How Elite Engg. Teams Deploy 208X More Frequently Compared to Us Mere Mortals?

Yes, you read the title right! 🤯

Elite engineering teams deploy 208 times more frequently than their low-performing peers.

That’s straight from the DORA's State of DevOps report, and it simply shows the game-changing potential of effective DevOps.

Yes, DevOps is a crucial practice that really affects software development, testing, and deployment. 

By building a culture of collaboration between development and operations teams, we can ensure faster, more reliable software delivery.

DORA Metrics on the other hand are one of the gold standards for measuring DevOps performance. 

They give you a clear picture of how well your team is doing and where there’s room for improvement.

In this article, we’ll share a few ideas on how to benchmark your organization’s DevOps practices using DORA metrics, with actionable insights to boost your performance.

I'll share ideas and flows to do this with and without Middleware, however, of course Middleware Open Source covers all these automatically taking out the manual labour of setting all these things up yourself.

With that being said let's dive in and see how you can transform your DevOps game.

First, Let’s Understand DORA Metrics 🧠

I’ll share a TL;DR version here since we already have a few blogs on DORA metrics going into depth of each metric which you can check out if you want more insights.

DORA are the key performance indicators that can make or break your DevOps strategy. 

The four primary DORA metrics are: Deployment Frequency, Lead Time for Changes, Mean Time to Restore (MTTR), and Change Failure Rate.

Let’s spend a few seconds on each one.

Deployment Frequency

What It Is: Deployment Frequency measures how often your team releases code to production.

Why It Matters: Frequent deployments mean your team is constantly delivering value, fixing bugs, and releasing new features. It’s a sign of agility and efficiency.

Example: Think of a team that deploys daily versus one that deploys monthly. The daily team can respond to customer feedback and market changes much faster.

Oh also, more deployments doesn’t necessarily mean better deployments - it’s well known that gaming DF is pretty easy btw.

Lead Time for Changes

What It Is: This metric tracks the time it takes for a code commit to go into production.

Why It Matters: Shorter lead times indicate a smooth and efficient development process. It means less waiting and more doing.

Example: Imagine you push a bug fix. If it takes weeks to go live, your users are stuck with the bug for far too long. 

Short lead times get fixes and features out quickly.

Mean Time to Restore(MTTR)

What It Is: MTTR measures how long it takes to recover from a failure in production.

Why It Matters: Downtime costs money and damages your reputation. The quicker you can recover, the less impact there is on your users and business.

Example: If your website goes down, every minute counts. A team with a low MTTR can get things back up and running quickly, minimizing disruption.

Change Failure Rate

What It Is: CFR shows the percentage of changes that fail in production.

Why It Matters: High failure rates can indicate problems with your testing or deployment processes. It’s important to keep this number low to maintain reliability.

Example: If 30% of your deployments result in bugs or downtime, it’s a sign that something’s broken in your pipeline. A low failure rate means more stable releases.

Why DORA Metrics Matter 🤔

Together, these metrics provide a comprehensive view of your DevOps health. 

High Deployment Frequency and low Lead Time for Changes mean you’re delivering quickly. Low MTTR and Change Failure Rate mean you’re delivering reliably.

Impact on Business Outcomes:

• Faster Time-to-Market: Quickly delivering features can give you a competitive edge.

• Improved Customer Satisfaction: Rapid, reliable updates keep users happy.

• Increased Efficiency: Streamlined processes reduce wasted time and effort.

• Happy Devs: A happier and not burnt out engineering team makes a huge difference in business success too!

Real-World Examples

Example 1: A financial services company reducing their MTTR from hours to minutes by implementing better monitoring and automated rollback processes. This can significantly improve their uptime and customer trust.

When it comes to financial services, a quick response counts a lot since people need to feel secure about their funds. Usually customers don’t mind a service outage as long as its quickly communicated and resolved!

Example 2: An e-commerce platform increasing their Deployment Frequency from monthly to weekly. This allows them to rapidly iterate on features and stay ahead of competitors especially during peak shopping seasons.

