Almost every click ends up in a data center. Most people never stop to think about that because the interaction feels immediate, almost effortless.

Step inside a data center and you’ll see rows of processors and networking hardware moving data across internal networks and remote locations. They support everything from basic unified communications as a service (UCaaS) platforms to artificial intelligence (AI) workloads around the clock. That constant flow of traffic across private and public data centers, as well as colocation facilities, comes with a cost: heat and power consumption.

You can partially blame ChatGPT and the watersheds of the Raccoon and Des Moines rivers behind it.

Data centers now consume as much electricity as 100,000 homes per facility, according to the International Energy Agency. Yet the global data center sector is poised for unprecedented expansion, with capacity expected to nearly double from 103 gigawatts to 200 gigawatts by 2030. Throw in talk of a $3 trillion supercycle as well as AI super factories and orbital data center networks, and you might begin to see evolution on a scale that would make even Charles Darwin and Alfred Russel Wallace faint.

So, WATT’S the problem?

Examining Five Data Center Trends

The industry’s hunger for capacity is changing how data centers operate and how teams troubleshoot them. Internal networks now carry far more traffic and support more GPU-intensive systems than just a few years ago. That means more complexity. 
For teams managing enterprise data centers, hyperscale cloud environments, and colocation facilities, that complexity is becoming harder to control. Suddenly, that “immediate, almost effortless” click isn’t so effortless to users anymore. Many data center trends point to something deeper than growth as the cause—something relevant to observability:

• GPU clustering: The deployment of dense GPU clusters will continue to rise as infrastructure scales to support large AI models. These tightly connected environments generate massive volumes of packet-level traffic as data moves continuously between processors.
• Liquid cooling: Increasing compute density is accelerating the move beyond traditional air cooling. Liquid cooling is gaining traction as organizations look to improve energy efficiency and support high-performance AI workloads, with AI-enabled systems optimizing cooling demand in real time.
• Power availability: Electricity access is now shaping where data centers can be built. Hyperscale facilities may require hundreds of megawatts, pushing operators toward regions with sufficient grid capacity to support growing AI demand. New data centers are projected to consume between 6.7 percent and 12 percent of U.S. electricity by 2028.
• Edge expansion: The distribution of applications across cloud, regional data centers, and edge environments, including colocation facilities, is expanding as compute moves closer to users and data sources. AI and Internet of Things (IoT) are increasing data generation outside traditional facilities, fueling growth in edge computing, with the market projected to reach approximately $249 billion by 2030.
• Modular construction: Demand for computing infrastructure is pushing developers to adopt modular and prefabricated designs to shorten deployment timelines, especially as grid connection wait times in some major markets now exceed four years.

Data center innovation is double-edged. It changes how data moves inside and between data centers. It’s not just whether systems are up or down, but how observable they are and how they behave under load. For organizations running critical workloads, one disruption can mean the difference between meeting service-level agreements (SLAs) or facing costly penalties, or between maintaining positive customer relationships or losing business to competitors.

Data Center Observability

It’s simple, really. Businesses need customers. Customers need services.

A typical data center might hold tens of thousands of servers, with hyperscale environments reaching hundreds of thousands, running countless services including storage, streaming, machine learning (ML) models, and corporate applications. Organizations are racing to include performance, compliance, and security data in their observability fabric to evolve and scale alongside data centers. Here are a few industry examples:

• Financial services: Banking and trading platforms process massive transaction volumes and microbursts, often in real time, funneling traffic to analytics and security systems. Payment systems, fraud detection, and digital banking are driving demand for larger, always-on data center environments.
• Healthcare: Medical imaging and patient care applications generate a lot of personally identifiable information (PII) that must be processed and securely stored. Hospitals and research institutions are expanding data center use to support AI-enabled diagnostics, treatment planning, and research systems.
  Manufacturing: Industrial systems continuously collect data from machines, production lines, and other systems and feed it to centralized analytics platforms. These data-intensive, often automated workloads are increasing data center requirements as manufacturers scale increasingly distributed digital operations.
  Across industries, being able to understand data in motion is critical to data center operations. That means observability is too.

NETSCOUT Visibility for Data in Motion

Look closely at any modern network, and something quickly becomes clear: streams of data in motion to keep digital services running. Whether that traffic flows through enterprise data centers, hyperscale cloud regions, or colocation sites, NETSCOUT’s nGenius solutions for observability and Omnis AI Insights solutions make that activity visible at the packet level by centralizing data from across the network to identify performance bottlenecks and security risks in real time at scale. This means that whenever you step inside a data center, now or in the future, you can be confident the infrastructure and services are protected.

Read this case study to see how NETSCOUT helped a large financial institution cut data center troubleshooting from two weeks to 15 minutes.