Data Analysis in the Era of Generative AI
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866914958488371200 |
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| author | Inala, Jeevana Priya Wang, Chenglong Drucker, Steven Ramos, Gonzalo Dibia, Victor Riche, Nathalie Brown, Dave Marshall, Dan Gao, Jianfeng |
| author_facet | Inala, Jeevana Priya Wang, Chenglong Drucker, Steven Ramos, Gonzalo Dibia, Victor Riche, Nathalie Brown, Dave Marshall, Dan Gao, Jianfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18475 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Analysis in the Era of Generative AI Inala, Jeevana Priya Wang, Chenglong Drucker, Steven Ramos, Gonzalo Dibia, Victor Riche, Nathalie Brown, Dave Marshall, Dan Gao, Jianfeng Artificial Intelligence Human-Computer Interaction 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. |
| title | Data Analysis in the Era of Generative AI |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.18475 |