1. Cycle Time
Cycle time is the duration required for a feature or a user story to complete the development process, from the beginning to the end- right from coding, to testing, and final deployment. A lengthy cycle time may signal issues with the development process. It might be ineffective collaboration or the absence of automation.
To shorten the cycle time, it is important to recognize and address the bottlenecks in your development process, which could include implementing automation tools, enhancing communication and collaboration between teams, or simplifying the development process.
[Read more: Software Development Cycle Time]
2. Change Failure Rate (CFR)
CFR, or Change Failure Rate, shows how often any new change causes disruption to your system. It is the ratio of total issues and number of changes over a time period. A high CFR indicates that there are issues with your deployment process, and can snowball into customer dissatisfaction, lost revenue, and even damage to a company's reputation.
To improve your CFR, it's essential to identify the root cause of your failures. This could be anything from insufficient testing to poorly designed infrastructure. Once teams have identified the problem, they can take the next steps, including adopting automated testing or redesigning infrastructure.
3. Mean Time to Restore (MTTR)
MTTR, or Mean Time to Restore, is the time taken to restore service after a failure. A high MTTR indicates issues with the incident response process, even leading to extended downtime, and software quality issues.
In order to decrease the Mean Time to Repair (MTTR), the DevOps teams need to implement a well-defined incident response plan, with carefully crafted roles, specified communication channels, and a protocol to track and document incidents. By establishing a clear-cut incident response strategy, engineering teams can promptly detect and resolve issues without having to compromise on software delivery.
4. Deployment Frequency (DF)
Deployment Frequency measures how often code changes are deployed to production. A high deployment frequency can indicate that the team’s development process is efficient and they are delivering features and functionality pretty quickly to customers. DF defines the final results of your SDLC, as it directly impacts how buyers/users consume a product.
To increase deployment frequency, it's essential to have an efficient and reliable deployment process in place.
Understanding how these metrics work together is essential to maximize their value and potential impact. For example, a team with a low deployment frequency but a high change failure rate is probably having difficulty getting their ideas integrated into production quickly. By understanding the four metrics, teams can quickly identify, and remove blockers, and build on productive work. Read here to find more about the four DevOps metrics, and how they impact software velocity, market time, and overall developer workflow.
Why Do DevOps Teams Need DORA?
The modern DevOps landscape is constantly evolving, with new issues stemming in every second deployment. However, most of these failures have common, fixed patterns. With a well-defined SDLC process, successful DevOps, and QA hand-offs, and data-driven metrics teams can double down on engineering effectiveness.
Let’s talk about the latter today. DORA was built on the principle of continuous improvement that binds all engineering teams together. The four indicators are a litmus test to an organization’s continuous delivery and software deployment efficacy. More so, DORA metrics provide research-based guidance, backed by team data into how organizations manage DevOps, measure their development progress, and develop effective technology pipelines.
What’s more is DORA doesn’t confine itself to examine the pipeline blockers, but are a clear reflection of a company’s progress across security, and reliability efforts. When teams integrate DORA evaluation to their CI/CD pipeline, they open themselves to look at their delivery side from a 360 degree view, and factual visibility. For example- Teams with DORA metrics have a higher chance of undergoing application-level security scanning regularly.
The teams also practice logging code history, and build scripts, right after they started getting value after continuous use of DORA. These practices, no matter how trivial, goes a long way in establishing an organization’s dominance as DevOps leaders.
Moreover, with the right use of DORA metrics, DevOps teams have seen a drastic increase in the software delivery rate, while experiencing a massive shift in downtime. This increased efficiency is a result of a well-orchestrated approach to DevOps.
DORA equips organizations with enough tools and visibility to implement a DevOps environment, through various assessments, capabilities, metrics, and reports, with Accelerate being one of them.
Teams can make use of DORA to continuously assess their progress, pinpoint areas of weakness, and adopt workflows that enhance overall team effectiveness. For example, DORA allows companies to base their lead times for changes against industry standard, and receive a breakdown of what’s working/not working in optimizing their lead time.
Ultimately, DORA helps engineering teams to stay on the leading edge in today’s volatile technology landscape. By taking advantage of DORA’s insights, teams can be sure to remain competitive, deliver high-quality software and services, and maximize operational efficiency in their DevOps environment.
While teams realize the benefits of using DORA for improving software delivery, they still lack a holistic understanding of what DORA exactly is.
5 Myths About DORA Metrics
While the four DevOps metrics help accumulate data-backed insights into the overall development process; sometimes teams can have several latent prejudices against using DORA metrics. It might be either because of lack of documentation around using DORA, or complexity of a team’s SDLC
Here is a list of five common myths about DORA metrics that most teams face:
Myth #1: DORA Metrics Are Only Used in Large Enterprises
This misconception is a common one, and finds its root in the first DORA assessment. The metrics were originally used in large-scale enterprises, including banks, telcos, and e-commerce businesses. Nevertheless, small and medium-sized enterprises can also take advantage of these benefits without any hindrance. With the right implementation, DORA metrics can give the same insights and performance results to companies of any size. All managers have to do is enroll team members into using the four metrics, with bi-weekly/monthly reviews.
