>> From Times Square, in the heart of New York City, it's theCUBE. Covering IMAGINE 2018. Brought to you by Automation Anywhere. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in Manhattan, New York City, at Automation Anywhere's IMAGINE 2018. We've never been to this show. Pretty interesting, about 1,100 people talking about Bots, but it's really more than Bots. It's really how do we use digital employees, digital programs, to help people be more efficient, and take advantage of a lot of the opportunities as well as the challenges that we're facing as we keep innovating, I'm really excited to have our next guest. Jeffrey Immelt, the former chairman and CEO of GE, great to see you Jeff. >> Good to see you. >> Absolutely, last I saw you I think, was at Minds and Machines, and we're huge fans, >> A couple years ago, yep. >> Beth Comstock, I loved Bill Ruh, so you know, what a fantastic team. >> A great team. >> But here you are talking about Bots, and it's interesting because at GE you guys have been involved in big industrial equipment, as well as a huge software business, so you really figured out that you've gotta have software and people to really work with these machines. >> So you know Jeff, I really am a big believer that productivity is the key, and that we, we're seeing a bow wave of technology that's really gonna impact the workplace in a meaningful way. The reason why I like RPA, what we call Bots-- >> Right, RPA. >> Is because it can happen so quickly. It can happen across the organization. It has great productivity associated with it. So I kinda view RPA as being really one of the uh, let's say early wave technologies in terms of how to drive more automation and productivity in the workplace. >> That's funny, because people ask me they're like, what's the deal with some of these stock evaluations, is it real, and think back to the ERP days right, ERP unlocked this huge amount of inefficiency. That was a long, long time ago, and yet we still continue to find these huge buckets of inefficiency over and over. >> I think it's, I mean I think to your point, the early days of IT, really if you look at ERP manufacturing systems, even CRM. They were really more around governance. They were kind of connecting big enterprises. But they really weren't driving the kind of decision support, automation, AI, that companies really need to drive productivity. And I think the next wave of tools will operate inside that envelope. You know, ultimately these will all merge. But I think these are gonna get productivity much quicker than an ERP system or an MES system did. Which are really, at the end of the day, driven by CFOs to drive compliance more than operating people to drive productivity. >> Right, but what's driving this as we've seen over and over, that consumerization of IT, not only in terms of the expected behavior of applications, you know you want everything to act like Amazon, you want everything to act like Google. But also, in terms of expectations of feedback, expectations of performance. Now people can directly connect with the customer, with companies like they never could before, and the customers, and the companies can direct with their customer directly. Where before you had channels, you had a lot of distribution steps in between. Those things are kind of breaking down. >> I think that's for sure. I mean I think that's sure. I would say beyond that is the ability to empower employees more with some of these tools so you know, an employee used to have to go to the CIO with a work ticket, hey here's what I need. You know these Bots grow virally inside organizations. They're easy to implement. They're easy to see an impact very quickly. So I just think the tools are becoming more facile. It's no longer kind of a hierarchical IT-driven technology base. It's more of a grounds-up technology base, and I think it's gonna drive more speed and productivity inside companies. >> Right, so really it's kind of, there's always a discussion of are the machines gonna take our jobs, or are they? But really there's-- >> Jeff, I'm not that smart really I mean-- >> Well, but it's funny because they're not right? I mean, everyone's got requisitions out like crazy, we need the machines to help us do the jobs. >> Nobody has, nobody has easy jobs. The fact of the matter is, nobody has easy jobs. You know, a company like GE would have 300 ERP systems right? Because of acquisitions and things like that. And the METs not a complexity, manual journal entries, things like that. So to a certain extent these, this automation is really helping people do their jobs better. >> Better. >> More than thinking about you know, where does it all go some day. So I think, I think we're much better off as an economy getting these tools out there, getting people experience with them and, and uh, seeing what happens next. >> Right, it's funny they just showed the Bot store in the keynote before we sat down, and when you look closely, a lot of them look like relatively simple processes. But the problem is, they're relatively simple, but they take up a lot of time, and they're not that automated, most of them. >> One of my favorites Jeff, is doing a quote for a gas power plant would take eight weeks. Because now we have Bots, that can draw data from different data sources, you can do it in two and a half days right? So that's not what you naturally think of for an automation technology like this. But the ability to automate from the different data sources is what creates the cycle of time reduction. >> Right, and you're fortunate, you've sat in a position where you can really look down the road at some interesting things coming forward. And we always hear kind of these two views, there's kind of the dark view of where this is all going with the automation, and the robots. And then there's the more positive view that you just touched on you know, these are gonna enable us to do more with less and, and free people up to actually be productive, and not do the mundane. >> I think productivity, productivity enables growth. The world needs more productivity. These tools are gonna be used to drive more productivity. I think many more jobs will be technically enabled, than will be eliminated by technology. Clearly there's gonna be some that are, that are, that are impacted more dramatically than others. But I would actually say, for most people, the ability to have technology to help them do their day-to-day job is gonna have a much higher impact. >> Right. What do you think is the biggest misperception of this of this combining of people and machines to do better? Where do you think people kind of miss the boat? >> Oh look I mean, I think it's that people wanna gravitate towards a macro view. A theoretical view, versus actually watching how people work. If you actually spent time seeing how a Service Engineer works, how a Manufacturing person works, how an Administrative person works, then I think you would applaud the technology. Really, I think we tend to make these pronouncements that are philosophical or, coming from Silicon Valley about the rest of the world versus, if everybody just every day, would actually observe how tasks actually get done, you'd say bring on more technology. Because this is just shitty you know, these are just horrible, you know, these are tough, horrible jobs right? A Field Engineer fixing a turbine out in the, in the middle of Texas right, a wind turbine. If we can arm them with some virtual reality tools, and the ability to use analytics so that they can fix it right the first time, that's liberating for that person. They don't look at that and say, "Oh my God, if I use this they're gonna replace me." >> Right, right. >> They really need me to do all this stuff so, I think not enough people know how people actually work. That's the problem. >> It's a tool right? It's as if you took the guy's truck away, and made him ride out there on a horse I mean-- >> It's just a, it's just a, you know look-- >> It's just another tool. >> I remember sitting in a sales office in the early 80s, when the IT guy came out and installed Microsoft Outlook for the first time. And I remember sitting there saying, who would ever need this? You know, who needs spreadsheets? >> Right, right. >> I could do it all here. >> Yeah, little did you know. >> So I just think it's kind of one of those crazy things really. >> Yeah, little did you know those spreadsheets are still driving 80% of the world's computational demands. >> Exactly. >> Great, well alright I wanna give you a last word again. You're here, it's a very exciting spot. We call 'em Bots, or robotic process automation for those that aren't dialed in to RPA stands for. As you look forward, what are you really excited about? >> Oh look, I mean I always think back to the, to kind of the four A's really, which is uh you know, kind of artificial intelligence, automation, additive manufacturing and analytics. And I think if everybody could just hone in on those four things, it's gonna be immensely disruptive, as it pertains to just how people work, how things get built, how people do their work so, when you think about RPA, I put that in the automation. It's kind of a merger of automation and AI. It's just really exciting what's gonna be available. But this, this bow wave of technology, it's just a great time to be alive, really. >> Yeah, it is. People will forget. They focus on the negative, and don't really look at the track, but you can drop into any city, anywhere in the world, pull up your phone and find the directions to the local museum. Alright, well Jeff, thanks for uh taking a few minutes of your time. >> Great. >> Alright, he's Jeff Immelt and I'm Jeff Frick, you're watching theCUBE from Automation Anywhere IMAGINE 2018. Thanks for watching. (jazz music)

