(upbeat music) >> From around the globe, it's theCUBE. With digital coverage of Postgres Vision 2021 brought to you by EDB. >> Well, good day, everybody. John Walls here on theCUBE. We continue our coverage here at Postgres Vision in 2021. Talking today with Andy Harris, who is the Chief Technology Officer at Osirium, a leader in the Privileged Access Management Space, and Andy, good day to you. Thanks for joining us here on theCUBE. >> Good morning to you and good afternoon, yes. >> That's right. Joining us from overseas over in England, we're on this side of the big pond, but nonetheless, we're joined by the power of Zoom. So again, thanks for the time. Andy, for those who aren't familiar who are watching about Osirium, share a little bit about your various service levels of what you provide, the kind of solutions you provide, and how you've achieved a great success in this space. >> Okay. I know these things, mine will be boring. So I'll just put a little slide up now, which is the minimum I think I can get away with which is that we're all about managing privilege. So that's privileged at the endpoint, Privileged Access Management, and Privileged Process Automation. So wherever a CIS admin has to do something on a machine that needs privilege, we like to be involved. Obviously, we like to be able to delegate all the way down to the business functions with Privileged Process Automation and with the EDB or the BDR part of that functionality in EDB that really fits in to our Privileged Access Management. So what I'll do just to take you away from our product. So I'll just quickly show you a slide of the architecture, which is as simple as we have these nodes. If you like the running ADB BDR and they can perform log-ins to a target device using privileged credentials, which we control when we might be really long up to about 128 characters. >> So Andy, if you would, I think you had put together a little show and tell you a demonstration for how when these systems are perhaps under siege if you will. That there are ways in which obviously you've developed to counter this and to be able to continue secure communications, which in the privilege assets world as you know is paramount. >> Yes, indeed. So I'll show you another slide, which gives you a kind of a overview of everything that's going on and you're going to see a little demonstration of two nodes here that has the BDL technology on and they can make these logins, and we have these characters, Bob and Allison. I've just noticed how it marks in department turn Alice to Allison. they should really be Alice because you get Bob, Alice, Carol, Dave, which are the standard encryption users. And what we're going to do is we're going to demonstrate that you can have breaks in the network. So I'm just sharing the network breaks slide. I'm showing the second network break slide. And then we have this function that we've built which we're going to demonstrate for you today, which is called evil beatings. And what it does is whilst there is a politician in the network, we are going to refresh many thousands of times the credentials on the target device. And then we're going to heal the break in the network and then prove that everything is still working. So right now, I'm going to zoom over to my live connection, terminal connections to the machine. And I'm going to run this command here, which is Python EV3. And I'm going to put a hundred cycles in it which is going to do around about 10,000 password refreshes. Okay. And I'm then going to go over to Chrome, and I should have a system here waiting for me. And in this system, you'll see that I've got the device demo and I've got this come online, SSH. And if I click on this I've got a live connection to this machine. Even whilst I have a huge number of queued up and I'll just show you the queued out connections through the admin interface. The system is working extremely hard at the moment. And in fact, if I show you this slide here, you can see that I have all of these queued credential resets and that is giving our system an awful lot of grief. Yeah. I can go back to the device connection and it is all here still top. Why not? And as you can see, it is all working perfectly. And if I was a user of EDB, I think this has to be one of the demonstrations I'd be interested in because it's one of the first things that we did when we dropped that functionality into our products. We wanted to know how well it would work under extreme conditions because you don't think of extreme conditions as normal working, but whenever you have 10 nodes in different countries, there will always be a network break somewhere and someone will always need to be refreshing passwords a ridiculous rates of knots. So Andy let's talk about this kind of the notion that you're providing here, this about accountability and visibility, audit-ability, all these insights that you're providing through this kind of demonstration you've given us how critical is that today, especially when we know there are so many possible intrusions and so many opportunities with legacy systems and new apps and all of this. I mean talking about those three pillars, if you will, the importance of that and what we just saw in terms of providing that peace of mind that everybody wants in their system. >> That's a cracking question. I'm going to enjoy that question. Legacy systems, that's a really good question. If you, we have NHS, which is our national health service and we have hospitals and you have hospitals every country has hospitals. And the equipment that they use like the MRI scanners, the electro-microscope, some of the blood analysis machines, the systems in those costs multiple Gillions of dollars or should use dollars euros, dollars, pounds and the operating systems running those systems, the lifetime of that piece of equipment is much much longer than the lifetime of an operating system. So we glibly throw around this idea of legacy systems and to a hospital that's a system that's a mere five years old and has got to be delivering for another 15 years. But in reality, all of this stuff gets, acquires vulnerabilities because our adversaries the people that want to do organizations bad things ransomware and all the rest of it they are spending all that time learning about the vulnerabilities of old systems. So the beauty of what we do is being able to take those old legacy systems and put a zero trust safety shell around them, and then use extremely long credentials which can't be cracked. And then we make sure that those credentials don't go anywhere near any workstations. But what they do do, is they're inside that ADB database encrypted with a master encryption key, and they make that jump just inside the zero trust boundary so that Bob and Alice outside can get administration connections inside for them to work. So what we're doing is providing safety for those legacy systems. We are also providing an environment for old apps to run in as well. So we have something called a map server which I didn't think you'd asked us that question. I'd have to find you some slides or presentations, which we want to do. We have a map server, which is effectively a very protected window server, and you can put your old applications on them and you can let them age gracefully and carry on running. Dot net 3.5 and all of those old things. And we can map your connection into the older application and then map those connections out. But in terms of the other aspects of it is the hospital stay open 24 hours a day banks run 24 hours a day and they need to be managed from anywhere. We're in a global pandemic, people are working from home. That means that people are working from laptops and all sorts of things that haven't been provisioned by centrality and could all have all sorts of threats and problems to them. And being able to access any time is really important. And because we are changing the credentials on these machines on a regular basis, you cannot lose one. It's absolutely critical. You cannot go around losing Windows active directory domain credentials it just can't be done. And if you have a situation where you've just updated a password and you've had a failure one of those 10 nodes has the correct set of credentials. And when the system heals, you have to work out which one of the 10 it is and the one that did it last must be the one that updates all the other 10 nodes. And I think the important thing is as Osirium we have the responsibility for doing the updates and we have the responsibility for tracking all those things. But we hand the responsibility of making sure that all the other 10 nodes are up to date which just drop it into bi-directional replication and it just happens. And you've seen it happen. I mean, might be just for the fun of it, We'll go back to that demonstration Chrome, and you can see we're still connected to that machine. That's all still running fine but we could go off to our management thing, refresh it and you see that everything there is successful. I can go to a second machine and I can make a second connection to that device. Yet, in the meantime that password has been changed, Oh, I mean, I wouldn't like to tell you how many times it's been changed. I need to be on a slightly different device. I was going to do a reveal password for you, I'll make another connection but the passwords will be typically, do a top on that just to create some more load. But the passwords will typically be... I'll come back to me. They'll typically be 128 characters long. >> Andy, if I could, I mean, 'cause I think you're really showing this very complex set of challenges that you have these days, right? In terms of providing access to multiple devices across, in multiple networking challenges, when you talk to your prospective clients about the kind of how this security perimeters changed, it's very different now than it was four or five years ago. What are the key points that you want them to take away from your discussion about how they have to think about security and access especially in this day and age when we've even seen here in the States. Some very serious intrusions that I think certainly get everybody's attention. >> That's a great question again. They're all... The way that I would answer that question would definitely depend on the continent that I was talking to. But my favorite answer will be a European answer, so I'll give you a European answer. One of the things that you're doing when you come along and provide Privileged Access Management to a traditional IT team, is your taking away the sysadmins right now, before privilege access, they will know the passwords. They will be keeping the passwords in a password vault or something like this. So they own the passwords, they own the credentials. And when you come along with a product like privilege access management you're taking over management of those credentials and you're protecting those systems from a whole wide range of threats. And one of those threats is from the system administrators themselves. And they understand that. So what I would say, it's an interesting question. 'Cause I'm like, I'm thinking I've got two ways of answering I can answer as if I'm talking to management or as if I'm talking to the people who are actually going to use the products and I feel more aligned with the, I feel more aligned with the actual users. >> Yeah, I think let's just, we'll focus on that and I'll let you know, we just have a moment or two left. So if you could maybe boil it down for me a little bit. >> Boiling it down, I would say now look here CIS admins. It's really important that you get your job done but you need to understand that those privileged accounts that you're using on those systems are absolute gold dust if they get into the hands of your adversaries and you need protections income away from those adversaries, but we trust you and we are going to get you the access to your machines as fast as possible. So we're a little bit like a nightclub bouncer but we're like the Heineken of nightclub bounces. When you arrive, we know it's you and we're going to get you to your favorite machine logged on as domain admin, as fast as possible. And while you're there, we're going to cut that session recording of you. And just keep you safe and on the right side. >> All right, I'm going to enjoy my night in the nightclub. Now I can sleep easy tonight knowing that Andy Harris and Osirium are on the case. Thanks, Andy. Andy Harris speaking with us. So the Chief Technology Officer from Osirium as part of our Postgres vision, 2021, coverage here on theCUBE. (upbeat music) >> From theCUBE studios in Palo Alto, in Boston connecting with thought leaders all around the world. This is theCUBE conversation.

