Big Data and Society: From Remarkable to Commonplace

By Nick Carney • December 4, 2013

The resiliency and adaptability of the human spirit is a favorite topic of philosophers, psychologists, and artists alike, but is on display perhaps nowhere more clearly than in our tendency to quickly forget how we are surrounded by remarkable technologies. Inventions or circumstances that were once miraculous quickly grow commonplace or even boring.

Why is it useful to be reminded of this tendency of human nature in the context of smart cities? Because reflecting on how this phenomenon occurs remind us that technological disruption requires not just the great ideas themselves, but successfully integrating those ideas into daily life and society at large; this thought exercise provides reference for how much has been accomplished with the Big Data space, and how much is yet left undone.

To get a better sense of what this means for the Big Data revolution, let’s first consider the development of another technological innovation: cell phones.

An Informative Detour

Over the span of two decades, mobile phones went from fantasy to necessity, achieving near-universal ownership and market saturation; 96 percent of the world’s population now owns a cellular device. Just as dramatically, in half that time those boring old boxes that once could merely place a call have transformed into handheld computers with a processing power four times greater (!) than that of the latest NASA Mars Rover.

And yet! The ubiquitous smart phone is no longer considered a technological miracle but a humdrum fixture of modern existence. How did we become so acclimated to this transformative technology?

On some level it is a result of humanity’s ability to acclimate to novel experiences, circumstances, and emotions, and to return a stable equilibrium (see how this phenomenon affects our overall happiness, for example, here).

Yet we also grow immune to revolutionary ideas and technology because the impact becomes seamlessly integrated into our daily experience. This occurs because:

  1. The idea or technology has reached sufficient refinement and maturity, such that the errors and bugs in the system have been largely eliminated.

  2. The societal structure in which the technology is being implemented has evolved, depending on this previously revolutionary device or technique as now simply one element its normal functioning.

Let’s turn back to cell phones to see this at work. To the first point, early cell phones looked like plastic bricks and weighed several pounds; not exactly convenient for slipping into your jeans pocket. Only as the design evolved into a truly practical device did mobile phones achieve large-scale adoption.

At the same time, a societal shift occurred. New etiquette developed for talking on cell phones in public (though some may argue that cell phone courtesy is an oxymoron); text messaging became an acceptable and efficient means of communication; telecommunications companies began to compete to offer the most attractive phone contracts; governments began to regulate the new wireless communications industry; privacy standards were adopted, debated, and ignored; map-producing companies went out of business for good as electronic maps became accessible on phones; and an entire generation of budding entrepreneurs dedicated their twenties to creating the next killer cell phone app. The process was organic, at times chaotic, and continues to churn, but we can no longer imagine life without the electronic slabs that we carry around in our pockets.

And Data Analytics?

In comparison with cell phones, the data analytics and Big Data revolution is less about creating flashy hardware than about developing groundbreaking software and applications of said software. Put another way, Big Data relies on powerful computers and sensors (and, yes, cell phones), but is about creating new ways in which those tools can be utilized. Despite this subtle difference, the same process of integration into our daily lives still applies.

If we examine the maturity of the technology, we can safely to say that we have not yet reached peak potential for data analytics. Novel uses continue to be dreamed up every day and we’ve only begun to scratch the surface, particularly in the realm of predictive analytics. Firms and governments will be discovering uses for the Big Data for years to come. So much for criterion number one.

Let’s turn to the second, and perhaps more interesting, condition: how much progress have we made in adapting our societal institutions to embrace these new applications of data?

On one hand, it is no longer uncommon to find newspaper articles discussing how Big Data is improving the efficiency of utilities, transportation systems, or policing. The notion of smart cities integrates itself more fully into the public consciousness with every passing day.

And as with cell phones, cars, and countless other innovations, at some point we’ll look back and forget what it was like to ever live without the efficiencies driven by data analytics.

Yet the job is far from completed. Take, for instance, an NPR piece examining the constitutional questions raised by preemptive analytics, in this case with software intended to preclude criminal activity. Programs like CompStat provide rigorous statistical analyses of crime patterns, but this new, cutting-edge program make forecasts of the precise location in which crime is most likely; the program stands at the vanguard of the Big Data world. While news sources like the New York Times, NBC News, and the AP have been heralding the arrival of preemptive policing methods for several years, with Santa Cruz and Los Angeles becoming early adopters of the PredPol software, earlier this year Seattle became the first municipality to use the program to predict gun violence.

With PredPol growing in popularity, legal scholars have begun to ask how it affects the constitutional requirements facing policemen. Does a prediction by a data-mining program constitute a “reasonable suspicion” (the legal barrier that the police must meet to stop someone on the street)? Is an individual reasonably suspicious if they are physically presence within a particular square footage deemed potentially problematic by PredPol? At what point to human judgment, statistical probability, and legal jurisprudence meet?

The exchange at play in the case of PredPol is an especially clear example of communities grappling with the impacts of Big Data, and in turn shaping the limits of what that technology will be permitted to do. But this is far from the only case in which the law and social mores find themselves in a dynamic dance with a particular application of Big Data. Throughout the next several decades, particularly as the idea of preemptive government grows more refined and commonplace, society will need to even more visibly wrestle with the balance between privacy and safety, information and secrecy, coincidence and statistical probability.

But during the process, we will grow accustomed to the presence of Big Data in our lives. And as with cell phones, cars, and countless other innovations, at some point we’ll look back and forget what it was like to ever live without the efficiencies driven by data analytics. When the benefits of Big Data are imperceptible to the average person but the idea of living without these programs is shocking, then this particular technological revolution will have come to full fruition. The only question is how soon before that moment occurs.


About the Author

Nick Carney

Nick Carney is a Masters in Public Policy candidate at the Harvard Kennedy School of Government and a Public Service Fellow. He is concentrating in Social and Urban Policy and has previously worked in clean energy policy and mixed-use, urban real estate development. Nick is particularly fascinated by transportation, land-use, and education policy, and with his sister runs a literacy nonprofit called Breaking the Chain.