Dell’s AI Data Innovation: When Storage Takes Command
In the not-so-distant past, data center storage was somewhat of an afterthought. You needed a place to gather data; you needed it to be reliable; and you needed it to be economical. And that’s pretty much where the conversation ended. Now in the era of AI workloads, storage is taking center stage for the critical role it plays in data activation. Having the right storage solutions in the right place provides the flexibility, efficiency, and security to feed AI at scale.
I recently had the opportunity to explore this transformation with Saif Aly, senior product marketing manager at Dell, and Scott Shadley, leadership marketing director at Solidigm, to explore how enterprise storage requirements are evolving in response to AI-driven workloads and data-intensive applications. During our TechArena Data Insights episode, it became clear that storage has evolved to the critical foundation enabling AI success.
The AI workload revolution has created unprecedented demands on storage infrastructure. As Saif explained, these workloads require sustained throughput, low latency, and massive scale simultaneously. The challenge extends beyond simple performance. Enterprises face data fragmentation across edge, core, and cloud environments, creating operational complexity that can lead to vendor lock-ins and underutilized graphics processing unit (GPU) resources.
Dell’s response centers on their AI Data Platform, built on the principle that modern storage must support the entire data lifecycle. The PowerScale platform serves as the foundation, delivering what Saif described as unmatched performance improvements: 220% faster data ingestion and 99% faster data retrieval compared to previous generations. The introduction of MetadataIQ further accelerates search and querying capabilities, directly supporting AI workload requirements.
Scott emphasized how customer conversations have evolved beyond traditional capacity discussions to focus on “time to first data”—how quickly organizations can access information when they need it. In AI application workloads, different data types require varying levels of accessibility and performance characteristics. The challenge lies in understanding what data needs to sit directly adjacent to GPUs versus what can be retrieved from more distant storage tiers.
The discussion revealed how inference workloads, particularly retrieval-augmented generation (RAG) architectures, create unique storage demands. These systems require large datasets to be readily accessible for real-time referencing while simultaneously managing active data processing next to compute resources. Success depends on optimizing the balance between high-performance local storage and efficient data movement from archive locations.
While flash storage dominates high-performance applications, both experts acknowledged that hard disk drives (HDDs) retain value for cold and warm datasets. The key insight: not all data is equal, and successful architectures blend flash-based solid-state drives (SSDs) and HDD storage within unified namespaces to balance performance and cost considerations.
The conversation highlighted remarkable capacity evolution, with Saif recounting his amazement at holding Solidigm’s 122 TB drive, a device containing massive data volumes in a small form factor. This density revolution, progressing from 30 TB to 60 TB to 122 TB drives just in the last year, enables dramatic improvements in rack space efficiency, power consumption, and cooling costs while maintaining the throughput AI workloads demand.
Scott connected this capacity evolution to practical customer needs, explaining how optimization now focuses on the right bandwidth, density, and time-to-data characteristics rather than simply maximum speed. As storage capacity per device increases, the focus shifts to infrastructure optimization that delivers customer value through improved total cost of ownership and operational efficiency.
Real-world impact emerged through customer examples Saif shared. Kennedy Miller Mitchell, the studio behind the Mad Max franchise, used PowerScale to enable pre-visualization of entire scenes before filming. That capability allows directors to iterate creatively and make real-time decisions. Subaru leveraged the platform to manage exponentially growing data volumes, handling 1,000 times more files than previously possible and directly improving their AI-driven driver-assistance technology accuracy.
Looking ahead, both experts see storage demands continuing to accelerate, driven by AI’s exponential data growth and evolving workload requirements. As Saif noted, “the data explosion is not going to stop,” with AI both consuming and creating massive amounts of data. The distributed nature of modern computing—spanning edge, core, and cloud environments—requires storage solutions that provide consistent experiences and seamless data mobility across all locations.
The TechArena Take
The convergence of AI workloads, massive data growth, and distributed computing architectures is fundamentally reshaping enterprise storage from a cost center to a strategic enabler. Dell and Solidigm’s partnership demonstrates how thoughtful collaboration can deliver solutions that scale from individual creators to global enterprises while addressing the critical balance between performance, capacity, and cost efficiency. As storage continues to assert its place as a foundation of modern workloads, organizations that invest in flexible, high-performance architectures today will be best positioned to capitalize on tomorrow’s AI-driven opportunities.
For more insights on Dell’s enterprise storage solutions, visit Dell.com/PowerScale or connect with Saif Aly on LinkedIn. Learn more about Solidigm’s AI-focused storage innovations at solidigm.com/AI or reach out via LinkedIn to Scott Shadley.
