One of the problems with data science is how black box it is compared to other corporate/software development functions: For many roles and positions in a comp

One of the problems with data science is how black box it is compared to other corporate/software development functions: For many roles and positions in a company, high and low, you can tell whether the person is doing a good (or at least competent) job. A sales rep can show leads he/she has followed up or converted, and $ made. A coder can point to functionality created and lines written. A CFO can point to deals done, documents prepared, etc.etc. With data science, sometimes you just have absolutely no idea whether the person is good or not. "This problem can't be solved". You just don't know whether it's true or not. "These are amazing insights". Do they have any practical value that can turn into $? Data science is from some perspectives the most black box part of a modern organization. And so much hype is put into it, so many people paid sky-high salaries in the belief that it is producing something. Is it usually? Not sure.