Strategic Analysis and Forecasting Solutions: Enrollment & Retention Intelligence Platform

Ruffalo Noel Levitz (RNL) tracks complex datasets across the student lifecycle, spanning enrollment (recruitment funnels, financial aid discount modeling) and retention (student success, drop-out risk warning). Although the backend data pipelines processed data via Azure Data Factory and Databricks, the consumption layer was highly fragmented. Individual university analysts were extracting files into Excel to manually clean, join, and compose reports. Scaling this operational flow to 110+ distinct university clients was labor-intensive, slow, and highly insecure without dynamic data isolation.

We designed a robust semantic modeling and reporting engine. First, we engineered a performant SQL Star Schema with conformed dimensions (Terms, Demographics, Majors) and fact tables (Admissions, Financial Aid). We applied advanced Power Query (M) transformations and leveraged Microsoft Fabric to automate scheduling. Using DAX Studio and Tabular Editor, we optimized VertiPaq engine caching, removing high-cardinality metadata and replacing bidirectional filters with direction-controlled constraints. We implemented a dynamic dynamic Row-Level Security (RLS) registry mapping user AD credentials to specific institution keys, maintaining a single central template for all clients.