Jeff Rick here with the cube we're in downtown San Francisco at the OSI soft Thai world 2018 they've been doing it for over 15 years is about 3,000 people here from all types of industries using this software solution and the data that comes out of it to basically find in efficiencies it is about solving some [Music] we started in San Francisco in 1990 we had 68 of our closest friends and it's just been an amazing journey some new players have grabbed onto 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 whether it's human or physical asset 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 the world has changed for most of our energy companies because their business models are under attack and so they are forced to transform digital transformation and energy so we think obviously every nua Buhl's right is growing like crazy and and the wind turbines are all over all over the place what are some of the other ways that they're really kind of under fire you have changing of regulation that takes place so they need to accommodate that in very short notice but you also have a very interactive environment where it used to be one way we're now two way and now you have communication coming from all of its participants in the market we're using PI as a data hub or like a bus a data bus essentially so for them it's good because we're saying look we're gonna have this one point of you know point-to-point system instead of having all of these individuals we're gonna connect to one system which will be easier for them to manage and maintain and will instruct staff to go to PI to get the data so that's a selling point for IT right more secure that's more manageable you know cybersecurity is gonna be forefront everybody's mind right how do we secure all this data so that our customers can really trust that their IP is being protected as everybody shares this data right sometimes companies by 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 one of the beautiful things about this conferences we see our partners we see our customers we see hundreds and thousands of different technologies and applications built around this disinformation that hasn't changed customers are demanding specific types of energy you may have customers at what clean energy they may want the cheapest they may want hydro so that interaction real-time is the world that we are in right now information which initiative is not connected can now be connected you have now full visibility into your entire systems and you can actually be able to control things it's really in any environment right businesses are gonna get more benefits it's not about sensors it's not about data collection is about business benefits the bottom line right the ability to see it and get insights with it does it make sense to put something new just to get another two percent maybe not but what about if you can now predict not just real time a predict what's gonna 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 if it takes me more time to do the analysis in the spreadsheet right or a kind of paper write to impact the outcome of the batch of mine I do it but against modern analytics hey I can get the insight quickly and I can make a change to what I'm doing and I or prevent something from happening and now it's worth doing with the rise of intelligent machines and artificial intelligence as you know other machines gonna take over the world but really consistency ly we hear it's really humans making better decisions with data that's provided by the machines and systems we're just automating your process make it better so that you could do more cool or better things so that you've actually analyzed the data set of inputting data right so that you can actually solve problems versus spending all your time trying to you know identify the data and collect information 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 overlay some of these new technologies that are coming from you know the giants of you know Google and Amazon is these we could take advantage of a lot of those tools with the data we've collected for 30 years that really drive outcomes of course the energy efficiency of all the machines are getting better and better but at some point you know it needs to be optimized right and that's where the software components it removed it of the human-in-the-loop really to optimize that that heats distribution and remove one of the next things always the next thing and that's blockchain the exchange of value would in you know a blockchain network also makes the the monetization of data very possible we have you know some assumptions of where blockchain might make sense to us as a company but especially to our customers so this year we really want to validate some of those assumptions digital transformation is more cultural transformation you know we all have these cool gadgets and a lot of these we we use it in our daily lives but how we can use these effectively in the mining world things like in iPads wireless technology and bring that in as I mentioned before on the table of the operator so that they are empowered now right now other departments in the city one it Public Works is asking for the city manager's office so it's really picking up you know some good buzz right we're kind of working our way down discussion of smart cities Hawks we don't have to worry about it we got it right on day one it's updatable and we know that the right solution for one customer and the right data is not necessarily the right data for the next customer right so we're not going to make the assumptions that we have it all figured out we're just trying to design the solution so that it's flexible enough to allow customers to do whatever they need to do if you just show them a white paper it's hard for them to say right this is what I need right you see once you see a suit to say I don't like that's high I don't like that shirt but something close yeah but something like that it's one thing to have scale in a data center it's another thing to have scale across the globe and this is where PI excels the idea doesn't have to generate in your industry could generate somewhere else then you can bring it back and that's what this conference really helps are our customers do is share those successful right people have been doing pilots until now maybe or files up to now are actually they've actually stepped in and we're seeing real purchase orders for real production applications and is happening in every industry so much easier right to get those efficiencies versus rip and replace or leave the data where it is that you're into you involve you are watching the cube from OSI soft hi world 2018 in downtown San Francisco [Music]

>> [Announcer] From San Francisco, it's theCUBE covering OSIsoft PIWORLD 2018, brought to you by OSIsoft. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE, we're in downtown San Francisco at the OSIsoft PIWorld 2018. They've been doing it for like 28 years, it's amazing. We've never been here before, it's our first time and really these guys are all about OT, operational transactions. We talk about IoT and industrial IoT, they're doing it here. They're doing it for real and they've been doing it for decades so we're excited to have our next two guests. Tyler Duncan, he's a Technologist from Dell, Tyler, great to see you. >> Hi, thank you. >> He's joined by Ed Watson, the global account manager for channels for Osisoft. Or OSIsoft, excuse me. >> Glad to be here. Thanks, Jeff. >> I assume Dell's one of your accounts. >> Dell is one of my accounts as well as Nokia so-- >> Oh, very good. >> So there's a big nexus there. >> Yep, and we're looking forward to Dell Technology World next week, I think. >> Next week, yeah. >> I think it's the first Dell Technology not Dell EMC World with-- >> That's right. >> I don't know how many people are going to be there, 50,000 or something? >> There'll be a lot. >> There'll be a lot. (laughs) But that's all right, but we're here today... >> Yeah. >> And we're talking about industrial IoT and really what OSIsoft's been doing for a number of years, but what's interesting to me is from the IT side, we kind of look at industrial IoT as just kind of getting here and it's still kind of a new opportunity and looking at things like 5G and looking at things like IPE, ya know, all these sensors are now going to have IP connections on them. So, there's a whole new opportunity to marry the IT and the OT together. The nasty thing is we want to move it out of those clean pristine data centers and get it out to the edge of the nasty oil fields and the nasty wind turbine fields and crazy turbines and these things, so, Edge, what's special about the Edge? What are you guys doing to take care of the special things on the Edge? >> Well, a couple things, I think being out there in the nasty environments is where the money is. So, trying to collect data from the remote assets that really aren't connected right now. In terms of the Edge, you have a variety of small gateways that you can collect the data but what we see now is a move toward more compute at the Edge and that's where Dell comes in. >> Yeah, so I'm part of Dell's Extreme Scale and Structure Group, ESI, and specifically I'm part of our modular data center team. What that means is that for us we are helping to deploy compute out at the Edge and also at the core, but the challenges at the Edge is, you mentioned the kind of the dirty area, well, we can actually change that environment so that's it's not a dirty environment anymore. It's a different set of challenges. It may be more that it's remote, it's lights out, I don't have people there to maintain it, things like that, so it's not necessarily that it's dirty or ruggedized or that's it's high temperature or extreme environments, it just may be remote. >> Right, there's always this kind of balance in terms of, I assume it's all application specific as to what can you process there, what do you have to send back to process, there's always this nasty thing called latency and the speed of the light that just gets in the way all the time. So, how are you redesigning systems? How are you thinking about how much computing store do you put out on the Edge? How do you break up that you send back to central processing? How much do you have to keep? You know we all want to keep everything, it's probably a little bit more practical if you're keepin' it back in the data center versus you're tryin' to store it at the Edge. So how are you looking at some of these factors in designing these solutions? >> [Ed] Well, Jeff, those are good points. And where OSIsoft PI comes in, for the modular data center is to collect all the power cooling and IT data, aggregate it, send to the Cloud what needs to be sent to the Cloud, but enable Dell and their customers to make decisions right there on the Edge. So, if you're using modular data center or Telecom for cell towers or autonomous vehicles for AR VR, what we provide for Dell is a way to manage those modular data centers and when you're talking geographically dispersed modular data centers, it can be a real challenge. >> Yeah, and I think to add to that, there's, when we start lookin' at the Edge and the data that's there, I look at it as kind of two different purposes. There's one of why is that compute there in the first place. We're not defining that, we're just trying to enable our customers to be able to deploy compute however they need. Now when we start looking at our control system and the software monitoring analytics, absolutely. And what we are doing is we want to make sure that when we are capturing that data, we are capturing the right amount of data, but we're also creating the right tools and hooks in place in order to be able to update those data models as time goes on. >> [Jeff] Right. >> So, that we don't have worry about if we got it right on day one. It's updateable and we know that the right solution for one customer and the right data is not necessarily the right data for the next customer. >> [Jeff] Right. >> So we're not going to make the assumptions that we have it all figured out. We're just trying to design the solution so that it's flexible enough to allow customers to do whatever they need to do. >> I'm just curious in terms of, it's obviously important enough to give you guys your own name, Extreme Scale. What is Extreme Scale? 