Taking a Look into the World of Real-Time Predictive Analytics for Business Development!

Real-time predictive analytics is the latest technology buzzword that tech fanatics cannot seem to get enough nowadays.
The uses of real-time predictive analytics are indeed amazing! It is used for credit card fraud detection where, when a credit card is swiped, all its information gets loaded in a pre-built predictive model to check if there are any unusual or suspicious attributes to the entire transaction or not. And all this happens within split seconds in real-time, while checking millions of transactions simultaneously.
There are numerous predictive analytics examples that is truly stunning. But what is this real-time analytics all about?
Let us try to unravel some of the questions surrounding this concept.

What is real-time predictive analytics?

The technology that is used to draw insightful details from real-time data sets for making business strategies is called
real-time predictive analytics
When a predictive model is built on historical data, and it can be used to conduct run-time predictions on a continuous stream of data that help in forming business decisions in real-time. There are two approaches to achieve this -
• In the first process, a predictive model is built using a standalone tool like SAS and R. It is then exported across machine learning environment in a compatible format, like PMML.

• The second process, although a bit complex is equally effective. Here streaming operational data analytics platform has to consume the PMML model to translate it to the predictive function.

Is it possible to build a predictive model on real-time data?

Real-Time Predictive modeling requires extensive analysis and investigation of historical data that creates the foundation of a predictive model. Without historical data for predictive analytics, it is impossible to unravel the data patterns and come up with forecasts. So in reality, a predictive analytics model cannot be developed on real-time data.

Is it possible to update a predictive analytics model on a real-time basis?

As mentioned above, building a real-time predictive analytics model is by no means a simple process. And updating it is also not quick and straightforward, because it involves changed data.
New data points acquired after the end of a survey or experimentation can indeed be highly voluminous. Studies suggest that 6500 people from all over the world can easily generate near about 600 million data points.
So, to update a real-time predictive analytics model, data scientists need to feed the base data with the newly found updated information which means they need to rebuild the model. Only if the model is simple rule-based, it can be updated incrementally with each observation.

Can technological innovations help rebuild predictive models in a jiffy?

Technological advancements might make this a reality in the near future. But experts feel that it would not benefit by rebuilding real-time predictive analytics models with new data points in a few seconds. It can only fetch benefits of technologies are used to rebuild the model after aggregating large volumes of data over a long time.
At present more than 70% of global organizations have hiked their investments for predictive analytics in marketing, as observed by Forbes.

Author's Bio: 

Ms. Aanchal Iyer is the Digital Marketing Manager and a Content Strategist for Aretove Technologies Pvt.Ltd. She has experience of 11+ years as a content consultant. Aanchal is actively involved in writing about the advancement of technologies in our everyday life.