1. Types & Typical Applications of DWH
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2. Types of data warehouse
Financial
Telecommunication
Insurance
Human Resource
Global
Exploratory
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3. Types of data warehouse
Financial
First data warehouse that an organization builds. This is appealing because:
Nerve center, easy to get attention.
In most organizations, smallest data set.
Touches all aspects of an organization, with a common denomination i.e. money.
Inherent structure of data directly influenced by the day-to-day activities of financial processing.
Word of caution, will discuss, if and when time permits.
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4. Types of data warehouse
Telecommunication
Dominated by sheer volume of data.
Many ways to accommodate call level detail:
Only a few months of call level detail,
Storing lots of call level detail scattered over different storage media,
Storing only selective call level detail, etc.
Unfortunately, for many kinds of processing, working at an aggregate level is simply not possible.
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5. Types of data warehouse
Insurance
Insurance data warehouses are similar to other data warehouses BUT with a few exceptions.
Stored data that is very, very old, used for actuarial processing.
Typical business may change dramatically over last years, but not insurance.
In retailing or telecomm there are a few important dates, but in the insurance environment there are many dates of many kinds.
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6. Types of data warehouse
Insurance
Insurance data warehouses are similar to other data warehouses BUT with a few exceptions.
Long operational business cycles, in years. Processing time in months. Thus the operating speed is different.
Transactions are not gathered and processed, but are in kind of “frozen”.
Thus a very unique approach of design & implementation.
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7. Typical Applications
Impact on organization’s core business is to streamline and maximize profitability.
Fraud detection.
Profitability analysis.
Direct mail/database marketing.
Credit risk prediction.
Customer retention modeling.
Yield management.
Inventory management.
ROI on any one of these applications can justify HW/SW & consultancy costs in most organizations.
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8. Typical Applications Fraud detection By observing data usage patterns.
People have typical purchase patterns.
Deviation from patterns.
Certain cities notorious for fraud.
Certain items bought by stolen cards.
Similar behavior for stolen phone cards.
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9. Typical Applications Profitability Analysis
Banks know if they are profitable or not.
Don’t know which customers are profitable.
Typically more than 50% are NOT profitable.
Don’t know which one?
Balance is not enough, transactional behavior is the key.
Restructure products and pricing strategies.
Life-time profitability models (next 3-5 years).
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10. Typical Applications Direct mail marketing Targeted marketing.
Offering high bandwidth package NOT to all users.
Know from call detail records of web surfing.
Saves marketing expense, saving pennies.
Knowing your customers better.
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11. Typical Applications Who should get a loan?
Credit risk prediction
Who should get a loan?
Customer segregation i.e. stable vs. rolling.
Qualitative decision making NOT subjective.
Different interest rates for different customers.
Do not subsidize bad customer on the basis of good.
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12. Typical Applications Yield Management
Works for fixed inventory businesses.
The price of item suddenly goes to zero.
Item prices vary for varying customers.
Example: Air Lines, Hotels etc.
Price of (say) Air Ticket depends on:
How much in advance ticket was bought?
How many vacant seats were present?
How profitable is the customer?
Ticket is one-way or return?
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13. Recent Application Agri and related data collected for decades.
Agriculture Systems
Agri and related data collected for decades.
Metrological data consists of 50+ attributes.
Decision making based on expert judgment.
Lack of integration results in underutilization.
What is required, in which amount and when?
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