  Selecting relevant data subsets from large, unfamiliar datasets can be
difficult. We address this challenge by modeling and visualizing two kinds of
auxiliary information: (1) quality - the validity and appropriateness of data
required to perform certain analytical tasks; and (2) usage - the historical
utilization characteristics of data across multiple users. Through a design
study with 14 data workers, we integrate this information into a visual data
preparation and analysis tool, DataPilot. DataPilot presents visual cues about
"the good, the bad, and the ugly" aspects of data and provides graphical user
interface controls as interaction affordances, guiding users to perform subset
selection. Through a study with 36 participants, we investigate how DataPilot
helps users navigate a large, unfamiliar tabular dataset, prepare a relevant
subset, and build a visualization dashboard. We find that users selected
smaller, effective subsets with higher quality and usage, and with greater
success and confidence.
