>> Narrator: From San Francisco, it's theCUBE. Covering OSIsoft PI World 2018. Brought to you by OSIsoft. >> Hey welcome back everybody, Jeff Frick here at theCUBE. We're in downtown San Francisco at OSIsoft's conference called PI Word, they've been doing it for at least 15 years. I see the pins walking around the hallways. Our first time here and it's pretty interesting because we talked about marriage of IT and OT all the time, and kind of the industrial internet of things, these guys having been coming out of it from the OT space for over 15 years, almost 20 years or no, 40 years, 1980 right? >> About 40 years. >> A long time so they even had it for a long time and we're excited to be here, 3,000 people. And we're joined in our next segment by Chris Nelson, he's a VP of software development for OSIsoft. And Penny Gunterman, group lead of product marketing. So welcome. >> Welcome, glad to be here. >> Thank you. >> So how many of these shows have you guys been to? >> I've been to every one since 1996 except for two when my two daughters were born. >> Pretty good, I've only been to fewer than 10 so that's make me a young. >> Well you're just a rookie in this crowd. >> Still wet behind the ears. >> Yes so for the folks that aren't familiar, give us a little bit more detail 'cause you're at a really interesting space. You're pulling all this data off sensors. You know, we talk about this all the time as if it's kind of a new and interesting and evolving thing, but you guys have been at it for decades and decades. >> Yeah, it's really just been kind of the press and some new players have grabbed on to it, but we've been doing this for 30 years and, you know, our goal is to collect operational data wherever it exists, reliably and securely persist that and deliver it to whoever or whatever needs it. We don't pretend to know how our customers and users are going to use the data. We just take care of that data flow and we really light them up by giving them their data, they can use it to drive outcomes for their companies and they are our data heroes. >> What's interesting, too, is a lot of times the sensor data gets tied back to big data and fast data and Hadoop and kind of all these technologies that are evolving around that type of data. You guys have been doing it long before there was you know, Hadoop out in the public sector, Flink or Spark or kind of all these new technologies and I think it's interesting because you're showing that you don't have to have big data for regular people to see trends and get value and get some real business benefits. >> Oh yeah, absolutely. I mean, really, when you think about it, it's like driving your car. In order to operate that car, you want to be able to get that information in, you need to make sense of it, and then you move forward with it, right? Now after the fact, you're going to do some analysis, maybe you want some other things, but in your day-to-day operations, when you're making sure that things are running, you want that dashboard, you want that real time visibility, and we've got folks out there that if they see a trend, they could tell you exactly what's wrong, they can tell you exactly where to pinpoint those issues. So what's interesting is seeing, finally, this emphasis on data and people kind of catching up, seeing what they could do, but now you take that natural intelligence that people have always had, pushing that into some of those advanced tools, doing what they couldn't do before, and that's what's really exciting. >> So are you integrating now more with some of the newer tools that are hitting the marketplace, as opposed to just kind of, I assume you're way tied into ERP and some of those type systems. >> It's really cool because we're in this technology and market change around digital transformation is the buzz word, but we can take everything that we've done in the past and then overlay some of these new technologies that are coming from, you know, the giants of Google and Amazon. We can take advantage of a lot of those tools with the data we've collected for 30 years, that really drive outcomes. I think the important part of the outcomes is we're really reducing a lot of the resources that are scarce in the world. You know, water, power, carbon footprint. That are the outcomes that, you know, people are trying to reduce with the data and it's really impactful in the world today. >> And it's funny too, you know, start ups often begin because somebody sees inefficiency, whether it's car ownership then you have Uber or it's, you know, empty rooms in a city like San Francisco and you have Airbnb. But you guys and your customers specifically, there's all types of inefficiencies still in old line industries, old line systems, old line infrastructure, that you're helping ring out all kinds of efficiency out of things that some people aren't paying attention, probably thought was already done. But there's still a lot of opportunity. >> Oh absolutely, you see it all the time especially with the older industries. They've been operating for hundreds, some even older, number of years, and so when you think about normalizing failure, a lot of them have just kind of, well, assumed that well, we're always going to have 30% loss or well, we're always going to have a 10% inefficiency. But I think we're really challenging some of that paradigm by being able to look at information and seeing well wait a minute, no we don't have to have that 30% lead, wait no, we can improve our goal extraction efficiency just with this simple tweak in the process. So I think what's exciting with conferences like these is you realize that you can challenge what used to be possible with these new tools, using that tribal knowledge that people have always had. >> And I think what, again hitting on what Penny said, the power of this conference is, especially today, we have industry tracks. So all colleagues across an industry will get together, share their success stories, and that will help those success stories get out to other customers, really helping the overall industry. So today is critical as that industry day where they all get together, share their expertise, and the other one is I've always found it interesting, I grew up in life sciences, you know, pharmaceuticals, going to other