Predictive, Prescriptive and Descriptive Analytics
In the present world, there are large volumes of data that need to be processed, and therefore, businesses have adopted various business intelligence tools. These intelligence tools include predictive, prescriptive, and descriptive analytics. These three analytics have some similarities, but their definition can also be easily understood from their differences. Descriptive analytics is used in summarizing data into a meaningful form that can be easily interpreted by humans. Predictive analytics help in predicting what might happen in the future. Prescriptive analytics, suggests, helps in prescribing possible solutions and courses of actions of a specific situation.
In a business operation scenario, descriptive analytics help describe the current state of the business by answering the “what” question. The predictive analytics help in the creation of data models by answering the ” what might happen” question. The prescriptive analytics help in giving suggestions about the result by answering the question” if, then.” Descriptive analytics mainly describes the past whereby no fixed time is defined as the past, and so it can be a few days ago or years ago. The predictive analytics is mainly focused on future outcomes. Prescriptive analytics uses algorithms and machine learning to analyze the effect of the decisions made in the future to come up with possible results. Descriptive analytics is practically useful in many businesses to undertake various functions, like calculating the total stock. Different fields also use predictive analytics for prediction operations, such as predicting the future behavior of customers. Although research has shown that not many businesses are using the prescriptive analysis because of its complexity, some enterprises use prescriptive analytics to ensure that operations are carried out effectively and that the customer’s experience is improved. A business needs to have good knowledge when selecting the analytics to use.