Metric misalignment is the dominant failure. The team optimises for a metric that was a good proxy when chosen and has since drifted from the underlying reality. Daily active users was a useful metric for social products until teams discovered they could inflate it with push notifications that users found annoying and eventually deleted the app to escape. The metric went up. Retention went down. The proxy had failed.
A second failure is metric isolation. Teams measure their subsystem’s metric without measuring its impact on downstream metrics. Infrastructure teams optimise for deployment frequency without asking whether the deployments change user outcomes. Product teams optimise for feature adoption without asking whether adopted features retain users. Each team’s metric looks good. The product stagnates.
Concept: Every metric is a proxy for user value. The quality of a measurement system depends on how well its proxies resist Goodhart corruption and how well they predict the outcomes they are supposed to represent.
Thread: T11 (Feedback) ← metrics create feedback loops that shape engineering behaviour → choose metrics where improvement requires improving the actual outcome
Core Idea: Leading indicators predict outcomes; lagging indicators confirm them. Technical decisions affect leading indicators. Goodhart’s Law corrupts metrics that become targets. Instrumentation is a first-class architectural requirement.
Tradeoff: AT9 (Speed vs Quality) — high-resolution metrics require instrumentation investment; teams that skip that investment operate faster but with lower visibility into whether speed is producing value
Failure Mode: FM11 (Observability Blindness) — without instrumentation at the task level, teams cannot distinguish between a product that is working and a product that looks like it is working
Signal: When a metric improves but the user outcome it is supposed to predict does not, the proxy relationship has broken down and the metric must be replaced
Maps to: Reference Book, Framework 9 (Laws)