Connect Jira to Scoop to analyze engineering velocity, track issue resolution patterns, and understand how work flows through your development and service teams. Snapshot issues to see exactly how they progress through statuses, measure cycle times, and identify bottlenecks.

What You Can Analyze

Connecting Jira to Scoop

Option 1: Native Connector

1. Create a new dataset in Scoop
2. Select Jira from the application list
3. Authenticate with your Atlassian account
4. Select the project(s) to sync
5. Choose which issue types to include

Option 2: Email Reports

Configure Jira to email reports to your Scoop data email address:

1. In Jira, create a saved filter for the issues you want
2. Set up a subscription to email the filter results
3. Use your Scoop dataset email as the recipient
4. Schedule daily or weekly delivery

Option 3: CSV Export

For one-time or periodic analysis:

1. Export issues from Jira as CSV
2. Upload to Scoop manually or via Google Drive

Recommended Data to Extract

Essential Fields

Valuable Additional Fields

Setting Up Snapshot Analysis

For powerful process analysis, configure your Jira dataset as a Snapshot type:

Recommended Filter

Include issues that are either open or recently resolved:

status != Done OR resolved >= -7d

This captures:

• All currently active issues
• Recently closed issues (to record final state change)

Key Metrics Enabled

With snapshotting, you can analyze:

See Snapshot Datasets for setup details.

Analysis Examples

Engineering Velocity Dashboard

Track sprint performance over time:

• Story points completed per sprint
• Bug vs. feature ratio
• Burndown patterns
• Velocity trend lines

Cycle Time Analysis

Understand how long work takes:

• Average time from creation to resolution
• Time spent in each status
• Comparison across issue types
• Identification of outliers

Team Workload

Analyze capacity and distribution:

• Issues per team member
• Work in progress limits
• Assignment patterns
• Bottleneck identification

Quality Metrics

Monitor quality trends:

• Bug creation rate over time
• Severity distribution
• Time to resolve by priority
• Reopening frequency

Blending Jira with Other Data

Jira becomes even more powerful when combined with other sources:

Jira + CRM Data

Goal: Understand which customers drive the most engineering work

Jira + Product Usage

Goal: Correlate product areas with engineering investment

• Which features generate the most bugs?
• Are high-usage areas getting appropriate attention?
• Where should engineering focus?

Jira + Financial Data

Goal: Calculate true cost of engineering work

• Cost per issue resolved
• Engineering investment by product area
• ROI on bug fixes vs. features

Best Practices

Data Hygiene

• Use consistent labeling for customers/products
• Ensure all issues have proper type classification
• Keep status workflows standardized

Snapshot Frequency

• Daily for active development tracking
• Weekly for longer-term trend analysis

Field Selection

• Include all fields you might want to analyze
• Add custom fields that contain business context
• Exclude sensitive personal data if sharing broadly

Troubleshooting

Missing Issues

• Check your Jira filter includes all needed statuses
• Verify permissions allow access to all projects
• Confirm date range covers expected data

Duplicate Records

• Ensure Issue Key is recognized as unique identifier
• Check for multiple projects with overlapping keys

Status Changes Not Tracking

• Confirm dataset type is "Snapshot"
• Verify daily data loads are occurring
• Check filter includes recently modified issues

Related Topics

• Snapshot Datasets - Track issue state changes
• Process Analysis - Visualize issue flows
• Blending Datasets - Combine with other sources