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Below: Comments Wall in

Below: Comments Wall in The Tate Modern’s “Art in Action” exhibit. In addition to being shared with visitors, tweets were used for “sentiment analysis.” © Tate Photography Big data analytics aren’t confined to marketing, either. Predictive policing modeled on earthquake prediction algorithms is being use to spot “fault lines” of crime, forecasting where and when criminal acts such as burglary and gun violence will occur, and who will become a repeat offender. Esri, which characterizes itself as a “Facebook for Maps,” integrates geographically tagged data with maps, social networks and statistical analysis to help with functions as diverse as finding lost hikers and mobilizing relief aid after natural disasters. Big data is being enlisted by the nonprofit humanitarian sector to do more and better good as well. “Big Data for Development” brings realtime monitoring and prediction to global aid programs: the United Nations Global Pulse project can analyze Twitter messages to predict spikes in unemployment, disease and food supply prices—what they call “digital smoke signals of distress.” At some point, this predictive software begins to look like precognition: a 27-year-old computer prodigy recently created an algorithm that mines news archives to predict possible disasters, geopolitical events and disease outbreaks with 70–90 percent accuracy. While for-profit companies are at the forefront of exploiting the potential of big data, nonprofit organizations are already creating data sets that draw on museum data, particularly from the arts. Examples include Fractured Atlas’ Archipelago data visualization software, Americans for the Arts’ Arts & Economic Prosperity Calculator and the University of Pennsylvania’s Social Impact of the Arts 26

Project (SIAP). These may only count as “medium large” rather than truly big data, but they point to how such tools may evolve as cultural organizations realize the power of pooled data resources. While some commentators are already saying Big Data is overhyped, there is no sign of it slowing down. Its growth is fueled in part by the power of combining the information collected via the “Internet of Things” (ubiquitous Internet-connected sensing and monitoring devices) with more “traditional” forms of data collection (the U.S. Census, mobile, landline, in-person intercept surveys, etc.). And it is driven by linking all these sources to rapid advances in computing intelligence that can recognize patterns and learn from its own mistakes. This in turn is supporting a growing workforce of programmers, analysts and dataliterate managers. What This Means for Society For decades, science fiction has speculated on whether human workers may be displaced by robots, whether humanoid or transformerlike. Now it seems that the disruption will be more subtle but just as profound. In 2011 IBM Watson caught our attention by beating human champions Ken Jennings and Brad Rutter at Jeopardy. That was just prologue to the real work of this artificial intelligence system. Watson’s ability to understand questions posed in natural language, mine huge data sets and learn from interacting with that data is now being harnessed to create a health-care Watson to improve diagnosis and treatment decisions, and a Wall Street Watson to give advice on investment choices, trading patterns and risk management. The question is not so much whether big data channeled through programs such as Watson will displace jobs, but how it will change the human role in decision making. Researchers point out that the biggest challenge facing doctors, investment analysts, engineers, policy makers and managers is learning to trust analytic algorithms rather than their own judgment. As our country faces an epidemic of obesity and attendant diseases, and as Boomers enter their retirement years projected to live longer and be sicker than any previous generation, we face a crisis in personal health management. Big data has something to contribute in this arena as well: the Quantified Self movement, consisting of people who believe that collecting obsessively detailed data about their own bodies can improve health and behavior. This movement is driven by increasingly affordable wearable biomonitors in the form of wristbands or sensors embedded in your shoes or sewn into your clothing that track how many steps you take, how much and how well you sleep, your heart rate and how many calories you consume. In the near future, these may be joined by biomonitoring implants tracking your body from the inside. These tools have been shown to affect physical health, and paired with interactive software and a diagnostic, cognitive system like Watson, such monitors could largely supplant traditional psychotherapy as well. Just as we begin to discover how much we can do with massive data mining, society is already struggling to decide how data should be used. As with all technology, analytic tools 27

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