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Anomaly Analytics

IBM Z Anomaly Analytics

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

Product documentation

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.

What's new Summary of changes and features for Z Anomaly Analytics
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.

Features

Machine learning system Metric and log analysis Incident notifications Impact visualization
Key components

Explore the data flow among the components of IBM Z Anomaly Analytics.

Z Common Data Provider

Provides the infrastructure for accessing IT operational data from z/OS® systems.

Log-based machine learning

Detects anomalies in z/OS systems log data.

Metric-based machine learning

Detects anomalies in the metric data from record types.

Ensemble

Correlates anomalies and scores event groups to alert teams of operational issues with high confidence.

See a visual representation of the data flow among components
Technical details
Planning for deployment

Help ensure that your environment meets the system requirements for deploying the software containers of IBM Z Anomaly Analytics on Linux® and IBM Z Common Data Provider on z/OS system.

Plan for deployment of IBM Z Anomaly Analytics
Next steps

Explore IBM Z Anomaly Analytics. Schedule a no-cost 30-minute meeting with an IBM Z representative.

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