How to Measure Success in DevOps

Published: June 23, 2020
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In the world of coding, the DevOps methodology is a powerhouse. While the name may imply a simple merging of Development and Operations, this only scratches the surface of what DevOps has helped organizations across virtually every industry to achieve.

DevOps creates permanent and ever-improving cultures of collaboration, efficiency, and reliability. With a focus on automation and testing, it triggers vast improvements in terms of both deployment times and code quality. At the same time, DevOps Leaders can ensure that IT work is fully aligned with the needs of stakeholders, as well as security specialists and end-users.

Still, as nice as this sounds, for a methodology to be truly beneficial, you must be able to find tangible evidence of its successes. How do the benefits of DevOps translate into practice? How do you measure improvements, and where? Is there a way to gauge exactly what kind of DevOps structure your business requires, or which areas are in direst need of improvement?

One does not necessarily require the help of a DevOps Leader to choose the most appropriate DevOps performance metrics. These will always be based on your own management and business goals. Once you have a clear idea of what you want to achieve, these metrics can help you understand what level your operations will need to be performing at.

The most important success metrics for DevOps

Team/ practitioner surveys

This is an excellent practice to adopt right after deploying DevOps. Have your teams record how often checks and tests are performed, as well as how well processes are being followed. Are they adhering to your chosen DevOps practices? As time goes on, this data will help you highlight areas for improvement.

If you are investing in DevOps training for your teams, you can also use an LMS to observe the success of students. This could include the frequency teams and individuals are accessing training materials, how well students are scoring on exercises, and so on.

Naturally, you will want to make this data anonymous before reporting your findings or sharing them in a group. The goal is not to point fingers, but to help induce further behavioral changes in order to get as much as possible from the DevOps methodology.

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Frequency of deployment

Put more simply, how often do you deploy finished code? This is a fairly standard metric to track, albeit a crucial one. Any business that knows its way around coding will want to optimize the rate of successful deployments whilst also minimizing the time required.

As DevOps is fairly agile, your teams will start focusing on smaller, more incremental goals. You will likely see a considerable short-term increase in your deployment rate at first. While this will become steady, continuous optimization combined with the continuous integration of new tools and practices should see the rate continue to rise.

It is important to observe this metric throughout your DevOps pipeline. As well as the rate of deployment for finished code, you can also examine the frequency of completion for specific stages, such as taking a lead through to production, or getting a new product to an app store – any process that creates something of value for end-users.

The ideal rate of deployment will vary from project to project. However, you should see greater efficiency overall, and your bird’s eye view across multiple stages will make it easy to spot areas for improvement.


The importance of compliance in IT cannot be understated. With regulations like the GDPR, businesses must be able to demonstrate not only that they are meeting requirements, but also that they have a strict methodology in place – complete with metrics that demonstrate reliable and consistent success.

DevOps encourages frequent testing throughout project pipelines, not only for bugs but also for security flaws. This is particularly prevalent in DevSecOps, which integrates security considerations into the methodology on a deeper level. As checks become increasingly automated, they will not only be faster but also more reliable, while testing throughout the pipeline will help you identify where compliance-related errors are occurring most frequently.

It is worth keeping in mind that, in addition to compliance regulations, you should also ensure your operations are in line with your Service Level Agreement (SLA). This identifies the level of performance and accountability that end-users should be able to expect from you. This can also be covered by automated testing in a DevOps culture.

Application performance

While measuring application performance is fairly common for companies that rely on code, DevOps takes it to another level. More frequent testing encourages practitioners to take a closer look at whether an application is functioning as intended, especially after the point of deployment. For example, they may look at usage in different areas of an application, as well as the frequency and primary focus points of customer support tickets.

Above all else, DevOps practitioners will look for consistency in terms of usage and traffic. When serious drops occur, they will react as swiftly as possible. They will also look out for patterns, such as the positive spikes that should be occurring following the point of certain new releases.

Frequency of downtime

Downtime is sometimes unavoidable when it comes to maintenance and particularly large releases. Still, it is undoubtedly a huge source of dissatisfaction for end-users, and any excesses can be disastrous. The metric is also linked to ‘Failed Deployments’, when errant code leads to serious errors.

DevOps encourages the use of cloud software to help limit the number of unplanned downtimes. Its emphasis on frequent automated testing also reduces the prospect of disaster-causing flaws and vulnerabilities slipping through the net.

In addition to the number of instances when downtime takes place, it is also important to track the length. This will demonstrate how quickly your teams report, react to, and solve such issues. In particularly damaging cases, you will want to investigate exactly why problems were more prevalent. Remember, some failures are unavoidable, and treating each as a case study is essential for preventing recurrences.

Mean Time to Detection (MTTD) and Mean Time to Recovery (MTTR)

‘Errors’ indicate issues in development and production. The number can be measured in relation to the size of the code in question, as well as the point they occur most often within a DevOps pipeline. Remember, reducing the number of flaws is essential for improving your rate of successful deployments.

After implementing DevOps testing, tools, and management practices, you should see errors start to get flagged earlier on in each pipeline stage. The ‘Mean Time to Detection (MTTD)’ should decrease, along with the ‘Mean Time to Recovery (MTTR)’, the average time for testing and operations teams to report that a problem has been solved.

Remember, errors can have a significant impact not only on client experiences but also on the efficiency of an operation. MTTR is often measured in lost business hours, and a particularly large average can indicate a culture of finger-pointing. Not only does DevOps emphasize the need to integrate tools that can highlight flaws, but its focus on collaborative cultures also ensures that fixes can be applied as quickly as possible.

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