Prescriptive Analytics

What Is Prescriptive Analytics?

Prescriptive Analytics is a type of advanced analytics that examines data or content to answer the question “What should be done?” or “What can we do to make ______ happen?” Graph analysis, simulation, complex event processing, neural networks, recommendation engines, algorithms, and machine learning are some of the techniques used. 

How Prescriptive Analytics Works

Prescriptive analytics is based on artificial intelligence techniques such as machine learning, which is the capacity of a computer program, without extra human input, to comprehend and develop from the data it collects while adapting. Machine learning enables the processing of massive amounts of data that are now available. When fresh or extra data becomes available, computer systems automatically change to take advantage of it, in a far faster and more complete manner than human skills could do.

Prescriptive analytics is a type of data analytics that complements predictive analytics, which uses statistics and modeling to predict future performance based on current and historical data. However, it goes a step further by recommending a future course of action based on the predictive analytics’ prediction of what is likely to happen.

The Benefits and Drawbacks of Prescriptive Analytics

1. Prescriptive analytics can break through the fog of current uncertainty and shifting situations. It can help with fraud prevention, risk reduction, company goals achievement, and the development of more loyal customers.
2. However, predictive analytics is not without flaws. Organizations can only be effective if they know what questions to ask and how to respond to the answers. If the input assumptions are incorrect, the output results will be incorrect as well.
3. However, when used correctly, prescriptive analytics can help businesses make decisions based on thoroughly researched data rather than rash assumptions based on instinct. 
4. Prescriptive analytics can simulate and display the probability of multiple outcomes, allowing businesses to gain a better understanding of the risk and uncertainty they face than they could by relying on averages. 
5. Organizations may learn more about the possibility of worst-case outcomes and plan accordingly.

Key Lessons

1. Machine learning is used in predictive analytics to help organizations make decisions based on computer program predictions.
2. Prescriptive analytics complements predictive analytics, which employs data to forecast near-term events.

3. When used correctly, prescriptive analytics can help businesses make decisions based on facts and probability-weighted forecasts rather than making rash decisions based on gut feelings.

Prescriptive analytics examples

1. Prescriptive analytics could help a variety of data-intensive businesses and government agencies, such as those in the financial services and health-care industries, where human error has a high cost.
2. When a fire is near, prescriptive analytics might be used to determine whether a local fire department should force citizens to evacuate a certain region. 
3. It could also be used to predict whether an article on a particular topic will be popular with readers, based on data from searches and social sharing for similar topics. 
4. Another use may be to adapt a worker’s training program in real-time based on how the worker responds to each lesson.

It’s important to note that, while algorithms can make data-driven recommendations, they can’t take the place of human judgment. Prescriptive analytics should be viewed as a tool for informing choices and plans. Your expertise is vital and required to add context and safeguards to algorithmic outcomes. 

If your company is new to prescriptive analytics, now is the time to learn more about how it can help you make better decisions. Begin with one question that needs to be addressed or one process that needs to be optimized. Gather data related to that topic or process and proceed through each sort of analysis to complete the picture.

Predictive analytics is an excellent technique to improve the understanding and clarity of your company’s decisions. Even if it takes a long time to collect valuable data and design a plan to filter through it, it will be worthwhile when you see the results of what it can achieve.