>> Narrator: From Las Vegas, it's theCUBE, covering Qualys Security Conference 2019, brought to you by Qualys. >> Hey, welcome back, everybody. Jeff Rick here with theCUBE. We're in Las Vegas at the Bellagio at the Qualys Security Conference. They've been doing this for 19 years. They've been in this business for a long time, seen a lot of changes, so we're happy to be here. Our next guest works for Caterpillar. He is Brian Rossi, the senior security manager vulnerability management. Brian, great to see you. >> Thanks for having me. >> So I was so psyched, they had an interview, a gentleman from Caterpillar a few years ago, and it was fascinating to me how far along the autonomous vehicle route Caterpillar is. And I don't think most people understand, right? They see the Waymo cars driving around, and they read about all this stuff. But Caterpillar's been doing autonomous vehicles for a super long time. >> A really long time, a really long time, 25-plus years, pioneering a lot of the autonomous vehicle stuff that's out there. And we've actually, it's been cool, had an opportunity to do some security testing on some of the stuff that we're doing. So, even making it safer for the mines and the places that are using it today. >> Yeah, you don't want one of those big-giant dump-truck things to go rogue. (laughing) >> Off a cliff. Yeah, no, bad idea. >> Huge. Or into a bunch of people. All right, so let's jump into it. So, vulnerability management. What do you focus on, what does that mean exactly? >> So, for me, more on the traditional vulnerability management side. So I stay out of the application space, but my group is focused on identifying vulnerabilities for servers, workstations, endpoints that are out there, working with those IT operational teams to make sure they get those patched and reduce as many vulnerabilities as we can over the course of a year. >> So we've done some stuff with Forescout, and they're the kings of vulnerability sniffing-out. In fact, I think they have an integration with Qualys as well. So, is it always amazing as to how much stuff that gets attached to the network that you weren't really sure was there in the first place? >> Yes, absolutely. (laughs) And it's fun to be on the side that gets to see it all, and then tell people that it's there. I think with Qualys and with some of the other tools that we use, right? We're seeing these things before anybody else is seeing them and we're seeing the vulnerabilities that are associated with them, before anyone else sees them. So it's an interesting job, to tell people what's out there when they didn't even know. >> Right, so another really important integration is with ServiceNow, and you're giving a talk I believe tomorrow on how you use both Qualys and ServiceNow together. Give us kind of the overview of what you're going to be talking about. >> Absolutely, so the overview is really what our motto has been all year, right? Is put work where people work. So what we found was that with our vulnerability management program, we're doing scanning, we're running reports, we're trying to communicate with these IT operational teams to fix what's out there. But that's difficult when you're just sending spreadsheets around and you're trying to email people. There's organizational changes, people are moving around. They might not be responsible for those platforms anymore. And keeping track of all that is incredibly difficult in a global scale, with hundreds of thousands of assets that people are managing. And so we turned to ServiceNow and Qualys to really find a way to easily communicate, not just easily, but also timely, communicate those vulnerabilities to the teams that are responsible for doing it. >> Right, so you guys already had the ServiceNow implementation obviously, it was something that was heavily used. You're kind of implying that that was the screen that a lot of people had open on their desktop all the time. >> We lucked out that we were early in the implementation with ServiceNow. So, Caterpillar was moving from a previous IT service management solution to ServiceNow so we got in on the ground floor with the teams that were building out the configuration management database. We got in with the ground floor with the teams who were operationalizing, using ServiceNow to drive their work. We had the opportunities to just build relationships with them, take those relationships, ask them how they want that to work, and then go build it for them. >> Right, it's so funny because everyone likes to talk about single pane of glass, and to own that real estate that's on our screens that we sit and look at all day long, and it used to be emails. It's not so much email anymore, and ServiceNow is one of those types of apps that when you're in it, you're working it, that is your thing. And it's one thing to sniff out the vulnerabilities and find vulnerabilities, but you got to close the loop. >> Brian: You got to, absolutely. >> And that's really where the ServiceNow piece fits. >> And it's been great. We've seen a dramatic reduction in the number of vulnerabilities that are getting fixed over the course of a 30-day period. And I think it simply is because the visibility is finally there, and it's real-time visibility for these groups. They're not receiving data 50 days after we found it. We're getting them that data as soon as we find it, and they're able to operationalize it immediately. >> Right, and what are some of the actions that are the higher frequency that you've found, that you're triggering, that this process is helping you mitigate? >> I would say, actually, what it's really finding is some of our oldest vulnerabilities, a lot of stuff that people have just let fall off the plate. And they're isolated, right? They may have run patching for a specific vulnerability six months ago, but there was no view to tell them whether or not they got everything. Or maybe it was an asset that was off the network when they were patching, and now it's back on the network. So we're getting them the real-time visibility. Stuff that they may have missed, that they would have never seen before, without this integration. >> So I'd love to get your take on one of the top topics that came in the keynote this morning, both with Dick Clark as well as Philippe, was IoT-5G and the increasing surface-area, attack surface area, vulnerability surface area. You guys, Caterpillar's obviously well into internet of things. You've got a lot of connected devices. I'm sure you're excited about 5G, and I'm sure in a mining environment, or those types of environments are just prime 5G opportunities. Bad news is, your attack surface just grew exponentially. >> Yeah. >> So you're in charge of keeping track of vulnerabilities. How do you balance the opportunity, and what you see that's coming with 5G and connected devices and even a whole other rash of sensors, compared to the threat that you have to manage? >> Certainly in the IoT space it's unique. We can't do the things to those devices that we would do with normal laptops' assets, right? So I think figuring out unique ways to actually deal with them is going to be the hardest part. Finding vulnerabilities is always the easiest thing to do, but dealing with them is going to be the hard part. 5G is going to bring a whole new ballgame to a lot of the technology that we use. Our engineering groups are looking at those, and we're going to be partnering with them all the way through their journey on how to use 5G, how to use IoT to drive better services for our customers, and hopefully security will be with them the whole way. >> Right, the other piece that didn't get as much talk today, but it's a hot topic everywhere else we go is Edge, right? And this whole concept of, do you move the data, do you move the data to the computer or the computer to the data? I'm sure you guys are going to be leveraging Edge in a big way, when you're getting more of that horsepower closer to the sites. There's a lot of challenges with Edge. It's not a pristine data center. There are some nasty environmental conditions and you're limited in power, connectivity, and some of these other things. So when you think about Edge in your world, and maybe you're not thinking of it, but I bet you are, how are you seeing that, again, as an opportunity to bring more compute power closer to where you need it, closer to these vehicles? >> So I think, I wish I had our other security division here with me to talk about it. We're piloting a lot of those things, but that's been a big piece of our digital transformation at Caterpillar, is really leveraging data from those connected devices that are out in the field. And we actually, our Edge has to be brought closer to home. Our engineers pack so much into the little space they have on the devices that are out there, that they don't have room to actually calculate on that data that's out in the field, right? So we are actually bringing the Edge a little closer to home, in order for us to provide the best service for our customers. >> Right, so another take on digital transformation. You talked about Caterpillar's digital transformation. You've been there for five years now. Before that you were at State Farm. Checking on your LinkedIn, right? State Farm is the business of actuarial numbers, right? Caterpillar has got big heavy metal things, and yet you talk about digital transformation. How did you guys, how are you thinking about digital transformation in this heavy-equipment industry that's in construction? Probably not what most people think of as a digital enterprise, but in fact you guys are super aggressively moving in that direction. >> Yeah, and for us, from a securities perspective, it's been all about shift-left, right? We have to get embedded with these groups when they're designing these things. We have to be doing threat models. We have to be doing pen testing. We have to be doing that secure life cycle the entire way through the product. Because with our product line, unlike State Farm where we could easily just make a change to an application so that it was more secure, once we produce these vehicles, and once we roll them out and start selling them, they're out there. And we build our equipment to last, right? So there's not an expectation that a customer is going to come back and say, "I'm ready to buy a new truck two years from now," because of security vulnerability. >> Jeff: Right, right. >> So, yeah, it's a big thing for us to get as early in the development life cycle as possible and partner with those groups. >> I'm curious in terms of the role of the embedded software systems in these things now, compared to what it was five years ago, 10 years ago 'cause you do need to upgrade it. And we've seen with Teslas, right? You get patches and upgrades and all types of things. So I would imagine you're probably a lot more Tesla-like than the Caterpillar of 20 years ago. >> Moving in that direction, and that is the goal, right? We want to be able to get the best services and the most quality services to our customers as soon as possible. >> Right, very cool. Well, Brian, next time we talk, I want to do it on a big truck. >> Okay. >> A big, yellow truck. >> Let's do it. >> I don't want to do it here at the Bellagio. >> Let's do it, all right. >> Okay, excellent. Well, thanks for-- >> Thank you. >> For taking a few minutes, really appreciate it. >> Absolutely. >> All right, he's Brian, I'm Jeff, you're watching theCUBE. We're at the Bellagio in Las Vegas, not on a big yellow truck, out in the middle of nowhere digging up holes and moving big dirt around. Thanks for watching. We'll see you next time. (upbeat techno music)

Hey welcome back everybody Jeff Frick here with theCUBE we're in San Francisco at Zuora Subscribed 2017 about 2,000 people talking about the subscription economy but what I liked is when Tien had some sample stories up he went with the big iron he went with GE and he went with Caterpillar companies that you probably don't think of a subscription economy like maybe do Spotify or Amazon Prime so we're really excited up Tom Buckler he's the Director of IOT and Channel Solutions for Caterpillar Tom welcome >> Yeah I appreciate it thanks for having me >> So I love that you had a ton of industrial internet stories I mean this is real this is not like coming down the road but it's here today >> no absolutely  you know we've I mentioned during the keynote that since the mid 90s we've been connecting equipment since the mid 90s we've been on our autonomous journey and and you know it's just today we can talk about the largest industrial fleets of over five hundred thousand assets connected all of that valuable information coming in to help our customers and then the fully autonomous fleets in the mines which it's pretty exciting stuff >>   right I mean you and you touched on something we talk about often in the context of GE we've had GE on a number of times where those GE sell engines or do they sell propulsion to the airlines and you talked about do we sell you know this big earth-moving equipment or do we sell X number I know how you measure big giant masses of rock and gravel move but really selling it as a service not necessarily just the truck >> yeah I think that you know that was an important part of our discussion because when we talk about IOT and digital it's really a very customer centric strategy so we're going to get into services like IOT type or digital based services which is our cat connect portfolio if it's going to help serve our customers that we have today in the industries we play be more successful increase their operations increase their efficiency so we're not looking to build a platform or be a software company right you know when we get into this space it's focused on those customers and increasing their profitability and that's what leads us into these areas we're going to be a heavy equipment manufacturer we're going to sell big iron that's what we do we're going to leverage digital to help our customers be more successful >> you say that but I'm telling you I can I can turn the lens a little bit I see a whole lot of software company behind that big iron >> so no I'm not you know and there's there's there's a lot of software on those machines right you know there is a lot of software that's coming off those machines and and certainly we want to take all of that information we want to put analytics on it helped our customers go from being reactive to predictive right and really that's why we're at this conference right because when you get to what we call our cat connect services a lot of those are subscription-based you know when we're connecting a you know five hundred thousand machines or we're able to go out and you know enable grade assist on a machine over the air or we're going to have these predictive health services to make sure uptime is maximized all of those are data driven services through CAD connect and they're all subscriptions right so it's a natural fit for us to migrate into that along with our product business it's um >> just interesting numbers that you shared in the keynote five hundred thousand connected machines you talked about you know the obvious stuff no and unplanned downtime these are huge assets that need to run as close to 24/7 as they predictably can but then you mentioned just looking at some other data and not even really heavy lifting data but customers getting tremendous utilization gains right by leveraging some of the software that you guys have incorporated in the machines >> yeah it's powerful stuff I mean if I talk about construction you know the customer we mentioned is they asked us to connect all 16,000 pieces their equipment you know 3,000 of those work at earthmoving machines you know the other 13,000 weren't they were a variety of other types of machines but with the customer with that information and when they can get put it on one screen and they can look at utilization they can look at location they can look at idle time they can increase their utilization significantly so basic data with a fleet that size can help customers realize 10 almost 20 percent utilization gains and across the fleet that size it's big money right and it's big customer value but even all the way down to the person who's got 10 machines you know they can start to look at idle time they can look at you know operator abuse and how you know where they can train their operators better to perform better so basic information on some of these machines is very valuable it's >> it's such an interesting concept because you keep talking about your customers doing better with the assets that you guys provide them you know when you're in a subscription relationship and you have this ongoing back and forth repetitive connection it's a very different relationship than when you just sell something and you ship it and you take the money and you go on to the other one it seems like that's really kind of the secret sauce of the subscripts economy that's not enough people really highlight yeah you know in some cases >> that's a great point and you know one of the strengths of Caterpillar is is our global dealer network and and so you mentioned about selling the product you know when when when we sell the product our dealers provide that product and sell it to our customers they generally have a long-standing relationship with that customer everything from helping them with uptime to machine selection to operations operator training so we're in the business of working with customers through the long haul but to your point that these digital services you know they create a digital relationship that's ongoing along with our dealers relationship that they've had for four decades right it's it's a really powerful kind of combination >> and then I  would imagine the data that you're now getting back off these machines which before before they were all connected you know you kind of saw them at the maintenance cycles and you could kind of see you know maybe what happened or didn't happen or maybe there's some patterns that are at Geographic or type of job or whatever but now you know I love that the quote we used to take a sample of old data and now we take all of current data it must be tremendous value for you guys to develop better products have better maintenance on your own products see how these things can do much much better >> yeah you're absolutely  right and you know when we talk about the data of the products a lot of people initially go to telematics and certainly when we talk about you know five hundred thousand assets where I'm talking telematics but we also do about 5 million fluid samples a year off different compartments we've got visual inspections they're on electronically through can't inspect that's all the data coming back so all of that information is really rich information and to your point we can take that all the way back to new product design and make sure that our next products are optimized >> pretty exciting stuff solutely and it who doesn't love a big yellow tractor truck actually all right Tom well thanks for a taking a few minutes out of your busy day and congratulations >> all right thanks for having me Tom Buckler I'm Jeff Frick you're >> he's  watching theCUBE we'll be back after this short break thanks for watching

(theCube jingle) >> Hey, welcome back everybody! Jeff Frick here with theCube. We are in San Francisco at Zuora Subscribe 2017, about 2,000 people talking about the subscription economy. But what I liked is when Tien had some sample stories up, he went with the big iron. He went with GE, and he went with Caterpillar, companies that you probably don't think of as subscription economy, like maybe you do Spotify or Amazon Prime. So, we're really excited to have Tom Bucklar. He's the director of IoT and Channel Solutions for Caterpillar. Tom, welcome. >> Yeah, I appreciate it, thanks for having me. >> So, I love that you had a ton of industrial Internet stories. I mean this is real. This is not coming down the road, but it's here today. >> No, absolutely. I mentioned during the keynote that since the mid 90s, we've been connecting equipment. Since the mid 90s, we've been on our autonomous journey. And just today we can talk about the largest industrial fleets of over 500,000 assets connected. All of that valuable information coming in to help our customers. And then the fully autonomous fleets in the mines. It's pretty exciting stuff. >> Right, and you touched on something we talk about often in the context of GE. We've had GE on a number of times where do they sell engines or do they sell propulsion to the airlines, and you talked about do we sell this big earth-moving equipment or do we sell x number of, I don't know how you measure big giant masses of rocking gravel move, but really, selling it as a service, not necessarily just the truck. >> Yeah, that was an important part of our discussion because when we talk about IoT and digital, it's really a very customer-centric strategy, so we're going to get into services like IoT type or digital based services, which is our Cat Connect portfolio, if it's going to help serve our customers that we have today in the industries we play, be more successful, increase their operations, increase their efficiency. So, we're not looking to build a platform or be a software company. When we get into this space, it's focused on those customers and increasing their profitability, and that's what leads us into these areas. We're going to be a heavy equipment manufacturer, we're going to sell big iron, that's what we do. We're going to leverage digital to help our customers be more successful. >> Yeah, you say that, but I'm telling you, I can turn the lens a little bit. I see a whole lot of software company behind that big iron, so... >> No, I'm not. You know, there's a lot of software on those machines. >> You're right. >> There's a lot of software that's coming off those machines. And, certainly, we want to take all of that information. We want to put analytics on it, and we want to help our customers go from being reactive to predictive. And, really, that's why we're at this conference, right, because when you get to what we call our Cat Connect services, a lot of those are subscription-based. You know, when we're connecting 500,000 machines, or we're able to go out and, you know, enable grade assist on a machine over the air, or we're going to have these predictive health services to make sure uptime is maximized. All of those are data-driven services through Cat Connect, and they're all subscriptions. So it's a natural fit for us to migrate into that along with our product business. >> Yes, some interesting numbers that you shared in the keynote. 500,000 connected machines. You talked about the obvious stuff, unplanned downtime, these are huge assets that need to run as close to 24/7 as they predictably can. But then, you mentioned looking at some other data, and not even really heavy-lifting data, but customers getting tremendous utilization gains by leveraging some of the software that you guys have incorporated in the machines. >> Yeah, it's powerful stuff. I mean, if I talk about construction, the customer we mentioned, they asked us to connect all 16,000 pieces of their equipment. You know, 3,000 of those were Cat earth-moving machines. You know, the other 13,000 weren't. They were a variety of other types of machines. But with the customer, with that information, and then when they can get put it on one screen, and they can look at utilization, they can look at location, they can look at idle time, they can increase their utilization significantly. So basic data, with a fleet that size, can help customers realize 10, almost 20% utilization gains, and across the fleet that size, it's big money, and it's big customer value. But even all the way down to the person whose got 10 machines. You know, they can start to look at idle time, they can look at operator abuse and where they can train their operators better, to perform better. So basic information of some these machines is very valuable. >> It's such an interesting concept because you keep talking about your customers doing better with the assets that you guy provide them. You know, when you're in a subscription relationship, and you have those ongoing back-and-forth repetitive connection, it's a very different relationship than when you just sell something, and you ship it, and you take the money, and you go on to the other one. And it seems like that's really kind of the secret sauce of the subscription economy that no enough people really highlight. >> Yeah, you know, in some cases, that's a great point. And, you know, one of the strength of Caterpillar is our global dealer network. And so you mention about selling the product. You know, when we sell the product, our dealers provide their product and sell it to our customers, they generally have a long-standing relationship with that customer. Everything from helping the with uptime to machine selection, to operation to operator training. So, we're in the business of working with customers through the long haul. But to your point that these digital services, you know, they create a digital relationship that's ongoing, along with our dealer's relationship that they've had for decades. So, it's a really powerful kind of combination. >> And then, I would imagine the data that you're now getting back off these machines, which before they were all connected. You know, you kind of saw them at the maintenance cycles, and you could kind of see maybe what happened or what didn't happen, or maybe there's some patterns that are geographic or type of job or whatever. But now, I love the quote, you used to take a sample of old data, and now we take all of current data. There must be tremendous value for you guys to develop better products, have better maintenance on your own products, see how these things can do much, much better. >> Yeah, you're absolutely right. And, you know, when we talk about the data off the products, a lot of people initially go to telematics, and certainly, when talk about 500,000 assets, I'm talking telematics. But we also do about 5,000,000 fluid samples a year of different compartments. We've got visual inspections that are on electronically through Cat Inspect, that's all the data coming back. So, all of that information is really rich information. And, to your point, we can take that all the way back to new product design and make sure that our next products are optimized. >> Pretty exciting stuff. >> Absolutely. >> And who doesn't love a big yellow tracker truck? (laughs) Absolutely. All right, Tom, well, thanks for taking a few minutes out of your busy day and congratulations. >> All right, thanks for having me. >> All right, he's Tom Bucklar, I'm Jeff Frick. You're watching theCube. We'll be back after this short break. Thanks for watching. (theCube jingle)

