9 Key Metrics for Businesses to Measure the Success of DevOps

Image Source: Cloud Google

As we look at today's applications, microservices and DevOps teams, we see leaders tasked with supporting complex applications using new technologies in systems distributed across multiple locations. Because of this, the way we measure and understand critical services and applications has also changed. Using DevOps metrics and KPIs is essential to ensure that your DevOps processes, pipelines and tools meet their intended goals. As with any IT or business project, you need to track key metrics.

Here are 9 key DevOps metrics and KPIs to help you achieve your goals.

Four DevOps Metrics: The Four Keys to DORA

Image Source: Cloud Google

Let's start with the 4 most common metrics established by Google's DevOps Research and Assessment (DORA) team, called the "Four Keys". Through six years of research, the DORA team has identified these four key metrics as indicators of DevOps team performance, ranking them from 'Low' to 'Elite', with elite teams more than twice as likely to meet or exceed their organizational performance. Let's take a closer look at how these DevOps KPIs can help your team perform better and deliver better codes.

1. Deployment Frequency

The deployment frequency measures how often teams successfully release to production environments.

As more organizations adopt continuous integration/continuous delivery (CI/CD), teams can release more frequently, usually multiple times per day. The high deployment frequency helps organizations deliver bug fixes, improvements and new features faster. It also means that developers receive valuable real-world feedback more quickly, which allows them to prioritize the fixes and new features that will have the greatest impact.

The frequency of deployments measures long-term and short-term efficiency. For example, by measuring deployment frequency daily or weekly, you can determine how efficiently your team is responding to process changes. Tracking deployment frequency over a longer period can indicate whether the speed of your deployments has improved over time. It can also indicate any bottlenecks or service delays that must be addressed.

2. Mean Lead Time for Changes

The lead time for changes is how long it takes a team to go from code committed to code successfully running in production.

Elite teams can complete this process in less than one day, while for low performers, this process can take anywhere between one and six months.

Since change lead time also considers cycle times, this metric helps you understand if your cycle time is efficient enough to handle a high volume of requests and prevent your team from becoming snowed under by requests or delivering poor user experiences.

Companies often experience longer lead times due to cultural processes like separate test teams, projects running with different test phases, shared test environments, complicated routes to live, etc., that have the potential to slow a team down.

The definition of lead time for change can also vary widely, which often creates confusion within the industry.

3. Change Failure Rate

Change Failure Rate measures the percentage of deployments that result in production failures that require repair or rollback.

Change failure rate looks at how many deployments are attempted and how many of these deployments result in failure when released to production. This metric measures the stability and efficiency of the DevOps process. To calculate the change failure rate, you need the total number of deployments and the ability to link them to bug-generated event reports, GitHub event tags, issue management systems, etc.

A change failure rate of more than 40% may indicate a flawed testing process, meaning the team will need to make unnecessary changes and thus be less efficient. The goal behind measuring change failure rates is to automate more of the DevOps process. Increased automation means more consistent and reliable software releases and a greater likelihood of success in production.

4. Time to Recovery

The Mean Time to Recovery (MTTR) service measures the time it takes for an organization to recover from a production failure.

In a world where 99.999% availability is the norm, measuring MTTR is a key practice to ensure resilience and stability. In an unplanned outage or service degradation, MTTR can help teams understand which response processes need improvement. To measure MTTR, you need to know when an incident has occurred and when it has been effectively resolved. To get a clearer picture, it's also helpful to know which deployments resolved the incident and to analyze user experience data to see if the service has recovered effectively.

For most systems, the optimal MTTR is likely less than an hour, while others have an MTTR of less than a day. Anything longer than a day may indicate poor alerting or monitoring and affect many systems.

To achieve fast MTTR metrics, deploy software in small increments to reduce risk and deploy automated monitoring solutions to pre-empt failures.

However, more than four DevOps metrics are required

The four keys to DORA provide a good foundation for improving the performance of your development practice, but they are only the beginning. Here are five more DevOps KPIs to help your team execute more optimally.

5. Defect escape rate

Defect escape rate measures the number of errors that "escape" the test and are released to the production environment.

This metric can help you determine the effectiveness of your test procedures and the overall quality of your software. A high defect escape rate indicates a process that needs improvement and more automation. In contrast, a low defect escape rate (preferably close to zero) indicates a well-run test program and high-quality software.

To understand this metric, you must track all released code and software defects. This means looking at development/QA and production defects to gain insight into any defects that have made it from development and QA into production. Generally, organizations should strive to find 90% of defects in QA before putting a release into production.

6. Mean Time to Detect (MTTD)

Mean Time to Detect (MTTD) measures the average time between the start of an event and its discovery.

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Among other DevOps metrics, this measurement helps determine the effectiveness of your monitoring and detection capabilities in supporting system reliability and availability. Measuring MTTD is influenced by other DevOps KPIs, including mean time to failure (MTTF) and mean time to recovery (MTTR). To calculate MTTD, add all incident detection times for a given team or project and divide by the total number of incidents.

The challenge with MTTD is knowing exactly when an IT incident starts, which requires the ability to analyze and evaluate historical infrastructure KPI data.

7. Percentage of Code Covered by Automated Tests

The percentage of code covered by automated tests measures the proportion of code that receives automated tests.

Automated testing usually indicates higher code stability, although manual testing can still play a role in software development. The goal is for automated tests to cover a higher percentage of the code. Although there will always be some damaged tests in good situations, the team must write codes to work as expected, not just to pass the tests.

8. Application Availability

Application availability measures the proportion of time an application is fully operational and accessible to meet the needs of end users.

High availability systems meet the five gold standard KPIs of 9 (99.999%). To measure application availability, ensure you can accurately measure the end-user experience, not just network statistics. While teams don't always expect downtime, they often plan for it for maintenance. Planned downtime makes communication between DevOps and SRE team members critical to resolving unforeseen failures and ensuring front and back ends run seamlessly.

9. Application Traffic Management (ATM)

Application usage and management monitor the number of users accessing your system and inform many other metrics, including system uptime.

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After deploying your software, you will want to know how many users are accessing your system and the number of transactions occurring to ensure that all is running properly.

For example, if your application gets too much traffic and usage, it could fail under constant pressure. Again, these metrics can be used for indirect feedback on deployments (new and existing). If usage or traffic drops, this may be feedback that end users are not accepting the changes you have made.

Having DevOps KPIs such as these application usages and traffic metrics allows you to see if there are issues and when there are unusual spikes in traffic or other unusual usage or traffic metrics. Similarly, you can monitor usage and traffic against microservices that support critical applications. So your DevOps team can use these metrics to ensure that the system is working as it should and take appropriate action, for example, reverting to a previous version to keep end users satisfied.

At The End

A successful DevOps practice requires teams to monitor a consistent and meaningful set of KPIs to ensure that processes, pipelines, and tools meet the desired goal of delivering better software faster.