Wrong but useful. Working @Twitter, tweeting about data science and people management.
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"Always has been"
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I will say, I like that article's recommendation to develop a taxonomy of your problem space. Imagine being able to map a DS project a problem space and automatically get relevant upstream knowledge and estimates of downstream impacts. I'd love a tool like that!
Although now that I'm thinking about it, that sounds a lot like having well-defined user journeys and funnels. Problems arise when two DS don't use the same funnels, or don't know where the funnels they care about overlap.
Which brings us back to communication. Making sure all the DS on your team have the same baseline mental models goes a long way towards enabling effective collaboration across seemingly unrelated work streams.
Anyway, developing a shared mental model of your company's problem space IS a serious part of the Data Science profession. I don't know if it's accurate to say knowledge sharing is to data science as peer review is to software engineering, but I feel like there's something there.
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