Building Data Products for AI Agents

Building data products is essential for AI agents, which rely on high-quality, structured, and contextual data to function effectively. Modern data architectures, particularly data lakehouses, provide the necessary foundation by combining the flexibility of data lakes with the reliability of data warehouses, using technologies like Apache Iceberg and Trino. These architectures support federated data access, comprehensive governance, and high performance, making them ideal for AI workflows. Key attributes of data products include accessibility, contextualization, governance, and quality, ensuring AI agents can operate autonomously and securely. Examples in sectors like finance, healthcare, and manufacturing illustrate how data products enable AI agents to perform tasks such as regulatory compliance monitoring, personalized patient care, and predictive maintenance. As organizations increasingly integrate AI into their operations, data products serve as a bridge between analytics, business intelligence, and AI, fostering faster insights and reducing friction across technical and business domains.