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Total 700 Wordsplagiarism Checkprovide Link To Resources
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.
As with the first discussion topic, it is not enough for you to simply create a own posting. You must read the postings of the other members of the class and comment on each of them. Please see Discussion Forum of the class syllabus for additional details on content.
Sample Paper For Above instruction
Introduction
Databases are integral to modern organizational operations, supporting decision-making, data management, and operational workflows. However, poorly implemented databases can pose significant challenges, leading to data inaccuracies, operational inefficiencies, security vulnerabilities, and ultimately, organizational failures. In this paper, we analyze an example of a poorly implemented database system, identify the causes of its failures, and suggest potential corrective measures based on scholarly literature and real-world case studies.
Case Study: The Healthcare Data Management System
One notable example of a poorly implemented database is an electronic health record (EHR) system used in a mid-sized hospital. The system was intended to streamline patient data management, support clinical decision-making, and facilitate billing processes. However, the implementation resulted in numerous issues, including data inconsistency, security breaches, and workflow disruptions. These problems severely impacted patient safety, regulatory compliance, and operational efficiency.
Causes of the Problems
1. Insufficient Planning and Requirements Gathering
The system was developed without comprehensive requirements analysis or stakeholder engagement. Many key functionalities, such as cross-departmental data sharing and real-time updates, were overlooked, leading to incomplete and incompatible data structures (Hovenga, 2010). This lack of planning contributed to data redundancy and inconsistencies.
2. Poor Database Design
The database schema lacked normalization, resulting in data duplication and storage inefficiencies (Elmasri & Navathe, 2015). The absence of proper indexing slowed query responses, hampering clinical workflows. Furthermore, inadequate constraints led to data integrity issues, with erroneous or incomplete records entering the system.
3. Inadequate Security Measures
The system lacked robust authentication protocols and encryption, making it vulnerable to unauthorized access and data breaches (van der Velden et al., 2019). This oversight compromised patient privacy and violated legal regulations like HIPAA.
4. Lack of Training and Change Management
Staff members were insufficiently trained on the new system, leading to improper data entry and user errors. Resistance to change further slowed adoption, resulting in inconsistent data entry practices and decreased system reliability (Kotter, 2012).
Potential Solutions
1. Comprehensive Planning and Stakeholder Engagement
Future projects should involve thorough requirements analysis, encompassing all stakeholders’ needs, to ensure the system supports all operational aspects effectively (Satzinger, Jackson, & Burd, 2014).
2. Proper Database Design and Normalization
Implementing a normalized schema with appropriate indexing and constraints will improve data integrity, reduce redundancy, and enhance query performance (Rob & Coronel, 2007).
3. Enhanced Security Protocols
Incorporating encryption, role-based authentication, and audit logs will strengthen security and ensure compliance with data protection regulations (Anderson, 2020).
4. User Training and Change Management
Comprehensive training programs and change management strategies are vital to facilitate system adoption and ensure data accuracy and consistency (Kotter, 2012).
Conclusion
The case exemplifies how poor database implementation can jeopardize organizational operations, especially in sensitive environments like healthcare. Addressing the root causes—insufficient planning, poor design, security lapses, and inadequate training—can significantly mitigate risks. Incorporating best practices from literature and previous experiences helps organizations develop robust, reliable, and secure database systems that support their strategic objectives.
References
- Anderson, R. J. (2020). Security engineering: A guide to building dependable distributed systems. Wiley.
- Elmasri, R., & Navathe, S. B. (2015). Fundamentals of database systems. Pearson.
- Hovenga, E. J. (2010). Healthcare data and information sharing: What we need to know. Journal of Clinical Nursing, 19(15-16), 2293-2299.
- Kotter, J. P. (2012). Leading change. Harvard Business Review Press.
- Satzinger, J. W., Jackson, R. B., & Burd, S. D. (2014). Object-oriented analysis and design with applications. Cengage Learning.
- Rob, P., & Coronel, C. (2007). Database systems: Design, implementation, and management. Cengage Learning.
- Van der Velden, M. W., et al. (2019). Enhancing electronic health records security: A systematic review. Journal of Medical Systems, 43, 263.