Medical University of South Carolina Expands Secure, Curated Data Access Across 35 Teams with Informer
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Informer
- Ellucian Colleague
技术栈
- CSV
- Excel
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
- Digital Expertise
技术
- 平台即服务 (PaaS) - 数据管理平台
- 应用基础设施与中间件 - 数据交换与集成
- 应用基础设施与中间件 - 数据可视化
适用行业
- 教育
适用功能
- 商业运营
- 质量保证
服务
- 软件设计与工程服务
- 系统集成
关于客户
The Medical University of South Carolina (MUSC) is the oldest medical school in the southern United States, located in Charleston, South Carolina. It has more than 3,000 students and 800 residents and is comprised of six colleges: Dental Medicine, Graduate Studies, Health Professions, Medicine, Nursing, and Pharmacy. MUSC also includes medical and research centers, along with a public hospital. The university is dedicated to providing high-quality education and healthcare services. Stan Sulkowski, the Director of University Reporting, leads a team responsible for analyzing student data across all six colleges. The team handles complex data structures and frequent data requests, making efficient data management crucial for their operations.
挑战
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.
解决方案
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.
运营影响
数量效益
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