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Monitor flow metrics

6 minute read

Navigate and analyze flow metrics to assess value stream efficiency, identify process bottlenecks, and optimize how work moves through your development lifecycle. This guide covers dashboard access, work item analysis, cycle time interpretation, and value stream improvement workflows.

Before you begin, ensure you have completed analytics dashboard setup with proper Jira integration and flow item mapping configuration.

Access and configure flow metrics view

Flow metrics analysis requires proper component scoping and time duration selection to provide meaningful value stream insights. To access and configure your flow metrics view:

  1. Navigate to Analytics  Flow metrics.

  2. Select FILTER to configure your analysis scope.

  3. Configure component selection:

    1. Select one or more Components for focused value stream analysis.

    2. Choose individual components for detailed examination or multiple components for comparative assessment.

  4. Set analysis duration from the following options:

    Table 1. Duration filter definitions
    Duration Definition

    Current week

    Current week in the month, Monday to Sunday schedule. For example, if current day is Tuesday, only data from Monday and Tuesday are displayed.

    Previous week

    Previous week in the month, Monday to Sunday schedule.

    Two weeks back

    Two weeks prior in the month, Monday to Sunday schedule.

    Current month

    First day of current month up to current day.

    Previous month

    First day to last day of previous month.

    Two months back

    First day to last day of two months prior.

    Last 7 days

    The past seven days.

    Last 30 days

    The past 30 days.

    Last 90 days

    The past 90 days.

    Custom range

    Select a custom range

  5. Select APPLY to update the dashboard with filtered data.

The dashboard displays five flow metrics showing work item progression through your configured value stream boundaries.

Set a custom date range

To set a custom date range:

  1. Select FILTER.

  2. Select Custom range.

  3. Select dates for the time frame start and end.

The custom date range is set accordingly and displayed in blue on the date picker. You can view the analytics data for any desired time frame.

Flow metrics require active Jira integration and analytics configuration that maps issue types to flow item categories.

Analyze work load and distribution

Work load and distribution metrics reveal current capacity allocation and organizational priority patterns. To assess work load and distribution:

  1. Review work load metrics showing current work items in progress.

  2. Select the work load number link to examine active work items details:

    1. Issue ID and Jira issue type information.

    2. Work item summary and assignment details.

    3. Issue creation date and configured flow item categorization.

    4. Current status within your configured Jira workflow.

  3. Analyze work items distribution chart showing capacity allocation:

    1. Feature development percentage indicating innovation investment.

    2. Defect resolution percentage revealing quality management effort.

    3. Risk mitigation work showing security and compliance focus.

    4. Technical debt percentage indicating maintenance investment.

  4. Identify organizational capacity patterns:

    1. High defect percentages (>50%) suggesting quality process issues.

    2. Low feature percentages (<30%) indicating innovation constraints.

    3. Minimal risk or tech debt work potentially creating future problems.

    4. Seasonal variations in work type priorities.

Work distribution reveals strategic focus and operational health through actual capacity allocation rather than planned priorities.

Monitor velocity and throughput

Velocity metrics track work completion rates and reveal productivity trends over time. To analyze velocity and throughput patterns:

  1. Review velocity metrics showing completed work items within the selected time period.

  2. Examine velocity by flow item type to identify:

    1. Feature delivery consistency indicating development predictability.

    2. Defect resolution rates showing quality management effectiveness.

    3. Risk and tech debt completion patterns revealing maintenance discipline.

  3. Analyze velocity trend charts to assess:

    1. Consistent throughput vs. variable completion patterns.

    2. Seasonal productivity variations or cyclical delivery patterns.

    3. Impact of process changes, tool adoptions, or organizational modifications on work completion.

  4. Hover over chart data points to view specific time period details:

    1. Exact completion counts by flow item type.

    2. Date range information for trend correlation.

    3. Patterns indicating team capacity or process efficiency changes.

  5. Use velocity data for:

    1. Sprint planning and capacity forecasting based on historical throughput.

    2. Process improvement impact assessment through before/after comparisons.

    3. Resource allocation decisions informed by actual delivery capacity.

Higher velocity indicates improved team productivity, but must be balanced with cycle time and quality metrics for complete performance assessment.

