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Most companies trying to build AI analytics start with Claude. Anthropic started with the data foundation. That’s the lesson. After reading how Anthropic built self-serve analytics internally, a few things stood out: 1️⃣ They didn’t let AI talk directly to raw data. They built a semantic layer first. 2️⃣ Metrics, dimensions, business definitions, and permissions lived in one place. One source of truth. 3️⃣ Claude became the interface. Not the intelligence layer. The intelligence was already embedded in the data foundation. 4️⃣ Governance wasn’t treated as a blocker. It was treated as a product feature. 5️⃣ The goal wasn’t replacing analysts. It was eliminating repetitive work so analysts could focus on higher-leverage decisions. This is the part many AI teams miss. Everyone wants the chatbot. Very few teams want to do the hard work: • Data modeling • Semantic layers • Governance • Permissions • Data quality • Lineage • Trust But that’s where the moat lives. Anthropic didn’t build an AI analytics tool. They built a trusted analytics foundation that AI could sit on top of. That’s a very different architecture. The future of AI analytics won’t be won by the best prompts. It will be won by the best data foundations. Follow @hackproduct for practical AI engineering, agent architectures, MCPs, data systems, and real-world AI implementations. #AI #DataEngineering #AnalyticsEngineering #AIEngineering #Anthropic

Most companies trying to build AI analytics start with Claude. Anthropic started with the data foundation. That’s the lesson. After reading how Anthropic built self-serve analytics internally, a few things stood out: 1️⃣ They didn’t let AI talk directly to raw data. They built a semantic layer first. 2️⃣ Metrics, dimensions, business definitions, and permissions lived in one place. One source of truth. 3️⃣ Claude became the interface. Not the intelligence layer. The intelligence was already embedded in the data foundation. 4️⃣ Governance wasn’t treated as a blocker. It was treated as a product feature. 5️⃣ The goal wasn’t replacing analysts. It was eliminating repetitive work so analysts could focus on higher-leverage decisions. This is the part many AI teams miss. Everyone wants the chatbot. Very few teams want to do the hard work: • Data modeling • Semantic layers • Governance • Permissions • Data quality • Lineage • Trust But that’s where the moat lives. Anthropic didn’t build an AI analytics tool. They built a trusted analytics foundation that AI could sit on top of. That’s a very different architecture. The future of AI analytics won’t be won by the best prompts. It will be won by the best data foundations. Follow @hackproduct for practical AI engineering, agent architectures, MCPs, data systems, and real-world AI implementations. #AI #DataEngineering #AnalyticsEngineering #AIEngineering #Anthropic

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