Hosts Sid Atkinson and Lee Harper engage with guests Gary Griffin and David Holcomb, authors of "Building a Data Culture: The Usage and Flow Data Culture Model", to explore the concept of data culture. They challenge the notion that culture is unmanageable, presenting frameworks and models that make culture visible and actionable. The discussion covers the three R's of data culture, the usage and flow model, the significance of microcultures, and the differences between public and private sector dynamics. The conversation culminates in the introduction of the Data Culture Institute, aimed at equipping leaders to build and sustain a data-driven culture.

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.