>> Narrator: Live from Las Vegas, it's theCUBE, covering UiPath Forward Americas 2019, brought to you by UiPath. >> Hi everybody welcome back to Las Vegas, we're at the Bellagio at UiPath Forward III, day two of theCUBE covers. theCUBE is a leader in live tech coverage. We go out to the events. We extract the signal from the noise. Erik Alexis here is the Vice President of Global Intelligent Process Automation at GE. Eric thanks for coming on. >> Yeah absolutely excited to be here. >> So, you guys have a COE, you're obviously heavily involved in essentially running the COE, is that right? >> Yeah that's my role at GE. I lead our Global Center of Excellence for intelligent process automation. Our journey started with UiPath a while back in 2016. So, it's been an incredible journey so far. >> And I want to get into that. So, before I do, I was struck by the Forrester analyst, Craig LeClair this morning made a statement. I don't know if you're in there, but he said, "Yeah COE, setting up a COE, "maybe that's asking too much." But I talk to a lot of people that have a center of excellence. Maybe it's definitional but what does your COE look like in terms of just it's role, size? >> Yeah it's a great question, so I think in terms of the role that we play more broadly, I mean we provide a lot of the technical expertise, the hands-on development and the operational support for our business units. And so we've really kind of developed that expertise over time, and we use our business units to really drive and identify the opportunities that come in through the COE. So, in terms of the size of the COE, we've got in total number of heads, we've got about 50 primarily technical resources there, that are supporting development as well as ongoing operation. >> Awesome, okay so let's talk about your journey. When did it start? What was the motivation behind it? How did you make the business case, and we'll get into it. >> Yeah so our journey started back in 2016, GE, we used to have a shared services organization that we had a very forward-thinking CEO at the time who wanted to really disrupt the way that we worked. And so RPA was something that was just coming out and kind of getting noticed by a lot of these shared services organizations. And so throughout the year we assessed a couple of technologies obviously landing on UiPath for a number of reasons. I would say in terms of our journey 2017 was kind of our year to prove the technology. We wanted to see if this stuff could really work long term and operate at scale. Given that I'm still here obviously, we proved that was correct and then 2018 was kind of the year of scaling and operationalizing kind of a sustainable model to support our business units across the board from an RPA standpoint. So, really building out a proper structure, building out the governance that goes along with building robots and building a kind of a resource team to continue to support the bots that we were at scale at that point, so maintaining those bots is critically important. And then 2019 has really been the year and I think the theme of this conference in general, a bot for every person I think that's the direction we're moving in 2019. We've kind of perfected the concept of the back office robot and the development of those, and running those at scale. And now we're moving towards a whole new market share when it comes to attended automation and citizen development. >> So, in '16 it was kind of kicking the tires it was almost like R&D. And then '17 was really essentially a proof-of-concept right so still a small team, a two piece kind of team kind of thing right? And then when you talked about scale, helped us understand what's involved in scale, I know it's also another big theme of this conference. What are the challenges of scaling and how did you resolve those? >> Yeah that's a very good question. I think it's a question that has been very common throughout this entire conference. I would say when I think about scaling what I've noticed over the past few years is that, the actual bot development is about 25% of the work that you need to do, right? When it comes to scale there is everything outside of the actual development is the important part. So, how are you funneling opportunities into a pipeline, how are you streamlining the entire process reengineering of fitting an RPA into an existing process, what's governance you have in place to make sure that the code of that development is clean and can be maintained long term? And then more importantly I think that people overlook, people think of scale as being able to develop a lot of bots. I think more importantly what scale is is being able to efficiently maintain a large portfolio of bots, and that's what I've realized this year. We've got now about 300 automations in production and your reputation as an organization is really on how well you maintain those bots, because if your bots are consistently failing, and you're not fixing them quick enough for your functional users to leverage them, then you lose a lot of credibility. So, I think that's been a big learning for us as we reach scale. >> That's interesting I mean I think about scripts, how fragile scripts are and you got a lot of 'em, and they almost always break. And so what is the discipline that allows you to have that quality of bot that is maintainable? Is it a coding discipline? Is it a governance? Is there other automation involved in maintaining those bots? >> No there is and I think the team that's under me, my technical team has done a phenomenal job of setting this up, but we've got some very rigorous standards that we've put in place around. We do have reusable components for example that need to be used on every single robot that goes into production, so that when I look at for example a bots login, that bots login is going to be the same across all my bots. So, every developer who's going to be maintaining that bot knows what it is and how to fix it. I think the standardized logging as well to make sure that we've got robust logging for every single robot is incredibly important because again that's going to be critical when somebody goes to try and fix the bot. >> So you are like an app store, you're enforcing rules like Apple for developers. >> Exactly. >> Okay so let me ask you a question. See now several years in if you had a mulligan, what would you do differently? >> Yeah I think that's another very good question. I think when you first start with this technology, it's unbelievably exciting, because it's something that you can immediately see the difference and the impact it can make, and so you want to try and apply it everywhere to everything, to solve every problem. And I think that's kind of where we got a little ahead of ourselves. We weren't as thoughtful as we should have been when we started taking in the use cases that we were bringing in and while I sit here and tell you that we've got 300 automations in production, I've also decommissioned about 90 automations as well. Because you kind of live and you learn as you go through that process on. This doesn't make sense for RPA. It's not driving the value anymore. It's not driving the right value for the company. >> And is that because the process needs to be reworked before it's automated or there are other factors? >> Yeah I think there's a couple of factors there. I think number one, some bots are intentionally just for short-term use. We look across the portfolio, some bots you design for to operate for two weeks for a massive for example document transition or something like that. So, that's a common reason for decommissioning. I would say secondly you just picked the wrong process. It's not big enough. You think this is perfect for RPA, but it's saving somebody maybe five or 10 minutes a week, which in reality do you really want to put all the effort and to continue to maintain something like that on a back office level? So, I think the size of the processes and the complexity you've got to be thoughtful about as well. >> Thinking about a bot for every worker, what does that actually look like? Is that like you get a laptop and you get a bot? How does that actually manifest itself? >> Yeah I think as I've talked to some of the teams and Daniel as well about this, it's really around I mean imagine opening it up just like any other application on your computer and Excel, you've got that sitting on your desktop and you use that for a number of different things. I think that's kind of how I envision it and everyone when they come into GE, they'll get their laptop and it's part of their kind of package of software that they get. One of them will be UiPath and I think again if GE where I see that as the future. We've got to be thoughtful about how that's rolled out because you want to make sure it's done the right way and you want to make sure that that succeeds and what comes along with that is a lot of education. There's a lot of people that need to be educated on the technology in order to roll that out effectively. >> It's part of the onboarding part, just part of the HR onboarding, and so you open up your laptop and based on your role you'll have a library of bots that are applicable for your job. Is that kind of what you envision? >> Again I think that's kind of the future state and so HR will have a common library that they can pull from and Finance will have a common library that they can pull from. And I think the announcement this weekend of or this week of our StudioX is going to make life significantly easier. So, if you need to kind of edit any of those components or make any custom steps, you can do that with StudioX, but I think having a pre-built set of bots by function would be extremely important. >> And StudioX is the citizen developer right? So, okay now how do you then enforce the edicts of the COE if Dave Vallente's writing automations. >> It's honestly a question that we haven't answered yet and I think that's the piece that we're trying to solve for now, to roll it out more broadly. And I think part of it's going to be training right? Educating the broader group, part of it is giving them access to front office robots and so you do have the code back at the orchestrator so that you can see kind of what's going on and make sure if there are massive changes that need to be made, you can make some of that centrally, so I think figuring out how to centrally maintain and store some of that code is going to be important. >> And the idea of moving beyond this what they call this morning the snowflake into the snowball. So, reusable components is something that I've heard a lot about. That's not trivial yeah right because mapping the right component for the right job is always going to be some kind of unique, not always, but there could be some unique element to put in words. So, what are your thoughts on kind of future? I mean we touched on some of them. It sounds like even though you started early, 2016, it sounds like you still got a long way to go. What's the roadmap look like for you guys? >> Well it's really never-ending because you know you see how quickly the industry is changing and how quickly these automation platforms. I think we're at the point now where these are no longer RPA platforms. They're automation platforms with all of the different features and you look at the broader ecosystem of the technologies being pulled into play. I think it's moving from robotics process automation into intelligent process automation. And that's really our goal and leveraging the ecosystem that the UiPath is built is I think what we want to do more of going forward. >> And the primary measurement of value to you, I'm inferring is time saved from doing non-differentiated tasks, is that really a key metric or are there others that you're looking at, bottom line dollars that you're saving or what? >> I think the way that we measure productivity is really in three major buckets. One is the hours saved so that employees can do other things and I would say that is far and away, the largest bucket that we have. But I think additionally you've got to think about direct cost out. I mean if my finance team comes to me and says, we're thinking about hiring a person to do this why not an RPA? Why can't we use an RPA to do that instead? So, it's not like anyone's losing their job over. It's just figuring out a better way to supplement your existing workforce. Then I would say the third way really is thinking about the compliance element of things. So, and that's often overlooked. You may not save anyone time. You may not save anyone hours or dollars, but what you can do is expand for example in your audit function, expand your testing or sampling of a certain criteria, instead of sampling maybe the top 20 risky units, you can now sample a 100% of a population, which fundamentally changes how you can get comfortable with your financial statements and other elements of the compliance. >> Talking earlier just I asked is sampling dead because of RPA right? >> It really feels like that you know. >> Dave: Eric it's super knowledgeable. I really appreciate you coming on. >> Absolutely. >> Dave: Congratulations on all your success really. >> Thank you very much Dave. I appreciate it. >> You're welcome. All right keep it right there everybody, we will be back with our next guest right after this short break. We're live from UiPath Forward III from Las Vegas. You're watching theCUBE. (upbeat music)

