Serious question: how much data do you need before data science is useful? I doubt there's a hard and fast rule, but I can't help remember that statistical significance testing, a cornerstone of modern statistics, was invented to make better decisions about industrial processeshttps://twitter.com/JohnDCook/status/1348658451665322005 …
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"Statistics" is especially useful with small data. Going from small-n survey to generalizing to a population is a great example. Not sure if that falls under "DS" but the small-data -> population inference I count as a very valuable skill.
Great point. That's definitely a place where some expertise adds a ton of value