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Data-Driven Decision Making — Turning Data into Confident Executive Decisions

Data-Driven Decision Making develops the executive discipline required to turn data and analytics into clearer, more defensible business decisions. Participants learn to frame decision questions, distinguish useful evidence from noise, interpret descriptive and predictive insights, evaluate uncertainty, and communicate findings effectively. Practical cases connect analytics, visualization, experimentation, and AI-supported analysis to real management decisions while highlighting data quality, cognitive bias, misleading correlations, and the limits of quantitative evidence.

Amsterdam

5 days

November 13, 2026 – November 19, 2026

Price

EUR 5,500

Initial Enrollment Request

Learning Objectives

Frame business decisions as clear analytical questions and define the evidence required.
Interpret descriptive, diagnostic, predictive, and prescriptive analytics in management contexts.
Evaluate data quality, uncertainty, assumptions, and common sources of analytical error.
Select appropriate metrics, visualizations, and analytical methods for executive decisions.
Recognize cognitive biases and distinguish correlation, causation, and credible evidence.
Combine data, analytics, AI-supported insight, and managerial judgment into defensible decisions.

Detailed Daily Schedule

Day 1 — Framing Decisions and Building the Evidence Base

Convert management challenges into clear decision questions.
Define outcomes, alternatives, constraints, and relevant metrics.
Identify appropriate internal and external data sources.
Assess data quality, completeness, representativeness, and timeliness.
Separate useful evidence from noise and vanity metrics.

Day 2 — Descriptive and Diagnostic Analytics

Use summaries, distributions, segmentation, and trends to understand performance.
Select KPIs that reflect business objectives and decision needs.
Diagnose variation, drivers, and potential root causes.
Design clear executive visualizations and dashboards.
Avoid misleading charts, averages, and interpretation traps.

Day 3 — Prediction, Uncertainty and Scenario Thinking

Interpret forecasts, probabilities, ranges, and model outputs.
Understand the business meaning of predictive accuracy and uncertainty.
Compare scenarios and sensitivity to key assumptions.
Recognize overconfidence, base-rate neglect, and other decision biases.
Use predictive insight without treating models as certainty machines.

Day 4 — Causality, Experimentation and Decision Quality

Distinguish correlation from credible causal evidence.
Understand the logic of controlled experiments and A/B testing.
Evaluate when experiments are practical and when observational evidence is necessary.
Combine quantitative findings with operational context and stakeholder knowledge.
Build a transparent decision rationale that can withstand challenge.

Day 5 — Analytics, AI and Executive Decision Practice