> if you want to succeed as a Data Scientist and be praised by management - you got to provide data analysis that supports managements ideas (however wrong or ineffective they might be).
> Data Scientist's job is to launder management's intuition using quantitative methods :)
It’s no different than the days when grey bearded wisemen would read the stars and weave a tale about the great glory that awaits the king if he proceeds with whatever he already wants to do.
The beards might be a bit shorter or nonexistent, but the story hasn’t changed.
Absolutely. If you don't like what K-Means is telling you, change a variable and re-run. (that's one great thing about business data: there's no shortage of variables! True, there's usually a shortage of independent variables, but fixing that is difficult and underfunded).
And you'd better hope the bones actually say something useful.
I was the infra lead on a data lake project and got take part all the way to breaking down the data and turning into PowerBI reports. The result was "sell more" and to clients who marketing already identified, years ago, as whales.
There were some interesting other insights, esp. w/r/t to niche products that sold around weird dates (Easter, Memorial Day, 4th July -- but not obvious gift days like Valentines or X-Mas), but it led to a lot of "you're doing it wrong!" recriminations and follow up projects.
> Data Scientist's job is to launder management's intuition using quantitative methods :)
It’s no different than the days when grey bearded wisemen would read the stars and weave a tale about the great glory that awaits the king if he proceeds with whatever he already wants to do.
The beards might be a bit shorter or nonexistent, but the story hasn’t changed.