Early data warehouses and marts used for business intelligence that targeted senior management reporting were called “decision support systems,” but what does it really mean to “support” a decision? More commonly these systems are used to facilitate decision-making in a broad array of scenarios, providing guidance for the decision before it is made and reinforcement of the process after the decision is made, all with the intent to help people make better decisions.

Understanding what type of information is expected to make a particular decision is one step in considering the variables that are relevant for improving the process as well as ways that the right amount of information can streamline or optimize those variables. This talk introduces a simple model that abstracts some of the key facets of a decision-making scenario and can help highlight where potential gaps in knowledge can impede optimal decision-making.

The talk will then consider how “decision quality” can be assessed using a number of dimensions such as informedness, timeliness, completeness, and optimality of outcome. In turn, we describe how a model for a decision-making scenario encompasses at the very least these characteristic features:

• Business use case
• Relevant actors
• Business question to be answered
• Inputs to the decision
• Decision points in the scenario
• Choices presented to the decision-maker
• Desired outcomes
• Performance measures

Attendees will learn about:

• Describing desired outcomes of a decision
• Identifying the decision points within a business process flow
• Assessing what types of decisions are properly informed
• How actors can be guided to make optimal choices
• Leveraging information that will lead to better overall optimal performance