For the complete documentation index, see llms.txt. This page is also available as Markdown.

The six data quality dimensions

Delpha uses six dimensions — completeness, validity, uniqueness, consistency, accuracy and timeliness — to score data quality. Each metric ensures your data is clean, consistent and accurate.

Delpha evaluates each field using six core data quality dimensions to provide a comprehensive health check:

Dimension

Meaning

Completeness

Is the value present or missing?

Validity

Does the data follow the correct format or business rules?

Uniqueness

Is the value duplicated elsewhere in the system?

Consistency

Is the data coherent across different fields or systems?

Accuracy

Is the value factually correct?

Timeliness

Is the information still relevant and up-to-date?

Accuracy is where clean ≠ correct: a value can be present, well-formatted, and unique — and still be factually wrong. Delpha verifies accuracy against external reality, with evidence.

Each use case scores against the subset of dimensions that applies to it — see, for example, Quality & confidence scoring for Ultimate Parent.

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