I gave the business what they asked for and they never used it

better.engineering

This is both amazing and hilarious. I linked to a Kenny Ning post last year about an ML project he did @ Better, and the post got plenty of attention elsewhere. Turns out though, the work got very little usage internally. This is a reflection of what went wrong—why didn't users find the project valuable?

Here's my favorite section:

We probably didn’t need a fully productionized ML solution to improve our understanding of conversion. For example, consider this much simpler solution: a) Collect a dozen candidate features and fit a model offline. You can do this using a fancy ML library, but logistic regression in Excel works fine too. b) Pick the top 3 most predictive features and sense-check with a domain expert. c) Track those 3 features as KPIs in a line chart.

I think this is often a valuable (although less flashy) approach to data science work. Generate a novel insight using ML but then present the insight using traditional descriptive statistics.

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