Final Data Analysis Project 22-665 Data Analytics Scenario. You are the owner of a minor league baseball team. You have seven employees working in the ticket sales area. They are all sales representatives and the current manager is retiring so you would like to promote one of the salespeople to be the new manager. Along with the general manager and human resources supervisor, you will choose the new ticketing manager from the current employees. Since you like data to drive your decision-making in all areas of your business, you have decided to develop an analytics system to evaluate the performance of the current employees to assist with the hiring decision. Before you examine the data presented by the human resources office and develop a system to determine analytically who should be the employee promoted to ticketing manager, you should consider some examples of how data have been applied successfully in human resources in the past. Once you have determined some ways that you might approach this process, apply that information to the Excel file relating to the seven current employees. Remember you cannot violate any laws so you have to be careful in your choice. Your final project should include the following components. Executive Summary and Background. An executive summary (written in narrative form) of necessary background research and the methodology you used to determine which employee to promote. This should include an explanation and justification of the method you used and a summary from at least three peer-reviewed journal article sources of how data analytics can be applied to human resource settings. This section should not exceed two pages, double-spaced, 12 point font, 1-inch margins. If you choose a presentation, this section should not exceed five slides. Cite all sources used for this section in APA style. Remember, this section should include appropriate background research you have conducted to inform your modeling and decision making process. For example, justify using research why you developed the analysis system you did. Modeling and rationale. Describe the data analysis process you used to determine how to best spend the money. Defend your process with evidence of why it would be effective. Include at least one APA style table and at least two APA style figures that display the results of your data analysis and brief narrative descriptions of the tables and/or figures using your best data-geek language that we have practiced in class. Conclusion and Reflection. Outline the strengths and weaknesses of your analytics system, including what other information would have been helpful in the development of your system. Also include a brief reflection of the process, including answers to these three questions: (1) what did you learn during this process, (2) what can you apply in the future, and (3) how have you changed your thinking about data analytics? General considerations. You have two options for completing this assignment. You can construct a Powerpoint Presentation or you can submit your work in the form of a Paper. If you write a paper, you should have 1-inch margins, use 12 point font and Times New Roman. If you choose a presentation, you should follow the best practices for presentations. In addition, you presentation should include a narrated voice-over presentation. For both options: All references should be in APA style. The project should be written professionally with no errors. Data analysis modeling is clear and data are displayed in both narrative format and with appropriate tables/figures/or other data visualization tools.

Executive Summary and Background The use of Business Analytics methods and tools in performance management is a relatively new context. Through the Performance Management Analytics (PMA), it is possible to create a more objective and scientific Performance Management System (PMS) which may also lead to a competitive advantage. Although conceptualizing the analytics constructs in performance management may not be challenging for modern managers, putting it into the execution and operational level could be the primary challenge to increase the effectiveness and efficiency of the PMS (Schläfke et al., 2013). In our context, we have seven potential sales representatives as potential candidates for promotion to the ticketing manager level. We have identified eleven criteria for selecting for ranking them and based on the top-most rank, we will select the one who is to be promoted to the next level. The situation is apt for the application of a multi-criteria decision-making (MCDM) model. One popular method of MCDM is the Analytical Hierarchy Process (AHP). In AHP, the pair-wise comparison is made for the criteria and also for the alternatives (i.e., the candidates, in this case) under each criterion separately. A priority vector of dimension equal to the number of alternatives is computed for each criterion. Also, a priority vector of length equal to the number of criteria is computed for the criteria. The final weight for each alternative is computed based on the interaction of these two priority vectors (Velasquez et al., 2013). The higher the final weight, the higher the rank of an alternative. The highest rank is selected for the top-most candidate. However, conducting an AHP using eleven criteria is not an efficient process. Instead, a data reduction technique may be employed in order to reduce the number of criteria. For example, the criteria ‘Gender’ must not be decided in this case. It will be an act of disparate treatment to decide the best candidate based on gender. So, the criteria gender must be omitted. Also, there is no specific reason to consider the group total sales value for 2018 and 2019 while deciding about individual performances. So, these two criteria are also removed. Furthermore, there is no need to consider individual game tickets for 2018 and 2019 separately. An average or total of the two years may be considered. After we implement the above steps, we are left with seven criteria. Two types of studies can be conducted to reduce the criteria further – the correlation study and the cluster analysis. In the correlation study, we compute the correlation coefficient between the criteria pairs and identify the pairs having a high correlation coefficient. The pairs having high correlation can be considered one variable instead of two. In the cluster analysis, the objective is to find similarities between two or more elements’ attributes. Based on the similarities, the clusters are formed. Then the decision-maker deals only with the cluster and not with the individual elements/ subjects. The simplest method of clustering is perhaps the single linkage clustering where the Euclidean distance between the objects is measured and the clusters are identified based on the minimum distance until only one cluster is left (Mehrizi et al., 2009). Since the data is not sufficient to conduct a correlational study, we have performed a single linkage cluster analysis and reduced the number of criteria from seven to and then conducted the AHP. Modeling and Rationale The first activity conducted was data reduction. Firstly, we have eliminated the criterion Gender from the list. Also, we have eliminated the group performance for 2018 and 2019. Moreover, we have considered the average individual game tickets for 2018 and 2019 instead of considering them separately. The reduced data looks like the following: Conclusion and Reflection In this project, we have developed a ranking scheme using the Analytical Hierarchy Process (AHP) for the seven potential candidates for promotion. In other to efficiently complete the process, we have also used the techniques of data reduction. We have used the single linkage clustering system using Euclidean distances and drawn a dendrogram to combine some of the criteria in order to reduce the size of the data. Essentially, we have reduced the data down to four criteria only and using the AHP, we have confirmed that ID #6 is the best candidate for the promotion. In the process, we have learned how to reduce the size of the data using the techniques of cluster analysis. Also, one of the multi-criteria decision-making (MCDM) models were learned in terms of AHP. Also, it was learned how to covert a process of judgment-based decision into a purely analytical decision-making process without getting involved in the emotional process. In the future, we can apply the techniques of AHP and Clustering analysis in other opportunities as well. Examples of such opportunities include training need analysis and training performance analysis. It can also be used to rank the suppliers and service providers, and also the mentors inside the company. The process has broken down the part of data analysis into building blocks that are small and manageable, hence it was easy to implement and also ready for future reference and implementation. So, the exercise made our outlook about data analysis from a difficult activity requiring skill and competence to a step-by-step procedure ready for implementation.

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Final Data Analysis Project 22-665 Data Analytics Scenario. You are the owner of a minor league baseball team. You have seven employees working in the ticket sales area. They are all sales representatives and the current manager is retiring so you would like to promote one of the salespeople to be the new manager.