A Good Data Die

In the world of data, everyone’s focused on one thing: the collection. “Gather the data! Store the data! Analyse the data!” These are the buzzwords, the mantras. We talk endlessly about how to squeeze every drop of value out of data, turning it into something smarter, faster, and more predictive.

But there’s a side of data that rarely gets any attention. What happens when the data is no longer useful? The end of the line. The moment when your data is outlived by time or relevance, and it needs to be retired, properly and securely.

And let’s face it. If there’s one thing that doesn’t get enough airtime in the data conversation, it’s knowing when to let go.

If there’s one thing that doesn’t get enough airtime in the data conversation, it’s knowing when to let go.

So, imagine for a second that you’re James Bond. You’ve got all this intelligence, documents, images, surveillance logs, highly classified information, and you’ve finished the mission. What happens next? Do you hand everything over to MI6, destroy it, and move on? Or do you keep it for... well, just in case?

Let’s talk about that just in case, and what could happen if Bond decided to hold on to everything.

Picture it. After every mission, Bond doesn’t just file his reports and clean up. No, instead, he keeps it all. A suitcase of files here, a few encrypted drives there, some photos in the cloud, an SD card in the glovebox of the Aston Martin.

Now imagine this information starts to pile up. At first it seems harmless. But time passes, and Bond forgets what he’s stored or why. The value fades, but the sensitivity doesn’t.

Now picture an old laptop left behind at a safe house. Or a dusty USB drive getting swiped by a low-level henchman during a scuffle in Venice. Before you know it, someone is decrypting the data and feeding it into a machine learning model designed to predict MI6 movements, analyse intelligence gaps, and identify every weakness across His Majesty’s secret service.

What should have been deleted becomes a threat. And not because it was malicious, but because it was simply... there.

What should have been deleted becomes a threat. And not because it was malicious, but because it was simply... there.

Once that data is out there, it is vulnerable. And artificial intelligence makes it worse. AI won’t discriminate between valuable and outdated. It takes what it is given and starts building. Patterns. Predictions. Profiles. Conclusions.

So, if an organisation is sitting on piles of outdated, inaccurate, or irrelevant data, and that data somehow gets scraped, leaked, or accessed by the wrong hands, the consequences go beyond the usual data breach narrative.

Now we are talking about AI being trained on junk. Or worse, on half-truths. And like any good spy, AI does not stop to ask whether its sources are telling the truth. It just absorbs and acts.

AI does not stop to ask whether its sources are telling the truth. It just absorbs and acts.

Imagine Ernst Stavro Blofeld, perched in some bunker, stroking that smug white cat of his while an AI built on your discarded records runs scenario after scenario. How to undercut your pricing. How to poach your customers. How to identify the weakest point in your security setup based on the behaviours you no longer even remember capturing.

The information you forgot you had becomes the foundation of your adversary’s strategy. You’ve not just lost control of your past. You’ve weaponised it.

But what if that data doesn’t leave your building? What if it stays inside your organisation, buried in internal systems, backup folders, or forgotten databases? Surely that’s not a problem.

Even then, the danger doesn’t go away. In fact, it multiplies.

AI systems trained on flawed data can go spectacularly wrong. You see, we understand data at the time we are using it. But as soon as it has served its purpose, and it is filed away we begin to forget the context. So, if your models are using historical records that are outdated, poorly labelled, coded in legacy formats, or riddled with bias, then every output they produce is compromised. Like a spy getting his orders from a faulty radio signal.

Imagine MI6 relying on a database of intel from 1983, built on assumptions and political dynamics that no longer apply. The missions would fail. The conclusions would be wrong. Agents would walk into traps because the models said the coast was clear.

That’s what happens when AI is trained on old or irrelevant data. It doesn’t just make poor decisions. It makes confident poor decisions. And that confidence is hard to spot until something breaks.

We keep data for all sorts of reasons. Future insights. Regulatory cover. Customer trends. Or sometimes, just because deleting feels risky. Maybe someone, someday, will ask for it.

But the more data you keep, the more risk you carry. It’s not the data you’ve been actively using that causes the most harm. It’s the neglected stuff. The backup of the backup. The spreadsheet from four marketing managers ago. The copy of a contract saved in six folders just in case.

the more data you keep, the more risk you carry. It’s not the data you’ve been actively using that causes the most harm. It’s the neglected stuff.

