Oversimplify

brohrer.github.io

I just ran across this; it's from a couple of years ago but I had never read it before. The post recommends that data scientists simplify their conclusions almost to an absurd degree. The entire post is fantastic; here's my favorite part:

Academia and industry have different goals. They are two worlds with different languages and currencies. The currency of academia is reputation, which you lose by being wrong. The currency of industry is currency, which you get by making decisions quickly and with conviction. Industry is the world of Gryffindor rather than Ravenclaw, more Kirk than Spock.
When a company officer asks for an analysis, they don't usually care about the answer. What they are really asking for is an answer to the question "What should I do?” The data scientist who is capable of bridging the gap from raw data to recommending a course of action is a rare asset. Recommending action is interpreted as a sign of leadership, and tends to be rewarded with raises and promotions. It shows that you are looking past your 37-inch monitor, to the well-being and future of the company. This is deeply reassuring to company leaders, and highly valued.

You're not being paid to say smart things, you're being paid to make (or help make) decisions. Decisions are ultimately binary.

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