(Our topic. Our tone. Sponsored by dataPARC *)
Back to our regular schedule, finally. Hannover Messe is in the rear-view mirror, the noise is fading, and we’re easing into one podcast a week again. To get us going, we sat down with Kevin Jones, Director of Partner and Product Strategy at dataPARC, for a conversation that took us all the way back to 1981 — and somehow ended up in a control room in the southeastern US, in the middle of the night, with a piece of black paper taped to a screen.
Meet Kevin (and dataPARC)
Kevin has spent 26 years at the same company. That’s not a typo. He’s worked on industrial data from every angle imaginable — applications, advanced process control, and for the last two decades, the data architecture and management layer underneath it all.
dataPARC itself is a 150-person, globally-distributed team that has stayed laser-focused on one thing: the industrial data stack. Connecting it. Storing it. Moving it between systems. Making it usable for analytics. As Kevin puts it, they’ve thought about expanding into other parts of the business over the years — and consciously decided not to.
“That’s been our focus from day one. We just work with industrial plants on their data stack.”
In a market where every vendor seems to be drifting into AI, into platforms, into “the data fabric for everything,” that kind of discipline is increasingly rare. And it’s exactly why this conversation went deep on the part of the architecture that gets the least attention but does the most heavy lifting: the historian.
What actually makes a historian
It’s tempting, especially if you come from the IT side, to assume any modern database can do the job. Why not just dump time-series data in a data lake and let the hyperscaler figure it out?
Kevin’s answer is one we’ll be borrowing for years.
“We were once in a meeting where one of the hyperscalers was advertising a use case. They said, ‘we stored 500 values and did all these great things.’ In the industrial world, a big fish tank can have 500 data points.”
Industrial historians aren’t dealing with thousands of data points. They’re dealing with thousands to millions of signals, sampled at up to millisecond frequency, retained for years. And — crucially — they’re built so that when an engineer says, give me a thousand tags for the last twelve months because I want to run a machine learning experiment, the answer comes back in seconds, not hours. That’s an architectural decision, not a configuration setting.
The other point worth flagging: the cloud-first mindset often forgets the egress side of the equation. Getting data into a cloud system is cheap. Pulling it back out, repeatedly, to compute weighted averages or run rolling analytics, can become eye-wateringly expensive very quickly.
From sensor data to semantic layer
Time-series data alone isn’t enough, and dataPARC realised this early. Their first commercial historian already had the basics of an asset model — process areas, an organising structure, a way to actually find data without knowing the cryptic tag name.
That principle has only grown more important.
With AI and large language models entering the picture, the semantic layer is no longer a “nice to have” for analytics teams. It’s the thing that determines whether your AI is repeatable or not.
“If we can have a good semantic layer, we can be more repeatable. The use cases are really prevalent and it’s becoming just as important as ever.”
Which, of course, brings us to the term that’s been impossible to avoid for the past two years: Unified Namespace.
Kevin’s view here is one we share. There’s a broad definition of UNS — having a universal, semantic naming for everything in your enterprise — and a narrower, more branded one that essentially says “MQTT broker on top of your data.” The broad version is foundational. The narrow version, on its own, gives you the most recent value. Useful, but limited.
“What happens if there’s a problem with this main flow pump? When did it start? Was it now, or did it start on the night shift? You want to look at your history. So having that universal semantic layer is key — but it should give you the current value and the historical values and be useful when you point a machine learning model at it.”
Closing thoughts
Forty-something years on from that first homegrown historian at Georgia Pacific, the historian has been declared dead more times than we can count. And every time, it has come back not as a relic but as something more central than before.
That’s because, as Kevin put it, all the AI in the world is only as good as the data feeding it. And the data feeding it has to be time-series, has to be contextualised, has to be reliable, and has to be retrievable on demand. Whatever you call that layer of your architecture — historian, time-series database, operational data store — the function isn’t going anywhere.
The harder, less glamorous truth is the human one. The piece of black paper taped over a monitor in the middle of the night is a more honest snapshot of where most plants are than any maturity model. Operators have already done the analysis in their heads. They’ve already decided what matters. The job of every IT/OT team is to listen to that, codify it, and build systems that respect it.
If your historian is doing its job, the next 2am phone call should be a sign of trust, not failure.
About DataParc
Founded in 1997, dataPARC is a comprehensive industrial analytics software suite that helps process manufacturers optimize operations, boost productivity, and drive sustainability. Featuring an enterprise data historian, embedded analytics, and tools for real-time data monitoring, trending, and reporting, dataPARC empowers manufacturers to make smarter, faster decisions that positively impact the bottom line. dataPARC serves customers across the process industries with deployments at thousands of sites globally.
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(*) At the IT/OT Insider we do value our independence and transparency. So as we look for ways to pay the bills we were looking for ways to work with sponsors without giving up on those principles. This is where the idea of sponsors comes from. Together with a few selected sponsors we’ll explore some topics that we both find interesting in the same way we write our normal articles. In the coming weeks you’ll find a couple of pieces that have been sponsored. Feel free to contact us if you are interested in a partnership as well.
