The continuous stream of information made available through the internet helps in achieving better customer service too. However, we must begin with an understanding of what real-time and streaming data analytics are, and in which fields they have produced exceptional results so far.
What is real-time analytics?
Real-time analytics is the ability of a computer to analyze data in real-time as soon as it becomes available on the plethora of devices connected to the big wide web through various sources – social media, sensors, machines,
smartphones and so many others. It is also known as streaming data analytics since most of the data is continuously streaming from various devices. As more and more organizations compete, they are
looking at real-time data analytics to gain a competitive advantage over other firms.
Examples of real-time analytics
GPS: GPS or the geographic positioning system, used in the transportation industry, can track traffic on routes as we travel to give us near accurate predictions of when we can reach our
destination, which routes are safe, and so on.
Web analytics: Web analytics helps to integrate data gained from customer interactions with social media, into marketing campaigns or sales pitch. Thus a customer who has browsed through “leather jackets” will be shown ads by sellers of winter-wear, thus optimizing the shopping experience of the customer as well as the money spent on an advertisement by the seller.
Manufacturing: All the data intelligence made available through real-time analytics helps companies to integrate and automate manufacturing workflow such that when one process is completed in one part of a country, another process automatically begins in part in another country.
Predicting equipment failure: Real-time analytics can predict equipment failure and thus save a lot of cost and hassle, by facilitating preventive maintenance. This is of special significance in the oil and gas industry.
Which streaming data analytics platform should you use?
Almost all enterprises today need to get on the real-time streaming analytics curve to make the best business
decisions. There are two options available in the market – expensive proprietary products or raw open source, which involves a lot of hit-and-trial and do-it-yourself. Both options come with their own set of advantages and
drawbacks. While one may prove too expensive for firms that do not have too much cash to spend on big data
technology, the other, though cheap, may not prove too efficacious.
The best alternative would be a third real-time data analytics option that combines the advantage of
enterprise-grade features with the power of open source engines.
Written By: – Priyanka Choudhury