'Cause you said it isn't necessarily because it's dirty data and hardened and kind of environmentally. What makes an Extreme Scale opportunity for you that maybe some of your cohorts will bring you guys into an opportunity? >> Yeah so I think for the Extreme Scale part of it is, it is just doing the right engineering effort, provide the right solution for a customer. As opposed to something that is more of a product base that is bought off of dell.com. >> [Jeff] Okay. >> Everything we do is solution based and so it's listening to the customer, what their challenges are and trying to, again, provide that right solution. There are probably different levels of what's the right level of customization based off of how much that customer is buying. And sometimes that is adding things, sometimes it's taking things away, sometimes it's the remote location or sometimes it's a traditional data center. So our scrimpt scale infrastructure encompasses a lot of different verticals-- >> And are most of solutions that you develop kind of very customer specific or is there, you kind of come up with a solution that's more of an industry specific versus a customer specific? >> Yeah, we do, I would say everything we do is very customer specific. That's what our branch of Dell does. That said, as we start looking at more of the, what we're calling the Edge. I think ther6e are things that have to have a little more of a blend of that kind of product analysis, or that look from a product side. I'm no longer know that I'm deploying 40 megawatts in a particular location on the map, instead I'm deploying 10,000 locations all over the world and I need a solution that works in all of those. It has to be a little more product based in some of those, but still customized for our customers. >> And Jeff, we talked a little bit about scale. It's one thing to have scale in a data center. It's another thing to have scale across the globe. And, this is where PI excels, in that ability to manage that scale. >> Right, and then how exciting is it for you guys? You've been at it awhile, but it's not that long that we've had things like at Dupe and we've had things like Flink and we've had things like Spark, and kind of these new age applications for streaming data. But, you guys were extracting value from these systems and making course corrections 30 years ago. So how are some of these new technologies impacting your guys' ability to deliver value to your customers? >> Well I think the ecosystem itself is very good, because it allows customers to collect data in a way that they want to. Our ability to enable our customers to take data out of PI and put it into the Dupe, or put it into a data lake or an SAP HANA really adds significant value in today's ecosystem. >> It's pretty interesting, because I look around the room at all your sponsors, a lot of familiar names, a lot of new names as well, but in our world in the IT space that we cover, it's funny we've never been here before, we cover a lot of big shows like at Dell Technology World, so you guys have been doing your thing, has an ecosystem always been important for OSIsoft? It's very, very important for all the tech companies we cover, has it always been important for you? Or is it a relatively new development? >> I think it's always been important. I think it's more so now. No one company can do it all. We provide the data infrastructure and then allow our partners and clients to build solutions on top of it. And I think that's what sustains us through the years. >> Final thoughts on what's going on here today and over the last couple of days. Any surprises, hall chatter that you can share that you weren't expecting or really validates what's going on in this space. A lot of activity going on, I love all the signs over the building. This is the infrastructure that makes the rest of the world go whether it's power, transportation, what do we have behind us? Distribution, I mean it's really pretty phenomenal the industries you guys cover. >> Yeah and you know a lot of the sessions are videotaped so you can see Tyler from last year when he gave a presentation. This year Ebay, PayPal are giving presentations. And it's just a very exciting time in the data center industry. >> And I'll say on our side maybe not as much of a surprise, but also hearing the kind of the customer feedback on things that Dell and OSIsoft have partnered together and we work together on things like a Redfish connector in order to be able to, from an agnostic standpoint, be able to pull data from any server that's out there, regardless of brand, we're full support of that. But, to be able to do that in an automatic way that with their connector so that whenever I go and search for my range of IP addresses, it finds all the devices, brings all that data in, organizes it, and makes it ready for me to be able to use. That's a big thing and that's... They've been doing connectors for a while, but that's a new thing as far as being able to bring that and do that for servers. That, if I have 100,000 servers, I can't manually go get all those and bring them in. >> Right, right. >> So, being able to do that in an automatic way is a great enablement for the Edge. >> Yeah, it's a really refreshing kind of point of view. We usually look at it from the other side, from IT really starting to get together with the OT. Coming at it from the OT side where you have such an established customer base, such an established history and solution set and then again marrying that back to the IT and some of the newer things that are happening and that's exciting times. >> Yeah, absolutely. >> Yeah. >> Well thanks for spending a few minutes with us. And congratulations on the success of the show. >> Thank you. >> Thank you. >> Alright, he's Tyler, he's Ed, I'm Jeff. You're watching theCUBE from downtown San Francisco at OSIsoft PI WORLD 2018, thanks for watching. (light techno music)

>> Announcer: From San Francisco, it's theCUBE covering OSIsoft PIWORLD 2018, brought to you by OSIsoft. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE, we're in downtown San Francisco at the OSIsoft PIWorld 2018. They've been doing it for like 28 years, it's amazing. We've never been here before, it's our first time and really these guys are all about OT, operational transactions. We talk about IoT and industrial IoT, they're doing it here. They're doing it for real and they've been doing it for decades so we're excited to have our next two guests. Tyler Duncan, he's a Technologist from Dell, Tyler, great to see you. >> Hi, thank you. >> He's joined by Ed Watson, the global account manager for channels for Osisoft. Or OSIsoft, excuse me. >> Glad to be here. Thanks, Jeff. >> I assume Dell's one of your accounts. >> Dell is one of my accounts as well as Nokia so-- >> Oh, very good. >> So there's a big nexus there. >> Yep, and we're looking forward to Dell Technology World next week, I think. >> Next week, yeah. >> I think it's the first Dell Technology not Dell EMC World with-- >> That's right. >> I don't know how many people are going to be there, 50,000 or something? >> There'll be a lot. >> There'll be a lot. (laughs) But that's all right, but we're here today... >> Yeah. >> And we're talking about industrial IoT and really what OSIsoft's been doing for a number of years, but what's interesting to me is from the IT side, we kind of look at industrial IoT as just kind of getting here and it's still kind of a new opportunity and looking at things like 5G and looking at things like IPE, ya know, all these sensors are now going to have IP connections on them. So, there's a whole new opportunity to marry the IT and the OT together. The nasty thing is we want to move it out of those clean pristine data centers and get it out to the edge of the nasty oil fields and the nasty wind turbine fields and crazy turbines and these things, so, Edge, what's special about the Edge? What are you guys doing to take care of the special things on the Edge? >> Well, a couple things, I think being out there in the nasty environments is where the money is. So, trying to collect data from the remote assets that really aren't connected right now. In terms of the Edge, you have a variety of small gateways that you can collect the data but what we see now is a move toward more compute at the Edge and that's where Dell comes in. >> Yeah, so I'm part of Dell's Extreme Scale and Structure Group, ESI, and specifically I'm part of our modular data center team. What that means is that for us we are helping to deploy compute out at the Edge and also at the core, but the challenges at the Edge is, you mentioned the kind of the dirty area, well, we can actually change that environment so that's it's not a dirty environment anymore. It's a different set of challenges. It may be more that it's remote, it's lights out, I don't have people there to maintain it, things like that, so it's not necessarily that it's dirty or ruggedized or that's it's high temperature or extreme environments, it just may be remote. >> Right, there's always this kind of balance in terms of, I assume it's all application specific as to what can you process there, what do you have to send back to process, there's always this nasty thing called latency and the speed of the light that just gets in the way all the time. So, how are you redesigning systems? How are you thinking about how much computing store do you put out on the Edge? How do you break up that you send back to central processing? How much do you have to keep? You know we all want to keep everything, it's probably a little bit more practical if you're keepin' it back in the data center versus you're tryin' to store it at the Edge. So how are you looking at some of these factors in designing these solutions? >> Ed: Well, Jeff, those are good points. And where OSIsoft PI comes in, for the modular data center is to collect all the power cooling and IT data, aggregate it, send to the Cloud what needs to be sent to the Cloud, but enable Dell and their customers to make decisions right there on the Edge. So, if you're using modular data center or Telecom for cell towers or autonomous vehicles for AR VR, what we provide for Dell is a way to manage those modular data centers and when you're talking geographically dispersed modular data centers, it can be a real challenge. >> Yeah, and I think to add to that, there's, when we start lookin' at the Edge and the data that's there, I look at it as kind of two different purposes. There's one of why is that compute there in the first place. We're not defining that, we're just trying to enable our customers to be able to deploy compute however they need. Now when we start looking at our control system and the software monitoring analytics, absolutely. And what we are doing is we want to make sure that when we are capturing that data, we are capturing the right amount of data, but we're also creating the right tools and hooks in place in order to be able to update those data models as time goes on. >> Jeff: Right. >> So, that we don't have worry about if we got it right on day one. It's updateable and we know that the right solution for one customer and the right data is not necessarily the right data for the next customer. >> Jeff: Right. >> So we're not going to make the assumptions that we have it all figured out. We're just trying to design the solution so that it's flexible enough to allow customers to do whatever they need to do. >> I'm just curious in terms of, it's obviously important enough to give you guys your own name, Extreme Scale. What is Extreme Scale? 