Data management purity and pragmatism built opposed camps starting in the 1990s, and echoes of those tenants present themselves today in Generative AI. Ontology purists and vector-based engineers appear at opposite ends, but how might we reframe so everyone's in the same picture? Tracy Talbot is a consummate data practitioner and has seen it all—from mainframes to machine learning. In this episode, she sits down with Sid Atkinson to reveal how the age-old Kimball vs. Inman debates mirror today’s AI struggles—and why her concept of “semantic patches” could be the key to keeping generative AI grounded in reality.

Building a data program isn't all about the tech—it's about getting people to actually work together. Carlos Rivero shares what it was really like starting as Virginia's first Chief Data Officer with no team, no budget, and a lot of skeptical agencies. He talks about how he learned to be more of a "people wrangler" than a data expert, turning critics into allies and figuring out that most of the job was just getting everyone on the same page.

Ever wonder why so many promising tech pilots never make it past the starting line? In this episode, Sid chats with Jonathan Alexander about breaking free from “pilot purgatory” and actually scaling innovation. From cavemen pushing carts with square wheels to global rollouts of cutting-edge solutions, Jonathan brings stories, strategy, and wisdom about how to get things done in a giant enterprise. They dive into building business cases that don’t flop, why change management beats the perfect tech stack, and how to avoid getting stuck with great ideas that go nowhere.