How Typo Uses DORA Metrics to Boost Efficiency?

DORA metrics are a compass for engineering teams striving to optimise their development and operations processes.

Consistently tracking these metrics can lead to significant and lasting improvements in your software delivery processes and overall business performance.

Below is a detailed guide on how Typo uses DORA to improve DevOps performance and boost efficiency:

What are DORA Metrics?

In 2015, The DORA (DevOps Research and Assessment) team was founded by Gene Kim, Jez Humble and Nicole Forsgren to evaluate and improve software development practices. The aim was to improve the understanding of how organisations can deliver software faster, more reliable and of higher quality.

They developed DORA metrics that provide insights into the performance of DevOps practices and help organisations improve their software development and delivery processes. These metrics help in finding answers to these two questions:

• How to identify organisations’ elite performers?

• What should low performers teams must focus on?

The Four DORA Metrics

DORA metrics helps in assessing software delivery performance based on four key (or accelerate) metrics:

• Deployment Frequency

• Lead Time for Changes

• Change Failure Rate

• Mean Time to Recover

Deployment Frequency

Deployment Frequency measures the number of times that code is deployed into production. It helps in understanding team’s throughput and quantifying how much value is delivered to customers.

When organizations achieve a high Deployment Frequency, they can enjoy rapid releases without compromising the software’s robustness. This can be a powerful driver of agility and efficiency, making it an essential component for software development teams.

One deployment per week is standard. However, it also depends on the type of product.

Why is it Important?

• It provides insights into the overall efficiency and speed of the DevOps team’s processes.

• It helps in identifying pitfalls and areas for improvement in the software development life cycle.

• It helps in making data-driven decisions to optimise the process.

• It helps in understanding the impact of changes on system performance.

Lead Time for Changes

Lead Time for Changes measures the time it takes for code changes to move from inception to deployment. The measurement of this metric offers valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies.

By analysing the Lead Time for Changes, development teams can identify bottlenecks in the delivery pipeline and streamline their workflows to improve software delivery’s overall speed and efficiency. Shorter lead time states that the DevOps team is more efficient in deploying code.

Why is it Important?

• It helps organisations gather feedback and validate assumptions quickly, leading to informed decision-making and aligning software development with customer needs.

• It helps organizations gain agility and adaptability, allowing them to swiftly respond to market changes, embrace new technologies, and meet evolving business needs.

• It enables experimentation, learning, and continuous improvement, empowering organizations to stay competitive in dynamic environments.

• It demands collaborative teamwork, breaking silos, fostering shared ownership, and improving communication, coordination, and efficiency.

Change Failure Rate

Change Failure Rate gauges the percentage of changes that require hot fixes or other remediation after production. It reflects the stability and reliability of the entire software development and deployment lifecycle.

By tracking CFR, teams can identify bottlenecks, flaws, or vulnerabilities in their processes, tools, or infrastructure that can negatively impact the quality, speed, and cost of software delivery.

0% — 15% CFR is considered to be a good indicator of your code quality.

Why is it Important?

• It enhances user experience and builds trust by reducing failures.

• It protects your business from financial risks which helps in avoiding revenue loss, customer churn, and brand damage by reducing failures.

• It helps in allocating resources effectively and focuses on delivering new features.

• It ensures changes are implemented smoothly and with minimal disruption.

Mean Time to Recovery

Mean Time to Recovery measures how quickly a team can bounce back from incidents or failures. It concentrates on determining the efficiency and effectiveness of an organisation’s incident response and resolution procedures.

A lower mean time to recovery is synonymous with a resilient system capable of handling challenges effectively.

The response time should be as short as possible. 24 hours is considered to be a good rule of thumb.

Why is it Important?

• It enhances user satisfaction by reducing downtime and resolution times.

• It mitigates the negative impacts of downtime on business operations, including financial losses, missed opportunities, and reputational damage.

• It helps meet service level agreements (SLAs) that are vital for upholding client trust and fulfilling contractual commitments.

• It provides valuable insights in day to day practices such as incident management, engineering team performance and helps elevate customer satisfaction.

The Fifth Metrics: Reliability

Reliability is a fifth metric that was added by the DORA team in 2021. It measures modern operational practices and doesn’t have standard quantifiable targets for performance levels.

Reliability comprises several metrics used to assess operational performance that includes availability, latency, performance and scalability that measures user-facing behaviour, software SLAs, performance targets, and error budgets.

How Typo Uses DORA to Boost Dev Efficiency?

Typo is an effective software engineering intelligence platform that offers SDLC visibility, developer insights, and workflow automation to build better programs faster. It offers comprehensive insights into the deployment process through key DORA metrics such as change failure rate, time to build, and deployment frequency.

Below is a detailed view of how Typo uses DORA to boost dev efficiency and team performance:

DORA Metrics Dashboard

Typo’s DORA metrics dashboard has a user-friendly interface and robust features tailored for DevOps excellence. This helps in identifying bottlenecks, improves collaboration between teams, optimises delivery speed and effectively communicates team’s success.

DORA metrics dashboard pulls in data from all the sources and presents in a visualised and detailed way to engineering leaders and development team.

DORA metrics helps in many ways:

• With pre-built integrations in the dev tool stack, DORA dashboard provides all the relevant data flowing in within minutes.

• It helps in deep diving and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency and much more from a single dashboard.

• The dashboard sets custom improvement goals for each team and tracks their success in real-time.

• It gives real-time visibility into a team’s KPI and lets them make informed decisions.

How to Build your DORA Metrics Dashboard?