>> Narrator: From around the globe, it's theCUBE with digital coverage of Exascale day, made possible by Hewlett Packard Enterprise. >> Hey, welcome back Jeff Frick here with the cube come due from our Palo Alto studios, for their ongoing coverage in the celebration of Exascale day 10 to the 18th on October 18th, 10 with 18 zeros, it's all about big powerful giant computing and computing resources and computing power. And we're excited to invite back our next guest she's been on before. She's Dr. Arti Garg, head of advanced AI solutions and technologies for HPE. Arti great to see you again. >> Great to see you. >> Absolutely. So let's jump into before we get into Exascale day I was just looking at your LinkedIn profile. It's such a very interesting career. You've done time at Lawrence Livermore, You've done time in the federal government, You've done time at GE and industry, I just love if you can share a little bit of your perspective going from hardcore academia to, kind of some government positions, then into industry as a data scientist, and now with originally Cray and now HPE looking at it really from more of a vendor side. >> Yeah. So I think in some ways, I think I'm like a lot of people who've had the title of data scientists somewhere in their history where there's no single path, to really working in this industry. I come from a scientific background. I have a PhD in physics, So that's where I started working with large data sets. I think of myself as a data scientist before the term data scientist was a term. And I think it's an advantage, to be able to have seen this explosion of interest in leveraging data to gain insights, whether that be into the structure of the galaxy, which is what I used to look at, or whether that be into maybe new types of materials that could advance our ability to build lightweight cars or safety gear. It's allows you to take a perspective to not only understand what the technical challenges are, but what also the implementation challenges are, and why it can be hard to use data to solve problems. >> Well, I'd just love to get your, again your perspective cause you are into data, you chose that as your profession, and you probably run with a whole lot of people, that are also like-minded in terms of data. As an industry and as a society, we're trying to get people to do a better job of making database decisions and getting away from their gut and actually using data. I wonder if you can talk about the challenges of working with people who don't come from such an intense data background to get them to basically, I don't know if it's understand the value of more of a data kind decision making process or board just it's worth the effort, cause it's not easy to get the data and cleanse the data, and trust the data and get the right context, working with people that don't come from that background. And aren't so entrenched in that point of view, what surprises you? How do you help them? What can you share in terms of helping everybody get to be a more data centric decision maker? >> So I would actually rephrase the question a little bit Jeff, and say that actually I think people have always made data driven decisions. It's just that in the past we maybe had less data available to us or the quality of it was not as good. And so as a result most organizations have developed organize themselves to make decisions, to run their processes based on a much smaller and more refined set of information, than is currently available both given our ability to generate lots of data, through software and sensors, our ability to store that data. And then our ability to run a lot of computing cycles and a lot of advanced math against that data, to learn things that maybe in the past took, hundreds of years of experiments in scientists to understand. And so before I jumped into, how do you overcome that barrier? Just I'll use an example because you mentioned, I used to work in industry I used to work at GE. And one of the things that I often joked about, is the number of times I discovered Bernoulli's principle, in data coming off a GE jet engines you could do that overnight processing these large data but of course historically that took hundreds of years, to really understand these physical principles. And so I think when it comes to how do we bridge the gap between people who are adapt at processing large amounts of data, and running algorithms to pull insights out? I think it's both sides. I think it's those of us who are coming from the technical background, really understanding the way decisions are currently made, the way process and operations currently work at an organization. And understanding why those things are the way they are maybe their security or compliance or accountability concerns, that a new algorithm can't just replace those. And so I think it's on our end, really trying to understand, and make sure that whatever new approaches we're bringing address those concerns. And I think for folks who aren't necessarily coming from a large data set, and analytical background and when I say analytical, I mean in the data science sense, not in the sense of thinking about things in an abstract way to really recognize that these are just tools, that can enhance what they're doing, and they don't necessarily need to be frightening because I think that people who have been say operating electric grids for a long time, or fixing aircraft engines, they have a lot of expertise and a lot of understanding, and that's really important to making any kind of AI driven solution work. >> That's great insight but that but I do think one thing that's changed you come from a world where you had big data sets, so you kind of have a big data set point of view, where I think for a lot of decision makers they didn't have that data before. So we won't go through all the up until the right explosions of data, and obviously we're talking about Exascale day, but I think for a lot of processes now, the amount of data that they can bring to bear, is so dwarfs what they had in the past that before they even consider how to use it they still have to contextualize it, and they have to manage it and they have to organize it and there's data silos. So there's all this kind of nasty processes stuff, that's in the way some would argue has been kind of a real problem with the promise of BI, and does decision support tools. So as you look at at this new stuff and these new datasets, what are some of the people in process challenges beyond the obvious things that we can think about, which are the technical challenges? >> So I think that you've really hit on, something I talk about sometimes it was kind of a data deluge that we experienced these days, and the notion of feeling like you're drowning in information but really lacking any kind of insight. And one of the things that I like to think about, is to actually step back from the data questions the infrastructure questions, sort of all of these technical questions that can seem very challenging to navigate. And first ask ourselves, what problems am I trying to solve? It's really no different than any other type of decision you might make in an organization to say like, what are my biggest pain points? What keeps me up at night? or what would just transform the way my business works? And those are the problems worth solving. And then the next question becomes, if I had more data if I had a better understanding of something about my business or about my customers or about the world in which we all operate, would that really move the needle for me? And if the answer is yes, then that starts to give you a picture of what you might be able to do with AI, and it starts to tell you which of those data management challenges, whether they be cleaning the data, whether it be organizing the data, what it, whether it be building models on the data are worth solving because you're right, those are going to be a time intensive, labor intensive, highly iterative efforts. But if you know why you're doing it, then you will have a better understanding of why it's worth the effort. And also which shortcuts you can take which ones you can't, because often in order to sort of see the end state you might want to do a really quick experiment or prototype. And so you want to know what matters and what doesn't at least to that. Is this going to work at all time. >> So you're not buying the age old adage that you just throw a bunch of data in a data Lake and the answers will just spring up, just come right back out of the wall. I mean, you bring up such a good point, It's all about asking the right questions and thinking about asking questions. So again, when you talk to people, about helping them think about the questions, cause then you've got to shape the data to the question. And then you've got to start to build the algorithm, to kind of answer that question. How should people think when they're actually building algorithm and training algorithms, what are some of the typical kind of pitfalls that a lot of people fall in, haven't really thought about it before and how should people frame this process? Cause it's not simple and it's not easy and you really don't know that you have the answer, until you run multiple iterations and compare it against some other type of reference? >> Well, one of the things that I like to think about just so that you're sort of thinking about, all the challenges you're going to face up front, you don't necessarily need to solve all of these problems at the outset. But I think it's important to identify them, is I like to think about AI solutions as, they get deployed being part of a kind of workflow, and the workflow has multiple stages associated with it. The first stage being generating your data, and then starting to prepare and explore your data and then building models for your data. But sometimes I think where we don't always think about it is the next two phases, which is deploying whatever model or AI solution you've developed. And what will that really take especially in the ecosystem where it's going to live. If is it going to live in a secure and compliant ecosystem? Is it actually going to live in an outdoor ecosystem? We're seeing more applications on the edge, and then finally who's going to use it and how are they going to drive value from it? Because it could be that your AI solution doesn't work cause you don't have the right dashboard, that highlights and visualizes the data for the decision maker who will benefit from it. So I think it's important to sort of think through all of these stages upfront, and think through maybe what some of the biggest challenges you might encounter at the Mar, so that you're prepared when you meet them, and you can kind of refine and iterate along the way and even upfront tweak the question you're asking. >> That's great. So I want to get your take on we're celebrating Exascale day which is something very specific on 1018, share your thoughts on Exascale day specifically, but more generally I think just in terms of being a data scientist and suddenly having, all this massive compute power. At your disposal yoy're been around for a while. So you've seen the development of the cloud, these huge data sets and really the ability to, put so much compute horsepower against the problems as, networking and storage and compute, just asymptotically approach zero, I mean for as a data scientist you got to be pretty excited about kind of new mysteries, new adventures, new places to go, that we just you just couldn't do it 10 years ago five years ago, 15 years ago. >> Yeah I think that it's, it'll--only time will tell exactly all of the things that we'll be able to unlock, from these new sort of massive computing capabilities that we're going to have. But a couple of things that I'm very excited about, are that in addition to sort of this explosion or these very large investments in large supercomputers Exascale super computers, we're also seeing actually investment in these other types of scientific instruments that when I say scientific it's not just academic research, it's driving pharmaceutical drug discovery because we're talking about these, what they call light sources which shoot x-rays at molecules, and allow you to really understand the structure of the molecules. What Exascale allows you to do is, historically it's been that you would go take your molecule to one of these light sources and you shoot your, x-rays edit and you would generate just masses and masses of data, terabytes of data it was each shot. And being able to then understand, what you were looking at was a long process, getting computing time and analyzing the data. We're on the precipice of being able to do that, if not in real time much closer to real time. And I don't really know what happens if instead of coming up with a few molecules, taking them, studying them, and then saying maybe I need to do something different. I can do it while I'm still running my instrument. And I think that it's very exciting, from the perspective of someone who's got a scientific background who likes using large data sets. There's just a lot of possibility of what Exascale computing allows us to do in from the standpoint of I don't have to wait to get results, and I can either stimulate much bigger say galaxies, and really compare that to my data or galaxies or universes, if you're an astrophysicist or I can simulate, much smaller finer details of a hypothetical molecule and use that to predict what might be possible, from a materials or drug perspective, just to name two applications that I think Exascale could really drive. >> That's really great feedback just to shorten that compute loop. We had an interview earlier in some was talking about when the, biggest workload you had to worry about was the end of the month when you're running your financial, And I was like, why wouldn't that be nice to be the biggest job that we have to worry about? But now I think we saw some of this at animation, in the movie business when you know the rendering for whether it's a full animation movie, or just something that's a heavy duty three effects. When you can get those dailies back to the, to the artist as you said while you're still working, or closer to when you're working versus having this, huge kind of compute delay, it just changes the workflow dramatically and the pace of change and the pace of output. Because you're not context switching as much and you can really get back into it. That's a super point. I want to shift gears a little bit, and talk about explainable AI. So this is a concept that a lot of people hopefully are familiar with. So AI you build the algorithm it's in a box, it runs and it kicks out an answer. And one of the things that people talk about, is we should be able to go in and pull that algorithm apart to know, why it came out with the answer that it did. To me this just sounds really really hard because it's smart people like you, that are writing the algorithms the inputs and the and the data that feeds that thing, are super complex. The math behind it is very complex. And we know that the AI trains and can change over time as you you train the algorithm it gets more data, it adjusts itself. So it's explainable AI even possible? Is it possible at some degree? Because I do think it's important. And my next question is going to be about ethics, to know why something came out. And the other piece that becomes so much more important, is as we use that output not only to drive, human based decision that needs some more information, but increasingly moving it over to automation. So now you really want to know why did it do what it did explainable AI? Share your thoughts. >> It's a great question. And it's obviously a question that's on a lot of people's mind these days. I'm actually going to revert back to what I said earlier, when I talked about Bernoulli's principle, and just the ability sometimes when you do throw an algorithm at data, it might come the first thing it will find is probably some known law of physics. And so I think that really thinking about what do we mean by explainable AI, also requires us to think about what do we mean by AI? These days AI is often used anonymously with deep learning which is a particular type of algorithm that is not very analytical at its core. And what I mean by that is, other types of statistical machine learning models, have some underlying theory of what the population of data that you're studying. And whereas deep learning doesn't, it kind of just learns whatever pattern is sitting in front of it. And so there is a sense in which if you look at other types of algorithms, they are inherently explainable because you're choosing your algorithm based on what you think the is the sort of ground truth, about the population you're studying. And so I think we going to get to explainable deep learning. I think it's kind of challenging because you're always going to be in a position, where deep learning is designed to just be as flexible as possible. I'm sort of throw more math at the problem, because there may be are things that your sort of simpler model doesn't account for. However deep learning could be, part of an explainable AI solution. If for example, it helps you identify what are important so called features to look at what are the important aspects of your data. So I don't know it depends on what you mean by AI, but are you ever going to get to the point where, you don't need humans sort of interpreting outputs, and making some sets of judgments about what a set of computer algorithms that are processing data think. I think it will take, I don't want to say I know what's going to happen 50 years from now, but I think it'll take a little while to get to the point where you don't have, to maybe apply some subject matter understanding and some human judgment to what an algorithm is putting out. >> It's really interesting we had Dr. Robert Gates on a years ago at another show, and he talked about the only guns in the U.S. military if I'm getting this right, that are automatic, that will go based on what the computer tells them to do, and start shooting are on the Korean border. But short of that there's always a person involved, before anybody hits a button which begs a question cause we've seen this on the big data, kind of curve, i think Gartner has talked about it, as we move up from kind of descriptive analytics diagnostic analytics, predictive, and then prescriptive and then hopefully autonomous. So I wonder so you're saying will still little ways in that that last little bumps going to be tough to overcome to get to the true autonomy. >> I think so and you know it's going to be very application dependent as well. So it's an interesting example to use the DMZ because that is obviously also a very, mission critical I would say example but in general I think that you'll see autonomy. You already do see autonomy in certain places, where I would say the States are lower. So if I'm going to have some kind of recommendation engine, that suggests if you look at the sweater maybe like that one, the risk of getting that wrong. And so fully automating that as a little bit lower, because the risk is you don't buy the sweater. I lose a little bit of income I lose a little bit of revenue as a retailer, but the risk of I make that turn, because I'm going to autonomous vehicle as much higher. So I think that you will see the progression up that curve being highly dependent on what's at stake, with different degrees of automation. That being said you will also see in certain places where there's, it's either really expensive or it's humans aren't doing a great job. You may actually start to see some mission critical automation. But those would be the places where you're seeing them. And actually I think that's one of the reasons why you see actually a lot more autonomy, in the agriculture space, than you do in the sort of passenger vehicle space. Because there's a lot at stake and it's very difficult for human beings to sort of drive large combines. >> plus they have a real they have a controlled environment. So I've interviewed Caterpillar they're doing a ton of stuff with autonomy. Cause they're there control that field, where those things are operating, and whether it's a field or a mine, it's actually fascinating how far they've come with autonomy. But let me switch to a different industry that I know is closer to your heart, and looking at some other interviews and let's talk about diagnosing disease. And if we take something specific like reviewing x-rays where the computer, and it also brings in the whole computer vision and bringing in computer vision algorithms, excuse me they can see things probably fast or do a lot more comparisons, than potentially a human doctor can. And or hopefully this whole signal to noise conversation elevate the signal for the doctor to review, and suppress the noise it's really not worth their time. They can also review a lot of literature, and hopefully bring a broader potential perspective of potential diagnoses within a set of symptoms. You said before you both your folks are physicians, and there's a certain kind of magic, a nuance, almost like kind of more childlike exploration to try to get out of the algorithm if you will to think outside the box. I wonder if you can share that, synergy between using computers and AI and machine learning to do really arduous nasty things, like going through lots and lots and lots and lots of, x-rays compared to and how that helps with, doctor who's got a whole different kind of set of experience a whole different kind of empathy, whole different type of relationship with that patient, than just a bunch of pictures of their heart or their lungs. >> I think that one of the things is, and this kind of goes back to this question of, is AI for decision support versus automation? And I think that what AI can do, and what we're pretty good at these days, with computer vision is picking up on subtle patterns right now especially if you have a very large data set. So if I can train on lots of pictures of lungs, it's a lot easier for me to identify the pictures that somehow these are not like the other ones. And that can be helpful but I think then to really interpret what you're seeing and understand is this. Is it actually bad quality image? Is it some kind of some kind of medical issue? And what is the medical issue? I think that's where bringing in, a lot of different types of knowledge, and a lot of different pieces of information. Right now I think humans are a little bit better at doing that. And some of that's because I don't think we have great ways to train on, sort of sparse datasets I guess. And the second part is that human beings might be 40 years of training a model. They 50 years of training a model as opposed to six months, or something with sparse information. That's another thing that human beings have their sort of lived experience, and the data that they bring to bear, on any type of prediction or classification is actually more than just say what they saw in their medical training. It might be the people they've met, the places they've lived what have you. And I think that's that part that sort of broader set of learning, and how things that might not be related might actually be related to your understanding of what you're looking at. I think we've got a ways to go from a sort of artificial intelligence perspective and developed. >> But it is Exascale day. And we all know about the compound exponential curves on the computing side. But let's shift gears a little bit. I know you're interested in emerging technology to support this effort, and there's so much going on in terms of, kind of the atomization of compute store and networking to be able to break it down into smaller, smaller pieces, so that you can really scale the amount of horsepower that you need to apply to a problem, to very big or to very small. Obviously the stuff that you work is more big than small. Work on GPU a lot of activity there. So I wonder if you could share, some of the emerging technologies that you're excited about to bring again more tools to the task. >> I mean, one of the areas I personally spend a lot of my time exploring are, I guess this word gets used a lot, the Cambrian  explosion of new AI accelerators. New types of chips that are really designed for different types of AI workloads. And as you sort of talked about going down, and it's almost in a way where we were sort of going back and looking at these large systems, but then exploring each little component on them, and trying to really optimize that or understand how that component contributes to the overall performance of the whole. And I think one of the things that just, I don't even know there's probably close to a hundred active vendors in the space of developing new processors, and new types of computer chips. I think one of the things that that points to is, we're moving in the direction of generally infrastructure heterogeneity. So it used to be when you built a system you probably had one type of processor, or you probably had a pretty uniform fabric across your system you usually had, I think maybe storage we started to get tearing a little bit earlier. But now I think that what we're going to see, and we're already starting to see it with Exascale systems where you've got GPUs and CPUs on the same blades, is we're starting to see as the workloads that are running at large scales are becoming more complicated. Maybe I'm doing some simulation and then I'm running I'm training some