With Big Data Comes Big Responsibility
Big Data is a terrific book for an elusive concept. Using simple illustrations, authors Viktor Mayer-Schönberger and Kenneth Cukier take on our information-rich future with bold assertions and a studied ease. Yet you may finish the book with some surprising questions, as we will soon see.
Just what is Big Data, anyway? As the book explains, it’s about extracting value from large data sets in ways that would be impossible at a smaller scale. Our world’s now moved from data scarcity to abundance—not because the information didn’t exist before, but because we now have the right tools to extract it. And in the right hands, data is becoming a “material of business, a vital economic input, used to create a new form of economic value.” Everything is quantifiable, therefore everything is valuable.
We live in a brave new world then. The authors see Big Data as akin to shifting from pictures to moving pictures. What at first seemed like a simple step—sequentially stringing together vast sums of photos—instead transformed our visual paradigm (and led to Hollywood, though that can be forgiven). Similarly, just changing the amount of data available stands to transform information’s essence and even how we relate to the world. Our world can now be rendered and quantified in data points for the first time ever; what the authors call “datafication.”
A number of implications loom large. For one thing, Big Data simply describes the what—it doesn’t tell us why. The authors believe that these new insights will be so valuable that many of us will move on from searching for causality and instead embrace simple correlations. Disorder and uncertainty, which have always been the hallmarks of human existence, will become acceptable and even welcome for decision-making.
Put another way, exactitude will become a relic of data scarcity. As long as you know that doing X is related to Y result and you can do something about it, who cares why? We won’t even need hypotheses or useful proxies to glean insight. Computer analysis will do that heavy lifting for us.
Big Data will also bring its own set of winners and losers. For companies, rewards will go mostly to larger firms that can readily scale their data and the smaller ones who can “scale without mass,” perhaps through a large online presence. Those in the middle will be hard-pressed to succeed. Three types of Big Data firms will do particularly well: companies offering data skills or analysis expertise, others with valuable ideas and unique insights, and, in particular, those with lots of data or access to it.
On the individual level, many of us will, in time, feel more empowered as our personal data grows in value and we are better able to leverage it for gain. Some readers may cast a skeptical eye on this assertion, but the authors seem confident of it. Whether Big Data helps us get good jobs is much more of an open question. Schönberger and Cukier believe that “many aspects of our world will be augmented or replaced by computer systems that today are the sole purview of human judgment.” Some will lose their jobs as a result, though other opportunities will appear too as the labor market shifts to accommodate Big Data.
Subject matter experts, in fact, may be in line for a big disruption. Just as Moneyball showed how baseball scouts had to contend with sabermetric analysis, the role of experts will have to adapt into being more of a data-driven decision maker. They may be better off as “algorithmists,” or impartial data auditors dealing in a currency of trust, filling a similar role as accountants for financial information.
For as good as Big Data is, I found myself increasingly skeptical of a few points. Let’s start with the author’s belief that answering why will be pushed aside for simply knowing what. As our Big Data tools improve, it seems to me that answering what will be increasingly commoditized, with ever-thinner margins. Answering why, on the other hand, will become more valuable and irreducibly human. Such inquiry serves as a source of innovation by connecting evidence with ingenuity. The authors seem to recognize this. They believe that “our creativity, intuition, and intellectual ambition” will be a key fount of progress. If so, we shouldn’t be surprised if more firms become dependent on the unique value of skilled human capital to interpret the what and answer the why.
It also strikes me as worrisome that society may adopt, as the book's authors believe, a “Big Data consciousness” that views the world through a quantitative lens. Everything’s worth would ultimately be derived from its status as a data point. Yet you and I consist of more than ones and zeroes. Decisions over our worth and even that of the world around us cannot be founded on algorithms alone. If Big Data is to truly be used to our benefit, then its advance must be matched in step by human institutions that understand it, mold it, and temper it.
That’s why I especially agree with one of the book’s key warnings on the “dictatorship of data.” As Big Data’s algorithms become more complex, they’ll be both more powerful as well as less understandable to the common person. The authors worry that we may just mindlessly accept Big Data’s answers, letting it blind us. Indeed, failing to appreciate the limitations of data may hamper our ability to fully realize its great potential. That should be reason enough to hope we do not move away from asking why or seeking to inform the American public, otherwise we simply become more susceptible to data’s soft despotism. Innovation and enterprise, on the other hand, flourishes in the presence of inquiry.
One could say that with Big Data comes big responsibility. As we move into a world of greater understanding, this book’s greatest lesson may be to never stop questioning.