Decision Intelligence Cheat Sheet

Back to Business IntelligenceUpdated 2026-05-15

Decision Intelligence represents the convergence of data science, social science, and managerial science to improve organizational decision-making at scale. It sits at the intersection of analytics and action, transforming insights from Business Intelligence tools into executable decisions that drive real business outcomes. Unlike traditional BI, which answers "what happened" or "what might happen," Decision Intelligence emphasizes "what should we do about it"—providing prescriptive guidance backed by causal reasoning, simulation, and optimization. The key mental model: Decision Intelligence treats decisions themselves as the unit of work, not just dashboards or reports, making the path from data to action explicit, testable, and continuously improvable.

What This Cheat Sheet Covers

This topic spans 16 focused tables and 118 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Analytics Maturity SpectrumTable 2: Decision Model and Notation (DMN) StandardTable 3: Causal AI and Root Cause AnalysisTable 4: Optimization and Prescriptive MethodsTable 5: Simulation and Scenario ModelingTable 6: Decision Loop ArchitectureTable 7: Key Driver and Contribution AnalysisTable 8: Decision Governance and AccountabilityTable 9: Decision Intelligence Platforms and ToolsTable 10: Measuring Decision Quality and OutcomesTable 11: Cognitive Biases in Decision-MakingTable 12: Machine Learning Decision TreesTable 13: Business Rule Engines and Decision AutomationTable 14: A/B Testing and ExperimentationTable 15: Decision Intelligence Certifications and LearningTable 16: Advanced Decision Intelligence Concepts

Table 1: Analytics Maturity Spectrum

Type	Example	Description
Descriptive analytics	`SELECT SUM(revenue) FROM sales` `WHERE year = 2026`	Summarizes historical data to reveal what happened; forms the foundation of BI with aggregations, KPIs, and dashboards.
Diagnostic analytics	`WHERE sales < target` `GROUP BY region, product`	Examines why outcomes occurred using drill-downs, root cause analysis, and variance decomposition to identify contributing factors.
Predictive analytics	`forecast_demand(X_train, y_train)` `predict(X_test)`	Uses statistical models and ML to estimate what will likely happen based on patterns in historical data; outputs probabilities or scores.

Table 1: Analytics Maturity Spectrum

Type	Example	Description
Descriptive analytics	`SELECT SUM(revenue) FROM sales` `WHERE year = 2026`	Summarizes historical data to reveal what happened; forms the foundation of BI with aggregations, KPIs, and dashboards.
Diagnostic analytics	`WHERE sales < target` `GROUP BY region, product`	Examines why outcomes occurred using drill-downs, root cause analysis, and variance decomposition to identify contributing factors.
Predictive analytics	`forecast_demand(X_train, y_train)` `predict(X_test)`	Uses statistical models and ML to estimate what will likely happen based on patterns in historical data; outputs probabilities or scores.