This is necessary because one of the key challenges of working with streaming data is acting on its potential insights quickly before the data, as it’s being generated or transmitted in real time, loses its value.

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Streaming analytics attempts to determine the data’s meaning and value, pinpoint relevance and generate instant alerts when there’s an urgency to take action. This analysis also enables enterprises to decide when streaming data should be stored, and therefore subjected to additional management and governance.

And the insights gleaned from streaming data analytics may also be discovered to have value as a complement or supplement for other enterprise applications.

My previous post discussed data preparation for streaming data. Streaming data analytics, also referred to as event stream processing, is becoming an increasingly critical component of how enterprises make the best use of all available data to move quickly from insight to action, make sound data-driven decisions, and adapt to changing business conditions.

This article was republished with permission from SAS.