An engineering team at a tech company was asked to speed up feature releases. They optimized for deployment velocity. Pushed more weekly updates. But soon, bugs increased and stability suffered. The company started getting more complaints.
The team had hit the target but missed the point—quality had taken a backseat to speed.
In engineering teams, metrics guide performance. But if not chosen carefully, they can create inefficiencies.
Goodhart’s Law reminds us that engineering metrics should inform decisions, not dictate them.
And leaders must balance measurement with context to drive meaningful progress.
In this post, we’ll explore Goodhart’s Law, its impact on engineering teams, and how to use metrics effectively without falling into the trap of metric manipulation.
Let’s dive right in!
Goodhart’s Law states: “When a metric becomes a target, it ceases to be a good metric.” It highlights how excessive focus on a single metric can lead to unintended consequences.
In engineering, prioritizing numbers over impact can cause issues like:
Understanding this law helps teams set better engineering metrics that drive real improvements.
Metrics help track progress, identify bottlenecks, and improve engineering efficiency.
But poorly defined KPIs can lead to unintended consequences:
When teams chase numbers, they optimize for the metric, not the goal.
Engineers might cut corners to meet deadlines, inflate ticket closures, or ship unnecessary features just to hit targets. Over time, this leads to burnout and declining quality.
Strict metric-driven cultures also stifle innovation. Developers focus on short-term wins instead of solving real problems.
Teams avoid risky but impactful projects because they don’t align with predefined KPIs.
Leaders must recognize that engineering metrics are tools, not objectives. Used wisely, they guide teams toward improvement. Misused, they create a toxic environment where numbers matter more than real progress.
Metrics don’t just influence performance—they shape behavior and mindset. When poorly designed, the outcome will be the opposite of why they were brought in in the first place. Here are some pitfalls of metric manipulation in software engineering:
When engineers are judged solely by metrics, the pressure to perform increases. If a team is expected to resolve a certain number of tickets per week, developers may prioritize speed over thoughtful problem-solving.
They take on easier, low-impact tasks just to keep numbers high. Over time, this leads to burnout, disengagement, and declining morale. Instead of building creativity, rigid KPIs create a high-stress work environment.
Metrics distort decision-making. Availability bias makes teams focus on what’s easiest to measure rather than what truly matters.
If deployment frequency is tracked but long-term stability isn’t, engineers overemphasize shipping quickly while ignoring maintenance.
Similarly, the anchoring effect traps teams into chasing arbitrary targets. If management sets an unrealistic uptime goal, engineers may hide system failures or delay reporting issues to meet it.
Metrics can take decision-making power away from engineers. When success is defined by rigid KPIs, developers lose the freedom to explore better solutions.
A team judged on code commit frequency may feel pressured to push unnecessary updates instead of focusing on impactful changes. This stifles innovation and job satisfaction.
Avoiding metric manipulation starts with thoughtful leadership. Organizations need a balanced approach to measurement and a culture of transparency.
Here’s how teams can set up a system that drives real progress without encouraging gaming:
Leaders play a crucial role in defining metrics that align with business goals. Instead of just assigning numbers, they must communicate the purpose behind them.
For example, if an engineering team is measured on uptime, they should understand it’s not just about hitting a number—it’s about ensuring a seamless user experience.
When teams understand why a metric matters, they focus on improving outcomes rather than just meeting a target.
Numbers alone don’t tell the full story. Blending quantitative and qualitative metrics ensures a more holistic approach.
Instead of only tracking deployment speed, consider code quality, customer feedback, and post-release stability.
For example, A team measured only on monthly issue cycle time may rush to close smaller tickets faster, creating an illusion of efficiency.
But comparing quarterly performance trends instead of month-to-month fluctuations provides a more realistic picture.
If issue resolution speed drops one month but leads to fewer reopened tickets in the following quarter, it’s a sign that higher-quality fixes are being implemented.
This approach prevents engineers from cutting corners to meet short-term targets.
Silos breed metric manipulation. Cross-functional collaboration helps teams stay focused on impact rather than isolated KPIs.
There are project management tools available that can facilitate transparency by ensuring progress is measured holistically across teams.
Encouraging team-based goals instead of individual metrics also prevents engineers from prioritizing personal numbers over collective success.
When teams work together toward meaningful objectives, there’s less temptation to game the system.
Static metrics become stale over time. Teams either get too comfortable optimizing for them or find ways to manipulate them.
Rotating key performance indicators every few months keeps teams engaged and discourages short-term gaming.
For example, a team initially measured on deployment speed might later be evaluated on post-release defect rates. This shifts focus to sustainable quality rather than just frequency.
Leaders should evaluate long-term trends rather than short-term fluctuations. If error rates spike briefly after a new rollout, that doesn’t mean the team is failing—it might indicate growing pains from scaling.
Looking at patterns over time provides a more accurate picture of progress and reduces the pressure to manipulate short-term results.
By designing a thoughtful metric system, building transparency, and emphasizing long-term improvement, teams can use metrics as a tool for growth rather than a rigid scoreboard.
A leading SaaS company wanted to improve incident response efficiency, so they set a key metric: Mean Time to Resolution (MTTR). The goal was to drive faster fixes and reduce downtime. However, this well-intentioned target led to unintended behavior.
To keep MTTR low, engineers started prioritizing quick fixes over thorough solutions. Instead of addressing the root causes of outages, they applied temporary patches that resolved incidents on paper but led to recurring failures. Additionally, some incidents were reclassified or delayed in reporting to avoid negatively impacting the metric.
Recognizing the issue, leadership revised their approach. They introduced a composite measurement that combined MTTR with recurrence rates and post-mortem depth—incentivizing sustainable fixes instead of quick, superficial resolutions. They also encouraged engineers to document long-term improvements rather than just resolving incidents reactively.
This shift led to fewer repeat incidents, a stronger culture of learning from failures, and ultimately, a more reliable system rather than just an artificially improved MTTR.
To prevent MTTR from being gamed, the company deployed a software intelligence platform that provided deeper insights beyond just resolution speed. It introduced a set of complementary metrics to ensure long-term reliability rather than just fast fixes.
Key metrics that helped balance MTTR:
By monitoring these additional metrics, leadership ensured that engineering teams prioritized quality and stability alongside speed. The software intelligence tool provided real-time insights, automated anomaly detection, and historical trend analysis, helping the company move from a reactive to a proactive incident management strategy.
As a result, they saw:
✅ 50% reduction in repeat incidents within six months.
✅ Improved root cause resolution, leading to fewer emergency fixes.
✅ Healthier team workflows, reducing stress from unrealistic MTTR targets.
No single metric should dictate engineering success. Software intelligence tools provide a holistic view of system health, helping teams focus on real improvements instead of gaming the numbers. By leveraging multi-metric insights, engineering leaders can build resilient, high-performing teams that balance speed with reliability.
Engineering metrics should guide teams, not control them. When used correctly, they help track progress and improve efficiency. But when misused, they encourage manipulation, stress, and short-term thinking.
Striking the right balance between numbers and why these numbers are being monitored ensures teams focus on real impact. Otherwise, employees are bound to find ways to game the system.
For tech managers and CTOs, the key lies in finding hidden insights beyond surface-level numbers. This is where Typo comes in. With AI-powered SDLC insights, Typo helps you monitor efficiency, detect bottlenecks, and optimize development workflows—all while ensuring you ship faster without compromising quality.
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