Requirements vs. Attributes in Quality Definitions

Data quality definitions take two fundamental forms:

Requirements-based definitions specify goals to be achieved. They articulate what quality should look like in declarative terms. Example: “Data shall accurately represent the real-world entities it models.”

Attribute-based definitions describe characteristics that data exhibits. They list quality properties, often with brief explanations. Example: “Accuracy: the degree to which data correctly reflects reality.”

The distinction matters for operationalization:

• Requirements translate more directly into quality goals and acceptance criteria
• Attributes often need additional interpretation to become actionable

Many frameworks blend both, listing attributes but defining some as requirements. The pattern: requirements specify the quality goal; attributes describe the qualities to measure. Neither is complete without the other for quality assurance.

Related: 04-atom—data-quality-consensus-gap, 04-atom—fitness-for-use-definition