Shaping The Future Of Data Science Through Business Alignment

David Rodrigues is Head of Data Analytics for Latin America and Canada at Philip Morris International, where he leads efforts to embed data-driven thinking into regional decision-making and business transformation. His career began in market research, working across sectors including finance, telecom and consumer goods, which gave him early exposure to how data informs diverse business models. Seeking to deepen his technical expertise, he pursued a master’s degree focused on data science, statistics and machine learning.

That shift led him into data science roles, including the pivotal position at Philip Morris International, where he applied advanced analytical methods to support commercial strategies. Over time, his scope expanded from modeling and insights to guiding broader data and analytics transformation efforts across markets. Today, Rodrigues bridges strategy and data execution, aligning analytics initiatives with core business priorities. In this feature, he reflects on how strategic alignment and trend awareness drive his approach to analytics, underscoring his leadership in scaling data initiatives across a complex regional landscape.

Aligning Data Initiatives with Business Goals

The most important part, often overlooked by data professionals, is starting with business priorities. Many CDOs and data leaders focus too much on tools and solutions, which can be risky. That approach often prevents data teams from delivering real value. Understanding the business endto-end should always come first. Once that is clear, data and technology can be used as enablers to help the business reach its goals, not as ends in themselves. For me, that is the foundation of impactful data work.

One of the biggest blockers today is data quality and governance. Many advanced tools, platforms and models are available, but without the right data, even the best solutions will fail. I have experienced this myself. Before I joined Philip Morris International, we launched a recommendation model using the latest technology. Everything seemed solid, but when we put it into production, the results made no sense. After some investigation, we discovered the issue was poor data quality and weak governance.

This gap between ambition and execution is very common. Many companies invest in sophisticated tools without first ensuring their data is ready. That is why I believe the next few years will see a growing focus on data readiness, governance and quality. These elements are not just technical necessities. They are critical for strategy. The ability to connect business priorities with strong data foundations and the right technology will define success in the analytics space.

Top Trends Shaping Data Science and AI

I think the most obvious trend right now is AI agents. This is where the cutting edge is, and many companies are starting to explore their potential. Of course, privacy is a top concern. We take data privacy, consumer protection and compliance very seriously. Strong safeguards and a responsible approach guide every step we take in the data space.

  ​Being able to speak both the data and business languages and

knowing how to connect them to solve real problems will be the most valuable

skill in the years ahead   

AI agents are different from the bots and solutions we have seen before. They are more autonomous. They can sense what is happening and make decisions or take action without being explicitly programmed for each step. That level of autonomy is what makes them so powerful and potentially transformative.

Without strong data governance and management foundations, companies will struggle to make real progress, even with the best tools. Many organizations will find their ambitions blocked by poor data readiness if they do not invest in quality and governance.

While AI agents are clearly a major trend, the ability to support them with solid data practices will define who succeeds in this next phase of innovation.

Leading Through Strategy and Execution

One area the company has been focusing on consistently is data governance. We already have strong foundations, especially around compliance and regulatory requirements. Working in a highly regulated industry, Philip Morris pays close attention to rules and data handling, often more than companies in other sectors. That focus is deeply embedded in how we operate.

As we continue investing in AI, governance becomes even more important. It is not just about managing data properly but also about how we govern AI itself. AI governance must ensure that our solutions are responsible, controlled and aligned with internal standards and external regulations.

We are continuously evolving this. As AI becomes more integrated into business processes, our approach to governance is expanding with it. We see it not only as a safeguard but also as a strategic element that ensures our AI efforts remain sustainable, efficient and aligned with our broader responsibilities.

Adapting to the Future of Data and Analytics

I believe technological solutions will become much easier to use in five to ten years. The technical complexity will be simplified, making tools more accessible. What will matter most is the ability to understand business strategy and translate it into data-driven solutions. The role of data professionals will shift from focusing on technical details to becoming strong connectors between business needs and data capabilities.

I see myself and others in this space becoming more involved in business discussions, speaking the language of strategy and aligning it with the right data approaches. The ability to bridge the gap will become essential.

At the same time, I believe data quality and data governance will remain critical. As companies continue to handle sensitive customer and employee data, the need for structured and compliant data practices will only grow. No matter how advanced the tools become, these areas will remain at the core of responsible and effective data work.

I advise anyone entering the data space to learn core languages like SQL and Python and build a strong understanding of logic and business. No matter how tools evolve, these foundations will remain essential. Being able to speak both the data and business languages and knowing how to connect them to solve real problems will be the most valuable skill in the years ahead.