'Monetising Digital Data in Higher Education: Analysing the Strategies and Struggles of EdTech Startups' by Janja Komljenovic, Kean Birch and Sam Sellar in (2024) Postdigital Science and Education comments
Digital data are perceived to be valuable in contemporary economies and societies. Since the 2011 World Economic Forum described personal data as a ‘new asset class’ that underpins the development of new products and services (World Economic Forum 2011), policymakers, economic and social actors, and scholars have sought to understand how data create both commercial and social value. For example, digital markets and data have become so important for our economies that in 2022–2023, the European Union introduced the Digital Markets Act to bring order to the digital economy, the Digital Services Act to harmonise rules for online intermediary services and create a safe online environment, and the European Data Act to facilitate the use and exchange of digital data for economic and social benefit.
However, digital data are neither inherently valuable nor exist ‘out there’ waiting to be collected and exploited. Instead, data and data products are constructs of political-economic and socio-technical arrangements, which also create conditions for data monetisation (Birch 2023). We are particularly interested in user data, i.e. digital data that are logged and collected as an outcome of an individual engaging with a digital platform. User data include, but are not limited to, personal data. Scholars have analysed how user data are imagined to be made valuable in various sectors, such as in healthcare via behavioural nudging (Prainsack 2020), in insurance via personalisation (McFall et al. 2020), or in the application of big data to food and agriculture (Bronson and Knezevic 2016). The literature also highlights the risks and adverse effects of datafication, including surveillance (Zuboff 2019) and various forms of population control and exploitation (Sadowski 2020). In each case, for digital user data to be made useful and valuable, data must be collected, analysed, and processed to produce various digital products and outputs, such as algorithms, analytics (e.g. scores, metrics), automated decisions, or dashboards (Mayer-Schönberger and Cukier 2013).
As the datafication of our economies and societies has expanded in general, so too has it impacted higher education (HE). Datafication refers to the ‘quantification of human life through digital information, very often for economic value’, with important social consequences (Mejias and Couldry 2019: 1). In education, datafication consists of data collection from all processes in educational institutions at all scales and levels, impacting stakeholder practices (Jarke and Breiter 2019). In HE, policymakers attempt to improve university quality, efficiency, and impact via datafication at the sectoral and institutional levels. For example, the UK Higher Education Statistics Agency (HESA) established a Data Futures programme as an infrastructure for datafying the sector and collecting and collating data from universities (Williamson 2018), with an alpha phase launched in 2021–2022. Moreover, Jisc, a HE sectoral agency providing network and IT services, supports universities with various initiatives, such as the Data Maturity Framework launched in 2024, which universities can use as a template to improve data capabilities and datafy their institutions. Digital data, then, is one of the foundational elements of postdigital education because digital technologies that staff and students use every day are increasingly data-based (Jandrić et al. 2024; Jandrić and Knox 2022).
User data in HE are not only valuable for universities and policymakers but also for the EdTech industry. Scholars aligning themselves with the field of critical data and platform studies in education (Decuypere et al. 2021) have already conducted excellent research into various aspects of data practices related to the economic value, such as EdTech’s commercial interest not always sitting well with user privacy (Hillman 2022) and the work needed to produce and manage school data (Selwyn 2020). Specifically in HE, emerging work has found that EdTech companies turn user data into assets they control (Hansen and Komljenovic 2023). EdTech incumbents such as Pearson have evolved into data organisations with intensive mobilisation of data analytics for impacting HE processes and governance (Williamson 2016, 2020). Research has also identified tensions and unintended consequences in relation to data work at universities (Selwyn et al. 2018), pedagogic, cultural, and social effects (Williamson et al. 2020), and the need for universities to pay greater attention to privacy issues and data standards in procurement processes (Ali et al. 2024). Thus, research in this field highlights (1) the relations between EdTech companies and universities as pivotal and (2) the dynamics of the EdTech industry as being highly relevant for the sector.
Data in HE are understood to be valuable in terms of their use, which is mostly the ambition of universities, and in economic terms, which is mostly the concern of the EdTech industry (Komljenovic et al. 2024a, b). In this article, we contribute to the literature by examining strategies employed by EdTech startups to make digital and personal data valuable in HE and the struggles that these startups confront. In other words, we examine the economic dimension of postdigital HE, which is co-constitutive of the socio-material assemblages of digital products and services (Knox 2019; Lupton 2018). Understanding how digital data can be made economically valuable is important because the monetisation of user data is consequential for university practices and the nature of postdigital HE, and because governments and organisations see digital data as the premise of contemporary economies in which HE is embedded. Moreover, we specifically focus on EdTech startups because of the promised transformation and disruption that they seek to achieve in HE (Decuypere et al. 2024; Ramiel 2020). As a result, we can reasonably expect these companies to be leaders of datafication processes.
In what follows, we first elaborate on our conceptual and empirical approach. We then move to discuss the economic construction of data value by EdTech startups and the challenges they confront, before concluding with some reflections on the impact that data monetisation has in HE