In real life, it’s not MI6 getting exposed. It’s customer addresses. Patient records. User behaviour logs. And when something goes wrong, a ransomware attack, a misconfigured server, a rogue employee, the data that spills is often the data that should have been deleted years ago.

This is not a theoretical risk. This is reputational damage. Legal penalties. Regulatory investigations. Not to mention the chilling effect it has on customer trust.

No one wants to find out their personal data is circulating in the wild because you forgot to clean out the digital attic.

Under GDPR, the rules on data retention are crystal clear. You cannot keep personal data for longer than necessary for the purposes you collected it for. That means you need a valid reason to keep it, and once that reason expires, so should the data.

There’s also the “right to erasure”; the so-called right to be forgotten. When individuals ask for their data to be removed, organisations must respond promptly. No excuses. No indefinite holding periods.

Failing to comply can mean large fines, legal action, and increased regulatory scrutiny. And the reputational cost of getting it wrong is often far worse than the financial one.

But Bond, ever the perfectionist, doesn’t just keep his data. Oh no, he starts duplicating it because…well, why not? Maybe for "safety". Maybe for "convenience". You know the drill. "Bond_Secrets.docx", "Bond_Secrets_Final.docx", "Bond_Secrets_Final_Final.docx", "Bond_Secrets_Final_Final_V2.docx"... and it goes on. Each one more hopelessly unorganised than the last.

Now, imagine someone at MI6 stumbles upon this mess. Maybe Q finds it on his lunch break while looking for a new gadget. How long do you think Bond would keep his 00 status after that?

If a spy can't be trusted with his own files, let alone sensitive intelligence, he’s more of a liability than a legend. His secrets aren’t just on the line, his entire network is. His cover’s blown. His reputation crumbles. He doesn’t just lose access to classified intel; he becomes the reason everything else unravels. And if that doesn’t spell the end of your career, what does?

That’s what happens when organisations treat data deletion as an afterthought. They turn themselves into liabilities.

when organisations treat data deletion as an afterthought. They turn themselves into liabilities.

And it doesn’t even take malicious intent. All it takes is inertia. The feeling that deleting something might be more trouble than it’s worth. The tendency to prioritise collection over closure.

So how do you avoid becoming the next Bond Villain? Here’s a practical list, shaken not stirred:

• Set expiry rules: Every mission has a time limit. So should your data. Define how long different categories of data should be kept. Be clear. Be consistent.

• Automate deletion: Bond doesn’t have time to manually shred every file. Neither do you. Build deletion into your workflows. Make it the default, not the exception.

• Respect consent: If someone says they want out, get them out. No dragging your feet. No “but what if” scenarios. Consent isn’t optional, it’s foundational.

• Secure your endings: Data should leave as cleanly as it arrived. Encrypt it when alive, destroy it properly when dead. Think bonfire, not recycle bin.

• Do regular audits: Be your own Q. Open the car boot, inspect the gadgets, check the files. Know what you’ve got. And don’t be afraid to let go.

• Tell people what you’re doing: Being transparent about how and when you delete data builds trust. It shows maturity. It signals that your organisation is serious about responsibility, not just reach.

• Cultivate data literacy: Even 007 needs to understand the gadgets he’s using. Make sure your team knows how to use, manage, and delete data effectively. Data literacy isn’t just for analysts; it's vital for everyone, from the top-level agents to the rookie in the field. Without it, even the best tools can turn into ticking time bombs.

The truth is, not every piece of data deserves a long life. Some files serve their purpose and should bow out with dignity. The same way Bond leaves the casino once the mission is complete. No dragging out the moment. No holding on to what no longer serves.

The truth is, not every piece of data deserves a long life. Some files serve their purpose and should bow out with dignity.

And if you manage your data like a good agent manages their intel, with discretion, respect, and a firm sense of timing, you’ll be better protected, more trusted, and far less likely to end up in the cat stroking villain’s training set.

In data, as in espionage, what you let go of can be just as important as what you keep.

So, if you want to be in control, let’s make it a good data die.