@hackproduct9: Most data teams don’t have a data problem. They have a definition problem. One dashboard says Revenue is $10M. Another says $11M. An AI agent says $9.8M. Who’s right? This is exactly why semantic models exist. A semantic model sits between raw data and business users, defining metrics, dimensions, and relationships once so everyone uses the same language. Instead of every team writing their own SQL: Revenue = SUM(order_amount) is defined once and reused everywhere. The result: ✅ Consistent metrics ✅ Trusted dashboards ✅ Faster analytics ✅ Better AI applications ✅ One source of truth As AI agents become consumers of enterprise data, semantic models are becoming one of the most important layers in the modern data stack. Raw tables tell you what happened. Semantic models tell everyone what it means. #DataEngineering #AnalyticsEngineering #SemanticLayer #DataModeling #AIEngineering

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