'Cause you said it isn't necessarily because it's dirty data and hardened and kind of environmentally. What makes an Extreme Scale opportunity for you that maybe some of your cohorts will bring you guys into an opportunity? >> Yeah so I think for the Extreme Scale part of it is, it is just doing the right engineering effort, provide the right solution for a customer. As opposed to something that is more of a product base that is bought off of dell.com. >> Jeff: Okay. >> Everything we do is solution based and so it's listening to the customer, what their challenges are and trying to, again, provide that right solution. There are probably different levels of what's the right level of customization based off of how much that customer is buying. And sometimes that is adding things, sometimes it's taking things away, sometimes it's the remote location or sometimes it's a traditional data center. So our scrimpt scale infrastructure encompasses a lot of different verticals-- >> And are most of solutions that you develop kind of very customer specific or is there, you kind of come up with a solution that's more of an industry specific versus a customer specific? >> Yeah, we do, I would say everything we do is very customer specific. That's what our branch of Dell does. That said, as we start looking at more of the, what we're calling the Edge. I think ther6e are things that have to have a little more of a blend of that kind of product analysis, or that look from a product side. I'm no longer know that I'm deploying 40 megawatts in a particular location on the map, instead I'm deploying 10,000 locations all over the world and I need a solution that works in all of those. It has to be a little more product based in some of those, but still customized for our customers. >> And Jeff, we talked a little bit about scale. It's one thing to have scale in a data center. It's another thing to have scale across the globe. And, this is where PI excels, in that ability to manage that scale. >> Right, and then how exciting is it for you guys? You've been at it awhile, but it's not that long that we've had things like at Dupe and we've had things like Flink and we've had things like Spark, and kind of these new age applications for streaming data. But, you guys were extracting value from these systems and making course corrections 30 years ago. So how are some of these new technologies impacting your guys' ability to deliver value to your customers? >> Well I think the ecosystem itself is very good, because it allows customers to collect data in a way that they want to. Our ability to enable our customers to take data out of PI and put it into the Dupe, or put it into a data lake or an SAP HANA really adds significant value in today's ecosystem. >> It's pretty interesting, because I look around the room at all your sponsors, a lot of familiar names, a lot of new names as well, but in our world in the IT space that we cover, it's funny we've never been here before, we cover a lot of big shows like at Dell Technology World, so you guys have been doing your thing, has an ecosystem always been important for OSIsoft? It's very, very important for all the tech companies we cover, has it always been important for you? Or is it a relatively new development? >> I think it's always been important. I think it's more so now. No one company can do it all. We provide the data infrastructure and then allow our partners and clients to build solutions on top of it. And I think that's what sustains us through the years. >> Final thoughts on what's going on here today and over the last couple of days. Any surprises, hall chatter that you can share that you weren't expecting or really validates what's going on in this space. A lot of activity going on, I love all the signs over the building. This is the infrastructure that makes the rest of the world go whether it's power, transportation, what do we have behind us? Distribution, I mean it's really pretty phenomenal the industries you guys cover. >> Yeah and you know a lot of the sessions are videotaped so you can see Tyler from last year when he gave a presentation. This year Ebay, PayPal are giving presentations. And it's just a very exciting time in the data center industry. >> And I'll say on our side maybe not as much of a surprise, but also hearing the kind of the customer feedback on things that Dell and OSIsoft have partnered together and we work together on things like a Redfish connector in order to be able to, from an agnostic standpoint, be able to pull data from any server that's out there, regardless of brand, we're full support of that. But, to be able to do that in an automatic way that with their connector so that whenever I go and search for my range of IP addresses, it finds all the devices, brings all that data in, organizes it, and makes it ready for me to be able to use. That's a big thing and that's... They've been doing connectors for a while, but that's a new thing as far as being able to bring that and do that for servers. That, if I have 100,000 servers, I can't manually go get all those and bring them in. >> Right, right. >> So, being able to do that in an automatic way is a great enablement for the Edge. >> Yeah, it's a really refreshing kind of point of view. We usually look at it from the other side, from IT really starting to get together with the OT. Coming at it from the OT side where you have such an established customer base, such an established history and solution set and then again marrying that back to the IT and some of the newer things that are happening and that's exciting times. >> Yeah, absolutely. >> Yeah. >> Well thanks for spending a few minutes with us. And congratulations on the success of the show. >> Thank you. >> Thank you. >> Alright, he's Tyler, he's Ed, I'm Jeff. You're watching theCUBE from downtown San Francisco at OSIsoft PI WORLD 2018, thanks for watching. (light techno music)

>> Narrator: From San Francisco, it's theCUBE covering OSIsoft PI World 2018 brought to you by OSIsoft. >> Hey welcome back, everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at OSIsoft PI World 2018 getting to the end of the day, it's been a very busy day, a lot of great conversations and about 3,000 people here talking about the industrial Internet of Things and IoT and really process improvement using data. They've been at it for almost four decades and we're excited to have a practitioner. He's Tara Rana, he is the Digital Transformation Process Control in Systems Engineering for Barrick Gold. Tara, good to see you. >> Oh, nice to meet you as well. >> Absolutely. >> Thank you. >> So, little bit of basics on Barrick Gold, kind of who are you guys, what's your business? >> All right, so, Barrick Gold Corporation, it's the largest gold producer as of today in the world. And we have about thirteen operating sites across the world. We are headquartered in Toronto, Ontario, Canada. >> Jeff: Okay. >> We are hugely focused in the Americas. About 75% of our revenue comes from the Americas, so that's North America and South America, and then we have other projects and mining operations across the world, to Australia, Chile, Zambia in Africa, Saudi Arabia, so it's global. >> So you are, you're basically getting the gold out of the dirt. >> Tara: From the rocks. >> From the rocks. >> Yeah. >> And it's pretty interesting right, we always think, we're here in San Francisco, right, in 1849 is when it all started, there was a guy with a pan, >> Tara: Oh yeah. (laughs) >> But that's not how it works anymore, right? >> Tara: No. >> Now it's a big industrial process that starts with lots of truckloads of ore, and then at the end of many many steps, out comes the gold. >> Tara: Yeah. >> And we've heard a number of times that there's so many process improvements that basically can increase the percentage of gold that you can extract out of that ore. >> So and to that note, there are a couple of things that we're actually looking at. So not only that but also as we're moving into the future, the gold grades from the ore is diminishing. And that's where I think we're at the right place, because we are looking at technology, we are looking at the buzzwords, like "artificial intelligence" to help us in that phase because all the good grades are almost gone, so to get that little gold that's in a big mass of rock, we definitely need to look at technologies. >> So the grade is the percentage of gold per unit of ore, right? Because the gold itself is the same gold, once you get it out. >> Correct, it's the ounce of gold in that mass of rock. >> So gold mining's been going on for a long time. What are some of the opportunities for you guys to use software to basically get your yield up? >> Okay, so there are a couple of things where we can look at technology. So number one is safety. So as the gold grade is going down, which also means we are actually going deeper in the mine, so as we go deeper in the mine, that means it's becoming unsafe for people operating underground. So we're looking at technology, we are looking at things like autonomous vehicles, artificial intelligence algorithms that can help us in exploration, and then other things like robotics, drones, all kinds of stuff. So, the technology space is huge for us to explore, to use. And then to go to safety, of course we're looking at reducing our operating costs, increasing productivity as much as we can, and hence, lower our AISC, which is the All-In Sustaining Cost. >> So the autonomous vehicles is an interesting one. I don't think most people are aware how many autonomous vehicles operate in mines. I don't know if it's gold mines specifically, but I think we've talked to Caterpillar before, and there's a lot of autonomous vehicles running around mining operations. >> That's the future definitely, so right now we are actually taking a couple of projects to run these autonomous mines. But yes, you're right, it's not only the gold industry, but across mining and metals industry. >> Right, and what is digital transformation in mining? 'Cause we think of big lumpy assets that are made out of rocks and steel and rubber, and you know, heavy heavy industry, heavy heavy machinery. So what does digital transformation look like in the gold industry? >> So, again, this is very interesting and also dangerous. Why I say that, because... I'll tackle the dangerous piece first. Because digital transformation is again a buzzword, we have gone through different ones in the past. What we are targeting to do through digital transformation is not new. We have attempted to do this in the past with some degree of success, but as you know, the mining industry's a very cyclical industry. So when we were in the peak of the cycle, we invested a lot of money, we did a lot of cool projects, but as soon as we moved into the downward cycle, the budgets were tighter, so some of those projects were taken off the table. But now what's happening is, we are taking it back, but we're looking at this as an enabler. What that means is we are democratizing the digital transformation laterally and vertically, which means, within the site, and also across the organization. So we are educating our operators, we are educating the metallurgists and all that, because digital transformation is more cultural transformation. You know, we all have these cool gadgets and a lot of these we use in our daily lives. But how we can use these effectively in the mining world, how we can use things like iPads, wireless technology, and bring that information, as I mentioned to you before, on the table of the operators so that they are empowered now to make decisions rather than waiting forever for their frontline supervisors to give them that information. So now with the use of digital transformation as an enabler we're hoping that A, we are making it safer, we are democratizing this, as well as making decisions faster efficient. >> So it's pretty interesting on the democratization. 