Product familyP3 · transformationOperationmany forms → one languageControlcatalog-defined schemaOutputpredictable product record

Product data standardization

Create One Consistent Data Language Across Your Catalog

A catalog can contain a lot of product information and still be difficult to use.

If every supplier uses different attribute names, units, spelling and value formats, the data remains fragmented.

ENRIVAQ standardizes enriched product information so products from different sources can fit the same catalog structure. The objective is consistency, not simply more data.

See Data Standardization in Action

Reference agricultural spare part AC820825

Reference product recordAC820825 · EXAMPLES ILLUSTRATE STANDARDIZATION RULES, NOT VERIFIED PRODUCT VALUES

Supplier variantsSAME PROPERTY · DIFFERENT LANGUAGE

Supplier 1WidthFan impeller

Supplier 2Product width4.2 cm

Supplier 3W42MM

CANONICALIZEschema rules

Canonical recordONE ATTRIBUTE · ONE UNIT

AttributeWidth

Value42

Unitmm

Schemacategory schema

The approved attribute name, unit and value representation are defined by the catalog data model — not invented by the interface.

Typical inconsistency

The same product property can appear in many forms

A multi-supplier catalog might contain:

Same dimension3 SOURCE FORMS

42 mm4.2 cm0.042 m

for the same dimension.

Same attribute3 LABELS

Outer DiameterOutside ØExternal diameter

for the same attribute.

Other inconsistencies may include:

capitalizationdecimal formatsabbreviationsvalue namingcategory-specific terminology

Without normalization, filters fragment and catalog maintenance becomes harder.

Unit normalization

Store comparable values in a consistent form

Unit normalization converts source values into the format defined by the catalog.

Supplier A4.2 cm

Supplier B42 mm

Supplier C0.042 m

Approved catalog unit42 mm

if mm is the approved catalog unit for that attribute

The target unit should be part of the schema. The system should not choose units arbitrarily.

That allows technically comparable products to use the same representation.

Value normalization

Different words may represent the same catalog value

Examples might include:

stainless steelStainless SteelINOX

Approved representationapproved material term

depending on product context and the terminology rules defined by the catalog

Naming conventions

One concept should have one canonical attribute

A structured catalog should avoid creating separate fields for every supplier vocabulary.

The same principle applies to:

category namesattribute groupsvalue labelstechnical terminology

Instead ofODOuter ØExternal Diameter

RULE
GATE

Canonical schemaOuter diameter

Source terminology can then map into that field. The target is defined by the catalog schema.

Before / after

Show how standardization changes catalog usability

Use a real catalog example here with several fields.

Before3 SOURCE FORMS

Source 1Fan wheel AC820825

Source 2Gebläserad AC820825

Source 3Koło wentylatora AC820825

IdentifierAC820825

SAME SKU · AC820825

AfterCANONICAL RECORD

Product typeFan impeller

OEM / MPNAC820825

EAN8716106986118

CompatibilityOptima / Optima HD

REFERENCE STANDARDIZATION · Fan wheel / Gebläserad / Koło wentylatora → Fan impeller · AC820825 retained as OEM / MPN

Export consistency

Give downstream systems a predictable record

Once values and attributes are normalized, the output becomes easier for other systems to consume. A standardized record can support:

Canonical product contract

Product typeFan impeller

OEM / MPNAC820825

CompatibilityOptima / Optima HD

Terminologycontrolled catalog vocabulary

PIM attributesconsistent field naming

ecommerce filterscomparable values

category templatespredictable schema

comparisonsconsistent units

product exportsstable field structure

subsequent content generationclean structured facts

The objective is for downstream systems to receive one consistent structure instead of repeating data cleanup again.

Show us where your supplier data stops being consistent

Start with a catalog sample from several suppliers or product groups.

See It on Your Data