From my experience, the issue is not the means to query the data, it's the actual idiosyncrasies of the data. The key metric is thus not "users seem to be more

From my experience, the issue is not the means to query the data, it's the actual idiosyncrasies of the data. The key metric is thus not "users seem to be more independent as we are receiving less inquiries for help", because there is a very high chance said users are pulling and interpreting completely bogus metrics from the data. I have seen this over and over. Once low tech users have access to the data, they start building pyramids of bogus analysis, somehow convinced that after all it's not that hard. The real blocker is all the context that is - let's be honest - always required to perform a correct analysis: - "Oh no, you cannot use the delivery date to compute monthly sales, since the finance team refill it for recurrent sales" - "Oh no the prices are stored in USD in the catalog but we actually adjust the rate monthly based on the `monthly_discount` table" - "Yeah you have to remove items that have a null purchase date from the sales report we have that convention to mark last year's unsold stock" - "No you cannot sum the sales without joining with the FX rate table since prices are in local currency" - Etc, etc, etc