I find iterative product development is as key in data as other fields given many data science tasks, e.g. model tuning or feature engineering, can be an endless pursuit: that extra 1% in accuracy is not always worth the effort and defining product increments helps establish the “good enough” point.
With that in mind, set as our delivery target a Minimum Viable Product (MVP) — a version of the product with just enough features to start learning from users with minimum effort.
There were 5 goals for the MVP: