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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Replying to @imightbemary
I think about it as Data-As-A-Service
Data-As-A-Partner
. The former involves lots of short-lived analyses that aren’t impactful in the long-term. Latter has Data Science as an equal partner on product direction, decision-making, embedding data in the product itself, etc1 reply 3 retweets 14 likes -
Replying to @catherinezh @imightbemary
Sayan Sanyal Retweeted Katie Bauer!
I love this phrasing. But, what are the tangible artifacts that help with setting product direction, decision making and the latter? Are these designing metrics, and providing live decision-making tools? like what
@imightbemary says here:https://twitter.com/imightbemary/status/1360379153661403136 …Sayan Sanyal added,
Katie Bauer! @imightbemaryAnd 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 focusShow this thread1 reply 0 retweets 1 like -
Replying to @shaayohn @imightbemary
Oh man, this would be such a great topic for a Data Science Spaces/Clubhouse!
I don’t know if I can write down all my thoughts in 280. Lots if solid examples of tangible artifacts (aka deliverables
) in the
2 replies 0 retweets 2 likes
we gotta make this Space happen

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