You just hit the chatGPT api for every row of data. Obviously. (Only 70% joking.)
Guys, ladies, meet Palantir Foundry ! #micDrop
Glean are already well established in that space.
Did not know that stack, thanks.
From my perspective as a data architect, I am really focused on the link between the data sources and the data lake, and the proper integration of heterogenous data into a “single” knowledge graph.
For Palantir, it is not very difficult to learn their way of working [their Pipeline Builder feeds a massive spark cluster, and OntologyManager maintains a sync between Spark and a graph database. Their other productivity tools then rely on either one data lake and/or the other].
I wonder how Glean handles the datalake part of their stack. [scalability, refresh rate, etc]
Unfortunately, those are 5+ year projects for a lot of F500 companies. And they'll have to burn a lot of political capital to get the internal systems under control. Meaning that the CXO that does get the SQL server up and running and has the clout to do something about non-compliance, that person is going to be hated internally. And then if it's ever finished? That whole team is gonna be let go too. And it'll all just then rot, if not implode.
The AI boom for corporations is really going to let people know who is swimming naked when it comes to internal data orderliness.
Like, you want to be the person that sell shovels in the AI boom here for enterprise? Be the 'Cleaning Lady' for company data and non-compliance. Go in, kick butts, clean it all up, be hated, leave with a fat check.