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As a tech leader or business owner, you understand that staying ahead of the competition requires more than just ambition — it demands data-driven decision-making. That's where DevOps tools come into play.

With just a little data, you can improve team performance, process efficiency, and customer satisfaction.

That’s not to say it’s easy. Implementing DevOps metrics has its challenges. It requires a strategic mindset, collaborative culture, and commitment to continuous improvement. However, the results are optimized workflows and processes on which you can build a solid foundation for scaling your business.

This article will explore key DevOps metrics and how to measure them to set relevant performance KPIs. We’ll then discover how to use these metrics to scale your business effectively.

The 5 Pillars of Process Maturity

DevOps combines and automates processes, practices, and tools used in software development and operations to increase the quality and speed of development life cycles.

Business process implementation has five key pillars, known as the Capability Maturity Model (CMM). This model focuses on five maturity levels for the implementation and continual improvement of service or product development into your business model.

The five pillars have many variations, but initial, repeatable, defined, capable, and efficient are the original stages. An alternative is culture, automation, measurement, sharing, and feedback. Each stage has the same concept; their labels have just evolved.

Here’s an example of the five pillars of process maturity with short descriptions for each:

A model showing what happens at each stage of maturity in DevOps
Five maturity model stages.

There are multiple metrics and frameworks that can be used to implement and measure the success of DevOps in your team. Let’s explore these in the section.

Principle-Based DevOps Frameworks

There are three main principle-based frameworks in DevOps. Principle-based refers to a practical approach with broad guidelines as opposed to a rules-based approach with prescriptive steps.

The Accelerate Framework

Authors Dr. Nicole Forsgren, Jez Humble, and Gene Kim explore what sets high-performing tech companies apart in their book, ‘Accelerate: The Science of Lean Software and DevOps: Building and Scaling High-Performing Technology Organizations.’

It is in this book that the Accelerate DevOps framework originated, which focuses on technical practices and management practices in DevOps teams for high-performing tech organizations. You can think of it as a study of the best practices of a DevOps team.

The three key areas of focus in technical practices are:

  • Continuous delivery: Continuous delivery includes areas like version control, continuous integration, deployment and test automation, and test data management. Although continuous delivery is a principle of its own, it is used as an umbrella under the Accelerate framework. The reason behind this approach is that DevOps teams performing at top levels are executing these elements simultaneously rather than in isolation from one another.
  • Architecture: High-performing DevOps teams were found to do most testing without the need for an integrated environment, deploy new applications independently of applications they depend on, and prioritize testing and deployability above the latest technology.
  • Product and process: Customer feedback is sought regularly throughout development, working in small batches, experimenting, and continually optimizing processes. This was found to deliver value, fix bugs quickly, and enable a customer feedback loop.

There are two key areas of focus in management practices. These are:

  • Lean management and monitoring: The Accelerate framework found that minimal approval processes improve software delivery performance than teams needing third-party approval. Monitoring capacities with work-in-progress limits and visual project management tools also improves performance.
  • Cultural: The framework puts a lot of emphasis on the importance of creating the right working environment for better DevOps performance. Collaboration and learning are two key ingredients to this, with a supportive and encouraging approach to teamwork. These teams should be supported by transformational leadership. People in this position tend to be motivational, intellectually stimulating, and recognize their team’s achievements.

‘Lead time for changes’ is a DevOps metric we’ll explore in more detail below. It measures how long a code change takes from a developer's commitment to being successfully deployed into production. Applying this metric within the Accelerate framework, you can measure and improve the speed and efficiency of your software delivery process, leading to faster and more reliable deployments.

The Three Ways Framework

Introduced in The Phoenix Project, authored by Gene Kim, Kevin Behr, and George Spafford, and appearing in The DevOps Handbook, the Three Ways framework improves DevOps performance by focusing on the principles of flow, feedback, and continuous learning.

Arrows showing how each of The Three Ways works
The Three Ways framework for improving DevOps performance.

Here’s a look at Three Ways framework:

  • The First Way: Flow Thinking:

The first way is to achieve an uninterrupted flow of work from development to operations and deliver value to customers quickly and reliably.

‘Lead time to production’ is a metric you can use here by measuring the time it takes for a code change to go from initial commit to being deployed into the production environment and aiming to reduce the lead time by optimizing the delivery pipeline, automating processes, and minimizing manual interventions.

  • The Second Way: Amplify Feedback Loops

The second way establishes fast and effective feedback loops throughout the software delivery process to enable continuous learning, improvement, and quality assurance.

Taking the ‘mean time to detect (MTTD)’ metric, you calculate the average time it takes to detect an issue or anomaly after a code change is deployed to production. Focus on reducing MTTD by improving monitoring, alerting systems, and feedback loops to quickly identify and address issues.

