Product · Check
Catch quality issues before they spread
When many people contribute to the same data, small problems travel fast. Automated checks with clear explanations help a team fix them at the source.
Common data-quality dimensions
| Dimension | Question it answers |
|---|---|
| Completeness | Are expected values and records present? |
| Validity | Do values follow the expected format, range and type? |
| Consistency | Do related datasets agree with each other? |
| Timeliness | Is the data as fresh as people assume it is? |
| Uniqueness | Are records duplicated? |
These dimensions are standard in data-quality practice; the exact checks offered by ex omnia are to be confirmed by the client.