How Design Researchers Make Sense of Data Visualizations in Data-Driven Design: An Uncertainty-aware Sensemaking Model

Abstract

While data is the cornerstone of modern design strategies, design researchers frequently struggle when performing data work. This creates a need to design tools that enable design researchers to actively engage with data. However, this presupposes understanding how design researchers create meaning from data representations, as the way of visualizing the data, along with other factors, can significantly impact the extracted insights, increasing uncertainty about the quality of the outcome. As a response to this problem, we explore how design researchers make sense of data in a case study: making sense of paired subjective and objective sleep and stress data visualizations. By synthesizing our findings from two user studies, we construct a sensemaking model which highlights how uncertainty related to data qualities, visualization parameters, and the viewer’s background, affects the insight-generation process. Our findings have implications for the future development of tools and techniques for visual data sensemaking for designers.