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Modern data analytics explained by Seeq VP

May 3, 2019
Michael Risse, VP and CMO at Seeq, talks with Jim Montague, Control’s executive editor, about data analytics in process automation and control

Michael Risse, VP and CMO at Seeq, talks with Jim Montague, Control’s executive editor, about data analytics in process automation and control, how they're evolving, the new forms they can take on, and how users can benefit from them.


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Jim Montague: Hi, this is Jim Montague, executive editor of Control magazine and ControlGlobal.com, and this is the sixth in our Control Amplified podcast series. In these recordings, we talk with expert sources about process control and automation topics, and try to get beyond our print and online coverage to explore some of the underlying issues impacting users, system integrators, suppliers, and other people and organizations in these industries.

For our sixth outing, we're talking to Michael Risse, VP and CMO at Seeq. Michael has been a huge help with several recent articles, and provided some excellent input for our March feature on historians and our upcoming May cover on data analytics, and so I thought might translate well from our usual print and online venues to this audio format. Well, Michael, sorry for the preamble, but thanks for joining us today.

Michael Risse: Thank you, Jim, very much for having me.

JM: Alright, let’s get started.

First of all, I’ve just got to tell you, I've been having a hard time getting good answers for that May cover article on data analytics, to the point that I suspect that the process control field maybe doesn't know what data analytics actually are. So, I guess my first question is what did data analytics used to be in the process industries, and then, what's it been turning into more recently? How many clipboards are still out there?

MR: Well, that’s a great question. There’s two things, I’m not sure yet that data analytics have or has changed. What it has been is pretty much what it still is. I do think that will change in the future, but right now, the present and past look pretty similar, and what that actually means depends on who you ask. Analytics is this wildly abused term, analytics means whatever you want it to in terms of getting insights from data. So, for some people, it might be the fiscal process control SVC, to some people it might be APM, asset performance management, to some people it might simply be visualization, like dashboards and ways to look at data, but I will tell you that for most people, the majority of the people the majority of the time, analytics means spreadsheets. It means the same thing today that it’s meant pretty much for the last 30 years, because you look at the history of the historian and you look at the history of the spreadsheet, they’ve kind of evolved together, and they’re still together. And so, what’s analytics? Well, it can be a lot of different things, but for most people most of the time, it’s the spreadsheet. The same answer they’ve had last year, 10 years ago, 20 years ago, in terms of getting insights out of data. So, we haven’t seen a switch yet, because that’s what it’s been, that’s what it is.

JM: So, in light of that history and this is just another area where things have somewhat calcified I guess, have the data analytics tools and techniques themselves been changing some? Is it just all software and all in the cloud, but what’s going on when you get there?

MR: Right, so there’s two things that are going to happen. One is as data moves to the cloud, or what we could say is replatform, as data is replatformed, which is becoming more and more common, when that happens what happens with customers is they tend to look at the whole stack - they tend to look at the storage and they tend to look at the analytics. So, as data gets replatformed to the cloud, aggregated to the cloud, collected to the cloud, sent to the cloud, people start thinking about the cloud as the place where data goes. They’re going to think about where should it get stored, in which public cloud, or which private cloud or which service, and how are they going to get those insights out, right? They’re going to rethink it. It’s just like if you move houses, you end up with new furniture. You get a bigger house,  you get new furniture, you get a small house, you downsize, you get less furniture, but your furniture moves with your house or changes with your house. Analytics changes with where your data is that’s one thing that’s going to happen, that’s classic. The other thing that’s going to happen and that is happening, and certainly we’re a part of this, is advanced analytics, and that’s a little silly that I just put advanced in front of analytics as if it’s something different, but advanced analytics is a specific term that McKenzie and Gardener and other analyst firms use to describe analytics which is powered by, enabled by, accelerated by, innovation - big data innovation, machine-learning innovation, advanced algorithm innovation, open source innovation. It’s bringing innovations from the computer science world, passing Silicon Valley, and bringing them into the process manufacturing analytics space. And those two things, the cloud/replatforming, a chance to take a fresh look at what you’re using for insights. No. 2, advanced analytics, bringing this innovation in to assist engineers and other subject matter experts in getting insights more quickly.

JM: So then, you know, as I understand it, I mean it’s much better obviously to go from the static spreadsheet that you can’t manipulate really and you can’t make better decisions with right away. So, we’ve got the cloud and having things online can help with that, but then maybe reiterate a little bit the advanced analytics would be then having some kind of algorithm. Is it using algorithms or other predetermined instructions to then get better data and make better decisions. Is that fair to say?

MR: Well, we can’t help you with better data, but we can certainly help with better decisions. And, the analogy I’d make is first of all, leave it in the hands and leave the investigations up to engineers, process engineers, subject matter experts, quality engineers, reliable engineers, instrument engineers, analysts, other people with expertise, education, experience in the plant and the processes. They’re the ones who know what’s going on, they’re the ones who know what they’re looking for. That’s Part 1. The second is let’s enable them to find those insights more quickly, leveraging those innovations and advanced analytics, and so forth. So, how will that change, right? That’s a lot of work. What does that really mean? We talk constantly to customers who say things like, “I’ve always wanted to study this,” or “I’ve always wanted to do this, and it’s just been too hard. It would take too long.” And so, there’s a set of analytics that are outside their grasp because it’d be so difficult to do with a spreadsheet, and then there’s another set which they do do, but still they deal with the analytics and the spreadsheets and so forth, but it’s taking them hours and weeks to accomplish this. So, you get two things, you get faster analytics than what you’re doing, better insights sooner, ability to make decisions and put them in place, and then there’s this second class of things that are currently outside the scope that are too difficult that you bring into scope, and that are now accomplishable, and what I mean by that is, if something was going to take 400 hours with a multi-person project in order to investigate some particular analytic on a particular type of asset, if  you can bring that down to within an hour, that just opens up all sorts of possibilities for what you can study and how quickly you can make an impact on a process or business outcome.

For more, tune in to the Control Amplified podcast.

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