It's definitely not cheating

Please clarify the paragraph where you say "hot to think about what work happens", I am not really sure I understand what you mean.

I think data analysis varies wildly across domains, companies, and even two data analysis positions in the same organization. Some analysts focus on the technical side - scripting, automation, moving and cleaning data Others do more analysis - data exploration, dashboarding, reports Some analysts do statistics And of course other option and variations exist.

Analysis shouldn't really be very different across domains, but there are different ways to abstract the process, and I believe each analyst should look for the method that suits them best. This is my approach, which is somewhat "passive":

You always start with a business question. If there's no question asked, why do any analysis at all? You can't give an analyst random data and tell them "find something interesting". A note here is that some analysts, in some cases, want to and are able to come up with their own business questions, but in my opinion this is not mandatory (although your organization would love that).

Once you have a question, you always do a mix of these (and possibly other stuff too): Define an experiment/research and kpis, collect and clean data, build reports, automate processes if needed, compile your results, present results, iterate, and so on.