Decision engineering (more recently called decision intelligence by The Decision Intelligence Institute International and companies like Quan telling ) is a framework that unifies a number of best practices for organizational decision making.
The basic idea: decisions are based on our understanding of how actions lead to outcomes.
Decision intelligence is a discipline for analyzing this chain of cause-and-effect, and decision modeling is a visual language for representing these chains.
DI is based on the recognition that, in many organizations, decision making could be improved if a more structured approach were used. Decision engineering seeks to overcome a decision making “complexity ceiling”, which is characterized by a mismatch between the sophistication of organizational decision making practices and the complexity of situations in which those decisions must be made. As such, it seeks to solve some of the issues identified around complexity theory and organizations.
In this sense, decision engineering represents a practical application of the field of complex systems, which helps organizations to navigate the complex systems in which they find themselves. Decision engineering can also be thought of as a framework that brings advanced analytics and machine learning techniques to the desktop of the non-expert decision maker, as well as incorporating, and then extending, data science to overcome the problems articulated in Black swan theory.
Decision engineering proponents believe that many organizations continue to make poor decisions. In response, decision engineering seeks to unify a number of decision making best practices, described in more detail below.
Decision engineering builds on the insight that it is possible to design the decision itself, using principles previously used for designing more tangible objects like bridges and buildings.
The use of a visual design language representing decisions is an important element of decision engineering, since it provides an intuitive common language readily understood by all decision participants. A visual metaphor improves the ability to reason about complex systems as well as to enhance collaboration.
In addition to visual decision design, there are other two aspects of engineering disciplines that aid mass adoption. These are: 1) the creation of a shared language of design elements and 2) the use of a common methodology or process.