What Is Data Center Modernization?
Data center modernization is the process of upgrading data center infrastructure to improve efficiency, increase performance, strengthen data security, and build in the flexibility needed to adopt new technologies, including artificial intelligence (AI) and machine learning (ML), and respond to market needs.
Modernization can take many forms, depending on business goals and unique circumstances, and is an ongoing process that can require substantial investments. However, in many cases, the cost of maintaining legacy equipment or delaying modernization—reduced productivity, greater security risks, and lack of innovation—can prove higher than the cost of evolving the technology infrastructure. This makes data center modernization a business imperative.
Corporate leaders recognize the need to revitalize their data center infrastructure. In a 2024 survey of technology leaders, only 38% of CIOs and CTOs said their company’s technology was prepared to support a new business model.1
Benefits of Data Center Modernization
Data center modernization can help companies maximize technology investments, increase performance and efficiency, improve security, and provide the flexibility to capitalize on new business opportunities.
Cost Reduction
Relying on outdated hardware and software infrastructure to handle modern workloads can lead to inefficient and underperforming systems, driving operating expenses higher and preventing organizations from leveraging the benefits of digital transformation.
By updating data center infrastructure and cloud investments businesses can lower operating costs, including energy expenses, and accomplish more with fewer servers. Selecting the right technologies can help to support the same or greater workload capacity in less space and with higher performance per watt for a more efficient data center footprint.
AI Deployment
Businesses can maximize AI’s vast and rapidly expanding capabilities with updated data center technology that supports the high-power densities and cooling requirements that AI applications demand.
A technology stack for artificial intelligence is a complete, end-to-end solution that consists of hardware, software, and tools that facilitate the development and deployment of AI applications. While similar to the technology stack used to build general-purpose software applications, the AI tech stack includes specialized tools to support the building of AI models that enable machine learning and deep learning.
It's important to note that many common AI workloads in the data center, cloud, and at the edge can be supported by AI processors with integrated AI accelerators. This removes the need for discrete GPUs or hardware AI accelerators, helping businesses save on hardware and software costs and reduce system complexity. Newer processors also offer enhanced security capabilities and more efficient architectures.
Upgraded Applications
Upgraded IT infrastructure yields better performance and enables organizations to support new deployment models and meet evolving application and workload requirements—unlocking the ability to roll out new services and customer experiences.
Applications can also be scaled up and down more quickly based on demand. This allows for faster adaptation to changing market conditions and integration of new technologies as needed.
Upgrading to newer technology can facilitate necessary end-of-life (EOL) transitions or application updates while also introducing advanced security measures to protect against cyber threats and ensure the reliability and availability of critical applications.
Improved Employee Productivity
Modernized data centers are better equipped to handle increasingly complex and demanding workloads, enabling employees to accomplish more in less time. Routine tasks such as server provisioning, storage management, and network configuration can be automated, reducing manual processes and errors and freeing up employee time and resources for more strategic initiatives.
Challenges of Data Center Modernization
Data center modernization is a complex and multifaceted process that impacts a business’s operational, technical, regulatory, environmental, and organizational aspects.
Organizations will face a range of challenges depending on the specific modernization project being implemented, including:
- Preventing data loss, minimizing downtime, and ensuring business continuity during migration.
- Mitigating risks to data integrity and security, and ensuring regulatory compliance.
- Managing hardware, software, and network infrastructure interoperability.
- Achieving the right balance between legacy infrastructure and new technologies.
- Accommodating the ever-increasing demand for processing power and storage capacity.
Careful upfront strategic planning is essential to address these issues.
Data Center Modernization Use Cases
Upgrading technology infrastructure enables a data center to incorporate new capabilities and expand existing ones to support ongoing business transformation:
- Virtualization: Replacing physical servers with virtualized environments improves resource utilization and flexibility.
- Software-Defined Networking (SDN): Managing network resources programmatically enhances agility and scalability.
- Hyperconverged Infrastructure (HCI): Combining compute, storage, and networking in a single integrated system simplifies IT resource management and enhances scalability.
- Edge Computing: Bringing compute resources closer to the data source reduces latency and enables near-real-time processing.
Developing a Data Center Modernization Strategy
To maximize modernization efforts, it’s important to assess potential opportunities and prioritize projects aligned with an overall business strategy.
The pace of progress will depend on the scope and timeline of the modernization project and how systems are currently integrated. The following steps can be implemented separately or together.
- Start: Identify and resolve potential barriers upfront to minimize implementation challenges.
- Create: Define business objectives and initiatives to help prioritize areas for modernization investment.
- Assess: Evaluate the current portfolio of applications and workloads, including dependencies, data accessed, connectivity requirements, and current platform support. For a holistic understanding of technology, capacity, and security requirements, be sure to include an audit of device fleets and cloud technologies and do an assessment of the current security strategy.
- Evaluate: Evaluate whether it is time to retire older applications, rehost them to a cloud operating model, replatform or refactor them, or replace them with a SaaS application to best achieve strategic business objectives. For employee devices, determine if a PC fleet refresh is needed for AI readiness, productivity and employee satisfaction improvements, or endpoint security reasons.
- Embrace: Learn and adapt for continuous improvement through agility and flexibility. As modernization projects progress, monitor and manage against objectives and success metrics, fine-tuning as needed. Scale the most successful approaches as subsequent workloads are modernized. Adjust operating models based on actual outcomes and evolving business needs to optimize results.
The Future of Data Centers
Exponential growth in the data center market is projected between 2025 and 2030,2 partially due to the push to harness the capabilities of artificial intelligence and machine learning and the increasing adoption of cloud computing, hyperscaling, and edge computing.
Data center modernization can help businesses capitalize on this growth, yielding significant returns in improved performance, greater efficiency and security, and reduced costs. However, data center modernization should not be considered a one-time exercise but an ongoing and incremental process.
With technology capabilities and competitive challenges continually evolving, businesses will need to ensure that their data centers’ capabilities can meet workload demands and strategic imperatives today and tomorrow.