MUSC Expands Secure, Curated Data Access Across 35 Teams
1. Student data requests could be bottlenecked, delaying access to end-users
2. Missing a solution that would prevent end-users from being inundated with irrelevant information
3. Inefficiencies and lack of flexibility in the scheduling of recurring jobs
The Medical University of South Carolina (MUSC) in Charleston, South Carolina is the oldest medical school in the southern United States with more than 3,000 students and 800 residents. The university is comprised of six colleges including Dental Medicine, Graduate Studies, Health Professions, Medicine, Nursing, and Pharmacy. The Medical University of South Carolina also includes medical and research centers, along with a public hospital. Stan Sulkowski is the Director of University Reporting and analyzes student data for all six colleges.
With his technical background, Sulkowski and his team had access to and understood the complex data structures of Ellucian Colleague, such as which very similarly named computed columns or fields to use, but it is difficult to transmit that knowledge to coworkers. Student data requests could be bottlenecked as the team frequently fields several concurrent demands for data. Most data transformations had to be completed post hoc, with data moving from Colleague to CSV to Excel, where formulas and pivot tables would then finally be run.
The university did not have a turn-key data filtering solution to supply end-users with only the information that was relevant to them. The Financial Aid team, for example, would have to manually comb through and collate data from multiple sources and years for some of their reporting, taking up valuable time. The university also had an inefficient system for recurring jobs that was especially cumbersome if run times needed to be changed.
With Informer, Sulkowski and his team create reports for end-users so that they can easily answer a few inputs and receive the exact data they need without additional intervention. When needed, he uses post-query Flow Steps for data transformations, arithmetic calculations, to combine and compare data from multiple sources, time calculations for tasks such as time to degree or how long a student has been in a program, and to flag records based upon certain characteristics. Other options available as Flow Steps include the find-and-replace feature and the omit feature which allows users to eliminate unnecessary records from the final results based on additional post-query criteria.
Informer Jobs, a widely used feature, provides automated workflows in scheduling and updating reports and Datasets. Jobs is being utilized by The Medical University of South Carolina and is particularly helpful for Jobs that need to be scheduled on custom intervals rather than just on a daily, weekly, or monthly basis. The school is using a new Dataset with a scheduled Job that uses email bursts to deliver Financial Aid counselors the information that targets their specific programs and colleges of responsibilities, so they only receive data they need. Jobs curate the data in a way that helps give end-users some relief from notification fatigue.
1. Expanded access grants end-users with the independence to answer their own data requests
2. Users receive the targeted data that they need, no longer having to manually filter information out
3. Scheduled Jobs are saving time by replacing manual Ad hoc processes
One of the top results that The Medical University of South Carolina has noticed as a result of using Informer is greater data democratization. “We have expanded access in a controlled and secured fashion,” commented Sulkowski. “It was relatively simple to give people access to get the data they needed.” Around 85 users across 35 teams with varying levels of access have gained data independence. The university has about 1,060 Datasets, 350 Ad hoc Queries, 36 Dashboards, and 150 Jobs currently in use.
Another benefit for the university is the cleansing and elimination of extra data. “Using Jobs has provided tremendous time savings and has been well received at MUSC,” said Sulkowski. The team has created dozens of sentinel Datasets/Ad hoc Queries to monitor applicant and student data for errors and inconsistencies with scheduled Jobs to keep them refreshed, saving time by replacing manual ad hoc processes. With Informer, the overall efficiency of the team has improved significantly.