The Challenge
Following a Student Information System migration, a mid-sized community college faced persistent data reliability issues driven by legacy data, inconsistent definitions, and fragmented reporting practices.
These gaps led to discrepancies in accreditation reporting, increased manual effort, and growing compliance risk. The institution lacked a structured framework for managing and monitoring data quality at scale.
The Approach
A structured data quality program was implemented using an AI-powered platform.
The engagement focused on standardizing critical data elements, establishing consistent definitions, and enabling continuous monitoring through automated quality rules. A governance foundation was also introduced to ensure accountability and long-term sustainability.
The Outcome
Within six months, the institution established a scalable data quality capability.
- Improved reliability of accreditation reporting
- Reduced manual reconciliation efforts
- Proactive identification of data issues
- Foundation for data governance and stewardship
