Decision making, - how individuals, groups and organizations go about selecting a course of action among several alternative scenarios, - has long been a subject of study. Given the explosive growth of big data over the past decade, it’s not surprising that data-driven decision making is one of the most promising applications in the emerging discipline of data science.
In a recently published article, Data Science and its Relationship to Big Data and Data-Driven Decision Making, Foster Provost and Tom Fawcett succinctly define data-driven decision making as “the practice of basing decisions on the analysis of data rather than purely on intuition.” Equally succinctly, they view data science “as the connective tissue between data-processing technologies (including those for big data) and data-driven decision making.”
Looking deeper at data-driven decision making, it’s important to understand not only the promise but also the limits. When can we embed decisions into well understood, automated processes? When does automation run into limits, and we should view data-driven decision making as a tool to help people make smarter, more effective decisions? And what are the prospects for its future, as technology, big data and data science continue to advance?