UK universities have been under serious pressure. Nearly half of universities in England expect to run a deficit this year, and student dropout numbers are at record highs. Despite this, some universities are finding a silver lining. They're using data analytics and AI not just to cope, but to improve how they operate. Here's how data is helping universities tackle financial headaches, boost student engagement, and run campuses more efficiently – all with examples from 2024 onwards.
Using data to manage tight university budgets
University finances are tight. With tuition fees frozen and costs rising, many institutions are scrambling to find savings and new income. Instead of guesswork, a few universities have turned to data analytics for answers.
Some universities are building AI-based forecasting tools into their budgeting processes, using machine learning to project different scenarios for income and spending. This helps leadership simulate 'what if' situations and take action early.
Another practical example: Anglia Ruskin University (ARU) found a straightforward way to improve planning – by giving all their managers one place to get the numbers they need. ARU set up a unified data hub using Qlik, bringing together over 9 million records from systems that were previously siloed. Within a month, they had a browser-based dashboard that 100+ staff could use for instant insights. The finance and recruitment teams, for instance, started using this system to track applications and enrolment in real time, instead of waiting weeks for static reports. This meant they could spot shortfalls in recruiting early and adjust marketing spend or course offerings accordingly. As Mike Frost, ARU's Assistant Director of Finance, noted, having that unified data available on demand made analysis capabilities "that were unthinkable in traditional setups" suddenly very normal. Essentially, ARU cut out the bottlenecks – any department can now get the data it needs in seconds, which has sharpened decision-making across the board.
These cases show that, even when money is tight, better data can point to solutions. Whether it's uncovering a spending leak or reallocating resources to a high-demand course, universities are finding that a single source of truth and some smart analytics go a long way. It's about making every pound count by basing decisions on facts, not hunches.
In plain terms, data is helping universities stay financially afloat by shining a light on where they can do better.
See all of our Higher Education case studies here.
Student engagement: from data to action
Student success is another big worry. More students are struggling due to cost-of-living pressures and other factors, and if they drop out, it's a lose-lose for everyone. Traditionally, universities noticed at-risk students only after they failed exams or stopped coming altogether. Now, data tools are changing that.
Take the University of Wolverhampton. In late 2024, a team of Wolverhampton's own MSc students built a Power BI dashboard to help the university improve attendance tracking. The university already recorded who showed up to class, but staff needed an easier way to spot patterns and intervene early. In a matter of weeks, the student interns produced a working prototype that any staff member could use. This dashboard lets tutors and support teams break down attendance by course, department, even by student characteristics like their home postcode or whether they have a part-time job. With a quick glance, they can identify if a particular group of students is slipping (maybe first-years from a certain region, or those juggling work). Instead of sifting through spreadsheets, staff get a clear visual cue of who might need help. Dr. Liam Naughton, who supervised the project, said the solution could "save staff countless hours" and make it easier to support students who are struggling to attend. In short, Wolverhampton used Microsoft's data tech – and a fresh pair of (student) eyes – to turn raw attendance data into an early warning system.
Beyond attendance, universities are also pooling data from multiple sources to gauge engagement. Think of all the digital footprints a student leaves: logging into the virtual learning environment, tapping into the library, submitting coursework, etc. In 2025, several universities started combining these signals to create a "engagement score" for each student. For example, one university integrated data from 30+ systems (including Moodle, library turnstiles, and even meal card swipes) into a single analytics dashboard. They set up alerts so that if a student withdraws from campus life – say, they haven't been to any classes or online modules in two weeks – a tutor gets notified to check in. This isn't about spying; it's about noticing a silent cry for help. Often the student is grateful someone noticed and can guide them to support (be it academic tutoring, mental health resources, or financial advice).
Even without fancy AI, just connecting the dots on existing data can make a huge difference. Nottingham Trent University (NTU) pioneered this approach a few years ago with their "traffic light" engagement dashboard, and they reported that over 90% of flagged students re-engaged after an advisor reached out. That success has inspired others. Now with tools like Qlik or Power BI, universities of all sizes are rolling out similar dashboards. The benefit? Staff spend less time trying to figure out who needs help and more time actually helping the right students. And students, for their part, feel more seen and supported, rather than slipping through the cracks.
In essence, data is making student support more proactive. Instead of waiting for a crisis, universities can offer a helping hand at the first sign of trouble – which can be the difference between a student staying or leaving.
Smarter everyday operations on campus
Running a university involves thousands of moving parts: managing facilities, scheduling classes, processing admin tasks, and so on. These operational areas might not be glamorous, but inefficiencies here can cost a lot (in money and in goodwill). Data analytics and automation are helping universities tidy up behind the scenes, making life easier for staff and students alike.
