In software development, technical leaders strive to boost productivity, shorten cycle times, and improve the developer experience. Traditionally, this journey differed significantly between organizations, with some focusing on cost-saving data points and others prioritizing employee experience. There was no consistent, data-driven approach applicable across industries and tech stacks.
That's where DORA metrics come in—they're a set of key performance indicators (KPIs) designed to measure the efficiency of software development teams. Through four key metrics, DORA indicators highlight your DevOps team's overall effectiveness. We'll guide you through understanding DORA metrics and their importance, as well as best practices for using them within your organization.
Table of contents:
DORA, or the DevOps Research and Assessment program, is a research initiative focused on uncovering the factors that influence software delivery and operations performance.
Each year, they conduct an in-depth analysis, published as the annual State of DevOps Report and led by Google, to collect data on critical engineering delivery and performance metrics.
The goal of this annual report is to ensure that quality software development ultimately leads to the successful delivery of profitable products and features to users. As an engineering leader, integrating DORA metrics into your team’s process can lead to more efficient DevOps practices by identifying areas for improvement.
DORA metrics are software engineering KPIs you can use to evaluate the effectiveness of your team's DevOps practices. Collecting the necessary data and analyzing it allows your development team to improve their software delivery process and create more reliable software, faster.
The four DORA metrics include:
Deployment frequency (DF): How often your team deploys to a production environment
Lead time for changes (LTFC): The time it takes for a commit to be deployed into production
Time to restore service (TTRS): The recovery time from system or product failure to full functionality
Change failure rate (CFR): The number of incidents resulting from deployed changes by your teams
DORA assesses team performance by categorizing performance within 4 categories (low, medium, high, and elite organizational performance). While aiming for an elite level may seem ideal, it's important to remember that every organization is unique, and context is key in interpreting these metrics.
For example, a large corporation with advanced automation may achieve an elite LTFC level, deploying changes within hours. In contrast, a smaller organization with fewer resources might take weeks, falling within the medium category. The medium rank isn't necessarily unfavorable, especially for a smaller business that doesn’t have much automation in place, but this medium ranking does highlight areas for improvement for their software development performance.
Understanding the organization's specific challenges and priorities helps teams effectively interpret these benchmarks and make informed decisions for continuous improvement in their delivery processes.
Deployment frequency is simply how often your team deploys. This directly impacts how often changes reach your end users. It's important to track not only how frequently you deploy but also the size of each deployment.
One strategy to improve deployment frequency is to minimize the size of your deployments. This approach reduces the possibility of errors by restricting the code your team can impact and permits more frequent releases. Smaller deployments simplify identifying a problem's origin if issues do occur.
How to measure DF:
You can measure deployment frequency manually or, preferably, using automated tools for greater efficiency. Manual tracking involves maintaining a detailed log of all deployments, including their date and time and any changes made. Although more efficient methods exist, spreadsheet applications can help record the necessary information and make ongoing calculations.
Continuous integration (CI) tools can help you analyze and build logs of needed data for DF calculations. Meanwhile, DORA metrics tools like Pluralsight Flow can automatically calculate your team's deployment frequency by dividing the total number of deployments within a specified date range by the number of weeks in that range.
Lead time for changes measures the time it takes for a commit to be deployed into production. This metric is a valuable tool for identifying and eliminating bottlenecks and inefficiencies that may slow down your team's operations.
How to improve LTFC:
To reduce your LTFC, consider automating your build, test, and deployment phases with CI for greater efficiency. You might also implement regular reviews by using a code review checklist to capture potential issues before they hit production. Ultimately, the best way to improve your LTFC is by using automated tools to reduce the number of steps your team needs to perform.
Measurement tools can help with process optimization by identifying friction points. For instance, Flow can flag prolonged waiting periods, like in testing or QA, which may extend to days or weeks. Addressing this type of low-hanging fruit enables informed decisions, like investing in automated testing or improving staging environments to alleviate bottlenecks during waiting states.
Time to restore service, also known as mean time to recovery, measures how long it typically takes for your team to recover from a system or product failure and restore full functionality.
Before focusing on improving metrics, it's important to understand the underlying issues. By analyzing time to restore service, your team can establish policies and procedures that minimize downtime and expedite recovery in the event of failures.
How to analyze TTRS:
Measure TTRS by tracking the time it takes for your team to identify and resolve downtime incidents. You can do this manually by interpreting incident reports and logs, but an automated solution can save your team time.
