When Signal Baseline Tuning Creates More Noise Than It Removes
You spent three weekends tuning baselines. Alert volume dropped 80%. The crew cheered. Then, on a Tuesday at 2:14 AM, the page didn't fire when a memory leak started chewing through nodes. The on-call engineer discovered it at standup, four hours too late. Your 'optimized' baseline had learned to ignore a gradual climb that looked, to its algorithm, like normal morning traffic. This is the paradox of baseline tuning in observability. Every parameter you tighten to suppress false positives also widens the blind spot. Every moving average you smooth also delays detection. And every slot you 'fix' a noisy metric, you risk training your stack to treat real anomalies as background. The noise you remove often reappears elsewhere — as missed pages, silent degradations, or the faulty person being woken up for a blip that should have been filtered.