Building a Future-Proof Engineering Metrics Strategy: Insights and Tools for 2025 and Beyond

As engineering teams grow, tracking productivity becomes a complex challenge. It can be a struggle to measure how to balance strategic and operational work and decide where resources and efforts should be focused. With strategic decisions like AI investment and team expansions at stake, an effective engineering metrics strategy can illuminate productivity issues and pave the way for data-driven decisions. Here, we dive into why a metrics strategy matters, the key data you need, best practices, and how to leverage these insights for powerful decision-making.

Why You Need an Engineering Metrics Strategy

In large organizations, understanding team performance is more than individual productivity. Managing various sub-teams, each with different goals and operational requirements, makes it challenging to gauge how effectively teams are distributed, especially across strategic and operational work. Without solid metrics, leaders can only make educated guesses regarding the optimization of their team, and this can have impact on their strategic planning. If your gut feel is wrong, this can have long-lasting and widespread impact, including delayed product launches, and missed innovation and R&D opportunities.

For instance, while it may be obvious when one person is overwhelmed with operations and maintenance (O&M) tasks, the broader impact across a team often goes unnoticed. This becomes a problem when it comes to R&D allocation and team distribution decisions across multiple teams. Many industry leaders we spoke to face similar challenges. As Ev Haus, Zenhub’s VP of Engineering, points out, “…figuring out which engineer is most needed where is an ongoing struggle…in larger organizations, [these decisions] can quickly become unmanageable without some tooling to guide you.”

This data gap leaves leaders asking fundamental questions, such as:

• Which teams and individuals are best suited for which specific projects?
• How much time is spent on strategic versus operational work, and how can we redirect efforts toward more strategic initiatives?
• Are our top engineers spending too much time on low-value tasks?
• Which projects are at risk due to resource constraints or underperformance?

George Champlin-Scharff, VP of Product at Zenhub, summarizes this challenge, saying, “You may not know who is best suited for what kinds of work. You may not know why a 10-person team is delivering 5 units of work regularly.” Implementing a metrics-driven strategy provides the transparency necessary to address these questions.

The Data You Need To Build an Engineering Metrics Strategy

Building an effective engineering metrics strategy starts with capturing the right metrics and breaking down work into clear categories. We’ve found that categorizing work into the “Big 4” — Admin & IT, Bugs, Operations & Maintenance, and Strategic Work — offers a solid foundation for tracking and optimizing productivity.

Traditional metrics, like team velocity, are often too broad and on large teams this often leaves far too much room for interpretation. As Ev Haus notes, without more detailed data, it’s challenging to answer questions like, “What’s the amount of work that somebody has in progress? How much work are they able to get through in a week?”

For large teams, instead of just velocity, you need data on the team’s true capacity. Breaking down work into different categories allows leaders to get a better idea of where time is really being spent so they can redirect teams to work on different areas. Without proper data categorization, this is impossible.

To be accurate, engineering metrics must capture 100% of the data on work being done. Many tools on the market, like Jellyfish and LinearB, base their data on Jira, and according to LinearB, “An average of 30% of work is unaccounted for in Jira–meaning there isn’t a ticket or issue linked to pull requests.” For most leaders, basing their decisions on just 70% of available data simply isn’t good enough.

The last piece of data is qualitative. It’s important to have a good grasp of the nuances of your teams before you start making strategic decisions, including individual skill sets and interpersonal relationships. For example, when we spoke to some of our customers, some teams were intentionally assigned to work on Admin and IT rather than strategic work because that’s simply what they were best at. These intricacies can often be overlooked when optimizing a team. Most of the tools on the market currently take into no account qualitative factors when it comes to generating engineering metrics.

Best Practices for Setting Up an Engineering Metrics Strategy

Creating a robust engineering metrics strategy starts with gathering the right data, and Zenhub’s Engineering Investment Report (EIR) is a valuable tool in this process. Here’s how EIR helps organizations gather the essential metrics we’ve discussed:

• EIR uses AI to automatically categorize your team’s work into the “Big 4” categories. Using AI means that the data can be generated with minimal manual input and no new processes for your teams. This means no manual tagging of issues, adding labels or clicking around your task management software to place things into the right categories.
• The reports generated as part of the EIR provide data both on the team and individual levels. This makes optimizing both levels possible, including moving team members to projects where their skills are most valuable, ensuring teams are properly sized, and more.
• EIR allows you to measure how much time and effort is spent on all categories of work so that you get a clear view of your team’s true capacity. Often, from top-level metrics, it can seem like teams have reached capacity when, in reality, teams are just focused on the wrong kinds of work. For example, in talks with many of our customers, we found that their teams unknowingly spent far more time on Operations and Maintenance work than they had thought.
• Zenhub’s EIR is truly based on 100% of data since it pulls data directly from GitHub. This means that there’s no manual cross-referencing to ensure you get a full picture of the work being done. As you might imagine, missing data can lead to severe miscategorization and interpretation of data.
• With EIR, we prioritize considering each team’s intricacies. This includes consulting with our customers and developing data that includes qualitative factors. We take the time to understand each unique case so that the data interpretation is based on what each team really looks like.
• When building EIR, we focused on evaluating all of the current options for gathering engineering metrics, and focused on building a robust tool that uses all of the data, while also being more cost-effective and time-saving than alternatives. Read more about the options for gathering engineering metrics in our previous blog.

With comprehensive data in hand, leaders can take action based on real insights. In the next section, we’ll look at how to apply this data to make impactful decisions.

Making Decisions Based on Engineering Metrics

During the development of EIR, Zenhub consulted with many customers to understand their productivity challenges. We learned that leaders often know that their teams have serious inefficiencies, but the challenge is figuring out where those challenges are without adding additional processes and significant manual work. However, the next steps became very clear once we ran an EIR for them.

George Champlin-Scharff explains, “With EIR, we help guide you towards unblocking people, breaking bottlenecks, and distributing work in a way that brings you closer to your strategic goals. EIR provides point-in-time checks to help you monitor improvements and identify dependencies you may not even know exist.”

An example of the impact of EIR can be seen in our work with the head of engineering at a leading market intelligence firm. He suspected that some of his top engineers were being diverted into support tasks, reducing their ability to focus on high-value strategic work.

He needed the data to prove that some of his best engineers were being bogged down by support work, “I’m trying to make the case that we need more support staff so not everything ends up with our senior tenured developers that should be focused on strategic work”

We ran an EIR for them and found that his hunch was right: their teams were spending far too much time on Operations & Maintenance work and far too little on strategic work. The problems extended beyond the team level. On the individual level, their most skilled and productive worker spent a disproportionate amount of work on bug fixes and admin work.

Engineering metrics, when properly gathered and interpreted, can drive transformative decisions. Check out our customer story here.

Incorporating an engineering metrics strategy means more than simply tracking performance; it’s about gaining deep insights to drive decisions that align with your organization’s strategic goals. By leveraging tools like EIR, leaders can move beyond intuition to make precise adjustments that enhance productivity, keep teams aligned, and ensure that everyone’s efforts contribute to meaningful outcomes.

Interested in getting these insights for your organization? Let’s talk.