kind of AI model, and then I'm inferring it on some other type, some other output of the simulation. I need to have the ability to do a lot of different things, and do them in at a very advanced level. Which means I need very specialized technology to do it. And I think it's an exciting time. And I think we're going to test, we're going to break a lot of things. I probably shouldn't say that in this interview, but I'm hopeful that we're going to break some stuff. We're going to push all these systems to the limit, and find out where we actually need to push a little harder. And I some of the areas I think that we're going to see that, is there We're going to want to move data, and move data off of scientific instruments, into computing, into memory, into a lot of different places. And I'm really excited to see how it plays out, and what you can do and where the limits are of what you can do with the new systems. >> Arti I could talk to you all day. I love the experience and the perspective, cause you've been doing this for a long time. So I'm going to give you the final word before we sign out and really bring it back, to a more human thing which is ethics. So one of the conversations we hear all the time, is that if you are going to do something, if you're going to put together a project and you justify that project, and then you go and you collect the data and you run that algorithm and you do that project. That's great but there's like an inherent problem with, kind of data collection that may be used for something else down the road that maybe you don't even anticipate. So I just wonder if you can share, kind of top level kind of ethical take on how data scientists specifically, and then ultimately more business practitioners and other people that don't carry that title. Need to be thinking about ethics and not just kind of forget about it. That these are I had a great interview with Paul Doherty. Everybody's data is not just their data, it's it represents a person, It's a representation of what they do and how they lives. So when you think about kind of entering into a project and getting started, what do you think about in terms of the ethical considerations and how should people be cautious that they don't go places that they probably shouldn't go? >> I think that's a great question out a short answer. But I think that I honestly don't know that we have a great solutions right now, but I think that the best we can do is take a very multifaceted, and also vigilant approach to it. So when you're collecting data, and often we should remember a lot of the data that gets used isn't necessarily collected for the purpose it's being used, because we might be looking at old medical records, or old any kind of transactional records whether it be from a government or a business. And so as you start to collect data or build solutions, try to think through who are all the people who might use it? And what are the possible ways in which it could be misused? And also I encourage people to think backwards. What were the biases in place that when the data were collected, you see this a lot in the criminal justice space is the historical records reflect, historical biases in our systems. And so is I there are limits to how much you can correct for previous biases, but there are some ways to do it, but you can't do it if you're not thinking about it. So I think, sort of at the outset of developing solutions, that's important but I think equally important is putting in the systems to maintain the vigilance around it. So one don't move to autonomy before you know, what potential new errors you might or new biases you might introduce into the world. And also have systems in place to constantly ask these questions. Am I perpetuating things I don't want to perpetuate? Or how can I correct for them? And be willing to scrap your system and start from scratch if you need to. >> Well Arti thank you. Thank you so much for your time. Like I said I could talk to you for days and days and days. I love the perspective and the insight and the thoughtfulness. So thank you for sharing your thoughts, as we celebrate Exascale day. >> Thank you for having me. >> My pleasure thank you. All right she's Arti I'm Jeff it's Exascale day. We're covering on the queue thanks for watching. We'll see you next time. (bright upbeat music)

(bright music) >> Hey welcome back, everybody. Jeff Frick here with theCUBE We are high atop San Francisco in the Sales Force Tower in the new Accenture offices, it's really beautiful and as part of that, they have their San Francisco Innovation Hubs. So it's five floors of maker's labs, and 3D printing, and all kinds of test facilities and best practices, innovation theater, and this studio which is really fun to be at. So we're talking about hybrid cloud and the development of cloud and multi-cloud and continuing on this path. Not only are customers on this path, but everyone is kind of on this path as things kind of evolve and transform. We are excited to have a couple of experts in the field we've got Larry Socher, he's the Global Managing Director of Intelligent Cloud Infrastructure Services growth and strategy at Accenture. Larry, great to see you again. >> Great to be here, Jeff. And Ajay Patel, he's the Senior Vice President and General Manager at Cloud Provider Software Business Unit at VMWare and a theCUBE alumni as well. >> Excited to be here, thank you for inviting me. >> So, first off, how do you like the digs up here? >> Beautiful place, and the fact we're part of the innovation team, thank you for that. >> So let's just dive into it. So a lot of crazy stuff happening in the marketplace. Lot of conversations about hybrid cloud, multi-cloud, different cloud, public cloud, movement of back and forth from cloud. Just want to get your perspective today. You guys have been in the middle of this for a while. Where are we in this kind of evolution? Everybody's still kind of feeling themselves out, is it, we're kind of past the first inning so now things are settling down? How do you kind of view the evolution of this market? >> Great question and I think Pat does a really nice job of defining the two definitions. What's hybrid versus multi? And simply put, we look at hybrid as when you have consistent infrastructure. It's the same infrastructure regardless of location. Multi is when you have disparate infrastructure, but are using them in a collective. So just from a from a level setting perspective, the taxonomy is starting to get standardized. Industry is starting to recognize hybrid is the reality. It's not a step in the long journey. It is an operating model that going to exist for a long time. So it's not about location. It's about how do you operate in a multi-cloud and a hybrid cloud world. And together at Accenture VMware have a unique opportunity. Also, the technology provider, Accenture, as a top leader in helping customers figure out where best to land their workload in this hybrid, multi-cloud world. Because workloads are driving decisions. >> Jeff: Right. >> We are going to be in this hybrid, multi-cloud world for many years to come. >> Do I need another layer of abstraction? 'Cause I probably have some stuff that's in hybrid and I probably have some stuff in multi, right? 'Cause those are probably not mutually exclusive, either. >> We talked a lot about this, Larry and I were chatting as well about this. And the reality is the reason you choose a specific cloud, is for those native differentiator capability. So abstraction should be just enough so you can make workloads portable. To be able to use the capability as natively as possible. And by fact that we now at VMware have a native VMware running on every major hyperscaler and on pram, gives you that flexibility you want of not having to abstract away the goodness of the cloud while having a common and consistent infrastructure while tapping into the innovations that the public cloud brings. So, it is the evolution of what we've been doing together from a private cloud perspective to extend that beyond the data center, to really make it an operating model that's independent of location. >> Right, so Larry, I'm curious your perspective when you work with customers, how do you help them frame this? I mean I always feel so sorry for corporate CIAOs. I mean they got security going on like crazy, they go GDPR now I think, right? The California regs that'll probably go national. They have so many things to be worried about. They go to keep up on the latest technology, what's happening in containers. I thought it was doc, now you tell me it's Kubernetes. It's really tough. So how do you help them kind of, put a wrapper around it? >> It's got to start with the application. I mean you look at cloud, you look at infrastructure more broadly I mean. It's there to serve the applications and it's the applications that really drive business value. So I think the starting point has to be application led. So we start off, we have our intelligent engineering guys, our platform guys, who really come in and look and do an application modernization strategy. So they'll do an assessment, you know, most of our clients given their scale and complexity usually have from 500 to 20,000 applications. You know, very large estates. And you got to start to figure out okay what's my current applications? A lot of times they'll use the six Rs methodology and they say hey okay what is it? I'm going to retire this, I no longer need it. It no longer has business value. Or I'm going to replace this with SaaS. I move it to sales force for example, or service now, etcetera . Then they're going to start to look at their workloads and say okay, hey, do I need to re-fact of reformat this. Or re-host it. And one of the things obviously, VMware has done a fantastic job is allowing you to re-host it using their software to find data center, you know, in the hyperscaler's environment. >> We call it just, you know, migrate and then modernize. >> Yeah, exactly. But the modernized can't be missed. I think that's where a lot of times we see clients kind of get in the trap, hey, i'm just going to migrate and then figure it out. You need to start to have a modernization strategy and then, 'cause that's ultimately going to dictate your multi and your hybrid cloud approach, is how those apps evolve and you know the dispositions of those apps to figure out do they get replaced. What data sets need to be adjacent to each other? >> Right, so Ajay, you know we were there when Pat was with Andy and talking about VMware on AWS. And then, you know, Sanjay is showing up at everybody else's conference. He's at Google Cloud talking about VMware on Google Cloud. I'm sure there was a Microsoft show I probably missed you guys were probably there, too. You know, it's kind of interesting, right, from the outside looking in, you guys are not a public cloud, per se, and yet you've come up with this great strategy to give customers the options to adopt VMware in a public cloud and then now we're seeing where even the public cloud providers are saying, "Here, stick this box in your data center". It's like this little piece of our cloud floating around in your data center. So talk about the evolution of the strategy, and kind of what you guys are thinking about 'cause you know you are clearly in a leadership position making a lot of interesting acquisitions. How are you guys see this evolving and how are you placing your bets? >> You know Pat has been always consistent about this and any strategy. Whether it's any cloud or any device. Any workload, if you will, or application. And as we started to think about it, one of the big things we focused on was meeting the customer where he was at in his journey. Depending on the customer, they may simply be trying to figure out working out to get on a data center. All the way, to how to drive an individual transformation effort. And a partner like Accenture, who has the breadth and depth and sometimes the vertical expertise and the insight. That's what customers are looking for. Help me figure out in my journey, first tell me where I'm at, where am I going, and how I make that happen. And what we've done in a clever way in many ways is, we've created the market. We've demonstrated that VMware is the only, consistent infrastructure that you can bet on and leverage the benefits of the private or public cloud. And I often say hybrid's a two-way street now. Which is they are bringing more and more hybrid cloud services on pram. And where is the on pram? It's now the edge. I was talking to the Accenture folks and they were saying the metro edge, right? So you're starting to see the workloads And I think you said almost 40 plus percent of future workloads are now going to be in the central cloud. >> Yeah, and actually there's an interesting stat out there. By 2022, seventy percent of data will be produced and processed outside the cloud. So I mean the edge is about to, as we are on the tipping point of IOT finally taking off beyond smart meters. We're going to see a huge amount of data proliferate out there. So the lines between between public and private have becoming so blurry. You can outpost, you look at, Antheos, Azure Stack for ages. And that's where I think VMware's strategy is coming to fruition. You know they've-- >> Sometimes it's great when you have a point of view and you stick with it against the conventional wisdom. And then all of a sudden everyone is following the herd and you are like, "This is great". >> By the way, Anjay hit on a point about the verticalization. Every one of our clients, different industries have very different paths there. And to the meaning that the customer where they're on their journey. I mean if you talk to a pharmaceutical, you know, GXP compliance, big private cloud, starting to dip their toes into public. You go to Mians and they've been very aggressive public. >> Or in manufacturing with Edge Cloud. >> Exactly. >> So it really varies by industry. >> And that's a very interesting area. Like if you look at all the OT environments of the manufacturing. We start to see a lot of end of life of environments. So what's that next generation of control systems going to run on? >> So that's interesting on the edge because and you've brought up networking a couple times while we've been talking as a potential gate, right, when one of them still in the gates, but we're seeing more and more. We were at a cool event, Churchill Club when they had psy links, micron, and arm talking about shifting more of the compute and store on these edge devices to accommodate, which you said, how much of that stuff can you do at the edge versus putting in? But what I think is interesting is, how are you going to manage that? There is a whole different level of management complexity when now you've got this different level of distributing computing. >> And security. >> And security. Times many, many thousands of these devices all over the place. >> You might have heard recent announcements from VMware around the Carbon Black acquisition. >> Yeah. >> That combined with our workspace one and the pulse IOT, we are now giving you the management framework whether it's for people, for things, or devices. And that consistent security on the client, tied with our network security with NSX all the way to the data center security. We're starting to look at what we call intrinsic security. How do we bake security into the platform and start solving these end to end? And have our partner, Accenture, help design these next generation application architectures, all distributed by design. Where do you put a fence? You could put a fence around your data center but your app is using service now and other SaaS services. So how do you set up an application boundary? And the security model around that? So it's really interesting times. >> You hear a lot about our partnership around software defined data center, around networking. With Villo and NSX. But we've actually been spending a lot of time with the IOT team and really looking and a lot of our vision aligns. Actually looking at they've been working with similar age in technology with Liota where, ultimately the edge computing for IOT is going to have to be containerized. Because you're going to need multiple modalware stacks, supporting different vertical applications. We were actually working with one mind where we started off doing video analytics for predictive maintenance on tires for tractors which are really expensive the shovels, et cetera. We started off pushing the data stream, the video stream, up into Azure but the network became a bottleneck. We couldn't get the modality. So we got a process there. They're now looking into autonomous vehicles which need eight megabits load latency band width sitting at the edge. Those two applications will need to co-exist and while we may have Azure Edge running in a container down doing the video analytics, if Caterpillar chooses Green Grass or Jasper, that's going to have to co-exist. So you're going to see the whole containerization that we are starting to see in the data center, is going to push out there. And the other side, Pulse, the management of the Edge, is going to be very difficult. >> I think the whole new frontier. >> Yeah absolutely. >> That's moving forward and with 5G IntelliCorp. They're trying to provide value added services. So what does that mean from an infrastructure perspective? >> Right, right. >> When do you stay on the 5G radio network versus jumping on a back line? When do you move data versus process on the edge? Those are all business decisions that need to be there into some framework. >> So you guys are going, we can go and go and go. But I want to follow up on your segway on containers. 'Cause containers is such an important part of this story and an enabler to this story. And you guys made and aggressive move with Hep TO. We've had Craig McLuckie on when he was still at Google and Dan, great guys. But it's kind of funny right? 'Cause three years ago, everyone was going to DockerCon right? That was like, we're all about shows. That was the hot show. Now Docker's kind of faded and Kubernetes is really taking off. Why, for people that aren't familiar with Kubernetes, they probably hear it at cocktail parties if they live in the Bay area. Why is containers such an important enabler and what's so special about Kubernetes specifically? >> Do you want to go on the general or? >> Why don't your start off? >> I brought my products stuff for sure. >> If you look at the world its getting much more dynamic. Particularly as you start to get more digitally decoupled applications, you're starting, we've come from a world where a virtual machine might have been up for months or years to all the sudden you have containers that are much more dynamic, allowed to scale quickly, and then they need to be orchestrated. And that's essentially what Kubernetes does, is really start to orchestrate that. And as we get more distributed workloads, you need to coordinate them. You need to be able to scale up as you need for performance etcetera So Kubernetes is an incredible technology that allows you really to optimize the placement of that. So just like the virtual machine changed how we compute, containers now gives us a much more flexible, portable, you can run on any infrastructure at any location. Closer to the data etcetera to do that. >> I think the bold move we made is, we finally, after working with customers and partners like Accenture, we have a very comprehensive strategy. We announced Project Tanzu at our last VM World. And Project Tanzu really focused on three aspects of containers, How do you build applications, which is what Pivotal and the acquisition of Pivotal was driven around. How do we run these on a robust enterprise class run time? And what if you could take every vSphere ESX out there and make it a container platform. Now we have half a million customers. 70 million VM's. All the sudden, that run time we are container enabling with a Project Pacific. So vSphere 7 becomes a common place for running containers and VMs. So that debate of VMs or containers? Done, gone. One place or just spend up containers and resources. And then the more important part is how do I manage this? As you have said. Becoming more of a platform, not just an orchestration technology. But a platform for how do I manage applications. Where I deploy them where it makes more sense. I've decoupled my application needs from the resources and Kubernetes is becoming that platform that allows me to portably. I'm the Java Weblogic guy, right? So this is like distributed Weblogic Java on steroids, running across clouds. So pretty exciting for a middleware guy, this is the next generation middleware. >> And to what you just said, that's the enabling infrastructure that will allow it to roll into future things like edge devices. >> Absolutely. >> You can manage an Edge client. You can literally-- >> the edge, yeah. 'Cause now you've got that connection. >> It's in the fabric that you are going to be able to connect. And networking becomes a key part. >> And one of the key things, and this is going to be the hard part is optimization. So how do we optimize across particularly performance but even cost? >> And security, rewiring security and availability. >> So still I think my all time favorite business book is Clayton Christensen, "Innovator's Dilemma". One of the most important lessons in that book is what are you optimizing for? And by rule, you can't optimize for everything equally. You have to rank order. But what I find really interesting in this conversation and where we're going and the complexity of the size of the data, the complexity of what am I optimizing for now just begs for plight AI. This is not a people problem to solve. This is AI moving fast. >> Smart infrastructure going to adapt. >> Right, so as you look at that opportunity to now apply AI over the top of this thing, opens up tremendous opportunity. >> Absolutely, I mean standardized infrastructure allows you, sorry, allows you to get more metrics. It allows you to build models to optimize infrastructure over time. >> And humans just can't get their head around it. I mean because you do have to optimize across multiple dimensions as performance, as cost. But then that performance is compute, it's the network. In fact the network's always going to be the bottleneck. So you look at it, even with 5G which is an order magnitude more band width, the network will still lag. You go back to Moore's Law, right? It's a, even though it's extended to 24 months, price performance doubles, so the amount of data potentially can exponentially grow our networks don't keep pace. So that optimization is constantly going to have to be tuned as we get even with increases in network we're going to have to keep balancing that. >> Right, but it's also the business optimization beyond the infrastructure optimization. For instance, if you are running a big power generation field of a bunch of turbines, right, you may want to optimize for maintenance 'cause things are running in some steady state but maybe there's an oil crisis or this or that, suddenly the price rises and you are like, forget the maintenance right now, we've got a revenue opportunity that we want to tweak. >> You just talked about which is in a dynamic industry. How do I real time change the behavior? And more and more policy driven, where the infrastructure is smart enough to react, based on the policy change you made. That's the world we want to get to and we are far away from that right now. >> I mean ultimately I think the Kubernetes controller gets an AI overlay and then operators of the future are tuning the AI engines that optimize it. >> Right, right. And then we run into the whole thing which we talked about many times in this building with Dr. Rumman Chowdhury from Accenture. Then you got the whole ethics overlay on top of the business and the optimization and everything else. That's a whole different conversation for another day. So, before we wrap I just want to give you kind of last thoughts. As you know customers are in all different stages of their journey. Hopefully, most of them are at least off the first square I would imagine on the monopoly board. What does, you know, kind of just top level things that you would tell people that they really need just to keep always at the top as they're starting to make these considerations? Starting to make these investments? Starting to move workloads around that they should always have at the top of their mind? >> For me it's very simple. It's really about focus on the business outcome. Leverage the best resource for the right need. And design architectures that are flexible that give you choice, you're not locked in. And look for strategic partners, whether it's technology partners or services partners that allow you to guide. Because if complexity is too high, the number of choices are too high, you need someone who has the breadth and depth to give you that platform which you can operate on. So we want to be the ubiquitous platform from a software perspective. Accenture wants to be that single partner who can help them guide on the journey. So, I think that would be my ask is start thinking about who are your strategic partners? What is your architecture and the choices you're making that give you the flexibility to evolve. Because this is a dynamic market. Once you make decisions today, may not be the ones you need in six months even. >> And that dynanicism is accelerating. If you look at it, I mean, we've all seen change in the industry, of decades in the industry. But the rate of change now, the pace, things are moving so quickly. >> And we need to respond to competitive or business oriented industry. Or any regulations. You have to be prepared for that. >> Well gentleman, thanks for taking a few minutes and great conversation. Clearly you're in a very good space 'cause it's not getting any less complicated any time soon. >> Well, thank you again. And thank you. >> All right, thanks. >> Thanks. >> Larry and Ajay, I'm Jeff, you're watching theCUBE. We are top of San Francisco in the Sales Force Tower at the Accenture Innovation Hub. Thanks for watching. We'll see you next time.