>> From Times Square, in the heart of New York City, it's theCUBE. Covering Imagine 2018. Brought to you by Automation Anywhere. >> Welcome back everybody, Jeff Frick here with theCUBE, we are at Automation Anywhere in midtown Manhattan, 2018, excited to have our next guest, he's Kevin Kroen, he's partner of financial services, intelligent automation leader at PWC, Kevin, great to see you. >> Thank you. >> So financial services seems to be a theme, we're here in Manhattan, why is financial services an early adopter or maybe a frequent adopter or an advanced adopter of the RPA technology? >> Sure, so I think as we see our financial services clients and their agendas, there's been a huge focus on productivity and simplifying their overall operating model over the past couple of years. Banks in particular have gone through several years of having to focus their spending on non discretionary manners like regulatory compliance and risk management. And what that's generated is a need, as they started looking towards the next generation to really start thinking about what they're gonna look like in a post regulatory environment. And automation has quickly risen to the top of the agenda. >> What they're gonna look like in a post regulatory environment. >> Yes. >> Why a post regulate? >> Well I mean if you look through, you know what banks have had to deal with in term of Dodd-Frank, in terms of CCAR, you know, the regulation from federal reserve, these are things that took a lot of spending both on implementing operational processes and on implementing technology. A lot of that work is starting to you know, the banks are putting that behind themselves and so as they look forward and look at how they're going to gain more profitability in the future, the challenge becomes, there's not necessarily a new set of product innovation coming in, and so you have to really look at the expense line. >> Right. >> And so because of that automation has risen to the top of that agenda and so this continues to be one of the top areas of interest that we're getting from our clients. >> Right, so when you say post regulatory, you mean like a new regulation that they have to respond to, not that they're suddenly not gonna be regulated. >> There's not a lot of new regulations coming in right now, especially- >> That pesky one last week, GDRP. >> Yeah but in the US we're in an environment right now, there was just, you know, the revisions to the Dodd-Frank bill that were passed a lot of regulatory rules were actually being loosened so you don't necessarily have an increase in dollars that are going to be going into that. >> Right right, so it just always fascinates me, right, I thought ERP was supposed to wring out all the efficiency in our systems but that was not the case, not even by a long shot and now we continue to find these new avenues for more efficiency and clearly this is a big one that we've stumbled upon. >> Yeah, you know I think it's interesting, when you look at big technology investment over the last decade or two, you could argue a lot of efforts been focused at what I call the kind of core infrastructure and core plumbing so you know, how do I consolidate data into a single location? How do I make sure that data reconciles into different parts of my organization but that like kind of last mile of what someone does as part of their day to day business process was never really addressed, you know or is only addressed in pieces, and so I think as you start looking at the productivity term and how you actually start getting efficiency, we have very few clients that are saying, I want to take on that next big ERP type of limitation or I'm ready to spend 300 million dollars on a new project, they're looking to try to get the most value out of what they already have and they're actually looking to look at that last mile and how can they actually gain some benefit off it so the RPA technologies I think we're one of the catalysts of just being the perfect technology in the right place at the right time from a current business environment, a current technology spend perspective. >> Yeah it's pretty interesting Mihir was talking about, you know one of the big benefits is that you can take advantage of your existing infrastructure, you know, it's not a big giant rip and replace project but it's, again, it's this marginal incremental automation that you just get little benefit, little benefit, little benefit, end of the day, turns into a big benefit. >> Yeah, and I think that's, you know, it's quick, it's fast, it's, you know it can be implemented in an agile manner and you know, our clients are continuously telling us over and over again, they're willing to invest, but they wanna invest where they're gonna see a tangible payback immediately. >> Right. >> And I think when you start to talk the concept of digital transformation, it can mean a lot of different things to a lot of different people but there are big picture changes that could be made, those may be longer term trends but they're more immediate things and more immediate benefits that could be gained and I think that's really the sweet spot of where RPA and Automation Anywhere fall into. >> I was just looking up Jeff Immelt in his key note said this is the easy fountain money of any digital transformation project, I think that was the quote, that you'll ever do. That's a pretty nice endorsement. >> Yeah and it's, as we go out, we talk to CFOs, COOs, CIOs, you know, it's, the value proposition is really attractive because, you know, there have been, there's a track record of failed, technology projects failed big transformation projects and, you know, no one wants to necessarily risk their career on creating the next big failure and so I think using technology like RPA almost as an entry point or kind of like a gateway drug into the digital world, see the benefits, start to understand what are some of the business problems and historical kind of, you know, things you're trying to untangle in your infrastructure, attack that and then, you know, start to layer on additional things on top of that, once you get good with RPA and then you can start figuring out, okay, that's they gateway to artificial intelligence, okay how do I start to apply AI across my organization? As you get beyond AI, okay, how do I get into, more advanced state infrastructure and you can start thinking about this world where you can, you know, rather than do the big, five year project where you're gonna try to solve world hunger, it gives you a chance to kind of incrementally go digital over time and I think that's definitely the direction we see a lot of our clients wanting to go in. >> Right, Kevin I want to get your feedback on another topic that came up again in the keynote, was just security, you know it was like the last thing that was mentioned, you know, like A B C D E F G and security, financial services, obviously security is number one, it's baked into everything that everyone's trying to do now, it's no longer this big moat and wall, but it's got to be everywhere so I'm just curious, from the customer adoption point of view, where does security come up in the conversation, has it been a big deal, is it just assumed, is there a lot of good stuff that you can demonstrate to clients, how does security fit within this whole RPA world? >> You know with security and I would just say the broader kind of risk management pieces to the operator infrastructure are one of the first questions we get asked and a highly regulated environment like financial services, you know, the technology is easy and powerful with RPA but you also have to take a step back and say okay, I can program a bot to go do anything in my infrastructure, and that could mean running a reconciliation or it could mean going to our wire system and trying to send money out the door. And so there's a lot of concern around, not only understanding the technical aspects to you know, how the tools work with different types of security technologies, but more looking at your approach to entitlements and your approach to how you actually manage who has access to code bots, deployed bots in production, the overtime, understand what happens, you know we did a presentation to a board of directors a couple months ago on kind of automation more broadly and you know this is, you know, senior level executives the first question we got was, you know, okay, how do I prevent the 22 year old kid that just came off of campus from building a bot that no one knows about, setting it loose in our infrastructure and it going rogue, right? And so I mean this group was pretty savvy, they caught onto it very quickly and you know, the CIO of this client was sitting next to me and she kind of didn't have an immediate answer to that and I think that was kind of the a-ha moment, this is something we really need to put some thought into around you know, who are we gonna let build bots, what policies are gonna be set around how bots get deployed into our production environment, how are we gonna monitor what happens? You know how are we gonna get our auditors, our operational risk folks, our regulators, how are we gonna get all our different stakeholder groups comfortable that we have a well controlled, well functioning bot infrastructure that exists? >> Right, cause the bots actually act like people, they're entitled as like a role right, within the organization? >> We have clients that have literally had to set bots up as new employees, like they get onboarded, they have a, you go to the corporate directory and you can see a picture of R2D2, right like and it's the way they get around how they get a bot intel to a system but it's still, it's not a human right, so you still have to have a policy for how you actually will get code that uses that bot entitlement to function right and so that has to be done in a well disciplined, well controlled manner. >> Right, because to give them the ability to provide information to help a person make a decision is very different then basically enabling them to make that decision and take proactive action. >> Exactly. >> Yeah, it's funny we talked to Dr. Robert