Prescriptive analytics is a method of finding the best course of action for a given situation based on the available data. This form of analytics uses technology, mathematical method and simulation algorithms to help a business make the best decision in specific situations.
Prescriptive analytics is the final phase of business analytics and draw inputs from both descriptive and predictive analytics. Prescriptive analytics offers computational advantages over other forms of analytics given that it factors in the possibilities of the present situation, past history and performance, the available resources and other data needed for a business to make the best decision.
A Little More on What is Prescriptive Analytics
Prescriptive analytics seeks to help businesses make optimal decisions amidst uncertainties and evolving situations. Thorough reliance on artificial intelligence measures, computational sciences, mathematical models and other statistics, prescriptive analytics determine what action a company ought to take given the available situation. This form of analytics also gives recommendations on the appropriate decisions a company must make in order to take advantage of results given by predictive and descriptive analytics.
Machine learning is a core part of artificial intelligence that prescriptive analytics uses to process large data which are important to the decision-making process of a company.
How Prescriptive Analytics Works
Businesses that have enormous data can use prescriptive analytics in processing the data and make informed decisions. This phase of business analytics offers lots of benefits such as the prevention of fraud, mitigation of risk, increased efficiency, enhancement of customers and achievement of business goals.
Businesses that use prescriptive analytics tend to make informed business decisions based on facts and empirical evidence than businesses that do not. This form of analytics aid optimal choice or decision, even when faced with unpalatable situations or in worst-case scenarios.
The output results that prescriptive analytics provide is determined by the accuracy of the inputs provided. For instance, of the inputs are laden with errors, the output given will be inaccurate.
Examples of Prescriptive Analytics
Below are some instances where prescriptive analytics can be utilized;
There is a pipeline explosion in one part of a neighborhood and the fire service department needs to decide where residents of close areas should be moved away from their homes to curtail the spread of the disaster or not. Using prescriptive analytics, the fire service department can make the best decision.
Another scenario is a situation where a private airline needs to increase profit margin and wants to m make a decision whether to adjust the prices of tickets or not.