For years, data governance has been about guiding people and their interpretations. We build glossaries, descriptions and documentation to keep analysts and business users aligned. But what happens when your primary “user” isn’t human? As agentic workflows, LLMs, and AI-driven decision systems become mainstream, the way we govern data must evolve. The controls that once relied on human interpretation now need to be machine-readable, unambiguous, and able to support near-real-time reasoning. The stakes are high: a governance model designed for people may look perfectly clear to us but lead an AI straight into hallucinations, bias, or costly automation errors.

This session explores what it really means to make governance “AI-ready.” We’ll look at the shift from human-centric to agent-centric governance, practical strategies for structuring metadata so that agents can reliably understand and act on it, and what new risks emerge when AI is the primary consumer of your data catalog. We'll discuss patterns, emerging practices, and a discuss how to transition to a new governance operating model. Whether you’re a data leader, platform engineer, or AI practitioner, you’ll leave with an appreciation of governance approaches for a world where your first stakeholder might not even be human.

Analytical Data Product success is traditionally measured with classic reliability metrics. If we were ambitious, we might track user engagement by dashboard views or self-serve activity; they are blunt, woolly indicators at best. The real goal was always to enable better decisions, but we often struggle to measure whether our data products actually help. Conversational BI changes this equation. Now we can see the exact questions users are asking, what follow-ups they need, and where the data model delights or frustrates them. This creates a richer feedback loop than ever before, but it also puts our data model front and centre, exposed directly to business users in a way that makes design quality impossible to hide.

This session will recap the foundations of good data product design, then dive into what conversational BI means for analytics teams. How do we design models that give the best foundation? How can we capture and interpret this new stream of usage feedback? What does success look like? We'll answer all of these questions and more.

Data’s not what it was. Analytics is shifting fast - so how should we rethink data product design before we’re left in the dust?