Using Velocity to Identify Patterns of Top Performing Teams - Code Climate Blog

Using Velocity to Identify Patterns of Top Performing Teams

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In this time of global and economic uncertainty, it’s never been more important to have a quick way of knowing which engineering processes are working and which are broken.

Nov 18, 2020

3 min read

In this time of global and economic uncertainty, it’s never been more important to have a quick way of knowing which engineering processes are working and which are broken.

And while it can be tempting to focus on bottlenecks and struggling team members, it can be even more useful to look at the practices, behaviors, and culture of your strongest teams.

This post runs through a framework for using Velocity, our Software Engineering Intelligence (SEI) platform, to identify and scale the most successful habits of high performing teams.

Finding the Top 5% of Your Organization

While all engineering organizations might define success slightly differently, there are two metrics within Velocity that indicate an extremely proficient engineering team:

In Velocity’s Compare report, you can select both of these metrics and compare them across your organization to identify the teams that are merging the most the fastest.

Once you’ve identified your strongest teams using success metrics like Throughput and Cycle Time, you’ll want to dig into what has made them successful. For this, you’ll need different, diagnostic metrics.

Identifying the Best Practices of Your Strongest Engineering Team

You can think of your software delivery process in three phases:

Typically, a strong engineering team will move faster in one of these stages.

Velocity makes it easy to look at these three metrics side-by-side. You can view them as bar graph clusters by week or by month.

Or, you can view these metrics by team.

Here, we can see that the top team — Gryffindor — is most distinguished by their extremely fast Review Speed. Although they have a long Time to Open and Time to Merge, this isn’t remarkable when looking at the other teams. The other teams (especially the Hogwarts team) frequently had work stuck in the review process.

Pair your quantitative analysis with qualitative information, and speak to the members of the Gryffindor team. Find out what makes their review process different from the other teams’ processes, and think about ways the other teams can apply those learnings.

DORA metrics are also useful to identify high performing teams within the organization.

Creating a Blueprint For Your Entire Organization

Now that you’ve identified your top-performing teams and their defining characteristics, you can create a blueprint for better processes across your organization.

One of our customers, a health data analytics solution, used Velocity following a Series B funding round to level-up the way they coached engineers at scale.

Their VP of Engineering had been brought on to help build out the engineering department. But after getting to know his teams, he realized that there wasn’t any consistency in how and when teams shipped features to end-customers.

The VP of Engineering worked with his engineering leads and product managers to identify agile practices that worked for his team, then shared them org-wide. Together, they created documentation and set up onboarding and mentoring around encouraging healthy coding habits at scale.

With stronger processes in place, the team was able to increase PR throughput 64%. With objective data and learnings from your highest performing teams, you’ll be able to replicate successful practices across your organization, and help boost productivity at scale.

Find out how Code Climate Velocity can help your team improve Cycle Time and PR Throughput by booking a demo .

Why a Strategy Matters

At the heart of every successful software engineering team is a drive for three things:

  1. A culture of continuous improvement
  2. The ability to move from idea to impact quickly, frequently, and with confidence
  3. 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.

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:

  1. Who will be using the developer productivity tool/insights platform?
  2. Are these hands-on developers or executives looking for high-level insights?
  3. 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.