Thinking about Data Mining As implied in the text, data mining boils down to making sense of bits of information embedded in a large mass of information. No matter how exquisite the software performing the data mining, the manager or professional must have good intuition about the potential value of information or patterns of information. To get you in the right mind-set for data mining, do the following puzzlers. • You are a manager in an insurance company. Your data mining software notes that people under 30 purchase less sun-blocking lotion, buy more cigarettes, and are more likely to let their auto inspection stickers expire. What sense do you make of these data that could help your insurance company? • You are a human resources professional. Your data mining software indicates that employees who purchase American flags, watch professional football on TV, and own an SUV tend to stay longer with the company. What implications might this information have for staffing your company? • You are a marketing specialist at a music company. Your data mining software indicates that people who purchase toothpaste with fluoride, own an umbrella, and give money to charity are more likely to pay to download music. What value for your company might you extract from this information?
Thinking about Data Mining As implied in the text, data mining boils down to making sense of bits of information embedded in a large mass of information. No matter how exquisite the software performing the data mining, the manager or professional must have good intuition about the potential value of information or patterns of information. To get you in the right mind-set for data mining, do the following puzzlers.
• You are a manager in an insurance company. Your data mining software notes that people under 30 purchase less sun-blocking lotion, buy more cigarettes, and are more likely to let their auto inspection stickers expire. What sense do you make of these data that could help your insurance company?
• You are a human resources professional. Your data mining software indicates that employees who purchase American flags, watch professional football on TV, and own an SUV tend to stay longer with the company. What implications might this information have for staffing your company?
• You are a marketing specialist at a music company. Your data mining software indicates that people who purchase toothpaste with fluoride, own an umbrella, and give money to charity are more likely to pay to download music. What value for your company might you extract from this information?
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