Background (from Weeks 1/2 and 3/4): You are working for a large hospital, Winterfell Health Systems, in the American Southwest. At Winterfell, expensive equipment is often shared between units and moved throughout the hospital facilities. One of the concerns expressed by medical professionals and managers in the organization is the difficulty of tracking specific pieces of equipment. As a result more units are purchased to make sure that at least one of them is always available when needed. From efficiency perspective there is no data to track usage of specific equipment. The machinery is also becoming more wirelessly connected and some security concerns have been raised. The BI team you are a part of is working with the technology infrastructure team on a system that will track the location and daily usage of every individual piece of equipment, including the new “smart” machinery devices that fall into the Internet of Things (IoT) category and communicate independently with other machines in the facility and patients’ electronic health records (EHR). New information: Two patients with identical names and birthdates but different medical profiles and needs checked into Winterfell at the same time. It was only through the diligence of a nurse that Patient A was not given Patient B’s treatment as specified by one of the new smart machines that had gotten its information from EHRs. Last week Winterfell had to rely on its backup generators for critical operations after a cyberattack against the city’s power grid was successful. Power was restored after an hour, but the culprits are still at large and it’s likely a matter of time before this happens again. During the blackout several pieces of equipment failed to upload their data completely and when the power came back on the machines restarted their uploading procedure from the beginning, resulting in partially duplicate data.
Task: While other parts of the team look into application prototyping and the meta data repository, it’s time for your group to turn to process independent logical data modeling and source data analysis. Given the business scenario you have, and the various players (including the smart machinery), what factors are involved in data modeling and source data analysis? Does it matter when meta data is considered? Do you care about what the meta data repository and application prototyping groups are doing?