Bi overview

Intelligent Ideas. Swift Execution.
What is BI
As per Wikipidea
“The term Business Intelligence (BI) refers to technologies,
applications and practices for the collection, integration, analysis,
and presentation of business information. The purpose
of Business Intelligence is to support better business decision
making.”

Why BI
• What happened?
• What is happening?
• Why did it happen?
• What will happen?
• What do I want to happen?
Past
Present
Future

Significance of BI
• Companies need to have accurate, up-to-date information on
customer preferences , that company can quickly adapt to
their changing demands
• BI applications can also help managers to be better informed
about actions that a company’s competitors are taking
• It help analysts and managers to determine which
adjustments are mostly likely to respond to changing trends
• IT can help companies develop a more consistent, data-based
decision, which can produce better results than making
business decisions by “guesswork”

Inmon approach/ Top Down

What is BI
a) ETL stands for Extract, transformation and Load
b) Pulling data out of the source systems and placing it into a data warehouse
Extract : The Extract step covers the data extraction from the source system (SAP, ERP
and other operational systems) and makes it accessible for further processing.
The most well known commercial tools are Ab Initio, IBM InfoSphere
DataStage, Informatica, Oracle Data Integrator and SAP Data Integrator.

What is BI
Transform
The transform step applies a set of rules to transform the data from the source to the
target.
• Applying business rules (e.g., calculating new measures and dimensions),
• Cleansing (e.g., mapping NULL to 0 or "Male" to "M" and "Female" to "F" etc.),
• Filtering (e.g., selecting only certain columns to load),
• Splitting a column into multiple columns and vice versa,
• Joining together data from multiple sources (e.g., lookup, merge),
• Transposing rows and columns,
• Applying any kind of simple or complex data validation (e.g., if the first 3 columns in
a row are empty then reject the row from processing)

What is BI
Load
Loading the data into the data warehouse.
a) Load is performed correctly and with as little resources as possible.
b) In order to make the load process efficient, it is helpful to disable any constraints
and indexes before the load and enable them back only after the load completes.
c) The referential integrity needs to be maintained by ETL tool to ensure consistency.

BUSINESS INTELLIGENCE AND VISUALISATION TOOLS
BI (ETL Tools)
•INFORMATICA
•SSDT (Sql Server Data Tools)
•BODS
DATA
WAREHOUSING
•SSAS (Sql Server Analysis Tools)
•SAP BW
•SAP HANA
•TERA DATA
•ORACLE
VISUALIZATION
TOOLS
•SSRS (SQL Server Reporting Tools)
•XCELISUS
•QLIKVIEW
•TABLEAU
•DESIGN STUDIO
•SAP LUMIRA
REPORTING
TOOLS
•BEX QUERY DESIGNER/Analyzer
•WEB INTELLIGENCE
•CRYSTAL REPORT

Overview of Datawarehousing
• The term "Data Warehouse" was first coined by Bill Inmon in 1990.
• “A data warehouse is a subject-oriented, integrated, time-variant, and
non-volatile collection of data. “
• Subject Oriented WH is organized around major subjects like Finance,
Marketing, Sales
• Integrated Presents unified view of data coming from various sources
like SAP, legacy systems
• Non-Volatile New data is added on an incremental basis and not as
replacement.
• Time Variant Data is accurate only for the some point of time and will
change for Daily, Weekly, Monthly

Difference Between OLAP and OLTP
Data warehouse (OLAP) OLTP
Historical processing of information Day to Day processing of transactions
Used by Executives, Managers and Analysts Used by clerks, DBAs or database professionals.
Used to analyze the business Used to run the business
Focuses on information out Focuses on Data in
Based on Star Schema, snowflake and Fact constellation Based on Entity Relationship Model
Provides multidimensional view of summarized and
consolidated data
Provides relational view of highly detailed data
No of records accessed are in millions. No of records accessed are in tens or hundreds
Database size is 100GB to 100TB Database size is 100MB to 100GB.

Terminologies in Datawarehousing
Terminologies
Data Cube: A data cube helps us represent data in multiple dimensions. It is defined by
dimensions and facts.

Terminologies in Data-warehousing
Terminologies
Data Mart : A subset of organisation wide data that is specific to a group of people
within the organisation.
• Data Marts are smaller in size.
• Data marts are customized for a
department.
• Data marts are flexible.
• Source of datamarts is
departmentaly structured DW.
• Implementation generally takes
weeks rather than months or
years.

Datawarehouse Architecture
Architecure of a typical Data warehouse
Operational
data source1
Query Manager
Warehouse Manager
DBMS
Operational
data source 2
Meta-data High
summarized data
Detailed data
Lightly
summarized
data
Operational
data store (ods)
Operational
data source n
Archive/backup
data
Load
Manager
Data mining
OLAP(online analytical processing) tools
Reporting, query,application development,
and EIS(executive information system) tools
End-user access tools
Operational data store (ODS)

Datawarehouse Architecture
Components of Data warehouse
• Query Manager
• Detailed and summarized data
• Archive/backup data
• End user access tools
Process flow in Data warehouse
• Extract and load data
• Cleaning and transformation of data
• Backup and archive data
• Managing queries

Relational v/s Dimensional Modelling
Relational Modelling Dimensional Modelling
Entity Relationship (ER) Model Facts and Dimensions, Star Schema
Normalization rules Less tables, but have duplicate/redundant data
Many tables using joins Easier for business user to understand
History tables using natural keys Slowly changing dimensions, surrogate keys
Good for indirect end-user access of data Good for end-user access of data

Kimball approach / Bottom Up

Facts and Dimensions
Facts Dimensions
Business facts (or measures), generally numeric Data set composed of individual, non-overlapping data
elements
It can be aggregated Commonly used dimensions are date, customer,
product
Examples are Sales price, quantity, revenue Customer dimension attributes may be first name, last
name, birth data, gender
Fact table contains keys to dimensional table as well as
measurable facts
Contains a hierarchy of attributes
They can grow very large in millions and billions Date Dimension : year > quarter > month > week > date
Going up a level in the hierarchy called rolling of data
Going down a hierarchy called drilling down

Facts and Dimensions
Dimensions

Different Kinds of schema
• Star Schema
• Snowflake Schema
• Fact Constellation Schema

Star schema
Star Schema
• One fact table and multiple dimensional
tables.
• Results can be retrieved easily.
• Dimension table holds descriptive data.
reflecting dimensions or attributes.
• A fact table in the middle surrounded by
dimension tables.

Snowflake Schema
Snowflake Schema
• A refinement of star schema
• Some dimensional hierarchy is
normalized into a set of smaller
dimensional table.
• Connects entities to dimensional
table rather than to fact tables.

Fact Constellation
Fact Constellation
• Multiple fact tables share
dimensional tables

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