What is data center modernization?

For most organizations, data center modernization is not a one-time project but an ongoing process. It focuses on moving away from hardware-dependent systems toward software-defined infrastructure that spans on-premises, private cloud and public cloud infrastructure and edge settings, all managed as one environment.

The need to derive value from artificial intelligence (AI), distributed applications and real-time processing at the edge has changed what organizations need from data centers. According to Goldman Sachs Research, data center power needs will rise 50% by 2027 and reach 165% of 2023 levels by 2030, driven largely by training and inference workloads.¹ Most legacy data centers were not built to meet those demands.

Data centers served a different era of computing where traditional infrastructure was built around CPU-based compute, predictable workloads and centralized storage.

Unlike model training, which runs in large centralized facilities, AI inference is increasingly running at the edge (for example, in factories, retail stores, remote sites). Modernization now extends beyond the core data center and into distributed locations.

Data sovereignty is also shaping where workloads run. Many industries and geographies require that certain data stays within specific jurisdictions, which means workload placement decisions have to account for where the data lives and where regulations apply.

Traditional data centers followed a centralized model. Physical servers ran individual applications, teams attached storage systems to specific hosts and network configurations required manual operation. This model worked well when workloads involved running enterprise applications like enterprise resource planning (ERP) and databases.

The shift to modern data centers began with server virtualization, which separated workloads from physical hardware and let multiple applications share resources on a single server. Software-defined infrastructure extended that to storage and networking.

The introduction of containers and Kubernetes increased portability, letting applications run consistently across on-premises servers, private cloud and public cloud platforms. This software-driven approach to managing the entire data center is referred to as the software-defined data center (SDDC).

Today, the modern data center is no longer a fixed location. It spans core facilities, cloud-based platforms and edge locations, with workloads moving based on performance, cost, latency and compliance rather than physical proximity to hardware. This distributed hybrid cloud model gives organizations the flexibility to run workloads where they perform best, while software and automation drive resource provisioning and management.

AI data centers go further, built for the scale and performance demands of AI training and inference workloads that traditional data centers were not designed to handle.

Traditional enterprise workloads still run in most facilities on virtualized, on-premises infrastructure. In contrast, cloud-native applications are designed to run across on-premises and public cloud systems simultaneously, rather than being tied to a single environment.

Today, AI-driven workloads put the heaviest demands on infrastructure, requiring GPU-dense compute, fast storage and low-latency networking. High-performance computing (HPC) workloads share many of these requirements.

Beyond the core data center, edge workloads are growing as machine learning (ML) and AI move to distributed locations closer to the source, running on remote servers and Internet of Things (IoT) devices.

Modern data center infrastructure covers systems that provide compute, storage and data protection. Here is a look at what those components include:

• Compute and AI-ready hardware: Modernizing compute means moving beyond CPU-only servers to support GPU-accelerated workloads for AI and HPC. Neural processing units (NPUs) handle inference at the edge, where power efficiency matters as much as raw performance. Enterprise hardware (for example, IBM Z®) combines AI accelerators with security capabilities required by regulated industries like finance and healthcare.

• High-performance storage: NVMe SSDs and all-flash arrays trim latency and increase throughput, while software-defined storage (SDS) decouples management from physical hardware. These capabilities support consistent operations across on-premises and cloud computing settings. In addition, object storage handles large volumes of unstructured data that AI training and edge workloads generate.

• Software-defined networking (SDN) and SDDC: Software-defined networking (SDN) applies software-driven management to networking, storage and compute, replacing manual hardware configuration across the entire data center.

• Virtualization and HCI: Server virtualization remains the foundation of most modernized data centers, letting multiple workloads share physical hardware. Hyper-converged infrastructure (HCI) goes further, combining compute, storage and networking into a single software-managed platform.

• Containers and Kubernetes: Containers package applications into portable units that run consistently across settings. Kubernetes manages their deployment and scaling across hybrid environments from a single platform.

• Automation and AIOps: As data centers grow more distributed, manual operations become harder to manage at scale. Automation and AIOps handle monitoring, incident response and capacity planning based on real-time data.

• Power and liquid cooling: High-density AI racks need far more power than traditional configurations, requiring many data center facilities to upgrade their power delivery systems. Liquid cooling handles heat that air cooling cannot manage at scale.

• Security and zero-trust architecture: As workloads spread across more settings and edge computing locations, there are more entry points to secure. Modernizing cybersecurity means moving away from perimeter-based models toward zero-trust frameworks, where no user, device or workload gets access by default, regardless of network location.

Data center modernization delivers a range of benefits that support today’s enterprise business needs:

• Support for AI and modern workloads: Data center modernization gives organizations the infrastructure needed to carry out AI training, inference or real-time analytics, eliminating bottlenecks that come with legacy systems.

• Greater agility: Software-defined infrastructure and automation cut the time that it takes to provision resources, deploy applications and respond to changing workload demands. Organizations can distribute resources across workloads without physical hardware changes.

• Lower operational costs: Modernizing the data center optimizes IT infrastructure so it runs more efficiently. Moving workloads to the most cost-effective setting, whether on-premises, cloud or edge, also reduces the overhead of maintaining underutilized infrastructure.

• Improved resilience: Modern data centers and distributed architecture reduce dependence on any single facility or connection. Workloads can continue across settings, and edge locations can keep running independently when central connectivity is lost.

• Better security: Data center modernization supports consistent policy enforcement across distributed settings and modern encryption reduces exposure across data centers, cloud platforms and edge locations.

• Easier compliance: Organizations can direct sensitive workloads to specific facilities or regions and enforce those policies through software-defined management rather than manual configuration.

• Sustainability: Modern infrastructure is more energy-efficient than legacy systems. Liquid cooling and energy-efficient hardware contribute to reducing the environmental footprint of data center operations.

• Application modernization: Containers, Kubernetes and hybrid multicloud platforms give development teams the tools to move from monolithic applications to microservices architectures. This method improves deployment speed, availability and uptime for better user experiences.
  
  

Data center modernization projects are complex and often a core part of a broader digital transformation strategy. Starting with a clear strategy and a roadmap that can adapt as technology and business requirements change matters more than most organizations expect.

Many enterprise organizations integrate consulting services from business technology providers (for example, IBM, HPE) to assess current infrastructure and manage the transition across architecture, security and operations.

Before making infrastructure decisions, organizations need to know what workloads they are running and what their performance, security and compliance requirements are. This reveals legacy dependencies and informs where each workload should go.

According to a Deloitte study, more than 60% of IT budgets still go toward maintaining legacy systems, which is often the first constraint modernization programs run into.²

Set specific goals, such as reducing infrastructure costs, supporting a specific AI use case or meeting a data residency requirement.

Specific goals give teams a way to measure progress and decide along the way.

Not every workload belongs in the cloud and not every workload belongs on-premises. Cloud solutions offer scalability and faster access to new services, while on-premises infrastructure gives organizations more control over performance and compliance.

For organizations whose facilities cannot support high-density AI infrastructure, colocation is worth considering as part of this decision.

A phased approach prioritizes the highest-value workloads first, reducing the risk of downtime and disruption to key operations as the initiative moves forward.

Security controls, compliance monitoring and cost management need to be part of the modernization architecture from the beginning. This approach includes backup and disaster recovery, and business continuity planning so that operations can keep running if a system fails or a migration goes wrong. 

Data center modernization does not end at deployment. Infrastructure requires continuous monitoring, patching, upgrades and lifecycle management.

Expertise in cloud service platforms, Kubernetes and AI infrastructure is often required, and IT teams need ongoing training as data center services and new technologies evolve.