Management jargon like ‘Extreme Experimentation’, ‘Fail Fast’ have been around for some time now. Much of this thinking and consequently, success has come from the software industry. But once you step outside the Silicon Valley, you will find hard pressed to find successful instances of experimentation translating to actual shareholder value. In my years of... Continue Reading →

In my role as Head of Data Science and Analytics, the single most important problem that I am trying to solve is: What does a world-class team look like? How should you build and more importantly, sustain such a team? It is always best to start with a few basic axioms (I call them so... Continue Reading →

It is very rare that any technology gets elevated to this status - most technologists however, do agree that AI should make the cut: along with the internal combustion engine, electricity and the internet. Many definitions exist, here's one: 1. Pervasiveness – The GPT should spread to most sectors. 2. Improvement – The GPT should... Continue Reading →

Data science has come through the world of statistics – where you are taught hypothesis driven feature selection and engineering. In other words, a priori business understanding drives the feature selection. That in itself, creates the problem of availability bias. And to top that, a typical statistical model would favor stronger features over weaker ones.... Continue Reading →

In my role as the Analytics Leader in a Fortune 500, I spend a lot of my time thinking about how to make Decision Sciences real in the Enterprise. The two main focus areas that Enterprises need to think about to start extracting value from Big Data and AI are: Moving from Discovery to Implementation:... Continue Reading →