Higher Ed’s Data Rush: Why Educators Are Rethinking Their Approach to Analytics

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By Andy MacIsaac, Director Of Industry Solutions For Higher Education And Public sector, Alteryx

The pandemic’s financial impact on U.S. higher education institutions is now reported to have surpassed a devastating $120 billion according to the American Council on Education. Still, some experts hold that many of the changes COVID-19 has imposed are long overdue. The sudden shift to online and hybrid learning is giving higher education leaders a surge of new data on each course and on the interactions between students and faculty – data that can yield the very insights they need to bolster both academic and financial performance. With tighter budgets, declining enrollment, quick pivots and new student behaviors spurred by the global pandemic, colleges and universities are feeling the pressure to make sense of it all – and to make big decisions faster than ever.

As the data sources and complexities multiply, educators are realizing that old approaches to problem solving and insights (e.g. spreadsheets and relying on a limited pool of data gurus) aren’t up to the task. Are we offering the right courses? How are students doing? What does the engagement data tell us? What changes should we make next year?

Increasingly, higher education institutions are modernizing their analytics to answer these questions and consciously democratizing data so that a broader array of staff can model and understand what actions will have the greatest impact. Technology is only part it. The bigger shift is in the cultural mindset, and the discovery aspects of what happens when more people have direct, self-service access to the insights they need, when they need them. No Ph.D. in coding or statistics required.

Improving Recruitment Outcomes & Marketing Yield

This year’s declining enrollment numbers mean that competition for prospective students is currently at an all-time high. If colleges and universities are not hyper targeted in their marketing and recruitment efforts, they risk missing out on good students who would thrive at their institutions. Indiana University Online found that trying to analyze prospective applicants using disparate, manual-based processes built on spreadsheets took several hours per applicant, significantly prolonging the process. But when the collection of applicant data was automated, the university accelerated its prospect-to-applicant analytic process from five hours to five minutes. Creating repeatable, automated analytic workflows helped hone recruitment targeting and improve marketing yield.

Being able to blend, process and analyze thousands of student data points from dozens of different systems allows institutions to extract and use data insights to optimize the admissions and enrollment process while improving engagement with prospective students. A similar strategy that brought self-service analytics to admissions staff helped Deakin University in Australia create an always-on marketing approach, netting a 21 percent conversion rate improvement.

These kinds of systems can also help universities more accurately project student behaviors and academic outcomes, ultimately improving the student experience, increasing retention rates, and ensuring students gain the skills they need for today’s workforce.

Increasing Student Retention

With the effort universities put into recruiting the best students, it makes sense that retaining those students should be top-of-mind. This is especially true for schools with selective admissions, such as the University of Dayton, which has a 14 to 1 student-to-faculty ratio and a 57 percent applicant acceptance rate. The university understood that having a better grasp of student needs and preferences would be a key factor in increasing retention. Like Indiana University Online, the University of Dayton needed to bring together thousands of data points from disparate systems. By doing just this, the University of Dayton was able to utilize strategic insights about student behaviors and outcomes and optimize the admissions and enrollment process, ultimately improving retention by three percent.

Forecasting A Sustainable Future

The University of Nottingham, one of the UK’s largest research institutions with 45,000 students from around the world, recently sought to build a robust model for student number planning and income forecasting. With variables such as how many students would enroll, how many would return and what they would study, the university automated its analytics workflow to handle these complexities.

Encompassing 10 years of historical student data, the university’s new analytics process started producing actionable insights in just two weeks. These sorts of operational efficiencies save staff a significant amount of time, allowing institutions to refocus on adding value to student services.

From admissions operations to alumni programs, automated analytics and self-service insights are becoming the new gold standard for better, faster decision making and creating new value for students. As the industry continues to digitize and transform, educators have an opportunity to rethink how their data, processes and people can converge in powerful new ways to fulfill their missions and emerge even stronger.

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