Monetising Digital Data in Higher Education: Analysing the Strategies and Struggles of EdTech Startups

Abstract Digital data are a building block of postdigital higher education and, as such, are believed to be economically and socially valuable. However, data need to be made valuable via a complex set of political-economic and socio-technical arrangements. While universities and policymakers aim to derive social benefits from digital data, we turn our attention to the economic value of digital data in the EdTech industry. In this article, we analyse the strategies and struggles of EdTech startup companies as they seek to monetise the user data they collect. Startups experiment with generating value by datafying their products, developing ever new data outputs and analytics, controlling data for matching services, building large datasets via company acquisitions, and developing data products as a service. However, they face important generic and sector-specific challenges that include high costs, building large datasets and managing sophisticated data processes, convincing customers to pay, demonstrating use-value for universities, lack of transparency of the premises that underpin product operations and impact, and managing investor relations. Navigating the experimental construction of value from data while managing these challenges creates many unknowns for the sector.