Catalog, Taxonomy, and Attribute Schema Are Not the Same Thing
A precise mental model for separating product records, categories, schemas, source documents, enrichment jobs, and review workflows.
Teams often use catalog, taxonomy, and attribute schema as if they were interchangeable. They are not. Collapsing them into one generic “product data architecture” makes governance harder because each layer answers a different question and fails in a different way.
| Layer | Question it answers | Common failure |
|---|---|---|
| Catalog | Which real products do we sell, buy, stock, or maintain? | Duplicates, stale records, unclear product identity. |
| Taxonomy | Where does each product belong? | Wrong or overly broad categories that make search and reporting weak. |
| Attribute schema | What must be known for products in this category? | Missing critical fields or generic fields that cannot support technical filtering. |
| Source documents | Where did the evidence come from? | Values copied without citation, context, or document version. |
| Enrichment jobs | How do sources become proposed values? | One-off scripts and manual spreadsheet work that cannot be repeated reliably. |
| Review | Which proposed values are allowed into the catalog? | Uncertain AI or supplier values are published without controls. |
Why the separation matters
A product can be correctly classified but still have poor attributes. A category can have a detailed schema while the catalog still contains duplicates. A record can have complete values while lacking provenance. Each problem requires a different workflow.
Claro’s model keeps the layers separate: product identity is resolved into canonical catalog records; taxonomy decides where the product belongs; the category schema defines what must be known; source documents provide evidence; enrichment jobs propose values; review decides what enters the trusted catalog.
A practical example
A stainless steel ball valve is the product record. “Valves > Ball valves” is taxonomy. Body material, connection type, pressure rating, bore, seal material, and temperature range are schema fields. A datasheet and supplier spreadsheet are source documents. Extraction and normalization propose values. Review approves the final record.
When those layers are explicit, teams can measure readiness precisely instead of debating whether the catalog is “clean.”
Claro
See how Claro handles this in production
This concept is one piece of keeping a catalog trusted. See how Claro resolves identity, enriches missing attributes, and validates every update before it reaches your PIM or ERP.
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