Historically, collaboration within and between academic institutions has been thwarted by a lack of a standardized data representation, even though most universities operate with very similar data needs. This makes tasks like integrating with generative artificial intelligence and constructing new software tools extremely difficult for university IT personnel due to the immense learning curve associated with each university’s unique data warehousing techniques. As a result, students are often deprived of the ability to extract useful insights/analysis from data the university makes available to them for the purposes of scheduling, planning, or understanding their academic journey. This paper proposes a standardized data picture to ontologize academic data in a platform-neutral way that accommodates the most common needs of universities. In doing so, we also present data specification design as an important steppingstone in paving the way for smoother data integration with modern technologies (like AI-assisted data analysis) both in the academic domain and beyond.