Top 4 Ways to Apply Big Data in Higher Education

With over half-a-million students set to enter the universities in the next few weeks, here’s how universities may seek help of big data to improve their processes:

Top 4 Ways to Apply Big Data in Higher Education

When did the last time you cleaned up the memory of your laptop? Was slow speed of your laptop the reason behind deleting those ‘unnecessary’ files? Did your laptop’s performance improve after cleaning up? Did you end up deleting some important files for which you had to regret?

Indeed, it’s very frustrating to work with a slow laptop and more frustrating is haphazardly deleting some files without even realizing the actual reasons behind the problem.

Related: Are companies really getting the Big Data

When you feel this trouble working with your mere single device, think about how troublesome it would be in a large scenario.

Universities and higher education institutes can be a great example – they have to deal with a high amount of data their last decade’s methods cannot handle. They deal with data coming from all horizons – online applications, online classroom assignments, test results, social media, blogs, surveys, and much more. Either they continue working with what we call a “slow systems”, or have to delete all valuable data.

Last decade’s software solutions are nowhere powerful enough to cope with such a high volume of data. Perhaps this is the reason why most of the higher education institutes are yet to harness big data – because they are not equipped with sufficient coping methods, and they fearfully avoid investing in better solutions.

Apparently, institutes with insufficient resources and an unclear plan are to be left behind in the race. The potential of data-informed strategies is the only way they can get fueled forward in the league tables.

So how can higher education institutes actually utilize the potential of big data analysis and use it to stop fire-fighting? Here are four ways:

Start Small:

The entire culture cannot be transformed in a day. A better option is starting small by finding the most data-intensive areas in your operations. Admissions or grading system can be the first thing to start with. Next is finding the problems that can be easily solved with data analytics. The success in the first venture will motivate you to find a new area, and replicate your success.

Predict the Desired Outcomes:

When you have a problem in mind, think about its desired solution. Your final outcomes should be clear in your mind – be it better student relationships, improved recruitment process or better grading system. Plan everything from the scratch and predict the future. It will help you finding the loopholes and investment requirements, both in forms of time & money. Once the outcomes are clearly planned, prepare a strategy and implement it from the roots.

Create a Data-driven Culture:

The biggest issue with higher education institutions is the lack of resources that prevents them from collecting & storing the data – leave aside analyzing it. With some cutting-edge technologies like Hadoop, this issue has been resolved to a great extent.

In this data-centric world, discarding the data is more expensive than keeping it. Vital is creating a data-driven culture that respects data and analyzes it for its proper implementation in the business perspective.

Team-up with Good Partners:

Buying all resources within the campus is indeed an expensive decision. Luckily, we have some organizations equipped with required resources & culture to utilize the data effectively. Teaming-up with one such organization will save you from the expenses. This partner will help you minimize your investments and build processes that are required for proper data analysis.

Related: Decision Latency in Big Data Analytics


With yesterday’s technologies and lack of awareness, higher education institutions are missing an edge. However, these issues can be easily avoided with some cutting-edge tools and subsequent resources. All it takes is implementing a culture where data remains at the top of priority list. Investments are there, but the long-run benefits pay for them.