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# Product Data Quality

> The degree to which product data is fit for its intended use, assessed across dimensions such as completeness, accuracy, consistency, timeliness, validity and uniqueness.

## Definition

The degree to which product data is fit for the purpose it is used for. It is assessed against
defined dimensions, commonly completeness, accuracy, consistency, timeliness, validity and
uniqueness, and it is meaningful only in relation to a stated use.

## Expanded Explanation

Quality is relative to purpose. A product description sufficient for an internal catalogue may be
inadequate for external publication, and a weight accurate to the nearest kilogram may be fine for
logistics and unusable for a footprint calculation. Defining the use before measuring the data is
therefore the first step, not an afterthought.

The dimensions each answer a different question. Completeness asks whether required values are
present. Accuracy asks whether they describe reality. Consistency asks whether related values agree
with one another and across systems. Timeliness asks whether they reflect the current state.
Validity asks whether they conform to the defined format, units and permitted values. Uniqueness
asks whether the same product is represented once rather than several times.

Validation is one control within this broader concept, not a synonym for it. Validation tests
information against defined rules and returns a pass or fail. Data can pass every validation rule
and still be poor quality, because it is out of date, internally inconsistent or simply wrong about
the physical product. Quality is measured; validation is enforced.

## Why It Matters

A Digital Product Passport exposes product data to parties who cannot check it against internal
context. Defects that were tolerable internally, such as placeholder values, mixed units or duplicate
records, become visible statements that may be relied upon. Measuring quality against the passport
use, before publication, is what prevents compliant publication of incorrect information.

## Common Misconceptions

#### Data quality means the data is complete

Completeness is one dimension. Fully populated data can still be inaccurate, stale, inconsistent
between systems or expressed in the wrong units.

#### A one-off cleanse fixes quality

Without governance and stewardship, corrected data degrades again as processes continue to produce
it in the same way. Quality is a maintained condition, not a project outcome.

## Related Terms

* [Product Data Governance](/docs/knowledge-base/glossary/data-governance)
* [Product Data Stewardship](/docs/knowledge-base/glossary/data-stewardship)
* [Golden Record](/docs/knowledge-base/glossary/golden-record)
* [Product Master Data](/docs/knowledge-base/glossary/product-master-data)
* [Product Data](/docs/knowledge-base/glossary/product-data)

## Related Knowledge Base Articles

* [What is Product Data Quality?](/docs/knowledge-base/enterprise-systems-and-data-architecture/what-is-product-data-quality)
* [What is Product Data Stewardship?](/docs/knowledge-base/enterprise-systems-and-data-architecture/what-is-product-data-stewardship)
* [How to Build a Trusted Product Data Foundation](/docs/knowledge-base/enterprise-systems-and-data-architecture/how-to-build-a-trusted-product-data-foundation)

## Related Articles

* [Authoritative Source](/docs/knowledge-base/glossary/authoritative-source)
* [Golden Record](/docs/knowledge-base/glossary/golden-record)
* [Product Data Governance](/docs/knowledge-base/glossary/data-governance)
* [Product Data Stewardship](/docs/knowledge-base/glossary/data-stewardship)
* [Source of Truth](/docs/knowledge-base/glossary/source-of-truth)
* [System of Record](/docs/knowledge-base/glossary/system-of-record)
* [Data Architecture](/docs/knowledge-base/glossary/data-architecture)
* [Master Data Management](/docs/knowledge-base/glossary/master-data-management)

## See Also

* [Enterprise Systems and Data Architecture](/docs/knowledge-base/enterprise-systems-and-data-architecture/overview)
* [Implementation](/docs/knowledge-base/implementation/overview)

## References

* ISO 8000, data quality: [https://www.iso.org/standard/81745.html](https://www.iso.org/standard/81745.html)
* ISO/IEC 25012, data quality model: [https://www.iso.org/standard/35736.html](https://www.iso.org/standard/35736.html)

## About This Article

About This Article

<dl>
  <dt>
    Last Reviewed
  </dt>

  <dd>
    2026-08-09
  </dd>

  <dt>
    Next Scheduled Review
  </dt>

  <dd>
    2027-08-09
  </dd>

  <dt>
    Article Version
  </dt>

  <dd>
    1.0
  </dd>
</dl>

tieback Knowledge is a continuously maintained reference library covering Digital Product
Passports, product traceability, product compliance and related regulations. Articles are
reviewed regularly as legislation, standards and implementation guidance evolve.

## Related Docs

* [Enterprise Systems and Data Architecture](/docs/knowledge-base/enterprise-systems-and-data-architecture/overview)
* [Implementation](/docs/knowledge-base/implementation/overview)
* [Digital Product Passports](/docs/knowledge-base/digital-product-passports/overview)
* [Glossary](/docs/knowledge-base/glossary/overview)