Reports 18 Min Read

6 Ways Big Data Analytics Fails to Work — and How Streaming Data Applications Fix Them

The rise of big data analytics has meant that organizations could inform their decision-making by analyzing massive volumes of disparate data. It wasn’t long, however, before organizations began bumping up against the limitations of this approach, especially as they tried to apply it to streaming data.

Here are six examples of such limitations and how modern streaming data applications overcome them with an approach that turns traditional big data analytics on its head.

Big data analytics was built for data at rest.

Big data analytics can’t scale.

Big data analytics “looks down” at the data.

Big data analytics is bound by the “tyranny of averages.”

Traditional analytics doesn’t support agility.

Traditional streaming data analytics is expensive.