1. Survey the literature from the past 6 months to find one application each for DSS, BI, and analytics, summarize the applications on one page, and submit it with the exact sources. DSS-Application Case 1.4 (page 26) In this case, Oklahoma State used data from football-related injuries to predict the healing time of certain injuries. In the study, they used CRISP-DM methodology to assist them in collecting the proper data for the predictive analysis. The actual process of formulating the prediction came from the usage of the IBM SPSS Modeler. Due to this analysis, specialists were used in place of training room staff to help athletes with the healing process. BI- Application Case 1.1 (page(s) 18-19) An application of BI would be case 1.1 “Sabre help its client through dashboards and analytics”. In this case, the company of Sabre realized that were beginning to have competition with other companies in the airline industry. At this point, the company realized that they needed to find a way to enhance the management of their business data. Therefore, Sabre created an Enterprise Travel Data Warehouse (ETDW). They were able to do this by using Teradata to hold the vast amount of data that the company had within their capacity. The creation of the ETDW was also a gateway to meeting the customer needs. Sabre used this new warehouse to configure a user-friendly dashboard for all of its customer. Due to these changes, they were able to increase their clientele as well as improve the company’s performance. Analytics-Application Case 1.3 (page 25) The company of Siemens was having issues with reporting solutions to all of the departments of the organization of Siemens. The company realized that they were in need of a platform that could assist with analyzing customer surveys, finance reports, and logistics process. In order to solve this issue, they implemented Dundas BI. This platform improved the dashboard and helped the company catch issues early than usual. 2. Distinguish BI from DSS. DSS systems are computer programs that analyze data points and provide solutions.BI provides users an additional step to users and is a broad category of applications that gather, store, analyze and provide access to the data point to help make better decisions. DSS has a much smaller architecture when compared to BI. BI has a vast architecture. 3. Compare and contrast predictive analytics with prescriptive and descriptive analytics. Use examples. Descriptive analytics asks the question “What has happened?” it basically turns the data collected into relevant information that can be used. For example, a company selling a product a can use this method to determine the average spend per customer. Predictive analytics asks “What could happen?” it is much more complex than descriptive analytics. It uses statistical or modeling techniques to allow analysts to make predictions. For example, the aforementioned company selling product A could use this method to determine future sales if the product is modified.?It helps forecast what might happen in the future. Prescriptive goes even further by asking “What should we do?” it?recommends?a course of action and the likely outcome of each decision. For example, a company can use this method when deciding to introduce a new product on the market hence giving the company options as to the approach to take and the outcome of each approach. The company can now use the information provided to weigh their pros and cons and choose the best method suitable.? 4. Discuss the major issues in implementing BI. Although implementing BI is beneficial, there are a few issues with it. One of these issues derives on the basis of advanced technology. As the usage of data analysis, is a new uprising in the economic world, many people do not know how to operate the new software. It has been seen that many companies have to use outside IT workers to use the advanced software. Another issue of implementing BI is the act of privacy. The use of computerized technology to store data leaves many companies susceptible to breaches and/or data loss.