11-07-2025
Why Quality Data Matters for AI Success
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Quality data is the foundation of AI success. Yasser Shawky explores why trusted, AI-ready data is essential for innovation, scalability, and long-term business value.
I was recently speaking to my mother about what she'd been cooking lately, and was amused to learn that she now uses ChatGPT to help her come up with new recipes. I couldn't help but smile at how pervasive AI has already become. When even our parents are comfortable treating LLMs as their culinary consultants, you know the technology has crossed a noteworthy threshold.
It also got me thinking. In this case, the advice she received was useful. But what if the AI hallucinated, as it sometimes does, and suggested swapping sumac for cinnamon in a delicate Middle Eastern dish? That would almost certainly ruin the flavour. And what if the same thing happened in a more serious context like a home remedy, financial advice, or supplement recommendation? The consequences could be dire.
That small, charming exchange has stayed with me, because it's a fitting metaphor for what I see happening across the Middle East as organisations rush to capitalise on AI. Like my mother with her recipes, businesses see the promise and want to seize it. But without proper visibility into the inputs AI models are drawing from, and without governance over the data that fuels them, they're exposing themselves to risk.
AI's Dot-Com Moment
With AI adoption proceeding at breakneck pace, it might feel like we're venturing deep into uncharted territory. And while that's undoubtedly true, the IT industry has stood at a similar inflection point before. Just over two decades ago, the world watched as the dot-com boom of the late 1990s rewrote the rules of business almost overnight. In that exhuberent environment, many crashed and burned, but those that invested in solid infrastructure, user experience, and sustainable business models emerged as titans, still standing strong today.
Now, we're living through AI's own dot-com moment and just as with the internet revolution, the winners won't be those who leap in first. Winners will be those who lay the right foundations, especially when it comes to data.
Why Data, Not Just AI, Should Be the Starting Point
It's easy to be dazzled by the tech itself. Large language models, generative AI, machine learning, have entered daily dialogue. But these are just enablers. The true foundation of any successful AI implementation is a strong data platform.
It's a truth the industry has long known but often skirts around, poor data quality destroys business value. According to Gartner, the average cost of bad data is estimated at US$10.8 million per year per organisation. The reasons? Duplicate entries, outdated records, inconsistent formatting, missing fields, lack of data governance and lineage, and fragmented systems. Every Chief Data Officer is all too familiar with these challenges.
But flip the equation and the potential is transformative. When data is accurate, complete, consistent, timely and relevant, what we at Informatica call 'AI-ready', then use cases that once seemed overly complex suddenly become achievable and durable. That's when data chaos is turned into business value. It's not just about better models. It's about creating a single, authoritative view of enterprise data that everyone, from executives to frontline staff, can trust. Like a chef who knows exactly what's in their pantry and how fresh it is, with data you can trust, you can cook up something extraordinary, without unpleasant surprises.
One Foundation, Many Use Cases
Let's explore this in practice by taking the example of an innovative bank. Its marketing team is keen to leverage AI to offer hyper-personalised financial products. They want to know when a customer might be buying a home, starting a business, or sending their child to university so they can provide timely, tailored offers. Meanwhile, their fraud detection team wants to use AI to spot suspicious patterns in real-time, saving customers the need to self-report.
Two vastly different use cases. But they share one foundational need: up-to-date, accurate behavioural and transactional data. The marketing team can't personalise without knowing the customer. The fraud team can't protect without understanding what 'normal' looks like. If each team tries to build its own data pipeline, you end up with duplication, inefficiency, and inconsistent insights.
Instead, by investing in a common, trusted data layer, like we at Informatica call turning chaos into business value, you empower every department across the organization to innovate, scale and advance. According to McKinsey, as much as 70% of the effort in developing AI solutions is spent on wrangling and harmonising data. Solve that once, and your entire organisation becomes more agile, more aligned and more AI-ready.
Getting Your Data AI-Ready
So how do you get there? It starts with acknowledging that data is not just an IT problem, it's a strategic asset belonging to the entire organisation. Preparing your data for AI involves more than cleansing spreadsheets. It requires a robust data governance framework that ensures integrity, compliance and accessibility. It requires scalable infrastructure, ideally cloud-based, that can grow with your ambitions. It requires investment in data literacy, so your people, across all functions, understand how to question, interpret and act on data.
Most of all, it requires trust. As Robin Miller, Group Data Manager at Lowell so perfectly put it, 'The vision I describe to my colleagues is that they'll be able to implicitly trust the data that informs them, no matter where in our organisation it comes from.' That trust is what enables action. And in a world moving as fast as ours, trusted action is everything.
Long-Term Value over Short-Term Buzz
There's no denying the momentum behind AI. But there's a real risk in chasing short-term wins — pilots that can't scale, models that aren't reproducible, projects that promise much but deliver little. What's needed is a long-term mindset. That begins with data.
By prioritising data quality and investing in AI-readiness from the ground up, organisations position themselves to move faster, not slower. They create the conditions for innovation to thrive. They accelerate business value by enabling data to flow freely, reliably and securely across systems, departments and applications. And most importantly, they ensure that when the hype cycle inevitably plateaus, their AI investments continue to pay dividends.
In the race for AI success, it's not the flashiest use cases that win, it's the strongest data foundation.
By Yasser Shawky, Vice President, Emerging Markets (MEA) at Informatica
