Speaker 1: To effectively manage student enrollment, it is important to establish a centralized data hub that accurately reflects each student's status within your student information system. This requires creating specific definitions for various situations reflected on a student's profile within your SIS. Older institutions may face challenges in implementing clear definitions due to the creation of too many specific statuses using a wide variety of additional program action and action reasons. However, a defined list of student statuses can help create structured applicant conversion rates, projected retention rates, and graduation rates. This can be further analyzed using Tier 1, Tier 2, and Tier 3 analytics. Tier 1 analytics could be the discussed projected retention rates, conversion rates, or graduation rates. Tier 2 can become country-specific or agency-specific. Tier 3, we can get into biographical data-specific inquiries, touching on marginalized groups, indigenous student groups, or a student group who was taught by one specific professor. Data can be exported from the SIS and stored or analyzed using tools such as Excel, Power BI, or Datamart. It is also important to conduct secondary research methods in the qualitative sense, such as conducting surveys to triangulate results for a better understanding of issues at your institution. Once core quantitative data is acquired through the SIS definitions, it should be shared between necessary departments to ensure everyone is operating on the same projections and conclusions. This can help increase transparency between recruitment goals, admission capacities, and academic staffing considerations, which leads to better outcomes for all stakeholders involved.