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ITDA1001 Database Fundamentals
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ITDA1001 Database Fundamentals
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Course Code: ITDA1001
University: Victorian Institute Of Technology
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Country: Australia
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Display all the details of all customers whose first name stars the letter J?
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Database legal issues
Database management should be government by legal rules and regulations which stipulates clearly storage and use of data. Database management involves collection of personal data from various customers which should be kept in secured IT infrastructure (George, Haas & Pentland, 2014). Security of collected data aims to achieve data confidentiality and privacy as requirement in any organization. It is important to note the issue of confidentiality and data privacy is very sensitive and should be enforced at all cost to avoid loss of organizational data. To achieve confidentiality and privacy of data, authorization and authentication to the systems should be implemented with strict conformity to set rules and regulations. Any organizational data storage rules and regulations should meet state level threshold which defines how organizations should set its infrastructure, the device conformity standards and approved means of securing data (Spiekermann, et al., 2015). In this regard, firewall authentication to the network should be enforced by setting up very strong filtering capabilities to all incoming and outgoing signals to the organizations. Firewalls should be configured to make sure only authenticated signals are allowed to enter and leave organizational network.
Next, authorization can be achieved by setting logical control parameters which limits authorized organizational employees to make any changes to the data once authorization/verification has been done to the system. When authorization is properly set, both creator of the data and the authorizer cannot be the same. This is set to make sure all data attributes; Atomicity, Consistency and Durability has been achieved (Mostert, et al., 2016). Finally, data encryption is one of the important practice which cannot be ignored by the organization as a means of securing data. It is an important aspect which protects data by converting it to unusable form unless accurate decryption key is provided. It is mainly done to secure data from interference while on storage as well as on transmission. George, Haas & Pentland (2014) argues that encryption algorithm chosen should provide difficult transformational matrix to the supplied key to make sure decryption process can only be done by the authorized person.
Entity Relationship Diagram (ERD) are created to present the actual database mapping of the proposed organizational database management system (DBMS). Database experts use ERD as a prototype of the database in order to make it possible to explain how database table would be mapped to each other (Fischer et al., 2018). It is used to define in advance names of tables, primary and foreign keys set ups to be implemented into the database during actual database design. Since actual database design and implementation is logical and cannot be presented as touchable evidence, it is the ERD which can be used to explain the actual database layout. Once database has been implemented, the entities can be exported to compare if it matches the actual design done at ERD level. It is a proof of what the DBMS experts will do or has translated into actual organizational database because. Specifically, according to Guidry, Stevens & Totaro (2011), it is used to capture requirements and specification of database as given by the customer’s request.
References
Fischer, P. M., Deshmukh, M., Maiwald, V., Quantius, D., Gomez, A. M., & Gerndt, A. (2018). Conceptual data model: A foundation for successful concurrent engineering. Concurrent Engineering, 26(1), 55-76.
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of management Journal, 57(2), 321-326.
Guidry, B. N., Stevens, D. P., & Totaro, M. W. (2011). The systems analysis and design course: An educators’ assessment of the importance and coverage of topics. Journal of Information Systems Education, 22(4), 331.
Mostert, M., Bredenoord, A. L., Biesaart, M. C., & van Delden, J. J. (2016). Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach. European Journal of Human Genetics, 24(7), 956.
Spiekermann, S., Acquisti, A., Böhme, R., & Hui, K. L. (2015). The challenges of personal data markets and privacy. Electronic Markets, 25(2), 161-167.
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