WIP: Effective use of eng metrics

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+---
+title: "Effective Use of Engineering Metrics"
+description: ""
+createdAt: 1741275333951
+updatedAt: 1741275333951
+authors: ["david"]
+category: "Learn Javascript"
+editors: ["velimir"]
+draft: false
+abstract: ""
+image: "/images/excluding-properties-typescript-omit.png"
+---
+
+Developer and engineering productivity metrics are an increasingly popular tool for software teams and leaders. When used correctly, these metrics help illuminate how work gets done, where bottlenecks occur, and how teams can improve over time.
+
+However, metrics can also be misused or misunderstood, leading to fear, gaming of the numbers, or a false sense of control.
+
+The key premise is that while team-level metrics are valuable for driving improvement, measuring individual developer "productivity" is problematic and counterproductive.
+
+## Why Engineering Metrics Matter
+
+At their core, engineering metrics provide **data-driven insight into the software development process**. They give teams and leadership a way to gauge progress and identify issues in an otherwise abstract workflow.
+
+For example, the popular **DORA metrics** (from Google’s DevOps Research and Assessment) measure aspects like deployment speed and reliability. Tracking such metrics over time can reveal a team’s overall agility and health, signaling where things are improving or where problems might be hiding.
+
+Metrics can highlight **bottlenecks**, perhaps code is written quickly but changes get stuck waiting for code review or deployment and by quantifying these process pains, organizations can pinpoint where to invest effort (e.g. improving CI/CD pipeline or adding reviewers) to accelerate delivery.
+
+Equally important, metrics allow engineering leaders to **make informed, strategic decisions**. In a retrospectives, teams can ask: Did our recent tooling change reduce the time it takes to release code? Is our incident response time trending down after adopting a new on-call rotation? Concrete metrics provide an objective answer to these questions. Especially with the rise of remote and asynchronous teams, having quantifiable measures helps leaders "take the pulse" of team performance even when they can’t directly observe daily work. 
+
+In short, good metrics give visibility into the otherwise invisible aspects of software development, helping teams celebrate improvements and detect regressions in how they build and ship products.
+
+That said, **the purpose of metrics is to guide improvement**, not to set arbitrary targets. Used as a compass, metrics can keep everyone aligned on important outcomes (like faster cycle times or fewer outages) and enable continuous improvement. 
+
+The **best metrics drive conversations and learning**, for instance, if deployment frequency drops one quarter, the data should spark a discussion about causes (maybe the team took on heavy refactoring or dealt with unforeseen obstacles) and possible remedies. 
+
+When treated as **indicators rather than absolute goals**, productivity metrics can be immensely helpful in making software delivery more efficient and resilient.
+
+## Principles for Using Metrics Effectively
+
+Implementing developer metrics requires careful thought. Here are some guiding principles to ensure metrics **help rather than hurt** your team:
+
+### Focus on Team-Level Insights, Not Individuals 
+
+Software development is a collaborative effort, and metrics should reflect that. It’s widely agreed that **measuring individual developer “productivity” by metrics like commit counts or lines of code is a terrible idea**. 
+
+One engineer cannot fully control all the factors (requirements clarity, code dependencies, review processes, etc.) that influence these numbers. As one engineering leader put it, applying metrics designed for team performance (like deployment frequency or story points completed) to an individual is unfair because an individual is only one part of a larger system. 
+
+If a particular metric is lagging, it’s far more productive to discuss how the **team** or process can improve than to blame a single developer. By keeping metrics aggregated at the team or organization level, you **foster collaboration over competition** and avoid pitting developers against each other.
+
+### Ensure Metrics Encourage the Right Behaviors
+
+Any metric can be gamed or twisted if taken as a strict target. This is a manifestation of *Goodhart’s Law*, which states that "when a measure becomes a target, it ceases to be a good measure". 
+
+To avoid this, be thoughtful about what behaviors your metrics incentivize. For example, measuring pure output (like lines of code written or tickets closed) might push people to favor quantity over quality. 
+
+A classic cautionary tale: one company tried to evaluate individual performance by counting Jira tickets, code pushes, and code review comments. The predictable result was developers writing scripts to auto-close trivial tickets and make superfluous commits, just to bump their numbers.  
