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Center for Continuous Intelligence

Successful Continuous Intelligence in Various Industries (eBook)

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At the heart of all continuous intelligence (CI) applications is the ability to perform analytics on real-time data streams.

One indication that businesses plan to use CI is the expected adoption of streaming analytics across all industries. The global streaming analytics market is expected to grow from $10.3 billion in 2019 to $35.5 billion by 2024, according to a report from MarketsandMarkets. That increase represents a compound annual growth rate (CAGR) of 28.2 percent for the next five years.

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Driving this growth is the shifting strategies that are moving businesses toward the real-time analysis of events. Information derived from real-time analytics can be used to identify anomalies and business changes as they occur. Rather than being reactive, streaming analytics lets companies take immediate corrective actions or seize opportunities that otherwise might have been missed.

Use cases that are particularly effective combine both the real-time analytics of streaming data and analysis of historical data. For example, in an industrial setting, CI applied to a key piece of equipment’s sensor data could be used to determine whether a critical part is likely to fail. The information is derived in milliseconds and available in time to gracefully shut down the device without causing damage. Historical analysis of the device’s IoT data could be used to answer questions such as “How many times has this device failed in the last weeks, month, or year?”

Such a combination requires different types of solutions to work together. First, a solution must be capable of ingesting the streaming data and performing real-time analytics on the data as it streams. A solution like this would require a data platform that unifies and simplifies the collection, organization, and analysis of data. It also would benefit from some type of event- or stream-processing engine such as those based on Apache Kafka. For the historical data analysis, a solution would require a database with advanced features such as in-memory technology that accelerates the analysis of large volumes of data.

To read more about how continuous intelligence is being used in different industries, read the full eBook here:
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