The future of big data analytics has arrived, and it’s called prescriptive analytics.

Gartner defines prescriptive analytics as a form of advanced data analytics that “examines data or content to answer the question ‘What should be done?’ or ‘What can we do to make _______ happen?'”

In other words, prescriptive analytics uses big data such as historical data and real-time data to not only anticipate what will happen and when but why something will happen and recommends actions to take based on those predictions. By acting on these insights, businesses can maximize an impending opportunity, optimize a situation, mitigate future risk, and gain a competitive advantage.

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As promising as prescriptive analytics seems, however, it remains a nascent technology, which can be confusing to deploy and manage. Before implementing prescriptive analytics in your business, it’s important to dispell misconceptions and understand what exactly prescriptive analytics is–and what it’s not.

5 prescriptive analytics myths

1. Prescriptive analytics is the same as predictive analytics.

Prescriptive analytics works with advanced data analytics such as descriptive analytics and predictive analytics and builds on it. For example, descriptive analytics provides insights into the past by answering “what happened.” Predictive analytics takes it a step further by forecasting “what is likely to happen” Whereas, prescriptive analytics prescribes an actual solution, as in “what we should do about it.”

2. Prescriptive analytics is foolproof.

Prescriptive analytics is only as effective as the data it receives. Many factors can affect data quality. For example, faulty data, bad assumptions, and poorly built models can all impact the reliability of prescriptive analytics’ insights.

3. Prescriptive analytics is easy.

Despite being faster and more comprehensive than human capabilities, you can’t just press a button and instantly retrieve insights. Prescriptive analytics relies on sophisticated analytics tools, techniques, and technology, like artificial intelligence, machine learning, heuristics, and algorithms to manifest solutions, which makes it challenging to implement and manage.

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4) Prescriptive analytics has limited use cases.

Many industries including operations, supply chain, sales, marketing, telecom, finance, and more can benefit from prescriptive analytics. For example, retailers like Amazon can use prescriptive analytics to improve customer service or recommend purchases; healthcare facilities can use prescriptive analytics to improve patient outcomes; oil companies can use prescriptive analytics to look for optimal drilling locations, and so on.

5. Prescriptive analytics offers one solution.

Prescriptive analytics works 24 hours a day and continually processes new data as it becomes available to re-predict and re-prescribe solutions.


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