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Data is transforming the landscape of industries. Technology has enabled the collection processing of large amounts of data — big data. Algorithms and high-speed computers enable the processing of data to spot trends and to make predictions — predictive analytics. The next step is prescriptive analytics.
Prescriptive analytics takes the results of predictive analytics, identifies potential outcomes, and then formulates a set of recommended actions that specify what to do and when to do it. The difference between predictive and prescriptive analytics is that the former produces a report, a graph or some sort of statistic, while the result of the latter is a decision. Two industries where prescriptive analytics are beginning to be applied are marketing and retail. Gartner predicts that the prescriptive analytics software market will reach $1.1 billion by 2019 and that by 2020 more than one third of organizations will be using it.
John Houston, analytics practice lead at Deloitte Consulting, said that “future adoption [of prescriptive analytics] will move into the day-to-day decision-making of knowledge workers, first with tactical, high-frequency decision types and then [to] more strategic, lower frequency decision types.”
Michelle Oxley, digital marketing analyst,wrote that “it appears prescriptive analytics will likely be common place in the not too distant future. Our desire to “get it right” with as little effort as possible makes this type of artificial intelligence highly attractive. Of course, it isn’t fool-proof. Just like humans, prescriptive analytics is subject to insufficient data and unpredictable future events… the competitive advantage prescriptive analytics provides to businesses dictates a prescription for it to be quite prevalent in everyday life in the near future.”