Graduate Program Insights Dashboard

Overview

A Power BI reporting workspace that unifies student inquiry, enrollment, program, and operational datasets into a single, coordinator-facing view of graduate program performance.

Business problem

Program coordinators tracked enrollment, inquiries, and operational KPIs in separate spreadsheets, which made institutional reporting slow, inconsistent, and error prone. Leadership needed one trusted view of program performance and analysts wanted to spend less time on manual roll-ups.

Responsibilities

• Analyzed student inquiry, enrollment, program, and operational datasets to identify trends
• Developed Power BI dashboards and automated Excel reporting workflows
• Cleaned, standardized, and validated data from multiple sources to improve reliability
• Defined KPIs, categorization rules, data-quality checks, and reporting documentation
• Translated stakeholder requirements into dashboards and analytical solutions
• Presented findings through reports, visualizations, and stakeholder summaries

Dataset

Approximately 5,000 anonymized institutional records covering graduate program inquiries, applications, enrollment, and operational activity across 20+ programs and multiple terms.

Approach

Audited existing spreadsheets, defined a shared schema with program owners, and used SQL and Power Query to clean and normalize the historical data. Modeled it in Power BI with clear measures for inquiries, yield, enrollment, and retention, then layered filters for department, term, and student type.

Solution

A Power BI workspace with a program overview page, a term-over-term trend page, and a drill-down page for individual programs. Refreshes are automated and data-quality checks are embedded in the pipeline.

Results

Coordinators self-serve most questions in meetings, weekly reporting effort dropped from about a day to under two hours, and the analytics team now focuses on deeper questions instead of data wrangling.

Challenges

Reconciling inconsistent field names, program codes, and manual categorizations across owners took multiple review rounds before the model could be trusted.

Limitations

The dashboard reflects the cadence and completeness of upstream data entry, so late updates and manual entries can still delay visibility into the newest term.

Lessons learned

Agreeing on shared definitions and KPI logic up front was more valuable than any visual choice; adoption followed the moment the numbers were trustworthy.

Future improvements

Add row-level security per department, layer in predictive enrollment forecasting, and extend the data model to cover retention and outcomes across cohorts.