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Data an organization may use to assess organizational performance.
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.
Sample Answer
That's a comprehensive request covering the differentiation of data types, the application of data analysis, and the impact of big data on organizational performance.
📊 Data Types for Organizational Performance Assessment
Organizations use three main types of data to assess performance, moving from understanding the past to predicting the future and guiding action.
Data Type
Purpose
Metric Example
Decision Justification
1. Descriptive
To understand past and current performance by summarizing what has happened.
Customer Churn Rate (Percentage of customers lost last quarter)
Justifies initiating a customer retention campaign aimed at groups with high churn.
2. Predictive
To forecast future outcomes by estimating what will happen.
Projected Inventory Shortfall (Estimated units that will be out of stock next month)
Justifies placing a large, expedited order for specific products to avoid stockouts.
3. Prescriptive
To recommend optimal actions by suggesting what should be done to achieve a goal.
Optimal Pricing Recommendation (Price point that maximizes revenue for a product given current demand and cost)
Justifies changing the current sales price to the optimal level suggested by the algorithm.
📈 Data Visualization Example (Descriptive Data)
Using the Descriptive Data example (Customer Churn Rate), a visualization can help an organization quickly identify performance trends and problem areas.1
That's a comprehensive request covering the differentiation of data types, the application of data analysis, and the impact of big data on organizational performance.
📊 Data Types for Organizational Performance Assessment
Organizations use three main types of data to assess performance, moving from understanding the past to predicting the future and guiding action.
Data Type
Purpose
Metric Example
Decision Justification
1. Descriptive
To understand past and current performance by summarizing what has happened.
Customer Churn Rate (Percentage of customers lost last quarter)
Justifies initiating a customer retention campaign aimed at groups with high churn.
2. Predictive
To forecast future outcomes by estimating what will happen.
Projected Inventory Shortfall (Estimated units that will be out of stock next month)
Justifies placing a large, expedited order for specific products to avoid stockouts.
3. Prescriptive
To recommend optimal actions by suggesting what should be done to achieve a goal.
Optimal Pricing Recommendation (Price point that maximizes revenue for a product given current demand and cost)
Justifies changing the current sales price to the optimal level suggested by the algorithm.
📈 Data Visualization Example (Descriptive Data)
Using the Descriptive Data example (Customer Churn Rate), a visualization can help an organization quickly identify performance trends and problem areas.1
Data Set Example: Monthly Customer Churn Rate by Subscription Tier
Month
Basic Tier Churn Rate (%)
Premium Tier Churn Rate (%)
Jan
5.5
2.1
Feb
5.2
2.5
Mar
6.1
2.3
Apr
7.8
2.9
A line chart visualizing this data clearly shows that the Basic Tier Churn Rate has experienced a sharp, undesirable increase in April, requiring immediate investigation.
🔬 Evaluation of Data Analysis Methods
Two valuable data analysis methods for assessing and improving organizational performance are A/B Testing and Regression Analysis.
Analysis Method
Example
How, When, and Why Used
1. A/B Testing (Statistical Experimentation)
Testing two versions of a website checkout page (Version A vs. Version B) to see which yields a higher conversion rate.
When: Used when comparing two or more alternatives to see which performs better on a single, clear metric. Why: To make data-driven decisions on user experience, marketing effectiveness, and product design by isolating the causal effect of a single change. How: Randomly assigning users to one of the versions and using statistical significance tests (like a t-test) to confirm the difference is not due to chance.
2. Regression Analysis (Statistical Modeling)
Using historical advertising spend, seasonal factors, and competitor pricing to forecast next quarter's sales volume.
When: Used when an organization needs to understand the relationship between one or more independent variables (causes/predictors) and a dependent variable (effect/outcome). Why: To identify the factors that most significantly drive performance, enable prediction, and justify resource allocation. How: Creating a mathematical model (e.g., linear regression) to quantify the impact of each independent variable on the dependent variable.
Justifying a Strategic Choice: Regression Analysis
Strategic Choice: To reallocate marketing budget to maximize Return on Investment (ROI).
Method Application: Multiple Regression Analysis is performed using past monthly sales (the dependent variable) and various marketing channel spends—Social Media, Search Engine Ads, and Email Marketing (the independent variables).
Result: The analysis reveals the following regression coefficients (the impact on sales for every dollar spent):
Social Media: $0.80
Search Engine Ads: $1.50
Email Marketing: $2.10
Justification: The $2.10 coefficient for Email Marketing indicates that it provides the highest return. Therefore, the strategic choice is to shift 30% of the Social Media budget to Email Marketing to leverage the channel with the highest proven ROI, thereby maximizing overall sales performance.