Myth #2: DORA Metrics Take Too Long To Implement
The implementation of DORA metrics can take a significant chunk of your time, depending on the size and complexity of the software platform. However, it’s important to keep in mind that a successful DORA metric implementation can actually save you time in the long run, once you start to experience the driver performance analytics. When it comes to execution, it is the initial setup, and team training taking the most of the chunk, rest comes to teams easily.
Myth #3: DORA metrics are too difficult to understand
Understanding and interpreting the data produced by DORA metrics can be a challenge. Traditionally, the data collection, cleaning, transformation and analysis can seem overwhelming, however, with the right engineering analytics tools, teams will be able to leverage the power of DORA metrics without having to become an expert in analytics.
Myth #4: DORA Metrics Are Expensive And Difficult To Maintain
In the past, DORA metrics were recognized as being expensive to build and difficult to maintain, especially because of their reliance on complex scripts and component monitoring. However, today there are simpler and cost-effective alternatives available allowing for simpler setups and efficient analysis.
Myth #5: Dora Metrics Are Used For Software Delivery Only
DORA metrics and analytics are commonly associated with software delivery, but they can also be used to examine, and improve performance of other applications and services. In fact, the insights gained by analyzing your applications and services can be extremely beneficial in helping to identify problem areas, prevent service interruption, and accelerate development.
Overall, understanding and leveraging the power of DORA metrics is crucial in this digital age. As the software development industry advances, so do the myths and prejudices surrounding the use of DORA metrics. With the right implementation practices, engineering teams can capitalize on the insights obtained through the right use of DORA metrics and optimize their software journey.
5 Challenges in Implementing DORA Metrics
The DORA metrics offer data-backed insights into existing and/or desired software delivery performance, equipping organizations with the ability to improve, and iterate continuously. Despite the availability of these metrics, most engineering teams still struggle to utilize them effectively, resulting in unnecessary challenges and missed prospects.
Here are some of the most common pitfalls, software teams are likely to encounter when using DORA metrics:
1. Mixing Up The Process and Outcome Metrics
The DORA metrics have two distinct categories – process and outcome metrics. Process metrics measure specific parameters within the software development cycle, including time required to fix bugs or how quickly new features are deployed.
Outcome metrics, on the other hand, measure the overall performance and success of the process, including factors like customer satisfaction with the product and the frequency of successful deployments. It’s important to understand the differences between these two categories of metrics to get an accurate picture of the impact of your software delivery processes.
For example, the metric “Lead Time For Changes” looks at the time it takes to complete changes from the moment they are identified until they are released, while the metric “Deployment Frequency” looks at the frequency of deployments. Although these two metrics evaluate similar aspects of the DevOps process, one of them focuses on the time needed to implement a change, while the other concentrates on the frequency of releases.
Teams need to find a balance between creating a watershed compartment between the four metrics, while combining the results to get the overall SDLC picture.
2. Focusing Too much on Outcomes Than Process Improvement
A common mistake among teams is to concentrate too much on the individual outcomes than focusing on the overall process improvement. Most teams we talk to have a common challenge: they keep chasing DORA results, and lose sight of the bigger picture. Instead of simply tracking development cycles, engineering teams should focus on quality assurance and customer satisfaction, to truly understand the performance of the project.
The best way to avoid this pitfall is to use additional DevOps metrics like PR size, sprint velocity, and developer well-being for complete factual visibility into the overall development cycle. DORA offers one piece of the SDLC puzzle, the rest is something teams need to figure out as they evolve- based on size, complexities and product development.
3. Using DORA Metrics Without Enough Context
It’s important to set realistic expectations when using DORA metrics. Too often, businesses set reasonable goals for their software development projects but fail to measure their progress against those goals. Use DORA metrics to track progress and measure success, but also move beyond these metrics to understand the context of the software development process.
Although the metrics offer a good base for assessing DevOps performance, they shouldn't be the sole source of truth for your team. The accuracy of the four metrics should be verified by feedback and interviews with stakeholders- right from devs, to team leads, and engineering managers, so everyone on the team gets a full picture of how the whole development cycle is moving.
For example, when considering the metric “Change Volume Per Day”, the number of changes made per day is only a surface-level metric. The actual quality of the changes, the time and effort breakdown, and their impact on the system require additional context that cannot be gleaned solely from the metric.
While the DORA team has already come up with enough benchmarks to base DevOps performances, it is advisable for teams to improvise on them, rather than chasing the elite team numbers impulsively. Each team is different, and so is their team size, the nature of work done, and type of products built. Customize DORA as per your team requirements, and not contrariwise.
4. Missing Out on Engineering Analytics
Data is useless unless put to use, with enough context, and clear team targets. The numbers cannot speak for themselves unless they are fueled with what teams need to achieve further, and define areas of improvement. Data analytics helps teams to extract insights from raw data, so teams have complete control over their process, and not the other way round. Make sure to leverage the data generated by the DORA metrics to plug SDLC bottlenecks, make better informed decisions, and drive improvements in the software delivery process.
