Product Data Quality
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 Data can pass every validation rule and still be poor quality, because it is out of date, internally inconsistent or simply wrong about the physical product.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
Completeness is one dimension. Fully populated data can still be inaccurate, stale, inconsistent between systems or expressed in the wrong units.
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
Related Knowledge Base Articles
- What is Product Data Quality?
- What is Product Data Stewardship?
- How to Build a Trusted Product Data Foundation
Related Articles
- Authoritative Source
- Golden Record
- Product Data Governance
- Product Data Stewardship
- Source of Truth
- System of Record
- Data Architecture
- Master Data Management
See Also
References
- ISO 8000, data quality: https://www.iso.org/standard/81745.html
- ISO/IEC 25012, data quality model: https://www.iso.org/standard/35736.html
About This Article
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.