In a Big Data presentation last year, MIT professor Erik Brynjolfsson pointed out that throughout history new tools beget revolutions. Scientific revolutions are launched when new tools make possible all kinds of new measurements and observations.
Big data is leading to such a measurement-driven revolution, brought about by the new digital tools all around us, including our mobile phones; searches and web links; social media interactions; payments and transactions; and the myriads of smart sensors keeping track of the physical world. These digital tools are enabling us to collect massive amounts of information on who we are, what we do and how we interact as individuals, communities and institutions.
The explosive growth of big data is in turn giving rise to data science, one of the most exciting new professions and academic disciplines. Data science is a mashup of several different fields. Its data part deals with acquiring, ingesting, transforming, storing and retrieving vast volumes and varieties of information. Its science part seeks to extract insights from the data by applying tried-and-true scientific methods, that is, empirical and measurable evidence subject to testable explanations and predictions.
One of the most exciting part of data science is that it can be applied to many domains of knowledge, given our newfound ability to gather valuable data on almost any topic. But, doing so effectively requires domain expertise to identify the important problems to solve in a given area, the kinds of questions we should be asking and the kinds of answers we should be looking for, as well as how to best present whatever insights are discovered so they can be understood by domain practitioners in their own terms.