  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.
