1. Day 1
SAP BI Training

2. 2
SAP Stands for
Systems Applications Products
In data processing

3. 3
What is SAP
• SAP stands for
Systems Applications and Products in Data Processing
• Name of the company
SAP AG
• Name of the software
SAP
• Founded in 1972 in Germany
• World’s fourth largest software provider
• World’s largest provider of ERP

4. 4
Why SAP
“SAP's solution is a best-practices approach based on collective experience with
thousands of Industries .
It delivers a totally integrated, robust and scalable product that empowers
the user,
instead of creating a costly dependence on the vendor”
• Simultaneous visibility across the whole enterprise
• Supports databases, applications, operating systems and hardware from
almost every major supplier
• Integrated Modules
• Extensive interfacing capabilities
• Designed for all business types
• Supports multiple languages and currencies
• Top ERP Provider

5. 5
3 Tier Architecture

6. 6
Introduction
Data
Integrating Data
Data Representation
TableField
Primary Key
Object Attribute
Company-Data / Department-Data
Metadata

7. 7
Introduction (2)
Database
Transactional Database
Data Warehouse
Master Data
Transaction Data
Data Mart
Data vs. Information
ERP – Enterprise Resource Planning
Actual Data
Plan Data

8. 8
ERP – Enterprise Resource Planning
An integrated system that operates in real time
A common database, which supports all applications.
A consistent look and feel throughout each module.
It’s a Complete integrated Application modules provided by SAP to
Integrate by the Information Technology (IT) Department
I

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R/3 OR ECC Core Business Process

10. 10
OLTP(ECC) Vs OLAP (BW)

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OLTP(ECC) Vs OLAP (BW)
OLTP System OLAP System
Online Transaction Processing Online Analytical Processing
(Operational System) (Data warehouse)
Source of data
Operational data; OLTPs are the original source of the
data.
Consolidation data; OLAP data comes from the various OLTP
Databases
Purpose of data To control and run fundamental business tasks To help with planning, problem solving, and decision support
What the data Reveals a snapshot of ongoing business processes Multi-dimensional views of various kinds of business activities
Inserts and Updates Short and fast inserts and updates initiated by end users Periodic long-running batch jobs refresh the data
Queries
Relatively standardized and simple queries Returning
relatively few records
Often complex queries involving aggregations
Processing Speed Typically very fast
Depends on the amount of data involved; batch data refreshes
and complex queries may take many hours; query speed can be
improved by creating indexes
Space Requirements Can be relatively small if historical data is archived
Larger due to the existence of aggregation structures and
history data; requires more indexes than OLTP
Database Design Highly normalized with many tables
Typically de-normalized with fewer tables; use of star and/or
snowflake schemas
Backup and Recovery
Backup religiously; operational data is critical to run the
business, data loss is likely to entail significant monetary
loss and legal liability
Instead of regular backups, some environments may consider
simply reloading the OLTP data as a recovery method

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Business Intelligence
Definition
Business intelligence (BI) is a broad category of
applications and technologies for gathering, storing,
analyzing, and providing access to information to help a
business make better business decisions.

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Evaluation of SAP BW/BI
Evaluation of SAP BI/BW
Name Version Release
BIW 1.2A Oct-1988
BIW 1.2B Sep-1999
BIW 2.0A Feb-2000
BIW 2.0B Jun-2000
BIW 2.1C Nov-2000
Name Change to BIW to BW
BW 3.0A Oct-2011
BW 3.0B May-2002
BW 3.1 Nov-2002
BW 3.1C Apr-2004
BW 3.3 Apr-2004
BW 3.5 Apr-2004
Name Change to BW to BI
BI 7 Jul-2005
Name Change to BI to BW
BW 7.3 Nov-2011

14. 14
Why it came that way?

15. 15
Solution needed

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Key Capabilities

17. 17
Key Capabilities

18. 18
SAP BI Architecture

19. 19
SAP BI Key Components

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Data Warehousing & ETL (Extract, Transform
& Load)

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Data Warehousing

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Operational Data Store and Data Warehouse layer

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Open Hub

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BI Suite

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For All User Types
Authors and analysts
■ need advanced analysis functionality and ad-hoc data exploration capabilities
■ require useful, manageable tools
Executives and knowledge workers
■ require personalized information in context via an intuitive user interface
■ want predefined analysis paths and the option of in-depth analysis of summary data.
Information consumers
■ need a snapshot of a particular data set to perform their operational tasks
■ do not interact extensively with the data.

