What are DORA metrics and why are they important for engineering teams?
DORA metrics are four key indicators—Deployment Frequency, Lead Time, Change Failure Rate, and Mean Time to Recovery (MTTR)—that measure the quality and velocity of software delivery. They are important because they correlate with actual business outcomes and employee satisfaction, providing industry standards for benchmarking engineering performance. Elite teams outperform others by orders of magnitude on these metrics, shipping more frequently and with higher quality. Source: State of DevOps Report 2024
How does Faros AI help organizations measure and improve DORA metrics?
Faros AI makes it easy for engineering teams to connect their data sources (e.g., GitHub, BitBucket, Jira, Jenkins) to its EngOps Platform. Faros automatically correlates data across CI/CD, artifact repositories, and source control, building a complete trace from idea to production. This enables out-of-the-box DORA dashboards with no change to development processes, helping organizations identify bottlenecks and drive continuous improvement. Source
What business impact can customers expect from using Faros AI for DORA metrics?
Customers using Faros AI have achieved a 50% reduction in lead time and a 5% increase in efficiency, accelerating time-to-market and improving resource allocation. Faros AI also ensures enterprise-grade scalability, handling thousands of engineers and hundreds of thousands of builds monthly without performance degradation. Source
Where can I learn more about DORA metrics and best practices?
What are the key capabilities and benefits of Faros AI?
Faros AI offers a unified platform that replaces multiple single-threaded tools, providing AI-driven insights, seamless integration with existing workflows, customizable dashboards, advanced analytics, and robust automation. It supports enterprise-grade scalability and security, and delivers measurable improvements in productivity, efficiency, and developer experience. Source
What APIs does Faros AI provide?
Faros AI provides several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling flexible integration and data access. Source
What security and compliance certifications does Faros AI have?
Faros AI is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR certifications, ensuring robust security and data protection for enterprise customers. Source
Pain Points & Use Cases
What core problems does Faros AI solve for engineering organizations?
Faros AI addresses challenges such as engineering productivity bottlenecks, software quality management, AI transformation measurement, talent management, DevOps maturity, initiative delivery tracking, developer experience insights, and R&D cost capitalization. It provides actionable data and automation to streamline processes and improve outcomes. Source
Who can benefit from using Faros AI?
Faros AI is designed for VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, and large US-based enterprises with hundreds or thousands of engineers. Source
What KPIs and metrics does Faros AI track for engineering teams?
Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), team health, tech debt, software quality, PR insights, AI adoption, talent management, initiative tracking, developer sentiment, and R&D cost automation. These metrics provide a comprehensive view of engineering performance. Source
Competitive Differentiation & Build vs Buy
How does Faros AI compare to DX, Jellyfish, LinearB, and Opsera?
Faros AI stands out by offering mature AI impact analysis, causal analytics, active adoption support, end-to-end tracking, and enterprise-grade customization. Unlike competitors, Faros AI provides actionable insights, flexible integration, and compliance-ready solutions for large enterprises. Competitors often offer limited metrics, passive dashboards, and SMB-focused features. Faros AI is available on major cloud marketplaces and supports advanced developer experience integration. Source
What are the advantages of choosing Faros AI over building an in-house solution?
Faros AI delivers robust out-of-the-box features, deep customization, proven scalability, and enterprise-grade security, saving organizations significant time and resources compared to custom builds. Its mature analytics and actionable insights accelerate ROI and reduce risk. Even large companies like Atlassian have found that building developer productivity tools in-house is complex and resource-intensive, validating the need for specialized platforms like Faros AI. Source
Support & Implementation
What customer support and training does Faros AI offer?
Faros AI provides robust support through an Email & Support Portal, a Community Slack channel, and a Dedicated Slack Channel for Enterprise Bundle customers. Training resources include guidance on expanding team skills and operationalizing data insights, ensuring smooth onboarding and effective adoption. Source
Faros AI Blog & Resources
What topics are covered in the Faros AI blog?
The Faros AI blog covers best practices, customer stories, product updates, engineering productivity, DORA metrics, developer experience, and AI transformation. It includes guides, news, and research reports such as the AI Productivity Paradox Report. Source
Where can I find more information and resources from Faros AI?
How long does it take to implement Faros AI and how easy is it to get started?
Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources through API tokens. Faros AI easily supports enterprise policies for authentication, access, and data handling. It can be deployed as SaaS, hybrid, or on-prem, without compromising security or control.
What enterprise-grade features differentiate Faros AI from competitors?
Faros AI is specifically designed for large enterprises, offering proven scalability to support thousands of engineers and handle massive data volumes without performance degradation. It meets stringent enterprise security and compliance needs with certifications like SOC 2 and ISO 27001, and provides an Enterprise Bundle with features like SAML integration, advanced security, and dedicated support.
What resources do customers need to get started with Faros AI?
Faros AI can be deployed as SaaS, hybrid, or on-prem. Tool data can be ingested via Faros AI's Cloud Connectors, Source CLI, Events CLI, or webhooks
Does the Faros AI Professional plan include Jira integration?
