Database Issues Report: Analysis and Mitigation Strategies

This report analyzes several critical issues that arise from improper database implementation. The student identifies three primary problems: incomplete, inaccurate, or absent database documentation, leading to difficulties in data centralization and impacting system change understanding; improper data normalization, resulting in data flexibility loss, coding for null values, and repetitive data storage; and the incorrect use of foreign keys and constraint checks, causing referential integrity issues and potential performance problems. The report proposes mitigation strategies for each issue, including centralizing models with automated report generation, proper data normalization using various forms, and performing ramification to validate data and improve error handling. The reflection section critiques the assignment's organization, clarity, and the vagueness of the proposed solutions, emphasizing the need for a more precise and well-structured presentation of the identified problems and their solutions. The report concludes with a list of relevant references.

Running head: DATABASE ISSUES
DATABASE ISSUES
Name of the Students
Name of the Universities
Author Note

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DATABASE ISSUES
Problems faced by organizations due to improper database implementation are as
follows:
a. Improper documentation of database in production sector: It is seen that there are three
major issues that there are causes due to improper and inefficient database management.
These issues are namely incomplete documentation, inaccurate documentation and none at all
documentation (Gulzar et al, 2017). Due to these issues centralization of the data is affected
and is noted that centralization of data is null at few times. In case centralization of data is not
done, understanding of the impact caused due to change inn the system gets difficult. This
creates an issue for the developers to scramble to the first page again.
Mitigation process: This issue can be mitigated with the help of placing the models
in central repository along with spawning the reports automatically which will be taking a
minimal effort. Once this model gets established with passing time the quality metrices will
be improving. Once the governance process increases, it can be stated that the extension of
metadata captured in the models can be made.
b. Another major issue that can be detected inn tis process of having a least efficient database
is improper normalization. Due to lack of normalization of the data the main issue that can be
considered is that the flexibility of the data will be absent. This absence of flexibility of data
is not desired (Connolly & Begg 2015). This will lead to a situation where there will be
several columns left empty and the programmer of the organization has to code for those null
values as well, making the entire process highly complex (Hogan 2018). Repetitive storage of
value is also seen in this case. This leads to the fact that the data that will be stored and used
will not get updated in a regular manner.
Mitigation process: The mitigation strategy that can be implemented in this process
is performing proper normalization of the data. Usage of first form normalization of the data

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ensures that proper elimination of duplicate columns will be made. This ensures that
repetitive values that are present in the table are eliminated. Usage of second form
normalization also acts as an important strategy. Removal of redundant data form multiple
columns can be made with this form. Usage of third from normalization ensures that the each
and every column of the table will be dependent on primary identifier.
c. In case the database is inappropriate another issue that might be getting detected is
improper usage of the foreign keys along with checking of constraints. In this case issues
regarding referential integrity is seen (Jukic, Vrbsky & Nestorov 2016). This presence of
referential integrity will be leading to lack of validation checks and hence wise affecting the
reverse engineering database. It is also experienced that this issue might lead to hampering of
performance of the foreign keys as well as the constraints.
Mitigation process: The main process that can be implemented is performing
ramification. Performing ramification will ensure that proper validation of the data can be
made despite having to compromise in quality of the project. Error handling will also be
getting affected in a positive manner.
Reflection
To start off with the reflection section, it can be stated that the data that are provided
needs to more precise than the way it is provided. The entire article has been performed in 3
sections, namely poorly implemented data base description, the issues that are faced due to
this improperly implemented database and the potential solutions that are to be provided. As
per my knowledge the assignment is about the issues that are faced due to poorly
implemented database and the potential mitigation strategies. However, the student has
created a separate section for describing the aspect that he/ she has faced the issues. This has
caused unnecessary elongation of the article. The issues are not very distinct and description

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of the issues are missing as well. The mitigation strategies provided by the student sounds
vague at points. Proper reading of the subject is highly recommended.

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References
Connolly, T., & Begg, C. (2015). Database systems. Pearson Education UK.
Gulzar, Y., Alwan, A. A., Salleh, N., & Al Shaikhli, I. F. (2017). Processing Skyline Queries
in Incomplete Database: Issues, Challenges and Future Trends. JCS, 13(11), 647-658.
Hogan, R. (2018). A practical guide to database design. Chapman and Hall/CRC.
Jukic, N., Vrbsky, S., & Nestorov, S. (2016). Database systems: Introduction to databases
and data warehouses. Prospect Press.