'Cause we see that in a lot of industries. So basically, giving the power, the tools, and the data to a broader group of people so they can make better decisions on the line. >> Correct. >> That's really the operator side. But you said something interesting, too, before we turned the cameras on, about transparency, not only at the site, but across the company, so that more people have more visibility into more pieces of the puzzle. >> Tara: Correct. >> So how's that been going? >> It has been going great so far. So what I meant by that was that the communities that we operate in, so Nevada in the States, Veladero, San Juan community in Argentina, communities like that... So now with the help of digital transformation we can also take this information to the community. Now they're more excited about what we're doing rather than being skeptical about us not sharing with them. >> Jeff: Right. >> So I think that is going great. The other aspect I should bring out is environmental. Environmental is a big piece. So, safety, health, and environment, we live by that because that's our license to operate. So with the help of digital transformation, and by sharing this information with our communities, I think we can reach our goal and bring everybody on board along this journey. >> Right, and I would imagine that ties directly back into trust. >> Correct, yeah. >> With the transparency, which I'm sure can be a big point of friction if you don't have that transparency. >> Tara: Absolutely. >> Especially on the environmental side, yeah. >> Tara: Yep, yeah. >> So what are you here for, what are you finding here at PI World? >> Okay, so I don't think I mentioned this, but along this journey, we are also looking for strategic partners. Because we cannot do this all by ourselves, right? And that was one of the reasons why digital transformation failed before, is we created silos, we didn't want to collaborate, we wanted to keep all the information within ourselves, and we were not sharing the information, not only publicly, but also within the organization. So what my role here in this conference is to share with all our peers in the industry what we have been doing, and also learn from others what they have been doing so that we can collaborate and make mining industry in general a very lucrative industry for everybody and make it safer and productive. >> So I would imagine there's probably a lot of sensitivity in sharing some of the operating processes, and I would imagine there's some proprietary technology in the way that you get your yield out of the ore. At the same time I would imagine safety and environmental can only benefit the industry if you share that information. >> Yes, absolutely. >> I would imagine that's not what you're going to build your competitive advantage on. >> No. >> And there's really more of an opportunity for industry sharing, if you will. >> Correct, so the point about... Sharing information about production. Yes, that is definitely sensitive, but I think what we are interested in sharing is the concepts, you know how we can do this digital transformation together, rather than the numbers that we're looking at. We're looking at percentage improvement. So even if I can share what we are doing with my peers in the industry in general, and if they are benefited, I think that's great. >> Jeff: Yeah... >> For the mining industry in general. >> Is the industry more receptive to that sharing than it has been in the past? >> Definitely there is more sharing now. But of course there are still some hurdles, and I'm hoping that attending conferences like this will make those hurdles smaller and smaller and we can do better. >> All right, well, Tara, thanks for taking a few minutes and sharing your story, and wish you obviously a lot of success on the safety and getting gold cheaper so we can all buy our wives bigger necklaces for Mother's Day, it's coming up, right? (laughs) >> Sure, absolutely, yeah. Thank you very much, and it's my pleasure to share, and let's enjoy the rest of the conference. >> Well, thanks a lot. He's Tara, I'm Jeff, you're watching theCUBE from OSIsoft PI World 2018 San Francisco, thanks for watching. (mellow techno music)

>> Announcer: From San Francisco, it's theCUBE, covering OSIsoft PI World, 2018. Brought to you by OSIsoft. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the OSIsoft show, it's called PI World. It's been going on for over 15 years. We've never been here before, we're excited to be here. Really is coming at it from the operations point of view, and they've been worrying about operations and operations efficiency for years. There's people walking around with 15-year pins, which is pretty amazing. I got my first one-year pin, so that's good. So we're excited to be here and dive into the details, because we've talked about IoT and industrial IoT, and kind of coming at it from the IT side, but these guys have been working at it from the OT side for years and years and years, almost 40 years. So our first guest is joining us. He's Remi Duquette, the Global Head - Applied AI and Datacenter at Clarity Lifecycle, it's a mouthful, for Maya Heat Transfer Technologies. Remi, nice to meet you. >> Very nice meeting you, thank you for having me. >> So, give us a little bit more detail on what Maya Heat Transfer is all about, and then we'll dive into some of the specific stuff you're working on. >> So Maya Heat Transfer started about 28 years ago in the simulation of heat and getting rid of all that heat that's being emitted by a lot of data centers, all the servers and the density that's occurring these days. And we've involved into developing a software solution, leveraging the PI infrastructure for real-time monitoring, and extended it beyond, for forecasting and doing all sorts of advanced analytics from that data. >> Right, so heat is the historical enemy of electronics, and has been forever. >> Yes, continuing to be so, for sure. >> And continuing to be so, and the data centers, you know, it's an interesting evolution in the data center space, because on one hand, they're consolidating data centers, or shutting down data centers, you've got this public cloud phenomenon. On the other hand, it's density, density, density, density, density, which probably is good opportunity for you guys. >> A great opportunity. Unfortunately, you know, the problems kind of are accentuated by exactly those phenomenon of consolidation, and the cloud, and the virtualization projects that are going on. So all of that combined, makes for a really big cocktail of heat and that heat needs to be dissipated somehow. And of course, the energy efficiency of all the machines are getting better and better, but at some point, it needs to be optimized, and that's where the software component, to remove the human in the loop, really to optimize that heat distribution and removal. >> So one of the big themes here at this show is finding inefficiency. This kind of continual quest for better efficiency and using data, and big data specifically, and sensor data, to be able to get that, find the inefficiency and act on the inefficiency. So what are some of the things that you guys look at? You've been at it for a long time, but there's still a lot more opportunities to find inefficiencies. Where are you still finding inefficiencies? >> Well, I mean, the main aspect is we have a lot of building automation systems and cooling loop systems, that have been programed to try and get to the best situation in any circumstances. And, really, when you look at what we're doing now, is applying artificial intelligence to augment the abilities of those systems, to better control and get to even a better place from an energy efficiency perspective. So that's really the latest evolution, to use that big data, to learn from that data, and then further optimize your cooling environment and your heat distribution. >> Right, now I'm curious what kind of new learnings came out of kind of the hyperscale players. Obviously, big public cloud players, Amazon and Azure, Google Cloud, have giant data centers, not only for their own core businesses, but now they're building them out as public clouds. Much bigger scale than the traditional corporate data centers. They're just operating at a whole different level. >> A whole new, yeah (laughs). >> So what are some of the things that have come out of those experiences that are different than the world pre-public cloud? >> Well, if you look at the pre-public, private cloud and public cloud, you had maybe, on average, five to six kilowatt per rack in a data center, was the average power consumed by those racks. Now we're looking, you know, some of our clients have up to 50 kilowatt per rack and now you need water-cooled elements into that rack, or other cooling elements that are being, helping the situation, 'cause those kinds of densities are producing a huge amount of heat, and that's really a big concern and a big shift from the enterprise level data center that was a little bit less of a consumer of that power. >> Right, now do you guys do anything outside of the data center? I know that's your area of specialties, but we've been doing a lot of autonomous vehicle shows, and one of the things that comes over and over and over is kind of the harsh environment for compute in a car or a truck or a bus or whatever. It's not a beautifully controlled with a lot of great backup power and diesel and air conditioning. Very rough environment. So what are some of the applications that you guys can use to help get that compute power in these vehicles? >> Well, actually the evolution for us more on the software side, was to apply our deep learning, artificial intelligence components and agents to other industries. So we're leveraging the forecasting capabilities of these deep learning agents to apply to other areas. So discrete manufacturing was one example, fleet optimization, so to go back to those edge devices, so we do a lot of fleet optimization, fuel optimization on these components. And that's completely outside the data center, but it's based on the same type of deep learning technologies that we've developed for the data center. >> And all the forecasts are, as more and more the compute and the store moves out to the edge, and you've got all the industrial devices running around in the centers, it's not new news for the group at this organization, >> No, clearly (chuckles). >> But you know, you're kind of shifting that load of the heat management from the data center out to the edge. >> To the edge, correct. So it does relieve a little bit of the, let's call it the pressure, inside the data center, but at the end of the day, the density of those cloud providers is just being accentuated by the sheer number of devices. So we thought there might be a shift towards the edge from a power, let's say a removal from the core data center, but in the end, it's actually the opposite that's happening. The power is really getting denser and denser inside the data center itself. >> So, last question before I let you go. What's your take on the vibe of the show, what's happening here at PI World? It's amazing, the international flavor as I'm walking around the halls. I'm seeing badges and hearing all kinds of languages. I mean, this is pretty hard-core, industrial internet happening right here. >> Oh yeah, I mean the operational technologies and the various applications and industries in which PI is used and leveraged worldwide is phenomenal. And it's a very vibrant show. It's actually quite good, when it comes down to it, a lot of people, the exchange between the end users together from different industries share their tips and tricks on how they've deployed, their various stories are just amazing. So a great, great, great PI World conference for sure. >> All right, good. Well thank you for taking a few minutes and sitting down and sharing the Maya story with us. >> Thank you for having me. >> Absolutely. All right, he's Remi, I'm Jeff. We are at OSIsoft PI World 2018 in downtown San Francisco, we'll be right back, thanks for watching. (electronic music)

>> 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.

