WGU C207 Study Guide: Understanding Analytics and
Data Measurement | Actual verified study set complete
solutions | 2026/27 Updates | Guaranteed Pass
1. Why Analytics Matter
• Scenario: “I want to buy a new car or house.”
In business, the question becomes: Where do I find pertinent information?
• Process:
o Do analytics BEFORE making decisions, then measure results AFTER.
• Goal:
o Use FACT-BASED information to build trust in decisions.
o Increases value of decisions for employees, customers, suppliers.
o Enables accurate predictions → reduces risk.
2. Types of Analytics
Ask yourself two questions:
1. Am I predicting?
2. Am I optimizing?
a) Descriptive Analytics
• Definition: Uses past data only.
• Example: “Car prices increased 2% in the past year.”
• Key:
o NO prediction, NO optimization.
b) Predictive Analytics
• Definition: Uses past data to predict future outcomes.
• Example: “Based on the past 10 years, car prices are expected to rise 2% next year.”
• Key:
o YES prediction, NO optimization.
c) Prescriptive Analytics
• Definition: Predicts future AND recommends actions to optimize outcomes.
,• Example: “By increasing electric charging stations by 7%, electric car sales are expected to
increase by 5% next year.”
• Key:
o YES prediction, YES optimization.
, Memory Trick:
• Descriptive = Describe past
• Predictive = Predict future
• Prescriptive = Prescribe action
3. Data Quality
Errors in data can distort analysis. Common issues:
• Omission: Missing data (find easily by sorting columns in Excel).
• Out of Range: Values outside expected limits (also found by sorting).
• Outlier: NOT an error (could be valid extreme value).
Error Types
• Systematic Error:
o Does NOT fix itself → skews data consistently.
• Random Error:
o Fixes itself with large sample sizes.
4. Reliability vs Validity
• Reliable:
o Consistent and repeatable measurement.
o Example: Thermometer gives same reading repeatedly.
• Valid:
o Measures what it is intended to measure.
o Example: Does a test score represent actual ability?
Tip:
• A reliable instrument → valid data results.
, 5. Bias
a) Measurement Bias
• Representative Sample:
o Every member of population has equal chance to be selected.
o Rule of thumb: At least 30 samples for statistical reliability.
• Random Selection:
o Eliminates bias.
b) Information Bias
• Occurs when:
o Ignoring the purpose of collected information.
• Examples:
o Asking irrelevant questions.
o Non-truthful answers.
• Best Practice:
o Record everything → weed out irrelevant data later.
1. What is Big Data?
• Definition:
Big Data refers to both structured and unstructured data in such large volumes that traditional
database and software techniques cannot easily process it.
Types of Data
• Structured Data:
o Organized in rows and columns (fits neatly into databases).
o Example: Grocery store checkout transactions.
• Unstructured Data:
o Does NOT fit into rows and columns.
o Examples: Social media posts, emails, photos, file notes.
Data Measurement | Actual verified study set complete
solutions | 2026/27 Updates | Guaranteed Pass
1. Why Analytics Matter
• Scenario: “I want to buy a new car or house.”
In business, the question becomes: Where do I find pertinent information?
• Process:
o Do analytics BEFORE making decisions, then measure results AFTER.
• Goal:
o Use FACT-BASED information to build trust in decisions.
o Increases value of decisions for employees, customers, suppliers.
o Enables accurate predictions → reduces risk.
2. Types of Analytics
Ask yourself two questions:
1. Am I predicting?
2. Am I optimizing?
a) Descriptive Analytics
• Definition: Uses past data only.
• Example: “Car prices increased 2% in the past year.”
• Key:
o NO prediction, NO optimization.
b) Predictive Analytics
• Definition: Uses past data to predict future outcomes.
• Example: “Based on the past 10 years, car prices are expected to rise 2% next year.”
• Key:
o YES prediction, NO optimization.
c) Prescriptive Analytics
• Definition: Predicts future AND recommends actions to optimize outcomes.
,• Example: “By increasing electric charging stations by 7%, electric car sales are expected to
increase by 5% next year.”
• Key:
o YES prediction, YES optimization.
, Memory Trick:
• Descriptive = Describe past
• Predictive = Predict future
• Prescriptive = Prescribe action
3. Data Quality
Errors in data can distort analysis. Common issues:
• Omission: Missing data (find easily by sorting columns in Excel).
• Out of Range: Values outside expected limits (also found by sorting).
• Outlier: NOT an error (could be valid extreme value).
Error Types
• Systematic Error:
o Does NOT fix itself → skews data consistently.
• Random Error:
o Fixes itself with large sample sizes.
4. Reliability vs Validity
• Reliable:
o Consistent and repeatable measurement.
o Example: Thermometer gives same reading repeatedly.
• Valid:
o Measures what it is intended to measure.
o Example: Does a test score represent actual ability?
Tip:
• A reliable instrument → valid data results.
, 5. Bias
a) Measurement Bias
• Representative Sample:
o Every member of population has equal chance to be selected.
o Rule of thumb: At least 30 samples for statistical reliability.
• Random Selection:
o Eliminates bias.
b) Information Bias
• Occurs when:
o Ignoring the purpose of collected information.
• Examples:
o Asking irrelevant questions.
o Non-truthful answers.
• Best Practice:
o Record everything → weed out irrelevant data later.
1. What is Big Data?
• Definition:
Big Data refers to both structured and unstructured data in such large volumes that traditional
database and software techniques cannot easily process it.
Types of Data
• Structured Data:
o Organized in rows and columns (fits neatly into databases).
o Example: Grocery store checkout transactions.
• Unstructured Data:
o Does NOT fit into rows and columns.
o Examples: Social media posts, emails, photos, file notes.
