Conversational Analytics: How to Get Data Answers in Plain English

This post details the technical architecture and trustworthiness mechanisms of conversational analytics, focusing on how natural language questions are translated into database queries. It highlights the importance of schema linking, business logic integration, and semantic models for generating accurate and actionable SQL. The post contrasts this with generic chatbots, emphasizing the need for direct database connection and context awareness. It also discusses the evolution from simple chatbots to integrated AI analytics platforms where conversational queries can lead to inspectable notebooks and dashboards.