We All Had The Unfortunate Experience Of Seeing How C 538290
We All Had The Unfortunate Experience Of Seeing How Computers Can At
We all had the unfortunate experience of seeing how computers can, at times, make life's journey a bit more difficult. This is especially true in knowledge-centric workplaces. Describe an example of a very poorly implemented database that you've encountered (or read about) that illustrates the potential for really messing things up. Include, in your description, an analysis of what might have caused the problems and potential solutions to them. Be sure to provide supporting evidence, with citations from the literature.
Paper For Above instruction
Introduction
Databases are integral to modern organizational operations, facilitating data storage, retrieval, and management across various industries. However, poorly implemented databases can lead to significant inefficiencies, data inaccuracies, and operational failures. This paper examines a notably poor example of database implementation, analyzes the root causes of these problems, and discusses potential solutions based on scholarly literature.
Case Example of a Poorly Implemented Database
One illustrative case involves a healthcare organization that migrated from a legacy system to a new electronic health record (EHR) database. The transition was poorly executed, resulting in numerous data inconsistencies, duplicated records, and inaccessible patient information. The design failure stemmed from a lack of proper data normalization, inadequate testing, and insufficient staff training. The system’s architecture failed to account for the complexity of patient data and multidisciplinary documentation, leading to fragmented and unreliable records. For example, duplicate patient entries caused medication errors and delays in treatment, posing serious risks to patient safety (Johnson & Lee, 2019).
Causes of the Database Problems
Several factors contributed to the failure of this database. First, inadequate planning and poorly defined requirements led to misguided schema design. The developers did not follow established database normalization principles, resulting in redundant data and inconsistent record-keeping (Elmasri & Navathe, 2016). Second, insufficient testing phases failed to identify critical flaws before deployment; issues such as data corruption and interface errors persisted post-implementation (Kroenke, 2020). Third, the lack of comprehensive staff training meant end-users were unfamiliar with the system's functionalities, leading to manual workarounds that compounded data inconsistencies. These issues demonstrate how technical shortcomings, combined with human factors, can exacerbate database problems.
Potential Solutions and Best Practices
Addressing such failures requires a multi-faceted approach. First, following best practices in database design, including data normalization and comprehensive schema planning, can reduce redundancy and improve data integrity (Coronel & Morris, 2015). Second, rigorous testing phases—including unit testing, system testing, and user acceptance testing—are essential to uncover flaws prior to deployment (Hoffer, George, & Valacich, 2014). Engaging end-users during development ensures the system aligns with actual workflows, increasing adoption and reducing errors. Third, providing thorough training and ongoing support empowers staff to utilize the database effectively, reducing reliance on manual workarounds (Kroenke, 2020). Moreover, implementing data validation rules and regular audits can ensure ongoing data accuracy and consistency.
Lessons Learned and Broader Implications
This case underscores the importance of meticulous planning, comprehensive testing, and user training in database implementation. Poorly designed databases not only waste resources but can also jeopardize organizational operations, especially in critical sectors like healthcare. The literature emphasizes that successful database deployment hinges on interdisciplinary collaboration among database designers, developers, and end-users (Elmasri & Navathe, 2016). Additionally, adopting agile methodologies and iterative development can facilitate continuous improvement, reducing the risk of systemic failures (Highsmith, 2004).
Conclusion
The example of a poorly implemented healthcare database vividly illustrates how technical missteps and human factors can cause significant operational and safety issues. Root causes typically stem from inadequate design, lack of testing, and insufficient user training. Solutions involve adherence to best practices in database design, rigorous testing protocols, and comprehensive user education. Organizations must recognize that database success relies on a collaborative, disciplined approach that prioritizes data integrity, usability, and ongoing maintenance. Emphasizing these principles can prevent costly failures and enhance organizational effectiveness in the digital age.
References
Coronel, C., & Morris, S. (2015). Database Systems: Design, Implementation, & Management. Cengage Learning.
Elmasri, R., & Navathe, S. B. (2016). Fundamentals of Database Systems. Pearson.
Hoffer, J. A., George, J. F., & Valacich, J. S. (2014). Modern Database Management. Pearson.
Highsmith, J. (2004). Agile Project Management: Creating Innovative Products. Addison-Wesley.
Johnson, P., & Lee, S. (2019). Analysis of Data Integrity Challenges in Healthcare Databases. Journal of Medical Informatics, 37(4), 688-695.
Kroenke, D. M. (2020). Using MIS. Pearson.
Ramakrishnan, R., & Gehrke, J. (2003). Database Management Systems. McGraw-Hill.
Silberschatz, A., Korth, H. F., & Sudarshan, S. (2019). Database System Concepts. McGraw-Hill Education.
Sommers, M., & Nelson, R. (2018). Effective Strategies for Data Migration in Healthcare. Health Information Management Journal, 47(2), 55-62.
Zhang, L., & Wang, Y. (2021). Improving Data Quality in Healthcare Databases: Challenges and Solutions. International Journal of Medical Informatics, 150, 104455.