I see this problem a lot, and in my experience there are at least two pieces to this puzzle - 1) many data science tools were originally desktop oriented or req

I see this problem a lot, and in my experience there are at least two pieces to this puzzle - 1) many data science tools were originally desktop oriented or required specialized, siloed engines to run, causing a parallel universe of data to need to be imported into those tools and environments (for example SPSS) and 2) traditional infrastructure teams need to think of data infrastructure and architecture as a different subdiscipline running at layers 5-7 of the OSI model. My problem as an infrastructure provider and data architect is how to provide a globally consistent, governed platform and model on top of which different classes of users have different levels of access rights to data in different forms and qualities, through different interfaces. My 2 cents, I accept silver bars too lol :) For what it’s worth, I don’t buy the argument that data folks should operate in an isolated infrastructure - we just need to adapt how we serve their needs, which can be quite extensive when you are talking about essentially anything ranging from someone writing highly complex algorithmic code to process large volumes of raw data (high level of support and access may be needed) vs. someone just designing a report or dashboard using just a graphical tool on top of a predefined data model (much lower infrastructure access needed).