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Group presentation2

  1. 1. Data Mining (DM)<br />Matthew Stanley<br />Cynthia Denise Williams<br />Cianti Williams<br />
  2. 2. Agenda<br /><ul><li>Background information
  3. 3. Real World Case 4- Applebee’s Travelocity, and Others: Data Mining for Business Decisions
  4. 4. Questions
  5. 5. Analysis
  6. 6. Final Remarks
  7. 7. References</li></li></ul><li>Background information<br />DM Encompasses<br /><ul><li>Statistics
  8. 8. Ability of systems to learn
  9. 9. Artificial Neural Networks Databases
  10. 10. Expert Systems
  11. 11. Data Visualization</li></li></ul><li>Background information<br />DM Past and Present<br /><ul><li>DM can be traced back to the late 1980’s
  12. 12. Early 1990’s DM recognized as a sub-application </li></ul>of KDD (Knowledge Discovery Database)<br /><ul><li>Notoriety greatly increased in the 1990 ’s
  13. 13. Secondary to advances in technology
  14. 14. DM process continue to increase
  15. 15. Will expand related to desire to collect electronic data</li></li></ul><li>Background information<br />DM Main Goals<br />Analyze<br />Predict future behaviors<br />Gain competitive advantages<br />Find patterns<br />Associations<br />Relationships<br />Summarize<br />Increase revenue, cut cost<br />
  16. 16. Real World Case 4<br /><ul><li>Analyzes three companies: Applebee’s, Travelocity, and VistaPrint uses of Data Mining
  17. 17. Applebee’s: restaurant
  18. 18. Analyzed operations at their restaurants
  19. 19. Used data to calculate how much time a customer spends in the restaurant (from time of order, to food service, to payment)
  20. 20. Result: Improved customer service</li></li></ul><li>Real World Case 4<br /><ul><li>Travelocity: Online travel agency
  21. 21. Using text analytics software (natural language engine) from Attensity
  22. 22. Identifies facts, opinions, trends, etc…
  23. 23. Result: effectively identifies trends which allows the company to prevent problems or anticipate customer needs more efficiently.
  24. 24. VistaPrint: online graphic design services
  25. 25. Improved their ability to retrieve trend information
  26. 26. Installed new technology->retrieved 1% of information
  27. 27. Result: Able to improve customer interaction with the website</li></li></ul><li>Advantages vs. DisadvantagesCreating business data warehousing<br />ADVANTAGES<br />DISADVANTAGES<br /><ul><li>Predict Future Behaviors
  28. 28. Gain Competitive
  29. 29. Advantages
  30. 30. Find Patterns
  31. 31. Summarize
  32. 32. Increase revenue, cut cost
  33. 33. Requires experience in</li></ul>statistics, domain<br />knowledge<br /><ul><li>Random fluctuations can </li></ul>be misinterpreted<br /><ul><li>Privacy concerns</li></li></ul><li>The Bandwagon effectWhy not jump on the data mining bandwagon?<br /><ul><li>Not for every business
  34. 34. Must be open minded
  35. 35. Need access to all phases of data for complete picture
  36. 36. Individual privacy
  37. 37. Data integrity</li></li></ul><li>Applebee’s<br />Other uses/questions while analyzing data<br /><ul><li>Total time to prepare meals and wait times
  38. 38. Compare drink choices with sport events
  39. 39. Zip codes on credit cards to create new </li></ul>locations<br /><ul><li> Blog content mining  advertise specialties in area using Smartphone technology</li></li></ul><li>Applebee’s<br />Other uses/questions while analyzing data<br />
  40. 40. Innovative ThinkingDoes data mining stifle creativity?<br />YES<br />NO<br /><ul><li>Become too </li></ul>heartless<br /><ul><li>All about numbers
  41. 41. Encourages innovation
  42. 42. Support for radical </li></ul>ideas<br /><ul><li>Undo bad choices </li></ul>Faster<br />Utilize technology such as a Creativity Engine<br />
  43. 43. Innovative ThinkingDoes data mining stifle creativity? (no)<br />Creativity Machine<br /><ul><li>Brings together libraries that were never intended to work together
  44. 44. Users become infinitely flexible with the ability to transform data.</li></li></ul><li>Learning Points<br /><ul><li>Data mining continuing to grow
  45. 45. More art than numbers</li></li></ul><li>Is this still a problem?<br /><ul><li>Becoming more standardized
  46. 46. Diversified not centralized</li></li></ul><li>Other examples in IT used for this case study<br /> example of company using data mining well. <br /><ul><li>Offers customized experience
  47. 47. Remember previous interests and display relevant items
  48. 48. Displays items that are popular and related
  49. 49. Shows items that were commonly purchased together</li></li></ul><li>Final Remarks<br /><ul><li>DM processes increased greatly over past ten years
  50. 50. DM will expand related to desire to collect electronic data</li></li></ul><li>References<br />Coenen, F., (2011). Data mining: past, present, future. The Knowledge Engineering Review:25th Anniversary Issue, 26(1), 25-29. doi: 2259819321.<br />Mining the Blogosphere to Generate Cuisine Hotspot Maps. (2010). Journal of Digital Information Management, 8(6), 396-401. Retrieved from EBSCOhost.<br />Shonle, M., & Yuen, T. T. (2010). Compose & Conquer: Modularity for End-Users. ICSE: International Conference on Software Engineering, 191-194. Retrieved from EBSCOhost.<br />
  51. 51. Thank you!<br />