Prescriptive analytics is a process that analyzes data and provides instant recommendations on how to optimize business [1] practices to suit multiple predicted outcomes. In essence, prescriptive analytics takes the (data), comprehensively understands that figure1 shows below data to predict what could happen, and suggests the best steps forward based on informed simulations.

Figure 1: Prescriptive Analytics

• Prescriptive analytics is the third and final tier in modern, computerized data processing. These three tiers include
• Descriptive analytics: Descriptive analytics acts as an initial catalyst to clear and concise data analysis. It is the “what we know” (current user data, real-time data, previous engagement data, and big data).
• Predictive analytics: Predictive analytics applies mathematical models to the current data to inform (predict) future behavior. It is the “what could happen.

Prescriptive analytics: Prescriptive analytics utilizes similar modeling structures to predict outcomes and then utilizes a combination of machine learning, business rules, artificial intelligence, and algorithms to simulate various approaches to these numerous outcomes. It then suggests the best possible actions to optimize business practices. It is the “what should happen.”

Prescriptive analytics is the natural progression from descriptive and predictive analytics procedures. It goes a step further to remove the guesswork out of data analytics. It also saves data scientists and marketers time in trying to understand what their data means and what dots can be connected to deliver a highly personalized and propitious user experience to their audiences.

Generating automated decisions or recommendations requires specific and unique algorithmic models and clear direction from those [2] utilizing the analytical technique. A recommendation cannot be generated without knowing what to look for or what problem is desired to be solved. In this way, prescriptive analytics begins with a problem.

Create a repeatable, scalable process: To accurately model complex scenarios for prescription analytics to be possible, it’s necessary to [3] create an accurate twin of the market. The simulated environment mimics current market conditions and consumer behavior for business users to run what-if scenarios in a matter of minutes. Prescriptive analytics acknowledges that the market is fluid, so a flexible, scalable approach to modeling is necessary.

Optimize business actions: The reason Forbes predicts that the future of data analytics is prescriptive analytics is because of its ability to go beyond forecasting what will happen in an organization, but how it could happen better by making certain strategic decisions.

Use near-time decision-making: With this agile and accurate model in place, business users at all levels of the organization are able to run scenarios in mere minutes. Since the model has been tested and validated multiple times, users trust that when they need answers to their complex questions quickly they receive accurate results without sacrificing the quality of the analysis.

Experience cost efficiencies with in-house capabilities: With self-service analytics tools such as a simulation platform that accommodates prescriptive analysis, businesses save money and also make cost-effective decisions. As opposed to outsourcing their analytics operations, businesses that invest in in-house solutions are able to keep profits within their organization while optimizing their decision making.

Improve productivity: It may go without saying, but the ability to achieve better, faster and cost-efficient decision-making made possible with prescriptive analytics benefits the entire business. The process brings teams together to collaborate who may not converse on business matters otherwise but it also allows departments to focus their efforts on their expertise. Self-service analytics are designed to be user-friendly so businesses users are empowered to run scenarios to help them determine what to do next.

References:

1. https://www.talend.com/resources/what-is-prescriptive-analytics/
2. https://www.valamis.com/hub/prescriptive-analytics
3. https://www.concentricmarket.com/blog/6-key-benefits-of-utilizing-prescriptive-analytics#:~:text=Here%20are%20the%20other%20key%20benefits%20of%20using, many%20fluid%20parts%20that%20business%20experience%20daily.%20

Cite this article:

Nandhinidwaraka. S (2021) The Methods of Prescriptive Analytics, Anatechmaz, pp. 42