Setting Up Your Benchmarking Process 📝

Now that we’ve got a handle on what DORA metrics are, let’s talk about how to set up a benchmarking process for your DevOps performance. 

This is where we get strategic and set ourselves up for success.

Define Goals

Why Benchmarking is Essential: Benchmarking your performance against industry standards or your past performance is critical. It helps you understand where you stand, identify areas for improvement, and track your progress over time.

Benchmarking properly with context has a few key advantages for our teams, such as:

• Informed Decision-Making: Understand what’s working and what’s not.

• Continuous Improvement: Identify bottlenecks and inefficiencies to improve over time.

• Competitive Advantage: Stay ahead by continually refining your processes.

Before diving into the metrics, it’s important to set clear, actionable goals. 

Here are some examples:

• Reduce Lead Time: Aim to decrease the time it takes for code changes to go live.

• Improve Deployment Frequency: Increase the number of successful deployments.

• Enhance MTTR: Minimize the time it takes to recover from incidents.

• Lower Change Failure Rate: Reduce the percentage of deployments that result in issues.

If you want to one up this then add specific KPIs to each of the goals as well. Example: Reduce lead time by 30% etc.

Select the Key Metrics

Choosing Relevant DORA Metrics: Not all metrics will be equally important to every org. Choose the ones that align most closely with your goals. 

For instance:

• If speed to market is critical, focus on Deployment Frequency and Lead Time for Changes.

• If reliability is your top concern, prioritize MTTR and Change Failure Rate.

Aligning Metrics with Business Goals: Make sure your chosen metrics support your broader business objectives. 

Here are some examples:

• Strategic Objective: Increase customer satisfaction.

  • Relevant Metric: Reduce MTTR to ensure quick recovery from service disruptions.

• Strategic Objective: Accelerate product development.

  • Relevant Metric: Increase Deployment Frequency to release new features faster.

Gather Data

Accurate data collection is the backbone of effective benchmarking. 

Here are some methods:

CI/CD Tools: Utilize Continuous Integration and Continuous Deployment tools to track deployments and lead times.

Monitoring Systems: Use monitoring solutions to track system performance and incident recovery times.

Tools and Platforms: There are tons of tools available to help you collect and analyze your data. 

Now it wouldn’t be fair if I didn’t talk about Middleware here. In fact we even have a Middleware Open Source version available that takes a few clicks to get you your DORA metrics!

And here is the kicker: it’s all done locally, so your data remains, you guessed it; YOURS!

Here are a few others:

• Jenkins: A popular CI/CD tool that can help you monitor deployment frequency and lead times.

• GitLab: Another excellent CI/CD tool with robust monitoring and reporting features.

• Prometheus: An open-source monitoring system that can track performance metrics and alert you to issues.

Data Accuracy: Data integrity is crucial. As they say, Garbage in, Garbage out. So maintaining high quality data sources is as important as anything else.

Here are some tips to ensure you’re getting accurate data:

• Automate Data Collection: Automating the data collection process reduces human errors.

• Consistent Metrics: Make sure that the same metrics are being measured consistently over time.

• Regular Audits: Periodically review your data collection processes to make sure everything is well oiled.

Comparing Against Industry Standards 📊

Once you’ve set up your benchmarking process and gathered your data, it’s time to see how you stack up against industry standards. 

Of course this needs to be taken with a grain of salt. A company with a 200 person engineering team, 12 product offerings and $2B in annual revenue is not going to need the same things as a 10 person engineering team doing $5M in revenue!

With that being said, this section will guide you through finding relevant benchmarks, conducting a gap analysis, and interpreting your results.

Research and Analysis

Identifying Industry Benchmarks: To gauge your DevOps performance, you need to know what ‘good’ looks like in your industry.

Here’s how to find relevant benchmarks:

• DORA Reports: Start with the annual State of DevOps reports from Google’s DORA team. They provide valuable insights into high, medium, and low-performing organizations across various industries. The 2024 version should be coming out soon as well.(depends on when you’re reading this)

• Industry Surveys: Look for surveys conducted by industry leaders or research firms that provide detailed benchmark data.

