DATA-DRIVEN DECISION MAKING

Competencies:

3009.1.1: The Case for Quantitative Analysis – The graduate uses decision-making methods to develop strategies for organizational decision processes.
3009.1.2: Statistics as a Managerial Tool – The graduate uses a variety of decision-analysis tools to evaluate alternatives during the decision-making processes.
3009.1.3: More Statistical Tools – The graduate uses quantitative techniques and statistical tools to identify the most appropriate decision alternatives.
3009.1.4: Quality Metrics and Tools –The graduate analyzes how work is accomplished and applies quality metrics and tools to increase efficiency, effectiveness, and quality.
3009.1.5: Real World Data-Driven Decisions – The graduate analyzes data from business intelligence and knowledge-management systems to make appropriate decisions.
3009.1.6: Improving Organization Performance – The graduate uses appropriate data to improve organizational performance.

Introduction:

In this task, you will identify a real-world business situation and use real data to perform a data analysis leading to an actionable recommendation. You are encouraged to select an issue in your workplace or program specialty area (e.g., IT management, HC management, or MBA). Publicly available data is also an option (see Course Tips).

Note: Work performed for a client or an employer is their property and should not be used without written permission. Fictionalize identifiable organizational information (i.e., make up information regarding the identity of the organization, but do not make up the data). Obtain written permission to use any information that would be considered confidential, proprietary, or personal in nature. To obtain an organization’s permission to use proprietary information, complete and submit the attached “Organization Verification Form.”

This business situation and data will be used to complete task 2. Do not work on task 2 until you have successfully passed task 1, indicating that the business situation and data analysis plan have been approved.

Use the “Determining the Appropriate Analytical Technique” presentation in the Attachment section below and/or speak with a course instructor to help you identify the appropriate analysis technique to analyze data for these tasks.

Approved data analysis techniques include the following:

Recommended Analysis Techniques:

• regression (linear regression, multiple regression, or logistic regression)
• time series or trend analysis (regression, exponential smoothing, or moving average)
• chi-square
• t-test (one sample, two independent samples, or paired)
• ANOVA
• crossover analysis
• break-even analysis

Additional Approved Analysis Techniques:

• statistical process control
• linear programming
• decision tree
• simulation

Requirements:

1. Complete and submit the attached “Waiver-Release Form,” along with your completed Task 1.

   Note: See the “Electronic Signature Instruction Sheet” for instructions on how to electronically sign the attached “Waiver-Release Form.”

   1. If you select the “includes Restricted Information” option in the Waiver-Release Form, complete and submit the “Organization Verification Form.”

      Note: If you will be submitting the attached “Organization Verification Form”, complete page 1 (letter template) and then submit the entire document to your chosen organization. The organization must complete page 2 of the “Organization Verification Form” and provide you with a copy. Submit the “Organization Verification Form” as a separate attachment with task 1 if you selected that your project IS based upon/includes restricted information in the “Waiver Form.” If this selection is made on the “Waiver Form,” and the completed “Organization Verification Form” is not included as a separate attachment, your submission will not be evaluated. See the attached “Electronic Signature Instruction Sheet” for instructions on how to electronically sign the attached “Organization Verification Form.” This form is not published and will remain confidential. The organization’s name should be used on this form but can be changed to a fictional name for the remaining tasks.

• Use the attached “Data-Driven Decision-Making Template” to complete the following prompts:

2. Describe a real-world business situation that could be addressed by collecting and analyzing a set of data.

   Note: The data can be from your workplace, from publically available sources, or primary data that you collect on your own (e.g., a survey or behavioral observations).

   1. 1. Summarize one question or decision relevant to the real-world business situation that you will answer by collecting and analyzing a set of data.
      2. Explain why the situation or question would benefit from a data analysis.
      3. Identify all of the data that you will need to collect that is relevant to the situation or question.

         Note: Identify the specific data relevant to part B1, such as time period, sample size, etc.

         Note: A sample size of 30 is the suggested minimum size.

   1. 1. Describe the data gathering method you will use to collect data.

         Note: The data gathering method can include data sources (e.g., databases, surveys, behavioral observations, online sources, etc.)

   1. Identify an appropriate data analysis technique from the approved list above to analyze this data.
      1. Explain why the data analysis technique you chose is an appropriate technique to analyze the data collected.

   Note: Use the “Determining the Appropriate Analytical Technique” presentation in the Attachment section below to help you determine the appropriate analysis technique for your data and business situation or speak with a course instructor.

3. When you use sources to support ideas and elements in a paper or project, provide acknowledgement of source information for any content that is quoted, paraphrased, or summarized. Acknowledgement of source information includes in-text citation noting specifically where in the submission the source is used and a corresponding reference, which includes the following:
   • author
   • date
   • title
   • location of information (e.g., publisher, journal, or website URL)

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