Abstract: Prior research highlights the growing importance of location-based data for business decision-making, while also documenting its limited integration into information systems curricula. Although students are routinely exposed to data analytics techniques, they rarely gain hands-on experience preparing and enriching datasets with location-based data. This teaching tip addresses that gap with a modular, instructor-ready exercise that introduces geospatial analytics through the data preparation process. Using a publicly available university innovation dataset derived from the Association of University Technology Managers (AUTM) survey data, enriched with geospatial data, the exercise guides students through a structured workflow that includes data cleaning, identification of location attributes, dataset enrichment using external sources, and transformation from panel to cross-sectional formats. The emphasis is on practical data-preparation decisions—such as handling missing values, resolving inconsistencies across datasets, selecting appropriate units of analysis, and integrating location-based variables—rather than on specialized geospatial functionality. Designed for use in information systems and analytics courses, the exercise supports core IS competencies in data management, analytics, and decision support while remaining adaptable across commonly used tools. By focusing on geospatial data preparation as an entry point, the teaching tip lowers the adoption barrier for instructors and provides students with foundational skills to incorporate location into business analysis.

Keywords: Geospatial analytics, Location analytics, Data cleansing, Geographic information system (GIS), Generative AI in education, University innovation

Download This Article: JISE2026v37n3pp360-396.pdf

Recommended Citation: Díaz López, A., Mamunuru, S., & Cazier, J. (2026). Teaching Tip: A Data Preparation and Enrichment Exercise for Location-Aware Analysis of University Innovation. Journal of Information Systems Education, 37(3), 360-396. https://doi.org/10.62273/AWXK7246