However, as I started interfacing with the team members of the data science team, one thing that came up repeatedly was that despite shipping some ‘good’ machine learning models, the adoption of those models among business stakeholder teams was almost zero. As per the team, there was just no enthusiasm for any machine learning models. In the last year alone, the team developed a propensity model, booking projection curve, demand forecasting, etc.

The real value of data science is rarely in snapshot but in motion

The self-serve model has its own risk. So before thinking of implementing that, it is important to evaluate the risk and its mitigation strategy.

Personas allow us to develop features that can best serve the users’ needs, give organizations a common language to talk about their users instead of using themselves or their friends as the user of the product

Transitioning organization culture from 'I believe' to 'my hypothesis' is

A deep dive into various aspects of self serve model such as when to use, risks and mitigation strategy, how to evaluate whether it is right for the business, how to launch, what to measure post launch, and what to do once the launch is successful.