This article was based on the interview with From Ai4: Coca-Cola FEMSA’s Jose Martinez on balancing continuous improvement and CX consistency by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
We’ve all been in the meeting where someone declares, with the gravity of a philosopher revealing a new truth, that “data is the new oil.” It’s a tired phrase, but it persists because it contains a kernel of truth. Like crude oil, raw data is valuable in its potential, but largely useless in its natural state. The real work—the work that separates market leaders from the laggards—is in the refining process. It’s in building the infrastructure, the processes, and the culture to turn a vast, messy resource into a high-octane fuel that powers intelligent business decisions. For enterprise marketing leaders, this is no longer a theoretical exercise. It’s the central operational challenge of our time.
This challenge is magnified to an almost unimaginable degree at an organization like Coca-Cola FEMSA, the world’s largest bottler of Coca-Cola products. Operating across 15 countries and serving millions of points of sale, from massive hypermarkets to small, independent retailers, the scale is staggering. I recently had a conversation with their Chief Data Officer, Jose Martinez, at the AI4 conference in Las Vegas, and his perspective is a masterclass in pragmatism. He’s not a futurist selling a vision of a fully autonomous enterprise; he’s an architect in the trenches, building a data foundation capable of supporting a global giant. His insights provide a clear-eyed playbook for any leader wrestling with the gap between data’s potential and its practical application.
Competitive Advantage Isn’t Found in Data, But in the Connections Between Data
The first mistake many organizations make is viewing data within departmental silos. The marketing team has its customer data, the operations team has its supply chain data, and the finance team has its transaction data. They all produce excellent reports, but they often fail to speak the same language. True competitive advantage, as Martinez explains, emerges not from perfecting these individual silos, but from building bridges between them to uncover patterns that no single department could see on its own. It’s about moving from domain-specific analytics to cross-functional intelligence.
“Are you looking at the data as a whole, or are you looking at the data as a single, in a single domain? How you interconnect the data. So… are you able to find patterns between one domain, let’s say operations and financial? Do you see the difference? Do you see the correlation? Do you see where they are getting mixed? That’s where you get the competitive advantage. If you are able to see that, and it’s not just the tools, it’s also the mindset.”
For a marketing leader, this is the holy grail. Imagine being able to correlate a regional marketing campaign not just with sales lift, but with the efficiency of last-mile delivery in that same region. What if you could see how a price promotion impacts not only revenue but also the operational cost of restocking specific retailers? This level of insight allows for a far more sophisticated allocation of resources. It transforms the marketing function from a cost center focused on campaigns to a strategic driver of overall business profitability. This, as Martinez notes, requires more than just a powerful tech stack; it requires a cultural shift toward a data-literate organization where everyone is empowered and expected to ask, “How does this connect to that?”
In the Age of AI, Bad Data is More Dangerous Than No Data
We’ve talked about data quality for decades. It was important when we were building dashboards and running statistical regressions. Now, with the proliferation of AI and LLMs, it’s existential. An incorrect dashboard might lead to a poor decision made by a human who can apply context and intuition. An AI model trained on flawed data, however, will make flawed decisions at machine speed and scale, without the benefit of common sense. Getting this wrong doesn’t just lead to inefficiency; it can actively damage the customer relationship and erode brand trust.
“Data quality was present in all that since then and before. So you need to have data that is clean, that is curated, that works… If you have tons of data that is not normalized, that is duplicated, that probably is not the right data you need to have, you will not get any result. You can have the biggest brain… you will not have the result you’re looking for… And it’s even more important today because we are… everybody is using AI somehow right now, and the usage of that has been increased. It’s faster. So you are getting also bad information faster.”
Martinez’s point about getting “bad information faster” should be a sobering thought for every marketer experimenting with AI-driven personalization. When an AI system incorrectly segments a customer, misinterprets their purchase history, or sends an irrelevant offer, the customer doesn’t see it as a data integrity issue. They see a brand that doesn’t know them, doesn’t listen, and ultimately, doesn’t care. The speed and automation that make AI so powerful also make its mistakes more pervasive. This underscores the critical, if unglamorous, importance of data governance, normalization, and building a reliable single source of truth before letting advanced algorithms loose on your customer base.
To Build a Data Culture, You Have to Sell the Business Value, Not the Technical Process
For many business leaders, terms like “data governance,” “stewardship,” and “normalization” sound like bureaucratic overhead. They sound slow. They sound expensive. And they sound like someone else’s problem. As a technical leader, a Chief Data Officer can’t simply walk into the boardroom and request resources for a governance initiative. To succeed, they must reframe the conversation entirely, moving from the technical “how” to the business “why.” This requires a different skillset—one that marketers should recognize immediately.
“You need to be a little bit of a seller. A little bit of a marketing person… If you go to the board of directors and say, ‘Hey, I need this team to do governance,’ they will tell you, ‘What? Why? What’s that for?’… But if instead of saying that, you say, ‘Hey, I think we can decrease costs of usage of data. I think we can get faster information. I think we can get more information… With that information we can create more use cases like this, this, this, this, that can give us an increase of revenue, cost savings, and stuff like that.’ If you are thinking about that, you are adding the value to the data.”
This is where marketing leaders can and should be the CDO’s greatest ally. We understand how to build a value proposition. We know how to translate features into benefits. Instead of seeing data governance as a tax on our agility, we should view it as the enabling foundation for the sophisticated, personalized experiences we want to create. By championing the business case alongside our technical counterparts, we can help the entire organization understand that this foundational work isn’t a detour from our goals; it’s the only sustainable path toward achieving them. It’s about securing the investment needed to build the refinery, not just admiring the oil.
The journey to becoming a truly data-driven enterprise is not a single project with a defined end date. It is a state of constant evolution, a commitment to what Martinez refers to as balancing continuous improvement with consistency. The goal is not to build a perfect, static system, but rather a resilient and adaptable one. The technology will continue to change at a dizzying pace, and the hype cycles will come and go. What will endure are the foundational principles of clean, interconnected data and a culture that knows how to derive value from it.
As leaders, our role is not just to chase the latest shiny object, whether it’s a new AI model or a new analytics platform. Our primary responsibility is to cultivate the environment—the people, the processes, and the mindset—that can harness these tools effectively. It’s about fostering a culture of curiosity, grounded in facts and fueled by the pursuit of better outcomes for both the business and the customer. The insights from practitioners like Jose Martinez remind us that at the highest levels, the greatest technological challenges are, and always will be, fundamentally human ones.
