Data Visualisation #9

My week of completed tasks (productivity)

This week I struggled to not only pick an area to track but for the first time I felt genuine discomfort throughout the whole data collecting, analysing and visualisation activity. The weekend before I kicked off my final data gathering exercise, I read one of the core texts for the block: Paul Prinsloo’s 2019 article. I was particularly struck by the discussion of the pervasive ‘data gaze’, its performative social presence, and data analytics as ‘performative politics’. By situating the discussion in relation to South Africa and the colonial/apartheid context, the impact and debate really came alive and made me reflect on the digital ecosystems that shape our world today. As I tracked my data throughout the week, I became increasingly aware of my own personal data leakage on a day to day basis, what systems these data feed and how this data can be used to control the narratives that shape our lives.

After much thought, I decided to stick with my original plan and track my productivity for the week. I defined productivity as the completion of a task or activity. I did not count all my email activity but focussed on important and crucial emails that either took some time to craft or signified the end of a particular part of a project. As well as tracking what tasks I completed, I tracked what time of the day I completed them, and I categorised the tasks into the different projects and work areas that they related to. The visualisation itself was loosely based on the idea of a clock with the beginning of the working day starting at the 12 o’clock mark and then the day progressing in a clockwise fashion. 

On a personal level, I am able to see how productive I was during the week; the peaks and troughs, and I can pinpoint times of the day or even whole days during the week where I could plan for particular activities to maximise my output and productivity. Yet reflecting on my visualisation, I am acutely aware that how I defined productivity from the outset has influenced the narrative I see in front of me.  On one hand, I can see the potential benefits of having access to this type of data. On the other hand, only half a story is being told; vital context, unseen work and progress is left untracked, and subsequently the narrative is incomplete.

Micro-management is a management style that frustrates me. Real-time tracking of this type of activity would impact my autonomy, potentially create tensions between colleagues, and put pressure on the organisation to perform within certain parameters. We are solely reliant on government funding that is granted on an annual basis and if we were scrutinised at this level with an analysis of productivity on quantitative output at a granular level, I believe it would impact the overall vision and strategic direction of the organisation. The adult learner would be pushed out from the centre of the equation. Our organisational focus would shift from quality to quantity and a numerical obsession that might not equate to impact on the ground. The power of data analytics to shape the narrative and subsequent future action of an individual and an organisation is clear. It is an area that is somewhat underdeveloped in Ireland within the adult education sector and it will be interesting to see what future data indicators and measurements are introduced and how this then impacts our strategic direction going forward.

Prinsloo, P., (2020) Data frontiers and frontiers of power in (higher) education: a view of/from the Global South,Teaching in Higher Education, 25:4, 366-383, DOI: 10.1080/13562517.2020.1723537

One Reply to “Data Visualisation #9”

  1. The “power of data analytics to shape the narrative and subsequent future action” is exactly the kind of observation we were hoping that this course would inspire. This is a really good set of reflections on visualizing your productivity, and considering the possible implications of this kind of data-centred “micromanagement” for educators’ professionalism. I wonder if micromanagement might be a useful lens to use to explore the datafication of education more broadly, eg educators micromanaged according to their measured performance, learners micromanaged by automated personalized learning platforms, institutions micromanaged and governed by policy centres etc. There is something intriguing here, which your post has raised, about increasing micromeasurement and micromanagement working in tandem. Generating data at increasingly granular, micro-scales (at the level of individual activities, keystrokes, seconds spent on tasks etc) also generates intensive micromanagement of those activities, with the goal of increasing performance, efficiency and productivity. The “narrative” you referred to above comes from the micromeasurement; the “future action” is shaped by the micromanagement. These are the kinds of practices that emerge from casting a “data gaze” over sectors such as education and work.

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