by Gary Worthington, More Than Monkeys

Measure what matters. Ignore what doesn’t. Here’s how to stay honest, adaptable, and focused on flow.

Why Agile Metrics Matter, And Why Most Teams Get Them Wrong

Agile delivery isn’t just about shipping faster. It’s about shipping smarter. Delivering value in a way that’s sustainable, predictable, and constantly improving.

But here’s the catch: most teams either ignore metrics completely (too busy delivering), or drown in dashboards that look impressive but tell them nothing useful. A burnup chart gathering dust in Jira isn’t going to unblock your pipeline or build stakeholder trust.

Metrics should spark conversations. They should help teams spot bottlenecks early, improve their ways of working, and build momentum across delivery cycles.They should not be tojust show off in stakeholder reviews.

When used well, these metrics help you answer the most important delivery questions:

• Are we building the right thing?
• Are we building it right?
• Can we get it to production reliably?
• Are we improving over time?

So let’s ditch the vanity stats and focus on ten metrics that actually help teams understand where they really are.

1. Velocity

How many story points your team typically completes in a timeboxed period. This works well in a Scrum like environment, where fixed Sprints are used.

Velocity isn’t about speed, it’s about predictability. When used well it helps teams plan with confidence and avoid overcommitting. Whatever you do, don’t use it to compare teams or judge individuals. That’s how you kill trust.

2. Burndown Chart

How much work remains each day in the current timeframe.

A healthy burndown curve shows steady progress. Seeing flat lines? That’s a signal that maybe there’s a blocker, maybe work hasn’t started. Either way, it’s your early warning system.

3. Burnup Chart

Tracks what’s been completed versus the total planned work.

Great for visualising scope creep. If the line of total work keeps rising midway through the timeframe, you’ve either got a moving target or someone keeps saying yes to extras. Useful for surfacing hidden process issues.

4. Cycle Time

The time it takes from starting work on a ticket to completing it.

Shorter cycle times = faster feedback loops = quicker learning. Long cycle times often point to hidden wait states, like PRs stuck in review or handoffs between teams.

5. Lead Time

Time from idea to delivery.

Lead time tracks your responsiveness. The quicker you can go from “We need this” to “It’s live,” the more competitive and adaptable your team becomes.

6. Cumulative Flow Diagram (CFD)

Visualises work in different stages of your delivery process.

CFDs make bottlenecks obvious. If work piles up in “Review” or “QA,” you’ve got a constraint to address. It’s like a real-time map of your delivery flow. Maybe it’s time to run a TOC session to understand your bottleneck?

7. Post release defect rate

Bugs found after release, and the frequency of bugs relative to work delivered.

High post release defects suggest you’re testing too late or not at all. (see my article on the test pyramid for some helpful ideas on how to prevent this) Tracking this helps you build quality in, rather than bolting it on afterwards.

8. Goal Success Rate

Whether or not the team delivered on what they intended to achieve during a defined delivery window.

Consistently hitting team-defined goals shows alignment and execution. Is your team seeing gegular misses? That’s a sign your planning needs work or your goals aren’t grounded in reality.

9. Time to Market / Feature ROI

How long it takes for a feature to go live, and what value it delivers.

Agile is about shortening the feedback loop between building something and learning if it works. Shipping sooner means you learn sooner and waste less on the wrong things.

10. DORA Metrics

The gold standard for engineering effectiveness:

• Lead time for changes
• Deployment frequency
• Change failure rate
• Mean time to recovery (MTTR)

These give you a clear picture of technical health and DevOps maturity. High-performing teams deploy often, recover quickly, and keep failure rates low, not because they work harder but because their system is resilient.

🎯 Why These Metrics Work (When Used Properly)

The magic isn’t in the numbers, like most of my musing, it’s in the conversations and collaborations they unlock.

Use them to:

• Improve predictability
• Surface bottlenecks early
• Build in quality
• Align around outcomes
• Encourage healthy delivery habits

But remember, metrics should serve the team, not the other way around.

• Don’t chase perfect burn charts
• Don’t game the velocity
• Don’t use metrics as performance reviews in disguise

The moment people feel measured by the data, they’ll either game it, or they’ll stop trusting it and you.

How to Use These in Practice

• Planning: Review past velocity and adjust upcoming work accordingly.
• Daily Standups: Check progress trends and flag stalls early.
• Team Reviews: Look at cycle time trends and defects to find improvement opportunities.
• Quarterly Check-ins: What are your DORA trends telling you? Are you getting faster? More reliable? Delivering more value?

If you measure the right things and use them to ask better questions, your team won’t just deliver more. They’ll deliver smarter, cleaner, and with more confidence.

And that’s what agility is really about.

Let me know in the comments below which ones you’re using, which ones you’ve binned, or what your team learned from tracking the wrong thing for too long.

Gary Worthington is a software engineer, delivery consultant and agile coach who helps teams move fast, learn faster, and scale when it matters. He writes about modern engineering, product thinking, and helping teams ship things that matter.

If you’re building a digital product and need clarity on testing strategy, team structure, or technical direction, visit morethanmonkeys.co.uk to find out how Gary and his team can help you ship faster with fewer surprises.

Follow him on LinkedIn for more practical insights into delivery strategy, architecture decisions, and scaling software teams with confidence.

(disclaimer: AI was used to improve syntax and grammar)