Continuous intelligence has entered the business intelligence (BI) and analytics lexicon. However, like many new tech terms it is somewhat shrouded in confusion. Some believe it is a byword for real-time analytics, while others argue that it is an entirely new approach to analytics. In this report, we attempt to unravel the phrase and its approach, as well as distinguish it from continuous data integration, which emerged almost four years ago.

The 451 Take

Continuous intelligence calls for a different mindset, where organizations need to think of data ingestion, integration and analytics as a continuous process, which is not the same as real-time analytics, even though there are similarities. Continuous intelligence is driven by enterprise demand to increase the frequency with which data is analyzed, in order to achieve greater business agility, as well as other business benefits such as improved customer service. Furthermore, this approach opens the door to carrying out new types of analysis, such as predictive maintenance. Indeed, some industry watchers believe predictive maintenance will be responsible for taking continuous intelligence prime time. For those vendors already addressing it, as well as those planning to do so, it should pay dividends by enabling them to gain a bigger share of the high-growth data science and analytics market, which 451 Research projects will increase at a compound annual growth rate of 10% from 2018-2023. The market for data science and analytics products was predicted to deliver revenue of $27.5bn in 2019, according to 451 Research's Data, AI & Analytics Market Monitor service.


Continuous Intelligence: Not just Real-time Analytics by Another Name

Some individuals describe continuous intelligence as a design pattern in which real-time analytics is integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events. This description implies that it is largely rebranded real-time analytics, which isn't the case, as we explain below.

Real-time analytics has always been a subjective phrase, open to interpretation – and debate – in large part because it is dependent on the vertical market using it and the regularity with which analysis is conducted. For instance, in financial services, real-time analysis is assumed to mean the ability to process and analyze data on a sub-millisecond basis, whereas in other industries it could mean each minute – or every 5, 10 or 15 minutes.

Continuous intelligence is therefore a better description because it is easier 'to get,' since it emphasizes the continuous nature of the process rather than the frequency at which it occurs. However, it still doesn't fully explain the term, which is important as some industry watchers have placed it as a top trend for 2020.

A better description might be to say it involves the ability to leverage decision automation, AI, real-time analytics and streaming event data. Why? Continuous intelligence relies on machine learning and AI to generate intelligence, rather than presenting analysis in numbers and visualizations – the classic way real-time analytics is served up. Additionally, continuous intelligence employs AI and machine learning for decision automation, which is another key feature that distinguishes it from pure real-time analytics offerings. Furthermore, the ability to support continuous data integration and analysis is another factor involved.

Continuous Integration is a Foundation for Continuous Intelligence

Continuous integration refers to the approach required to integrate, monitor and manage in-motion data flows composed of streaming and live information sources. Continuous intelligence is therefore much more than merely embracing streaming event data – although that is certainly a core part of this discipline.

As we have previously noted, stream-processing deployments take what is essentially a batch-based approach to data ingestion that assumes there is a specific beginning and an end. That approach isn't continuous data integration because it involves batch processes, and therefore can't support continuous intelligence either.

Furthermore, just as is the case with continuous data integration, continuous intelligence also requires integration with live information sources, as well as historical data sources on an ongoing basis – otherwise, the insight generated by it will be incomplete. In other words, continuous data integration means continuously integrating various data sources when they are updated, which is the reason continuous intelligence can't occur without continuous data integration.

In short, continuous data integration is a core enabler for continuous intelligence – just as classic data integration involving ETL (extract, transform, load) processes into a data warehouse – and is a linchpin behind traditional business intelligence and analytics.

Continuous Intelligence Vendor Landscape

The current vendor landscape marketing around continuous intelligence is fairly sparse. However, we expect it to blossom as the year unfolds, in part because of the new use cases it can engender, such as predictive maintenance, as well as the existing one in analytics.

ClearStory Data – acquired by data science, analysis and management vendor Alteryx in April 2019 – was an early vendor to espouse continuous intelligence. It makes sense for ClearStory Data to use the phrase because it elucidates many of the vendor's core smarts – and moreover the reason Alteryx acquired it to fill gaps in its own portfolio.

ClearStory sums up its definition of continuous intelligence as a modern machine-driven approach to analysis that enables you to quickly get all of your data in order to get the analysis you need – no matter how off-the-beaten-track it is, no matter how many data sources there are or how vast the volumes. ClearStory also notes that it's not about doing this once, but letting the machine automate it, so it is continuous and frictionless.

TIBCO Software is another vendor in the data science and analytics sector that we expect to position more fully around continuous intelligence in 2020. TIBCO already has the technology in place to support it, including the ability to engender streaming data science and analytics, as well as generate AI-enabled insight, by applying machine learning to essential data management and analysis processes, including data prep, insight discovery and analysis. Furthermore, TIBCO's strong heritage in real-time analysis makes it a great candidate for continuous intelligence, in addition to giving it competitive differentiation in the crowded data science and analytics market. 451 Research is currently tracking 96 data science and analytics vendors in our Data, AI & Analytics Market Monitor service.

Sumo Logic, which we examine more closely here, is another type of continuous intelligence vendor. As 451 Research notes, Sumo is primarily thought of as a log analytics vendor, serving both IT operations and security use cases. Sumo's continuous intelligence approach involves an 'always on,' current, scaling, elastic and AI architecture involving machine learning algorithms, in addition to capabilities for uncovering patterns and anomalies across a company's infrastructure or application stack.

Sumo Logic exemplifies another form of continuous intelligence involving proactive push systems, which are essentially always-on monitoring. They 'listen' to events as they occur, and when a threat or opportunity is detected that requires a response, they push updates to dashboards or trigger automated responses.

Finally, while it's hard to predict how the continuous intelligence market will play out, some vendors will serve it up as a new homegrown offering. These vendors are likely to be startups positioning themselves as continuous intelligence pure plays in order to ride the momentum created by it as a top trend for 2020.

Other companies will make acquisitions to move into this territory, following Alteryx's lead in its pick-up of ClearStory Data. Additionally, continuous intelligence is likely to be sold as an add-on (or upgrade to existing BI and data-driven systems), providing an opportunity for other vendors in the data science and analytics sector that don't already market around it specifically – such as IBM, Oracle, SAP, SAS Institute and Microsoft – but do have the technology in their portfolios to do so, to enter the continuous intelligence fray.
 Krishna Roy
Senior Analyst, Data Science & Analytics

As a Senior Analyst for the Data, AI & Analytics team, Krishna Roy is responsible for the coverage of self-service analytics, predictive analytics and performance management. Prior to joining 451 Research, Krishna held a number of positions as a journalist in London and the US, including several years writing for Computergram International.

Matt Aslett
Research Vice President

Matt Aslett is a Research Vice President with responsibility for 451 Research’s Data, AI and Analytics Channel – including operational and analytic databases, Hadoop, grid/cache, stream processing, data integration, data governance, and data management, as well as data science and analytics, machine learning and AI. Matt's own primary area of focus currently includes distributed data management, data catalogs, business intelligence and analytics, data science management, and enterprise knowledge graphs.

Keith Dawson
Principal Analyst

Keith Dawson is a principal analyst in 451 Research's Customer Experience & Commerce practice, primarily covering marketing technology. Keith has been covering the intersection of communications and enterprise software for 25 years, mainly looking at how to influence and optimize the customer experience.

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