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Data an organization may use to assess organizational performance.

Respond to the following five (5) questions related to one of the learning objectives covered in this course.
For question 2, confirm your answers with examples of data sets and/or visualizations.
While you may choose these from the sample data sets provided in the resources listed for this course, It is strongly recommended that you search for new data sources to use as examples.
Questions:

Differentiate between various types (Descriptive, Predictive, or Prescriptive) of data an organization may use to assess organizational performance.
Provide an example for each data source.
Highlight the purpose of the data sources, the metric(s) it explains, and what kind of decision it would help justify.
Create a data visualization graphic that incorporates appropriate data sets for one of the three types.
Consider one of the data sets you have shared in question number 1 of this workbook.
Evaluate the benefits of at least two different data analysis methods.
Share an example of each.
Explain how, when, and why these methods have been used in a business situation.
Justify a strategic choice based on a data analysis method.
Use the data analysis method in Week 3 or another example of your choice.
Assess how big data can influence organizational performance.
You may consider using an example if you find that helpful to support your argument.
Consider how data can create insight into a business problem and provide a sense of decision-making justification.

We will justify a strategic choice using Regression Analysis.

Strategic Choice Justification:

A national gym chain uses Multiple Linear Regression to analyze branch-level performance. They use branch size, local population density, and local competitor density as independent variables to predict the dependent variable: Average Monthly Member Retention Rate.

Analysis: The regression results show that, controlling for size and population, a high local competitor density is the single greatest negative predictor of member retention. Specifically, every additional competitor within a 1-mile radius is associated with a 2% drop in retention.

Strategic Choice: The company justifies a strategic choice to invest 20% of its new marketing budget exclusively in enhanced loyalty programs and high-end amenity upgrades for branches identified as having high competitor density.

How, When, and Why:

How: Regression established the causal relationship between competition and retention loss.

Sample Answer

Evaluating Data Analysis Methods

Effective organizational assessment requires various methods to interpret data. Two valuable methods are Regression Analysis and Time Series Forecasting.

Analysis Method	Example Application	Business Situation	Benefit
Regression Analysis	Example: A retail company analyzes how advertising spend (Independent Variable) influences weekly sales revenue (Dependent Variable).	Situation: Used to determine the strength and direction of a relationship between variables. The business uses it to confirm if increased spending on one platform (e.g., social media) actually causes a measurable increase in revenue, controlling for other factors like seasonality.	Causality and Contribution: Quantifies the marginal return on investment (ROI) for specific business inputs, allowing for more efficient resource allocation.
Time Series Forecasting	Example: A SaaS company analyzes its monthly customer subscriptions over the past five years to predict the volume for the next year.	Situation: Used when data is collected sequentially over time. The business uses it to identify patterns like seasonality (e.g., higher sales in Q4), trends (steady annual growth), and cycles, providing an accurate prediction of future demand.	Planning and Budgeting: Enables accurate production planning, staffing forecasts, and financial budgeting by providing a reliable outlook on future demand or resource needs.