industries, seeing what they're focused on, you can learn from them and bring it back to your industry. So the idea doesn't have to generate in your industry. It could generate somewhere else, and you can bring it back and that's what this conference really helps our customers do, share those success stories. >> I can't help but think of a bourbon or scotch commercial where they talk about you know, the angels share. When they take it out of the barrel after so many years, there's some percentage, which is kind of cute and quaint for a commercial, not necessarily if it's a big municipal water district. Somebody said in the hall, some of these big ones are losing as much as 50% of the water leaks out of the system. That's crazy. So this is the type of tool, what, or how, do they use it? So they're just, you're looking for inconsistencies in the data, is it just kind of classic pattern recognition? How are you helping the people find these inefficiencies so they can bring new solutions? >> Yeah, it's a little bit of both. Some of it is just surfacing that data. It's almost like I said, if you never even knew on your car dashboard that your oil was looking low, you wouldn't even know to go in and service it. So level one is just surfacing that information. I would say that's going from zero to 50. But if you go from 50 to 100, you talked about whiskey, I'm a beer fan, so we've got customers like Deschutes, who were going through and they were trying to figure out when the fermentation was done. They just have to go around and pipette when it was completed. The problem was you get your rounds maybe once an hour maybe fewer, less than that and by the time you get back around to that batch, you could have lost long passed that fermentation point where that beer needed moved on to the next process. It could mean either bad beer or it could mean that you reduce the amount of throughput that you could have. So they've used the data that they were collecting from the PI system, trained their models, be able to predict when that fermentation was going to be complete, and know exactly when they should be moving over to their next batch. >> Right, and I'll share one from my knowledge that I worked on from pharmaceuticals where, just creating a new drug, there's lots of iterative processes that goes through that. We monitor that manufacturing process to give that data to the process engineers so as that iterative processes, they know exactly what they're building is according to how they filed to their regulatory companies. So that's all great and they use the PI system to do that and they've been doing that for 20 years. This one particular drug that this manufacturer was making, they wanted to go into a new market. And that new market was they had to provide enough yield product to the whole population. And they couldn't make enough. So then they took and applied Big Data Analytics and they found a process problem that they could optimize which allowed them to get enough product to go into this new population. So it's really, like Penny said, from zero to 100, just getting the data unlocked and providing it to these companies it's valuable right now. So we believe the PI system once you install it brings value to those customers and then you can overlay projects over time and really drive the value up over time. So like you said, a customer that's had PI for 30 years is still going through optimizations. They're still bringing value to their company through those optimization techniques. >> I'm curious, how many of these kind of opportunities for say the individual you just mentioned, did they know or there they just couldn't they just couldn't put a data point on it, they couldn't put their finger on it? Versus how many of them are oh my gosh I had no idea this complete green field opportunity for efficiency that we never even thought of. How does that kind of break down? >> I definitely think it's 50/50, it varies by customer. You'll see a lot of customers that start off with a very known problem. So let's say they know they've been having challenges with transformer failures, right? So they go in, they look at the data, they can find a signature, and they deploy it. But then the next group comes along and say oh hey wait I could use that data, too. I could use that to prevent parallel cycles so we could improve the efficiency of that conversion. And it becomes almost more this culture to say well wait a minute, if I could do that, actually you know we're collecting information from our security substations. We could compare logs of who's entering against who's supposed to be in there. So I say that first one tends to be very directed, but then it becomes contagious and people realize that, what else could I doing? >> And you really see it just spread through so at our conference last year in London, a water company deployed the PI system to basically manage how the water was flowing throughout their utility. Once they finished that project, the customer was so happy he goes, why am I not utilizing this to monitor my network? So a secondary project that he did not have funding for he deployed it to monitor his network the same way he's monitoring water flow throughout his complex and he was able to say wow, I love it as a network monitoring tool. It really speaks to the approach that we take which is this infrastructure approach. We focus on moving the data and marry that with our customers' creativity to use that data for things we never even thought they could do. So it's this infrastructure approach where we take care of the data flow and then marry our customers on top of that where they just light up that creativity. >> So speak a little bit about the opportunity and the challenge that now all this stuff's going to be connected. It's all going to be IP based. We're going to have PHI-g coming out over the next couple of years so the speed and the quantity of the data that's now available. So huge opportunity for you guys but obviously a huge change in the marketplace as well, where you've been dealing with, I assume, a whole lot of proprietary and you know, individual systems for all these sensors that weren't necessarily built to IP protocol. So great opportunity, got to be a little scary as well, I imagine. >> Oh absolutely