Identify data sources and analytic structures that generate business value. a. Describe how the given data sources could potentially provide business value for your organization. Include an explanation of how the CRISP-DM process will enable proper execution. b. Given the organizational context and the list of available data and sources, describe the purpose behind the descriptive and predictive analytic structure you will use. Explain how the structure fits the organization and how it could provide additional support, benefits, and values for the organization as a whole. c. Based on your experience and knowledge, propose analytic tools to use in your data research and modeling. What elements factor into your consideration in choosing this tool? d. What additional data fields would you like to include in your model that are not in the given data set? How will these fields add value in combination with your current data? Be sure to cite examples and support your reasoning. II. Evaluate potential ethical implications for the chosen data analytic structure. a. Evaluate the ethical implications of the data set for the organization. In other words, what fields could potentially be ethically damaging to the organization if used as a part of your model (e.g., "marriage" or "gender" fields)? b. Recommend a strategy for addressing the ethical implications. Given the implications that you have identified, how would you highlight the ethical aspects for the organization's review? Describe how you would address using those fields in light of these ethical implications. III. Model Creation a. Evaluate existing data analytic strategies in terms of their use for data model creation. How useful would these strategies be within the chosen organizational environment? b. What value does your analytic structure and strategy hold for model creation for your organization? Be sure to provide examples and support with resources when appropriate. c. Create a pilot plan in which your strategy will be implemented and test your strategy using the available data. In other words, run the available data through your architecture to ensure that the process is smooth and the results are as needed. d. Create visual and text-based reports showing pilot results. In other words, create reports from the pilot implementation of your strategy. IV. Presentation: Create a project proposal for the full implementation of your proposed data analytic solution. This executive presentation is your opportunity to convince your executives of the value that can be added by your proposal. a. Extrapolate pilot results to expected full implementation results and estimate reasonable return on investment for full implementation. In other words, given the pilot results, how feasible is full implementation and what is the estimated return on investment? b. Articulate how full implementation would meet the needs of the organization. Be mindful of your audience (organization executives, in this case), as they are the ultimate decision makers that you need to convince. c. Create a data analytic architecture pattern with details for full implementation. In other words, create a model or image that represents the overall architecture or structure of the data analytic solution that helps to communicate a plan for implementation.