Data portraits are representations of individuals made by visualizing data produced by and about them. As visualizations based on personal data, these portraits can function as data mirrors in that they reveal behavioral patterns of the subject portrayed. Dear Self explores the potential of data portraits as a tool for self-knowledge through the representation of personal experience, from an auto ethnographic perspective. To this end, it draws on biometric, behavioral, and social data, and follows the Observe – Collect – Draw method, proposed by Lupi and Posavec, in order to portray the psychological and emotional state of the subject. It is devised as a self-portrait in which the resulting visualizations represent my experience during two equivalent 15-day periods, enabling comparison between the days prior to my burnout diagnosis and, in the following year, the recovery and vacation context.

The visualization strategy is based on a mandala, evoking its spiritual role as a therapeutic resource oriented toward an encounter with the Self. This visual metaphor is explored in the form of small multiples that allow me to confront the impact of daily activities on my state of mind. The geometrization of the forms creates an abstraction suggestive of the difficulty of accessing the inner Self. In this manner, and by emphasizing the combination of manual and digital processes in its elaboration, Dear Self materializes as a personal data diary, in a printed publication and a web page.

The project’s intention is to reveal how, within the current context of proliferation of personal data that raises questions about privacy and surveillance, we can also take advantage of the collection and analysis of our personal data for self-knowledge purposes.

Keywords: Data Portrait, data humanism, personal data, visualization, autoethnography.