Engineering metrics tools won’t solve problems if there isn’t communication about expectations in place. Learn how leaders are connecting engineering metrics with business outcomes.
Engineering organizations are waking up to something that used to be optional: measurement.
Not vanity dashboards. Not a quarterly “engineering metrics review” that no one prepares for. Real measurement that connects delivery speed, quality, and reliability to business outcomes and decision-making.
That shift is a good sign. It means engineering leaders are taking the craft seriously.
But there are two patterns I keep seeing across the industry that turn this good intention into a slow-motion failure. Both patterns look reasonable on paper. Both patterns are expensive. And both patterns lead to the same outcome: a metrics tool becomes shelfware, trust erodes, and leaders walk away thinking, “Metrics do not work here.”
Engineering metrics do work. But only when leaders use them the right way, for the right purpose, with the right operating rhythm.
Here are the two patterns, and how to address them.
Pattern #1: “We bought the tool, gave it to leaders, and expected behavior to change”
This is the silent killer.
An engineering executive buys a measurement platform and rolls it out to directors and managers with a message like: “Now you’ll have visibility. Use this to improve.”
Then the executive who sponsored the initiative rarely uses the tool themselves.
No consistent review cadence. No decisions being made with the data. No visible examples of metrics guiding priorities. No executive-level questions that force a new standard of clarity.
What happens next is predictable.
Managers and directors conclude that engineering metrics are optional. They might log in at first. They might explore the dashboards. But soon the tool becomes “another thing” competing with real work. And because leadership is not driving the behavior, the culture defaults to the old way: opinions, anecdotes, and local optimization.
If leaders are not driving direction with data, why would managers choose to?
This is not a tooling problem. It is a leadership ownership problem.
What to do instead: make metrics executive-owned, not manager-assigned
If measurement is important, the most senior leaders must model it.
That does not mean micromanaging teams through numbers. It means creating a clear expectation that engineering metrics are part of how the organization thinks, communicates, and makes decisions.
Here is what executive ownership looks like in practice:
- The executive sponsor uses the tool publicly. In staff meetings, in reviews, in planning, in post-incident discussions.
- Metrics show up in decision moments. Prioritization, investment tradeoffs, risk calls, capacity conversations.
- Leaders ask better questions because they have data. Not “Why are you slow?” but “What is slowing you down, and what would move it?”
- A consistent cadence exists. Not random dashboard reviews. A repeatable operating rhythm.
When executives do this, managers follow. Not because they are told to, but because the organization has made measurement real.
Pattern #2: “Buying a measurement tool will fix our engineering problems”
This is the other trap, and it is even more common.
There is a false belief that if an organization has DORA metrics, improvements in throughput and quality will automatically follow. Like measurement itself is the intervention.
But measurement does not create performance. It reveals performance.
A tool can tell you:
- how long changes take to reach production
- how often you deploy
- how frequently you experience failure
- how quickly you recover
Those are powerful signals. But they do not change anything on their own.
If the system that produces those numbers stays the same, the numbers stay the same.
This is why organizations buy tools, instrument everything, and still feel stuck. They measured the pain, but never built the discipline to diagnose and treat the cause.
What to do instead: treat engineering metrics as instrumentation, not transformation
If you want metrics to lead to improvement, you need two things:
- Clear definitions and shared understanding
- A metrics practice that turns numbers into decisions and experiments
Without definitions, metrics turn into arguments. Everyone interprets the same number differently, then stops trusting the system.
Without a practice, metrics turn into observation. You notice, you nod, then you go back to work.
The purpose of measurement is not to create pressure. It is to create clarity. Clarity about where the system is constrained, what tradeoffs you are making, and whether your interventions actually helped.
The real goal: measure change, not teams
Here is the shift that unlocks everything:
The goal is not to measure engineers.
The goal is to measure the system.
More specifically, the goal is to prove whether a change you made actually improved outcomes.
A change could be:
- a tooling change
- a process change
- a policy change
- a staffing or org change
- a reliability investment
- a platform improvement
- a CI/CD modernization effort
If you cannot measure movement after you make a change, you are operating on opinions and hope.
If you can measure movement, you can run engineering like a disciplined improvement engine.
This is where DORA metrics become extremely valuable, when they are used as confirmation and learning, not as a scoreboard.
Engineering metrics should confirm reality, not replace judgment
The best leaders I have worked with do not hand leadership over to dashboards. They use metrics as confirmation of what they already sense, and as a way to test assumptions.
- “We believe code reviews are a bottleneck. Do we see it in cycle time breakdowns?”
- “We believe flaky tests are slowing delivery. Do we see increased rework or longer lead time?”
- “We believe incident recovery is too manual. Do we see MTTR improve after automation?”
- “We believe our deployment process is too risky. Does change failure rate drop after we change release strategy?”
That is the role of measurement. It turns gut feel into validated understanding, then turns interventions into provable outcomes.
A practical operating model that works
If you want measurement to drive real improvement, here is a straightforward structure that scales.
1) Define what “good” means in your context
Use DORA as a baseline, but make definitions explicit:
- What counts as a deployment?
- What counts as a production failure?
- How do you define lead time?
- How do you define recovery?
This prevents endless debates and keeps the organization aligned.
2) Establish a simple cadence
You do not need a heavy process. You need consistency.
A strong starting point:
- Weekly: team-level review of flow and reliability signals, focused on removing friction
- Monthly: leadership review focused on trend movement, constraints, and investments
- Quarterly: strategic review to decide where to focus improvement efforts next
3) Pair every metric with a lever
A metric without a lever becomes a complaint.
Examples:
- If lead time is high, what levers do you pull?
- reduce batch size, improve trunk-based practices, improve test speed, remove manual approvals
- reduce batch size, improve trunk-based practices, improve test speed, remove manual approvals
- If change failure rate is high, what levers do you pull?
- improve testing strategy, release safety patterns, observability, rollback mechanisms
- improve testing strategy, release safety patterns, observability, rollback mechanisms
- If MTTR is high, what levers do you pull?
- better alerting, runbooks, ownership clarity, automated remediation, incident practices
- better alerting, runbooks, ownership clarity, automated remediation, incident practices
4) Run experiments and measure outcomes
This is the part most organizations skip.
Pick one change. Implement it. Measure before and after. Learn. Repeat.
Improvement becomes a system, not a motivational speech.
5) Make leaders the model
This brings us back to Pattern #1.
If executives use the tool and drive decisions with it, measurement becomes real. If they do not, the tool becomes optional, and optional always loses.
Where the best organizations land
The organizations that do this well eventually stop talking about “metrics adoption.” They talk about “how we run the business.”
Measurement becomes part of how engineering communicates with leadership, how priorities get set, how teams remove friction, and how investment decisions are made.
And the biggest shift is this:They stop expecting a measurement tool to fix problems.They use measurement to prove that the problems are being fixed.
That is the point. Not dashboards, not reporting, not performance theater: Clarity, decisions, experiments, and outcomes.
In the end, measurement is not the transformation. It is the instrument panel that tells you whether your transformation is working.
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I’m the Director of EngX Advisory, where I help engineering organizations measure what matters and turn delivery signals into meaningful improvement. My work focuses on building practical metrics practices, aligning leadership around clear definitions, and using data to validate assumptions and guide better decisions. I’m passionate about making measurement trustworthy, actionable, and tied to real outcomes like throughput, reliability, and quality.
