| Start with a simple JQL query | Before building complex queries, test with `project = "YOUR_PROJECT"` or `assignee = currentUser()` to understand the data. Once the data flow runs successfully, refine your query to add filters like status, date ranges, or custom fields. |
| Use "Detailed data" for analysis | If you're joining Jira data with other sources or building advanced reports, choose the "Detailed data" export format to preserve IDs and metadata. This makes it easier to join issues with other datasets later. |
| Select only the columns you need | If your Jira instance has many custom fields, explicitly list the columns you need in the "Columns" field. This reduces export size, speeds up the data flow, and makes your destination cleaner and easier to read. |
| Schedule daily refreshes for active projects | If your team moves issues frequently, schedule daily refreshes to keep your reporting sheets up to date. For slower-moving projects, weekly refreshes are usually sufficient. |
| Use date ranges in JQL to limit historical data | Instead of pulling all issues from the beginning of time, filter by creation date (e.g., `created >= -90d`) to keep exports manageable and faster. Archive old data separately if needed for historical analysis. |
| Filter by project or sprint first | Queries like `project = "PROJ"` are much faster than open searches. If using sprints, add `sprint = "Sprint Name"` to the query to significantly reduce the dataset. |
| Avoid overly broad JQL searches | Queries like `type != Epic` or `priority is not empty` scan your entire Jira instance. Add positive filters (e.g., `project in (PROJ1, PROJ2)`) to narrow scope and speed up execution. |