Leaders across the institution must invest in data governance efforts and establish a baseline understanding of the important role of quality data in campus operations and innovation. First, educate stakeholders about the avoidable costs and risks that bad data governance perpetuates, then focus on high-performing peers and best practices to build consensus for action.
Review the data governance process overview to get started.
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.
Most institutions have some data governance processes in place, but each university or college varies in its areas of strength and weakness. We identified hallmarks of high-performing institutions, noting tight interdependency between some areas of maturity. Fill out the brief diagnostic to find your institution’s areas for focus.
If you’d like to understand multiple perspectives on your current data governance maturity, our maturity self-test is available as an administered survey of select campus community members.
The structure of data governance oversight is the single most important factor in success. When designing your capability oversight structures, aim to create responsive, agile workflows able to withstand campus change—whether from strategic direction shifts, leadership transitions, or technology and asset overhauls.
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.
When building out an institution-wide data dictionary, leaders should first assess campus strategic priorities and tackle high-use data. Focus on high-priority data early in the process to demonstrate early value and secure lasting resources for ongoing data governance efforts.
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.
Finally, leaders should ensure that clean, appropriate enterprise data is made available to the right decision-makers. To create a seamless, responsive delivery mechanism data governance groups must provide reliable data access, monitor data sources for quality, and review emerging needs to guide future data strategy.
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.
