(upbeat music) >> theCUBE presents UiPath FORWARD5 brought to you by UiPath. >> Welcome back to Las Vegas, everybody you're watching theCUBE's coverage of UiPath FORWARD5. We're here at the Venetian Convention Center Dave Vellante with Dave Nicholson this morning. Dave, we heard these boomers, these thunder boomers. We thought it was the sound system. (Dave laughing) >> Thought it was something fake. >> But it was actually some crazy weather out here in Vegas. It's rare to see that kind of nuttiness out here. John Morrison is the director of Product and Technology at T-Mobile and Naga Chakravarthy is the Chief Digital Officer at iOpex. Guys, welcome. >> Thanks for having us. >> Next, so John, (commentator booming) so okay, we're serving automation. I don't know if you guys can hear that S0 let's just give him a second here. >> (Commentator) Three different tracks >> I think it's pretty loud. Probably coming through. Usually we don't get that. >> It's live. >> But, it is live. So John, we, we've interviewed a lot of customers that have automation in their title. Your title's, Director of product and technology. Obviously you're here 'cause you have an affinity to automation. But talk about your role and how automation fits into it. >> Sure. Well, I'm the director of product and technology and I oversee what we call the communication, collaboration and productivity applications and services for T-Mobile. Reason I'm here is we took over the automation program and automation is falling within to our productivity portfolio. So I'm here to learn about, from these experts and all these leaders within the UiPath and from our vendors as well. >> Okay. Now tell us about iOpex. So kind of an interesting name. Where'd that come from? I think cloud. When I think opex, but, get rid of my cap. Where's the name come from and what do you guys do? >> Actually we thought hard about what to name about 13 years back. You know, I think all of us, the whole team comes from a service background and then I think we believe that you need to have people and as a lot of operational activities were increasing, you know the dependency on people was also increasing. And we thought that there has to be an angle for us to be very unique in the market. So we thought, you know, I would say iOpex is currently at 3.0 and if you look at what 1.0 was, it's all about driving innovation in operation excellence, right? And the medium was technology. And today, if you ask me from operation excellence that is the base, we are actually looking at how do you drive innovation in operating experiences. That's where automation and all these things becomes very native to us. >> So the market just went right, right to you guys you were ahead of the game. And then, wow, now, >> I have to brag that we fortunately named it Opex, which can be interchangeably used for operation excellence or operating experience. >> Got it. >> So, so John, where did, where did it start? What was the catalyst for your automation journey? How did, was it the, was it the, the merger? Take us through that. >> Sure. So I look at our automation journey, like a crawl, walk, run journey for sure. It started with the partnership of UiPath and iOpex. We had an innovation lab. They came, they set up a proof of concept. Proof of concept was successful. I was then asked to build out an automation program for the T-mobile enterprise. Not having any experience within automation as we had discussed before usually you have automation within the title. We leaned heavily on our partners iOpex being main critical partner in that evolution. And so iOpex came in and helped us build that center of excellence and really helped us put that support team together so that we could be successful as we moved forward. Now, when we had both of those in place, we were able to go to the businesses and find opportunities and showcase what automation was all about. The problem is we were so green is that, you know, we'd go and we'd look at an opportunity, but that opportunity we'd deliver and then our pipeline would be empty and we'd have to go look for other opportunities. So we really had to present and get that executive sponsorship of automation for the enterprise. And I'm going to do a few shoutouts here. Giao Duong, John Lowe and our CIO Brian King, were critical in giving us what we needed to be successful. They gave us the expertise, the funds to do what we needed to, to build out this program. We utilized iOpex, UiPath to really get that expertise in place. And today, our pipeline, we have about 300,000 manual hours of labor savings that we'll deploy by the end of the year. That's a huge success. And that's where we're at right now. The run part of it is going to be, I'll wait. >> Wait. No, it's okay. So you went, you went from hunting to fishing in a barrel? >> Absolutely. Absolutely. So the, our next is focused on citizen development, building out that citizen development program, where we will be partnering with UiPath and iOpex to get that in place. And once we have that in place I feel like we're going to be ready to run and we'll see that program just kick off. But like I said before, 300,000 hours of savings in the first year of that program. That's incredible. And we're a large company and we'll, I mean we're just starting so it's going to be fun. >> So many questions. So Naga, is the COE where people typically start or is it sometimes a grassroot effort and then the COE comes later? How do you typically recommend approaching it? >> I think the fact that we started very small there was a clear mandate that we have