Q12: Next please explain when and how we can use prescriptive analytics?

Prescriptive analytics goes one step further and finds the best course of action for a given situation. Its primary goal is to enhance decision-making by giving multiple outcomes based on multiple variables.   The analogy of how doctors prescribe medicine to patients based on a wide range of variables in a patient’s health, using an equally wide range of treatment options.

“Prescriptive tells you the best way to get to where you want to be,” says Anne Robinson, director of supply chain analytics at Verizon Wireless and a past president of INFORMS, a society for analytics and operations research professionals.  “If you want to differentiate yourself, the next step is the prescriptive tool box.

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Predictive analytics answers the question what will happen. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option.

Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics allows us to handle blended data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead. It also allows to take advantage of this predicted future without compromising other priorities.

In addition, most prescriptive analytics efforts require a predictive model with two additional components: actionable data and a feedback system that tracks the outcome produced by the action taken. It is simply too much data and too many outcomes to track if you haven’t invested in the right people and the right technology.

To really be impactful, this type of analytics also requires more data integration then the other types. “Data scientists typically spend about three-quarters of their time preparing data sets and only a quarter running analysis”, says Forrester Research analyst Mike Gualtieri. The need to not only blend and integrate data, but to constantly be looking at ways to keep the good and toss out the bad.

There is also a lot of discussion ongoing about the role prescriptive analytics actually replacing human decision-making. Advances in machine learning have gotten to a point where many routine business decisions can be made automatically.

We are currently seeing a lot of buzz in the industry about how far can an automated predictive analytics solution take us in freeing up time and resources. Currently we are finding ways to spend less time data blending and integrating and more analyzing and taking action. But soon it may be the whole analytics process that is managed by artificial intelligence.

Prescriptive analytics is the way of the future for those with the resources to apply it. However, for those who do not have those resources, prescriptive analytics is out of reach. This to me is a huge challenge for the analytics industry to solve.

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