Automatically correlate, group, contextualize and identify common underlying issues and probable root cause.
When incidents occur, operations teams must react quickly to identify and remediate issues. However, trying to determine the root cause is often difficult with traditional tools and methods. With multiple devices and various systems creating massive volumes of events and alerts, it is nearly impossible to determine which are related and which initiated the incident. Lengthy response times are often the result of manual troubleshooting activities and time-consuming bridge calls. Services and applications could be unavailable or severely degraded while customers and businesses suffer the immediate effects.
Grok AIOps provides IT teams with a powerful solution to help when quick response times are critical. Grok AIOps leverages real-time machine learning algorithms to develop multi-layer and multi-stack representation models of the IT infrastructure. When incidents occur, like and related events are automatically correlated and clustered together based on similarity, relationship, time, and historical classifications. These groupings serve as indicators of one or more probable root causes. Grok displays this information in a contextual timeline with other ITSM or change management information to provide further situational clarity.
Once the probable root cause is identified, Grok can trigger intelligent ticketing with an ITSM solution by assigning the correct ticket type, category, criticality, group, response action, and other predefined attributes. This process ensures that ITSM solutions can be updated and remain as the system of record and that operational workflows are uninterrupted. Grok can also initiate intelligent automation to execute the appropriate response action based on the combination of probable root cause and the classification of the incident. With Grok AIOps, operations teams can reduce troubleshooting and response times from hours down to minutes or seconds.