Generative AI: The Magic Above the Surface and Data Iceberg Beneath It

There’s no denying that AI is one of the most mind-blowing technologies of our time. One of its better known children – Generative AI – is everywhere: writing text, creating images, coding… even attempting jokes (though I don’t think we’re quite there yet with this one). These models can whip up everything from human-like text to lifelike images and even code, leaving many of us wondering whether we’re witnessing a touch of digital magic. But behind the curtain of AI wizardry lies an even more impressive story—the data. Beneath every awe-inspiring output is a mountain of meticulously curated datasets, quietly working to make all this possible. Without it, AI is like a car without an engine—all shiny on the outside but not going anywhere fast.

The Real Star: Data

While AI models hog the headlines (and the spotlight these days), they’re really just the shiny tip of the iceberg. What powers these innovations are vast, robust, and very well-maintained data operations. Think of it this way: If generative AI is the bit of iceberg you can see, your organisation’s data is the bulk beneath the surface—what really keeps everything afloat.

As a leader, understanding the role data plays in generative AI is critical. It’s simple: world-class AI needs world-class data. No cutting corners here. And trust me, if you’ve got your eyes set on riding the generative AI wave, you’d better get a handle on the data iceberg before your ambitions hit an unpleasant bump.

If you’re treating your data like a glorified landfill, i.e. just dumping it into a storage space then you’re probably doing it all wrong. To truly compete, you need to think of data as a product, not just a side-effect of your operations. Nurture it, maintain it, and give it the respect it deserves. Treating your data as a valuable asset elevates its role within the business. Just like a well-known brand commands respect, well-managed data does the same. No one ever built a skyscraper on a dodgy foundation, and no one builds great AI on rubbish data so your data isn’t just a stepping stone. It’s the whole foundation.

Data Junkyard to Data Treasure Trove

You can’t just shovel all your data into a digital junkyard and hope for the best. If you want your AI to work wonders, you need to treat data like the goldmine it is. These are some principles you could consider:

• Think of Data Like Fine Wine: Don’t treat data like the random stuff you find down the back of the sofa. Think of it more like a fine wine— it needs to be cared for, curated, and stored properly. Treat your data as a product, not a byproduct. Version it, govern it, and make sure it evolves with your business. Just as you wouldn’t let the wine go off, don’t let your data go stale either.
• Diverse Data Is the Spice of Life: Few people I know would want a diet of only beige food (unless you’re really into rice and toast). Your AI doesn’t want a one-dimensional dataset. It needs diversity—different accents, backgrounds, contexts, and experiences to reflect the real world. This isn’t just about fairness (though that’s important), it’s about giving your AI enough variety to make it useful. Think of it like a well-stocked kitchen — you need more than just salt to cook up a storm.
• Governance Isn’t a Bad Word—It’s the Secret Sauce: Hear me out first. Governance doesn’t have to mean layers of boring paperwork and endless meetings. Think of this more like making sure your data is easy to use without breaking everything in the process. Give people access, but do it smartly—like giving your kids the TV remote but not the credit card. It’s about enabling, not restricting. Keep it simple and efficient, not locked down like a museum exhibit.
• Make Documentation Useful (Not a Sleep Aid): Documentation shouldn’t read like a 900-page legal contract. It’s there to help, not send you into a deep slumber. Keep it concise, clear, and relevant—like IKEA instructions (but hopefully with fewer arguments). Your AI team needs to know where the data came from, what it’s meant for, and any potential biases or issues it might carry. If it feels like a manual anyone could follow, you’ve nailed it.
• “Garbage In, Garbage Out”—But Make It Less Garbage: If you’ve ever tried cooking with expired ingredients then you will know exactly how an AI would feel when injected with bad data. If your data is messy, incomplete, or just plain wrong, the outputs will be equally chaotic. AI needs high-quality inputs to generate anything useful. So, think of it like quality control at the factory—check for errors, fix what’s broken, and keep the production line running smoothly.
• Privacy: Handle With Care: Data privacy is like the seatbelt in your car. You could technically drive without it, but we all know that’s a terrible idea. Protecting user privacy and securing data isn’t optional—it’s mandatory. Anonymise what needs to be anonymous, keep sensitive info locked up tight, and follow the rules like GDPR unless you fancy a few hefty fines. Cutting corners here will land you in trouble faster than you can say “data breach.”
• Don’t Boil the Ocean—Focus Your Data: When it comes to data, more isn’t always better. Think of it like packing for a holiday—you don’t need every outfit you own, just the essentials for where you’re going. Focus on curating the data that matters, the stuff that directly supports your business goals. Don’t try to gather everything under the sun. It’ll just make your AI as confused as you are when you pack six pairs of shoes for a weekend trip.
• Scalability: Data That Grows with You: Imagine buying a suit that only fits you today. Next year, it’s either too tight or too loose, and now you’re back to square one. Your data strategy should be scalable, ready to grow with your organisation. Build flexible architectures that can handle the growing demands of your business without turning into a patchwork mess. Think of it as stretchy pants—comfy now and ready for the extra room when you need it!
• Collaboration: Share the Toys in the Sandbox: Data doesn’t like to live in silos, all locked away and only accessible to a select few. That’s like hoarding all the toys in the sandbox and refusing to share. Encourage cross-functional collaboration within your organisation. The more people who can access and use your data, the better your insights will be. Just make sure everyone plays nicely by following the same data governance rules.
• Ethical AI: Don’t Build a Robot Overlord: Just because you can do something doesn’t mean you should. AI’s power is immense, but it also comes with great responsibility (cue the Spider-Man quote). Build your AI systems ethically, ensuring they are transparent, fair, and socially responsible. Avoid creating a biased, rogue AI that’s more HAL 9000 (or Frankenstein) than helpful assistant. Think of it like building a robot that serves coffee, not one that takes over the world.

Lead with a Solid Strategy

To conclude, building successful AI solutions requires more than just clever algorithms and having all the data in the world. You will need to build a rock-solid data strategy and by focusing on such things as data quality, governance, and privacy, you will ensure that your AI doesn’t just work, it thrives as the good data does the heavy lifting behind the scenes whilst your shiny AI solution dazzles the crowd.