Google Cloud’s Nathen Harvey on Maximizing Engineering Impact with DORA Metrics [WEBINAR]
Read on for highlights from the conversation, and watch the full webinar on demand here.
In an effort to understand the markers of a productive engineering team, the DORA (DevOps Research and Assessment) group, founded by Dr. Nicole Forsgren, Jez Humble, and Gene Kim, designed their research to answer core questions about DevOps:
Does technology matter for organizations? If so, how can we improve software delivery and engineering performance?
Through rigorous academic and analytical research, the group was able to demonstrate that improving software delivery and engineering performance leads to increased profitability and customer satisfaction. They identified four key metrics, now known as the DORA metrics, which address the areas of engineering most closely associated with success, and established benchmarks, enabling engineering teams to improve performance and balance the speed and stability of their software delivery.
How can organizations get started with DORA metrics and turn those insights into action? Code Climate’s Director of GTM Strategy and Intelligence, Francesca Gottardo, sat down with DORA expert Nathen Harvey to discuss how leaders leverage DORA metrics to improve engineering team health and truly drive change in their organization.
Francesca Gottardo: How did the DORA team choose between metrics that measure the quality of software versus how quickly it was getting shipped?
Nathen Harvey: Delivering technology can enable and accelerate any business. We all want to accelerate the delivery of that technology to enable great customer experiences.
So we look at two metrics: Deployment Frequency, how frequently are you pushing changes out to your users? And Lead Time for Changes, how long does it take for code to go from committed to actually in the hands of your users? The challenge there is that moving fast is good, but not enough, so we also have two stability metrics that complement that. The stability metrics are your Change Failure Rate, what I like to call the ‘Oh, Expletive’ rate — when you push a change to production and someone shouts out an expletive – and Time to Restore, how do we as a team quickly respond and recover when there is an incident or an outage that impacts our users?
That traditional thinking leads us to believe that these two are trade-offs of one another: we can either be fast or we can be stable. But what the data shows us over almost a decade now, is that these two ideas move together in lockstep. There are teams that are good, are good at all four metrics and the teams that are performing poorly are performing poorly across all four metrics as well.
Francesca: Who are DORA metrics for? Are they best suited for a specific type or size of company?
Nathen: We've seen teams of all shapes and sizes using DORA and insights from it successfully. But there were also some challenges there. First, measuring those metrics at an organizational level doesn't really give you a whole lot of value. How frequently does Google deploy? A lot, but what are we going to learn from that?
We really want to look at an application or a service, a particular set of capabilities, if you will, that we deliver to our customers, so first we have to measure at that level. And let's also make sure that we're using or getting insights across the entirety of the team that's responsible for prioritizing, building, deploying, and operating that application or service — it often takes a cross-functional team focused on one application or service.
The technology really doesn't matter. You can use those four metrics to look at how you’re doing with the custom application that you’re building for customers, but you could also do that for the commercial off-the-shelf software that you're using to deliver to your customers, or a SaaS that you're using.
Francesca: Is there a specific type of view or a few specific metrics that a leader of a few teams should look at?
Nathen: From a leadership perspective, I think the best insights you can get from the DORA metrics are just really to understand how your teams are doing. But here's the pitfall there: you're not using this to weigh teams against one another. Instead, what DORA really tries to get at is embracing a practice and a mindset of continuous improvement.
You might want to look across your teams to understand how each team is doing, find those teams that are doing really well, and identify what lessons you can learn from that team. Of course, the context is so important here. If you're shipping a mobile application or if you're working on the mainframe, we can use those same four measurements, but we don't expect the values are going to be the same across those teams. As a leader, I think that there are really good ways to have insight into what sort of investments you need to make in the team, and what sort of support each of your teams need.
Francesca: What are some other common pitfalls you find when people start using DORA metrics?
Nathen: The biggest one is this idea that we have to reach peak performance. Really, the goal is improvement. Don't worry about how other teams are doing. It's nice to have a benchmark to understand where you sort of fit, but the more important thing is, how do you get better? In fact, looking at the four metrics, it's difficult to say, ‘How do I get better? My Deployment Frequency isn't what I want, so I need to get better.’ You don't get better just by mashing the deploy button more frequently. That's not the right approach. The research actually goes a little bit deeper beyond those four key metrics into some capabilities — practices or tools or things that your team does regularly.
The capabilities that the research investigates are technical capabilities like version control, continuous integration, and continuous testing. There are also process capabilities: How much work do you have in-flight at any given time? Maybe shrink down your amount of work-in-progress. What does that change approval look like? Focusing on that change approval process is maybe the thing that's going to unlock value.
Most important of the capabilities are the cultural capabilities. How do the people in your team show up? How do they communicate and collaborate with one another? How are they rewarded? What's incentivized? All of these things really matter, and DORA is really about taking that comprehensive view of what capabilities a team needs to improve in order to drive those four metrics.
Francesca: What is the starting point that you recommend leaders look at?
Nathen: One of the beautiful things about these four metrics is thinking about them holistically. You may want to improve Deployment Frequency, but do you know how you're going to get there? You're going to make your lead times shorter. You're going to make your Change Failure Rate go down and you're going to restore service faster. It doesn't matter which one you focus on; changes are likely going to have good impacts across all four, and we really encourage you to look at all four as a whole.
How do you get started from there? You really then need to go deeper into the capabilities. Start with the capabilities where your teams have a lot of opportunity for growth. It's really about finding your constraint and making improvements there.
Francesca: And you would say as you're measuring that opportunity for growth, it's really relative to the benchmark, correct?
