Why data governance matters for university strategy—and why most data governance projects fail
Getting the most out of your data begins with good governance
January 15, 2020
Colleges need consistent, reliable data to make strategic decisions that support student success and the institution’s financial sustainability. Yet leaders struggle to make data-informed decisions, partly because of poor data governance.
At its core, data governance is about making sense of data that is produced on campus and ensuring that its meaning is consistent as it moves around the organization. This has become an increasingly difficult technical and cultural task, given the expanding ecosystem of technologies on college campuses and the growing number of people relying on data to support strategic decision-making.
Most colleges and universities manage data in a way that confines its value to operational siloes, prioritizing focus on department-level needs over institutional decision-making. As schools attempt to implement analytics initiatives, they’re faced with several challenges—including inconsistent data definitions, poor data collection, and suboptimal systems architecture.
Historically, units have had their own way of defining broadly applicable terms, such as “student” or “section fill rate.” While these definitions may fit a group’s specific need, they don’t consider the broader need for consistency across campus. How do you make strategic decisions about student success when every department defines “student” differently?
Key challenges to successful data governance
Data definitions
- Varying definitions specific to each unit
- Data definitions for internal eyes only
- Staff only involved with data in their unit
Challenges:
- Multiple different definitions of “student” between departments
- Data definitions not publicly accessible or hidden unintentionally
Data collection
- Data used for single unit purposes and value
- Placeholder data used for convenience of unit
- Data quality assumed and unverified by institution
Challenges:
- Workarounds use open fields to record advisor names
- Low adoption of central data and reporting tools, leading to data denial
Data systems
- Static system aligned to business unit
- Inconsistencies among system implementation
- Siloed suboptimal shadow systems
Challenges:
- Excel spreadsheets stored on local desktops of analysts
- Data errors only corrected in frozen data, not in source system
Despite their importance, data governance initiatives can easily lose momentum. The best-laid plans to “tackle” data issues are often overwhelmed by the magnitude of the project. For instance, a majority of Chief Information Officers interviewed by EAB reported that their aspirations to resolve data quality and access issues resulted in short-term enthusiasm and action, followed by a swift deceleration of all progress and ultimately a collapse of the project.
Facing projected enrollment declines, many institutions want to harness the collective power of campus-wide data to inform strategic decisions that achieve results. Getting the most out of your data begins with good governance, laying the groundwork needed to manage university data as it grows in volume and complexity.
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A majority of CIOs reported difficulties in completing data governance initiatives
Institutions need a solution that scales IT resources and helps hardwire data governance decisions while being flexible enough to adapt to the evolving architecture of the university technology ecosystem. Though not common in higher education, one successful solution used widely in other industries is a Data Management Platform (DMP). To learn more about how DMPs can help universities make data-driven strategic decisions, read our blog post here.
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