Establishing sustainable, broad-reaching data governance is a campus-wide challenge that requires executive support and dedicated resources in perpetuity. Bad data management means increased costs, widespread risks, and poor return on investment from analytics initiatives.
Conversely, strong data governance builds a shared language for understanding and evaluating campus problems and provides a reliable foundation for institutional problem solving. Download the plug-and-play executive presentation to educate your campus about data governance.
Due to the magnitude of an institutional data governance effort, many colleges and universities find that their best-laid plans collapse under their own ambition. Single-committee structures become paralyzed by competing priorities, and poor investment in organizational continuity often leads to disbandment. Instead, establish bicameral committees to separate strategy setting from implementation workflows.
To ensure success, select the appropriate staff for the right work, establish leadership and organizational continuity practices, and determine data stewardship accountability models.
Creating data dictionaries that support widespread use and understanding across campus requires thorough definitions, accessible and jargon-free metadata, and a user-friendly format.
Strategy committees should isolate data domains for working groups to address. Then, working groups should use role-based decision frameworks and opt-out opportunities to speed consensus when writing enterprise definitions. Finally, groups should incorporate user-centric design principles when creating a data dictionary. Create a searchable, central repository that includes intelligible metadata and groups like-terms for ease of navigation.
Data governance oversight groups can remediate data quality issues while expanding data availability with two key steps: provide principled data access to all members of campus, then monitor usage and quality.
Leaders should apply data sensitivity frameworks to segment institutional data and provide role-based access to appropriate terms through automated single sign on. Error checking should also be automated, with units held accountable for remediating poor data entry or capture mechanisms. Finally, consult heavy data users to determine areas of insufficient data provision and opportunities for expanding available data through future governance efforts.