(upbeat music) >> Announcer: Live from Atlanta, Georgia, it's theCUBE. Covering Citrix Synergy Atlanta 2019. Brought to you by Citrix. >> Hello, and welcome to theCUBE's coverage of Citrix Synergy 2019 from Atlanta, Georgia. I'm Lisa Martin with my co-host Keith Townsend, the CTO Advisor. Keith, it's so great to see you. >> Lisa, good to be on the show with you again. >> So we're going to geek out the next two days. >> Oh isn't it so good? >> We've been geeking out already just coming from the keynote. This is ... >> Yeah This is, it was really good there was meat, there was announcements, there was news, partnerships. Citrix is a 30 year old company, who's done a lot in the last 12/18 months, to transform. From rebranding, product names, et cetera, lots of launches and announcements. And something that really peaked my interest as a marketer this morning, is hearing the influence of consumerization. Them talking about leveraging Citrix Workspace, and the things that they have done to beef it up which we'll talk about, to deliver a stellar employee experience, to delight the users. And those are words that we hear often in the marketing space, like customer lifetime value, they talked about the employee lifetime value because employee attraction, talent attraction and retention, is critical for every business. Really meaty stuff. What was some of your take on some of the announcements on Workspace? >> So I was really interested because as I'm coming off of SAP SAPPHIRE, where I'm accustomed to hearing terms like customer experience, employee experience, you know, the kind of X-data versus O-data conversation. We heard a lot of that here today. And it's weird coming from an infrastructure company. Citrix in the past I like to put into a box, it's about VDI, application virtualization and networking, and that's pretty much the conversation, it stayed at the IT infrastructure leader perspective. Today we heard a lot that broke out of that, and it was going into the C-Suite and delivering not just technology results, but business results. There was a lot about making transformation real. >> You're right it was about making it real, and if you think at the end of the day, I think I heard a stat the other day, that by 2020, which is literally around the corner, 50% of workers are going to be remote. You and I are great examples of that, we're on the road all the time, we have multiple devices we need to have connectivity that ... to all the apps, SAS apps, mobile apps, web, that allow us to be productive from wherever we are, done in a way that our employers, are confident there is security behind this. But delivering that exceptional employee experience is absolutely business critical. They gave some stats today about the trillions of dollars that are spent, or rather work that's lost, with employees that have so many apps each day that they're working with that really distract from their actual day to day function. >> Yeah I think one of the stats that they gave from an ambitious perspective, they want to give one day back to every employee, 20% of their time, back, I think the stat you referred to some seven trillion dollars of productivity is lost from just hunting and pecking inside of applications. Both of us work remotely, you work from your tablet, I work from a tablet or my phone a lot. Because I just, you know, it's low power to, it lasts the day, but yeah I still need to edit video, I need to sign invoices, I need to create statements that worked. I need to be just as creative on the road as I am at home. It helps me to compete against larger competitors, but more importantly, offer a different customer experience and this is what Citrix was talking about today, was more than just VDIs, about picking up any device asking basic logical questions like what is the status of the latest deal, the big deal, and getting that status from Salesforce without again hunting and pecking, from whatever device you're on. >> Which is critical, especially to have that seamless experience going from desktop to mobile. I think they also said ... there was a lot of stats this morning, which I really geek out on. But that the average person is using seven to 10 apps a day and I loved the video that they showed this morning that really brought that to life. Looking at a senior marketing manager for some enterprise company, who, as she starts her day, there's 10 minutes that goes by which is lie, oh, I forgot I got to log into Workday and request my PTO, oh, one of my employees needs me to approve an expense report, and oh, my boss wants to know about this big deal that's closed. And the time that is spent logging into different applications is really as you mentioned that number seven trillion dollars lost, what they're doing with Citrix, with the intelligent, the workspace intelligence experience is bringing all of that to the end user. So it's much more an activities focus rather than an app focus experience. And I loved what you said that they're very ambitiously aiming to give each person back one day a week, yes please. I will take that. In any organization. >> So I was at a government conference a few weeks ago and they talked very much about this CFO of GSA presented to a crowd of fellow government workers, and they were talking about eliminating waste, they were talking about automating processes, taking the PDF, taking a document and scanning it into a system, and then kicking off a real workflow. And this is done, the industry's been working on this problem for the past 10 years, it's called RPA, Robotic Process Automation. One of Citrix's partners and I guess now competitors in that space just received $560,000,000 in funding, in a single round, to enable artificial intelligence to do this. What I thought was interesting, is that Citrix didn't use the term bots, I think other than one time ... >> Lisa: That's right. ... on the stage. But these are essentially bots, that take redundant processes, automates them, to ultimately add value. I'm anxious to dive in and talk about how Citrix is taking stuff like, they mentioned Mainframe, AS/400 applications, integrating that in Salesforce without having this huge multi-million dollar project to re-write these core business applications and processes. So, you know it's a really exciting time in the industry Citrix has really stepped up in saying, you know what, we won't settle for just having a good business, and this application virtualization and network space, we're going to go all in. >> So one of the things I saw in Twitter this morning, is you and I are both tweeting during the keynote, which we just came from is you talked about PRA right away on Twitter and it's something that you heard instinctively with what they were saying. What are your thoughts as to why RPA as a term wasn't discussed? Did you think it's the type of audience that's here? Is it just not a term that resonates as well as AI and machine learning, which are buzz words at every event we go to? >> And I think a good portion of that is a mix. We're at a conference that's very IT-centric. Citrix is a you know, one of the core IT infrastructure vendors. So when you throw out a term like Robotic Process Automation you constantly, you instantly think, you know, gain of productivity from me or your level maybe, but from an IT infrastructure practitioner perspective, Robotic Processing Automation has a resonance with being equal to eliminating jobs. If, you know, you're going to automate the integration between VMware VSphere and Citrix desktop virtualization and that administration piece, which these solutions definitely can do that, what's left for me to do the work on. If you're going to automate the provisioning of DNS and IP addressing and all these mundane tasks that administrators probably spend 50-60% of their day doing, you know what, that's threatening. To say that you know what, we're going to give you the same tools that we give to make the workspace available today from an application perspective and to tackle that from the concept of this is just extending that ideal and you're a what, your job and what you do today to adding true business value, I think it was smart on their part to kind of avoid the bot conversation. >> Okay, I'm glad that you shared that insight, that makes perfect sense. So, PJ Hough was up there, the Chief Product Officer, who's going to be on tomorrow, talking about what Citrix is doing to distill apps and make this experience much more personalized. And of course he was joined on stage with a big Microsoft announcement today. I think I've been to so many shows this year I've lost count but I think Satya Nadella has either been on stage, he was at Dell Technologies World with Michael Dell and Pat Gelsinger, or in a video like he was today. So the partnership with Microsoft expanding here a little bit of a teaser at Microsoft Ignite a couple of months ago. Gimme your thoughts on what Microsoft, I should say what Citrix is doing to facilitate their users being much more proficient at using Microsoft Team, which I believe the gentleman from Microsoft said there's over 300,000 active users already. Fastest growing product in Microsoft's history. >> So when you talk about collaboration, you can't collaborate without these tools, whether Teams, Slack, whatever, it's become an integral part of how we communicate, how we interact, I know a lot of friends that I have are moving from Slack to Teams, just because of the integration with Office365 they can collaborate around, and I think here on theCUBE we talk about data as being the key. You have to talk about data. One of the things that was prepared to go kind of head on with Citrix today, and tomorrow about, was about data. You know it's great to present applications, but how are you helping to help users collaborate and use and access data and the combination of RPA with the intelligent experi- intelligent, it's going to take us some time to used to this ... >> I keep wanting to say enterprise. >> Yeah enterprise >> Intelligent experience >> Experience product, with Teams, with the Azure announcement, integration with Azure and full support of the Citrix platform inside Azure will just make the employee experience at least potentially seamless, a lot more seamless, I'm super excited about, you can't tell in my voice, I haven't gotten excited about Citrix in a long time. And this is the first time they've had theCUBE at Synergy since 2011, I think it was a great time to reignite that partnership, and this coverage is going to be an interesting two days. >> It is. So we talked about digital workspace, the other two areas of Citrix's business that you touched on a little bit, security and analytics. Let's talk about