By Laura Jackson, Principal Consultant / 19 February 2024
Sucker Punch
It’s been another difficult month for universities, with yet another newspaper story using the sector’s own data to expose another perceived failing in university operations. This time it was a poorly researched article in the Sunday Times about international student recruitment. And again, the sector has, as is its strength, fought back with due diligence. Mission Groups, VCs, and impassioned individuals have rallied to provide insight, context, data and detail to evidence the big picture, to demonstrate the truth behind the numbers, and highlight the complexity of using data to craft a narrative about higher education.
But in the responses certain phrases keep appearing: ‘it’s complicated’, ‘trends are unclear’, and ‘we won’t see the full picture until….’ – and this is where it gets interesting. Because as we all know, the higher education sector has for some time now collected rich and magnificent data. Providers are required to collect and submit data on the full range of their activities, and much of this is available as open-source data. Institutions are regulated and held to account on their basis of the data they submit. So why is it so complicated to penetrate? And how can the same data give rise to such difference of opinion?
It's... Complicated
There is a running joke amongst the data wranglers of HE along the lines of, ‘Ask a simple question get a complex answer’. Anyone asking, ‘How many students do we have on a course?’ is generally met with return questions of, ‘What do you mean by student?’, and ‘What do you mean by course?’. A lot of this stems from the increasing complexity of provision – a ‘student’ can be many things – an undergraduate, a postgraduate, a researcher, a visitor to name but a few. The courses they study (which might not even be called courses – they may be called programmes, or pathways for instance) are equally diverse – 3 years, 4 years, apprenticeships, industrial, short courses, microcredentials. And this is before we even start getting into any of the rich detail about the who, what, where, when and how all of this activity is provided.
Then, throw in the regulatory framework, which is not renowned for its accessibility and succinctness, and we add another layer of complexity onto the data. The latest guidance for providers on completing the HESES return – meant to be an early indication of the number of students studying in a given year – runs to 114 pages. The Teaching Excellence Framework (TEF) metrics documentation is a relatively svelte 80 pages, whereas the Data Futures guidance consists of a microsite, training videos, many blog posts and a collective crisis. The data from providers is there, and it is available, but is it accessible?
All of this begs the question - in the quest for clarity and transparency in our data, have we actually created a monster? If collectively we as a sector struggle with this, how can we expect the wider public to make any sense of it?
Can't See the Wood for the Trees
Which brings us back to our old friend Data Futures. Inarguably, more timely and quality data on the sector will be an asset to those who already access and understand the data as it is. Yet Data Futures has also added another level of complexity to already complicated sector data. If the data available to the sector now is this difficult to navigate, how will this look in the future? It isn’t hard to envisage a future where the additional segmentation added across the regulatory board will divide the data up into parts which are not easy to put back together to see the ‘whole’. In the quest to provide more quality and granularity, are we actually preventing the big picture stories from being seen and heard?
It is, of course, entirely right that as we strive for quality, equity and actionable insight from our data we take full account of those things which we know make a difference. Universities, their students, their academic and civic missions are not homogenous and should not be treated as such. But by adding layer upon layer of granularity, do we lose the ability to pick out those factors which enable us to make a real difference? What are we actually going to be able to do with all this rich data anyway?
After all, we don’t even know what a course is.
If you’re interested in having a conversation with us about transformational change or professional development for specialists, get in touch.