WGU C207
DATA-DRIVEN DECISION MAKING
Objective Assessment Enhanced Study Guide • 2025/2026 Topics • 2026 Edition
Original educational companion
Built from the public preview of the supplied Stuvia listing and publicly visible C207 topic coverage. It does not reproduce
paid/locked exam questions or claim access to an actual WGU assessment.
WGU C207 Data-Driven Decision Making • Enhanced Study Guide 2026 Page 1
, 1. C207 Blueprint
The supplied Stuvia listing is a 21-page 2025/2026 C207 Objective Assessment resource. Its public description identifies descriptive and
inferential statistics, hypothesis testing, regression analysis, probability, data visualization and decision-making strategies.
■cite■turn0search2■
Additional public C207 previews show recurring emphasis on analytics types, measurement levels, data quality, the Davenport-Kim three-stage
model, z-scores, variance/standard deviation, confidence intervals, probability rules, chi-square, ANOVA, correlation, regression, KPIs and
decision-support methods. ■cite■turn0search5■turn0search8■
A newer public C207 preview also expands coverage into the analytics lifecycle, data cleaning, exploratory analysis, visualization, modeling,
validation, deployment, monitoring and iteration. ■cite■turn0search0■
Priority Master
Analytics Descriptive vs diagnostic vs predictive vs prescriptive
Statistics Center, spread, distributions, z-scores, probability
Inference Sampling, confidence intervals, hypothesis tests, errors
Relationships Correlation, regression, interpretation, causation
Decision-making Problem framing, alternatives, evidence, communication
Data literacy Measurement scales, quality, visualization, bias and ethics
2. Analytics: What Happened, Why, What Next?
Type Question answered Typical output
Descriptive What happened? Reports, dashboards, summaries
Diagnostic Why did it happen? Root-cause analysis, drill-downs
Predictive What is likely to happen? Forecasts, models, probabilities
Prescriptive What should we do? Recommendations, optimization, decision rules
The public C207 preview uses prescriptive analytics for managerial decisions that recommend an action. ■cite■turn0search12■
Memory chain
Past → Cause → Future → Action: descriptive → diagnostic → predictive → prescriptive.
3. Davenport-Kim Three-Stage Model
The public C207 preview identifies the Davenport-Kim model as framing the problem → solving the problem → communicating results.
Problem recognition belongs in framing, while data collection is part of solving the problem. ■cite■turn0search0■
• Framing: recognize the problem, define objectives, identify stakeholders and determine what decision must be made.
• Solving: gather/analyze relevant evidence, develop alternatives and evaluate results.
• Communicating: translate findings into a decision-useful message for the audience.
Exam trap: do not confuse the analytical work with the final communication stage. A correct statistical result that cannot be communicated to
decision-makers has limited practical value.
4. Data Types & Levels of Measurement
Level Meaning Example Arithmetic
Nominal Labels/categories; no inherent order Department, product category Counts/modes
Ordinal Ordered categories; unequal gaps Satisfaction: poor→excellent Ranks/medians
Interval Equal intervals; no true zero Temperature °C Differences/means
Ratio Equal intervals + meaningful zero Revenue, weight, age All standard arithmetic
The public C207 preview explicitly identifies the four levels as nominal, ordinal, interval and ratio. ■cite■turn0search8■
Fast test
WGU C207 Data-Driven Decision Making • Enhanced Study Guide 2026 Page 2
DATA-DRIVEN DECISION MAKING
Objective Assessment Enhanced Study Guide • 2025/2026 Topics • 2026 Edition
Original educational companion
Built from the public preview of the supplied Stuvia listing and publicly visible C207 topic coverage. It does not reproduce
paid/locked exam questions or claim access to an actual WGU assessment.
WGU C207 Data-Driven Decision Making • Enhanced Study Guide 2026 Page 1
, 1. C207 Blueprint
The supplied Stuvia listing is a 21-page 2025/2026 C207 Objective Assessment resource. Its public description identifies descriptive and
inferential statistics, hypothesis testing, regression analysis, probability, data visualization and decision-making strategies.
■cite■turn0search2■
Additional public C207 previews show recurring emphasis on analytics types, measurement levels, data quality, the Davenport-Kim three-stage
model, z-scores, variance/standard deviation, confidence intervals, probability rules, chi-square, ANOVA, correlation, regression, KPIs and
decision-support methods. ■cite■turn0search5■turn0search8■
A newer public C207 preview also expands coverage into the analytics lifecycle, data cleaning, exploratory analysis, visualization, modeling,
validation, deployment, monitoring and iteration. ■cite■turn0search0■
Priority Master
Analytics Descriptive vs diagnostic vs predictive vs prescriptive
Statistics Center, spread, distributions, z-scores, probability
Inference Sampling, confidence intervals, hypothesis tests, errors
Relationships Correlation, regression, interpretation, causation
Decision-making Problem framing, alternatives, evidence, communication
Data literacy Measurement scales, quality, visualization, bias and ethics
2. Analytics: What Happened, Why, What Next?
Type Question answered Typical output
Descriptive What happened? Reports, dashboards, summaries
Diagnostic Why did it happen? Root-cause analysis, drill-downs
Predictive What is likely to happen? Forecasts, models, probabilities
Prescriptive What should we do? Recommendations, optimization, decision rules
The public C207 preview uses prescriptive analytics for managerial decisions that recommend an action. ■cite■turn0search12■
Memory chain
Past → Cause → Future → Action: descriptive → diagnostic → predictive → prescriptive.
3. Davenport-Kim Three-Stage Model
The public C207 preview identifies the Davenport-Kim model as framing the problem → solving the problem → communicating results.
Problem recognition belongs in framing, while data collection is part of solving the problem. ■cite■turn0search0■
• Framing: recognize the problem, define objectives, identify stakeholders and determine what decision must be made.
• Solving: gather/analyze relevant evidence, develop alternatives and evaluate results.
• Communicating: translate findings into a decision-useful message for the audience.
Exam trap: do not confuse the analytical work with the final communication stage. A correct statistical result that cannot be communicated to
decision-makers has limited practical value.
4. Data Types & Levels of Measurement
Level Meaning Example Arithmetic
Nominal Labels/categories; no inherent order Department, product category Counts/modes
Ordinal Ordered categories; unequal gaps Satisfaction: poor→excellent Ranks/medians
Interval Equal intervals; no true zero Temperature °C Differences/means
Ratio Equal intervals + meaningful zero Revenue, weight, age All standard arithmetic
The public C207 preview explicitly identifies the four levels as nominal, ordinal, interval and ratio. ■cite■turn0search8■
Fast test
WGU C207 Data-Driven Decision Making • Enhanced Study Guide 2026 Page 2
