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Your monitoring tools are not the problem. The problem is that six of them fire simultaneously when one switch fails, and a human has to work out that it was one switch. Ramisun integrates your monitoring estate into ServiceNow Event Management and configures correlation against a real service topology, so alert storms collapse into single incidents with impact already calculated.
Event Management integration connects your monitoring and observability tools — Datadog, Splunk, Dynatrace, SolarWinds, Nagios, Prometheus, SCOM, and others — into ServiceNow ITOM Event Management, where raw alerts are normalised, deduplicated, and correlated against the CMDB service topology. Instead of hundreds of alerts arriving in an inbox, a handful of alert groups become incidents with probable root cause identified and business service impact already calculated.
"Nobody has ever solved an alert storm by adding another monitoring tool. Correlation is a data problem, and it needs a topology to correlate against."
Datadog, Splunk, Dynatrace, SolarWinds, Nagios, Prometheus and more, normalised.
Storms collapsed into alert groups with probable root cause surfaced.
Correlation against a real CMDB topology, so impact is calculated not guessed.
Known fixes executed automatically under policy, with full audit trail.
More tools produce more alerts. Only correlation produces fewer incidents.
Figures shown are industry benchmarks and illustrative placeholders — replace with sourced, dated statistics before publication.
From a raw alert to a restored service, with correlation doing the work a human used to do.
Monitoring tools push events into ServiceNow via connector or webhook
Different formats mapped to one event schema with consistent severity
Repeat and flapping alerts collapsed into a single event record
Related events grouped against topology to surface probable root cause
Service maps identify affected business services and SLAs
Auto-remediation runs, or an incident is raised with full context
The difference between watching alerts and operating a service.
| Area | ❌ Typical Approach | ✅ Ramisun Approach |
|---|---|---|
| Alert Volume | Thousands per day across separate consoles | Correlated into a handful of actionable alert groups |
| Duplicate Alerts | Same issue reported by four tools, four times | Deduplicated into one event record |
| Root Cause | Worked out manually by whoever is on shift | Probable root cause surfaced by correlation |
| Business Impact | Unknown until someone escalates | Calculated from service maps automatically |
| Incident Creation | Manual, after a human triages the noise | Automatic, with topology and impact attached |
| Known Fixes | Runbook in a wiki someone has to find | Automated remediation executed under policy |
| Flapping Alerts | Ignored, and eventually so are the real ones | Suppressed with the pattern surfaced for fixing |
Each capability maps to real delivery work — with outcomes and the Ramisun approach.
We connect what you already run rather than asking you to replace it. Each tool is integrated so its events arrive normalised into a common schema with consistent severity mapping.
Correlation is where the value is, and it only works if the topology underneath is accurate. We tune correlation rules against your real service maps so alert groups reflect actual dependency, not coincidence in time.
An alert on a server tells you nothing about whether the business cares. Service maps turn infrastructure events into business impact, so prioritisation reflects consequence rather than alert severity.
A meaningful share of alerts have a known, safe fix that a human executes identically every time. Those are automation candidates. Ramisun implements them with policy gates so autonomy is earned scenario by scenario.
Event Management is only as good as the topology beneath it, and most correlation disappointments are really CMDB problems. Where the foundation is weak we fix that first rather than tuning rules against bad data.
Once correlation is working against a trustworthy topology, machine learning can add real value on top — anomaly detection, alert clustering, and similar-incident suggestions. Before that point, it mostly adds confident noise.
Correlation is a data problem before it is a tooling problem, and we sequence the work accordingly.
Correlation quality is bounded by topology quality. Where Discovery and service mapping are weak, we fix that first rather than tuning rules against unreliable relationships.
We prove correlation on a single network or application domain before extending across the estate. A tuned rule set for one domain beats an untuned one everywhere.
We baseline alert volume before starting and report reduction against it. If correlation is not measurably reducing noise, the rules are wrong and we say so.
Auto-remediation starts with the safest, most repetitive scenarios and expands as confidence is evidenced. Nothing risk-bearing runs without a policy gate and a defined rollback.
We integrate your monitoring estate rather than replacing it. Rip-and-replace is expensive, slow, and rarely the actual source of the problem.
Correlation rules are reviewed against real incidents on an ongoing basis. An event management deployment that is never tuned degrades back into noise within a year.
Tell us how many alerts you handle daily and which tools produce them. We will show you what correlation could realistically reduce that to.
"Nobody has ever solved an alert storm by adding another monitoring tool."
Effectively any tool that can emit an alert over a webhook, REST call, SNMP trap, or file. ServiceNow ships connectors for common platforms including Datadog, Splunk, Dynatrace, SolarWinds, Nagios, Prometheus, and SCOM, and custom connectors handle the rest. We normalise all of them into one event schema so correlation works consistently regardless of source.
No, and it should not. Your monitoring tools detect; Event Management correlates and decides what to do about it. Replacing working monitoring is expensive and rarely addresses the actual problem, which is that nothing joins their outputs together.
Well-tuned correlation against a good CMDB commonly reaches 70–85% reduction in actionable items, though it varies with estate complexity and starting alert hygiene. We baseline your current volume before starting so the improvement is measured rather than claimed.
For topology-based correlation, yes — and this is the honest constraint most vendors underplay. Correlation quality is bounded by relationship quality. If your CMDB is weak, we recommend fixing that first, and we will tell you that during scoping rather than after a disappointing go-live.
For approved scenarios, yes. Orchestration and Flow Designer can execute known remediation such as restarting a service, clearing a queue, or expanding disk space. We start with the safest and most repetitive cases, require policy gates on anything risk-bearing, and define a rollback path before enabling any automation.
A first domain typically reaches production in 6–10 weeks, assuming the CMDB is in reasonable shape. Where Discovery and service mapping need work first, that becomes a preceding phase and we scope it separately rather than absorbing the risk silently.
Every quarter without the right platform is revenue and efficiency left on the table. Tell us your challenge — get a real plan back, not a sales script.
A ServiceNow consultant will reach out within one business day to schedule your session.