DSS methodologies and tools originated largely in academia, whereas arose largely from the software industry. Many bI tools, such as data mining and predictive analysis, have come to be considered dSs tools as well 3. Compare and contrast pred ictive analytics with predictive and descriptive analytics Use examples Predictive analytics is the use of statistical techniques and data mining to determine what is likely to happen in the future. For example, an airline might use predictive analytics to forecast the impact on sales and profits if it raises baggage fees by $10. It applies information from descriptive analytics, applying historical or real-time data to know what is happening in the organization and understand some underlying trend and causes of such occurrences. In the airline example, descriptive analytics would include data about ticket prices, baggage fees, ticket sales, baggage volume, and so on, applied to find relationships among these variables. Predictive analytics may be applied to prescriptive analytics, which is a set of techniques that use descriptive data and forecasts to identify the decisions most likely to result in the best performan For example, predictive analytics could forecast the impact on profits of different ggage fees. It might show, for example, that raising baggage fees by $5 will lead to the greatest profits after the airline takes into account fee revenues, ticket sales, the amount of baggage carried, and the cost to transport the baggage.( Students may use different examples, as long as they illustrate the definitions. 4. Discuss the major issues in implementing BI Major issues in implementing bi are Properly appreciating the different classes of potential users of the bI Properly aligning BI with the business strategy eveloping BI applications that me Determining whether to develop or acquire BI systems, and how to do so Justifying the bi investment using cost-benefit analysis nsuring the security and privacy protection Integrating BI applications with organizational systems, databases, and e Copyright C2018 Pearson Education, Inc.16 Copyright © 2018Pearson Education, Inc. • DSS methodologies and tools originated largely in academia, whereas BI arose largely from the software industry. Many BI tools, such as data mining and predictive analysis, have come to be considered DSS tools as well. 3. Compare and contrast predictive analytics with predictive and descriptive analytics. Use examples. Predictive analytics is the use of statistical techniques and data mining to determine what is likely to happen in the future. For example, an airline might use predictive analytics to forecast the impact on sales and profits if it raises baggage fees by $10. It applies information from descriptive analytics, applying historical or real-time data to know what is happening in the organization and understand some underlying trends and causes of such occurrences. In the airline example, descriptive analytics would include data about ticket prices, baggage fees, ticket sales, baggage volume, and so on, applied to find relationships among these variables. Predictive analytics may be applied to prescriptive analytics, which is a set of techniques that use descriptive data and forecasts to identify the decisions most likely to result in the best performance. For example, predictive analytics could forecast the impact on profits of different baggage fees. It might show, for example, that raising baggage fees by $5 will lead to the greatest profits after the airline takes into account fee revenues, ticket sales, the amount of baggage carried, and the cost to transport the baggage. (Students may use different examples, as long as they illustrate the definitions.) 4. Discuss the major issues in implementing BI. Major issues in implementing BI are: • Properly appreciating the different classes of potential users of the BI applications. • Properly aligning BI with the business strategy. • Developing BI applications that meet users’ needs for real-time, on￾demand capabilities. • Determining whether to develop or acquire BI systems, and how to do so. • Justifying the BI investment using cost-benefit analysis. • Insuring the security and privacy protection • Integrating BI applications with organizational systems, databases, and e￾commerce