>> Announcer: From San Francisco, it's The Cube. Covering, OSIsoft, PI World 2018. Brought to you by OSIsoft. >> Hey, welcome back everybody, Jeff Frick here with the Cube. We're at OSIsoft in downtown San Francisco for PI World 2018. About 3,000 people talking about really, OT, operations as it slowly marries with IT. Industrial Internet of Things, they've been doing it here for a long time and we're excited to have our next guest and practitioners out in the field who got solutions and they are actually doing good work, so, Sanket Amberkar, he's the SVP of Falkonry, sorry about that, welcome. >> No problem. >> And Michael Risse the VP at Seeq, welcome. >> Yes. Thank you very much. >> So, before we get started just a quick summary of what your companies do. >> Sure, so in the case of Falkonry we do what is called operation machine learning, and what it means is really applying machine learning to business operations and data analytics to really drive improvement and efficiencies in their operations. And what's unique about that is it's kind of like a data scientist in a box, so you don't require a data scientist on your side, you can actually have your own practitioners and operations people use it. >> And just plug into your algorithms? >> And just plug it into what exists in their infrastructure. >> Okay, and what about Seeq? >> So, Seeq is an analytics application for process manufacturing data, for example OSIsoft PI, and really what our focus is, there's incredible innovation out there, the open source and the machine learning and the big data and so forth, and we're about closing the gab between what's possible and what's practical, in terms of the applications that people use everyday in process manufacturing. >> So, it's just funny cause big data is all the rage and machine learning is all the rage and AI and the Industrial Internet of Things and IoT, and yet these guys have been doing it for like 40 years. (laughs) Without IP based sensors, without 5G, without Hadoop 40 years ago. So, why have we not heard about this and what kind of opportunities now open up when the rest of kind of the IT infrastructure space and we do get 5G and we do have IP connected devices and everybody's ready to get this censored data it's a whole new revolution. >> Exactly, because what we're seeing right now is people have data in their systems, they just haven't leveraged it to the full capability. So, as you start getting more and more data and especially if you have a PI system, you have access to all that data now. How can you fully leverage what you have and really drive new insights from that, and that's really what's driving all this stuff and you know you brought up some good points with wifi and 5G and other sources where information which was initially not connected can now be connected. You have now full visibility into your entire systems, and you can actually be able to control things that before you had to send a person out there and kind of go and tweak and turn and get working. So, it's really changing how you digitize your infrastructure, its become a bit of a buzzword unfortunately, but digitization of your industrial operations is actually real and it's happening right now. >> Right. >> It's funny you bringing that up, because you could argue that original big data was manufacturing data, they were just missing a branding team to call it something cool, right? (laughs) So, the original big data was manufacturing data, there's a lot of it, there's been a lot for a long time. Now, they are ahead in the sense that they know how to store it and do a great job at the PI infrastructure, for example, and now as you said, it's about that next step. Not only for the manufacturing environment but for those IoT environments that are just starting to collect and process data. So, now if we can close the gap on modern analytics, right and with the modern analytics capabilities with the data they've collected, what that means is businesses are going to get more benefits. It's not about sensors, it's not about data collection, it's about business benefits to the bottom line. >> Jeff: Right, right. >> The ability to see then get insights from data. >> So, it's really interesting you know because so many start-ups get started because they see some inefficiency. Whether it's empty rooms that can be Air B&B or it's cars that sit 90 percent of the time that can turn into an Uber or a Lyft. You would think that in some of these old line manufacturing that a lot of that inefficiency would have already been rung out but as we keep hearing stories here time and time again, whether it's getting better yield out of your gold ore, or getting better yield out of your water systems, there's still a ton of inefficiencies and opportunity yet to be extracted and that's before we add machinery. >> Well, that's the difference between I've got the data and I've got the science or I've got the calculations. It's too hard and takes too long to get the insight to impact the outcome, if that makes sense. It takes me more time to do the analysis in a spread sheet, right, or a pen or paper, >> Jeff: Right. >> Then to impact the outcome the batch, I'm not going to do it, but with these modern analytics, I can get the insight quickly and I can make a change to what I'm doing or prevent something from happening and now it's worth doing. So, I've got the data, got the insights. >> And if you think about like today, for example, you have controls systems in place that have been there for 20 years, that basically do what we call, Real-Time Control, so, you're doing a batch process and you're monitoring that stuff, it can do that stuff perfectly well. Does it make sense to put something new just to make another two percent, maybe not, but what about if you can now predict not just real time but predict what's going to happen six