Extended Star Schema

© 2008 MindTree Consulting© 2008 MindTree Consulting

Introduction to BI, Data warehouseDay 1

© 2008 MindTree Consulting

Introduction to BI, Data warehouse

BI concepts

Data warehouse concepts

Introduction to BIW

Advantages of BIW over other data warehouse tools

Concept of star schema architecture

Introduction to Administrator workbench (All buttons in AWB)


Introduction

Success in a competitive business environment needs more than just good information. Ability to derive meaningful, timely and readily accessible insights from the information is the need of the hour.

Insights into the business are the key to define effective strategy, align business operations to the strategy and improve the efficiency and effectiveness of execution.


Is your enterprise set up to win?


Business needs

The ability to take actions based on complete, timely, relevant insights.

A fast accurate way to pinpoint root causes.

The ability to track and manage the alignment of strategic objectives and business activities

Easy access to information

Support for legal compliance


Why are today’s insights not enough?

75% of business users do not use analytic applications

Analytics are disconnected from business processes.

Business processes are disconnected from corporate strategy.

90% of organisations fail to execute their strategies- Fortune magazine


Introducing SAP BI


SAP BI’ s Approach

1. Establish one foundation to run your business - providing integrated consistent data and metrics


Today


With SAP BI -Establish a Foundation


SAP BI’ s Approach


2. Bring decision making to the business process


Bring decision making to the business process


Bring decision making to the business process


SAP BI’s Approach



3. Align execution with strategy across organizations to achieve corporate goals


Align execution to strategy


Deliver actionable insights


SAP BI’s Approach



3. Align execution with strategy across organizations to achieve corporate goals

4. Profit from the immediate action on insights within the business process with clear options and explanation of potential results


Profit from timely action


Improve business process


What does this mean for the future


Business benefits

Better-informed decisions with faster corrective actions.

Better business performance as a result of strategy-guided actions.

Faster innovation.

Faster response to changing business conditions.

Increased competitive advantage.


Business Intelligence

Defined as:

“Business Intelligence is a technology based on customer and profit oriented models that reduce operating costs and provide increased profitability by improving productivity, sales, and service and help to make decision-making capabilities at no time. Business Intelligence Models are based on multi dimensional analysis capabilities.”


BI solutions differ from and add value to standard operational systems (OLTP systems – Online Transaction Processing systems) in three ways

By providing the ability to extract, cleanse and aggregate data from multiple operational systems into a separate data mart or data warehouse

By storing data often in a star or multi dimensional cube format, to enable rapid delivery of summarized information and drill down to detail

By delivering personalized, relevant informational views and querying, reporting and analysis capabilities for gaining deeper business understanding and making better decisions faster


To implement BI, the following technologies are used-

Data Marts/ Data Warehouses - A data warehouse is a subject

oriented, integrated, time variant, non-volatile collection of data in

support of management's decision-making process. To facilitate

data retrieval for multi dimensional analytical processing a star

schema is used very often.

Extraction, Transformation and Loading (ETL) - Data is extracted

from multiple source systems. Data is cleansed and transformed

and into a consistent format and structure. The cleansed data is

loaded into the data warehouse.

On-Line Analytical Processing (OLAP) and Data Mining - Analysis

tools are applied against the data warehouse to analyze and mine

the data.


Key differentiators

●SAP BI supports key business processes.

●SAP BI reflects SAP’s industry-leading business

process expertise.

●SAP BI provides complete visibility across the entire

value chain.

●SAP BI is delivered on the most robust and scalable

technology platform.

●SAP BI delivers the most relevant set of predefined

content.SAP BI is easy to deploy and extend.


Key features of SAP BI


Introduction to Data Warehouse


What is a data warehouse?

Term Data Warehouse coined by Bill Inmon in 1990

Bill Inmon ’s definition

A warehouse is a Subject-oriented, Integrated, Time-variant and Non-volatile collection of data in support of management’s decision making process

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Subject-Oriented Data that gives information about a particular subject instead of about

a company's ongoing operations

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Invoices Orders

DespatchPlan

Operational Data Warehouse

Customers Products

Regions Time



IntegratedData that is gathered into the data warehouse from a variety of sources

and merged into a coherent whole.

