Prescriptive analytics is the area of business analytics (BA) dedicated to finding the best intervention for a given situation.
Prescriptive analytics is related to both descriptive and predictive analytics. While descriptive analytics is designed to provide insight into what has occurred and predictive analytics helps design and forecast what might occur, prescriptive analytics seeks to determine the best answer or outcome among various options, given the known parameters.
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Prescriptive analytics can also suggest decision options for the way to take advantage of a future opportunity or reduce a future risk, and illustrate the particular implications of each decision option. In practice, prescriptive analytics can continuously and automatically process new information to improve the accuracy of forecasts and provide better decision options.
The process-intensive task, the prescriptive method analyzes potential decisions, the relationships between decisions, the influences that will bear upon these decisions as well as the bearing all of the above has on a good outcome to ultimately prescribe a good optimal course of action in real time. Prescriptive analytics is not failproof, however , but will be subject to the same distortions that can upend descriptive and predictive analytics, which includes data limitations and unaccounted-for exterior forces. The effectiveness of predictive analytics also depends on how properly the decision model captures the influence of the decisions being analyzed.
Advancements in the speed of computing as well as the development of complex mathematical algorithms placed on the data sets have made prescriptive evaluation possible. Specific techniques used in prescriptive analytics include optimization, simulation, sport theory and decision-analysis methods.
An organization called Ayata holds the brand for the (capitalized) term Prescriptive Analytics. Ayata is the Sanskrit word with regard to future.