The field of data analysis is undergoing a significant transformation with the integration of artificial intelligence (AI) and agent technologies. The use of large language models (LLMs) and agent-driven data analysis is revolutionizing traditional database applications and system deployment, enabling new pathways for semantic querying and improving analytical efficiency. The focus is shifting towards developing flexible, semantic-aware data analytics systems that can capture query semantics and provide actionable insights. Noteworthy papers in this area include:

• A Multimodal Conversational Agent for Tabular Data Analysis, which presents a system that lets users query datasets with voice or text instructions and receive answers as plots, tables, statistics, or spoken explanations.
• Beyond Relational: Semantic-Aware Multi-Modal Analytics with LLM-Native Query Optimization, which introduces a multi-modal data analytics framework that incorporates programmable semantic operators and leverages logical and physical query optimization strategies.