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Appl A - m,fAppl B - 1,0

Appl C - male,female

Appl A - balance dec fixed (13,2)Appl B - balance pic 9(9)V99

Appl C - balance pic S9(7)V99 comp-3Appl A - bal-on-hand

Appl B - current-balanceAppl C - cash-on-hand

Appl A - date (julian)Appl B - date (yymmdd)Appl C - date (absolute)

m,f

date (julian)

balance dec fixed (13,2)

Current balance



Time VariantAll data in the data warehouse is identified with a particular time

period.

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Operational Data Warehouse

Current Value data• time horizon : 60-90 days• key may not have element of time

Snapshot data• time horizon : 5-10 years• key has an element of time• data warehouse stores historical data



Ralph Kimball’s Definition

“A copy of transaction data specifically structured for query and analysis.”

Basically - “Snapshots of business events at regular intervals”

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How is a DW different from OLTP?

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OLTP DW

Business event / transaction oriented

Decision oriented

Supports Operations“Making the Wheels turn”

Decision support“Watching the Wheels turn”

View NarrowLooking ‘within’...

Broadlooking ‘across’...

Usage patterns

Stable, predictable Variable, Unpredictable

Time Limited time frame Historical data

Data Detailed only Detailed / Summarized and Derived



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OLTP DW

Typical Operation

Insert/Update intensive(A “twinkling” database)

Read intensive(A quiet data store )

Age of Data Current Historical

Data Required/ Queried

Minimal Extensive

Table structure

Normalized.Minimum redundancy

De-normalized. Controlled redundancy

Scope of data Internal Internal+external

Data Reacts to events Can anticipate events



To Summarize

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OLTP Systems are used to “run” a business and are based on ER Model

The Data Warehouse helps to “optimize” the business and is based on OLAP (dimensional model)


What is OLAP

Stands for OnLine Analytical Processing

OLAP tools aid users in quick and easy multi dimensional analysis to get insights into what’s happening

Supports features for the following

Slice and dice along the dimensions Drill up and drill down through hierarchies

Types of OLAP ROLAP – Relational OLAP

Data always comes from relational tables

MOLAP – Multidimensional OLAP Data always comes from multi-dimensional cubes

HOLAP – Hybrid OLAP Data always comes from both relational as well as multi-dimensional cubes Aggregated data comes from multi-dimensional cubes Detailed data comes from relational tables

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What is OLAP

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Product Manager View Regional Manager View

Financial Manager View Ad Hoc View

ProductFilmLensesCamerasFilm

RegionEastWestCentralWest

MonthDecJanFebMar

Sales240250690425

Record#001#002#003#004

Relational Model:

Product

Reg

ion

Time

Sales

Multidimensional Model:

Slice and Dice


EDW – Dimensional Model

Originated in the mid seventies by A.C.Nielson

Made popular by Ralph Kimball

Dimensional Model divides the world into

Measurement : Sales, Cost, Stock, Yield

Context (Dimensions) surrounding these measurements : Customer, Time, Service, Region

Two Variants of dimensional model

Star Schema

Snow Flake Schema

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Typical OLTP Model

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Payment

Mode

Payment Denial Product

Location Property Agent Product Line

Contract

Booking Business

Unit

Contact

Sales rep

Franchisee Customer

Division

Product Group

Data is S C A T T E R E D across !!!



Star Schema

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Site

Rate Plan

Channel

Date

# of new bookings

# of booking nights

# of rooms for bookings

# of guaranteed bookings

Site key

Site desc

Chain code

Site status

Rooms available

Mgmt company

Marketing area

Site QA Score

Lodge Score

Rest Score

Site

Bookings

Dimension Fact

MeasureHierarchy

Channel key

Channel desc

Original channel

Source id

Source desc

Source type

Channel

Rate plan key

Rate plan desc

Rate plan type

Brand

Rate Plan

Date key

Week

Month

Quarter

Year

Weekend flag

Time


Dimensions - Definition

●Contain descriptors of the business using which analysts

view data by.

