Proactive Semantic Modelling: Make your Data Products AI-Ready

Enterprise teams are under pressure to deliver trusted data products faster for analytics, self-service BI, and AI. But too often, the semantics that give a model its purpose live outside the model, scattered across documentation, tribal knowledge, and downstream BI logic.

The result: business rules reconstructed after the fact, inconsistent metrics across consumers, and AI systems that inherit ambiguity instead of intent.

That gap isn't a tooling problem. It's an architectural one. Relational models describe how data is stored. Semantic models describe what it means. 

SqlDBM and Snowflake are bridging the gap by embedding semantic properties — like formulas and synonyms — into the data modeling process, allowing teams to capture business meaning during design rather than after deployment.

This session is designed for both strategic and hands-on audiences: enterprise architects shaping platform standards, and practitioners building the models and data products that the business depends on every day.

What you’ll learn and see in this session:

• Why semantic models are becoming essential for modern data products

• How SqlDBM approaches semantic model design and management

• How semantic definitions can connect cleanly into Snowflake workflows

• What enterprise architects and practitioners should consider when designing for scale, reuse, and change

• Where semantic foundations can support better analytics experiences and AI outcomes

• How a semantic model allows you to “talk to your data” using Snowflake Cortex

Who should attend?

This webinar is ideal for:
 

• Enterprise architects defining data platform and modelling standards

• Data architects and data modellers designing reusable business-ready structures

• Analytics engineers and data product owners responsible for trusted downstream consumption

• Data and AI leaders looking to improve consistency, governance, and readiness for AI-driven use cases