Business Intelligence Integration Joel Da Costa, Takudzwa Mabande, Richard Migwalla Antoine Bagula,...
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Transcript of Business Intelligence Integration Joel Da Costa, Takudzwa Mabande, Richard Migwalla Antoine Bagula,...
Business Intelligence IntegrationJoel Da Costa, Takudzwa Mabande, Richard MigwallaAntoine 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
•Previously ‘one size fits all’•Which are actually the most effective ?
▫Bayesian Belief Networks (GA)▫Neural Networks (GA)▫Artificial Immune Systems
Cases
•Profiling Customers•Predictive Sales Forecasting
Aim
•See variance of results on same data•Define strengths and weaknesses in BI
technologies
ApproachBrief Look into the rationale behind our proposed solution
Overview
(Yet to add Diagram here…)
Input
•Previous Works▫S. Mahfoud and G. Mani▫P.-C. Chang
•Sanlam Specification▫Sales▫Income
Interface
•Simplified Interface▫Graphical Display ▫Relevant information▫Technical Data Hiding
System Approach 1:
Bayesian Belief Networks• Joel De Costa
(Diagram here)
System Approach 2:
Neural Networks (NN)• Takudzwa Mabande
System Approach 3:
Artificial Immune Systems (AIS)•Richard Migwalla•Overview
▫Abstraction of Human immune System(Diagram here)
Output:
•Sanlam Specification▫Predicted sales▫Customer Profile
Likely Purchase based on current income
Division Of Work
Bayesian Networks
Joel
Neural Networks Takudzwa
Artificial Immune SystemRichard
Connecting To
Database Joel
Customer DB
Interface
Richard
Sales DB Interface Takudzw
a
Sales & Customer
Visualisation
Takudzwa
GUIRichard
Timeline
RisksRisk Matrix Evaluation Avoidance Mitigation
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 deadlinesC. 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.
Resources
•Lab PC’s•Access to Sanlam Database•Java Development Enviroment•Project team
DB
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.
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)