Most organizations are inundated with a huge volume of IT data in the form of KPIs and logs. Finding meaningful inferences from so much data is a struggle for ITOps teams. AI allows organizations to cut through all the data and capture the most impactful deviations from normal behaviour. AI finds the anomalies worth investigating so your teams can spend on remediation than on investigation.
AI-based anomaly detection trains machine learning algorithms on the steady state of the system using KPIs, traces, and log data. AI can correlate multiple KPIs and understand the interdependence for anomaly detection. By using technical language processing techniques, AI intelligently isolates issues in log data and finds meaningful log anomalies.
Alert noise is a serious challenge impeding IT Operations teams from being effective in dealing with issues. Rule-based event thresholds lack the sophistication to evaluate when something is wrong instead of just above the threshold. They also cannot react to changing resource requirements and require constant updates and maintenance. AI-based anomaly detection instead captures the steady state of the system and reacts to high-value deviations. It can also automatically adapt to changing conditions with constant retraining.
MTTDs define how effective an organization is in identifying issues before they become a major problem. By correlating events across multiple KPIs from different sources and telemetry data, AI combines events that are related to each other. This enables quick detection and diagnosis of incidents as ITOps teams have all information ready at hand. By cutting down on Mean time to Detect, AI enables IT Operations teams to be more effective while handling huge workloads.
Tool sprawl is the other great bane of IT Operations. Dependence on tools for monitoring is only growing as cloud operations, containerization, and edge computing take center stage in industry trends. ITOps teams become less efficient when each diagnosis involves using many tools to correlate and understand what went wrong. By enabling a single AIOps platform to do that for your teams, you can save them time, effort and the frustration of trawling through many tools for diagnosis.
