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Bw training 3 data modeling

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1 Data Modelling and Loading
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Page 1: Bw training   3 data modeling

1

Data Modelling and Loading

Page 2: Bw training   3 data modeling

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Data Modeling and Loading- First Steps

• Data Modeling

• ERM model

• MDM / Star Schema model

• BW Extended Star Schema

• BW Master Data

• InfoObjects

• Attributes

• Hierarchies

• Text

• Loading Master Data via Flat Files

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5. Transactional Data

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One “business process” is modeled at a time

Data storage optimized for reporting by a “Star Schema”

Characteristics are structured together in related branches called “Dimensions”

The key figures, KPI's, and other calculations form the “Facts”

This structure is the same for all sources

SAP BW Data Model

Dimension 2

Facts

Dimension 1 Dimension 3

Dimension 4 Dimension n

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Example: Sales

Who did we sell to? What did we sell? Who sold it? How much did we sell? Who did we compete

against? When did we sell?

Product Dimension

Quantities Revenues

CostsRev./Group

Customer Dimension

Sales Dimension

Competition Dimension

Time Dimension

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Time dimensionProduct dimension

Customer dimension

P Product # Product group …

2101004 Displays ...

C Customer # Region …

13970522 West ...

T Period Fiscal year …

10 1999 ...

Dimensions

Dimension tables are groupings of related characteristics.

A dimension table contains a generated primary key and characteristics.

The keys of the dimension tables are foreign keys in the fact table.

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CustomerCustomer number

Customer name

Cust Category

Cust Subcategory

Division

Industry

Revenue Class

Transportation zone

Currency

VAT #

Legal Status

Regional market

Cust Statistics group

Incoterms

Billing schedule

Price group

Delivering plan

ABC Classification

Account assignment group

Address

State

Country

Region

ProductMaterial number

Material text

Material type

Category

Subcategory

Market key

MRP Type

Material group 1

Planner

Forecast model

Valuation class

Standard cost

Weight Volume

Storage conditions

Creation Date

SalesSalesperson

Rep group

Sales territory

Sales region

Sales district

Sales planning group

Distribution key

CompetitionNielsen indicator

SEC Code

Primary competitor

Secondary Competitor

Time

Date

Week

Month

Fiscal Year

Dimensions Example: Sales

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P C T Quantity Revenue Discount Sales overhead

250 500,000 $ 50,000 $ 280,000 $

50 100,000 $ 7,500 $ 60,000 $

… … … ...

Fact table

Fact Table

A record of the fact table is uniquely defined by the keys of the dimension tables

A relatively small number of columns (key figures) and a large number of rows is typical for fact tables

A fact table is maintained during transaction data load

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C Customer # Region …

13970522 west ...

P C T Quantity Revenue Discount Sales overhead

250 500,000 $ 50,000 $ 280,000 $

50 100,000 $ 7,500 $ 60,000 $

… … … ...

Time dimensionProduct dimension

T Period Fiscal year …

10 1999 ...

P Product # Product group …

2101004 displays ...

Fact table

Customer dimension

Star Schema The combination of Fact and Dimension Tables is

called a Star Schema.

P C T Quantity Revenue Discount Sales overhead

250 500,000 $ 50,000 $ 280,000 $

50 100,000 $ 7,500 $ 60,000 $

… … … ...

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Example Star Schema: Sales

Facts

Qty soldList priceDiscountsInvoice priceFixed mfg costVariable costMoving average priceStandard costContribution marginExpected ship dateActual ship date

