Until this semester, I had not encountered the term “postdemographic.” First hearing it, I assumed it referenced the more individuated way that data collection could take place, putting less emphasis on the demographic that one belonged to and more emphasis on individuals themselves and what they did. In a sense, this understadning approaches the term, but Rogers completes it: “Postdemographics could be thought of as the study of the data of social networking platforms, and in particular, how profiling is or may be performed” (153).

Rogers also stresses the shift from the “biopolitical” to the “info-political,” explaining a shift from embodied attributes, like age and race, to the information that these bodies generate and consume. This explains a shift in focus on the data. Though the sites of data collection are not necessarily limited to “social networking platforms,” these sites tend to abound and automatically collect the sorts of data that postdemographics focuses on: taste, pop culture influences, political leanings, groups, associations, and the way that these may line up with people that you know or interact with.

These are the curated ads and Amazon book recomendations. The “you-might-also-likes” and “people-who-liked-this-also-likeds.” In my case, an odd mix of books on wine, Zen philosophy, critical theory, composition theory, and research methods.

Rogers also notes that many of these spaces, particularly in platforms like Facebook,  the space itself reaches out for us to detail our preferences and network our contacts and interests through pre-set categories like books and movies that we like or more open-ended notes. It wants our data, takes that data, and constructs our experience accordingly.

That, very roughly, introduces postdemographics.

Contrasting the postdemographic with demographic, I see a complication: how do demographics impact postdemographics? As the Pew research reports note, demographic issues, like age and gender, connect with who uses a platform. For example, Pinterist users tend to be women: 42% of women online users, compared with 12% of men. Also, wealth and access still play a role in usage, which are again traditional demographic categories.

These potential considerations have both ethical and practical implications. On the more ethical side, I think it forces us to look at the dangers of essentialism and the issues of representation. Demographics may have a link to postdemographics–with certain demographics tending to prefer certain media–but this trend does not necessary make a truism. It is just a trend. And when value judgements and hierarchies enter the equation, as the often do with taste, attention to the fragility and complications of trends becomes more important. One must check assumptions and sloppy reasoning all the more, as more potential connections get put on the table and our pattern-pushing brains have more to work with.

This points to larger issues of big data and postdemographic data more generally. Though “the garbage in garbage out” caveat is common when it comes to the data itself, it also has some connection to our interpretations. When making claims and synthesizing findings, research requires self-critical analysis of our own thinking and transparency in our reasoning. One can easily see connections that a correlation may draw, but as often noted, correlation does not equal causation.

In a similar way, similar interest does not mean similar postdemographic–or demographic. Just because I like Doc Martin doesn’t mean I like Dr. Who. And just because people who like classical music may have similar browsing habits, values, or memberships does not mean I do.

On the one hand, this is obvious. But its obviousness should not detract from its importance.