Prescriptive analytics is a part of business analytics that is used to find the most appropriate course of action in various situations. Prescriptive analytics is associated with predictive and descriptive analytics. Prescriptive analytics finds the best outcome among many choices when all the parameters are known. Descriptive analytics provides an understanding of what has happened.
As a process-intensive task, the prescriptive method analyses possible decisions, the connections between those decisions, and the resulting consequences. When all that is examined, an outcome with an optimal course of action is prescribed in real time. However, prescriptive analytics is not foolproof. It is subject to the distortions that upend predictive and descriptive analytics such as data limitations and unforeseen external factors. The efficacy of predictive analytics also relies on how the decision model internalises the impacts of the decisions at hand.
The numerous improvements in computing speeds and the advancement of sophisticated mathematical algorithms used in the data sets have enhanced prescriptive analysis. Some of the techniques used in prescriptive analytics include simulation, optimisation, decision analysis, and game theory.
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