Natural Language and Conversational Analytics enable business users to query enterprise data using plain English questions instead of SQL or BI dashboards, receiving answers as visualizations, tables, or narrative summaries. The category spans text-to-SQL engines that translate queries into database commands, conversational interfaces supporting multi-turn dialogues with follow-up questions, and agentic systems that autonomously execute multi-step analytical workflows. Originally built on rule-based semantic parsing, these platforms increasingly leverage large language models to interpret intent and generate queries, though challenges around accuracy, hallucination prevention, and semantic grounding in business context remain central. Key technical considerations include query auditability, integration with data governance frameworks, handling ambiguous requests, and maintaining context across conversation threads to ensure reliable, trustworthy analytics accessible to non-technical users.