Simply “doing DevOps” is not sufficient. For teams to succeed, they need to understand what is effective, what isn’t, and how to enhance their processes, this is where DevOps research and assessment play a crucial role.
Supported by data and established frameworks like DORA (DevOps Research and Assessment), organizations are now evaluating their DevOps performance to achieve quicker deployments, improved reliability, and increased rates of innovation.
In this guide, I’m explaining the most effective strategies for DevOps research and assessment to assist you in making genuine improvements that lead to business success, rather than just superficial metrics.
What Is DevOps Research and Assessment?
DevOps Research and Assessment (DORA) offers a standard collection of DevOps metrics that are used to assess process performance and maturity. These metrics give insights into how fast DevOps can adapt to changes, the average time it takes to deploy code, the frequency of iterations, and details about failures.
DORA began as a team at Google Cloud focused on assessing DevOps performance using consistent metrics. Their goal is to improve performance and teamwork while increasing speed. These metrics help DevOps teams set goals based on current performance and track their progress.
Core Components of DevOps Assessment (Key Metrics and KPIs That Matter
DORA emphasizes 5 (initially 4) essential performance metrics that collectively offer a clear view of an organization’s DevOps effectiveness.
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The 5 DORA metrics include:
Deployment frequency
This metric assesses how often code is deployed to production. A high deployment frequency signifies continuous delivery and quick iteration capabilities, showcasing agility and responsiveness to business requirements.
Deployment Frequency is easy to collect, requiring just one table, but calculating its frequency buckets can be tricky. Although it’s simple to show daily deployment volume or average weekly deployments, the focus is on frequency, not volume.
Mean lead time for changes (MLT)
It measures the time from code commitment to successful production. Short lead times indicate efficient processes and the ability to quickly deliver features and fixes, reflecting responsiveness to business needs. For software leaders, lead time for changes demonstrates the efficiency of CI/CD pipelines and indicates how quickly updates reach customers. Ideally, this lead time should decrease over time, signaling improved team performance and more efficient pipelines.
Mean time to recover (MTTR)
`How long it takes an organization to recover from a failure in production`
MTTR calculates the average time required to recover from a production failure. Shorter MTTRs indicate a better ability to identify and resolve issues, reducing downtime and customer impact.
To measure the Time to Restore Services, you need to know two key timestamps: when the incident was created and when it was resolved. Additionally, it’s important to track when the incident was first created and when a deployment addressed that incident. This data can typically be obtained from any incident management system.
Change the Failure Rate
This rate represents the percentage of deployments to production that fail or need remediation. A lower change failure rate indicates a stable and dependable deployment process, which is crucial for maintaining customer trust and operational efficiency.
A measure of overall software development performance, reliability targets the health and stability of software products. It includes indicators such as availability, latency, error rates, and mean time between failures (MTBF).
Change failure rate benchmarks are as follows:
- Elite: 0-15%
- High: 16-30%
- Medium: 16-30%
- Low: 16-30%
This metric serves as a strong indicator of:
- Your code quality
- The efficiency of your testing methods
For effective DevOps practices, your team’s CFR should ideally be between 0-15%. Implementing practices such as trunk-based deployment, test automation, and working in small increments can enhance this metric.
Advantages of DORA Metrics for DevOps Teams
DORA uses metrics to assess and rank team performance as Low, Medium, High, or Elite. For example, a team earns an Elite ranking in change failure rate by maintaining a 0-15% failure rate and resolving issues within one hour for time to restore. The overall team ranking is based on combined performance across all metrics.
- Simplifies software delivery, speeding up time to market and boosting customer satisfaction.
- Enhances decision-making by monitoring the status of the software development process.
- Provides clear evidence of business value and positive impacts on customers.
- Supports continuous improvement by identifying performance benchmarks and establishing new objectives.
How does it benefit companies?
- Promotes effective benchmarking and comparison of software development performance.
- Increases business agility through agile DevOps methodologies.
