Company’s Tuning-Free Unsupervised Machine Learning Empowers IT Teams to Resolve IT Incidents Quickly

(PR Newswire article)

InsightFinder, the first AIOps tool built on top of unsupervised machine learning, today announced the availability of RCA Focus, which helps organizations get to the heart of complicated performance issues or outages quickly. With RCA Focus, InsightFinder is the only AIOps platform / solution that turns any monitoring tool into an engine for Root Cause Analysis. Here’s a demo that shows how it works.

“With today’s complex IT environments, an incident can be caused by practically anything – including buggy software releases, hardware failures and human misconfiguration,” said Helen Gu, InsightFinder founder and CEO. “RCA Focus facilitates fast remediation of incidents such as a system degradation or application outage by combining all the data related to the incident in one place, automatically identifying the root cause and pinpointing how to resolve it.”

According to Gu: “RCA Focus empowers companies to find and remediate root cause analysis as quickly as possible. This feature allows customers to see all events surrounding a system degradation in one place – including buggy release, hardware failures, or human misconfigurations. Rather than looking at a number of different tools, they can rely on InsightFinder to quickly comb through all their data, determine the root cause and pinpoint how to resolve the incident.”

InsightFinder’s RCA Focus feature is the best way to find the root cause of an incident, and view the context surrounding it. Unlike alternatives, users only have to look at one tool to understand the problem and perform remediation with confidence. Key benefits include:

  • Click and magnify function, allowing users to zoom in on the key time period surrounding the incident
  • Detailed timeline of all relevant anomalies and change events around the time of the selected incident
  • Ranked list of probable root causes of the incident

RCA Focus helps businesses on the path to zero downtime. Michael Sao Pedro, CTO of ed tech company Apprendis, uses InsightFinder with his CloudWatch log and metric data. “RCA Focus enables our dev team to quickly see all events surrounding a system degradation. We receive intelligent suggestions about which application logs and system performance metrics across all our services could cause system degradation or errors. Triangulating application log data and system performance metrics across services would otherwise be untenable without InsightFinder, and the RCA focus greatly speeds up how quickly we can isolate and solve problems.”

From InsightFinder’s Unified Health View, users simply click and magnify on the desired area surrounding an incident. Detailed metric anomalies and application error logs are collected and can be sent to the responsible software developers who committed this buggy version for fixing the problem. By using InsightFinder’s RCA focus feature, the system operator can quickly pinpoint the root cause and recover the system by reversing buggy code.

InsightFinder seeks to revolutionize the way operators and developers solve problems by turning any observability tool into a root cause analysis engine. This allows users to efficiently understand and take action on a problem while minimizing or eliminating impact on users.

About InsightFinder

InsightFinder is the provider of the world’s first AIOps platform completely built on unsupervised machine learning technology. Founded by renowned machine learning and distributed system expert Dr. Helen Gu, InsightFinder analyzes data streams to detect anomalies, predict incidents and automate root cause analysis, helping organizations of all sizes automate data analysis from all their data streams including observability, infrastructure, cloud and business data sets. Learn more about InsightFinder and the Unified Intelligence Engine at

Other Resources

A major credit card company’s mobile payment service experienced severe performance degradation on a Friday afternoon.
InsightFinder utilizes the industry’s best unsupervised multivariate machine learning algorithms to analyze a large amount of production system data.