Key DevOps Metrics to Assess Efficiency and Quality Improvement

Micro Focus Contributor
Micro Focus Contributor
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chicago-fast-light-streaks-2622848 (1).jpgIn today’s competitive world, businesses look for more agile and faster ways to develop and deploy applications to meet the rapid pace of changing customer demands. Enterprises employing traditional methods of software delivery struggle to keep up with the changing ecosystem, and are looking for new techniques and practices to stay relevant. DevOps is the new IT frontier that aims at overcoming the complexities of traditional methods of software development and help businesses speed up the delivery.

Organizations are adopting DevOps to meet their critical business goals of higher efficiency and superior quality by ensuring:

  • Instant and continuous release of new features
  • Ability to incorporate changes faster
  • Stable operating environment
  • Reduced change management complexity
  • Improved team collaboration
  • Higher productivity and innovation

Measuring DevOps Improvement: Key DevOps Metrics

You can’t improve what you can’t measure, and therefore, it is crucial to measure business improvements post DevOps implementation. A data-driven approach with standard metrics is vital to ensure DevOps success. Continuous KPI measurement and tracking help businesses take important decisions to improve processes and practices that ultimately enhance efficiency and quality.

Data-driven metrics focusing on two primary aspects of DevOps journey: efficiency and quality help enterprises gauge the improvement levels with better clarity. Efficiency metrics determine the time it takes to launch new features or update changes, while quality metrics reveals the extent of errors or failures in every iteration. 

Let’s deep dive into the key DevOps metrics that are worth tracking for enterprises that strive to improve efficiency and overall quality:

Deployment Frequency 

To continuously improve efficiency, it is crucial for businesses to track how often a new feature or an updated product version is released to the end-customers. Enterprises measure deployment frequency on a daily, weekly, or monthly basis. In an ideal scenario, this metric remains stable or witness a steady upward trend over time. A sudden decrease in this metric helps enterprises proactively identify workflow bottlenecks and initiate faster resolutions.

This metric resonates with continuous improvement, the ultimate essence of DevOps. Frequent deployments imply continuous enhancements in products and services to keep them updated and competitive. Frequent updates also indicate that the IT team is highly responsive to business requirements and customer demands. However, businesses need to keep a check in case the metric is too high as it might be the resultant of a higher failure rate due to deploying defective build into production.

Deployment Speed

Enterprises deploy builds at a higher frequency if they are quick to implement. Therefore, tracking how long it takes to roll out single deployment once it’s approved for production becomes critical for any business. High performing organizations take around an hour for a typical deployment and experience higher consistency across the DevOps pipeline. Short deployment time is imperative for business but not at the cost of accuracy. Higher error rate indicates highly quick and haphazard deployments.  On the other hand, a drastic increase in deployment time coupled with reduced deployment volume demands a further audit.

Tracking this metric helps IT teams identify problems that take up their maximum time and subsequently choose tools that automate tasks to eliminate manual, error-prone jobs, and improve efficiency. Automation helps in improving deployment speed by reducing unnecessary errors and rework while employing additional time to develop more value-added services.

Lead Time for Changes 

Change lead time determines how long it takes for a change or new feature to go from the initialization of the development cycle to deployment and production. Businesses track this metric to gain valuable insights into development process efficiency, code and development systems complexity, and the team’s capabilities to meet the evolving customer demands.

While short lead times imply immediate feedback incorporation, long change lead time indicates an inefficient deployment system with severe performance bottlenecks hampering change implementations. Tracking this metric helps teams to discover patterns that indicate prevailing complexity at various stages of the delivery pipeline.

Change Failure Rate 

One of the primary goals of DevOps is to ensure frequent deployment with the least failure rate. The change failure rate metric measures the percentage of releases that results in some failures such as unexpected outages, hard performance degradations, or other unplanned failures.

Enterprises aim to have a low change failure rate to ensure quick and frequent deployments. Conversely, a high failure rate indicates unstable DevOps process and results in a negative end-user experience. Organizations with more mature DevOps practices and processes ensure sufficient pre-deployment test coverage and application stability during the production stage to curb the failure rate. It is crucial for enterprises to closely track this metric as failed deployments may lead to service downtimes causing revenue loss and frustrated customers.

Mean Time to Detect (MTTD) 

Maintaining the low change failure rate is significant for businesses, but what differentiates a mature DevOps process from a relatively nascent one is the ability to quickly detect failures. MTTD measures the amount of time between the point when an issue occurs and the moment when the team discovers the problem. This metric helps to ensure faster response to failures to avoid customer frustration and loss of business value.

High MTTD further escalates the failure rate, as the bottlenecks causing current issues propagates and interrupts the entire DevOps workflow. Quick error detection is ideal for security failures to minimize an attack’s reach and avoid any data loss.

Mean Time to Recovery (MTTR) 

Faster error detection should be followed up with even prompt recovery to minimize application downtime and customer frustration. MTTR is an essential quality metric that measures the average time it takes to restore a system or service to its original state from the moment an error is identified. Failures are inevitable; however, resilient applications and team’s capability have a crucial role to play in developing a fix and rolling out to production.

Teams strive to improve MTTR since it serves as the barometer for multiple aspects. It highlights the agility level of an organization by tracking how fast it can respond to changes pertaining to process, technology, and people. Shorter recovery time also suggests the efficiency of the feedback loop between the customers, operations, and developers.

Conclusion: Start Data-driven DevOps Journey

The overall objective of DevOps is to generate value for the business by continuously delivering products and services to satisfy customer demands. Businesses measure value in terms of economic benefit and therefore, data-driven metrics form an essential part to assess the value added by DevOps.

It is important for organizations to continuously track the key metrics highlighted above to ascertain what is working for them and identify gaps for improvement. In addition to helping enterprises realize the impact of several DevOps benefits, measuring KPIs drives improvement in application development, deployment, and production. Every business has different goals and requires specific metrics to measure the success rate of its DevOps journey, but the key is to start measuring results from the very first day of DevOps implementation.

 

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