  How do analysis goals and context affect exploratory data analysis (EDA)? To
investigate this question, we conducted semi-structured interviews with 18 data
analysts. We characterize common exploration goals: profiling (assessing data
quality) and discovery (gaining new insights). Though the EDA literature
primarily emphasizes discovery, we observe that discovery only reliably occurs
in the context of open-ended analyses, whereas all participants engage in
profiling across all of their analyses. We describe the process and challenges
of EDA highlighted by our interviews. We find that analysts must perform
repetitive tasks (e.g., examine numerous variables), yet they may have limited
time or lack domain knowledge to explore data. Analysts also often have to
consult other stakeholders and oscillate between exploration and other tasks,
such as acquiring and wrangling additional data. Based on these observations,
we identify design opportunities for exploratory analysis tools, such as
augmenting exploration with automation and guidance.
