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Continuous Intelligence: The Newest Trend in Data Analysis

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Jenna Delport
Jenna Delport
I’m a tech writer, world traveller, avocado-eater and dog lover, not always in that order.

Continuous intelligence (CI) provides organisations across industry sectors with a more effective way of conducting data analysis. Being able to do so at greater scale, with higher volumes, and in near real-time will ensure their data will deliver even more business value.

CI refers to the design pattern in which real-time analytics are integrated within the workflow of the business, analysing current and past data to suggest actions in response to events. Think of it as automating data analysis on a large scale.

Gartner predicts that by 2022, more than half of major new business systems will incorporate CI using real-time contextual data to improve decisions. In basic terms, this enables organisation to improve the cycle time of its data to derive continuous business benefit from it. Furthermore, they will no longer be limited by previous manual-driven processes from data analysts who can now focus on delivering other, more strategic opportunities for the organisation.

Automating for value

In part, the exponential growth of data has contributed to the emergence (and growth) of CI. Given the volume of data available to organisations, it is no longer possible to perform effective manual analysis across disparate sources. Additionally, CI can deliver immediate benefit on the huge data volumes currently untouched by human interaction.

As a result, organisational systems can react to new information as soon as it becomes available. By interpreting and responding to data in near real-time, CI can respond to any business question as it is shaped by evolving market forces.

With speed being one of the defining characteristics of the digital revolution, CI can assist executives to create value that differentiates the organisation from its competitors. While business intelligence (BI) will still mostly be derived from information workers, CI can leverage artificial intelligence (AI) and machine learning algorithms from a more analytical perspective.

Key considerations

What makes CI such a compelling value proposition is that its effectiveness is not impacted by how complex the data becomes. Given its high-speed capabilities of analysing volumes of data from any number of sources, CI can greatly enrich the analytical environment of an organisation.

And unlike people that generally tend to think about an answer before even examining the data, CI systems are completely unbiased. Considering how technology has evolved and become more accessible to a variety of end-users, the insights derived from CI systems can benefit a broader cross-section of business users.

A new way of dealing with data

CI deals with data in a way that the human mind simply cannot process. It automatically creates data stories that become an integral part of the analytical process. This assists in guiding decision-making on all the latest available information.

Considering the growth of the Internet of Things (IoT) and how 5G technology is collating data even faster at the edge, the increase in connected devices is creating a data-rich environment for CI systems to benefit from. Organisations can now interpret data in near real-time at the edge – becoming more agile in the process.

Before long, CI will likely replace a significant portion of manual data analysis. By leveraging advanced and evolving AI and machine learning algorithms, organisations can capitalise on how their data presents value for growth in a digitally focused landscape.

By Andreas Bartsch, Head of Service Delivery at PBT Group
Edited by Jenna Delport
Follow Jenna Delport on Twitter

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