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Financial Broker Data Warehouse and Mining Strategies

Enhance revenue tracking, client investment protection, and customer risk awareness for financial brokers using data warehousing and mining techniques. Report on revenue by state, investment, high-risk customers, and employee sales. Analyze customer and employee data to improve business decisions.

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Financial Broker Data Warehouse and Mining Strategies

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  1. Benchmark financial brokers Data Warehousing and Data Mining Terry Lee Sandeep Udhani Tran Ngo Navin Negi UNIVERSITY OF HOUSTON-CLEAR LAKE

  2. Business Need • Helps owner and regional managers keep track of revenue (sales commission) from each branch. • Strategic information needed in order to protect client investments. • Strategic information needed to better inform customers about the risk of their investments.

  3. Business Measurements • Revenue by State • Revenue by Investment • Customers with High Risk Investments • High Risk Investments by Employee

  4. Initial Snowflake Design

  5. Revised Star Schema

  6. Relational Implementation - Access

  7. Physical Data – Transaction Table

  8. Physical Data – Investment Table

  9. Physical Data – Employee Table

  10. Physical Data – Customer Table

  11. Physical Data – Date-Time Table

  12. Attribute Relationships

  13. Creating Dimensional Hierarchies

  14. Cube Implementation in SQLServer

  15. Browsing The Cube

  16. Create Reports with Report Services

  17. Designing Reports

  18. Designing Reports

  19. Designing Reports

  20. Customers with High Risk Investments

  21. Sales Revenue by State

  22. Revenue by Investment

  23. High Risk Investments by Employee

  24. General Conclusions • There are a few employees who are regularly selling High Risk Investments • Texas is our highest grossing State

  25. The END • Any Questions or Comments?

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