Data and AI are intrinsically linked, with AI algorithms processing large datasets to identify patterns and generate insights. As generative AI advances, the demand for high-quality, diverse data has become critical. Traditional data procurement methods are reaching their limits, necessitating innovative approaches: data spaces.

These collaborative models offer a solution by enabling data sharing under three key conditions: data sovereignty, trust, and mutual benefit. By providing a framework that respects individual and organizational data rights, ensures transparency, and creates value beyond monetary exchange, data spaces can unlock new data sources for AI development. Research indicates that current data collection practices will soon exhaust available sources, making collaborative data sharing models not just beneficial, but urgent for the continued advancement of AI technologies.