> For the complete documentation index, see [llms.txt](https://docs.collibra.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.collibra.com/data-quality-self-hosted/core-concepts/findings/data-quality-score-meter.md). # Data Quality Score Meter The data quality score is an aggregated value between 0-100 that represents a summary of the integrity of your data. A score of 100 means that Collibra DQ does not detect data quality issues in your dataset. Conversely, a score of 0 indicates that Collibra DQ has observed enough potential data quality issues that it has down-scored the findings to the lowest possible value. {% hint style="info" %} The findings from each data quality dimension where Collibra DQ detects potential data quality issues contribute to the overall data quality score. {% endhint %} Below the score meter are five clickable options plus a time stamp. ![Data Quality Score meter](https://4121921244-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FWCrP7Albs8DfJjIQxUxw%2Fuploads%2Fgit-blob-391d33e616d317da8a849e64ef910e9ccb3ce1d3%2Fdq-score-meter.png?alt=media) | No. | Component | Description | | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Number 1 | Pass | Runs that failed but after investigation were valid. Train the model to pass future runs that are similar. | | Number 2 | Fail | Runs that passed but after investigation were invalid. Train the model to fail future runs that are similar. | | Number 3 | Off-Peak | A valid run that does not align with peak time runs, for example, over a weekend or a holiday. Label as Off Peak to train the model to recognize the pattern for future runs. | | number 4 | Ignore | A valid run that fits the run cycle profile but should not be used for training. You simply want to ignore. | | Number 5 | Delete | Runs that triggered by mistake and should be removed. | | Number 6 | Timestamp | The runId of the current DQ Job. When there is only one DQ Job run on a given runId date, the timestamp is always 00:00. However, when there are multiple DQ Job runs on a given runId date, the timestamp reflects the time each DQ Job ran, for example, 01:00 or 10:45. |