Assess cycle time and efficiency

Cycle time and efficiency metrics identify value stream bottlenecks and reveal time-to-value optimization opportunities. To analyze cycle time and work efficiency:

  1. Review cycle time measurements showing average completion duration by flow item type:

    1. Feature cycle times indicating development process efficiency.

    2. Defect cycle times revealing troubleshooting and resolution effectiveness.

    3. Risk and tech debt cycle times showing maintenance process maturity.

  2. Examine cycle time variations to identify:

    1. Consistent completion times indicating predictable processes.

    2. Cycle time increases suggesting process degradation or complexity growth.

    3. Flow item type differences revealing process specialization effectiveness.

  3. Analyze work efficiency metrics separating active work from wait time:

    1. Active work time percentage by flow item type showing actual development effort.

    2. Wait time breakdown revealing process bottlenecks (code review delays, approval bottlenecks, blocked status duration).

    3. Efficiency variations between different work types indicating workflow optimization opportunities.

  4. Select efficiency percentage links to drill into detailed analysis:

    1. Individual issue cycle times and efficiency calculations.

    2. Active vs. waiting time breakdown for specific work items.

    3. Flow item patterns revealing systematic bottlenecks or process inconsistencies.

  5. Identify improvement opportunities through efficiency analysis:

    1. High wait times (>40%) suggesting process automation or approval optimization needs.

    2. Low efficiency variations between work types indicating well-balanced workflows.

    3. Efficiency degradation over time requiring process investigation and refinement.

Shorter cycle times with higher efficiency percentages indicate mature value stream processes that minimize waste and maximize value delivery speed.

Compare flow metrics across components

Component comparison reveals performance variations and identifies best practices across teams or applications. To conduct comparative flow analysis:

  1. Configure multi-component analysis using component filter selection.

  2. Compare work distribution patterns between components:

    1. Different team capacity allocation strategies (feature-focused vs. maintenance-heavy).

    2. Component-specific work type requirements based on technology or domain.

    3. Organizational priority variations across different product lines or customer segments.

  3. Analyze cycle time and efficiency differences across components:

    1. Teams achieving consistently faster value delivery cycles.

    2. Process maturity variations indicating workflow optimization opportunities.

    3. Technology stack or architectural patterns affecting delivery efficiency.

  4. Examine velocity patterns between components:

    1. Consistent high-throughput teams suitable for knowledge sharing.

    2. Variable velocity patterns requiring process stabilization.

    3. Capacity planning insights based on proven delivery rates.

  5. Identify performance patterns for organizational learning:

    1. Components with optimal work distribution balance between innovation and maintenance.

    2. Teams with superior cycle time performance indicating effective processes.

    3. Efficiency leaders demonstrating bottleneck resolution strategies.

  6. Export comparative data for:

    1. Team coaching initiatives based on proven performance patterns.

    2. Process standardization using high-performing team workflows.

    3. Strategic resource allocation informed by actual delivery capabilities.

Performance comparison enables data-driven process improvement and knowledge transfer across organizational boundaries.

Correlate flow metrics with development execution

Flow metrics validation through development activity correlation ensures measurement accuracy and process effectiveness. To correlate flow data with development patterns:

