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.