BizTech: Business Intelligence And Analytics Systems For Decision Support. Business Technology Today by Jubi Gloria & Odame Paul

Author:Jubi, Gloria & Odame, Paul [Jubi, Gloria]
Language: eng
Format: epub
Publisher: Litmux.com
Published: 2019-10-25T16:00:00+00:00

Prescriptive analytics relies on artificial intelligence techniques, such as machine learning — the ability of a computer program, without additional human input — to learn from and adapt to new data. Machine learning makes it possible to process the tremendous amount of data available today so that companies can learn from it. As new or additional data becomes available, computer programs adjust automatically to make use of it. This process far exceeds human capabilities because it is much faster and more comprehensive. Prescriptive analytics makes use of machine learning to help businesses decide what the importance of a new development is and how to react to it based on a computer program’s predictions.

How Prescriptive Analytics Works

Numerous types of data-intensive businesses can benefit from using prescriptive analytics, including financial services, healthcare and government. It is especially valuable in fields like these because of the high cost of human errors. Prescriptive analytics is not foolproof, however. It is only effective if organizations know what questions to ask and how to react to the answers. If the input assumptions are invalid, the output results will not be accurate.

Prescriptive analytics can simulate the probability of various outcomes and show the probability of each, helping organizations to better understand the level of risk and uncertainty they face than they could be relying on averages. Organizations can gain better understandings of the likelihood of worst-case scenarios and plan accordingly.

When used effectively, prescriptive analytics can help organizations make decisions based on highly analyzed facts rather than jump to under-informed conclusions based on instinct.

Suppose you are the CEO of a transportation company and you want to maximize your company’s profits. Prescriptive analytics can help you do this by automatically adjusting ticket price and availability based on numerous factors, including customer demand, weather and oil prices. When the algorithm identifies that this year’s Winter ticket sales from Los Angeles to New York are lagging last year’s, for example, it can automatically lower prices, while making sure not to drop them too low in light of this year’s higher oil prices. At the same time, when the algorithm evaluates the higher than usual demand for tickets from St. Louis to Chicago because of icy road conditions, it can raise ticket prices automatically.

The CEO doesn’t have to stare at a computer all day looking at what’s happening with ticket sales and market conditions and then instruct workers to log into the system and change the prices manually; a computer program can do all of this and more at a faster pace.

Prescriptive analytics also relies on artificial intelligence techniques, such as machine learning — the ability of a computer program, without additional human input — to learn from and adapt to new data. Machine learning makes it possible to process the tremendous amount of data available today so that companies can learn from it. As new or additional data becomes available, computer programs adjust automatically to make use of it. This process far exceeds human capabilities because it is much faster and more comprehensive.

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