Root-Cause Analysis (RCA)

Challenges

  • Root-Cause Analysis (RCA) is becoming more complex due to vast and diverse data volumes in a cloud-centric virtualized world.
  • Traditional IT methods are limited in analyzing large real-time infrastructure data, resulting in high Mean Time to Acknowledge (MTTA) and Mean Time to Repair (MTTR).
  • This leads to poor performance and lack of actionable data in the RCA process for quick remediation actions.

Solution

  • Ennetix’s xVisor AIOps platform streamlines RCA with an integrated approach, examining every aspect of the application-delivery infrastructures.
  • It uses AI/ML algorithms to automate IT event correlation and speed up anomaly detection, thereby cutting manual labor.
  • This enables faster detection and prevention of issues and supports automation of IT Service Management (ITSM) and Security Orchestration, Automation, and Response (SOAR) processes.

Conclusion

xVisor’s innovative AI/ML-based automated root-cause analysis significantly cuts MTTA and MTTR, provides preemptive notifications, and enables smoother integration of network management and cybersecurity in today’s complex IT landscapes.

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