Enterprise Wide Data Warehousing With SAP BW

8/8/2019 Enterprise Wide Data Warehousing With SAP BW

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Ent erpr ise Wide Dat aWarehousing w i t h SAP BW

SAP NetWeaver Regional Implementation Group -BISAP AG


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SAP AG 2004, Enterprise Wide BW, J. Haupt / 2

Content

Enterprise Wide BWEnterprise Wide BW -- BackgroundBackground

Elements of a Corporate BW StrategyElements of a Corporate BW Strategy

The BW Data Model Prevents SilosThe BW Data Model Prevents Silos

BW Data Layers Guarantee Reliability and FlexibilityBW Data Layers Guarantee Reliability and Flexibility

BW Corporate Landscape PatternsBW Corporate Landscape Patterns

Content


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4/38 SAP AG 2004, Enterprise Wide BW, J. Haupt / 4

Ent erpr ise Perspec t ive on BW

Enterprise perspective on BW:

Higher level organizational units want

to harmonize the lower level BW initiatives

Architecture-based BW implementation

from the very beginning Stabilize a mature BW landscape

(Overhaul the architecture)

GroupGroup

Division ADivision A

Division BDivision B

Division CDivision C

Division DDivision D

............ RetailRetailBusiness

MarketingBusinessMarketing ..............

EuropeEurope

AmericasAmericas

Asia/

PacificAsia/

Pacific

GermanyGermany

SpainSpain

UKUK

FranceFrance

NordicNordic



Mul t ip le BW Im plement a t ions Cha llenges

Source

Source

Source

Source

Source

Source

Source Source

Local

BW

LocalBW

Source

Source

SourceLocal

BW

Unit 2BW

Successful Data Warehousing means: controlled redundancy !

Local

BW

Local

BW

Stream BBW

Stream CBW

Group

BW Stream ABW

Unit 1BW

Consistency issues:

Uncontrolled data flows

Multiple extractions of same data

Costly development

Not aligned data models

Danger of high level Silos



Loc a l BW Im plement a t ion Cha l lenges

.... Real WorldInformationrequirementsSchema-Modeling

Data Marts-InfoCubes

Extraction

Sources

Business Rules

Transformation

B

WProjectTe

am

Observat ions: Incremental set up

Solution-Focus (Data Marts)

Departmental view

Project is responsiblefor the entireData Warehouse Process

Pot ent ia l c onsequenc es:

Departmental islands (silos)

Consistency problems Completeness not sufficient

Flexibility restricted

Trace back not possible

Successful Data Warehousing means: controlled redundancy !



Corporat e Dat a Warehousing St rat egy

Missing corporate strategy and guidelinesPotential consequences: Silo solutions Doubtful reliability

Restricted flexibility High costs

Means: Uncontrolled Redundancy

Limited Solution Focus

Prerequisites to set up a proper strategy: Awareness

about accepted data warehousing concepts and their benefits

Support of corporate management (Sponsorship)

If there is no organizational momentum toward a common goal, then the bestarchitecture, the best framework in the world is bound to fail.

W.H. Inmon

Who needs a Corporat e BW St rat egy?



Who needs a Corporat e BW St rat egy?Al l , bu t t he prerequ is i t es are d i f fe rent !

Group

Stream A Stream B Stream C Stream D

...... RetailBusiness

Marketing......