+
+In another real example, a team tied promotions to meeting certain productivity OKRs, which led developers to hurriedly ship code right before review deadlines, **and caused a spike in production incidents as a result**.  
+
+These cases highlight the danger of **perverse incentives**: if hitting the metric becomes more important than the work it’s supposed to represent, the team will **optimize for the metric at the expense of actual value**. To use metrics effectively, choose measures that align with healthy engineering practices (for instance, you might track how often code is released *with zero rollback* rather than just how often code is released). Always ask, "If we focus on this metric, what behaviors will it encourage, and are those what we want?"
+
+### Use Metrics as a Starting Point, Not the Final Judgment
+
+Metrics are **indicators** and conversation starters—they are not infallible measures of reality. It’s crucial to combine quantitative metrics with qualitative context. Numbers alone can be misleading if viewed in isolation or without understanding the full context behind them.
+
+For example, if your team's **deployment frequency** suddenly drops over a quarter, the numbers alone won’t explain *why* this happened. There could be numerous reasons: perhaps the team shifted their focus to paying down technical debt, onboarded new team members, or worked on complex infrastructure changes that aren’t easily measured. 
+
+Metrics should serve as **the beginning of an investigation**, not the final verdict.
+
+Encourage open discussions around anomalies or trends—if your **code review turnaround time** spikes, ask the team what's happening rather than making assumptions. Maybe the delays arose from tackling unexpected production issues rather than inefficiencies or negligence. 
+
+Ensuring that developers know metrics are there to facilitate discussion rather than judge their performance is essential for maintaining trust.
+
+
+### Prefer Actionable Metrics Over Vanity Metrics 
+
+Good engineering metrics should tie to outcomes the team cares about (quality, speed, customer impact) and be something the team can influence through their actions. 
+
+Avoid vanity metrics, numbers that look impressive but don’t drive decisions. For instance, “hours spent coding” or “number of commits” might sound like productivity measures but have little correlation with real outcomes and can be easily skewed. 
+
+Instead, focus on metrics that, when observed to change, lead the team to consider specific actions. If **code review turnaround time** (how long it takes for a pull request to get reviewed and merged) is high, that’s actionable: the team can experiment with smaller pull requests, more reviewers, or dedicated review time to improve it. 
+
+If **cycle time** from development to deployment is long, the team can investigate which stage (coding, testing, deploying) is the slowest and target improvements there. 
+
+Every metric you track should have a purpose – ask “What will we do if this metric goes up or down?” If you don’t have a clear answer, that metric might not be worth tracking. 
+
+By keeping metrics **meaningful and actionable**, you ensure they remain a tool for positive change rather than noise.
+
+
+## Best Practices for Rolling Out Metrics Dashboards
+
+Introducing a new metrics program or dashboard into an engineering organization is not just a technical implementation – it’s **a cultural change management exercise**. Developers may be naturally skeptical or nervous about being measured, and leaders might have concerns about what the data will reveal. To ensure a smooth rollout, consider these best practices:
+
+### Engage Key Stakeholders Early and Often
+
+Don’t surprise your leadership or teams with a new metrics dashboard out of the blue. **Nothing derails a rollout faster than blindsiding leaders**.  Long before the dashboards go live, have conversations with engineering managers, senior engineers, and executives like the CTO or VP of Engineering. Explain what you intend to measure and *why* it matters. When stakeholders are involved early, you can address their questions and turn them into champions of the initiative. One-on-one discussions tend to work better than large presentations initially – in a big meeting, a single vocal skeptic could create resistance or fear in others. Instead, meet individually with key influencers to build buy-in and incorporate their feedback. By the time you roll out, these stakeholders should feel ownership and support the metrics program.
+
+## Be Transparent About Purpose and Usage.
+
+Transparency is critical to avoid misunderstanding and fear. Make it crystal clear **what the metrics will measure, how the data will be used, and how it will **not** be used**. A great way to do this is to publish an **internal FAQ or documentation** for all engineers. In this FAQ, describe the metrics being collected (e.g., deployment frequency, lead time, etc.), define each one, and explain why it’s important. Also explicitly state that the metrics are **focused on processes and team outcomes – not individual performance evaluations**. For example, clarify that you’re measuring things like pull request flow or deployment frequency to improve the system, not to scrutinize who committed how many lines of code. This level of openness helps prevent rumors and anxiety. When everyone understands the intent (to help the team improve, not to punish), it builds trust. Encourage questions and be honest about the limitations of metrics. The goal is to eliminate ambiguity and assure the team that there’s **no “gotcha” hidden agenda** in the metrics initiative.
