We All Had The Unfortunate Experience Of Seeing How Computer ✓ Solved

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 about 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 Search the peer-reviewed literature for examples of this. You may select any topic relating to technology 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 . 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

In many organizations, poorly designed or poorly implemented databases can lead to significant operational inefficiencies, data integrity issues, and even organizational crises. One illustrative example can be found in a healthcare setting where patient data was stored in a non-normalized database structure. This database was originally created without following best practices for relational database design, leading to redundancy, inconsistency, and difficulty in updating records.

The primary problem in this scenario stemmed from the lack of normalization, which is a fundamental principle to organize data efficiently in relational databases. Without proper normalization, the same patient data might be stored multiple times across different tables, increasing the risk of inconsistent information. For example, a patient’s address and contact details might be duplicated in various records, and updating these details universally was nearly impossible due to the distributed nature of the data. Consequently, clinicians relied on outdated or conflicting information, adversely impacting patient care quality.

The root causes of these problems can be traced to inadequate planning during database design, often driven by time constraints, lack of expertise, or a misunderstanding of normalization principles among database developers. The absence of data validation and referential integrity rules further compounded the issues, leading to orphaned records and data corruption. These deficiencies compromised the reliability of the database, making it a poor foundation for decision-making and reporting.

To address these issues, several solutions could be implemented. First, a comprehensive database normalization process should be undertaken to eliminate redundancy and enforce data integrity. Designing appropriate primary and foreign keys can ensure referential integrity, preventing orphaned records and inconsistent data. Moreover, implementing robust data validation rules at the application and database levels can minimize entry errors.

Training personnel in best practices for database design is also critical, alongside continuous monitoring and auditing of the database’s performance and integrity. Adopting database management systems that support version control and change tracking can aid in detecting anomalies early. Additionally, involving stakeholders from different departments during the design phase can ensure the database structure aligns with actual operational needs, reducing the likelihood of poor design choices.

Research supports these strategies; normalization and proper database schema design have been shown to significantly improve data quality and system reliability (Codd, 1970; Date, 2004). Implementing referential integrity constraints reduces data inconsistency, while user training enhances understanding and adherence to best practices (Fitzgerald & Dennis, 2008). Legacy systems with flawed designs serve as cautionary tales emphasizing the importance of investing in thorough initial planning and ongoing maintenance.

References

• Codd, E. F. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6), 377-387.
• Date, C. J. (2004). An Introduction to Database Systems (8th ed.). Pearson Education.
• Fitzgerald, G., & Dennis, A. (2008). Business Data Communications and Networking. Wiley.
• Elmasri, R., & Navathe, S. B. (2015). Database Systems: Fundamentals (7th ed.). Pearson.
• Maier, D. (2013). The Theory of Relational Databases. Springer.
• Kroenke, D. M. (2019). Using MIS. Pearson.
• Silberschatz, A., Korth, H. F., & Sudarshan, S. (2019). Database System Concepts (7th ed.). McGraw-Hill Education.
• Harrington, J. L. (2016). Relational Database Design: Applying Normalization and Domain Constraints. Morgan Kaufmann.
• Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit. Wiley.
• Abiteboul, S., Hull, R., & Vianu, V. (1995). Foundations of Databases. Addison-Wesley.