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Introduction BIM. Objectives Nature of Data Mining Data Mining Tools Ethics Online Survey Techniques Interpret Data.

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Presentation on theme: "Introduction BIM. Objectives Nature of Data Mining Data Mining Tools Ethics Online Survey Techniques Interpret Data."— Presentation transcript:

1 Introduction BIM

2 Objectives Nature of Data Mining Data Mining Tools Ethics Online Survey Techniques Interpret Data

3 Overview Data Mining (data or knowledge discovery) Definition - the process of analyzing data from different perspectives and summarizing it into useful information Uses - increase revenue, cut costs, or both. Examples of data mining tools Database software programs - Access or Oracle Online programs - Survey Monkey or GoogleDocs

4 Example Kroger card Point-of-Sale Records Targeted Promotions Develop Products Increase Revenue Promotions Coupons

5 Vocabulary Data - any facts, numbers, or text that can be processed by a computer Operational or Transactional Data – Sales, Cost, Inventory, Payroll, Accounting Nonoperational Data – Industry Sales, Forecast Data, Macro Economic Data Meta Data - data about the data itself Demographic – a vital or social statistic of a human population (see example: City of Allen Demographics – population, age, male/female, education)

6 Relationships Internal Price Product Positioning Staff skills External Economic Indicators Competition Customer Demographics Analyze Sales Customer Satisfaction Corporate Profits Detailed Transactional Data Data Mining Video

7 Five Major Elements Extract Store and manage Share information Analyze Present

8 Analysis Analyzes relationships and patterns using queries Classes Clusters Associations Sequential patterns

9 Issues & Ethics Data Integrity Cost Individual Privacy Fair Use Guidelines Federal Trade Commission Examples Survey Monkey Privacy Policy Survey Monkey Terms of Use

10 Data Mining for Retail Store Setup an account on Survey Monkey Create Online Survey Name of Store Items to Sell Hours of Operation Dates of Operation Customer Demographics Name Grade Gender

11 Sources Overview – accessed on 2/18/2011 http://www.anderson.ucla.edu/faculty/jason.frand /teacher/technologies/palace/datamining.htm– http://www.anderson.ucla.edu/faculty/jason.frand /teacher/technologies/palace/datamining.htm Ethical Issues – accessed on 2/18/2011 http://www.springerlink.com/content/m13883x46 5627814/– http://www.springerlink.com/content/m13883x46 5627814/ Customer privacy – accessed on 2/18/2011 http://www.exforsys.com/tutorials/data- mining/data-mining-privacy-concerns.html http://www.exforsys.com/tutorials/data- mining/data-mining-privacy-concerns.html

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