Data Warehousing and SAP BW

Aug 16, 2002

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

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SAP BW: A Step-by-Step Guide: A Step-by-Step Guide

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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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