This activity focuses on decision-making under conflicting analytical evidence. Students are given multiple datasets from different sources sales, customer feedback, finance, and operations that appear to point in different directions. Some data suggests growth, others signal risk. The scenario mirrors real organizations where data does not produce a single clear answer.
Initially, students apply analytical techniques to clean, visualize, and summarize each dataset independently. This technical engagement aligns with the Apply level of Bloom’s Taxonomy. Faculty emphasize disciplined analysis over selective interpretation.
In the next phase, students analyze contradictions between datasets and explore why different measures tell different stories. They evaluate data quality, context, timing, and bias, aligning this phase with the Analyze and Evaluate levels. Students must confront the discomfort of ambiguity rather than forcing premature conclusions.
In the final phase, students create an integrated decision recommendation that acknowledges uncertainty and proposes adaptive strategies. They design decision scenarios, risk contingencies, and monitoring frameworks. This synthesis aligns with the Create level of Bloom’s Taxonomy.
Learning outcomes include the ability to apply analytics critically, evaluate conflicting evidence, and create decision frameworks that respect uncertainty. Students develop analytical maturity, learning that data informs judgment but does not replace it.
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