Data Warehousing

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Data Warehousing

• An Overview

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Outline

• What is Data Warehousing? (Definition)
• Why does anyone need it? (Applications)
• How is the data organized? (Star Schema)
• Implementation Issues.

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Data Warehouse Definitions

• Dyche Used for decision making- duplicates
  existing data - Combination of hardware,
  specialized software and data extracted from
  other corporate systems.
• Inmon Subject-oriented, integrated, non-volatile
  and time-variant collection of data in support of
  management decisions.

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Why Warehouse?

• Provide single view of customers across
  enterprise
• Improve turnaround time for common reports
• Monitor customer behavior
• Predict future purchases
• Improved responsiveness Business issues.

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Coca Cola IBM

• IBM helping Coca Cola with warehouse.
• Deal with Global companies like McDonalds
  support for negotiating global contracts.

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Financial Services Example Credit Life Cycle
Product Planning
Customer Acquisition
Collections
Customer Management
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Customer Acquisition
Product Planning

• Support for Marketing
• Market Segmentation
• Plus Forecasts with
• Response Models
• Risk / Bankruptcy Models
• Profitability Models

Customer Acquisition
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Customer Management
Who gets a credit increase? Which of delinquent
customers is likely to default? What do you do
(call, send letter, do nothing?) Decision
Support Forecast Customer Behavior (Behavior
Models)
Customer Acquisition
Customer Management
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Collections/Recovery
What is the likelihood of recovering money from
an account sent to collections? Decision
Support Collections models
Collections
Customer Management
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Other Questions

• How can we reduce attrition?
• How can we activate inactive accounts?
• How well are my current strategies performing?
• How do we detect Fraud?

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Where is the data?

• Transaction Systems
• Marketing Database
• Credit Reports
• Customer Service

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How is it Organized?

• Separate from transactional data
• Contains Historical data
• Generally aggregated to some extent
• Optimized for flexible querying of large volumes
  of data

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Star Schema

• Fact Table plus several dimensional tables
• Un-normalized
• Less flexible than normalized tables
• Faster retrieval than normalized tables for large
  volumes of data

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Implementation

• Start with the Business Issues
• Project Planning/Human Resources
• Database design / data sources
• Application Development

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Business Analysis

• What is the problem?
• Who owns the problem?
• Will data help solve it?

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When can data be used to Predict?
High
Coupling
Low
Low
High
Randomness
Source www.butlergroup.com
Also read article in Wired Magazine on Data
Mining and Terrorism
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