What is intelligent storage?

Put another way, it is a smart software layer that provides broad storage management across an enterprise ecosystem. Key features include predictive analysis, autonomous data tiering, automatic software updates and proactive troubleshooting.

Organizations today face ever-expanding data-intensive workloads, particularly as artificial intelligence (AI) and machine learning (ML) adoption accelerates. They are modernizing their data storage infrastructure to support these advanced analytics workloads and harness new business opportunities.

The need for intelligent storage is growing rapidly. According to Fortune Business Insights, the global data storage market is projected to grow from USD 298.54 billion in 2026 to USD 984.56 billion by 2034, with a compound annual growth rate (CAGR) of 16.10%.¹

This expansion is driven by vast amounts of data from Internet of Things (IoT) devices, cloud computing and AI applications, as well as data at the edge that requires local data processing capabilities. In response, organizations are turning to advanced solutions like software-defined storage (SDS) and intelligent storage systems to manage this complexity.

There are three main types of data storage:

• File storage organizes data in a hierarchical structure of files, folders and directories. This technology works well for collaboration, document management and situations where data needs to be easily shared among users. File storage is typically used with hard disks and network-attached storage (NAS) devices.

• Block storage divides data into fixed-size blocks, each with a unique identifier, and stores them independently. Block storage delivers high performance and low latency, making it ideal for mission-critical workloads, such as databases, containers and backend storage for virtual machines (VM).

• Object storage manages data as discrete objects with unique identifiers and rich metadata. Designed for massive scalability, object storage excels at handling large volumes of unstructured data (for example, images, videos, sensor data) across distributed, cloud-based environments. It’s particularly well suited for AI/ML workloads, data lakes and generative AI applications.

Traditional data storage management consists of the processes used to securely retain, organize and access data assets while optimizing storage resources and providing data protection. It relies on manual provisioning, monitoring and optimization, which becomes increasingly challenging as data volumes grow and workloads diversify across hybrid cloud environments.

Intelligent storage builds upon this approach by integrating AI, ML and automation directly into the storage infrastructure. Rather than requiring manual intervention, intelligent storage manages data placement, predicts capacity needs, optimizes performance and protects against threats within a unified management system.

While closely related, intelligent storage and AI storage are not the same.

AI storage refers to data storage systems designed for the large datasets, high-speed data access and intense compute needed for AI/ML workloads. These systems are purpose-built to support AI training, inference and data lake capabilities to meet the performance demands of AI applications.

Intelligent storage embeds AI and ML into storage systems to automate management and optimize performance across all an organization’s data. Such systems analyze data patterns, automate data tiering, predict storage capacity needs and reduce operational complexity across the entire IT ecosystem.

These technologies often work together with intelligent storage systems that are also optimized for AI workloads and AI storage systems that incorporate intelligent management features.

Traditional enterprise storage technology centered on monolithic storage area networks (SANs) for archiving data and files. However, SANs became increasingly expensive for organizations to maintain as their data storage needs grew, requiring more physical servers and other IT infrastructure to meet their demands.

Cloud storage emerged with the elasticity needed to scale data volumes up or down based on business requirements. By storing data in the cloud, organizations leverage pay-as-you-go pricing, optimizing costs. Today’s cloud-based solutions involve hybrid multicloud approaches, where organizations can combine storage from multiple cloud providers to meet various requirements, such as performance, cost and compliance.

As AI and ML technologies matured and became more accessible, embedding this intelligence directly into storage infrastructure became possible. Intelligent storage has evolved as an optimized solution for scalable, data-intensive environments.

Intelligent storage goes beyond traditional cloud solutions, with features like continuous data pattern analysis, automatic data tiering and predictive maintenance. It has become an essential component in supporting today’s IT requirements, including AI training workloads, edge computing and massive data generation from IoT devices.

Intelligent storage manages data throughout its lifecycle by using several core technologies:

• Software-defined storage (SDS): SDS plays a key role in intelligent storage by decoupling storage software from physical hardware, enabling flexible, automated resource management. This software layer manages core storage functions (for example, RAID configurations, resource allocation), allowing organizations to scale storage resources independently of underlying hardware.

• Artificial intelligence (AI) and machine learning (ML): AI and ML analyze data access patterns, performance metrics and usage trends by using rich metadata to make intelligent predictions about storage needs. ML algorithms forecast capacity requirements, detect potential hardware failures and automatically optimize data placement, classification and policy enforcement.

• Real-time analytics: Real-time analytics continuously monitor storage system performance and workload patterns, enabling dynamic adjustments and identifying anomalies that might indicate security threats or performance issues.

• Automatic tiering and caching: High-speed intelligent and caching algorithms store frequently accessed data for faster retrieval and reduce CPU wait times, while automatically moving data between storage tiers based on access patterns. “Hot” data resides on high-performance flash storage, while “cold” data shifts to cost-effective options.

• Data reduction technologies: Deduplication, compression and other techniques minimize the storage footprint and reduce costs while maintaining high-performance access. These technologies identify and eliminate redundant data, storing only unique data blocks to maximize storage efficiency.

• Predictive maintenance: Intelligent storage uses ML models to analyze system health indicators and predict potential failures, enabling proactive, predictive maintenance that minimizes downtime. The system triggers alerts, initiates failover procedures or schedules maintenance automatically.

• Integrated security and resilience: This storage solution integrates automated threat detection and response along with disaster recovery and built-in redundancy for robust security and resilience. An intelligent storage system can isolate compromised data, secure access pathways and maintain immutable backup copies to enable rapid cyber recovery.

Intelligent storage offers benefits that help enterprises achieve their business and IT goals, including the following:

• Provides faster data access: Enables organizations to scale data while maintaining high throughput, low latency and managed data access with limited to no downtime.

• Simplifies operations: Eliminates data silos, reduces complexity and accelerates time to value for digital transformation initiatives with seamless compatibility across environments.

• Increases productivity: Automates overall management, removing silos to help teams improve workflows and reduce focus on manual tasks.

• Optimizes costs: Reduces application and data sprawl to support cost-efficiency while moving infrequently accessed data to cheaper storage tiers.

• Reduces risk: Safeguards apps and data with AI-driven threat detection and automated encryption, enabling quick recovery from data breaches and attacks with integrated ransomware protection functions for cyber resilience.

• Ensures business continuity: Uses data replication and synchronization to achieve business continuity and maintain consistent, available copies across locations for rapid recovery.

• Supports compliance: Manages data sovereignty and compliance needs automatically, allowing organizations to meet requirements like GDPR.

Organizations can access intelligent storage through multiple deployment options. Major public cloud providers (for example, Amazon Web Services (AWS), Google Cloud, IBM Cloud®, Microsoft Azure) offer intelligent storage capabilities as fully managed services. Enterprise storage vendors including Dell, Hewlett-Packard Enterprise and NetApp provide intelligent storage platforms that can be deployed in an on-premises data center or integrated with cloud environments.

Many organizations adopt strategies to optimize data placement across distributed hybrid infrastructures based on performance, compliance and cost requirements. These solutions support a range of business use cases including data lakes, high-performance computing (HPC), virtual desktop infrastructure (VDI) and edge computing.