Engineering Metrics That Actually Help Your Team Improve
Not just dashboards. Actionable insights with root cause analysis. Know WHY metrics changed and HOW to fix them.
The Problem with Most Engineering Metrics Tools
Dashboard fatigue and metrics theater
Most engineering metrics tools show you pretty dashboards. Your velocity is 42. Your cycle time is 5.2 days. Great. Now what? Metrics without action just create compliance theater.
The Real Problem: Observation, Not Action
LinearB, Jellyfish, Swarmia—they all show you the same DORA metrics everyone else has. But they don't tell you WHY your deployment frequency dropped or HOW to fix your cycle time. You get metrics without insights.
Actionable Engineering Metrics
Metrics + Root Cause Analysis + Suggested Actions
DORA Metrics (All 4)
• Deployment Frequency
• Lead Time for Changes
• Change Failure Rate
• Mean Time to Recovery
+ WHY they changed + HOW to improve them
PR & Review Metrics
• PR Cycle Time (median & P95)
• Time to First Review
• Review Bottlenecks
• Review Distribution
+ Automatic bottleneck detection
Team Health
• Work Distribution Balance
• Context Switching Overhead
• On-Call Load
• Burnout Risk Indicators
+ 1:1 conversation starters
Sprint Predictability
• Commitment Accuracy
• Velocity Trends
• Scope Creep Detection
• Dependency Blockers
+ Predictive sprint risk alerts
The Difference: Action Over Observation
Other Tools:
"Your deployment frequency is 2.3 per week and trending down."
TeamOnTrack:
"Your deployment frequency dropped 60% after adding manual security reviews. Teams who automated these checks using GitHub Actions deploy 3x more. Here's how they set it up: [specific steps with code examples]"