Data Overview

When you pull Jira issues via JQL, you get a flat list of issues with columns representing different fields. The data structure depends on your export format choice.

Export formats

Format	Description	Best for
Jira CSV export	Standard Jira format with commonly-used fields	Reports, dashboards, familiar layout
Detailed data	All metadata including IDs, URLs, and custom fields	Advanced analysis, data warehousing, joining with other sources

Common fields

Standard fields

Field	Description	Type
Issue key	Unique issue identifier (e.g., PROJ-123)	Text
Summary	Issue title	Text
Description	Full issue description	Text
Status	Current workflow status	Text
Assignee	User assigned to the issue	Text
Reporter	User who created the issue	Text
Priority	Priority level (e.g., High, Medium, Low)	Text
Type	Issue type (Bug, Task, Story, Epic, etc.)	Text
Created	Issue creation timestamp	Date/Time
Updated	Last update timestamp	Date/Time
Due date	Target completion date	Date

Sprint & board fields (Scrum/Kanban)

Field	Description	Type
Sprint	Sprint the issue belongs to	Text
Epic	Parent epic link	Text
Story points	Estimated effort in story points	Number
Components	Technical components involved	Text

Custom fields

Any custom fields configured in your Jira instance (e.g., "Client Name", "Environment", "Root Cause") will be included in the detailed data export.

Common metric combinations

Here are useful analysis patterns you can build with Jira data:

• Issues by status over time — Track workflow progress by counting issues per status

• Burndown analysis — Combine story points with sprint dates for velocity tracking

• Bug lifecycle — Calculate average time from creation to resolution by comparing created and updated dates

• Workload by assignee — Count or sum story points grouped by assignee

• Issue resolution rate — Calculate percentage of issues moved to "Done" in a time period

Use cases by role

Engineering managers

Monitor sprint progress, identify bottlenecks, and track team velocity. Pull issues grouped by sprint and status to build burndown charts. Use story points to forecast capacity and plan upcoming sprints.

Product managers

Operations/support teams

Data analysts

Platform-specific notes

• JQL macros supported — Coupler.io supports Jira macros like currentUser(), now(), and relative date ranges (e.g., -7d, -30d) in JQL queries

• Custom fields — Custom fields appear in the detailed data export; column names match your Jira configuration

• Permissions — You can only pull issues you have permission to view in Jira

• Rate limits — Jira Cloud has API rate limits; large exports may take longer but will respect throttling

• Field differences — Different issue types may have different available fields; not all columns will have values for every issue