Nathen: Oh, absolutely. Let's say that continuous integration popped up as the thing that you should focus on. Now we have to figure out how we get better at continuous integration. Let's go put some new things in place. Those new things might be new measures, so we can test how well we're doing with continuous integration. There's certainly going to be new practices, maybe even new technologies, but after you've made some of that investment, you have to go back to those four key metrics, back to the benchmarks. Did this investment actually move those metrics in the way that we expected it to?
Francesca: Sometimes leaders can have a hard time getting buy-in for new forms of measurement, or the individual developers on a team have seen a lot of flawed measurement and can be skeptical. How do you suggest that leaders get their teams on board to be measured like this?
Nathen: Yeah, I don't like to be measured either. I get it. I think honestly, the best way to help teams get on board with this is for leaders to share the idea of these metrics and then step out of the way and give the teams the autonomy that they need to make the right choices. If a leader comes to me and says, ‘I'm going to measure your team's performance based on these four metrics,’ that's fine, but what I don't want that leader to do is tell me exactly how to improve those four metrics, because the leader isn't attached to the daily work of our team. But if that same leader says, ‘These are the metrics by which you'll be measured and we want to improve these metrics, what can we do?’ Now, as a team, we’ve been given that trust and the autonomy to select where we should invest and what we should do. A leader's job really is to support that investment, support that learning of the team.
Francesca: How could you ensure that you're comparing kind of apples to apples as you're looking at DORA metrics for teams that may be looking or working in different platforms?
Nathen: You are in fact comparing apples to oranges, and so the thing that I encourage folks to do is celebrate the teams that make the most progress. Maybe you can get to a derivative: This team increased their Deployment Frequency by 10%, this team increased their Deployment Frequency by 50%. Maybe that 50% team went from annual deployments to twice a year, but that's still a 50% improvement, and that's worthy of celebration. I think really looking at the progress that you're making instead of the raw numbers or that sort of benchmark data is the best way to go.
Francesca: One thing I've heard is that it's really important for teams to improve to a higher performance bucket, rather than stay within that bucket.
Nathen: We put out an annual report and people are hungry for benchmarks, and they really want those benchmarks and want to understand how they measure up to peers, to others in the industry. And each year, we do a cluster analysis of those four key measures, and these clusters emerge from the data. We don't set in advance what it means to be a low performer or medium performer. We let the data answer that question for us, but then we have to put labels on those clusters to make them consumable by a leader and by teams, and unfortunately, we use labels like low or medium or high or elite.
Nobody wants to be a low performer. It's not very encouraging to show up to work as a low performer. But I try to encourage folks to recognize that this is not a judgment and maybe just discard the label; it's about that improvement. How are you making progress against that? As you're making changes, you're likely to have some setbacks as well.
In 2020, we did an investigation into reliability practices, and we saw that some teams, as they began their journey changing some of their reliability practices, the reliability of their systems dropped. But over time, as they stayed committed and got more of their teams involved and more of the practices honed within their team, they saw this J curve illustrating impact across the team. So I think the important takeaway there is that this requires commitment. We're asking people to change process and technology. It's going to take some commitment.
Francesca: DORA metrics have had a huge impact in this space and are a popular starting point for taking a data-driven approach. What are your thoughts on how popular they’ve become?
Nathen: It's really exciting for me and for my team, and of course for the researchers, to see that it's had such a lasting and big and expanding impact on the industry. I think that it is important, though, to remember that the research is focused on that process of software delivery and operations. Oftentimes people ask about developer productivity or developer experience. This isn't particularly measuring that, although I would say that a developer is going to have a much better experience knowing that the code that they wrote is actually in the hands of users.
So it's not a direct measure there, it is an outcome of that process. When it comes to any sort of metric that we are looking at, it is important to remember which of these measures are inputs, which of these measures are outcomes. Even something like software delivery as an outcome is an input to organizational performance. It's really important just to understand the full context of the system, which of course includes the people in the system.
Francesca: If you're looking at DORA metrics in a tool like ours, there's also the context available so that you can have those conversations upward and people aren't going to be using that data in the ways that it wasn't intended.
Nathen: Absolutely. And with tools like Code Climate, you can go beyond those four keys. What are the inputs that are driving that? As an example, what is the quality of the code that's been written? Is it following the practices that we've set within our team? How long does a peer review of this code take? All of these things are really, really important and drive those overall metrics.
Francesca: We've seen that Deployment Frequency really is closely related to PR Size. So that's a great place to look first.
Nathen: Yeah, I think that one in particular is interesting because those four measures really, I think what the researchers really wanted to measure were batch sizes.
But how do we ask you, ‘What's the size of your batch?’ Smaller for you might be a medium for me. So those four metrics can really be used as a proxy to get at batch size and you're going to improve if you make that batch size smaller.
What is the size of our PRs? We can actually look across teams and say, ‘This team that has large PRs, lots and lots of code changes, they tend to go out slower.’ We could also start to look at things like from the time the change is committed, that lead time, what does it do for our Change Failure Rate? We've worked with customers who can pull out data and show us on a dashboard that the longer this change takes to get to production, the higher chance it's going to fail when it reaches production.
Francesca: Can you talk a little bit more about the importance of having metrics be standardized or making sure that Deploy Frequency, for example, means the same thing to everybody in the organization?
Nathen: I think it can be a real challenge, and I think that one of the values of DORA is that it gives us a shared language that we're communicating with one another. Deployment Frequency is a really interesting one. Of course, it's just how frequently you’re pushing changes out to your customers, but then there can be a lot of nuance.