to take a very strategic approach while we are solving a tactical problem to show that automation is the future and you need to solve using automation, right? And we not only looked at it just from a task automation standpoint, we were starting to look at it from a process, entire end to end process automation. And when we started looking at it, though we were tactically automating it, COE naturally fell in place. So, which means you need to evangelize this across multiple departments. So when you have to have, when you have to have evangelize across multiple departments, what is very important is you need to have the pod leaders identified let's say if you have to go to different departments it is somebody from John's team who's very capable of navigating through different departments' problem statements and how when you, when you navigate it you can rightly evangelize what is the benefit. And when it comes to benefit, right? You need to look at it from both the angles of operation excellence and what is it going to do from a growth standpoint of solving a future problem. So somebody internally within T-Mobile we were able to use very nice, you know John's team, you know, the COE naturally fell in place. All of them were at some point in time doing automation. And slowly it was a path that they took to evangelize and we were able to piggyback and scale it bigger. >> So in the world we're in, whether you're talking about cloud services that are created by hyper scale cloud providers or automation platforms from UiPath, between those shiny toys and what we want to accomplish with them in the world of business and everything else there are organizations like iOpex and you and John are working together to figure out which projects need to be done in a strategic, from a strategic viewpoint but you're also addressing them tactically. I'm curious, >> Yeah. >> How does that business model from an iOpex perspective work do you have people embedded at T-Mobile that are working with John and his folks to identify the next things to automate? Is it a, is it, where is the push and where is the pull coming from in terms of, okay now what do we do next? Because look, let's be frank, in the, from a business perspective, iOpex wants to do as much as it can a value for T-mobile because that's what, that's the business they're in. But, so tell me about that push pull between the two of you. Does that make sense? Yeah, So I'll say real fast that, yeah iOpex is actually part of the T-mobile team. They are embedded. >> Nicholson: Okay. >> We work with them daily. >> Nicholson: Okay. >> Right. They had the expertise they're passing along the expertise to our full-time employees. And so it's like we're all one team. So that should answer that one for sure there. >> Absolutely. Let me add one more point to it. See if, you know, I think with respect to T-Mobile I would say it's a little bit of a special case for us. Why I say that is, when we started the whole conversation of we need to drive automation with you there was a natural way to get embedded, you know as part of their team. Normally what happens is a team, a COE team works and say I will do the discovery and you guys can come and do the solution design. That was not the case, right? I think it was such a strategic investment that T-Mobile made on us, right? We were part of the discovery team. So, which means that we were able to take all the best practices that we learned from outside and openness to accept and start looking at it what's in it for us for the larger good that made us to get to what we call it as building a solution factory for T-Mobile. >> Vellante: I got a lot of questions. >> John: Yeah. >> John, you mentioned your CIO and a couple of other constituents. >> Yes. >> What part of the organization were they from? They helped you with funding, >> Yep. >> And maybe sort of gave you a catalyst. How did this all get funded? If I, if you could, Cause a lot of people ask me well how do I fund this thing? Does it fund itself? Do I do, is it an IT driven initiative line of business? >> So those executives were from the IT team. >> Vellante: Okay. For sure. But a lot of our programs start from grassroots ground up and you know a lot of vendors say, hey, you need it from the top down. This was a perfect example of getting it from the top down. We were working it, it was fine, but it wouldn't have taken off if we didn't have, you know, Brian King and John Lowe providing us that executive sponsorship, going to their peers and telling them about the program and giving us the opportunity to showcase what automation can do. >> How do you choose, I got so many questions I'm going to go rapid fire. How do you choose your automation priorities? Is it process driven? Is it data led? What's the right approach? >> I think it's a combination, right? One fundamentally guiding principle that we always look at is let it not be a task automation, right? Task automation solves a particular problem, but maybe you know, if you start looking at it from a bigger, you need to start looking at it from process angle. And when it comes to process, right? There are a lot of things that gets executed in the systems of record, in the form of workflow. And there's a lot of things that gets executed outside the systems of record, which is in people's mind. That's when data comes in, right? So let's say you use process mining tool of UiPath, you will get to know that there is a bottleneck in a particular process because it's cluttered somewhere. But you also have to look at why is this clutter happening, and you need