1. DATA WAREHOUSING

2. Legacy System
Systems that were developed in the early years of business processing
Rich source of historical data, but it’s difficult to retrieve, because of non-standard features
This is why we need data warehouse

3. Problems with Legacy System
Access data from a legacy system may be difficult for several reasons:
Developed for a different hardware or software platform
Use a different data model
Use a different DBMS
Use a different data definitions
Use a different data format
All these make difficulty in integration and sharing data

4. Data Definitions Problems
Homonyms – use different field names to store the same data in the different database
Synonyms - use the same field names to store different data in the different database
Domain integrity – domain for the same field may be different
Business rules – may be different in different database
Referential integrity – may be problems linking related records from different databases
Concurrency control – when multiple users access a database that design for single user

5. Data Warehouse Concepts
Technique of extracting and filtering data from diverse database and use this data to build a new database
Stores information extracted from historical, operational and external databases
The primary purpose : to provide information for management decision making

6. Database vs data warehouse
Activity
Database
Data warehosue
Function
Support business operation
Support decision making
Data
Process oriented
Subject-oriented
Usage
Structured, repetitive
Unstructured, repetitive
Processing
Data entry
End user initiated queries

7. Data Warehouse Architecture
Operational database / external database layer
Information access layer
Data access layer
Metadata layer
Process management layer
Application messaging layer
Physical layer
Data staging layer

8. Data Warehouse Implementation
Data – includes operational, historical and external data
Extraction and transformation – extract and transform data in different table
Data warehouse storage – store the extracted and transformed data in different table
Historical data – used for forecasting purposes
Reports, statistics, data analysis and presentation – output from data warehouse to make a decision

9. Data Warehouse : Benefits and Risks
Reduces reporting cost
Reduces data consolidation and integration cost
Increase efficiency and decision making capabilities
Risks
House the wrong data
Expensive to build and maintain
Require organizational changes

10. Online Analytical Processing
Support data modeling and multidimensional data analysis
Share the characteristics :
Provide user-friendly interface
Use multidimensional data analysis technique
Provide advanced database support
Support client/server architecture

11. Online Analytical Processing
Can be classified :
Relational Online Analytical Processing – use RDBMS
Multidimensional Online Analytical Processing – extension of RDBMS

12. Data Mining
Data mining is a decision support tools that enables a user to access directly large amount of data and analyzes the data
Data mining is the set of activities used to find new, hidden, or unexpected patterns in data

13. Data Mining Technique Data mining process has four phases :
Data preparation – main data sets to be used are identified and cleaned
Data analysis and classification – identify common data characteristic or pattern
Knowledge acquisition – develop a model resemble target data
Prediction – used to predict future behaviour and forecast business outcomes

14. Data Mining Tools
Data mining tools today has this following characteristics :
Data preparation facilities
Selection of data mining operations
Product scalability and performance
Facilities for visualization of results

15. END