How universities can use data to improve decision making
Head of Business Intelligence, Lety Kemp
Data, our world is driven by it. Today’s institutions both big and small, public and private, are being extensively guided by data when it comes to decision making. They use data to set goals, evaluate progress, measure growth, among an endless number of other things.
However, it has been estimated that less than 50% of universities are analysing key student data for business intelligence purposes and that just over 25% have the means to do this1.
This is in spite of the fact that universities now have access to more data than ever about their:
- Current and prospective students
- Admissions and learning trends
- Student progress
And a whole range of other incredibly valuable metrics.
So, what exactly is the problem? Why, despite having an inherent need to, and there existing a huge benefit to be gained from the leverage of data, have universities seem to have fallen behind other industries in the uptake of data analytics practice?
Competition and workload
One possible explanation is the organisational paradigm shift universities are currently experiencing. HE institutions have traditionally been not-for-profit with a clear societal mission; however, they are becoming a global service delivered by corporation-like divisions and operating in a highly competitive marketplace2.
This certainly places unprecedented strains in professional and academic teams. On top of developing and delivering academic programmes, they need to be on top of marketing strategies to lure students before their competitors do. For many UK universities, this is not a small task as their target market spans the entire world!
Despite being overworked and having missions growing beyond borders, our experience shows that universities’ teams are very much interested in leveraging the power of data analytics. In fact, the great majority of them understand that they already have data that is a treasure trove of valuable information, and our mission is to help those first steps towards data insights heaven – with self-service analytics!
Hundreds of thousands of spreadsheet rows are combined to visually represent relevant metrics during a 7-year period in this single-page dashboard. Users can get a holistic overview overtime or point and click to obtain details on demand.
Busting self-service analytics myths
By now it is not huge news that data visualisation has enormous potential to help people extract meaningful knowledge out of big data. The figure above gives you an idea of how much information can be neatly packed in one single screen. Hundreds of thousands of spreadsheet rows are combined to visually represent relevant metrics spanning a 7-year period.
For example, if you’re in HE recruitment, keeping this dashboard updated every semester means that you and your team will have audience details relating to programme enrolment for over a 7-year period on your fingertips. Constantly interacting with its many options will help you understand many different facets in this subject and also allow you to start making inferences about possible correlations between the main variables; are there any visible patterns? That’s where informed decision making comes from in this context.
However, real client experience tells us that there are a few hurdles stopping university teams getting knowledge from data. Usually, and most, unfortunately, we come across a range of myths regarding the use of data analytics, so if you can relate to them, you are not alone:
- I don’t have data scientists in our team
- I don’t have a budget for IT infrastructure, nor time to wait for them to implement it
- I don’t know what questions to ask the data
We’ll start by tackling the first one, nowadays there are several business intelligence (BI) solutions that allow for self-service analytics to be utilised by anyone, you don’t need data science training in order to visually explore data. Some BI software (Tableau, QlikView, Looker, Power BI, and many others) also eliminate the need for any extra IT infrastructure or man-hours, so there’s the second myth done.
As for what questions to ask, universities report that they are currently inundated with data, so you have a paradise waiting to be discovered. A simple exploration of a mixture of existing departmental datasets can easily start uncovering patterns otherwise unknown to you and your team.
When you begin visualising data, you start getting very familiar with it much faster. You’ll then start thinking more critically about interesting factors, that’s when you start asking and pursuing more complex questions. So, third myth busted: explore first, get familiar with your data, and interesting hypothesis will certainly come to the fore.
Start small, start local
Whether you’ve experienced the power of data analytics or not, our experience tells us that data, particularly in large amounts, is something that can leave many people feeling confused and overwhelmed, if that’s happening to you too, don’t be discouraged. You now know that current BI technology allows you to get real meaningful insights out of your data pretty easily.
Most importantly, you don’t have to wait for that university-wide data project to be implemented (that might take some time!) You can start small with locally stored historical data that is relevant to what you do.
In no time, you’ll start ripping these benefits plus many more as you keep adding to your analysis.
Quick team wins with data
Having data scattered around many spreadsheets is one thing. Having it already summarised in a visually digestible format is another. Research shows3 that our human perception abilities are enhanced when quantities are represented by shapes and colours. This is the point that your data becomes real information at a much faster rate, eventually how it will help you make better decisions.
One source of truth
When data is easily accessible and simple to digest, workflows become more streamlined and there is less back-and-forth between teams because two people have acted upon two different and conflicting datasets or files. And, let’s face it, how can conflicting versions of spreadsheets be controlled within large institutions (particularly universities?) Simple, they can’t!
If you find yourself hovering over spreadsheets and having to manually update them every so often, you’ll appreciate having dashboards. You’ll prepare it once, then all updates are performed in the data layer level. By using a central dashboard you’ll only save a huge chunk of time because everything is seen, managed, and responded to from one place rather than multiple verticals.
I hope these facts about analytics and big data inspire you and your team to start exploring some data. Happy visualising!
If you would like to find out how data visualisation can help universities and colleges, please get in touch – firstname.lastname@example.org – 01792 709184
1 Milford McGuirt, M., Gagnon, D., Meyer, R., (2015) 2015–2016 Higher Education Industry Outlook Survey https://assets.kpmg/content/dam/kpmg/pdf/2016/06/co-gv-4-2015-2016-higher-education-industry-outlook-survey.pdf
2 Francesca Pucciarelli, F. Kaplan, A. (2016) Competition and strategy in higher education: Managing complexity and uncertainty https://www.sciencedirect.com/science/article/pii/S0007681316000045?via%3Dihub
3 Keim, D et al., (2008) Visual Analytics: Scope and Challenges. https://link.springer.com/chapter/10.1007/978-3-540-71080-6_6
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