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Data analysis is challenging as analysts must navigate nuanced decisions thatmay yield divergent conclusions. AI assistants have the potential to supportanalysts 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 forboth analysis execution and planning. In this work, we characterize helpfulplanning suggestions and their impacts on analysts' workflows. We first reviewthe analysis planning literature and crowd-sourced analysis studies tocategorize suggestion content. We then conduct a Wizard-of-Oz study (n=13) toobserve analysts' preferences and reactions to planning assistance in arealistic scenario. Our findings highlight subtleties in contextual factorsthat impact suggestion helpfulness, emphasizing design implications forsupporting different abstractions of assistance, forms of initiative, increasedengagement, and alignment of goals between analysts and assistants.

How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study