Section 4.7 Review Questions What are the privacy issues in data mining? Data that is collected, stored, and analyzed in data mining often contains information about real people. This includes identification, demographic financial, personal, and behavioral information. Most of these data can be accessed through some third-party data providers. In order to maintain the privacy and protection of individuals rights, data mining professionals have ethical(and often legal) obligations 2. How do you think the discussion between privacy and data mining will progress? Why? As technology advances and more information about people becomes easier to get the privacy debate will adjust accordingly. People's expectations about privacy will become tempered by their desires for the benefits of data mining, from dividualized customer service to higher security. As with all issues of social import, the privacy issue will include social discourse, legal and legislative decisions, and corporate decisions. The fact that companies often choose to self- society be able to find a happy med ium between privacy and data mining. a regulate(e.g, by ensuring their data is de- identified) implies that we may (Answers will vary by student.) What are the most common myths about data mining? Data mining provides instant, crystal-ball predictions Data mining is not yet viable for business applications Data mining requires a separate, dedicated database Only those with ad vanced degrees can do data mining Data mining is only for large firms that have lots of customer data 4. What do you think are the reasons for these myths about data mining? Students'answers will differ. Some answers might relate to fear of analytics, fear of the unknown, or fear of looking dumb 5. What are the most common data mining mistakes/blunders? How can they be minimized and/or eliminated? Selecting the wrong problem for data mining Ignoring what your sponsor thinks data mining is and what it really can and cannot do Copyright C2018 Pearson Education, Inc.11 Copyright © 2018Pearson Education, Inc. Section 4.7 Review Questions 1. What are the privacy issues in data mining? Data that is collected, stored, and analyzed in data mining often contains information about real people. This includes identification, demographic, financial, personal, and behavioral information. Most of these data can be accessed through some third-party data providers. In order to maintain the privacy and protection of individuals’ rights, data mining professionals have ethical (and often legal) obligations. 2. How do you think the discussion between privacy and data mining will progress? Why? As technology advances and more information about people becomes easier to get, the privacy debate will adjust accordingly. People’s expectations about privacy will become tempered by their desires for the benefits of data mining, from individualized customer service to higher security. As with all issues of social import, the privacy issue will include social discourse, legal and legislative decisions, and corporate decisions. The fact that companies often choose to self￾regulate (e.g., by ensuring their data is de-identified) implies that we may as a society be able to find a happy medium between privacy and data mining. (Answers will vary by student.) 3. What are the most common myths about data mining? • Data mining provides instant, crystal-ball predictions. • Data mining is not yet viable for business applications. • Data mining requires a separate, dedicated database. • Only those with advanced degrees can do data mining. • Data mining is only for large firms that have lots of customer data. 4. What do you think are the reasons for these myths about data mining? Students’ answers will differ. Some answers might relate to fear of analytics, fear of the unknown, or fear of looking dumb. 5. What are the most common data mining mistakes/blunders? How can they be minimized and/or eliminated? • Selecting the wrong problem for data mining • Ignoring what your sponsor thinks data mining is and what it really can and cannot do