What are DevOps Metrics?
DevOps metrics help engineering teams make data-driven decisions in order to continuously improve practices, deliver software faster, and ensure that it remains reliable. The four metrics are:
Below, we’ll break down the four most important DevOps Metrics:
4 DevOps Metrics To Track
Elite performing software teams – those who deliver the most value, fastest, and most consistently – will optimize for four metrics in particular:
Change Lead Time
Change lead time measures the total time between when work on a change request is initiated to when that change has been deployed to production and thus delivered to the customer. Lead time helps you understand how efficient our development process is. Long lead times may be the result of some inefficient process or bottleneck along the development or deployment pipeline.
The most common way of measuring lead time is by comparing the time of the first commit of code for a given issue to the time of deployment. A more comprehensive (though also more difficult to pinpoint) method would be to compare the time that an issue is selected for development to the time of deployment.
Deployment Frequency measures how often a team pushes changes to production. This indicates how quickly your team is delivering software – your speed. High performing teams endeavor to ship smaller and more frequent deployments. This has the effect of both improving time to value for customers and decreasing risk (smaller changes mean easier fixes when changes cause production failures) for the development team.
Change Failure Rate
Change Failure Rate is a measurement of the rate at which production changes result in incidents, rollbacks, or failures. This tells you the quality of code you are pushing to production. The lower the rate here the better (higher performing teams have a change failure rate of 0-15%), but the ultimate goal of the team here should be to decrease the change failure rate over time as skills and processes improve.
The trickiest piece for most teams is in defining what a failure is for the organization. Too broad or too limiting of a definition and you will encourage the wrong behaviors. In the end, the definition of failure is and needs to be unique to each organization, service, or even team.
Mean Time to Recovery (MTTR)
MTTR is about resolving incidents and failures in production when they do happen. It is the measurement of the time from an incident having been triggered to the time when it has been resolved via a production change. The goal of optimizing MTTR of course is to minimize downtime and, over time, build out the systems to detect, diagnose, and correct problems when they inevitably occur.
Why are DevOps Metrics Important?
Engineering leaders are in the position to empower teams with the direction and the tools to succeed. By measuring and tracking DevOps metrics and trends over time, developers, teams, and engineering leaders can make more informed decisions about what needs to be improved and how to make improvements to the development process. When your DevOps metrics improve, you can be confident that you’ve made good decisions to enable your team, and that you are delivering more value to your customers.
Here are some additional ways teams can value from tracking DevOps metrics:
- Visibility and Transparency: DevOps metrics provide visibility into the software development and deployment processes. They allow teams to see what is happening in their pipelines, identify bottlenecks, and understand where improvements are needed. This transparency is essential for informed decision-making.
- Continuous Improvement: Metrics help organizations continuously improve their DevOps processes. By measuring key performance indicators, teams can set specific, measurable goals and track progress towards those goals. This data-driven approach facilitates ongoing process optimization.
- Quality Assurance: Metrics related to deployment success rate and change failure rate are crucial for ensuring the quality of software releases. High-quality software with fewer defects improves user satisfaction and reduces the need for post-release fixes.
- Risk Reduction: By tracking metrics like mean time to recovery (MTTR) and deployment success rate, organizations can reduce the risks associated with incidents and failures in production. Rapid incident response and quick recovery times minimize the impact on users and the business.
- Resource Optimization: Metrics related to resource utilization can help organizations manage their infrastructure efficiently. This includes optimizing the use of computing resources, which can lead to cost savings and improved system performance.
- Data-Driven Decision-Making: DevOps metrics provide a solid foundation for data-driven decision-making. Instead of relying on intuition or anecdotal evidence, organizations can make informed choices based on quantifiable data.
- Alignment with Business Objectives: DevOps metrics should be aligned with an organization’s broader business objectives. This ensures that DevOps efforts support the company’s goals, such as revenue growth, cost reduction, or market expansion.
- Cultural Change and Collaboration: Implementing DevOps metrics can foster a culture of collaboration and accountability. When teams have visibility into their performance, they are more likely to work together to improve processes and deliver better results.
- Benchmarking and Comparison: Organizations can benchmark their DevOps performance against industry standards or competitors. This benchmarking can reveal areas where the organization lags behind or excels, providing insights for further improvement.
Want to learn how Jellyfish can help you to continuously improve your software delivery effectiveness to achieve fast and reliable results using DevOps metrics? View the DevOps Metrics Product Tour or Request a Demo!