• Peer Comparisons: Network with other companies in your sector to share and compare performance metrics.(this is a double edged sword and tough to execute, but helpful nonetheless)

A Few Sources for Benchmark Data:

• DORA Reports: These are comprehensive and widely regarded as the gold standard in DevOps performance benchmarking. Check out the 2023 DORA report.

• Industry Whitepapers: Publications by firms like Gartner, Forrester, and IDC often contain relevant benchmarks.

• Professional Communities: Forums and groups on platforms like LinkedIn or specialized DevOps communities can be rich sources of comparative data.

Gap Analysis

Conducting a Gap Analysis: Once you have your benchmark data, it’s time to compare your performance:

• Collect Your Data: Gather your internal metrics from the benchmarking process you set up. Psssttt…use Middleware!

• Compare with Benchmarks: Look at each DORA metric and compare it to the industry benchmarks you’ve identified.

• Identify Gaps: Note where your organization’s performance falls short, meets, or exceeds the benchmarks.

Interpreting the Results: Understanding what your gaps mean is crucial for driving improvements:

• Below Benchmark: Areas where your metrics are lower than the benchmark indicate opportunities for improvement. For example, a longer lead time for changes might suggest bottlenecks in your development process.

• At Benchmark: Metrics that match the benchmark are areas where you’re performing well, but continuous improvement should still be a focus.

• Above Benchmark: Metrics exceeding the benchmarks are your strengths. Leverage these to drive further improvements and innovation in other areas.

Visualizing Data: Use charts and graphs to make your comparisons clear and actionable. Once again, even better is simply just looking at the Cockpit view in Middleware. If you’re using the cloud version then you’d have access to flow metrics as well as bottleneck insights along with everything else you’d need.

Examples

Example 1: Company A’s Benchmarking Process and Outcomes:

•Background: Company A is a mid-sized tech firm.

•Benchmarking Process: They used DORA metrics and compared their performance against the latest DORA report.

•Findings: They identified longer lead times and higher change failure rates.

•Actions Taken: Implemented CI/CD tools and improved testing protocols.

•Outcomes: Reduced lead time by 20% and change failure rate by 15%.

Example 2: Company B’s Experience and Lessons Learned:

•Background: Company B is a large enterprise in the financial sector.

•Benchmarking Process: Used a combination of DORA metrics and industry whitepapers.

•Findings: Discovered their deployment frequency was lower than industry leaders.

•Actions Taken: Adopted feature flagging and improved their deployment pipeline.

•Outcomes: Achieved a 30% increase in deployment frequency and faster customer feedback loops.

The point is simple: “What gets measured, gets managed”

Analyzing and Interpreting Results 🥇🥈

After collecting and benchmarking your DORA metrics, it’s time to analyze and interpret the results effectively. 

I’ll quickly take you through things like identifying strengths and weaknesses, conducting root cause analysis, and extracting actionable insights.

Identify Strengths and Weaknesses

Highlighting Areas of Strength: Recognize and leverage your strengths to drive continuous improvement:

• High Deployment Frequency: If you have a high deployment frequency, it simply says that your team can deliver features and updates quickly. Leverage this by experimenting with new features or conducting A/B testing to enhance your product. The point being your team may have some leeway to try experimenting on new features or tests.

• Low Change Failure Rate: A low CFR means robust testing and quality assurance processes. Use this to confidently deploy more frequently or tackle more complex changes.

Spotting Areas for Improvement: Identifying weaknesses is the first step towards improvement:

• Long Lead Time for Changes: If your lead time for changes is longer than industry benchmarks, look into your development and review processes for bottlenecks.

• High Time to Restore Service (MTTR): A high MTTR may indicate issues in your incident response process. This is critical to address to reduce downtime and improve user satisfaction.

Root Cause Analysis

Techniques for Root Cause Analysis: Use structured frameworks to identify the underlying causes of performance issues.

