Anomalies detected by Corvil Intelligence Hub are automatically deduplicated, consolidated and filtered to provide actionable alerts to stakeholders while minimizing noise and alert fatigue. Diverse machine learning approaches can also be correlated to reduce the noise in detecting and presenting anomalies for reporting and action.
Customizable management policies deliver complete control over alert propagation and workflows, for example:
- Grouping and tuning alerts into logical subsets that are unique for specific stakeholders, services, or types of applications
- Diverse correlation options (such as across multiple metrics, frequency of occurrence, or time of day suppression, among others) adds context that prioritizes troubleshooting efforts more effectively
- Repetitive, manual alert triage tasks such as cross-checking other metrics or data points can be automated, reducing investigation times from hours to a few seconds