Unfortunately, your reality-driven approach has ~zero emotional appeal for most managers, exec's, and alpha-data-scientist wanna-be's.
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.
I wonder if that's the quote they mean?
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.
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.
This is the key issue. Solving it isn't easy -- it requires people who are wise, and wisdom is a scarce commodity.
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.