>> 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)

>> Announcer: From San Francisco, it's theCUBE. Covering OSIsoft PI World 2018. Brought to you by OSIsoft. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at the OSIsoft PI World 2018. About 3,000 executives here. Downtown San Francisco. Talking about operational technology. We talk a lot about IT on theCUBE, and the merger of IT and OT, but these guys are really coming at it from an OT point of view first. They've been at it for 40 years. So we're excited to be here, talk to some of the partners, practitioners, and really get insight as to what's going on in this industrial IT because a lot of it's happening here. And our next guest is Todd Nate, he's a global VP of energy programs for Nokia. Todd, great to see you. >> Thank you very much, Jeff. >> Jeff: And we're also joined by Michael Kanellos, the IoT analyst from OSIsoft. Michael, great to see you. >> Oh, thank you very much. >> Absolutely. So, first off, Nokia. We all know Nokia phones. We all had the flip. It's still everybody's favorite. (laughing) What are you doing in the energy business? >> Well, believe it or not, we have been in the energy business for many, many decades. And we work on the OT side of the house, in the mission critical environment. Which is why we're not often seen, but very mission critical. We work across both energy companies that are in the mining oil gas, as well as the electric utilities sector, encompassing distributed energy resources, generation, transmission, distribution, and the like. >> Right. So I know what GE does in those spaces. They make the turbines, they make the trains. What do you guys do? Where do you play in that ecosystem? >> We provide the pervasive connectivity for all of the mission critical communications that allow them to run efficiently. Today, the world has changed for most of our energy companies because their business models are under attack. And so they are forced to transform. And what we do is we're allowing them the ability to have a technology platform off which they can pivot, not only to be able to respond to the threats to the market, but also the opportunities in a very quick fashion. >> Right. So it's interesting. Digital transformation and energy. So we think obviously of renewables, right, is growing like crazy and the wind turbines are all over the place. What are some of the other ways that they're really kind of under fire? Is it, you know, emission standards that are getting tougher? What are some of the things that they're telling you they need help with? >> Yeah, well you mentioned regulation. So obviously regulation has gone up. You have changing of regulation that takes place so they need to accommodate that in very short notice. But you also have a very interactive environment. Where it used to be one way, we're now two way. And now you have communication coming from all of its participants in the market. So these participants are not only their customers, these participants are also third parties that are now come to play in their market, which used to be a captive market. >> Jeff: Right. >> So for them, it is an environment that is two way and a large volume of data and information transacting. And they need to be able to make sense of that data and be able to act on that data. And what we do is we provide that pervasive connectivity so that they can have that communication. >> And they're also looking for new revenue and business models. If you think of utility, everyone's getting more efficient. So actually the sale of electronics is actually going down in a lot of areas, but they just can't go to the next city over. So doing things like doing consulting services or doing like even selling their own software. To just new days to develop, you know, take that know-how they have, and see if they can get revenue that way. >> Right. So we talk, we do a IT shows, not as many OT shows, and, obviously, cloud data centers is a big topic. And we've all seen pictures of the beautiful colored pipes inside of a Google data center or an Amazon data center that they share every now and then. It's not quite the same in the industrial IoT space, right? These machines, you talk about environment. It's like literally environment. It's rain and storms and hail, bad weather, no connectivity. So I imagine you guys, I think you said before we turned on the cameras, you guys are offering private LTE and all sorts of solutions to help get that connectivity out of the data center and really out to the edge and these big devices on the edge, like locomotives and turbines. >> Yeah, absolutely. Yeah, so what we've found is there's been a confluence of many things on the market. We've seen the price of technology has gone down significantly. You have a scenario where the cost of technology and the feature functionality, so the cost of technology has gone down, but the feature functionality has gone up. And we see a disruption in the market with regard to how their business models for our customers are coming to play. So they're adding and subtracting assets. The key right now for our customers is they got to get a volume of data. They got to get the volume of data in so they can process it. We're involved not only in the cores of the network, but on the edge with machine edge computing. We have the visualization of data that has become very much important to our customers so they can make decisions. This is not only with regard to current assets, but then you also have your DER assets that can take many forms. Those DER assets can be around >> Jeff: What's a DER asset? >> A DER asset: wind, pv, you know, solar. >> Michael: Distributing energy resources. >> Jeff: Okay, okay. >> Storage, it can be a micronuclear facility. It can be a combined heat and cycle plant, for example. Gas plant. >> Right. >> So these are more distributed and they are more voluminous and they need to be able to communicate with those entities and those assets, not only for their health, but also to be able to manage the grid for which they're responsible. >> Right. So really interesting things. We've heard a number of times, as we always hear, and more today, about preventative maintenance. It's still unplanned downtime is still a big giant issue and still costs more, probably costs more than it ever has because of the efficiency. You lose something, don't quite have as much backup and redundancy as you used to have back in the day. So it's amazing that that's still such a big business use case to get out ahead of the curve on these assets. >> Wind is like the poster child for that. If you think about a big wind turbine. There's like thousands of moving parts inside there, right? Any of those could break. >> Jeff: Right. >> And if they do break, you have to sometimes take the entire turbine down. >> Jeff: Right. >> Take it back to the shop and bring it back up. So you do it well enough, if you can do it in advance >> Jeff: Right. >> Without doing major heart surgery on it, that's fantastic. >> The other thing is that it's just adding more sophistication into the generation and to the consumption based on the broader demand so that you can take advantage of cheaper rates at night or, you know, pump back into the grid when the rates are high, so. It's the amount of technology out to the edge to start to control these devices to pump that energy back into the grid. It's got to have changed significantly over the last couple years. >> Yeah, absolutely, and it's disrupting, it's not only disrupting the energy companies themselves, it's disrupting the client. Because you got to remember the energy companies, they don't want to be caught without enough power >> Jeff: Right. >> So they have to buy that power. They have to manage many more varied assets. Again it's two way. And then you also have the customer experience, where customers are demanding specific types of energy. So you may have customers that want clean energy. They may want the cheapest. They may want hydro. So that interaction real-time, is the world that we are in right now. >> Jeff: Right. >> It's not a future world. It's the world that we're in right now. So the retention of my customers as an energy company, very, very important because they're the ones that pay the bill. >> Jeff: Yeah. >> We, that environment, is where we are living today. Highly interactive environment, highly autonomous environment, and providing that connectivity, the pervasive connectivity, to enable that, whether it's machine to machine, whether it is client to customer, and vice versa, it's really an any to any environment, and that's what we set up. >> Jeff: Right. >> It's costing, to just add on that, like to energy storage, a battery system. If you have a lot of data, you can actually install a smaller battery system and then take little tinier sips so it actually lasts longer. >> Jeff: Right. >> So actually your return on investment, things could go massively way up if you're actually using the data correctly. >> Well and the funny thing, when you said clean energy, I thought you meant clean like clean for the machines to be able to execute their operation well. (laughing) Like they get at a data center, which is very different than out on the edge in a field, you know, an energy field or on the edge of a turbine where you don't have all that control like you have in a beautiful pristine data center. So it's a very different world out on the edge. >> Absolutely. >> So, Todd, last question. What are you doing here at PI World? What's kind of the vibe? What are you hoping to accomplish? What are you, you know, what have you seen and heard in the hallways that has Nokia here at PI World? >> Yeah, PI World's very interesting for us. And the reason it is is because the conversation here is different. When we're dealing with clients here at PI World, it is about solving something, right? It is about the use cases, it is about their business, it's about their balance sheet. And less about what you manufacture or sell. And so these conversations are driving what it is we do. We are very much engaged with OSIsoft when it comes to the visualization of data. And in the enablement of our customers to be able to access their data, share their data, anywhere anytime to any asset, whether it's human or physical asset. In order for them to not only thrive in this market, but be able to adapt their business models. So for us, a very exciting and much appreciated. >> Yeah, it's great. Thank you. >> Alright, we'll Michael and Todd thanks for spending a few minutes with us, sharing the story. >> Thank you, Jeff, appreciate it. >> Alright, I'm Jeff Frick. We are at OSIsoft PI World 2018, downtown San Francisco. Thanks for watching. (upbeat music)

