This document discusses the importance of data warehousing for multinational corporations (MNCs). It notes that MNCs often struggle to find, understand, and use the data they need because data is scattered across networks in many different versions and formats. A data warehouse integrates data from various sources into a single consistent store that is easy for end users to access and understand. It allows historical data analysis and "what if" scenario planning to help answer business questions and make better decisions. The document outlines key components and architectures of data warehouses and how they can provide summarized data to different departments through data marts.

1. Data WarehousingData Warehousing
and its Importanceand its Importance
in MNCsin MNCs

2. A producer wants to knowA producer wants to know
2
Which are our
lowest/highest margin
customers ?
Which are our
lowest/highest margin
customers ?
Who are my customers
and what products
are they buying?
Who are my customers
and what products
are they buying?
Which customers
are most likely to go
to the competition ?
Which customers
are most likely to go
to the competition ?
What impact will
new products/services
have on revenue
and margins?
What impact will
new products/services
have on revenue
and margins?
What product prom-
-otions have the biggest
impact on revenue?
What product prom-
-otions have the biggest
impact on revenue?
What is the most
effective distribution
channel?
What is the most
effective distribution
channel?

3. Data, Data everywhereData, Data everywhere
• An MNC can’t find the data it needs
o data is scattered over the network
o many versions, subtle differences
3
MNC can’t get the data it needs
need an expert to get the data
MNC can’t understand the data found
available data poorly documented
MNC can’t use the data found
results are unexpected
data needs to be transformed from one
form to other

4. What are the users saying...What are the users saying...
• Data should be integrated
across the enterprise
• Summary data has a real value
to the organization
• Historical data holds the key to
understanding data over time
• What-if capabilities are
required
4

5. What is a Data Warehouse?What is a Data Warehouse?
A single, complete and
consistent store of current
and historic data, obtained
from a variety of different
sources, and made available
to end users in a way they
can understand and use in
business context.
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6. What is Data Warehousing?What is Data Warehousing?
A process of transforming data
into information and making it
available to users in a timely
enough manner to make a
difference.
Technique for assembling and
managing data, (from various
sources) for the purpose of
answering business questions.
Thus making decisions that were
not previous possible
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Data
Information

7. Warehouses are very LargeWarehouses are very Large
Data BasesData Bases
• Terabytes -- 10^12 bytes:
• Petabytes -- 10^15 bytes:
• Exabytes -- 10^18 bytes:
• Zettabytes -- 10^21 bytes:
• Zottabytes -- 10^24 bytes:
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Walmart -- 24 Terabytes
Geographic Info. Systems
National Medical Records
Weather images
Intelligence Agency Videos

8. Data Warehouse PropertiesData Warehouse Properties
o Subject Oriented
o Used to analyze business
o Summarized and refined
o Snapshot data
o Integrated Data
o Ad-hoc access
o Knowledge User (Manager)
o Thousands of Users
8

9. Components of the WarehouseComponents of the Warehouse
• Data Extraction and Loading
• The Warehouse
• Analyze and Query -- OLAP Tools
• Metadata
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10. Data Warehouse ArchitectureData Warehouse Architecture
10
Data Warehouse
Engine
Optimized Loader
Extraction
Cleansing
Analyze
Query
Metadata Repository
Relational
Databases
Legacy
Data
Purchased
Data
ERP
Systems

11. Data Warehouse and Data MartsData Warehouse and Data Marts
11
OLAP
Data Mart
Lightly summarized
Departmentally structured
Organizationally structured
Atomic
Detailed Data Warehouse Data

12. From the Data Warehouse toFrom the Data Warehouse to
Data MartsData Marts
12
Departmentally
Structured
Individually
Structured
Data Warehouse
Organizationally
Structured
Less
More
History
Normalized
Detailed
Data
Information

13. Application AreasApplication Areas
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Industry Application
Finance Credit Card Analysis
Insurance Claims, Fraud Analysis
Telecommunication Call record analysis
Transport Logistics management
Consumer goods promotion analysis
Data Service providers Value added data
Utilities Power usage analysis

14. Thank You!!!Thank You!!!
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A Presentation by:A Presentation by:
Abhitanjay Chaudhary (C23)Abhitanjay Chaudhary (C23)
Owais Ashraf (C41)Owais Ashraf (C41)
Anant Prakash Gupta (C24)Anant Prakash Gupta (C24)
Rajat Sharma (C20)Rajat Sharma (C20)