This article constitutes the conceptual and empirical perspective on creating a foundation for enterprise-wide data intelligence and how AI and ML can permanently transform data integration.

, KDnuggets on April 19, 2022 in Artificial Intelligence   elaborates further the aformentioned conceptual and empirical perspective.

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The world of data integration is one that's been changing for years. Particularly in light of a greater number of employees worldwide working remotely, businesses now more than ever need real-time access to their data. With artificial intelligence (AI), organizations can more efficiently analyze large sets of information and share their analyses across their business.

AI and machine learning (ML) are now making it more viable for businesses to create platforms for data integration that cut down on the time it takes to make data-driven organizational decisions. With these platforms, businesses can also do a better job of securing sensitive user data from breaches perpetrated by bad actors. AI and ML also make it easier for companies to be more compliant with important data privacy and usage regulations like the GDPR and HIPAA.

In order to maximize AI and ML's potential to analyze lots of data at once, businesses must leverage their data intelligence capabilities to create and expand their platforms for data integration. Let's take a look at what goes into creating a foundation for enterprise-wide data intelligence and how AI and ML can permanently transform data integration.

AI Enhances Quality of Information

Artificial intelligence has been shown to expedite the process with which a business performs a use case's sequence of specific actions to generate value for business actors. This automation of processes, though, is only one of many benefits AI offers when it comes to using data intelligence to deliver consistent, reliable results; AI can be used to greatly improve data quality and resolve problems predicated on the quality of data, too.

Artificial intelligence lets organizations make their consistency of data more reliable for the sake of ultimately enhancing their enterprise-wide data management capabilities. With AI as well as ML, organizations can proactively respond to issues related to their data quality rather than reacting in an ad-hoc, unstructured manner. An organization could, for instance, continuously write large amounts of data to user devices and use AI to better anticipate when users would turn their devices offline and make themselves vulnerable to bad actors.

Only that, but AI lets businesses also monitor user devices - even those that go offline - to signal to them when to stop sending data to those devices. The more devices that a business's AI system can monitor, the better it can predict user patterns in terms of device usage to anticipate when users will go offline, know when to cease the transmission of data, and thus reduce overall repair costs. This monitoring strategy also provides businesses with the protections they need to detect anomalies in device usage, considering data privacy regulations prevent them from controlling how users interact with their devices.