●Dimensions sets the context for asking questions about the

facts in the fact table.

●SPEAKS BUSINESS LANGUAGE !!!

●Dimensions have multiple levels

●A combination of levels participate in a hierarchy

●Hierarchies are logical structures that use ordered levels as

a means of organizing data.

●A hierarchy can be used to define data aggregation.

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Dimension - Characteristics

●The tables contains all the textual descriptors of the

business.

●Dimensions supply the context in which a measurement was

made

●They correspond to the entities by which you want to

analyze the business

●Many columns

●Fewer rows

●Are linked to a fact table through a foreign key reference to

their primary keyPage 42


Dimensions – Examples

●Franchisee

●Consumer

●Property

●Car

●Channel

●Channel-Travel Agent

●Site

●Rate plans

●Brand

●Business unit

●Entity

●Entity group

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Fact - Definition

●Fact tables contain the measures related to a process or

event

●measures are analyzed by the various dimensions contained

in the dimension tables

●Each row in a fact table corresponds to a measurement.

●Fact tables have a few columns and lots of rows

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Fact - characteristics

They

●Are usually the largest tables

●Are usually appended to

●Can grow quickly

●Can contain either detail or summarized data

●Are joined to dimension tables through foreign keys

●It is always sparse – no rows are stored to represent ‘nothing happened’.

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Fact – examples

●Sum insured

●Amount Approved

●Claims ratio (derived fact)

●Premium Paid

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EDW – Dimensional Model Advantages

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4 * 4 * 5 * 6 = 480 reports

Actual salesSales ForecastReturnsComplaints

PRODUCTAll Products

Category

Brand

Product

All CustomersAll Depots

Region

Depot

Territory

Customer

Region

State

Time point

Area

CUSTOMERDEPOTAll Periods

Year

Month

Quarter

Day

PERIOD


EDW – Dimensional Model Advantages

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SELECT Channel_Desc,

year,

month,

'TotSales' = sum(Total_Sale)

FROM Arrivals st,

Channel_Dimension pd,

Time_Dimension td

WHERE Channel_Desc = ‘Agent'

and month = 2

and year in(1992,1994)

and st.Product_Key = pd.Product_Key

and st.Time_Key = td.Time_Key

GROUP BY

Channel_Desc,

year,

month

SELECT Channel_Desc,

‘Year’ = DATEPART(year,oht.book_Date),

‘Month’ = DATEPART(month,oht.book_Date),

‘TotRevenue’ = sum(DISTINCT(1+Tax_Rate)

*(days_booked*olt.rate_per_night))

FROM book_Header_Table oht,

book_Line_Table olt,

Property_Table st,

Product_Table pt,

SubChannel_Table sct,

Channel_Table ct

WHERE oht.book_Number = olt.book_Number

and oht.Property_Number = st. Property _Number

and olt.product_code = pt.product_code

and pt.product_code = sct.product_code

and sct.subChannel_code = ct.subChannel_code

and Channel_Desc = 'Agent'

and DATEPART(year,oht.book_Date) IN (1992, 1994)

and DATEPART(month,oht.book_Date) = 2

GROUP BY

Category_Desc,

DATEPART(year,oht.book_Date) ,

DATEPART(month,oht.book_Date)

Using OLTP Database Using Star Schema

5 Joins !!!

2 Joins !!!

Intensive computation

Less intensive


Typical Stages in the evolution of a DW

Stage 1: Reporting

Biggest challenge : Data Integration + Data quality

Example

Retail : what products does he buy ?

HealthCare : Which area contribute to maximum Claims?

Stage 2: Analysis

Less focus on what happened ?

More focus on why it happened ?

Iterative refinement of questions ( Q&A Map ) – support “chain of thought” analysis and questions

Example

Why did expenses increase by 10% compared to last quarter?

