Louis Dreyfus Company

Daniel Braga

Braga is Leading Data and Analytics at Louis Dreyfus Company in Brazil. With a background in data science and strategic leadership, he drives the company’s digital transformation by embedding AI, analytics and data productization across global operations, which enhances decision-making, sustainability and resilience in the agribusiness supply chain.

From Insights to Impact in Agribusiness

 My career has been shaped by a deep belief that when well-governed and insightfully applied, data can unlock transformative outcomes, especially in industries as dynamic and complex as agribusiness. I started in the data analytics space, working closely with supply chain operations and commercial teams, where I saw the challenges of disconnected systems, fragmented information and delayed decision-making first-hand. Over time, my roles evolved from technical delivery to strategic leadership, culminating in my current position at Louis Dreyfus Company.

At LDC, the responsibility is to manage data infrastructure and embed analytics and AI into the very fabric of decision-making across trading, logistics, finance and risk. It’s about transforming data into scalable, governed and reusable products that drive business value. Most recently, this vision has expanded to include establishing a Weather Expertise Center—strategic initiatives that will enhance our risk management capabilities, support precision agriculture and ultimately deliver a trading edge in weather-sensitive markets.

Technology Trends That Are Reshaping Digital Transformation

 Several technology trends are converging to create powerful opportunities for digital transformation in agro-trading:

Data Productization & AI at Scale: The shift from siloed dashboards to reusable data products is changing how organizations generate insights. At LDC, we implement operational and analytical squads to deliver these products using multiple platforms, ensuring scalability and interoperability.

AI-Driven Optimization: Predictive models are now embedded into core pricing and logistics routing functions. We’re leveraging machine learning to forecast yields, anticipate market movements and recommend timely sourcing decisions.

 IoT & Real-Time Weather Intelligence: Integrating sensors, satellite data and advanced forecasting models unlock new value. Our Weather Expertise Center combines this data with AI to anticipate disruptions, improve crop resilience and inform trading decisions.

 Sustainability Tech & Traceability: With increasing regulatory demands like EUDR, robust traceability systems and ESG analytics are becoming non-negotiable. We're embedding traceability into our data layer to ensure compliance and partner transparency.

Biggest Challenges on the Horizon Looking ahead, the biggest challenge will not be technology—but alignment and adoption. Scaling innovation requires more than tools; it requires cultural readiness, upskilling and clarity of ownership. As data leaders, we must bridge the gap between IT and business, ensuring that digital investments are grounded in measurable outcomes.

Another critical challenge is data quality and governance. As AI models proliferate, the quality of the input data becomes missioncritical. We are addressing this through strong domain ownership, a federated governance model and clear stewardship embedded into our squads.

 Lastly, climate volatility and geopolitical complexity will increasingly disrupt supply chains. Our work with weather analytics is part of our response, equipping our teams with early signals to navigate uncertainty with confidence.

Advice for Organizations Embracing Emerging Tech

My advice for organizations on the path to transformation is this: start with the problem, not the platform. Too often, technology is adopted without a clear link to business outcomes. Begin by identifying pain points and value-added. Then, build data products that address them—iteratively, transparently and collaboratively.

Also, invest in talent and change leadership. Technology without capability is noise. Foster multidisciplinary squads that blend engineering, analytics and business knowledge. And don’t underestimate the role of evangelism, storytelling is as important as statistics.

Finally, avoid pitfalls like overengineering, lack of sponsorship, or one-size-fits-all approaches. The best strategies are agile, domainaware and scalable by design. At LDC, our dual-squad delivery model and phased data-product roadmap ensure we deliver early and quick wins while building toward long-term transformation.

Our mission remains clear as we continue to evolve: to position Louis Dreyfus Company as a global leader in digital agriculture— combining data, technology and human expertise to drive sustainable, intelligent growth.