DevOps and its practices are highly admired across the software development industry for accelerating and streamlining the software delivery process. Though it’s a new kid, DevOps has proven its value by allowing engineers to build better product within less time.

It transforms the way development and operation departments interact with each other by bringing next level transparency in communication at the organization level. More than just driving the digital transformation to a company, it nurtures a culture of collaboration across the organization.

Well, DevOps enables businesses and IT service providers with a lot of benefits. But, the question is, how to ensure the success of DevOps implementation. This post will give you a list of important metrics and key performance indicators that are worth to track to ensure DevOps success as you strive to improve both user experience and efficiency.

8 Metrics and Key Performance Indicators to Judge Success of DevOps as a Service

#1. Deployment Frequency

Deployment frequency is how frequently new code, features, function, and capabilities are launched in the software. Companies can track deployments on a daily or weekly basis to improve the efficiency of deployments. It is good if you do smaller deployment as often as possible. Small sized deployments make it easy for developers to test and release the build.

With rapid deployments, the frequency metric remains stable or increases steadily. The sudden reduction in deployment frequency means desperation results. More deployments are better, but up to an extent. Extremely high frequency takes more time in deployment and increases the failure rate. It is preferable to hold off the deployments until the existing issues get resolved.

#2. Deployment Time

It is very important to know how long it will actually take to roll out the deployments. If the deployments are quick to implement, they can be released with greater frequency. Shorter deployment time is better, but make sure it doesn’t come at the cost of accuracy. Longer deployment time definitely requires a detailed investigation of the flow to know the causes of delayed and reduced deployment volume. Continuous increase in error rates can be occurred due to the extreme frequency of deployments.

#3. Deployment Failure

Also known as mean time failure, this metric enables businesses to learn about how often the deployment efforts fail. More frequent and quick deployments are great, but with the same frequency if the changes fail, then the result will be zero. Failed deployments result in loss of revenue, increased customer frustration and take the services down.

#4. Change Volume

Tracking and measuring the change volume, IT service provider can push the team towards high deployment frequency. Leverage the large deployment frequency by releasing small but valuable changes rapidly that are inconsequential to the market trend and user experience.

While considering frequent deployment, it is equally important to track the amount of change within the deployment needs for a meaningful comparison. Adopting DevOps as a service, deployment changes come often and in small pieces.

#5. Change Failure Rate

The number of expected and unplanned outages occurred in the release can be considered as change failure. The higher the change failure rate, the poor the application stability. It ultimately ends up by delivering negative user experiences and end-user outcomes. The lower change failure rate proves that the deployment occurred regularly and quickly.

#6. Lead Time

The metric allows to track how long it will take to deploy a change. This is one of the very important DevOps metrics that track the time an idea or task takes from inception to implementation. It is very important to get valuable insights into the efficiency of the end-to-end software development process. Moreover, it ensures the software’s capability whether it will be able to meet the users constantly emerging needs or not.

Short lead time suggests that feedback is addressed effectively and quickly. While long lead time indicates a harmful bottleneck.

#7. Error Rates

Every software product runs with more or less errors or defects. They can be detected during acceptance testing. And if these errors are discovered by the end users, then nothing can be worse than that. Errors are a natural part of any software development process so that it should be planned accordingly. The error rate is all about accepting the reality that the issues will occur and developers need to discover and resolve it as soon as possible.

The metric measured how often the errors have occurred during the development process and in the pre-development phase. This comparison provides valuable insights into software quality before it releases.

#8. Mean Time to Recovery

It helps in measuring an average time the development team takes to recover from a failure, bug, and issue. It helps judge the competence of your team in managing the process and handling the faults with ease. It depends on the ability and flexibility of the team in terms of tackling the unique issues that arise frequently.

Key implications of the mean time to recovery involves a change in the environment of operations, coding platform complexity, new features of the system and server upgrades. The metric analyzes the uptime and downtime of the app along with traffic volume in order to track app performance.

Closing Lines

While measuring the success of DevOps, do not focus on only one or two key performance indicators. Analyze the results generated by all the metrics to get a holistic view of how DevOps improves your software development life cycle by driving the technical and cultural shift.

Consider a leading IT service provider who has proven experience and expertise in offering DevOps as a Service. They promise to deliver the best software product by implementing industry best DevOps practices and considering all the above-mentioned metrics and key performance indicators.