Connect Jira to Count
Jira Analytics with Count
Transform your Jira project data into actionable insights with Count's AI-powered jira kpi dashboard. Instead of wrestling with rigid templates or spending hours building manual reports, Count's AI agent writes custom SQL and Python analysis tailored to your exact questions about sprint performance, team productivity, and delivery metrics.
Beyond Basic Jira Reporting
Jira holds the complete story of your development process — sprint velocity, cycle times, backlog health, defect patterns, and team capacity. But Jira's built-in reports offer limited segmentation and can't answer follow-up questions like "Why did our cycle time spike in Q3?" or "Which story point estimates are consistently off, and by how much?"
Count runs hundreds of queries in seconds, uncovering patterns in your sprint data that manual analysis would never find. Whether you're tracking Sprint Velocity, analyzing Cycle Time trends, or measuring Team Capacity Utilization, Count delivers bespoke analysis instead of generic templates.
Complete Project Intelligence
Unlike spreadsheet analysis that becomes error-prone with complex Jira datasets, Count automatically handles data quality issues and messy project structures. It connects your Jira data with other sources — your database, GitHub, Slack — for complete project intelligence.
Every analysis is transparent and collaborative. Count shows its methodology, so you can verify assumptions about Sprint Burndown Analysis or Epic Progress Tracking. The result? Presentation-ready insights that turn your Jira data into strategic advantage, not just another jira reporting tool.
Stop Reading About Jira Analytics. Start Analyzing.
Connect your Jira data directly to Count's AI-powered canvas. Go from sprint questions to actionable insights in one session—no more waiting weeks for custom reports.
Metrics & Analyses You Can Run
Sprint Velocity
Measure your team's delivery capacity by tracking story points or issues completed per sprint over time.
Cycle Time
Track how long it takes for issues to move from in-progress to done in your Jira workflow.
Lead Time
Measure the total time from issue creation to completion to understand your team's responsiveness.
Sprint Burndown Analysis
Visualize daily progress against sprint goals to identify potential delivery risks early.
Defect Density
Track the ratio of bugs to features across your Jira projects to monitor code quality trends.
Epic Progress Tracking
Monitor completion status and timeline progress of large initiatives broken down into epics.
Team Capacity Utilization
Analyze how effectively your team's available time is being used based on logged work and sprint commitments.
Issue Resolution Rate
Track how quickly your team resolves different types of issues to identify bottlenecks and improvement opportunities.
Backlog Health Analysis
Evaluate the age, priority distribution, and size of your product backlog to maintain optimal workflow.
Sprint Commitment Accuracy
Compare planned versus actual sprint deliveries to improve future sprint planning accuracy.
Code Review Cycle Time
Measure time spent in code review status to optimize your development workflow efficiency.
Priority Distribution Analysis
Analyze how work is distributed across priority levels to ensure proper focus on critical items.
Blocked Time Percentage
Track how much time issues spend in blocked status to identify and eliminate workflow impediments.
Release Burnup Analysis
Monitor progress toward release goals by tracking completed work against total scope over time.
Component Quality Trends
Analyze defect patterns across different components or modules to focus quality improvement efforts.
Worklog Accuracy
Compare estimated versus logged time on issues to improve future estimation and planning accuracy.
Cross Team Dependency Analysis
Identify and track dependencies between teams to minimize coordination bottlenecks and delays.
Story Point Estimation Accuracy
Analyze how well your team's story point estimates align with actual delivery to refine estimation practices.
Technical Debt Ratio
Track the proportion of technical debt items versus new features to maintain healthy codebase balance.
User Productivity Analysis
Measure individual team member contribution patterns and workload distribution across your Jira projects.
Flow Efficiency
Calculate the ratio of active work time to total cycle time to identify workflow optimization opportunities.
Version Release Success Rate
Track the percentage of planned features successfully delivered in each version release.
Issue Age Distribution
Analyze how long issues remain open to identify stale work and prioritization problems.
Sprint Goal Achievement Rate
Measure how often your team successfully meets defined sprint objectives and goals.
Escalation Pattern Analysis
Track when and why issues get escalated in priority to improve initial triage and planning.
Team Collaboration Index
Measure team interaction patterns through issue assignments, comments, and handoffs to optimize collaboration.
Stop Reading About Jira Analytics. Start Analyzing.
Connect your Jira data directly to Count's AI-powered canvas. Go from sprint questions to actionable insights in one session—no more waiting weeks for custom reports.