[philipodonnell]

I think a lot of times this is not about skill sets but more that data engineers don’t build datasets with UX in mind. The examples in this piece are not what show up when a leader browses a real database with hundreds of tables stuffed with abbreviations and numeric/short codes. If you want your leaders to use your data, you have to design your data to be used by leaders, not teach them SQL and statistics.

[PaulHoule]

You have to design your data tables in an operational system to support operations, in particular, to not get corrupted, which tends to lean towards

https://en.wikipedia.org/wiki/Database_normalization

but means you have to write queries with joins to get answers and many people find that difficult. Tooling to provide a better view for analytics is an interesting question.

As for statistics I think anybody making decisions should know a little about them. Myself I am a fan of "nonparametric" methods because they only make me learn one probability distribution so if I was stuck on a desert island (with pencil and paper) I could compute my own tables for methods like

https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test

[philipodonnell]

I’ve been working on some tooling to create better views of analytics based on the idea that people don’t like to write joins so why not create logical views that do all possible joins. Appreciate any feedback. https://github.com/eloquentanalytics/pyeloquent