Smaller institutions have less access to resources and support for analytics. Majorities of respondents said their institution offers access to analytics tools/software (77%), technical support (59%), training and professional development opportunities (54%), and access to policies and/or guidelines for data collection, storage, and ethical use (51%). Only 30% overall said they have access to communities of practice. Notably, access to these resources and support varies by institution size. Respondents from smaller institutions (student enrollments under 5,000) reported having less access to all resources/forms of support compared to institutions with enrollments larger than 5,000 (see figure 11). Interestingly, with the exception of access to communities of practice, institutions with more than 20,000 students were similar to those with enrollments between 5,000 and 19,999.

Smaller institutions have fewer technologies to support analytics. Overall, respondents indicated that the technologies most commonly available to support analytics at their institution included learning management systems (LMSs) that have analytics capabilities integrated (75%), BI tools (e.g., dashboard-building platforms such as Tableau, Power BI) (73%), and statistical analysis software (e.g., SPSS, SAS, R) (69%). Similar to the previous findings, respondents from smaller institutions reported having fewer technologies available to support analytics at their institutions (see figure 12). Despite having fewer resources and support, smaller institutions are just as engaged in using analytics as larger ones—reporting comparable levels of usage for each of the analytics types. This suggests that smaller institutions are making the most of what they have, demonstrating efficiency and a strong commitment to leveraging analytics, even with limited resources.

Only some institutions are making use of newly integrated AI capabilities. Approximately one-third or less of respondents indicated that their institution is making use of AI capabilities that have recently been integrated into existing analytics technologies (see figure 13). The most common uses of new AI capabilities were those integrated with big data platforms (34%), machine learning tools (33%), and cloud computing (30%).