to start collecting data. So a combination of a data science as well as a process science blends together. And that's when you'll start deciding, hey this is repetitive in nature, this is going to scale, this is an optimization problem. And then you build a scorecard and that scorecard naturally drives the, you know decision making process. Hey, it's going to drive operation excellence problem for me or is it going to be a true business benefit of driving growth? >> So I was going to ask you how you visualize it. You visualize it through, I guess, understanding of the organization, anecdotal comments, research digging, peeling the onion, and then you do some kind of scorecard like approach and say, okay these are the high, high opportunity areas. Okay. So combination. Got it. How about change management? Because Dave, you and I were talking about this before, big organizations that I know they have IT, they got an application portfolio. That application portfolio the applications have dependencies on each other. And then they have a process portfolio that is also related. So any change in process ripples through the applications. Any change in application affects other applications and affects processes. So how do you handle change management? >> So we actually have a change management team and we make sure that before we go forward with anything it's communicated what changes would be in place. And this change management team also does communications broadly for any of our applications, not just automation. So they partner close with iOpex, with our development teams on opportunities that are going out. You want to add anything? >> Yeah. So when it comes to change management, right? Well, John is front-ending all the changes relating to apps and stuff like that by having a steering committee, what really is the proactive thing that we end up doing is right when a bot goes live, there is a life support that we provide for the entire bot that's gone live. And the fundamentally core principle for that entire support to work good is you start looking at what's the benefit that the bot is giving more than that when a bot fails. Right? Why is the bot failing? Is it because the systems of records on which the bot is running? Is it that is failing? Or the inputs that is coming to the systems of record the data format, is it changing or the bot logic is failed? And once we set up a constant monitoring about that we were able to throw insights into the change management team saying that the bot failed because of various reasons. And that kind of compliments the whole change management process. And we get earlier notifications saying, hey there's going to be changes. So which means we go proactively look at, hey, okay fair enough, this systems of records, this data is going to change. Can we test this out in staging before you hit the production? So that way the change becomes a smoother process. >> And how quickly can you diagnose that? Is it hours, minutes, days, weeks, months? >> So, >> Vellante: Depends. >> It's a very subjective question. Right. If we know the pattern early then the SWAT team quickly gets into it and figure out how we could stop something, you know, stop the bot from failing. The moment the bot fails, you know, you need to basically look at how the business is going to going to get affected. But we try to do as much as we could. >> So Naga, I'm going to put you on the spot here. >> Please. >> As a partner of UiPath, this question of platform versus product. In order to scale and survive and thrive into the future UiPath needs to be able to demonstrate that it's more than a tool set, but instead a platform. What's your view on that in general? What differentiates a platform from a product? Does it matter to your organization whether UiPath moves in the direction of platform or not? >> I think, it is, it's undoubtedly platform, right? And a platform in my mind will constantly evolve. And once you think about it as a platform you will end up having a lot of plug and place. If you look at the way UiPath is evolving it is evolving as a platform. It used to be attended bot and unattended bot and plugged with Orchestrator. And if you look at it, the problem of solving the up chain and the down chain naturally came in process mining, task capture, made it up chain, a platform that solves the up chain. And then it slowly evolved into, hey I'm actually doing business process automation. Why could I not do test automation with the same skillset? So a platform will try to look at what is that, you know I've got in myself and how can I reuse across the enterprise? I think that is deeply embedded in the UiPath culture. And that's the kind of platform that, you know anybody like a system integrator like us, we do not have to multi-skill people. You just have to skill in one and you can interchange. That I would say is a good approach. >> So John, what's the future look like? What's the organization's appetite for automation? You know, is there an all you could eat kind of enterprise license approach? >> John: Yeah, so we are enterprise license. >> You are? Okay. >> So, and iOpex helped us move to the cloud so we can move quickly. That was definitely a benefit. The future of it, I would say citizen development is going to be key. Like I want citizen development within every business organization. I want them to be able to discover, deploy, you know, and and just use us, the center of excellence as support as needed. The appetite's there. Every group has automation within their goals or KPIs right? So it's there. We just need to be able to get in front of 'em. It's a large company. So I'm, '23 is going to be huge for us. >> Another fantastic story. I love that UiPath brings the customers to theCUBE. So thank you guys for telling your story. Congratulations on all your success. Good luck in the future. >> Yeah. Thank you. >> All right. Okay. Thank you for watching. This is Dave Vellante for Dave Nicholson UiPath FORWARD5. The bots are running around Dave. We're going to have to get one of the bots to come up here and show people a lot of fun at FORWARD. We're here in Vegas, right back, right after this short break.

