Dealing with uncertainty

A really excellent blog post by Gavan Conlon on wonkhe got me thinking about uncertainty within the higher education sector. Gavan’s post was about the RAB charge for tuition fee loans, which turns out to be higher (at the moment) than had been forecast. But also about the longer term changes to the higher education sector which arise from the policy changes.

This is just one example of the uncertainties around UK higher education at the moment. Let’s name check a few of them:

• Will international students keep coming to the UK given growing HE sectors elsewhere, and the current government’s hostile stance to migration?
• Will universities be able to regain steady patterns of student recruitment, or is the current system volatility set to continue?
• How much further will research funding be concentrated after REF, and will this make research unsustainable in some universities?
• What will the disruption to established patterns of higher education from the internet be? Is it MOOCs, or some other disruption yet to come?

There’s lots to be said about each of these, but that isn’t the point of what I want to say (not today, anyway!). The point is, that no-one who works in or cares about universities can act as if some basic assumptions won’t ever change. And this is a problem, because universities operate on a long cyclical model. For example, the students graduating in summer 2014 with an undergraduate degree, after 3 years of study, entered university in 2011, on promises made in a prospectus which was signed off in autumn 2009. Before Browne, before tuition fees, before austerity budgets.

So universities have to adapt to events, but they carry a heavy burden of commitments which make his hard, and which place burdens on staff who are very busy just delivering the day-to-day. (If universities are sometimes seen as slow to change, I think this is one of the reasons)

There’s no magic wand which will protect a university, or a team, or a person in a university, from change. But there are things you can do to help you prepare. Here’s three things you can do

1. Keep reading news.  And thinking about it.  By the time something is a headline it’s too late to avoid it, but by looking into what’s behind the headlines, and thinking a bit about what factors are driving developments, you can see further into the future.  The film Armageddon is a bit like this (honestly!)  If you nudge the asteroid far enough away from earth, it flies past harmlessly, but if you wait too long it’s gonna get you.

2. Scenario planning: imagine a few futures – in the five year horizon works well – and think about what would have to happen for that to come true, and what would be the implications if it did. So, for instance, suppose that in five years MOOCs are a dominant form of learning in higher education: a higher completion rate; reliable ways found to assess performance. If it were like this, who wouldn’t follow courses from Harvard and Yale? So universities would need to think about changing the teaching model, to focus perhaps on small group teaching as an adjunct to online lectures (welcome back, blended learning!) And to find ways to award credit for MOOCs. Will this happen? Personally, I doubt it, but if you were, for instance, responsible for quality assurance processes in a university, you might want to look at your APL rules to see how much use they would be in this scenario.

3. Keep yourself lean: I don’t mean exercise more, but lean in the sense of the processes that you use. Do you know why you’re doing what you’re doing, and have you thought about what effort you might be wasting doing things that don’t need to be done? Some of that’s about priorities, but some if it is just about being efficient. Here’s a clear introduction from the Cardiff University's Lean University team about what lean is and is about – there’s lessons and benefits for all of us.

So there’s three steps: read the future, think about it in a structured way, clear the decks so you can react.

Big data, small budgets - 7 ways to make a difference

You don't need a big budget to get more from the data you have.

I saw this morning an advert from a company specialising in big data for universities – how to join together the data that universities already hold, turn it into useful information and get value from it in making a university more effective. There were some very impressive applications on display, enough to make any university management green with envy. All very good – but the client list is the big beasts of the higher education jungle: University of Michigan (income $3.4bn, 60k students), Oxford University (income £1bn, 22k students), Cornell University (income $3.1bn, 22k students), Brown University (income $700m, 8k students), Texas A&M (income $4.1bn, 53k students), Berkeley (income $2.1bn, 35k students). It can take big bucks to get big data.

In many UK universities budgets are tighter, and investment in the databases and analytics software that frees up big data isn’t this year’s (or next’s) priority. But it isn’t a lost cause: here are seven ideas which can make a real difference.


1. Know what data you have. Universities will have systems to record information and transactions about admissions, enrolments, exams, staff, space, finance, timetabling, learning resources, alumni, donors, research and more. Some of these systems may only be a spreadsheet, or paper-based files stored in one place, but knowing what is there can make a real difference. University management teams will see the possibilities of combining information; planning professionals will want to know what is there, make sure that its meaning is understood, and what the limits are on sharing the data.

2. A focus on data quality can be a real help. Look at where errors are creeping in to your data. Are you double-entering data because systems are not set up to be compatible? Have you got good documentation – with clear, unambiguous and relevant definitions of data fields, and good guidance for users – for all of the IT systems which you use to manage your background processes? A data quality policy will get you a tick from the governing body when it comes to the annual return to the funding council, and it can help you identify where you need to address problems.

3. Use the expertise you have. Universities have plenty of people who understand data and statistics – within the professional services, but also amongst the academic staff. Often these people will be only too pleased to be involved in making the data work better for their university. For staff in a professional services team, being part of a wider group looking at data can be a way to get a glimpse beyond the silo of their current role; and for academic staff, the chance to contribute on an institution-wide basis can be good for career development and professional recognition.  

4. Get in training. Train people in what data you have – sharing this knowledge opens up possibilities.  Train people in using the functionality of spreadsheet software – there’s power in these tools, for analysis and for presentation, which might surprise you. And train people in numerical reasoning – we all know an otherwise-high-performing-professional who has a real block with numbers, and overcoming this can be very empowering for them and for you. 

5. Use the data you have. It’s always possible to want better quality data, in different formats, and bringing together data sets which don’t match. And there are some questions where you do need real accuracy. But the data you have is good enough to help answer an awful lot of questions: focus on what you can say, rather than what you can’t, and don’t let the quest for perfect data get in the way of effective use of data. Read ‘How to measure anything’ by Douglas Hubbard to get a sense of what is possible. And think about letting a postdoc scientist loose on the data – it’s their capacity to see and understand the numbers that matters, not their knowledge of the underlying business. You’ll be surprised at what a data scientist can do!

6. Look for bottlenecks in your systems.  Do you have a colleague whose job it is to manage data requests, or is it a little bit of many people’s jobs?  Is the data team in IT and disconnected with users, making prioritisation difficult?  Sometimes sorting out one or two little problems can have a dramatic effect on how data can be made available and shared.

7. Spring clean your reports. Many data systems have reporting functions which require knowledge of SQL, for instance, to generate a report. Is the library of reports which have been coded a manageable size, and they reports which you still need? Find out what reports have already been written, remove duplicates, specify what you need now, and share the menu with others. Manage requests for new reports – if there’s real value in a new report, then it’s worth coding, but sometimes a colleague can happily use what already exists.


These seven tips won’t give you big data – you’ll still be casting longing glances at the analytics some universities use – but they will help you make an impact. And once the management team gets an appetite for data, who knows where that will go?