tailwindlabs / tailwindcss
github.com·21h ago
Developer Tools
Databricks Blog·Jun 11·5 min read
Unlocking semantics for AI: How Mercedes-Benz Korea built trusted “Talk to Data” at scale
TL;DR
Mercedes-Benz Korea faced challenges in providing reliable self-service analytics due to inconsistent data semantics. They built a governed semantic layer using Unity Catalog and Databricks tools to standardize over 500 key performance indicators (KPIs).
✦ Why It Matters
Engineers can leverage unified semantic layers to enhance the reliability of AI insights in their analytics projects.
Key Takeaways
- Utilize Unity Catalog for a governed semantic layer.
- Automate KPI migration with a DAX-to-Metric-View transpiler.
- Implement persona-based access control for tailored insights.
- Follow a structured process for onboarding KPIs into AI systems.
How It Works
Mercedes-Benz Korea's 'Talk to Data' initiative integrates a unified architecture on the Databricks Data Intelligence Platform. It combines Lakeflow for data ingestion, Unity Catalog for KPI governance, and Genie for natural language queries.
The automated DAX-to-Metric-View transpiler converts existing Power BI definitions into metric views, ensuring that AI agents can access consistent business logic. This architecture allows for tailored responses based on user roles, enhancing the overall analytics experience.