>>The Cube presents UI Path Forward five. Brought to you by UI Path. >>Hi everybody. We're back at UI path forward to five. This is Dave Ante with Dave Nicholson. Derek Mu is here. He's automation product line lead for Merck. Thank you, by the way, for, you know, all you guys do, and thank you Dave for having in the, in the, in the vaccine area, saving our butts. And Bill Engel is back on the cube. He's the director at cgi. Guys, good to see you again. >>Good to see you. Thank >>You. So Merrick, Wow, it's been quite a few years for you guys. Take us through Derek, what's happening in sort of your world that's informing your automation strategy? >>Well, Dave, I mean as you know, we just came out of the pandemic. We actually have quite a few products like Gabriel Antiviral Pill. Obviously we worked, you know, continue to drive our products through a difficult time. But, you know, is during these can last few years that, you know, we've accelerated our journey in automation. We're about four years plus in our journey, you know, so just like the theme of this conference we're we're trying to move towards, you know, bigger automations, transformational change, continue to drive digital transformation in our company. >>Now Bill, you've been on before, but CGI tell people about the firm. It's not computer graphics imaging. >>Sure. No, it's, it's definitely not. So cgi, we're a global consultancy about 90,000 folks across the world. We're a, we're both a product company and a services company. So we have a lot of different, you know, software products that we deliver to our clients, such as CGI Advantage, which is a state local government EER P platform. And so outside of that, we, my team does automation and so we wrap automation around R IP and deliver that to our clients. >>So you guys are automation pros, implementation partners, right? So, so let's go back. Yep. Derek said four years I think. Yep. Right, You're in. So take us through what was the catalyst, how did you get started? Obviously it was pre pandemic, so it's interesting, a lot of companies pre pandemic gave lip service to digital transformation. Sounds like you guys already started your journey, but I'll come back to that. But take us back to the Catalyst four years ago. Why automation? We'll get into why UI path, >>Right. So I, I would say it started pretty niche in our company. Started first in our finance area. Of course, you know, we were looking in technology evaluating different companies, Blue Prism, ui P. Ultimately we chose UI p did it on-prem to start to use automation in sort of our invoice processing, sort of our financial processes, right? And then from there, after it was really when the pandemic hit, that's when sort of we all went to remote work. That's when the team, the COE continued to scale up, especially during pandemic. We were trying to automate more and more processes given the fact that more and more of our workers are remote, they reprocesses. How, how do you do events? You know, part of our livelihood is, is meeting with engaging with customers. Customers in this case is, are doctors and physicians, right? How do you engage with them digitally? How do you, you know, you know, a lot of the face to face contact now have to kind of shift to more digital, digital way. And so automation was a way to kind of help accelerate that, help facilitate that. >>You, you, I think you mentioned COE as in center of excellence. Yep. So, so describe your approach to implementing automation. It's, that sounds like when you say center, it sounds like something is centralized as, as opposed to a bunch of what we've been hearing a lot about citizen developers. What does that interaction >>Look like? We do have both. I would say in the beginning was more decentralized, but over time we, over the few years as, as we built more and more bots, we're now at maybe somewhere between four to 500 bots. We now have sort of internal to the company functional verticals, right? So there's an animal health, we have an animal health function. So there's, there's a team building engaging with the animal health business to build animal health box. There's human health, which is what I work on as well as hr, finance, manufacturing, research. And so internally there's engagement leads, one of the engagement leads that interact with the business. Then when there's an engineering squads that help build and design, develop and support and maintain those as well as sort of a DevOps team that supports the platform and maintains all the bot infrastructure. >>So you started in finance common story, right? I'm sure you hear this a lot Belt, How did you decide what to target? Was it, was it process driven decision? Was it, was it data oriented? Like some kind of combination? How did you decide, Do you remember? Or do you, could you take >>Us back to Oh yeah. So for, for cgi how we started to engage with MER is, you know, we, we do a lot of other business with Merck. We work on all their different business