Data Warehouse Management Using SAP BW Alexander Prosser

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Data Warehouse Management

Using SAP BW

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Overview

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Overview

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CEO: “We may have a problem in procurement.

I have a feeling that we employ too many suppliers. We are not focused enough, and probably the issue is even growing. Give me a flexible analysis tool to check that out.

We should have all data in our SAP ECC system.”

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Overview

What can I expect of a Data Warehouse ?

What not ?

What can I expect of my consultants/IT professionals ?

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Overview

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1980-ies: Functional software

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Overview

1990-ies: Process orientation:

Business Process

Value for the customer

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Overview

SEITE 7 V e rt ic a l in te g ra ti o n Office automation

Cross-functional base applications Functional applications Reporting, Analysis, Controlling Decision Support Horizontal integration Operational systems EIS Reporting

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Overview

Cross-functional base applications Functional applications

Reporting, Analysis, Controlling

Decision Support

Cross-functional base applications Functional applications Reporting, Analysis, Controlling Decision Support Organisation #1 Organisation #2

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Overview

SEITE 9 Operational IS External Sources DW OLAP Data Mining KBS

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Overview

Operational system Data warehouse

Procurement Vendor assessment

Sales and order processing Analysis of customer behaviour

Production planning and shop floor control

Analysis of rework/reject and overdue production orders

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Overview

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A data warehouse is NOT a list generator.

A data warehouse is NOT an address database for mail merge operations.

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Overview

O p e ra tio n a l s ys te m

U sa ge T ra n sa ctio n -inte n sive (re a d an d w rite )

U se rs R e la tive ly la rge n u m ber

C o ve ra ge (In m o st ca se s) cu rre n t d a ta o n ly

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Overview

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O perational system Data w arehouse

Usage Transaction-intensive

(read and write)

Q uery-intensive (read only)

Users Relatively large num ber Relatively sm all num ber,

unless used as a general reporting tool

Coverage (In m ost cases) current data

only

Current & historical data; tim e-dependent

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Overview

Operational system

Model (typical)

Data is organised according to a process

Data

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Overview

SEITE 15 items _customer period .... .. oranges bananas milk tomatoes Woolworths K-Mart Mc Donalds 01/ 02/ 03/ 04/ 01/ 01/ 01/ 01/ 09 09 09 09

Operational system Data warehouse

Model (typical)

Data is organised according to a process

Data is organised according to a subject matter

Data

structure Flat

Multi-dimensional according to the subject matter

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Modeling a Data Warehouse

Total Item type Total I t e m

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Modeling

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You have to unequivocally specify what you want … before you sign the contract.

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Modeling

You have to unequivocally specify what you want … before you sign the contract.

Otherwise, you will not get what you want.

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Modeling

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Let‘s design a data warehouse:

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Modeling

STEP 1:

What is the fact I want to analyze ?

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Modeling

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Nominal: numerical coding without meaningful values

Ordinal: coding represents >< relationships, no meaningful sum

Interval: metric, but have a “beginning” and/or “end”, hence, no meaningful sum

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Modeling

STEP 2:

What are the axes in my analyses ?

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Modeling

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STEP 3:

Are the axes of aggregation independent of one another ?

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Modeling

()

Operator Nominal Ordinal Interval Rational Sum No No No Average No Minimum No Maximum No

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Modeling

SEITE 25 Additivity Stock_ level Y M W Storage_location Plant * Material Material_group * => AVG Σ Σ Σ

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Modeling

x Σ AVG min max

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Modeling

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STEP 4:

Are there any non-aggregation attributes ?

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Modeling

STEP 5:

Where does the data come from ?

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Modeling

SEITE 29 Operational IS DW Key Integration Key_1 Key_2 Example: • Accounts receivable • Customer • Transport destination one object in DW

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Modeling

Operational IS Field Integration

DW

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Modeling

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Operational IS

Content Integration

• Material classes the same ?

• Account assignment the same ?

• Data maintenance discipline/rules the same ? • … ?

DW

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Modeling

Transfer Rules Update Rules InfoCubes Communication structure Communication structure BW Server Transfer Structure Transfer Structure Data Sources (replicas)

Data Sources and Info Sources

Transfer Structure Transfer Structure Transfer Structure Transfer Structure Data Sources (user-defined) Transfer Structure Transfer Structure Communication structure Communication structure Transfer Rules Master Data Transfer Structure Transfer Structure Transfer Structure Transfer Structure Master Data Communication structure Communication structure DataSources Master Data InfoSource Update Rules

Transfer Rules Transfer Rules

Transaction InfoSource Master Data InfoSource Customer data Product data Delivery plant data Sales data

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Case Study

SEITE 33 day region nr. transactions price qty. revenue delivery plant product product group year month customer

Case Study Umbrella Sales:

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Case Study

day costs

nr. transactions price

product

product group customer group

customer

quarter cost element

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Case Study

SEITE 35 Source system Transfer data Transfer data Transfer rules Transfer rules Info source Info source Update rules Update rules Info cube

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Case Study

day costs

nr. transactions price

product

product group customer group

customer

quarter cost element

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Institut für Produktionsmanagement

Institute of Production Management Augasse 2-6, 1090 Vienna, Austria

Alexander Prosser prosser@wu.ac.at http://prodman.wu.ac.at http://erp.wu.ac.at http://e-voting.at

Kontaktdaten ergänzen

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