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Data Warehousing and SAP BW

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Learn the concept of data warehousing and how SAP BW (Business Information Warehouse) can help you by examining its architecture.
This chapter is from the book

The objective of data warehousing is to analyze data from diverse sources to support decision making. To achieve this goal, we face two challenges:

  • Poor system performance. A data warehouse usually contains a large volume of data. It is not an easy job to retrieve data quickly from the data warehouse for analysis purposes. For this reason, the data warehouse design uses a special technique called a star schema.

  • Difficulties in extracting, transferring, transforming, and loading (ETTL) data from diverse sources into a data warehouse. Data must be cleansed before being used. ETTL has been frequently cited as being responsible for the failures of many data warehousing projects. You would feel the pain if you had ever tried to analyze SAP R/3 data without using SAP BW.

SAP R/3 is an ERP (Enterprise Resources Planning) system that most large companies in the world use to manage their business transactions. Before the introduction of SAP BW in 1997, ETTL of SAP R/3 data into a data warehouse seemed an unthinkable task. This macro-environment explained the urgency with which SAP R/3 customers sought a data warehousing solution. The result is SAP BW from SAP, the developer of SAP R/3.

In this chapter we will introduce the basic concept of data warehousing. We will also discuss what SAP BW (Business Information Warehouse) is, explain why we need it, examine its architecture, and define Business Content.

First, we use sales analysis as an example to introduce the basic concept of data warehousing.

1.1 Sales Analysis—A Business Scenario

Suppose that you are a sales manager, who is responsible for planning and implementing sales strategy. Your tasks include the following:

  • Monitoring and forecasting sales demands and pricing trends

  • Managing sales objectives and coordinating the sales force and distributors

  • Reviewing the sales activities of each representative, office, and region

Suppose also that you have the data in Tables 1.1 through 1.3 available about your firm's materials, customers, and sales organization.

Table 1.1 Materials

Material Number

Material Name

Material Description

MAT001

TEA

Ice tea

MAT002

COFFEE

Hot coffee

MAT003

COOKIE

Fortune cookie

MAT004

DESK

Computer desk

MAT005

TABLE

Dining table

MAT006

CHAIR

Leather chair

MAT007

BENCH

Wood bench

MAT008

PEN

Black pen

MAT009

PAPER

White paper

MAT010

CORN

America corn

MAT011

RICE

Asia rice

MAT012

APPLE

New York apple

MAT013

GRAPEFRUIT

Florida grapefruit

MAT014

PEACH

Washington peach

MAT015

ORANGE

California orange


Table 1.2 Customers

Customer ID

Customer Name

Customer Address

CUST001

Reliable Transportation Company

1 Transport Drive, Atlanta, GA 23002

CUST002

Finance One Corp

2 Finance Avenue, New York, NY, 10001

CUST003

Cool Book Publishers

3 Book Street, Boston, MA 02110

CUST004

However Forever Energy, Inc.

4 Energy Park, Houston, TX 35004

CUST005

Easy Computing Company

5 Computer Way, Dallas, TX 36543

CUST006

United Suppliers, Inc.

6 Suppliers Street, Chicago, IL 61114

CUST007

Mobile Communications, Inc.

7 Electronics District, Chicago, IL 62643

CUST008

Sports Motor Company

8 Motor Drive, Detroit, MI 55953

CUST009

Swan Stores

9 Riverside Road, Denver, CO 45692

CUST010

Hollywood Studi10 Media Drive,

10 Los Angeles, CA 78543

CUST011

One Source Technologies, Inc.

11 Technology Way, San Francisco, CA 73285

CUST012

Airspace Industries, Inc.

12 Air Lane, Seattle, WA 83476


Table 1.3 Sales Organization

Sales Region

Sales Office

Sales Representative

Sales Representative ID

EAST

ATLANTA

John

SREP01

 

NEW YORK

Steve

SREP02

 

 

Mary

SREP03

MIDWEST

DALLAS

Michael

SREP04

 

 

Lisa

SREP05

 

CHICAG

Kevin

SREP06

 

 

Chris

SREP07

WEST

DENVER1

Sam

SREP08

 

LOS ANGELES

Eugene

SREP09

 

SEATTLE

Mark

SREP10


You also have three years of sales data, as shown in Table 1.4.

Table 1.4 Sales Data

Customer ID

Sales Representative ID

Material Number

Per Unit Sales Price

Unit of Measure

Quantity Sold

Transaction Date

CUST001

SREP01

MAT001

2

Case

1

19980304

CUST002

SREP02

MAT002

2

Case

2

19990526

CUST002

SREP02

MAT003

5

Case

3

19990730

CUST003

SREP03

MAT003

5

Case

4

20000101

CUST004

SREP04

MAT004

50

Each

5

19991023

CUST004

SREP04

MAT005

100

Each

6

19980904

CUST004

SREP04

MAT005

100

Each

7

19980529

CUST005

SREP05

MAT006

200

Each

8

19991108

CUST006

SREP06

MAT007

20

Each

9

20000408

CUST007

SREP07

MAT008

3

Dozen

10

20000901

CUST007

SREP07

MAT008

3

Dozen

1

19990424

CUST008

SREP08

MAT008

3

Dozen

2

19980328

CUST008

SREP08

MAT009

2

Case

3

19980203

CUST008

SREP08

MAT010

1

U.S. pound

4

19991104

CUST009

SREP09

MAT011

1.5

U.S. pound

5

20000407

CUST010

SREP10

MAT011

1.5

U.S. pound

6

20000701

CUST010

SREP10

MAT011

1.5

U.S. pound

7

19990924

CUST010

SREP10

MAT012

2

U.S. pound

8

19991224

CUST010

SREP10

MAT013

3

Case

9

20000308

CUST011

SREP10

MAT014

1

U.S. pound

10

19980627

CUST012

SREP11

MAT014

2

U.S. pound

1

19991209

CUST012

SREP11

MAT015

3

Case

2

19980221

CUST012

SREP11

MAT015

2

Case

3

20000705

CUST012

SREP11

MAT015

3.5

Case

4

20001225


The data in these tables represent a simplified business scenario. In the real world, you might have years of data and millions of records.

To succeed in the face of fierce market competition, you need to have a complete and up-to-date picture of your business and your business environment. The challenge lies in making the best use of data in decision support. In decision support, you need to perform many kinds of analysis.

This type of online analytical processing (OLAP) consumes a lot of computer resources because of the size of data. It cannot be carried out on an online transaction processing (OLTP) system, such as a sales management system. Instead, we need a dedicated system, which is the data warehouse.

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