Prescriptive analytics is a form of data analytics that helps businesses make better and more informed decisions by suggesting decision options for how to take advantage of a future opportunity or mitigate a future risk, and shows the implication of each decision option. It is the third and final phase of business analytics, which also includes descriptive and predictive analytics. Prescriptive analytics uses algorithms and machine learning models to simulate various scenarios and predict the likely outcomes of different decisions. It then suggests the best course of action based on the desired outcome and the constraints of the situation. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Prescriptive analytics incorporates both structured and unstructured data, and uses a combination of advanced analytic techniques and disciplines to predict, prescribe, and adapt.

Some examples of prescriptive analytics include identifying and preventing fraud in finance, predicting equipment failures in manufacturing, optimizing supply chain operations, and improving customer experience in retail. Prescriptive analytics can also inform product development and improvements by analyzing user data to identify trends, discover the reasons for those trends, and predict whether the trends are predicted to recur.

Its important to note that while algorithms can provide data-informed recommendations, they cant replace human discernment. Prescriptive analytics is a tool to inform decisions and strategies and should be treated as such. Human judgment is valuable and necessary to provide context and guard rails to algorithmic outputs.