
LogicMonitor announced general availability of its AIOps early warning system, which marks the latest enhancement to its AIOps solution, LM Intelligence.
The new system is designed to provide actionable warning indicators for imminent issues. Combined with the single pane of glass view enabled by LogicMonitor’s unified IT infrastructure monitoring platform, LM Intelligence helps enterprises predict and prevent IT outages, saving IT teams time, money and avoiding potential damage to their brand reputation.
“In the same way that a doctor identifies symptoms that if left untreated might lead to major complications, LM Intelligence’s AIOps early warning system will detect the signs that precede IT performance problems, and warn users accordingly,” said Tej Redkar, Chief Product Officer at LogicMonitor. “96% of IT decision makers report experiencing at least one major outage during the past three years. By providing our customers with AIOps functionality that warns them about issues before they happen, teams can prevent problems and outages instead of reacting to them. This ultimately prevents service-level agreement failure and helps teams deliver a consistently positive customer experience.”
The new AIOps early warning system within LM Intelligence features root cause analysis and dynamic thresholds, which intelligently identify signal from noise so IT teams can focus on the most important issues in complex on-premises and multi-cloud IT infrastructures. With dynamic thresholds, LogicMonitor uses anomaly detection to define an expected performance range for specific IT resources based on historical performance and ensures that alerts are only sent to IT operations teams for anomalies outside of that normal range.
Root cause analysis (RCA) uses automatically-discovered infrastructure topology to find the root cause for performance anomalies, and automatically limits sent notifications to only those that are relevant to the root cause, ensuring that IT operations teams aren’t flooded with alerts for downstream symptoms.
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