> ## Documentation Index > Fetch the complete documentation index at: https://docs.heygarth.ai/llms.txt > Use this file to discover all available pages before exploring further. # Dora metrics *** # DORA Metrics Dashboard ## Overview The DORA Metrics Dashboard provides insights into engineering performance using industry-standard DevOps metrics. It helps track deployment speed, system stability, and recovery efficiency, enabling teams to measure and improve software delivery performance. ## Key Performance Indicators ### What Is It? A summary section displaying high-level DORA metrics that reflect overall engineering performance. ### Why Is It Important? Provides a quick snapshot of delivery efficiency, system reliability, and operational performance without needing to analyze detailed charts. ### Where Does It Exist? Top section of the DORA Metrics dashboard. ### Metrics Included #### Deployment Frequency Number of deployments performed within a given time period. #### Lead Time for Changes Average time taken from code commit to production deployment. #### Change Failure Rate Percentage of deployments that result in failures. #### Mean Time to Recovery (MTTR) Average time taken to recover from system failures. #### Failed Deployment Recovery Time Time taken specifically to recover from failed deployments. ## Industry Benchmark Comparison ### What Is It? A comparison table mapping current performance against industry-standard DORA benchmarks. ### Why Is It Important? Helps teams understand where they stand relative to high-performing organizations and identify areas for improvement. ### Where Does It Exist? Below the KPI summary section. ### Metrics Included | Metric | Benchmark Comparison | | ------------------------------- | -------------------- | | Deployment Frequency | Elite, High, Medium | | Lead Time | Elite, High, Medium | | Change Failure Rate | Elite, High, Medium | | MTTR | Elite, High, Medium | | Failed Deployment Recovery Time | Elite, High, Medium | ### Use Cases * Benchmark team performance * Identify gaps in delivery efficiency * Set improvement targets ## Deployment Trends ### Number of Deployments #### What Is It? A time-series chart showing the number of deployments over time. #### Why Is It Important? Helps track release frequency and consistency. #### Where Does It Exist? First chart under the trends section. #### Use Cases * Monitor release cycles * Identify spikes or slowdowns in deployments * Track delivery velocity ### Mean Lead Time for Changes #### What Is It? A trend line showing how long it takes for changes to move from commit to production. #### Why Is It Important? Helps identify bottlenecks in the development and release pipeline. #### Where Does It Exist? Below the deployment frequency chart. #### Use Cases * Improve delivery speed * Detect process inefficiencies * Optimize CI/CD workflows ## Stability and Recovery Metrics ### Failed Deployment Recovery Time #### What Is It? A chart showing the time taken to recover from failed deployments over time. #### Why Is It Important? Measures system resilience and incident response efficiency. #### Where Does It Exist? Mid-lower section of the dashboard. #### Use Cases * Improve incident response * Reduce downtime * Track recovery improvements ### Change Failure Rate (%) #### What Is It? A chart showing the percentage of deployments that failed over time. #### Why Is It Important? Indicates release quality and stability. #### Where Does It Exist? Bottom section of the dashboard. #### Use Cases * Improve code quality * Reduce production issues * Monitor release risk ## Controls and Filters ### What Is It? Interactive controls to customize the data view. ### Why Is It Important? Allows users to analyze metrics at different levels of granularity. ### Where Does It Exist? Top section of the dashboard. ### Features * Project / Repository selection * Time granularity toggle (Daily / Weekly / Monthly) ## Actions ### Executive Summary #### What Is It? A high-level summary view of engineering performance. #### Why Is It Important? Provides quick insights for stakeholders and leadership. ### Export Report #### What Is It? An option to export dashboard data. #### Why Is It Important? Enables reporting, sharing, and offline analysis. ## Key Use Cases ### Performance Benchmarking Compare team performance against industry standards. ### Delivery Optimization Improve deployment speed and efficiency. ### Stability Monitoring Track failure rates and system reliability. ### Incident Management Measure and reduce recovery time. ### Process Improvement Identify bottlenecks in the development lifecycle. # DORA Metrics Executive Summary ## Overview The Executive Summary provides a high-level, leadership-focused view of engineering performance based on DORA metrics. It consolidates key insights, benchmarks, trends, and recommended actions into a single view for quick decision-making. ## Overall Status ### What Is It? A top-level classification of engineering performance, such as Elite Performer, based on DORA metrics. ### Why Is It Important? Gives an immediate understanding of overall engineering maturity and performance level. ### Where Does It Exist? Top-right section of the Executive Summary. ## Performance Summary ### What Is It? A narrative summary describing current performance across all four DORA metrics. ### Why Is It Important? Provides context and interpretation of metrics instead of just raw numbers. ### Where Does It Exist? Below the Executive Summary heading. ## Key Metrics Snapshot ### Metrics Included | Metric | | --------------------------- | | Deployment Frequency | | Lead Time for Changes | | Mean Time to Restore (MTTR) | | Change Failure Rate | ## Additional Performance Indicators ### Metrics Included | Metric | | ----------------------- | | DORA Classification | | Deployment Success Rate | | Deployments This Week | | Average PR Merge Time | ## Actions This Quarter ### What Is It? A prioritized list of recommended actions to maintain or improve DORA performance. ### Types of Actions * High Priority Actions * Medium Priority Actions ### Use Cases * Scale best practices across teams * Improve monitoring and alerting * Enhance observability and incident response ## DORA Performance Tier ### Metrics Included | Metric | | -------------------- | | Deployment Frequency | | Lead Time | | MTTR | | Change Failure Rate | ## Monthly Trends ### Metrics Included | Metric | | -------------------- | | Deployment Frequency | | Lead Time | | MTTR | | Change Failure Rate | | Test Coverage | ## Risk Watch ### Risk Areas * Scaling Risk * Deployment Complexity * Incident Response Fatigue ### Use Cases * Prepare for team expansion * Manage system complexity * Prevent burnout in incident response teams ## Bottom Line ### What Is It? A final summary statement combining performance, risks, and recommended focus areas. ### Why Is It Important? Provides a clear takeaway for leadership and decision-makers. ### Where Does It Exist? Bottom section of the page. # Filters: Project and Repository Selection ## What Is It? A dropdown filter that allows users to select a specific project or repository to view DORA metrics. ## Why Is It Important? Ensures that metrics are scoped to a relevant dataset, enabling accurate analysis for a specific team, service, or codebase. ## Where Does It Exist? Top section of the DORA Metrics dashboard. ## How It Works ### Project Selection Users can choose from a list of available projects such as backend services, engineering teams, testing environments, or specific initiatives. ### Repository Selection Within the selected project, users can select a repository to further narrow down the metrics view. ### Multi-Context Data The dropdown includes various types of sources such as: * Engineering projects * Testing environments * CI/CD pipelines * Tool-specific integrations