Week 9: My diet

For my 7th visualization I’ve chosen to track what I eat and drink during the day and when. At first sight, it seems that this kind of personal data can hardly be linked to the topic of digital governance. However, I could relate them to two issues: teachers’ or students’ well-being and their efficiency. Interestingly, that these data can be interpreted in opposite ways. On the one hand, my diet looks more or less balanced, and it’s obvious that I eat regularly (even more often than needed:). This may suggest, quite indirectly though, that I have the potential to be efficient at work, since hunger is not what will take away my attention. On the other, I eat or drink almost every hour during the working day, which is distracting and can play against my efficiency. So what decisions can these data inspire? Or would it be necessary to link these numbers to another data set, like my performance at work, to be able to conclude something?

In 2020, many people switched to working from home, and some companies, especially in IT, are now struggling to decide whether WFH is as productive as working from the office to adapt their policies by the time the pandemic fades away. So by gauging employees’ behavior (engagement) when working from home, for instance, quantifying it and comparing it to the times in the office, could help institutions arrive at more ‘objective’ decisions that people would trust. In the time of ‘fast policies’ (Williamson, 2017), you can never predict what data will become part of the algorithm that will determine the fate of millions.

p.s. They say ‘we are what we eat’. I felt quite uneasy sharing my personal data with the public. I would most probably feel the same if I’m expected to log information about myself or my work so that it is used by ‘I don’t know who’ and ‘I don’t know how’. It made me think of teachers who are often forced to live and work ‘behind the glass wall’ to serve the current values of transparency, openness and participation.

2 thoughts on “Week 9: My diet

  1. This visualization is a really good way of reflecting on several issues, not least personal sensitivity about sharing individual information. I also like how you’ve extrapolated to employee engagement and productivity tracking, which is very definitely a live issue right now, e.g. https://www.theguardian.com/technology/2020/nov/26/microsoft-productivity-score-feature-criticised-workplace-surveillance. This kind of performance measurement is certainly a key technique of governing, in the sense of the purposeful management of behaviours towards certain outcomes.

    Your visualization also made me think about the expanding scope of datafication in education. Increasingly, various organizations are seeking to quantify students’ well-being, physical health, and other ‘social-emotional skills’ – often because they’re assumed to be ‘proximal’ to learning in some way, e.g. well-being as a prerequisite for learning. Indeed, there is growing awareness of how nutrition and metabolic processes are linked to learning, and a long history of policy interventions targeting ‘obesity’, so it’s not inconceivable that dietary tracking could be used in an education study of some kind. And, as with interventions in ‘obesity’, this could lead to attempts to ‘govern’ students’ ‘healthy’ behaviours more effectively.

  2. Thanks for your feedback and the link, Ben! Having read the article, I’ve checked my weekly My Analytics (by Microsoft) mail that I usually ignore. It aggregates hours, tools and media and gives me some tips which are not particularly useful. Interestingly, it must be drawing on the same algorithm (IT worker-oriented) for different roles, so this is some kind of ‘one-size-fits-all personalization’. My manager has no access to these data, so I don’t feel that my privacy is somehow threatened. Do I find these data collecting exercises useful? Not really. Threatening? No, as long as I’m doing my work well.

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