A tool like Flow provides in-depth insights that boost confidence when implementing fixes and procedures, decreasing room for error. This process gives your team a clear roadmap for responding to incidents and outages.
The change failure rate measures the number of incidents resulting from deployed changes by your teams. Put simply, CFR is the ratio of deployments to failure.
Change failure rate can be used as a control metric while working on improving overall DORA metrics. This metric helps you identify when there is an overemphasis on speed, reminding your team to maintain a balance between speed and quality to provide a better product for users.
How to reduce CFR:
One key to reducing your CFR is improving your team's code quality and review processes. Use integration and end-to-end testing to test different parts of your system in real-world scenarios. You can't manually catch every error, so integrating monitoring and alert systems within your development process can be vital to reducing CFR.
Flow provides a closer look into critical aspects like pull requests, code reviews, QA time, and backflow, offering valuable insights into each step. This process helps keep everyone aware of changes, understand why failures happen, and know how to address them in the future for the best possible results.
DORA metrics help teams work smarter and deliver better software faster. By assessing these metrics, you can:
Quantify process changes: DORA metrics provide concrete data for assessing and improving software delivery performance.
Monitor progress: They allow teams to set achievable goals and track progress toward improving delivery capabilities.
Enhance collaboration: DORA metrics align teams around common goals, fostering collaboration and accountability.
Reduce lead times: Tracking metrics like deployment frequency and lead time for changes helps streamline processes for faster delivery. Helping your team to deliver more, quicker.
Minimize failure rates: DORA metrics like change failure rate highlight areas for improving quality assurance practices, reducing failures and service disruptions.
Improve customer satisfaction: Faster and higher-quality software delivery enhances customer satisfaction and trust in products and services.
DORA metrics provide useful insights into software delivery performance, but proper interpretation is essential. Consider these four best practices for DORA metrics interpretation and usage:
DORA metrics evaluate how your team performs as a whole. Never use DORA metrics to measure the performance of an individual—this can lead to misunderstandings and disrupt teamwork.
With a team-based approach, leaders facilitate a collaborative mindset, creating a culture where everyone works together toward a shared goal. DORA metrics measure the system that developers work in—a system created by engineering leaders. So, it's crucial for leaders to view DORA metrics solely as measures of systems and processes, not as assessments of individuals or teams.
DORA metrics are designed to work together rather than in isolation. Each metric provides valuable insights into different aspects of software delivery performance, and they often influence one another. For example, a team that focuses solely on increasing deployment frequency without enough attention to change failure rate may experience higher failure rates due to rushed deployments.
While speed is important for software delivery, it shouldn’t come at the expense of quality. Similarly, focusing only on quality may result in slower delivery times. It’s important to strike a balance between these metrics to optimize performance.
Recognize benchmarks as reference points, not rigid goals. Each team has unique challenges and capabilities. Rather than comparing to external benchmarks, focus on continuous improvement based on your team’s past performance.
Consider context when comparing organizations using DORA metrics. Simply comparing metrics without understanding underlying factors can lead to misleading conclusions. Team size, project complexity, tech stack, and organizational culture significantly impact software development KPIs. Provide context and nuance to comparisons for more informed decisions and meaningful improvements.
While DORA metrics offer valuable insights into software delivery performance, you need to effectively interpret them to use them correctly. Utilizing a software development analytics tool like Flow can help you understand the underlying causes for the outcomes of your DORA metrics.
Pairing DORA metrics with Flow's actionable insights empowers engineering leaders to advocate for their teams and make informed decisions. With the help of Flow's DORA metrics dashboard, engineering leaders can delve deeper into performance metrics, identify areas for improvement, and implement strategies to enhance software delivery.
While DORA metrics are powerful tools for engineering teams striving to improve, they often lack granularity and context, only highlighting surface-level issues without providing guidance on improving organizational performance.
To fully leverage DORA metrics and identify root causes, engineering leaders must delve deeper into their engineering data. Pluralsight Flow offers actionable insights that drive enhanced delivery, better decision-making, and the development of high-impact teams. To discover how Pluralsight Flow can elevate your processes, schedule a demo with our team today.
The above is the detailed content of DORA metrics: Best practices for engineering leaders. For more information, please follow other related articles on the PHP Chinese website!
How to solve parse error
What are the common linux systems?
wirelesskeyview
How to run code with vscode
The difference and connection between java and javascript
Do you know if you cancel the other person immediately after following them on Douyin?
js method to generate random numbers
What equipment is needed for Douyin live broadcast?
![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