>> Hey, welcome back already, Jeffrey. Here with the Cube, we are high top San Francisco in the Salesforce Tower in the newest center offices. It's really beautiful and is part of that. They have their San Francisco innovation hubs, so it's five floors of maker's labs and three D printing and all kinds of test facilities and best practices Innovation theater and in this studio, which is really fun to be at. So we're talking about hybrid cloud in the development of cloud and multi cloud. And, you know, we're, you know, continuing on this path. Not only your customers on this path, but everyone's kind of on this path is the same kind of evolved and transformed. We're excited. Have a couple experts in the field. We got Larry Soccer. He's the global managing director of Intelligent Cloud Infrastructure Service's growth and strategy at a center. Very good to see you again. Great to be here. And the Jay Patel. He's the senior vice president and general manager, cloud provider, software business unit, being where enemies of the people are nice. Well, so, uh so first off, how you like the digs appear >> beautiful place and the fact we're part of the innovation team. Thank you for that. It's so let's just >> dive into it. So a lot of crazy stuff happening in the market place a lot of conversations about hybrid cloud, multi cloud, different cloud, public cloud movement of Back and forth from Cloud. Just wanted. Get your perspective a day. You guys have been in the Middle East for a while. Where are we in this kind of evolution? It still kind of feeling themselves out. Is it? We're kind of past the first inning, so now things are settling down. How do you kind of you. Evolution is a great >> question, and I think that was a really nice job of defining the two definitions. What's hybrid worse is multi and simply put hybrid. We look at hybrid as when you have consistent infrastructure. It's the same infrastructure, regardless of location. Multi is when you have disparate infrastructure. We're using them in a collective. So just from a level setting perspective, the taxonomy starting to get standardized industry starting to recognize hybrid is a reality. It's not a step in the long journey. It is an operating model that's gonna be exists for a long time, so it's no longer about location. It's a lot harder. You operate in a multi cloud and a hybrid cloud world and together, right extension BM would have a unique opportunity. Also, the technology provider Accenture, as a top leader in helping customers figure out where best to land their workload in this hybrid multicolored world, because workloads are driving decisions right and one of the year in this hybrid medical world for many years to come. But >> do I need another layer of abstraction? Cause I probably have some stuff that's in hybrid. I probably have some stuff in multi, right, because those were probably not much in >> the way we talked a lot about this, and Larry and I were >> chatting as well about this. And the reality is, the reason you choose a specific cloud is for those native different share capability. Abstraction should be just enough so you can make were close portable, really use the caper berry natively as possible right, and by fact, that we now with being where have a native VM we're running on every major hyper scaler, right? And on. Prem gives you that flexibility. You want off not having to abstract away the goodness off the cloud while having a common and consistent infrastructure. What tapping into the innovations that the public cloud brings. So it is a evolution of what we've been doing together from a private cloud perspective to extend that beyond the data center to really make it operating model. That's independent location, right? >> Solarium cures your perspective. When you work with customers, how do you help them frame this? I mean, I always feel so sorry for corporate CEOs. I mean, they got >> complexities on the doors are already going on >> like crazy that GDP are now, I think, right, The California regs. That'll probably go national. They have so many things to be worried about. They got to keep up on the latest technology. What's happening in containers away. I thought it was Dr Knight. Tell me it's kubernetes. I mean, it's really tough. So how >> do you help them? Kind of. It's got a shot with the foundation. >> I mean, you look at cloud, you look at infrastructure more broadly. I mean, it's there to serve the applications, and it's the applications that really drive business value. So I think the starting point has to be application lead. So we start off. We have are intelligent. Engineering guys are platform guys. You really come in and look And do you know an application modernisation strategy? So they'll do an assessment. You know, most of our clients, given their scale and complexity, usually have from 520,000 applications, very large estates, and they got to start to freak out. Okay, what's my current application's? You know, you're a lot of times I use the six R's methodology, and they say, OK, what is it that I I'm gonna retire. This I'm no longer needed no longer is business value, or I'm gonna, you know, replace this with sass. Well, you know, Yeah, if I move it to sales force, for example, or service now mattress. Ah, and then they're gonna start to look at their their workloads and say OK, you know, I don't need to re factor reform at this, you know, re hosted. You know, when one and things obviously be Emily has done a fantastic job is allowing you to re hosted using their softer to find a data center in the hyper scale er's environments >> that we called it just, you know, my great and then modernized. But >> the modern eyes can't be missed. I think that's where a lot of times you see clients kind of getting the trap Hammer's gonna migrate and then figure it out. You need to start tohave a modernisation strategy and then because that's ultimately going to dictate your multi and your hybrid cloud approaches, is how they're zaps evolve and, you know, they know the dispositions of those abs to figure out How do they get replaced? What data sets need to be adjacent to each other? So >> right, so a j you know, we were there when when Pat was with Andy and talking about, you know, Veum, Where on AWS. And then, you know, Sanjay has shown up, but everybody else's conferences a Google cloud talking about you know, Veum. Where? On Google Cloud. I'm sure there was a Microsoft show I probably missed. You guys were probably there to know it. It's kind of interesting, right from the outside looking in You guys are not a public cloud per se. And yet you've come up with this great strategy to give customers the options to adopt being We're in a public hot. And then now we're seeing where even the public cloud providers are saying here, stick this box in your data center and Frank, this little it's like a little piece of our cloud of floating around in your data center. So talk about the evolution of the strategy is kind of what you guys are thinking about because you know, you're cleared in a leadership position, making a lot of interesting acquisitions. How are you guys see this evolving? And how are you placing your bets? >> You know, that has been always consistent about this. Annie. Any strategy, whether it's any cloud, was any device, you know, any workload if you will, or application. And as we started to think about it, right, one of the big things be focused on was meeting the customer where he's out on its journey. Depending on the customer, let me simply be trying to figure out looking at the data center all the way to how the drive in digital transformation effort in a partner like Accenture, who has the breadth and depth and something, the vertical expertise and the insight. That's what customers looking for. Help me figure out in my journey. First tell me where, Matt, Where am I going and how I make that happen? And what we've done in a clever way, in many ways is we've created the market. We've demonstrated that VM where's the omen? Consistent infrastructure that you can bet on and leverage the benefits of the private or public cloud. And I You know, I often say hybrids a two way street. Now, which is you're bringing Maur more hybrid Cloud service is on Prem. And where is he? On Premise now the edge. I was talking to the centering folks and they were saying the mitral edge. So you're starting to see the workloads, And I think you said almost 40 plus percent off future workers that are gonna be in the central cloud. >> Yeah, actually, is an interesting stat out there. 20 years 2020 to 70% of data will be produced and processed outside the cloud. So I mean, the the edges about, you know, as we were on the tipping point of, you know, I ot finally taking off beyond, you know, smart meters. You know, we're gonna see a huge amount of data proliferate out there. So, I mean, the lines between public and private income literary output you look at, you know, Anthony, you know, as your staff for ages. So you know, And that's where you know, I think I am where strategy is coming to fruition >> sometime. It's great, >> you know, when you have a point of view and you stick with it >> against a conventional wisdom, suddenly end up together and then all of a sudden everyone's falling to hurt and you're like, This is great, but I >> hit on the point about the vertical ization. Every one of our client wth e different industries have very different has there and to the meeting that you know the customer, you know, where they're on their journey. I mean, if you talk to a pharmaceutical, you know, geekspeak compliance. Big private cloud started to dip their toes into public. You know, you go to minds and they're being very aggressive public. So >> every manufacturing with EJ boat back in >> the back, coming to it really varies by industry. >> And that's, you know, that's a very interesting here. Like if you look at all the ot environment. So the manufacturing we started see a lot of end of life of environment. So what's that? Next generation, you know, of control system's gonna run on >> interesting on the edge >> because and you've brought of networking a couple times where we've been talking it, you know, and as as, ah, potential gate right when I was still in the gates. But we're seeing Maura where we're at a cool event Churchill Club, when they had Xilinx micron and arm talking about, you know, shifting Maur that compute and store on these edge devices ti to accommodate, which you said, you know, how much of that stuff can you do at the adverse is putting in. But what I think is interesting is how are you going to manage that? There is a whole different level of management complexity when now you've got this different level of you're looting and security times many, many thousands of these devices all over the place. >> You might have heard >> recent announcements from being where around the carbon black acquisition right that combined with our work space one and the pulse I ot well, >> I'm now >> giving you a management framework with It's what people for things or devices and that consistency. Security on the client tied with the network security with NSX all the way to the data center, security were signed. A look at what we call intrinsic security. How do we bake and securing the platform and start solving these end to end and have a park. My rec center helped design these next generation application architectures are distributed by design. Where >> do you put a fence? You're you could put a fence around your data center, >> but your APP is using service now. Another SAS service is so hard to talk to an application boundary in the sea security model around that. It's a very interesting time. >> You hear a lot of you hear a >> lot about a partnership around softer to find data center on networking with Bello and NSX. But we're actually been spending a lot of time with the i o. T. Team and really looking at and a lot of our vision, the lines. I mean, you actually looked that they've been work similarly, agent technology with Leo where you know, ultimately the edge computing for io ti is gonna have to be containerized because you can need multiple middleware stacks supporting different vertical applications, right? We're actually you know what we're working with with one mind where we started off doing video analytics for predictive, you know, maintenance on tires for tractors, which are really expensive. The shovels, It's after we started pushing the data stream up it with a video stream up into azure. But the network became a bottleneck looking into fidelity. So we gotta process there. They're not looking autonomous vehicles which need eight megabits low laden C band with, you know, sitting at the the edge. Those two applications will need to co exist. And you know why we may have as your edge running, you know, in a container down, you know, doing the video analytics. If Caterpillar chooses, you know, Green Grass or Jasper that's going to co exist. So you see how the whole container ization that were started seeing the data center push out there on the other side of the pulse of the management of the edge is gonna be very difficult. I >> need a whole new frontier, absolutely >> moving forward. And with five g and telco. And they're trying to provide evaluated service is So what does that mean from an infrastructure perspective. Right? Right, Right. When do you stay on the five g radio network? Worse is jumping on the back line. And when do you move data? Where's his process? On the edge. Those all business decisions that need to be doing to some framework. >> You guys were going, >> we could go on. Go on, go. But I want to Don't fall upon your Segway from containers because containers were such an important part of this story and an enabler to the story. And, you know, you guys been aggressive. Move with hefty Oh, we've had Craig McCloskey, honor. He was still at Google and Dan great guys, but it's kind of funny, right? Cause three years ago, everyone's going to Dr Khan, right? I was like that were about shows that was hot show. Now doctors kind of faded and and kubernetes has really taken off. Why, for people that aren't familiar with kubernetes, they probably here to cocktail parties. If they live in the Bay Area, why's containers such an important enabler? And what's so special about Coburn? 80 specifically. >> Do you wanna go >> on the way? Don't talk about my products. I mean, if you >> look at the world is getting much more dynamics on the, you know, particularly you start to get more digitally to couple applications you started. You know, we've gone from a world where a virtual machine might have been up for months or years. Toe, You know, obviously you have containers that are much more dynamic, allowed to scale quickly, and then they need to be orchestrated. That's essential. Kubernetes does is just really starts to orchestrate that. And as we get more distributed workloads, you need to coordinate them. You need to be able to scale up as you need it for performance, etcetera. So kubernetes an incredible technology that allows you really to optimize, you know, the placement of that. So just like the virtual machine changed, how we compute containers now gives us a much more flexible portable. You know that, you know you can run on anything infrastructure, any location, you know, closer to the data, et cetera. To do that. And I >> think the bold movie >> made is, you know, we finally, after working with customers and partners like century, we have a very comprehensive strategy. We announced Project Enzo, a philosophy in world and Project tansy really focused on three aspects of containers. How do you build applications, which is pivotal in that mansion? People's driven around. How do we run these arm? A robust enterprise class run time. And what if you could take every V sphere SX out there and make it a container platform? Now we have half a million customers. 70 million be EMS, all of sudden that run time. We're continue enabling with the Project Pacific Soviets. Year seven becomes a commonplace for running containers, and I am so that debate of'em czar containers done gone well, one place or just spin up containers and resource is. And then the more important part is How do I manage this? You said, becoming more of a platform not just an orchestration technology, but a platform for how do I manage applications where I deploy them where it makes most sense, right? Have decoupled. My application needs from the resource is, and Coburn is becoming the platform that allows me to port of Lee. I'm the old job Web logic guy, right? >> So this is like distributed Rabb logic job on steroids, running across clouds. Pretty exciting for a middle where guy This is the next generation and the way you just said, >> And two, that's the enabling infrastructure that will allow it to roll into future things like devices. Because now you've got that connection >> with the fabric, and that's working. Becomes a key part of one of the key >> things, and this is gonna be the hard part is optimization. So how do we optimize across particularly performance, but even costs? >> You're rewiring secure, exact unavailability, >> Right? So still, I think my all time favorite business book is Clayton Christians. An innovator's dilemma. And in one of the most important lessons in that book is What are you optimizing four. And by rule, you can't optimize for everything equally you have to you have to rank order. But what I find really interesting in this conversation in where we're going in the complexity of the throughput, the complexity of the size of the data sets the complexity of what am I optimizing for now? Just begs for applied a I or this is not This is not a people problem to solve. This is this >> is gonna be all right. So you look at >> that, you know, kind of opportunity to now apply A I over the top of this thing opens up tremendous opportunity. >> Standardize infrastructural auditory allows you to >> get more metrics that allows you to build models to optimize infrastructure over time. >> And humans >> just can't get their head around me because you do have to optimize across multiple mentions. His performances cost, but then that performances gets compute. It's the network, I mean. In fact, the network's always gonna be the bottlenecks. You look at it even with five G, which is an order of magnitude, more bandwidth from throughput, the network will still lag. I mean, you go back to Moore's Law, right? It's Ah, even though it's extended to 24 months, price performance doubles. The amount of data potentially can kick in and you know exponentially grow on. Networks don't keep pays, so that optimization is constantly going to be tuned. And as we get even with increases in network, we have to keep balancing that right. >> But it's also the business >> optimization beyond the infrastructure optimization. For instance, if you're running a big power generation field of a bunch of turbines, right, you may wanna optimize for maintenance because things were running at some steady state. But maybe there's oil crisis or this or that. Suddenly the price, right? You're like, forget the maintenance. Right now we've got you know, we >> got a radio controlled you start about other >> than a dynamic industry. How do I really time change the behavior, right? Right. And more and more policy driven. Where the infrastructure smart enough to react based on the policy change you made. >> That's the world we >> want to get to. And we're far away from that, right? >> Yeah. I mean, I think so. Ultimately, I think the Cuban honeys controller gets an A I overlay and the operators of the future of tuning the Aye aye engines that optimizing, >> right? Right. And then we run into the whole thing, which we've talked about many times in this building with Dr Room, A child re from a center. Then you got the whole ethics overlay on top of the thing. That's a whole different conversation from their day. So before we wrap kind of just want to give you kind of last thoughts. Um, as you know, customers Aaron, all different stages of their journey. Hopefully, most of them are at least at least off the first square, I would imagine on the monopoly board What does you know, kind of just top level things that you would tell people that they really need just to keep always at the top is they're starting to make these considerations, starting to make these investments starting to move workloads around that they should always have kind of top >> of mind. For me, it's very simple. It's really about focused on the business outcome. Leverage the best resource for the right need and design. Architectures are flexible that give you a choice. You're not locked in and look for strategic partners with this technology partners or service's partners that alive you to guide because the complexities too high the number of choices that too high. You need someone with the breath in depth to give you that platform in which you can operate on. So we want to be the digital kind of the ubiquitous platform. From a software perspective, Neck Centuries wants to be that single partner who can help them guide on the journey. So I think that would be my ask. It's not thinking about who are your strategic partners. What is your architecture and the choices you're making that gave you that flexibility to evolve. Because this is a dynamic market. What should make decisions today? I mean, I'll be the one you need >> six months even. Yeah. And And it's And that that dynamic that dynamics is, um is accelerating if you look at it. I mean, we've all seen change in the industry of decades in the industry, but the rate of change now the pace, you know, things are moving so quickly. >> I mean, little >> respond competitive or business or in our industry regulations, right. You have to be prepared for >> Yeah. Well, gentlemen, thanks for taking a few minutes and ah, great conversation. Clearly, you're in a very good space because it's not getting any less complicated in >> Thank you. Thank you. All right. Thanks, Larry. Ajay, I'm Jeff. You're watching the Cube. >> We are top of San Francisco in the Salesforce Tower at the center Innovation hub. Thanks for watching. We'll see next time. Quick

