Before You Scale AI, Fix Your Data

Episode notes

AI is working. Your data probably isn’t. As enterprise AI moves into production, a new constraint shows up fast. Not models. Not compute. Data. In this episode, recorded live at NVIDIA GTC, NetApp’s Tore Sundelin and WWT’s Derek Elbert get into what’s actually slowing teams down. The shift from clean, structured data to messy, high-value, unstructured data that’s harder to find, govern and use in real time. This is where things start to break. Data spread across systems. Inconsistent policies. No clear way to trust what’s being used. And once AI depends on live enterprise data, those gaps don’t stay hidden for long. Because at this stage, AI doesn’t fail at the model. It fails at the data. Support for this episode provided by: Riverbed More about this week's guests: Derek Elbert is an AI Practice leader at World Wide Technology focused on hybrid cloud AI and high-performance architecture. He specializes in networking and storage within modern AI stacks, helping organizations design and scale infrastructure to support data-intensive, production-grade AI workloads. Tore Sundelin is a product leader at NetApp focused on enterprise AI and data platforms.

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