1. Database Processing for Business Intelligence Systems
DAVID M. KROENKE and DAVID J. AUER
DATABASE CONCEPTS, 4th Edition
Chapter Eight
Database Processing for Business Intelligence Systems

2. Chapter Objectives
Learn the basic concepts of data warehouses and data marts
Learn the basic concepts of dimensional databases
Learn the basic concepts of business intelligence (BI) systems
Learn the basic concepts of OLAP and data mining
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

3. Heather Sweeney Designs: Database Design
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

4. Heather Sweeney Designs: HSD Database Diagram in SQL Server 2005
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5. Business Intelligence Systems
Business intelligence (BI) systems are information systems that
Assist managers and other professionals in the analysis of current and past activities and in the prediction of future events
Do not support operational activities, such as the recording and processing of orders
These are supported by transaction processing systems
Support management assessment, analysis, planning and control
BI systems fall into two broad categories
Reporting systems that sort, filter, group, and make elementary calculations on operational data
Data mining applications that perform sophisticated analyses on data, analyses that usually involve complex statistical and mathematical processing
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6. The Relationship Among Operational and BI Applications
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

7. Characteristics of Business Intelligence Applications
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

8. Characteristics of a Data Warehouse
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

9. Problems with Operational Data
“Dirty Data”
Example – “G” for Gender
Example – “213” for Age
Missing Values
Inconsistent Data
Example – data that have changed, such as a customer’s phone number
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

10. Problems with Operational Data (Continued)
Nonintegrated Data
Example – data from two or more sources that need to be combined
Incorrect Format
Example – time data in hours when needed in minutes
Too Much Data
Example – An excess number of columns
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

11. ETL Data Transformation
Data may need to be transformed for use in a data warehouse
Example
{CountryCode  CountryName}
“US”  “United States”
address to domain
 “somewhere.com”
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12. Characteristics of a Data Mart
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

13. Enterprise Data Warehouse (EDW) Architecture
Combines the data warehouse structure and the data mart structures shown above
Expensive to create, staff and operate
Smaller organizations use subsets of the EDW architecture
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

14. Dimensional Databases
A non-normalized database structure used for data warehouses
May use slowly changing dimensions
Values change infrequently
Phone Number
Address
Use a Date or Time dimension
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

15. Star Schema
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16. HSD-DW Star Schema
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17. Two-Dimensional Matrix
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18. Three-Dimensional Matrix
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19. Conformed Dimensions and the Extended HSD-DW Schema
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20. Reporting Systems
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21. Reporting Systems: RFM Analysis
RFM Analysis analyzes and ranks customers according to purchasing patterns:
R = Recent (most recent order)
F = Frequent (how often an order is made)
M = Money (dollar amount of orders)
Customers are sorted into five groups, each containing 20% of the customers.
Each group is given a numerical value:
1 = Top 20%
2, 3, 4 = Each 20% in between top and bottom 20%
5 = Bottom 20%
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22. The RFM Score Report
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23. Reporting Systems: Report Characteristics
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24. Reporting Systems: Report System Functions
Report Authoring:
Connect to data sources
Create the report structure
Format the report
Report Management:
Defines who receives what reports when and by what means
Report Delivery:
Push reports or allow them to be pulled
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

25. OLAP and Data Mining
OnLine Analytical Processing (OLAP) is a technique for dynamically examining database data
OLAP uses arithmetic functions such as Sum and Average
Data Mining is a mathematically sophisticated technique for analyzing database data
Data mining uses mathematical and statistical techniques
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

26. OLAP OLAP systems produce an OLAP report, also know as an OLAP cube
The OLAP report uses inputs called dimensions
The OLAP report calculates outputs called measures
Excel PivotTables can be used to create OLAP reports
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27. Excel PivotTable OLAP Report I
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28. Excel PivotTable OLAP Report II
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29. Excel PivotTable OLAP Report III
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30. Data Mining Applications: The Convergence of the Disciplines
KROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall

31. Data Mining Applications: Popular Data Mining Techniques
Cluster analysis – Identifies groups of entities that have similar characteristics
Decision tree analysis – Classifies entities into groups based on past history
Logistic regression – Produces equations that offer probabilities that certain events will occur
Neural Networks – Complex statistical prediction techniques
Market Basket Analysis – Determines patterns of associated buying behavior
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32. Data Mining Applications: Cluster Analysis I
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33. Data Mining Applications: Cluster Analysis II
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34. Data Mining Applications: Cluster Analysis III
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35. Data Mining Applications: Market Basket Analysis
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36. Database Processing for Business Intelligence Systems
DAVID M. KROENKE and DAVID J. AUER
DATABASE CONCEPTS, 4th Edition
End of Presentation on Chapter Eight
Database Processing for Business Intelligence Systems

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