Although big data applications in education have different usage areas, they can make important contributions to the evaluation of universities and education systems, decision-making and strategy development, and improvement of the education system. In this study, which is considered to contribute to the academicians who will work on big data in higher education, the subject of big data in higher education has been examined with the bibliometric analysis method. The aim of this study is to reveal the intellectual structure of the studies in the literature on big data in higher education and its development over time. The research method of the study is based on the bibliometric analysis technique, which enables the evaluation and interpretation of the data obtained by examining the literature with different parameters. The research data consists of 477 articles scanned in Scopus and Web Of Science databases published between 2014 and 2023. The articles examined in the study were analyzed under the titles of authors, keywords, the density of the words used, and the journals that published. R Bibliometric analysis program RStudio interface and Biblioshiny package were used for data analysis. The findings of the study are expected to provide guiding information to researchers in the field of big data in higher education. As a result of the study, especially in recent years, educational big data, online learning, industry 4.0 themes have emerged as current subject areas that can be studied. It has been concluded that the big data theme has changed as an online learning, moocs, educational big data theme in recent years. It was concluded that the researchers who published and cited the most in this field were Siemens, Daniel, Chen, Wang and Zhang.