Conversational Analytics: Turning Enterprise Data into Instant Business Insights with LLMs

Abstract

Conversational analytics is transforming how business users interact with enterprise data by enabling natural language queries instead of complex SQL, reducing reliance on technical expertise and accelerating decision-making. However, traditional approaches often produce inconsistent or unreliable results due to ambiguous schemas, lack of standardized metric definitions, and limited understanding of business context. These challenges increase dependency on data engineering teams to build pipelines, curated views, and reports, slowing down insight generation. This paper proposes a modern approach that combines a governed semantic model with large language models (LLMs) to deliver accurate and reliable conversational analytics. By positioning the semantic model closer to the data layer on top of curated fact and dimension tables, it standardizes metrics, relationships, and business logic, enabling LLMs to generate context-aware SQL with high accuracy. This approach reduces turnaround time, minimizes dependency on data engineering teams, and enables scalable self-service analytics while maintaining governance, consistency, and a single source of truth across the organization.