What Is Prescriptive Analytics?

Prescriptive analytics is a type of data analytics—the use of technology to help businesses make better decisions through the analysis of raw data. Specifically, prescriptive analytics 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 immediate to long term.

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

How Prescriptive Analytics Works

Prescriptive analytics relies on artificial intelligence 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 the tremendous amount of data available today. As new or additional data becomes available, 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 and government agencies can benefit from using prescriptive analytics, 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 statistics and modeling to determine future performance, based on current and historical data. However, it goes further: Using the 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 cut through the clutter of immediate uncertainty and changing conditions. It can help prevent fraud, limit risk, increase efficiency, meet business goals, and create more loyal customers.

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

When used effectively, however, prescriptive analytics can help organizations make decisions based on highly analyzed facts rather than jump to under-informed conclusions based on instinct. Prescriptive analytics can simulate the probability of various outcomes and show the probability of each, helping organizations to better understand the level of risk and uncertainty they face than they could be relying on averages. Organizations can gain a better understanding of the likelihood of worst-case scenarios and plan accordingly.

Key Takeaways

  • Prescriptive analytics makes use of machine learning to help businesses decide a course of action based on a computer program’s predictions.
  • Prescriptive analytics works with predictive analytics, which uses data to determine near-term outcomes.
  • When used effectively, prescriptive analytics can help organizations make decisions based on facts and probability-weighted projections, rather than jump to under-informed conclusions based on instinct.

Examples of Prescriptive Analytics

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

Prescriptive analytics could be used to evaluate 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 about searches and social shares for related topics. Another use could be to adjust a worker training program in real time based on how the worker is responding to each lesson.

Prescriptive Analytics for Hospitals and Clinics

Similarly, prescriptive analytics can be used by hospitals and clinics to improve the outcomes for patients. It puts healthcare data in context to evaluate the cost-effectiveness of various procedures and treatments and to evaluate official clinical methods. It can also be used to analyze which hospital patients have the highest risk of re-admission so that healthcare providers can do more, via patient education and doctor follow-up to stave off constant returns to the hospital or emergency room.

Prescriptive Analytics for Airlines

Suppose you are the CEO of an airline and you want to maximize your company’s profits. Prescriptive analytics can help you do this by automatically adjusting ticket price and availability based on numerous factors, including customer demand, weather, and gasoline prices. When the algorithm identifies 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 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.