A few weeks ago I participated in an online debate sponsored by The Economist around the question: Are Smart Cities Empty Hype?. I argued the case against the motion. In my opening statement I said that I strongly believe that digital technologies and the many data services they are enabling will make cities smarter and help transform them over time. Later, in my closing remarks, I pointed out that since the qualifier smart essentially means information-based or data-driven, the promise of smart cities is inexorably linked to the general promise of big data and data science, which some have felt are themselves being over-hyped.
Over the holidays, I learned about an intriguing debate that took place this past year in the New Republic. Billed as Science vs the Humanities, it raised a number of important questions. To what extent does the promise of data science apply to different kinds of disciplines, including the humanities? Can big data tools and related methods help the humanities achieve a more scientific understanding of human nature? Let me make some general comments before getting to the debate itself.
Scientific revolutions are launched when new tools lead to new measurements and observations. Early in the 17th century, Galileo made major improvements to the recently invented telescope which enabled him to make discoveries that radically changed our view of the universe. Over the centuries we have seen that new tools, measurements and discoveries precede major scientific breakthroughs in physics, chemistry, biology and other natural sciences disciplines.
Similar breakthroughs have been much harder to achieve beyond the natural sciences, especially when it comes to the study of human nature, that is, the way we feel and behave. But, the situation has been rapidly changing in the past few decades. First of all, advances in behavioral science, neuroscience, genomics, evolutionary biology and related disciplines have shed significant new light on such people-oriented studies. And then there is the advent of big data.