A business of

Overview of SAP BW – with particular reference to release 3.0A

Briony Dobbs, PwC ECOE Walldorf.

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1. Agenda

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Contents

The rationale behind Datawarehousing and the SAP BW Introducing the Administrator Workbench Data Modeling and Loading (Master Data) Data Extraction (OLTP and Remote Systems) The ODS Business Content Special Topics Production Support

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SAP BW 3.0A - New Features

InfrastructureInstallation

Archiving

Open Hub

XML Exchange

WorkbenchODS

Aggregates

MOLAP

Master Data

Transformation Engine

Hierarchy Upload

Process Chains

Info Sets

Data Mining

ReportingBEx

Web-Reporting

Crystal Reports

Document Integration

Mobile Intelligence

Portal Integration

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2. Data Warehousing and the SAP BW Overview and Concepts

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Globalization

Faster reaction to market

Decentralization

Increasing importance of service

Smaller margins

Business Trends

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Immediate, single-point access to all relevant information regardless of the source

Coverage of entire business processes

High quality of information

Shorter implementation time with fewer resources

Sophisticated decision support

Consequences for Information Systems

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Operational Environment Decision Support Environment

Detailed, transaction data Current data with minimal

history Changing with business events Minimally integrated with data

from other modules/applications Highly normalized for

performance

Update/Insert/Delete 30-60 days of data Only data for specific function

of application

Often summarized data Significant history required Fixed as of a specific point in

time Significant integration with data

from other modules/applications Often highly denormalized or

restructured for queries Read only 2-7 years of data Data not associated with an

application, but rather integrated from various applications

Operational vs. Decision Support Environments

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Data Warehouse Objectives

• Standardized structures and representation for all enterprise information

• Easy-to-use access, single-point of access to all enterprise information

• Self-service, high quality business reporting and analysis on all levels

• Fast and cost-effective to deploy

• High performance environment fed from heterogeneous sources

• Freed-up systems and IT resources in OLTP environment

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SAP Business Information Warehouse

• Data Warehouse system with optimized structures for reporting and analysis

• OLAP engine and tools

• Integrated meta data repository

• Data extraction and data staging in OLTP

• Preconfigured support for data sources from R/3 Systems

• BAPIs for data sources from non-SAP systems

• Automated Data Warehouse management

• Administrator Workbench for controlling and managing content

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BW and the Business Framework

Data Sources

- Separate shipment- Separate release cycle- Separate maintenance- Available for R/3 3.0D and later

Preconfigured withSAP´s business

process know-how

BusinessInformation Warehouse

Server

BusinessInformation Warehouse

Server

BusinessExplorerBusinessExplorer

3rd party3rd party

R/2R/3R/2R/3 external

sourcesexternalsources

Data Presentation

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Business Content Overview – find new

Preconfigured, best practice business objects that reduce the time and expense of data warehouse implementations

Includes a wide range of roles, industries and applications Evolutionary in nature

R/3 Data elements,Properties,Relationships

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Business Information Warehouse Architecture

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What is an InfoObject ?

The various OLTP data models are unified for BW Business objects / data elements become

InfoObjects

InfoObject “0COSTCENTER”

InfoObjects are unique across application components !

R/3 OLTP

COCOControllingControlling

HRHRHuman Human

ResourcesResources

KOSTL ...

Table of cost centers

Table of employeesEMPLO COST_CENTER ...

BW Extractor

DataSourcefor

Cost Center

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Types of InfoObjects

Characteristics: evaluation groups like “Cost Center”, “Product group”, “Material”

Have discrete values stored in their master data tables(e.g. the characteristic “Region” has the values “North”, “South”, ... )

Special types of characteristics: Time characteristics like “Fiscal period”, “Calendar

year”, ... Unit characteristics which comprise currencies and

units of measure like “Local currency” or “Sales quantity”

Keyfigures: continuously valued numerical fields like amounts and quantities (e.g.: “Revenue” and “Sales quantity”)

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Transfer Rules

Update Rules

InfoCubes

Communication structureCommunication structure

Transfer StructureTransfer Structure

Extract Source StructureExtract Source Structure

Business InformationWarehouse Server

Staging Engine

OLTP System 1 OLTP System 2

Extract Source StructureExtract Source Structure

Transfer StructureTransfer Structure

Transfer StructureTransfer StructureTransfer StructureTransfer Structure

Extract Source StructureExtract Source Structure

Transfer StructureTransfer Structure

DataSource

Transfer StructureTransfer Structure

InfoSource

Transfer RulesTransfer Rules

(Replicated)

DataSource and InfoSource

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InfoCube: SAP BW Design

Central data stores for reports and evaluations Contains two types of data:

Key Figures Characteristics

1 Fact Table and up to 16 Dimension Tables 3 Dimensions are predefined by SAP

Time Unit Info Package

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Customer group

Reg

ion

InfoCube: Example

Division

Dept. Stores

Wholesale

Retail

Glass- Ceramics Plastics Pottery Copper Pewter ware

No

rth

So

uth

Eas

t

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Reg

ion

Nor

thS

outh

Eas

t

Glass-ware

Ceramics

Customer group

Division

RetailWholesale

DeptStores

Analysisof Ceramicsdivision

Analysisof Plasticsdivision

Analysis of Plastics divisionand Southern region

Reg

ion

Nor

thS

outh

Eas

t

Glass-ware

CeramicsPlastics

Customer group

Division

RetailWholesale

DeptStores Reg

ion

Nor

thS

outh

Eas

t

Glass-ware

Ceramics Plastics

Customer group

Division

RetailWholesale

DeptStores

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Reg

ion

Nor

thS

outh

Eas

t

Glass-ware

Ceramics Plastics

Customer group

Division

RetailWholesale

DeptStores

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Product groupCustomer groupDivisionAreaCompany codeRegionPeriodProfit CenterBus. Area

Plastics

Characteristics:Query Cache InfoCube

InfoCube: Multi-dimensional analysis

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Division

1100RT-0001

NorthPlasticsRetail Trade

SalesHours worked

4,000,0001,300,000 Key Figures

Character- istics

Customer group

Reg

ion

Key Figures are stored for a unique combination of Characteristic Values Number of dimensions is degree of granularity / summarization level of the

dataset

InfoCube: Characteristics and Key Figures