The university’s Data Issue Management Process enables the identification, intake, examination, prioritization, tracking and resolution of data issues. 

The following examples illustrate the types of data issues that fall within the scope of the Data Issue Management Process. This list is not exhaustive, but it demonstrates the types of issues most likely to be addressed. Issues will be escalated through the Data Issue Management Process to the appropriate Data Governance entity depending on whether the scope crosses domains or exceeds the authority of a single role.

Potential issues within their purview include:

• Conflicts in decision rights or authority (e.g., overlap or gaps in stewardship of University Data);
• Questions about Data Classification (e.g., whether data should be designated as Not Sensitive (green), Moderately Sensitive (yellow), Highly Sensitive (red) or Ultra-sensitive (purple);
• Conflicts in interpretation and application of policies, procedures, standards or business rules affecting the use, access or management of University Data;
• Compliance questions and issues with laws, regulations, university PRRs, policies, standards and procedures;
• Persistent data integrity issues;
• Access control inconsistencies or conflicts (e.g., access not aligning with steward-approved guidelines);
• Implementation disputes over business processes that affect the confidentiality, integrity or availability of University Data;
• Disagreements over data definitions or standards that affect reports, analytics or decision making.

Exclusions:

The following types of issues fall outside the scope of the Data Issue Management Process and should not be submitted. These issues are expected to be resolved through existing operational, technical or compliance channels.

• Data validation issues: Record-level accuracy, formatting or completeness of data entries (e.g., missing values in a form, typos or incorrect codes).