Companies seeking to adapt and thrive in complex and uncertain environments
are embracing decision analysis to navigate risk and optimize value. Business
analytics empowers decision analysis by clarifying opportunities for
managerial decision making and specifying uncertainty in value measures.
Decision analysis specifies methods to guide decision making in projects and
initiatives to exploit opportunities associated with uncertainty. Unstructured
approaches to decision making in environments of uncertainty put companies at
risk of making suboptimal decisions and missing value-creating opportunities.
In this course, you will explore the concepts of business analytics-driven
decision analysis with a focus on process and practical organizational
implementation. You will then examine the organizational drivers and benefits
with attention to identifying and exploiting uncertainty as opportunities
associated with managerial decision making. With a focus on practical
organizational implementation, you will define a four-step decision analysis
process that includes framing, valuation, business analytics, and
implementation. Core valuation, as enhanced by business analytics, leads to
decision tree analysis, a central method for determining the optimal decision
path to employ given uncertainties. Finally, you will apply core underlying
decision analysis principles, components, and practices to a fictional
consumer electronics company, eWidget.
After completing this course, you should be able to:
Define the decision analysis process.
Identify the importance of framing a business problem.
Outline the valuation process.
Define the central role of business analytics.
Apply decision specification and implementation to identify the best decision.