Why AI is only as good as the data that feeds it

Evan Smith, a Technical Content Manager at Starburst Data, discusses the critical role of data quality and architecture in the successful implementation of AI, particularly generative AI (GenAI). He emphasizes that AI's effectiveness is contingent on the quality and accessibility of the data it uses, aligning with the longstanding computing principle "Garbage In, Garbage Out" (GIGO). The article highlights the challenges of curating high-quality data, including issues with data access, collaboration, and governance, and the impact of dark data and data silos on AI outcomes. Smith proposes solutions such as data products and the Icehouse architecture, which incorporates Trino and Iceberg, to enhance data interoperability and governance. These approaches aim to improve data quality and flexibility, ensuring that AI systems can evolve effectively alongside rapidly changing technologies without requiring a complete overhaul of existing data architectures. Starburst's Icehouse architecture offers a robust framework for managing AI data, supporting the development of data products to promote better data access and collaboration.