Higher education institutions are facing a "perfect storm" of challenges in 2026. From decreasing enrollment rates and declining government funding to a persistent mental health crisis, where 51% of students now report that financial pressures negatively affect their wellbeing, the need for a data-driven evolution is clear. The educational experience is a complex process that involves multiple stakeholders, including students, faculty, administrators, and support staff. Each of these stakeholders has a role to play in ensuring that students receive the best possible education and support. Analysing big data in education can help universities better understand the needs and preferences of their students, as well as the factors that contribute to their success.
To prove their value to stakeholders, universities must move beyond traditional reporting. The answer lies in higher education data analytics: leveraging the power of data to improve student outcomes and drive institutional success.
What types of data do universities use?
To truly understand the importance of data analysis in education, we have look at the two primary types of data collected:
- Quantitative Data: Numerical measures like test scores, grades, and attendance. This allows for education data analysis that identifies objective patterns, such as which students are at high risk of not completing their degree.
- Qualitative Data: Subjective information like student feedback on university culture or mental health. This provides higher ed data analytics teams with a deeper understanding of the "why" behind the numbers.
In 2026, universities are also increasingly looking at student data analytics from social media and VLE (Virtual Learning Environment) engagement to get a 360-degree view of the student experience.
Benefits of big data analysis in higher education
1. Insights into student behaviour and performance
In the context of higher education, data analysis can provide universities a wealth of information about student demographics, academic performance, and engagement, among other things. For instance, universities can use data analysis to identify the academic performance of students in specific courses or programmes. This can help identify which courses or programmes are successful and which need improvement.
Institutions can also analyse student demographics to determine the most effective enrollment and retention strategies. Additionally, data analysis can be used to test the effectiveness of different support services, such as academic advising and tutoring, and to identify areas for improvement.
Research from UCLA found: “University leaders said they could make better strategic decisions about hiring and curriculum if they had more comprehensive data on faculty research, prospective students, research funding, higher-education policy trends and competitive intelligence about other universities.”
2. Improving student retention and identifying "at-risk" students
One of the main benefits of data analysis in higher education is its ability to improve student retention and graduation rates. By adopting a proactive approach through student data analytics, universities can identify students who are "at-risk" of dropping out and offer the specific support they need to succeed.
An "at-risk" student is defined as someone with a higher probability of failing a course or withdrawing entirely. This is often driven by a mix of academic and personal factors. According to the Office for Students (2025/26), a significant performance gap persists:
- The Continuation Gap: Only 81.6% of students from the most deprived backgrounds completed their courses, compared to 92.2% of their most advantaged peers.
- Attainment Trends: The gap between students from the most and least deprived areas gaining a 1st or 2:1 recently increased to 17.8 percentage points, up from previous years.
By leveraging education data analysis and higher education data analytics, universities can develop predictive models that flag these disparities in real-time. This allows institutions to move beyond general academic support and provide targeted, personal interventions, such as financial guidance or specialised tutoring, that are essential for closing these persistent equality gaps.
3. Personalised learning and adaptive education
Another benefit of data analysis in higher education is its ability to support personalised learning and adaptive education. By analysing data on student performance and behaviour, universities can develop personalised learning plans that are tailored to each student's individual needs and learning style.
Analysing data from previous semesters also means universities can identify trends in the types of courses and subjects that students are interested in. This information can be used to develop new courses or change existing ones to better meet the needs of students. If there is data showing that a particular course isn’t as successful, the university can bring in extra resources to improve its offering.
4. Optimising course offerings
Data analysis can also help universities optimise their course offerings by identifying courses that are in high demand and courses that are not meeting student needs and need improving. By analysing data on enrollment patterns, student demographics, and course evaluations, universities can make informed decisions about which courses to offer and when. It also gives the opportunity for universities to recognise course redesign opportunities.
By identifying where students are disengaged or where performance may be dropping, institutions can redesign courses to better meet the needs of their students. Similarly, they can get a better understanding of their most popular courses and ensure resources are being used cost-effectively.
5. Improving teaching effectiveness
Data analysis can also help universities improve teaching effectiveness by providing insights into instructional practices and student learning outcomes. By analysing data on student engagement, performance, and satisfaction, universities can identify areas where teaching can be improved and provide targeted professional development opportunities for faculty. Data analysis can be used to evaluate the effectiveness of different teaching strategies and interventions. By analysing data on student outcomes and feedback, teachers can determine which strategies are most effective and make data-driven decisions about how to adjust their teaching approaches.
6. Predicting student success
By analysing historical data on student performance, including grades, attendance, and demographic information, predictive models can be built to forecast student outcomes such as graduation rates, academic success, and retention rates.
A predictive data analysis model is a statistical tool that uses data analysis to predict future outcomes based on historical data. These models are built using algorithms and mathematical formulas that analyse patterns and relationships between variables to make predictions about future events.
7. Enhancing overall institutional performance
Data analysis can help universities enhance their institutional performance by providing insights into resource allocation, financial performance, and other key areas. By analysing data on budgetary trends, enrolment patterns, and student outcomes, universities can make informed decisions about resource allocation and strategic planning.
👉 RECOMMENDED READING: 
Overcoming challenges universities face when analysing data
While data analysis has great potential to improve student outcomes, universities also face several challenges when analysing data. These challenges can include:
1. Data quality
Data quality is a critical issue for universities as the accuracy and completeness of the data can impact the validity of the insights gained from analysis. Incomplete or inaccurate data can lead to incorrect conclusions or ineffective interventions.
2. Data security and privacy
Universities need to ensure that student data is secure and protected in compliance with relevant data privacy regulations. This can be challenging when working with sensitive data, such as personally identifiable information or health records.
3. Data consistency across departments
Universities often have a complex organisational structure, with different departments and units collecting and managing their own data. This can result in data silos, where data is not shared or integrated across the organisation, making it difficult to gain a comprehensive view of student performance and engagement. McKinsey states that there is “much reluctance” across departments in Higher Education Institutions to share data and that they lack set policies for how data can be shared across departments.
However these challenges can be overcome with the right upskilling in data analysis throughout workforces in all industries, not just education. With sufficient data training, it’s possible to overcome inconsistencies across departments and build a more data-driven culture within an organisation.
Ready to solve the challenges of big data in education through upskilling?
Did you know that public funding for apprenticeships is already available through the Apprenticeship Levy? This is a UK tax on employers that pay a wage bill of £3 million per year or more. The money is then used to fund apprenticeship training throughout the UK and can be reinvested back into the workforce in the form of apprenticeship training.
If you’re looking to upskill your workforce in data analysis, we would be happy to help. Our apprenticeship programmes empower you to harness the power of data.