It's very complicated

Take a look at this article in the University World News, reporting work done by Claud Xiao and Rob Downs at Palo Alto Networks, a US internet-security company. It seems that it usernames and passwords, valid at universities across the world, are available on Taobao, the Chinese version of e-Bay. Accounts were available to buy at universities in Australia, Canada, China, Denmark, Italy, Singapore, Sweden, Switzerland, the UK and the USA.

I’m not an IT security expert, and this post isn’t about the technologies behind this: if you need counsel in that regard, don’t talk to me! My observations here are more about how complex the world is becoming for universities.

The accounts for sale were, reportedly, valid current student accounts – often accounts which were being used without the knowledge or permission of the student. “Don’t change the password” was the vendors’ advice, so as not to raise suspicions.

The most popular type of account was one which enabled the purchaser to unlock their Windows phone. This won’t be good news for the universities concerned, which will probably have a bit of explaining to do when the renegotiate their license fee with Microsoft. But at least in that case the use was external to the university. But also flagged were accounts which got access to research and library databases, and support. A university’s knowledge is the core of its value to the world, so this is looking difficult (albeit probably marginal for now) for universities.

But here’s a couple of bigger thoughts.

Firstly, universities are places which work on trust. Once you’re in, the culture is that you’re an equal member of the family, and that people will treat you as such. Does this sort of problem nudge universities even more to regarding their students instead as customers who perhaps aren’t what they seem? My guess is that the human factor (easily guessable passwords; risky online behaviour) is behind these hacks, so it isn’t about the unwitting students here trying to be bad. But the consequences of their behaviours gnaw away at trust.

And secondly, how much of our view of the world is conditioned by the language in which we browse? The world has lots of different alphabet types, let alone languages, and whilst I can’t speak and translate Danish, for instance, without help, I can at least recognise it as Danish and I can use Google to help. But my keyboard doesn’t do other sorts of alphabets – logographies, syllabaries, abjads. (If you want a good distraction check out this Wikipedia page on alphabet types, which enabled me to write the last sentence; you can find out what an abjad is too, aside from being a great Scrabble word). How can I even begin to understand what is going on with web pages which I don’t know how to read?

I don't know what this says ...

Many universities seek to act on a world stage, but how many are really equipped to do this? The IT account issue above shows that on the web, some of your neighbours speak and write in ways you can’t understand. This might worry you. But to push the neighbour metaphor a bit more, when you live in a multicultural place, you can either get suspicious, lock the doors and grumble about how it isn’t like it used to be; or you can accept change, have fun, and learn a bit of the lingo, so your neighbours become less like strangers.

If universities really want to internationalise then perhaps there’s a need to have more language fluency within management teams.

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?