Trust
Data quality
Data quality is about fitness for purpose: whether data is good enough for the decision or analysis it is used for.
Common quality dimensions
| Dimension | Question |
|---|---|
| Accuracy | Does the data reflect reality? |
| Completeness | Are required values present? |
| Consistency | Do related values agree across datasets? |
| Timeliness | Is it up to date for the use case? |
| Validity | Does it follow the expected format and rules? |
| Uniqueness | Are there unwanted duplicates? |
Quality in shared data
When many people contribute, quality depends on shared rules: automated tests on every change, clear definitions, and a visible way to report problems. Publishing known limitations is part of quality too: users can only work around issues they know about.
Work with ex omnia
Contact details for ex omnia will be published here once confirmed.