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) Takudzwa Mabande

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 Neural Networks Takudzwa Artificial Immune System Richard Connecting To Database Joel Customer DB Interface Richard Sales DB Interface Takudzwa Sales & Customer Visualisation Takudzwa GUI Richard

16. Timeline

19. Risks RiskMatrix EvaluationAvoidanceMitigation 1. Loss of a project team member. D. Serious/ Low Probability Pressure to stay on the project as failure to do so means not graduating. Have sufficiently independent deliverable modules for each team member. 2. Delay in Delivery of test data. C. Disastrous/ Low Probability Pressure Sanlam to provide data as soon as possible. Create random test data or use alternative available data. 3. Scope creep (Plan too many tasks, Cannot complete tasks in time) E. Marginal/ Low Probability Project planned in detail with supervisor and department approval. Start with fundamental features first and leave other things to the end. 4. Data loss due to hardware failure, (External Factor) C. Serious/ Medium Probability Frequent backups of all progress on different machines or storage devices. Roll back to last backup. 5. Missing project deadlines C. Serious/ Medium Probability Constant reference to the project timeline and clear communication between project members Review and reassess deadlines; readjusting where necessary- as cost-effectively as possible. 6. Misunderstanding User requirements. (Resultant of miscommunication/ ambiguity in user-team interaction) D. Serious/ Low Probability Constant communication with Sanlam and providing them with project plan and design in order to detect flaws. Iterations through development so that inconsistencies can be detected early.

20. Resources Lab PC’s Access to Sanlam Database Java Development Enviroment Project team

21. 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.

22. 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)