metrics and KPIs in DevOps: how to evaluate your DevOps efforts (and why it matters)

The DevOps methodology has increased the success of IT organizations, with 99% of companies recording significant growth after its implementation. Despite being a relatively new methodology, tech giants like IBM, Microsoft, and Atlassian have adopted DevOps practices across the board.

But the DevOps strategy is not a plug-and-play approach that guarantees better and faster deliverables; instead, you need to track the metrics in order to streamline operations and deliver successful products.

Companies also need to measure KPIs to understand the ROI, as DevOps salaries are quite high and they need to make sure the efforts of those employed actually improve development and operations.

In this article, we’ll cover DevOps KPI measurement for teams, as well as explore the essential metrics that managers can track without being part of the process.

Tracking DevOps metrics gives control over the workflow and helps managers run processes and assign tasks efficiently. Measuring performance indicators will also help understand the impact of individual engineers and interdependent teams within the product development pipeline.

Moreover, you will be able to scale operations while maintaining optimum customer satisfaction across the board. These DevOps KPIs will also give you a better estimation of the expected ROI on every project so that you will know where to cut costs and where to spend more.

However, despite the success rate of DevOps, only 74% of organizations have a system for tracking and measuring key DevOps metrics and KPIs. The rest have yet to adopt a clear strategy to measure any given KPI for a DevOps team.

How to measure DevOps metrics

Sometimes, the metrics for success become evident in the way customers appreciate your products. If the feedback for the solution is positive, then the customer satisfaction metric is hitting the right benchmarks.

Also, if your stakeholders and C-level executives are happy with the results of the DevOps practices in place, then you are doing something right.

However, this human-centric feedback mechanism has a few drawbacks: humans lie, but numbers don’t. To eliminate potential bias, you need to adopt a data-driven model to analyze the performance of your DevOps strategy within the production pipeline.

Engineering managers can track a DevOps KPI and metrics using advanced tools and automation. These tools will help your organization gather crucial information about deployment speed, application performance, and other essential DevOps key metrics.

In the current digital age, businesses have unlimited access to data from which they can synthesize DevOps key metrics. Most of these performance indicators focus on efficiency and productivity, while the rest focus on successes, failures, and customer satisfaction.

Here are the core DevOps KPI examples you should track for your organization.

Deployment speed

The time it takes for you to start a project from scratch and release it accounts for the deployment speed (deployment time). This metric is important because it determines the efficiency of your DevOps methodology.

As a result, engineering managers should try to boost the deployment speed without compromising the quality of the product’s final version. You should also pay attention to uncharacteristic spikes in deployment speed, as they can point to further problems down the pipeline.

Deployment frequency

Most people confuse deployment frequency with deployment speed, but these metrics differ. While development speed measures the average completion time for projects, deployment frequency measures how often the DevOps team deploys components.

This KPI is important for business success because it shows the team’s capability to deploy stable features and components — mostly at weekly or daily intervals. So, the DevOps team should focus on increasing the deployment frequency while keeping changes to a minimum.

Deployment success rate

In the race to increase deployment speed and frequency, some engineering managers and CTOs often abandon successful practices. Eventually, this negligence comes back to haunt the company in the form of deployment failures and software flaws.

To avoid this problem, managers should track the deployment success rate to determine how many of their deployments are successful or result in failure.

But first, the manager must establish the criteria for success. For instance, the rollback procedure could be deemed successful even though it means the deployment didn’t go through. Alternatively, the deployment could be deemed unsuccessful since a variable (rollback procedure) is hindering progress.

Cycle time

Cycle time tracks the amount of time the team spends from pushing a commit to deploying it into production. This metric tells team managers how to improve productivity in the development cycle.

By reducing the cycle time for individual projects, managers can align their teams’ goals with the stakeholders' expectations.

Lead time (for changes)

Lead time for changes is a velocity metric, measuring the time that elapses committing new code (from initiating a commit to getting it into production) when it is already in a deployed state.

This metric relies on automation to determine how long it takes a DevOps team to implement new changes, especially for products that require constant tweaks to meet consumer needs. Long lead times often signify frustrating bottlenecks, while short lead times signify efficient delivery and fast-paced innovation.

In high-performing teams, the average lead time never exceeds a few hours. Conversely, medium and low-performing teams measure the lead time in days, weeks, or months. Regardless of your team’s performance level, you should aim to decrease the lead time as much as possible.

Use Azure’s Lead Time and Cycle Time widgets to track both of these essential metrics.

Change volume

This metric tracks the number of changes the codebase undergoes before deployment. By tracking the change volume, you can keep an eye on the overall progress of the development process.

High change volumes signify that your engineers are making frequent errors — or that the process is still in the initial stages. Subsequently, the change volume should tail off as you break down the release into small sets.

Change failure rate (CFR)

The change failure rate shows the number of changes that failed to meet expectations. This metric is black-and-white because it relies on established outcomes to determine if the process is a success or failure.

For instance, a change failure in the software development pipeline could occur when the application delivers a different output than what is expected.

To calculate your project’s change failure rate, divide the problematic deployments by the total number of deployments. If the resulting figure is in the 0-15 percentile, then your engineers are elite performers.

Failed deployment rate

Unlike the deployment success rate, the failed deployment rate only tracks failures during the deployment stage. As the manager or product owner, you should aim to reduce the failed deployment rate as much as possible.

Defect escape rate

This metric reveals how frequently engineers push buggy code into production. The defect escape rate shows managers the effectiveness of their testing and debugging processes.

When combined with the change failure rate, your team will get actionable insights to help improve the accuracy of QA processes. As a result, you will be able to spot vulnerabilities and errors before they slip further into the production pipeline.

