The increasing availability of digital data on inputs and outputs in scholarly activities is bound to have powerful impact on academic career research: it allows to measure the academic profession with ever more precision. The pressure to quantify more intensively the research on academic careers is easily understandable in the context of the declining attractiveness of small-scale academic surveys in generally increasingly data-sensitive global academic environment. Higher education research faces huge opportunities if only the field understands how globalization-related opportunities are already used by its competitors for scholarly and policy attention.

Digital data on research funding and its types, productivity by publication type and journal profile, collaboration by its major types, citations, and the various categories of academic mobility – can be explored today at a scale unimaginable in higher education research a decade ago. The point is, however, not to lose its identity which makes the field distinct. The unprecedented opportunities to study science and scientists and to explore the structure and evolution of science (Fortunato et al. 2018) come at a cost to higher education research. New competition to study science, scientists and their institutional settings emerges between the various fields and disciplines, and higher education research becomes one among many. Equipped with mostly traditional tools and generally staying away from big data (and especially from large commercial bibliometric datasets like Scopus and the Web of Science), higher education research may be losing the competition about where data, interpretation and knowledge of the academic sector will be located in the future.

Specifically, the traditional issues examined in higher education research (such as collaboration, publishing, productivity, and mobility patterns) are being increasingly studied in scientometrics and quantitative studies of science and related fields (see e.g. Fortunato et al. 2018; Wang and Barabàsi 2021; Clauset, Larremore and Sinatra 2017; Zeng et al. 2017; Ioannidis 2018; Glänzel, Moed, Schmoch & Thelwall 2020). Social science in general enters a golden age and scientists involved in the above fields use available big data and computational power and skills – as part of the big data revolution in social science research. Studies in academic profession has been focused on similar issues for decades and the field needs to be well aware of what competing fields offer today to academic and policy communities.

The keyword for higher education research in the future is thus complementarity: in the case of academic profession studies, big data can accompany surveys and interviews; and macro-level studies can accompany micro-level studies. I will assess the advantages and disadvantages of traditional vs. new data-intensive approaches based on current global research.