Data Mining Presented By: Sean T. Ryan

Data Mining

Presented By:Sean T. Ryan

April 23, 2001CSMN 601

Agenda Introduction Data Mining Techniques Technology Results Conclusion

Challenges Faced by Businesses

Understanding CustomersCharacteristics & Profiles

Preferences

Know what they want to buy

Identifying attracting and returning profitable customers

Identifying opportunities

Customer Data

Sales ForceInformation

Market Information

OrderEntry

CustomerService

Inventory

MarketingDatabase

Data Integration => Mining

Sales Force information ->

Market information ->

Order Entry ->

Customer Service ->

Inventory ->

Marketing ->

DatabasDatabasee

Data Mining

Definition:

Data mining is used to discover [hidden] patterns and relationships in your data in order to help you make better business decisions.

Data Mining Introduction Data Mining Techniques Technology Results Conclusion

Data Mining Techniques Include:

Sequences Associations Predictions Clustering Classification

Sequences

One event leading toA latter event

Example: Customer purchases

A rugCustomer purchases

curtains

Associations

One event is correlatedto another event

Example:

Beer purchasers

Will purchase peanuts aCertain percentage of the time

Predictions

Discovering patterns in data that can lead to

predictions about the future

Examples:

Anticipate loan defaults& fraudulent behavior

Recommend products & service Offerings that match customer needs

Identifying the Strategic Value of Customers

Valuable Growable 3rd Tier 4th Tier Nth Tier

Actual Value Strategic Value Data Mining Strategic Costs

Clustering

Finding and visualizing groups of facts not previously known

Example:

Who is likely to Buy a Buick?

Entire Population

Age<45Age>45

FemaleMale

Parents ownedBuick

Parents ownedForeign

Classification

Classification by recognition of patterns

Example:

Detailed Profile of customer

Personalization

Data Mining Introduction Data Mining Techniques Technology Results Conclusion

Accrue Decision Series

KnowledgeSTUDIOKnowledgeSEEKER

Data Junction Integration Studio

Data Mining Software

SuperQuery.

BusinessMiner

The Data Mining Group The data mining group is an

independent vendor group which develops data mining standards

PMML

Data Mining Introduction Data Mining Techniques Technology Results Conclusion

Florida Hospital improves operations with DB2 Intelligent Miner                                                                                                                                                                                                                                                                                                      

"By using DB2 Universal Database and DB2 Intelligent Miner for Data, not only are we operating more efficiently, but we're getting people out of the hospital faster and back to their normal, healthy lives."

--Alex Veletsos, Director of Information Systems, Florida Hospital

                    Potential savings of $1.5 million per year

DM tools Investigate Patterns of care given by individual physicians & the total charges they generated

Standardized approaches to specific diagnoses

Efficiency in Accounts Payable

Establishment of training programs targeted to increase Medicare and insurance reimbursement

The Human Genome Project

International research program designed to construct detailed genetic and physical maps of the human genome, to determine the complete sequence of DNA

Human Genome Project helps child birth defects

Professor Peter Scambler, Institute of Child Health in London

Utilize data mining applications to detect ‘bad’ genes that may play a role in certain birth defects

Information is deposited into public databanks accessible to other researchers, physicians, and drug developers

Control or alleviate symptoms

Determination of prognosis

National Basketball Association

NBA coaches score big with IBM data mining application.                                                                                                                                                                                                                                                                                                      

"By helping us make better decisions, Advanced Scout is playing a huge role in establishing incredible fan support and loyalty--that means millions of dollars in gate traffic, television sales and licensing."

--Tom Sterner, Assistant Coach, Orlando Magic

Optimizes Line ups

Real time statistical evaluations

Organizes & reveals patterns of a vast array of statistics

Data Mining Introduction Data Mining Techniques Technology Results Conclusion

In 2001, Do You Expect The Data Mining Industry To:

0 50 100 150 200

Have No Idea

Decline Significantly

Decline Slightly

Stay the Same

Grow Slightly

Grow Significantly

334 Votes Total

Data Mining

Hidden Patterns Various Solutions Wide Variety of products Utilize Customer Data Know your Business needs Results!

Questions