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Typical Stages in the evolution of a DW

Stage 3: Prediction Org is now well entrenched in the “whys” Build predictive models Regression ( linear/non linear), decision trees, Neural

Stage 4: Operational Insight Stage 13 on strategic decision making Process reengineering Example

Retail : Inventory management with JITHealthCare : Generating Preventive Campaigns well before time.

Stage 5: Activate Sense and respond layer sits on top of BI Example

Order raw material if inventory below threshold value

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Typical architecture of a DW

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Building Blocks - Component 1: Source Systems

●Operational systems that run the business SAP Siebel JD Edwards BAAN Point of sales application Oracle applications Home grown systems Excel spreadsheets

●Optimized for inserts and updates

●Very less redundancy of data by design …

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Building Blocks – Component 2 : ETL

●ETL stands for Extract Transform Load

●The action of Extracting information from one or more Source

Transforming it mid stream

Aggregation

Business Rules

Code normalization/cleansing

Loading it into a central database

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Building Blocks - Component 3: Staging Area

●Refers to both the storage area and a set of ETL processes

●Raw data is “massaged” and made ready for loading using ETL tools, scripts, SQL, etc Rules checking Re formatting / Re structuring etc

●Should NOT be exposed to business users

●May use flat files, relational tables or both

●Is the ‘black box’ that converts raw input data into finished data for the presentation layer

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Building Blocks - Component 4: Storage Layer

●This is where the data is organized, stored and made available for querying by users and tools

●This is the data warehouse for the business users

●Usually based on the dimensional model

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Building Blocks - Component 5: Reporting Layer

●Comprises tools and applications that present the data to end users for decision making.

●Could consist of:

Pre-canned reports (optionally web-enabled)

Ad-hoc query tools

Data mining applications

Budget Planning and forecasting applications, etc

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Building Blocks – Component 6 : The Metadata Layer

●The glue that binds the data warehouse components

●An encyclopedia of the data warehouse

●Crucial for maintaining the warehouse

●One of the hardest thing to manage in a warehouse !!!

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Other Building Blocks

Calculation Engines

For deriving new measures using the base measures

DW is the ideal place for calculating Key Performance Indicators

Extractors

For distributing data to other applications

MRDR (Master Reference Data Repository)

Also termed MDM – Master Data Management

Managing master data in the enterprise

Best place to implement conformed dimensions

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Tools required for a DW solution?

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To extract & transform data

baseSAS Datastage Informatica DTS PL-SQL Pro*C

To extract & transform data

baseSAS Datastage Informatica DTS PL-SQL Pro*C

To Store data Oracle SQL

Server DB2

To Store data Oracle SQL

Server DB2

To present data Business

Objects Cognos Microstrategy OLAP services Express Hyperion Brio SAS-EIS

To present data Business

Objects Cognos Microstrategy OLAP services Express Hyperion Brio SAS-EIS

Data cleansing Trillium I-Spheres

based solution

Data cleansing Trillium I-Spheres

based solution


SAP Business Information Warehouse - BW

What is SAP BW ? Data warehouse System with optimized

structures for reporting and analysis

OLAP engine and Tools

Integrated Meta Data Repository

Data Extraction and Staging Preconfigured support for data sources from R/3

System Business Application Programming Interfaces

(BAPI’s) for non-SAP systems

Automated Data Warehouse Management

Administrative Workbench for controlling and managing

DataExtraction

Transformation

Data Warehouse

Reports

Data Sources

Reporting and Analysis

Data Access


SAP BW Architecture


SAP BW Components

Info-objects

DataSources

Persistent Staging Area (PSA)

ODS objects

Infocubes

Master data

InfoProviders

Query and query views

InfoSpokes and Open-hub destination

Business Content


Star schema

The Star schema offers comprehensibility for software. The Star schema is the most popular way of implementing a Multi-Dimensional Model in a relational database


Star schema

The key elements of a Star schema are:

Central fact table with dimension tables shooting off from it

Fact tables typically store atomic and aggregate transaction information, such as quantitative amounts of goods sold. They are called facts.

Facts are numeric values of a normally additive nature.

Fact tables contain foreign keys to the most atomic dimension attribute of each dimension table.

