Sounds a bit of gate keeping to me. I mean it isn't magic, it's still 0s and 1s which yield results that are of some use to someone. Sure, it would be nice if everything were masters of DS but in reality, people make mistakes and sadly, or luckily depending how you look at it, you still can get useful results even when some of your assumptions about the data are wrong.

But I understand what you mean, and completely agree that poseur amateurs who treat DS as just easy "number fluff" and expect fancy ML frameworks to solve all their problems are cancerous and ruin the reputation of DS as a field.

Yet the more I have worked with regular people at work, the more I have moved from the camp of always doing the "right thing" to hoping that people would just "do something". I can't fix every problem so maybe I'll just deal with the reality as it is and try to make best out of it. I won't start quizzing people at work about DS know-how but maybe then silently guide them towards understanding what they are doing instead of driving them out of the room and keeping them located somewhere far away from the data science team.