Layered data architecture turns enterprise data into a system of intelligence
The knowledge graph is fast becoming a foundational layer for enterprise AI, as organizations race to turn scattered data into answers that leaders can trust. As IT stacks that went cloud-native now go AI-native, a new class of layered data architecture is taking shape — one built to give models the context they need to reason, not just retrieve.
That shift is playing out across a fragmented data landscape of lakehouses, operational databases and customer profile stores, where the hard problem is accurately describing an enterprise’s information so AI can make sense of it. Graph technologies are increasingly serving as the connective tissue for AI agents and GraphRAG architectures, according to Tristan Baker (pictured), senior director and head of data architecture at Salesforce Inc.
“It’s becoming that critical piece that helps tie the end agentic experience that you want to deliver,” Baker said. “It’s almost like the glue that stitches what the customer or the person is asking to the data in the context that is needed in order to answer that question.”
Baker spoke with theCUBE’s John Furrier at the Neo4j GraphTalk event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how knowledge graphs, ontologies and layered data architecture are reshaping the pursuit of an enterprise system of intelligence. (* Disclosure below.)
