Properly implemented, the DQ process enables capturing maximum value in uncertain and complex scenarios.
[3] They are different because of the uncertainties when making a choice—a high-quality decision can still result in a poor outcome, and vice versa.
A wide variety of tools exist to improve the quality of the information used in the decision problem.
Quality in this element requires the identification of the right decision criteria and the definition of trade-off rules among them.
Quality in this element is achieved when the value and uncertainty of each alternative are understood, and the best choice is clear.
At the end of the process, quality is characterized by buy-in across all stakeholders and an organization that is ready to take action and commit resources.
Decision quality concepts were first developed in 1964, building on developments in statistical decision theory and game theory by Professor Howard Raiffa of Harvard University, and dynamic probabilistic systems by Professor Ronald A. Howard of Stanford University.
[6] Beyond organization-wide implementation, decision quality concepts can also be applied on multi-company projects.