While the convergence of information technology (IT) with industrial operational technology (OT) has been underway for decades, of late it seems to have taken on a new dynamic.
Back in the day, IT/OT convergence implied our selective adoption of commercial, off-the-shelf (COTS) IT technologies to replace or complement legacy OT system components—Windows-based PCs instead of purpose-built operator consoles, for example. The industrial control realm simply wasn’t large enough to support the type of R&D cycles that the much larger IT realm could afford, so we borrowed what COTS technologies made sense and adapted them to our needs. We were just one of many tails to the tech industry’s dog.
But in recent years, Big Tech has expanded its focus from dealing mostly with abstracted data and information, pushing ever closer to real-time interactions with the physical world. (Enter the Internet of Things, and its Industrial counterpart.) That new focus has necessitated an increased focus on messy issues like availability and determinism—longtime hallmarks of OT reality. In the process, they’ve identified industrial automation and controls as a market worthy of specific attention and development.
The time-sensitive networking (TSN) extensions of the IEEE’s Ethernet 802 standards is one example that comes to mind. I recall visiting the Cisco booth at the 2017 Hannover Messe trade fair in Germany, where the networking giant was highlighting the importance of TSN to the manufacturing sector. Indeed, industrial automation stands out on a rather short list of key use cases for the technology, and I remember Paul Didier, an IoT solutions architect with Cisco, at the time stressing to me how they were working to extend the standard “just for you.”
And on Sept. 30, Amazon Web Services (AWS)announced the general availability of Amazon Timestream, the company’s new cloud-based time-series database for IoT and operational applications. It can scale to process trillions of time-series events per day, up to 1,000 times faster than relational databases, and at as low as 1/10th the cost, according to an AWS press release.
“What we hear from customers is that they have a lot of insightful data buried in their industrial equipment…but managing time series data at scale is too complex, expensive and slow,” said Shawn Bice, VP, databases, AWS, highlighting the industrial implications of the new offering. “Solving this problem required us to build something entirely new. Amazon Timestream provides a serverless database service that's purpose-built to manage the scale and complexity of time series data in the cloud, so customers can store more data more easily and cost effectively, giving them the ability to derive additional insights and drive better business decisions from their IoT and operational monitoring applications.”
Not to be outdone, Microsoft announced in 2018 the acquisition of machine learning (ML)specialist Bonsai, which had been developing a deep reinforcement learning platform, especially suited for "enterprises leveraging industrial control systems." Fast-forward to present day, and Project Bonsai, a low-code artificial intelligence (AI) platform to speed the development of increasingly autonomous control systems is available to preview on Microsoft Azure. “Project Bonsai is the first service component of Microsoft's vision to empower customers to build, operate and manage autonomous systems,” the company said in a May 19, 2020, press release. “With Project Bonsai, subject matter experts can innovate their most dynamic physical systems and processes without needing a background in AI.”
So, if it seems lately that the technology options available to the industrial automation space is harder than ever to keep up with, the increasingly focused attention of Big Tech may be part of the reason why.
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