A great example is how universities are handling the massive puzzle of timetabling. Creating a timetable used to be like playing 4D chess with paper pieces – try to schedule hundreds of classes for thousands of students and hope nothing clashes. Now, data-driven scheduling systems can do the heavy lifting. Several institutions (including the University of Leicester and Edinburgh Napier University) adopted modern scheduling software that uses algorithms to auto-generate timetables. While some of these systems are proprietary, others have been built in-house using tools like Python or cloud data platforms. The impact is clear: at Leicester, the timetabling team can produce a full exam schedule at the click of a button and then use analytics to adjust it. They've cut weeks of manual work, and the final timetables are better – fewer room clashes, more efficient use of space, and consideration for things like students' part-time work commitments. Students now often get their timetables earlier and with fewer strange gaps, which lets them plan the rest of their life (important when so many have jobs).
Another area is estate management – keeping campus buildings and facilities in good shape. While some universities turned to specialised platforms (we won't name them here), others leveraged existing data tools to streamline maintenance. For instance, a university might use Microsoft Power Apps and Power BI to create a simple system where anyone on campus can report an issue (like a broken door or malfunctioning projector), and the maintenance team can track and prioritize tasks. One university in the North West did exactly this in 2024: they replaced an old email-based maintenance log with a Power BI dashboard. Now they have real-time data on how many repair tickets are open, average fix times, and which buildings have the most issues. This helped them spot repeat problems (they found one lecture hall's projector broke down 5 times in a term – time to replace it) and allocate their maintenance staff more efficiently throughout the week. Over the year, they resolved about 30% more issues than the previous year, thanks to a clearer view of the workload and process tweaks informed by the data.
We should also mention the backbone of all these efforts: having the right data infrastructure. Big universities like the University of Birmingham have started using cloud data platforms (e.g. Snowflake) to bring all their information together. Birmingham, for example, moved heaps of data (student records, HR info, research data, you name it) into Snowflake's cloud. This gave them a single, scalable place to run analytics. They then use BI tools on top of that to build dashboards for different needs – whether it's monitoring energy usage across campus or tracking research grant spending. By modernising their data infrastructure in this way, they not only improved day-to-day reporting (faster queries, fewer errors), but also set the stage for more advanced projects like machine learning. In other words, they cleaned and connected their data so they can start doing the fancy AI stuff on a solid foundation.
What all these operational wins have in common is simplification. Universities are taking processes that were manual or fragmented and making them smarter with data. The immediate results are things like time saved (staff not doing data grunt-work) and better service (students seeing quicker responses and more convenient schedules). But beyond that, it changes the culture: staff see that data isn't just an IT thing – it's something that can help in their job, whether they're a timetabler, a facilities manager, or a course leader.
Data as a bright spot in challenging times
It's easy to be pessimistic given the challenges in higher education right now. Budgets are under strain, and students have it harder than before. Yet, the examples above show a more optimistic story alongside the grim headlines. Data, analytics, and AI are proving to be crucial allies for universities. They're helping institutions spend wisely, support students proactively, and run operations smoothly.
What's notable is how quickly some of these changes have happened. Anglia Ruskin stood up a university-wide analytics dashboard in one month. Wolverhampton's students built a viable attendance tool in a matter of weeks. Bath Spa's team started creating new data apps in hours once the right platform was in place. We're not talking about five-year IT overhauls here – these are agile projects that deliver results within a semester or two. That speed of implementation has been key to getting buy-in. When people see a new data tool actually making their job easier, they're more open to the next idea, and the next.
The upshot for university leaders and planners is clear: investing in data capability pays off quickly and tangibly. It's not about fancy dashboards for the sake of it; it's about finding the stuck points in your institution and using data to unknot them. That could mean automating a tedious report, or using AI to mark the spots on a heatmap where you're likely to lose students. It's often a series of small fixes and improvements that add up to big gains.
Most importantly, this approach keeps the focus on what matters – delivering a good education and experience. When a finance system is smarter, it frees funds for teaching. When a student gets help in week 3 instead of dropping out in week 10, that's a life potentially changed. When an admin process speeds up, staff have more time for students and research.
In a way, the current crises are pushing universities to innovate more than they might have in calmer times. What we're seeing is a shift: from working harder to working smarter with data. And that gives a reason to be upbeat. It means a university can face difficulties and still strive toward its goals, armed with better insight and agility than it had before.
In the face of budget crunches and rising expectations, UK universities are discovering that data and AI can be the heroes of their story. It's not about tech for tech's sake – it's about using the information at hand to make better decisions, help people, and do more with what you have. That truly turns a crisis into a catalyst for positive change.
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