Decision-Driven Data Analytics

By James Taylor

Bart de Langhe and Stefano Puntoni recently published a great article in the MIT Sloan Management Review called “Leading With Decision-Driven Data Analytics”. In contrast to so much of the literature that focuses first on data, they focus on decision-making. In fact, they go so far as to say that :

“Leaders need to make sure that data analytics is decision-driven.”

They describe how focusing on data and on insights can lead companies down blind alleys and is not really a way to become “data-driven” at all. We like to say that companies should do analytics backwards. The authors focus on the purpose of data:

“Instead of finding a purpose for data, find data for a purpose. We call this approach decision-driven data analytics.”

They contrast this decision-centric approach to traditional data-centric ones very nicely:

“Data-driven decision-making anchors on available data. This often leads decision makers to focus on the wrong question. Decision-driven data analytics starts from a proper definition of the decision that needs to be made and the data that is needed to make that decision.”

This has been our experience also. Companies that focus on the decision they want to improve before doing their analytics work are much more likely to succeed in operationalizing an analytics or data-driven approach. Bert and Stefano are focused on management decisions, whereas we focus on operational ones, but the conclusions are the same.

They identify three steps to success:

  1. Identify the alternative courses of action.
  2. Determine what data is needed in order to rank alternative courses of action.
  3. Select the best course of action.

I would add to this only that building a decision model is a critical step between steps 1 and 2, especially for decisions you are going to make more than once. Defining a decision as a question and possible (alternative) actions is the right first step. To get from that to the data and analytics you need often involves breaking down the decision into sub-decisions and considering each of them independently. This is what a decision model is particularly good at. Applying their steps 2 and 3 to each sub-decision naturally leads “up” the model to a successful step 3 for the main decision.

It’s a great paper, and you should definitely read it. You might also enjoy these papers on decision modeling and on framing analytic requirements using decision modeling.