Building a DevOps Dashboard: What Metrics to Include and Why

By almost every count on the board, your team is thriving. Commits are up, tickets are closing, and the panels are green. Yet releases keep slipping, and nobody can point to exactly where in the pipeline things are breaking down. 

The problem usually traces to the same place. Most DevOps dashboards are built around outputs rather than how work actually flows through your delivery system. They don’t provide enough visibility into delivery outcomes, productivity, and predictability, so engineering leaders can’t use them to improve engineering performance or business outcomes.

Below, we explain why most dashboards mislead organizations, what good dashboards actually do, and how to build a DevOps dashboard that’s optimized for decision-making, rather than just reporting.

Why most DevOps dashboards fail

The failure starts with how the board gets built: from the data that just happens to be available, not from the signals leaders need to make decisions. For example, you’ll often find that a metric exists in Jenkins, so it gets a panel, no questions asked. 

Avitesh Kesharwani, Transformation Delivery Leader at Genpact, discusses this problem. 

“The biggest mistake I’ve seen is building dashboards around what tools can measure instead of what teams actually need to know,” he says. 

During a large cloud migration, his boards showed green across infrastructure metrics, while business processes kept failing due to downstream dependencies and integration gaps that leaders had no clear visibility into.

Goodhart’s Law also points to a common mistake in how teams use dashboards. Once teams treat a metric as a goal, they optimize for the metric rather than the outcome it represents. For example, a team rewarded for shipping more often can split a single release into 5 or push trivial changes just to move the number. The panels stay green, deployment frequency improves, but delivery doesn’t.

Denis Tiumentsev, Lead DevOps Engineer at Integro Technologies, asks this question before adding any panel. “What would I do differently if this number doubled?” he says. “If there’s no answer to that, then the panel isn’t needed.”

What a good DevOps dashboard actually does

A good dashboard acts less like a history book and more like a navigation system. Instead of simply reporting what’s already happened, it provides complete visibility into how work flows through the SDLC, so you can understand what needs improvement before focusing on optimization. 

Before a metric earns a place on the board, it should pass a short test.

Dashboard usefulness checklist

Put your dashboard through these 4 questions:

• Does it change a decision when it moves, or just confirm activity?

• Does it surface a constraint?

• Does it read the same way across roles?

• Does it work without a walkthrough?

A dashboard that fails these is noise, however good it looks.

Core DevOps metrics to include and why they matter

Ultimately, a dashboard is only as good as its metrics. The metrics worth tracking reveal how work flows through the system rather than focusing on the individuals or teams inside it, and they connect delivery performance and real-world business outcomes.

Plandek’s 4 Pillars framework covers both these qualities. It provides benchmarks for measuring and improving productivity across 4 main pillars: focus, speed, predictability, and quality. 

Focus

Focus measures whether teams are working on the right things.

• Planned work % (planned versus unplanned work) shows how much time gets pulled into firefighting. 

• Work in progress (WIP) per person exposes whether developers are overloaded or working on too many things at the same time. 

• Context switching indicates whether teams are too thinly spread across competing priorities to finish projects efficiently.

When these metrics trend in the wrong direction, the system is likely absorbing more low-value unplanned work than it can handle, and leaders will need to address this to improve delivery outcomes.

Speed

Speed is about flow, not hustle.

• Lead time to value tracks how long an idea takes to go from defined to live in production.

• Cycle time measures the time from the start of development to the deployment of a change.

• Time to merge PRs is worth tracking on its own, since reviewing and merging pull requests can absorb close to a third of the delivery life cycle.

When speed metrics stall, the delay is usually waiting between stages, code sitting in review, or changes queued for deployment, rather than slow coding. That’s where to look first.

Predictability

Predictability is about asking, “Will it ship?”

• Sprint capacity accuracy compares what a team planned to take on against what it actually delivered.

• Sprint target completion shows how reliably teams hit the commitments they set.

• Scope change tracks how much planned work shifts mid-flight, which is usually what breaks a forecast.

When these metrics slip, the forecast becomes untrustworthy, and the gap points to where commitments and actual capacity have drifted apart.

Quality

Quality asks whether speed is costing you stability.

• Bug resolution time captures how quickly defects are fixed once they surface.

• Stories delivered to bugs raised ratio shows whether new features are outrunning defect creation.

• Bugs resolved to bugs raised ratio shows whether the team is clearing defects faster than they accumulate or falling behind in technical debt.

When quality metrics decline as speed rises, that’s the signal to slow down and steady the delivery path before pushing for more speed.

Which views matter for different engineering roles

In addition to capturing the right metrics, an effective dashboard excels in usability. Engineers should be able to quickly identify the metrics relevant to them, understand what these signals mean, and know what to do next. That’s why one board for everyone serves no one. 

