Engineering analytics that drive action (not vanity metrics)
A practical approach to measuring productivity using throughput, flow, and reliability, without incentivizing the wrong behaviors.
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.
Use qualitative feedback to explain the numbers
Even great dashboards cannot tell you why things changed. Pulse surveys fill in the missing context by capturing sentiment and drivers like clarity, tooling, on-call load, and process friction.
If a metric can be gamed, it will be. Prefer system-level measures that are hard to game and easy to improve collaboratively.
GitView is built around this idea: unify delivery metrics and pulse feedback so leaders can diagnose constraints, not just track activity.
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