lines and we, we understand the business process. So we, we knew where there was potential for automation. So we brought those ideas to Merck and, and really kind of landed there and helped them realize the value from automation from that standpoint. And then from there the journey just continued to expand, you know, looking for those use cases that, that, you know, fit the mold for, for, for RPA to start. And now the evolution is to go to broader hyper automation. >>And, and was it CFO led into the finance department and then, or was it sort of more bottoms >>Up? Yeah, so, so I think it started in, in finance and, and, but we actually really started out in the business line. So out in regulatory clinical, that's, that's where we, we have the life science expertise that are embedded. And so I partnered with them to come up with, hey, here's a real solution we could do to help streamline, say submission archiving. So when, when submissions come back from the fda, they need to be archived into, you know, the, their system of record. So that's, those are the types of use cases that, that we helped automate. >>Okay. Cause you're saying a human had to sort physically archive that and you were able to sort of replicate that. Okay. And you started with software robots, obviously rpa and now you're expanding into, we we're hearing from UI this the platform message. How does that coincide Derek, with what you guys are doing? Are you sort of adding platform? What aspects of the platform are, are you adding? >>Yeah, no, I mean we are, we are on-premise, right? So we have the platform, but some of the cool things we just had, another colleague of mine presented earlier today. Some of the cool things we're, we're doing ephemeral infrastructure. So infrastructure as code, which essentially means instead of having all these dedicated bot machines, that that, you know, cuz these bots only in some cases run 10 minutes and they're done. So we're, we're soon of doing all on demand, you know, start up a server, run the bot when it's finished, you know, kill the server. So we only pay for the servers that we use, which allows us to save a whole >>Lot of money. Serverless bots. So you, but you're doing that OnPrem, so you >>No, >>No, but >>That's >>Cloud. We, >>We, we we're doing it OnPrem, but our, our bot machines that actually run the, let's say SAP process, right? We spin that machine up, it's on the cloud, it runs it finish, Let's say it's processed in one hour and then when it's done, we kill that machine. So we only play for that one hour usage of that bot machine. >>Okay. So you mentioned SAP earlier you mentioned Blue Prism when you probably looked at other competitors too. You pull the Gartner Magic quadrant, blah, blah, you know, with the way people, you know, evaluate technology, but SAP's got a product. Why UI path mean? Is it that a company like SAP two narrow for their only sap you wanted to apply it other ways? Maybe they weren't even in the business that back then four years ago they probably weren't. Right? But I'm curious as to how the decision was made for UiPath. >>Well, I think you hit it right on the nail. You know, SAP sort of came on a little later and they're specific to sort of their function, right? So UiPath for us is the most flexible tool can interact by UI to our sales and marketing systems, to, to workday, to service Now. It's, it cuts across every function that we have in the company as well as you're the most mature. I mean, you're the market leader, right? So Right. Definitely you, you continue to build upon those capabilities and we are exploring the new capabilities, especially being announced today. >>And what do you see Bill in the marketplace? Are you, are you kind of automation tool agnostic? Are you more sort of all in on? I >>Would say we are, we are agnostic as a company, but obviously as part of a, as an automation practice lead, you know, I want to deliver solutions to my clients that are gonna benefit them as a whole. So looking at UI path, you know, that this platform is, it covers the end to end spectrum of, of automation. So I can go really into any use case and be able to provide a solution that, that delivers value. And so that's, that's where I see the value in UI path and that's why CGI is, is a customer as well. We automate our internal processes. We actually have, we just launched probably SALT in the, in the market last week, expanded partnership with UiPath. We launched CGI, Excel 360. That's our fully managed service around automation. We host our clients whole UI path infrastructure and bots. It's completely hands off to them and they just get the value outta >>Automation. Nice, nice. Love >>It. Derek, you mentioned, you mentioned this ephemeral infrastructure. Yeah. Sounds like it's also ethereal possibility possibly you're saying, you, you're saying you have processes that are running on premises, right? But then you reach out to have an automation process run that's happening off pre and you're, and you're sort of, >>It's on the cloud, so, so yeah, so we have a in-house orchestrator, so we don't, we're not using your sort of on the cloud orchestrator. So, so we brought it in-house for security reasons. Okay. But we use, you know, so inside the vpn, you know, we have these cloud machines that run these automations. So, so that's, that's the ephemeral side of the, of the >>Infrastructure. But is there a financial angle to that in terms of when you're spinning these things up, are you, is it a, is it a pay by the drink or by the, by the CPU >>Hours, if you can imagine like we, you