Yet another way is that data analytics platforms are built from the ground up with hard-wired priorty given to scaling out the ability to test multiple hypotheses without any attempt to correct the significance metrics for the multiplicity of testing (or, even subtler, for subject researcher degrees of freedom that further affect the multiplicity of testing). Often, the business stakeholders who are demanding such an "analytics" system aren't even aware of the statistical fallacies they are inexorably baking right into the platform itself (one might call this the "Hadoop disease", though it's not stricly the fault of Hadoop or Hadoop-like tools).
At any rate, I would say in the current climate of "analytics" in business environments, to a good first approximation, one can assume that "make it easy to understand" is exactly equivalent to "throw out any and all difficult yet rigorous science until the thing is cheap and easy, and then just use that."
What I've seen around this is analytics professionals hired under the pretense that their skills to produce accurate scientific conclusions will be used for the good of a business, yet having their conclusions and efforts dismissed for no good reason other than decision makers 'didn't get it' or otherwise just refused to heed the results. So why did they hire experts in the first place, then? To lend the company credibility that it doesn't really deserve? I'm sure lots of the reason for this type of thing is politically motivated, as you previously mentioned.
The problem is to replace inept employees who believe "business decisions" are not scientific questions, so that over time there is a convergence to using the scientific method, with legitimate statistical rigor, when making a so-called business decision.
Generally speaking, the only people who want for there to be a distinction between a "business question" and a "scientific question" are people who can profit from the political manipulation that becomes possible once a question is decoupled from technological and statistical rigor. Once that decoupling happens, you can use almost anything as the basis of a decision, and you can secure blame insurance against almost any outcome.
This is why many of the experiments testing whether prediction markets, when used internally to a company, can force projects to be completed on time and under budget are generally met with extreme resistance from managers even when they are resounding successes.
The managers don't care if the projects are delivered on time or under budget. What they care about is being able to use political tools to argue for bonuses, create pockets of job security, backstab colleagues, block opposing coalitions within the firm. You can't do that stuff if everyone is expected to be scientific, so you have to introduce the arbitrary buzzword "business" into the mix, and start demanding nonsense stuff like "actionable insight" -- things that are intentionally not scientifically rigorous to ensure there is room for pliable political manipulation for self-serving and/or rent-seeking executives, all with plausible deniability that it's supposed to be "quantitative."