The rapid adoption of artificial intelligence and machine learning (AI/ML) over the past year has transformed just about everything – ushering in a new era of innovation and growth the world has never seen. The same goes for data storage, where the technologies’ impact will be transformative, enabling greater business agility that companies need to compete in today’s data-driven world.
What might the change look like? Here are seven ways AI/ML could transform the storage landscape in 2024:
1. Tiered storage: Tiered storage has been around for decades, allowing organizations to leverage different types of storage media based on requirements around capacity, speed, cost, security, and other factors. More recently, pre-written scripts introduced some level of process automation, but implementing tiered storage still remains a relatively manual effort. AI/ML can provide the opportunity to fully automize tiered storage by analyzing access and usage patterns to intelligently allocate data to the optimal storage tier. And, because models are always learning, AI/ML will be able to adapt to changing conditions in real time – ultimately driving optimal price-per-performance capabilities.
2. Classification and tagging: Classification and tagging is another important yet manual task that offers opportunity for further automation. AI/ML can analyze the context of the data and the associated metadata to automatically categorize large volumes of information into neat, organized consumable units. The potential use cases for classification and tagging are intriguing – including commerce, content management, and any other process that relies on search functionality.
3. Security and threat mitigation: Threat actors have been using AI/ML to create more sophisticated attacks at scale, and cybersecurity teams are starting to fight back by integrating AI/ML capabilities throughout the enterprise security stack. This includes more advanced threat detection, better visibility into vulnerable assets, and increased productivity and efficiency across security operations. Highly reliable data storage will be required to manage and parse through the mountains of security data that is generated from dozens of monitoring tools.
4. Deduplication and compression: Deduplication and compression can help organizations maximize storage resources in today’s data-driven world. AI/ML could be leveraged to review huge datasets across the organization and identify opportunities to dedupe or compress datasets, which will enable organizations to more effectively leverage their total storage capacity.
5. Predictive maintenance: Unplanned downtime costs organizations billions of dollars in lost productivity each year. AI/ML-powered predictive maintenance could allow organizations to analyze storage performance and health in real time to head off and fix issues before they become a major problem that leads to downtime. AI/ML could also help with planned downtime, identifying the most optimal time to take systems down for regular, preventative maintenance.
6. TCO analysis: Insight into the true cost of storage deployments could transform how organizations allocate storage resources across the organization. Hidden costs such as energy consumption, networking, and maintenance are nearly impossible for humans to figure out on their own. AI/ML, on the other hand, could learn how to take these costs into account over the entire lifecycle of storage systems. Understanding TCO for these systems would enable informed decision-making for cost optimization and resource allocation.
7. Resource management: AI/ML could also help with the configuration, optimization and reallocation of compute and storage resources. This can lead to significantly improved asset utilization, enhanced system performance, and increased data availability. These benefits can be amplified when combined with NVMe over fabrics (NVMe-oF), which can enhance data transfer speeds, efficiency, scalability, and resource utilization. AI algorithms could dynamically adjust storage resources and parameters in real time, intelligently manage data across different tiers or resources, and predictively maintain system health to ensure efficient and effective data management. Pairing AI with NVMe-oF could be a crucial step toward managing the rapidly expanding volume of data to offer more reliable and scalable shared storage solutions.
AI/ML Is Ushering in a New Era of Intelligent Storage
The implications of AI/ML on storage will be profound. From intelligent data management and optimized resource allocation to enhanced security and storage efficiency, AI will fundamentally reshape how organizations perceive and utilize storage systems.
Storage will become more than just capacity and performance. It will be more about intelligence and efficiency. As organizations continue to harness the power of AI/ML in their storage strategies, we can anticipate a data-centric landscape that is more responsive, secure, and cost-effective.
The emergence of AI in storage is exciting and transformative, offering a glimpse into the limitless possibilities of smart, adaptive storage solutions.