Course Outline

1. The Promise of BI
   • Data Challenges
   • The BI Organization
   • Actionable Insight
   
   
2. The BI Gap
   • Traditional Disparate Data Stores
   • A Pie Chart Is Not Enough
   
   
3. Filling The BI Gap
   • Foundations for Business Intelligence
   • Leading RDBMS Vendors
   • The BI Fabric
   • Information vs. Simple Data
   • Filling The BI Gap
   
   
4. Data Mining
   • Overview and Terms
   • What Is Data Mining
   • Data Mining Scenarios
   • Market Segmentation
   • Basket Rules Analysis
   • Classification
   • Value Prediction
   • Complete The Analytic Landscape
   • The BI Organization & Data Mining
   
   
5. Data Mining Labs: The objective of the lab exercises is to use data mining techniques to explore data sets and establish mining models in traditional ETL flow for data quality.
   (total lab time: 45 minutes)
   • Exercise #1:  Using Microsoft SQL 2005 Data Mining functions - Gain insight for running decision trees and cluster algorithms to enhance your BI solutions.
   
   
6. Human-Machine Intelligence
   • Decision Support Systems
   • Decision Paths
   • Human-Machine Relationships
   • Agent (Softbot) Characteristics
   • Business Rule Architecture
   
   
7. Business Rule Engine Lab: The objective of the lab exercises is to expose participants to the core components of leading rule engine products and the solutions they support.
   (total lab time: 45 minutes)
   • Exercise #1:  Using Blaze Business Rule Engine - Modify and publish and existing business rule decision path using a high-end software product from Fair Isaac called, Blaze.
   
   
8. Visualization
   • Overview and Terms
   • We Think Visually
   • Great Body of Evidence
   • Advanced Histograms
   • 3-Dimensional Intersections
   
   
9. Visualization Lab: The objective of the lab exercises is to demonstrate advanced visualization technology and how it impacts the insight gleaned from complex data.
   (total lab time: 60 minutes)
   • Exercise #1:  Using Tableau - Import traditional financial data into the Tableau software and perform several visual queries against the data set.
   • Exercise #2:  Using PolyVista - Examine complex, multi-dimensional OLAP data in a single, advanced visualization
   
   
10. Spatial Analysis
    • Overview and Terms
    • Beyond Simple Characterization
    • Complete & Comprehensive BI
    • Location Intelligence
    • Information Enhancement
    • Blending Space with BI
    • Information-rich Warehouse
    • The BI Organization & Spatial Data
    
    
11. Spatial Analysis Lab: The objective of the lab exercises is to demonstrate the enhancement of traditional warehouse data and reports with spatial data and analysis.
    (total lab time: 60 minutes)
    • Exercise #1:  Import, geocode and map traditional, structured data.
    • Exercise #2:  Experience the use of Business Analyst for advanced business intelligence applications.

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