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Acct 6910 Building Business Intelligence Systems

Acct 6910 Building Business Intelligence Systems. Class Introduction – From Data to Knowledge. Business Intelligence. We are drowning in data , but starving for knowledge Business intelligence (BI) is knowledge extracted from data to support better business decision making.

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Acct 6910 Building Business Intelligence Systems

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  1. Acct 6910Building Business Intelligence Systems Class Introduction – From Data to Knowledge

  2. Business Intelligence • We are drowning in data, but starving for knowledge • Business intelligence (BI) is knowledge extracted from data to support better business decision making.

  3. Data, Information, Knowledge • Data is a set of discrete, objective facts about events. Ex., Mark’s GPA is 2.9 in Fall 2001. • Information is meaningful data. Ex., how is Mark’s performance in Fall 2001? • Knowledge is hidden patterns extracted from data. Ex., how to improve Mark’s academic performance?

  4. Data, Information, Knowledge Online bookstore Example: July’s revenue is $2 million Information July’s sale is bad. Knowledge Suggest marketing strategy to boost sales Data

  5. Building BI Systems • Data Warehouse: A huge and integrated data base. • Data mining: Techniques to extract hidden patterns from data.

  6. What’s the Excitement About Data Warehouse? • Internet • Data warehouse • E-commerce The top three most important technologies ranked by IT managers in 2000-2001 (Recent surveys by The Data Warehousing Institute and Deloitte Research)

  7. Brain-machine interfaces Flexible transistors Data mining Digital rights management Biometrics Natural language processing Microphotonics Untangling code Robot design Microfluidics What’s the Excitement About Data Mining? 10 emerging technologies that will change the world (MIT’s Magazine of Innovation, 2001 Annual Innovation Issue)

  8. Data Mining Applications • Finance and Insurance • Marketing: Target Marketing, Cross Selling • E-commerce: personalization, recommendation, web site design • Crime Detecting

  9. Course Objectives • Concept Learning • Hands on experience • Real world oriented learning • Promoting data warehouse and data mining career interests and opportunities

  10. Course Structure • Data Warehouse • Logical design of data warehouse • Physical design of data warehouse • Data preparation and staging • Data analysis (OLAP)

  11. Course Structure • Data Mining • Association rules – Cross Selling • Clustering – Target Marketing • Classification – Credit Card Approval • Advanced issues – web mining, personalization

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