SI2 : Monetizing Data ~ How To Drive Value From Analytics

Source: McKinsey Global Institute Analysis

To accelerate value with machine learning, it is imperative that the right use-cases such as those tied directly to a business problem are identified to result in strong business outcomes with high technical feasability and low data risk. This is nicely put across by Charles Kettering, Scientist- “A problem well stated is a problem half solved ”. The entire lifecycle of machine learning from ideation to implementation at scale and the downstream monitoring matters.

When we visit consumer facing services like Netflix and Amazon or Shopee, we are shown suggestions about what we should watch or buy next. This is made possible because algorithms have accessed data to learn what you would most likely be interested in and the subsequent high-quality recommendations are personalized and displayed to just you. In fact, MxKinsey has reported that 35% of what consumers purchase on Amazon and 75% of what they watch on Netflix comes from product recommendations based on machine learning algorithms.