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Use of student data: dashboards

The University's Student Systems team have built a prototype dashboard for our academic schools, to display information about students on the schools' programmes and courses.  The prototype has several tabs, presenting information about applications for programmes, completion results, survey results, demographics, and assessment.  The team consulted with other universities about what works for them, took the best results we found and applied that experience to the needs of our University.

The next stage is to turn this into a robust service and this where the architecture team have a role.  We are currently working to define an architecture for the university's BI/MI data, both at the technology level (e.g. a data warehouse) and at the modelling level.  Much of the data used in the prototype has been scraped together using a combination of existing data marts and Excel, with significant effort needed to write complex reports across these multiple sources.  That is fine for a prototype but needs refinement for a production service.
The architecture team are working with the Student Systems team to define a roadmap for the BI data that underlies the dashboards.  There are some immediate tasks that the dashboard designer has already identified, such as standardising certain values in order to simplify table joins, and building BI universes for the data that is currently in Excel.  Moving beyond this, we are defining a target architecture based on a single data mart that will incorporate all the necessary data and be more flexible than the existing database.  Business logic will be moved from the BI universes into the data mart, reducing the complexity of the presentation layer and opening the way for adopting different dashboard tools.

In the long term, it would be desirable to link to data from other systems.  For example, a school may wish to know the current cost of a course, including data about the costs of the space and other resources used.  This cross-unit reporting is a particular focus of our wider BI architecture work, which I'll write about later.

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