  • The Third Way: Culture of Continual Experimentation and Learning

The third and final way encourages a culture of continuous learning, experimentation, and risk-taking to drive innovation, adaptability, and organizational growth.

The ‘change failure rate’ metric is ideal to apply here. You track the percentage of code changes that result in failures or issues after deployment. Create a culture of experimentation by setting a safe environment for testing, learning from failures, and continuously improving processes to reduce the change failure rate.

The CALMS Framework

The CALMS framework embodies the principles of culture, automation, lean, measurement, and sharing. It is an ideal option for teams looking to transition to a DevOps approach to software development and eliminate development and operations teams working in silos.

You’ve already heard of the creator of the CALMS framework because he co-authored Accelerate and The DevOps Handbook, detailing the Accelerate and Three Ways frameworks, respectively. Jez Humble created the CALMS framework as a suitability assessment for whether companies are set up and ready to implement DevOps processes.

Similar to the CMM, but specifically for the administration of DevOps, you can use the CALMS framework to test if your business is ready.

These are the five pillars to follow:

  • Culture: Your development and operations teams have been working within their groups, each with their own processes and communication styles. You must nurture a sense of shared responsibility to ensure a DevOps-ready culture.
  • Automation: DevOps is about streamlining processes, speed, and efficiency. The key? Automation. Your teams need to prepare to automate manual tasks and procedures wherever possible. This step will support the continuous building, testing, deployment, and monitoring of software releases that comprise the DevOps lifecycle.
  • Lean: The principles of the lean methodology, a business and project management approach, are used to minimize waste and optimize value streams. You must keep working capacities realistic and visualize project statuses to speed up processes.
  • Measurement: Your company may be ready to roll out DevOps if you’ve got this far and your teams are committed to collecting and analyzing data to improve processes.
  • Sharing: In addition to a culture of shared responsibility, your teams need to be open and willing to share data and information, ensuring everyone aligns with the same shared goals and is not competing. This final assessment point ensures smooth hand-offs and fast growth.
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Challenges with DevOps Implementation

One of the biggest challenges with implementing DevOps is the resistance to culture change. You have two teams, each with their own processes, tools, and communication styles. Transforming them into one cohesive unit with shared goals, responsibilities, and metrics is a significant transformation. Maintaining high morale and a transparent approach to each step of the implementation process is vital to ensure smooth sailing. The cost of new tools and methods also needs to be considered.

If your team prefers following rules-based frameworks, it may be quite the shift to begin working with principle-based frameworks. It is fast-paced and often evolves with the team, so it can be complex to navigate for workers who have used traditional approaches for many years or new team members fitting into the dynamic.

Finally, the continuous flow of work can cause various vulnerabilities with software releases deployed without data encryptions or authentications and buffer overflows. This can put DevOps teams at risk of security breaches, so it’s essential to tighten processes around this while your DevOps team matures. This is also why implementation shouldn’t begin until the CALMS framework has been used to assess if you’re ready.

Why Are DevOps Metrics Important?

As discovered above, shared goals and metrics can be challenging for teams rolling out DevOps within a business. Without DevOps metrics, there would be no testing or measuring and no improvements to the development. It would just be iterations of the same software based on hunches and ideas, throwing it out into the abyss, and then trying something new. Without feedback or product success metrics to understand how something performs, there is no direction.

A DevOps metrics pie chart showing how a typical developer spends their time
Reasons why you need DevOps metrics.

For example, finance departments want to keep costs as low as possible, whereas developers want to keep performance as high as possible. These two goals may not align unless risks have been assessed and the teams understand each others’ targets.

Where your developers may choose to ship an incomplete product and deal with it later, the cost of this impact causes technical debt. Implementing DevOps can align the goals and strategies of both teams, and you can help your developers understand the cost impact of their technical decisions.

This is just one small part of DevOps and the importance of shared metrics.

Understanding DevOps Metrics

Implementing DevOps and understanding principle-based frameworks is one thing, but DevOps metrics are where you really start to see the benefits of this collaborative approach to the software development life cycle. Like most business strategies, there are endless metrics you could choose to measure for growth, depending on your industry, audience, and goals.

Google Cloud’s DevOps Research and Assessment (DORA) team is the longest-running research team of its kind. They originally found four key metrics to measure the performance of the ‘elite’ in DevOps. However, there has since been a fifth key metric added to the mix, and their cluster analysis only detects three levels of DevOps team: high, medium, and low, with ‘elite’ being a thing of the past.

The 4 Key DORA Metrics

Here’s a closer look at the four DORA metrics:

1. Deployment Frequency (DF)

DF measures how often you successfully release to production. This metric is about consistency and is an excellent indication of goal completion.

Teams under the ‘elite’ category would consistently deploy to production multiple times daily, whereas low-performing teams would be closer to once every six months.