know, like I mentioned where somewhere between four to 500 bots and every bot has a time slot to run and takes a certain amount of time. And so that's hundreds and hundreds of bot machines that we in the old days have to have to buy and procure and, you know, staff and support and maintain. So in this new model, and we're just beginning to kind of move from pilot into implementation, we're moving all, all of bots this in ephemeral infrastructure, right? So these, okay, these machines, these bot machines are, you know, spun up. They run the, they, they run their automation and then they spin >>Down. But just to be clear, they're being spun up on physical infrastructure that is in your >>Purview and they spun up on aws. Yeah. Okay. And then they spin down. Okay, got >>It. Got it. Interesting. Four >>To 500 bots. You know, Daniel one point play out this vision of a bot chicken in every pot, I called it a bot for every employee. Is that where you're headed or is that kind of in this new ephemeral world, not necessary, it's like maybe every employee has access to an ephemeral bot. How, how are you thinking about that? >>That's a good question. So obviously the, the four to 500 is a mix of unattended bonds versus attended bonds, right? That, that we also have a citizen developer, sort of a group team. We support that as well from a coe. So, you know, we see the future as a mix. There's, there's a spectrum of, we are the professional development team. There's also, we support and nurture the personal automation and we provide the resources to help them build smaller scale automations that help, you know, reduce the, you know, the mundaneness and the hours of their own tasks. But you know, for us, we want to focus more and more on building bigger and bigger transfer transformational automations that really drive process efficiencies and, and savings. >>And what's the, what's the business impact been? You mentioned savings and maybe there's other sort of productivity. How do you measure the benefit, the ROI and, and >>Quantify that we, you know, I, I don't, I don't profess I don't think we have all the right answers, but yeah, simple metrics like number of hours saved or other sort of excitement sort of in like an nps, internal NPS between the different groups that we engage. But we definitely see automation demand coming from our, our functional teams going up, driving up. So it's, it's continued to be a hot area and hopefully we, we can, you know, like, like what the key message and theme of this, of this conference. Essentially we want to take and build upon the, the good work that we've done in terms of rpa and we want to drive it more towards digital transformation. >>So Bill, what are you seeing across the, your customer base in terms of, of, of roi? I'm not looking for percentages there. I'm sure they're off the charts, but in terms of, you know, you can optimize for fast payback, you know, maybe lower the denominator, you know, or you can optimize for, you know, net benefit over time, right? You know, what are you seeing? What are customers after they want fast payback and little quick hits? Or are they looking for sort of a bigger enterprise wide impact? >>Yeah, I think it's, it's the latter. It's that larger impact, right? Obviously they, you know, they want an roi and just depending upon the use case, that's gonna vary in terms of the, the benefits delivered. And a lot of our clients, depending on the industry, so in in life sciences it may be around, you know, compliance like GXP compliance is huge. And so that may may not be much of a time saver, but it ensures that they're, they're running their processes and they're being compliant with, you know, federal standards. So that's, that's one aspect to it. But you know, to, you know, a bank, they're looking to reduce their overall costs and and so on. But yeah, I think, I think the other, the other part of it is, you know, impacting broader business processes. So taking that top down approach versus kind of bottom up, you know, doing ta you know, the ones you choose the tasks is not as impactful as looking at broader across the entire business process and seeing how we can impact >>It. Now, Derek, when you guys support a citizen developer, how does that work? So, hey, I got this task I want to automate, I'm gonna go write a, you know, software robot. I'm gonna go do an automation. Do I just do it and then throw her to the defense? You guys, you guys send me a video on how to do it. Hold my hand. How's that work? >>Yeah, I mean, good question. So, so we obviously direct them to the UI path Academy, get some training. We also have some internal training materials to how to build a bot sort of internal inside Merck. We, we go through, we have writeups and SOPs on using the right framework for automations, using the right documentation, PDD kind of materials, and then ultimately how do we deploy bot inside the MER ecosystem. But I, I, maybe I'll just add, I think you asked the point about ROI before. Yeah. I'll also say because we're, we're a pharmaceutical company. I think one of the other key metrics is actually time saved, right? So if, if, if we have a bot that helps us get through the clinical process or even the getting a, a label approved faster, even if it's eight days saved, that's eight days of a product that can get out to the market faster to, to our patients and, and healthcare professionals. And that's, that, that's immeasurable benefit. >>Yeah, I bet if you compress that ELAP time of, of getting approval and so forth. All right guys, we've gotta go. Thanks so much. Congratulations on all the success and appreciate you sharing your story. Thank >>You so much. Appreciate it. You're welcome. >>Appreciate it. All right. Thank you for watching this Dave Ante for Dave Nicholson, The cubes coverage, two day coverage. We're here in day one, UI path forward, five. We'll be right back right after the short break. Awesome. >>Great.

