This document provides an overview of Business Intelligence (BI) and SAP BI. It defines BI as gathering, storing, analyzing, and providing access to data to help organizations make better decisions. The document then discusses SAP BI specifically, describing it as a data warehousing solution that integrates, transforms, and consolidates business data for flexible reporting and analysis. It provides historical details on the evolution of SAP BI and describes the typical data flow and architecture within SAP BI including extraction, transformation, loading, data storage, and analysis tools.

1. -by
Rahul Sindhwani

2.  What is BI ?
 SAP BI
 History of SAP BI
 ETL Process
 Architecture of SAP BI
 Typical Data Flow in SAP BI
 Data Model – Info Object, Infocube(Star Schema
Extended Star Schema ,DSO etc
 Business Explorer (Bex Analyser , Query Designer)

3. What is Business Intelligence?

4.  Gathering
 Storing
 Analyzing
 Providing Access to data
 Make better Decisions

5.  What is the current status of the business?
–
–
What’s going well?
What needs improvement?
 What are the business’ strengths and weaknesses?
 How do we improve our decision making?

6. Decision
Knowledge
Information
Data

7.  SAP BI  Data Warehousing Solution by SAP
 Flexible reporting and analysis tool for evaluating and
interpreting the data.
 Business data integrated, transformed, and
consolidated in Sap BI.

8.  SAP launched the product in 1997 by the name
“Business information Warehouse (BIW), Version 1.
2A
 Product Name Changed to “Business Warehouse”
(BW) with version 3.0A
 Named “Business Intelligence “BI” with version 7.0

9.  ETL (Extraction, Transformation, Loading)
 Data Analysis & Planning
 Tools for accessing and visualizing data
 Publishing content from SAP BI
 Performance
 Security
 BI Content

10.  The process of the extracting data from Source systems
and making it useful for our needs is ETL

11.  SAP systems (S-API Service Application Programming
Interface)
 BI systems
 Flat files
 Database management systems (DB Connect)
 Relational or multidimensional sources (UD Connect)
 Web Services

12.  Direct assignment
 Constants
 Reading master data
 Routines
 Formula
 Initial

13. DATA STORAGE
AND
DATA FLOW

14. Transformation 2
DTP
Transformation 1
DTP
Infopackage

15.  Data Source is a set of fields that are provided to
transfer data into BI
1) DataSource for transaction data
2) DataSource for master data

16.  The Persistent Staging Area (PSA) is the storage area
for data from the source systems in BI.
 The requested data is saved, unchanged from the
source system.
 Starting point (entrance) of data into BI system

17.  A DataStore object serves as a storage location for
consolidated and cleansed data.
 The data in DataStore objects is stored in transparent,
flat database tables.
 This data can be evaluated using a BEx query.
 Contain 1) Key Fields (Ex Doc number, item etc
2) Data Fields

18. Dimensions
/Characteristics
Determine the sales amount for customers located in ‘New York’ with Material
Group “ABC” in the year 2011

24. Load into PSA
3
Data Load
Monitor
Drop Indices
2
Load into ODS
4
Start 1
Roll up
Aggregate
9
Activate
Data in
ODS
5
Build DB
Statistics
8
Data Target
Maintenance
Further update
6
Build Indices
7

25.  Covers Major Business Processes
 Simple access to business information via a single
point of entry
 High performance environment.
 Standardized structuring and display of all business
information

26.  Infosets now can include Infocubes as well
 Remodeling. This is only for info cube.
 The BI accelerator (for now only for infocubes) helps in
reducing query run time
 Search functionality hass improved. You can search any
object.
 The Data Warehousing Workbench replaces the
Administrator Workbench

27. THANK YOU

28.  Transfer and Update rules replaced by Transformation
 Load through PSA has become a mandatory
 Introduction of "end routine" and "Expert Routine“
 Renamed ODS as DataStore.
 Introduction of Write optimized DSo