Regions/ countries

Headquarter

Region A Region B Region C

countries

Architecture-,

SAP-based,single Instance

Legacy R/3

Legacy R/3

Legacy

Legacy

Legacy

Organization,Business

OLTP landscape

Necessity of a corporate BW Strategy



Content



The BW Data Model Prevents SilosThe BW Data Model Prevents Silos

BW Data Layers Guarantee Reliability and FlexibilityBW Data Layers Guarantee Reliability and Flexibility


Content



Elem ent s of a Corporat e BW St rat egy

BW Enterprise Data Warehousing

Decision Areas

ArchitectureApplicationDevelopment

IntegrationStrategySupport

Data Layer Data Model Landscape

Tactical/Strategic

Decision-Making

OperationalDecision-Making

Think o f : Bu i ld ing a Corpora te In form at ion Fac t ory *- Avoid Si los

*The Corporate Information Factory: introduced by Inmon, Imhoff



The Role of a Consist ent Dat a Warehouse Dat a Model

Consistent Data Warehouse

Data Model

long term success

A challenge forData Warehouse

consistency

MaterialManagement

OLTP

SalesOLTP

Consistent corporate Data Model

?

HROLTP

Not aligned operationalData Models

Inconsistent Data Warehouse Data Models

Island-solutions (silos, stovepipes)

?


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The BW Dat a Warehouse Dat a Model

BW of fers as par t o f t heBusiness Cont ent

a predef ined ex pandable

Dat a Warehouse Data Model

BW Data Warehouse DataModel for SAP und other

applications(InfoObjects & InfoSources)

SAP Corporate

Data Model

Legacy

Data Models

Consistent mapping of operative

data models For all SAP applications For SAP industrial solutions

(e.g. Retail, Utilities..)

For non SAP application Ascential Partnership Siebel, Oracle ....

BW Ex t ended St ar Sc hem a


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BW Ex t ended St ar Sc hem abui l t on t he BW Dat a Model

BW Extended Star Schemas

built on BW Data Model

0SALESPERS0SALESORG.....

0CUSTOMER00CUSTGR......

0MATERIAL

0MATGR0MATTYPE......

BW InfoObject Master Data:

0CUSTOMER0MATERIAL0AMOUNT

BW InfoCube:

Customer Material Sales Person

Sales

Transaction

Materialgroup SalesORG

MaterialType

Corporate Data Model:SAP Components

Shared/ ConformedDimensions

scope-specific

BCT Extractors/ DataSources

BCT InfoSources

Subject AreasMaster Data Transaction Data

0MATERIAL 0MATGR 0MATTYPE 0DOCNO 0CUSTOMER 0SALESPERS 0MATERIAL 0AMOUNT

BW Data Model definedby Business Content

BW E t d d St S h P t Si l


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BW Ex t ended St ar Sc hem as Prevent Si los

Conformed dimensions enable virtual BW MultiProvider scenariosConformed dimensions enable virtual BW MultiProvider scenarios

FACT Table

Material_Dimension_ID..........._Dimension_ID.............Dimension_ID.............Dimension_ID

Key figure 1Key figure 2InfoCubeInfoCube

BB

LocalLocal Part ofMaterial Dimension

Material_Dimension_ID

0MATERIAL

0PROD_HIERMaterial Dimension

Table

FACT Table

Material_Dimension_IDSalesOrg_Dimension_IDTime_Dimension_IDCustomer_Dimension_ID

Sales AmountQuantity InfoCubeInfoCube

AA

Material_Dimension_ID

0MATERIAL

Material DimensionTable

G eb ie t 1 G eb i et 2 G eb ie t 3

Bezirk 1

Gebiet 3a

Bezirk 2

Region 1

G eb ie t 4 G eb ie t 5

Bezirk 3

Region 2

Gebiet 6

Bezirk 4

G eb ie t 7 G eb ie t 8

Bezirk 5

Region 3

Vertriebsorganisation

Material HierarchyTable

0MATERIAL

Language Code

Material Name

Material Text Table

Material Master Table

0MATERIAL

Material Type

LocalLocal Part ofMaterial Dimension

Conformed, SharedConformed, SharedPart of

Material Dimension

Material Dimension:Material Dimension:

local & conformed partlocal & conformed part

BW DWH D t M d l d E t i Wid BW


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BW DWH Dat a Model and Ent erpr i se Wide BW

The BW Dat a Warehouse Dat a Model is t he g lue,w h ich t ies eve ry th ing toget her !

