Definition of prescriptive analysis


What is prescriptive analysis?

Prescriptive analytics is a type of data analytics – the use of technology to help companies make better decisions by analyzing raw data. Specifically, prescriptive analysis factors information about possible situations or scenarios, available resources, past performance and current performance, and suggests a course of action or strategy. It can be used to make decisions on any time horizon, from the immediate to the long term.

The opposite of prescriptive analytics is descriptive analytics, which examines decisions and results after the fact.

How Prescriptive Analytics Works

Prescriptive analytics is based on artificial intelligence techniques, such as machine learning, the ability of a computer program, without additional human intervention, to understand and advance from the data it acquires, adapting all the time. Machine learning makes it possible to process a huge amount of data available today. As new or additional data becomes available, computer programs automatically adjust to make use of it, in a process that is much faster and more comprehensive than human capabilities could handle.

Many types of data-intensive businesses and government agencies can benefit from the use of prescriptive analytics, including those in the financial services and healthcare sectors, where the cost of human error is high.

Prescriptive analytics works with another type of data analytics, predictive analytics, which involves using statistics and models to determine future performance, based on current and historical data. However, it goes further: using predictive analytics estimation of what is likely to happen, it recommends what future course to take.

The pros and cons of prescriptive analytics

Prescriptive analytics can eliminate the clutter of immediate uncertainty and changing conditions. It can help prevent fraud, limit risk, increase efficiency, meet business objectives, and create more loyal customers.

However, prescriptive analytics is not foolproof. It is only effective if organizations know what questions to ask and how to react to the answers. If the input assumptions are not valid, the output results will not be accurate.

However, when used effectively, prescriptive analytics can help organizations make decisions based on highly analyzed facts rather than jumping to ill-informed conclusions based on instinct. Prescriptive analytics can simulate the likelihood of multiple outcomes and display the likelihood of each, helping organizations better understand the level of risk and uncertainty they face than they might be relying on averages. Organizations can better understand the likelihood of worst-case scenarios and plan accordingly.

Key takeaways

  • Prescriptive analytics uses machine learning to help companies decide on a course of action based on the predictions of a computer program.
  • Prescriptive analytics works with predictive analytics, which uses data to determine short-term results.
  • When used effectively, prescriptive analytics can help organizations make decisions based on facts and probability-weighted projections, rather than jumping to ill-informed conclusions based on instinct.

Examples of prescriptive analysis

Many types of data-intensive businesses and government agencies can benefit from the use of prescriptive analytics, including those in the financial services and healthcare sectors, where the cost of human error is high.

Prescriptive analyzes could be used to assess whether a local fire department should require residents to evacuate a particular area when a wildfire is burning nearby. It could also be used to predict whether an article on a particular topic will be popular with readers based on data on searches and social shares of related topics. Another use could be to adjust a real-time worker training schedule based on how the worker is responding to each lesson.

Prescriptive analysis for hospitals and clinics

Similarly, hospitals and clinics can use prescriptive analytics to improve patient outcomes. It puts health data into context to assess the cost effectiveness of various procedures and treatments and to evaluate official clinical methods. It can also be used to analyze which hospital patients are at the highest risk of readmission so that healthcare providers can do more, through patient education and physician follow-up to avoid constant returns to the hospital or emergency room.

Prescriptive analysis for airlines

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

At the same time, when the algorithm assesses the higher-than-usual demand for tickets from St. Louis to Chicago due to icy road conditions, it can automatically increase ticket prices. The CEO does not have to stare at a computer all day looking at what is happening with ticket sales and market conditions and then instruct workers to log into the system and change prices manually; a computer program can do all of this and more, and at a faster rate too.

www.investopedia.com

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Mark Holland

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