>>live from Orlando, Florida It's the cue covering Microsoft Ignite Brought to you by Cohee City. >>Welcome back, everyone to the cubes. Live coverage of Microsoft ignite here in Orlando, Florida. I'm your host, Rebecca Night, along with my co host Stew Minimum. We are joined by David Noi. He is the VP of cloud at cohesively, which is where we are. We're in the Coe City boots. So I should say thank you for welcoming us. >>My pleasure they found over here. >>So you are pretty brand new to the company. Ah, long time Tech veteran but new newish to Cohee City. Talk a little bit about what made you want to make the leap to this company. >>Well, you know, as I was, it was it was time for me to move from. My prior company will go to the reasons they're but a CZ. I looked around and kind of see who were the real innovators, right? You were the ones who were disrupting because my successes in the past have all been around disruption. And when I really looked at what these guys were doing, you know, first, it's kinda hard to figure out that I was like, Oh my gosh, this is really something different, Like it's bringing kind of the cloud into the enterprise and using that model of simplification and then adding data service is and it is really groundbreaking. So I just like, and the other thing was, I'll just throw this point out there. I read a lot of the white papers of the technology, and I looked at it and having been, you know, Tech veteran for a while, it looked to me like a lot of people who have done this stuff before we got together and said, If I had to do it again and do it right, what were things I wouldn't d'oh! And one of the things I would do, right? Right, so that was just fascinating. >>So, David, I was reading a Q and A recently with Mohit, founder of Cohee City, and it really is about that data you mentioned. Data service is, Yeah, bring us inside a little bit way in the storage and I t industry get so bogged down in the speeds and feeds and how fast you can do things in the terabytes and petabytes and like here. But we're talking about some real business issues that the product is helping to solve. >>I totally agree. Look, I've been in the in the storage industry for a while now, and you know, multi petabytes of data. And the problem that you run into when you go and talk to people who use this stuff is like old cheese. I start to lose track of it. I don't know what to do with it. So the first thing is, how do you search it? Index it? That's, you know, so I can actually find out what I have. Then there's a question of being able to go in and crack the date open and provide all kinds of data. Service is from, you know, classifications. Thio. Uh oh. Is this Ah, threat or business? Have vulnerabilities in it. It's really a data management solution. Now, of course, we started with backup, right? But then we're very quickly moving into other. Service is back on target file an object. You'll see some more things coming out around testing dead. For example, if you have the world's data is one thing to just keep it and hold it. But then what do you do with it. How do you extract value out of it? Is you really gotta add data management Service is and people try to do it. But this hyper converge technology and this more of a cloud approach is really unique in the way that it actually goes about it. >>I speak a little bit of that. That that cloud approach? >>Yeah, So I mean, you know, But he comes from a cloud background, right? He wrote was big author of the Google foul system. The idea, basically is to say, Let's take a look at a global view of how data is kept. Let's basically be ableto actually abstract that with the management layer on top of that and then let's provide service is on top of that. Oh, by the way, people now have to make a decision between am I gonna keep in on premise or keep it in the cloud? And so the data service is how to extend not just to the on Prem, but actor actually spend Thio. Pod service is as well, which is kind of why I'm here. I think you know what we do with Azure is pretty fascinating in that data management space, too. So we'll be doing more data management. Is the service in the cloud as well? >>So let's get into that a little bit. And I'm sure a lot of announcements this week with your arc and another products and service is. But let's dig into how you're partnering and the kinds of innovative things that go he see a Microsoft are doing together >>what we do. A lot of things. First of all, we weave a very rapid cadence of engineering, engineering conversations. We do everything from archiving data and sending long term retention data into the cloud. But that's kind of like where people start right, which is just ship it all up there. You know, Harvard, it's held right. But then think about doing migrations. How do you take a workload and actually migrated from on Prem to the cloud hold? We could do wholesale migrations of peoples environments. You want to go completely cloud native, weaken, fail over and fill back if we want to as well so we can use the cloud is actually a D. R site. Now you startle it. Think about disaster. Recovery is a service. That's another service that you start to think about what? About backing up cloud