16742716 SAP BW Training 3 Data Modeling and Loading Httpsapdocsinfo

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Data Modelling and Loading

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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 aStar Schema

Characteristics are structured together in

related branches called Dimensions

The key figures, KPI's, and othercalculations form the Facts

This structure is the same for all sources

SAP BW Data Model

Dimension 2

Facts

Dimension 1 Dimension 3

Dimension 4Dimension 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?

ProductDimension

QuantitiesRevenuesCosts

Rev./Group

CustomerDimension

SalesDimension

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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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

Product

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

Salesperson

Rep group

Sales territory

Sales region

Sales district

Sales planning group

Distribution key

CompetitionNielsen indicator

SEC Code

Primary competitor

Secondary Competitor

Time

DateWeek

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 ...


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.


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

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

...


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Example Star Schema: Sales

Facts

Qty sold

List price

Discounts

Invoice price

Fixed mfg cost

Variable cost

Moving average price

Standard cost

Contribution marginExpected ship date

Actual ship date

Customer

Material

Competition

Sales

Time

Competition

Nielsen indicatorSEC Code

Primary

competitor

Secondary

Competitor

Sales

Salesperson

Rep group

Sales territory

Sales region

Sales district

Sales planning

group

Distribution key

Time

Date

Week

MonthFiscal Year

Customer

Customer number

Customer name

Cust. Category

Cust. Subcategory

Division

Industry

Revenue Class

Transportation zone

Currency

VAT #

Legal Status

Regional market

Cust. Statisticsgroup

IncoTerms

Billing schedule

Price group

Delivering plan

ABC Classification

Account assignmentgroup

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 P T quantity sold revenue discount sales overhead stock value

T period fiscal year

P product-no product group brand category

product-no language product description

Time dimension

Product dimension

Customer dimension

Product master data: Text

Fact table

Territory 1 Territory 2 Territory 3District 1

Territory 4District 2

Zone 1

Territory 5 Territory 6District 3Zone 2

Territory 7District 4

Territory 8 Territory 9District 5

Zone 3Sales hierarchy

Sales InfoCube

Customer-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)

Customer Dimension

ProductDimension

ProductDimension

QuantitiesRevenues

CostsRev./Group

CustomerDimension

SalesDimension

Competition

Dimension

Time

Dimension

Terminology used to discuss theMDM modeling of a business process.

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


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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 CenterPerson Resp

1000000000 32245 Maria2000000000 65465 Ming

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

2930000007890 5454Joe2940000006123 6547BjorneSAP1000000000 32245MariaSAP2000000000 65465Ming

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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