AI is democratizing data analysis by enabling natural language interaction with data. Text-to-SQL with LLMs: provide schema as context, user writes natural language question, LLM generates SQL. Implementation: schema_context = get_schema_description(database); prompt = f"Given this database schema: {schema_context}

Generate SQL for: {user_question}

Return only the SQL query."; sql = llm.complete(prompt); results = execute_sql(sql). Key challenges: 1) Schema complexity: large schemas require selective context - use embedding similarity to retrieve relevant tables. 2) Ambiguous questions: ask clarifying questions before generating SQL. 3) SQL safety: validate generated SQL (no DDL/DML), limit result rows, use read-only database user. 4) Error handling: if SQL fails, send error back to LLM for correction. Automated insights: after generating chart data, use LLM to identify anomalies, trends, correlations and generate narrative summaries. "Sales dropped 15% in Q3. The decline was concentrated in the West region (-32%) while East region maintained growth (+8%)." Conversational analytics: maintain conversation context allowing follow-up questions ("what about last year?", "break this down by category"). Tools: Langchain SQL Agent, Vanna.ai (specialized text-to-SQL), Defog (enterprise text-to-SQL). Evaluation: benchmark accuracy against human SQL with business stakeholder test questions.