It protects against silos (stove pipe solutions, islands) Within a BW Instance

Within a BW Landscape

It is the basis for communication

Drill Thru between BW solution structures (InfoCubes, ODS-Objects)

within a BW Instance within distributed BW Instances

Drill Thru between BW solution structures and SAP applications

Between people

Enterprise BW Strategy: Usage of delivered BW DWH data model whenever possible

Central controlled guidelines for modifications and expansions

Content


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Content



The BW Data Model Prevents SilosThe BW Data Model Prevents Silos BW Data Layers Guarantee Reliability and FlexibilityBW Data Layers Guarantee Reliability and Flexibility


Content

In t roduc ing a BW Layer Arc h i tec t u re to Guaran t ee


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g yRel iab i l i t y and Flex ib i l i t y

PrimaryPrimary

DataData

ManagementManagement

DataData

AcquisitionAcquisition

DataData

DeliveryDelivery

SAP Business Information Warehouse andthe Corporate Information Factory (CIF)*

OperationalOperational

DataData

StoreStore

StagingStaging

AreaArea

EraoOSan

asoce

CleansingCleansing

TransformTransform

MasterReference

Data

DataData

WarehouseWarehouse

TransactionData

aag

ODisrbo

ArchitectedArchitected

Data MartsData Marts

Finance

Logistic

The Data MartThe Data Mart is customized and/or

summarized data derived from thedata warehouse

* Source: Bill Inmon

BW Arch i t ec t ed Dat a Mart Layer :


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yThe Depart m enta l Layer What c an w e ex pec t ?

0CUSTOMERmaster

0MATERIALmaster

BW Extended Star Schema: InfoCubes

InfoObject Master Data(conformed dimensions)

0CUSTOMER

0MATERIAL

InfoCube A

0CUSTOMER

InfoCube B

0CUSTOMER

0MATERIAL

InfoCube C

The BW Data Mart Layer based on the BW Data Model and a

proper Project Methodoffers Consistency within the Data Marts (InfoCubes, ODS-Objekte)

Control of data redundancy (MultiProvider, Queries)

All desired historical views

...

BW Arch i t ec t ed Dat a Mart Layer :


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yThe Depar t m enta l Layer Corporat e Needs ?

It is not the task of the scope-oriented Data Mart layer to anticipate

all kinds of future arising needs this would overload the schemasand corrupt performance

It cannot be expected that the data mart project teams have the 360

corporate view

We are limited from a corporate perspective in terms of Flexibility, Completeness

Reliability, Consistencyif we concentrate ourselves just on Data Marts.

BW Data Warehouse Layer the corporate layer

The BW (Ent erpr i se) Dat a Wareho use Layer:


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The Corporat e Layer


ODisrbo

StagingStaging

AreaArea

CleansingCleansing

TransformTransform

MasterReference

Data

DataData

WarehouseWarehouse


DataData

StoreStore

PrimaryPrimary

DataData


EraoOSan

DataData


DataData

DeliverDeliver

aag

asoce Transaction

Data




Finance

Logistic

The result of the data transformationand cleansing process is storedpersistently: Subject oriented Integrated Granular Non volatile (historical) Not scope-specific (not flavored)

The BW (Ent erpr i se) Dat a Warehou se LayerTh C L Wh ?


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The Corporat e Layer What c an w e expec t ?

Rel iab il i t y , Trace bac k Prevent Si los Single point of truth

All data have to pass this layer on its path from the source to the summarizededw-managed data marts

Contro l led Ex t rac t ion and Dat a St ag ing (t ransform at ion,c leans ing) Data are extracted only once and deployed many

Merging data that are commonly used together

Flex ib i l i ty , Reusabi l i t y , Com pleteness

The data are not manipulated to please specific project scopes (unflavored) Coverage of unexpected ad hoc requirements (Support the Unknown) The data are not aggregated Old versions are not overwritten or changed but useful information may be added

In tegra t ion Data are integrated (as far as possible)

Realization of the corporate data integration strategy

...

