But like the "AI lawyer", the "AI doctor", the "AI coder", it doesn't replace the analyst, it just improves the quality and output of their work.

It might replace analyst interns, or some entry-level positions that were mostly low-level tasks and data entry, but a data analyst at a company who knows how to put data into a LlamaIndex for querying is a very useful analyst - think of what can be built on top of that domain-specific LLM for the company, for the analyst. It makes non-technical analysts who don't use AI seem incredibly passe and useless in comparison.

Same with auto mechanics, lawyers, doctors, HVAC techs, construction workers - AI tooling will even make its way into police work (even in the field, running on their little computer in-car) as well as dispatching.

Doesn't replace anyone, but has the ability to drastically improve our quality of work. Turns the expert into a super-expert.

Plus, it helps to have a neck to wring when it turns out that a slightly wrong left join deep in the data reporting pipeline means that you just told the CEO/Board/Wall Street materially incorrect numbers...

Also the part about data analysis is understanding the data and any nuances. Databases tend to be the place where all of an organization's pathologies over time get encoded, so sometimes column names mean a totally different thing then their name, and it's stupid, but it would take too much politics to change so nobody does, and updating the documentation would mean admitting the work around so it's all informally passed around amongst the analysts...

I could see LLMs as query-suggestors, but it doesn't make much sense to feed them data that isn't more "language." Their fundamental design is not conducive to doing math or solving logic constraints.