April 12, 2024

Database DevOps metrics: How DevOps teams can ditch change management delays

In the pursuit of bringing the best and brightest digital experiences to the market, modern organizations look to DevOps (philosophy, processes, teams, and tech) for its critical ability to measure, analyze, and improve every aspect of the application development process.

Yet, one foundational element of that process typically lags behind in this data-driven continuous-improvement approach: database change management.

The lack of DevOps observability metrics for creating and executing structural changes to the organization’s myriad data stores creates a problematic blind spot. This hinders database and DevOps teams' ability to understand, analyze, track, and improve the bottlenecked process in their otherwise rapid delivery cycle.

Without visibility and measurements on the database change process, teams not only miss opportunities to optimize this major cause of slowdowns in the release cycle but also operate at an increased risk of errors, deployment delays, and database rollbacks. These risks not only jeopardize the pace of delivery but also data integrity and performance of the final product. In environments where competitive edge is measured in seconds, such vulnerabilities can be pivotal – especially when database change SLAs are still measured in hours or days.

The DevOps team also has its hands tied when it comes to procedurally and technologically aligning database updates with the agile application development workflow, leaving the handoff of changes frustrating and inefficient.

It’s possible to unlock the full potential of the entire pipeline by removing the obstacles that appear when application releases require changes in the database structure. At the same time, procedural metadata around change management can be captured to inform a DevOps approach to improvement that pursues peak efficiency — and even unlocks the ability for DevOps, database, and application teams to spend less time on change management and instead focus more on value-driving innovations to data structures and movements.

What’s causing the visibility gap?

Application development pipelines are stacked with analytics, monitoring, and observability capabilities. DevOps engineers can pore over these, drawing out optimization insights and solving root causes to iterate better and better cycles.

Database change management remains veiled, with any visibility into workflow performance opaque at best. Why?

Outdated processes and legacy systems – managed manually

Databases serve a stateful purpose in the development and execution of digital experiences, so on one hand, it makes sense for systems to stick around long enough to be considered “legacy” – designed in a bygone era of software development, yet so foundational and critical, they’re resistant to procedural evolutions.

These systems often lack the integrations and interfaces necessary for the real-time tracking and analytics that modern DevOps practices depend on. While application code changes are meticulously and automatically tracked through version control systems, database changes frequently bypass these mechanisms, leading to a significant gap in control and visibility.

The database change process is instead manual, from scripting changes to reviewing and deploying them. This could be because of a prevailing fear that automating database changes could lead to catastrophic failures, data loss, or breaches. This fear often leads to conservative approaches to database change management, where visibility and automation get sacrificed for perceived control and safety.

This manual approach, often necessitated by the complexity and customization of database environments, introduces several critical challenges. It heightens the risk of human error, with manual scripting, reviewing, and deploying making the process prone to oversights that can lead to everything from minor glitches to major system outages. Such errors not only disrupt operations but can also undermine trust in the database management process.

It also slows down the DevOps pipeline, makes it harder to scale database management alongside organizational growth, and lacks the flexibility needed to accommodate new data store technologies. Tracking and auditing also remain overly complicated and tedious without providing the granularity needed for efficient and impactful remediations. Instead, piecing together the history and rationale behind changes becomes a detective mission through scattered communications and disjointed tech stacks, hindering speed, visibility, and other important elements like security and compliance.

How can DevOps metrics inform DCM optimizations?

When DevOps and database teams collaborate to bring more visibility and measurement to the change management workflow, they can do more than just monitor for major issues. With initial DevOps metrics setting a baseline, teams can then map out a road of process improvements to address the procedure's most inefficient or problematic elements. This database observability holds the key to solving the DCM-DevOps dilemma.

This can start with the key DORA (DevOps Research and Assessment) metrics:

Deployment frequency
Lead time for changes
Change failure rate
Time to restore service

These can offer a quantitative framework to assess change management workflow performance. Deployment frequency helps gauge the agility of the database change process, while change lead time indicates the efficiency from change initiation to deployment. The change failure rate reveals the reliability of changes, and mean time to recovery measures the resilience of the process in addressing failures.

Another metric DORA recommends is about reliability – the ability of an organization to keep its services operational and accessible to users – which is likely to vary in definition based on the organization, team, application, and pipeline being measured.

Applying these metrics allows DevOps teams to pinpoint bottlenecks and areas of risk within the database change management practice. For instance, a high change failure rate could indicate the need for more robust testing or review processes before changes are applied. Conversely, lengthy lead times might reveal overly cautious manual review processes that could benefit from automation to reduce delays and increase the pace of delivery.

Actually, the process won’t just benefit from automation. It needs to be automated – that’s a mission-critical directive if there’s any hope of bringing DevOps metrics to the database.

Automation + integration = continuous improvement

There is a winning formula for extending DevOps visibility and measurement to the database workflow. By automating database change management, integrating it into the CI/CD pipeline, and observing workflow analytics, DevOps and database teams unlock continuous improvement for DCM.

With automated change requests, scripts, reviews, and deployments, opportunities for human error and inconsistency essentially disappear. Change management becomes more reliable and efficient, allowing teams to focus on innovation rather than getting caught in the cycle of manual tasks, boosting productivity and job satisfaction.

Integrating DCM with the wider CI/CD pipeline ensures database changes are consistently tested and deployed, mirroring the application development cycle. This synchronized process provides real-time insights into database updates, allowing for swift adjustments and improvements before a change makes it to production.

The whole process then – once slow, manual, tedious, and outdated – can be guided by DevOps metrics like deployment frequency and change failure rates to inform strategic productivity and efficiency analysis.

Automating and integrating database change management enables a continuous cycle of refinement, enhancing not only the speed and reliability of database updates but also improving the overall agility of the development pipeline. Organizations that adopt this strategy can expect a more cohesive, efficient, and innovation-focused database environment, where change management is no longer a bottleneck but a catalyst for growth.