>> 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)

(can opening) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are live in downtown San Francisco at Zuora Subscribe 2017. 2,000 people talking about the subscription economy and subscription equals freedom, and coming right off the keynote, we're excited to have the founder and CEO, Tien Tzuo, founder of Zuora. >> Great to be here. >> Jeff: Well first of all, great job on the keynote. >> Oh, thanks. Thanks for having me on the show. >> Great energy. You know, we hear a lot of about subscription economy. Obviously, a lot of people have Amazon Prime, a lot of us subscribe at Costco. We've got streaming music services, like Spotify. But I don't think people think of companies like Caterpillar, or Fender Guitar, as companies that have a subscription-based relationship with their customer. So before we get into the specifics, I want to talk to you, how is the subscription relationship different than a regular, one-off transactional relationship in the way that you are connected to your customer? >> Right, well, we all know that the world has changed. And we're even evangelizing at this event. This is the sixth year we're having this event. There's over 7,000 people that actually come to these events around the world. That the world is moving to a subscription economy. Starting two years ago, people said, "You know what, we get it. "This is a subscription economy. "I can feel myself, I don't buy products anymore. "I simply tap into services that I use." And the great thing about these services is the provider of these services really care about you. They want you to come back and use their services. They're constantly updating it. And it really frees us all from the shackles of product ownership when we want to get from point A to point B today. We don't have to worry about cars. We pull out our phone, tap into our service, and we're able to get what we need when we want it. >> Yeah, you have that as a really big theme. Kind of shackles of ownership, shackles of obsolescence. This idea that if you have a subscription to a service, you don't have to worry about the oil change. You don't have to worry about whether it's last year's model. You've pulled up some funny pictures of CDs and the CD wasn't even in the CD case. >> The CD, and that wasn't that long ago that we had these CD cases. >> I have empty CD cases all over my garage. I'm guilty as charged. Let's dive into this specific example. So Caterpillar is a cool example. Already having autonomous vehicles driving these big mining. That's all right, but let's talk about the Fender example, 'cause I think that's a really interesting one. What is Fender doing in terms of a subscription relationship with their customers to change who they are and what they are as a business? >> Well, we talk to companies that are going through this transformation. What they bring it down to is the shift in mindset of selling a product to thinking about customers. And so when Fender did this, an amazing transformation happened, right? They sell a lot of guitars. And when they look at shipping products out, how do I sell more guitars? And they said, "Let's not look at it that way. "Let's look at our customers." And what they found is that over 40% of their customers, guitar purchases, are first-time customers. And then 90% of the customers quit after about three months because it's just too hard. It's just too hard. And so when they look at it that way, they say, "Gosh, we have a 10% retention rate "for our customers after 90 days. "Now, if we can just extend that, "extend that out," and, oh, by the way, the 10% of customers that stay, they stay for life. They buy additional guitars. They buy additional amps. They buy sheet music. They buy picks. And so that's how we have to think. We have to not think about selling more guitars. We have to think about how to hold on to our customers for life. If we could just go from 10% to 15% to 20%, we are going to find so much more revenue and we're going to double or triple the size of our company. >> So how do they execute that with your guys' software. >> So what they need to do is they need to establish a subscriber ID. So when you buy a Fender now, there's a whole set of digital technologies that they draw you into. There's a tuning app that you can use, 'cause it's hard to tune your guitar. There's applications that teach you how to play a guitar. There's applications that you can use to play like The Edge, or play like Flea, or play like your favorite guitarist so they draw you into the process that creates social community, social networks. And what we do is we help them turn a guitar purchase into a subscription service that the customers opt into for life. >> So interesting, right? 'Cause this is not a transaction; it's an experience. And it's an engagement. And what are the other things you said in the keynote that got my attention? That there's all these other transactions now. You can buy, you can upgrade, you can pause, you can turn off, you can turn on, you can change the level. So it's this much more dynamic, engaged process and relationship between person selling the service and, arguably, guitar enjoyment, not a Fender Guitar versus an actual piece of wood and some metal strings and everything else. >> Right, what we try to talk about is this whole world of subscriptions. Ultimately, when you're successful is when you deliver freedom to customers, right? Freedom to customers that didn't have it before, right? The story of Netflix is if you have, or let's say Spotify, so you have $20 to spend, you don't have to buy one song, one album, one CD. You can access the whole library of music ever created. And there's a freedom to that. Now, what that means for businesses to react to that is that puts a lot of constraints on businesses, right? Before, they just simply take orders, give me a guitar; give me a song; give me five units of this Widget. Now they have to react to what customers want. I want this; I want this now; I want it like this; I want to upgrade; I want to downgrade. And so this creates all these constraints on businesses and what we want to talk about today was in this new world, businesses need freedom too, right? Businesses need freedom to price, to experiment, to design customer experiences, to get the information they need and what's holding them back is their IT architectures are the past. These ERP systems, and so what we presented this morning was an alternative view. A post-CR view, P, ERP view, of a new set of systems that we provide that help companies be successful and grow in this new subscription economy. >> That's a linear. Basically, that was your theme, right? Not because it's linear. >> That's right. >> It's those transaction types. >> These linear systems passed, they don't work anymore. >> Well the other thing I think is really compelling that I think needs more attention is now, if I have to pay $20 a month to Spotify, which I do. We're on the family plan. I love the service. But they have to keep delivering new value, because for me to keep paying every month, it's a much deeper relationship because they got to keep keeping me on the hook. They got to keep innovating. They got to keep delivering new things and so that's what I think is really interesting about this is the relationship between the buyer and the seller when you have an ongoing touch point every single month versus that one-time transaction. >> Well the keyword there is relationships, right? In the old model, which I'll call an asset transfer model, let me convince you to buy my product. Now you own it. I've gotten your money and I'm going to go focus on the next customer. This new model really requires me to care about the relationship, to care about the value that I'm creating, to continue to add to it to make sure that there's not an alternative out there that's moving faster and delivering things that I'm not. That relationship becomes really, really important. And that's why this model is better and that's why when you use services like a Salesforce, like an Uber, a Spotify, a Netflix, an Amazon Prime, you get the feeling that the other person, the vendor on the other side, really cares about you because, of course, they do. >> All right, so I know you're super busy. You got a lot to do but before you leave, just give your impression, you've been at this for a while, how this has grown. Has it grown faster than you expected? Is it about the same line? As you've seen the subscription economy grow from your initial vision six, seven years ago, what's your, kind of, takeaway as you sit here amongst 2,000 people that are in, arguably, the center of this universe right now? >> Gosh, when you look at this subscribed event, when you look at the energy here, And then when you look at the companies here, I would say five, six, seven years ago, we had a lot of software as service companies here. Box, they're great customers. They continue to be customers. But did we think that we would have Ford, right? Showcasing their electric bikes here? Or Caterpillar showcasing their autonomous vehicles? And these are gigantic vehicles that are carrying 200 cars in what they talked about on stage. And the world's clearly being transformed. Did I think it was going to happen? You know, we always knew the subscription economy was going to be here. We always knew the size and scope. But once you hear the stories, right, you can really tell how much our world is going to change and how much it's going to become just, simply, a better place. >> All right Tien, well congratulations to you and thanks for taking a few minutes to stop by the table. >> Thanks a lot. Thanks for having me. >> All right, he's Tien, I'm Jeff. You're watching theCUBE from Zuora Subscribe. Thanks for watching. (can opening)

>> Announcer: Live from the Javits Center in New York City, it's the theCUBE, covering Inforum 2017. Brought to you by Infor. (bright electronic music) >> We are back with theCUBE's coverage of Inforum 2017. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by Barig Siraj and Nasser Bayram. They are both of the Zahid Group, out of Saudi Arabia. Thank you both so much for joining us. >> Good to be here, thank you for having us. >> So I want you to start out by just explaining to our viewers a little bit about what Zahid Group and Zahid Tractor, what you do. >> We are a large group based in Saudi Arabia. We're very diversified. We are mainly in heavy equipment, capital equipment business. We are the importer of Caterpillar machinery and Volvo trucks, Renault trucks, and many other products. More than 40 franchises. We have locations in more than 40 locations, or branches, more than 40 locations for their area, and we have about 4,000 employees, and we mainly focus on providing sales and after-sales services in the kingdom, with a big focus on after-sales. We pride ourselves to be the second to none when it comes down to after-sales services, and we strongly believe in technology and in digital transformation that is sweeping the world of business, and thus far, we embarked on this journey five years ago. >> So what does that digital transformation mean for your business, and generally, and then specifically for IT. Maybe you can start, Nasser. >> Well, first, we have to agree. The business model has changed. There are new business models that has disrupted every single industry landscape out there, and you have to be ready to change and accept that transformation, otherwise, you'll be left behind. The digital transformation takes you beyond managing an organization introducing an IT platform or technology. You have to change the way you think and your readiness to be able to manage where the future is going. If we look, we just attended this session, 52% of Fortune 500 companies in year 2000 no longer exist. They went out of business. In 2015, 55% of Fortune 500 companies lost money. There was no economic crisis or downfall. It simply missed the boat, or they did not, they were not very innovative in their digital strategy or thinking ahead, allowing their industry to be disrupted by people like Uber, Amazon, Alibaba, Souq, an other new entrants with very great innovative ideas and technologies. The old business model of cutting cost or restructuring an organization no longer works. You need to think differently and act differently, and hence, digital transformation becomes critical for your organization, and implementing an ERP platform, standardizing rationalization of your ERP platform, if you have more than one, like in our case, we have more than one, you have to have one standardized platform, one standardized processes, business processes, so that we have one source of data in order to be ready for the future where you can mine that data, have it be by analytics or business intelligence, in order to be able to better serve your customers and learning on about their behavior, about their trends, and how you can better position production services for them in the future to buy, and for you to remain profitable. >> So Barig, okay so now, that's, what Nasser just described, I'm inferring, is much more real-time, much faster, and more data. Your ability to analyze that data wherever it is, how do you, and the processes and people behind that as we talked about technology's the easy part even though some technology's even more complicated than ever. So what does that mean for the IT organization? >> Well for IT organization, we had, and we still have a legacy application built over 30 years. Now, and there we could not reap the benefits of the data mining, the standardization, even that just from AI capabilities on top of that. We cannot reap that until we have that standardized ERP Platform across all our companies. So basically, that's the tall order that was put on our plate, and what we have done, we started the journey. We're partly through it. We went live with two of our companies. We still have three more to go, and we've done it with lesser volume, allowing us to learn and therefore, once we reach our biggest volume company, we would have learned as an organization, not just applying the technology that even the personnel, the change management, the resistant pockets have to deal with all of that. >> Can you give an example of what you've learned along the way, becoming, as we said, it is so much about change management, and it's about getting people over this fear of change. Can you give an example of what you've learned, of what you're doing differently for the companies that have yet to have the rollout? >> The biggest learning experience we had, we just went live with one of our companies, called EJAR, which is a rental company. The success there of the learning, the success is a learning experience. We have a long journey for to go live with five companies, and this is the first one to go live. What we learned by doing that company first is the challenges of change management, how to support on live, challenge of data migration, data cleansing, readiness of the organization, not simply from change management perspective, but also from IT, legal, readiness of your documentation, the contracts, et cetera. It's a vast learning curve to overcome, and we're very happy that we took the strategic decision to go live once more company, so that we gain that experience, and that is the real success we got out of this project now. Now we better we feel we are in better position for the new companies to go forward with, when we go live, we learn so much about change management, where we failed and where we succeeded, we learn better about our readiness, whether it is Zahid Tractor, or Infor, or our IT, our infrastructure, our training program, our after go-live support, the war room was set up to support the go-live, and go in production. We've been two month in production. We're still having some challenges, but nothing that, there are no showstoppers, however, more and more every day, we learn more and more, and we are better positioned to go live with a bing bang on the big company. >> Nasser, as the executive in sort of leading this transformation, do you look for and demand new metrics, new types of KPIs that you want to see? >> Well, definitely, you do the whole thing because of the new metrics. The new metrics have to have built into it, not simply the traditional KPIs of your GPs and revenue and discount and so on, you need to look at customer behavior, customer analytics, pricing positioning, where you are going forward. In the old days, everybody would sit down around the table, say, "Hey, we're number one, okay?" That doesn't hold water anymore. You're number one in what? It's about number one in responding to customer requirements on that customer behavior. Today, with Amazon.com, many retail businesses are challenged, they're going out of business. How do you stop that business model? You can't. So how do you compete? You can. To do that, you have to have the right data in place, the right organization in place, and the right mindset to be able to lead your organization to compete in the new market space. >> Can you give our viewers some examples of the kind of data that you are deriving, in terms of this business analytics, in terms of understanding and deepening your understanding of customer behavior, and what customers want, and how it's changing, how you approach your customers and what you do for them. >> I'll give you a comparison. When we have a legacy systems, what you do at end of day, you extract your data, you transform it and you load it up to your data mart or data warehouse, and then you run your report, and if you're lucky, you have savvy users who can create their own reports on the fly, but with the way we're going with an integrated ERP solution and one standardized platform, we do hope we have the right analytics in place, and business intelligence in place, that we give our management the right data to make decisions, ready to make decisions. Not filtered data, not reports designed, and that takes me straight into your question on IT and ability to IT to deliver. There is no way for any IT organization to cope with the changes. Nowadays, when Amazon went live recently with Whole Food, it took them three to six, three to four months to deal with legal, to deal with retail, with pricing, with the announcement, the whole nine yards of marketing. How did they have their IT ready? That's a challenge. How can you do that in four to six month? That is the challenge in the future. If you don't have the right platform to do that, you will never be able to compete, and data analytics are critical for you to respond or predict the behavior of customer, so before a customer comes next time to the counter, you already have certain statistics that tell you what that person is ready for, and that takes you straight also into IoT. Your products, or our products now, are connected to the Internet. If you don't have IoT in place, connected to your back end, and your analytics, you won't be able to compete, and that would be the differentiator in the future. Those who could do that versus those who will continue to follow the old brick and mortar business model, restructuring and cost-cutting and whatnot. >> So your instrumenting your heavy equipment in the field, presumably, and that's, you're well down the road with that. That changes the data model, it changes the analytics model so I wonder if you could describe that a little bit. I mean, obviously you're processing data at the edge. How much data stays at the edge versus comes back to your central location, maybe you could add some color to that whole equation. >> Well the devices that are put on the machines, there are several ways of putting. The older models, you have, actually the PSSR has to actually go with his laptop, hook it up, suck the data, and bring it back for analytics. The newer models are more, are sending it to, directly to us, and enabling our, what I call tower, to do equipment monitoring, and be able to anticipate, we call up the customer and saying, "By the way." Actually tell the salesmen to call up the customer and saying, "You need to bring your machine in "because it's, you might face a failure "in so amount of time." So improving the customer side, that is, that is that part, but coming back to the organization change issue, we went from a legacy application that the branch managers waited until the end of the month to get the truth, to now being able to, seeing the performance on a daily basis, because they're seeing the truth because everything is connected, whereas before, whatever they did, they don't, their piece of the puzzle, they have a lot missing, and they, information that they waited until it show, send them back there, a report. >> And none of this takes place in the public cloud, is that right? >> No, it does, to add to that, the data is stored in the cloud. Customers have access to it, along with our SOS lab, which is oil sampling lab. They have access to the data to see what is happening, like predictive analysis of their machine performance, and as a result of analyzing the oil, plus any data collected from these machines. We do have cloud implementation. We just went live with our treasury management system. It is on the cloud, and it was our first deployment on the cloud, though the implementation of Infor today is still on-premise. Long-term, down the road, we may be looking at the cloud. >> I got to ask you, we hear Infor messaging about microspecialization, that last mile, all the hard stuff that nobody else wants to do. Is that something that you take advantage of in your industry, or is it? >> I'll give you an example. We utilize the implementation accelerator from Infor for the rental, and it's 77% of our processes map directly into that, so we, that enabled us, that, to have EJAR, which is a rental company, go much smoother. Now, we're working with Infor to enhance their equipment implementation accelerator, and it will be partly the same ratio, around 70% of the processes that we're going to go live with, are the standard processes in the product, out of the box, for the equipment rental, for the equipment business space. >> Our objective is to reduce customization as much as possible, go out of the box, or native, out of the box, as much as possible, but you have to accept the fact, depending on your business environment and some localization requirement, you have to do some customization. However we do have a governance in place, to make sure it's to the minimal. Otherwise, long-term, you'll be challenged with release management and change management and so on, and when you speak of the cloud, if you ever elect to go to the cloud, you can kiss customization goodbye. (Dave laughs) You have to be ready to adopt and adapt. >> And how about your security regime, as a result of the edge and IoT and now, cloud, how is that evolving? >> That's close to my heart. (laughs) >> Yeah, I'll bet, and probably the board's. >> Actually, well, (laughs) actually, interesting enough, many organization, like ourselves included, we invested so much money in building firewalls and security systems to protect what's behind the wall. Now with the cloud, well your most important data is no longer behind the wall. >> Rebecca: It's right there. >> It's outside the wall, so you have to have some kind of a hybrid security system, and you really have to pick the right partner who is hosting your cloud application, leasing your cloud application to you, so the challenge or the perspective of security, cybersecurity, changes drastically and totally, and your understanding of it has to change, otherwise, you just stay behind your own wall and guess what? You can end up locking yourself behind the wall, and you're going to miss the boat, but this does not mean that you'll let down your guard. You have to maintain your security awareness, you have to maintain your security diligence, and you should not underestimate the threats out there, because even if you are on the cloud, the biggest threat nowadays is through phishing. That's what we call the human firewall. Relegating the right awareness, the right education to your organization from within, to understand the threats and the danger of such a threat, otherwise, your password, that's how you access the cloud, you'll end up be compromised and guess what? So will be your data. >> Yes, so, Barig, Nasser, thank you so much for joining us. It's been great to have you on the program. >> Our pleasure. >> Thank you. >> Nasser: Thank you for hosting us, thank you. >> See you guys again, great, thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from Inforum after this. 