As enterprise AI matures, connected and trusted data is becoming the foundation for real business value. Graph technologies are helping organizations turn fragmented information into actionable intelligence.

Fragmented enterprise data remains one of the biggest barriers to extracting value from AI. Most companies have data everywhere, completely fragmented, making graphs a natural fit because they are built to connect data and its relationships, according to Tim Gosnell (pictured), chief executive officer of CommonThread AI Inc.

“A lot of people spend their time pulling data out of one system and trying to understand it in another system. There’s no relationship there,” he said. “Graphs literally are built for this idea of connecting data and their relationships and representing that. For us, this is the most natural fit.”

Gosnell spoke with theCUBE’s John Furrier for an exclusive AI Luminaries interview series on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how graph technologies can create the foundation for enterprise AI and why trusted, connected data pipelines built on graph databases are essential for delivering reliable business outcomes. (* Disclosure below.)

Graph technologies close the go-to-market gap

Go-to-market teams all have different tools for different parts of the process, but those systems often don’t talk to one another. Even with companies working to connect them, many organizations still lack a cohesive system for bringing their data together, Gosnell noted.

“When you talk to data engineers, most of the time you see a lot of relational databases. These days you see a lot of vector databases, but you don’t see a lot of this component that we use, which is the graph database,” he said. “That literally brings that relationship structure to the forefront and allows those teams to talk to one another using a common definition.”

The AI era will open up opportunities businesses haven’t even imagined yet. Much like the rise of e-commerce, its biggest use cases may be the ones no one can predict today, according to Gosnell.

“We’re in that phase right now where AI is about to change things in ways we cannot comprehend. Part of that is getting rid of or helping us solve that data silo piece and getting people to actually talk to one another,” he said. “If that includes not having to actually do the work to figure out how to talk to one another, I think that is a plus.”

Companies need to unify their segmentation, ideal customer profiles, messaging and markets into a single strategy rather than treating them as separate pieces, Gosnell explained. Lasting growth comes from reading the economic and political forces shaping the world and letting that insight drive messaging.

“The only way we can do that is by having a larger context window. Let’s build that larger context window,” he said. “The more we build that larger context window, the more we understand what’s going on across our business, and then we keep updating that. As things change, we keep getting new and adaptive context.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s exclusive coverage of the AI Luminaries interview series:

(* Disclosure: TheCUBE is a paid media partner for the Neo4j AI Luminaries interview series. Neither Neo4j, the sponsor of theCUBE’s coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE