Proactively identify operational issues and avoid costly incidents by detecting anomalies in both log and metric data

IBM Z® Anomaly Analytics is software that provides intelligent anomaly detection and grouping to proactively identify operational issues in your enterprise environment.

IBM Z Anomaly Analytics uses historical IBM Z log and metric data to build a model of normal operational behavior. Real-time data is then scored against the model to detect anomalous behavior. A correlation algorithm then groups and analyzes anomalous events to proactively alert operation teams of emerging problems. 

Your essential services and applications must always be available in today's digital environment. For enterprises with hybrid applications, including IBM Z, detecting and determining the root cause of hybrid application issues has become more complex with rising costs, skill shortage and changing user patterns.

Features

• **Comprehensive model-building with machine learning: **The solution continuously monitors real-time metric and log data, detecting deviations in frequency, occurrence or sequence patterns to provide immediate insights into emerging anomalies.
• **Real-time metric and log analysis: **The platform continuously monitors real-time operational data and log messages, detecting deviations in frequency, occurrence or sequence patterns to provide immediate insights into emerging anomalies.
• **Prioritized incident notifications with ensemble event grouping: **IBM Z Anomaly Analytics correlates and prioritizes anomalous event groups, helping ensure that IT teams are alerted only to high-confidence issues, which streamlines the response process and reduces false positives.
• **Impact visualization with topology service: **Correlate and analyze anomalous event groups to help IT operators and system programmers prioritize which operational issues to address. This helps ensure that your team is only alerted to high-confidence event groups, reducing false positives.

Benefits

• **Proactive incident detection: **Enhances operational efficiency by providing real-time notifications of correlated and grouped anomalous behavior, enabling IT teams to respond swiftly and proactively. By assessing the impact of these anomalies, the system prioritizes responses, helping ensure that resources are efficiently allocated to address critical issues and minimize disruptions.
• **Enhanced detection accuracy: **Improves detection accuracy by building comprehensive models of regular operations across multiple subsystems, allowing for precise identification of deviations from the norm. By correlating and grouping metric and log anomaly events, the system further reduces false positives, helping ensure that true anomalies are accurately detected.
• **Data-driven decision-making: **Empowers data-driven decision-making by providing detailed visualizations of anomalous activity within a topological context, making it easier to interpret complex data and diagnose issues. Coupled with real-time data analysis against established operational models, the system helps ensure timely, informed decisions based on the most current and actionable insights.