Turn Jira sprint data into metric trees that connect engineering output to business impact.

Backlog Health Analysis evaluates the overall quality and manageability of a Jira backlog. It considers factors such as issue age distribution, prioritisation consistency, estimation coverage, and the ratio of groomed to ungroomed items.

Blocked Time Percentage calculates the proportion of an issue's total cycle time spent in a blocked or impeded state in Jira. It quantifies how much delivery time is consumed by waiting rather than active work.

Code Review Cycle Time measures the elapsed time between when a Jira issue enters a code review state and when it exits, either approved or requiring changes. It captures the efficiency of the review process as reflected in Jira workflow transitions.

Component Quality Trends analyses the volume and severity of defects and issues filed against specific Jira components over time. It identifies components with improving or deteriorating quality trajectories, guiding targeted investment in testing and refactoring.

Cross-Team Dependency Analysis maps the network of issue links, blockers, and handoffs between different Jira projects and teams. It quantifies how often teams depend on each other and measures the impact of those dependencies on delivery timelines.

Cycle Time measures the elapsed time from when work actively begins on a Jira issue (typically moving to "In Progress") to when it is marked done. It captures the actual working duration, excluding backlog waiting time, and is a key indicator of process efficiency.

Defect Density measures the number of defects found per unit of delivered work, such as per story point, per feature, or per release in Jira. It provides a normalised quality indicator that accounts for the volume of work delivered.

Epic Progress Tracking monitors the advancement of Jira epics by measuring the proportion of child issues completed, story points delivered, and time elapsed against planned timelines. It provides feature-level visibility into delivery progress.

Escalation Pattern Analysis examines how issues are escalated in Jira, including priority changes, reassignments to senior staff, and cross-team handoffs. It identifies recurring escalation triggers and measures the impact of escalations on delivery timelines.

Flow Efficiency measures the ratio of active work time to total cycle time for Jira issues. It reveals what percentage of an issue's lifecycle is spent in value-adding states versus waiting states such as review queues, approvals, or blocked status.

Issue Age Distribution analyses the age profile of open issues in Jira, categorising them into age brackets to reveal how much of the backlog is fresh versus stale. It tracks the number and proportion of issues in each age bracket over time.

Issue Resolution Rate measures the number or percentage of Jira issues resolved within a given time period. It serves as a fundamental throughput metric that reflects the team's ability to close out work items across all issue types.

Lead Time measures the total elapsed time from when a Jira issue is created to when it is resolved. Unlike cycle time, lead time includes the time an issue spends waiting in the backlog before work begins, providing a customer-centric view of delivery speed.

Priority Distribution Analysis examines how issues are distributed across priority levels in Jira. It tracks the proportion of issues at each priority level over time and identifies trends such as priority inflation, where an increasing percentage of issues are marked as high or critical.

Release Burnup Analysis tracks the cumulative amount of work completed for a Jira release version over time, plotted against the total release scope. Unlike burndown charts, burnup charts also visualise scope changes, making them ideal for releases where requirements evolve.

Sprint Burndown Analysis tracks the remaining work in a Jira sprint over time, comparing actual progress against the ideal burndown trajectory. It reveals whether the team is on track to complete their sprint commitment and surfaces mid-sprint risks.

Sprint Commitment Accuracy measures the percentage of committed sprint scope that a team actually delivers by the end of the sprint in Jira. It compares what was planned at sprint start against what was completed at sprint end, excluding work added mid-sprint.

Sprint Goal Achievement Rate measures the percentage of sprints in which the stated sprint goal is achieved in Jira. Unlike commitment accuracy which focuses on scope completion, this metric evaluates whether the overarching sprint objective was met.

Sprint Velocity measures the total story points or issue count completed by a team in each Jira sprint. It provides a rolling baseline of team capacity that is used for forecasting future delivery and calibrating sprint commitments.

Story Point Estimation Accuracy compares the story points assigned to Jira issues at estimation time against the actual effort required, measured through cycle time or time tracking. It identifies systematic over- or under-estimation patterns across issue types and teams.

Team Capacity Utilisation measures the proportion of available team capacity that is actively allocated to Jira issues. It compares assigned workload against available capacity, accounting for team member availability, leave, and non-project commitments.

Team Collaboration Index quantifies the quality and frequency of collaboration across Jira teams. It combines signals such as cross-team issue assignments, comment interactions on shared issues, linked issues between projects, and collaborative resolution of blockers.

Technical Debt Ratio measures the proportion of engineering effort in Jira that is allocated to addressing technical debt, including refactoring, upgrading dependencies, and fixing architectural issues, relative to total development effort.

User Productivity Analysis examines individual contributor patterns in Jira, including throughput, cycle time, issue type distribution, and work consistency. It identifies productivity trends and factors that influence individual effectiveness, supporting coaching and team optimisation.

Version Release Success Rate measures the percentage of Jira version releases that are delivered on time, within scope, and without critical post-release defects. It provides a holistic view of release quality and predictability.

Worklog Accuracy compares the time logged against Jira issues with the actual time spent, derived from calendar data, workflow state durations, or team surveys. It measures the reliability of worklog data for planning, billing, and process analysis purposes.