Sharing our implementation of proactive incident management that reduced MTTR by 67% and improved SLA compliance from 94% to 99.2%. We integrated Azure Monitor with Application Insights and ServiceNow to create an automated anomaly detection and response system.
The challenge was reactive firefighting - incidents were only detected when users reported issues. By the time our team investigated, SLA breaches had already occurred. We needed automated anomaly detection that could identify performance degradation before it impacted users, with seamless ITSM integration to route incidents to the right teams immediately.
Key components: smart detection rules in Application Insights, custom metric alerts in Azure Monitor, and Logic Apps for ServiceNow automation. The system now detects anomalies in response times, failure rates, and dependency failures, automatically creates prioritized incidents with full diagnostic context, and tracks everything against our SLA commitments.
Great question. We implemented a three-tier filtering approach in Logic Apps before ServiceNow ticket creation. First tier: severity-based routing where only high and critical alerts create incidents immediately. Medium severity alerts aggregate over 15-minute windows. Second tier: correlation logic that groups related alerts from the same application component into single incidents. Third tier: suppression rules during deployment windows and scheduled maintenance. This reduced ticket volume by 73% while maintaining comprehensive coverage. The Logic App also enriches incidents with application topology from Azure Resource Graph and recent deployment history from Azure DevOps.
We rely primarily on Application Insights’ built-in smart detection for anomaly patterns. It uses adaptive machine learning that learns normal behavior over time and alerts on deviations. For custom metrics, we implemented dynamic thresholds in Azure Monitor that adjust based on historical patterns and time-of-day variations. This eliminated 80% of false positives we experienced with static thresholds. The key is tuning sensitivity settings during the learning period and excluding known maintenance windows.
How did you handle the ServiceNow integration? We’re looking at similar ITSM automation but concerned about alert noise creating too many tickets. Do you have any filtering or aggregation logic before incidents are created?
This is exactly what we need. Can you share more details on the availability calculation methodology and how you’re measuring MTTR improvements? Also curious about the cost implications of this monitoring setup.