Day 1 SAP BI Training

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SAP Stands for

Systems Applications Products

In data processing

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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

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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

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3 Tier Architecture

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Introduction

Data

Integrating Data

Data Representation

TableField

Primary Key

Object Attribute

Company-Data / Department-Data

Metadata

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Introduction (2)

Database Transactional Database

Data Warehouse Master Data

Transaction Data

Data Mart

Data vs. Information

ERP – Enterprise Resource Planning

Actual Data

Plan Data

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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

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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

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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

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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

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Why it came that way?

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Solution needed

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Key Capabilities

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Key Capabilities

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SAP BI Architecture

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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.

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Business Explorer

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Query, Analysis and Reporting

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Web Application Framework

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Information Broadcasting

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Authorization

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Open Analysis Interfaces

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Business Content

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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

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Business Content Benefits

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Road Map

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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

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Products Directions for BI solutions Richest offering for all business users

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Data Warehouse

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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

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Data Warehouse Star Schema

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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.

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Infocube - Extended Star Schema

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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

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Summary

Thank You.