the security piece first as it relates to Microsoft Teams and Azure. SD-WAN is becoming more and more absolutely critical to ensure that because as people we are the number one threat vector in any organization. Not that we're all bad actors. >> Keith: Right. >> But because we need to get things done, as frictionless or seamless, as you said, as possible, and efficiently as possible. What did you hear today with respect to security, that might really make some of those IT folks take notice? >> Well, we want to work from any device. Like, I want to be able to, ideally if I say, you know what, I want to pick up a new Surface tablet, when I go to Atlanta I don't want to pack my iPad. I want to be able to pick that up, and work. If I go to a kiosk, I want to be able to, even if it's running Windows XP, I want to be able to do my work, I want to be able to do my work from any device. This is a nightmare for system administrators to say how do I control security, while making the experience frictionless? Those two things don't seem to go together. So Citrix, whether it's with this new announcement with Microsoft with Teams, it's traditional applications around SD-WAN, enabling access from remote locations, and Citrix is kind ... this is their bread and butter, offering remote access to applications securely and fast, this is you know, Citrix is starting to formulate a really great end to end story about making applications, data and more importantly, business answers and capability available anywhere securely, so it's a great story. >> It really is. So if you're excited, you already know how excited I am. I think we're going to have a fantastic day today, and tomorrow. We've got a whole bunch of the C-Suite from Citrix on, we're also going to be talking with some partners and customers, and interestingly as a marketer this peaked my interest as well, they have the innovation awards. There are three finalists, we will be talking with all three over the next two days, and this is a customer awards program, that anybody can vote on. So I haven't seen that before, so I'm excited to understand how Citrix is enabling them to have this great employee experience which is more and more critical as the shortages and the gaps are becoming more and more prevalence. And also, how these customers are reacting to just some of the news announced today, with Microsoft, the intelligent enterprise, and how they see their employees, and attracting and retaining top talent as actually really mission critical. So we're going to have fun Keith. >> I agree. >> All right, you're watching Keith Townsend and Lisa Martin live from theCUBE, we are on the show floor at Citrix Synergy 2019 from Atlanta, Georgia. Stick around, Keith and I will be right back with our first guest after a short break. (upbeat electronic music)

>> Hi, I'm Peter Burris, Chief Research Officer for Wikibon Cube and welcome to another special digital community event. Today we are going to be presenting Wikibon's 2019 trends. Now, I'm here in our Palo Alto Studios in kind of a low tech mode. Precisely, because all our our crews are out at all the big shows bringing you the best of what's going on in the industry, and broadcasting it over The Cube. But that is okay because I've asked each of our Wikibon analysts to use a similar approach to present their insights into what would be the most impactful trends for 2019. Now the way we are going to do this is first we are going to use this video as base of getting our insights out, and then at the end we are going to utilize a crowd chat to give you an opportunity to present your insights back to the community. So, at the end of this video, please stay with us, and share your insights, share your thoughts, your experience, ask your questions about what you think will be the most impactful trends of 2019 and beyond. >> A number of years ago Wikibon predicted that cloud, while dominating computing, would not feature all data moving to the cloud but rather, the cloud experience and cloud services moving to the data. We call that true private cloud computing, and there has, nothing has occurred in the last couple of years that has suggested that we were, anyway, wrong about this prediction. In fact, if we take a look at what's going on with Edge, our expectations that increasingly Edge computing and on Premise technology, or needs, would further accelerate the rate at which cloud experiences end up on Premise, end up at the Edge, and that would be the dominant model for how we think about computing over the course of the next few years. That leads to greater distribution of data. That leads to greater distribution of places where data actually will be used. All under the aegis of cloud computing but not utilizing the centralized public cloud model that so many predicted. >> A prediction we'd like to talk about is how multi-cloud and orchestration of those environments fit together. At Wikibon, we've been looking for many years at how digital businesses are going to leverage cloud, and cloud is not a singular entity, and therefore the outcomes that you are looking for, often require that you use more than one cloud, specially if you are looking at public clouds. We've been seeing the ascendance of Kubernetes as a fundamental foundational piece of enabling this multi-cloud environment. Kubernetes is not the sole thing, and of course, you don't want to overemphasize any specific tool, but you are seeing, driven by the CNC AFT in a broad ecosystem, that Kubernetes is getting into all the platforms, both public and private cloud, and that we predict that by 2020, 90% of multi-cloud enterprise applications will use Kubernetes to lead for the enablement of their multicloud strategies. >> One of the biggest challenges that the industry is going to face over the next few years is how to deal with multi-cloud. We predict, ultimately, that a sizable percentage of the marketplace, as much as 90%, will be taking a multi--cloud approach first to how they conceive, build, and operate their high, strategic value applications that are engaging customers, engaging partners, and driving their businesses forward. However, that creates a pressing need for three new classes of Technology. Technology that provides multi-cloud inter-networking; Technology that provides orchestration services across clouds, and finally Technologies that ensure data protection across multi-cloud. While each of these domains by themselves is relatively small today, we think that over the next decade they will, each, grow into market that are tens of billions if not hundreds of billions of dollars in size. >> The picture I'd like to talk about a very few, the Robotic Process Automation, RPA. So we've observed that there's a widening gap between how many jobs are available world wide and the number of qualified candidates to fill those jobs. RPA, we believe, is going to become a fundamental approach to closing that gap, and really operationalizing artificial intelligence. Executives that we talk to in The Cube; They realize they just can't keep throwing bodies at the problem, so this so called "software robots" are going to become increasingly easy to use. And we think that low code or no code approaches to automation and automating work flows are going to drive the RPA market from its current position, which is around a billion dollars to more than ten X, or ten billion dollars plus by 2023. I predict that in 2019 what we are going to see is more containerization of AI machine learning for deployment to the Edge, throughout the multi-cloud. It's a trend that's been going on for some time. In particular, what we are going to be seeing is a increasing focus on technologies, or projects in code base such as Cube flow, which has been established in this year just gone by to support that approach for containerization of AI out to the edges. In 2019, we are going to see the big guys, like Google, and AWS, and Microsoft, and others in the whole AI space begin to march around the need for a common delatched framework suck such as Cube Flow, because really that is where many of their customers are going. The data scientists and App developers who are building these applications; They want to manage these over Kubernetes using these CNC stacks of tooling and projects to enable a degree of supportability and maintain ability and scalability around containerized intelligent applications. >> My prediction is around the move from linear programming and data models to matrix computing. This is a move that's happening very quicly, indeed, as new types of workload come on. And these workloads include AI, VR, AR, Video Gaming, very much at the edge of things. And ARM is the key provider of these types of computing chips and computing models that are enabling this type of programming to happen. So my prediction is that this type of programming is gonna start very quickly in 2019. It's going to rule very rapidly about two years from now, in 2021, into the enterprise market space, but that the preparation for this type of computing and the movement of work right to the edge, very, very close to the senses, very, very close to where the users are themselves is going to accelerate over the next decade. >> The prediction I'd like to make in 2019 is that the CNCF, as the steward of the growing cloud native stack, they'll expand the range of projects to include the frontier topics, really the frontier paradigms, in micro sources in cloud computing; I'm talking about Serverlus. My prediction is that virtual Kubelets will become an incubating project at CNCF to address the need to provide Serverlus event driven interfaces to containerize orchestrated micro sources. I'd also like to predict that VM and container coexistence will proceed apace in terms of a project such as, specially Kubevirt. I think will become also a CNCF project. And I think it will be adopted fairly widely. And one last prediction, in that vein, is that the recent working group that CNCF has established with Eclipse, around IOT, the internet of things. I think that will come to fruition. There is an Eclipse project called Ditto that uses IOT, and AI, and digital twins, a very interesting way for industrial and other applications. I think that will come under the auspices of CNC in the coming year. >> Security remains vexing to the cloud industry, and the IT industry overall. Historically, it's been about restricting access, largely at the perimeter, and once you provide through the perimeter user would have access to an entire organization's resources, digital resources, whether they be files, or applications, or identities. We think that has to change, largely as a consequence of businesses now being restructured, reorganized, and re-institutionalizing work around data. That what's gonna have to happen is a notion of zero trust security is going to be put in place that is fundamentally tied to the notion of sharing data. So, instead of restriction access at the perimeter, you have to restrict access at the level of data. That is going to have an enormous set of implication overall, for how the computing industry works. But two key technologies are essential to making zero trust security work. One is software to find infrastructure, so that you can make changes to the configuration of your security policies and instances by other software and to, very importantly, high quality analytics that are bringing the network and security functions more closely together and through the shared data are increasing the use of AI, the use of machine learning, etc and ensuring higher quality security models across multiple clouds. It's always great to hear from the Wikibon analysts about what is happening in the industry and what is likely to happen in the industry. But now, let's hear from you, so let's jump into the cloud chat as an opportunity for you to present your ideas, your insights, ask your questions, share your experience. What will be the most important trends and issues in 2019 and beyond, as far as you are concerned. Thank you very much for listening. Now let's cloud chat.

