Summary
This technical report discusses the key elements of automated root-cause analysis utilizing AIOps, which applies machine learning and artificial intelligence for the rapid identification of incident root causes. The document outlines the significant financial impact of unplanned downtime, estimating costs for large enterprises at nearly $1.5 million per hour. It details how AIOps can reduce mean time to resolution (MTTR) by up to 50%, thereby saving thousands of labor hours annually. The report describes the process of real-time event correlation, which uses pattern matching to create a unified view of event data, reducing alert noise and simplifying incident management. Additionally, it explains how AIOps can analyze system changes to identify potential causes of incidents and prioritize them accordingly. The use of generative AI is also highlighted, showcasing its ability to suggest root causes quickly, enhancing the efficiency of incident response. The report concludes with statistics indicating a 95% reduction in alert noise for users of BigPanda's solutions.
