In many of the enterprise orgs I've worked in, the two tech teams that are chronically understaffed are 1) info sec, 2) DBA/ data architecture/ data science. I'm lumping those 3 together on purpose, because they're always understaffed and typically not empowered to build anything.
You're right to group Data teams together. They seem to share a common plight.
In my experience, internal employees outside Data have a funny relationship with Data. They hate to manage it but they love to blame it, especially in analytical / decision-making scenarios. Teams that "own" the data usually get the blame, on top of having to deal with a mass of rotting pipes and noncompliant teams, while also losing out on credit when non-Data teams report big wins.
Based on what the GP says, it sounds like Palantir knows how to exploit common internal politics around Data. They build up technical & social expertise in ETL'ing disparate data sources, and they can avoid blame by being hired by executives as an external third party.
This is exactly what I thought TFA was getting at when it brought up politics being a problem at companies and in sectors Palantir engages with, but instead it went a much more general direction.
> Why is data integration so hard? The data is often in different formats that aren’t easily analyzed by computers – PDFs, notebooks, Excel files (my god, so many Excel files) and so on. But often what really gets in the way is organizational politics: a team, or group, controls a key data source, the reason for their existence is that they are the gatekeepers to that data source, and they typically justify their existence in a corporation by being the gatekeepers of that data source (and, often, providing analyses of that data). [3] This politics can be a formidable obstacle to overcome, and in some cases led to hilarious outcomes – you’d have a company buying an 8-12 week pilot, and we’d spend all 8-12 weeks just getting data access, and the final week scrambling to have something to demo.
I think he's seen more companies without talented Data experts than companies with that talent.
They should help the business with the evidence to make all kinds of decisions, and in a platform-team kind of way help you self serve data needed to make decisions in your team.
