The State of Play
Student income is the largest income stream for the University of Nottingham, and each year the university must forecast its student population and the related income in order to allocate departmental budgets. The planning and performance team is responsible for collecting and analyzing up to nine million records of data on thousands of students across the globe to model expected student numbers and activity accurately.
However, with 56 departments requiring deep levels of analytical granularity and five-year forecasts, calculations are complex. By 2015, the team desperately needed a more dynamic student planning model that would allow increased granularity and subsequently improve decision making, whilst working alongside Tableau’s data visualization. The task would be to handle all reporting for student planning and forecasting as well as verifying data for HESA (Higher Education Statistics Agency), the body that collects quantitative data about higher education in the UK.
The team had earlier replaced Microsoft Excel with IBM Cognos planning and business intelligence software to cope with scalability needs but keeping the data warehouse relevant was difficult and expensive. Utilizing Tableau was relieving some limitations but querying and data preparation were still problematic.
Davidson’s team initially took advantage of Alteryx to clean and prepare the data for Tableau by completely scrapping the student elements of the data warehouse and rebuilding a new dataset in Alteryx, comprising 10 years of student data.
“Coding wasn’t required so we were able to build the system ourselves,” Davidson says. “Within two weeks we had a proof of concept and Alteryx was handling the volume with ease. So, we kept developing and within two months we’d built an entire student number planning and income model. There was excitement within the team because using the product had opened the door to many new and exciting possibilities. In addition to our student number planning model, we have managed to recreate our data warehouse from scratch, as well as beginning to leverage data on applicants’ A-level grades to model which students we want to actively recruit.”
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