Among the industries that require high-quality data for the improved efficiency of their processes, higher education is one of the more prominent ones. However, some of the more automated data management practices haven’t caught on as they have in other industries, making it more prone to data quality issues.
As data analysis is getting more complex and requiring more and more data, higher education needs to work on fixing issues that are frequently seen. Let’s have a closer look at the most common data quality issues in higher education to get a better idea of where improvements can be made:
Another frequently seen issue, according to Experian, is outdated information. If a student changes their contact information, and this change is not reflected in the higher education’s system, problems arise.
For example, Experian reports that 70% of higher education institutions frequently experience e-mail deliverability issues. This issue is also not that hard to remedy, either with timely updates or by automating the process of data management.
Inconsistencies when Documenting Maximum Course Capacity
Higher education institutions also tend to have difficulties establishing their course enrollment caps, which leads to inaccuracies in their fill-rate analysis. This process is essential for determining whether or not a course covers an excessive amount of material.
Without the ability to get accurate results from the fill-rate analysis, institutions might end up wasting resources on a course that’s just too big. On the other hand, they might make it harder for students to graduate if the course does not cover as much as it should.
Tracking Tenure and Rank Information
When it comes to internal processes in higher education institutions, there are frequent data quality issues regarding tenure and rank information of the instructors. This makes it difficult to perform many analyses that help run faculty operations smoothly.
For example, higher education institutions that don’t track tenure or have incorrect information regarding tenure and rank can’t calculate faculty headcount accurately. It also prevents them from taking a critical look at instructors’ workloads to see whether there are areas where they should make changes.
Improving Data Quality for Higher Education
The quality of data that a higher education institution works with is essential to keeping both internal and external processes running efficiently. Runner EDQ’s software integration solutions provide enterprise data quality. Higher education institutions can keep their student and alumni data automatically updated, as well as keep track of faculty employee data.