Here are a couple of frameworks that are frequently used:

• 5 Whys: Ask “Why?” repeatedly (usually 5-7 times) to drill down to the root cause of a problem. This technique helps uncover deeper issues that might not be immediately apparent.This framework is usually also used to understand the key value prop of a feature or a product.

• Fishbone Diagram: Also known as the Ishikawa or cause-and-effect diagram, this framework maps out the potential causes of a problem, categorizing them to identify the root cause.

Example Analysis: 

Let me walk you through a root cause analysis for a specific metric:

• High Change Failure Rate:

  • Observation: Your team has a high CFR.

  • 5 Whys Analysis:

    • Why are changes failing? Because they are not adequately tested.
    • Why are they not adequately tested? Because we lack comprehensive automated tests.
    • Why do we lack comprehensive automated tests? Because we have limited resources and expertise in writing tests.
    • Why do we have limited resources and expertise? Because training has not been a priority.
    • Why has training not been a priority? Because we focused more on feature delivery than skill development.
    • Action Plan: Increase focus on training our devs in automated testing and allocate resources specifically for this purpose.

Actionable Insights

Extracting Insights from Data: All of this is no use if we don’t turn our data into clear, actionable recommendations.

• Patterns and Trends: Look for patterns in your data to identify recurring issues. For example, if you consistently see high MTTR on weekends, you may need to improve your on-call processes.

• Correlations: Identify correlations between different metrics. For example, see if high lead times correlate with certain stages in your development process.

Examples of Actionable Insights: 

• Improving CI/CD Processes: If your deployment frequency is low, explore integrating more automation in your CI/CD pipeline. Tools like Jenkins or GitLab CI can help streamline deployments and reduce manual intervention.

• Enhancing Testing Protocols: If you have a high CFR, you can invest in comprehensive automated testing. Tools like Selenium for web applications or JUnit for Java can help ensure that changes are thoroughly tested before deployment.

• Optimizing Incident Response: If your MTTR is high, improve your incident response protocols. Implement tools like PagerDuty for alerting and on-call management, and conduct regular incident response drills to ensure your team is prepared.

Implementing Improvements 🛠️

Now that you’ve analyzed your DevOps performance using DORA metrics and identified areas for improvement, it’s time to take action. 

This section will guide you through developing actionable plans, prioritizing tasks, and continuously monitoring progress to ensure sustained improvement.

Develop Action Plans

Creating a Roadmap: Steps to develop a detailed improvement plan:

• Set Clear Goals: Define what success looks like for each improvement area. For example, aim to reduce lead time for changes by 30% within six months.

• Break Down Goals: Divide each goal into smaller, manageable tasks. For example, improving lead time might involve streamlining code reviews, automating testing, and optimizing deployment pipelines.

• Assign Responsibilities: Allocate tasks to specific team members, ensuring everyone knows their role in achieving the goals.

Prioritizing Actions:

• Impact Assessment: Evaluate the potential impact of each action on your DevOps performance. Focus on actions that offer the greatest improvements in key metrics like deployment frequency or MTTR. Play around with these one at a time, A/B testing works wonders.

• Feasibility Analysis: Think about the resources, time, and effort required for each action. Prioritize actions that are both high-impact and feasible within your current constraints.

• Quick Wins: Identify and implement quick wins that can show immediate improvements. This boosts team mood and shows the effectiveness of your action plans.

Continuous Monitoring

Importance of Ongoing Monitoring:

• Track Progress: Regularly monitor your DORA metrics to track progress toward your goals. This helps make sure that your improvements are having the desired effect.

• Identify New Issues: Continuous monitoring can help you spot new issues as they arise, allowing for timely intervention before they escalate.

• Sustain Gains: Regular assessment helps sustain the improvements you’ve made by making sure that the positive change based culture becomes part of your team’s routine.

Some Arbitrary Case Based Examples

Example 1: How A Company Implemented Improvements Based on DORA Metrics

• Initial Challenge: Company C struggled with a high change failure rate and long lead times.

• Action Plan: They implemented automated testing, improved code review processes, and streamlined their CI/CD pipeline.

