for access to or analysis of more data that they can sell on. At the same time, new business models emerge from the possibilities for recombining and reusing data, examples being “data intermediaries” that utilize data acquired from different sources in an innovative way.4
The inevitable flip side of data love is an indifferent, if not hostile, relationship with the world of human privacy. At the precise point when data entrepreneurs dig into the vast depth and extent of the accumulated data in order to claim the treasure of a promising competitive advantage, petty, individual concerns of privacy will be in the way of their spirit of discovery, just as the aged couple Philemon and Baucis, in Goethe’s Faust, stood in the way of modern-day business practices. An important part of big-data business is, therefore, the management of mood. The subjects or objects of the data—our choice of terminology depending on whether, for instance, one attributes the production of GPS data to the user of a mobile phone or to its provider—have to be convinced of the entrepreneurs’ good intentions, namely, that their goal is to develop better products and to offer improved customer care, as the announcement of the Data Love conference declared. One has to convince the potential customer to see big-data mining as the great adventure of our times, an adventure in which everybody is obliged to participate. For, in contrast to former times, when courageous businessmen embarked on dangerous voyages, risking their lives for potential gains, the entrepreneurs of big-data business must take the entirety of society along for the ride.
At the same time, it should be stressed that it is not only business that may accrue profit from the data of their customers; customers can also profit from the data of businesses. A good example is the computer specialist Oren Etzioni, who reacted to the realization that he had paid more for his plane seat than his neighbors by creating the startup Farecast, a program that uses statistical analysis to predict variations in fares. Anyone who helps others to save money in such a way will, in the end, make money for himself—or as soon as his company, as happened with Farecast, is bought for 110 million dollars by Microsoft.5 The story of Farecast points to the fact that data mining does not thrive on personalized advertisement alone; it shows that in an information society profit flows from the trade of information in general and that, quite naturally, it is made by those who are able to broker it.