How to Improve DORA Metrics in Modern DevOps Pipelines

Today, software is produced at lightning speed, but speed without quality can create chaos in production. That’s why high-performing teams rely on DORA metrics to assess the speed and efficiency at which they are delivering their changes, while still being able to maintain a stable environment. The DORA metrics can allow engineers to take their software delivery process and convert it from an "on-demand" to a "data-driven" model. However, simply measuring DORA metrics is not sufficient to create improvements. Engineers should focus on analysing key indicators and acting on the data collected, as well as continuously improving their process. This guide will give you a complete overview of the DORA metrics, how to measure them, and what best practices exist to systematically improve upon them within an engineer’s typical DevOps workflow.

What are DORA Metrics?

DORA Metrics are industry-standard measurements of software delivery performance. They were developed through DevOps Research and Assessment, and help organizations benchmark their software delivery performance against others in order to identify areas of improvement in their engineering outcomes with regard to speed of delivery and reliability of delivery. DORA Metrics consist of four metrics that represent key performance indicators for software delivery:

• Deployment Frequency

This indicates the number of times per day/week/month that an organization deploys code to production successfully. A high number of deployments indicates a mature state of automation, an organization with a strong CI/CD pipeline, and therefore a team that is able to deliver code in smaller batch sizes. Higher deployment frequency is an indicator of greater ability to innovate quickly, and reduced risk of deploying code into production. Therefore, it is one of the best metrics of how agile an organization’s DevOps practices are.

• Lead Time for Changes

This measures the amount of time it takes to move a change from code commit to deployment. The lead time provides insight into how quickly the organization is able to convert an idea into value for customers. An organization with a short lead time has streamlined its review process, has implemented testing automation, and has a strong branching strategy.

• Change Failure Rate

This is the percentage of successful deployments that result in an issue being created. An organization with a high change failure rate demonstrates a high level of failure during the release process, while a low change failure rate indicates better testing of the releases, safer deployment processes and better DevOps practices that minimize the chance of customer disruption due to release failures.

• Mean Time To Recover (MTTR)

MTTR indicates how fast teams can recover from an incident. It indicates the organization’s capability for incident response, as well as the efficiency of monitoring and building out rollback or remediation mechanisms when incidents occur. The lower the MTTR for an organization, the faster that organization can restore its services and, thus, the greater overall reliability and trust for users.

Using the above four DORA metrics provides teams with a clear definition of DORA metrics and a common criterion for measuring success across modern engineering teams.

Why DORA Metrics Matter for Modern DevOps Pipelines?

By requesting DevOps teams to provide product and service delivery (through evolution) in greater speed and timeframe, DORA metrics provide the means for organizations to:

• Identify inefficiencies in the planning, coding, testing and deployment process

• Provide sustainable, measurable insight(s) that enable organisations to improve their teams’ accountability

• Connect/align engineering productivity and business performance

• Provide a framework for organisations to optimise their DevOps and the related KPIs based upon measurable outcomes

• Enable an organisation to make data-driven rather than intuition-based (decisions on continuous process improvement).

How to Measure DORA Metrics?

To effectively assess DevOps DORA metrics, teams should:

• Collect commit/build/deploy data.

• Track incident history and automate reporting using CI/CD pipelines on-call, and monitoring tools.

• Assessment of DORA requires that teams align metrics with their version control system and production environment.

• Perform metric assessments on a per-service/module basis; ideally, for microservices, the data should be sufficiently granular to allow this.

• The amount of detail contained in the collected metrics must allow teams to establish accurate data traceability from code changes to actual production impacts.

Good measurement requires high data accuracy and traceability from code changes to production impact.

Tools and Platforms to Capture DORA Metrics

A variety of options exist for tools and platforms that can be used to obtain DORA metrics via automation. The following examples illustrate some of those options:

• CI/CD (Continuous Integration/Continuous Delivery) Solutions: GitLab, GitHub Actions, Azure DevOps

• Monitoring & Incident Management Solutions: Datadog, PagerDuty, Splunk

• Engineering Analytics Solutions: Waydev, LinearB

• Dashboarding Solutions: Grafana, Looker, Datadog Dashboards

The best choice for capturing DORA metrics is likely to work in concert with the existing DevOps Metrics environment.

