1. Data Mining: Software Helping Business Run
Group 4
Austin Beam, Brittany Dearien, Warren Irwin, Amanda Medlin, Rob Westerman

2. Introduction Data Mining defined Basic Facts Goals of data mining
Steps to data mining
What data mining can do
Data mining in business
Advantages/Disadvantages
Data Mining Software
Data Mining in the future

3. Data Mining Data mining is defined as
The science of extracting useful information from large data sets or databases
Also known as Knowledge-Discovery in Databases (KDD)

4. Basic Facts
Data mining presents information that would not be available otherwise
The more data the better!
Must have good data or the solutions are irrelevant

5. Automated Hidden Predictive
Goals of Data Mining
Simplification and automation of the overall statistical process from data sources to model application
This means:
The automated extraction of hidden predictive information from large databases
Automated Hidden Predictive

6. Steps to Data Mining
Data mining relieves the pressure and need for as many statisticians
Begin with a Predictive Model: take various information such as family, age, income to answer a question
Mathematical Algorithms

7. How the data is scored Qualitative view: Quantitative view:
The way data is received/recorded
Qualitative view:
provides insight into the data you are working with, but requires interaction capabilities and good visualization
Quantitative view:
more of an automated process and a bottom line orientation

8. Decision Trees
A series of ‘if/then’ questions that reach a final solution

9. Convergence of 3 Technologies
Increased
Computing
Power
Improved Data
Collection
and Management
Statistical
Algorithms DM

10. What can Data Mining do?
Other than help find new information, data mining can assist in
Finding new patterns
Recognizing significant facts
Valuing customer loyalty
Following new and changing trends

11. Data Mining in Business
Market segmentation
Customer churn
Fraud detection
Direct marketing
Interactive marketing
Market basket analysis
Trend analysis

12. Advantages of Data Mining
Automated predictions of trends and behaviors
Discovery of previously unknown patterns
More time/cost efficient than statisticians
Competitive advantage
Increased Profitability

13. Disadvantages of Data Mining
Is the data correct?
Who has the right to this information?
Privacy
Ethics

14. Data Mining Software SAS SPSS Insightful (formerly Mathsoft/S-Plus)
800 Pound Gorilla in the data analysis space
SPSS
Insightful (formerly Mathsoft/S-Plus)
Well respected statistical tools, now moving into mining
Oracle
Integrated data mining into the database
Angoss
One of the first data mining applications (as opposed to tools)
HNC
Very specific analytic solutions
Unica
Great mining technology, focusing less on analytics these days

15. Data Mining in the Future
Growing Trends
Data Mining market size of software has grown from $540M in 2002, $1.5B in 2005
Endless possibilities for everyday life!