Business Intelligence Systems Appendix J DAVID M. KROENKE and DAVID J. AUER DATABASE CONCEPTS, 6 th Edition

Business Intelligence Systems

Appendix J

DAVID M. KROENKE and DAVID J. AUER

DATABASE CONCEPTS, 6th Edition

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Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Chapter Objectives

• Learn the basic concepts of business intelligence (BI) systems

• Learn the basic concepts of reporting systems and data mining

KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

J-3

Heather Sweeney Designs Review:Database Design


J-4

Heather Sweeney Designs Review:HSD Database Diagram in SQL Server 2012


J-5


• 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; and

– Data mining applications that perform sophisticated analyses on data that usually involve complex statistical and mathematical processing.


J-6

The Relationship Among Operational and BI Applications


J-7

Figure J-1: Characteristic of Business Intelligence Systems

Characteristics of Business Intelligence Applications Review


J-8

Characteristics of aData Warehouse


J-9

Figure J-2: Components of a Data Warehouse

Problems with Operational Data

• “Dirty Data”– Example: “G” for Gender– Example: “213” for Age

• Missing Values• Inconsistent Data

– Example: data that has changed, such as a customer’s phone number


J-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


J-11

ETL Data Transformation

• Data may need to be transformed for use in a data warehouse– Example:

• {CountryCode CountryName}• “US” “United States”

– Example:• Email address to Email domain• [email protected] “somewhere.com”


J-12

mailto:[email protected]

Characteristics of aData Mart


J-13

Figure J-3: Data Warehouses and Data Marts

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.


J-14

Dimensional Database Review

• 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


J-15

Star Schema Review


J-16

HSD-DW Star Schema Review


J-17

Conformed Dimensionsand the Extended HSD-DW Schema


J-18

Figure J-4: Dimensional Databases and the Star Schema

The RFM Score Report


J-19

Figure J-5: The RFM Score Report

Reporting Systems


J-20

Figure J-6: Components of a Reporting System

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%


J-21

Reporting Systems: Report Characteristics


J-22

Figure J-7: Report Characteristics

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


J-23

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.– Discussed in Chapter 8

• Data Mining is a mathematically sophisticated technique for analyzing database data.– Data mining uses mathematical and statistical

techniques .KROENKE and AUER - DATABASE CONCEPTS (6th Edition) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

J-24

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.


J-25

Data Mining Applications:The Convergence of the Disciplines


J-26

Figure J-8: Convergence of Disciplines for Data Mining

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

J-27

Data Mining Applications:Cluster Analysis I


J-28

Figure J-9:The Microsoft Excel 2010 with the Microsoft SQL Server 2012 Data Mining Add-ins

Data Mining Applications:Cluster Analysis II


J-29

Figure J-10: The Cluster Analysis Results

Data Mining Applications:Cluster Analysis III


J-30

Figure J-11: The Cluster Analysis Results (cont’d)

Data Mining Applications:Market Basket Analysis


J-31

Figure J-12: A Market Basket Analysis Example


End of Presentation on Appendix J

DAVID M. KROENKE and DAVID J. AUER

DATABASE CONCEPTS, 6th Edition

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Business Intelligence Systems Appendix J DAVID M. KROENKE and DAVID J. AUER DATABASE CONCEPTS, 6 th Edition