Data Edibilization
Yun Wang, Xiaojuan Ma, Qiong Luo, Huamin Qu · 2016
Data communication is critical in data science. We propose data edibilization, i.e., encoding data with edible materials, as a novel approach to leverage multiple sensory channels to convey data stories. We conduct a preliminary data tasting workshop to explore how users interact with and interpret data edibilization. Based on the participants' feedback, we summarize the advantages of edibilization in terms of attractiveness, richness, memorability, affectiveness, and sociability. We also identify several challenges with data edibilization. We discuss possible pragmatic processes, enabling technologies, and potential research opportunities to provide insights into the design space of data edibilization and its practicality.