Data Warehousing

An Overview

Outline

• What is Data Warehousing? (Definition)

• Why does anyone need it? (Applications)

• How is the data organized? (Star Schema)

• Implementation Issues.

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.

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.

Coca Cola & IBM

• IBM helping Coca Cola with warehouse.

• Deal with Global companies like McDonalds – support for negotiating global contracts.

Financial Services Example – Credit Life Cycle

Product Planning

CustomerAcquisition

CustomerManagement

Collections

Customer Acquisition

Product Planning

CustomerAcquisition

Support for Marketing• Market Segmentation

Plus Forecasts with:• Response Models• Risk / Bankruptcy Models • Profitability Models

Customer Management

CustomerAcquisition

CustomerManagement

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)

Collections/Recovery

CustomerManagement

Collections

What is the likelihood of recovering money from anaccount sent to collections?

Decision Support:Collections models

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?

Where is the data?

• Transaction Systems

• Marketing Database

• Credit Reports

• Customer Service

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

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

Implementation

• Start with the Business Issues

• Project Planning/Human Resources

• Database design / data sources

• Application Development

Business Analysis

• What is the problem?

• Who owns the problem?

• Will data help solve it?

When can data be used to Predict?

Chaotic Markets

(fashion driven)

Real-Time Markets

(Stock Market)

Linear Markets (Local authority - # of trash cans)

Statistical Markets (retail)

Low High

Low

High

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RandomnessSource: www.butlergroup.com

Also read article in Wired Magazine on Data Mining and Terrorism