The Enterprise Storage Scaling Playbook: How To Help Customers Scale Without Overbuilding

Data is growing at an unprecedented pace. The AI data rush, increased demand for cyber recovery and greater workload intensity across emerging business applications have all put new pressures on storage infrastructures.

IT leaders need to scale storage capacity while managing costs and sustaining performance, at the same time they avoid storage buildouts they don’t need and that can’t easily expand when required.

Solution providers play an essential role in helping clients design architectures that align with today’s more complex workload requirements and clients’ long-term business goals.

Guide Customers Through The AI Storage Rush

AI sits at the center of the current storage boom. Model training, GenAI inferencing and agentic AI all require access to enterprise data while simultaneously creating data. Other forces impacting storage:

• Increased value of unstructured data like documents, images, telemetry, which makes file and object storage more important
• Data resilience and recovery speed has become as business critical as data location and storage capacity
• Supply constraints driven by data center infrastructure growth is changing storage economics

The Top Causes Of Overbuilding Storage Infrastructure

Overbuilding often begins when infrastructure is designed around uncertainty. Customers want enough storage to handle future growth, but this can lead to costly infrastructure that often remains untapped. Common practices that compound overbuilding:

• Focusing on expanding capacity over improving performance.
• Sizing around peak workload demand that does not reflect day-to-day operations.
• Storing datasets that don’t require instant accessibility on premium, flash storage.
• Retaining unnecessary duplicate data, backups and inactive files.

Match Storage Architecture To The Workload

Not all storage architectures are built for the same purpose. AI, analytics and unstructured data workloads each place different demands on performance, scalability, accessibility and cost. Aligning storage infrastructure with workload requirements helps organizations maximize performance, control costs and ensure their data environments can scale as business needs evolve.

What Storage Architectures Are Best For AI Workloads?

The best storage architectures for AI workloads combine scale-out file storage and object storage.

AI training and inferencing have intense storage speed and accessibility requirements. High-performance scale-out file storage architectures with non-volatile memory express (NVMe) provide the high availability, high throughput, low-latency and concurrent access that AI workloads need. Object storage is also necessary, providing API-based access and horizontal scalability that needed for data lakes, RAG systems and long-term model data.

What Storage Architectures Are Best For Data Analytics?

Analytics environments benefit from compute and storage that scale independently. Many analytics engines can effectively access data lakes and run with compute that expands or contracts based on processing demand without additional storage. Latency-sensitive analytics workloads, however, require high-performance file or flash storage. Analytics needs can vary significantly between organizations and industries, which makes data assessments and decisions critical to ensure storage economics align with business requirements.

What Storage Architectures Are Best For Unstructured Data?

Scale-out file storage and object storage are both important for unstructured data, which can grow unpredictably and overwhelm traditional architectures. Scale-out file storage has the flexibility to grow incrementally with multiplying datasets. Object storage environments are necessary for cost-effective, long-term storage that delivers cloud-like agility with stronger operational controls.

Modernize Storage Without Disrupting Client Operations

A phased approach that improves performance while prioritizing security is the ideal strategy.

• Discovery – Identify applications, datasets and dependencies throughout the organizations’ infrastructure.
• Real-world baseline – Determine the organizations’ current capacity consumption, latency and throughput needs.
• Workload classification – Clarify mission-critical databases, active AI datasets, high-performance analytics applications, growing file repositories, archives, cold data.
• Incremental infrastructure deployment and migration – Begin with lower-risk datasets and validate performance in the new environment.
• Expand – Migrate increasingly essential workloads.
• Optimize – Ongoing storage performance and security optimization is an operating discipline that should include capacity management and monitoring, recovery strategy updates and data lifecycle policies.

Key Guiding Factors For Storage Modernization Investments

Flexibility. Storage needs forecasting became an even greater challenge after AI entered the scene. Help clients see storage investments through a change-readiness lens. Can capacity expand without replacement? Does their infrastructure span on-premises and cloud resources?

Software defined storage (SDS) establishes versatile IT environments quickly and helps control public cloud spending by separating storage software from hardware. With SDS, providers can deliver storage that adjust capacity automatically and reduces infrastructure overhead.

Performance. Peak Input/Output Operations Per Second (IOPS) should not be the lone guiding metric for storage buying decisions. Storage should match workload requirements: throughput for AI, latency for databases, concurrent access for analytics and economical capacity for large data repositories.

Resilience. Storage architectures should be designed to meet recovery point and recovery time goals, with regular testing to ensure these targets are achievable. Today’s unpredictable AI threats make built-in, automated storage security features and capabilities essential. These include synchronous and asynchronous replication, immutable snapshots, anomaly detection and isolated recovery environments.

ROI. Storage ROI extends beyond acquisition costs. Infrastructures that prioritize flexibility and performance and resilience over time are more likely to meet storage ROI targets like total cost of ownership (TCO) and data lifecycle value.

Combine Storage Consolidation And Flexibility For Future-Ready Client Infrastructures

Siloed storage environments make it difficult to reassign unused capacity and can lead to overprovisioning due to limited visibility. By consolidating storage resources and adopting a software-driven disaggregated infrastructure model, organizations can improve utilizations while maintaining the flexibility to adapt as AI, analytics and application requirements evolve.

Storage consolidation supports the use of tools that automate thin provisioning, deduplication, compression and tiering, while providing the visibility required to optimize utilization and make more informed capacity planning decisions.

A software-driven, disaggregated infrastructure complements storage consolidation by creating a shared, adaptable resource foundation. By separating compute, storage, networking and acceleration resources, organizations can scale each independently based on business needs while maintaining centralized visibility and utilization efficiencies.

Modern storage strategies should prioritize consolidation without rigidity. With this framework, solution providers reduce storage sprawl and stranded capacity for clients while delivering the architectural flexibility needed to support future business growth. Learn more about Storage at Dell