Prescriptive analysis is a type of data analytics — that facilitates the use of intelligent AI Technology  to help businesses make better decisions through the analysis of raw quantum data. Specifically, prescriptive analytics systems factor in information about possible situations or scenarios, available peripheral resources, past performance, research necessity, and current performance, and suggests a course of action or strategy. It can be used to make intelligent decisions on any time horizon, from immediate to long term in a variety of systems.

Prescriptive analytics relies on intelligent techniques, such as machine learning—the ability of a computer program, without additional human input, to understand and advance from the data it acquires, adapting all the while. Machine learning makes it possible to process a tremendous amount of data available today. As new or additional data, research necessity and systems become available, intelligent AI Technology (Artificial Intelligence) based computer programs adjust automatically to make use of it, in a process that is much faster and more comprehensive than human capabilities could manage. Numerous types of data-intensive businesses (Business Intelligence) and government agencies can benefit from using prescriptive analytics and its smart systems, including those in the financial services and health care sectors, where the cost of human error is high.

Prescriptive analytics works with another type of data analytics, predictive analytics, which involves the use of statistic systems, AI technology (Artificial intelligence) and modeling to determine future performance, based on current and historical data. However, it goes further: Using the predictive analytics‘ intelligent systems to make an estimation of what is likely to happen, it recommends what future course to take, a very useful tool in the Business Intelligence ecosystem for any business or organisation.

Suppose you are the CEO of an airline and you want to maximize your company’s profits. Prescriptive analytics and AI technology can help you do this by automatically adjusting ticket prices and availability based on numerous factors, including customer demand, Research Necessity, weather, and gasoline prices. When the Artificial Intelligence enables its algorithm, it will identify that this year’s pre-Christmas ticket sales from Los Angeles to New York are lagging last year’s, for example, it can automatically lower prices, while making sure not to drop them too low in light of this year’s higher oil prices.

At the same time, when the intelligent AI technology algorithm evaluates the higher-than-usual demand for tickets from St. Louis to Chicago because of icy road conditions, it can raise ticket prices automatically. The CEO doesn’t have to stare at a computer all day looking at what’s happening with ticket sales and market conditions and then instruct workers to log into the system and change the prices manually; a computer program can do all of this and more—and at a faster pace, too.