DORA Metrics: Weve Been Using Them Wrong

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. This measures how long it takes to have a change, starting from when the developer works on it all the way till it gets into production. This looks at the ratio between how many times you’ve deployed and how many times those deployments are unsuccessful. MTTR is the average time it takes your team to recover from an unhealthy situation.

In order to make that work, you need to change the batch size to be as small as possible. In other words, ship as few changes to production at a time as you can. Increasingly, organizations are investing in proactive monitoring and alerting tools to monitor their DORA metrics on an ongoing basis. Lead time for changes is a measure of how long it takes between receiving a change request and deploying the change into production. It’s an important metric because it’s related to both customer experience and cost efficiency.

DORA metrics core objectives

Various tools measure Deployment Frequency by calculating how often a team completes a deployment to production and releases code to end-users. 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. The insights they each provide give specific meaning to people in different roles, such as team leads, VPs of engineering, CTOs and the C-suite.

This empowers engineering leaders, enabling them to benchmark their teams against the rest of the industry, identify opportunities to improve, and make changes to address them. There is a need for a clear framework to define and measure the performance of DevOps teams. In the past, each organization or team selected its own metrics, making it difficult to benchmark an organization’s performance, compare performance between teams, or identify trends over time.

Two Non-DORA Metrics Great for Showing Engineering Outcomes

These metrics are designed to provide insight into the speed and reliability of the organisation’s software delivery life cycle (SDLC), from development to deployment and all the way to incident recovery. By using these metrics, ITOps teams gain insight into where their processes need improvement, allowing them to focus their efforts on specific areas. The ability to monitor progress towards goals, identify opportunities for improvement, and optimize existing processes is essential for successful DevOps initiatives. Ultimately, the use of DORA metrics by ITOps teams helps them become more efficient and effective at delivering value to customers. By comparing all four key metrics, one can evaluate how well their organization balances speed and stability. 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.

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DORA metrics are four indicators used to calculate DevOps team efficiency. They measure a team’s performance and provide a reference point for improvements. That allows engineering leaders to continuously streamline processes and increase the speed of delivering customer value, which is crucial for a product to remain competitive. Since the goal is to deliver on promises and deliver more features faster, our process for improvement has to start with our projects. Projects, initiatives, epics, features—whatever you call them at your organization—are the shared language between engineering and business.

Code of conduct

In order to count deployments that failed in production, you need to track deployment incidents. These might be logged in a simple spreadsheet, bug tracking systems, a tool like GitHub incidents, etc. Wherever the incident data is stored, the important thing is to have each incident mapped to an ID of a deployment. This lets you identify dora 4 the percentage of deployments that had at least one incident—resulting in the change failure rate. This metric refers to how often an organization deploys code to production or to end users. Successful teams deploy on-demand, often multiple times per day, while underperforming teams deploy monthly or even once every several months.

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Learn how each of the metrics works and set the path to boosting your team’s performance and business results. These two metrics aren’t just good proxies for measuring customer experience. Yes, having fewer production incidents and fixing them faster will make customers happy. But, low CFR and MTTR translate to less unplanned work which leads to higher planning accuracy and more time spent on new features.

How to calculate Time to Restore Service?

For example, let’s say your customer hits a bug, how quickly can your team create a fix and roll that fix all the way out to production? Or if you need a new feature or a small improvement, how quickly can you deliver that as well? A company that’s able to deliver changes quicker tend to be more successful than a company that takes two to three months to get any kind of change out to production. As with any data, DORA metrics need context, and one should consider the story that all four of these metrics tell together.

  • For software leaders, Time to restore service reflects how long it takes an organization to recover from a failure in production.
  • But, low CFR and MTTR translate to less unplanned work which leads to higher planning accuracy and more time spent on new features.
  • If you get asked to take on a new project, you can point to the project allocation report and say “Absolutely.
  • The DevOps Research and Assessment (DORA) team is a research program that was acquired by Google in 2018.
  • Additionally, a lower Build Failure Rate (as a result of a lower Change Failure Rate) means it’s easier to isolate issues and optimise specific pipelines.
  • If you prefer to watch a video than to read, check out this 8-minute explainer video by Don Brown, Sleuth CTO and Co-founder and host of Sleuth TV on YouTube.

Real change comes from optimizing workflows where developers spend all their time – in the IDE and in Slack. To connect the dots between DORA metrics and better business outcomes, we have to stop treating them like the be-all-end-all and look at the bigger picture of engineering improvement. In order to actually improve them, there’s an additional set of leading indicator metrics we need to measure and improve like pull request size, review time and code churn.

Lead time for changes

Mean time to resolution (MTTR) is a measure of the time it takes from initially detecting an incident to successfully restoring customer-facing services back to normal operations. This is a measurement of the overall effectiveness of an organization’s Incident Response and Problem Resolution Process. For IT operations teams, MTTR is an important metric that can provide insight into how efficiently they can identify and fix problems as soon as possible. The company provided assessments and reports on organizations’ DevOps capabilities. They aimed to understand what makes a team successful at delivering high-quality software, quickly. Their annual reports present their findings, a combination of industry trends and learnings that can help other teams improve performance.

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The DORA metrics provide a standard framework that helps DevOps and engineering leaders measure software delivery throughput (speed) and reliability (quality). They enable development teams to understand their current performance and take actions to deliver better software, faster. For leadership at software development organizations, these metrics provide specific data to measure their organization’s DevOps performance, report to management, and suggest improvements. This metric captures the percentage of changes that were made to a code that then resulted in incidents, rollbacks, or any type of production failure. According to the DORA report, high performers fall somewhere between 0-15%. Change Failure Rate is calculated by counting the number of deployment failures and then dividing it by the total number of deployments.

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As with lead time for changes, you don’t want to implement sudden changes at the expense of a quality solution. Rather than deploy a quick fix, make sure that the change you’re shipping is durable and comprehensive. You should track MTTR over time to see how your team is improving and aim for steady, stable growth. This metric is important because it encourages engineers to build more robust systems. This is usually calculated by tracking the average time from reporting a bug to deploying a bug fix.

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