Introducing New Data Antiperspirant™ and Data Body Wash™

In her discussion of big data, Melissa Gregg argues that we should think about our relation to data according to the concept of “data sweat” (44). For Gregg, sweat illustrates the “existence of data that is essential to us” (45). Like sweat, data can unintentionally seep out, can be an annoyance or an accomplishment (depending on the context), and leaves behind a trace. While we may try to clean up our online image, Gregg claims that this is merely an “attempt to control what is ultimately out of our control” (45). Gregg sees this as problematic because powerful interests benefit from the lack of control that we have over our data sweat.

While Gregg briefly notes that there is an industry of perfumes and deodorants that accompany the need to disguise sweat, she does not fully develop this part of the metaphor in relation to data. Accordingly, I want to extend her analysis by offering two ways that we can control data sweat: data antiperspirant and data body wash.

First, data antiperspirant refers to a preventative measure that one can use in order to reduce or eliminate their data sweat. One example of a data antiperspirant is the software program Disconnect. In short, Disconnect blocks companies, governments, and individuals from tracking and collecting your data. In other words, Disconnect prevents you from sweating data while you are active online. In this case, “data antiperspirant” is more appropriate than “data deodorant” because the former prevents or reduces sweating, whereas the latter merely conceals unpleasant odours.

Second, data body wash refers to software programs that help one clean oneself of data sweat that has already formed. One example of a data body wash is the software Repnup, which helps people clean up their social media profiles by flagging potentially inappropriate content. Users can look over what has been flagged and delete the content that they want. Like an exfoliating body wash, Repnup helps users clean away their “inappropriate” data sweat. Repnup, however, is not a deep cleaning body wash. Repnup neither removes data sweat that has already been obtained by third parties, nor helps users deal with data sweat that is not considered inappropriate.

  

These two concepts – data antiperspirant and data body wash – draw attention to the ways that users can presently exercise some control over their data sweat. In addition, the idea of data body wash highlights the need for something that can clean away the sweat that has seeped into the crevices of third parties.

Discussion Questions:

• What are some additional ways that people can reduce their data sweat?
• Do software programs that embody the concepts of data antiperspirant and data body wash adequately deal with the problems of data sweat? Is there a need for a different solution?