Data Warehousing
Prepared By:
Dr. Radhika Kotecha

Case Study: The Need for Data Warehousing
• Pallav Raj is the CEO of a large garments retail chain
called JRTs.
• JRTs has approximately 100 stores spread
throughout the country.
• Pallav Raj asks one of his employees to provide him:
1) A status report on the business as he wishes to
know if the company was making an overall profit or
loss
2) A detailed product report of the previous year as
he wishes to know which products sold well and
those that did not even have a marginal sale

Case Study: The Need for Data Warehousing
• How does the employee calculate if the company
was making an overall profit or loss ???
– Manually !!
– Tedious task !!
• And further, how does the employee find a
detailed product report of the previous year ???
– ???

Increasing Demand for Strategic Information
• Strategic information: The information needed for
formulating and executing business objectives.
• Critical for the survival of the corporation in a highly
competitive world
• Example business objectives:
– Retain the current customers of the business.
– ƒAdd to the customer base by at least 10% over the next 3
years.
– ƒEnhance the market share by 15% in the next 2 years.
– ƒLaunch new and better products in the market by the next
year.
– ƒImprove product quality of top five selling products.
– ƒIncrease sales in the north west region.

Information Crisis
• Organizations have a huge amount of data.
• Information systems that they have are
ineffective at turning this into useful strategic
information

Inability of Past Decision-support Systems
• Most of these attempts by IT in the past ended in failure as the
users could not clearly define what they wanted in the first
place.
• The users indulged themselves into the spiral of asking for
more and more supplementary reports thereby increasing the
IT load even further.
• ƒThe users depended on IT professional to provide the
information as they could not access the information directly in
!an interactive manner.
• IT received too many ad hoc requests for a variety of reports. IT
was not able to generate all the reports in the requested
manner within the assigned timeframe.
• Requests were not only numerous, but also kept changing over
time with users wanting more reports subsequently to expand
and understand earlier reports

Expectations from the Decision-support System
• Designed for analyzing large volumes of data
• Data extracted from multiple applications
• ƒUser friendliness
• Periodically updating of data content
• Contain current as well as historical data
• ƒAbility for users to formulate and execute queries
and get results online

Operational vs. Decision-support System
• Identify the differences between the two systems:
Attribute Operational
System
Decision-support
System
Data Content
Access Frequency
Access Type
Response Time
Users
No. of Users
Summarization
Database Size
Database Design

Data Warehouse Defined
• Data warehouse is a:
– Subject-oriented (for example, customer, product)
– Integrated (data cleansing & transformation)
– Non-volatile (Read-only)
– Time-variant (current + historical)
collection of data in support of management’s
decisions

Data Warehouse Elaborated
• The data warehouse is an informational environment
which:
– ƒprovides an integrated view of the enterprise
– renders the enterprise’s current as well as historical data
readily
– available for making strategic decisions
– ƒmakes decision making possible without hindering
operational systems
– ƒmakes the organization’s information consistent and easily
accessible
– provides a flexible, conducive and interactive source of
strategic information

What Can a Data Warehouse Do?
• Immediate information delivery
• Integration of data from within and outside the
organization
• Provides an insight into the future
• Enables users to look at the same data in different
ways
• Provides freedom from the dependency on IT
professionals

What Can a Data Warehouse NOT Do?
• Cannot create additional data on its own.
• For example, if a manager wants to analyze the
sales of a product based on customer’s income
level, and if the income of the customer is not
captured by the source systems, then the data
warehouse will not be able to help the manager

Data Warehouse—An Environment or a Product
• An Environment: That needs to be created
• Not a Product: That can be purchased

Industry Applications
Retail Customer Loyalty Categorization, Target Marketing
Finance & Banking Risk Management, Fraud Detection
Airlines Route Profitability Identification, Promotional Schemes
Identification
Manufacturing Cost Reduction, Resource Management
Applications of Data Warehouse System

Benefits of Data Warehousing
• Data warehouses enable end-users to access a wide
variety of data
• Business analysts and decision makers can analyze
the current trends in the market to predict future
trends
• Data warehouse provides consistent data
• ƒIt helps to increase productivity and decrease
computing costs
• Data warehouses contain data that has been
integrated from a number of different sources
• The results obtained can be presented in a variety of
formats in the form of reports, graphs, etc.

Benefits of Data Warehousing [Contd.]
• Tangible Benefits
– For a retail business with $200 million in annual sales,
a 1% improvement in sales can yield additional annual
revenue of $2 million
• Intangible Benefits
– Enhanced customer relations through improved
knowledge of individual customer’s requirements and
trends in the market

Case Study [Contd.]
• Should Pallav Raj go for data warehouse?

Case Study [Contd.]
• Suppose Mr. Pallav Raj decides to go for data
warehouse - Maintains monthly basis data
granularity
– Efforts are now reduced, but issues don’t end here
• If Pallav Raj asks one of his employees to provide:
1) Sales of particular brand of T-shirt for 2nd
quarter: Possible Easily
2) Sales of particular brand of T-shirt in last week
of December: ?????

Case Study [Contd.]
• Next Lecture:
– Discussion on data warehouse need & fundamentals
described in previous slides
– Discussion on solution for the issue of data
warehouse highlighted in previous slide
– Architecture of Data Warehouse

Reference:
• Reema Thareja, “Data Warehousing”, Oxford
University Press, 1st Edition, 2009.