Correct product?
Does the value describe the correct product?
Evidence requiredSolutions / Catalog AssemblyQuality dossier · four-lens inspection
Product data quality
Make Product Data More Complete, Consistent and Verifiable
Product data quality is not one number.
A record can be complete but wrong, accurate but poorly structured, or consistent while still missing the specifications buyers need. ENRIVAQ supports product data quality improvement by identifying gaps, standardizing inconsistent information, surfacing conflicts and validating enriched records before they move downstream.
The goal is a catalog that is easier to use, review and maintain.
Required fields?
Are the fields required by the category available?
Schema checkSame representation?
Are equivalent values represented the same way?
Conflict visibleFits the rules?
Does the data fit the expected schema and format?
Rule checkReference productFan impellerAC820825
IdentityKnown reference
Quality stateBy dimension
Single scoreNot published
Quality is not one numberOne record · four explicit verdicts
Definition
Quality means more than the field is not empty
ENRIVAQ separates product-data quality into distinct dimensions so success in one area cannot hide a failure in another.
Is it correct?
Does the value describe the correct product and is it supported by relevant evidence?
Evidence lensIs it sufficient?
Are the fields required by the product category and target schema available?
Schema lensIs it comparable?
Are equivalent attributes, units and values represented in the same canonical way?
Conflict lensDoes it fit?
Does the data satisfy the expected schema, format and validation rules?
Rules lensQuality score
Use a score only if the product actually calculates one
A quality score can be useful when it is based on transparent rules, such as required attribute completeness or validation results.
It becomes misleading when a single percentage hides very different issues.
Quality is shown through explicit checks and states rather than an unexplained composite score.
versus
AccuracyEvidence and identitySUPPORTED
CompletenessCategory requirementsPARTIAL
ConsistencyCanonical representationCONFLICT
ValiditySchema and format rulesPASS
Missing data
Find the fields that prevent a product record from being useful
Missing data should be evaluated against the product category and target schema. A blank optional marketing field is different from a missing technical dimension required for product selection.
technical_dimensionRequired
materialRequired
manufacturer_refRequired
marketing_claimOptional
secondary_noteOptional
technical_dimensionMissing
materialMissing
manufacturer_refAC820825Present
marketing_claimOptional blank
Conflicts
Treat disagreement as a data-quality issue, not an inconvenience
Two sources may provide different values for the same attribute. A quality workflow should make these conflicts visible so they can be resolved or reviewed.
Source claim A
Source claim B
Historical record
Visible
conflict
conflict
Do not silently choose a value
Field
weightStateReview / hold
Decision
HOLD WEIGHT · EXCLUDE FROM OUTPUTThe system should not select the first available value simply to maximize completeness.
Standardization
Make comparable products comparable
Normalization and standardization bring attribute names, units, values and formats into the target catalog structure. This supports cleaner filters, easier comparison and more predictable downstream processing.
Koło wentylatora / Gebläserad / Fan wheel
mixed terminologyOEM / Part No. / manufacturer reference
mixed labelsAC820825
same identifierEAN 8716106986118
stable identifiercanonical
mapping
mapping
canonical_product_nameFan impeller
manufacturer_part_numberAC820825
ean8716106986118
compatibilityOptima / Optima HD
Validation
Check enriched data before treating it as accepted catalog data
Validation is where source relevance, schema rules, formats, conflicts and uncertainty are considered together. It separates enrichment from blind generation.
Source relevanceSchema rulesFormatsConflictsUncertainty
AcceptedTreated as catalog data
ReviewRouted to a person for a decision
HeldLeft unresolved rather than generated
Reporting
Make quality visible at product and catalog level
A quality initiative needs reporting that helps the team decide what to work on next.
Reporting can expose the product states available in the implemented workflow, such as missing required fields, validation failures and review queues.
Catalog quality recordRun-specific quality status
Measured metrics onlyProduct statusAccepted / Review / Rejected
Missing required fieldsTracked against target schema
Validation failuresRecorded with reasons
Completeness changesAvailable when a baseline is defined
Review queuesException records separated for follow-up
Use a representative catalog sample to establish a baseline
Start by measuring what is missing, inconsistent or uncertain before discussing how much can be improved.