Gates at a show a little while ago and he said the only place in the US military where a machine can actually shoot a gun is on the Korean border, but every place else they can make suggestions but ultimately it's gotta be a person that makes the decision to push the button. >> And we're seeing, you know, trying to equate that to financial services, you see a similar pattern where there are certain areas where people are very comfortable playing this technology, you know you get into accounting and reporting and you know more back office type processes, you got other areas that people are a little less comfortable, you know anything that touches kind of wire systems or touches things that, you know, going out the door, touches kind of core trading processes, things like that there's a different risk profile associated with it. I think the other challenge is too is RPA is getting the gateway drug into this going back to my previous point, as you start to layer additional technologies into this, you might have less transparency over understanding clearly what's happening, especially as artificial intelligence takes a much broader role in this and so there's gonna be a lot of scrutiny I think over the next couple years put into like how do I understand the models that are created by artificial intelligence technologies and those decisions that are being made because you, if your regulator says, okay, why did you make this decision, you have to be able to explain it as the supervisor of that intelligent bot, you can't just say, oh it's cause what the machine told me to do, as so, that'll be one of the interesting challenges that's ahead of us. >> Yeah it's good, I mean it's part of the whole scale of conversation, I had interesting conversation with a guy, talking about really opening up those AI boxes so that you have an auditable process, right, you can actually point to why it made the decision even if you're not the one that made it in real time and it's doing it really really quickly so. >> Exactly. >> Really important piece. >> Yeah and as PWC, it's one of our challenges, as a consultant I'm helping clients implement this, my colleagues in our audit practice are now grappling with that same question because we're increasingly being asked to audit that type of infrastructure and have to prove that something did what it was suppose to have done. >> Right, right, alright Kevin, well nothing but opportunities for you ahead and thanks for taking a few minutes to stop by. >> Okay, thank you for having me. >> Alright, he's Kevin, I'm Jeff, you're watching theCUBE from Automation Anywhere, Imagine 2018 in Manhattan, thanks for watching. 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>> From Times Square, in the heart of New York City, it's theCUBE. Covering Imagine 2018. Brought to you by, Automation Anywhere. >> Welcome back everybody, Jeff Frick here with theCUBE. We're in Manhattan, New York City, at Automation Anywhere's Imagine Conference 2018. About 1,100 professionals really talking about the future of work bots, and really how automation is gonna help people do the mundane a little bit easier, and hopefully free us all up to do stuff that's a little bit more important, a little higher value. We're excited to have our next guest, he's Craig Le Clair, the VP and Principal Analyst from Forrester, and he's been covering this space for a long time. Craig, great to see ya. >> Yeah, nice to see you, thanks for having me on. >> So, first off, just kind of general impressions of the event? Have you been to this before? It's our first time. >> Yes, I did a talk here last year, so it was a little bit smaller then. There's obviously more people here today, but it's pretty much, I think it was in Brooklyn last year. >> It was in Brooklyn, okay. >> So, this is an upgrade. >> So, RP Robotic Process Automation, more affectionately, probably termed as bots. >> Yeah. >> They're growing, we're seeing more and more time and our own interactions with companies, kind of on the customer service side. How are they changing the face of work? How are they evolving as really a way for companies to get more leverage? >> Yeah, so I'll make one clarification of your sentence, and that's, you know, bots do things on behalf of people. What we're talking to in a call center environment is a chat bot. So, they have the ability to communicate or really, I would say, attempt to communicate with people. They're not doing a very good job of it in my view. But, bots work more in the background, and they'll do things for you, right? So, you know, they're having a tremendous effect. I mean, one of the statistics I was looking at the other day, per one billion dollars of revenue, the average company had about 150 employees in finance and accounting ten years ago. Now, instead of having 120 or 130, it's already down to 70 or 80, and that's because the bots that we're talking about here can mimic that human activity for posting to a general ledger, for switching between applications, and really, move those folks on to different occupations, shall we say. >> Right, right. >> Yeah. >> Well it's funny, Jeff Immelt just gave his little keynote address, and he said, "This is the easiest money you'll find in digital transformation is implementing these types of technology." >> Yeah, it's a good point, and it was a great talk, by the way, by Jeff. But, you know, companies have been under a lot of pressure to digitally transform. >> Right. >> You know, due to really the mobile, you know, mobile peaked around 2012, and that pushed everyone into this gap that companies couldn't really deal with the consumer technology that was out there, right? So then you had the Ubers of the world and digital transformation. So, there's been a tremendous focus on digital transformation, but very little progress. >> Right. >> When we do surveys, only 11% are showing any progress at all. So, along comes this technology, Robotic Process Automation that allows you to build bots without changing any of the back end systems. There's no data integration. You know, there's no APIs involved. There's no big transformation consultants flying in. There's not even a Requirements Document because you're gonna start with recording the actual human activity at a work station. >> Right. >> So, it's been an elixir, you know, frankly for CIOs to go into their boss and say, "You know what, we're doing great, you know, I've just made this invoice process exist in a lot better way." You know, we're on our path to digital transformation. >> And it's really a different strategy, because, like you said, it's not kind of rip and replace the old infrastructure, you're not rewriting a lot of applications, you're really overlaying it, right? >> Which is one of the potential downfalls is that, you know, sometimes you need to move to that new cloud platform. You don't want, to some extent, the technology institutionalizes what could be a very bad process, one that needs to be modernized, one that needs to be blown up. You know, we're still using the airline reservation systems from 1950s, and layers, and layers, and layers and layers built upon them. At some point, you're gonna have to design a new experience with new technology, so there's some dangers with the seduction of building bots against core systems. >> Right, so the other thing that's happening is the ongoing, I love Moore's Law, it's much more about an attitude then the physics of a microprocessor, but you know, compute, and store, and networking, 5Gs just around the corner, cloud-based systems now really make that available in a much different way, and as you said, mobile experience delivers it to us. So as those continue to march on and asymptomatically approach zero and infinite scale, we're not there yet, but we're everyday getting a little bit closer. Now we're seeing AI, we're seeing machine-learning, >> Yes. >> We're seeing a new kind of class of horsepower, if you will, that just wasn't available before at the scale it's at today. So, now you throw that into the mix, these guys have been around 14 years, how does AI start to really impact things? >> It's a fascinating subject and question. I mean, we're, at Forrester, talking about the forces of automation. And, by the way, RPA is just a subset of a whole set of technologies: AI, you mentioned, and AI is a subset of automation, and there's Deep Learning, is a subset of AI and you go on and on, there are 30, 40 different automation technologies. And these will have tremendous force, both on jobs in the future, and on shifting control really to machines. So, right now, you can look at this little bubble we had of consumer technology and mobile, shifting a lot of power to the consumer, and that's been great for our convenience, but now with algorithms being developed that are gonna make more and more decisions, you could argue that the power is going to shift back to those who own the machines, and those who own the algorithms. So, there's a power shift, a control shift that we're really concerned about. There's a convergence of the physical and digital world, which is IOT and so forth, and that's going to drive new scale in companies, which are gonna further dehumanize some of our life, right? So that affects, it squeezes humans out of the process. Blockchain gets rid of intermediaries that are there to really transfer ideas and money and so forth. So, all of these forces of automation, which we think is gonna be the next big conversation in the industry, are gonna have tremendous effect societally and in business. >> Right. Well, there's certainly, you know, there's the case where you just you can't necessarily rescale a whole class of an occupation, right? The one that we're all watching for, obviously, is truck drivers, right? Employs a ton of people, autonomous vehicles are right around the corner. >> Right. >> On the other hand, there's going to be new jobs that we don't even know what they're gonna be yet, to quote all the