The most important thing there is that you have consistency over time within a team. And then the second most important thing is across an organization as you're looking across teams, even if you can't get to a consistent definition, at least you can publish or write down and probably store in version control, how are we measuring this thing so that it's clearly communicated across those teams?
Francesca: So how do people connect something like Deployment Frequency and Lead Time to higher levels of work? For example, a story, a project, or business feature where you're really delivering that end-user value.
Nathen: DORA metrics are really focused on this idea of software delivery. We are looking only at code commit through to code deploy, but of course there's a lot of stuff that happens before we even get to code commit. Teams want to know things like feature velocity. How fast am I able to ship a feature? That's a different question than ‘How fast am I able to ship a change?’ because a feature is likely multiple changes that get rolled up together. This is where other metrics frameworks, like the flow metrics, might start to come in, where we look at a broader view of that entire value chain. And I think that it can be very difficult. Is a feature brand new thing that we're launching from fresh, or is a feature changing the location of this particular button? They're both features, they both have very different scales. One of the reasons that the DORA research really focuses on that software delivery process is it gives us a little bit more sort of continuity. A change, is a change, is a change, is a change. If we're shipping a change, it should follow the same process. There should be less variability in the size of that or in the duration of how frequent or how long that takes.
Francesca: A lot of the questions that we've been getting from the audience are more about digging deeper into the context of each of these metrics because they're very big picture outcomes. So let's take Mean Time to Recovery, for example. How do you suggest digging into this one?
Nathen: I think the best thing to do is look at something that just happened. So let's say you just had an incident or an outage, something that you recovered from. First and foremost, make sure you're recovered, make sure your users are happy again. Now that we're there, let's take some time to learn from that incident or from that outage. And that's where the investment really starts to take place.
One of the first things we have to do is go talk to people. We have to understand your mindset during this incident or outage, and really try to unwind what led to this, not in a way that we're looking for what things we should blame, but instead just to get a better understanding of the system overall. Let's ask really good questions, and involve the right people in those conversations.
Francesca: Your research is in the abstract, but when you’re seeing DORA metrics practices in actuality, what were some surprises?
Nathen: What I so often see is a thing that you mentioned earlier: it really comes down to the process and the people. The people really matter.
The truth is that as we're trying to change culture and the way that teams and people show up at work, it is oftentimes the case that you have to change how they work to change how they think. Technology and culture are kind of stuck together. You can't just change out a tech and expect that the culture's going to change, nor can you just change the culture and expect that the technology's going to follow. These two amplify and reinforce one another. I think that we're reminded again and again and again that there is not a magic wand. There are no silver bullets. This takes consistent practice. It takes commitment and it takes looking at the entire system if you want to improve.
Francesca: If the need and desire for measuring DORA isn't coming from leadership, how do you suggest a team goes about implementing it?
Nathen: In my opinion, these measures matter for the teams that are building the software. And in fact, I don't mind if a leader isn't pushing me to measure DORA metrics. What I really want is for the team of engineers, the team of practitioners that come together to ship that software, to care about these metrics. Because at the end of the day, the other thing that we know is that these teams are more productive when they're able to hit those metrics. And a productive team is a happy team.
I often ask this question: is a happy team productive or is a productive team happy? I think the answer is yes, right? As an engineer, when I'm productive, when I'm able to be in the flow of my work and get fast feedback on the work that I'm doing, that makes me happier. I have better days at work. There's even research from GitHub that looks exactly at that. What does a good day look like? It's when that engineer, that developer is in the flow doing the work that they love to do, getting that fast feedback. So these metrics really matter for the team.
Francesca: It's what we see in how people use our tool as well, and that so often people or customers will come to us either wanting to fix some inefficiencies in their SDLC or wanting to improve team health, and they may be one and the same when you're really looking at the big picture.
Nathen: Absolutely. I don't know of any CEO who has come to a team and said, ‘Wow, you've deployed more frequently this year. Congratulations. Here's a big bonus.’ The CEO cares about the customer. These metrics can help reinforce that. As technologists, it's easy for us to get caught up in the latest, greatest new technology, this new microservices framework, this new stuff from AI. But at the end of the day, we're here to deliver value to our customers and really understand what they need out of this. That's what our CEO cares about. Frankly, that's what we should care about. We're using technology to further those goals and to keep our customers happy.
Francesca: In the polls, 26% of people said that they have philosophical or cultural barriers to implementing DORA. If leadership doesn't see it as a priority, how can managers still motivate the team?
Nathen: I think that one approach that is successful is to — and this pains me a little bit — stop using the word DORA. Stop using the word DevOps. Don't talk about those things, don't talk about those labels. Turn to a more curious mindset and a questioning mindset. What would it be like for our customers, for our organization, for our team, if we were able to deliver software faster? Imagine that world because we can do that. We can get there and do that in a safe way. Imagine or ask the question, ‘What happens if we don't improve? What happens if we stay stagnant and our practices aren't improving?’ And really start at digging into some of those questions to find that intrinsic motivation that we have. As an engineer, like I said, I want to ship more things. I want to get feedback from that faster. I want to do the right thing for my customers.
Francesca: What is the future of DORA? Are there additional metrics you think of exploring or avenues outside of stability, outside of speed, that you think are important to include in the future?
