Governed by default: how data teams at Nordstrom turn dbt governance into an AI advantage
One question reframed how Nordstrom thinks about dbt: "What would it take for our data to answer questions on its own, and hold up under interrogation?"
That shift turned dbt from pipeline plumbing into the control surface for reliable AI. This session covers how Nordstrom's retail data teams are using dbt governance and semantics to build an AI-ready analytics foundation at scale: treating freshness as an AI SLA, using tests, contracts, and lineage as guardrails, and designing a "Data Concierge" conversational agent that answers merchants' questions in plain English against governed data.
You'll get a realistic view of the challenges, tradeoffs, and patterns a large enterprise is using to make its analytics platform ready for LLMs, not as slides, but as production decisions made by a team doing this work right now.
Check out more sessions
- Lightning talks
Retention metrics break in production: Modeling them correctly with dbt
Bao Nguyen / AdobeView session - Breakout session
Data share as a service — generalizing Snowflake DataShare across Mitratech products
Manju Choudhary / MitratechRitika Naidu / MitratechView session - Hands-on lab
Migrate stored procs with dbt Wizard
Raini Laughlin / dbt LabsSteven Nguyen / dbt LabsView session
