Data-Driven Decision Making: Promises and Limits

Citation: In a recently published article, “Data Science and its Relationship to Big Data and Data-Driven Decision Making,” Foster Provost and Tom Fawcett 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.”
. . .
We can think of decision making as lying across a broad spectrum.  At one end of the spectrum are operational decisions, which are generally highly structured, routine, short-term oriented and increasingly embodied in sophisticated software applications. At the other end of the spectrum are strategic decisions. These are usually taken by high levels of management as they set the long-term directions and policies of a business, government or other organizations. They tend to be complex, and unstructured because of the uncertainty and risks that generally accompany longer term decisions.

In between are many kinds of decisions, including non-routine ones in response to new or unforeseen circumstances beyond the scope of operational processes, and tactical decisions dealing with the necessary adjustments required to implement longer term strategies.
. . . 
The more data we gather, and the more sophisticated the analysis, the more such decisions can be made with little or no human intervention. Over time, Big Data and advanced data science applications will enable us to take operational decision making to a whole new level in a wide variety of disciplines. 

#DecisionMaking   #BigData  

Posted by +Dan Durrant 

Cause Analytics is here to help you navigate through Business Intelligence, understand today's challenges and tomorrow's technologies.
Shared publicly