Intrinsic vs. Contextual Data Quality

Data quality dimensions can be classified by their relationship to context:

Intrinsic quality refers only to the data itself, properties assessable without knowing how the data will be used. Accuracy and consistency are typically considered intrinsic.

Contextual quality relates data to something external:

• User context: Can the data be understood without ambiguity? Is it credible and trustworthy to the user?
• System context: Is the data available, secure, and timely within the system that provides it?
• Societal context: Is the data free from bias? Does it reflect diverse perspectives?

The classification is not always clean. Completeness appears in both categories depending on the author, some see it as intrinsic (does the data have all required fields?), others as contextual (does the data have everything needed for this specific use?).

The implication: quality assessment methodologies must specify which relationships they’re evaluating.

Related: 04-atom—fitness-for-use-definition