Operations | Monitoring | ITSM | DevOps | Cloud

Data center decisions are only as reliable as the information behind them. An asset inventory can accurately describe the equipment it records while still omitting assets that have not been documented. Power readings can be current for monitored equipment while other gaps go unnoticed. And when infrastructure data is spread across disconnected systems, getting a complete picture becomes harder.

Uptime Institute's Global Data Center Survey 2026 found that the average modal rack density exceeded 11 kW in 2026, up from 9 kW in 2025. “Modal rack density” means the rack density that is most common within a data center. Arguably, the more important trend is that rack densities are becoming more varied, with more facilities supporting a mix of conventional and high-density racks.

Data center power distribution delivers electricity from the facility's power source to the IT equipment that consumes it. Data center monitoring provides visibility into how much power is being used, where it is being used, and how much capacity remains available to improve uptime and efficiency.

Many universities and academic institutions use data centers for a wide range of purposes: secure data storage for student and faculty information, learning management systems, and administrative operations. Universities also use data centers for research and data analysis. Research, simulations, machine learning models, and data analysis all require high-performance compute.

Data center capacity planning concerns are growing. The Uptime Institute Global Data Center Survey 2026 reveals that the share of data center managers and operators who are at least somewhat concerned about forecasting future capacity requirements has risen from 68% in 2024 to 76% in 2026. Data center professionals are caught between accelerating business demands, aggressive artificial intelligence rollouts, higher-density GPU clusters, and intense scrutiny over power and cooling.

Power Usage Effectiveness has long been used as a metric to calculate data center efficiency. For roughly the last two decades, this popular metric has been used to manage data centers throughout the world. In Singapore, arguably one of the leading data center hubs in Southeast Asia with 1.4 gigawatts capacity, future PUE limits are likely to force operators of both new and existing data centers to modify their facilities and how they are managed.

Managing AI infrastructure in a traditional enterprise data center comes down to validating that sufficient capacity exists before hardware arrives, then maintaining accurate infrastructure data to support planning, deployment, troubleshooting, and ongoing operations. This is because AI has changed what enterprise data centers were built to handle.

NetBox is an open-source platform used for network infrastructure management and documentation, helping organizations track networks, devices, IP addresses, circuits, and racks. Sunbird’s new NetBox connector programmatically pulls device, port, and cabling data from NetBox into dcTrack using a preconfigured base XML connector, driving automation and giving you a single pane of glass and single source of truth across your entire infrastructure.

Across the United States, data center projects are being stalled, local governments are putting ordinances in place to limit data center buildouts, all because people hate data centers. Due to community pushback, in Q1 2026 alone, over $130 billion’s worth of AI data center projects has been blocked or delayed.

Idle resources in your data center are a liability. Every unused server drawing power, every underutilized rack consuming cooling capacity, and every stranded port sitting dark on a switch represents money spent with no operational return. That's always been true for traditional compute. It's even more consequential as organizations invest in GPU infrastructure for AI workloads, where the hardware is an order of magnitude more expensive and the cost of underutilization scales accordingly.