How to Improve Each DORA Metric?

Improve Deployment Frequency

• Automate testing and release processes

• Shift from large releases to smaller, modular deployments

• Implement feature flags for gradual rollout

Reduce Lead Time for Changes

• Automate build and test pipelines

• Remove approval bottlenecks where appropriate

• Maintain clean branching strategies (e.g., trunk-based development)

Reduce Change Failure Rate

• Strengthen pre-production testing and observability

• Encourage root cause analysis for every failure

• Conduct risk-based impact assessments on each change

Improve Mean Time to Restore (MTTR)

• Implement clear incident response runbooks

• Improve monitoring and alerting for quicker detection

• Enhance rollout/rollback automation for fast recovery

Integrating DORA Measurement into CI/CD Pipelines

Continuous improvement can be achieved through continuous capture of pipeline data. Teams can:

• Label deployments with commit data for tracing.

• Include testing and performance data into pipelines.

• Deploy only when metrics pass automated quality gates.

Example:

Keploy helps teams create and validate test cases automatically during the deployment process, leading to lower rates of change failure and greater confidence in the deployment process, and ultimately an increase in the overall DORA Metrics.

Using DORA Metrics to Drive DevOps Success

Utilizing DORA Metrics can enhance DevOps practices and help teams measure success through different means. Teams should leverage metrics in the following way:

• As reference material for retrospectives & OKR’s in the engineering department.

• As signals of where they should invest in automated solutions.

• As a reference point for enhancing the organization’s culture, such as implementing blameless postmortems.

Regular review of these metrics will also help to ensure that delivery performance aligns continuously with the business requirements of the organization.

Considerations When Choosing a DORA Metrics Solution

Before selecting a DORA metrics solution, examine the following factors:

• Compatibility with Current CI/CD and Monitoring Tools;

• Ability to Grow Across Services and Environments;

• Real-Time Dashboards for Software Development Teams;

• Ability to Customise Your Alerts and Targets;

• Security Compliance and Data Governance Options.

The objective is to obtain rapid insights into the development process with minimal effort from developers.

Practices to Avoid with DORA Metrics

Some of the key practices you should avoid are:

• Treating the metrics as an assessment of individual performance (the potential to game the system).

• Measuring and doing nothing with the measurements (metrics will only be viewed as worthless charts).

• Failing to account for cultural change when implementing DevOps transformation tools (the tools alone have no impact on improving results).

• Over-optimizing one metric while neglecting others to achieve desired outcomes (i.e., easier and faster deployments but establishing a higher rate of failed deployments).

As a rule, balancing all of the DORA metrics is essential for continued progress.

Conclusion

DORA metrics offer an effective tool for enhancing both the efficiency of engineering and the stability of delivery channels. Through the measurement of automated metrics, application of tactical improvements to engineering practices, and inclusion of insight from analytics into the decision-making process, DevOps teams can accelerate the speed at which they release products while maintaining high levels of trust in the reliability of those releases. With tools such as Keploy that evaluate the actual impact of changes on business outcomes based on testing data, organisations can increase the pace of innovation and provide customers with superior experiences.

FAQs

What is the difference between DORA metrics and traditional DevOps KPIs?

DORA metrics are based on 4 common standards and are based on what you can measure and improve with delivery performance, whereas the development of KPIs may vary across organisations.

Do DORA metrics apply to small teams or startups?

Yes, DORA metrics can provide rapid insights into what could be slowing down your organisation and help you scale faster.

How long does it take to see improvements after adopting DORA metrics?

In general, how long it takes will depend on the current level of maturity of your development processes. Most teams will see very significant improvements within a few release cycles.

How often should teams review and update their DORA metrics strategy?

At a minimum, development teams should revisit their DORA Metrics Strategy on a quarterly basis, so it remains aligned with the development team and also the business objectives as they change.

Can DORA software metrics be customized for non-DevOps teams?

Yes, DORA Metrics can extend beyond DevOps to other groups like SRE, QA, and Platform Engineering to allow those teams to work in coordination and use the same metrics.

What role does AI and automation play in improving DevOps DORA metrics?

Artificial Intelligence allows you to make decisions faster, automate the tasks of finding and fixing issues, reduce your lead time and increase your ability to achieve operational efficient resilience.