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Data

Robert Miller is vice president for enrollment management of Centenary University.

Ubiquitous in the business world, big data is being adopted by higher education, particularly in the area of recruitment. With stagnant or declining recruitment budgets and increased competition for students, leveraging the data most colleges capture is a cost-effective approach that can yield significant results.

Clockwise from top left: Lisa Daniels (Excelsior College), Vince Kellen (University of Kentucky), Thomas Blum (Sarah Lawrence College) and Elaine Lewis (Washburn University).

To get a picture of who is responsible for predictive analytics on campuses and what their jobs look like, University Business interviewed four campus “data czars” to learn more about their work, how it impacts their institutions and how they make it all happen.

6/21/2016

Institutional research is crucial to providing college and university leadership with the data required to make the most informed strategic decisions, across a broad range of areas including admissions, academics, finance, enrollment, retention, staffing, facilities and more. In order for this research to be effective, however, it is also important to have a strategy and process to translate the data gathered into actionable information.

4/21/2016

Why do students persist? Although there are some commonalities, the answer is different at every institution. Predictive modeling seeks to answer the question of why students persist by discovering hidden relationships in data. By leveraging a clear picture of past and present behavior, predictive modeling uses statistical analysis to generate a confident simulation of future behavior. Higher education institutions can then use that insight to positively impact student trajectories and influence outcomes.

3/22/2016

The Payment Card Industry Data Security Standard (PCI DSS) was developed to encourage and enhance cardholder data security and facilitate the broad adoption of consistent data security measures globally. Version 3.2 is being released now and includes a number of updates.

3/29/2016

When the Office of Undergraduate Admissions at Clark University in Massachusetts experienced an 87 percent increase in application volume between 2012 and 2015, administrators recognized that they needed to overhaul their yield forecasting and admitted student engagement efforts. They knew that personalization would be key to achieving their enrollment goals, but it would only be possible in the context of such an enlarged applicant pool if they could effectively target the right students.

Artificial intelligence has come out of research labs and onto college and university campuses to aid students and faculty. It remains in the very early stages of making education more effective, accessible and affordable—but it’s beginning to transform learning environments and campus services.

Colleges and universities are under intense pressure to maintain enrollment, increase student retention and ensure student success. Predictive analytics can play a crucial role in these efforts at institutions of any size, by providing actionable data that can drive more effective strategic decision-making.

Colleges and universities are under intense pressure to boost retention and completion rates, and national research has shown that students who are highly engaged on campus are more apt to graduate. By using student data and predictive analytics effectively, institutions can improve retention rates by identifying students needing early intervention and proactively helping them succeed.

In just three years, enrollment at Lone Star Community College grew by about 50 percent. The six-campus system, located in the north Houston metro area, now has more than 95,000 students and has experienced explosive data growth, as well—from 40 terabytes to 1.6 petabytes.

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