Data governance and quality for AI within the European context

This document serves as a technical report focused on the management and quality assurance of data utilized within artificial intelligence systems, specifically tailored to the European context. It addresses the critical need for structured approaches to handling AI-related data throughout its lifecycle, emphasizing aspects such as data collection, processing, storage, and validation. The content provides guidance on ensuring data integrity, reliability, and consistency, which are essential for developing robust and trustworthy AI solutions. It outlines best practices for governance frameworks that support high-quality data environments, facilitating compliance with broader regulatory expectations while promoting interoperability across member states. The report aims to assist organizations in implementing effective data strategies by offering detailed methodological recommendations. It covers various dimensions including ethical considerations, technical specifications, and operational procedures relevant to maintaining data standards. By establishing a common reference point, this publication supports the alignment of national practices with European-level objectives, fostering a cohesive approach to AI data quality across diverse sectors.