Predictive Analytics and Financial Aid Offices: A Win-Win

By Chris Chumley, Chief Operating Officer

Chris.ChumleyThe article “Who has ownership of campus analytics,” got me thinking. Predictive analytics has been a buzz word for several years, along with “big data” and “data science.” But gaining insights from historical data through analysis and modeling is really not new.

What is new is the fact that we have access to greater and greater amounts of data, and we have more and more powerful tools analyzing that data. This has opened up the possibilities for embedding analytics into everything we do—including administering financial aid. Just think of how predictive analytics could impact access to financial aid, help reduce borrowing, or cut the costs of administration. But in order for analytics to be useful to financial aid, the administrators on the front lines need to be more involved in identifying the right questions that need to be asked.

What Are Predictive Analytics?

Institutions have always (generally) had institutional research departments that trended, classified, and clustered data. The goal of predictive analytics is to use data to identify the probability of events occurring in the future, and to put those probabilities in the hands of staff who can make that information actionable. Making data actionable means it needs to be fast, easy to understand, and easy to access.

Why Ownership of Analytics Is Shifting

More and more, business users other than IT-based are being tapped to lead analytics projects. It’s a move that makes sense: the individuals within the business—those on the frontlines—are best-positioned to understand how relevant any piece of data is to the business. An IT team can absolutely help extract, qualify, and validate data. But when it comes to using that data to identify future probabilities, when it comes to whether the data extracted is relevant or simply noise, that’s where the business unit needs to be involved.

What Role Should FinAid Offices Play?

FinAid professionals are uniquely qualified to take advantage of analytics—and to use predictive analytics to drive success across an institution. Great student outcomes require that students with financial need have access to aid. FinAid offices deal with a lot of data, mountains of it, in fact. Buried in that data are insights that fall within a variety of categories, like these ones pulled from our CampusMetrics Financial Aid Insights Kit:

  • ISIR Volume: Insights for comparing and trending the number of ISIRs received over time periods, geographies, and/or types of students.
  • Verification: Insights regarding data relevant to the verification selection process including volumes, trends, groupings, and background.
  • Pell Information: Insights related to Pell Grant information including Pell projections, eligibility, and recipient profiles.
  • Loan Information: Insights from loan-related data, eligibility, and recipient profiles.
  • IRS: Insights from data related to income and IRS processing flags.
  • Demographics: Insights from student population and profile data.

Are you owning providing context for FinAid analytics? If not, you should be.

Tips for researching a FinAid data solution >

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