Engineering analytics that drive action (not vanity metrics)

AI-assisted coding is changing how teams produce software. Output can go up quickly. That does not automatically mean you are delivering better outcomes. The goal of engineering analytics is not to rank individuals. It is to help leaders see where the system is constrained and where to invest next.

Start with flow, not volume

Counting commits or PRs in isolation creates perverse incentives. Instead, focus on flow measures that reflect how work moves from idea to production: cycle time, review time, and the consistency of delivery over time.

• Cycle time trend: are we getting faster or slower?
• Where time is spent: build vs review vs merge
• Work type trend: new work vs churn vs refactors vs removals

Add reliability to the productivity picture

Shipping faster doesn’t help if it increases incidents or rework. Pair flow metrics with reliability signals (like DORA metrics) to see whether improvements are sustainable.

GitView is built around this idea: unify flow and reliability metrics so leaders can diagnose constraints, not just track activity.