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Prescriptive Analytics

Prescriptive Analytics is the area of data analytics that focuses on finding the best course of action in a scenario given the available data. It’s related to both descriptive analytics and predictive analytics but emphasizes actionable insights instead of data monitoring. Whereas descriptive analytics offers BI insights into what has happened, and predictive analytics focuses on forecasting possible outcomes, prescriptive analytics aims to find the best solution given a variety of choices.

What is prescriptive modeling?

Essentially they predict multiple futures and allow companies to assess a number of possible outcomes based upon their actions. Prescriptive analytics use a combination of techniques and tools such as business rules, algorithms, machine learning and computational modelling procedures.

What is the difference between Prescriptive Analytics and Data Science?

Prescriptive analytics gathers data from a variety of both descriptive and predictive sources for its models and applies them to the process of decision-making. This includes combining existing conditions and possible decisions to determine how each would impact the future. See the full article for more details.

What is prescriptive research?

Prescriptive research, like Evaluative research, is applied rather than theoretical. It differs from Evaluative research in that it goes a step further, beyond identifying success or performance or outcomes, and actually recommends solutions or new ideas.

What is prescriptive data analytics?

Prescriptive analytics is a type of data analytics – the use of technology to help businesses make better decisions through the analysis of raw data. It can be used to make decisions on any time horizon, from immediate to long term.

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