"Managers will say they want to make data-driven decisions, but they really want decision-driven data"
Ooofff. This is too true. How often is the case that data is collected to test hypotheses vs confirming priors?
Ooofff. This is too true. How often is the case that data is collected to test hypotheses vs confirming priors?
The preceding sentence is a hilariously cynical zinger:
“Those who have seen my Twitter posts know that I believe the role of the data scientist in a scenario of insane management is not to provide real, honest consultation, but to launder these insane ideas as having some sort of basis in objective reality even if they don’t.”
Find me some evidence of WMDs in Iraq! Yessss Sir!
Of course, in many situations the business totally lacks what it needs to correctly do the "data-driven" stuff they want to, and it'd take a good deal of up-front effort by competent people to get it, amounting to entire new projects or deep modification of existing projects.
So, given the choice between: going without that stuff and acknowledging that a lot of what they're doing is guesswork and gut decision making, or simply arbitrary; putting a smaller but still-large amount of work into finding out what they can glean from what's available; spending the time and money to collect what they need, the right way, to do the data-driven decision making they claim to want to do; and insisting they're doing things "data driven" but having all their data hopelessly ruined by e.g. selection bias and comically-bad experimental construction that can't possibly be yielding reliable results, so they can cheap out and get no actual "data-driven" benefits aside from falsely claiming that's what they're doing—they tend to go with that last option, nearly every time!
I've seen so many analysis tasks where data scientists without questioning went away for a few weeks to crunch data and come back with some random graphs and statistics that are completely useless as decision support.