Predict and prevent incidents by connecting New Relic to InsightFinder
Turn New Relic’s market leading observability tool into an incident prediction engine by connecting New Relic to InsightFinder. With a few easy clicks, New Relic users can now stream their data to InsightFinder’s Unified Intelligence Engine. Our award winning technology detects anomalies, predicts and prevents incidents, and shows the root cause of a potential incident all within one tool. InsightFinder ingests all data through standard APIs and analyzes it using our patented, unsupervised, neural network algorithms.
Because InsightFinder analyzes logs, metrics, and events all together, it has a holistic view of how systems are working and connected with each other. As a result, InsightFinder anomaly detection reduces noise, and is more accurate than alternative approaches. In addition, InsightFinder helps turn insights into action by highlighting where and when future incidents are predicted to occur, as well as providing a path to rapid correction via root cause analysis so the problem can quickly be identified and fixed.
New Relic users benefit because the InsightFinder tool detects anomalous alerts, and prioritizes them based on severity. This allows users to prioritize action based on system and user impact. In addition, InsightFinder uses real-time, multivariate data analysis to provide the most accurate insights to users. Users can add outside data not included in New Relic, such as change events, to InsightFinder, to create the most accurate view of a system’s activity as possible. This allows for more precise results, and a system that is best equipped to predict incidents.
Learn more about the InsightFinder / New Relic integration today.
Other Helpful Resources
Unified Intelligence Engine (UIE): A Technical Deep Dive Paper
InsightFinder utilizes the industry’s best unsupervised multivariate machine learning algorithms to analyze a large amount of production system data.
Root Cause Analysis
A major credit card company’s mobile payment service experienced severe performance degradation on a Friday afternoon.