There is a growing urgency to adopt cloud services to buttress today's rapidly growing inventories of high-end analytic and AI applications. This is fueled by massive volumes of data that are spilling over the capacities and capabilities of on-prem systems. It works the other way, as well: AI is paving the way for more robust cloud services, offering ever-expanding real-time automated provisioning and scalability.
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Data drives everything, and businesses must prepare their data to fully leverage technologies, including generative AI (GenAI), cloud computing, and advanced applications. Archiving is a key strategy in this process, ensuring data is clean, accessible, optimized, and cost-efficient. By consolidating and archiving inactive or redundant data, companies can create the high-quality datasets that are essential for training GenAI models and achieving reliable outcomes.
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In the AI era, we're constantly talking about how important data is—storing data, disseminating data, and protecting data. As data specialists, we understand bad data management leads to bad use of AI which leads, quite frankly, to bad business outcomes.
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The claim that "AI projects are failing" has become a familiar headline—and a valid one. But while the failure rate may be high, it's not necessarily cause for alarm. In fact, understanding why these initiatives fall short is key to making them succeed.
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