industrial IoT platforms overview of capabilities

Industrial IoT Platforms: What You Need to Know from the Gartner Magic Quadrant 2020

Gartner’s Magic Quadrant for Industrial Internet of Things (IIoT) platforms was released in October 2020. Looking at the current market and the required capabilities, Gartner defines the industrial IoT platform as follows:

“…. a set of integrated software capabilities to improve asset management decision making and operational visibility and control for plants, infrastructure and equipment.”

Industrial IoT platforms are different from generalized IoT platforms. Here the IIoT technologies are specifically conceptualized and built for use within asset-intensive, highly regulated environments. Within such environments, the industrial IoT platform takes care of the harvesting, aggregation, orchestration, and analysis of data. This takes place with an eye towards rapid decision-making and enhanced process transparency. 

Mapping out the industrial IoT platform

Gartner’s new Magic Quadrant for industrial IoT platforms speaks for an emphasis on inter- and intra-platform integration, as well as a focus on data and device management coupled with the capacity to scale. At the same time, the research indicates that complex IT/OT integration is still emergent. In what follows, we take on these insights to offer an overview of the capabilities and requirements for IIoT platforms. We also touch on some of the latest trends in IoT platform development.

Depiction of Gartner's Magic Quadrant
Image 1. Based on Gartner’s Magic Quadrant for Industrial IoT Platforms 2020 that ranks vendors as Leaders, Challengers, Niche Players, or Visionaries based on criteria such as ability to execute and completeness of vision, among others.

Capabilities and requirements for industrial IoT platforms

In defining and positioning the industrial IoT platform within an array of services, Gartner outlines the key differentiators that make IIoT platforms superior to legacy operational technology (OT). The industrial IoT platform capabilities encompass the cost-effective harvesting of high volumes of high-velocity, high-complexity data from IoT devices, orchestrating historically siloed data sources for more accessibility, and improving insights and actions through specialized data analysis that takes place across diverse assets.

The functions of an IIoT platform cover the monitoring of IoT devices and streams, the facilitation of app development and IoT deployment, and the integration of data analysis at the IoT gateway and/or in the cloud. Added to this are the support of various vendor and industry protocols, the integration of IT and OT systems, as well as being a supplement to OT functions. In short, we have the following industrial IoT platform capabilities:

  • Monitoring of IoT endpoints
  • Supporting a variety of protocols
  • Integrating data analytics capabilities both at the edge and in the cloud
  • Integrating IT and OT systems in data sharing and consumption
  • Enabling application development and IoT deployment
  • Supplementing OT functions to improve asset management life cycles

Industrial manufacturers can utilize the  IIoT platform as a technology suite, as a generic, open application platform, or as a combination of both. Being aligned with the mission to protect industry assets and the operating environment, IIoT platforms support the necessary security requirements for industrial manufacturers and address regulatory concerns wherever applicable.

Integration enablement and hybrid deployment named key requirements

In defining the IIoT platform further, we have a combination of a number of core functions. All in all, an IIoT platform offering is expected to combine the following:

  • Analytics capabilities
  • Data management
  • Edge device management
  • Integration tools and management
  • Application enablement and management
  • Security

Key for the IIoT platform is the capability to integrate analytics. This encompasses data stream processing, monitoring, and tracking patterns, as well as the implementation of things as diverse as machine learning, data visualization, and event stream processing.

Another key functionality is data management. This involves the capability to ingest edge device data, feed into and store the harvested IoT data to platforms, manage the accessibility of the data plus the data flow, and enforce policies for analytics governance. 

Enhanced device management functionalities have become an indispensable component of the industrial IoT platform. These entail software that enables the remote and secure creation, configuration, troubleshooting, and management of large numbers of IoT devices and gateways, which can be manual and/or automated.

With application enablement and application management, we have software that enables business applications to analyze IoT data and perform related tasks. Then we have integrations—these involve software, tools, communication protocols, APIs, etc. 

Security is also identified as a vital capability. This is the umbrella term for the software, tools, and practices in place to ensure compliance and establish controls and actions that protect data and its privacy across the IIoT solution.

The broader picture

All in all, Gartner identifies the core requirements and industrial IoT platform capabilities as follows. First, they need to be “integrable with both OT and IT applications” as well as “resilient and reliable”. In terms of deployment requirements, IIoT platforms have to address both cloud and on-premises deployment options. Along with this, IoT edge computing with services from IoT endpoints and the cloud has become increasingly relevant. Data needs to be processed and analyzed as close to the IoT edge as possible to minimize data loss. 

Looking at IIoT platform trends

Across specificities, Gartner could identify key tendencies among customers asked about their reasons for deciding on an industrial IoT platform. Some 21% of the respondents said they want to “augment legacy automation and control systems” with IIoT. Further 16% said they opted for an IIoT platform in an effort to “replace automation and control systems.”

Further, according to Gartner, container-based IIoT solutions are now a preferred approach for on-premises implementation where platforms have to partly operate in a disconnected mode. User reports have revealed that containers are simpler to build and deploy. Container-based technology is also the preferred approach to push intelligence to the edge of the IoT network. By moving intelligence out of the cloud and into edge devices, IIoT platforms are better suited to provide real-time analytics, more quickly and reliably.

The future of the IIoT edge is container-based.

Gartner believes that many industrial companies are open to the promise of the industrial IoT platform to combine the assets of software-defined architecture, superior data harvesting and analytics, as well as enhanced condition monitoring. The projection is that IIoT will continue to augment legacy control systems to eventually replace them over the long term. 

By 2025, 50% of industrial companies will use IIoT platforms to improve operations, an increase from 10% in 2020.

Further, 25% of large global industrial companies will acquire or invest in an IIoT platform by 2025, according to Gartner.

Can I use the Record Evolution platform for my industrial enterprise?

The Record Evolution platform combines data science and IoT capabilities in bringing together the following functions: 

  • Device management — the platform enables both manual and automated tasks. You create, configure, troubleshoot, and manage IoT device groups remotely and securely while allowing for enhanced scalability;
  • Integration — the platform brings together software, tools, communications protocols, APIs and more. You cover an array of integration requirements within the IIoT ecosystem;
  • Data management — the platform helps you oversee the harvesting and storing of IoT device data. It provisions data accessibility, tracking the data flow, as well as considering security and privacy issues;
  • Data analytics — the platform covers the entire data journey from data ingestion through data transformation to advanced analytics and visualization, and facilitates the building of machine learning models;
  • Application development and enablement — the platform makes it possible to easily package your built machine learning models as IoT apps and roll them out on multiple IoT devices. You program directly on the device, receiving live feedback.

You have a use case in mind? Contact us for more information on using the Record Evolution platform for your industrial enterprise and a demo.

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About Record Evolution

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