Data, Models and Decision-Making
This MBA foundation subject illustrates the School’s emphasis on quantitative management.
At a glance
Data, Models and Decision-Making
This MBA foundation subject illustrates the School’s emphasis on quantitative management.
Model-based analysis supports structured reasoning about enterprise choices.
Core concepts and course context
Data, Models and Decision-Making | Core concepts and course context
Data, Models and Decision-Making develops a disciplined relationship between evidence and managerial choice. Different models are suitable for different problems: forecasting helps estimate demand, optimization compares resource allocations, and simulation explores complex systems. Interpreting results requires awareness of missing data, unstable parameters and competing objectives. In the 2025 MBA context, this subject supports quantitative reasoning that can then be applied across finance, marketing and organizational decisions.
Decision variables, models and sensitivity analysis
Data, Models and Decision-Making is among the 2025 MBA professional foundation subjects. The subject links descriptive evidence to decision alternatives through quantitative representations.
A model needs identifiable variables, assumptions, constraints and outcomes. Sensitivity analysis tests whether a preferred decision changes when uncertain inputs move; this is especially relevant when management decisions must be made before complete information is available.
A closer look at Data, Models and Decision-Making
Data, Models and Decisions links information quality with analytical choices. A model makes assumptions explicit, defines variables and translates an operational question into a form that can be compared or optimized. Sensitivity analysis then tests how recommendations change when the underlying forecasts, costs or constraints change.
Independent course study guide
Translate a managerial decision into variables, constraints and sensitivity tests.
Download PDF learning guide ↓