“Talk to Data” is rapidly becoming an important capability across industries, and delivering it at enterprise quality requires a strong semantic foundation. Answer reliability is highest when AI can draw on clearly governed business logic rather than inferring it from complex schemas, report-specific KPI logic, or disconnected dashboards. Consistent KPI definitions, aligned business logic, and well-defined joins and aggregations are what give executives the explainable answers they need.

Mercedes-Benz Korea and Databricks approached this together. Rather than treating “Talk to Data” as a chatbot project, Mercedes-Benz Korea extended its existing analytics foundation with a governed semantic layer for enterprise AI. To enable semantics that can power both BI and AI, Mercedes-Benz Korea made KPI logic available in Unity Catalog Business Semantics in addition to Power BI. Drawing on Metric Views, Genie, and Agent Bricks on the Databricks Data Intelligence Platform, Mercedes-Benz Korea piloted a unified architecture for data, semantics, and agentic AI. Learnings from the Korea pilot can serve as a reference for other Mercedes-Benz markets.

Mercedes-Benz is a market leader in the high-end luxury automotive segment, operating a global sales network in which data-driven, market-specific decision-making is a continuous priority. “Talk to Data” self-service analytics is one capability being explored to further support this priority.

Mercedes-Benz Korea has a mature data foundation. Over time, Mercedes-Benz Korea established gold-layer reporting data, a master KPI catalog, and shared definitions in the Lakehouse and Unity Catalog on Databricks. This foundation serves as the single source of truth for BI reporting, automation, and other data products, covering more than 500 KPIs across business domains such as sales, product, marketing, customer service, and finance. Given this foundation, Mercedes-Benz Korea was selected to pilot the “Talk to Data” approach.

At the same time, a significant share of the business semantics at Mercedes-Benz Korea was defined in Power BI. As part of preparing for AI use cases, these definitions were complemented by an open, AI-ready semantic layer in the Lakehouse.

The broader vision of Mercedes-Benz Korea for “Talk to Data” was to establish a unified, AI-ready, and governed semantic foundation for enterprise decision-making that can support reporting, self-service analytics, and AI experiences on a consistent set of business definitions. In line with this vision, Mercedes-Benz Korea did not approach “Talk to Data” as a migration away from Power BI, but pursued three key objectives: