AI Data Management: Tackling Unstructured Data Challenges and Enhancing Governance

AI data management drives efficiency in organizing unstructured data - SiliconANGLE

AI data management challenges: Tackling the explosion of unstructured information 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 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 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:

The need for governance and security in data storage for AI - SiliconANGLE

Navigating data storage for AI: Insights from Dell and NVIDIA experts Artificial intelligence may be the buzzword of the moment, but its power lies in the data it consumes and the processes it drives. Each company's approach to AI differs, shaped by unique goals, risk tolerance and operational philosophy. Given its variability and versatility, how can companies harness data storage for AI? "[AI is] a big buzzword, but it's also a market that's in flux. Everyone's trying to figure out what it means to them," said Geeta Vaghela (pictured, left), senior director of product management, unstructured data solutions, at Dell Technologies Inc. "One thing we're taking away is that there isn't a one-size-fits-all. Everyone's doing it a slightly different way. They're trying to solve a slightly different problem. What Dell has been looking to do is, one, provide some of that strategic advisory for when enterprises or customers need it to pick apart what your true goals are and how we can help you get there." Vaghela and Premal Savla (right), senior director of product management, deep learning systems, at NVIDIA Corp., 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 need for companies to understand data storage for AI, from navigating complex infrastructures to building scalable solutions. (* Disclosure below.) Data governance and security are crucial to the longevity of AI systems. As organizations process vast amounts of data, ensuring data sovereignty -- maintaining the origin, integrity and security of that data -- becomes increasingly important. Data stewardship has fundamentally changed the AI game, especially as companies seek to manage and protect massive amounts of information, according to Vaghela. "We want to be able to build the Lego blocks that can fit into the various environments," she said. "And then, scale those as many of these enterprises go from test dev to production, and that becomes a whole different world, especially when you think about data. I live in storage, so data stewardship is a big part of what I think about, and that entirely changes the game for them." Besides an aligned approach to thought leadership in data management, Dell relies on NVIDIA's technology to simplify AI adoption, with NVIDIA providing the compute power through its DGX SuperPOD infrastructure. Leveraging the system, Dell can facilitate seamless storage integration, supporting large-scale data management for AI applications, according to Savla. "What Dell brought to the table was a lot of capabilities around what it's already been doing with the enterprise, and that allowed us to partner together to create a solution with Ethernet-enabled storage so that we can take it to our customers," he said. Alongside storage, data stewardship and governance, there's also the need for continuous education and awareness around AI technologies, Savla added. Many companies are still learning about how AI fits into their existing systems, the massive processing power AI requires, and how businesses need to rethink their storage strategies to handle such large volumes of data. From governance to data security, understanding the complex relationship between storage and AI is crucial for successful AI adoption. "[We are] talking about all the different teams within a company and how they're approaching these different projects is critical," Savla said. "They have a certain way they have been doing things, but AI makes you think differently. The amount of processing power that it requires, the amount of data that is consumed is significant. They have to think through how this is going to be used as they move forward with whatever initiatives that they're taking with AI in their particular company." Here's the complete video interview, part of SiliconANGLE's and theCUBE Research's coverage of the Making AI Real With Data event: