Needing to CYA also has pretty low emotional appeal for managers, exec's, and alpha-data-scientist wanna-be's. (Until it's just about too late, obviously.)
And recall Mark Twain's old quip about lies & statistics. The more & bigger data that the folks who control the data & analysis have, the easier it is to make sure that those meet their own emotional & political needs.
One possible reason: no one whose job it is to write Python scripts was ever promoted for making an Excel spreadsheet when that is the simpler and more practical approach. And no manager of people who write Python scripts is going to be able to use that Excel spreadsheet to sell "I need more responsibility and head count." People tend to follow incentives, rather than focusing on making wise decisions.
Even wise people likely follow the incentives. What is wise about doing something that your employer doesn’t reward in exchange for doing something that they will reward?
Excel has a history of forced format updates, breaking incompatibility. I know people who banned it because they got tired of marching to MSs upgrade beat.
Python 2 to 3 upgrade aside, can’t really say the same about the language.
There are a number of good arguments out there that might violate an engineers perception, which one might call a cognitive data model built through training and experience.
There is no theory that makes any given engineering path “wiser” than others. Just engineers chasing incentives to be engineers.
Libraries introduce breaking changes, too. I’ve been bit by silent default changes in Pandas, for example. To me that’s kind of striking because I also wouldn’t consider myself a major user of the library.
- "You just talked to them and concluded this? What certainty you can have on this conclusion, and how can we trust you just didn't want it to be true from the start?"
A few slides showing the data, a boring 10 minutes about methodology, and finally the conclusion brings an air of reliability that you can't replicate for knowledge instead of data.