Improving DF is as simple as releasing several minor updates. The main benefit of doing this is highlighting any process blockers, bottlenecks, or complex projects requiring attention. Larger teams may prefer deploying at regular intervals by building Agile release trains; this will help to remove the overwhelm of extreme pace and many people.

2. Lead Time for Changes (LTC)

LTC refers to the time it takes a commit to get into production. This metric is a good indicator of team responsiveness and agility as it measures how quickly they can act on user needs and demands.

The legacy ‘elite’ standard would aim for less than one day for LTC, whereas lower-performing teams could take more than six months. Performing at the lower end of the scale for LTC is likely due to inefficient processes.

You can improve this metric by improving automation processes, especially testing. By enhancing your continuous integration and continuous delivery (CI/CD) pipeline, you can get updates to production faster. However, one risk to watch out for here is sustainability. If your team cannot sustain this improved pace, you could face poor user experience and potential security vulnerabilities.

3. Change Failure Rate (CFR)

CFR is the percentage of deployments causing a failure in production. Failure could be downtime, rollbacks, or degraded service. This metric shows you the effectiveness of your team when deploying changes.

Elite performance benchmarks are 0-15%, where high, medium, and low performance all fall under 16-30%.

Improving CFR is about quality over quantity. Companies release varying numbers of changes and, therefore, have varying numbers of failures. However, the highest-quality modifications will result in fewer failures, whether they deploy changes twice a year or twice a day.

4. Mean Time to Recovery (MTTR)

MTTR is how long your team takes to restore service from a failure or disruption. Not only does this metric measure your team's agility, but it’s also a good gauge of the stability of your software.

If you’re aiming for an ‘elite’ level, you’d need to be looking at less than an hour for MTTR. Low-performing teams can take over six months.

You can boost MTTR by focusing on minor, quick releases, making failures easier to find and fix. You could also explore feature flags to give your team more control — especially if they’re experimental.

A table showing the DORA metrics split by speed and quality
The four DORA metrics include DF, LTC, CFR, and MTTR.

There’s one more metric to cover.

5. Reliability

Bonus metric number five is reliability. This metric was discovered later by the DORA team (2021) due to previously measuring availability as a benchmark for reliable software. However, it was decided that reliability better encompasses availability, latency, performance, and scalability. It is essentially the inclusion of measuring operational performance alongside development.

Other Notable DevOps Metrics

  • Cycle time: Cycle time is the total time from starting a task to final delivery. On the surface, it tracks the working speed of your team. However, you can dive deeper into this metric to find bottlenecks like long queue times and long-running pull requests.
  • Mean Time Between Failures (MTBF): MTBF measures the average time elapsed between system failures, downtime, or incidents. This metric evaluates the reliability and stability of your software systems, highlighting the effectiveness of your preventive maintenance and error mitigation strategies.
  • Mean Time to Detect (MTTD): This is the average time it takes for your team to acknowledge a failure. A low MTTD indicates efficient monitoring and alerting systems, allowing for quick incident response and resolution.
  • Time to Mitigate (TTM): The time taken to fix an issue once detected is the TTM. This metric helps assess incident response and resolution processes, indicating the efficiency of your teams in addressing and recovering from issues.
  • Change Lead Time (CLT): CLT provides a benchmark for the end-to-end change implementation process, including development, testing, review, and deployment. A shorter CLT signifies faster delivery cycles and increased agility.
  • Defect Escape Rate: The defect escape rate is how many bugs are missed during testing and released into production – how many ‘escaped.’ This metric is ideal for tracking if you want to improve testing and automation processes.

How to Set KPIs With DevOps Metrics

Now that you have a good understanding of the metrics you can use to implement and optimize DevOps in your business, you’lll want to explore key performance indicators (KPIs) to set some targets and benchmarks for your team.

Metrics vs. KPIs

You may be wondering what the difference between metrics and KPIs is. A metric is what you measure. It’s a quantifiable measurement that provides data about a specific performance aspect. Not all metrics are necessarily tied to specific goals or targets.

On the other hand, KPIs are a type of metric strategically selected and defined to reflect the most critical aspects of performance and progress toward strategic goals. KPIs are typically tied to objectives and often have associated targets, thresholds, or benchmarks that need to be achieved.

Setting KPIs for Your DevOps Team

The first step to setting KPIs for your DevOps team is linking DevOps metrics with your business strategy and goals. By prioritizing the most essential metrics to track, you can begin to establish targets and benchmarks for continuous improvement. Selecting the right metrics requires considering your company size, product, and market. Focus on what will provide the most actionable insights, avoiding the common pitfall of metric overload.

Monitoring your KPIs is just as important as setting them. Explore data visualization tools to provide your team real-time data on intuitive dashboards. This ensures complete visibility, provides accountability, and allows everyone to work together on shared goals, aligning development and operations teams and supporting the adoption and maturity of DevOps.

