The surest sign that a company has no idea how to work with Data Science is requesting Insights™ as its primary output. You can tell just from the word--it's vague and sort of mystical, which is not exactly how you want to describe your quantitative teams
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That's definitely been the case for me, at least. I've done a lot of analysis over the course of my career, and I can only think of a handful that meaningfully changed the conversation around a subject
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I've been mulling over what I think the alternative to Insights™ should be and while I have yet to come up with a satisfying conclusion, I do think one of the biggest flaws of the DS-As-Insights-Generators model is that it's a narrow strategy
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If your job is to ensure data is a part of the decision making process at your company, you probably won't achieve it with artisanal Insights handcrafted by a data scientist. Maybe if you work for a magical company where the DS-stakeholder ratio is 1-to-1, and if you do--hey hmu
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One of our unique strengths as DSes is getting data into a shape where the signal is differentiable from the noise, so maybe we should direct our energy towards creating environments where data is widely accessible and hard to use wrong
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That's the promise of self-serve analytics, I suppose. But IMO such initiatives focus too much on making it possible for everyone to be a data scientist, when in actuality they should make data scientists unnecessary in as many situations as possible
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And don't get me wrong, data scientists still have tremendous influence in a world like that. You make what you measure, so designing the datasets, metrics, procedures, dashboards, etc. that other teams use gives a DS tremendous influence over where the company puts its focus
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Stepping up as owner of these things, really taking responsibility for their quality, reliability and improvement over time--it gives you way more leverage than a dropping few Insights every quarter, even if the Insights are Actionable®
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End of conversation
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