and you see definitely industries that are on different parts of that spectrum. So let's say you think about shipping. When that ship is out at sea, they've got maybe satellite and that's it. But the people on shore still want to be able to monitor, right? So you have to get very diligent about what pieces of information you're going to send over while you're in that constraint of being out at sea. Now once you come into port, no problem, hook right up, and you can do that full dump and come back out. So I think what we're going to see in the next five, 10 years is a very deliberate selection of what we send and what we decide to move on with. >> I'll add on top of this is our CEO and founder, Patrick Kennedy, has very much kept us focused on this data infrastructure approach. And the reason why I bring that up is we're always looking several years out. In order to provide this robust infrastructure, we're constantly looking at the market and technology and trying to project where out customers are going to be so that we can provide them the tools. So right now, absolutely. We see lots of challenges, or maybe opportunities, coming into the market. >> Same coin, right? Same coin, different sides of the same coin. >> Yeah, as everybody connects, let's say cyber security has got to be forefront in everybody's mind, right? How do we secure all this data so that our customers can really trust that their IP is being protected? One, data ownership, right? So that's another one that's coming out is as everybody shares this data, right, sometimes companies buy companies. Who owns that data? So data ownership is going to be critical and these are the things that internally we are already trying to, you know, build solutions for because of our singular focus on this data infrastructure around the PI system. So it's really that approach of our job is to collect this data and share it with everybody. It's fantastic. Me and Penny often say, there's no better time to be in the operation space with all this new technology and also the disruption in a lot of the business models that these companies are going through, right? Deregulation, a lot of the things that are happening in business are directly related to a need for data and really driving value from that data. >> Well it's just so interesting, we cover a lot of big tech shows and everyone's so excited for the marriage of IT and OT and you know, we've covered GE. We've covered Ford, so we've covered some of the, more of the industrial side as well but it's just funny that you guys have been kind of silently doing your thing for years and years. But I would imagine the opportunities now to integrate with, I see the SAP, as a gold sponsor and some of the classic big IT companies love to get connected with you guys and have you feed all their analytic system and all this stuff they're working on as well. 'Cause it is a marriage of these two systems which is so important. >> Oh, absolutely and I mean you think about how dirty a lot of this sensor data is, right? It's coming raw, it's real time. There are no do-overs. There's communication gaps and so how do you prepare that, cleanse it, because I think a lot of times the operational environment, you think about dusty, dirty. It kind of matches the type of data, right? And you think of IP systems and they're nice, clean, temperature controlled server rooms so somehow, you're going from this really dusty, dirty data to something that needs to be able to be brought into it a very sanitized environment. So a lot of what we've been focusing on is around being able to clean that data and massage it, take the gaps out. That's where the PI integrators have worked out really well, I mean we have customers that have been able to get value out of these big data projects six months faster than what they would have done otherwise. And it's really then when the data scientists pick up. Picking up at a point that now they're doing the stuff you paid them to do, right? They're not cleaning, they're not doing the janitorial work, they're actually creating the models, training it, and helping drive forward. So I think it's an interesting dichotomy to see and I think IT folks are also starting to get excited because finally this dirty, dusty data is now becoming accessible to them and I've talked to a couple of folks that get really excited when they look at the PI system and they see how the PI system can help also reference all these other data sources they are dealing with. We can touch into ERP but we don't have to fully expose that. They look at the PI system as almost a data directory, that switchboard that allows people to come in, one-stop-shop, and get everything they need. For IT that means that they just have to manage that one point of entry, not the 10, 20 that they would otherwise be dealing with. >> Yeah, and if we look at it as let's put the customer at the front and center, right? They are trying to do something to drive value. We don't determine their partners or who they use or what technology they use, so we want to bring a rich infrastructure of partnerships to really go to the user, focus on the user, right? So whether or not that be SAP, Microsoft, Google, all these ones, whatever the customer wants to use, we want to light up. And that's really our partner strategy and it's again, us being the technology guy, I get excited because these partners are also bringing their expertise to the table. So some of the technology that they're working on we just love because we can apply it against the data. It really is this rich ecosphere where we're putting the customer at the center so they can drive a lot of this value. You can see my energy. >> Yeah, no it's a cool story and all the use cases, you know, are just fantastic. There's so, so many they're household names. They're doing really simple things in terms of being able to recognize the value you know reducing loss in the water system you know increasing efficiency in the gold output and it's all very discrete and easy to understand stuff. So exciting times and congratulations to you both. >> All right, thank you. >> Thank you. >> All right, so Chris and Penny, thanks for stopping by. I'm Jeff, you are watching theCUBE from OSIsoft in downtown San Francisco. Thanks for watching.