College libraries have been natural repositories of data for thousands of years. Now in the Digital Age, libraries have extended their function as the aggregator and curator of digital information.

Libraries at universities around the country are using data to measure the outcome they have on students. It’s not just for pride; the data is quite useful when libraries need to justify their budgets.

Jeffrey Young of tech and education website EdSurge writes:

These days, though, libraries are finding new ways to measure their worth. They’re counting how many times students use electronic library resources or visit in person, and comparing that to how well the students do in their classes and how likely they are to stay in school and earn a degree. And many library leaders are finding a strong correlation, meaning that students who consume more library materials tend to be more successful academically.

“University libraries are having to explain their place in the world of degree completion,” says Alan Bearman, dean of university libraries and the Center for Student Success and Retention at Washburn University. “As we all know, there’s this big push in the nation about on-time degree completion, and libraries are trying to see where they fit in that world.”


Perhaps the biggest change for libraries is creating systems to make library-use data part of the dashboard of options available to administrators across campus.

At Mercer University, for instance, that has meant a partnership between the library, which knows what electronic materials students use, and the technology office, which manages other campus data such as usage of the course-management system. The university is doing a study to see whether library usage there also equates to student success.

Scott Gillies, associate dean of university library at Mercer University, says that having the data at the ready has come in handy when explaining the library’s value on campus.

The movement towards open data from both private and government sources can only accelerate this process. Linked data standards will allow the combination of any number of discrete data sets that would promote insights previously unknown.

Boris Zetterland of the tech solutions provider Axiell Group thinks that library-use data can help libraries manage their resources, benchmark their performance, predict shifts in users’ demands, and personalize the library experience for individual users.

Zetterland adds:

“Big Data is not a new concept, but libraries certainly have some catching up to do. Organisations the world over have been using Big Data to better understand their audiences, tailor make their solutions and services, and deliver them in an effective, personal and timely way. Moreover, the exploitation of Big Data is driving literally £Billions in savings through efficiency gains across every industry that adopts practices that look to embrace it.

“Other industries aside, there is a huge opportunity for libraries; using data ‘mash-ups’ libraries can remove any kind of guess work associated with library strategy. Decisions will be entirely evidence-based, and by virtue libraries can organically become much more streamlined and efficient. They can’t fail to be; efficiency is driven by accuracy, and the data doesn’t lie.”

In his book, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Viktor Mayer- Schonberger writes:

“If millions of electronic medical records reveal that cancer sufferers who take a certain combination of aspirin and orange juice see their disease go into remission, then the exact cause for the improvement in health may be less important than the fact that they lived. Likewise, if we can save money by knowing the best time to buy a plane ticket without understanding the method behind airfare madness, that’s good enough.”

With big data, Mayer-Schonberger suggests that we already have the answers in the dataset and we just have to ask the appropriate questions to find out what is it we want to discover. With the ability of digest the entire dataset (no need for random sampling and its attendant errors), we can make out patterns and derive conclusions without ever knowing precisely why it does what it does.