As artificial intelligence continues to transform industries, the role of AI data management has become crucial in ensuring these systems operate efficiently.

The true challenge for enterprises isn’t just in accumulating data, but in organizing, tagging and analyzing vast volumes of information across diverse architectures. Managing unstructured data and seamlessly integrating it into AI models is now seen as a key factor for business success. With advancements in data lakehouses, metadata management and next-generation storage solutions, organizations are better equipped to unlock the full potential of AI and stay competitive in a changing landscape.

“I think everybody is super focused on their data strategy in every single vertical. I don’t think anybody’s left out,” said Elizabeth Carbone (pictured, center), senior marketing manager of unstructured data storage at Dell Technologies Inc. “I think that’s just magnified when it comes to AI. What we’re hearing a lot of is focusing quality to make sure that our AI models are as accurate as possible.”

Carbone was joined by Dell’s Vrashank Jain (left), senior product manager of data management, and Martin Glynn (right), senior director of product management, unstructured data solutions. They spoke with theCUBE Research’s Rob Strechay at the Making AI Real With Data event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the increasing importance of AI data management and how enterprises are tackling the challenge of organizing, tagging and analyzing unstructured data. (* Disclosure below.)

The rise of unstructured data and AI data management

The explosion of unstructured data is reshaping the way enterprises approach AI implementation. Businesses are discovering new use cases by leveraging unstructured data and sophisticated metadata management. These capabilities allow organizations to identify, tag and process complex data types, making them more useful in AI applications such as generative AI and vector search.

“They want to be able to use the tool sets that they’re comfortable with and that are changing rapidly in this new generative AI world,” Glynn said. “They don’t want to be locked into a specific way to query a system or have to do something on a specific system. They want to be able to do it outside the system.”

Unstructured data has long been undervalued but is now seen as a high-value asset, according to Jain. Enterprises have invested heavily in refining structured data for years, but unstructured data, including documents and multimedia, has the potential to bring even more value when properly managed. By improving how metadata is handled, businesses can better organize this AI data management, leading to enhanced AI model accuracy and effectiveness.

“I think in the initial gen AI craze, suddenly we thought the old structured data side of the world became also old,” Jain said. “I think what enterprises are now starting to realize, that is a very high value data set because we’ve spent years and tons of dollars of investment in refining it. We want to bring that into the generative AI fold, and we want to start asking questions to that data.”

Dell’s Data Lakehouse and PowerScale: A new era of flexibility and performance

Dell Technologies is stepping up to meet the data challenges of the AI era through its innovative solutions, such as the Data Lakehouse and PowerScale storage platform. The Dell Data Lakehouse, built with Starburst Data Inc.’s advanced querying technology, allows enterprises to access and process data seamlessly, no matter where it resides. This approach breaks down data silos, providing the flexibility needed for AI-driven businesses to scale effectively, according to Glynn.

“PowerScale as a storage platform, hands down, offers the best set of capabilities,” he said. “Building on that, we also extended the platforms so that you could run it in the cloud. What customers are really excited about there, from a generative AI perspective, is there are some tool sets you can only access in the cloud. They want to take advantage of tool sets inside the cloud.”

Designed to handle everything from small deployments to vast datasets, PowerScale enables organizations to manage petabytes of data with ease. Dell has expanded PowerScale with all-flash nodes, enhanced ethernet capabilities and even cloud compatibility, allowing businesses to integrate on-premises and cloud infrastructures. These developments enable companies to optimize their AI models by easily accessing high-value data wherever it is stored, Glynn added.

“When you talk about your storage platform, you have to start with, ‘Do you have the right amount of flexibility built in?’” he said. “PowerScale has proven over and over again, it can start really small, three nodes. Tens of terabytes can go to hundreds of nodes and hundreds of petabytes. We continue to expand on that and make it more flexible and offer new levels of performance, so end capacity and density.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the Making AI Real With Data event:

Watch the complete episode here:

(* Disclosure: TheCUBE is a paid media partner for the “Making AI Real With Data” event. Neither Dell Technologies Inc., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE