This is the second entry in our Data in Action series, highlighting the many ways Unizin members leverage data from the Unizin Data Platform (UDP) to inform decision-making across their institutions.
The University of Wisconsin-Madison has utilized the Unizin Data Platform (UDP) for several years to deliver data to advisers and faculty and inform important decisions around student success. The recent availability of Unizin data marts has quickly altered how UW-Madison manages UDP data, reducing the cost of creating and delivering data-rich applications to faculty and advisors while enabling the faster and more precise development of new prototypes to serve different audiences.
“When we first started, we were picking up data from the UDP and moving it to an S3 bucket in AWS to run many of our calculations. While this worked, it was costly,” said Brian Ploeckelman, Technical Lead at the University of Wisconsin-Madison’s Division of Information Technology (DoIT).
This two-stage model required UW-Madison to maintain and run its own cloud infrastructure, and moving data from the UDP to AWS for calculations was an inefficient use of resources.
The release of student success data marts by Unizin enabled UW-Madison to streamline the process and better utilize R&D resources. With access to pre-rendered analytics similar to their previous calculations, UW-Madison has now shifted the majority of the technical infrastructure powering its applications to the UDP, eliminating the cost of maintaining a separate cloud environment to perform calculations to support data-rich applications.
Learner Activity for Advisors (LAVA) screenshot, an application built by UW-Madison that leverages UDP mart data to help represent student activity patterns.
The continued expansion of Unizin data marts has improved UW-Madison’s R&D efforts as well. “The continued evolution of the available data marts has allowed us to shift a great deal of our efforts from data engineering activities to working directly with end users. We can now collaborate more closely to quickly identify the data fields that best support their workflows and decisions. We can test new data fields with end users and iterate through different design ideas faster than ever before,” said Chris Lalande, Learning Analytics Architect in the Learning Analytics Center of Excellence.
UW-Madison has utilized data marts to reduce infrastructure costs, simplify R&D, and quickly iterate different models and visualizations with data consumers. Unizin continues to publish new data marts to expand the utility of the UDP, including data marts that incorporate calculated data fields representing years of R&D from Unizin member institutions to benefit the entire consortium.