CustomerMaterialCompetitionSalesTime

Competition

Nielsen indicator

SEC Code

Primary competitor

Secondary Competitor

Sales

Salesperson

Rep group

Sales territory

Sales region

Sales district

Sales planning group

Distribution key

Time

Date

Week

Month

Fiscal Year

Customer

Customer number

Customer name

Cust. Category

Cust. Subcategory

Division

Industry

Revenue Class

Transportation zone

Currency

VAT #

Legal Status

Regional market

Cust. Statistics group

IncoTerms

Billing schedule

Price group

Delivering plan

ABC Classification

Account assignment group

Address

State

Country

Region

Material

Material number

Material text

Material type

Category

Subcategory

Market key

MRP Type

Material group 1

Planner

Forecast model

Valuation class

Standard cost

Weight Volume

Storage conditions

Creation Date

Sales

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Extended Star Schema (Functional View)

C customer-no territory chain office head office C customer-no territory chain office head office

C P T quantity sold revenue discount sales overhead stock valueC P T quantity sold revenue discount sales overhead stock value

T period fiscal yearT period fiscal year

P product-no product group brand categoryP product-no product group brand category

product-no language product descriptionproduct-no language product description

Time dimension

Product dimension

Customer dimension

Product master data: Text

Fact table

Territory 1 Territory 2 Territory 3

District 1

Territory 4

District 2

Zone 1

Territory 5 Territory 6

District 3

Zone 2

Territory 7

District 4

Territory 8 Territory 9

District 5

Zone 3

Sales hierarchy

Sales InfoCube

Customer-no Name Location Industry keyCustomer-no Name Location Industry key

Customer master data: Attributes Sales hierarchy

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From Data Model to Database

Star Schema(Logical)

InfoCube(Physical)

T

ime

Customer Dimension

Pro

du

ct

Dim

en

sio

n

Product Dimension

Quantities Revenues

CostsRev./Group

Customer Dimension

Sales Dimension

Competition Dimension

Time Dimension

Terminology used to discuss the MDM modeling of a business process.

Terminology used to discuss the MDM modeling of a business process.

Real data base tables linked together

and residing on a BW database server.

Real data base tables linked together

and residing on a BW database server.

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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

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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Data Granularity

Data Granularity is defined as the “detail” of the database, the characteristics which describe our key figures.

Fundamental atomic level of data to be represented The “by” words - for example, Sales by customer, by

material It determines how far you can “drill down” on the data. Example: Time Granularity

Day versus Month A customer buys the same product 2 to 3 times a month With time granularity of Day : 2 or 3 fact table entries With time granularity of Month : 1 record in the fact

table but a loss of information (i.e. number of orders on different weekdays).

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Performance versus Disk Space

• The decision on granularity has the biggest impact on space and performance

• Reducing granularity means losing information

• With ‘normal’ star schemas (i.e. big fact table and small dimension tables) the design of dimensions is primarily guided by analytical needs.

• Large dimension tables have a big impact on performance

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6. Master Data

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Characteristic InfoObject

BW term for Business Evaluation Object A unique name containing technical information and

business logic InfoObject components:

Technical Definition (length, format, check routines, etc.)

Master Data, Texts Attributes Hierarchies Compound Information

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Scenario for New InfoObject

LEGACY COSTCENTER TABLECost Center#(13 char.) Profit CenterPerson Resp

2930000007890 5454 Joe2940000006123 6547 Bjorne

R/3 System (SYSTEM NAME = SAP…..)Cost Center#(10 char.) Profit Center Person Resp

1000000000 32245 Maria 2000000000 65465 Ming

BW InfoObject COSTC00 Master Data TableCost Center#(13) Profit CenterPerson Resp

2930000007890 5454 Joe 2940000006123 6547 BjorneSAP1000000000 32245 Maria SAP2000000000 65465 Ming

The legacy system and R/3 system have cost center numbers of different lengths

A new InfoObject (COSTC##) is needed with a length of 13 characters.

R/3 data will take the first 3 characters of the system ID as a prefix for identification purposes.

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Creating a New InfoCube – Already Covered?

1. Create New InfoCube Name in Selected InfoArea

2. Choose Characteristics Specified in Data Model

4. Assign Characteristics to Dimensions

3. Create Necessary User-Defined Dimensions

5. Choose Time Characteristics

6. Choose Key Figures

7. Activate


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