graduating seniors, it's graduation season, most of them are going to work in jobs that don't even exist 10 years from now. >> Correct, correct, very true. >> And the other thing is every company we talk to has got tons of open reqs, and they can't get enough people to fulfill what they need, and then Mihir, I think touched on an interesting point in the keynote, where, ya know, now we're starting to see literal population growth slow down in developed countries, >> Yes. >> Like in Japan is at the leading edge, and you mentioned Europe, and I'm not sure where the US is, so it's kind of this interesting dichotomy: On one side, machines are going to take more and more of our jobs, or more and more portions of our job. On the other hand, we don't have people to do those jobs necessarily anyway, not necessarily today, but down the road, and you know, will we get to more of this nirvana-state where people are being used to do higher-value types of activities, and we can push off some of this, the crap and mundane that still, unfortunately, takes such a huge portion of our day to day world? >> Yeah, yeah. So, one thought that some of us believe at Forrester, I being one of them, is that we're at a, kind of, neutral right point now where a lot of the AI, which is really the most disruptive element we're talking about here, our PA is no autonomous learning capability, there's no AI component to our PA. But, when AI kicks in, and we've seen evidence of it as we always do first in the consumer world where it's a light version of AI in Netflix. There's no unlimited spreadsheets sitting there figuring out which one to watch, right? They're taking in data about your behavior, putting you in clusters, mapping them to correlating them, and so forth. We think that business hasn't really gotten going with AI yet, so in other words, this period that you just described, where there seems to be 200,000 people hired every month in the ADP reports, you know, and there's actually 50,000 truck driver jobs open right now. And you see help-wanted signs everywhere. >> Right, right. >> We think that's really just because business hasn't really figured out what to do with technology yet. If you project three or four years, our projections are that there will be a significant number of, particular in the cubicles that our PA attacks, a significant number of dislocation of current employment. And that's going to create this job transformation, we think, is going to be more the issue then replacement. And if you go back in history, automations have always led to transformation. >> Right. >> And I won't go through the examples because we don't have time, but there are many. And we think that's going to be the case here in that automation dividends, we call them, are going to be, are being way underestimated, that they're going to be new opportunities, and so forth. The skills mis-match is the issue that, you know, you have what RPA attacks are the 60 million that are in cubicles today in the US. And the average education there is high school. So, they're not gonna be thrown out of the cubicles and become data scientists overnight, right? So, there's going to be a massive growth in the gig economy, and there's an informal and a formal segment of that, that's going to result in people having to patch together their lives in ways they they hadn't had before, so there's gonna be some pain there. But there are also going to be some strong dividends that will result from this level of productivity that we're gonna see, again, in a few years, cause I think we're at a neutral point right now. >> Well, Amara's Law doesn't get enough credit, right? We overestimate in the short-term, and then underestimate the long-term needs affect. >> Absolutely. >> And one of the big things on AI is really moving from this, in real time, right? And all these fast databases and fast analytics, is we move from a world where we are looking in the rear view mirror and making decisions on what happened in the past to you know, getting more predictive, and then even more prescriptive. >> Yes. >> So, you know, the value unlock there is very very real, I'm never fascinated to be amazed by how much inefficiency there still is every time we go to these conferences. (Craig laughs) You know we thought we solved it all at SAP and ERP, that was clearly-- >> Clearly not the case. Funny work to do. >> But, it's even interesting, even from last year, you mentioned that there the significant delta just from year to year is pretty amazing. >> Yes, I've been amazed at the level of innovation in the core digital worker platforms, the RPA platforms, in the last year has been pretty amazing work. What we were talking about a year ago when I spoke at this conference, and what we're talking about now, the areas are different. You know, we're not talking about basic control of the applications of the desktop. We're talking about integration with text analytics. We're talking about comp combining process mining information with desktop analytics to create new visions of the process. You know, we weren't talking about any of that a year ago. We're talking about bot stores. They're out there, and downloadable robots. Again, not talking about last year at all. So, just a lot of good progress, good solid progress, and I'm very happy to be a part of it. >> And really this kind of the front end scene of so much of the development is manifested on the front end, where we used to always talk about citizen developers back in the day. You know, Fred Luddy, who was just highlighted Service Now, most innovative company. That was his, you know, vision of Citizen Developer. And then we've talked about citizen integrators, which is really an interesting concept, and now we're talking about really citizens, or analysts, having the ability via these tools to do integrations and to deliver new kind of work flows that really weren't possible before unless you were a hardcore programmer. >> Yeah, although I think that conversation is a little bit premature in this space, right? I think that most of the bot development requires programming skills today, and they're going to get more complicated in that most of the bot activities today are doing, you know, three decisions or less. Or they're looking at four or five apps that are involved, or they're doing a series of four or five hundred clicks that they're emulating. And the progression is to get the digital workers to get smarter and incorporating various AI components, so you're going to have to build, be able to deal statistically with algorithm developments, and data, and learning, and all of that. So, it's not.... The core of this, the part of it that's going to be more disruptive to business is going to be done by pretty skilled developers, and programmers, and data scientists, and statistical, you know, folks that are going through. But, having said that, you're going to have a digital workforce that's got to be managed, and you know, has to be viewed as an employee at some level to get the proper governance. So you have to know when that digital worker was born, when they were hired, who do they report to, when were they terminated, and what their performance review is. You gotta be doing performance reviews on the digital workers with the kind of dashboard analytics that we have. And that's the only way to really govern, because the distinction in this category is that you're giving these bots human credentials, and you're letting them access the most trusted application boundaries, areas, in a company. So, you better treat them like employees if you want proper governance. >> Which becomes tricky as Mihir said when you go from one bot to ten bots to ten thousand. Then the management of this becomes not insignificant. >> Right. >> So Craig, I want to give you the last word. You said, you know, big changes since last year. If we sit down a year from now, 2019, _ Oh. >> Lord knows where we'll be. What are we gonna talk about? What do you see as kind of the next, you know, 12-month progression? >> You know, I hope we don't go to Jersey after Brooklyn, New York, and-- >> Keep moving. >> I see Jersey over there, but it's where it belongs, you know, across the river. I'm from Jersey, so I can say that. You know, I think next year we're gonna see more integration of AI modules into the digital worker. I think with a lot of these explosive markets, like RPA is, there's always a bit of cooling off period, and I think you're going to see some tapering off of the growth of some of the platform companies, AA, but also their peers and compatriots. That's natural. I think that the area has been a little bit, you know, analysis and tech-industry loves change. If there's no change, there's nothing for us to write about. So, we usually over-project. Now, in this case, the 2.8 billion-dollar market project five years out that I did is being exceeded, which is rare. But I expect some tapering off in a year where there's not a ceiling hit, but that, you know, you end up with going through these more simple applications that can be robotized easily. And now you're looking at slightly more complicated scenarios that take a little more, you know, AI and analytics embedded-ness, and require a little more care, they have a little more opaque, and a little more thought, and that'll slow things down a bit. But, I still think we're on our way to a supermarket and a lot of productivity here. >> So just a little less low-hanging fruit, and you gotta step up the game a little bit. >> I guess you could, you said it much simpler then I did. >> I'm a simple guy, Craig. >> But that's why you're the expert on this panelist. >> Alright, Craig, well thanks for sharing your insight, >> Alright. >> Really appreciate it, and do look forward to talking to you next year, and we'll see if that comes true. >> Alright, appreciate it, take care now. >> He's Craig Le Clair and I'm Jeff Frick. You're watching theCUBE from Automation Anywhere Imagine 2018.