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>> Narrator: From the Fairmont Hotel in the heart of Silicon Valley, it's theCUBE. Covering When IoT met AI: The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back here everybody Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel. At a little event it's When IoT Met AI: The Intelligence of Things. As we hear about the Internet of Things all the time this is really about the data elements behind AI, and machine learning, and IoT. And we're going to get into it with some of the special guests here. We're excited to get the guy that's going to kick off this whole program shortly is Tom Stuermer. He is the I got to get the new title, the Global Managing Director, Ecosystem and Partnership, from Accenture. Tom, welcome-- >> Thank you, Jeff. >> And congrats on the promotion. >> Thank you. >> So IoT, AI, buzz words, a lot of stuff going on but we're really starting to see stuff begin to happen. I mean there's lots of little subtle ways that we're seeing AI work its way in to our lives, and machine learning work our way into its life, but obviously there's a much bigger wave that's about to crest here, shortly. So as you kind of look at the landscape from your point of view, you get to work with a lot of customers, you get to see this stuff implemented in industry, what's kind of your take on where we are? >> Well, I would say that we're actually very early. There are certain spaces with very well-defined parameters where AI's been implemented successfully, industrial controls on a micro level where there's a lot of well-known parameters that the systems need to operate in. And it's been very easy to be able to set those parameters up. There's been a lot of historical heuristic systems to kind of define how those work, and they're really replacing them with AI. So in the industrial spaces a lot of take up and we'll even talk a little bit later about Siemens who's really created a sort of a self-managed factory. Who's been able to take that out from a tool level, to a system level, to a factory level, to enable that to happen at those broader capabilities. I think that's one of the inflection points we're going to see in other areas where there's a lot more predictability and a lot of other IoT systems. To be able to take that kind of system level and larger scale factors of AI and enable prediction around that, like supply chains for example. So we're really not seeing a lot of that yet, but we're seeing some of the micro pieces being injected in where the danger of it going wrong is lower, because the training for those systems is very difficult. >> It's interesting, there's so much talk about the sensors, and the edge, and edge computing, and that's interesting. But as you said it's really much more of a system approach is what you need. And it's really kind of the economic boundaries of the logical system by which you're trying to make a decision in. We talk all the time, we optimizing for one wind turbine? Are you optimizing for one field that contains so many wind turbines? Are you optimizing for the entire plant? Or are you optimizing for a much bigger larger system that may or may not impact what you did on that original single turbine? So a systems approach is a really critical importance. >> It is and what we've seen is that IoT investments have trailed a lot of expectations as to when they were going to really jump in the enterprise. And what we're finding is that when we talk to our customers a lot of them are saying, look I've already got data. I've got some data. Let's say I'm a mining company and I've got equipment down in mines, I've got sensors around oxygen levels, I just don't get that much value from it. And part of the challenge is that they're looking at it from a historical data perspective. And they're saying well I can see the trajectory over time of what's happening inside of my mind. But I haven't really been able to put in prediction. I haven't been able to sort of assess when equipment might fail. And so we're seeing that when we're able to show them the ability to affect an eventual failure that might shut down revenue for a day or two when some significant equipment fails, we're able to get them to start making those investments and they're starting to see the value in those micro pockets. And so I think we're going to see it start to propagate itself through in a smaller scale, and prove itself, because there's a lot of uncertainty. There's a lot of work that's got to be done to stitch them together, and IoT infrastructure itself is already a pretty big investment as it is. >> Short that mine company, because we had Caterpillar on a couple weeks ago and you know their driving fleets of autonomous vehicles, they're talking about some of those giant mining trucks who any unscheduled downtime the economic impact is immense well beyond worrying about a driver being sick, or had a fight with his wife, or whatever reason is bringing down the productivity of those vehicles. So it's actually amazing the little pockets where people are doing it. I'm curious to get your point of view too on kind of you managed to comment the guy's like I'm not sure what the value is because the other kind of big topic that we see is when will the data and the intelligence around the data actually start to impact the balance sheet? Because data used to be kind of a pain, right? You had to store it, and keep it, and it cost money, and you had to provision servers, and storage, but really now and the future the data that you have, the algorithms you apply to it will probably be an increasing percentage of your asset value if not the primary part of you asset value, you seeing some people start to figure that out? >> Well they are. So if you look, if step back away from IoT for a minute and you look at how AI is being applied more broadly, we're finding some transformational value propositions that are delivering a lot of impacts to the bottom line. And it's anywhere from where people inside of a company interact with their customers, being able to anticipate their next move, being able to predict given these parameters of this customer what kind of customer care agent should I put on the phone with them before you even pick up the phone to anticipate some of those expectations. And we're seeing a lot of value in things like that. And so, excuse me, and so when you zoom it back in to IoT some of the challenges are that the infrastructure to implement IoT is very fragmented. There's 360 some IoT platform providers out in the world and the places where we're seeing a lot of traction in using predictive analytics and AI for IoT is really coming in the verticals like industrial equipment manufacturers where they've kind of owned the stack and they can define everything from the bottom up. And what they're actually being able to do is to start to sell product heavy equipment by the hour, by the use, because they're able to get telemeter off of that product, see what's happening, be able to see when a failure is about to come, and actually sell it as a service back to a customer and be able to predictably analyze when something fails and get spares there in time. And so those are some of the pockets where it's really far ahead because they've got a lot of vertical integration of what's happening. And I think the challenge on adoption of broader scale for companies that don't sell very expensive assets into the market is how do I as a company start to stitch my own assets that are for all kinds of different providers, and all kinds of the different companies, into a single platform? And what the focus has really been in IoT lately for the past couple of years is what infrastructure should I place to get the data? How do I provision equipment? How do I track it? How do I manage it? How do I get the data back? And I think that's necessary but completely insufficient to really get a lot of value IoT, because really all your able to do then is get data. What do you do with it? All the value is really in the data itself. And so the alternative approach a lot of companies are taking is starting to attack some of these smaller problems. And each one of them tends to have a lot of value on its own, and so they're really deploying that way. And some of them are looking for ways to let the battles of the platforms, let's at least get from 360 down to 200 so that I can make some bets. And it's actually proving to be a value, but I think that is one of the obstacles that we have to adoption. >> The other thing you mentioned interesting before we turned on the cameras is really thinking about AI as a way to adjust the way that we interact with the machines. There's two views of the machines taking over the world, is it the beautiful view, or we can freeze this up to do other things? Or certainly nobody has a job, right? The answer is probably somewhere in the middle. But clearly AI is going to change the way, and we're starting to see just the barely the beginnings with Alexa, and Siri, and Google Home, with voice interfacing and the way that we interact with these machines which is going to change dramatically with the power of, as you said, prescriptive analytics, presumptive activity, and just change that interaction from what's been a very rote, fixed, hard to change to putting as you said, some of these lighter weight, faster to move, more agile layers on the top stack which can still integrate with some of those core SAP systems, and systems of record in a completely different way. >> Exactly, you know I often use the metaphor of autonomous driving and people seem to think that that's kind of way far out there. But if you look at how driving an autonomous vehicle's so much different from driving a regular car, right? You have to worry about at the minutia of executing the driving process. You don't have to worry about throttle, break. You'd have to worry about taking a right turn on red. You'd have to worry about speeding. What you have to worry about is the more abstract concepts of source, destination, route that I might want to take. You can offload that as well. And so it changes what the person interacting with the AI system is actually able to do, and the level of cognitive capability that they're able to exercise. We're seeing similar things in medical treatment. We're using AI to do predictive analytics around injury coming off of medical equipment. It's not only starting to improve diagnoses in certain scenarios, but it's also enabling the techs and the doctors involved in the scans to think on a more abstract level about what the broader medical issues are. And so it's really changing sort of the dialogue that's happening around what's going on. And I think this is a good metaphor for us to look at when we talk about societal impacts of AI as well. Because there are some people who embrace moving forward to those higher cognitive activities and some who resist it. But I think if you look at it from a customer standpoint as well, no matter what business you're in if you're a services business, if you're a product business, the way you interact with your employees and the way you interact with your customers can fundamentally be changed with AI, because AI can enable the technology to bend it to your intentions. Someone at the call center that we talked about. I mean those are subtle activities. It's not just AI for voice recognition, but it's also using AI to alter what options are given to you, and what scenarios are going to be most beneficial. And more often than not you get it right. >> Well the other great thing about autonomous vehicles, it's just a fun topic because it's something that people can understand, and they can see, and they can touch in terms of a concept to talk about, some of these higher level concepts. But the second order impacts which most people don't even begin to think, they're like I want to drive my car is, you don't need parking lots anymore because the cars can all park off site. Just Like they do at airports today at the rental car agency. You don't need to build a crash cage anymore, because the things are not going to crash that often compared to human drivers. So how does the interior experience of a car change when you don't have to build basically a crash cage? I mean there's just so many second order impacts that people don't even really begin to think about. And we see this time and time again, we saw it with cloud innovation where it's not just is it cheaper to rent a server from Amazon than to buy one from somebody else? It's does the opportunity for innovation enable more of your people to make more contributions than they could before because they were too impatient to wait to order the server from the IT guy? So that's where I think too people so underestimate kind of the big Moore's Law my favorite, we overestimate in the short term and completely underestimate in the long term, the impacts of these things. >> It's the doubling function, exactly. >> Jeff: Yeah, absolutely. >> I mean it's hard for people, human kind is geared towards linear thinking, and so when something like Moore's Law continues to double every 18 months price performance continues to increase. Storage, compute, visualization, display. >> Networking, 5G. >> You know the sensors in MEMS, all of these things have gotten so much cheaper. It's hard for human of any intelligence to really comprehend what happens when that doubling occurs for the next 20 years. Which we're now getting on the tail end of that fact. And so those manifest themselves in ways that are a little bit unpredictable, and I think that's going to be one of our most exciting challenges over the next five years is what does an enterprise look like? What does a product look like? One of the lessons that, I spent a lot of time in race car engineering in my younger days and actually did quants and analytics, what we learned from that point is as you learned about the data you started to fundamentally change the architecture of the product. And I think that's going to be a whole new series of activities that are going to have to happen in the marketplace. Is people rethinking fundamental product. There's a great example of a company that's completely disrupted an industry. On the surface of it it's been disrupted because of the fact that they essentially disassociated the consumption from the provision of the product. And didn't have to own those assets so they could grow rapidly. But what they fundamentally did was to use AI to be able to broker when should I get more cars, where should the cars go? And because they're also we're on the forefront of being able to drive, this whole notion of consumption of cars, and getting people's conceptual mindset shifted to having owned a car to I know an Uber's going to be there. It becomes like a power outlet. I can just rely on it. And now people are actually starting to double think about should I even own a car? >> Whole different impact of the autonomous vehicles. And if I do own a car why should it be sitting in the driveway when I'm not driving it? Or I send it out to go work for me make it a performing asset. Well great conversation. You guys Accenture's in a great spot. You're always at the cutting edge. I used to tease a guy I used to work with at Accenture you've got to squeeze out all the fat in the supply chain (laughs) your RP days and again a lot of these things are people changing the lens and seeing fat and inefficiency and then attacking it in a different way whether it's Uber, Airbnb, with empty rooms in people's houses. We had Paul Doherty on at the GE Industrial Internet launch a few years back, so you guys are in a great position because you get to sit right at the forefront and help these people make those digital transformations. >> I appreciate that. >> I will tell you I mean supply chains is another one of those high level systems opportunities for AI where being able to optimize, think about it a completely automated distribution chain from factory all the way to the drone landing at your front doorstep as a consumer. That's a whole nother level of efficiency that we can't even contemplate right now. >> Don't bet against Bezos that's what I always say. All right, Tom Stuermer thanks for spending a few minutes and good luck with the keynote. >> I appreciate it Jeff. >> All right, I'm Jeff Frick you're watching theCUBE. We are at The Intelligence of Things, When IoT met AI. You're watching theCUBE. Thanks for watching. (upbeat music)

