Generative AI-based Conversational Interfaces for Unified Data Catalog Navigation
TL;DR
This paper discusses the design, implementation, and evaluation of a conversational interface that provides users with real-time, context-aware responses to queries, and demonstrates how such AI-powered systems enhance user experience, improve query accuracy, and streamline data discovery processes.
Abstract
Data catalogs are essential tools for managing and discovering organizational data, but traditional navigation methods can be cumbersome, especially as data volumes grow. This paper explores the integration of generative AI-based conversational interfaces to enable intuitive and efficient navigation of unified data catalogs. By leveraging advanced natural language processing (NLP) models, users can interact with data catalogs through human-like dialogues, eliminating the need for complex query languages and manual search processes. We discuss the design, implementation, and evaluation of a conversational interface that provides users with real-time, context-aware responses to queries. Through a case study and usability testing, we demonstrate how such AI-powered systems enhance user experience, improve query accuracy, and streamline data discovery processes. Finally, we address the challenges and future directions of deploying generative AI in data catalog systems, emphasizing the need for scalable and secure solutions.