SUSE this week revealed it has acquired Losant, a provider of a platform for normalizing data collected from Internet of Things (IoT) devices running at the very edge of a network.
Werner Knoblich, chief revenue officer for SUSE, said the acquisition of Losant extends the reach of the company’s portfolio to a wide range of devices that are typically connected to the Internet using numerous protocols. Losant makes it possible to centralize the collection of data across all those protocols in a way that can now more easily surface insights in real time, he added.
For example, a manufacturer can collect real-time sensor data from production equipment that is then passed on to a Visual Workflow Engine that enables organizations to employ dashboards to automate workflows, noted Knoblich.
The Losant platform was created to normalize 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 makes analyzing the data they generate using a unique set of tools for each data format costly. Losant 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.
According to a market forecast from IoT Analytics, the number of connected IoT devices is forecast to reach 39 billion in 2030 and more than 50 billion by 2035. There are, of course, two classes of IoT devices. The first is made up of consumer-class devices while the latter tends to be deployed in some type of manufacturing, healthcare or retail environment. Historically, most of the data collected by organizations from an IoT device has involved some type of vertical industry application, but increasingly organizations are now also collecting massive amounts of data generated from consumer applications involving devices connected to the Internet.
Traditionally, much of that IoT 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 services being delivered over 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. Before any of the data can be fed into an 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 connected to the Internet using everything from a Wi-Fi network to a cellular service.
