Enterprise Data Unlocks Generative AI Potential with Vertex AI + Denodo

Leveraging enterprise data for generative AI and large language models presents significant challenges related to data silos, quality inconsistencies, privacy and security concerns, compliance with data regulations, capturing domain-specific knowledge, and mitigating inherent biases. Organizations must navigate the complexities of consolidating fragmented data sources, ensuring data integrity, and addressing ethical considerations.

Techniques like Retrieval Augmented Generation (RAG), help bridge the gap between enterprise gen AI apps and the actual enterprise data. Although RAG is a great tool, and LLMs have enabled natural language to SQL translation, these capabilities fall short in situations where enterprise data is scattered in a complex, heterogeneous data landscape. It’s fairly easy to extend your chatbot to query one database, but how to deal with a complex system with an EDW, data lake, several applications on prem and SaaS? How to ensure security is consistent across that ecosystem? How to bring forward governance, lineage, documentation or data quality?

Read more in https://www.datamanagementblog.com. Originally published on April 08, 2024.