Data and AI Lessons from Ana Tralle, CEO of ANATEQS

In this special edition of The Executive Outlook , I sat down with Ana Tralle , Founder and CEO of ANATEQS , and recognized among the top 1% of global experts in data and AI. Ana’s career spans more than a decade, during which she has worked with Fortune 500 companies and fast-growing startups, advising leaders on how to harness the full potential of data. Today, she leads ANATEQS with a mission that goes beyond technology: to challenge the potential of both people and businesses.

Ana’s journey began over ten years ago, in what she fondly calls the “good old days” when Power BI had just entered the market and the world was starting to discover data visualization. She remembered how she was drawn to the possibilities. “I started digging deeper into the topic, and after some time, I could work with data almost with my eyes closed,” she laughed. 

That early fascination grew into something larger as she took on roles at global companies, eventually becoming an Innovation Lead and Data & Analytics Manager before taking the leap to start her own company. For Ana, founding ANATEQS was not just about building a business but about shaping a vision—a company designed to help people and organizations unlock their hidden potential through data.

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When asked what lessons she carries into every engagement, Ana didn’t hesitate. “The number one lesson is simple: you have to use data,” she explained. “Every business today has data, but not everyone is using it. And using it doesn’t just mean looking at it—it means trusting it. And you can only trust it if the quality is right.” 

Trust in data is at the heart of Ana Tralle’s philosophy. She has seen too many businesses fall back on gut feelings, even when the data is right there. “Gut feeling is important, yes, but not always enough. Sometimes the real clues are hidden in the data, and they can help you move faster. But if the quality isn’t there, people stop believing in it, and then the data becomes useless.” 

Ana has worked with organizations where entire departments held different truths because their systems didn’t align. Marketing had one number, sales had another, HR a third. “When departments see different data, it means they’re not speaking the same language. It creates barriers inside the business. That’s why data architecture is so critical—you need to make sure the systems connect, the definitions match, and the flows are clear. Without that, trust breaks down.”

She pointed out that this challenge looks different depending on the size of the business. For smaller companies, it’s often easier because the data volumes are manageable. But for global corporations operating in dozens of countries with hundreds of systems, the complexity can be overwhelming. “That’s why finding the right data architecture strategy for your organization is non-negotiable. It has to fit your needs, scale with your operations, and above all, give you a single version of truth.” 

For Ana, the issue of data quality is not an abstract debate—it’s something she has seen play out in real, sometimes painful, ways. At a global data summit, she once heard a question posed to a room full of executives: How many of you believe your data is accurate and of good quality? Not a single hand was raised. She wasn’t surprised. “I don’t know a single business that has 100% perfect data. It doesn’t exist. There’s always something missing, whether it’s unstructured data like images and videos, or overlooked signals like sentiment. Businesses may rely on key numbers, but if you ignore the surrounding context, you’re still missing a piece of the picture.”

Watch the full conversation on YouTube by clicking the link below:

When asked about fixing data quality issues, Ana emphasized documentation and monitoring as the first steps. “You can’t clean what you don’t understand. First, map the data—know what you have and where it sits. Then set up monitoring. Find out what percentage of your data is clean, what’s missing, and where errors appear. Once you know, you can prioritize. Which processes bring the most value? Start there. Monitoring is the moment of truth. Once you see the problem, you’re obligated to solve it.” 

She illustrated this with a story from early in her career, one that still stands out. During the Christmas season, the company she worked with was shipping large numbers of holiday packages. Customers expected gifts to arrive before Christmas Eve, and the stakes were high. But delays threatened customer trust. Ana and her team built a system to track shipping parcels. “We could see which packages were delayed, contact the delivery companies or customers. It saved both financials and customer relationships.”

This led to another important question: how do businesses measure the cost of bad data? Ana was clear in her response. “I believe monitoring is crucial, but measuring the full cost of bad data is almost impossible. Data is everywhere—it touches sales, marketing, customer support, HR, leadership decisions. It’s like air: you can’t live without it, but you can’t always measure its exact weight. Trying to put a number on bad data’s cost often misses the point. Instead, focus on finding the problems and fixing them.” 

As our conversation shifted to AI, Ana’s excitement was obvious. “AI is no longer just another box in the business architecture—it’s everywhere. It’s not what it was even five years ago. It’s embedded in workflows, reshaping how we operate.” She described how tools like Copilot now allow employees to query reports with natural language. “You don’t need to open a dashboard and dig through charts. You just ask the question, and AI gives you the answer. It’s faster, simpler, and far more accessible.”

For Ana, this democratization of technology is one of AI’s biggest impacts. “We’re seeing the rise of citizen developers—people outside of tech who can now create solutions using AI. Maybe it’s not as complex or scalable as a full engineering project, but it solves real problems quickly. That’s powerful.” 

Of course, she acknowledged that AI comes with challenges. Processes ingrained for decades are being disrupted. Legal, contractual, and ethical questions arise when AI creates or modifies content. “But disruption isn’t bad—it forces us to rethink, to innovate, to adapt. That’s how progress happens.” 

What excites her most is how fast this evolution is happening. “In the past, we thought about learning as something you did when you wanted to reach a new level. Today, learning is constant. Tools change monthly, sometimes weekly. A feature you relied on a month ago may already be outdated. That means upskilling is no longer optional—it’s part of everyday life. And businesses that embrace this mindset will stay ahead.”

As our conversation ended, Ana left me with a thought that captured the heart of her philosophy: “Data is like air—it’s everywhere. You can’t escape it, and you can’t ignore it. But if it’s polluted, if the quality is bad, then everything built on it suffers. The key is not perfection—it’s the relentless pursuit of better. Clean it, monitor it, use it. That’s how businesses grow. That’s how people unlock their potential.”

For Ana, data is not just about numbers—it’s about trust, speed, and the belief that improvement is always possible. Her journey, from those early days exploring Power BI to leading ANATEQS as one of the top global voices in data and AI, is proof that with vision, expertize, and curiosity, data can truly transform the way we work and live.

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