Your organization uses Tableau for executive dashboards, Power BI for operational reports, and Python notebooks for data science. Revenue is defined in Tableau's calculated field, Power BI's DAX measure, and a SQL query inside a Jupyter notebook. Three tools. Three definitions. None of them match.
This is what happens when semantic models are locked inside BI tools. Headless BI fixes it by pulling the definitions out.
The Problem with Tool-Specific Semantic Models
Every major BI tool comes with its own modeling layer. Looker has LookML. Tableau has the Data Model. Power BI has DAX and the tabular model. Each one defines metrics, relationships, and calculated fields in a proprietary format.
This creates three problems:
