Cloud PC Analytics — Utilization, Connection Quality & Resource Performance
Built the analytics suite for Windows 365 in Microsoft Intune — covering Cloud PC utilization, connection quality, and resource performance reporting to help IT administrators optimize deployments, reduce costs, and identify underperforming devices.
Managing a fleet of Cloud PCs without usage and performance data is like managing physical hardware with no monitoring. This suite of reports gave Windows 365 administrators the visibility they needed to operate Cloud PC environments confidently at scale.
The problem
Enterprise IT teams deploying Windows 365 needed answers to three distinct questions: Are users actually using their Cloud PCs? Are connections performing well? Are the devices provisioned with the right amount of compute? None of these had good answers without purpose-built reporting.
What we built
Three interconnected reports surfaced through Microsoft Intune and Endpoint Analytics:
Cloud PC Utilization Report — shows time connected per Cloud PC over 28, 60, or 90-day windows, with a histogram bucketing devices into High (80+ hours), Average (40–80 hours), Low (<40 hours), and None. The key operational use case: identify underutilized licenses for reassignment or deprovisioning to reduce costs.
Cloud PC Connection Quality Report — per-device metrics including round-trip time (RTT), available bandwidth, remoting sign-in time, UDP utilization, and connection gateway. Filters let admins identify devices failing to meet their organization’s performance criteria and evaluate root causes — network configuration, geographic distance, authentication architecture, client/host appropriateness.
Resource Performance Report — built on Endpoint Analytics, tracking vCPU and RAM spike time percentages (usage over 50% is a spike) across Cloud PC SKUs and individual devices over 14-day windows. A composite Resource Performance Score feeds into Microsoft’s Productivity Score. When a device scores below baseline, the report surfaces resize recommendations.
What I learned
The most impactful design decision across all three reports was the drill-down to individual device context. Aggregate metrics tell you something is wrong. Per-device detail tells you where to look and what to do. Building that navigation path — from fleet-level summary to specific device to remediation action — was the difference between a report that informed and one that actually changed behavior.