How Artificial Intelligence Can Transform Data Integration - KDnuggets

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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.

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.

  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.

  A big part of real-time data-driven decision-making is mapping customer data. With AI, businesses can now map that data faster than ever before. Faster customer data mapping expedites the rate at which organizations can transform their data and make the corresponding data-driven decisions. 

Tools such as AI mapping systems let their users outline complex mappings of customer data that rely on ML algorithms. These AI-enabled tools let business leaders direct more accurate processes with which they can map out customer data -- even non-technical users can use these innovative tools to map customer data while their information technology colleagues can pursue other, more technical tasks.

AI can also hasten the rate at which businesses process large sets of data. ML algorithms make the analysis of data faster, even when that analysis accounts for big data on an enterprise-wide scale. This improved processing of big data is most commonly applied to legacy solutions, but it can also be used to parse through more modern business solutions like business texting; improved, AI-enabled big data processing can even be used to create data models with the help of ML algorithms when applied to an organization's internal structures of big data.

  It wasn't all that long ago that most businesses handled their sets of big data manually. Modern sources of large sets of data typically come from the Internet of Things (IoT) and streaming -- large volumes of data from sources like these, simply put, cannot be handled with conventional data integration processes. Thankfully, AI that relies on ML techniques can improve the flow of data integration when applied to sources like IoT and streaming.

There are several other benefits that AI and ML offer that address common problems with business data integration. For one, AI and ML make for a reduced level of usage complexity while making it easier for non-technical professionals to handle data integration tasks without needing to call on the help of others. This scenario results in lower costs of ownership that data integration can impose on users who are given data integration responsibilities. 

Additionally, artificial intelligence and machine learning techniques grant access to relatively easy-to-use DI templates that process configurable data. With the help of AI, these data integration templates become suited to provide an intuitive, step-by-step process that non-technical professionals can follow to carry out data integration responsibilities.

  Moving forward, it's worth noting that artificial intelligence and machine learning techniques will inevitably increase the already high demand that exists for data engineering professionals. Businesses should prepare for their future data engineering roles to require professionals who understand how to train machine learning models in order to identify data quality-related anomalies in customer devices and data usage.

What this means is that the data engineering role is poised to become a position of machine supervisor -- data engineers will be responsible for training machines and ensuring that they accurately associate and classify assets that belong to big structures of data. Thankfully, AI is able to reduce time spent on manual data integration tasks so that data engineers can become better able to oversee their data classification tasks in the context of training machines.  

  Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed — among other intriguing things — to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.