A research-intensive university 

INFORMATION, DATA AND AI GOVERNANCE 

Student and curriculum data governance 

The requirement. Substantial investment in new platforms and a central data lake had not resolved underlying weaknesses in governance, ownership and data quality. The university wanted an independent assessment of where the risk actually sat. 

The approach. A strategic review and risk assessment combining document analysis, benchmarking against institutions with mature integrated analytics, and extensive stakeholder interviews. The review mapped governance structures and accountability gaps, assessed data quality risk across the lifecycle, and examined system architecture and integration points. 

The outcome. Eight core risks identified across governance, quality, reporting and culture, with a risk-based mitigation strategy: a university-wide governance model with defined stewardship roles and a data council able to enforce standards, a data quality roadmap using entry controls and exception reporting, rationalisation of the system estate, automation of statutory returns, and role redesign to remove single points of failure.