Most enterprises have hundreds of data pipelines and few reliable data products. The difference: a pipeline moves data from A to B. A data product delivers trustworthy, documented, and governed data to a consumer who uses it to make decisions. This shift from pipeline thinking to product thinking is what separates data-driven organisations from data-collecting ones.
What Makes a Data Product
A data product has: (1) A named owner responsible for quality and evolution. (2) A service level agreement — freshness, accuracy, availability. (3) Documentation that a non-technical user can understand. (4) Governance controls — who can access it, for what purpose. (5) Discoverability — users can find it through a catalog. (6) Consumer feedback loop — the owner knows who uses it and how.
The Operating Model
Data products require cross-functional teams: a product manager (prioritises features based on consumer needs), a data engineer (builds and maintains the pipeline), a data steward (ensures quality and governance), and domain experts (define business semantics). These teams operate like software product teams — with roadmaps, sprints, and user feedback.
From Pipeline to Product: The Transformation
Start by auditing existing pipelines. For each, ask: who consumes this data? What decisions does it inform? Who owns it? Is there an SLA? Pipelines without clear consumers or owners are candidates for retirement. Pipelines with active consumers are candidates for productification — adding documentation, SLAs, governance, and ownership.
The MCP Semantic Layer as a Data Product Platform
The MCP semantic layer is, in essence, a data product platform. Each metric in the semantic layer is a data product: it has a definition, an owner, a data source, governance rules, and consumers. When a user asks 'What is our revenue?', they're consuming a data product — and the semantic layer ensures it's trustworthy, governed, and consistent.
Key Takeaways
- What Makes a Data Product
- The Operating Model
- From Pipeline to Product: The Transformation
- The MCP Semantic Layer as a Data Product Platform
Conclusion
The MCP semantic layer is, in essence, a data product platform. Each metric in the semantic layer is a data product: it has a definition, an owner, a data source, governance rules, and consumers. When...
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