Data Programs Gain Traction on Campuses, but Complexity Remains

With cheaper storage and better collection methods, it’s much easier for higher education institutions to amass mountains of data. Chipping away at that mountain to make sense of the data is another story.

Anywhere from about 40 percent to 60 percent of colleges and universities have a data analytics program in place, but a survey by Ovum reports that many of those initiatives aren’t fully matured. Only 27 percent of universities consider their data analytics programs to be advanced or complete, and more than half (54 percent) are in the planning or early implementation stages.

MORE FROM EDTECH: University leaders discuss how best to use data analytics.

Data Initiatives Drive Improvements in Academic Performance

Despite the promise of Big Data, there are well-known barriers to gaining insights. Campus staff may disagree on how to best use it: for teaching and learning, student retention or operational efficiency? Leadership buy-in and financial concerns also come into play.

Institutions may lack good data hygiene, making it difficult for data from one source to work with data from another. And finally, there’s the human factor: Sometimes stakeholders from different departments may simply not want to share data.

When universities do Big Data right, the results can make a significant difference to students and to institutional success. Learning from and reacting to data as it’s generated allows universities to create timely, campuswide initiatives that can be scaled up or down and continuously measured and refined for even better outcomes. Here are some ways universities are using data analytics to produce actionable results:

MORE FROM EDTECH: See how Kennesaw State University cut failure rates in half through a data-driven program.

Strong Foundations Help Data Programs Succeed

Even institutions that are fully committed to expanding the use of data analytics programs can’t implement them without crucial technical help, such as hardware, software and human resources training. Foundational support can include:

  • Strong, standardized infrastructure: In an article for Higher Education Today, authors Jonathan Gagliardi and Philip Wilkinson note that campuses need both a technical backbone for data analytics and skilled professionals.
  • Intuitive user interface and reporting: In its report “Increasing Insights Across the Institution,” Ovum recommends researching a wide range of data analytics products. Today’s software tools are designed to be user-friendly, making it possible for a broader range of campus staff to be involved in designing and presenting data analysis.
  • Vendor transparency: Authors of the “Predictive Analytics in Higher Education” report emphasize that leaders need to thoroughly understand what they are buying. “To ensure models and algorithms are sound, transparent and free from bias, you must be intimately involved with or knowledgeable about how predictive models and algorithms are built,” they write.

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