>> 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 downtown Manhattan, actually midtown Manhattan, at Automation Anywhere Imagine 2018, 1100 people talkin' about bots, talkin' about Robotics Process Automation, or RPA. And we're excited to have the guy that counts the money at the end of the day; it's important part of any business. He's a co-founder, Ankur Kothari, Chief Revenue Officer and Co-Founder, Automation Anywhere. Ankur, great to see you. >> Great to be here, Jeff, thanks for having me. >> So, first off, as a co-founder, I think you're the third or fourth co-founder we've had on today. A little bit of reflection since you guys started this like 14 years ago. >> Yeah. Here we are, there's 1100 people, the room is packed. They had the overflow, they're actually all over us out here with the overflow for the keynote. Take a minute and kinda tell us how you feel about how this thing has evolved over time. >> It feels like a great party to be part of. Always, you're always happy. >> Right. >> One of the traits that you'll find a lot of co-founders is that they are always happy, never satisfied. They're always looking for the next big one. >> Right. >> But it's amazing to be part of Imagine because we learn so much from our customers and our partner as well. It's not just that we bring them together and we're talking. We're learning every time. It's becoming a big ecosystem. >> Right. >> And, an idea as big as a bot or a future of work is too big an idea for one company to continue. You want as many people to come. >> Right. >> So, our idea of Imagine was a little bit like Field of Dreams, you build and they'll come and they'll collaborate and it'll become bigger and bigger. >> And look all around us. I mean, we're surrounded by people and really, the ecosystem. >> And the bots as well, there are bots on the walls and everything else. >> Bots on the walls, partners everywhere. So let's dive into it a little bit. I mean, one of the ways that you guys participate in the ecosystem, and the ecosystem participates, is the Bot Store. >> Yes. >> So it's just like any other kind of an app store. >> Exactly. >> You've got people contributing. I assume you guys have contributed stuff. But we saw earlier in the keynote by Accenture, and EY, and Deloitte. And all types of companies are contributing bots into this ecosystem for lots of different functions or applications. So really, an interesting thing. How's that workin' out? Where'd you come up with the idea? And why's that so important? >> At Automation Anywhere we like to ask ourselves hard questions, as the leaders in this space. And we asked ourselves this question, "What can we now do to further accelerate our journey of all our customers to become a digital enterprise?" The answer came that we are to share in the new bot economy. Now once that answer was clear, every economy requires a marketplace. >> Right. >> And that's where the Bot Store came. It's a marketplace where producers meet the consumers, and you connect them. All we do is, we curate and make sure that the right things go up. But other than that, it's just like any other marketplace. And we thought that if we'll build the right marketplace where the producers meet consumers, we have thousands of customers and large companies looking at it. It will allow perfect place where all the right ideas get converted into product. >> Right. >> We have tons of partners who have domain expertise, functional expertise, vertical expertise; they can prioritize their expertise, they can convert it into IP. >> Right. >> They can do it for free, they can monetize it. So there's lots to gain for producers of all these bots. And if I am a consumer, now suddenly my time clock to make further shrinks, because instead of creating these bots all from scratch, I can download them from this Bot Store and snap them together like a Lego block. >> Right. >> So that's how the whole idea came. We launched it just two months ago and we have hundreds-- >> You just launched it two months ago? >> Yeah! And we have hundreds of bots in it. More than 80-100 partners have participated. We are getting at least 20-30 more submissions coming every day, and we have few hundred submissions coming every week. So, just like any free marketplace, it has an exponential nature. And that's the thing we are counting on. >> That's amazing, that you've got that much traction in such a short period of time. >> Thousands of downloads on a daily basis. Thousands of users just in two month's time. >> You know, we go to a ton of shows. We do over a hundred shows a year. And once shows get to a certain size, it starts to change a little bit. But when they're small like this, it's a very intimate affair on a couple floors here at the Sheraton, everyone is still really involved. They're really sharing. >> Yes. >> There's so much sharing of information. Not so much, you know ... Because they're not really competitors. Within their own companies, they're all part of this same team that are trying to implement this new thing. >> Exactly. >> And you really feel it. >> Exactly. >> So, the store's cool, but the bot economy. When you talk about the bot economy, we talk about API economy a lot. >> Yes. >> How do you see the bot economy? What are the factors that drive the bot economy, and how's it gonna evolve over time? >> We look at it as a few elements. The current version, we think that bot economy, like any economy, has a marketplace, which is our Bot Store. We have a program which we call Bot Games, because any good economy, any new economy, one of the trait is that the good idea can come from anyone. >> Right. >> It can come from anyplace. Like, any customers, any partner, anyone can bring. A good economy, what it does is it brings that idea from anyone, and it gives these vehicles for good ideas to take flight. If the idea is good, it becomes viral, and it has vehicles where those ideas can go to market. What we did was, we created a program called Bot Games. Yesterday on May 29th, we had the 1st Inaugural Bot Games. We invited developers, people who are part of these programs and their companies. And we gamified and created different games. And we thought that if we bring all these champions and pioneers and like-minded people in the same room, give them certain same problem, and then gamify it, put a clock on it, a lot of great ideas will come out of it. >> Right. >> And that came. And some of those ideas will make it to the marketplace, like a Bot Store, like an Imagine. >> Right. >> So that's where all the ideas connect to the customers. And the people who bring those ideas, they also come up. So that's the other aspect. So the Bot Games is where the ideas, you can crowdsource from places. Bot Store is where they go to the market. In between there is a gap. And we are trying to remove that gap by creating a stimulus package for this new bot economy. Like any economy time and again requires a stimulus pack, and we have created one. What we have done is that if you want to learn Automation Anywhere, right? If you want to understand, because that gap is you're to understand Automation Anywhere. We have created Automation Anywhere University a year ago. And now anyone can take courses for free to learn how to create bots. Whether they are customers or partners. And then, if you purchase these bots through one of our certified partners, the first three bots in year one are free. So we are removing the friction in between. If you have not started on this journey, your learning is free, you get ideas from different places, we can get these prebuilt bots, and the first three bots, if you purchase it through our partners, they are free. So we are removing that friction. And then, we are supporting that whole economy with the industry's largest customer success program. >> Right. So I'm curious if you know, maybe you don't know, of the bots in the bots store, how many are free and how many are paid, as a percentage? >> Interestingly, I don't have that stat because we don't actually worry about that. We let all our partners and people who are contributing to this Bot Store decide that. >> Right. >> Some bots they may decide to monetize, some they may not. It's listed on the Bot Store. Offhand, I would say-- >> Take a guess. Is it 50/50? A third? Two-thirds? >> The nature of it looks like 50/50. >> That's a good guess. Full caveat, it's a guess. We didn't do the analysis. >> Exactly. But here is the unique aspect. Yesterday we had a Bot Game, and the winner had an amazing idea that none of us had ever think of. He created this bot that automates the COE of all these programs. Now, we are talking. He is thinking of putting that on Bot Store. That's the power of bringing multiple people together. >> Right. >> That's the power of free economy, where the exponential nature of it is what we are counting on. And we are getting on a daily basis these new bot ideas, these new bots that are making it to the Bot Store. Just like your App Store. I go to App Store to get ideas what I can do on my phone. >> Right, right. >> Just like that, now we are finding our customers are going to Bot Store to figure out what else can they automate. >> Right, right. >> And that's been another amazing part of it. >> You know, it's so consistent. All these shows we go to, right? How do you unlock innovation? There's some really simple ways. One is, give more people the power, give more people the tools, and give more people the data. >> Exactly. >> And you'll get stuff out of it that the small subset of people that used to have access to those three things, they never found. They just didn't think of it that way, right? >> Exactly. And then we firmly believe that any technology, anything, once you democratize it, you give it in hands of everyone-- >> Right, right. >> You can't have a thriving economy unless everyone forms their own point of view. Unless everyone creates their own perspective. And that's our vision of this bot economy. We are bringing everyone and giving them these vehicles to try it out. Look, the technology has reached a stage where it's cheaper to try it out than talk about it. >> Yes. >> And we are doing that so that everyone forms their own unique point of view, and then they express that point of view and we connect those points of view to these thousands of customers worldwide. >> Right. >> Good ideas take flight, and all we have to do is create vehicles for those good ideas to take flight. >> Alright. So, Ankur, I gave you the last word before we wrap up here. If we come back next year, a year from now, inspired 2019, what are we gonna be talking about? What's on your roadmap? What're some of the priorities that you guys are workin' on over the next 12 months? >> We are talking about ... The next 12 months, we are looking at how to further accelerate this journey. Because what people are in this, the real problem people are trying to achieve is how to become a digital enterprise. Not just to automate, but how do you create a digital enterprise? You cannot become a digital enterprise unless your operations are digital. You cannot make your operations digital unless your processes are digital. And you cannot do that unless your workforce is digital. So we are trying to create technologies, vehicles, platforms, so that everyone can scale their program. Where pretty much everyone should have a digital colleague. Everyone should be able to create a bot. Everyone should be able to work with a bot. Every process, every department, every system should have a digital workforce working in it and that can allow you to create a digital enterprise that can scale up and scale down with the demand and supply. >> Alright-- >> That's what we are trying to start. >> Well, we look forward to gettin' the update next year. >> Exactly. >> Alright, Ankur, thanks for taking a few minutes out of your busy day with us. >> Thanks for having me here, and I appreciate and enjoy the conversation. >> Alright, he's Ankur, I'm Jeff. We're at Automation Anywhere Imagine 2018. Thanks for watching theCUBE. See you next time.

>> 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.

