Agree with some of what you've said, but disagree with a lot:
> Most corporate decision making is highly political, the needs of/whats best for the business is just one parameter in a complex equation.
100% Individual humans are emotional creatures with their own wants and needs, and it's important to understand how organizational incentives drive decision making.
> Data Scientists are commonly disregarded by VPs in large corporations, despite the claims about being "data driven".
This has not been my experience, though. The more common thing I've seen is that, sometimes data is boring and doesn't really show much actionable insight, but as everyone wants to justify their job, I've seen data scientists come up with really questionable conclusions that fell apart on further inspection (call it "p-hacking the enterprise").
Plus, a lot of this data in these data wearhouses is messy. Often times data scientists are siloed at the end of the process, but then you get "garbage in/garbage out" results, where there is some bug in data tracking that isn't understood until it's too late. Much better in my opinion to have data engineers and data scientists work much more closely with product engineering teams up front so they can help ensure the data they collect is accurate.
> Most corporate decision making is highly political, the needs of/whats best for the business is just one parameter in a complex equation.
100% Individual humans are emotional creatures with their own wants and needs, and it's important to understand how organizational incentives drive decision making.
> Data Scientists are commonly disregarded by VPs in large corporations, despite the claims about being "data driven".
This has not been my experience, though. The more common thing I've seen is that, sometimes data is boring and doesn't really show much actionable insight, but as everyone wants to justify their job, I've seen data scientists come up with really questionable conclusions that fell apart on further inspection (call it "p-hacking the enterprise").
Plus, a lot of this data in these data wearhouses is messy. Often times data scientists are siloed at the end of the process, but then you get "garbage in/garbage out" results, where there is some bug in data tracking that isn't understood until it's too late. Much better in my opinion to have data engineers and data scientists work much more closely with product engineering teams up front so they can help ensure the data they collect is accurate.