Data visualization has become an integral part of governing education, greatly expanding its reach and influence in the digital age. From tracking the performance of individual students to monitoring the overall success of educational systems, data visualization serves as a powerful tool for informing policy and decision-making on both global and local levels. By providing an easy-to-understand representation of numerical data, it helps governments to quickly identify patterns and trends over time, make calculations about the future and communicate complex information in ways that are both informative and aesthetically pleasing. While there has recently been an increased interest in understanding the role of numbers in shaping education policy (e.g., Pettersson, 2020), visual representations have so far received little attention. Given the importance attached to data in education governance (Williamson, 2016), this gap is surprising. The aim of this paper is, therefore, to contribute insights on how images, words and numbers work together to produce knowledge that makes educational systems amenable to analysis, comparison, and governance (Decuypere & Landri, 2021; Williamson, 2016). More precisely, we explore how quantitates are transformed into geometric shapes, arrows, bars, and vectors to create persuasive accounts of what ‘works’ and what needs to be fixed. We do so by analyzing abstract non-representational pictures employed by international education agencies (such as OECD and UNESCO) in their reports from the last three decades. Inspired by Science and Technologies Studies (Daston & Galison, 2007; Latour, 2012; Lynch & Woolgar, 1990), we consider data visualization a specific technique of knowledge production that structures our understanding of educational spaces and temporalities (cf. Decuypere & Simons, 2020). Although data visualization is often assigned the role of ‘cognitive aid’, the preliminary results of our study indicate that it is not as transparent and self-evident as it is widely believed. By allowing the viewer to ‘see’ the past and present and to imagine the future, graphs, charts, and diagrams convey the impression as if they were entirely devoid of politics. With this promise of objectivity visual representations turn invisible phenomena into ‘noisy’ but ‘beautiful’ evidence (Halpern, 2015; Lynch, 1991). Nevertheless, data visualization presupposes filtering of what can be seen, in what ways and for what purposes. As such, it operates as a mode of preemptive governance (cf. Massumi, 2007), whereby the visualized pasts and projected (un-)desirable futures are brought into and organize the present.

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