What is Streaming Analytics? Streaming analytics, also known as event stream processing (ESP), refers to the analyzing and processing of large volumes of data through the use of continuous queries. Traditionally, data is moved in batches. While batch processing may be an efficient method for handling huge pools of data, it is not suitable for time-sensitive, “in-motion” data that could otherwise be streamed, since that data can expire by the time it is processed. By using streaming analytics platforms, businesses across a wide range of industries can easily identify new opportunities, and can also recognize risks before they even occur.
Real-time analytics offers many advantages over traditional analytics. It enables companies to make immediate changes when needed, to customize monitoring of behavior, and to share information through transparent dashboards. What’s more is that real-time analytics provides the ability to create custom interactive analytics tools, and it can also be applied to machine learning. In contrast to traditional analytics, real-time analytics help in detecting anomalies and can pinpoint statistical outliers that are caused by technological failures or security breaches. It also improves marketing efforts by managing location data, in addition to finding insights in demographics that help identify audience targeting and determine appropriate pricing strategies.
Streaming analytics gives companies the ability to analyze data as soon as it becomes available. With immediate real-time data at their fingertips, businesses can identify new revenue streams, resulting in an increase in profits. Other advantages include new customers, improved customer service, and also a way to analyze risks before they even occur. Because data is processed before it even lands in a database, streaming analytics supports much faster decision-making than using traditional data analytics technologies. Streaming analytics also guarantees security protection by detecting threat patterns and their risks, since it provides security monitoring of network and physical assets. By using real-time analytics, organizations can both predict and detect significant business events the moment they occur, making it possible to minimize risk while simultaneously maximizing gains.
Using real-time analytics, data can either be pushed or pulled into the system. However, streaming must be in place to push that data into the system which can sometimes be impractical and can also require a lot of resources. While it is not a complicated process, real-time data analytics involves different key components.
Other factors that make real-time analytics possible include:
As big and fast data proliferates and more data streams are generated, companies are relying on real-time analytics more and more. Real-time data monitors any number of event streams and event data of any kind, allowing for a quick response to events at the very moment they happen - or even sooner when using predictive models.
Real-time analytics makes data more organized, useful, and accessible from the instant it’s generated. Here is a look at some of the incredible benefits it provides:
For companies, real-time analytics can help businesses comprehend what’s happening across their business units, improve the relationship between sales and marketing, enhance workflows, understand customer behavior, and much more. Real-time data can be used for nearly every type of business and industry ranging from financial services, healthcare, information technology, advertising, and transportation.
