CTO.ai insights offer a comprehensive analytics DORA metrics dashboard that helps engineering teams better to understand their build, test, and deployment processes by providing detailed metrics and insights; CTO.ai insights enable teams to identify bottlenecks, optimize performance, and improve their overall software delivery pipeline.
Measuring deployment performance is a key component in helping teams identify the gaps in their delivery processes so they can adapt and improve their releases in an environment. Deployment frequency refers to the number of times a team or organization deploy, on demand, new software versions, updates, or features into a production environment within a specific period (e.g., per day, week, or month). Elite teams are expected to make multiple deployments per day which means they can perform multiple releases daily, allowing them to solve bugs or launch new features as soon as the end users request them.
This metric indicates the team's ability to deliver value to customers and stakeholders quickly and efficiently. Here’s how CTO.ai insights can improve your workflow:
- Faster time to market: A higher deployment frequency means that new features, bug fixes, and improvements are delivered to customers more quickly, providing a competitive advantage.
- Incremental changes: Higher deployment frequency usually correlates with smaller, more incremental changes, which are easier to manage and troubleshoot. This reduces the risk of errors and failures during deployment.
- Continuous improvement: Regular deployments encourage a culture of continuous improvement, as teams are more likely to learn from mistakes and iterate on their processes to achieve better results over time. With this, you can identify the blockers your team is facing during the execution of their deployments and create a workaround plan for it.
- Enhanced feedback loop: Frequent deployments help teams receive feedback from users more quickly, enabling them to make data-driven decisions and prioritize the most valuable features and improvements.
Check out this video to learn more about Deployment Frequency
Lead Time for Changes
Lead Time for Changes refers to the time it takes for a code commit to being deployed into production. In other words, it measures the duration between a change to the codebase (e.g., a new feature, bug fix, or improvement) and when that change is successfully running in the production environment. Elite teams must take less than one hour to deliver committed code to a production environment running successfully. This metric is important for several reasons:
- Speed of delivery: A shorter LTC indicates a faster and more efficient software development process, showing that the team can quickly deliver new features, improvements, and bug fixes to end users.
- Agile development: A shorter LTC supports the principles of Agile software development, which emphasizes the ability to respond quickly to customer needs and market changes.
- Competitive advantage: Companies with a shorter LTC are better positioned to innovate and adapt to changing market conditions, giving them a competitive advantage over organizations with longer lead times.
- Quality and reliability: Tracking LTC can help identify bottlenecks and inefficiencies in the development and deployment process, enabling teams to optimize their workflows, improve quality, and reduce the likelihood of production issues.
Lead Time for Changes is useful for identifying gaps in the delivery process that could affect your team's ability to deliver the code efficiently.
Check out this video to Lead Time for Changes on how you can evaluate your resource velocity.
Mean Time to Recover(MTTR)
Mean Time to Recover (MTTR), also otherwise known as Time to Restore Service, is a key performance metric used in various industries, particularly in IT and engineering, to evaluate the efficiency and effectiveness of a system or service. It measures the average time to restore a system, service, or component to full functionality after a failure, downtime, or disruption. MTTR is typically expressed in minutes or hours, depending on the context. Elite teams must take less than one hour to get their resources back to business. MTTR is calculated by dividing the total time spent on recovery during a specific period by the number of incidents or failures that occurred during that period.
MTTR is a critical metric for engineering teams, as it directly impacts system reliability, customer satisfaction, and operational efficiency. By focusing on reducing MTTR, teams can improve their systems' resilience and deliver better-quality services to their users. Benefits of MTTR to engineering teams:
- Improved system reliability: Focusing on reducing MTTR helps engineering teams to identify and address potential bottlenecks in their incident management processes, leading to more robust and reliable systems.
- Enhanced customer satisfaction: A lower MTTR means that customers experience shorter downtime or service disruption periods, which can improve customer satisfaction and loyalty.
- Faster problem resolution: A lower MTTR can encourage engineering teams to develop more efficient problem-solving techniques, leading to faster resolution of incidents and reduced downtime.
- Better resource allocation: Understanding MTTR can help teams identify areas where resources should be allocated more effectively, such as automating repetitive tasks or investing in better monitoring tools to prevent failures in the first place.
Check out this video to learn more about MTTR
Change Failure Rate
Change Failure Rate (CFR) is a key performance indicator (KPI) used to measure the percentage of changes or updates made to a system, software, or service that result in failures, incidents, or the need for immediate rollback. Development teams need to have metrics to measure the success of deployments, velocity, or code quality. For that, developers can definitely benefit from utilizing the four key metrics from DORA. Change Failure rate means how many of your deployments to production lead to a crash on the application that affects end users. Consequently, developers need to work on a quick fix to do a rollback to a previous stable version of the App.
CFR is calculated by dividing the number of unsuccessful changes by the total number of changes made during a specific period. Benefits of Implementing Change Failure Rate.
- Elite teams can analyze and evaluate the delivery performance to become masters in software deliveries. With this, you can see new features in a production environment faster with less or zero impact on users, which enables fast scalability and happy end users.
- Performance benchmarking: CFR compares the performance of different engineering teams, departments, or organizations. This can help identify best practices and areas for improvement.
- Informed decision-making: CFR provides valuable data that can be used by management and stakeholders to make informed decisions about system upgrades, maintenance, and resource allocation.
To learn more about Change Failure rate kindly watch this video
Unlock the Power of DORA Metrics with CTO.ai Insights and Accelerate Your Engineering Performance
CTO.ai developer control plane automatically calculates the four key metrics from DORA according to the deployment data you send. With CTO.ai insights and DORA metrics, developers can get information about the build duration, event activity, and success rates.
CTO.ai insights allow users to filter and segment data; this enables teams to focus on specific aspects of their CI/CD pipeline and gain deeper insights.