Data products solved ownership. They solved pipelines. They never solved meaning.
In a world where humans, dashboards, copilots and agents all query at once, a static data product collapses.
What data products got right
Clear ownership, defined contracts, discoverability, SLAs. Those were real advances over an undifferentiated lake, and the operating model they introduced was correct.
Where they stop
| Assumption | Why it breaks |
|---|---|
| Consumers are known in advance | Agents ask unanticipated questions |
| A contract is a schema | A schema says nothing about what a field means |
| Definitions live in documentation | Documentation decays silently |
| One product per domain | Real questions cross domains |
| Governance is upstream | Agents reach the data through new doors |
The second row is the crux. A data contract will guarantee the revenue column exists, is a decimal, and arrives by 06:00. It guarantees nothing about whether it includes intercompany, at what recognition point, in which currency.
What replaces them
A semantic product: the same ownership discipline, but the unit is a governed concept rather than a dataset.
- Typed and versioned, so
active customerhas one definition with a history - Relationships proven, not documented — join paths are part of the product
- Policy attached to the concept, travelling into every query
- Self-maintaining, with drift detection instead of a quarterly review
- Consumable by humans and agents through the same compiled path
Data products made data available. Semantic products make it usable by machines that can't ask a colleague for clarification — which is now most of your query volume.
The full breakdown — the semantic product model, how it composes with mesh thinking, and the migration path — is here:
👉 Data Products Are Dead: The Era of Semantic Products
Originally published at colrows.com/blogs/data-products-are-dead-long-live-semantic-products
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