Special Feature
Part of a ZDNet Special Feature: How to Win with Prescriptive Analytics

5 myths about prescriptive analytics

​Want your business to gain a competitive advantage? If so, learn what prescriptive analytics is--and is not.

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The future of big data is here, and it's called prescriptive analytics.

Gartner defines prescriptive analytics as a form of 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 not only anticipates what will happen and when but why something will happen and recommends specific actions to take based on those predictions. By acting on these insights, companies can maximize an impending opportunity, mitigate future risk, meet deliverable milestones, or gain a competitive advantage, to name just a few advantages.

SEE: Prescriptive analytics: An insider's guide (free PDF)

As promising as prescriptive analytics seems, it still remains a nascent technology, which can be confusing to deploy and manage. Before implementing prescriptive analytics in your organization, it's important to dispel 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 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 business solution, and answers "what we should do about it." Think of prescriptive analytics as a more augmented approach to predictive analytics.

2. Prescriptive analytics is foolproof

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

"Big data must be cleaned, prepped, secured, vetted for compliance, and continuously maintained. The problem with these tasks is that data comes in so fast companies find it difficult to perform all of the data preparation steps to ensure optimum data quality," writes Mary Shacklett in Big data's biggest challenges: 3 solutions, published by ZDNet's sister site TechRepublic.

3. Prescriptive analytics is easy

Despite being faster and more comprehensive than human capabilities, prescriptive analytics still takes time to deploy and manage. You can't just press a button and instantly retrieve actionable insights. Prescriptive analytics relies on sophisticated tools, algorithms, and technologies, like artificial intelligence. According to Gartner, "Prescriptive analytics is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning."

Before implementing prescriptive analytics at you company ensure that you have knowledgeable IT teams and vendors in place to operate and manage prescriptive analytics deployment and processes, supportive executives, a budget to spend on any necessary analytics software, and of course, clearly defined goals. A lack of big data business savvy can quickly foil the plans for a successful prescriptive analytics project.

4. Prescriptive analytics has limited use cases

If your industry collects data and uses advanced data analytics to describe and predict possible outcome, there's most likely a use case at the ready for prescriptive analytics.

Many industries including operations, sales, supply chain, marketing, telecom, and utilities, and many more can benefit from prescriptive analytics. Retailers can leverage prescriptive analytics by analyzing different types of data such as geo-location, trends, product availability and shopping peak hours. Google's YouTube may use prescriptive analytics to figure out what it can do to make people watch more YouTube videos. Healthcare facilities can use prescriptive analytics to improve patient outcomes. Oil companies can use prescriptive analytics to look for optimal drilling locations. Banks can use prescriptive analytics to increase business, cut customer and operational costs, and improve marketing efforts, processing speed, and security.

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.

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