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