The Importance of Data in Higher Education

Posted on Posted in Sample Term Papers

Higher education institutions face an increasingly complex challenge today to increase their efficiency and maintain sustainability. As costs rise and public funding decreases each year, institutions are forced to look at ways of providing a quality and affordable learning experience for students while keeping their own selves competitive and sustainable.

Data analysis, used wisely, can help institutions face this challenge by turning the huge volume of raw information that they possess into a meaningful and actionable knowledge base. Data analysis processes this information to provide the latest trends and dynamics that institutions can use to understand student needs, streamline procedures and improve overall efficiencies.

Some uses of data in higher education are given below.

 Evaluating Strengths and Weaknesses

Data analysis enables universities and business schools, to evaluate their own strengths and weaknesses as well as those of their competitors. Stakeholders can utilize data analytics available to them in order to assess their institution in detail and find room for improvements.

Better Marketing Strategies

Data metrics can be used to create an effective marketing strategy. University rankings and league tables can be used to build a strong brand identity and influence important policies and decisions. Data about an institution’s league or ranking position can help establish a sound reputation and develop a brand image. Almost 77% of students in the UK and Australia listed university rankings as ‘very important’ in determining their choice of institution. This highlights the need to use data analytics to exploit a ranking advantage in a competitive business environment.

Retaining Students

Recently, data has been used to directly improve the academic performance of students and reduce dropout rates. Innovative digital techniques using learning data analytics are increasingly being used by universities to compare present and past data and identify which students may require help and of what type. Such predictive models promise even more benefits and reliability in the future.