The term "data warehouse" conveys many different meanings, which this course will examine in some depth. Whether the warehouse provides a historical look back through the organization?s transaction histories, or acts as an amalgam of many different data sets, from many different organizations, the key question the organization has to ask is why. Why build a data warehouse? Many different business processes are involved with and affected by the accumulation, extraction (mining), and interpolation of data that might exist between the real data points (data farming). Information quality, as a design discipline and as a management attitude, provides essential emphasis on assuring the right data comes in, to properly-designed and verified correct business decision processes, so that the right decisions can come out of the data warehouse and its operational use. Of all the many attributes and facets of data warehousing, data quality is undoubtedly the utmost significant one. Basically, this is because if the data is wrong, we place our organization at greater risk if we rely on the data warehouse for decision support.Over the past decade, a large number of vendors have saturated the market with several data warehousing products, and it is difficult to evaluate their offerings and help one?s organization choose wisely. Students will explore these issues by looking at selected steps in typical data warehousing projects, focusing on organizational objectives and needs, while examining the details of how data warehouses are designed, built, used and maintained. Administration, security, information quality and other key issues will also be placed in this project framework.
Data warehousing and business intelligence environments are growing into successful information delivery systems. Some companies such as American Airlines RADAR data warehouse and Ingenix, a wholly owned subsidiary of United Health Group Galaxy enterprise data warehouse, are fulfilling the massive benefits transformed into positive returns at the bottom line. This course puts successes like these -- and failures as well -- into an overall project planning context, by guiding the student through the key activities of project planning, requirements identification and analysis, and the design and development from both architectural and infrastructure perspectives. Data preparation, information quality and delivery, deployment and maintenance of a data warehousing project are also examined.
Upon successful completion of this course, the student will be able to: Identify the drivers and compelling need for data warehousing. Explain the relationship between risk, data warehousing, data and information quality. Plan the development and operational use of a data warehousing project to fit organizational needs. Describe the data warehouse infrastructure supporting architecture and the importance of metadata. Explain data and physical design concepts, including the structure of business dimensions, the architectural plan and dimensional modeling. Describe the extraction, transformation, and loading (ETL) process. Identify ETL tools and describe the functions they perform. Explain the many challenges of data and information quality. Identify indicators that can predict the success or failure of a data warehousing project, the major deployment activities and the various considerations for implementing, managing and maintaining a data warehouse.
Located on the Daytona Beach Campus, the Jack R. Hunt Library is the primary library for all students of the Worldwide Campus. The Chief Academic Officer strongly recommends that every faculty member, where appropriate, require all students in his or her classes to access the Hunt Library or a comparable college-level local library for research. The results of this research can be used for class projects such as research papers, group discussion, or individual presentations. Students should feel comfortable with using the resources of the library.
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