Evaluate organizational risk preferences by plotting utility curves and interpreting their strategic implications.

Apply OODA or Cynefin frameworks to structure unstructured problems and evaluate framework suitability for different contexts.

Evaluate alternative decision frameworks for suitability using contextual criteria and strategic alignment principles.

This course objective equips learners to critically examine past business decisions, uncover hidden cognitive biases that shaped outcomes, and recommend practical debiasing techniques to improve judgment, decision quality, and future organizational performance.

Design and implement bias-mitigation checklists for recurring reviews and pilot in workplace meetings.

Learn to generate descriptive statistics with data quality flagging for robust analytics foundations.

Master layout and visual design principles to rebuild cluttered dashboards with measurable improvements.

Learn to evaluate dashboard usage patterns and identify low-value elements for strategic optimization.

Synthesize analysis findings into strategic recommendations that align with stakeholder decision-making needs.

Learners will apply the 5 Whys and Pareto analysis to identify dominant drivers of cost-overrun incidents and validate findings with data.

Learners will evaluate multiple RCA techniques (Ishikawa, RCA tree) on a sample issue and document the most explanatory approach.

You will explore how generative AI tools can accelerate decision frameworks, data analysis, and root-cause diagnostics — and practice the critical validation habits that keep AI-assisted outputs accurate and trustworthy.