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Big Data Analytics – Prescriptive Analytics

Learn about the prescriptive analytics, how prescriptive analytics works, its advantage and disadvantages.
Submitted by IncludeHelp, on December 31, 2021

What is Prescriptive Analytics?

Prescriptive analytics is concerned with determining the most appropriate course of action in a particular situation based on the information provided. It is related to both descriptive analytics and predictive analytics, but it places a greater emphasis on actionable insights rather than data monitoring and analysis. In the field of data analytics, predictive analytics is a sort of data analysis that involves the use of technology to assist organizations in making better decisions based on the examination of raw data. Prescriptive analytics, in particular, takes into account information about probable events or scenarios, available resources, prior performance, and current performance, and then recommends a course of action or strategy. The method can be used to make judgments across any time horizon, from the immediate to the long term.

How Prescriptive Analytics Works?

In order to be effective, predictive analytics must rely on artificial intelligence techniques such as machine learning—the ability of a computer program, without the assistance of a person, to understand and progress from the data it collects, while adjusting as it does so. With the help of machine learning, it is now possible to process the massive amount of data that exists today. New or extra data becomes available, computer systems automatically change to take advantage of it in a process that is considerably faster and more complete than anything human beings could accomplish.

Prescriptive analytics is embedded into the majority of modern business intelligence solutions, allowing users to receive actionable insights that enable them to make better decisions. In the oil and gas industry, one of the more fascinating applications of prescriptive analytics is in the control of oil and gas prices, which are continually fluctuating due to constantly changing political, environmental, and demand variables.

It is possible for a wide range of data-intensive enterprises and government agencies to benefit from the use of prescriptive analytics, including those in the financial services and health care sectors, where the cost of human error is particularly high.

When used in conjunction with another sort of data analytics, such as predictive analytics, which involves the use of statistics and modeling to forecast future performance based on current and historical data, prescriptive analytics can be very effective. However, it goes even beyond that: Based on the predictive analytics' prediction of what is likely to happen, it makes recommendations on how to proceed in the future.

Advantage and Disadvantages of Prescriptive Analytics

Businesses may exploit information obtained to generate actual business value because of the vast amount of data now available to them, making it easier than ever to do so. However, determining the most effective method of analyzing this data might be difficult.

Prescriptive analytics is one alternative that can aid your company in discovering data-driven strategic decisions and in avoiding the limits of typical data analytics practices, which include the following:

  • Using up costly resources on housing data that isn't useful for corporate decision-making
  • Investing effort in sorting through data sets that aren't being used
  • Ignoring opportunities to generate new revenue sources and get new knowledge

It is possible to break through the clutter of immediate uncertainty and shifting conditions using predictive analytics. It can aid in the prevention of fraud, the mitigation of risk, the improvement of efficiency, the achievement of company objectives, and the creation of more loyal customers.

Prescriptive analytics, on the other hand, is not without its flaws. Organizations can only be effective if they know what questions to ask and how to respond to the answers they receive. It is impossible to obtain reliable output results if the underlying assumptions are incorrect.

Prescriptive analytics, on the other hand, can assist organizations in making judgments based on extensively examined data rather than jumping to uninformed assumptions based on instinct when employed properly. In order to better understand the level of risk and uncertainty that organizations face, predictive analytics can simulate various outcomes and show the probability of each. This is more accurate than relying on averages alone, which can help organizations better understand the level of risk and uncertainty they face. Organizations can acquire a better knowledge of the likelihood of worst-case events occurring and can plan for them more appropriately.


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