>> Narrator: From the Computer History Museum in the heart of Silicon Valley, it's theCUBE, covering food IT: Fork to Farm, brought to you by Western Digital. >> Hey, welcome back here and ready, Jeffrey Frick with theCUBE. We are in Silicon Valley at the Computer History Museum at a really unique event. It's food IT: Fork to farm, not the other way around, which you might think, "Hm, that doesn't make sense," but actually it does, really by the consumer-driven world that's hitting everything including the food and agriculture and we're really excited to have the guys running this show, representing The Mixing Bowl. Rob Trice is the founder and Michael Rose, partner, of The Mixing Bowl. Gentlemen, welcome. >> Thank you for having us. >> Thank you. >> So, first off, a little history on this event, it's the first time we've been here. I think you said there's about 350 people, really a broad spectrum: academe, technology, farmers, from New Zealand, I think was the one I heard from the furthest place. What's kind of the genesis of this show? >> So, my background is 15 years in mobile internet, telecom venture capital and my wife, actually, a couple of years ago, started running a cattle ranch out on the Pacific Coast and through that I saw how little technology was being used on the ranch and amongst local food producers. I came back to Silicon Valley and none of the big food or ag. players were here then, four years ago. Monsanto just had up a venture group, Unilever and Nestle had one person each here, but by and large, Silicon Valley's IT innovation ecosystem was not focused on food and agriculture. So I started The Mixing Bowl as a little bit more than just a Meetup group and we did it a couple of times and then somebody said, "You know, we should do a conference on this topic." So the first year we did it at Stanford with a partner of ours, and we thought might have 150 people come. We had over 300 people come and it was this kind of audience, kind of cross-section of technologists, food and agriculturalists. So that's when I said, "You know, I'm done with telecom. I want to go ride this food tech, ag. tech wave and see where the heck this comes to roost." So, it's been four years now and I'm pleased to be working not only with Michael, but then our colleagues Seana and Brita, and having a blast, learning a lot. >> Okay, so that's the conference. What about for The Mixing Bowl specifically, what is your charter as an organization? >> Well we've got three aspects of our business, so the first one is information sharing, so doing events like this. We do themed events, we did a water-tech for agricultural event down in Fresno. And then we also are contributing writers for Forbes. We also have an advisory business where we work with large corporates who are seeking innovation and trying to bring innovation to the food and ag. Sector, trying to bring technology and innovation. And then we have an investment side of our business, out of the brand Better Food Ventures. So we invest in the space as well, we have about 12 companies in our portfolio. >> That's interesting that you said there wasn't a lot of tech in ag. here and yet, we talked to Paul from Ford, we talked about their conference that they have at Salinas and of course, Sacramento Valley, San Fernando Valley, or not San Fernando Valley, San Joaquin Valley is a huge producer of food. So why do you think it was so late to come here? >> Well, I think that there have been other opportunities and I think that there's a misperception that agriculture doesn't need IT and I think what we've now realized is there's a huge opportunity, whether it is Internet of Things or looking at tracking and transparency, there's a lot of inefficiencies in our food production system and there also are a lot of societal challenges that we have. Everyone talks about feeding nine billion people by 2050, but then also we look at food safety, we look at what the consumer wants, which is why we're here today, talking about the fork to the farm. Consumers want change in food. They want different kinds of food. They want it delivered to them in different ways. All of these are opportunities for tech to be applied to food and agriculture. >> So we love being here. Go ahead, Michael. >> No, I was just going to say, I think it's like any other vertical in any other sector that starts to adopt technology over time. And even in the ag. sector, you've seen in the commodity crops in the Midwest with the automation that they adopted technology early but you've got other sectors, whether it's the specialty crops down in Salinas or people who are doing almonds, etc. Those people are starting to adopt technology, they're just a little further behind than you are with commodity crops. >> Right. It's funny, we interviewed the guy from Caterpillar a few weeks ago, and they are already running huge fleets of autonomous vehicles in mining. Obviously they have a lot of equipment involved in agriculture as well, so it seems kind of start and stop depending on the vendors that you're talking about. But one of the big themes we talk about, we go to a lot of platform shows, right? It's Cloud, it's edge, it's connectivity, it's big data, drones, I mean, as you look at some of these big classifications of technology that are now being applied in ag. are there any particular ones that kind of jump out as either the catalyst or the leading edge of adoption that's really helping drive this revolution? >> I guess, if you think about the fact that we're kind of looking at this staircase of adoption. One thing that we need to do is actually digitize information and that's one of the challenges that we have. Once we digitize, then we can start to manage operations based on that data, then we can start to optimize, and then we can automate. So it's a four-step staircase that we look at and I think in a lot of cases, even at restaurants, a lot of them are still placing orders via fax and telephone. We need to get off of that and start getting them to order online through online platforms and so forth. So, at any rate, one area that I'm particularly excited about is aerial imaging for agriculture because I think you are instantaneously, by just doing a flyover, providing farmers with more information than they've ever had. In some cases, I think you could actually argue, you're going from a data desert to a data flood. Now the challenge is moving up that staircase to go make sense of that data and then ultimately be able to give prescriptive machine-learning or artificial intelligence-based recommendations to that farmer on how to do a better job, whether that is increasing sustainability, maximizing yield, looking at pricing, any of those kind of things. >> Right, one of the things you hear real often in every industry, is kind of the old guy using intuition versus becoming really a data-driven organization. Are you seeing that classic conflict, or do people get it pretty quickly when you can provide the data to show them things that they could never really see before? >> I was going to say, one of the biggest challenges that's also dictating the market timing is the fact that average American farmer is about 65, so we now are having this turn as the kids are coming back who are tech-enabled back to the production point, back to the farm and starting to take over farms from their parents. And their parents, of course, have just been maybe a little slower to adopt new technology. So it's just a timing issue. I think the other thing is, there are all the different pieces, whether it's the sensors or whether it's the connectivity of data or whether it's the storage of data, there needs to be a solution and they need to be integrated. And so we see this on the farm, getting that data off and then getting it stored and then how to use it. But then you also see this in restaurants. In restaurants, you have all of the delivery services coming in, so a restaurant can have seven different delivery services picking up from the restaurant. And they have seven different iPads that they have to manage with their point of sales system and very few of them currently will integrate with a POS, right? >> Right. And I think whether it's in a restaurant or on a farm, this lack of integration, API integration, making it a usable solution as opposed to a number of features, is where we're probably going to see a lot more tech innovation. I think unfortunately what you're probably also going to see is a lot of consolidation because you've had venture capital-backed companies with solutions for food and agriculture that have their own proprietary solution, their own OS. And we know that, from other tech sectors, that's not a long-term viable strategy. Ultimately, the data will be free, it will open up, it will interconnect, and we just need to happen in food and in agriculture. >> And are they getting that? Because the classic farmer dilemma that you learn in economics 101 is they have a great crop, crap prices go in the toilet. They have a crappy crop, price is up but they don't have enough quantity to share and gaming the system, and who's going to plant what? Do they start to see the value of sharing some level of data aggregation for the benefit of all? >> I think there's a misperception out there that farmers won't share their data. The reality is they're willing to share their data, if it's providing some value to them. A lot of people want to charge these farmers for their data without any demonstrable benefit to using that data. And I think where you can find a solution, I think the farmers are, speaking generally here, I think the other thing is, farmers know, if you're not paying for the data, you probably are the product, right? And they're smart enough to figure that out, so they don't want people misusing their data for reasons that aren't clear to them. And they've had bad experiences with that in the past. >> It's not any different than any other sector. I mean, go back seven years ago when people said, "Well, we're going to mix your data up with somebody else's data, but it's not a problem, right? Zeros and ones, it's bits." And they were both like, "Nooo," and they got over it, right? >> Right, but the other thing I'll say is I think that the challenges are changing and this is not just standard commodity ups and downs, particularly if you look at here in California, the specialty crops. We have lost access to what has been a cheap labor pool historically and we need to automate. So now we need to go where northern Europe has already gone, in terms of automating production for specialty crops and then things like climate change are causing different crops to grow in different seasons and we need to be able to predict that, we need to take more of it indoors as a nice complement to outdoor growing. So there's a lot of different things that farmers are dealing with now that they really haven't had to deal with in the future. And I think the same is true on the restaurant side. >> Yeah, and the predictability of understanding what your needs are going to be is going to be so important here, particularly because we need to see more automation, both on the farm and production and the restaurants. I know a lot of people talk about being concerned about losing their jobs to automation or robotics, but the reality is, the National Restaurant Association says in the next 10 years, we have a shortage of 200,000 line cooks. >> Jeff: Just line cooks? >> Just line cooks, right. So when you see someone like Chowbotics who's here showing the automated customized salad maker, there's clearly a need in the market place for these kind of approaches. >> The other thing too is you touch on such big, global societal issues. Obviously we're in California here, water. We had a really wet winter, but you know, I'm looking for the water track, I mean that's got to be a huge piece of this whole thing. You have the environmental concern, again, in California, there's always the fight between the farmers that want the water in the rivers and the environmentalists who want to keep the salmon swimming upstream. These are not simple problems that have an obvious solution, and as I think somebody said in they keynote, there's no free trade-off. You've got to make decisions based on values and they're not simple problems. So you guys are right in the middle of a lot of big society changes. >> Yeah, and I think that's one of the things. This is not just a US or a California thing. Globally, things are changing. And whether it is China having more disposable income available to eat more meat and what the ramifications of that are versus other societies with more environmental challenges moving front and center to them, the labor challenge. There's a lot of different things that are happening globally and we don't really have that connectivity layer globally to share this innovation to find the right solutions and get them addressing these market challenges. >> Right. >> Yeah, I would say the thing is, it is complex, so they're going to be talking about tomato growth later on today, and the example somebody was giving is we went to precision watering instead of spray, well, when you go to drip irrigation, you actually have to pressurize an entire system so you actually use more energy. So we use less water but we burn more coal, more oil, whatever it may be, to pressurize the system. And then if it produces a product that has more water content, you spend more energy drying it on the backend. So there's trade-offs. I would say the other thing that we found is really interesting is people ask us if we're social impact investors and we aren't but we have a social impact consideration about what we do, but pretty much everything that you see in this space right now from an innovative side is moving the ball forward, either it's better nutrition, it's less input, it's less chemicals, less water. So this innovation in food and ag. is just by its nature having a very positive impact. >> Right, two years ago, we called food IT macro to micro, and fundamentally what we believe at The Mixing Bowl is, as Michael said, at Better Food Ventures, we don't consider ourselves social impact investors, first and foremost, we want to keep financial grounding. However, I think at a core level, we all believe that harnessing IT to go address these societal challenges in food and agriculture is the biggest thing that we can make. So the reality is we're not going to be able to do much more with the chemical era, we've maximized the yield that we can get there. So now we are going to be looking at IT and how can we actually apply IT to these different challenges and I'm going to cough now. (Jeff laughs) (Rob coughs) >> Well, even something, people think IT and they think highly technical and they think of Cloud, they think of data connections, well look at food waste. The bulk of food waste that happens in our society happens at the home to the restaurant. So even if it's an iPhone app that's teaching our children how to deal with food waste in their home, it's a technical approach, it's hugely impactful. And it's those kind of touch points that will make a difference. >> Right, right. Well, Rob, Michael, thanks for inviting us, it's really fun to come to more of an application-centered show than an infrastructure show and see how the impact of Cloud and big data and sensors and IOT and drones and all of these things are having material impact on us day by day. So congratulations on the event and we'll let you go back to the keynote stage, they're waiting for you. >> Thank you. >> Thank you. >> All right, I'm Jeff Frick, you're watching theCUBE. We are at the Food IT show in Mountain View, California. We'll be right back with the next guest after this short break. Thanks for watching. (electronic music)

>> Hey, welcome back everybody! Jeff Frick here with theCUBE. We're at Zuora Subscribe at downtown San Francisco, and every time we go out to conferences, there's a pretty high probability we're going to run into this Cube alumni. Sure enough, here he is, Ray Wang. He's the founder and principal of Constellation Research. Ray, always great to see you. >> Hey Jeff, this is awesome, thanks for having me. >> And close to your hometown, what a thrill! >> This is, it's a local conference! What else can I ask for? >> So what do you think? Subscription economy, these guys have been at it for a while, 1200 people here, I'm a big Spotify fan, Amazon Prime, go back to Costco if you want to go back that far. But it seems to really be taking off. >> It is. About three years ago, digital transformation became a hot topic. And because it became a hot topic, it's really about how do I get products to be more like services. How do I get services to get into insights, and how do I make insights more like experiences and outcomes? And that natural transition as companies make a shift in business models is what's driving and fueling the subscription economy. >> It's interesting. Do you think they had to put the two and two together, that once the products become services now you can tap into that service, you can pull all kinds of data after that thing, you can have analytics, as opposed to shipping that product out the door it goes and maybe you see it every 15,000 miles for a checkup? >> You know what it is? It's basically, about three years ago, people started to realize this. Tien's been talking about this for ages, right? He's been talking about everything's a subscription economy, everything is going to be SAS-ified. And in tech world, everybody got that. But it was when companies like GE, which we saw together, a Caterpillar or a Ford, started to realize, "Hey we can do remote monitoring and sensing "with IOT on our cars, "and I can now figure out what's going on "and monitor them or give an upgrade, "or give a company an upgrade on their appliance, "or give an upgrade on their vehicle, "or do safety and compliance." Then people started realizing, "Oh, wow. "We're not just selling products. "We're in the services business." >> Right. It's funny, if you read the Elon Musk book, how the model years of Teslas, there's no such thing as a model year. It's what firmware version are you on, and then they upgrade. >> Oh, no, that's what we do all the time. You click on a little T, and it's like, boom, firmware. Oh, I get a new upgrade. Only the other day, you touch your head seat, there's like a lumbar support thing, the software popped up for headrest! I never knew I could change the headrest! It literally showed up two months ago. It's unbelievable. >> So, the cool thing, I think, that doesn't get enough play is the difference in the relationship when now you have a subscription-based relationship. That's a monthly recurring or annual recurring, you got to keep delivering value. You got to keep surprising you every morning, when you come out and get in your car, as opposed to that one time purchase. "Adios, we'll see you in however many years "until you get your next vehicle." >> Oh, that's a great example. And the Tesla, we got the Easter eggs over Christmas, right? So the Christmas holiday thing with the Model X that actually did Trans-Siberian Express to the Bellagio fountains with the doors that popped up. You're like, "Hey, what is this thing?" It's just an upgrade that shows up. You're like, "Okay." But you do. You do have to delight customers, you're always capturing their attention, and the fact is, hey, I might buy a toaster. And in that toaster, I might get an upgrade two to three years out. Or maybe, I just buy toasters, and I subscribe to them. And every three years, I get a new toaster. And I can choose between a model L or I can go upsell, get a different color, or I can change out a different set of features, but we're starting to see that. Or maybe, I get a hotel room or a vacation. And that hotel room is at level X, and if I get a couple more members of my family, I get to level Z, and I get to another level, where I lose all the kids, I go back to level A. But the point being is I'm buying a subscription to having an awesome vacation. And that is the type of things that we're talking about here. It's that freedom that Tien was talking about. >> Because he talked about the freedom from obsolescence, freedom from maintenance. There's a whole bunch of benefits that aren't necessarily surfaced when you consume stuff as a service versus consuming it as a product. >> It does. And sometimes it may cost more, but you're trading the convenience, you're trading the velocity of innovation, right? For some people, they just want to own the same thing, they're not going to make the move, but for other people, it's about getting the newest thing, getting delighted, having a new feature. And in some cases, it's about safety, right? This is regulatory compliant or I'm actually doing rev rec correctly, as they were talking about, ASC606. >> Alright, so you're getting out on the road a lot, it's June 6, and I won't tell anyone on air how many miles you already have, because Tamara is probably watching, and she'll be jealous, but biggest surprise is you see here or recently as this digital transformation just continues to gain speed. I'm doing a little research now, and maybe you can help me out. Looking back at digital photography, because it's like, "No, no, no, no, no." for the film, and then it's like, boom. I think these really steep inflection points, or up if you're on the right side, are coming. >> Let's stick to digital photography, that was a great one. There was the point, remember, where we actually had all those disposable cameras at parties that'd get developed, one hour developing. Then we get to back to the point where you just showed up at Costco, dropped something off, you'd get the disk and the photo. Then we had O-Photo, and now we have nothing. Everything just went away because of the phones. These things changed everything, right? I mean, they changed the way we look at photography to the point where, do we even have an album? I was breaking out albums basically three weeks ago, showing my kids, like "Hey, this is what a photo album looks like." And they were completely mystified. "Oh, you print these, how do they get printed?" I mean, they're asking the basic questions. That transformation is what we're having right now. "You own a car?" "You actually buy a PC?" I'm buying compute power. Kilowatts per hour for artificial intelligence in the next year. It's not going to be, I bought the server, I loaded it up, I got it tuned, I got it ready. So yeah, we are in the middle of that shift. But it's the fact that companies are willing to change their business models, and they're willing to break free in the post ERP era. A lot of this is just, my old ERP does not do billing, it doesn't understand the smallest unit of something I sell, and I've got to fix that. And more importantly, my customers, they want to buy it today. The want to buy it in pieces. They want to buy it even smaller pieces. They might buy it every other week, they might buy it-- we have no idea. Yeah, I've got to make sure I can do that. >> It's just interesting too that this is happening now. We're talking about autonomous cars. We see the Waymo cars all the time. The guy from Caterpillar, he's got to a whole autonomous fleet of mining vehicles that are operating today. >> 500,000! He's got 500,000 little trucks. Well, they're not little trucks, they can't fit in this building. >> They're big trucks. Apparently, they tried. >> But they're trying to get these trucks in. We used to think about, like "Hey, these are agricultural vehicles that can be remotely controlled by GPS, they also work for tanks." These are things that are actually doing runs. Now, it's a great reason. Think Australia. Out in Perth, it's about $150,000 to hire a driver. Just to go back and forth. So they figured, "This is just getting ridiculous. "We don't have enough people out here. "We can't convince enough people "to come drive these trucks. "Let's go automate that." That's a lot of the story of where a lot of this came from. >> Or he had a bad night, or broke up with his girlfriend, or distracted about this or that. The whole autonomous vehicle versus regular people driver-- all you've got to do is ride around on your bicycle in your neighborhood, and watch how many people stop at stop signs. Should we answer that question real fast? >> Oh, I do that in California. That's kind of bad, actually. >> Alright Ray. Well, thanks for taking a few minutes. I'm glad you get a weekend at home. Where you off to next, I should ask? >> Oh, it's going to be a crazy next few weeks. I'm going to be in London and Paris and Boston all next week. >> Oh, you're going to eat well. >> I'll try. >> Alright, he's Ray Wang. I'm Jeff Frick. You're watching the Cube from Zuora Subscribe. Thanks for watching.

