AI-Driven Data Marketplaces: Autonomous Pricing, Trading, and Quality Assurance

Abstract

The rapid expansion of data-driven ecosystems has led to the emergence of data marketplaces as critical platforms for exchanging high-value datasets across organizations. However, traditional data marketplaces rely heavily on manual pricing, limited quality evaluation, and static trading models, resulting in inefficiencies, trust deficits, and market asymmetries. This research examines an AI-driven architecture for next-generation data marketplaces that autonomously determine pricing, execute trading decisions, and ensure data quality through continuous evaluation mechanisms. Leveraging machine learning, multi-agent systems, and automated semantic assessment, the proposed framework introduces dynamic pricing algorithms, quality assurance intelligence, and autonomous negotiation strategies. The study presents a comprehensive methodology supported by an experimental case analysis using synthetic supply-chain datasets to evaluate pricing accuracy, quality scoring precision, and trading efficiency. Results demonstrate that AI-enabled mechanisms outperform conventional methods in adaptability, fairness, transparency, and operational efficiency, positioning intelligent data marketplaces as a foundational component of future digital economies.