The Smart Data Blog

Creating an Intelligent Internet of Things with Data Fabrics

October 31, 2019 | By: Kirk Newell

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For most organizations, the vast potential of machine learning and Artificial Intelligence is still limited to fringe use cases. These back-office deployments are typically silos restricted to data preparation basics like data formatting, tagging or cataloging, providing little tangible value to front office users.

Enterprise-scale cognitive computing applications require swift, expedient means of integrating data for full-fledged service automation. Many organizations remain stuck in batch processing, making machine learning necessities like feature engineering too time-consuming for lucrative digital transformation initiatives like the Internet of Things.

The modern data fabric architecture handily resolves these issues with a unified semantic layer that issues two critical benefits. It enables organizations to use any data management tool they need for their businesses, and integrates them—and their data—for arbitrary queries and real-time AI deployments.

This approach creates an Intelligent Internet of Things in which the IoT’s low latency data is coupled with instant AI processing for core business use cases. The transformative business value it provides throughout the enterprise empowers front office workers to achieve organizational objectives, easily justifying investments in these technologies.    

Enterprise Data Fabrics

Semantic graph technology is the essence of the comprehensive integration capacity of data fabrics supporting the IIoT. The aforementioned unified semantic layer links any type of data together for contextualized (human and machine) understanding of how they interrelate to support business objectives. This approach gives data universal identifiers that are machine-understandable and Web-accessible for machine-to-machine communication in the IoT. Semantic standards ensure data are described in business terms so they’re also human-readable.

Most importantly, those standards enable data preparation for timely integrations to be done upfront, minimizing costly and time-consuming data processing activities. Thus, the semantic foundation of data fabrics serves as a global access platform for the instantaneous automation necessary for combining and scaling machine learning, AI, and the IoT. This synthesis is mutually beneficial. The real-time data streams of the IoT are ideal for training and then utilizing machine learning models. In turn, machine learning and AI can accelerate the processing required for the IoT, creating multiple ways to profit from the IIoT.

The Intelligent Internet of Things

The use cases for the IIoT’s competitive edge are most readily available in the Industrial Internet. IoT deployments in the industrial sector largely revolve around equipment asset management—such as machines for digging oil, mining for gas, or transmitting petroleum. Running machine learning analytics on this IoT data for predictive maintenance significantly decreases costs. In the aviation industry, for example, intelligent predictions about jet engine data can indicate when maintenance is needed and how to preserve the longevity of these assets—instead of unnecessarily purchasing new ones.

In manufacturing, the IIoT is pivotal to developing new products and services. Because many manufactured devices (like washing machines) are now connected through the IoT, organizations can sell new services like options for predictive maintenance to preserve the longevity of these goods. Moreover, continual insight into how consumers are using machines and their various features provides visibility into which new product features will create greater consumer satisfaction. In healthcare, real-time integrations of data from wearables with medical care facility data is useful for informing decisions about patient care and issuing timely alerts to achieve patient objectives.

Intelligent Integrations

The semantic integration layer of data fabrics is indispensable for implementing AI into front office use cases. It makes the IIoT a reality by automating many of the services stemming from combining the IoT with cognitive computing. Timely data integrations of these applications is the crucial first step in justifying investments in these areas. They increase IT proficiency in general and usher in the era of full digital transformation.


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