Number one function used. Export to Excel, and normally it is on reports of basic table join queries, none of the advanced things it can do with the data
Turns out lots of people have decided what the conclusion is and are taking that conclusion and the data, and then blindly munging until they get a path which connects them.
But that's a lot of work so businesses don't want to pay for it.
Business data is full of minor inconsistencies which are not obvious until you sit in front of it. Products are sold by different units. Reporting ranges and aggregates are slightly different. Subsidiaries use categories which are close but not exactly identical.
There is generally plenty of massaging to do before you can get the information you need.
AWS is pushing this message on every NFL broadcast with their Next Gen Stats ads. "The data tells us all"
Of course you want to be intellectually honest about it, and I agree that cherry-picking the "right" data can be a big problem in the wrong kind of organization. I remember one time a Jr analyst I managed was asked by the CEO to create a certain chart. I was not looped in. The CEO then used that chart to convince himself and many others on the executive team to make a huge product change that ended up being a complete disaster.
Often, this is learning for me. I have a bunch of Stephen Few’s books and use exported CSV files to figure out which reports are useful to me and my org. When I find them, I do make a request for standardized reporting. These often become the basis for regular review meetings. In those meetings, we still come up with instances where we need to export to CSV and get into the data to understand what we are looking at.
Our work is going through a big shift this year that means our historic data is not helpful in predicting trends. That’s increased the need for this kind of engagement with the underlying data.