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In this session, we will take a clear and practical look at how Big Data, Data Science, and Artificial Intelligence complement one another to solve real-world challenges across both technical and business environments. We will distinguish the value that each discipline brings, when they operate independently, and when combining them becomes essential -especially in a world where data volumes grow continuously and decisions must be faster and more precise than ever.

We will review applied examples and current challenges -such as data quality, MLOps, and algorithmic ethics- and examine key technological trade-offs that are often overlooked but critical when designing an effective architecture (e.g., Spark vs. DuckDB, SQL vs. Python, custom models vs. pretrained models). Building on these cases, we will offer general recommendations and a practical decision-making framework to help you choose the right technology for different scenarios. Finally, we will explore the most relevant trends of the coming years, from Generative AI to Edge AI.

This talk should be of interest to professionals curious about how data and AI technologies fit together, and especially to those who want a clearer, more structured view of the landscape without needing deep prior specialization. The goal is to equip attendees with enough context and direction so they can explore the ecosystem more confidently and identify opportunities relevant to their own work.

About Martín Hernández Navarro
He is a mathematician and physicist with a strong business orientation, specializing in product development, software engineering and technology strategy. He began his career at Accenture Madrid in 2016, moved to investment banking at UBS Zurich in 2018, and founded TIDI Systems in 2020, where he develops software solutions and technology prototypes in areas such as big data, data science, applied artificial intelligence, recommender systems, retail profitability analysis, and both front-end and back-end systems using Python, Rust, React JS and modern cloud architectures.

He also works as a consultant for SMEs and growing companies, supporting them in operational improvement, financial analysis and the design of business strategies. He combines technical depth with business insight to help organizations optimize processes, make data-driven decisions and translate complex challenges into impactful technological solutions.

Artificial Intelligence Machine Learning Robotics
Professional Development
Big Data
Data Science
Professional Networking

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