What is Real-Time Analytics?
Real time analytics refers to the process of preparing and measuring data as soon as it enters the database. 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:
Benefits of real-time data
Real-time analytics can be incredibly useful for today’s businesses. Here are some of the benefits that these tools can offer.
Tracking customer data. See the latest time-sensitive customer data and craft an immediate response. Real-time analytics reveals when and why your customers behave as they do, and how to optimize their satisfaction.
Cost efficiencies. Real-time analytics can help improve profitability by saving money across the organization in areas like hiring and retention, employee engagement, and of course, reducing the workload of the IT department.
that will give you a competitive advantage.
Faster response time. A sudden market fluctuation can mean big opportunities for businesses. Real-time analytics can ensure that you get ahead of situations that might cost money or conversely, could be a big money-maker.
Real-time testing. With immediate answers at their fingertips, businesses can forecast with confidence and optimize their data to find the best options. Split-testing or A/B testing can be carried out with ease to make big decisions clearer.
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.
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.