hours, 12 hours, two days, a week ahead of time, that's entirely brand new. And the problem is looking at your data you have today, there's just way too much data for you to humanly possibly do that. So, therefore it never really got touched as much. Now is you have the tool sets that have come from the IT side, have come from (unintelligible), now you apply them over here. Suddenly, you're uncovering basically net new benefits that you can get, that just before were not easily accessible. >> Jeff: Right, right. >> I was just going to say 30 years after all the data was created and collected, unplanned downtime, right, is still a bugaboo of so many of these industries. Unplanned downtime means whoops, we didn't expect that to happen. Machine failure, something going down, another set of analytics is going to be required to really stamp that out >> Jeff: Right. >> And know things in advance as Seeq just pointed out. >> So, what are the notions that gets kicked around a lot right as data's the new oil rut, And I'm not going to go there but one thing that is clear is that data used to be a liability, it used to be expensive to store, expensive to keep and you hear time, I mean there's a really great movie, was sponsored by EMC, big data movie that they did and they talked, it was a horrible story about these EKG machines that would be kicking out data all the time on a tape that would go to the floor with predictive data that could tell you when someone's having an issue but the nurses only came in and checked at once an hour or whatever the protocol was. It's just horrible. So, have the industrial companies now realized that beyond what's on their balance sheet and their capital expense and these huge infrastructure projects, they actually have a lot of value in their data. We see it in tech companies all the time. Why do these companies have this valuation? It's not a multiple revenue, it's because they got the data. But we haven't really seen it morph into more old line asset-based companies where there isn't a line in them yet. Soon, it's going to be interesting to see how the accounting principles change where you get credit for this data. People getting it now, are they seeing the value? >> Absolutely, they're getting it. The pressure that they have to now realize the benefits of the data possibility, mean that they recognize that look, my next benefit out of my balance statement comes from my, Mackenzie calls it competing on analytics, my ability to do analytics drives that balance sheet results. Okay, now what are the right analytics and what am I looking for in terms of outcomes? So, they absolutely get it. It's just been too hard, the gap between the innovation and our consumer and IT lives and what's been generally available and the OT space has been too high for too long. >> Jeff: Right. >> And that's what we're working on closing. >> And there's two things actually, you bring up a good point with the Mackenzie article because Mackenzie's predicting that 20 percent, actually, the next 20 percent increase in productivity rises actually come from data analytics being applied to manufacturing and being flied to process operations. >> Jeff: Right, right. >> And it's interesting because it's not like this stuff did not exist before, if you look at it right now, there's about 15 percent adoption rate of advanced analytics in manufacturing, and I'm not talking about your standard real time stuff, I'm talking more the advanced. But, if you look at the adoption, what's expected by 2020 they're saying that's going to go up to 53 percent, of all manufacturing out there, all process of each other. So, what it means is right now, this year 2018 and 2019 is we're going to see a huge amount of adoption where people have been doing pilots until now maybe or doing a little big of trials up to now, actually, they have stepped in and we're seeing real purchase orders for real production applications and it's happening in every industry, that's interesting thing too, it's not just, before it used to be semiconductors are leading or automotive is leading or maybe oil and gas. We're seeing it in pretty much every single one right now because everyone has the data, everyone knows it's not being utilized and they're saying, "Where can I get my next advantage from?" because it is a competitive advantage now. If your competitor is doing a better job at their data than you are, then you want to make sure that you are able to leverage it yourself. >> Goldman Sachs actually wrote an article on productivity on (unintelligible) and shell from brawn to bites to brains and the whole point was the next chunk of innovations is going to come from the brains and the analytics that are possible and how to optimize those outcomes. >> Jeff: Right. >> So, it's very clearly seen. >> So, the other buzz that's happening writers of the all the machines are going to take our jobs and the universal basic income will lay on the beach or being laying out and you're on market street one of the three, I'm not sure which. But, clearly the evidence is contrary and really we're seeing that here especially with some of the stuff even without the analytics, it's a combination of the machine with the data and a little bit of an application on top of that to an able people to make their decisions and some of used cases that have been coming out of this show are fascinating to me. The scale of impact, one of the water companies that are losing like 50 percent of the water between the time it goes out of the processing plant to the spicket at the house. 