Data analytics has been a hot topic in higher education for many years, but there always seem to be challenges to navigate when implementing a data analytics program. Thinking through and addressing these barriers can help drive insights into both financial and operational metrics for institutions to help make data-informed decisions.
Barrier – Silos Exist, Causing Data Quality Concerns
The complexity of the university organizational structure creates separation between administration, faculty, staff, and even students. The university is set up in divisions, colleges, and other groups, which can impact the centralization and consistency of processes, policies, and data collection across campus. Finding pockets of standardization, collaboration opportunities, and shared services or centralization is essential to gaining enhanced perspective on effective policies, procedures, and stories as told by data analysis.
Silos can contribute to issues with data quality, which feeds the concept of garbage in, garbage out. The quality of the analytics is driven heavily by the caliber of data used as an input. Connecting or removing silos can help increase data quality and reduce the risk posed by garbage in, garbage out.
Barrier – Budget Limitations
According to the 2024 Annual Higher Education Outlook from FORVIS, in 2022, public colleges and universities obtained an increase in state appropriations for the first time since the Great Recession. The appropriations increased nearly 5% beyond inflation. However, it may take time for the overall budget to feel the positive impact of the increase.
Many universities—especially public universities—have suffered from budget constraints for several years, which means that one year of an increase may not cover the needs they have accumulated over time. The prioritization of hiring the right staff and procuring a data analysis software tool may continue to occupy the “back burner” while other tasks and needs take precedence.
Barrier – Mismatched Skill Sets
Whether using Microsoft Excel or some other data analysis tool, employees experience a learning curve related to what inputs are needed, how scripts are designed, and how results are interpreted. Current staffing may not have the skills needed to incorporate data analysis into strategy and goal-setting conversations or decision-making efforts. Understanding the story that data can tell the reader and articulating the story to others may be a missing skill for university employees.
How FORVIS Can Help
FORVIS brings a history of serving higher education institutions to help solve complex problems and assist in reducing some of the barriers associated with starting a data analytics program. Our higher education knowledge can be paired with the knowledge from our Analytics team to help implement data analytics tools.
If you have any questions or need assistance, please reach out to a professional at FORVIS.