What is prescriptive analytics? Prescriptive analytics is a branch of data science that uses mathematical models to make hypotheses about future events. Unlike other types of analytics, prescriptive analytics not only tells you what has happened in the past and what is happening now but also what you should do in the future to achieve the desired result. In this article, we will take a closer look at what prescriptive analytics is and how it differs from other types of analytics.
How do prescriptive analytics differ from other types?
Prescriptive analytics is a newer form of analytics that uses machine learning and optimization techniques to provide recommendations for actions that will achieve specific business goals. It differs from other types in that it not only identifies what has happened and why but also recommends specific actions to improve later outcomes. Prescriptive analytics can be used for things like recommending the best price point for a product or the most efficient way to allocate resources across different areas of a business.
What are the advantages of prescriptive analytics?
The advantages of prescriptive analytics are:
- Increased efficiency and effectiveness – By predicting future outcomes, prescriptive analytics can help organizations optimize their operations for maximum efficiency and effectiveness. For example, it can help identify areas where resources could be better allocated or where processes could be improved.
- Improved decision-making – The recommendations provided by prescriptive analytics can help organizations make better decisions based on data-driven evidence rather than intuition or guesswork. This leads to more accurate predictions and increased ROI from strategic decisions.
- Enhanced customer experience – By understanding how customers interact with products and services, prescriptive analytics can help organizations deliver a better customer experience. This includes recommending changes to products or service offerings, as well as optimizing delivery channels and timing for maximum impact.
What data is needed for prescriptive analytics?
Prescriptive analytics requires a significant amount of data to be effective on a work computer, as it relies on past events to build its models. The data must be cleansed and sorted so that the most relevant information can be extracted. Once the models have been created, they can be used to make predictions about future outcomes and suggest courses of action based on those predictions.
Who uses prescriptive analytics?
Prescriptive analytics is used by a variety of different organizations in a number of different ways. Some of the most common users of prescriptive analytics include insurance companies, health care providers, and banks. Insurance companies use prescriptive analytics to help them identify and prevent potential risks. They can use this information to make more informed decisions about pricing, underwriting, and product development. Health care providers use prescriptive analytics to help them identify patients who are at risk for developing certain diseases. They can also use this information to help them develop treatment plans that are specific to each patient. Banks use prescriptive analytics to help them identify and prevent potential financial risks. They can use this information to make more informed decisions about lending, investment, and fraud prevention.
What are the challenges of using prescriptive analytics?
One of the biggest challenges is the fact that prescriptive analytics requires a lot of data. Businesses need to be able to collect and analyze data at scale in order to get meaningful results. Another challenge is the complexity of the algorithms involved. Businesses need to have the resources to develop and implement these algorithms. There is also a risk that the recommendations generated by prescriptive analytics will be ignored or not implemented correctly. Businesses need to ensure that they have the necessary governance and controls in place to ensure that the recommendations are properly implemented. Overall, the challenges of using prescriptive analytics can be overcome with the right resources and planning. Prescriptive analytics has the potential to revolutionize how businesses operate, but there are several hurdles that need to be overcome first.
Overall, prescriptive analytics is important because it can help businesses make better decisions by predicting outcomes and prescribing specific actions to achieve desired results. This type of analytics differs from other types of analytics because it can take into account a company’s specific goals and constraints, as well as the current state of the business.