In the not-so-distant past, data center storage was somewhat of an afterthought. You needed a place to gather data; you needed it to be reliable; and you needed it to be economical. And that’s pretty much where the conversation ended. Now in the era of AI workloads, storage is taking center stage for the critical role it plays in data activation. Having the right storage solutions in the right place provides the flexibility, efficiency, and security to feed AI at scale.
I recently had the opportunity to explore this transformation with Saif Aly, senior product marketing manager at Dell, and Scott Shadley, leadership marketing director at Solidigm, to explore how enterprise storage requirements are evolving in response to AI-driven workloads and data-intensive applications. During our TechArena Data Insights episode, it became clear that storage has evolved to the critical foundation enabling AI success.
The AI workload revolution has created unprecedented demands on storage infrastructure. As Saif explained, these workloads require sustained throughput, low latency, and massive scale simultaneously. The challenge extends beyond simple performance. Enterprises face data fragmentation across edge, core, and cloud environments, creating operational complexity that can lead to vendor lock-ins and underutilized graphics processing unit (GPU) resources.
Dell’s response centers on their AI Data Platform, built on the principle that modern storage must support the entire data lifecycle. The PowerScale platform serves as the foundation, delivering what Saif described as unmatched performance improvements: 220% faster data ingestion and 99% faster data retrieval compared to previous generations. The introduction of MetadataIQ further accelerates search and querying capabilities, directly supporting AI workload requirements.
Scott emphasized how customer conversations have evolved beyond traditional capacity discussions to focus on “time to first data”—how quickly organizations can access information when they need it. In AI application workloads, different data types require varying levels of accessibility and performance characteristics. The challenge lies in understanding what data needs to sit directly adjacent to GPUs versus what can be retrieved from more distant storage tiers.
The discussion revealed how inference workloads, particularly retrieval-augmented generation (RAG) architectures, create unique storage demands. These systems require large datasets to be readily accessible for real-time referencing while simultaneously managing active data processing next to compute resources. Success depends on optimizing the balance between high-performance local storage and efficient data movement from archive locations.
While flash storage dominates high-performance applications, both experts acknowledged that hard disk drives (HDDs) retain value for cold and warm datasets. The key insight: not all data is equal, and successful architectures blend flash-based solid-state drives (SSDs) and HDD storage within unified namespaces to balance performance and cost considerations.
The conversation highlighted remarkable capacity evolution, with Saif recounting his amazement at holding Solidigm’s 122 TB drive, a device containing massive data volumes in a small form factor. This density revolution, progressing from 30 TB to 60 TB to 122 TB drives just in the last year, enables dramatic improvements in rack space efficiency, power consumption, and cooling costs while maintaining the throughput AI workloads demand.
Scott connected this capacity evolution to practical customer needs, explaining how optimization now focuses on the right bandwidth, density, and time-to-data characteristics rather than simply maximum speed. As storage capacity per device increases, the focus shifts to infrastructure optimization that delivers customer value through improved total cost of ownership and operational efficiency.
Real-world impact emerged through customer examples Saif shared. Kennedy Miller Mitchell, the studio behind the Mad Max franchise, used PowerScale to enable pre-visualization of entire scenes before filming. That capability allows directors to iterate creatively and make real-time decisions. Subaru leveraged the platform to manage exponentially growing data volumes, handling 1,000 times more files than previously possible and directly improving their AI-driven driver-assistance technology accuracy.
Looking ahead, both experts see storage demands continuing to accelerate, driven by AI’s exponential data growth and evolving workload requirements. As Saif noted, “the data explosion is not going to stop,” with AI both consuming and creating massive amounts of data. The distributed nature of modern computing—spanning edge, core, and cloud environments—requires storage solutions that provide consistent experiences and seamless data mobility across all locations.
The TechArena Take
The convergence of AI workloads, massive data growth, and distributed computing architectures is fundamentally reshaping enterprise storage from a cost center to a strategic enabler. Dell and Solidigm’s partnership demonstrates how thoughtful collaboration can deliver solutions that scale from individual creators to global enterprises while addressing the critical balance between performance, capacity, and cost efficiency. As storage continues to assert its place as a foundation of modern workloads, organizations that invest in flexible, high-performance architectures today will be best positioned to capitalize on tomorrow’s AI-driven opportunities.
For more insights on Dell’s enterprise storage solutions, visit Dell.com/PowerScale or connect with Saif Aly on LinkedIn. Learn more about Solidigm’s AI-focused storage innovations at solidigm.com/AI or reach out via LinkedIn to Scott Shadley.
.png)
