Architecture and Design Implications for Data Usage | Free Essay Example

Architecture and Design Implications for Data Usage

Words: 599
Topic: Tech & Engineering

Comparisons between the architecture alternatives covered in readings and the two schools

In both cases, the architecture allows entry of data at one point and the output of the computed data (information) at another point. In both cases, data are cleansed to ensure that data computed are free from missing data and other errors. In both cases, there are special identifiers used to identify the entries. These identifiers are unique, and they ensure there is no data redundancy. In both cases, there is a repository, which is a central store for data collected. It is from this repository that data are searched and extracted (De Chaves, Uriarte & Westphall, 2011).

Evaluation of the alternatives

It would be prudent to adopt an architecture that will satisfy the data usage needs of the merged organization (Loshin, 2008). The college and school were previously independent, thus had different data usage needs. The system being used in the community college manages students’ information. Besides, it has the traditional accounting, inventory, payroll, and human resources components. The features included in this architecture are good but lack customization.

Besides, the traditional features make it unfit for the merged organization. The system being used in the technical school is better than the one being used in the community college. This system has been customized by students to satisfy the needs of their data usage. The system has better applications that have been customized over time. If it is adopted by the new institution (after merging the two), then it can efficiently serve the institution and ensure proper data usage. Both systems can also be replaced with a commercial off-the-shelf (COTS) system. The COTS systems are sold by software vendors who design software. They are customized to satisfy the needs of a client.

Recommendation for the alternative

The system being used by the technical school could have been a perfect alternative were it not for its support and compatibility challenges. A commercial off-the-shelf (COTS) system would be the most likely alternative to meet the data usage needs of the merged organization. This would be bought from software vendors, and it will not solve the challenges that were experienced with the systems in the two institutions. The COTS system would be customized to meet the needs of the organization (De Chaves et al., 2011).

The customization would be done by the data usage requirements. These requirements are student information, human resources, payroll, and accounting systems. Also, the COTS system would not have compatibility, support, and maintenance challenges. The system would be used, in the merged organization, to meet the needs of students, personnel, and stakeholders. The COTS system to be adopted must be chosen wisely because this is a new organization with enhanced data usage needs different from those in previously independent schools and colleges.

Potential benefits and challenges associated with adopting the COTS system

Implementing the COTS system would meet the data usage needs of the merged organization. The implementation process would also ensure that all stakeholders are involved, thus its full adoption and utilization (Loshin, 2008). The students’ information, payroll, human resources, and accounting information would be automated and ease the process of data usage in the merged organization. However, the adoption would face the challenge of facing resistance from some members who were used to the old systems (Loshin, 2008). Also, it may prove difficult to implement the COTS system, especially when the developers of the system are not directly involved. It is a technical challenge that must be addressed right from the planning stage.


De Chaves, S. A., Uriarte, R. B., & Westphall, C. B. (2011). Toward an architecture for monitoring private clouds. Communications Magazine, IEEE, 49(12), 130-137.

Loshin, D. (2008). Master data management. Burlington, MA: Morgan Kaufmann.