Real-time predictive analytics is the process of extracting useful information from data sets in real time. This process is based on a predictive model which is designed to determine and predict future outcomes in order to enable decision-making processes in real time. RTP Analytics does not precisely predict what will happen in the future; instead, it forecasts what might happen on the basis of certain "if" scenarios.
The predictive model could be built in two ways: by a data scientist or through a streaming operational data analytics platform. The whole process is iterative by nature and involves rigorous experimentation, historical data, and other similar processes. It enhances the customer experience and in turn, helps businesses to increase earnings.
In the RTP Analytics process, each single observation (raw data point) has to be used to compute the predicted outcome. This is a continuous process as a stream of new observations continuously arrives and business decisions based on the predicted outcomes are made in real-time. In the domain of credit card fraud detection, when a credit card is swiped, all the data pertaining to the said transaction is fed to a pre-built predictive model in order to identify if the transaction is fraudulent and if so, deny it. This process has to occur in a split second considering the the millions of transactions being undertaken each second, in real-time.
Another field of application is the preventive maintenance in Internet of Things (IoT), wherein continuous streaming data from thousands/millions of smart devices has to be leveraged to predict any possible failure in advance to prevent/reduce downtime.
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