50 percent! >> Michael: Right. >> These are humongous. Huge inefficiencies. So, the opportunity just seems endless. I was just going to say, do you have any of your favorite stories where it's just mind blowingly, in hindsight maybe obvious but it wasn't at the time until they actually dug into this data a little bit. >> Sure. So, you bring up a really good point because it's not really about replacing any work, it's actually augmenting what the work can do. You're making them much more efficient with what they're able to do because they're the ones making the decisions at the end of the day. There's a couple of interesting news cases that we've been seeing and I'll give you one coming from the mining side, where for example, they've been having an issue where on the conveyor belt depending on the quality of the ore that ore was starting to get blocking into the part of the machine that does the crushing and does the grinding and that when it goes down is about 30 thousand dollars per hour, takes them somewhere between five hours to a full day, so that can be like 720 thousand dollars per day and it happens twice a week so you can do the math, >> Jeff: That's loss of productivity. >> That's loss of productivity right off the bat. >> And it happens twice a week. >> And this is not a massively large company, this is like a mining company on Wyoming having an issue like this. So, obviously there's a big problem over there to solve, and the beauty of it is, you can take the data, the data can absolutely anticipate and say three steps before it reaches that grinding part of the cycle that dispatch of ore which is moving through right now has a problem and therefore what they're able to do is they're able to go and slow the process down so you're still having output and productivity, have the ore removed, and then basically continue the process on. They got to the point where they're so confrent now that the actual operater now is able to close that loop remotely and basically whenever the warning happens, they can say yes, here's the bad batch, automatically get it taken off and it keeps going on. But you have the operate in the loop. The operate is the one making the decision, what to do about this. This is not being done for them. And while it helps in automating, it's not an automation, it's still a person in the loop. And that's always going to be the case. >> I just think one of the things that Falkonry and Seeq have in common is that focus on the engineer or the operator, the person and then taking advantage of their expertise, their experience, their education, they know a lot about those plans and assets. It's just too hard to do the analytics by hand. So, if they can use the Falkonry or Seeq to get the insight more quickly, then they get the better production result. But tapping rather than replacing that expertise and that engineering or that frontline worker absolutely critical because there's 20 or 30 years of experience in some of these plants and some of these assets. You want to tap what they know cause they've seen it. Just help them do something more quickly. >> That institutional law just really hard throughout the cake, and I still keep hearing about everything on Excel too. It's just fascinating, the market penetration of Microsoft Excel. >> 30 years later. >> I have my data on a CSV file, can you do something with it? >> Yes, can you do something with it. >> And it's from three weeks ago and I finally threw it out the export. So, before I let you guys go, thoughts on the show, we're here at OSIsoft. Have you been here before, >> Yep. >> It's our first time, I see people walking around with 15 year badges which is amazing, it's like the most successful company you've never heard about that's right across the bay and operating for 40 years. So, general impressions, some takeaways from some of the sessions, what are you guys here for? >> So, OSIsoft does a really great value for essentially the Industrial Operations Team because basically, they're bringing them data that actually can really change what they do in their operations, can really make a big difference and in terms of the users, they're really sophisticated, you don't have to convince them and say hey data is important, they know that data is important, they have been doing stuff with their data and they're able to actually show really good views cases. If you go into any of these, I was sitting in the transmission distribution one and it's amazing even in industry like transmission distribution which you think is a regulated industry, have been doing a tremendous amounts of stuff in terms of how they have been using the data or their PI system and improving operations and actually making things much more efficient for you and I to your point that there's so much of loss between the energy generated to finally reaching your light bulb at home and imagine them making significant improvements in that so that there's less loss of power when it comes to you. I mean it's more benefits for all of us. >> Oh, for sure. >> It's funny you mention the OSIsoft, is it known and I can see and understand that but this is the largest user conference they've had, they doubled the partner space that they've got. >> 3,000 people. >> People here so I think the recognition of, before I can get the insights from the data, I got to have it well-stored in that PI infrastructure, is growing among organizations, so that's why you see the growth in the user conference and once it's there, then we can kick in. The advanced analytics on top to go from the data collections stored and managed to the insights that drive better business outcomes. >> It's so much easier to get those efficiencies versus rip and replace or >> Leave the data where it is get your engineers' involved >> Leave the infrastructure. Fix the leak. 50 percent of my water is coming out that leak, it's crazy. All right, Sanket and Michael, we got to leave it there, thanks for sharing a few minutes with us. >> Sure, thanks for having us. >> Very much appreciate it. >> All right, I'm Jeff Frick, you're watching The Cube from OSIsoft 2018 Thanks for watching. (upbeat music)