Prescriptive analytics is the area of business analytics (BA) dedicated to finding the best course of action for a given situation.
Prescriptive analytics is related to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters.
Prescriptive analytics can also suggest decision options for how to take advantage of a future opportunity or mitigate a future risk, and illustrate the implications of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve the accuracy of predictions and provide better decision options.
A process-intensive task, the prescriptive approach analyzes potential decisions, the interactions between decisions, the influences that bear upon these decisions and the bearing all of the above has on an outcome to ultimately prescribe an optimal course of action in real time. Prescriptive analytics is not failproof, however, but is subject to the same distortions that can upend descriptive and predictive analytics, including data limitations and unaccounted-for external forces. The effectiveness of predictive analytics also depends on how well the decision model captures the impact of the decisions being analyzed.
Advancements in the speed of computing and the development of complex mathematical algorithms applied to the data sets have made prescriptive analysis possible. Specific techniques used in prescriptive analytics include optimization, simulation, game theory and decision-analysis methods.
A company called Ayata holds the trademark for the (capitalized) term Prescriptive Analytics. Ayata is the Sanskrit word for future.