Define your objectives

Firstly, define clear and measurable objectives. Consider KPIs that align with your organisational goals. Whether it’s improving deployment speed, reducing failure rates, or enhancing overall efficiency, having a well-defined set of objectives will help guide your implementation of the dashboard.

Understanding DORA metrics

Gain a deeper understanding of DORA metrics by exploring the nuances of Deployment Frequency, Lead Time, Change Failure Rate, and MTTR. Then, connect each of these metrics with your organisation’s DevOps goals to have a comprehensive understanding of how they contribute towards improving overall performance and efficiency.

Dashboard configuration

Follow specific guidelines to properly configure your dashboard. Customise the widgets to accurately represent important metrics and personalise the layout to create a clear and intuitive visualisation of your data. This ensures that your team can easily interpret the insights provided by the dashboard and take appropriate actions.

Implementing data collection mechanisms

To ensure the accuracy and reliability of your DORA Metrics, establish strong data collection mechanisms. Configure your dashboard to collect real-time data from relevant sources, so that the metrics reflect the current state of your DevOps processes.

Integrating automation tools

Integrate automation tools to optimise the performance of your DORA Metrics Dashboard.

By utilising automation for data collection, analysis, and reporting processes, you can streamline routine tasks. This will free up your team’s time and allow them to focus on making strategic decisions and improvements.

Utilising the dashboard effectively

To get the most out of your well-configured DORA Metrics Dashboard, use the insights gained to identify bottlenecks, streamline processes, and improve overall DevOps efficiency. Analyse the dashboard data regularly to drive continuous improvement initiatives and make informed decisions that will positively impact your software development lifecycle.

Comprehensive Visualization of Key Metrics

Typo’s dashboard provides clear and intuitive visualisations of the four key DORA metrics:

Deployment Frequency

It tracks how often new code is deployed to production, highlighting the team’s productivity.

By integrating with your CI/CD tool, Typo calculates Deployment Frequency by counting the number of unique production deployments within the selected time range. The workflows and repositories that align with production can be configured by you.

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Cycle Time (Lead Time for Changes)

It measures the time it takes from code being committed to it being deployed in production, indicating the efficiency of the development pipeline.

In the context of Typo it is the average time all pull requests have spent in the “Coding”, “Pickup”, “Review” and “Merge” stages of the pipeline. Typo considers all the merged Pull Requests for the main/master/production branch for the selected time range and calculates the average time spent by each Pull Request in every stage of the pipeline. No open/draft Pull Requests are considered in this calculation.

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Change Failure Rate

It shows the percentage of deployments causing a failure in production, reflecting the quality and stability of releases.

There are multiple ways this metric can be configured:

• A deployment that needs a rollback or a hotfix: For such cases, any Pull Request having a title/tag/label that represents a rollback/hotfix that is merged to production can be considered as a failure.

• A high-priority production incident: For such cases, any ticket in your Issue Tracker having a title/tag/label that represents a high-priority production incident can be considered as a failure.

• A deployment that failed during the production workflow: For such cases, Typo can integrate with your CI/CD tool and consider any failed deployment as a failure.

To calculate the final percentage, the total number of failures are divided by the total number of deployments (this can be picked either from the Deployment PRs or from the CI/CD tool deployments).

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Mean Time to Restore (MTTR)

It measures the time taken to recover from a failure, showing the team’s ability to respond to and fix issues.

The way a team tracks production failure (CFR) defines how MTTR is calculated for that team. If a team considers a production failure as :

• Pull Request tagging to track a deployment that needs a rollback or a hotfix: In such a case, MTTR is calculated as the time between the last deployment till such a Pull Request was merged to main/master/production.

• Tickets tagging for high-priority production incidents: In such a case, MTTR is calculated as the average time such a ticket takes from the ‘In Progress’ state to the ‘Done’ state.

• CI/CD integration to track deployments that failed during the production workflow: In such a case, MTTR is calculated as the average time between that deployment failure to its being successfully deployed.

Benchmarking for Context

• Industry Standards: By providing benchmarks, Typo allows teams to compare their performance against industry standards, helping them understand where they stand.

• Historical Performance: Teams can also compare their current performance with their historical data to track improvements or identify regressions.

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Find out what it takes to build reliable high-velocity dev teams:

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How Does it Help Engineering Leaders?

• Typo provides a clear, data-driven view of software development performance. It offers insights into various aspects of development and operational processes.

• It helps in tracking progress over time. Through continuous tracking, it monitors improvements or regressions in a team’s performance.

• It supports DevOps practices that focus on both development speed and operational stability.

• DORA metrics help in mitigating risk. With the help of CFR and MTTR, engineering leaders can manage and lower risk, ensuring more stability and reliability associated with software changes.

• It identifies bottlenecks and inefficiencies and pinpoints where the team is struggling such as longer lead times or high failure rates.

How Does it Help Development Teams?

• Typo provides a clear, real-time view of a team’s performance and lets the team make informed decisions based on empirical data rather than guesswork.

• It encourages balance between speed and quality by providing metrics that highlight both aspects.

• It helps in predicting future performance based on historical data. This helps in better planning and resource allocation.

• It helps in identifying potential risks early and taking proactive measures to mitigate them.

Conclusion

DORA metrics deliver crucial insights into team performance. Monitoring Change Failure Rate and Mean Time to Recovery helps leaders ensure their teams are building resilient services with minimal downtime. Similarly, keeping an eye on Deployment Frequency and Lead Time for Changes assures engineering leaders that the team is maintaining a swift pace.

Together, these metrics offer a clear picture of how well the team balances speed and quality in their workflows.

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