The Myth of Objective Data

This two-cultures thinking, moreover, distorts the empirical realities of data collection, the challenging work of forcing unruly phenomena to speak in clean, distinct, ideally quantitative phrases

From the perspective of scientific visualization, the idea that machines allow us to see true has long been outmoded. In everyday discourse, however, there is a continuing tendency to characterize the objective as that which speaks for itself without the interference of human perception, interpretation, judgment, and so on.

The students think that data is a matter of describing things as they are , and that there is no art to it and certainly no fashion. They very much want to let things speak for themselves when they approach a project like designing a schema to describe a set of things. What my students paradoxically fail to realize, in their zeal to be responsible,

It teaches them that data is created, not found; and that creating it well demands humanity, rather than objectivity

despite the undeniably consistent picture that we see across studies of scientific data collection, the desire to remove the human from the data in order to enhance objectivity remains very strong. Invariably, it seems like the ethical move.

Changing a culture is a major undertaking, and a data culture is no exception. But thinking of these issues as cultural in the first place can help to open the imagination

If in our daily lives we tend to overlook the diverse, situationally textured sense-making actions that information seekers, conversation listeners, and other recipients of communicative acts perform to make automated information systems function, we are even less likely to acknowledge and value the interpretive work of data collectors, even as the