10 essential database CI/CD metrics to gauge your team’s readiness for advanced DevOps automation (DB continuous delivery metrics)

(Updated April 10, 2024)

As your organization aims to complete the CI/CD pipeline – extending it to include database change management (DCM) – there’s a clear set of metrics that will help measure and optimize implementation and ongoing advancements. These metrics, inspired by Michael Bowler, an expert in Agile methodologies and continuous delivery, help an organization take a sobering look at the state of their deployment pipeline, then charge forth with reasonable goals to bring the pipeline’s productivity and efficiency into the modern age – and up to speed with application deployments. 

Making the organizational shift to database CI/CD is no light lift, even if everyone can agree it’s a long overdue and clearly beneficial change. As you plan and execute the implementation of database change management automation, governance, and observability, these metrics track progress along the way and guide any additional initiatives or targeted refinements. 

Don’t wait until your automated DCM process is already up and running. These are metrics you should start to measure now, to get a clear understanding of the true state of your database deployments. Yes, it might be difficult to track some of these down. That gets incredibly easier once automation and tracking is in place and database DevOps observability metrics feed right into your existing optimization dashboards. 

Analyzing these metrics now also informs your teams if they're ready for database change automation or if other, perhaps cultural or organizational, updates need to happen first. It’s critical to have the culture, buy-in, and skill sets in place to ensure the change management automation initiative gets off the ground and succeeds across the first, and then the rest, of the company’s data stores. 

Let’s get to it – here are the 10 CI/CD metrics to track before, during, and after your shift to an automated, governed, and observable database change management workflow.

1. Lead time to production (lead time for changes)

The clock starts ticking on this one as soon as enough is known about the work to begin. It ends when the work is live in production. Sounds simple, right? Familiar, of course. But there’s a catch. 

When teams are first starting on their database automation initiatives, lead time for changes might be so long – months, even – that it makes more sense to track just the cycle time. A cycle would be the time between when work starts and when the change is “done” by the team’s definition, whether or not that means it’s fully deployed and debugged. 

Getting valuable insight and reliable feedback during a months-long change process can be a big challenge, so teams might just measure cycle time in the interim and extend the measurement to a more true definition when they have a better handle on reliable, speedier deployments. Those speedier deployments with shorter lead times enable teams to respond quicker to market changes, customer demands, or new industry developments. It accelerates the feedback loop between development and deployment. 

Shorter lead times can also lead to more collaboration between developers, operations, and quality assurance teams, fostering a culture of continuous improvement and innovation.

2. Number of bugs (change failure rate)

The percentage of deployments causing a failure in production, such as bugs or system outages, is a critical measure of software quality and pipeline effectiveness. A lower change failure rate signifies a more stable and reliable delivery process, highlighting the success of your testing, quality assurance, and risk management practices.

Shipping buggy code is no good, especially when that code changes the very foundation of your data-driven business – its data stores. Document the number of bugs before taking on the automation initiative and try to get a handle on what that means for the business. Identify the problems that need to be solved, lest they worsen when put into the rapid-fire automated cycle. 

After all, if buggy code is bad – quickly and continuously delivery buggy code is even worse. If there are quality issues with the code, then continuous delivery is only going to get more bugs out into the world faster, causing more headaches later on. Ideally, the solution reduces bugs and kicks off continuous feedback regarding them, too, for a continually decreasing frequency and severity. 

Your team can decide how nuanced this measurement becomes. You can categorize and segment bugs based on severity, type, or business impact. This can help prioritize fixes and focus efforts on bugs that significantly affect functionality, security, or user experience. You could also categorize by root cause, if you can dig into the details to find out how they originate. 

3. Defect resolution time (time to restore service)

Measuring how long it takes to recover from a failure in production is essential for assessing your team's responsiveness and operational resilience. Faster recovery times demonstrate a high level of preparedness and capability in incident management, crucial for maintaining trust with your users and minimizing the impact of disruptions.

Again, quality code is a prerequisite to successful database CI/CD. One way to measure quality, along with the team’s commitment to quality, is to track the life of bugs. Is your team already high performing, squashing bugs as fast as they appear? Or is your team saddled with bug lists that stretch on for pages, in which the oldest is measured in years? You need to know the caliber of the challenge ahead of you and how buggy the current process is before you add fuel to the flames with CI/CD automation.  

