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Continuous Intelligence vs. Traditional Streaming Analytics (Special Report)

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It’s no longer enough to simply ingest streaming data. Companies need to transition to developing insights that help them understand events in real time.

In the age of digital transformation, it’s no longer enough to simply ingest streaming data; organizations need to transition to developing insights that help them understand events in real time. When businesses have such analytics capabilities, they can use that information to decide plans of action.

Beyond Streaming Analytics

Continuous intelligence (CI) produces insights from streaming data to take actions in milliseconds to minutes. It frequently makes use of artificial intelligence (AI) and machine learning (ML) models to
perform the real-time analysis and make connections between different events as they are happening.

Such rapid analysis of streaming data requires significant and highly variable amounts of compute and data storage capacity. And in many cases, the various stages of the process (e.g., model training and tuning, data ingestion, and analysis) have distinctly different compute requirements, which can vary over time.

Traditional compute infrastructures for high-performance computing and big data analysis will not do the job. What’s needed is a highly scalable infrastructure that supports very dynamic development
environments, deployment scenarios, and integration of new technologies.

CI goes beyond streaming analytics, which generally only applies a few filters, transformations, or aggregations to data. For example, a retailer analyzing site visitor click streams might derive information to make the site easier to navigate. By contrast, like the nervous system, CI propels people to act immediately. In the same example, CI might be used to make a personalized recommendation to a customer based on their journey on the site for that day’s visit.

To read more about the benefits of continuous intelligence and streaming analytics, read the full report here:
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