Framing a Business Problem as a Data Science Problem


There is an industry agnostic pattern - Spurred by a penchant for action, managers tend to switch quickly into solution mode without checking whether they really understand the problem. As per a Harvard study, 85% strongly agreed that their organizations were bad at problem diagnosis, and 87% strongly agreed that this flaw carried significant costs. 

Problems are typically multicausal and can be addressed in many ways. One won’t know which problems can benefit from being reframed until one tries them. The point of reframing is not to find the “real” problem but, to see if there is a better one to solve. Alongside, one also need to identify the appropriate tools to solve these reframed problems most effectively and efficiently.. 

Key Takeaways:

  Understand and apply the 7 golden principles for effective problem formulation
  Understand the pre-requisites to effective solutioning and knowing which data / analytical model would solve the reframed problem in the most efficient manner.
  Understand CRISP_DM; a robust industry process model with six phases of a data science life cycle
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Krishna Raha
Senior Manager - 

Krishna Raha is currently a Senior Manager – Sales Reporting & Analytics at PepsiCo. She works with senior business leaders to understand business challenges and reframe them into pieces that can be solved using data and analytics.

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