# 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

{% tabs %}
{% tab title="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.
{% endtab %}

{% tab title="Product managers" %}
Track feature requests, prioritize backlog items, and monitor issue resolution times. Export issues by type and priority to understand feature delivery rates and bug response times.
{% endtab %}

{% tab title="Operations/support teams" %}
Monitor bug reports and support tickets. Filter for critical issues and track resolution times. Analyze incident trends by priority and component to identify areas needing attention.
{% endtab %}

{% tab title="Data analysts" %}
Combine Jira data with other sources (CRM, time tracking, surveys) for deeper insights. Use detailed data export to preserve metadata for complex joins and aggregations in BigQuery or your data warehouse.
{% endtab %}
{% endtabs %}

## 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