Control Amplified: Exploring historians & data analytics

March 4, 2019
Panacea Technologies' Will Aja joins Jim Montague to discuss the "modern historian," the evolution of data analytics and more.

Will Aja, customer operations VP at Panacea Technologies Inc., a CSIA-member system integrator in Montgomeryville, Pa., talks with Jim Montague, Control’s executive editor, about the "modern historian" in process automation and control applications, the evolution of data analytics and the new forms it can take on, and how users can take advantage of them.


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Jim Montague: Hi, this is Jim Montague, executive editor of Control magazine and, and this is the fourth in our new Control Amplified podcast series. In these recordings, we're talking with experts about important topics in the process control and automation field, and trying to go beyond our print and online coverage to explore some of the underlying issues affecting users, system integrators, suppliers and other people and organizations in these industries.

For our fourth outing, I thought it might be useful to talk to Will Aja, vice president of customer operations at Panacea Technologies, in Montgomeryville, Pennsylvania, and a also member of the Control System Integrators Association. Will has been one of Control magazine's most interviewed system integrators in the past few years, and has given us lots of expert insight into in-the-trenches system integration projects, issues and solutions. One of his areas of expertise is historians and data analytics, which we'll be covering in two upcoming stories. And, as is so often the case with my sources, he's going to clear these topics up for me, so I can actually produce stories about them.

Oh, and one more thing. Since we’ve got two stories, we may be able to two podcasts on these topics. So, this may just be Part 1.

OK, Will, sorry for the lengthy preamble, and thanks for joining us today.

Will Aja: Yeah, I’m excited to be on the podcast.

JM: Alright, let’s get started then. I guess first up, if historians have evolved to the point that any device with a microprocessor and software can be set to collect and store long-term data, then what the heck characterizes a "historian" these days?

WA: That’s interesting, because I think at their core, historians are still what they were when they first developed, which is a way to store data, a way to historize data. I think that’s really what characterizes a historian and still holds true. And they’ve gone through a lot of changes, but I think the big thing that we’re seeing is that there’s more of the way that a client would classify what their historian is, rather than what the software is itself, and I think that they used to classify it very much as you throw the data in there and it doesn’t really matter, just collect everything and put it somewhere, and we’ll run reports on it later. And I think that they’re starting to classify a historian in the same way that you would classify a utility. So, data is a utility, it’s no different than chilled water or steam or wifi. It’s now a utility, and I think that’s been the big change. There’s a lot of value in it, and if you kind of view it as this stream of value and stream of importance, historians are being treated much differently because of that.

Read more: Modern data historians provide for optimization, savings

JM: So, given how much historians have changed, in what kinds of new ways are they being applied where they maybe couldn't be used before?

WA: So, I think one of the big ones is obviously, there’s more data being collected. The storage used to be a very expensive issue for a historian. Memory wasn’t cheap, so to speak, so you weren’t likely to collect absolutely everything, as quickly as possible, because it just didn’t make sense from a cost perspective. Now, one of the big changes has been that you can really collect everything as quickly as you want and it kind of goes along with that IoT and by year whatever-it-is, there will be this much data, which brings its own issues with collecting all the data, but that’s the big one. The change to be able to just collect a bunch of data all at once and be able to store it long term.

But, some of the really interesting ways that historians are changing is, one, they’re changing, from what we see, the way that processes are designed and physically coded, and what I mean by that is that there’s a lot of effort that’s going into creating code and specifically creating a batch applications, batch models that have triggers in them that can show you when certain processes start and stop, because as anyone listening to this podcast has probably been through, when you run a batch and you’re doing some sort of investigation on data, it can be very difficult to go, ‘OK, when did we run the batch for that product?’ ‘Well, it started on this day.’ ‘OK, so go pull all the data from this day at, let’s just take a guess and say 6 a.m., to let’s say four days later when it ended at 4 p.m., and then we’re going to have to go through and clean and actually figure out when the is data that we’re looking for.’ So, being able to characterize that data has been something that is new, and you’re able to start setting up, very intelligently, reports and be able to pull reports in a way that’s a lot easier. So, when you’re doing investigations or you want to get some data out, it’s a lot simpler than it used to be.

The other one is the things like PAT modeling, which PAT stands for “process analytical technology.” It’s the same kind of concept as multi-variate analysis, and that’s something that historical data and data historians are enabling in a way that we haven’t seen used as widely as it is now. So, being able to take data that exists and take real-time data that’s being put into a historian and actually crunch it into high-level calculations and be able to make changes to your model or make changes to your control as data is being collected is just huge, and that’s something that we’re seeing.

And really the final one, I would say, and the big one that we get excited about is that historians are being used more for using data. So, putting data to use and making actions upon data, and making decisions upon data and changes upon data, rather than just simply grabbing it.

JM: So, then is that a part of the overall mindset of thinking of data as a utility stream like chilled water? How do those come together?

WA: Yeah, of course. Because now data becomes useful. Now, it’s not just something that you store on some disk that gets backed up and get’s put in some storage facility so you can have it if a regulatory agency comes knocking or you want to look at it later. Now, it’s utility. Now, it provides value. Now, it’s something that every one of your clients wants access to and wants to be able to use it in some way, and make decisions upon it in some way. And that’s been the big change. It’s just as important as electricity or just as important as chilled water. It really is a utility because it’s something that runs through your facility and it’s something that everybody can use and everyone can use in a productive way.

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