Defect volume

Like the defect escape rate, the defect volume focuses on issues resulting from substandard QA processes. However, the defect volume measures the actual number of errors and bugs rather than the frequency at which they occur.

Of course, bugs are integral to software development processes, but you need to keep the defect volume minimal.

Mean time to recovery (MTTR)

This metric refers to the average duration of any effort to fix an issue during the software development lifecycle. Factors that determine the mean time to recovery include:

• The speed of identifying the failure

• The complexity of the issue

• The time it takes to roll back changes

• The time it takes to return operations to normal

If your mean time to recovery (MTTR) is less than an hour, your team is high-performing. But if you take days or weeks to fix an error, then you should work on improving your performance.

Mean time to failure (MTTF)

This metric tracks the time it takes from the last instance of the code functioning properly to the first detection of the issue.

A high MTTF indicates a lack of rigorous testing practices in the production pipeline. Sometimes, the MTTF might be high because of poor-quality engineers. Either way, keep an eye on this metric to ensure it stays low.

Mean time to detection (MTTD)

This metric outlines your DevOps team’s ability to detect problems in the development pipeline. The mean time to detection (MTTD) is similar to the mean time to failure in the way both KPIs focus on problems and errors.

But the key difference is that the MTTD highlights the effectiveness of your monitoring mechanisms and testing practices in detecting issues before they cause your product to fail.

With that in mind, you should always try to improve testing time using the proper testing and monitoring tools to keep your MTTD low.

Mean time between failure (MTBF)

As the name suggests, this KPI measures the average time that elapses between software failures. In essence, the mean time between failure (MTBF) determines the stability of components in production.

You can calculate the mean time between failure by dividing the total uptime by the number of failures within that period. If the MTBF is high, then the component is relatively stable. But if the MTBF is too low, you need to revamp the component’s core architecture.

Unplanned work rate (and volume)

This metric tracks all miscellaneous expenses in time and resources outside the project's preliminary budget. Unplanned work can take the form of process optimization and unprecedented changes to the codebase.

If the unplanned work rate and volume continue to increase, your managers should create better plans for scheduling and reshuffling tasks in order to mitigate these extra expenses.

Repository speed

The repository speed metric tracks the time from submission to merge of GitHub pull requests over the previous 30-day period.

If you can’t track old pull requests, the repository speed will decrease significantly., especially when handling multiple repositories at once. To avoid this issue, managers should encourage DevOps engineers to highlight old pull requests for review. This technique will boost your repository speed.

Application performance

Managers can evaluate an application to determine its performance in real-life scenarios and how it meets users’ demands without minimal downtimes. Before deployment, the testing team will conduct test runs to determine how the application handles high server demands.

Once the software infrastructure meets these performance benchmarks, the team can deploy it. But if the application’s performance continues decreasing before it gets to deployment, the team needs to roll back changes and fix these issues immediately.

Application availability

As the name indicates, this metric measures the system’s availability in terms of uptimes and downtimes. In DevOps, managers and QA specialists must ensure the version of the application going into deployment will stay online frequently.

If your DevOps services are top-notch, your application will stay online most of the time. Conversely — with sub-optimal DevOps practices in place — you will struggle to keep your application online due to frequent downtimes.

Customer ticket volume

This performance indicator highlights the frequency at which customers create tickets to address problems with the product.

Your DevOps team should keep the customer ticket volume to an acceptable minimum. A low customer ticket volume means that:

• The app delivers a top-notch end user experience.

• Your testing procedures are effective in detecting bugs.

• Your DevOps engineers and QA specialists are highly skilled.

If the ticket volume for a particular issue is uncharacteristically high, address the problem as fast as possible to avoid losing users.

Response time

Your product’s response time measures the number of seconds or minutes it takes for the application to respond to a request. This metric shows your system’s speed in handling common requests. It also shows your system’s capability to handle increasing workloads.

If your system has a high response time, it will take longer to process user requests, affecting the overall user experience.

Gathering DevOps business metrics is one thing, but applying them to gain tangible results and benefits is an entirely different ball game.

According to Atlassian, 85% of organizations have faced DevOps implementation barriers stemming from untrained staff and outdated technology. So, let’s share tips to help you implement DevOps KPIs and metrics.

1. Train your engineers, managers, and QA specialists to know what to track as well as how what’s being tracked affects the product development pipeline.
2. Modernize legacy infrastructure to cope with the rigors of modern DevOps strategies. If you want to improve repository speed, you need to embrace the transformation of the existing architecture to handle the massive workloads now and in the future.
3. Use automation such as Docker, Selenium, Zabbix, Kubernetes, Splunk, Git, Ansible, Jenkins, CircleCI, AWS ECR, Azure DevOps, Cloud Foundry, and Bamboo to improve testing, monitoring, and development results.
4. Hire DevOps engineers with hands-on industry experience to work on your project. Having the right people on board will help you shift to an agile approach. Project managers can also use Scrum and Kanban methodologies to track and measure results.
5. Change the corporate culture to accommodate modern DevOps methodologies. Values like collaboration, transparency, trust, and empathy are essential for the success of your DevOps initiative.
6. Implement user feedback to back up the data findings. You should also establish an internal feedback loop between teams to ensure everyone is on the same page at every stage of the project.
7. Use the information to improve infrastructure security. If your application experiences numerous downtimes or has several vulnerabilities, tracking the application performance and availability will help you spot things requiring change.

The DevOps approach presents a lot of issues that require the attention of seasoned engineers and competent managers. By tracking the right metrics, you can improve the quality of the product while reducing the average time to deployment, thereby saving operational costs. Use DevOps automation tools to track the right metrics as well as improve the overall quality of the insights obtained from massive data repositories.