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

Database Processing forBusiness Intelligence Systems

Chapter Eight

DAVID M. KROENKE and DAVID J. AUER

DATABASE CONCEPTS, 4th Edition

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 8-2

Heather Sweeney Designs:Database Design


Heather Sweeney Designs:HSD Database Diagram in SQL Server 2005


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


The Relationship Among Operational and BI Applications


Characteristics of Business Intelligence Applications


Characteristics of aData Warehouse


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


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

columnsKROENKE and AUER - DATABASE CONCEPTS (4th Edition) © 2010, 2008 Pearson Prentice Hall 8-10

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”


Characteristics of aData Mart


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


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


Star Schema


HSD-DW Star Schema


Two-Dimensional Matrix


Three-Dimensional Matrix


Conformed Dimensionsand the Extended HSD-DW Schema


Reporting Systems


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%


The RFM Score Report


Reporting Systems: 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


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


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


Excel PivotTableOLAP Report I


Excel PivotTableOLAP Report II


Excel PivotTableOLAP Report III


Data Mining Applications:The Convergence of the Disciplines


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

Data Mining Applications:Cluster Analysis I


Data Mining Applications:Cluster Analysis II


Data Mining Applications:Cluster Analysis III


Data Mining Applications:Market Basket Analysis


Database Processing forBusiness Intelligence Systems

End of Presentation on Chapter Eight

DAVID M. KROENKE and DAVID J. AUER

DATABASE CONCEPTS, 4th Edition

8-37

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