(clicks) >> Hey welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco with Zuora Subscribe. About 2,000 people all focused on the subscription economy. And we're looking at some really cool products. We've had GE on, we're going to have Caterpillar on, but this is something new. You know, kind of these medical devices. Fitbit on steroids. I don't know how you describe it. Dr Sumon Pal. He is the cofounder and Chief Scientific Officer for Thync. Welcome. >> Thank you, thank you for having me. >> Absolutely. So give us a little bit of background on Thync, and then we'll jump into the device. >> Absolutely. So we're the first subscription service for wellness and better mental health. >> Okay. >> And the way it works is that there's hardware which is a neuromodulator, and they interface with your skin which is some pads and basically you put this on the back of your neck. There's software, there are programs that come along in the app and what these are are algorithms that have been developed to stimulate certain nerves in the right way. Those nerves in turn connect with your brain stem and that is the center for stress, for sleep cycles, for mood in general. And over the last five years we've developed a way to safely stimulate those nerves, such that you can sleep better, your mood is improved and you can de-stress. >> Okay, so let's back, back way up. You covered like a, you went the whole enchilada there. So you basically did some research. You guys figured out that nerve can stimulation can give better wellness. >> Right. >> And is that just during sleeping hours, during waking hours, all the above, kind of? >> Yeah, so it's both. I mean a session lasts about 10 to 15 minutes. >> Okay. >> In that time, what's happening is that it's dampening the stress response in your body. >> Jeff: Okay >> So if you do this on a daily basis or you do this in the evenings when you come home from work, you are kind of detaching from that stress that's built up during the day. >> Jeff: Without drinking a glass of wine or a bottle of beer. >> Absolutely. Without really any toxicity, without any side effects, without any addiction. Without any of the issues that come along with pills and substances. >> All those other things. Okay so then you put this thing on. >> That's right. >> So you put it on like right after you get home from work, or? >> Sure. >> Or when you go to sleep? Does it make a difference? >> Or if you just had a bad meeting. You had a rough morning. If there's kind of an acute occasion where you're anxious or highly stressed then you can use it then, too. >> Okay, so it's kind of yoga in a box. If I would be so presumptuous. >> Without any effort, right. >> No damage to the knees. >> Right, right. >> All right, super. So, a little bit in terms of the history of the company, so you said this is version two that you just came out with >> Right. Yeah, we've been developing the product, the technology in general for about five years. We've done three published studies. We've tested thousands of subjects. The first product has over two million minutes of use without any adverse side effects or, you know, we know that it's a really safe and powerful method to help people. >> Okay, and what does it retail for? >> So the hardware costs 149. >> Okay. >> And then there's the subscription. And the subscription is because there's a consumable involved, which are these pads. Which are actually a proprietary formulation so that this is absolutely painless, absolutely comfortable. And we have algorithms, so you're actually streaming these programs and those programs are highly complex, changing over time and constantly being updated. So for the software, service, and for the pads you pay either $29.99 a month or you pay $19.99 a month depending on a longer commitment. >> Okay. And when you decided to go with the subscription pricing, versus just selling it and if I need more pads, I order a 12-pack of pads or whatever. What were some of the things you thought about and then what are some of the outcomes that you have found? Both kind of expected and unexpected in having a subscription relationship with your customers. >> Yeah, it's a great question. So, one of the things that's really important about, so stress leads to a huge number of health issues. Everything from cardiovascular issues to being linked with diabetes, to being linked with premature aging. And so it's important to chronically reduce your stress levels. And you want to have all the components around when you need it. It's not one of those things where you've had a terrible day, you're extremely anxious. You know, you want everything to be there. You don't want to go and then order some pads online, order what you need online. >> Right, right. >> So that's one aspect, and the second is that you want access to the programs that are being updated all of the time. And what we find is that when people are on a subscription service, that kind of constant use which is so critical for your health, mental health, general well-being, is maintained in a better way than if you're kind of having to reorder these things or buy them. So really it's about supporting and promoting this kind of continuous regular use and routines. >> And I would presume that then you also get the benefit too 'cause you're getting all those data. >> Absolutely. >> Points that are feeding your algorithms. >> Absolutely. >> So you can make changes to the application, changes to the algorithm. >> That's right and also we have a library, about a thousand programs. And it's also about, we can, for any customer switch out the programs that they have if it's not working for whatever reason. So to kind of rescue people, it's also important to get that data of, what is happening month to month. >> So is a program the sequence of, of, I don't want to say charges but stimulations or whatever. >> Yeah, that's right. >> That set a different pattern, a different frequency and that creates like a program. >> That's right. >> And you experiment to find out what works best for you? >> That's right, it's a lot like music. It's a stimulation pattern that's built in blocks and those blocks change over time. And that is one of the things that we figured out how to do, that no one really had done before. >> Alright, well, pretty exciting stuff. >> Thank you. >> I look forward to watching you guys grow and see how things continue to progress. >> Absolutely, thank you very much. >> Alright, thanks for stopping by theCUBE. Alright, he's Dr. Sumon Pal, I'm Jeff Rick. You're watching theCUBE from Zuora Subscribe. Thanks for watching. (clicks)

>> Announcer: Live from Santa Fe, California, it's the Cube covering Big Data Silicon Valley 2017. >> Welcome, back, everyone. We're here live in Silicon Valley in San Jose for Big Data SV in conjunct with stratAHEAD Hadoop part two. Three days of coverage here in Silicon Valley and Big Data. It's our eighth year covering Hadoop and the Hadoop ecosystem. Now expanding beyond just Hadoop into AI, machine learning, IoT, cloud computing with all this compute is really making it happen. I'm John Furrier with my co-host George Gilbert. Our next guest is Arik Pelkey who is the senior director of product marketing at Pentaho that we've covered many times and covered their event at Pentaho world. Thanks for joining us. >> Thank you for having me. >> So, in following you guys I'll see Pentaho was once an independent company bought by Hitachi, but still an independent group within Hitachi. >> That's right, very much so. >> Okay so you guys some news. Let's just jump into the news. You guys announced some of the machine learning. >> Exactly, yeah. So, Arik Pelkey, Pentaho. We are a data integration and analytics software company. You mentioned you've been doing this for eight years. We have been at Big Data for the past eight years as well. In fact, we're one of the first vendors to support Hadoop back in the day, so we've been along for the journey ever since then. What we're announcing today is really exciting. It's a set of machine learning orchestration capabilities, which allows data scientists, data engineers, and data analysts to really streamline their data science processes. Everything from ingesting new data sources through data preparation, feature engineering which is where a lot of data scientists spend their time through tuning their models which can still be programmed in R, in Weka, in Python, and any other kind of data science tool of choice. What we do is we help them deploy those models inside of Pentaho as a step inside of Pentaho, and then we help them update those models as time goes on. So, really what this is doing is it's streamlining. It's making them more productive so that they can focus their time on things like model building rather than data preparation and feature engineering. >> You know, it's interesting. The market is really active right now around machine learning and even just last week at Google Next, which is their cloud event, they had made the acquisition of Kaggle, which is kind of an open data science. You mentioned the three categories: data engineer, data science, data analyst. Almost on a progression, super geek to business facing, and there's different approaches. One of the comments from the CEO of Kaggle on the acquisition when we wrote up at Sylvan Angle was, and I found this fascinating, I want to get your commentary and reaction to is, he says the data science tools are as early as generations ago, meaning that all the advances and open source and tooling and software development is far along, but now data science is still at that early stage and is going to get better. So, what's your reaction to that, because this is really the demand we're seeing is a lot of heavy lifing going on in the data science world, yet there's a lot of runway of more stuff to do. What is that more stuff? >> Right. Yeah, we're seeing the same thing. Last week I was at the Gardener Data and Analytics conference, and that was kind of the take there from one of their lead machine learning analysts was this is still really early days for data science software. So, there's a lot of Apache projects out there. There's a lot of other open source activity going on, but there are very few vendors that bring to the table an integrated kind of full platform approach to the data science workflow, and that's what we're bringing to market today. Let me be clear, we're not trying to replace R, or Python, or MLlib, because those are the tools of the data scientists. They're not going anywhere. They spent eight years in their phD program working with these tools. We're not trying to change that. >> They're fluent with those tools. >> Very much so. They're also spending a lot of time doing feature engineering. Some research reports, say between 70 and 80% of their time. What we bring to the table is a visual drag and drop environment to do feature engineering a much faster, more efficient way than before. So, there's a lot of different kind of desperate siloed applications out there that all do interesting things on their own, but what we're doing is we're trying to bring all of those together. >> And the trends are reduce the time it takes to do stuff and take away some of those tasks that you can use machine learning for. What unique capabilities do you guys have? Talk about that for a minute, just what Pentaho is doing that's unique and added value to those guys. >> So, the big thing is I keep going back to the data preparation part. I mean, that's 80% of time that's still a really big challenge. There's other vendors out there that focus on just the data science kind of workflow, but where we're really unqiue is around being able to accommodate very complex data environments, and being able to onboard data. >> Give me an example of those environments. >> Geospatial data combined with data from your ERP or your CRM system and all kinds of different formats. So, there might be 15 different data formats that need to be blended together and standardized before any of that can really happen. That's the complexity in the data. So, Pentaho, very consistent with everything else that we do outside of machine learning, is all about helping our customers solve those very complex data challenges before doing any kind of machine learning. One example is one customer is called Caterpillar Machine Asset Intelligence. So, their doing predictive maintenance onboard container ships and on ferry's. So, they're taking data from hundreds and hundreds of sensors onboard these ships, combining that kind of operational sensor data together with geospatial data and then they're serving up predictive maintenance alerts if you will, or giving signals when it's time to replace an engine or complace a compressor or something like that. >> Versus waiting for it to break. >> Versus waiting for it to break, exactly. That's one of the real differentiators is that very complex data environment, and then I was starting to move toward the other differentiator which is our end to end platform which allows customers to deliver these analytics in an embedded fashion. So, kind of full circle, being able to send that signal, but not to an operational system which is sometimes a challenge because you might have to rewrite the code. Deploying models is a really big challenge within Pentaho because it is this fully integrated application. You can deploy the models within Pentaho and not have to jump out into a mainframe environment or something like that. So, I'd say differentiators are very complex data environments, and then this end to end approach where deploying models is much easier than ever before. >> Perhaps, let's talk about alternatives that customers might see. You have a tool suite, and others might have to put together a suite of tools. Maybe tell us some of the geeky version would be the impendent mismatch. You know, like the chasms you'd find between each tool where you have to glue them together, so what are some of those pitfalls? >> One of the challenges is, you have these data scientists working in silos often times. You have data analysts working in silos, you might have data engineers working in silos. One of the big pitfalls is not really collaborating enough to the point where they can do all of this together. So, that's a really big area that we see pitfalls. >> Is it binary not collaborating, or is it that the round trip takes so long that the quality or number of collaborations is so drastically reduced that the output is of lower quality? >> I think it's probably a little bit of both. I think they want to collaborate but one person might sit in Dearborn, Michigan and the other person might sit in Silicon Valley, so there's just a location challenge as well. The other challenge is, some of the data analysts might sit in IT and some of the data scientists might sit in an analytics department somewhere, so it kind of cuts across both location and functional area too. >> So let me ask from the point of view of, you know we've been doing these shows for a number of years and most people have their first data links up and running and their first maybe one or two use cases in production, very sophisticated customers have done more, but what seems to be clear is the highest value coming from those projects isn't to put a BI tool in front of them so much as to do advanced analytics on that data, apply those analytics to inform a decision, whether a person or a machine. >> That's exactly right. >> So, how do you help customers over that hump and what are some other examples that you can share? >> Yeah, so speaking of transformative. I mean, that's what machine learning is all about. It helps companies transform their businesses. We like to talk about that at Pentaho. One customer kind of industry example that I'll share is a company called IMS. IMS is in the business of providing data and analytics to insurance companies so that the insurance companies can price insurance policies based on usage. So, it's a usage model. So, IMS has a technology platform where they put sensors in a car, and then using your mobile phone, can track your driving behavior. Then, your insurance premium that month reflects the driving behavior that you had during that month. In terms of transformative, this is completely upending the insurance industry which has always had a very fixed approach to pricing risk. Now, they understand everything about your behavior. You know, are you turning too fast? Are you breaking too fast, and they're taking it further than that too. They're able to now do kind of a retroactive look at an accident. So, after an accident, they can go back and kind of decompose what happened in the accident and determine whether or not it was your fault or was in fact the ice on the street. So, transformative? I mean, this is just changing things in a really big way. >> I want to get your thoughts on this. I'm just looking at some of the research. You know, we always have the good data but there's also other data out there. In your news, 92% of organizations plan to deploy more predictive analytics, however 50% of organizations have difficulty integrating predictive analytics into their information architecture, which is where the research is shown. So my question to you is, there's a huge gap between the technology landscapes of front end BI tools and then complex data integration tools. That seems to be the sweet spot where the value's created. So, you have the demand and then front end BI's kind of sexy and cool. Wow, I could power my business, but the complexity is really hard in the backend. Who's accessing it? What's the data sources? What's the governance? All these things are complicated, so how do you guys reconcile the front end BI tools and the backend complexity integrations? >> Our story from the beginning has always been this one integrated platform, both for complex data integration challenges together with visualizations, and that's very similar to what this announcement is all about for the data science market. We're very much in line with that. >> So, it's the cart before the horse? Is it like the BI tools are really driven by the data? I mean, it makes sense that the data has to be key. Front end BI could be easy if you have one data set. >> It's funny you say that. I presented at the Gardner conference last week and my topic was, this just in: it's not about analytics. Kind of in jest, but it drove a really big crowd. So, it's about the data right? It's about solving the data problem before you solve the analytics problem whether it's a simple visualization or it's a complex fraud machine learning problem. It's about solving the data problem first. To that quote, I think one of the things that they were referencing was the challenging information architectures into which companies are trying to deploy models and so part of that is when you build a machine learning model, you use R and Python and all these other ones we're familiar with. In order to deploy that into a mainframe environment, someone has to then recode it in C++ or COBOL or something else. That can take a really long time. With our integrated approach, once you've done the feature engineering and the data preparation using our drag and drop environment, what's really interesting is that you're like 90% of the way there in terms of making that model production ready. So, you don't have to go back and change all that code, it's already there because you used it in Pentaho. >> So obviously for those two technologies groups I just mentioned, I think you had a good story there, but it creates problems. You've got product gaps, you've got organizational gaps, you have process gaps between the two. Are you guys going to solve that, or are you currently solving that today? There's a lot of little questions in there, but that seems to be the disconnect. You know, I can do this, I can do that, do I do them together? >> I mean, sticking to my story of one integrated approach to being able to do the entire data science workflow, from beginning to end and that's where we've really excelled. To the extent that more and more data engineers and data analysts and data scientists can get on this one platform even if their using R and WECCA and Python. >> You guys want to close those gaps down, that's what you guys are doing, right? >> We want to make the process more collaborative and more efficient. >> So Dave Alonte has a question on CrowdChat for you. Dave Alonte was in the snowstorm in Boston. Dave, good to see you, hope you're doing well shoveling out the driveway. Thanks for coming in digitally. His question is HDS has been known for mainframes and storage, but Hitachi is an industrial giant. How is Pentaho leveraging Hitatchi's IoT chops? >> Great question, thanks for asking. Hitatchi acquired Pentaho about two years ago, this is before my time. I've been with Pentaho about ten months ago. One of the reasons that they acquired Pentaho is because a platform that they've announced which is called Lumata which is their IoT platform, so what Pentaho is, is the analytics engine that drives that IoT platform Lumata. So, Lumata is about solving more of the hardware sensor, bringing data from the edge into being able to do the analytics. So, it's an incredibly great partnership between Lumata and Pentaho. >> Makes an eternal customer too. >> It's a 90 billion dollar conglomerate so yeah, the acquisition's been great and we're still very much an independent company going to market on our own, but we now have a much larger channel through Hitatchi's reps around the world. >> You've got IoT's use case right there in front of you. >> Exactly. >> But you are leveraging it big time, that's what you're saying? >> Oh yeah, absolutely. We're a very big part of their IoT strategy. It's the analytics. Both of the examples that I shared with you are in fact IoT, not by design but it's because there's a lot of demand. >> You guys seeing a lot of IoT right now? >> Oh yeah. We're seeing a lot of companies coming to us who have just hired a director or vice president of IoT to go out and figure out the IoT strategy. A lot of these are manufacturing companies or coming from industries that are inefficient. >> Digitizing the business model. >> So to the other point about Hitachi that I'll make, is that as it relates to data science, a 90 billion dollar manufacturing and otherwise giant, we have a very deep bench of phD data scientists that we can go to when there's very complex data science problems to solve at customer sight. So, if a customer's struggling with some of the basic how do I get up and running doing machine learning, we can bring our bench of data scientist at Hitatchi to bear in those engagements, and that's a really big differentiator for us. >> Just to be clear and one last point, you've talked about you handle the entire life cycle of modeling from acquiring the data and prepping it all the way through to building a model, deploying it, and updating it which is a continuous process. I think as we've talked about before, data scientists or just the DEV ops community has had trouble operationalizing the end of the model life cycle where you deploy it and update it. Tell us how Pentaho helps with that. >> Yeah, it's a really big problem and it's a very simple solution inside of Pentaho. It's basically a step inside of Pentaho. So, in the case of fraud let's say for example, a prediction might say fraud, not fraud, fraud, not fraud, whatever it is. We can then bring that kind of full lifecycle back into the data workflow at the beginning. It's a simple drag and drop step inside of Pentaho to say which were right and which were wrong and feed that back into the next prediction. We could also take it one step further where there has to be a manual part of this too where it goes to the customer service center, they investigate and they say yes fraud, no fraud, and then that then gets funneled back into the next prediction. So yeah, it's a big challenge and it's something that's relatively easy for us to do just as part of the data science workflow inside of Pentaho. >> Well Arick, thanks for coming on The Cube. We really appreciate it, good luck with the rest of the week here. >> Yeah, very exciting. Thank you for having me. >> You're watching The Cube here live in Silicon Valley covering Strata Hadoop, and of course our Big Data SV event, we also have a companion event called Big Data NYC. We program with O'Reilley Strata Hadoop, and of course have been covering Hadoop really since it's been founded. This is The Cube, I'm John Furrier. George Gilbert. We'll be back with more live coverage today for the next three days here inside The Cube after this short break.