What is a data center?

Modern data centers have evolved quickly. Infrastructure has shifted from traditional on-premises physical servers to virtual networks that support applications and workloads across pools of physical infrastructure and into a multicloud environment.

Today, data is distributed across a wide range of environments—including on-premises data centers, edge locations, and both public and private clouds. To function effectively, the data center must be able to communicate seamlessly across all these environments. For example, when applications run in the cloud, they rely on the cloud provider’s data center infrastructure. At the same time, organizations may choose to maintain certain workloads, sensitive data, or legacy systems on-premises for reasons such as security, compliance, or performance.

The latest progression in the data center landscape centers on cloud-native architectures. These data centers are purpose-built to host cloud-native applications, which are designed to maximize the unique capabilities of distributed, scalable cloud environments. By leveraging technologies like containers and microservices, cloud-native applications enable businesses to innovate rapidly, adapt to changing demands, and deliver seamless user experiences. This shift is critical as organizations look to streamline their operations, ensuring that their infrastructure is ready to support the dynamic and competitive demands of the digital age.

Why are data centers important to business?

Businesses depend on data centers that support business applications and activities, including:

• Email and file sharing
• Productivity applications
• Customer relationship management (CRM)
• Enterprise resource planning (ERP) and databases
• Virtual desktops and communications and collaboration services
• Big data, artificial intelligence, and machine learning
• AI workloads, such as model training, retrieval augmented generation (RAG), and inference

Data center design includes routers, switches, firewalls, storage systems, servers, and application delivery controllers. Because these components store and manage business-critical data and applications, data center security is critical in data center design. Together, they provide three necessary elements.

Network infrastructure. This connects servers (physical and virtualized), data center services, storage, and external connectivity to end-user locations.

Storage infrastructure. Data is the fuel of the modern data center. Storage systems are used to hold this valuable commodity.

Computing resources. Applications are the engines of a data center. These servers provide the processing, memory, local storage, and network connectivity that drive applications.

How do data centers operate?

Data center services are typically deployed to protect the performance and integrity of the core data center components.

Network security appliances. These include firewall and intrusion protection to safeguard the data center.

Application delivery assurance. To maintain application performance, these mechanisms provide application resiliency and availability through automatic failover and load balancing.

What is data center security?

Data center security encompasses the technologies, policies, and practices designed to protect the infrastructure, workloads, and applications that power modern businesses. It involves safeguarding physical assets, such as servers and storage systems, as well as virtualized environments and cloud-based operations. Key elements include robust access controls to prevent unauthorized entry, advanced threat detection to monitor and mitigate cyberattacks, and encryption to secure data in transit and at rest.

As data centers grow to support hybrid and multicloud models, security must also adapt to protect distributed environments. Effective data center security maintains business continuity, protects sensitive information, and supports compliance with industry regulations. Security is foundational for resilient, agentic operations.

Data center components require significant infrastructure to support the center's hardware and software. These include power subsystems, uninterruptible power supplies (UPS), ventilation, cooling systems, fire suppression, backup generators, and connections to external networks.

The most widely adopted standard for data center design and data center infrastructure is ANSI/TIA-942. It includes standards for ANSI/TIA-942-ready certification, which ensures compliance with one of four categories of data center tiers rated for levels of redundancy and fault tolerance.

Tier 1: Basic site infrastructure. A Tier 1 data center offers limited protection against physical events. It has single-capacity components and a single, nonredundant distribution path.

Tier 2: Redundant-capacity component site infrastructure. This data center offers improved protection against physical events. It has redundant-capacity components and a single, nonredundant distribution path.

Tier 3: Concurrently maintainable site infrastructure. This data center protects against virtually all physical events, providing redundant-capacity components and multiple independent distribution paths. Each component can be removed or replaced without disrupting services to end users.

Tier 4: Fault-tolerant site infrastructure. This data center provides the highest levels of fault tolerance and redundancy. Redundant-capacity components and multiple independent distribution paths enable concurrent maintainability and one fault anywhere in the installation without causing downtime.

Types of data centers

Many types of data centers and service models are available. Their classification depends on whether they are owned by one or many organizations, how they fit (if they fit) into the topology of other data centers, what technologies they use for computing and storage, and even their energy efficiency. The six main types of data centers include the following.

AI data centers

An AI data center is specifically designed to support the demanding computational requirements of artificial intelligence workloads. These facilities use advanced infrastructure—like software-defined networking, built-in security, and high-performance GPUs and TPUs—to efficiently process vast data and run complex AI algorithms. Unlike traditional data centers, AI data centers prioritize parallel processing and optimized workflows to train and deploy machine learning models at scale.

Minimizing latency is a key focus in AI data centers, because real-time AI applications—predictive analytics and natural language processing—require rapid data processing and decision making. By combining powerful compute resources with low-latency architectures, AI data centers enable organizations to unlock the full potential of artificial intelligence in dynamic and data-intensive environments.

Enterprise data centers

Enterprise data centers are company owned and operated, typically located on corporate campuses and tailored to serve internal users.

Managed services data centers

Managed services data centers are managed by a third party (such as a managed services provider) on behalf of a company. The company leases the equipment and infrastructure instead of buying it.

Colocation data centers

In colocation ("colo") data centers, a company rents space within a data center owned by others and located off company premises. The colocation data center hosts the infrastructure: building, cooling, bandwidth, security, and so on, while the company provides and manages the components, including servers, storage, and firewalls.

Cloud data centers

In this off-premises form of data center, data and applications are hosted by a cloud services provider such as Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft (Azure), IBM Cloud, or other public cloud provider, often within hyperscale data centers designed to efficiently support massive workloads and global scalability.

Edge data centers

An edge data center is a smaller, decentralized facility located closer to end users and devices, designed to process data locally, reduce latency, and support real-time applications in industries like IoT, autonomous vehicles, and content delivery. Edge computing is especially useful when latency needs to be kept to a minimum, such as with AI processing.

Infrastructure evolution: from mainframes to cloud applications

Computing infrastructure has experienced three macro waves of evolution over the last 65 years:

• The first wave saw the shift from proprietary mainframes to x86-based servers, based on premises and managed by internal IT teams.
• A second wave saw widespread virtualization of the infrastructure that supported applications. This allowed for improved use of resources and mobility of workloads across pools of physical infrastructure.
• Today, the third wave reflects the move to cloud, hybrid cloud, and cloud-born applications.

This evolution has given rise to distributed computing. This is where data and applications are distributed among disparate systems, connected and integrated by network services and interoperability standards to function as a single environment. It has meant the term "data center" is now used to refer to the department that has responsibility for these systems irrespective of where they are located.

Organizations can choose to build and maintain their own hybrid cloud data centers, lease space within colocation facilities (colos), consume shared compute and storage services, or use public cloud-based services. The net effect is that applications today no longer reside in just one place. They operate in multiple public and private clouds, managed offerings, and traditional environments. In this multicloud era, the data center has become vast and complex, geared to drive the ultimate user experience.

Can data centers use less power?

Data centers are significant energy consumers, and their power demands are rising as digital transformation accelerates. The GPUs that power AI data centers, consume 10 to 15 times more power per processing cycle than CPUs that power traditional data centers because of the computational intensity of training and running AI models.

To reduce power costs and sourcing challenges, data centers can implement advanced energy strategies—such as optimizing power use, adopting clean energy, and enhancing cooling efficiency. These efforts can help meet rising digital demand while aligning with global energy and environmental regulations.