When customers talk about AI or the potential to start using their data for it, the topic of data inevitably comes back to storage. But in my experience, the real challenge isn’t simply where to store data. It’s how to cleanse, prepare, and tidy up your and data and then manage it properly once it’s there.
Over the years I’ve seen how quickly data environments can become fragmented. Information ends up spread across on-prem systems, cloud platforms, SaaS applications, and sometimes even physical archives (which to be fair a lot of vertical industries are still suffering by).
When that happens, organisations often lose visibility over what data they actually have, where it lives, and how it should be governed – and essentially the value of that data remains limited (and certainly untapped for data insights). That’s where things start to become difficult from a compliance, operational, and risk perspective but then cripples the potential for a real AI strategy.
For me, storage infrastructure should be more than just a place to keep data. It should form part of a broader data management strategy. That means supporting things like classification, retention policies, and lifecycle management so organisations can maintain control over both structured and unstructured information.
When storage and data management are aligned, organisations gain much better visibility into their information landscape. They can ensure data is properly governed, remain compliant, accessible when needed, and managed throughout its lifecycle.
Ultimately, I see storage infrastructure as the core foundation that enables organisations to manage data responsibly. When it’s done well, it not only reduces risk but also helps businesses get more value from the information they already have.