This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow by translating high-level user intentions into executable code, charts, and insights. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps. Finally, we discuss the research challenges that impede the development of these AI-based systems such as enhancing model capabilities, evaluating and benchmarking, and understanding end-user needs.

翻译：本文探讨了人工智能驱动工具重塑数据分析的潜力，重点关注设计考量与挑战。我们研究了大型语言模型与多模态模型的出现如何通过将高层级用户意图转化为可执行代码、图表与洞见，为增强数据分析工作流各阶段提供新机遇。随后，我们考察了以人为本的设计原则，这些原则有助于实现直观交互、建立用户信任，并在跨多个应用程序中优化人工智能辅助分析工作流。最后，我们讨论了阻碍此类基于人工智能的系统发展的研究挑战，包括增强模型能力、评估与基准测试，以及理解终端用户需求。