When a core switch fails, every system behind it reports trouble: servers unreachable, backups failed, applications timing out. A naive monitoring stack turns this into thirty tickets. An operations platform turns it into one incident with thirty correlated signals.
The mechanics matter. Correlation works when signals share a topology: the platform knows which assets sit behind which switch because discovery and the CMDB maintain those relationships continuously. Suppression follows correlation — duplicate tickets are never created, so the SLA clock runs on the incident, not on the noise.
The response changes too. Instead of thirty technicians triaging thirty tickets, one technician sees one incident, its blast radius, and the change that likely caused it. Mean time to understand drops from hours to minutes.
This paper walks through the correlation model, the suppression rules that keep queues clean, and the metrics that show it working: alert-to-incident ratio, duplicate suppression rate, and time-to-context.