Leaving insufficient time for data preparation. It takes more effort than Looking only at aggregated results and not at individual records Being sloppy about keeping track of the mining procedure and result Ignoring suspicious findings and quickly moving on Running mining algorithms repeatedly and blindly. (It is important to think hard enough about the next stage of data analysis. Data mining is a very hands-on activity. Believing everything you are told about data Believing everything you are told about your own data mining analysis Measuring your results differently from the way your sponsor measures them Ways to minimize these risks are basically the reverse of these items ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION O Application Case 4.1: Visa Is Enhancing the Customer Experience while reducing Fraud with Predictive Analytics and Data mining What challenges were Visa and the rest of the cred it card industry facing? Visa was facing twin challenges. The first was the growing rates of credit card fraud, while the second was inaccurate fraud identification systems that created customer issuc 2. How did visa improve customer service while also improving retention of fraud? By creating more accurate fraud identif ication systems, Visa was able to decrease the number of false positives, and the resulting customer concerns and complaints that went along with them What is in-memory analytics, and why was it necessary? Copyright C2018 Pearson Education, Inc.12 Copyright © 2018Pearson Education, Inc. • Leaving insufficient time for data preparation. It takes more effort than one often expects • Looking only at aggregated results and not at individual records • Being sloppy about keeping track of the mining procedure and results • Ignoring suspicious findings and quickly moving on • Running mining algorithms repeatedly and blindly. (It is important to think hard enough about the next stage of data analysis. Data mining is a very hands-on activity.) • Believing everything you are told about data • Believing everything you are told about your own data mining analysis • Measuring your results differently from the way your sponsor measures them Ways to minimize these risks are basically the reverse of these items. ANSWERS TO APPLICATION CASE QUESTIONS FOR DISCUSSION  Application Case 4.1: Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive Analytics and Data Mining 1. What challenges were Visa and the rest of the credit card industry facing? Visa was facing twin challenges. The first was the growing rates of credit card fraud, while the second was inaccurate fraud identification systems that created customer issues. 2. How did Visa improve customer service while also improving retention of fraud? By creating more accurate fraud identification systems, Visa was able to decrease the number of false positives, and the resulting customer concerns and complaints that went along with them. 3. What is in-memory analytics, and why was it necessary?