(upbeat orchestral music) >> Welcome back everybody. Jeff Frick here with the CUBE. We're at our Palo Alto studios having a CUBE conversation about digital transformation, industrial internet, AI, ML, all things great, and we're really excited to have a representative of GE, one of our favorite companies to work with because they're at the cutting edge of old industrial stuff and new digital transformation and building a big software organization out in San Ramon. So we're so happy to have here first time Jeff Erhardt. He is the VP Intelligent Systems from GE Digital. Jeff, great to see you. >> Pleasure to be here. Thanks for having me. >> Absolutely, so how did you get into GE? You actually, a creature of the valley, you've been here a little while. How did you end up at GE? >> I have. I'm a new guy, so I've been here about a year and a half, I came in via the acquisition of a company called Wise IO where I was the CEO, so I've spent the last 10 years or so of my life building two different analytic startups. One was based around a very popular and powerful open source language called R and spent a lot of time working with much of the Fortune 500. Think the really data driven companies now that you would think of, the Facebooks, the Goldman Sachs, the Mercks, the Pfeizers helping them go through this data driven journey. Anyway, that company was acquired by Microsoft and is embedded into their products now. But the biggest thing I learned out about that was that even if you have really good data science teams, it's incredibly hard to go from white board into production. How do you take concepts and make them work reliably repeatably, scalably over time? And so, Wise IO was a machine learning company that was a spin out from Berkeley, and we spent time building what I now refer to as intelligent systems for the purposes of customer support automation within things like the sales force and Zendesk ecosystem, and it was really that capability that drew us to GE or drew GE to approach us, to think about how do we build that gap not just from algorithms, but into building true intelligent applications? >> Right, so GE is such a great company. They've been around for a hundred years, original DOW component, Jeff Immelt's not there now, but he was the CEO I think for 16 years. A long period of time. Beth Comstock, fantastic leader. Bill Ruth building this great organization. But it's all built around these industrial assets. But they've started, they did the industrial internet launch. We helped cover it in 2013. They have the Pridix Cloud, their own kind of industrial internet cloud, had a big developer conference. But I'm curious coming from kind of a small Silicon Valley startup situation. When you went into GE, what's kind of the state of their adoption, you know, kind of how had Bill's group penetrated the rest of GE and were they making process? We're people kinda getting it, or were you still doing some evangelical work out in the field? Absolutely both, meaning people understand it are implementing yet I think there was maybe misunderstandings about how to think about software data in particular analytics and AI machine learning. And so a big part of my first year at the company was to spend the time coming in really from the top down, from sort of the CEO and CDO levels across the different business understanding what was the state of data and data driven processes within their businesses. And what I learned really quickly was that the core of this business, and this is all public information been well publicized, is in things like GE Aviation. It's not necessarily the sale of the engine that is incredible profitable, but rather it's maintaining and servicing that over time. >> Right. >> And what organizations like them, like our oil and gas divisions, with things like their inspection capabilities like our power division had really done is they had created as a service businesses where they we're taking data across the customer base, running it through a data driven process, and then driving outcomes for our customers. And all of a sudden the aha moment was wow, wait a minute. This is the business model that every startup in the valley is getting funded to take down the traditional software players for. It's just not yet modern, scalable, repeatable, with AI machine learning built in, but that's the purpose and the value of building these common platforms with these applications on top that you can then make intelligent. >> Right. >> So, once we figure that out it was very easy to know where to focus and start building from that. >> So it's just, it's kinda weird I'm sure for people on the outside looking in to say data driven company. We all want to drive data driven companies. But then you say, well wait a minute, now GE builds jet engines. There's no greater example that's used at conferences as to the number of terabytes of data an engine throws off on a transcontinental flight. Or you think of a power plant or locomotion and you think of the control room with all this information so it probably seems counterintuitive to most that, didn't they have data, weren't they a data driven organization? How has the onset of machine learning and some of the modern architectures actually turned them into a data driven company, where before I think they were but really not to the level that we're specifying here. >> Yah, I-- >> What would be your objective, what are you trying to take on this? >> Absolutely, machine learning, AI, whatever buzz words you want to use is a fascinating topic. It's certainly come into vogue. like many things that are hyped, gets confused, gets misused, and gets overplayed. But, it has the potential to be both an incredibly simple technology as well as an incredibly powerful technology. So, one of the things I've most often seen cause people to go awry in this space is to try to think about what is the new things that I can do with machine learning? What is the green field opportunity? And whenever I'm talking to somebody at whatever level, but particularly at the higher levels of the company is I like to take a step back and I like to say, "What are the value producing, data driven workflows within your business?" And I say define for me the data that you have, how decisions are made upon it, and what outcome that you are driving for. And if you can do that, then what we can do is we can overlay machine learning as a technology to intelligently automate or augment those processes. And in turn what that's gonna do is it's gonna force you to standardize your infrastructure, standardize those workflows, quantify what you're trying to optimize for your customers. And if you do that in a standardized and incremental way, you can look backward having accomplished some very big things. >> Right, and those are such big foundational pieces that most people I think discount again, just the simple question of where is your data. >> That's right. >> What form is it in? So another interesting concept that we cover all the time with all the shows we go to is democratization, right? So it seems to me pretty simple, actually. How do you drive innovation, democratize the data, democratize the tool to manipulate the data, and democratize the ability to actually do something about it. That said, it's not that easy. And this kind of concept that we see evolving from citizen developer to citizen integrator to citizen data scientist is kinda where we all want to go to, but as you've experienced first hand it's not quite as easy as maybe it appears. >> Yah, I think that's a very fair statement and you know, one of the things, again I spend a lot of time talking about, is I like to think about getting the right people in the right roles, using the right tools. And the term data scientist has evolved over the past five plus years going from to give Drew Conway some credit of his Venn diagram of a program or a math kinda domain expert, into meaning anybody that's looking at data. And there's nothing wrong with that, but the concept of taking anybody that has ability to look at data within something like a BI or a Tableau tool, that is something that should absolutely be democratized and you can think about creating citizens for those people. On the flip side, though, how do you structure a true intelligent system that is running reliably, robustly, and particular in our field in mission critical, high risk, high stakes applications? There are bigger challenges than simply are the tools easy enough to use. It's very much more a software engineering problem than it is a data access or algorithmic problem. >> Right. >> And, so we need to build those bridges and think about where do we apply the citizens to for that understanding, and how do we build robust, reliable software over time? >> Right, so many places we can go, and we're gonna go a lot of them. But one of the things you touched on which also is now coming in vogue is kind of ML that you can, somebody else's ML, right? >> Mhmm. >> As you would buy an application at an app store, now there's all kinds of algorithmic equations out there that you can purchase and participate in. And that really begs an interesting question of kinda the classic buy versus build, or as you said before we turned on the cameras buy versus consume because with API economy with all these connected applications, it really opens up an opportunity that you can use a lot more than was produced inside your own four walls. >> Absolutely. >> For those applications. >> Yep. >> And are you seeing that? How's that kinda playing out? >> So we can parse that in a couple of different ways. So the first thing that I would say is there's a Google paper from a few years back that we love and it's required reading for every new employee that we bring on board. And the title of it was machine Learning is the High Interest Credit Card of Technical Debt. And one of the key points within that paper is that the algorithm piece is something like five percent of an overall production machine learning implementation. And so it gets back to the citizen piece. About it's not just making algorithms easier to use, but it's also about where do you consume things from an API economy? So that's the first thing I would think about. The second thing I would think about is there's different ways to use algorithms or APIs or pieces of information within an overall intelligent system. So you might think of speech to text or translation as capabilities. That's something where it probably absolutely makes sense to call an API from an Amazon or a Microsoft or a Google to do that, but then knowing how to integrate that reliably, robustly into the particular application or business problem that you have, is an important next step. >> Right. >> The third thing that I would think about is, it very much matters what your space is. And there's a difference between doing things like image classification on things like Imagenet which is publicly available images which are well documented. Is it a dog versus a cat? Is it a hot dog versus not? Versus some of the things that we face with an industrial context, which aren't really publicly available. So we deal with things like within our oil and gas business we have a very large pipeline inspection integrity business where the purpose of that is to send the equivalent of an MRI machine through the pipes and collect spectral images that collect across 14 different sensors. The ability to think that you're gonna take a pre trained algorithm based on deep learning and publicly available images to