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.
@article{arxiv.2409.18475,
title = {Data Analysis in the Era of Generative AI},
author = {Jeevana Priya Inala and Chenglong Wang and Steven Drucker and Gonzalo Ramos and Victor Dibia and Nathalie Riche and Dave Brown and Dan Marshall and Jianfeng Gao},
journal= {arXiv preprint arXiv:2409.18475},
year = {2024}
}