Self-Heal Analytics & Monitoring

Overview of Self-Heal Analytics

Self-Heal Analytics is designed to provide a data-driven approach to IT issue resolution. It enables IT administrators to:

• Monitor self-heal execution in real-time across all devices.
• Identify frequently occurring issues and their resolution trends.
• Assess the impact of self-heal automation on system compliance and performance.
• Generate reports that help IT teams optimize self-heal strategies.

By leveraging these insights, IT teams can proactively manage IT infrastructure, reducing the number of support tickets and improving system efficiency.

2. Host Summary & Execution Status

The Host Summary & Execution Status section provides a device-level view of self-heal execution. It helps IT teams monitor the effectiveness of self-heal actions across all endpoints.

Key Metrics Tracked in Host Summary:

3.Total Number of Self-Heal Executions

• Displays the number of times self-heal rules have been triggered on a device.
• Helps IT teams identify high-risk endpoints that frequently require self-heal interventions.

2.Success vs. Failure Rate of Self-Heal Actions

• Success Rate: Indicates how many self-heal executions resolved the issue successfully.
• Failure Rate: Shows instances where self-heal was unable to resolve the issue, requiring manual intervention.

3.Status of Devices

• Provides a real-time compliance status of each device.
• Highlights endpoints that need further investigation due to repeated self-heal failures.

Why is Host Summary Important?

• Helps IT teams identify problematic devices and take proactive actions.
• Reduces the need for manual troubleshooting by understanding which issues occur most frequently.
• Allows IT admins to prioritize remediation efforts for critical endpoints.

3. Rule-Level Insights and Performance Tracking

The Rule-Level Insights & Performance Tracking section provides a deep dive into individual self-heal rules to assess their effectiveness and optimize their performance.

Key Data Tracked at Rule Level:

1. Most Frequently Triggered Self-Heal Rules
   • Identifies which self-heal rules are executed most often.
   • Helps IT teams spot recurring issues that may require further investigation.
2. Success vs. Failure Rate for Each Rule
   • Tracks the effectiveness of each self-heal rule in resolving issues.
   • Helps IT teams identify underperforming rules that may need updates.
3. Impact of Self-Heal Rules on System Compliance
   • Measures how well self-heal rules are enforcing IT compliance policies.
   • Ensures that devices remain secure and meet compliance standards.
4. Execution Time and Efficiency
   • Tracks how quickly each rule is executed and resolves an issue.
   • Helps IT teams fine-tune execution parameters for better efficiency.

Why is Rule-Level Insights Important?

• Enables continuous improvement of self-heal automation.
• Helps IT teams eliminate ineffective rules and enhance well-performing ones.
• Reduces the likelihood of recurring IT issues by refining the automation process.