Assignment Requirements
Data Mining
The development of complex algorithms that can mine mounds of data that have been collected from people and digital devices have led to the adoption of data mining by most businesses as a means of understanding their customers better than before. Data mining takes place in retailing and sales, banking, education, manufacturing and production, health care, insurance, broadcasting, marketing, customer services, and a number of other areas. The analytical information gathered by data-mining applications has given some businesses a competitive advantage, an ability to make informed decisions, and better ways to predict the behavior of customers. Write a four to five (4-5) page paper in which you:
1. Determine the benefits of data mining to the businesses when employing:
1. Predictive analytics to understand the behavior of customers
2. Associations discovery in products sold to customers
3. Web mining to discover business intelligence from Web customers
4. Clustering to find related customer information
2. Assess the reliability of the data mining algorithms. Decide if they can be trusted and predict the errors they are likely to produce.
3. Analyze privacy concerns raised by the collection of personal data for mining purposes.
1. Choose and describe three (3) concerns raised by consumers.
2. Decide if each of these concerns is valid and explain your decision for each.
3. Describe how each concern is being allayed.
4. Provide at least three (3) examples where businesses have used predictive analysis to gain a competitive advantage and evaluate the effectiveness of each business’s strategy.
5. Use at least three (3) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources.
Your assignment must follow these formatting requirements:
Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow APA or school-specific format. Check with your professor for any additional instructions.
Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.
The specific course learning outcomes associated with this assignment are:
• Explain how information technology systems influence organizational strategies.
• Evaluate the ethical concerns that information technologies raise in a global context.
• Outline the challenges and strategies of e-Business and e-Commerce technology.
• Use technology and information resources to research issues in information systems and technology.
• Write clearly and concisely about topics related to information systems for decision making using proper writing mechanics and technical style conventions.
**1 of the 5 references must be:
Turban, E., & Volonino, L. (2011). I nformation technology for management (8th ed). Hoboken, NJ: John Wiley & Sons.
Below is the grading rubric:
Points: 75 Assignment 4: Data Mining
Criteria Unacceptable
Below 70% F Fair
70-79% C Proficient
80-89% B Exemplary
90-100% A
1a. Determine the benefits of data mining to the businesses when employing predictive analytics to understand the behavior of customers.
Weight: 5% Did not submit or incompletely determined the benefits of data mining to the businesses when employing predictive analytics to understand the behavior of customers. Partially determined the benefits of data mining to the businesses when employing predictive analytics to understand the behavior of customers. Satisfactorily determined the benefits of data mining to the businesses when employing predictive analytics to understand the behavior of customers. Thoroughly determined the benefits of data mining to the businesses when employing predictive analytics to understand the behavior of customers.
1b. Determine the benefits of data mining to the businesses when employing associations discovery in products sold to customers.
Weight: 5% Did not submit or incompletely determined the benefits of data mining to the businesses when employing associations discovery in products sold to customers. Partially determined the benefits of data mining to the businesses when employing associations discovery in products sold to customers. Satisfactorily determined the benefits of data mining to the businesses when employing associations discovery in products sold to customers. Thoroughly determined the benefits of data mining to the businesses when employing associations discovery in products sold to customers.
1c. Determine the benefits of data mining to the businesses when employing Web mining to discover business intelligence from Web customers.
Weight: 5% Did not submit or incompletely determined the benefits of data mining to the businesses when employing Web mining to discover business intelligence from Web customers. Partially determined the benefits of data mining to the businesses when employing Web mining to discover business intelligence from Web customers. Satisfactorily determined the benefits of data mining to the businesses when employing Web mining to discover business intelligence from Web customers. Thoroughly determined the benefits of data mining to the businesses when employing Web mining to discover business intelligence from Web customers.
1d. Determine the benefits of data mining to the businesses when employing clustering to find related customer information.
Weight: 5% Did not submit or incompletely determined the benefits of data mining to the businesses when employing clustering to find related customer information. Partially determined the benefits of data mining to the businesses when employing clustering to find related customer information. Satisfactorily determined the benefits of data mining to the businesses when employing clustering to find related customer information. Thoroughly determined the benefits of data mining to the businesses when employing clustering to find related customer information.
2. Assess the reliability of the data-mining algorithms. Decide if they can be trusted and predict the errors they are likely to produce.
Weight: 20% Did not submit or incompletely assessed the reliability of the data-mining algorithms. Did not submit or incompletely decided if they can be trusted and did not submit or incompletely predicted the errors they are likely to produce. Partially assessed the reliability of the data-mining algorithms. Partially decided if they can be trusted and partially predicted the errors they are likely to produce. Satisfactorily assessed the reliability of the data-mining algorithms. Satisfactorily decided if they can be trusted and satisfactorily predicted the errors they are likely to produce. Thoroughly assessed the reliability of the data-mining algorithms. Thoroughly decided if they can be trusted and thoroughly predicted the errors they are likely to produce.
3a. Choose and describe three (3) concerns raised by the collection of personal data for mining purposes.
Weight: 5% Did not submit or incompletely chose and described three (3) concerns raised by the collection of personal data for mining purposes. Partially chose and described three (3) concerns raised by the collection of personal data for mining purposes. Satisfactorily chose and described three (3) concerns raised by the collection of personal data for mining purposes. Thoroughly chose and described three (3) concerns raised by the collection of personal data for mining purposes.
3b. Decide if each of these concerns is valid and explain your decision for each.
Weight: 5% Did not submit or incompletely decided if each of the concerns is valid and did not submit or incompletely explained your decision for each. Partially decided if each of the concerns is valid and partially explained your decision for each. Satisfactorily decided if each of the concerns is valid and satisfactorily explained your decision for each. Thoroughly decided if each of the concerns is valid and thoroughly explained your decision for each.
3c. Describe how each concern is being allayed.
Weight: 5% Did not submit or incompletely described how each concern is being allayed. Partially described how each concern is being allayed. Satisfactorily described how each concern is being allayed. Thoroughly described how each concern is being allayed.
4. Provide at least three (3) examples where businesses have used predictive analysis to gain a competitive advantage and evaluate the effectiveness of each business’s strategy.
Weight: 30% Did not submit or incompletely provided at least three (3) examples where businesses have used predictive analysis to gain a competitive advantage and did not submit or incompletely evaluated the effectiveness of each business’s strategy. Partially provided at least three (3) examples where businesses have used predictive analysis to gain a competitive advantage and partially evaluated the effectiveness of each business’s strategy. Satisfactorily provided at least three (3) examples where businesses have used predictive analysis to gain a competitive advantage and satisfactorily evaluated the effectiveness of each business’s strategy. Thoroughly provided at least three (3) examples where businesses have used predictive analysis to gain a competitive advantage and thoroughly evaluated the effectiveness of each business’s strategy.
5. 3 references
Weight: 5% No references provided Does not meet the required number of references; some or all references poor quality choices. Meets number of required references; all references high quality choices. Exceeds number of required references; all references high quality choices.
6. Clarity, writing mechanics, and formatting requirements
Weight: 10% More than 6 errors present 5-6 errors present 3-4 errors present 0-2 errors present
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