A table showing the benchmark KPIs for the DORA DevOps metrics
Benchmark KPIs for DORA DevOps metrics.

Taking the DORA core metrics, we can see the benchmarks for each metric based on the elite, high, medium, and low-performing teams. This table can help you set KPIs for your own business, depending on what you qualify as a priority.

When setting your KPIs, considering your team and business carefully without comparing them to other companies is best. If we look at CFR as an example, the benchmark is the same for high, medium, and low performance. This will depend heavily on your deployment frequency but can also affect it the other way around. If your team is spending all their time fixing failures, they spend less time developing and releasing updates. KPIs may also change as your DevOps team matures. Airtight automation and testing processes should automatically mean you can raise the bar on your targets.

How to Use DevOps Metrics to Scale

With the global DevOps market expected to hit $24.71 billion by 2027 (a compound annual growth rate of 22.9%, based on the $10.84 billion market size in 2023), take this as your sign if you’re looking for the best time to begin implementing it into your business.

DevOps not only accelerates development, reducing time-to-market, but it also improves collaboration by eliminating silos, enhances quality with continuous testing and feedback loops, and utilizes resources efficiently, which saves money. Finally, it is easily scalable, supporting business growth to new limits, with 83% of IT decision-makers accessing higher business value by implementing DevOps in 2021.

Performance Measurement and Continuous Improvement

Monitoring performance is the key to scaling a business that utilizes DevOps. It’s a fast-paced approach with a constant production workflow, so measuring and reporting should be just as frequent. DevOps metrics allow you to track the performance of your development and delivery processes. By quantifying key aspects such as deployment frequency, lead time, and change failure rate, you can identify bottlenecks, inefficiencies, and areas for improvement. This enables you to optimize workflows and processes often.

A loop showing how development and ops teams integrate responsibilities
Optimize workflows with DevOps.

Tracking metrics like change failure rate and mean time to recovery helps you identify trends that allow you to detect issues early. This proactive approach means you can take corrective measures and minimize the impact on customers, providing reliability – another vital ingredient for scaling at high quality.

Finally, using DevOps metrics will help to mature your DevOps team by opening a culture of continuous improvement. Feedback loops promote learning and growth; there must always be room for experimenting with different approaches.

Which DevOps Metrics Support Business Growth?

When setting KPIs, most metrics should be considered within your team and positioned for growth rather than compared with other businesses and groups at different maturity levels.

For example, a low LTC could show that your team is efficient, but if they cannot maintain the pace, it’s not sustainable and could eventually affect user experience. This metric should be measured over time rather than setting a KPI to match a high-performing, or even elite, mature DevOps team. Setting KPIs to reduce LTC month-on-month, quarterly, or yearly demonstrates growth within your team and business.

CFR is a valuable metric because not everyone has the same number of failures or issues, but by putting a percentage on this, you can measure how successful your deployments are. Your team may have very few failures if they release changes infrequently, but if each release is causing an issue, the CFR will be very high. If you follow CI/CD practices, you may see a higher number of failures, but if your CFR is low, you will have an advantage because you have speed and quality supporting growth. MTTR should also be measured over time to ensure steady growth.

DORA found that teams of all development performance levels saw better results when focusing on operational performance. This could mean setting KPIs for incident reports or open tickets, application uptime, and availability.

A high number of open tickets could represent an issue with your customer satisfaction, but aiming for a decrease over time reflects growth in this area. You could delve deeper and measure response and queue times to accelerate the improvement of this metric.

The one metric that combines both development and operations wholly as a DevOps team is the cycle time. This opens the floor for building a culture of feedback and growth. As other metrics improve and automation matures, you should expect cycle time to reduce. If your customer service is high and development failures low, you’ve found a winning combination to scale your business.

Embrace A Metrics-Driven Culture For Long-Term Success

We’ve discovered how data-driven decision-making can be implemented by tracking and measuring appropriate DevOps metrics. The correct approach can benefit team performance, process efficiency, and customer satisfaction.

While all DevOps frameworks revolve around culture, collaboration, and continuous improvement, identifying which framework and metrics apply to your growth strategy will support your business to scale most effectively.

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By Paulo Gardini Miguel

Paulo is the Director of Technology at the rapidly growing media tech company BWZ. Prior to that, he worked as a Software Engineering Manager and then Head Of Technology at Navegg, Latin America’s largest data marketplace, and as Full Stack Engineer at MapLink, which provides geolocation APIs as a service. Paulo draws insight from years of experience serving as an infrastructure architect, team leader, and product developer in rapidly scaling web environments. He’s driven to share his expertise with other technology leaders to help them build great teams, improve performance, optimize resources, and create foundations for scalability.