Unified data platforms reshape AI storage – News

Unified data platforms are reshaping the foundation of enterprise storage as AI adoption accelerates, forcing businesses to overhaul aging architectures and rethink how data is managed, scaled and consumed.

Faced with mounting data sprawl and the demands of GPU-accelerated workloads, organizations are turning to storage solutions that unify block, file and object workloads under one streamlined framework. The shift is urgent: Legacy systems can’t keep pace with AI-driven environments that require flexibility, centralized control and the ability to seamlessly handle unstructured datasets across hybrid cloud deployments. At the heart of this transformation is a growing push to simplify complexity, reduce operational costs and unlock the full potential of AI at scale, according to Rob Strechay (pictured, right), principal analyst at theCUBE Research.

“The growth has been phenomenal with the Alletra MP and having come out 18 to 24 months ago,” Strechay said. “Really what they’re doing with it … the unified data services bringing together a full lifecycle management of the hardware and making that easy, as well as giving that cloud consumption model under GreenLake.”

Strechay spoke with fellow analyst Savannah Peterson (left), as they provided analysis of their recent interview with Patrick Osborne interview for the “Cloud AI Journey With HPE” interview series, during an exclusive broadcast on theCUBE, News Media’s livestreaming studio. They discussed how unified data platforms are enabling AI scalability, the growing role of hybrid deployments and the increasing importance of metadata and inference in modern storage strategies. (* Disclosure below.)

Unified data platforms support scalability and hybrid flexibility

AI’s integration into modern IT environments is highlighting the need for unified data platforms that can deliver flexibility without sacrificing performance. Organizations are moving away from piecemeal storage solutions in favor of workload-oriented systems that streamline management and simplify infrastructure, according to Strechay.

“This is why people like cloud and really enjoy buying in that methodology,” he said. “I think customer adoption … like Patrick [Osborne] said, they’ve been voting with their dollars, which has been great to see.”

This shift is also tied to enhancing the developer and customer experience, Peterson underscored. Unified data services not only provide technical scalability but also create smoother, more intuitive user journeys.

“You highlighted the three things that stood out to me in that particular part of the [Osborne] interview as well,” Peterson noted. “It’s a unique value proposition that’s really mindful of the experience of those developers and of their customers.”

The discussion also covered how hybrid and on-premises deployments are seeing renewed attention, especially as AI models require high-speed access to vast datasets, much of which still resides within enterprise walls. Data privacy, regulation and performance concerns are keeping significant AI workloads on-prem, according to Strechay.

“I think also a lot of it was about being hybrid,” he explained. “The hybrid and on-prem deployments, we see … 85% of data being used for AI is actually on-premise these days.”

Metadata, inference and simplifying AI storage complexity

Another key takeaway revolved around metadata and how enterprises are prioritizing not just the data itself, but the information about that data. As AI-driven workloads scale, metadata management becomes a critical differentiator, enabling faster retrieval, smarter organization and more efficient processing.

TheCUBE’s Savannah Peterson talks about the importance of data.

“One quote I want to call out,” Peterson said. “[Osborne] said something that’s really important that I think sometimes people miss when they’re starting to think about their strategy around this. He said, ‘That the data about the data is almost as important or more important than the data itself.’”

Metadata has become vital in modern storage systems. AI inferencing is moving closer to the data itself, allowing enterprises to reduce latency and avoid unnecessary data movement — an approach that Strechay fully supports. He remarked on the shift toward embedding inference and retrieval-augmented generation directly within storage platforms to improve efficiency and performance.

“What [Osborne’s] talking about is the two different types of metadata that people are utilizing for building out these AI systems,” he said. “When you start to look at it, he was really on top of it. Enterprise data is essential. It’s the core of the enterprise; it’s the core of organizations and it’s their intellectual property.”

While AI often feels like magic, it’s the result of extensive planning, development and engineering behind the scenes, according to Peterson. Achieving simplicity in today’s highly complex data infrastructures requires significant effort and expertise.

“I think that is a key … that making something simple that is as complex as these storage and data platforms is not easy in the least,” Strechay added. “It is about making it a simple customer experience.”

The conversation is set to continue at HPE Discover.

Here’s theCUBE’s complete video interview, part of News’s and theCUBE’s coverage of the “Cloud AI Journey With HPE” interview series:

(* Disclosure: TheCUBE is a paid media partner for the “Cloud AI Journey With HPE” interview series. Neither Hewlett Packard Enterprise Co., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or News.)

Photo: News