Are Metrics Fragmenting Across Your Snowflake Environment?

Snowflake scales analytics effortlessly, but it does not govern what data means. As usage grows, teams write SQL independently, reuse datasets in different ways, and build dashboards with conflicting logic. The result is different teams defining the same business concept differently, leading to confusion, duplicated work, and slower decisions.

Is Business Logic Getting Lost Between the Data and the Query?

Relationships between datasets in Snowflake are reconstructed every time analysts write a query. Analysts navigate joins manually, embed business logic in SQL scripts, and rely on institutional knowledge that lives in individual notebooks rather than a shared model. This complexity grows with every new team, tool, and data source added to the platform.

Is Your Snowflake Data Ready for AI?

When LLMs and AI agents attempt to generate SQL or answer business questions from Snowflake, they encounter the same ambiguity as human analysts. Without explicit relationships and governed definitions, they guess at joins and metrics, producing results that may look reasonable but are difficult to trust at scale.