Nathen: Yeah, absolutely. I think this is really where we get back to those capabilities. Number one, we will continue this research and continue our ongoing commitment to that research being program and platform-agnostic, really trying to help teams understand what capabilities are required to drive those metrics forward. Number two, like you mentioned earlier, we're seeing more and more teams that are trying to use the DORA framework and the metrics and the research to drive those improvements. In fact, we've recently launched dora.dev as a place where the community can start to come together and learn from one another and collaborate with one another and answer these questions.
Don't ask Nathen, let's ask each other. Let's learn from each other and the lived experiences of everyone here. And then, of course, I mentioned that the research will continue. So stay tuned. In the next couple of months, we'll launch the 2023 state of DevOps survey. And the best thing about the survey is the questions that it asks. And I think that teams can really get a lot of value by carefully considering the questions that are posed in the survey. Just by looking at those questions, a team is going to start to identify places where they could make investments and improvements right now.
Francesca: Something that is going to stick with me from what you've said today is that the benchmarks that are standardized may not be what we should all be spending so much time on. It's really about how we compare to ourselves and where we can improve.
Nathen: It is that practice and mindset of continuous improvement. Now, look, the benchmarks are still important because we have to have dashboards, we have to have ways to report, we have to have ways to test that what we're doing is making things different. Whether that's good, better, or worse, at least these benchmarks give us a way to test that and prove out the theories that we have.
To learn how your engineering team can implement DORA metrics to drive improvement, request a consultation.
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Navigating the world of software engineering or developer productivity insights can feel like trying to solve a complex puzzle, especially for large-scale organizations. It's one of those areas where having a cohesive strategy can make all the difference between success and frustration. Over the years, as I’ve worked with enterprise-level organizations, I’ve seen countless instances where a lack of strategy caused initiatives to fail or fizzle out.
In my latest webinar, I breakdown the key components engineering leaders need to consider when building an insights strategy.
Why a Strategy Matters
At the heart of every successful software engineering team is a drive for three things:
- A culture of continuous improvement
- The ability to move from idea to impact quickly, frequently, and with confidence
- A software organization delivering meaningful value
These goals sound simple enough, but in reality, achieving them requires more than just wishing for better performance. It takes data, action, and, most importantly, a cultural shift. And here's the catch: those three things don't come together by accident.
In my experience, whenever a large-scale change fails, there's one common denominator: a lack of a cohesive strategy. Every time I’ve witnessed a failed attempt at implementing new technology or making a big shift, the missing piece was always that strategic foundation. Without a clear, aligned strategy, you're not just wasting resources—you’re creating frustration across the entire organization.
Sign up for a free, expert-led insights strategy workshop for your enterprise org.
Step 1: Define Your Purpose
The first step in any successful engineering insights strategy is defining why you're doing this in the first place. If you're rolling out developer productivity metrics or an insights platform, you need to make sure there’s alignment on the purpose across the board.
Too often, organizations dive into this journey without answering the crucial question: Why do we need this data? If you ask five different leaders in your organization, are you going to get five answers, or will they all point to the same objective? If you can’t answer this clearly, you risk chasing a vague, unhelpful path.
One way I recommend approaching this is through the "Five Whys" technique. Ask why you're doing this, and then keep asking "why" until you get to the core of the problem. For example, if your initial answer is, “We need engineering metrics,” ask why. The next answer might be, “Because we're missing deliverables.” Keep going until you identify the true purpose behind the initiative. Understanding that purpose helps avoid unnecessary distractions and lets you focus on solving the real issue.
Step 2: Understand Your People
Once the purpose is clear, the next step is to think about who will be involved in this journey. You have to consider the following:
- Who will be using the developer productivity tool/insights platform?
- Are these hands-on developers or executives looking for high-level insights?
- Who else in the organization might need access to the data, like finance or operations teams?
It’s also crucial to account for organizational changes. Reorgs are common in the enterprise world, and as your organization evolves, so too must your insights platform. If the people responsible for the platform’s maintenance change, who will ensure the data remains relevant to the new structure? Too often, teams stop using insights platforms because the data no longer reflects the current state of the organization. You need to have the right people in place to ensure continuous alignment and relevance.
Step 3: Define Your Process
The next key component is process—a step that many organizations overlook. It's easy to say, "We have the data now," but then what happens? What do you expect people to do with the data once it’s available? And how do you track if those actions are leading to improvement?
A common mistake I see is organizations focusing on metrics without a clear action plan. Instead of just looking at a metric like PR cycle times, the goal should be to first identify the problem you're trying to solve. If the problem is poor code quality, then improving the review cycle times might help, but only because it’s part of a larger process of improving quality, not just for the sake of improving the metric.
It’s also essential to approach this with an experimentation mindset. For example, start by identifying an area for improvement, make a hypothesis about how to improve it, then test it and use engineering insights data to see if your hypothesis is correct. Starting with a metric and trying to manipulate it is a quick way to lose sight of your larger purpose.
Step 4: Program and Rollout Strategy
The next piece of the puzzle is your program and rollout strategy. It’s easy to roll out an engineering insights platform and expect people to just log in and start using it, but that’s not enough. You need to think about how you'll introduce this new tool to the various stakeholders across different teams and business units.
The key here is to design a value loop within a smaller team or department first. Get a team to go through the full cycle of seeing the insights, taking action, and then quantifying the impact of that action. Once you've done this on a smaller scale, you can share success stories and roll it out more broadly across the organization. It’s not about whether people are logging into the platform—it’s about whether they’re driving meaningful change based on the insights.