Foreign keys tie the fact table rows to specific rows in each of the associated dimension tables.

The points of the star are dimension tables.

Dimension tables store both attributes about the data stored in the fact table and textual data.

Dimension tables are de-normalized.

The most atomic dimension attributes in the dimensions define the granularity of the information, i.e. the number of records in the fact table.


Extended Star Schema

Attributes of the dimension tables are called characteristics. The meta data objects for these are infoobjects

Hierarchies of characteristics or attributes may be stored in separate hierarchy tables. Therefore these hierarchies are named external hierarchies

Textual descriptions of a characteristic are stored in a separate text table. The system runs in different languages at a time.

Dependent attributes of a characteristic can be stored in a separate table called the Master Data Table for the characteristic


Extended Star Schema - Continued

Text

SID Tables

Master

Hierarchies

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Text

SID Tables

Master

Hierarchies

Text

SID Tables

Master

Hierarchies

Text

SID Tables

Master

Hierarchies

DimensionTable

Text

SID Tables

Master

Hierarchies

DimensionTable

DimensionTable

DimensionTable

DimensionTable

Hierarchies

Master

SID Tables

Text

FACT

Solution Dependent Schema

The InfoCube, which describes the process-oriented part of the solution.

An InfoCube consist of One fact table andSeveral dimension tables

Solution Independent Schema

The Shared Master Table valid for use with any info cube or ODS object

These master tables are the glue that binds the data warehouse

pointer or translation tables called SID (Surrogate-ID) tables are used in the BW schema to link the solution-independent master tables of the BW schema to InfoCubes


Comparison

Slide 67


Extended Star Schema – Key Elements

Attributes located in the dimensions are called Characteristics.

Attributes located in a master data table of a Characteristic are called attributes of the Characteristic.

SID tables (pointer tables) provide the technical link to the Master Data (attribute, text and hierarchy) tables that are outside the dimension of a star schema.

Dimension tables are built using the combination of numeric SID values of each Characteristic in the Dimension.

External information (attributes of the Characteristics, text descriptions and external hierarchies) is stored separately (shared) and linked to the InfoCubes.

Historical relationships as well as the current state of the data can be maintained and reported on

Multiple languages are supported for text / description


Administrator Workbench

The Data Warehousing Workbench (DWB) is the central tool for performing the tasks in the data warehousing process

It provides data modeling functions as well as functions for control, monitoring and maintenance of all processes in SAP NetWeaver BI having to do with data procurement, data retention, and data processing.

Functional Areas of the Data Warehousing Workbench: Modeling

Administration

Transport connection

Documents

Business Content

Translation

Metadata Repository


Modeling

Used to create and maintain

(meta) objects relevant to the

data staging process in SAP BW.

Objects are displayed in a tree

structure, in which the objects are

ordered according to hierarchical

criteria.

To access the Modeling function

area, choose transaction RSA1.


Administration

Functional areas is used to

display the navigational area

and, if applicable, the

corresponding object tree in

the left hand area of the

screen when applications are

called.

This means that you can use

the tree to start new

application you are in


Transport Connection

Used to collect newly created or

changed objects in the SAP BW

system.

You can use the Change and

Transport Organizer (CTO) to

transport them into other SAP

BW systems.


Documents

The Documents function area

enables you to insert, search

in, and create links for one or

more documents in various

formats, versions and

languages for SAP BW objects.


BI Content

BI Content provides pre-

configured information models

based on metadata.

It provides users in an

enterprise with a selection of

information they can use to

fulfill their tasks.

To access the BI Content

function area, choose the

transaction RSORBCT


Translation

In the Translation function area,

you can translate short and

long texts belonging to SAP

BW- objects.


Metadata Repository

All SAP BW Meta objects and

the corresponding links to

each other are managed

centrally.

In addition, metadata can

also be exchanged between

different systems, HTML

pages can be exported, and

graphics for the objects can

be displayed.

To access the Metadata

Repository function area,

choose the transaction RSOR.


Thank You

© 2008 MindTree Consulting© 2008 MindTree Consulting© 2008 MindTree Limited

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