26. 26
Business Explorer

27. 27
Query, Analysis and Reporting

28. 28
Web Application Framework

29. 30
Information Broadcasting

30. 31
Authorization

31. 32
Open Analysis Interfaces

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Business Content

33. 34
Business Content
Predefined, role-based and task-oriented information models
♦ Provide technical definitions, such as extraction and
transformation rules
♦ Predefined templates for reporting and analysis.
For various industries and business areas

34. 35
Business Content Benefits

35. 36
Road Map

36. 37
SAP Approach: ASAP Methodology
ASAP(Accelerated SAP)
The implementation of your SAP System covers the following
phases:
1. Project Preparation
2. Business Blueprint
3. Realization
4. Final Preparation
5. Go Live & Support

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ASAP: 1. Project Preparation
In this phase you plan your project and lay the foundations for
successful implementation. It is at this stage that you make the
strategic decisions crucial to your project:
Define your project goals and objectives
Clarify the scope of your implementation
Define your project schedule, budget plan, and implementation sequence
Establish the project organization and relevant committees and assign
resources

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ASAP: 2. Business Blueprint
In this phase you create a blueprint which
Documents your enterprise’s requirements and
establishes how your business processes and organizational structure are to
be represented in the SAP System.
You also refine the original project goals and objectives and
revise the overall project schedule in this phase.
 Define your project goals and objectives

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ASAP: 3. Realization
Configure the requirements contained in the Business Blueprint.
 Baseline configuration (major scope) is followed by final
configuration (remaining scope)
Conducting integration tests and
Drawing up end user documentation.

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ASAP: 4. Final Preparation
Complete your preparations, including testing, end user training,
 System management, and cutover activities.
Resolve all open issues in this phase.
Ensure that all the prerequisites for your system to go live have
been fulfilled..

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ASAP: 5. Go Live & Support
Moved from a pre-production environment to the live system.
Setting up production support,
Monitoring system transactions, and
Optimizing overall system performance.

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The new intelligence platform
Value added within an SAP landscape

43. 44
Products Directions for BI solutions
Richest offering for all business users

44. 45
Data Warehouse

45. 46
Basics of Star Schema
Types of Data:
1. Master Data
2. Transation Data
Master Data:
Master data is data that remains unchanged over a long period of
time. Master data contains information that is needed again and again
in the same way
Example:
Customer ID Customer Name Customer Address Customer Phone
C01 John north America 90001234
C02 Cater Uganda 90001234
C03 Robert USA 90001234
C04 Philips UK 90001234
C05 Rakul Singapore 90001234

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Basics of Star Schema
Transactional Data
Data relating to the day-to-day transactions is the Transaction data
Customer ID Customer
Name
Customer
Address
Quantity Price
C100 John USA 10 100
C200 Cater UK 20 200
C300 Cooper UAE 30 300
C400 Scot DNM 40 400

47. 48
Data Warehouse Star Schema

48. 49
Classic Star Schemas
A schema is called a star schema if all dimension tables can be joined directly to the fact table.
The following diagram shows a classic star schema.

49. 50
Infocube - Extended Star Schema

50. 51
Enterprise Data Warehouse Architecture
Consolidating data warehouse layers that were not developed
together may produce following inconsistencies
Uncontrolled data flows
Multiple extraction of the same data
Local BI initiatives (without a global agreement)
Several inconsistent data models
Silos, standalone systems
An unreliable corporate information basis (unreliable headquarter reporting)
Overall: Redundant, expensive development

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Why EDW?
All decisions made for the entire company
To produce a valid and stable corporate Data Warehouse solution
that satisfies all of the demands for integrated and consistently
structured information.
For this, it is necessary to adhere to generally accepted
guidelines.
The Enterprise Data Warehouse architecture reflects all of these
decisions.
The architecture is a "system design" decision that is valid and
stable for a specified timeframe.

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Master Data / Transactional Data
InfoArea
 InfoObject Catalog
 Application Component
 InfoObject
 DataSource
 InfoCube
 DataStore Objects (DSO)
 Characteristics
 Key Figures
 Dimension Table
 Fact Table
 SID Table
TERMINOLOGY - 1

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TERMINOLOGY - 2
Attributes
 Text
 Hierarchy
 InfoProvider
 Source System
 Data Targets
 Transformation
 Data Transfer Process
 BEx Suites
 BEx Web
 BEx Analyzer

54. 55
Summary

55. Thank You.