Yes, the Faros AI Professional plan includes Jira integration. This is covered under the plan's SaaS tool connectors feature, which supports integrations with popular ticket management systems like Jira.
The DORA metrics are a set of metrics that measure the quality and velocity of software delivery of an engineering organization. By measuring and continuously iterating on these metrics, engineering teams can deliver better software to their customers faster and achieve significantly better business outcomes.
Where did the DORA metrics come from?
The DORA metrics were put forth by the DevOps Research and Assessment (DORA) organization that synthesized several years of research studying engineering teams and their DevOps processes. The group publishes a yearly report called the State of DevOps Report, and was acquired by Google in 2018. In 2018 the group also published a widely acclaimed book called Accelerate on building and scaling high performing technology organizations.
Why are the DORA metrics interesting?
The DORA metrics are especially interesting because they correlate with actual business outcomes and employee satisfaction. In addition, they finally give the software engineering world a set of industry standards to benchmark against. It’s not an overwhelming set of indicators either. Turns out, just 4 key metrics are sufficient to distinguish truly elite engineering teams from mediocre ones.
As the infographic taken from the State of DevOps Report 2024 depicts, elite engineering teams differ from mediocre ones by orders of magnitude on the DORA measures. Further, there isn’t necessarily a trade-off between quality and velocity as widely assumed. Elite performers both ship more frequently and with higher quality!
So what are the DORA metrics exactly?
The DORA metrics were inspired by lean manufacturing principles. The first two metrics are measures of software delivery velocity. They are:
1. Deployment frequency (DF): “How often an organization successfully releases to production”
This metric measures the frequency at which an organization successfully releases code to production. There is some latitude in how “production” is defined, depending on a team’s individual business requirements. But in essence, smaller, more frequent releases incur less risk and indicate a more predictable, consistent delivery of value to customers. Elite teams are able to deploy on-demand, typically several times a day, while lower-performing teams make more big-bang releases once every several months.
2. Lead Time: “The amount of time it takes for changes to get deployed to production”
This metric measures how long it takes on average for committed code to reach production. The metric is thus a measure of the efficiency of the DevOps toolchain and processes in an organization. Quicker deployments mean faster value delivery to customers. For elite teams, it typically takes less than an hour from when code gets checked in to when it gets deployed in production.
The next two metrics are measures of quality and stability in software delivery. They are:
3. Change Failure Rate (CFR): “The percentage of deployments that cause a failure in production”
This metric measures the quality and stability of the code that a team is shipping. It is calculated as the percentage of deployments that result in severe service degradation and require immediate remediation such as a rollback or a hotfix. For elite engineering teams, no more than 15% of their deployments result in degraded services.
4. Time to Restoration (MTTR): “How long it takes an organization to recover from a failure in production”
And finally, unplanned outages always happen. This last metric measures the time to recover from them and restore service availability for the end user. Elite teams typically take less than an hour to restore degraded services.
The table below taken from the State of DevOps Report 2024 summarizes four distinct performance profiles for engineering teams, with statistically significant differences in measures among them.
How can you measure your DORA metrics?
Measuring and monitoring an organization’s DORA metrics can be difficult because the underlying data needed to compute them often comes from many different systems and isn’t always easy to correlate. For instance, in order to measure the average lead time for changes, you need to be able to compute the delta of all the changes that got shipped to production since the last release to production and average all of their lead times. This requires tracing data across your CI/CD systems, your artifact repositories, and your source control system for all the many applications that your organization deploys. This is hard enough to do for one application, but as organizations grow and tooling and pipelines explode, this can be an entirely non-trivial endeavor.
At Faros AI, we put a lot of thought into making it super easy for engineering teams to connect up their individual data sources to our EngOps Platform. Faros then does the hard work of connecting the dots between the data sources automatically. Hooking up known vendors such as GitHub, BitBucket, Jira, Jenkins etc. to the Faros AI Platform is as simple as clicking a button on the UI; custom home-grown systems can also be easily integrated with the Faros SDK. Faros AI manages all the data, imputes change-sets, correlates incidents with deployments, and so forth, to build a complete trace of every change from idea to production and beyond (and every stage in between). The result is DORA dashboards out of the box with no change in the development process.
Continuous improvement with data
With live DORA dashboards in place, engineering organizations can start to see where they stand relative to other engineering organizations, and what the scope for improvement is in their software delivery processes. The ability to slice and dice lead time or failure recovery time by application, DevOps team, and stage helps in identifying bottlenecks in processes — whether in code review, QA, build times, or triage. At the same time, trends over time enable organizations to assess the true impact of interventions — with data. More generally, engineering organizations can finally start to take a data-informed approach to improving the efficiency and effectiveness of their operations.
Ready to see Faros AI's DORA metrics dashboards in action?
Shubha Nabar is the Co-founder of Faros AI. Prior to Faros AI, she was part of the founding team of the Einstein machine learning platform at Salesforce and built data products and data science teams at LinkedIn and Microsoft.
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