Submit your data warehouse pros and cons analysis. Review the scenario for the final assessment. Using the scenario, develop a pros and cons analysis of implementing a data warehouse. Include the following elements:
1) Cost and return on investment (ROI),
2) Required resources,
3) Informational value, and
4) Limitations.
The final project for this course is a two-part project: an executive presentation and a technical proposal. The final project presents a detailed scenario regarding the merger of two insurance companies. For the project, the student is positioned as the chief information officer (CIO) and is asked to leaden initiative to merge the data infrastructures of both insurance companies into a single consolidated data warehouse. For this milestone (due in Module Two), you will submit your data warehouse pros and cons analysis. Review the scenario for the final assessment. Using the scenario, develop a pros and cons analysis of implementing a data warehouse. Include the following elements:
1) Cost and return on investment (ROI),
2) Required resources,
3) Informational value, and
4) Limitations.
The following critical elements will be addressed in this submission:
Pros and Cons:
a) Cost and Return on Investment: How is the cost of a data warehouse worth the investment? What type of information can a data warehouse provides that would make the cost more acceptable? How will the organization benefit from a data warehouse? Are there any negative consequences of having a data warehouse? Which specific operational areas will feel the benefits?
b) Required Resources: What are the costs associated with a data warehouse? Will any additional staff be required to maintain and support the data warehouse? Be sure to explain the importance of each resource you identify.
c) Informational Value: How can the information in a data warehouse add value to the organization? What specific business opportunities could be illuminated and how would the use of a DBMS help solve business problems?
d) Limitations: What are some functions that a data warehouse cannot perform? How scalable is a data warehouse? How can the organization overcome these obstacles to ensure data quality? Support your conclusions.