For a growing number of organizations, the rise of real-time analytics couldn't come a moment too soon.

Today’s business environment in many cases demands not just data analytics, but real-time analytics. This is when organizations apply logic and mathematics to data virtually as soon as it comes into a system.

As research firm Gartner Inc. has noted, in some use cases the analytics is completed within a few seconds or minutes after the arrival of new data.

“Real-time analytics are insights gained from a single data source or multiple data sources as transactions occur,” said Don Loden, managing director of data management & advanced analytics—business intelligence at global consulting firm Protiviti. “This type of analysis is very helpful for inventory movement, looking at on-hand merchandise, or other use cases in which data is [highly] volatile and there is a great benefit to seeing up-to-the-second information.”

There’s rising demand for real-time analytics, Loden says.

“Modern data platforms are making this approach more of a reality, with better means of streaming data and change data capture mechanisms that are much more sophisticated than in recent times,” he said. “The market is responding by taking advantage of these capabilities, moving quickly from proof of value/proof of concept to fully productive use.”

Real-time analytics does not apply to all scenarios, Loden said.

See Also Big data has a problem: How to support real-time analytics

“It’s important to always consider the use case,” Loden said. “For example, in financial applications, real-time data changes in the GL [general ledger] for postings that are occurring throughout the day can cause numbers to shift, creating mistrust in the data even though it is correct.”

It’s important to consider whether or not it makes sense to implement real-time analytics. “Don’t just do it because you can,” Loden said.

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