>> 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 in downtown San Francisco at the OSIsoft called PI World 2018. They've been doing it for over 15 years. There's about 3000 people here from all types of industries really, sharing best practices about using this software solution and the data that comes out of it to basically find inefficiencies. And we're excited to have for our next guest, he's Josh Raines, a senior metering engineer from MIT all the way in Boston, and Josh, I'm glad we could get you out of the snow. >> Great to be here. >> Jeff: Absolutely. So a little bit about what you do at MIT. >> I deal with the campus energy metering. MIT brings in electricity and natural gas and then makes electricity, chill water, and steam and then distributes that to the rest of the campus. I deal with all of the physical meters in the building, the (mumbles) acquisition hardware, as well as the PI system that historizes all of that information for ever and a day. >> And just for people, kind of general scope, how many buildings? What are some of the kind of top level numbers of all the systems that you guys are keeping track of, buildings, etc.? >> We've got about 120 buildings, I believe. At the moment, we aren't metering that many, however we are pushing out a lot more meters within the next three years to do exactly that, to really get a good solid grasp on exactly what every single building is using, every watt that goes through the wall, every BTU that goes through the wall. >> So it's interesting 'cause buildings are kind of a living organism. I think most people, if you're not in that business, you see the walls, you see the glass, you think it's pretty static. But there's actually a whole lot of stuff going on and I wonder if you can talk to some of the obvious inefficiencies and opportunities to make those buildings perform better, if perform is the right word, and maybe some of the less obvious ones that you've discovered using the PI software or just other ways that you've discovered opportunities. >> Calling a building a living entity is actually a really great example. We'll have buildings that will almost completely shut down between the hours of about 10 o'clock at night and maybe about six or seven o'clock in the morning, and then you can actually watch, and using the PI software is phenomenal for this, you can watch the building wake up in the morning and come alive. You can see, in the summer, the doors start to open, the internal temperatures start to rise as people come in and out, and the chill water usage go up as that air conditioning starts to kick on. In some of our newer buildings, we have done some predictive analysis on, in the building management side of things, so the air conditioning will actually come on about 30 minutes before people start to come into the building and try and pre-cool and get ready for that influx of heat as people start arriving. It helps maintain the overall temperature of the building and you don't get some of those big swings that would then propagate back to the central utility plant. This allows the central utility plant to even out their chillers, maybe bring on a larger chiller a little bit ahead of time and not have to then bring on two or three chillers in order just to deal with that surge of heat coming back in. >> Just curious, one of the really interesting topics that's happening all over right now, with the rise of intelligent machines and artificial intelligence as you know, are the machines going to take over the world, but really consistently we hear it's really humans making better decisions with data that's provided by the machines and the systems. So I wonder if you could share some examples of that where you've been able to take some data, find the pattern without some really crazy big data analytics or running all kinds of crazy analysis, but actually relatively straightforward trend lines or anomalies that really pop out of the data once you have the data presented in an easy way to consume? >> There are actually two scenarios that we had on campus within the last year that pop to mind real quick. One of them was in a building where we had simultaneous heating and cooling, we found. And we found that-- >> In the same building? >> In the exact same building. >> How big was the building? >> It's actually one of the central buildings on campus. I can't remember the square footage. >> Jeff: But like two stories, eight stories? >> Oh, at about four stories-- >> Jeff: Okay. >> With a large mechanical sub-basement to it. >> Jeff: Okay. >> And we actually found the simultaneous heating and cooling and were then able to track it back and find a three-way valve that was completely broken and allowing both hot and chilled water to flood into the coils at the exact same time. Just by finding that, fixing the valve, we were able to bring that under control and reduce the wasted energy going into that building. >> By how much? Orders of magnitude? 10%, 100%? >> I want to say it was five percent on that one. >> Okay. >> One of the larger improvements we made, we had a building that was returning chill water delta tap of somewhere in the 0.2 to 0.4 degree range. So we were supplying chill water at 42 degrees and getting it back at roughly 42 1/2 degrees. Ideally we're striving for a 12 degree differential, to actually pull the heat out of the building and bring it back to the plant. >> So it should be hotter water coming back-- >> Josh: Exactly. >> to the air conditioning. >> Once we found this, we realized that the control valve was not working in any way, shape, or form the way it was supposed to. It was basically stuck open. Once we were able to identify that, we were able to fix the valve, start controlling the building better, the savings actually necessitated ... The amount of chill water, gallon per minute basis, going into the building was roughly 1200 gallons a minute full flow, we dropped that down close to 150 gallons a minute. That necessitated almost shutting off a chill water pump at the plant. Estimated savings over the course of a single year, I believe were anywhere from $60,000 to $80,000. >> Wow. And what's interesting about that story, 'cause the actual valve itself that was broken, it had no censor on it, right? >> Josh: Correct. >> It was just a static old piece of equipment? >> Josh: Correct. >> But you were able to determine, based on the other data, to track it down? >> Yep, correct. >> That's a great story. It really ties to another factor which I'm sure, you already talked about kind of evening things out and we hear a lot now in the popular media about Tesla batteries, you stick them on the side of your house, and now you can kind of manage your consumption off the grid when it's cheaper, and you know, put it back on when it's expensive. It's not a single price that you pay for that kilowatt, right? >> That is correct. >> It is highly variable. So I wonder if you've really been able to take advantage there too to avoid some of that peak consumption pattern that's going to cost you a lot more than if you can even it out? >> Actually utilizing the PI data in the past was one of the pushes towards MIT creating, or revitalizing, their Cogen system and bringing in an entire new Cogen building increasing their existing electrical output from 25 megs up to a theoretical 40 megs in order to reduce how much we are pulling off the grid at any one time. >> Wow. So what's next? What's next? What are some other opportunities that you see that you can leverage these tools to go find some more inefficiency? >> One of the things, and actually one of the reasons that I'm here at this conference this year, is to work on a way to pull in high speed PMU data and be able to analyze that after an incident happens or as an incident is happening to determine where an electrical fault may be occurring, whether it's in our system or whether it's coming off the grid, and make determinations as to do we need to replace equipment? Do we need to go into island mode? And do we need to disconnect and just source all the power directly? Are there particular buildings that we need to isolate and figure out why are they performing so badly immediately? It could be a detrimental cost to the campus. >> So it's really interesting because you're finding all kinds of opportunities just to fix things versus, I would imagine at some point, somebody looked at a number and said, "This is completely inefficient," like your other building, "We need to overhaul the whole system." >> Yes, and we've got an entire system engineering group that is doing exactly that. They are taking data after the fact, they are analyzing it over the last year, two years, 10 years, and determining how the building was operating 10 years ago. We may have made a full building renovation. How is it operating now? Did we do better? If this building is almost equivalent in usage, in size, in location on campus, direction of where the sun is, and they renovated this building, but they haven't done this one yet, can we expect to see the same energy improvements on this other building or should we do this other building in order to get to the same energy profile? >> Right, really cool stuff. Josh, I really appreciate you taking a few minutes and stopping by. >> Happy to be here. >> Alright, he's Josh, I'm Jeff. You are watching the Cube from OSIsoft PI World 2018 in downtown San Francisco. Thanks for watching. (light techno music)

>> Announcer: From San Francisco, it's theCUBE! Covering OSIsoft PI World 2018. Brought to you by OSIsoft. >> Hey welcome back everybody Jeff Frick here with theCUBE. We're at OSIsoft's PI World 2018 in downtown San Francisco, they've been at it for decades and decades and decades talking really about OT and efficiency. And we're excited to be here it's our first time, and really want to talk to a customer, excited to have our next customer CJ Smith, She's a Project Manager for the city of Riverside CJ great to see you. >> Thank you, hi! >> So you represent a whole slew of mid-sized US cities, so how big is Riverside for people that aren't familiar? >> We serve 120,000 customers so we're not too small, but we're definitely not as big as some of the other cities. >> Right and then as we said before we turned on the cameras, you guys have a whole department for utilities, you have your own utility as well. >> Yes we do have a public utility division within the city, also an IT and public works, parks and recs like other cities as well. But we do have the utility, which is different than some of the stand along utilities, like LADWP for example. >> Right but it's good you were saying off camera that that gives you guys a nice revenue source, so it's a nice asset for the city to have. >> Yeah the utility is revenue generating department. >> Okay so what are you doing here at PI World, how are you guys using OSI software? >> So we started down PI back in August 2016, as an enterprise agreement customer, and at that time we really lacked visibility into our system so we needed something to help us gather the data and make sense of it, because we had data all over the place, and it was hard to answer simple questions it was hard to find simple data. And so we started down the PI journey at that time, and we basically used it like a data hub to aggregate data, turn that data into information, and then we disseminate it using dashboards. So PI Vision dashboards which used to be PI Coresight, as well as reports. >> So what were some of the early data sources that you leveraged, that you saw the biggest opportunity to get started, or yet even more importantly your earliest successes where'd your early success come from? >> So our very first work group that we worked with was our Water Operations and our Water SCADA team. >> Seems to be a pattern here a lot of water talk here at OSIsoft. >> Yeah I'll talk about electricity too. But we started on water and the first thing we did was implement their data, it was called a Water Operations dashboard, and they were doing it manually in Excel, and it would take a staff person over eight hours to do it. And they would do it the next day for the previous day data. So imagine how opposite of real time that is right? So we integrated that data with PI. >> And how many data elements? How big is the spreadsheet this poor person is working on? >> So the Water SCADA tags that we brought in were near 1500 tags, so you imagine that much data and calculations with over 1500 calculations behind it. So it was a ton of effort. >> Right. >> And a huge quick win for them! So it's saved staff time, they now have actual intelligence, real time data, the managers get alerts to their phones about the status of wells, and so it was really helpful to that work group. So that one was one of our first and earliest wins on PI. >> Was it a hard sell? To those people to use it? It wasn't because we did find a champion in that group, someone that would help us. Actually the manager he was very interested in technology and automation. And they understood that even though it would be a time investment up front, it would save them a ton of time in the long run, for the rest of the year. And so one of the things that helped us get buy-in early on is that we used an Agile approach. So we would tell the manager, I only need you for five weeks. I need you and your staff for five weeks, and then you don't have to talk to us anymore. We will deliver the product in five weeks, we will do all the work, but if you could give us five weeks of your time, then you could have all your time back the rest of the year. And that helped us get buy-in from the managers and a commitment, because