>> Announcer: From San Francisco, it's theCUBE. Covering Red Hat Summit 2018 brought to you by Red Hat. >> Hello everyone and welcome back to see CUBE's exclusive coverage of Red Hat Summit 2018 live in San Francisco, California at Moscone West. I'm John Furrier, your cohost of theCUBE with John Troyer co-founder of Tech Reckoning advisory and community development firm. Our next two guests Mike Piech Vice President and General Manager of middleware at Red Hat and Mark Little, Vice President of Software Engineering for middleware at Red Hat. This is the stack wars right here. Guys thanks for coming back, good to see you guys again. >> Great to see you too. >> So we love Middleware because Dave Vellante and I and Stu always talk about like the real value is going to be created in abstraction layers. You're seeing examples of that all over the place but Kubernetes containers, multi-cloud conversations. Workload management and all these things are happening at these really cool abstraction layers. That's obviously you say global I say middleware but you know it's where the action is. So I got to ask you, super cool that you guys have been leading in there but the new stuff's happening. So let's just go review last year or was it this year? What's different this year, new things happening within the company? We see core OS' in there, you guys got OpenShift is humming along beautifully. What's new in the middleware group? >> There's a few things. I'll take one and then maybe Mike can think of another while I'm speaking but when we were here this time last year we were talking about functions as a service or server-less and we had a project of our own called Funktion with a K, between then and now the developer affinity around functions as a service has just grown. Lots of people are now using it and starting to use it in production. We did a review of what we were doing back then and looked around at other efforts that were in the market space and we decided actually we wanted to get involved with a large community of developers and try and move that in a direction that was pretty beneficial for everybody but clearly for ourselves. And we've decided, and we announced this publicly last year but we're now involved with a project called Apache OpenWhisk instead of Funktion. And OpenWhisk is a project that IBM originally kicked off. We got involved, it was tied very closely to cloud foundering so one of the first things that we've been doing is making it more Kubernetes native and allowing it to run on OpenShift. In fact we're making some announcements this week around our functions are service based on Apache OpenWhisk. But that's probably one of the bigger things that's changed in the last 12 months. >> I would just add to that that across the rest of the middleware portfolio which is as you know, a wide range of different technologies, different products, in our integration area we continue to push ahead with containerizing, putting the integration technologies in the containers, making it easier to basically connect the different components of applications and different applications to each other together through different integration paradigms whether it's messaging or more of a bus style. So with our Jboss Fuse and our AMQ we've made great progress in continuing to refine how those are invoked and consumed in the Openshift environment. Forthcoming very shortly, literally in the next week or two is our integration platform as a service based on the Fuse and AMQ technologies. In addition we've continued to charge ahead with our API management solution based on the technology we acquired from Threescale a couple of years ago. So that is coming along nicely, being very well adopted by our customers. Then further up the stack on the process automation front, so some of the business process management types of technologies we've continued to push ahead with containerizing and that was being higher up the stack and a little bit bigger a scale of technology was a little bit more complex in really setting it up for the containerized world but we've got our Process Automation 7.0 release coming out in the next few weeks. That includes some exciting new technology around case management, so really bringing all of those traditional middleware capabilities forward into the Cloud Native, containerized environment has been I would say the most significant focus of our efforts over the last year. >> Go ahead. >> Can you contextualize some of that a little bit for us? The OpenShift obviously a big topic of conversation here. You know the new thing that everyone's looking at and Kubernetes, but these service layers, these layers it takes to build an app still necessary, Jboss a piece of this stack is 17, 18 years old, right? So can you contextualize it a little bit for people thinking about okay we've got OpenStack on the bottom, we've got OpenShift, where does the middleware and the business process, how has that had to be modernized? And how are people, the Java developers, still fitting into the equation? >> Mark: So a lot of that contextualization can actually, if we go back about four or five years, we announced an initiative called Xpass which was to essentially take the rich middleware suite of products and capabilities we had, and decompose them into independently consumable services kind of like what you see when you look at AWS. They've got the simple queuing service, simple messaging service. We have those capabilities but in the past they were bundled together in an app server, so we worked to pull them apart and allow people to use them independently so if you wanted transactions, or you wanted security, you didn't have to consume the whole app server you actually had these as independent services, so that was Xpass. We've continued on that road for the past few years and a lot of those services are now available as part and parcel of OpenShift. To get to the developer side of things, then we put language veneers on top of those because we're a Java company, well at least middleware is, but there's a lot more than Java out there. There's a lot of people who like to use Pearl or PHP or JavaScript or Go, so we can provide language specific clients for them to interact. At the end of the day, your JavaScript developer who's using bulletproof, high performing messaging doesn't need to know that most of it is implemented in Java. It's just a complete opaque box to them in a way. >> John F: So this is a trend of microservices, this granularity concept of this decomposition, things that you guys are doing is to line up with what people want, work with services directly. >> Absolutely right, to give developers the entire spectrum of granularity. So they can basically architect at a granularity that's appropriate for the given part of their job they're working on it's not a one size fits all proposition. It's not like throw all the monoliths out and decompose every last workload into it's finest grain possible pieces. There's a time and a place for ultra-fine granularity and there's also a time and a place to group things together and with the way that we're providing our runtimes and the reference architectures and the general design paradigm that we're sort of curating and recommending for our customers, it really is all about, not just the right tool for the job but the right granularity for the job. >> It's really choice too, I mean people can choose and then based on their architecture they can manage it the way they want from a design standpoint. Alright I got to get your guys' opinion on something. Certainly we had a great week in Copenhagen last week, in Denmark, around CUBECon, Kubernetes conference, Cloud NativeCon, whatever it's called, they're called two things. There was a rallying cry around Kubernetes and really the community felt like that Linix moment or that TCPIP moment where people talk about standards but like when will we just do something? We got to get behind it and then differentiate and provide all kinds of coolness around it. Core defacto stand with Kubernetes is opening up all kinds of new creative license for developers, it's also bringing up an accelerated growth. Istio's right around the corner, Cubeflow have the cool stuff on how software's being built. >> Right. >> So very cool rallying cry. What is the rallying cry in middleware, in your world? Is there a similar impact going on and what is that? >> Yeah >> Because you guys are certainly affected by this, this is how software will be built. It's going to be orchestrated, composed, granularity options, all kinds of microservices, what's the rallying cry in the middleware? >> So I think the rallying cry, two years ago, at Summit we announced something called MicroProfile with IBM, with Tomitribe, another apps vendor, Piara and a few quite large Java user groups to try and do something innovative and microservices specific with Enterprise Java. It was incredibly successful but the big elephant in the room who wasn't involved in that was Oracle, who at the time was still controlling Java E and a lot of what we do is dependent on Java E, a lot of what other vendors who don't necessarily talk about it do is also dependent on Java E to one degree or another. Even Pivotal with Springboot requires a lot of core services like messaging and transactions that are defined in Java E. So two years further forward where we are today, we've been working with IBM and Oracle and others and we've actually moved, or in process of moving all of Java E away from the old process, away from a single vendor's control into the Eclipse Foundation and although that's going to take us a little while longer to do we've been on that path for about four or five months. The amount of buzz and interest in the community and from companies big and small who would never have got involved in Java E in the past is immense. We're seeing new people get involved with Eclipse Foundation, and new companies get involved with Eclipse Foundation on a daily basis so that they can get in there and start to innovate in Enterprise Java in a much more agile and interesting way than they could have done in the past. I think that's kind of our rallying call because like I said we're getting lots of vendors, Pivotal's involved, Fujitsu. >> John F: And the impact of this is going to be what? >> A lot more innovation, a lot quicker innovation and it's not going to be at the slow speed of standards it's going to be at the fast, upstream, open source innovative speed that we see in likes of Kubernetes. >> And Eclipse has got a good reputation as well. >> Yeah, the other significant thing here, in addition to the faster innovation is it's a way forward for all of that existing Java expertise, it's a way for some of the patterns and some of the knowledge that they have already to be applied in this new world of Cloud Native. So you're not throwing out all that and having to essentially retrain double digit millions of developers around the world. >> John F: It's instant developer actually and plus Java's a great language, it's the bulldozer of languages, it can move a lot, it does a lot of heavy lifting >> Yep. >> And there's a lot of developers out there. Okay, final question I know you guys got to go, thanks for spending the time on theCUBE, really appreciate certainly very relevant, middleware is key to the all the action. Lot of glue going on in that layers. What's going on at the show here for you guys? What's hot, what should people pay attention to? What should they look for? >> Mark: I'll give my take, what's hot is any talk to do with middleware >> (laughs) Biased. >> But kind of seriously we do have a lot of good stuff going on with messaging and Kafka. Kafka's really hot at the moment. We've just released our own project which is eventually going to become a product called Strimsy, integrated with OpenShift so it's coognative from the get-go, it's available now. We're integrating that with OpenWhisk, which we talked about earlier, and also with our own reactive async platform called Vertex, so there's a number of sessions on that and if I get a chance I'm hoping to say into one >> John F: So real quick though I mean streaming is important because you talk about granularity, people are going to start streaming services with service measures right around the corner, the notion of streaming asynchronously is going to be a huge deal >> Absolutely, absolutely. >> Mark: And tapping into that stream at any point in time and then pulling the plug and then doing the work based on that. >> Also real quick, Kubernetes, obviously the momentum is phenomenal in Cloud Native but becoming a first class citizen in the enterprise, still some work to do. Thoughts on that real quick? Would you say Kubernetes's Native, is it coming faster? Will it ever be, certainly I think it will be but. >> I think this is the year of Kubernetes and of enterprise Kubernetes. >> Mike: I mean you just look at the phenomenal growth of OpenShift and that in a way speaks directly to this point >> Mike, what's hot, what's hot? What are you doing at the show, what should we look at? I'd add to, I certainly would echo the points Mark made and in addition to that I would take a look at any session here on API management. Again within middleware the three-scale technology we acquired is still going gangbusters, the customers are loving that, finding it extremely helpful as they start to navigate the complexity of doing essentially distributive computing using containers and microservices, getting more disciplined about API management is of huge relevance in that world, so that would be the next thing I'd add. >> Congratulations guys, finally the operating system called the Cloud is taking over the world. It's basically distributed computer all connected together, it sounds like >> All that stuff we learned in the eighties right (laughs) >> It's a systems world, the middleware is changing the game, modern software construction of Apple cases all being done in a new way, looking at orchestration, server lists, service meshes all happening in real time, guys congratulations on the all the work and Red Hats. Be keeping it in the open, Java E coming around the corner as well, it's theCUBE bringing it out in the open here in San Francisco, I'm John Furrier with John Troyer we'll be back with more live coverage after this short break

