Data analysis is challenging as analysts must navigate nuanced decisions that may yield divergent conclusions. AI assistants have the potential to support analysts in planning their analyses, enabling more robust decision making. Though AI-based assistants that target code execution (e.g., Github Copilot) have received significant attention, limited research addresses assistance for both analysis execution and planning. In this work, we characterize helpful planning suggestions and their impacts on analysts' workflows. We first review the analysis planning literature and crowd-sourced analysis studies to categorize suggestion content. We then conduct a Wizard-of-Oz study (n=13) to observe analysts' preferences and reactions to planning assistance in a realistic scenario. Our findings highlight subtleties in contextual factors that impact suggestion helpfulness, emphasizing design implications for supporting different abstractions of assistance, forms of initiative, increased engagement, and alignment of goals between analysts and assistants.

翻译：数据分析极具挑战性，分析师需处理可能导致不同结论的细微决策。AI助手有潜力支持分析师规划分析流程，从而实现更稳健的决策。尽管针对代码执行的AI辅助工具（如GitHub Copilot）备受关注，但针对分析执行与规划双重辅助的研究仍有限。本研究旨在描述具有实用性的规划建议及其对分析师工作流程的影响。我们首先回顾分析规划相关文献与众包分析研究，对建议内容进行分类。随后开展巫师实验（n=13），在真实场景中观察分析师对规划辅助的偏好与反应。研究结果揭示了影响建议实用性的上下文因素细微差异，强调了支持不同辅助抽象层级、主动性形式、增强参与度以及协调分析师与助手目标的设计启示。