Keep in mind that the DORA metrics solely present a high-level outline of DevOps performance. In order to truly assess the health and performance of a DevOps process, teams need to take a closer look at additional variables, including environmental factors (including organizational culture), infrastructure complexity and scalability, tooling, and team dynamics.
An engineering analytics combines all available team and process indicators at one place by collating all related data. That way, engineering teams can have complete visibility into how their DevOps pipeline is moving, the blockers, and what needs to be done at individual contributor and team level.
How to Measure DevOps Performance With DORA?
Teams do not have to confine DORA to just process outcomes, rather they can be used to examine the overall team health, and even the extent of collaboration, and even individual blockers.
Teams can use DORA metrics by integrating all their digital toolstack, including VCS, CI/CD toolstack, REGEX for failure tracking, messaging and conferencing apps with each other, or using an engineering analytics tool to do the heavylifting of connecting all tools. DORA takes into account deployments occurring in your code base and the way fixes are implemented, through analyzing repository, change failure, and deployment base.
After setting up DORA, here is a common SoP for teams to follow:
- Start with the aliveness metrics: As its name suggests, this metric measures the “aliveness” of your DevOps team — how well they collaborate, user responsiveness, customer experience, and overall team productivity.
- Use the stability metrics: Move over to indicators like change fail rate, automation, service level objectives, and mean time to restore. See the differences in how aliveness metrics rippled into higher/lower software stability. Looking at the two metrics in a single pane is the first step to optimizing your SDLC.
- Access the DevOps performance: Evaluates the time it takes to execute tasks, detect problems, and resolve them. Do an A/B testing for different environments, and find out a process that works for you.
- Offer extra attention to security: DORA, if looked closely offers invaluable insights into your security practices. The four indicators, when coupled with additional security indices, can tell teams why, and how their security protocols lack integrity. The recent Accelerate report too combines DORA with security, where teams leverage DORA to plug loopholes: metadata inspections, two person reviews, and build test fields.
[Read more: How to use DORA for software delivery]
Best Practices for Using DORA Metrics
Now that we know what DORA metrics are, and how to use them for assessing DevOps execution, let’s see the best practices industry leaders follow while using DORA metrics:
1. Expand Automation and Agile Development Processes
Using agile and automation together is your silver bullet in improving the speed of the development and deployment cycle. Automation can save administrators time on mundane tasks and create better results. Moreover, Agile development processes can increase collaboration, improve communication and enhance decision-making in the development process.
When teams combine DevOps, and agile together, they create room for more iterative processes. Using DORA metrics on top of your agile+automation combination can help teams build better workflows.
2. Don’t Forget to Secure Your DevOps Pipeline
Security is an integral part of DevOps processes. The idea is to protect the system from attack, any kind of breach, and identify errors and defects more quickly. Engineering teams can build on the organizational security by incorporating access control and authentication processes, Multi-Factor Authentication (MFA), system monitoring, following a least privilege model, and constantly updating and patching software.
The metrics can work wonders when the system is breach-proof, even reducing downtimes, and in turn, MTTR.
Tools like Docker can create a platform-agnostic environment and help automate the configuration and deployment processes. To maintain a secure and updated code, teams should carry out continuous testing and eliminate any potential bugs or malicious software. It circumvents unplanned downtime and prevents critical errors from reaching the market.
4. Create a Culture of Collaboration and Innovation
A DevOps culture encourages continuous innovation, feedback and collaboration between developers, operations, and other stakeholders. This involves celebrating successes, fail-forward learning, experimentation, and embracing a ‘fail fast’ attitude. Moreover, encouraging team training, sharing of knowledge, respect, and transparency boosts motivation and engagement. Smooth team hand-offs are prerequisite to any elite team.
DevOps benefits from cross-functional integration- developers, operations engineers, and other personnel should be encouraged to share knowledge and insights in order to gain a better understanding on project status.
Creating an atmosphere of education fosters collaboration and incentivizes team members to work more productively and efficiently together.
5. Monitor the Complete Agile Cycle
Organizations should actively monitor all stages of the Agile cycle for any issues.
This includes the elicitation, analysis, design, coding, implement and testing stages. Knowing how these stages operate makes it easier to foresee any possible problems and to make sure that all parties involved are on the same page.
6. Automate the DORA Metrics
Engineering teams should consider automating their DORA Metrics to ensure accurate and consistent data. This can be done by deploying automated monitoring systems or through an engineering analytics platform.
7. Establish a Baseline
Organizations should also establish a baseline for their metric measurements to ensure that there are valid points of comparison when assessing the performance of the DevOps initiatives. This baseline will provide context and enable organizations to easily identify changes and trends over time.
8. Streamline the Reporting
To maximize the value of the data collected through the metrics, teams should streamline their reporting process to enable faster access to insights and improved decision making. Engineering analytics platforms can transform raw information into meaningful insights by collating all data from multiple sources, for improved visibility into the development process.
Viewing DORA Metrics With Hatica
Hatica offers a comprehensive view of the four DORA metrics by collating inputs across digital toolstack and offer data-driven insights to understand the DevOps bottlenecks of a team.