Step 5: Choose Your Platform Wisely
And finally, we come to the platform itself. It’s the shiny object that many organizations focus on first, but as I’ve said before, it’s the last piece of the puzzle, not the first. Engineering insights platforms like Code Climate are powerful tools, but they can’t solve the problem of a poorly defined strategy.
I’ve seen organizations spend months evaluating these platforms, only to realize they didn't even know what they needed. One company in the telecom industry realized that no available platform suited their needs, so they chose to build their own. The key takeaway here is that your platform should align with your strategy—not the other way around. You should understand your purpose, people, and process before you even begin evaluating platforms.
Looking Ahead
To build a successful engineering insights strategy, you need to go beyond just installing a tool. An insights platform can only work if it’s supported by a clear purpose, the right people, a well-defined process, and a program that rolls it out effectively. The combination of these elements will ensure that your insights platform isn’t just a dashboard—it becomes a powerful driver of change and improvement in your organization.
Remember, a successful software engineering insights strategy isn’t just about the tool. It’s about building a culture of data-driven decision-making, fostering continuous improvement, and aligning all your teams toward achieving business outcomes. When you get that right, the value of engineering insights becomes clear.
Want to build a tailored engineering insights strategy for your enterprise organization? Get expert recommendations at our free insights strategy workshop. Register here.
Andrew Gassen has guided Fortune 500 companies and large government agencies through complex digital transformations. He specializes in embedding data-driven, experiment-led approaches within enterprise environments, helping organizations build a culture of continuous improvement and thrive in a rapidly evolving world.
Most organizations are great at communicating product releases—but rarely do the same for process improvements that enable those releases. This is a missed opportunity for any leader wanting to expand “growth mindset,” as curiosity and innovation is as critical for process improvement as it is product development.
Curiosity and innovation aren’t limited to product development. They’re just as essential in how your teams deliver that product. When engineering and delivery leaders share what they’re doing to find efficiencies and unclog bottlenecks, they not only improve Time to Value — they help their peers level up too.
Create Buzz Around Process Wins
Below is a template leaders can use via email or communication app (Slack, Microsoft Teams) to share process changes with their team. I’ve personally seen updates like this generate the same level of energy as product announcements—complete with clap emojis👏 and follow-up pings like “Tell me more!” Even better, they’re useful for performance reviews and make great resume material for the leads who author them (excluding any sensitive or proprietary content, of course).
Subject: [Experiment update]
[Date]
Experiment Lead: [Name]
Goal: [Enter the longer term goal your experiment was in service of]
Opportunity: [Describe a bottleneck or opportunity you identified for some focused improvement]
Problem: [Describe the specific problem you aimed to solve]
Solution: [Describe the very specific solution you tested]
Metric(s): [What was the one metric you determined would help you know if your solution solved the problem? Were there any additional metrics you kept track of, to understand how they changed as well?]
Action: [Describe, in brief, what you did to get the result]
Result: [What was the result of the experiment, in terms of the above metrics?]
Next Step: [What will you do now? Will you run another experiment like this, design a new one, or will you rollout the solution more broadly?]
Key Learnings: [What did you learn during this experiment that is going to make your next action stronger?]
Please reach out to [experiment lead’s name] for more detail.
Sample Use Case
Subject: PR Descriptions Boost Review Speed by 30%
March 31, 2025
Experiment Lead: Mary O’Clary
Goal: We must pull a major capability from Q4 2024 into Q2 2025 to increase our revenue. We believe we can do this by improving productivity by 30%.
Opportunity: We found lack of clear descriptions were a primary cause of churn & delay during the review cycle. How might we improve PR descriptions, with information reviewers need?
Problem: Help PR Reviewers more regularly understand the scope of PRs, so they don’t need to ask developers a bunch of questions.
Solution: Issue simple guidelines for what we are looking for PR descriptions
Metric(s): PR Review Speed. We also monitored overall PR Cycle Time, assuming it would also improve for PRs closed within our experiment timeframe.
Action: We ran this experiment over one 2 week sprint, with no substantial changes in complexity of work or composition of the team. We kept the timeframe tight to help eliminate additional variables.
Result: We saw PR Review Speed increase by 30%
Next Step: Because of such a great result and low perceived risk, we will roll this out across Engineering and continue to monitor both PR Review Speed & PR Cycle Time.
Key Learnings: Clear, consistent PR descriptions reduce reviewer friction without adding developer overhead, giving us confidence to expand this practice org-wide to help accelerate key Q2 2025 delivery.
Please reach out to Mary for more detail.
How Make This Process Stick
My recommendation is to appoint one “editor in chief” to issue these updates each week. They should CC the experiment lead on the communication to provide visibility. In the first 4-6 weeks, this editor may need to actively solicit reports and coach people on what to share. This is normal—you’re building a new behavior. During that time, it's critical that managers respond to these updates with kudos and support, and they may need to be prompted to do so in the first couple of weeks.
If these updates become a regular ritual, within ~3 months, you’ll likely have more contributions than you can keep up with. That’s when the real cultural shift happens: people start sharing without prompting, and process improvement becomes part of how your org operates.
I’ve seen this work in large-scale organizations, from manufacturing to healthcare. Whether your continuous improvement culture is just getting started or already mature, this small practice can help you sustain momentum and deepen your culture of learning.
Give it a shot, and don’t forget to celebrate the wins along the way.
Jen Handler is the Head of Professional Services at Code Climate. She’s an experienced technology leader with 20 years of building teams that deliver outcome-driven products for Fortune 50 companies across industries including healthcare, hospitality, retail, and finance. Her specialties include goal development, lean experimentation, and behavior change.
