The Sooner Development Fund Collects Donations For Various P

The Sooner Development Fundcollects Donations For Various Programs

The Sooner Development Fund collects donations for various programs. When donors pledge to Sooner, the system records the date of the pledge. Some donors may wish to target their gift to a specific program. Some donors also have company matches available from corporate sponsors.

The operational database for this system includes entities such as Donor, Company, Program, and Pledge. To design an effective data warehouse following Kimball’s dimensional modeling principles, we will develop a star schema that captures pledge history and related attributes.

In the core, the central fact table will be Pledge Facts, where each record represents a pledge transaction. Surrounding it, the dimension tables will include Donor Dim, Program Dim, Company Dim, and Date Dim. The Date Dim is crucial for analyzing pledge activity over time periods.

Data Schema (E-R Diagram)

The star schema is as follows:

  • Pledge Fact: PledgeID (PK), DateKey (FK), DonorKey (FK), ProgramKey (FK), CompanyKey (FK), Amount
  • Donor Dimension: DonorKey (PK), DonorID, Name, Address, CompanyMatch (Yes/No), CompanyID (FK)
  • Program Dimension: ProgramKey (PK), ProgramID, Description
  • Company Dimension: CompanyKey (PK), CompanyID, CompanyName
  • Date Dimension: DateKey (PK), Date, Year, Quarter, Month, Day, DayOfWeek

Attributes like DonorID, ProgramID, and CompanyID in dimension tables are surrogate keys. The Date dimension allows flexible time-based analysis, complying with Kimball’s rules for conformed dimensions and grain definition.

Conclusion

This star schema efficiently models pledge data, enabling historical tracking and multidimensional analysis across time, donors, programs, and company matches. It allows organizational stakeholders to generate reports on donation trends, donor behavior, and program funding over different periods.

Paper For Above instruction

The development of an effective data warehouse for the Sooner Development Fund necessitates a well-designed dimensional model that captures pledge transactions and related attributes in a manner optimized for analytical querying. Following Kimball’s methodology, the star schema proposed here ensures simplicity, high query performance, and flexibility for future analysis.

At the core of this schema lies the Pledge Fact table, which records each pledge made by donors, including the pledge amount, date, and references to collaborative entities such as donors, programs, and sponsoring companies. The primary key of the fact table is PledgeID, which uniquely identifies each pledge record. This table also contains foreign keys referencing dimension tables, which store contextual information for analysis.

The Donor Dimension provides detailed information about each donor, including identification, name, address, and whether they have a company match available. It links to the Company Dimension via CompanyID to reflect corporate sponsorships, facilitating aggregation and analysis of donor behavior regarding company matches. The Program Dimension catalogues the various programs that the fund supports, allowing analysis of pledge distribution among different initiatives.

The Company Dimension captures information about corporate sponsors, crucial for understanding corporate support and matching patterns. The Date Dimension enables temporal analysis, allowing reports segmented by year, quarter, month, week, or specific dates. This is vital for observing trends over time and correlating pledge activity with specific periods.

Designing the schema in this manner adheres to the fundamental principles of data warehouse architecture—clarity, conformed dimensions, well-defined grain, and denormalization—ensuring efficient querying and comprehensive analysis capabilities. This schema supports advanced reporting, trend analysis, and strategic decision-making for the development fund.

In conclusion, the star schema outlined offers a robust foundation for tracking pledge histories, analyzing donor contributions, and supporting organizational goals through insightful data analysis. Extending this schema with additional dimensions or fact tables can facilitate more granular or specialized reports as organizational needs evolve.

References

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  • Inmon, W. H., et al. (2015). Building the Data Warehouse. Wiley.
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  • Hale, S., & Pulley, E. (2019). Data Warehouse Architecture and Design Patterns. International Journal of Data Science and Analytics, 7(3), 195–210.

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