The future of BIG Data

Prescriptive analytics can be seen as the future of Big Data. If we see descriptive analytics as the foundation of Business Intelligence and we see predictive analytics as the basis of Big Data, than we can state that prescriptive analytics will be the future of Big Data. Earlier, I already explained the difference between these three types of analytics, but let’s have a small recap: descriptive analytics means looking at historic data, ranging from 1 minute ago to years ago. It can be compared as looking in the rear mirror while driving. Predictive analytics means using all that data to make a prediction about where to go; it is the navigation that tells you how to drive and when you will arrive. Prescriptive analytics is the self-driving car, that knows exactly what the best route is based on infinite data points and calculations. Not surprisingly, Google’s self-driving car makes extensive use of prescriptive analytics.

Prescriptive analytics uses the latest technologies such as machine learning and artificial intelligence to understand what the impact is of future decisions and uses those scenarios to determine the best outcome. With prescriptive analytics it becomes possible to understand and grasp future opportunities or mitigate future risks as predictions are continuously updated with new data that comes in. Prescriptive analytics basically offers organizations a crystal ball. Prescriptive analytics will become really powerful when it has developed into a stage where decision makers can predict the future and make prescriptions to improve that predicted future, without the needs for Big Data scientists.

Although prescriptive analytics is really still in its infancy, we see more and more use cases being developed. Also several Big Data startups focus especially on prescriptive analytics. The most well know is Ayata. They use patented software to predict what is going to happen, when it is going to happen and why it is going to happen. They focus primarily on the oil and gas industry, but there are more use cases of prescriptive analytics. Prescriptive analytics is used in scenarios where there are too many variables, options, constraints and data sets. Without technology it is too complex for humans to efficiently evaluate those scenarios. Also when experimenting in real-life is too risky or expensive, prescriptive analytics can come to rescue. Let’s have a look at three of the possible use cases: