DORA Metrics for DevOps Performance

1 post · Dec 2019 – Nov 2024

What this trail covers

GitLab's Value Stream Analytics provides a unified view of the DevOps lifecycle, measuring time spent across stages from planning to monitoring. It helps identify bottlenecks and optimize the software delivery flow. The platform tracks key DevOps stages (Issue, Plan, Code, Test, Review, Staging) and integrates DORA metrics (Deployment Frequency, Lead Time for Changes, Time to Restore Service, Change Failure Rate) to assess team velocity and delivery performance. This post highlights the introduction of customizable Insights Dashboards leveraging labels for granular tracking of merge requests by squad and requirement type, enabling more precise measurement of team productivity and work distribution.

All GitLab trails

Nov 2024Data-driven DevSecOps: Exploring GitLab Insights DashboardsThis post details how to leverage GitLab's Insights Dashboards and Labels to create custom metrics for tracking merge request performance by squad and requirement type. It provides a concrete example of configuring `.gitlab/insights.yml` with specific chart definitions for bar and stacked-bar charts, demonstrating how to filter and group merge requests by labels like `squad::alpha` and `type::feature`. It also hints at using the GitLab API and Triage Bot for automated labeling.
Apr 2022How the DORA metrics can help DevOps team performanceThis post introduces and explains the four key DORA metrics: Deployment Frequency, Lead Time for Changes, Time to Restore Service, and Change Failure Rate. It highlights their importance for measuring DevOps performance and driving continuous improvement. The post also references Zoopla's use of these metrics with the GitLab DevOps platform to boost deployments and automation, and mentions the availability of DORA metrics via an API in GitLab.
Apr 2022The top DevOps tooling metrics and targets at GitLabThis post details specific targets for seven key DevOps metrics at GitLab: master pipeline stability (>95%), review app deployment success rate (>99%), time to first failure (<15 minutes), open S1 bug age (<100 days), open S2 bug age (<300 days), merge request pipeline duration (<45 minutes), and MR pipeline costs (<$7.50). It explains the rationale behind each metric and its importance for engineering efficiency and productivity.
Jan 2022The role of Value Stream Analytics in GitLab's DevOps PlatformThis post introduces and elaborates on GitLab's Value Stream Analytics feature. It explains the concept of Value Stream Management, details the default DevOps stages tracked (Issue, Plan, Code, Test, Review, Staging), and demonstrates how to customize these stages at the group level. It also highlights key metrics, including the DORA 4 metrics, and emphasizes the benefit of integrated data analysis within GitLab's single application for the DevOps lifecycle.
Jan 2022Zoopla Boosts Deployments & Automation with DORA MetricsThis post details Zoopla's implementation and use of DORA metrics to measure and improve engineering performance. It outlines the specific DORA metrics used (production deploy frequency, lead time, MTTR, change fail rate, time to onboard), how they are collected using GitLab, Blameless, and Jenkins APIs, and how they are utilized for decision-making and cultural transformation. Zoopla reports significant improvements in all measured metrics, including a shift from weekly to multiple daily deployments and a reduction in lead time from 10 days to under two days. The post also highlights cultural changes such as increased automation, improved incident response, and standardized IaC.
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Dec 2021Understand how your teams adopt DevOps with DevOps reportsThis post introduces DevOps Adoption Reports and DevOps Score as new features for analyzing DevOps performance. DevOps Adoption Reports provide insights into feature usage across Development, Security, and Operations categories, helping identify lagging subgroups and successful practices. DevOps Score offers a comparative analysis of feature usage against top-performing instances, calculated from Service Ping data. Cohorts are also presented as a tool for administrators to view user retention and manage seats.
Oct 2021How to make your DevOps team elite performersThis post introduces the DORA metrics (Deployment Frequency, Lead Time for Changes, Time to Restore Service, Change Failure Rate) as key indicators of DevOps team performance. It highlights the significant performance gap between elite and low-performing teams based on these metrics and provides six actionable tips for teams to improve their performance: smart use of hybrid/multi-cloud, proper cloud implementation (NIST essentials), complementarity of DevOps and SRE, thorough documentation, integrated security throughout development, and attention to team culture.
Jul 2021Optimize DevOps with enhanced visibility tools in GitLab 14This post introduces the availability of Deployment Frequency and Lead Time for Changes metrics within GitLab 14, building upon the existing Value Stream Analytics capability. It explains how these DORA metrics help users understand DevOps maturity, benchmark progress, identify bottlenecks, and improve decision-making by providing actionable insights into the software delivery lifecycle.
Dec 2019How to optimize your value exchange systemThis post introduces the concept of the Value Exchange System as a framework for understanding and optimizing the flow of value in software development and business. It contrasts output-based metrics with outcome-based metrics, highlighting the importance of customer value and business value. The post uses a fictitious company, Acme Co., to illustrate the problem of focusing solely on output (work items delivered) leading to declining revenue growth due to a disconnect between R&D delivery and customer expectations. It introduces the Improvement Kata as a management process for continuous improvement and proposes a Value Stream Map to visualize the flow of work items from requirements to production features, emphasizing the need to measure cycle time and active work time to identify bottlenecks.