* Conway's law causing multiple different data science toolchains, different philosophies on model training, data handling, schema and protocol, data retention policies, etc.
* Coming up with tech solutions to try to mitigate the impact of multiple silos insisting on doing things their own way while also insisting that other silos do it their way because they need to access other silos' data.
And the reason standardization won't happen: the feudal lords of each of those branches of the hierarchy strongly believe their way is the only way that can meet their business/tech needs. As someone who gets to see all of those approaches - most of their approaches are both valid and flawed and often not in the way their leaders think. A few are "it's not going to work" levels of flawed as a result of an architect or leadership lacking operating experience.
So yeah, it might look like technical problems on the surface, but it's really people problems.
To solve this, one can be an instrument for change. Network, band people together, evangelize better ways forward, all while not angering management by operating transparently.
Sometimes, that can work... up to a point. To broadcast real change, quickly, you really need anyone managing all the stakeholders to lead the charge and/or delegate a person or people to get it done. So the behavior of directors and VPs counts a lot for both the problem and the solution. It's not impossible to manage up into that state with a lot of talking and lobbying, but it's also not easy.
I'll add that technological transformation of the workplace is so hard to do, Amazon published a guide on how to do this for AWS. As a blueprint for doing this insanely hard task, I think it holds up as a way to implement just about any level of tech change. It also hammers home the idea that you need backing and buy-in from key players in the workforce before everyone else will follow. https://docs.aws.amazon.com/prescriptive-guidance/latest/clo...