DORA (DevOps Research and Assessment) Metrics are a set of metrics that measure software delivery performance. Software development teams have a natural affinity to DORA metrics, especially because of their role in optimizing DevOps performance, and plug SDLC gaps. By comparing all four key metrics, one can evaluate how well their organization balances speed and stability. If the LTC is within a week with weekly deployments but the it consulting rates change failure rate is high, then teams may be rushing out changes before they’re ready, or they may not be able to support the changes they’re deploying. If they are deploying once a month, on the other hand, and their MTTR and CFR are high, then the team may be spending more time correcting code than improving the product. But these improvements are only possible when engineers are empowered by beneficial processes and tools.

• You can plan for different performance levels from a team working on an active product to one working on a new product.
• We’ve broken these sections into metrics for tests and code quality, deployment, continuous integration, customer satisfaction and, lastly, monitoring practices.
• Applying all 5 DORA metrics will make you more likely to achieve your commercial and non-commercial goals.
• By knowing what data to track over time, engineering leaders can measure how efficiently their DevOps teams are operating and enable them to maximize their value stream to deliver the best possible product to end users.
• Each plays a distinct role in helping organizations define their vision, measure their progress, and improve their performance.
• Respondents who worked from home because of the pandemic experienced more burnout than those who stayed in the office (a small portion of our sample).

Being able to connect data
from all of these sources in one place is a fundamental requirement in modern
observability tools. Metrics can be collected from the application, as well as from its underlying
systems, such as the JVM, guest OS, hypervisor, node OS, and the hardware
itself. Note that as you go further down in a stack, you might start conflating
metrics that are shared across workloads. For example, if a single machine
serves several applications, watching the disk usage might not correspond
directly to the system under observation. Correlating issues across applications
on a shared system, however, can help you pin down a contributing factor (such
as a slow disk). Drilling down from a single application to its underlying
system metrics, then pulling up to show all similarly affected applications can
be very powerful.

Data extraction and transformation

Lead time is the time a code commit requires to become production-ready after passing all the necessary tests in the pre-production environment. Calculate this metric using the times of the code commit and the start of the release. The CFR of your team should sit between 0-15% if you are following effective DevOps practices.

This could indicate that the team needs to invest in new tools or processes to improve their efficiency. Often, you’ll have a well-established and business-critical system not meeting the goals of the flowing group. This indicates critical gaps in your deployment pipeline or opportunities to adopt more capabilities to improve performance. A valuable way to use deployment frequency in your organization is to track the number of weekly deployments per developer. Using per-developer numbers helps you see problems as you scale and manage expectations when a developer leaves.

Expand Automation and Agile Development Processes

The DevOps team should collect and track data efficiently to ensure that DORA metrics deliver accurate results. In 2021, the DORA team added a new metric – ‘Reliability’ to the list that helps DevOps team meet the reliability targets for the software they operate. In broader terms, this metric measures how well you can meet your user’s expectations, such as availability, latency, scalability and performance. DevOps metrics and DevOps KPIs are essential for ensuring your DevOps processes, pipelines, and tooling meet their intended goal. The less the value of LTTC is, the higher the performance of the team responsible for implementing it. When the time elapsed between the first commit and release is too long, this can be an indication of certain issues, such as bottlenecks that delay deployment or an inefficient workflow.

Instead, the groups reflect an appropriate level of performance based on a classification of the software system. DORA takes into account deployments occurring in your code base and the way fixes are implemented, through analyzing repository, change failure, and deployment base. Overall, understanding and leveraging the power of DORA metrics is crucial in this digital age.

Calculating the metrics

The DORA group outlines specific metrics to track and work towards in order to get the most out of your product. Monitoring is tooling or a technical solution that allows teams to watch
and understand the state of their systems. DevLake now supports Jenkins, GitHub Action and GitLabCI as data sources for deployments data; Jira, GitHub issues, and TAPD as the sources for incidents data; Github PRs, GitLab MRs as the sources for changes data. GitLab enables retrieval and usage of the DORA metrics data via GraphQL and REST APIs for analytics and reporting best suited for your team. You can empower your business teams to utilize metrics data through APIs, without technical barriers.

Moreover, with the right use of DORA metrics, DevOps teams have seen a drastic increase in the software delivery rate, while experiencing a massive shift in downtime. This increased efficiency is a result of a well-orchestrated approach to DevOps. What’s more is DORA doesn’t confine itself to examine the pipeline blockers, but are a clear reflection of a company’s progress across security, and reliability efforts.

Break the black box of software delivery with GitLab Value Stream Management and DORA Metrics

The deployment frequency metric measures the number of deployments your team makes. Once you have an easy and often used deployment pipeline in place, it has a positive impact on Lead Time for Changes and Mean Time to Recover. Ideally, high-performing companies tend to ship smaller and more frequent deployments. Next up is the change failure rate, or, simply stated, a measurement of the percentage of deployments that cause failures in production. DevOps Research and Assessment (DORA) is a DevOps research team that Google acquired in 2018.

If your CI/CD tools are not listed on the Supported Data Sources page, have no fear! DevLake provides incoming webhooks to push your deployments data to DevLake. Deployment Frequency is the easiest metric to collect, because it only needs one table. However, the bucketing for frequency is also one of the trickier elements to calculate.

Metrics

A technology company’s most valuable assets are its people and data, especially data about the organization itself. By knowing what data to track over time, engineering leaders can measure how efficiently their DevOps teams are operating and enable them to maximize their value stream to deliver the best possible product to end users. Feature flags allow teams to control the deployment of new features or changes to their product. When properly implemented they help teams iterate on new features faster and with less risk when features are deployed behind feature flags. It’s challenging to use one set of metrics for different products and teams because no two products or teams are the same. Every team operates within its own context and circumstances, so it may be more challenging for certain teams to become an elite performing group.

DORA eases the collaboration and decision-making processes, creating an environment where leaders can consistently and effectively make data-driven improvements. DevOps Research and Assessment were founded with the objective of studying and measuring what it takes for DevOps teams to become top performers. The researchers also wanted to come up with a model that would identify the specific capabilities teams could leverage in order to improve software delivery performance in an impactful way. Over the years, many industry experts have tried to devise ways of predicting performance with more or less success. One widely-accepted conclusion is that to improve a process, you first need to be able to define it, identify its end goals, and have the capability of measuring the performance.

How the DORA metrics can help DevOps team performance

Overcoming the challenges of DORA metrics for DevOps highly depends on the software development process and the business context. While measuring performance within an organization is no easy task, many metrics can provide data to inform an overview of the matter. One way of assessing performance in DevOps teams is using the four DORA Metrics that speak about both velocity and quality of delivery.

Master Kubernetes Monitoring with these Must-Track Metrics

All managers have to do is enroll team members into using the four metrics, with bi-weekly/monthly reviews. MTTR, or Mean Time to Restore, is the time taken to restore service after a failure. A high MTTR indicates issues with the incident response process, even leading to extended downtime, and software quality issues. CFR, or Change Failure Rate, shows how often any new change causes disruption to your system.

Importance of DORA Metrics for DevOps Teams

There are a few keys to effective implementation of monitoring and
observability. First, your monitoring should tell you what is broken and help
you understand why, before too much damage is done. The key metric in the event
of an outage or service degradation is time-to-restore (TTR). A key contributor
to TTR is the ability to rapidly understand what broke and the quickest path to
restoring service (which may not involve immediately remediating the underlying
problems).