AI and the Art of Digital Hoarding

If you’ve ever cleaned out an old cupboard, you know how easy it is to accumulate things you don’t need. A stack of receipts from five years ago, cables for devices you no longer own, a mystery key with no known lock.

Businesses do something similar with data, collecting everything, just in case.

AI has supercharged this tendency. With models that thrive on vast amounts of information, companies are storing more data than ever, often without a clear reason beyond ‘it might be useful one day.’ But is this approach really helping?

The Data Hoarding Problem

Holding onto every bit of information seems like a good idea—more data means better insights, right? Not always. Storing excessive amounts of data can lead to:

• Increased storage costs – Cloud services and data centres don’t come cheap.

• Security risks – The more data you have, the more attractive you are to hackers.

• Regulatory challenges – Privacy laws like GDPR and CCPA require businesses to justify what they store and for how long.

• Inefficiency – More data doesn’t always mean better decisions. Too much noise can obscure what’s actually important.

  
  

Do We Really Need It All?

AI models are improving in how they handle smaller, more relevant datasets. Instead of feeding them everything, companies could:

• Use targeted data collection: storing what’s truly valuable.

• Apply data lifecycle management: deleting what’s no longer useful.

• Prioritise privacy-first approaches: reducing liability by only keeping necessary information.

  
  

The Way Forward

Hoarding data ‘just in case’ isn’t sustainable. Businesses need to shift towards a more mindful approach—one that values quality over quantity. AI might thrive on information, but it works best when given the right data, not just more of it.

Maybe it’s time to treat digital storage like that overflowing cupboard—take a step back, assess what’s actually useful, and let go of the rest.