Decision intelligence

A related field, decision engineering, also investigates the improvement of decision-making processes but is not always as closely tied to data science.

These are: The need for a unified methodology of decision-making is driven by a number of factors that organizations face as they make difficult decisions in a complex internal and external environment.

Examples: The car is becoming an expression of identity, values, and personal control in ways that move far beyond traditional segmentation and branding.

Are fair labor practices employed?We live in a dynamic world in which the pace, scope, and complexity of change are increasing.

During the decision execution phase, outputs produced during the design phase can be used in a number of ways; monitoring approaches like business dashboards and assumption based planning are used to track the outcome of a decision and to trigger replanning as appropriate (one view of how some of these elements combine is shown in the diagram at the start of this article).

[4] Decision intelligence seeks to create a visual language that serves to facilitate communication between them and quantitative experts, allowing broader utilization of these and other numerical and technical approaches.

If a pattern from previous industries holds, such a methodology will also facilitate technology adoption, by clarifying common maturity models and road maps that can be shared from one organization to another.

The decision intelligence approach is multidisciplinary, unifying findings on cognitive bias and decision-making, situational awareness, critical and creative thinking, collaboration and organizational design, with engineering technologies.

The movement from these largely informal structures to one in which a decision is documented in a well understood, visual language, echoes the creation of common blueprint methodologies in construction, with promise of similar benefits.

Many of its elements—such as the language of assessing assumptions, using logic to support an argument, the necessity of critical thinking to evaluate a decision, and understanding the impacts of bias—are ancient.

Research centering on decisions, defined broadly as biological and nonbiological action selection, is considered part of the discipline.

The basic idea is that a visual metaphor enhances intuitive thinking, inductive reasoning, and pattern recognition—important cognitive skills usually less accessible in a verbal or text discussion.

[7] Decision intelligence recognizes that many aspects of decision-making are based on intangible elements, including opportunity costs, employee morale, intellectual capital, brand recognition and other forms of business value that are not captured in traditional quantitative or financial models.