• Results: Within six months, they reduced their change failure rate by 30% and cut lead times in half, leading to faster and more reliable deployments.

Example 2: Success Story of Company D’s DevOps Transformation

• Initial Challenge: Company D had a low deployment frequency and high MTTR, causing frequent disruptions and slow feature releases.

• Action Plan: They adopted continuous integration practices, improved incident response protocols, and invested in tools like Middleware to measure and impact..

• Results: Over a year, they increased their deployment frequency by 120% and reduced MTTR by 60%, resulting in more agile and resilient engineering processes.

Let’s Wrap This Up 🎁

Now that you have a solid understanding of DORA metrics and the benchmarking process, it’s time to take action. 

Benchmarking your performance is not just a one-time task but an ongoing journey, cliché, but it’s a marathon not a sprint!

Start small, measure your progress, and iterate. 

Here are some resources to help you get started:

Middleware Open Source: Use Middleware’s platform to track and analyze your DORA metrics effortlessly.

DORA Metrics Guide: Learn more about DORA metrics and their impact on DevOps performance.

Books and Articles:

• Accelerate: The Science of Lean Software and DevOps by Nicole Forsgren, Jez Humble, and Gene Kim

• The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win by Gene Kim, Kevin Behr, and George Spafford

• The DevOps Handbook: How to Create World-Class Agility, Reliability, & Security in Technology Organizations by Gene Kim, Patrick Debois, John Willis, and Jez Humble

Too little time, too much to do! Happy benchmarking my friend! 🚀

middlewarehq / middleware

✨ Open-source DORA metrics platform for engineering teams ✨

Open-source engineering management that unlocks developer potential

Join our Open Source Community

Introduction

Middleware is an open-source tool designed to help engineering leaders measure and analyze the effectiveness of their teams using the DORA metrics. The DORA metrics are a set of four key values that provide insights into software delivery performance and operational efficiency.

They are:

• Deployment Frequency: The frequency of code deployments to production or an operational environment.
• Lead Time for Changes: The time it takes for a commit to make it into production.
• Mean Time to Restore: The time it takes to restore service after an incident or failure.
• Change Failure Rate: The percentage of deployments that result in failures or require remediation.

Table of Contents

• Middleware - Open Source
  • Features
  • Quick Start
    • Installing Middleware
    • Troubleshooting
  • Developer Setup
    • Using Gitpod
    • Using Docker
    • Manual Setup
  • Usage
  • How we Calculate DORA
  • Roadmap
  • Contributing guidelines
  • …

View on GitHub

Comments

[Dhruv Agarwal]

Are DORA metrics really omnipotent for the dev teams? I understand it's unbiased and stuff but what more should we measure along with it for effective results? I can't expect to just handover these metrics and boom results happen. What am I missing?

[Shivam Chhuneja]

DORA metrics provide a structured approach to measure key aspects of DevOps, but great results also require thinking about adding metrics aligned to business outcomes, quality, team dynamics, financial impact, user experience, compliance, and qualitative insights and more.
DORA is a great start but definitely not to be looked at or built upon alone.

[Jayant Bhawal]

The title definitely got me there!
But the answer seemed so straightforward too!

[Shivam Chhuneja]

good things usually tend to be straightforward I guess :p

[Martin Baun]

Agreed, the best things are often the simplest!

[allen_z]

Great article on the deployment frequency of elite engineering teams! At Enginuity, we completely resonate with the importance of deploying frequently and efficiently. Our focus has always been on empowering teams with the right tools and insights to make continuous deployment a seamless process.

With our Leiga Insights platform, we provide engineering teams with comprehensive analytics and data-driven insights that help in identifying bottlenecks and optimizing workflows. Our platform supports integration with popular tools like JIRA, GitHub, and GitLab, ensuring that teams have a unified view of their development and deployment processes.

We believe that with the right insights and tools, every team can achieve elite-level performance. Feel free to check out more about how we support engineering excellence on our Leiga Insights page.

Looking forward to more discussions and insights from this community!