You Can't Build an AI Strategy Without a Data Strategy
Tech Field Day, Thursday, August 28th, 2025
At their foundation, AI systems are massive data engines. Training, deploying, and operating AI models requires handling enormous datasets-and the speed at which data moves between storage and compute can make or break performance.
In many organizations, this data movement becomes the biggest constraint. Even with better algorithms, companies frequently point to limitations in data infrastructure as the top barrier to AI success.
During the recent AI Infrastructure Field Day, Solidigm-a maker of high-performance SSDs built for AI workloads-shared how data travels through an AI training workflow and why storage plays an equally important role as compute. Their central point: AI training succeeds when storage and memory work in sync, keeping GPUs fully fed with data. Since high-bandwidth memory (HBM) can't store entire datasets, orchestrating the flow between storage and memory is essential.
The takeaway: Well-designed storage architecture ensures GPUs can run at peak capacity, provided data arrives quickly and efficiently.