something that is noisy, dirty, has 14 different types of sensors on it and get a good answer-- >> Right. >> Is ridiculous. >> And there's not that many, right? >> And there's not that many. >> That's the other thing I think people underestimate the advantage that Google has we're all taking pictures of dogs and blueberries-- >> Correct. >> So that it's got so much more data to work with. >> That's right. >> As opposed to these industrial applications which are much smaller. >> That's right. >> Lets shift gears again, in terms of digital transformation one of the other often often said examples is when will the day come that GE doesn't sell just engines but actually sells propulsion miles? >> Yep. >> To really convert to a service. >> Yah. >> And that's ultimately where it needs to go cause it's kinda the next step beyond maintenance. >> Yep. >> How are you seeing that digital transformation play out? Do people kinda get it? Do the old line guys that run the jet engine see that this is really a better opportunity? >> Mhmm. >> Cause you guys have, and this is the broader theme, very uniques data and very unique expertise that you've aggregated across in the jet engines base all of your customers in all of the flying conditions and all of the types of airplanes where one individual mechanic or one individual airline just doesn't have an expertise. >> Yep. >> Huge opportunity. >> That's exactly right, and you can say the ame thing in our power space, in our power generation space. You can say the same thing in the one we we're just talking about, you know things like our inspection technology spaces. That's what makes the opportunity so powerful at GE and it's exactly the reason why I'm there because we can't get that any place else. It's both that history, it's that knowledge tied to the data, and very importantly it's what you hinted at that bares repeating is the customer relationships and the customer base upon which you can work together to aggregate all that data together. And if you look at what things are being done, they're already doing it. They are selling effectively, efficiency within a power plant. They are selling safety within certain systems, and again, coming back to why create a platform. Why create standardized applications? Why put these on top? Is if you standardize that, it gives you the ability to create derivative and adjacent products very easily, very efficiently, in ways that nobody else can match. >> Right, right. And I love the whole, for people who aren't familiar with the digital twin concept, but really leveraging this concept of a digital twin not to mimic kinda the macro level, but to mimic the micro level of a particular part unit engine in a particular ecosystem where you can now run simulations, you can run tests, you can do all kinds of stuff without actually having that second big piece of capital gear out there. >> That's right, and it's really hard to mimic those if you didn't start from the first phase of how did you design, build, and put it in to the field? >> Right, right. So, I want to shift gears a little bit just on to philosophical things that you've talked about and doing some research. One of them is that tech is the means to an end, and I know people talk about that all the time, but we're in the tech business. We're here in Silicon Valley. People get so enamored with the technology that they forget that it is a means to an end. It is now the end and to stay focused. >> That's right. >> How are you seeing that kind of play out in GE Digital? Obviously Bill built this humongous organization. I'm super impressed he was able to hire that many people within the last like four years in San Ramon. >> Yah. >> Originally I think just to build the internal software workings within the GE business units, but now really to go much further in terms of industrial internet connectivity, etc. So how do you see that really kinda playing out? >> Yah, I think one of my favorite quotes that I forget who it came from but I'll borrow it is, "Customers don't want to buy a one inch drill bit, they want to buy a one inch hole." >> Right. >> And I think there is both an art and a science and a degree of understanding that needs to go into what is the real customer problem that they are trying to solve for, and how do you peel the onion to understanding that versus just giving what they ask for? >> Right. >> And I think there's an organizational design to how do you get that right. So we had a visitor from Europe, the chairman of one of our large customers, who is going through this data driven journey, and they were at the stage of simply just collecting data off of their equipment. In this case it was elevators and escalators. And then understanding how was it being used? What does it mean for field maintenance, etcetera? But his guys wanted to move right to the end stage and they wanted to come in and say, "Hey, we want to build AI machine learning systems." And we spent some time talking through them about how this is a journey, how you step through it. And you could see the light bulb go off. That yes, I shouldn't try to jump right to that end state. There's a process of going through it, number one, and then the second thing we spent some time talking about was how he can think about structuring his company to create that bridge between the new technology people who are building and doing things in a certain way, and the people who have the legacy knowledge of how things are built, run, and operated? >> Right. >> And it's many times those organizational aspects that are as challenging or as big of barriers to getting it right as a specific technology. >> Oh, for sure, I mean people process and tech it's always the people that are the hard part. It's funny you bring up the elevator or escalator story, We did a show at Spunk many moons ago and we had a person on from an elevator company and the amazing insight they connected Spunk to it. They could actually tell the health of a building by the elevator traffic. >> Yah. >> Not the health of it's industrial systems and it's HVAC, but whether some of the tenants were in trouble. >> Yep. >> By watching the patterns that were coming off the elevator. While different kinda data driven value proposition than they had before. >> Yep. So again, if you could share some best practices really from your experiences with R and now kinda what you're doing at GE about how people should start those first couple of steps in being data driven beyond kinda the simple terms of getting your house in order, getting your data in order, where is it. >> Yah. >> Can you connect to it? Is it clean? >> Yah. >> How should they kinda think about prioritizing? Ho do they look for those easy wins cause at the end of the day it's always about the easiest wins to get the support to move to the next level. >> Yah, so I've sorta got a very simple Hilo play book and you know the first step is you have to know your business. And you have to really understand and prioritize. Again, sometimes I think about not the build, buy decision per say, but maybe the build consume decision. And again, where does it take the effort to go through hiring the people, understanding building those solutions, versus where is it just best to say, "I'm best to consume this product or service from somebody else." So that's number one, and you have to understand your business to do that, really well. The second one is, and we touched on this before, which is getting the right people in the right seats of the bus. Understanding who those citizen data scientists are versus who your developers are, who your analytics people are, who your machine learning people are, and making sure you've got the right people doing the right thing. >> Right. >> And then the last thing is to make sure, to understand that it is a journey. And we like to think about the journey that we go through in sort of three phases, right? Or sort of three swim lanes that could happen, both in parallel, but also as a journey. And we think about those as sort of basic BI and exploratory analytics. How do I learn is there any there there? And fundamentally you're saying, I want to ask and answer a question one time. Think about traditional business reporting. But once you've done that, your goal is always to put something into production. You say, "I've asked and answered once, now I want to ask and answer hundreds, millions, billions of times-- >> Right, right. >> In a row." And the goal is to codify that knowledge into a statistic, an analytic, a business role. And then, how do you start running those within a consistent system? And it's gonna do and force exactly what you just said. Do I have my data in one place? Is it scalable? Is it robust? Is it queryable? Where is it being consumed? How do I capture what's good or bad? And once I start to then define those, I can then start to standardize that within an application workflow and then move into, again, these complex, adaptive, intelligent systems powered by AI machine learning. And so, that's the way we think about it. Know your business, get the people right, understand that it's a systematic journey. >> Right, and then really bake it into the application. >> That's right. >> That's the thing, we don't want to make the same mistake that we do with big data, right? >> Yep. >> Just put it into the application. It's not this stand alone-- >> Correct. >> You know, kinda funny thing. >> Exactly. >> Alright, Jeff, I'll give you the last work before we wrap for the day. So you've been with GE now for about a year and a half, about halfway through 2018. What are your priorities for the next 12 months? If we sit down here, you know June one next year, what are you working on, what's kinda top of mind for you going forward? >> Yah, so top of the line for me, so as I mentioned sort of our first year here was really surveying the landscape, understanding how this company does business, where the opportunities are. Again, where those data driven work flows are. And we have an idea of of that with the core industrial. And so what we've been doing is getting that infrastructure right, getting those people right, getting the V ones of some very powerful systems set up. And so, what I'm gonna be doing over the next year or so is really working with them to scale those out within those core parts of the business, understand how we can create derivative and adjacent products over those, and then how we can take them to market more broadly based upon that, exactly as you said earlier, large scale data that we have available, that customer insight, and that knowledge of how we've been building the stuff, so. >> Alright, I look forward to it. >> I look forward to being back in a year. >> All right, Jeff Erhardt. Thanks for watching. I'm Jeff Frick. You're watching the CUBE from our Palo Alto studios. See you next time. (upbeat orchestra music)