Output is not the same as impact. Flow is not the same as effectiveness. Most of us would agree with these statements—so why does the software industry default to output and flow metrics when measuring success? It’s a complex issue with multiple factors, but the elephant in the room is this: mapping engineering insights to meaningful business impact is far more challenging than measuring developer output or workflow efficiency.
Ideally, data should inform decisions. The problem arises when the wrong data is used to diagnose a problem that isn’t the real issue. Using misaligned metrics leads to misguided decisions, and unfortunately, we see this happen across engineering organizations of all sizes. While many companies have adopted Software Engineering Intelligence (SEI) platforms—whether through homegrown solutions or by partnering with company that specializes in SEI like Code Climate—a clear divide has emerged. Successful and mature organizations leverage engineering insights to drive real improvements, while others collect data without extracting real value—or worse, make decisions aimed solely at improving a metric rather than solving a real business challenge.
From our experience partnering with large enterprises with complex structures and over 1,000 engineers, we’ve identified three key factors that set high-performing engineering organizations apart.
1. Treating Software Engineering Insights as a Product
When platform engineering first emerged, early innovators adopted the mantra of “platform as a product” to emphasize the key principles that drive successful platform teams. The same mindset applies to Software Engineering Intelligence (SEI). Enterprise organizations succeed when they treat engineering insights as a product rather than just a reporting tool.
Data shouldn’t be collected for the sake of having it—it should serve a clear purpose: helping specific users achieve specific outcomes. Whether for engineering leadership, product teams, or executive stakeholders, high-performing organizations ensure that engineering insights are:
- Relevant – Focused on what each audience actually needs to know.
- Actionable – Providing clear next steps, not just numbers.
- Timely – Delivered at the right moment to drive decisions.
Rather than relying on pre-built dashboards with generic engineering metrics, mature organizations customize reporting to align with team priorities and business objectives.
For example, one of our healthcare customers is evaluating how AI coding tools like GitHub Copilot and Cursor might impact their hiring plans for the year. They have specific questions to answer and are running highly tailored experiments, making a custom dashboard essential for generating meaningful, relevant insights. With many SEI solutions, they would have to externalize data into another system or piece together information from multiple pages, increasing overhead and slowing down decision-making.
High-performing enterprise organizations don’t treat their SEI solution as static. Team structures evolve, business priorities shift, and engineering workflows change. Instead of relying on one-size-fits-all reporting, they continuously refine their insights to keep them aligned with business and engineering goals. Frequent iteration isn’t a flaw—it’s a necessary feature, and the best organizations design their SEI operations with this in mind.
2. The Value of Code is Not the Code
Many software engineering organizations focus primarily on code-related metrics, but writing code is just one small piece of the larger business value stream—and rarely the area with the greatest opportunities for improvement. Optimizing code creation can create a false sense of progress at best and, at worst, introduce unintended bottlenecks that negatively impact the broader system.
High-performing engineering organizations recognize this risk and instead measure the effectiveness of the entire system when evaluating the impact of changes and decisions. Instead of focusing solely on PR cycle time or commit activity, top-performing teams assess the entire journey:
- Idea generation – How long does it take to move from concept to development?
- Development process – Are teams working efficiently? Are bottlenecks slowing down releases?
- Deployment & adoption – Once shipped, how quickly is the feature adopted by users?
- Business outcomes – Did the feature drive revenue, retention, or efficiency improvements?
For example, reducing code review time by a few hours may seem like an efficiency win, but if completed code sits for six weeks before deployment, that improvement has little real impact. While this may sound intuitive, in practice, it’s far more complicated—especially in matrixed or hierarchical organizations, where different teams own different parts of the system. In these environments, it’s often difficult, though not impossible, for one group to influence or improve a process owned by another.
One of our customers, a major media brand, had excellent coding metrics yet still struggled to meet sprint goals. While they were delivering work at the expected rate and prioritizing the right items, the perception of “failed sprints” persisted, creating tension for engineering leadership. After further analysis, we uncovered a critical misalignment: work was being added to team backlogs after sprints had already started, without removing any of the previously committed tasks. This shift in scope wasn’t due to engineering inefficiency—it stemmed from the business analysts' prioritization sessions occurring after sprint commitments were made. A simple rescheduling of prioritization ceremonies—ensuring that business decisions were finalized before engineering teams committed to sprint goals. This small yet system-wide adjustment significantly improved delivery consistency and alignment—something that wouldn’t have been possible without examining the entire end-to-end process.
3. Shifting from Tactical Metrics to Strategy
There are many frameworks, methodologies, and metrics often referenced as critical to the engineering insights conversation. While these can be useful, they are not inherently valuable on their own. Why? Because it all comes down to strategy. Focusing on managing a specific engineering metric or framework (i.e. DORA or SPACE) is missing the forest for the trees. Our most successful customers have a clear, defined, and well-communicated strategy for their software engineering insights program—one that doesn’t focus on metrics by name. Why? Because unless a metric is mapped to something meaningful to the business, it lacks the context to be impactful.
Strategic engineering leaders at large organizations focus on business-driven questions, such as:
- Is this engineering investment improving customer experience?
- Are we accelerating revenue growth?
- Is this new approach or tool improving cross-functional collaboration?
Tracking software engineering metrics like cycle time, PR size, or deployment frequency can be useful indicators, but they are output metrics—not impact metrics. Mature organizations go beyond reporting engineering speed and instead ask: "Did this speed up product releases in a way that drove revenue?"