they can identify with okay just five weeks. >> Right so those were probably the operational folks, what about on the IT folks how was getting buy-in from the IT folks? >> The funny thing is and the thing we did different is, we have a great relationship with IT, and we really forged a partnership with them early on, even from the very beginning when we were just reviewing the agreement. We got their buy-in early on to say okay, this is what we're thinking about doing, we want you to be part of the team, and we really built a partnership with this project so that it could be successful. So they work hand in hand with our PI implementation team every step of the way. They've been on this journey every step of the way with us. So we don't have some of the challenges that other companies that I hear are talking a lot about here with IT and it kind of being a bottleneck, we didn't have that same experience because we really worked hard up front to have the buy-in with them and really build a partnership with them, so that they're implementing PI with us. And another selling point with that is, we're using PI as a data hub or like a bus, a data bus essentially. So for them it's good because we're saying look we're only going to have this point to point system, instead of having all of these individual points we're only going to connect to one system, which will be easier for them to manage and maintain, and we'll instruct staff to go to PI to get the data. So that's a selling point for IT it's more secure, it's more manageable. >> And did you use an outside integrator, or did you guys do it all in house? >> Our implementation team is a combination of in house staff and a consulting firm as well. >> And then it's curious 'cause then you said once you add all the data it's kind of a data bus, how long did it take for somebody to figure out hmmm this is pretty cool maybe there's data set number two, data set number three, data set number four? >> So right after our first six week implementation, we rolled out a new implementation every four to six weeks. >> Every four to six weeks? >> Yeah so we did a sprint cycle the whole first year, and actually the whole second year we're currently in right now, and so we touched a different work group every single time, delivering a new solution to them. So we picked up a lot of traction so much that now, other departments in the city want it, public works is asking for it, the city manager's office so it's really picking up some good buzz, and we're kind of working our way down discussion of smart city talks, and seeing how PI can support smart city, big data advanced analytic initiatives at the city. >> So what are some of the favorite examples of efficiency gains, or savings that department A got that now department B sees and they want to get a piece of that what are some of your favorite success stories? >> I would say two of mine, I shared one on the big stage yesterday about the superpower I talked about our operations manager, who started receiving actionable intelligence overnight. And he got an alert around midnight, and he called his operator and said hey, what's going on with that well? And the operator said very puzzled, how do you know that there's something going on with this well? And he replied and said because I have superpowers. And so his superpower was PI, and that's one of my favorite stories because it's just simple and it resonates with people, because he is receiving alerts and push notifications that he never had before to his mobile device at home. So that's a huge win. >> Was the operator tied in to that same notification, or did that person know before the operator? >> The manager knew before the operator. So the operator didn't know about PI at the time and we had just rolled it out. And so the manager was just kind of testing it and adopting it, and so it was kind of like he had a leg up a little bit and they were confused like how do you know you're at home? >> Man: Right. >> He's like I have superpowers. (laughing) It's probably my funniest and best story, and one that I always tell because it helps everyone, no matter if it's an executive to a field person, really understand the power behind PI. I think another one if I had to pick another example of a win that I think was powerful is, our work order and field map. So we have our field crews right now that have a map, that's powered from our work order and asset management system pushing data to PI, which then pushes it to Esri through the PI integrator, and they're out using it in the field and it helps them route their work, they can see where their workers are, they can see customer information. And that map is really changing the way the field crews work. So imagine a day before this system where, they would go in and have to print every work order from the system. And not all asset management systems are really user friendly. They're kind of archaic a little clunky, so I won't say the name of our system. >> And doesn't work well if there's a change right? >> Yeah and they're not really mobile friendly. So that's part of the challenge, but because of that now public works wants that map, parks and rec every department that has field forces, they want something similar so that they can get all the data from all the other systems in one app in one location on their device. >> And do you find that's kind of a system pattern, where often department A needs very similar to what department B needed with just a slight twist? So it's pretty easy to make minor modifications to leverage work across a bunch of different departments? >> Absolutely a lot of work groups are similar, maybe a little different like you said, but especially those that have field forces. Sometimes it makes it easy to sell it to the next group, it's like look this is what we've done, is this something that you kind of need? Or what would you need differently? Like we've developed field collection tools. That's easy to replicate. Once you see it it's easy to say you know what that works but I need it to say this and I need it to say this. If you just show them a white paper, it's hard for them to say this is what I need. Most people just don't know, but it's easy once you see a suit to say oh I don't like that tie I don't like that shirt, I don't like those pants. >> But something close. >> Yeah but something like that right? So that's the benefit once you start having a solution to easily modify and reproduce. And then the good thing about Agile, you're running sprints so you're learning every sprint. You're kind of learning as you go, and you're able to refine it and refine it and make the process that much better. >> Right. On the superpower thing employee retention is a challenge, getting good people is a challenge, I'm just curious how that impacts the folks working for you, that now suddenly they do have this new tool that does allow them to do their job better, and it's not just talk it's actually real and gave that person a head up on the actual operation person sitting on the monitor devices. So as it proliferates what is the impact on morale, and are more people rising up to say hey, I want to use it for this I want to use it for that. >> Yeah we are getting a lot of interest, and I think the challenge is, and I talked about this a little bit during my session, is change management and culture. Some people see automation and technology as sometimes a threat because of job security, or the I've always done it this way type of mentality. >> Man: Never a good answer. >> Right but once you kind of get them to see that we're just automating your process to make it better so that you can do cooler and better things, so that you can actually analyze the data instead of inputting data. So you can actually solve problems versus spending all your time trying to identify the data and collect information. So staff are starting to see the value, and after the first year and a half, we've gotten a lot of traction. I don't really have to sell it as much, it's now such a huge part of our culture that the first question when we want to implement a new system is does that integrate with PI? I don't even have to ask them. Everyone else is asking well have you thought about using PI for that? So we always kind of look to PI first to say, can we create this solution in PI? And then if not we look at other solutions and if we're looking at other solutions we say, does that solution integrate with PI? So that's become part of our norm to make sure that it plays nice with what we're calling our foundational technology which is PI. >> Right so you talked a lot about departments. Is there kind of a cross-department city level play that you're rolling data and or dashboards into something that's a higher level than just the department level? >> Yeah so far the only thing that we have done that's kind of cross divisional not just in one division, is our overtime dashboards. So we recently created overtime dashboards throughout the entire city so that executive level department heads have visibility into overtime, which just gives them trends so that they can know what departments are receiving the most overtime? Is that overtime associated with what type of cause? Was it something outside of our control? Was it a planned overtime? And then most importantly where we're trending. Where are we on track to be by the end of the year, given our current rate so that they can be proactive in making changes. Do we need to do something different? Do we need to hire more people in this department? Do we have too many people in this department? Can we make shifts? So it's giving that level of visibility, and that's a new rollout that we just have completed, but it's something that we're already seeing a lot of interest in doing more of. Cross divisional things so that the city manager's office and that level has more view into the whole city. >> Right well CJ it sounds like you're doing a lot of fun stuff down at Riverside. >> Woman: We are we are! >> And you can never save enough water in California, so that's very valuable work. >> Woman: That's true! >> Well thanks for taking a minute and sharing your story, I really enjoyed it. >> Thank you for having me. >> Absolutely she's CJ Smith I'm Jeff Frick, you're watching theCUBE from OSIsoft PI World 2018 in San Francisco, thanks for watching. (upbeat music)