>> Announcer: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Welcome back to New York City. Along with Dave Vellante, I'm John Walls. We continue our coverage here on theCUBE of machine learning everywhere. Build your ladder to AI, IBM our host here today. We put together, occasionally at these events, a panel of esteemed experts with deep perspectives on a particular subject. Today our influencer panel is comprised of three well-known and respected authorities in this space. Glad to have Colin Sumpter here with us. He's the man with the mic, by the way. He's going to talk first. But, Colin is an IT architect with CrowdMole. Thank you for being with us, Colin. Jennifer Shin, those of you on theCUBE, you're very familiar with Jennifer, a long time Cuber. Founded 8 Path Solutions, on the faculty at NYU and Cal Berkeley, and also with us is Craig Brown, a big data consultant. And a home game for all of you guys, right, more or less here we are in the city. So, thanks for having us, we appreciate the time. First off, let's just talk about the title of the event, Build Your Path... Or Your Ladder, excuse me, to AI. What are those steps on that ladder, Colin? The fundamental steps that you've got to jump on, or step on, in order to get to that true AI environment? >> In order to get to that true AI environment, John, is a matter of mastering or organizing your information well enough to perform analytics. That'll give you two choices to do either linear regression or supervised classification, and then you actually have enough organized data to talk to your team and organize your team around that data to begin that ladder to successively benefit from your data science program. >> Want to take a stab at it, Jennifer? >> So, I would say, compute, right? You need to have the right processing, or at least the ability to scale out to be able to process the algorithm fast enough to be able to find value in your data. I think the other thing is, of course, the data source itself. Do you have right data to answer the questions you want to answer? So, I think, without those two things, you'll either have a lot of great data that you can't process in time, or you'll have a great process or a great algorithm that has no real information, so your output is useless. I think those are the fundamental things you really do need to have any sort of AI solution built. >> I'll take a stab at it from the business side. They have to adopt it first. They have to believe that this is going to benefit them and that the effort that's necessary in order to build into the various aspects of algorithms and data subjects is there, so I think adopting the concept of machine learning and the development aspects that it takes to do that is a key component to building the ladder. >> So this just isn't toe in the water, right? You got to dive in the deep end, right? >> Craig: Right. >> It gets to culture. If you look at most organizations, not the big five market capped companies, but most organizations, data is not at their core. Humans are at their core, human expertise and data is sort of bolted on, but that has to change, or they're going to get disrupted. Data has to be at the core, maybe the human expertise leverages that data. What do you guys seeing with end customers in terms of their readiness for this transformation? >> What I'm seeing customers spending time right now is getting out of the silos. So, when you speak culture, that's primarily what the culture surrounds. They develop applications with functionality as a silo, and data specific to that functionality is the component in which they look at data. They have to get out of that mindset and look at the data holistically, and ultimately, in these events, looking at it as an asset. >> The data is a shared resource. >> Craig: Right, correct. >> Okay, and again, with the exception of the... Whether it's Google, Facebook, obviously, but the Ubers, the AirBNB's, etc... With the exception of those guys, most customers aren't there. Still, the data is in silos, they've got myriad infrastructure. Your thoughts, Jennifer? >> I'm also seeing sort of a disconnect between the operationalizing team, the team that runs these codes, or has a real business need for it, and sometimes you'll see corporations with research teams, and there's sort of a disconnect between what the researchers do and what these operations, or marketing, whatever domain it is, what they're doing in terms of a day to day operation. So, for instance, a researcher will look really deep into these algorithms, and may know a lot about deep learning in theory, in theoretical world, and might publish a paper that's really interesting. But, that application part where they're actually being used every day, there's this difference there, where you really shouldn't have that difference. There should be more alignment. I think actually aligning those resources... I think companies are struggling with that. >> So, Colin, we were talking off camera about RPA, Robotic Process Automation. Where's the play for machine intelligence and RPA? Maybe, first of all, you could explain RPA. >> David, RPA stands for Robotic Process Automation. That's going to enable you to grow and scale a digital workforce. Typically, it's done in the cloud. The way RPA and Robotic Process Automation plays into machine learning and data science, is that it allows you to outsource business processes to compensate for the lack of human expertise that's available in the marketplace, because you need competency to enable the technology to take advantage of these new benefits coming in the market. And, when you start automating some of these processes, you can keep pace with the innovation in the marketplace and allow the human expertise to gradually grow into these new data science technologies. >> So, I was mentioning some of the big guys before. Top five market capped companies: Google, Amazon, Apple, Facebook, Microsoft, all digital. Microsoft you can argue, but still, pretty digital, pretty data oriented. My question is about closing that gap. In your view, can companies close that gap? How can they close that gap? Are you guys helping companies close that gap? It's a wide chasm, it seems. Thoughts? >> The thought on closing the chasm is... presenting the technology to the decision-makers. What we've learned is that... you don't know what you don't know, so it's impossible to find the new technologies if you don't have the vocabulary to just begin a simple research of these new technologies. And, to close that gap, it really comes down to the awareness, events like theCUBE, webinars, different educational opportunities that are available to line of business owners, directors, VP's of systems and services, to begin that awareness process, finding consultants... begin that pipeline enablement to begin allowing the business to take advantage and harness data science, machine learning and what's coming. >> One of the things I've noticed is that there's a lot of information out there, like everyone a webinar, everyone has tutorials, but there's a lot of overlap. There aren't that many very sophisticated documents you can find about how to implement it in real world conditions. They all tend to use the same core data set, a lot of these machine learning tutorials you'll find, which is hilarious because the data set's actually very small. And I know where it comes from, just from having the expertise, but it's not something I'd ever use in the real world. The level of skill you need to be able to do any of these methodologies. But that's what's out there. So, there's a lot of information, but they're kind of at a rudimentary level. They're not really at that sophisticated level where you're going to learn enough to deploy in real world conditions. One of the things I'm noticing is, with the technical teams, with the data science team, machine learning teams, they're kind of using the same methodologies I used maybe 10 years ago. Because the management who manage these teams are not technical enough. They're business people, so they don't understand how to guide them, how to explain hey maybe you shouldn't do that with your code, because that's actually going to cause a problem. You should use parallel code, you should make sure everything is running in parallel so compute's faster. But, if these younger teams are actually learning for the first time, they make the same mistakes you made 10 years ago. So, I think, what I'm noticing is that lack of leadership is partly one of the reasons, and also the assumption that a non-technical person can lead the technical team. >> So, it's just not skillset on the worker level, if you will. It's also knowledge base on the decision-maker level. That's a bad place to be, right? So, how do you get into the door to a business like that? Obviously, and we've talked about this a little bit today, that some companies say, "We're not data companies, we're not digital companies, we sell widgets." Well, yeah but you sell widgets and you need this to sell more widgets. And so, how do you get into the door and talk about this problem that Jennifer just cited? You're signing the checks, man. You're going to have to get up to speed on this otherwise you're not going to have checks to sign in three to five years, you're done! >> I think that speaks to use cases. I think that, and what I'm actually saying at customers, is that there's a disconnect and an understanding from the executive teams and the low-level technical teams on what the use case actually means to the business. Some of the use cases are operational in nature. Some of the use cases are data in nature. There's no real conformity on what does the use case mean across the organization, and that understanding isn't there. And so, the CIO's, the CEO's, the CTO's think that, "Okay, we're going to achieve a certain level of capability if we do a variety of technological things," and the business is looking to effectively improve some or bring some efficiency to business processes. At each level within the organization, the understanding is at the level at which the discussions are being made. And so, I'm in these meetings with senior executives and we have lots of ideas on how we can bring efficiencies and some operational productivity with technology. And then we get in a meeting with the data stewards and "What are these guys talking about? They don't understand what's going on at the data level and what data we have." And then that's where the data quality challenges come into the conversation, so I think that, to close that cataclysm, we have to figure out who needs to be in the room to effectively help us build the right understanding around the use cases and then bring the technology to those use cases then actually see within the organization how we're affecting that. >> So, to change the questioning here... I want you guys to think about how capable can we make machines in the near term, let's talk next decade near term. Let's say next decade. How capable can we make machines and are there limits to what we should do? >> That's a tough one. Although you want to go next decade, we're still faced with some of the challenges today in terms of, again, that adoption, the use case scenarios, and then what my colleagues are saying here about the various data challenges and dev ops and things. So, there's a number of things that we have to overcome, but if we can get past those areas in the next decade, I don't think there's going to be much of a limit, in my opinion, as to what the technology can do and what we can ask the machines to produce for us. As Colin mentioned, with RPA, I think that the capability is there, right? But, can we also ultimately, as humans, leverage that capability effectively? >> I get this question a lot. People are really worried about AI and robots taking over, and all of that. And I go... Well, let's think about the example. We've all been online, probably over the weekend, maybe it's 3 or 4 AM, checking your bank account, and you get an error message your password is wrong. And we swear... And I've been there where I'm like, "No, no my password's right." And it keeps saying that the password is wrong. Of course, then I change it, and it's still wrong. Then, the next day when I login, I can login, same password, because they didn't put a great error message there. They just defaulted to wrong password when it's probably a server that's down. So, there are these basics or processes that we could be improving which no one's improving. So you think in that example, how many customer service reps are going to be contacted to try to address that? How many IT teams? So, for every one of these bad technologies that are out there, or technologies that are not being run efficiently or run in a way that makes sense, you actually have maybe three people that are going to be contacted to try to resolve an issue that actually maybe could have been avoided to begin with. I