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Business Intelligence Integration

Business Intelligence Integration. Joel Da Costa, Takudzwa Mabande, Richard Migwalla Antoine Bagula, Joseph Balikuddembe. Business Intelligence. What How Why. Current BI Trends. Predictive Analysis Real-Time Monitoring In-Memory Processing Software as a Service. Problem Statement.

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Business Intelligence Integration

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  1. Business Intelligence Integration

    Joel Da Costa, Takudzwa Mabande, Richard Migwalla Antoine Bagula, Joseph Balikuddembe
  2. Business Intelligence What How Why
  3. Current BI Trends Predictive Analysis Real-Time Monitoring In-Memory Processing Software as a Service
  4. Problem Statement Previously ‘one size fits all’ Which are actually the most effective ? Bayesian Belief Networks (GA) Neural Networks (GA) Artificial Immune Systems
  5. Cases Profiling Customers Predictive Sales Forecasting
  6. Aim See variance of results on same data Define strengths and weaknesses in BI technologies

  7. Approach

    Brief Look into the rationale behind our proposed solution
  8. Overview (Yet to add Diagram here…)
  9. Input Previous Works S. Mahfoud and G. Mani P.-C. Chang Sanlam Specification Sales Income
  10. Interface Simplified Interface Graphical Display Relevant information Technical Data Hiding
  11. System Approach 1: Bayesian Belief Networks Joel De Costa (Diagram here)
  12. System Approach 2: Neural Networks (NN) TakudzwaMabande
  13. System Approach 3: Artificial Immune Systems (AIS) Richard Migwalla Overview Abstraction of Human immune System (Diagram here)
  14. Output: Sanlam Specification Predicted sales Customer Profile Likely Purchase based on current income
  15. Division Of Work Bayesian Networks Joel Customer DB Interface Richard Sales & Customer Visualisation Takudzwa GUI Richard Connecting To Database Joel Artificial Immune System Richard Sales DB Interface Takudzwa Neural Networks Takudzwa

  16. Timeline

  17. Risks
  18. Resources Lab PC’s Access to Sanlam Database Java Development Enviroment Project team
  19. Anticipated Outcomes We will create a package that will: Read in data from the Sanlam database. Use different machine learning techniques to profile customers and forecast sales. Compare the accuracy of the different techniques using actual data. Identify the best technique for use in each particular scenario.
  20. Key Success Factors Identifying the best technique for Customer Profiling Identifying the best technique for Sales Forecasting All techniques performing approximately the same amount of work (i.e. same data, about the same time, relatively the same complexity)
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