Building Semantic Data Models: From BI to AI

AI has become increasingly integrated into business intelligence (BI) tools and analytics workflows, but its effectiveness is often limited by a lack of business-specific context, such as metric definitions and valid join paths, which are typically embedded in SQL, dashboards, and tribal knowledge. A semantic data model can address this issue by consolidating these definitions and rules into a single framework that both humans and AI can interpret consistently. This concept was discussed by Alec Bialosky in a talk at dbt Coalesce, emphasizing the importance of semantic data models for enhancing AI's utility in analytics through shared meaning, query correctness, reasoning context, and automation foundations. The process of building a semantic data model involves identifying key business questions, aligning on core metrics, leveraging existing trusted definitions, making ownership explicit, documenting in plain language, and iterating with feedback. Automating the generation of semantic data models can further streamline this process, with tools like Select Star offering capabilities to reverse engineer and extract metric logic from existing BI tools, thereby accelerating model development and deployment.