Confira a entrevista "Transformando Dados em Valor Estratégico" realizada com Gabriel Vernalha Ribeiro

Gabriel Vernalha Ribeiro é Vice-presidente de Operações, Desenvolvimento Profissional e Estudos Técnicos na DAMA Brasil.

Entrevista concedida ao portal CXO Outlook em março/2026.

Gabriel Vernalha Ribeiro graduated in Electrical Engineering, MBAs and Executives certifications in Project Management, Business intelligence, Finance, Leadership, Data Science and IA in many institutions. He has always worked in the Data and Analytics field, over 20 years, since EDS at GM in Detroit, through HP and later as COO of a portuguese consultancy, responsible for planning, managing and deploying data projects in many big companies in several industries like finance, retail, education, health and telecom, until returning to the healthcare market in 2018, where he had the opportunity to create and lead a data team from scratch at Unimed Nacional, the largest cooperative system in the world, with more than 20 million clients, for over 6 years, and recently accepted the challenge of restructuring the data area of ​​the largest diagnostics company in Latin America and the fourth largest in the world, DASA in Brazil. Regarding his volunteer work, Gabriel served at PMI Brazil for 7 years, since Project Manager till Director and also Governance Council until the end of 2024, and since then he is dedicated to DAMA Brazil volunteering as Director as well, In January of this year, he began serving on the Dama International Board of Directors.

Recently, in an exclusive interview with CXO Outlook Magazine, Gabriel shared insights into his passion for transforming data into strategic value and tangible impact. He emphasized the importance of emerging trends like Generative AI, Data Fabric, and Responsible AI, and highlighted the need for leaders to foster a culture of data-driven innovation within their teams. Gabriel also discussed his admiration for Leonardo da Vinci’s interdisciplinary approach and curiosity, and stressed the importance of curiosity, business translation, resilience, and ethics in emerging leaders. He shared his excitement about structuring a data governance workshop focused on Data Owners and offered advice to emerging leaders in data and analytics. The following excerpts are taken from the interview.

Hi Gabriel. As a seasoned Data & Analytics professional, what do you enjoy most about working in the field of data science and AI?

In the field of Data Science and AI, what fascinates me most is the ability to transform the raw potential of data into strategic value and tangible impact. It’s about designing an architecture that allows data to be turned into a clear and actionable narrative for the organization. It’s not just about building models or optimizing algorithms; it’s about uncovering hidden patterns, predicting future trends, and fundamentally empowering decision-making with intelligence and precision.

It is incredibly rewarding to witness the journey of an insight, which often begins as a hypothesis or a data anomaly, evolving into a transformative business initiative. Whether optimizing operational processes, personalizing the customer journey, or identifying new market opportunities, the impact is direct and measurable. Furthermore, the constantly evolving nature of data science and AI ensures a continuous learning environment. Each new challenge presents an opportunity to explore new technologies, methodologies, and approaches, keeping the mind engaged and creativity flowing. It is a field where curiosity is not only encouraged but essential for success.

As data and analytics continue to transform industries, what emerging trends should leaders prioritize to stay competitive?

To successfully navigate the constantly changing landscape, leaders must look beyond the hype and focus on trends that offer strategic leverage and long-term resilience. The crucial areas are:

Generative AI & LLMs:

Generative AI represents a leap in the ability of machines to create, synthesize, and interact, increasing human creativity and productivity. Leaders should prioritize controlled experimentation and business applications, creating safe environments (sandboxes) for teams to explore the potential of LLMs. The focus should be on high-value use cases, such as content automation, developer assistance, customer experience improvement, and product innovation. It is equally crucial to establish specific governance and ethics for Generative AI, developing clear policies for responsible use, transparency, and auditability, given the complexity of authorship, bias, and intellectual property.

Data Fabric & Data Mesh

With the increasing distribution and volume of data, traditional architectures become bottlenecks. Data Fabric, which integrates data from various sources, and Data Mesh, a decentralized approach that treats data as products, are crucial for scalability and agility. They enable data decentralization and ownership, empowering domain teams to manage their own data and reducing central dependency. They promote self-service and democratization, providing intuitive platforms for business users to access and utilize data independently. Finally, they ensure standardization and interoperability through rigorous rules, ensuring that data is easily discovered and consumed throughout the organization.

Responsible AI and Ethics

As AI becomes ubiquitous and impacts critical decisions, responsibility and ethics are no longer optional but a strategic imperative. Leaders must prioritize the construction of AI governance frameworks, establishing policies and processes to mitigate risks such as algorithmic bias and privacy. Inclusive design and bias mitigation are fundamental, ensuring representative training data and rigorous testing. Transparency and explainability (XAI) are essential to explain how models make decisions, building trust. Finally, privacy by design must be incorporated from the outset, protecting sensitive information with advanced techniques.

