Why “old” data is the new gold in the age of AI

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 here’s the real shift, those insights don’t just come from yesterday’s data. They come from everything an organisation has ever captured. Every byte could hold the next breakthrough.

That’s 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 doesn’t 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. It’s no longer just about speed, it’s 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 isn’t cost savings, it’s 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.

There’s also a compliance aspect. Regulators increasingly demand auditability in AI decision-making. If you can’t trace your training data, you can’t prove accountability.

Deleting historical data is like erasing institutional memory. You lose the raw material for innovation. Once it’s gone, its value is gone.

Years ago, customers asked, “Why are we storing all this data?” Today, they’re asking, “Why are we deleting it? Help us store it.”

What solutions can reduce the OPEX of storing old data?

The goal isn’t just to store data cheaply, it’s 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 aren’t paying for performance they don’t 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. It’s about building flexible, scalable infrastructure that adapts as workloads evolve.

The old “store and forget” model doesn’t work anymore. Think of data as capital, it's dynamic, and so is the technology that powers it. Organisations that rethink lifecycle management today aren’t just keeping up, they’re building a foundation that scales with them.