Parag Shah

Chat interfaces and coding agents are quickly becoming the primary way people consume analytics inside organizations. The new world lowers the barrier to access and shortens the path from question to insight. But what does this mean for how data teams operate? Data leaders share how they’re adapting, how teams across the business are using AI to get answers, how data leaders build trust in AI over time, and what iterative feedback and tuning look like when AI becomes the front door to your data. AI may be the new interface, but dashboards, models and even Excel still matter. The challenge is connecting these foundations to AI without losing trust.

CarGurus runs a hub-and-spoke data organization — a central team with embedded analytics teams across the business. That model created friction: PR bottlenecks, inconsistent standards and inefficient backfills consuming engineering capacity. This session covers how CarGurus deployed the dbt Fusion engine on Snowflake to intelligently optimize builds, reclaim 24,000 annual engineering hours and maximize business value, achieving ROI in just five months. We also cover how that recovered capacity is fueling AI experimentation with Snowflake Cortex AI, Streamlit and the dbt semantic layer.

In this session, a representative from Fortune Brands Innovations Inc. will share their experience accelerating their Snowflake implementation. They will discuss challenges; strategies to improve modeling, version control and governance; and how they streamlined workflows for faster deployment. Joining them, SqlDBM's head of product will provide insights into best practices for modeling and governance. Attendees will gain insights into strategies and lessons from their implementation.