While challenging to measure, this is where true business value lies. A 10% improvement in cycle time may indicate progress, but if sales remain flat, did it actually move the needle? Instead of optimizing isolated metrics, engineering leaders should align their focus with overarching business strategy. If an engineering metric doesn’t directly map to a key strategic imperative, it’s worth reevaluating whether it’s the right thing to measure.
One of our retail customers accelerated the release of a new digital capability, allowing them to capture additional revenue a full quarter earlier than anticipated. Not only did this directly increase revenue, but the extended timeline of revenue generation created a long-term financial impact—a result that finance teams, investors, and the board highly valued. The team was able to trace their decisions back to insights derived from their engineering data, proving the direct connection between software delivery and business success.
Understanding the broader business strategy isn’t optional for high-performing engineering organizations—it’s a fundamental requirement. Through our developer experience surveys, we’ve observed a significant difference between the highest-performing organizations and the rest as it relates to how well developers understand the business impact they are responsible for delivering. Organizations that treat engineers as task-takers, isolated from business impact, consistently underperform—even if their coding efficiency is exceptional. The engineering leaders at top-performing organizations prioritize alignment with strategy and avoid the distraction of tactical metrics that fail to connect to meaningful business outcomes.
Learn how to shift from micro engineering adjustments to strategic business impact. Request a Code Climate Diagnostic.
Code Climate has supported thousands of engineering teams of all sizes over the past decade, enhancing team health, advancing DevOps practices, and providing visibility into engineering processes. According to Gartner®, the Software Engineering Intelligence (SEI) platform market is expanding as engineering leaders increasingly leverage these platforms to enhance productivity and drive business value. As pioneers in the SEI space, the Code Climate team has identified three key takeaways from partnerships with our Fortune 100 customers:
- Engineering Metrics Are Not Enough
- Engineering leaders that adopt a Software Engineering Intelligence (SEI) platform without a proper SEI strategy fail to extract value from the data
- Engineering leaders that adopt quantitative metrics without qualitative measures are missing the full picture
- Engineering leaders that adopt a Software Engineering Intelligence (SEI) platform without a proper SEI strategy fail to extract value from the data
- Hands-Off Approach Falls Short
- Approaching an SEI platform as a traditional turnkey SaaS product does not ensure team success
- Organizations that lack collaboration with an SEI solutions provider often struggle to drive adoption and understanding of engineering insights
- Approaching an SEI platform as a traditional turnkey SaaS product does not ensure team success
- Insights Alone Do Not Drive Outcomes
- Engineering leaders often struggle to translate insights from an SEI platform into actionable steps
- Engineering leaders often struggle to align engineering performance to meaningful business outcomes
- Engineering leaders often struggle to translate insights from an SEI platform into actionable steps
Empowering Engineering Leaders at Enterprise Organizations
The above takeaways have prompted a strategic shift in Code Climate’s roadmap, now centered on enterprise organizations with complex engineering team structure and workflows. As part of this transition, our flagship Software Engineering Intelligence (SEI) platform, Velocity, is now replaced by an enhanced SEI platform, custom-designed for each leader and their organization. With enterprise-level scalability, Code Climate provides senior engineering leaders complete autonomy over their SEI platform, seamlessly integrating into their workflows while delivering the customization, flexibility, and reliability needed to tackle business challenges.
Moreover, we understand that quantitative metrics from a data platform alone cannot transform an organization, which is why Code Climate is now a Software Engineering Intelligence Solutions Partner—offering five key characteristics that define our approach
- Tailored Solutions: We provide engineering solutions via quantitative insights and qualitative workshops that are specifically designed to meet the unique needs of enterprise engineering teams—moving beyond standard, black-box solutions.
- Strategic Collaboration: We enable our enterprise customers to build an SEI strategy, engaging with key stakeholders to align Code Climate’s solution and services with their broader business goals.
- Long-Term Partnership: Our strategic partnership with our enterprise customers is typically ongoing, focusing on long-term value rather than offering a standard insights platform. As an enterprise-level SEI solutions partner, we are invested in the sustained success of our customers.
- Expert Guidance: We offer expert guidance and actionable recommendations to help our enterprise customers navigate challenges, optimize performance, and achieve business goals.
- End-to-End Support: We provide comprehensive services, from advisory support and implementation to ongoing support and optimization.
A Message from the New CEO of Code Climate
"During my time at Pivotal Software, Inc., I met with hundreds of engineering executives who consistently asked, “How do I improve my software engineering organization?” These conversations revealed a universal challenge: aligning engineering efforts with business goals. I joined Code Climate because I'm passionate about helping enterprise organizations address these critical questions with actionable insights and data-driven strategies that empower engineering executives to drive meaningful change." - Josh Knowles, CEO of Code Climate
Ready to make data-driven engineering decisions to maximize business impact? Request a consultation.
Today, we’re excited to share that Code Climate Quality has been spun out into a new company: Qlty Software. Code Climate is now focused entirely on its next phase of Velocity, our Software Engineering Intelligence (SEI) solution for enterprise organizations
How It Started
I founded Code Climate in 2011 to help engineering teams level up with data. Our initial Quality product was a pioneer for automated code review, helping developers merge with confidence by bringing maintainability and code coverage metrics into the developer workflow.
Our second product, Velocity, was launched in 2018 as the first Software Engineering Intelligence (SEI) platform to deliver insights about the people and processes in the end-to-end software development lifecycle.
All the while, we’ve been changing the way modern software gets built. Quality is reviewing code written by tens of thousands of engineers, and Velocity is helping Fortune 500 companies drive engineering transformation as they adopt AI-enabled workflows.
