> For the complete documentation index, see [llms.txt](https://help.delpha.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.delpha.io/core-concepts/the-six-data-quality-dimensions.md).

# 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](/use-cases/ultimate-parent/quality-and-confidence-scoring.md).


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