When people ask me about my job, I tend to mutter about pivot tables and conditional formatting. I’ve never really been the Marketing kind; I can’t tell you what you should or shouldn’t buy or buy into on the basis of a snap discussion or gut feeling. And don’t even get me started on accolades, awards ceremonies and the like.

Talking about pivot tables and conditional formatting usually has the desired effect of moving people on, although I’m occasionally sucked in to conversations about Excel, or data.

I’ve been thinking about data, as over the last week or so, I’ve managed to get my hands on the Uni’s HESA 2017 student dataset. (Thanks Lesley!). I now have anonymised data for every UK HEI student from 2013-2017. For every enrolment, at every HEI, I have:

  • Course (title, award etc) and JACS details (thus making the bought in data more detailed than HEIDI data);
  • Broad demographic information (e.g. outer postcode area, nationality, age on enrolment);
  • Previous school (where appropriate).

These data answer all comers and all questions when it comes to new course requests. Or do they?

Pluggin’ in the the 1992 main frame

There are restrictions and caveats – and there’s nothing I love more than a restriction or a caveat:

  • Are student enrolments a measure of demand? I advise my ‘internal customers that UCAS hold better data on pure demand – i.e. applications. UCAS don’t, however, make data available in the same way that HESA do. Specs and lead times are often a factor. As well as fees – UCAS applicant data seems significantly more expensive than HESA student data. I always concede that UCAS has better data, but HESA’s is – for the Uni – better value;
  • HESA student data is always at least a year out of date. And always retrospective. My projections always reach into the past with ease and aplomb, but I can’t access this year’s enrolment figures at other unis;
  • Data can’t factor in current market conditions. It can suggest a profitable direction in terms of new courses, although it can’t always show sudden market jumps, blips and the actions and reactions of competitors.

This last point lies at the heart of my work anguish. I can accurately reflect, but struggle with projections. I can answer ‘which HEI DID students with a Luton postcode enrol at; but not, crucially, which HEI WILL students with a Luton postcode enrol at. Historical trend data can inform, but never answer this question.

Data anguish 2019

This is when faculties and people who don’t spend most of their time sitting in front of computer screens can get a helping hand from other colleagues across the University; both in helping with well thought through questions, and/or additional market and subject insight, which I’m not going to be in a position to talk about.

Anyway, I’m off to our UCAS fair tomorrow. No rest for the wicked, and all that.