How Big Data Analytics is Transforming Decision-Making in Higher Ed

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In our last post, we discussed the growing number of government and industry regulations that affect colleges and universities. Many institutions are spending millions of dollars annually to meet regulatory compliance requirements — money that’s diverted from educational, research and financial aid programs.

These regulations run the gamut from institutional accreditation to student loan verification to the reporting of crimes on campus. Many require that colleges and universities take steps to ensure the security and privacy of student data — education records, health records and financial records, to name a few.

Regulatory requirements are creating a conundrum as institutions of higher education seek to leverage the data they’ve collected to improve decision-making and streamline their operations. The concept of “big data” arose in recent years to describe the vast amounts of data that organizations are looking to mine and analyze, as well as advanced tools and techniques for manipulating massive datasets.

Colleges and universities have always been awash in data, but it traditionally has been disjointed. For example, course registrations and grade records had no relationship to financial aid information or student profiles, making it difficult to measure and assess the success rates of at-risk student populations. Reports were static snapshots of historical data at a particular point in time.

Today, real-time analyses of diverse datasets can be performed with just a few mouse clicks. As a result, a growing number Institutions are using big data analytics to drive a wide range of initiatives across every aspect of campus life. For example:

  • Student recruitment efforts can be fine-tuned by creating profiles of applicants and using social media to target potential candidates and meet diversity goals.
  • Academic advising processes are enhanced by information from a variety of sources, including high school records, aptitude test scores and student interests as well as external data related to job opportunities and earnings potential.
  • By monitoring not only educational performance but student behaviors, schools can recommend supplemental materials, tutoring or even course changes to help increase student success rates.
  • Retail analytics and inventory management tools can help boost the profitability of campus-based and online stores.

Of course, “garbage in, garbage out,” as the saying goes. Inaccurate data can lead to poor decisions, limiting the return on investment in analytics tools. In order for big data analytics to be successful, colleges and universities must have an effective means of entering, validating and correlating data. Institutions that have launched data-based initiatives also stress the importance of goal-setting and collaboration across departmental and hierarchical boundaries.

Ironically, regulatory compliance is partly responsible for the rise of big data analytics in higher ed. Increased reporting requirements brought an added incentive for schools to adopt centralized student information systems linking disparate data sources. It will be interesting to see how regulatory requirements evolve to address big data analytics. Will big data be viewed as a beneficial tool that enables colleges and universities to deliver better outcomes more efficiently? Or will security and privacy concerns hamper data-driven initiatives?