The data can stay shared, but the view should change based on the question each reader is answering. Here is how one system can look from 4 different vantage points.

The rule that keeps this manageable: separate views off one shared dataset, not separate dashboards off separate data. Otherwise, the numbers stop agreeing, and trust in all of them erodes.

Using dashboards to identify bottlenecks and delivery risk

One of the most valuable things a dashboard can do is show where work is actually stuck, which is rarely where teams assume it is. For most teams, 75-85% of a change’s lead time is spent waiting between stages, not in active work. The constraint usually lives in the gaps. 

The view, then, has to span the whole delivery path, not just development. On one modernization program, Kesharwani saw development teams finish on schedule while releases kept slipping. Mapping the full delivery life cycle exposed the real constraint. “The delays were occurring in security reviews, infrastructure provisioning, environmental readiness checks, and approval workflows rather than software development itself,” he says.

Catching these degradations also requires watching how the system behaves over time. Trends beat snapshots. “I looked at the weekly metrics and saw that our deploy stage duration had tripled over three weeks,” recalls Tiumentsev, who traced the regression to a caching change that triggered no alert. 

Map your stages, plot stage-duration trends, and measure wait time at each handoff. That’s where the recoverable time hides. 

Learn more from our complete guide to identifying engineering bottlenecks.  

Metrics and anti-patterns to avoid

Most boards carry metrics that look productive and say almost nothing about the system.

The usual suspects are activity counts: commits, lines of code, story points, build success percentage, and tickets closed. They reward motion over outcomes and invite local optimization that can hurt the whole. Jitesh Keswani recalls a team that “closed 437 tickets in the quarter” to general acclaim while, as he puts it, “their cycle time had doubled, and nobody noticed because it wasn’t on the dashboard.”

There are 2 cuts worth naming. 

The first is deployment frequency in isolation, the metric leaders most often overvalue. Shipping more often is not the same as shipping better, and the count says nothing about whether customers gain.

The second is wait time: the hours engineers lose on reviews, CI results, approvals, and decisions. That wait is often the larger drag on delivery.

How AI and predictive analytics are changing DevOps dashboards

The adoption of AI across software engineering shifts DevOps dashboards in 3 ways.

Reporting → prediction

AI-powered dashboards are moving from reporting what happened to flagging what’s coming. Across large estates of repositories and pipelines, AI can reveal patterns that may take time to analyze manually and suggest ways to improve outcomes. 

Garima Agarwal, Software Developer at Bank of America Merrill Lynch, describes boards that now surface recommendations like, “This change has 65% higher failure probability based on similar past patterns.” 

Output → outcomes

Traditionally, DevOps dashboards have emphasized output and adoption metrics, but AI is challenging that approach. Plandek’s Engineering Productivity Benchmarks Report has shown that while AI increases output, it also exposes existing bottlenecks without fixing them. 

The last 2 years of DORA research have found that increased AI adoption correlates with lower delivery stability. Meanwhile, GitClear’s code-quality analysis of 211 million changed lines found that churn roughly doubled relative to its 2021 baseline, as copy-paste began to outpace refactoring. 

Engineering leaders are starting to realize that their dashboards should look beyond output and toward impact (flow, constraints, and delivery outcomes).

Developer impact → AI impact

Most engineering dashboards were designed around measuring human developer productivity. With AI in the picture, those same metrics can mislead. Cycle times may compress and commit volumes may rise, but that doesn’t mean delivery is improving.  

METR’s 2025 trial found that experienced developers worked 19% slower with AI while believing they were 24% faster, the kind of gap a board built for human output won’t reveal.

Plandek’s RACER framework helps organizations decide what to measure in the AI era. With this lens, a DevOps dashboard shows whether AI is actually removing constraints, improving flow, and producing measurable results at the system level, rather than merely raising activity.

How Plandek supports operational DevOps dashboarding

A good dashboard helps you understand the delivery system well enough to act. Most DevOps dashboards fail that test because they track output and activity instead of how work flows, so leaders watch green panels while releases keep slipping.

Plandek provides configurable dashboards with metrics that matter across the full delivery cycle, along with custom views, external benchmarking, and predictive AI insights, so you can see what’s blocking delivery, what’s at risk, and where to act next. 

Contributors

• Avitesh Kesharwani, Transformation Delivery Leader and Technical Architect, Genpact

• Denis Tiumentsev, Lead DevOps Engineer, Integro Technologies

• Jitesh Keswani, CEO and Founder, e intelligence

• Garima Agarwal, Software Developer, Bank of America Merrill Lynch