+
+### Lead with the Benefits for Developers and Teams
+When introducing metrics, frame them in terms of **how they will help the developers** doing the work. Engineers will be more receptive if they see metrics as a tool to make their day-to-day easier, rather than another report card. For instance, communicate that by tracking cycle time and code review delays, the team can identify pain points and make changes that allow code to get to production faster with less frustration. Emphasize wins: maybe the metrics will reveal repetitive manual steps that could be automated, freeing developers from boring tasks. If the data shows, say, too many incidents happening on Fridays, the team can adjust release schedules to reduce firefighting. **Leading with these positive, concrete benefits** helps developers view the metrics as *their* tool for continuous improvement, not just leadership’s surveillance mechanism. It’s also useful to share success stories or case studies. For example, “Team X reduced their average PR wait time by 50% after measuring it and allocating focus time for reviews, which eliminated a major source of frustration.” This kind of narrative reinforces that the metrics program is about empowerment and success, not blame.
+
+### Invest in Change Management and Education. 
+
+Rolling out metrics is as much about culture as it is about numbers. Don’t underestimate the need for training, communication, and iteration. Plan a **phased rollout** if possible: perhaps pilot the dashboard with a small team or two, learn from their feedback, then expand. Provide training sessions or demos on how to interpret the dashboards. Engineers who are unfamiliar with certain metrics (for example, not everyone has seen lead time or DORA metrics before) may need context to understand what the numbers mean and how to act on them. **Over-communicate** during the rollout – use multiple channels like email, Slack, team meetings, and town halls to keep everyone informed. Remind people before and after launching the metrics: for instance, send a heads-up (“Metrics dashboard goes live next week, here’s what to expect...”) and follow up later (“It’s been a month, here’s how teams are using it...”). Reiterate the purpose frequently, so it stays fresh that this is about improving our processes and results. Also, invite continuous feedback. Maybe an anonymous survey after a quarter of using the metrics to ask developers if the dashboards are helpful, or if any metric is causing confusion or unintended stress. Show that you’re willing to adjust the program based on the team’s input – this flexibility demonstrates that the goal is **truly to help the team**, not to rigidly enforce something. In short, treat the rollout itself as an agile iteration: communicate, listen, and refine. By devoting time and effort to change management (instead of treating it as an afterthought), you’ll build much stronger acceptance and even enthusiasm for the metrics initiative.
+
+## Conclusion: Metrics Should Serve Developers, Not Control Them
+
+In conclusion, developer productivity metrics can be a powerful ally in improving how software is built – **if we use them with care, intent, and empathy**. The purpose of tracking these numbers is not to turn human creativity and collaboration into a sterile scoreboard. Rather, it’s to shine a light on areas where teams can streamline workflow, eliminate pain points, and celebrate progress. Effective metric programs are characterized by **transparency, trust, and continuous learning**. When done right, engineers see metrics as helpful feedback, much like automated tests or linters – a mechanism that catches issues early and guides them to be better at their craft.
+
+Always remember that **people build software, not metrics**. The moment metrics start to drive a wedge between team members or create anxiety, their value evaporates. As an engineering leader or change agent introducing metrics, keep reiterating: **metrics exist to serve the team, not to control it**. They should inform decision-making, not dictate it. They should prompt questions, not stifle them. And they should *never* substitute for human judgment. The best organizations use metrics as a mirror for the team – sometimes it highlights things we didn’t notice, sometimes it confirms what we suspected – and then the team, empowered with that knowledge, takes action to improve. 
+
+In the end, the success of a developer metrics initiative isn’t measured by hitting a vanity target or producing fancy dashboards. It’s measured by whether the engineering **culture improves**: Are deployments safer and more frequent? Are developers happier and more productive because blockers are being removed? Are customers getting value faster and with higher quality? Those are the outcomes that matter. By focusing on principles and avoiding pitfalls, you can ensure that productivity metrics become a boon to your engineering organization – **a tool for empowerment and insight, rather than a source of control or fear**.