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Big Data Brings Big Changes to Higher Ed

The term “big data” is thrown around a lot. We even use it when we talk about ReachBright and its power to make big data manageable. But what exactly is “big data” and why is it so important for colleges and universities to collect, understand, and make use of?

Defining Big Data

A firm definition of “big data” is hard to come by.

Even Forbes struggles to come to a definition. Is big data something to worry about? Or is big data simply old data with a new name? Can it be controlled? Or is it nothing more than chaos contained in a database, threatening to break free in an unmanageable flurry?

Oxford English Dictionary attempts to come to a consensus by defining big data as data so large, it presents “logistical challenges.” That’s because big data is being shared every second. As technology has modernized, data has expanded. According to the Harvard Business Review, more data is passed across the internet per second than what was stored in the entire internet just 20 years ago. Every time you open an email, click on a Facebook link, sign up for a newsletter, or download a song, you’re sharing data.

Hence, “big data” really is just data. It’s simultaneously so large, yet so detailed, that it helps us identify patterns, run reports, and make important decisions. It helps businesses market their products to targeted audiences. It helps companies identify demographics. It helps political groups conduct polls. It even helps sports teams predict upcoming seasons.

Every industry benefits from big data, but perhaps no industry relies on it more to meet its evolving landscape than the higher ed industry. Big data can bring big changes to higher ed. But how?

Enrolling the Perfect Fit

Some schools are using big data to predict whether prospective students are a good match for their campus and whether current students have the tools they need to graduate. Retention rates at schools around the country are kept low by the fact that about 1 in 3 first-year college students will drop out before their second year, costing schools millions.

What if there was a way for schools to predict which students will drop out and which will go on to find success? With big data, there is.  

Through predictive analysis, schools can study data they’ve collected on past students who’ve succeeded and others who haven’t. What classes did history majors do well in? What was the average grade of a math major? How does doing poorly in a science class affect the graduation time of a nursing major?

Using this past information, schools will know if and when behavior by current students falls in line with the risks students have seen in the past. This data can also detect abnormal behavior, such as a sudden fall in grades or a lack of attendance, and bring it to an end before it results in the student potentially dropping out of college.  

Georgia State University was perhaps one of the first schools to use this kind of technology. The institution ran reports on students, their classes, and their grades. They could detect an anomaly in student behavior and automatically alert advisors, who would schedule an appointment with the student and help address their problems. Perhaps the student was in a class that was too difficult or a class that didn’t fit their major. Whatever the issue, advisors could address it and remediate it.

At Georgia State, this system paid off both figuratively and literally. It helped decrease the average time it took students to graduate by one semester and returned millions of dollars to the school. If your institution is struggling with enrolling quality students, maintaining their retention, and ensuring their graduation, sometimes to look forward, all it takes is a little bit of looking back.  

Increasing Alumni Giving

There’s another reason retention and graduation should be top priority: when students drop out of college, potential donations also disappear. When more students graduate, alumni giving increases. So, it’s important that your school do everything it can to keep students both in class and on track to graduate.

However, big data can also help higher ed predict which alumni are more likely to give back to the school, from running reports on information as general as age and major to running reports on data as personal as estimated annual income and home value. This type of big data will help your school identify who its most generous prospective donors may be and how much those donors may have to give.

ReachBright, for example, helps track the giving history of alumni. It tells you which alumni come from “legacy families.” It lets you see how much each donor gives and which donors make recurring donations. In addition, its tagging capabilities can help schools easily identify alumni’s interests and shape giving campaigns around them, making alumni want to donate. Meanwhile, highly personalized, yet automated, communications based on those interests keep alumni connected with the school and keep an engaging conversation moving along, ensuring that the alumni stay informed and updated on their alma mater.

Keeping in contact with alumni is a sure way to keep them engaged. The more involved alumni feel toward their campus, the more likely they are to donate. Big data can help make sure both the content and the news you’re sharing line up with their interests.

The Downfalls of Big Data

Not everyone is pleased with how big data is being used. Some argue that big data is invading privacy, so much so that it has been compared to something out of one of Orwell’s novels. The education industry, critics say, is akin to “Big Brother” watching prospects’, students’, and alumni’s every move. However, (for now, at least) there are still limits as to what kind of data schools can collect (credit card limits, for example, are private).    

Meanwhile, Slate blames big data for overcomplicating the college admissions process. Big data, the online publication says, forces schools to rely on algorithms, which make the application process “tougher, crueler, and more expensive.” Data can be helpful if it is scrutinized, but it shouldn’t be blindly followed. It should be used to help find patterns and drive decisions, but it shouldn’t be the only deciding factor, especially in college admissions.

Finding a Balance

Although we are a company that specializes in big data software, we can agree with some of what Slate says. Nothing can ever replace human interactions, especially at smaller universities where interaction and communication are vital to growing the relationships that help these schools thrive.

Enrollment and engagement shouldn’t be left entirely to what a database says. Database figures and scores can’t replace an admissions interview and graduation rates will never increase if advisors don’t take the time to sit down with students and personally work them through the issues they’re facing. Data should be used as a means of cross-checking what you discover through more traditional interactions, but it shouldn’t replace interactions altogether. It can help you improve retention, graduation, and engagement, but it can’t improve them for you.

If you’re looking for a “big data” software that strikes the perfect balance—one that gives you the data you need to launch those meaningful interactions—look no further than ReachBright.

Filed Under: Alumni DevelopmentEnrollment Management

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