Understanding defect resolution time isn’t just about tracking how long it takes to fix a bug, though. It's about assessing the overall health and efficiency of your change management pipeline, including developers, database administrators, DevOps teams, and cross-functional managers. A shorter defect resolution time indicates a well-oiled machine in which teams can quickly identify, prioritize, and rectify issues without significantly impacting the application experiences or reliability. 

Monitoring and improving time to restore service encourages a culture of accountability and continuous improvement. By setting benchmarks and goals for defect resolution, teams are motivated to not only address issues quickly but also to proactively identify potential areas of improvement in the development process that can prevent problems in the first place. This proactive stance towards quality assurance can lead to more stable releases, fewer disruptions in service, and a better overall user experience.

Incorporating tools and practices such as automated testing, continuous integration, and effective incident management processes can significantly aid in reducing the time to restore service. Automated testing platforms can ensure that new database code changes don’t introduce issues, continuous integration allows for quicker issue identification by integrating code changes more frequently, and workflow visibility and monitoring ensures that when bugs do slip through, they are addressed as efficiently as possible.

To truly leverage defect resolution time as a metric for continuous improvement, it's essential to delve into the data behind the numbers. Analyzing patterns in defect origins, types, and resolution processes can unveil insights into systemic issues within the database CI/CD pipeline or specific areas where additional training or resources may be required. For instance, a recurring type of defect might point to a need for better coding practices in a particular area, while consistently slow resolution times for certain kinds of bugs might highlight bottlenecks in the testing or deployment processes.

4. Regression test duration and other automated test coverage

Is it reasonable and feasible to run a regression test before any and every production deployment? Tracking the time it takes to complete a full regression test lets you know where the bar is currently set. For teams with manual testing practices, this metric will be measured in weeks or months. 

Automated testing can take that down to a “minutes or hours” type of measurement. When regression testing is easier, faster, and more accessible, teams can run these tests within the automated pipeline before production, ensuring a smaller rate of failed deployments. This, in turn, supports the overall success of the database CI/CD automation initiative by removing barriers to smooth operations.  

The extent to which automated tests cover your codebase is an important metric for understanding the safety net available for catching bugs before they reach production. Higher test coverage indicates a lower risk of defects in deployed applications, contributing to higher quality and stability. Automated tests serve as a guardrail, ensuring that new code commits, schema updates, or data migration scripts don’t introduce unexpected behaviors or degrade the performance of the database. 

The automated testing approach embodies the principles of shift-left testing, where testing is performed earlier in the software development life cycle. This approach allows for the early detection of defects, reducing the cost and effort required to resolve issues that might have compounded over time. They provide immediate feedback to developers, enabling quick corrections that adhere to the continuous delivery model’s demand for speed and efficiency.

It also supports continuous optimization. Encouraging collaboration between developers, QA engineers, and database administrators (DBAs) in creating and maintaining regression test processes can lead to more robust and comprehensive knowledge sharing about common failure points, critical database functionalities, and pipeline analytics so they more accurately reflect real-world scenarios and solutions.

5. Broken build time

Everyone makes mistakes, it’s true, even on the most advanced application, database, and data teams. But this metric asks, “How important is it to the teams to get that build fixed?” It’s another way to understand the teams’ tolerance for errors and commitment to quality. If these teams live with broken builds for days at a time, they’re likely not in a mature enough DevOps posture to take on advanced automation, governance, and observability tools. 

The duration a broken build remains unresolved directly impacts the team's ability to deliver consistent, quality updates to users. A prolonged broken build time not only signifies a bottleneck in the development process but also reflects a potential undercurrent of complacency towards errors. In contrast, a quick turnaround in fixing broken builds exemplifies a robust DevOps culture that prioritizes agility, quality, and continuous improvement.

Quickly fixing broken builds is essential for keeping a team’s innovation and adaptability at peak performance. Swift action ensures teams can respond rapidly to changes and challenges, maintaining the momentum demanded by application and data pipelines. It also builds confidence among collaborators, from product owners and developers to end-users, by showing a commitment to delivering and maintaining a reliable product. 

