Semantic Modeling: How to Build a Single Source of Truth

This post details the concept and implementation of semantic modeling as a critical component of a data integration and semantic layer strategy. It explains how semantic models define business meaning for raw data, translating between technical schemas and business language. The post outlines the building blocks (entities, dimensions, measures, metrics), the hub-and-spoke architecture, and the use of compilation engines like MetricFlow to generate SQL. It provides a practical guide on how to build a semantic model by starting with contentious metrics, building on normalized staging models, adopting incrementally, validating through CI, and closing the feedback loop with tools like Hex's Context Studio. It emphasizes that semantic models are one layer within a broader semantic context, working alongside schema metadata, endorsed tables, and workspace rules to guide AI agents and analysts.