I recently participated in an industry panel on Redefining BI & Analytics in the Cloud at a conference in New York. Among other questions, moderator Michael Hickins, - a senior editor at the Wall Street Journal and editor of its online CIO Journal, - asked the panel to discuss the difference between the business intelligence and analytics approaches of the past twenty years and the emerging discipline of data science.
In my opinion, data science should be viewed as a multidisciplinary evolution from business intelligence and analytics. In addition to having a solid foundation in statistics, math, data engineering and computer science, data scientists must also have expertise in some particular industry or business domain, so they can properly identify the important problems to solve in a given area and the kinds of answers one should be looking for. Domain expertise is also needed to be able to draw the proper conclusions from their analysis, and to communicate their findings to business leaders in their own terms.
The emergence of data science is closely intertwined with the explosive growth of big data over the past several years. Institutions are now wrestling with information coming at them in volumes and varieties never encountered before. In addition to their multidisciplinary skills, data scientists bring increased breadth and depth to the analysis of these different sources of information compared to traditional analyst roles, as described in this short article in IBM’s website, So what does a data scientist do?:
“Whereas a traditional data analyst may look only at data from a single source - a CRM system, for example - a data scientist will most likely explore and examine data from multiple disparate sources. The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data.”