Perhaps a more useful characterization would be that it's what happens when you take work that would traditionally belong to statisticians and actuaries with only limited programming and IT skills, and give it to someone who's a more of a jack-of-all-trades programmer/statistician.

I don't think that this leads to changes in the traditional ways of handling IT because of any fundamental change in a data analyst's needs. It's more that, historically they were outside the formal tech sphere, and therefore lusers who had to make do with whatever resources they were able to squeeze out of IT. Now, armed with better technical chops, they're able to make a stronger case for their needs, which means that more is being done to meet those needs.

Which is not to say that your skepticism is unwarranted. "Data science" is still a catch-all term for a new and ill-defined way of working, and things are still pretty wild west. Anyone who tells you they're sure they know what the best practices are is probably trying to sell you their product, which happens to be the best practice. Not many people have the benefit of 20 years of hard-won experience from which to draw a set of really strong guidelines.