  Unfamiliar measurements usually hinder readers from grasping the scale of the
numerical data, understanding the content, and feeling engaged with the
context. To enhance data comprehension and communication, we leverage analogies
to bridge the gap between abstract data and familiar measurements. In this
work, we first conduct semi-structured interviews with design experts to
identify design problems and summarize design considerations. Then, we collect
an analogy dataset of 138 cases from various online sources. Based on the
collected dataset, we characterize a design space for creating data analogies.
Next, we build a prototype system, AnalogyMate, that automatically suggests
data analogies, their corresponding design solutions, and generated visual
representations powered by generative AI. The study results show the usefulness
of AnalogyMate in aiding the creation process of data analogies and the
effectiveness of data analogy in enhancing data comprehension and
communication.
