* 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.
I work in implementation of large enterprise wide systems. When I do projects that span departments/divisions/agencies what you’re describing is the biggest hurdle. The project always starts with “we’re bringing everyone together into one solution” but as time goes on it starts to diverge. It’s so easy to end up with a project per department vs one project for all. You have to have someone with the authority to force/threaten/manipulate all the players onto the same page. It’s so easy to give in to one groups specific requirements and then you’ve opened Pandora’s box as word spreads. It’s very hard to pull off.
I think public sector (governments) is the hardest because the agencies seem to sincerely hate each other. I’ve been in requirements gathering meetings where people refused to join because someone they didn’t like was on the invite. At least in a for profit company the common denominator for everyone is keeping their job.