feel like it's optimistic to say that robots are going to take over, because you're probably going to need more people to put band-aids on bad technology and bad engineering, frankly. And I think that's the reality of it. If we had hoverboards, that would be great, you know? For a while, we thought we did, right? But we found out, oh it's not quite hoverboards. I feel like that might be what happens with AI. We might think we have it, and then go oh wait, it's not really what we thought it was. >> So there are real limits, certainly in the near to mid to maybe even long term, that are imposed. But you're an optimist. >> Yeah. Well, not so much with AI but everything else, sure. (laughing) AI, I'm a little bit like, "Well, it would be great, but I'd like basic things to be taken care of every day." So, I think the usefulness of technology is not something anyone's talking about. They're talking about this advancement, that advancement, things people don't understand, don't know even how to use in their life. Great, great is an idea. But, what about useful things we can actually use in our real life? >> So block and tackle first, and then put some reverses in later, if you will, to switch over to football. We were talking about it earlier, just about basics. Fundamentals, get your fundamentals right and then you can complement on that with supplementary technologies. Craig, Colin? >> Jen made some really good points and brought up some very good points, and so has... >> John: Craig. >> Craig, I'm sorry. (laughing) >> Craig: It's alright. >> 10 years out, Jen and Craig spoke to false positives. And false positives create a lot of inefficiency in businesses. So, when you start using machine learning and AI 10 years from now, maybe there's reduced false positives that have been scored in real time, allowing teams not to have their time consumed and their business resources consumed trying to resolve false positives. These false positives have a business value that, today, some businesses might not be able to record. In financial services, banks count money not lended. But, in every day business, a lot of businesses aren't counting the monetary consequences of false positives and the drag it has on their operational ability and capacity. >> I want to ask you guys about disruption. If you look at where the disruption, the digital disruptions, have taken place, obviously retail, certainly advertising, certainly content businesses... There are some industries that haven't been highly disruptive: financial services, insurance, we were talking earlier about aerospace, defense rather. Is any business, any industry, safe from digital disruption? >> There are. Certain industries are just highly regulated: healthcare, financial services, real estate, transactional law... These are very extremely regulated technologies, or businesses, that are... I don't want to say susceptible to technology, but they can be disrupted at a basic level, operational efficiency, to make these things happen, these business processes happen more rapidly, more accurately. >> So you guys buy that? There's some... I'd like to get a little debate going here. >> So, I work with the government, and the government's trying to change things. I feel like that's kind of a sign because they tend to be a little bit slower than, say, other private industries, or private companies. They have data, they're trying to actually put it into a system, meaning like if they have files... I think that, at some point, I got contacted about putting files that they found, like birth records, right, marriage records, that they found from 100-plus years ago and trying to put that into the system. By the way, I did look into it, there was no way to use AI for that, because there was no standardization across these files, so they have half a million files, but someone's probably going to manually have to enter that in. The reality is, I think because there's a demand for having things be digital, we aren't likely to see a decrease in that. We're not going to have one industry that goes, "Oh, your files aren't digital." Probably because they also want to be digital. The companies themselves, the employees themselves, want to see that change. So, I think there's going to be this continuous move toward it, but there's the question of, "Are we doing it better?" It is better than, say, having it on paper sometimes? Because sometimes I just feel like it's easier on paper than to have to look through my phone, look through the app. There's so many apps now! >> (laughing) I got my index cards cards still, Jennifer! Dave's got his notebook! >> I'm not sure I want my ledger to be on paper... >> Right! So I think that's going to be an interesting thing when people take a step back and go like, "Is this really better? Is this actually an improvement?" Because I don't think all things are better digital. >> That's a great question. Will the world be a better, more prosperous place... Uncertain. Your thoughts? >> I think the competition is probably the driver as to who has to this now, who's not safe. The organizations that are heavily regulated or compliance-driven can actually use that as the reasoning for not jumping into the barrel right now, and letting it happen in other areas first, watching the technology mature-- >> Dave: Let's wait. >> Yeah, let's wait, because that's traditionally how they-- >> Dave: Good strategy in your opinion? >> It depends on the entity but I think there's nothing wrong with being safe. There's nothing wrong with waiting for a variety of innovations to mature. What level of maturity, I think, is the perspective that probably is another discussion for another day, but I think that it's okay. I don't think that everyone should jump in. Get some lessons learned, watch how the other guys do it. I think that safety is in the eyes of the beholder, right? But some organizations are just competition fierce and they need a competitive edge and this is where they get it. >> When you say safety, do you mean safety in making decisions, or do you mean safety in protecting data? How are you defining safety? >> Safety in terms of when they need to launch, and look into these new technologies as a basis for change within the organization. >> What about the other side of that point? There's so much more data about it, so much more behavior about it, so many more attitudes, so on and so forth. And there is privacy issues and security issues and all that... Those are real challenges for any company, and becoming exponentially more important as more is at stake. So, how do companies address that? That's got to be absolutely part of their equation, as they decide what these future deployments are, because they're going to have great, vast reams of data, but that's a lot of vulnerability too, isn't it? >> It's as vulnerable as they... So, from an organizational standpoint, they're accustomed to these... These challenges aren't new, right? We still see data breaches. >> They're bigger now, right? >> They're bigger, but we still see occasionally data breaches in organizations where we don't expect to see them. I think that, from that perspective, it's the experiences of the organizations that determine the risks they want to take on, to a certain degree. And then, based on those risks, and how they handle adversity within those risks, from an experience standpoint they know ultimately how to handle it, and get themselves to a place where they can figure out what happened and then fix the issues. And then the others watch while these risk-takers take on these types of scenarios. >> I want to underscore this whole disruption thing and ask... We don't have much time, I know we're going a little over. I want to ask you to pull out your Hubble telescopes. Let's make a 20 to 30 year view, so we're safe, because we know we're going to be wrong. I want a sort of scale of 1 to 10, high likelihood being 10, low being 1. Maybe sort of rapid fire. Do you think large retail stores are going to mostly disappear? What do you guys think? >> I think the way that they are structured, the way that they interact with their customers might change, but you're still going to need them because there are going to be times where you need to buy something. >> So, six, seven, something like that? Is that kind of consensus, or do you feel differently Colin? >> I feel retail's going to be around, especially fashion because certain people, and myself included, I need to try my clothes on. So, you need a location to go to, a physical location to actually feel the material, experience the material. >> Alright, so we kind of have a consensus there. It's probably no. How about driving-- >> I was going to say, Amazon opened a book store. Just saying, it's kind of funny because they got... And they opened the book store, so you know, I think what happens is people forget over time, they go, "It's a new idea." It's not so much a new idea. >> I heard a rumor the other day that their next big acquisition was going to be, not Neiman Marcus. What's the other high end retailer? >> Nordstrom? >> Nordstrom, yeah. And my wife said, "Bad idea, they'll ruin it." Will driving and owning your own car become an exception? >> Driving and owning your own car... >> Dave: 30 years now, we're talking. >> 30 years... Sure, I think the concept is there. I think that we're looking at that. IOT is moving us in that direction. 5G is around the corner. So, I think the makings of it is there. So, since I can dare to be wrong, yeah I think-- >> We'll be on 10G by then anyway, so-- >> Automobiles really haven't been disrupted, the car industry. But you're forecasting, I would tend to agree. Do you guys agree or no, or do you think that culturally I want to drive my own car? >> Yeah, I think people, I think a couple of things. How well engineered is it? Because if it's badly engineered, people are not going to want to use it. For instance, there are people who could take public transportation. It's the same idea, right? Everything's autonomous, you'd have to follow in line. There's going to be some system, some order to it. And you might go-- >> Dave: Good example, yeah. >> You might go, "Oh, I want it to be faster. I don't want to be in line with that autonomous vehicle. I want to get there faster, get there sooner." And there are people who want to have that control over their lives, but they're not subject to things like schedules all the time and that's their constraint. So, I think if the engineering is bad, you're going to have more problems and people are probably going to go away from wanting to be autonomous. >> Alright, Colin, one for you. Will robots and maybe 3D printing, for example RPA, will it reverse the trend toward offshore manufacturing? >> 30 years from now, yes. I think robotic process engineering, eventually you're going to be at your cubicle or your desk, or whatever it is, and you're going to be able to print office supplies. >> Do you guys think machines will make better diagnoses than doctors? Ohhhhh. >> I'll take that one. >> Alright, alright. >> I think yes, to a certain degree, because if you look at the... problems with diagnosis, right now they miss it and I don't know how people, even 30 years from now, will be different from that perspective, where machines can look at quite a bit of data about a patient in split seconds and say, "Hey, the likelihood of you recurring this disease is nil to none, because here's what I'm basing it on." I don't think doctors will be able to do that. Now, again, daring to be wrong! (laughing) >> Jennifer: Yeah so--6 >> Don't tell your own doctor either. (laughing) >> That's true. If anything happens, we know, we all know. I think it depends. So maybe 80%, some middle percentage might be the case. I think extreme outliers, maybe not so much. You think about anything that's programmed into an algorithm, someone probably identified that disease, a human being identified that as a disease, made that connection, and then it gets put into the algorithm. I think what w6ll happen is that, for the 20% that isn't being done well by machine, you'll have people who are more specialized being able to identify the outlier cases from, say, the standard. Normally, if you have certain symptoms, you have a cold, those are kind of standard ones. If you have this weird sort of thing where there's n6w variables, environmental variables for instance, your environment can actually lead to you having cancer. So, there's othe6 factors other than just your body and your health that's going to actually be important to think about wh6n diagnosing someone. >> John: Colin, go ahead. >> I think machines aren't going to out-decision doctors. I think doctors are going to work well the machine learning. For instance, there's a published document of Watson doing the research of a team of four in 10 minutes, when it normally takes a month. So, those doctors,6to bring up Jen and Craig's point, are going to have more time to focus in on what the actual symptoms are, to resolve the outcome of patient care and patient services in a way that benefits humanity. >> I just wish that, Dave, that you would have picked a shorter horizon that... 30 years, 20 I feel good about our chances of seeing that. 30 I'm just not so sure, I mean... For the two old guys on the panel here. >> The consensus is 20 years, not so much. But beyond 10 years, a lot's going to change. >> Well, thank you all for joining this. I always enjoy the discussions. Craig, Jennifer and Colin, thanks for being here with us here on theCUBE, we appreciate the time. Back with more here from New York right after this. You're watching theCUBE. (upbeat digital music)