>> Narrator: Live from Las Vegas, it's theCUBE covering HPE Discover 2017 brought to you by Hewlett Packard Enterprise. >> Welcome back, everyone. We are here live in Las Vegas for HPE Discover 2017 exclusives look at angle cube coverage, our seventh year. I'm John Furrier with my co-host Dave Vellante. Our next guest is Tripp Partain, HPE CTO for GE General Electric and Anthony Rokis, VP of Software Engineering at Predix with GE Digital. Guys, welcome back to theCUBE. Good to see you. >> Thanks guys. >> Thanks for coming on. Obviously, GE has really been on the front end of IOT. You guys have been doing extremely well and changing over, bringing digital to analog, kind of connecting those worlds. What's your take on this intelligent Edge? You got to love the messaging. You got to love the messaging with HP. >> God, it's great. I think this is really starting to take off. If you look at our positioning, we really are going after the Edge, right. And with Predix being our forefront in the Predix system, we really believe in the opportunity here. I think, as you heard Meg speak yesterday, the engagement between GE Digital and HPE is getting stronger, we're finding more and more synergies over time. And both our strategy and their strategy are really starting to line up very nicely, both Edge and computing in general. >> I had a chance a couple years ago to host a panel with your CEO Jeff Amels, and United Airlines, Hospital in Chicago and at that time it was really hardcore, tangible dollars on the line. I mean, we're talking highly instrumented devices and machinery that you guys are in and there were some significant dollars involved. Just getting the data is a very low-hanging fruit, but big numbers, this is now going mainstream where everyone's kind of having this awakening moment, Tripp, where it's kind of like, "Hey, we're just going mainstream." So what's next for you guys, as the world starts getting up to speed on IOT, what's next for GE? What are you guys doing now to go onto the next level? What's that next tier of digital IOT for you guys? >> Yeah, honestly in my view and GE's view if you look at what we've done in the past, it's really the foundations getting in place. It's censor-enabled devices getting assets. The censor is more progressive, and that's kind of been the first sort of step, right. Then we get into how do we collect that data? Where you think GE is headed now, are the smart analytics. It's the outcomes that are going to drive those big dollars in productivity. It's really getting into the digital industrial revolution area. To date, it's been a lot of the foundation getting in place, and I think that's where you're going to see tremendous growth over time is. When you unleash data scientists on wealth of information, the outcomes in the productivity, the world and the economy is going to see is going to be great. >> I love that quote, with Jeff Immelt. We refer to it all the time. I went to bed an industrial giant, and woke up, you know a software company. And so it clearly underscores the transformation. We were talking off-camera about the study that we did many, many years ago. I mean, the numbers are staggering. It's in the trillions. But one of the things that we found was this notion of, and we talk about it all the time and I'd love to get your take on it is the IT and OT. They're not talking to each other. Typically, they're not birds of a feather. What are you seeing, Tripp, in your experience with customers in terms of those organizational, let's start with the IT side and we can talk about the OT side. >> Yeah, and as we've had our partners show up with GE continue to develop, the one thing we've found is we have a lot of similar customers. And in these same customers are extremely large customers, but what's interesting we don't talk to any of the same people. Right, on the HPE side we tended to talk to the IT teams and data center and GE would be out in the factory floor or out in the field and more industrial. But in order to really fulfill the IOT promise, the two groups are really having to come together and I think it's taking time for messaging to really sort out there. And one of the things that we're really doing, taking advantage of our partnership to help solve the problem is when we have IT teams come to visit HPE, we bring along GE operational experts to actually talk about the business side of the outcome so it's not just an IT conversation. And really intentionally crossing those paths and leveraging our partner in GE to bring that capability to us so we can have a holistic conversation to the customer. >> So who's in charge here, who's driving the bus? Is it the OT guys, or the IT guys, or somebody above? >> It's both, there's two drivers. >> Uh oh. >> Two hands, four hands on the wheel almost. If you look at the OT side, there's a lot of challenges we're facing where HPE and the IT community is coming to help. For instance, data sovereign team, right. So one of the challenges we have is a lot of our companies, our customers, want data sovereignty and this is where IT has solved that problem for us and on the OT side, we need to figure out how do we store, maintain, analyze that data within a country. And again, that's why we're bringing the IT companies with us and partner to help us. >> So when a plane flies from Spain, crosses France, Germany, and ends up in Ireland, where is the data? (laughs) >> Very good question. >> Well it infringes the data, because there's sort of a data love triangle going on. You've obviously got devices installed, HPE brings equipment, and the customer. So, talk about the conversations that you're having with your customers. I mean, who owns the data. The factory says, "Hey, wait a minute it's a system. That's my data." GE obviously has to do predictive maintenance and same with HPE. There's all this data flowing. What about data, I don't know, ownership or IP, what are those conversations like? >> Yeah, I can say certainly from the GE side it's always been our stance that the customer owns their data, right. We are running a multi-tenency environment and a platform. And they own that data. How that data is stored, we can help facilitate, right. We offer Cloud store and a couple other technologies that allow that. But at the end of the day in a multi-tenent environment, the customer owns that data. And we will facilitate with HPE where that data needs to reside based on the customer's need. >> So you're not trying in any way to monetize that data? I mean, I'm astounded, why not? >> I think the monetization really comes in with how you empower the customer to get the value out of the data. And in a former life, I worked through the data monetization world and there is certain amounts of value in the data itself. There's also value in helping the customer determine what their data can offer to them and the business cases that we're able to jointly present to the customer and the value that that generates still allows for us to monetize the process by which we help enable the customer to really bring these data assets together. Really understand areas that they may have seen silos of the data before, but they weren't looking holistically at it and being able to, in a very timely fashion correlate between that and then actually see a different answer to a problem where yes, this meter may be reading 80 and it should be 60, but if I throttle it to 70 and I get 10% more output, it's worth running at 70 because of the benefit on the revenue. So you actually can make trade offs across certain areas where you weren't able to do that. >> But Predix is informing models, is it not, I mean. >> Yeah, I mean at the end of the day, we're taking that data and for the customer created an outcome. Right, the analytic, the information that we can derive out of it to make a more productive or a more efficient outcome of running operation, that's where we get the monetization from. >> If data's a new oil then you need to refine it, was your point about the monetization question. That's interesting because we see the same thing where if you make the data freely available or you treat it as an asset to the customer, it's how it's monetized in its effect. Or there's a tacticle, let's monetize our data. So depending on how you look at it, there's different approaches, right? I mean this is kind of the key thing. >> Right, and even though this is not the way now, if you follow the history of how other industries have dealt with the data. So I came out of credit services long ago, and it's very common now, in the credit services industry for data to be monetized and leveraged like for credit reports and for that whole banking financial process to take place, but it didn't start that way. So my guess is, as we continue to show value to the customer of their own data as they then start to think about, "Wow but if I could do comparables between my data and industry data that would help me even more." I expect that the customers that today that are worried about who owns the data, will eventually start asking players like HPE and GE Digital to help them solve that problem. And they'll evolve to that sort of data monetization like a lot of the other industries have. >> A whole new digital just creates a whole new way to look at things, it's not a linear supply chain anymore whether relative to data or what not, so super cool. Final question for you guys and I appreciate you coming on theCUBE and sharing your insights. What's next for the partnership with HPE and GE Digital? Obviously, the digital transformation's in full swing impacting business transformation, impacting the Dev Ops aspect of Cloud. All this cool stuff's happening, true private Cloud's on fire, hybrid's the doorway to Multi Cloud. A lot of cool stuff happening, what's next for you guys? >> Yeah I think from our side we're really excited about the partnership on the Edge, right. When we start looking at the computing requirements and needs at the Edge, close to the asset, low latency that's where HPE and GE are really going to start to partner very heavily and you're going to see a lot more engagement at that level. So I think the Edge is going to be our focal point. >> Oh absolutely, and I think the uniqueness we bring to the market with our Edge line converged systems, we're able to do things at the Edge, leveraging GE Predix and then also bringing in other third party partners in conjunction and now you have enough computer power in the right form factor that can all sit and reside at the Edge, process at the Edge and solve the problems there locally. Doesn't take away from the Cloud aspect, doesn't take away from being able to have a macro view across multiple scenarios. But if I'm on an oil rig in the middle of the North Sea, you know it's going to be very important for me to have everything I need in the right form factor at the lowest power utilization possible and still solve my problems. >> And can process all the data right there. Guys, we are pushing it to the Edge here theCUBE goes out to the events, that's the Edge of the action. We'll bring you all the great videos. Thanks for coming on, this is theCUBE live coverage from the Edge at HPE Discover 2017. I'm John Furrier, Dave Vellante. Be right back with more, stay with us. (digital music)

