...probably true to some extent, but not all leaders are self important ass hats who refuse to acknowledge they are simply “making decisions” not “making good decisions”. Most leaders are doing the best they can (often even very well) with the insights available to them.
I don’t think most data teams are really at fault; they’re just doing what they’re told.
The problem imo lies with the analysts who fail to do anything useful with the data they’re given, and demand constant changes from the data team because they want to deliver silver bullet results to the leadership level.
That’s the problem layer; people who want to be important but have nothing to offer, whipping their data team to produce rubbish and then blaming them for either a) not producing anything fast enough or b) not making the numbers big enough.
"Data science"/analytics groups are a cost center telling management things they do not want to hear, and disrupting management narrative (with receipts). There's no point to it; either you deliver "moneyball"-like opportunities that are ignored, or torture the data to fit existing narratives. In both cases, you eventually get stabbed by an experienced bureaucratic knife fighter.
The place I currently work is data-driven (perhaps to a fault). Every change is wrapped behind an experiment and analyzed. Engineers play a major role in this process (responsible for analysis of simple experiments), whereas the data org owns more thorough, long-term analysis. This means there are a significant number of people invested in making numbers go up. It also means we're very good at finding local maxima, but struggle greatly shipping larger changes that land somewhere else on the graph.
Some of the best advice I've heard related to this is for leadership to be honest about the "why". Sometimes we just want to ship a redesign to eventually find a new maximum, even through we know it will hurt metrics for a while.