Subj : Why old data is the new gold in the age of AI To : All From : TechnologyDaily Date : Fri Nov 14 2025 09:45:07 Why old data is the new gold in the age of AI Date: Fri, 14 Nov 2025 09:30:26 +0000 Description: We speak to Seagate's Melyssa Banda to find out more FULL STORY ====================================================================== For nearly three years now, Generative AI (GenAI) has captured the imagination of enterprises worldwide, promising to transform customer experiences , boost productivity and unlock new revenue streams. However, today, many large organizations find themselves grappling with the reality behind the hype. Market research and advisory firms have placed GenAI firmly in the Trough of Disillusionment phase, as firms come to grips with its true potential and limitations. Isn't old data just big data in new clothes? AI innovation is driving exponential growth in the volume and value of data. More specifically, Generative AI is finally delivering on what big data promised turning information into actionable intelligence. But heres the real shift, those insights dont just come from yesterdays data. They come from everything an organisation has ever captured. Every byte could hold the next breakthrough. Thats why companies are rethinking data as a long-term strategic asset, not something to discard. Big data gives you the now. Historical data gives you the why. Together, they fuel intelligence. Why is old data so important (AI, ML) and where is it mostly located? (tape? old hard drives? paper?) AI doesnt exist without data, and the most powerful models are built on patterns that span time. Historical data gives AI context, transforming predictions into precision and ideas into innovation. Think of it this way, humanity has always preserved information from clay tablets in Mesopotamia to punch cards for the U.S. Census. The difference today is that the stakes are higher. AI thrives on volume and diversity. More data means better results, giving organizations a competitive edge. As for where that data lives, the vast majority of it, roughly 87% in large-scale deployments, is stored on HDDs. Modern AI workloads demand scalable, high-capacity hard drives optimised for sustained throughput and durability. Its no longer just about speed, its about handling massive volumes, ensuring long-term retention, and doing so at scale. Maintaining old data carries a cost. What can happen if businesses decide to delete old data altogether? Deleting data isnt cost savings, its erasing potential value. Every byte erased is a missed opportunity to train better models and build proprietary insights. In industries like finance, healthcare, and manufacturing, historical data is essential for anomaly detection, predictive maintenance, and trend analysis. Without it, AI becomes less accurate, less transparent, and less trustworthy. Theres also a compliance aspect. Regulators increasingly demand auditability in AI decision-making. If you cant trace your training data, you cant prove accountability. Deleting historical data is like erasing institutional memory. You lose the raw material for innovation. Once its gone, its value is gone. Years ago, customers asked, Why are we storing all this data? Today, theyre asking, Why are we deleting it? Help us store it. What solutions can reduce the OPEX of storing old data? The goal isnt just to store data cheaply, its to store it intelligently. Many organisations are shifting to tiered storage architectures, where frequently accessed data sits on high-performance systems, while older or less-accessed data moves to cost-optimised tiers. This approach ensures businesses arent paying for performance they dont need. In short, store smarter, not just cheaper. In a statement, you/Seagate said organizations must rethink data lifecycle management - but with technology moving as fast as it currently does, is it actually possible? AI has redefined the value of data, which means data lifecycle management can no longer mean archiving. Its about building flexible, scalable infrastructure that adapts as workloads evolve. The old store and forget model doesnt work anymore. Think of data as capital, it's dynamic, and so is the technology that powers it. Organisations that rethink lifecycle management today arent just keeping up, theyre building a foundation that scales with them. ====================================================================== Link to news story: https://www.techradar.com/pro/why-old-data-is-the-new-gold-in-the-age-of-ai --- Mystic BBS v1.12 A49 (Linux/64) * Origin: tqwNet Technology News (1337:1/100) .