A university with student records spread across a 20-year-old SIS, a separate LMS, and multiple departmental Access databases needed a unified data model. AI-assisted schema mapping made it possible in 90 days.
The university had student records in a legacy SIS (20 years old), a separate LMS, and 14 departmental Microsoft Access databases built over the years. No two systems used the same student ID format, and institution-wide reporting was impossible. Accreditation reviews required data that simply could not be assembled without weeks of manual effort.
Used Azure Purview to automatically scan and catalog the SIS, LMS, and all 14 Access databases — generating a unified data dictionary and identifying overlapping student fields across systems.
Designed a normalized SQL Server data model with a central Student entity and related tables for Enrollment, Courses, Grades, Financial Aid, and Engagement — accommodating all source system variations.
Used Azure Machine Learning-assisted schema mapping to automatically suggest source-to-target field mappings across all 16 source systems, reducing manual mapping effort by an estimated 70%.
Converted all 14 Access databases to SQL Server using SSMA (SQL Server Migration Assistant) with AI-assisted query translation, preserving all historical data and relationships.
Created a Power BI semantic model on top of the unified student data model, enabling institution-wide enrollment, retention, and academic performance reporting for the first time.
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