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

Analysis Type	Questions Answered
Velocity	How much work is the team completing per sprint?
Cycle Time	How long do issues take from creation to resolution?
Bottlenecks	Where do issues get stuck? Which statuses have the longest dwell time?
Quality	What's the bug rate? How often do issues reopen?
Capacity	How is work distributed across team members?
Predictability	Are estimates accurate? What affects delivery?

AI-Assisted Setup

When connecting Jira to Scoop, choose "Guide me with AI" for an intelligent, guided setup experience.

Available Analysis Templates

Template	Object	Best For	What You'll Analyze
Issue Tracking (Default)	issues	Bug & feature tracking	Issue key, type, status, priority, assignee
Sprint Analysis	issues	Sprint velocity	Sprint field, story points, completion
Team Workload	issues	Capacity planning	Assignee distribution, work in progress

Example Questions You Can Answer

Issue Management:

"How many bugs were created vs resolved this week?"
"What's the average time to resolve by priority?"
"Which components have the most open issues?"
"What's our bug escape rate to production?"
"How does resolution time vary by issue type?"

Sprint & Velocity:

"What's our average velocity over the last 5 sprints?"
"How accurate are our story point estimates?"
"What percentage of sprint work carries over?"
"Which sprints had the most scope changes?"
"What's the correlation between team size and velocity?"

Team Workload:

"How is work distributed across team members?"
"Who has the most work in progress?"
"What's the aging distribution of assigned issues?"
"Which team members have the highest throughput?"
"How does workload vary by issue priority?"

Need Something Different?

If the templates above don't match your needs, select "Something else" and describe what you want to analyze. For example:

"I want to analyze issues by a custom field"
"I need to track specific status transitions"
"I want to focus on a particular project or component"

Scoop's AI will recommend the right configuration for your specific use case.

Connecting Jira to Scoop

Option 1: Native Connector

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

Option 2: Email Reports

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

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

Option 3: CSV Export

For one-time or periodic analysis:

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

Recommended Data to Extract

Essential Fields

Field	Analysis Use
Issue Key	Unique identifier for snapshotting
Status	Track lifecycle progression
Created Date	Measure age and volume trends
Resolution Date	Calculate cycle time
Issue Type	Segment by bugs, stories, tasks
Priority	Analyze priority distribution
Assignee	Workload analysis

Valuable Additional Fields

Field	Analysis Use
Sprint	Velocity tracking
Story Points	Effort estimation accuracy
Components	Area-based analysis
Labels	Custom categorization
Time in Status	Bottleneck identification
Customer/Account	Cost-to-serve analysis

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:

Metric	What It Shows
Stage conversion rates	% of issues that reach each status
Average time in status	Days spent in each stage
Velocity trends	Work completed over time
Aging analysis	Issues stuck for too long
Reopening rates	Quality issues requiring rework

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 Field	CRM Field	Insight
Customer label	Account ID	Issues per customer
Story points	Contract value	Cost to serve ratio
Priority	Customer tier	Priority alignment

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

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

Jira