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Insights Dashboard

The Insights Dashboard is a highly customizable operational environment for real-time monitoring and predictive analysis. It offers a “Build-Your-Own” experience, enabling users to curate widgets across three intelligence categories: Graphs (AI-powered diagnostics), Cost Project Graphs (financial governance), and Kubernetes (cluster health). Based on comprehensive statistical calculations, it serves as a unified hub for tracking system health trends, incident patterns, and downtime/labor costs, while providing actionable metrics like prediction accuracy and alert suppression counts.

1. Dashboard Management & Creation

Navigate to the Insights Dashboards section.From the “All dashboards” landing page, users can oversee their entire monitoring ecosystem:

• Create New Dashboards:Click the “+ New dashboard” button to build a clean-slate view for specific systems or projects.
• System Health At-a-Glance:Track the real-time Health Score (0-100) and Popularity of each dashboard to prioritize critical environments.
• Search & Filter:Quickly locate specific views by Dashboard Name, System, or Author.

2. Dynamic Customization (Edit Mode)

Once inside a dashboard, users have full authority over the interface layout through an intuitive sidebar workflow:

• Enter Edit Mode:Click the Edit icon next to the dashboard name to unlock the customization layer.
• The Widget Library (Automatic Sidebar):Entering edit mode automatically triggers the right-side management panel. Here, you can directly search for and toggle specific components on or off. This allows you to instantly visualize changes and keep your workspace focused.
• Interactive Controls:Add new insights or close unnecessary tiles in real-time, then click “Save” to lock your personalized configuration

3. Monitoring Categories & Widget Definitions

A. AIOps & Diagnostic Graphs: Operational & AI Diagnostics

This category focuses on System Health Monitoring, Rapid Incident Identification, and AI-driven Root Cause Analysis.

• Incident & Anomaly Identification
  • Incident statistics: Visualizes the frequency trend of alerts to identify sudden system spikes.
  • Top anomalous (Components/Instances): Pinpoints specific instances with the highest failure rates.
  • Top anomalous/incidents (Patterns): Uses AI to cluster recurring issue signatures, helping users extract systemic risks from scattered alerts.
• AI Diagnostic & Analysis Performance
  • Top Root Cause Categories: Automatically classifies complex failures (e.g., Network, Resource Exhaustion) to accelerate initial troubleshooting.
  • RCA / Prediction confidence score: Indicates the reliability percentage of the AI’s diagnostic and predictive findings, quantifying analytical precision.
  • Auto-fixed incidents: Tracks the number of issues resolved via automated remediation scripts, highlighting self-healing efficiency.
  • Alert/Log compression ratio (Signal Extraction): Measures how effectively the system extracts valid signals from raw data. A higher ratio reflects superior identification precision.
  • Incident consolidation ratio: Measures the system’s ability to merge related failure points into a single logical incident, significantly reducing information overload.
• Volume & Spatial Metrics
  • Total metric/log count: Monitors the total data throughput being processed.
  • System instance / Instances / Containers: Real-time inventory of active operational assets.
  • World map: Spatial visualization of service across global regions.
  • Honeycomb map: A high-density heat map to instantly identify “sub-health” nodes in large-scale clusters.
  • Prediction statistics: Forecasts future system behavior based on AI algorithms for proactive risk warning.

B. Cost Project Graphs: Financial & Resource Governance

Used for cloud cost auditing and monitoring resource ROI.

• Cost statistics: Time-series trend of total financial expenditure.
• Top cost (Projects/Accounts/Services): Identifies the largest spenders across different organizational dimensions.
• Trend top cost: Monitors the growth slope of spending to detect budget drifts caused by misconfigurations or traffic spikes.

C. Kubernetes: Container Orchestration

Dedicated status and resource monitoring for K8s-native environments.

• Hosts / Pods: Real-time tracking of the deployment and operational status of K8s nodes and individual pods.