This week’s readings opened my eyes to the complexities of collecting and visualizing data, and helped me to understand that data I’ve taken to be neutral is anything but. Many of the questions raised for me in Johanna Drucker’s “Humanities Approaches to Graphical Display” were answered in the rest of the readings, but I want to linger on some of these questions and their solutions in my post. 

Drucker introduces the idea that capta should be interpreted by a factor of “x”, where is x can be the point of view of the data collector, agendas, presumptions, assumptions, or other conventions that lend a subjective dimension to the capta. This made me wonder if advanced digital technologies can capture the complex, interpretive qualities of data by letting us view datasets with different factors of “x” highlighted through animations. If qualitative data could be displayed in a way that brings attention to the variable priorities of the data collectors, viewers, and content, then maybe our general conception of data could be loosened from the rigid idea that data is self-evident and even approach the understanding that our positionality influences how we interpret the statistical world around us. 

The complexities and necessary considerations of data visualization gave me pause: if graphical representations of interpretive data are so insufficient when it comes to representing nuance, then why do we even need them? Jennifer Guiliano and Carolyn Heitman’s “Difficult Heritage and the Complexities of Indigenous Data” helped me to understand that recognizing the messiness of data is essential in turning a critical eye towards data. When describing ethical modes of accessing information about Indigenous communities, the authors state that “there is no expedient or universal solution” (15). This is a good thing, because it means that those holding and accessing information are forced to consider the impact of their data use on Indigenous communities that have a right to safeguard information. Guiliano and Heitman’s assertion applies to the complexities of data visualization raised by Drucker, too: the fact that it’s difficult to display context makes both the creator and the viewer of a data visualization think deeply about the assumptions that underlie a more straightforward graph. By embracing the fact that there is no universal or expedient way to display capta, we can draw attention to the shortcomings of non-humanist means of collecting, analyzing, and displaying data and interrogate what’s at stake – what is sacrificed – in the pursuit of expediency.