Data

Overview

From the earliest stages of training to ongoing refinement, AI systems rely on vast and diverse datasets to learn patterns, make decisions, and improve over time. 

This chronological journey - starting with raw data collection, followed by preprocessing, model training, and real-world validation - shows how essential data is at every phase of AI development. Without high-quality, representative data, even the most sophisticated algorithms cannot perform effectively or responsibly. The use of large amounts of data by AI systems also pose complex intellectual property questions, as well as personal data protection challenges. 

Explore our insights on intellectual property, personal data protection and non-personal data available below.

For more information or a tailored discussion, reach out to our team via the contacts listed on each pages.