>> Hey welcome back everybody. Jeff Frick here with the Cube. We're in San Francisco at the Professional Business Women of California Conference, the 28th year, I think Hillary must be in the neighborhood because everyone is streaming up to the keynote rooms. It's getting towards the end of the day. But we're excited to have Adam Smiley on. He's the author of The Quarter-Life Breakthrough. Welcome Adam. >> Great to be here, thanks for having me. >> Absolutely. So you gave a talk a little bit earlier on, I assume the theme of kind of your general thing. Would you just, Quarter-Life Breakthrough, what is Quarter-Life Breakthrough? >> So this is a book about how to empower the next generation. How young people can find meaning in their careers and their lives. So the subtitle of the book is Invent Your Own Path, Find Meaningful Work, and Build a Life That Matters. So everyone talks about millennials, you hear them in the news, "Oh they're the lazy generation," >> Right, right. >> "The entitled generation." The Me, Me, Me generation. I actually think that couldn't be further from the truth. So the truth is that actually 50% of millennials would take a pay cut to find work that matches their values. 90% want to use their skills for good. So my book is a guide for people to find purpose in their careers and really help them find meaning at the workplace and help companies empower that generation at work. >> So from being the older guy, so then is it really incumbent, you know, because before people didn't work for good, they worked for paycheck, right. They went, they punched in, they got paid, they went home. So is it really incumbent then on the employers now to find purposeful work? And how much of it has to be purposeful? I mean, unfortunately, there's always some of that, that grimy stuff that you just have to do. So what's the balance? >> Yeah and it's not to say that millennials don't want a paycheck, everyone wants money. I obviously want to make more money than less money. But it's also that this generation is really looking for meaning in the workplace. And one of the main things, if you look at all the studies, whether it's the Deloitte Millennial Study or the IBM Study, this is a generation that wants to move the needle forward on social issues at work. Not just after work or on the weekends, but at the workplace. And I think it's incumbent upon companies to really think about how they're providing those opportunities for purpose. Both in the mission of the company, what someone's doing every day, and opportunities outside of work, whether it's service projects, paid sabbaticals for people to do purpose-driven projects, really thinking about how someone is inspired to do mission during work every day. >> Right, it's interesting, Bev Crair at the keynote talked about, the question I think was, do you have to separate, kind of your personal views from your professional views and your social life? And she made a very powerful statement, she's like, "I'm comfortable enough with my employer that I can say what I feel and if there's ever a question they can ask me about it. But I don't gait what I say based on my employer as long as I'm being honest and truthful." So you know it's an interesting twist on an old theme. Where before you kind of had your separate worlds. You know, you had your work life and your home life, but now between email and text and social media, there is no kind of they're there for work and it's really invaded into the personal. So is that why the personal has to kind of invade back into the work? >> And when it comes to millennials, one word that always comes up is authenticity. People do not want to separate who they are at home from who they are at work. They want to be their whole person. Now obviously there's a line you don't cross. I'm not going to tell someone exactly what I think of them or tell the boss to go screw themselves or insult somebody or put on social media something that's secret that we're doing at the company. But I think that people want to feel that they get to show up who they are, have their beliefs echoed at the workplace, be able to be their full self, their full values, their mission, their goals, have that reflected in what they do, and have people at the company actually acknowledge that. You're not just an employee, I actually know what's going on in your life. I know what your dreams are, I know what your family's going through. I care about where you're headed, not just today or while you work here, but when you leave the company. Because that's the other thing, is that we're accepting that most of the people entering the workforce now or starting a new job, they're going to be there on average two to three years, maybe four, five, or six years. They're not going to be there ten, fifteen, twenty years like they used to be. So how do you actually empower someone to make an impact while they're there. And help them find the next lily pad, as they call it. The next opportunity. Because they're going to have a lot of those lily pads as they go throughout their career. >> It's interesting. We interviewed a gal named Marcia Conrad at an IBM event many years ago. She just made a really funny observation, she's like, "You know, people come in and you interview them and they're these really cool people and that's why you hire them, because they've got all these personality traits and habits and hobbies and things that they do, and energy." And then they come into the company, and then the old-school, you drop the employee, you know manual on top of them, basically saying stop being you. Stop being the person that we just hired. So that's completely flipped up on its head. >> Right, one of the things I talked about in the session today was this idea of stay interviews versus exit interviews. Normally when we do performance managements, kind of like, okay, you're leaving, what did you think? Why are you doing that when someone leaves? Do it to be like, what would make you stay? What do you want to accomplish while you're here? And you're not being graded against what everyone else is being graded on, what do you want to be graded on? What are your goals? What are your metrics for success? Performance achievement versus just performance measurement. I think is very important for this generation, because otherwise it's like, well why am I being judged on the standards that were written in 1986? This is what I'm trying to do here. >> It's interesting, even Jeff Immelt at GE, they've thrown out the annual review because it's a silly thing. You kind of collect your data two weeks before and the other fifty weeks everybody is just working. I have another hypothesis I want to run by you though. On this kind of purpose-driven. Today so many more things are as a service, transportation as a service, you know there seems to be less emphasis on things and more emphasis on experiences. It also feels like it's easier to see your impact whether it's writing a line of code, or doing something in social media. And you know there was an interesting campaign, Casey Neistat did, participated a couple weeks ago, right. They raised $2 million and basically got Turkish Airlines to fly in a couple hundred thousand metric tons of food to Somalia. And my question is, is it just because you can do those things so much easier and see an impact? Is that why, kind of this, increased purposefulness, I'm struggling on the word. >> I think the tools are certainly more available for people to take action. I think the connection is there. People are seeing what's going on in the world in a way that they've never been exposed to before with social media, with communication technology. It's up front and center. I think also that as technology takes over our lives, you see this with kind of statistics around depression and anxiety, people are starved for that in-person connection. They're starved for that meaning, that actual conversation. We're always doing this, but really a lot of data shows that people experience true joy, true fulfillment, true connection, true experience is what you're talking about, when they're in a room with someone. So people want that. So it's kind of a return back to that purpose-driven life, that purpose-driven tribe, village experience because the rest of the time we're on our phones. And yeah, it's cool, but something's missing. So people are starting to go back to work and be like, "I want that inspiration" that other generations may have gotten from church or from outside of work, or from their community, or from their village, or from the elders, or from a youth group or something. They're like, "I want that in the workplace. I want that everyday." >> Well so this is more top-down right? I mean I just think again, kind of the classic, back in the day, you were kind of compelled to give x percentage of your pay to United Way or whatever. And that was like this big aggregation mechanism that would roll up the money and distribute it to God-knows-where. Completely different model than, and you can see, because of social media and ubiquitous cell phones all over the place, you can actually see who that kid is, that's getting your thing on the other side. >> And it's empowering someone to say, "Okay this is what's important to me. These are the causes that I'd like to support. This is where I want my money to go and here's why." >> So what do you think's the biggest misunderstanding of millennials from old people like me or even older hopefully? >> Well one thing that I do think that millennials don't get right is the importance of patience. I think a lot of times people say, you know, "oh millennials, they want things to happen too quickly." I think that that's true. I think that my generation, I'm going to be the first to admit and say that we need to do a better job of being patient, being persistent. You can't expect things to happen overnight. You can't expect to start a job and in two months get promoted or to feel like you're with the Board of Directors. Things take time. At the same time, it's incumbent upon older generations to listen to these young people, to make them feel like they have a voice, to make them feel like they're heard and that their ideas matter, even if they don't have the final say, to make them feel like they actually matter. Because I think sometimes people assume that they don't know anything. They don't know everything, but they have some really brilliant ideas and if you listen to those ideas they might actually be really good for the company both in terms of profit and purpose. So that's one thing I would say. >> Okay, just, so first time with this show, just get your impressions of the show. >> Oh it's great. This is a great show. You all are doing a great job, a great interview. >> No not our show. The PBWC, I mean of course we're doing a good job, we have you on. I mean the PBWC. >> It's a great, you know for me, it's real exciting to be at the end of an event where I'm one of the only male speakers. Because usually, I've been doing the speaking circuit thing now for a year or two. And I go to these events, I go to panels, I go to conferences, keynotes, and it's mostly male speakers, which is a huge problem. There's far far far fewer women and people of color speaking at these events than men. And one of the things I'm really trying to change is that but also pay equity around speaking, because I talked to some of my female colleagues about what they were paid for a specific event, and they'll say, "Well they covered my transportation, they covered my lift and a salad, or my hotel maybe." I'm like, well I got paid $5000. That's messed up. We did the same amount of work. We did the same panel or doing the same keynote, similar experience levels. That's messed up. And so I'm trying to change that by doing this thing called the Women Speaker Initiative. Which is a mentorship program to empower more women and people of color to be speakers and then to make sure that they're paid fairly when compared to men. >> So how do people get involved with that? >> They should just got to my website, smileyposwolsky.com and check out Women Speaker Initiative. >> Alright, well Adam, thanks for taking a few minutes out of your day. Great great topic and I'm sure, look forward to catching up again later. >> Thanks so much for having me. >> Alright. He's Adam, I'm Jeff. You're watching theCube. We're at the Professional Business Women of California conference, twenty eighth year. Thanks for watching.