- Lowers risks linked to software deployments by using metrics such as failure rates and MTTR.
- Fosters improved collaboration between development and operations teams.
Top DevOps Maturity Models to Use in 2025
As teams kick off their DevOps journey, using DORA metrics is crucial for their success. Open DevOps assists teams in monitoring DORA metrics to evaluate their DevOps health.
With the native integrations of Open DevOps, teams can create a comprehensive toolchain for complete software development and apply DORA metrics to gauge their success. Some of the top DevOps tools are:
Jira Software is the top pick for agile software development teams when it comes to scheduling and tracking tasks.
Bitbucket enables development teams to store, track code, and manage changes effectively.
Confluence offers knowledge management and collaboration tools that help teams capture, analyze, and share information seamlessly.
Jira Service Management aids DevOps teams in tracking and managing incidents while capturing essential DORA metrics.
Enhance your software delivery process and equip your team for success with Open DevOps, which provides everything you need to start developing and operating right away.
How DevOps Research and Assessment Can Help With Different Goals!
DevOps Metrics Overview
Metrics for Testing & Code Quality
- Defect escape rate: This measures how many defects slipped through lower-level testing and made it to production.
- CI test failure rate: This shows code quality by calculating the ratio of failed tests in the CI pipeline to the total tests run.
- Code Coverage: This indicates how much of the code is tested by the automated test suite.
Metrics for Deployment & Workflows
- Cycle time: This measures the duration from when work starts on a specific item until it is ready for end-users.
- Deployment size: This is determined by the number of features, stories, and bug fixes implemented.
- Deployment time: This measures how efficient the deployment pipelines are.
Flow Metrics
- Flow velocity: This measures how many flow items are completed over a certain period.
- Flow time: This measures the time taken from the start to the finish of a flow item.
- Flow efficiency: This measures the ratio of active time to total flow time.
- Flow load: This measures how many flow items are present in a value stream.
Metrics for Optimizing Continuous Integration (CI)
- CI runs per day: This counts how many times the CI pipeline is executed daily.
- CI success rate: This is calculated by dividing the total number of successful CIs by the total CI runs.
Metrics to Measure Customer Satisfaction
- Customer ticket volume: This is the number of incidents or support tickets reported by customers.
- Application availability/uptime: This measures the time the application is fully functional and available to end-users.
- Application performance: This assesses how the application performs under stress and different user loads.
Metrics for Optimizing Monitoring Practices
- Mean time to detection (MTTD): This is the time taken to identify a production failure and mark it as an issue.
- Application usage and traffic: This indicates how many users are accessing the systems and the number of transactions occurring in real-time.
Conclusion: Turn Research Into DevOps Impact
Measuring DevOps is more than just tracking KPI’s, you need it for continuous improvement. Merge DORA’s speed and stability metrics with the best practices explained, automate gathering data, disseminate results, and progress consistently. This is the way to elevate good DevOps to greatness, turning software delivery into a key business benefit.
FAQ’s
1. What is DevOps Research and Assessment (DORA)?
DevOps Research and Assessment (DORA), which is now part of Google Cloud, is a well-known research program that focuses on four main DevOps metrics—deployment frequency, lead time for changes, change failure rate, and MTTR—that assist teams in evaluating their delivery and operational performance.
2. What tools help with DevOps research and evaluation?
Common platforms are:
- GitLab DevOps Analytics
- Azure DevOps/Jira reports
- Google Cloud’s DORA Metrics Dashboard
- DuploCloud and LaunchDarkly for insights based on features
3. How frequently should assessments take place?
Monthly or quarterly allows for timely changes; it’s frequent enough to identify trends without overloading teams.
4. Are DORA metrics applicable to all industries?
Yes, they are relevant across different sectors, but the targets differ based on risk levels and size.
5. Is it possible to misuse metrics?
Yes, if they are used as punishment or to make unfair comparisons between teams, it can lead to a lack of trust.