Today, Quality and Velocity serve different types of software engineering organizations, and we are investing heavily in each product for their respective customers.
Where We're Going
To serve both groups better, we’re branching out into two companies. We’re thrilled to introduce Qlty Software, and to focus Code Climate on software engineering intelligence.
Over the past year, we’ve made more significant upgrades to Quality and our SEI platform, Velocity, than ever before. Much of that is limited early access, and we’ll have a lot to share publicly soon. As separate companies, each can double down on their products.
Qlty Software is dedicated to taking the toil out of code maintenance. The new company name represents our commitment to code quality. We’ve launched a new domain, with a brand new, enhanced edition of the Quality product.
I’m excited to be personally moving into the CEO role of Qlty Software to lead this effort. Josh Knowles, Code Climate’s General Manager, will take on the role of CEO of Code Climate, guiding the next chapter as an SEI solutions partner for technology leaders at large, complex organizations.
We believe the future of developer tools to review and improve code automatically is brighter than ever – from command line tools accelerating feedback loops to new, AI-powered workflows – and we’re excited to be on that journey with you.
-Bryan
CEO, Qlty Software
Technology is evolving very quickly but I don't believe it's evolving as quickly as expectations for it. This has become increasingly apparent to me as I've engaged in conversations with Code Climate's customers, who are senior software engineering leaders across different organizations. While the technology itself is advancing rapidly, the expectations placed on it are evolving at an even faster pace, possibly twice as quickly.
New Technology: AI, No-Code/Low-Code, and SEI Platforms
There's Generative AI, such as Copilot, the No-code/Low-code space, and the concept of Software Engineering Intelligence (SEI) platforms, as coined by Gartner®. The promises associated with these tools seem straightforward:
- Generative AI aims to accelerate, improve quality, and reduce costs.
- No-code and Low-code platforms promise faster and cheaper software development accessible to anyone.
- SEI platforms such as Code Climate enhance productivity measurement for informed decisions leading to faster, efficient, and higher-quality outcomes.
However, the reality isn’t as straightforward as the messaging may seem:
- Adopting Generative AI alone can lead to building the wrong things faster.
- No-code or Low-code tools are efficient until you hit inherent limitations, forcing cumbersome workarounds that reduce maintainability and create new challenges compared to native code development.
- As for SEI platforms, as we've observed with our customers, simply displaying data isn't effective if you lack the strategies to leverage it.
When I joined Code Climate a year ago, one recurring question from our customers was, "We see our data, but what's the actionable next step?" While the potential of these technologies is compelling, it's critical to address and understand their practical implications. Often, business or non-technical stakeholders embrace the promises while engineering leaders, responsible for implementation, grapple with the complex realities.
Navigating New Technology Expectations and Realities
Software engineering leaders now face increased pressure to achieve more with fewer resources, often under metrics that oversimplify their complex responsibilities. It's no secret that widespread layoffs have affected the technology industry in recent years. Despite this, the scope of their responsibilities and the outcomes expected from them by the business haven't diminished. In fact, with the adoption of new technologies, these expectations have only increased.
Viewing software development solely in terms of the number of features produced overlooks critical aspects such as technical debt or the routine maintenance necessary to keep operations running smoothly. Adding to that, engineering leaders are increasingly pressured to solve non-engineering challenges within their domains. This disconnect between technical solutions and non-technical issues highlights a fundamental gap that can't be bridged by engineering alone—it requires buy-in and understanding from all stakeholders involved.
This tension isn't new, but it's becoming front-and-center thanks to the promises of new technologies mentioned above. These promises create higher expectations for business leaders, which, in turn, trickle down to engineering leaders who are expected to navigate these challenges, which trickle down to the teams doing the work. Recently, I had a conversation with a Code Climate customer undergoing a significant adoption of GitHub Copilot, a powerful tool. This particular leader’s finance team told her, "We bought this new tool six months ago and you don't seem to be operating any better. What's going on?" This scenario reflects the challenges many large engineering organizations face.
Navigating New Technology Challenges and Taking Action
Here's how Code Climate is helping software engineering leaders take actionable steps to address challenges with new technology:
- Acknowledging the disconnect with non-technical stakeholders, fostering cross-functional alignment and realistic expectations. Facilitating open discussions between technology and business leaders, who may never have collaborated before, is crucial for progress.
- Clearly outlining the broader scope of engineering challenges beyond just writing code—evaluating processes like approval workflows, backlog management, and compliance mandates. This holistic view provides a foundation for informed discussions and solutions.
- Establishing a shared understanding and language for what constitutes a healthy engineering organization is essential.
In addition, we partner with our enterprise customers to experiment and assess the impact of new technologies. For instance, let's use the following experiment template to justify the adoption of Copilot:
We believe offering Copilot to _______ for [duration] will provide sufficient insights to inform our purchasing decision for a broader, organization-wide rollout.
We will know what our decision is if we see ______ increase/decrease.
Let’s fill in the blanks:
We believe offering Copilot to one portfolio of 5 teams for one quarter will provide sufficient insights to inform our purchasing decision for a broader, organization-wide rollout.
We will know what our decision is if we see:
- An increase in PR Throughput
- A decrease in Cycle Time
- No negative impact to Rework
- No negative impact to Defect Rate
Andrew Gassen leads Code Climate's enterprise customer organization, partnering with engineering leaders for organization-wide diagnostics to identify critical focus areas and provide customized solutions. Request a consultation to learn more.