The behind-the-scenes work of producing school data (part 1) … Brookdale’s ‘Student Attendance’ app

The flow of data around a school relies on considerable amounts of behind-the-scenes ‘invisible’ work.  Following the lead of Susan Leigh Star and others, one obvious focus for our investigations of this invisible labour is the continuous infrastructure work required to keep school data systems operational. Less obvious, perhaps, is the invisable work relating to the production of school data.

For example, Brookdale High School has a neat ‘Student Attendance’ App that allows parents to inform the school when their child will not be attending school. Rather than go through the rigmarole of phoning the school up, the parent can click the App and update the school’s attendance data accordingly.

At first glance, this App represents the subtle out-sourcing of data-entry work to the parent. In effect, the parent is undertaking unpaid data-entry work for the school. This replicates a common feature of the digital economy, which increasingly requires end-users and consumers to deal with all manner of personal data tasks – “thus dispensing with the need for a variety of paid data-entry and verification labour” (Huws 2019, p.17).

Yet, Brookdale’s App is not all that it seems. This is certainly not a fully-automated system. Instead, parental input simply triggers a prompt for the schools’ administrative staff to manually input the data into the system themselves (in the same way that they are expected to do if a parent informs the school by telephone):

“There’s a feature where parents can go on the app and say, “My son is ill today.”  So if they do that we get an email …so we’re looking that up and then we have to go in and manually enter them into the system as absent”  (Brookdale Office administrator)


Astra Taylor (2018) refers to this as ‘fauxtomation’ – the illusion of having online systems that run automatically but actually require large amount of concealed human labour to carry out their function (in Brookdale’s case, the labour of mostly female, low-paid administrative staff).

In one sense, this example simply reminds us how data-driven technologies do not simply ‘function’ of their own accord in settings such as schools – rather, the introduction of a new system or technology requires an extensive ‘human infrastructure’ to mediate, moderate and troubleshoot its demands and quirks  (see Mateescu and Elish 2019)

Yet, this example raises a number of possible concerns. For example, aside from the doubling-up of parental and administrative efforts, this also has the diminishing effect of making human workers appear more vulnerable and placing them in a weakened (replaceable) position, while simultaneously demanding additional work from them.

Thus, it is important to continue to explore the human labour that lies beneath seemingly automated and ephemeral data processes in schools. As Denis and Goeta (2014) remind us, in contrast to commonplace talk of ‘raw data’ and ‘data collection’ … “data are not ready-at-hand resources … naturally ready for public diffusion … Data’s very existence is far from being obvious, and instead of being an intrinsic property … is the outcome of numerous operations”.