Why is decision modeling the foundation of decision intelligence?

Decision modeling provides the "blueprint" design for decision intelligence. Before a platform can provide insights, a human with subject matter expertise must define the logic. The decision logic includes what inputs are important, how they relate, what patterns indicate, and the outputs that trigger an action.

Decision modeling helps set-up decision intelligence processes by:

• Structuring expert knowledge. To apply expert knowledge, the subject matter experts encode what they know about a domain — patterns, scores into models for predicting risk or opportunities.
• Enabling Explainability. The reason the decision logic is trusted and explainable is due to it being modeled rather than inferred by a black-box algorithm. This helps analysts and decision makers trace event findings back to the source. This functionality is critical for high-consequence decisions.
• Supporting continuous intelligence. Once a decision model is deployed, it can run continuously against live data streams, enabling the proactive, real-time risk and opportunity assessment that separates decision intelligence from traditional reporting or batch analytics.
• Bridges human expertise and artificial intelligence. Decision modeling is where the human expert judgment gets operationalized — the model reflects how a skilled analyst thinks, but it scales far beyond what any individual could provide coverage for. Thereby, risks are lowered, and opportunities are maximized by enabling expert models within decision intelligence platforms.