Here are a few suggestions for keeping track of data quality and automation metrics for custom fields in Jira:
- Set up regular audits of your custom fields to spot check data accuracy and look for anomalies. You can pull reports in Jira to analyze custom field usage and data over time.
- For critical custom fields, build validations into the creation/edit forms to prevent bad data from being entered. Jira has various validators you can use.
- Monitor errors in your automation logs to see if any automations are failing or behaving unexpectedly. Set up alerts for increased errors.
- Track key metrics over time - like number of issues with a custom field populated, number of automations run, number of errors, etc. Use this to spot trends.
- Survey teams on their experience with custom fields and automations. Gather feedback on data quality or issues.
- Set SLAs for resolution time on identified data quality issues. Track how long it takes your team to investigate and fix problems.
- Consider adding data quality tests in your QA process when releasing changes that may impact custom fields or automations.
The key is taking a proactive approach to monitoring custom field data and automation performance. Leverage Jira's reporting capabilities, track key metrics, and stay on top of user feedback. This will allow you to catch any data quality issues early.
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