The BW Data Warehouse Layer is the corporate memory, thecorporate information repository

In t roduc ing t he BW Dat a Warehouse Layer


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In t roduc ing t he BW Dat a Warehouse Layer

Business Logic

Web templates,ReportsQueries

Data MartsData Marts(InfoCubes)(InfoCubes)

(E)DW Layer(E)DW Layer(ODS(ODS--Objects)Objects)

Extraction

Sources

Staging-Transformation,

Cleansing

Proje

ctTeam

(E)DW

AdminTe

am

1

1

1 111

22

Business Logic

Set upIncremental set up in parallel

with data marts guaranteesbuy in from business users

Technology

BW ODS-Objects Expanded InfoSources

define ODS-Objectstructures

BW flexible staging for

Master Data

Ownersh ip

Corporate Guidelines must be definedand administrators must be establishedto achieve the goals of a DataWarehouse layer

1 Increment

BW and Operat ional Dec is ion-Mak ing


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BW and Operat ional Dec is ion Mak ing


ODisrbo

StagingStaging

AreaArea

CleansingCleansing

TransformTransform

MasterReference

Data

DataData

WarehouseWarehouse


DataData

StoreStore

PrimaryPrimary

DataData


EraoOSan

DataData


DataData

DeliverDeliver

asoce Transaction

Data



aag

Finance

Logistic


Operat iona l , Tac t ic a l and St ra t eg ic Dec is ion-Mak ing inTrad i t iona l BW Im plem ent a t ions


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Trad i t iona l BW Im plem ent a t ions

Most BW customers do not distinguish between strategic/tactical and operational decision-making using BW features: Precalculated aggregates of InfoCubes (aggregation awareness) Drill thru

tactical/ strategiconsummarized data:Data Marts

operationalon detailed, slightlysummarized data:Standard Collections

InfoCube withprecalculatedaggregates

Two InfoCubeswith Drill Thru

ODS-Object

InfoCube andODS-Objectwith Drill Thru

BWAchteeDaMs

Flex ib i l i t y o f BW Arc h i tec t ed Dat a Mar ts :D t M t & St d d C l l t i


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Data Mar t s & St andard Co l lec t ions

Focus of traditionalFocus of traditional

BW implementations:BW implementations:

supportsupport

the unknownthe unknown

supportsupport

the knownthe knowndata martscenarios InfoCubes

summarized

tactical / strategic

decision-making

edw base tables

granular complete history ODS-Objects

flexibility &

reliability

standard collectionsdetailed, slightly summarized InfoCubes, ODS-Objects

operational

decision-making,flexibility

Lim i t s o f Us ing BW t he Trad i t iona l Way:In t roduc ing BW Operat iona l Data St ores


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In t roduc ing BW Operat iona l Data St ores

The traditional usage of BW (with or without dwh-layer), which supports all

kinds of decision-making using Architected Data Marts has its limits If we want to provide event-level or close to event-level operational

reporting (near real time data warehousing) and/ or

If we are confronted with high data volumes (e.g. Retail)

s ev era -> ev ent l oadsG ranul arity XP SA

Sou rce G ranul arity X

DWHWHG ranul arity >=X

DWHOD SOb j ect

Data Martsata MartsG ranul arity > XI n oCub e

d aly-w eekly

Dedicated ODS-Objects (orInfoCubes) with granular data

support operational decision making Directly loaded from PSA

Short life cycle of data(Archiving, Near line Storage)

They build a BW Operational DataStore

G ranul arity X

I n oCub eOD SOb j ect

BWWOperationalperationalData Storeata Store

Abstrac t Layer Arc h i t ec t ure and t he Real i ty o f BW as Pac k agedSolut ion


