Kverneland fan impeller AC820825 for Optima / Optima HD
meaning is embedded in proseProduct familysoft signal · crisp product dataP3structure / transformationStructure layermachine-readable record12 scenesnarrative → downstream use
Knowledge hub · definition
What Is Structured Product Data?
Structured product data represents product information in explicit fields and relationships that systems can interpret.
Instead of storing everything inside a description, structured data separates the product into defined elements such as identifier, category, attribute, value, unit and relationship.
This makes the record easier to validate, filter, compare, transform and reuse across PIM, ecommerce and other systems.
materialSteel
outer_diameter42 mm
facts separated by meaning{ material: "Metal", outer_diameter_mm: 42 }ready for governed downstream useSTRUCTURE LAYER
Structured vs unstructured
The same information can exist in text or as machine-usable fields
“Kverneland fan impeller AC820825 for Optima / Optima HD” contains identity and compatibility information that becomes more useful when stored as separate structured fields.
With structured data, those facts are stored separately and can be queried directly.
Kverneland fan impeller AC820825 for Optima / Optima HD
→- material
- Steel
- outer_diameter_mm
- 42
Record anatomy
Structured product records combine several field types
A complete product record is not a flat list of specifications. It combines identity, classification, technical facts, relationships and content fields.
Identifiers
SKU, manufacturer part number and other reference fields.
Category
The product family that determines relevant fields.
Attributes
Explicit technical and commercial properties.
Units
The measurement context that makes a value usable.
Relationships
Variants, compatibility, replacements and related products.
Descriptions
Human-readable product content that complements facts.
Channel fields
Presentation fields required by a specific destination.
Schema hygiene
Each field should express one stable concept
Combined values, inconsistent labels and missing unit context make structured records difficult to validate and reuse.
Attribute extraction and normalization separate those concepts and map them to canonical fields.
dimensions = “128 × 64 × 8 mm”→length_mm · width_mm · thickness_mm
spec = “steel, zinc plated”→material · coating
OD + outside_diameter + ext_dia→outer_diameter_mm
value = “42”→outer_diameter_mm = 42
Identifier map
Identifiers connect internal records to external product references
Different identifiers serve different purposes. Internal SKUs and external references are not interchangeable.
skuinternalCatalog operations and ordering
manufacturerexternal brand entityGrouping and research
mpnmanufacturer part numberMatching and deduplication
oem_referenceoriginal equipment referenceCompatibility and replacements
supplier_codeper supplier feedOnboarding and reconciliation
Structure contract
Structured data allows systems to process product facts without re-reading prose
Once facts have stable names, value types and units, downstream systems can work with the record predictably.
This is foundational for automation.
canonical product fieldsEcommerce filters
Find products by explicit values.
PIM validation
Check required fields and formats.
APIs
Exchange predictable field structures.
Analytics
Group and compare product facts.
Ecommerce use
Structured data supports product discovery and comparison
Customers can use filters, comparisons, variant selectors and specification tables when product facts are stored consistently.
Customer-facing prose still matters, but it should be supported by structured facts rather than used as their substitute.
Filters
Narrow a catalog by material, dimension or connection type.
Comparisons
Align the same facts across similar products.
Variant selectors
Expose the properties that distinguish family members.
Specification tables
Present product facts in a readable, consistent order.
PIM governing model
PIM systems use structured product models to govern fields and relationships
The exact schema differs by company and platform.
Enrichment writes into the existing model instead of proposing a new one.
01Familydefines applicable fields
02Required fieldsdefine record completeness
03Value rulesdefine format and unit context
04Enriched recordwrites into this model
AI and search context
Structured product data gives AI and search systems clearer context
Explicit product facts are easier to interpret than values hidden inside inconsistent prose.
Clearer entities
Identifiers and categories help separate identity from description.
Explicit facts
Attributes and units expose the values a query may depend on.
Better context
Relationships and descriptions add meaning around those facts.
Machine-readable example
A structured record keeps values typed and field meanings explicit
In this compact example, measurements remain numeric and the unit context is part of the field definition.
This object is illustrative. It is not an API contract and the values do not describe a verified product.
{
"sku": "AC820825",
"manufacturer": "Example",
"mpn": "AC820825",
"category": "Bearings",
"attributes": {
"outer_diameter_mm": 42,
"inner_diameter_mm": 20,
"material": "Metal"
}
}FAQ
Common questions about structured product data
Four practical boundaries around descriptions, markup, enrichment and human-readable content.
{{ r.answer }}
{{ r.tag }}Next step