Pred ictive analytics is the use of statistical techniques and data mining to determine what is likely to happen in the future. Businesses use predictive analytics to forecast whether customers are likely to switch to a competitor, what customers are likely to buy, how likely customers are to respond to a promotion and whether a customer is creditworthy. Sports teams have used predictive analytics to identify the players most likely to contribute to a teams success 6. What is prescriptive analytics? What kind of problems can be solved by prescriptive analytics? Prescriptive analytics is a set of techniques that use descriptive data and forecasts to identify the decisions most likely to result in the best performance. Usually, an organization uses prescriptive analytics to identify the decisions or actions that will optimize the performance of a system. Organizations have used prescriptive analytics to set prices, create production plans, and identify the best locations for facilities such as bank branches 7. Define modeling from the analytics perspective As Application Case 1.6 illustrates, analytics uses descriptive data to create models of how people, equipment, or other variables operate in the real world These models can be used in predictive and prescriptive analytics to develop forecasts. recommendations and decisions 8. Is it a good idea to follow a hierarchy of descriptive and predictive analytics before apply ing prescriptive analytics? As noted in the analysis of Application Case 1.5, it is important in any analytics project to understand the business domain and current state of the business oblem. This requires analysis of historical data, or descriptive analytics Although the chapter does not discuss a hierarchy of analytics, students may observe that testing a model with predictive analytics could logically improve prescriptive use of the model How can analytics aid in objective decision making? As noted in the analysis of Application Case 1.4, problem solving in organizations has tended to be subjective, and decision makers tend to rely on familiar processes. The result is that future decisions are no better than past decisions Analytics builds on historical data and takes into account changing conditions to arrive at fact-based solutions that decision makers might not have considered Section 1.6 Review Questions 1. Why would a health insurance company invest in analytics beyond fraud detection? Why is it in their best interest to predict the likelihood of falls by An insurance company would potentially want to evaluate analytics to both quantify the risk of a potential incident category (like falls)and to help identif subgroups of the population that are at-risk for this type of injury. With this type Copyright C2018 Pearson Education, Inc.8 Copyright © 2018Pearson Education, Inc. Predictive analytics is the use of statistical techniques and data mining to determine what is likely to happen in the future. Businesses use predictive analytics to forecast whether customers are likely to switch to a competitor, what customers are likely to buy, how likely customers are to respond to a promotion, and whether a customer is creditworthy. Sports teams have used predictive analytics to identify the players most likely to contribute to a team’s success. 6. What is prescriptive analytics? What kind of problems can be solved by prescriptive analytics? Prescriptive analytics is a set of techniques that use descriptive data and forecasts to identify the decisions most likely to result in the best performance. Usually, an organization uses prescriptive analytics to identify the decisions or actions that will optimize the performance of a system. Organizations have used prescriptive analytics to set prices, create production plans, and identify the best locations for facilities such as bank branches. 7. Define modeling from the analytics perspective. As Application Case 1.6 illustrates, analytics uses descriptive data to create models of how people, equipment, or other variables operate in the real world. These models can be used in predictive and prescriptive analytics to develop forecasts, recommendations, and decisions. 8. Is it a good idea to follow a hierarchy of descriptive and predictive analytics before applying prescriptive analytics? As noted in the analysis of Application Case 1.5, it is important in any analytics project to understand the business domain and current state of the business problem. This requires analysis of historical data, or descriptive analytics. Although the chapter does not discuss a hierarchy of analytics, students may observe that testing a model with predictive analytics could logically improve prescriptive use of the model. 9. How can analytics aid in objective decision making? As noted in the analysis of Application Case 1.4, problem solving in organizations has tended to be subjective, and decision makers tend to rely on familiar processes. The result is that future decisions are no better than past decisions. Analytics builds on historical data and takes into account changing conditions to arrive at fact-based solutions that decision makers might not have considered. Section 1.6 Review Questions 1. Why would a health insurance company invest in analytics beyond fraud detection? Why is it in their best interest to predict the likelihood of falls by patients? An insurance company would potentially want to evaluate analytics to both quantify the risk of a potential incident category (like falls) and to help identify subgroups of the population that are at-risk for this type of injury. With this type