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Solut ion

Focus of traditionalFocus of traditional

BW implementations:BW implementations:

supportthe unknown

supportthe known

data martscenarios InfoCubes

summarized

tactical / strategic

decision-making

edw (base tables)

granular complete history ODS-Objects

flexibility,

reliability &data mining

standard collectionsdetailed, slightly summarized InfoCubes, ODS-Objects

operational

decision-making& flexibility

operational

decision-making& data mining

support

low latency &high volume

Operational Data Stores BW ods / BW PIPE for retail others most granular

Content


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The BW Data Model Prevents SilosThe BW Data Model Prevents Silos BW Data Layers Guarantee Reliability and FlexibilityBW Data Layers Guarantee Reliability and Flexibility


Content

Ent erpr ise Dat a Warehouse as Single Point o f Trut h


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DSS ApplicationsDepartmental Data Marts

EDW

MarketingAcctg Finance

SalesERP

ERPERP

CRM

eComm.

Bus. Int.

ETL

Global

ODS

Oper.

Mart

Explorationwarehouse/data mining


Staging

Area

local

ODS

DialogueManager

Cookie

CognitionPreformatted

dialogues

Cross media

Storage ManagementNear line

Storage

Web Logs

SessionAnalysis

Internet

ERP

CorporateApplications

Changed

Data

GranularityManager

Archives

The CorporateInformation Factory*

Basic Landsc ape Types


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The idea l c orpora te BW landsc ape tha t suppor ts a l l k ind

of dec is ion-m ak ing needs ?

EnterpriseBW

Hub & SpokeBW Enterprise

Data Warehouse SpokeBW

SpokeBW

Others

SpokeBW

Local

BW

Local

BW

Local

BW

GlobalBW

Outside-INMultiple Hubs &

Global BW

Others

Select the [IT] approach that fits most closely with corporatestrategy Prof. Sethi, University of Texas

Information&Management, 2/01

There is no one s ize f i t s a l l BW landsc ape!

Som e Inf luenc ers of a Corporat e BW Landsc ape


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More and more operational, low-latency decision-making using BW

operationaldecision making

tactical/ strategicdecision making

edw &edw managed

data marts

operationalBW Objects

Mission critical BW applications in BW

mission criticalapplications

business criticalapplications

Local interests

central governancelocal freedom

Flexibility for time of migration and future acquisitions

Single Corporat e Wide BW Inst anc e


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Parameters Company time zones

Mission critical dominates others

Workload from operational reporting

Scalability

BW EDWW EDW

BWWArchitected Data Martsrchitected Data MartsPSASA

SanAe

BW ODSW ODSlatency: near real time

External Data Martsxternal Data Marts

mission critical business critical others

StandardtandardCollectionsollectionsDataataMartsarts

mission critical business critical

latency: >= 1 day

Handl ing Miss ion Cr i t ic a l and Operat ive Dec is ion-Mak ingSeparate ly


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p y

mission critical strategic

BW EDWW EDW

BWOBWO


BWOBWO

business critical others

BWOBWO

Parameters

Company time zones

Mission critical dominates

EDW

Migration path from singleInstance

EDW separated from

majority of data marts

business critical others

BW EDWW EDW

BWOBWO

Parameters

Company time zones

EDW close to majority of

data marts

Mul t ip le Cent ra l Managed Loca l BWs t o Suppor tBus iness Cr i t ic a l Sc enar ios


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Asia Europe Americas

EDWDWOO

business critical others business critical others business critical others


Local BWocal BW Local BWocal BW Local BWocal BW

Global BWlobal BWParameters

Scalable

Mission critical dominates

EDW

EDW separated from

majority of data marts

OO OO OO

Summary


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BW offers a wide range of features that supportenterprise wide data warehousing needs.

Nevertheless a strategy and an organizational momentummust exist to realize an enterprise wide BW strategy.

Quest ions


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Any

Questions?


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Copyr ight 2004 SAP AG. Al l Right s Reserved


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Enterprise Wide Data Warehousing With SAP BW