To improve how teams handle broken builds, adopting comprehensive testing strategies and continuous monitoring helps spot potential issues early. Establishing clear communication channels ensures that the right team members know about broken builds as soon as they happen, allowing for immediate action. Empowering developers to take charge of fixing broken builds promotes a proactive approach. This empowerment helps maintain code quality and pipeline integrity, reinforcing a culture of operational excellence fundamental to successful DevOps practices.

6. Number of code branches and version control

CI/CD advocates for main trunk development so that everything is maintained in the same pipeline. Tracking the number of branches you have to the code now, and continuing to track this number as it fluctuates over time, gives indication of team preparedness to begin continuous delivery. 

But even before measuring branches, teams need to ensure their code is in database version control so it can be managed and tracked properly. These systems are foundational for tracking changes, managing code branches effectively, and ensuring that every piece of code is accounted for and can be seamlessly integrated into the main development pipeline. Ensuring code is under version control is the first step towards achieving a mature and efficient CI/CD environment, setting the stage for successful continuous integration and delivery.

7. Production downtime during deployment

While applications like batch processes won’t need zero-downtime deployments, some absolutely do, like web apps. And there’s a cost to that downtime, sometimes thousands of dollars per minute and hundreds of thousands per hour. Any customer-facing or revenue-producing application incurs a cost when it goes down and is unavailable, so for those, the goal should be zero downtime during deployments.

Once you achieve zero-downtime deployments with seamless, automated, governed, and observable database change management, there’s no need to continue measuring this (it will just be zero).

8. Deployment frequency

This metric tracks how often your organization successfully releases to production. High deployment frequency is a cornerstone of a mature CI/CD pipeline, indicating that your team is capable of delivering new features, fixes, and updates to customers quickly and efficiently. It underscores the agility of your database change management process and your ability to respond to market changes.

Prior to automation, though, your deployment frequency is probably a couple of times a week, maximum. Many organizations are stuck in biweekly or monthly database deployment cadences while application updates tick off daily. For the database workflow to operate nearly as quickly, teams need to embrace a blend of automation, strategy, and cultural shift. Implementing specialized automation tools for database CI/CD is crucial. 

These tools streamline testing and deployment, approaching the agility seen in application development. Adopting a database DevOps platform integrates database changes seamlessly into the broader CI/CD pipeline, fostering a cohesive workflow between database and application updates. This strategic alignment ensures database changes complement application innovations, facilitating a synchronized, agile release process that meets business objectives and maintains a competitive edge.

9. Impact on application performance, feature usage, and adoption rate

Observing the impact of changes on application performance post-deployment can provide insights into the efficiency and effectiveness of the CI/CD pipeline. Metrics such as response times, error rates, and user satisfaction scores can help identify whether recent changes have positively or negatively affected the application, guiding future optimizations.

Beyond performance, understanding how swiftly and effectively users adopt new features post-deployment shines a light on the alignment between development efforts (including database changes) and user expectations. This metric is crucial for validating the entire development process, ensuring features meet user needs, driving continuous feedback, and refining the CI/CD pipeline even at the database, which might not be typically considered in this light. High adoption rates of new features signal not only a successful deployment but also a strong connection with user demands, underscoring the importance of a user-centric approach in application and database development.

10. Frequency of database rollbacks

Monitoring how often deployments are rolled back due to issues offers a window into the reliability of the DCM process. A low rollback rate indicates a high success rate of deployments on the first attempt, showcasing the effectiveness of your testing and quality assurance strategies. It reflects the maturity of your database change process and your team's ability to implement with minimal disruption. 

Frequent rollbacks, however, may signal the need for improvements in the pre-deployment testing phase, a deeper integration of automated testing, or a reevaluation of the deployment strategies. Successfully reducing the frequency of rollbacks enhances pipeline reliability, boosts team confidence, and ensures a smoother, more predictable release process, aligning closely with the goals of continuous improvement and efficiency that are central to CI/CD philosophy.

Once your teams have rallied around these metrics and painted a clear picture of their readiness for database change management automation, Liquibase is at the ready with database CI/CD tools that help your organization deploy faster and safer.