Case Study
First read the St. Rita’s vignettes (green boxes) on pages 172, 181-182, and 184 I have attached St. Ritas
The case study begins with Val reminding the EIM team on its primary charge: developing a proposal about the efficacy of an EIM program at St. Rita’s. This small dialogue refocuses the students’ attention on the bigger picture, keeping in mind that EIM is bigger than the current domain being examined. Data quality challenges and their consequences are introduced in the first vignette when Shirlee and Monte remind the team about quality problems in the MPI and how interfaces with multiple systems are complicated by poor quality master data. In further chapter vignettes, Shirlee and Monte connect the dots between the relationships among master data management, metadata management, and data architecture and begin to see how these subsystems are dependent upon each other. In the final vignette, the EIM team acknowledges that cleansing master data is a back-end process and, if more focus was put on gathering better requirements, developing data models, and designing systems, data quality would be better. At the end, the EIM team unanimously agrees that master data management will likely be one of the top considerations for the launch of an EIM program at St. Rita’s.
Please address following questions:
What are the key takeaways from the vignettes in this chapter?
Why do you think the EIM team decided that master data management will be a top consideration for the launch of an EIM program at St. Rita’s?
Expert Solution Preview
Introduction:
The given case study revolves around EIM team’s proposal to assess the efficacy of an EIM program at St. Rita’s. Quality issues in master data and its impact on interface with multiple systems are the primary challenges. The following questions address the key takeaways and why master data management will be a vital consideration for launching an EIM program at St. Rita’s.
1) What are the key takeaways from the vignettes in this chapter?
The key takeaways from the vignettes in this chapter are the challenges associated with the quality of master data. It was acknowledged that poor quality of master data could impact interface with multiple systems, which could cause administrative challenges. The EIM team realized that subsystems, including master data management, metadata management, and data architecture, are dependent on each other and play a crucial role in ensuring data quality.
2) Why do you think the EIM team decided that master data management will be a top consideration for the launch of an EIM program at St. Rita’s?
The EIM team decided that master data management would be a top consideration for the launch of an EIM program at St. Rita’s because it is a back-end process that could significantly impact the efficacy of the overall EIM program. The team concluded that if more focus is put on gathering better requirements, developing data models, and designing systems, data quality would improve. Thus, master data management is critical to ensuring accurate and reliable data, which is essential for successful implementation of EIM programs.