How can leaders foster a culture of data-driven innovation within their teams?

Fostering a data-driven innovation culture is a challenge that requires more than just investment in technology; it demands a cultural and organizational transformation. The key is to create an environment where data is seen as a strategic asset and curiosity is encouraged. The most effective strategies include:

Democratization of Access and Data Literacy

It’s not enough to have data; people need to know how to access, interpret, and use it. This involves providing intuitive self-service platforms (such as interactive dashboards and BI tools) that allow business users to explore data independently, without constantly relying on data and IT teams. In parallel, data literacy programs are essential to develop the analytical skills of the entire workforce, from basic concepts to understanding AI application. Furthermore, establishing open communication channels (forums, communities of practice) fosters a sense of community and collective learning, where insights are shared and collaboration is stimulated.

Encouraging Experimentation and Continuous Learning

Innovation is born from experimentation. Leaders must create an environment where intelligent failure is seen as a learning opportunity, not a mistake to be punished. This translates into a Test & Learn culture, encouraging hypothesis formulation and experimentation (A/B testing, proofs of concept) to validate ideas and document results, whether successes or failures, for knowledge sharing. Allocating time for innovation is also crucial, dedicating part of team time to exploratory projects, such as hackathons or innovation days. Finally, recognition and reward should celebrate not only successes but also significant learnings, reinforcing that the innovation process is as important as the final outcome.

Leadership by Example and Strategic Vision

Data culture starts at the top: leadership must be the primary advocate and user of data. This manifests in data-driven decision-making, where leaders consistently demonstrate the use of analytics to inform their strategies, making the question “What do the data tell us?” the norm. Clear communication of vision is essential, articulating how data and AI will drive business strategy, engaging teams, and demonstrating the impact of their work. Finally, continuous investment in people, technology, and training for data and AI initiatives demonstrates a long-term commitment to data-driven transformation.

If you could have dinner with any historical figure, who would it be and why?

My choice would be, without hesitation, Leonardo da Vinci. He was a genius who transcended the boundaries between art and science, engineering, and philosophy, seeing knowledge as an interconnected universe. This perspective is incredibly relevant to current data science, which requires a fusion of technical skills, creativity, and deep business understanding. I would invite him to explore his methodology of thought and observation. How would he approach the complexity of modern data? I believe he would be fascinated by the ability to process and analyze information on a scale he could only imagine. His empirical approach, dedication to experimentation, and relentless pursuit of the ‘why’ are qualities every data and AI professional should seek. Furthermore, his ability to communicate complex ideas through drawings and detailed annotations is a valuable lesson in the art of visual communication and data storytelling. In short, a dinner with Leonardo da Vinci would be a masterclass on how to integrate different forms of knowledge, cultivate curiosity, and apply an analytical and creative mind to solve complex challenges, the backbone of innovation in Data and AI.

What’s the most important quality you look for in a team member or colleague?

In the dynamic and constantly evolving landscape of Data and AI, where tools and technologies change rapidly, the most important quality I seek in a team member or colleague is unwavering curiosity. This characteristic goes beyond specific technical skills and becomes the fundamental driver for individual growth and collective success. Curiosity is vital because it drives continuous learning, this area of data and AI does not allow for stagnation, and a curious colleague actively seeks new knowledge and stays updated. It also fosters creative problem-solving, leading to deep questions and the identification of the root cause of anomalies, developing innovative solutions. Furthermore, it promotes data exploration, where the professional delves deeper, seeking patterns and insights that a superficial approach would miss, seeing data as stories waiting to be told.

What’s something on your bucket list that you’re excited to tackle soon?

I’ve been structuring a data governance workshop focused on Data Owners. The goal is to transform the perception of bureaucracy in data management activities into business value, engaging leaders in the management of their data assets, formalizing existing responsibilities, and connecting them to human and strategic purpose.

What advice would you give to emerging leaders in data and analytics?

For emerging leaders in data and analytics, the core advice centers on three fundamental pillars for lasting impact: mastering business translation to connect technical insights with tangible business value; cultivating resilience and adaptability to navigate the field’s constant evolution; and prioritizing ethics and governance from the outset to build trust and ensure responsible AI. Ultimately, leaders are reminded that they are not just building models, but shaping the future with purpose, intelligence, and integrity.

A entrevista original pode ser acessada no link:https://www.cxooutlook.com/transforming-data-into-strategic-value/

*Gabriel Vernalha Riveiro é Vice-presidente de Operações, Desenvolvimento Profissional e Estudos Técnicos na DAMA Brasil.