Visualizing Junk

Abstract

The datafication of culture has led to an increase in the circulation of data visualizations. In their production, visualizers draw on historical antecedents which define what constitutes a good visualization. In their reception, audiences similarly draw on experiences with visualizations and other visual forms to categorize them as good or bad. While there are often sound reasons for such assessments, the gendered dimensions of judgments of cultural artifacts like data visualizations cannot be ignored. In this article, we highlight how definitions of visualizations as bad are sometimes gendered. In turn, this gendered derision is often entangled with legitimate criticisms of poor visualization execution, making it hard to see and so normalized. This, we argue, is a form of what Gill calls flexible sexism, and it is why there is a need not just for feminist critiques of big data but for feminist data studies–that is, feminists doing big data and data visualization.