Decision support and BI systems ch08

Learning Objectivestransformation, and load ETL processes active data warehousing and security issues... Data MartA departmental data warehouse that stores only relevant data A subset t

Business Intelligence and Decision Support Systems

(9 th Ed., Prentice Hall)

Chapter 8:

Data Warehousing

 Describe the processes used in developing and managing data warehouses

 Explain data warehousing operations

 Explain the role of data warehouses in decision support

Learning Objectives

transformation, and load (ETL) processes

active) data warehousing

and security issues

Main Data Warehousing (DW) Topics

Data Warehouse Defined

are specially organized to provide wide, cleansed data in a standardized format

integrated, subject-oriented databases design

to support DSS functions, where each unit of data is non-volatile and relevant to some

moment in time”

Data Mart

A departmental data warehouse that stores only relevant data

A subset that is created directly from a data warehouse

A small data warehouse designed for a strategic business unit or a department

Data Warehousing Definitions

A type of database often used as an interim area for a data warehouse

An operational data mart

A data warehouse for the enterprise

Data about data In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use

A Conceptual Framework for DW

Data Sources

ERP

Legacy

POS

Other OLTP/wEB

External data

Select

Transform Extract

Integrate Load

ETL Process

Enterprise Data warehouse Metadata

Replication

Data/text mining

Custom built applications

OLAP, Dashboard, Web

Routine Business Reporting

Applications (Visualization)

Data mart (Engineering)

Data mart (Marketing)

Data mart (Finance)

Data mart ( ) Access

No data marts option

Generic DW Architectures

software

access and analyze data from the warehouse

A Web-based DW Architecture

Web Server

Client (Web browser)

Application Server

Data warehouse

Web pages

Internet/

Intranet/

Extranet

Alternative DW Architectures

SourceSystems

Staging Area

Independent data marts(atomic/summarized data)

End user access and applications

ETL(a) Independent Data Marts Architecture

SourceSystems

Staging Area

End user access and applications

ETL

Dimensionalized data marts linked by conformed dimentions(atomic/summarized data)

(b) Data Mart Bus Architecture with Linked Dimensional Datamarts

SourceSystems

Staging Area

End user access and applications

ETL

Normalized relational warehouse (atomic data)

(c) Hub and Spoke Architecture (Corporate Information Factory)

Alternative DW Architectures

SourceSystems

Staging Area

Normalized relational warehouse (atomic/some summarized data)

End user access and applications

ETL (d) Centralized Data Warehouse Architecture

End user access and applications

Logical/physical integration of common data elements

Existing data warehousesData marts and legacy systmes

Data mapping / metadata (e) Federated Architecture

Which Architecture is the Best?

Empirical study by Ariyachandra and Watson (2006)

Data Warehousing Architectures

1 Information interdependence between organizational units

2 Upper management’s information needs

3 Urgency of need for a data warehouse

4 Nature of end-user tasks

5 Constraints on resources

6 Strategic view of the data

warehouse prior to implementation

7 Compatibility with existing

Ten factors that potentially affect the

architecture selection decision:

Enterprise Data Warehouse (by Teradata Corporation)

Data Integration and the Extraction, Transformation, and Load (ETL) Process

Integration that comprises three major processes:

data access, data federation, and change capture

A technology that provides a vehicle for pushing data

from source systems into a data warehouse

An evolving tool space that promises real-time data integration from a variety of sources

A new way of integrating information systems

Extraction, transformation, and load (ETL) process

Data Integration and the Extraction, Transformation, and Load (ETL) Process

Packaged application

Legacy system

Other internal applications

Transient data source

Extract Transform Cleanse Load

Datawarehouse

Data mart

ETL

learning curve

number of data sources/architectures

functional user

Benefits of DW

 Allows end users to perform extensive analysis

 Allows a consolidated view of corporate data

 Better and more timely information

 Enhanced system performance

 Simplification of data access

 Enhance business knowledge

 Present competitive advantage

 Enhance customer service and satisfaction

 Facilitate decision making

 Help in reforming business processes

Data Warehouse Development

 There is no one-size-fits-all strategy to DW

DW Development Approaches

(Inmon Approach) (Kimball Approach)

DW Structure: Star Schema (a.k.a Dimensional Modeling)

Claim Information

Time Location

Start Schema Example for anAutomobile Insurance Data Warehouse

Dimensions:

How data will be sliced/

diced (e.g., by location, time period, type of automobile or driver)

Facts:

Central table that contains (usually summarized) information; also contains foreign keys to access each dimension table

object in which each

dimension of the data

Best Practices for Implementing DW

business professionals (a business–supplier relationship must be developed)

Risks in Implementing DW

(Continued …)

Risks in Implementing DW – Cont

Things to Avoid for Successful Implementation of DW

because it is available

design is the same as transactional DB design

technology oriented rather than user oriented

(…see more on page 356)

Real-time DW (a.k.a Active Data Warehousing)

real-time analysis and real-time decision making is growing rapidly

Evolution of DSS & DW

Active Data Warehousing (by Teradata Corporation)

Comparing Traditional and Active DW

Data Warehouse Administration

DW requires especially strong monitoring in order to sustain its efficiency, productivity and security.

management of a data warehouse entails skills and proficiency that go past what is required of a traditional database

administrator.

hardware, and networking technologies

DW Scalability and Security

 The amount of data in the warehouse

 How quickly the warehouse is expected to grow

 The number of concurrent users

 The complexity of user queries

data-access functions will grow linearly with the size of the warehouse

BI / OLAP Portal for Learning

End of the Chapter