SUSE today launched an industrial Internet of Things (IoT) platform following its acquisition of Losant, a provider of a platform for normalizing data collected from devices running on the network edge, earlier this year.

Announced at the SUSECON 2026 conference, the SUSE Industrial Edge platform provides a protocol-agnostic means to collect data from edge computing devices. It also includes a set of no-code/low-code tools to invoke a workflow engine, along with templates and dashboards that streamline implementation.

At the same time, SUSE also revealed it has become a member of the steering committee for the Margo Community, which promotes interoperability across industrial automation ecosystems. Previously, SUSE has committed to also making the core Losant platform available under an open source license.

Keith Basil, general manager for edge at SUSE, said collectively these initiatives enable SUSE to extend the reach of its portfolio to include edge computing applications using a platform that is built around a drag-and-drop interface. The goal is to provide a single, unified view of normalized data from numerous diverse sources, he added

The Losant platform was created to streamline the collection of IoT data in a way that doesn’t require organizations to standardize on a specific data format or class of devices. It normalizes the collection of data from IoT devices that today are based on a wide range of processors. Each of those devices generates data in different formats, which can make analyzing that data challenging in the absence of some way to normalize it.

In general, more workloads than ever are being distributed to the network edge as part of an effort to process and analyze data closer to the point where data is being created and consumed. However, there is still a need to aggregate the data being collected, especially in industrial environments where there may not be enough processing power available on a device to do anything more than collect data that is passed on to another platform to analyze.

Historically, industrial data has been collected by operational technology (OT) teams that have specialized expertise in analyzing telemetry data collected from, for example, an oil well. However, as more artificial intelligence (AI) is being applied to that data, much of it is now also being fed back to platforms managed by internal IT teams. The overall goal is to enable organizations to better predict and prevent outages while at the same time improving the quality of the applications and services connected to the Internet.

Of course, the degree to which organizations have been able to meld their IT and OT teams varies widely. However, as organizations increasingly operationalize AI the need to integrate those teams is becoming more apparent. After all, before any of the data can be fed into an analytics application or AI model, the first step of that journey is being able to normalize the collection of massive amounts of data being generated by thousands of devices and platforms that are using everything from a Wi-Fi network to a cellular service to transfer what is becoming massive amounts of data.