I also believe that company context matters a lot. I think so much of getting started with extracting value from data is getting up the learning curve of understanding what it means (which columns have the truth). One of the reasons that we don’t have a lot of canned reports is that understanding these edge cases within a company often matters a lot (and that not accounting for the nuance can often lead to a misinference). With this in mind, the explosion of ETL solutions and products like Mozart Data means that others at the company can specialize in their business context, as opposed to needing someone who can do all aspects of data including engineering, data science, analysis, and communicating/presenting it.
the consulting "data scientist" is likely able to do better job if they have experience with the idiosyncracies of the individual company's operations. If you get a fresh data scientist every time they need to repeat the ramp-up period before they are in a position to maybe add value.
Suggests a model where company keeps the same consultant on retainer and brings them on board each time a situation pops up where the consultant may be able to assist
(this isn't a particularly novel suggestion, the same suggestion is made in a 60s/70s era thesis investigating how applicable operations research is to small businesses)
I think the most efficient way is to have inhouse staff for visualizing data and extracting insights. They should be able to cover 70-90% of the demand. The remaining part and possible peaks in demand should be covered by a contractor(s).
In the long run, this makes sure you have a reliable contractor, who already knows you (the company) and also the infrastructure and meaning of your data. It helps a lot for example when an employee is sick or has left the company. So you can bridge the gap with almost no delay.
Most employees don't want (and often don't have time) to learn additional tools for anayltics & data visualization next to their daily business. And the data models quickly become too complex for "casual users". To make "self-service BI" really possible it needs a lot of work upfront (to prepare data etc.). I think I never saw "self-service BI" working in the real world. (maybe some "poweruser" in finance & controlling)
Imho the best case would be to have a specialized BI team which works together with the domain experts to create insights. Normally the persons from specific departments know their data very well and they are very helpful in the process of finding insights. They often have already done reports/calculations etc. before. But the manual process is just too complicated, too slow or whatever.
I'm not sure this is even doable without a dedicated data scientist, but a potential solution is a two-way marketplace that connects companies with data scientists to help make heads or tails of the data they're storing. Otherwise, it's just sitting in a data lake somewhere. (Not sure if something like this exists already, I'm just thinking out loud.)