>> Announcer: From San Francisco, it's theCUBE covering OSIsoft PI World 2018, brought to you by OSIsoft. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at OSIsoft's PI World. It's been going on for 28 year, I think. I saw some 15 year pins. It's my first year pin, but I just heard that 28 years, 68 people. This year 3,000 people talking about the industrial internet, the internet of things, and it's happening here. A lot of places talk about it's coming, it's happening here. We're really excited to have two guests on from OSIsoft Richard Beeson. He's a CTO. Richard, great to see you. >> Yeah, thank you. >> And Michael Van Der Veeken, he's a senior developer. Welcome. So first off, impressions on this year's PI World compared to when you started out 28 years ago. >> Yeah, you said it. We started in San Francisco in 1990 at a small hotel down by Fisherman's Wharf, and we had 68 of our closest friends. And it's just been an amazing journey, an amazing journey to see the customer base just continue to appreciate the message, appreciate the value and the consistency that we've bene bring, and most recently just seeing this incredible explosion around the value of information in operations, in IoT and the time-space. >> It's funny because we usually cover it from the IT side and a lot of the IT players are excited now to be bringing IT and connecting it with OT and, in fact, I can show you very formal handshakes and exchanges of pleasantries around that. But you guys have been coming at it from the OT side for a very long time, before there was IP sensors on all these machines, before there was 5G, before there was saduke, before there was all these kind of enabling technologies for what people are talking about now for the industrial internet, but you guys have been doing it for a very long time with the existing infrastructure that was already in place at these places >> Yeah, it is kind of funny. Sometimes we'll say, hey, we've been doing this IoT or industrial IoT for the last 30 years. It's what process control engineers have been doing. You need to get the data from the sensors, from the operation to be able to control it. So the act of control, the act of optimization, the act of running a plant, of running any kind of operation requires that. >> Jeff: Right. >> The big shift has just been fundamentally in the scale, the cost point and just the general availability of that kind of information. It's really changing the game. >> Right. >> And a lot of the same principles still apply. And we've had experience here for 30 years now. And with the whole IoT boom, a lot of the same principles still apply to streaming data, to real-time data, and the PI system is able to support that. >> Right, but it's interesting because now you have a whole new level of computer horsepower that you did have many years ago. You've have a whole new level of networking speed which is even going to go up again with 5G on the mobile side shortly which is going to give massive amounts of more data, and the, of course, to store and everything else just gets cheaper, cheaper and cheaper so you're kind of enabling technologies under the cover or probably just allowing you to explore and expand dramatically the value that you guys are able to generate. >> Yeah, on one had it changes how we do what we do, but, fundamentally, you go back to the original proposition. For our customers, it's all about getting all of the information into the system, no matter where it's coming from, traditionally DCSs, now IoT devices and beyond. And it then becomes all about making that data available in the way, in the place, in the form that they will value it, and there's a myriad. One of the beautiful things about this conference is we see our partners, we see our customers. We see hundreds and thousands of different technologies and applications built around this information. That hasn't changed. It think that's one of the things Michael was eluding to. >> Yeah and you mentioned more available computing power and things like that, but what we see is that using that, people can get much more actionable information out of their data, things or types of analyses that were previously, we were unable to do that because we didn't have the right technology or the right computing power. >> Jeff: Right. >> But now we do. And especially if you can combine different sources of data and people are starting to share that data, you can get way more value out of that raw data that comes from those sensors. >> Right, but now we're going to talk about kind of the next thing, one of the next things. There's always the next thing. And that's blockchain. A lot of talk about blockchains. There's talk about bitcoin and cryptocurrencies. We're going to just put that on the side for now, and really talk about the fundamental technology under the covers which is this blockchain. We see IBM making big investments in it. We hear about it all the time. What are you guys doing in blockchain? And what do you kind of see as an opportunity that you hope that you eventually you'll be able to execute on using blockchain technology? >> Right so we have been researching blockchain for a little while now, and we're still kind of in exploration phase. We first wanted to really get a good understanding of the technology. Mainly to be able to separate the hype from the hope. There is a big hype around everything that is blockchain. But we really want to start looking at where does it actually make sense. Where does it actually add value? Are there situations where a centralized system might actually make much more sense? Or are there actually situations where this decentralized shared ecosystem makes more sense. So I think we have a decent understanding of the technology now, and we're starting to have those conversations with customers. Where should this make sense to you? So this week at PI World, we had our first conversations about that. We had our first session The session was very well attended. There was very good feedback. We'll have a more of a deep dive session this Thursday. And, yeah, we're really looking for those different use cases and to identify patterns within those different use cases across our different industries basically. >> And are you getting pull from the industries. Are they asking you for you guys to do this? Do they see either the curiosity or the opportunity or, I don't want to say hope, that's not a good word, to use blockchain in this distributed, trusted, non-centralized transaction engine to take care of some big issues that are out there right now. >> When I get out and I talk to executives around our customer base, I'm hearing at least three things, multiple times. It's a bit of a pattern. One is how could we use or would it be possible to use blockchain or some other technology in protecting or verifying the consumption or the use or the sharing of data, so kind of the outbound field. Another thing that I'm hearing frequently is most of our customers have very complex supply chains, very complex distribution chains, and as materials that they either depend on or create flow through these supply chains, there's often data around the conditions or the volumes or the paths that they take. And as that information transitions across various ownerships, various boundaries, how do they guarantee the authenticity, the availability and where that information can go in conjunction with that product. And then another one I've been hearing recently which was, I guess, not surprising, but it was novel when I first heard it is one of the activities in operations that every operator goes through is they send instructions or commands or settings or operational conditions down into their factory. How do you know if you can trust the instruction that has been delegated down? How do you know who did it? How do you know how long that instruction is valid for? All different aspects around that. So those are just three very, very significant challenges that our customers are surfacing for which this may be a solution. >> Right. >> And that's some of the fun, I think in going to this research path that we're going down. >> And I want to add to that the whole concept of the exchange of value within a blockchain network also makes the monetization of data very possible. People are starting to realize that the data they're collecting or the information they collected out of that data actually has value to other people. So can we find an easy way for them to monetize on that so see the data as an asset. And that's something that, you know, there are a number of startup projects that focus around that, and they're really looking into that, okay, would that make sense for our customers and how could we potentially tie into that or make that available to our customers. >> Right, the balance sheet value of data is an interesting topic because, you know, before data was just expensive because we had to store it and we had to keep it and we threw most of it away because we had to buy servers and machines to store it. Now, obviously, on the consumer side, you see the valuation of the data with companies like Google and Facebook whose valuation is a function of the value of that data even though its not reflected on their balance sheet and it's an interesting concept. How do you not only monetize it, but eventually get it on the balance sheet so that there is all the benefits that come by having that on the balance sheet with the value of that data. And that's the first time I've ever heard of using blockchain potentially as a way to capture, track and extract that value from that data. >> Exactly, and there are many different applications. It could be, for instance, a renewable company that has a wind farm that is monitoring the environment or monitoring the weather. That data is something that they use. But that data could potentially be very interesting to other companies or maybe to local governments as well. So is that data that they can monetize on? Another aspect could be, for instance, in autonomous vehicles where you're driving past somewhere and you want to get information about what are the gas prices or where can I get something to eat or things like that. So those could be really quick even microsecond transactions >> Jeff: Right. or interactions between a vehicle and whatever is in its environment. But maybe there are some way to do some quick micropayments of that data because that is valuable to that vehicle, and, in turn, that vehicle could also sell some of the data that it is collecting about the weather, about the road conditions, about traffic. So, in general, potentially we could see this whole economy around data arising. >> Right. >> And there's also a lot of cost in validating the trust now. We talked to some of the shipping lines and like 50% of the cost of shipping is the processing of the paperwork that basically does the validation that you just kind of outlined. Is it what it's supposed to be? Did it come from where it's supposed to be coming from? It is going to where it's supposed to be going to? And literally it's like 50% of the cost of shipments is processing this paper. So not only does it provide value, but it unlocks another whole set of value that currently is just getting eating up by super inefficient, still paper-based not even Excel, right. They probably still have copy machines. >> Transportation is one of the worse. (Jeff laughs) >> But you look at that scenario and a number of these others, immediately you go to this notion of data ownership. You eluded to it. Philosophically and practically, OSI is firmly committed to all of the information that we manage for our customers is our customer's data. They own that. But even as they get into these complex landscapes, then there really is that question. As materials flow through these supply chains, who owns the data associated with that. So this is going to be an interesting frontier >> Right. where these things have to get resolved and understood. And most of our customers consider the 10, 20, 30 years of operational data that they've preserved one of their more valuable IP assets. It's both an amazing frontier and amazing opportunity and something that's going to stir up some emotions as well. >> Right. And then you got the geopolitics of it as well because of the disparate laws all over the place about data, data treatment and exactly where was the data generated. That's always one of my favorite things when you really dig down as to where was that data actually generated. And it's not necessarily an easy thing to determine. So here we are 2018, what are you guys working on this year? If we come back a year from now, what are we going to be talking about? >> So right now, we are starting the conversation. We are starting to have this discussion. We have some assumptions where blockchain might make sense to us as a company especially to our customers. So this year, we really want to use this year to validate some of those assumptions, to really work with our customers but also with academia to find out where does this actually make sense. How can we get the most value out of this amazing new technology that has a lot of promise. And maybe we'll see us starting prototyping some of these solutions together with our customers. >> You going with that? >> Yeah, I'm going with that. >> All right, Richard's going with Michael, all right. So we're going to leave it there. And thanks for taking a few minutes and congratulations. I don't know if you've been here for all 28 years, Michael. >> Seven years. >> Seven years, pretty good. But what a great story, what a great success and really happy to come here and learn some of the story. >> Yeah, I'm honored every year. It just blows me away what I get to see and listen to and the people I get to meet so thank you. >> Thank you. All right, and he's Richard. >> Thank you. >> And he's Michael, I'm Jeff. You're watching theCUBE from OSIsoft PI World 2018 in downtown San Francisco. Thanks for watching. (upbeat music)