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Prescriptive analytics is a type of data analytics that attempts to answer the question What do we need to do to achieve this? It involves the use of technology to help businesses make better decisions through the analysis of raw data.
Prescriptive Actions are about implementing a pit-stop mentality to effectively solve alerted problems before they cause equipment failure.
Email automation is a clear-cut example of prescriptive analytics at work. Marketers use email automation to sort leads into categories based on their motivations, mindsets, and intentions and deliver email content to them based on those categories.
The key difference is that predictive analytics simply interprets trends, whereas prescriptive analytics uses heuristics (rules)-based automation and optimization modeling to determine the best way forward.
Prescriptive analytics tries to answer the question How do we get to this point? It relies on artificial intelligence (AI) techniques, such as machine learning (the ability of a computer program without additional human input), to understand and advance from the data it acquires, adapting all the while.
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People also ask

The Prescriptive Method provides engineering tables showing reinforcement specifications for common wall heights and openings, as well as connections between walls and footings, walls and floors, and walls and roofs. The Prescriptive Method, once adopted into the IRC has not been revised or updated.
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.

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