Real Time Analytics

What is Real-Time Analytics?

Real time analytics is the analysis of data as soon as that data becomes available. In other words, users get insights or can draw conclusions immediately (or very rapidly after) the data enters their system.

Real-time analytics allows businesses to react without delay. They can seize opportunities or prevent problems before they happen. By comparison, batch-style analytics may take hours or even days to yield results. Consequently, batch analytical applications often yield only “after the fact” insights (lagging indicators). BI Insights from real-time analytics can allow businesses to get ahead of the curve.

Who Uses Real-Time Analytics?

Examples of real-time analytics include:

  • Real time credit scoring, helping financial institutions to decide immediately whether to extend credit
  • Customer relationship management (CRM), maximizing satisfaction and business results during each interaction with the customer
  • Fraud detection at points of sale
  • Targeting individual customers in retail outlets with promotions and incentives, while the customers are in the store and next to the merchandise.

See it in action:

Retail Analysis Dashboard

What is Real-Time Analytics’ Biggest Challenge?

To be immediately useful, real-time analytics applications should have high availability and low response times. They should also be able to handle large amounts of data, up to and including terabytes. Yet they should still return answers to queries within just seconds.

“Real-time” also means handling changing data sources, which may spring up as market and business factors change. In short, they should also handle big data. (Learn more about big data basics here). Real-time big data analytics are already used in financial trading. They use data from financial databases, social media, and satellite weather stations to instantly inform buying and selling decisions.

Empowering End-Users

Businesses are becoming increasingly digital. Real-time big data analytics must handle growing quantities and diversity of data. Different technologies exist to help meet these demands. Some are based on specialized appliances (hardware and software systems). Others use special processor/memory chip combinations, or in-database analytics (the database has analytics capabilities embedded in it).

However, it is also possible to use ordinary computer systems and any data source. The real-time analytics application must simply be designed to leverage the full power of standard processors and memory.

This makes real-time analytics more affordable. When the application is also user-friendly, it puts the power of real-time business intelligence directly into the hands of business users. This is also where it should be, for the greatest business benefit.

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