Data governance is often seen as restrictive—but what if it’s actually the key to making data work?

Bob Seiner joins Sid Atkinson to challenge how organizations think about governance, accountability, and behavior change. Drawing from decades of experience, Bob explains why governance efforts stall, what shifts when organizations focus on outcomes instead of activity, and how ownership, not tooling, drives real adoption.

He also introduces the concept of data fluency, exploring why the ability to communicate with data is becoming just as important as understanding it.

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.