  1. Analyze correlation between Jira issue progression and actual commit activity:

    1. Issues marked "In Progress" should correlate with active development work.

    2. Issue completion should align with merge and deployment activities.

    3. Status transition timing should reflect actual development workflow patterns.

  2. Examine relationship between issue complexity and cycle time:

    1. Story point estimates vs. actual completion time correlation.

    2. Issue type complexity patterns affecting delivery predictability.

    3. Development effort estimation accuracy revealed through cycle time analysis.

  3. Identify workflow configuration optimization opportunities:

    1. Jira status mappings that don’t reflect actual work patterns.

    2. Missing intermediate statuses causing efficiency measurement gaps.

    3. Status transition automation opportunities to improve data accuracy.

  4. Validate flow item categorization accuracy:

    1. Issue types correctly mapped to flow item categories (Feature, Defect, Risk, Tech Debt).

    2. Flow item classification reflecting actual work value and organizational impact.

    3. Analytics configuration adjustments needed for more accurate measurement.

  5. Optimize development team Jira usage based on flow insights:

    1. Consistent status updates to improve cycle time accuracy.

    2. Proper issue linking to enable comprehensive flow analysis.

    3. Workflow discipline that supports accurate value stream measurement.

Regular correlation analysis ensures flow metrics reflect actual development patterns and guide effective process improvements.

Use flow metrics for value stream optimization

Flow metrics provide strategic guidance for value stream improvement through bottleneck identification and capacity optimization. To transform flow analysis into improvement initiatives:

  1. Assess current value stream performance across all five metrics:

    1. Work load sustainability (avoiding excessive work in progress).

    2. Work distribution balance appropriate for organizational goals and product lifecycle stage.

    3. Velocity consistency and improvement trends over time.

    4. Cycle time efficiency and predictability across different work types.

    5. Overall efficiency indicating process maturity and waste elimination.

  2. Prioritize improvement areas based on business impact:

    1. Cycle time reduction for faster time-to-market and improved customer responsiveness.

    2. Work distribution optimization for strategic capacity allocation.

    3. Velocity improvement for increased delivery throughput and capacity.

    4. Efficiency gains through bottleneck elimination and process automation.

  3. Set realistic improvement targets using flow metrics baselines:

    1. Incremental cycle time reduction goals based on current performance patterns.

    2. Work distribution targets that balance innovation with operational requirements.

    3. Velocity improvement expectations accounting for team capacity and organizational constraints.

    4. Efficiency improvement through systematic bottleneck resolution.

  4. Implement measurement-driven improvement cycles:

    1. Regular flow metrics review to track improvement initiative impact.

    2. Process experiments validated through before/after flow analysis.

    3. Continuous optimization based on flow data insights and trend analysis.

  5. Document value stream optimization results:

    1. Baseline flow metrics for future improvement comparison.

    2. Process changes and their measured impact on value stream performance.

    3. Best practices identified through successful flow optimization initiatives.

Flow metrics enable data-driven value stream improvement that connects process changes to measurable business outcomes.

Troubleshoot flow metrics issues

Address common issues when Flow metrics data is not appearing or appears incomplete in the dashboard.

Missing Jira integration data

Flow metrics depend on active Jira integration and proper analytics configuration mapping.

Problem: Flow metrics display incomplete data

Solution: Confirm Jira integration is active and analytics configuration maps issue types correctly. Verify commit messages include Jira issue keys for correlation.

To resolve missing Jira integration data:

  1. Navigate to Configurations  Integrations.

  2. Check that Jira integration shows "Active" or "Connected" status.

  3. Refresh authentication credentials if the connection appears inactive.

  4. Test the integration connection to confirm data access.

Flow metrics cannot populate without an active Jira integration.

Incorrect analytics configuration mapping

Flow metrics require explicit mapping between Jira issue types and flow item categories.

To check analytics configuration mapping:

  1. Navigate to analytics configuration for your project.

  2. Verify issue types are mapped to flow item categories:

    1. Feature: Story, Epic

    2. Defect: Bug, Issue

    3. Risk: Security, Compliance

    4. Tech Debt: Technical Task, Improvement

  3. Confirm mappings cover all relevant Jira issue types in your projects.

Missing or incorrect mappings prevent flow metric calculations.

Poor commit message correlation

Flow metrics depend on correlation between Jira issues and development activity.

To improve commit message correlation:

  1. Review recent commit messages for Jira issue key inclusion.

  2. Confirm issue keys match active Jira issues in your configured projects.

  3. Verify commit authors have appropriate Jira access for issue linking.

Include Jira issue keys in commit messages to enable correlation between development activity and work item tracking.
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