Product Data Quality API

How Good Is Your Product Data Really?

Evaluate product data in real time using your own business rules. Calculate quality scores, information levels, and approval statuses from product descriptions, technical attributes, images, documents, and compliance data.

Measure, evaluate, and expose product data quality as an API.
Model in Excel. Deploy as an API.

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Calculate Product Data Quality in Real Time

Not all product data is equally valuable. Two products may be complete yet still differ significantly in quality. A record that contains images, technical specifications, product descriptions, documents, and compliance information delivers far more value than a record that contains only mandatory fields.

With the Contene Logic Engine (CLE), companies can evaluate product information using configurable data quality rules, calculate data quality scores, and expose the results through an API. Applications send product data as JSON and receive a quality score, approval status, or information level in real time.

Why Use a Product Data Quality API?

In many organizations, product data quality is assessed manually or through rigid validation rules. A simple pass/fail validation is often not enough.

The real questions are:

How good is the product data actually?
Is the data ready to be published?

A Product Data Quality API evaluates records against configurable data quality rules and calculates measurable data quality scores and quality indexes.

Typical evaluation factors include:

  • Product names
  • Product descriptions
  • Multilingual content
  • Technical attributes
  • Images
  • Documents
  • Safety data sheets
  • Compliance information
  • Classifications
  • Master data fields

Instead of checking completeness alone, product data quality is measured and scored. Many organizations use Product Data Completeness Scores to measure publication readiness across large product catalogs.

Product Data Completeness Scores provide an objective indicator of how complete product information is before publication.

A Product Data Completeness Score answers whether information exists.
A Product Data Quality Score answers how useful that information actually is.

This creates a more granular view than simple complete/not complete indicators and provides an objective basis for publication decisions, quality monitoring and continuous improvement.

Example: Product Data Quality Assessment

In a production PIM environment, product data can be evaluated against predefined quality criteria.

Typical criteria include:

  • Product names
  • Product descriptions
  • Multilingual content
  • Images
  • Documents
  • Safety data sheets
  • Customs information
  • Technical attributes

Each criterion contributes a defined number of points to the overall result. The resulting score is used to derive information levels, approval decisions, and other business outcomes. These data quality rules form a scoring model that automatically calculates a product data quality score for every product record.

This creates an objective and measurable indicator of product data quality.

POST /api/decision
Content-Type: application/json

[{
  "articleNumber": "98F9934",
  "hasDescriptionDE": true,
  "hasDescriptionEN": true,
  "imageCount": 2,
  "documentCount": 1,
  "technicalAttributesFillRate": 0.84,
  "hasRoHS": true,
  "hasCountryOfOrigin": false
}]

The API evaluates the data using the configured decision model:

HTTP 200 OK 

[{
  ...
  "qualityScore": 92,         
  "informationLevel": 3,      
  "approved": false           
}]

Applications can immediately use these data quality results for publication decisions, workflows, dashboards, and downstream processes.

Quality Assessment Instead of Simple Validation

Traditional validation only answers one question:

Valid or invalid

A Product Data Quality API goes significantly further:

Quality Score = 92
Information Level = 3
Approval Status = No

This creates a measurable and comparable KPI for product data quality.

Product Data Quality as the Basis for Publication Decisions

In many organizations, product data quality directly determines whether a product can be published. Missing product descriptions, images, documents, or technical attributes may prevent publication even when all required master data exists.

As a result, assessing product data quality becomes a business decision in its own right.

The real question becomes:

Is the product information good enough to be published?

Model Quality Rules in Excel

Data quality rules and scoring models are defined in Excel.

The Excel model represents a real-world decision model for assessing product data quality. Individual quality criteria receive specific weights and contribute to an overall quality score. From that score, information levels, approval decisions, and additional business outcomes are derived.

Business users can modify data quality rules, scoring weights, approval thresholds, and information levels without changing application code:

  • How many points a product description receives
  • How important images are
  • Which mandatory information must be present
  • Which thresholds are required for approval
  • How information levels are calculated

Excel serves as the modeling language for data quality rules, scoring models, approval thresholds, and publication decisions.

The Contene Logic Engine compiles the decision model into an executable dependency graph and exposes the evaluation as an API.

Common Use Cases

Evaluate Product Data in a PIM System

Calculate quality scores for individual products or entire product catalogs.

Automate Publication Decisions

Products are published only after defined quality thresholds have been reached.

Guide Content Teams

Editorial teams receive transparent quality scores and can immediately identify areas for improvement.

Evaluate Supplier Data

Automatically assess the quality of incoming supplier information.

Build KPI Dashboards

Visualize product data quality metrics in BI systems, PIM platforms, and business portals.

A Data Quality API does more than validate data. It applies configurable data quality rules, calculates data quality scores, and returns measurable quality results that applications can use for automated business decisions.

Works With Existing Systems

A Product Data Quality API is independent of the system that stores the product data.

Applications simply provide product data as JSON and receive quality scores, information levels, and approval decisions in return.

Typical systems include:

  • Pimcore
  • Akeneo
  • Contentserv
  • Stibo Systems
  • Informatica PIM
  • ERP systems (e. g. Business Central, Odoo)
  • Product portals
  • Ecommerce platforms
  • Custom applications

CLE does not replace these systems:
The system remains responsible for storing and managing product data.
CLE evaluates the product information and returns business decisions through a REST API.

Whether product data originates from Pimcore, Akeneo, Contentserv, an ERP system or a custom application, the same decision model evaluates product data quality consistently across all systems.

Business Decisions as APIs

Evaluating product data quality is ultimately a business decision. The question is not whether a field is technically valid, but whether the available information is sufficient for publication, commerce, compliance, or customer experience.

The organization decides:

  • Which information matters
  • How many points should be awarded
  • Which criteria are required
  • When a product is considered publication-ready

With CLE, these rules remain maintainable in Excel and become available as APIs for any application.

Developers build applications. Business experts define data quality.
CLE executes the decision.

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