native workloads? Well, you don't just want to back up your work Clothes that are in the on Prem data certainly want to back him up also in the cloud. And that includes even office 3 65 So you just look at all of what you know. That means that the ability then could practice that data open and then provide all these additional when I say service is I'm talking about classifications, threat analysis, being able to go in and identify vulnerabilities and things of that nature. That's just a huge, tremendous value on top of just a basic infrastructure capabilities. >>David, you've been in the industry. You've seen a lot of what goes on out there, help us understand really what differentiates Cohee City. Because a lot of traditional vendors out there that are all saying many of the same word I hear you're Clough defying enters even newer vendors. Then go he sitio out there >>totally get it. Look, I mean, here's here's kind of what I find really interesting and attractive about the product. I've been in the storage history for a long time, so many times, people ask me, Can I move my applications to the storage? Because moving the data to the application that's hard. But moving the application to the data Wow, that makes things a lot easier, right? And so that's one of the big things that actually we do that's different. It's the hyper converged platform. It's a scale out platform. It's one that really looks a lot more like some of the skill of platforms that we've done in the past. But it goes way beyond that. And then the ability. Then say, OK, let's abstract that a ways to make it as simple as possible so people don't have to worry about managing lots of different pools and lots of different products for, you know, a service one versus service to versus service three, then bringing applications to that data. That's what makes it really different. And I think if you look around here and you talk to other vendors, I mean don't provide a P eyes. That's one thing that's great and that's important. But it actually bring the applications to the data. That's you know, that's what all of the cloud guys dont look a Google Gmail on top. They put search on top. They put Google translate on top. Is all of these things are actually built on top of the data that they store >>such? Adela This morning in the Kino talked about that there's going to be 500 million knew at business applications built by 2023. How is cohesive? E position to, you know, both partner with Microsoft and everyone out there to be ready for that cloud native >>future. That's a great question. Look, we're not gonna put 500 million applications on the product, right? But we're gonna pick some key applications that are important in the top verticals, whether it's health care, financial service is public sector and so long life sciences, oil and gas. But in the same time, we will offer the AP eyes extensions to say anything about going into azure if we can export things is as your blobs, For example, Now we can start to tie a lot of the azure service's into our storage and make it look like it's actually native as your storage. Now we can put it on as your cold storage shed, a hot storage. We can decide how we want to tear things from a performance perspective, but we can really make it look like it's native. Then we can take advantage of not just our own service is, but the service is that the cloud provides is well on. That makes us extraordinarily powerful >>in terms of the differentiator of Cohee City from a service of standpoint. But what about from a cultural standpoint we had sought Nadella on? The main stage is turning. Talking a lot about trust and I'm curious is particularly as a newer entrants into this technology industry. How how do you develop that culture and then also that reputation. So >>here's one of the interesting things when when I joined the company and I've been around for a while and I've been in a couple of very large brand names, I started walking down the holes and I'm like, Oh, here, here. Oh, you're here. Wait, you're here. It's like an old star cast, and when you go into, you know, some of the customer base and it's like, Hey, we know each other for a long time. That relationship is just there. On top of that, I mean the product works, it's solid. People love it. It's easy to use, and it actually solves riel problems for them. On Dhe, you know, we innovate extraordinarily fast. So when customers find a problem, we're on such a fast release cadence. We can fix it for them in extraordinarily, uh, in times that I've never seen before. In fact, is a little bit scary how fast the engineering group works. It's probably faster than anything I've ever seen in the past. And I think that helps that build the customers trust because they see that if we recognize there's a problem, we're gonna be there to soldier for >>them. There's trust of the company when we talk about our data. There's also the security aspect. Yes, cohesive. He fit into the A story with Microsoft and beyond. >>The security part is extraordinarily important. So look, we've already, as I said, built kind of our app marketplace and we're bringing a lot of applications to do things like Ransomware detection, um, vulnerability detection day declassification. But Microsoft is also developing similar AP eyes, and you heard this morning that they're building capabilities for us to be able to go and interact with them and share information. So we find vulnerabilities because share it with Ambika. Share with us so we could shut them down. So way have the native capabilities built in. They have capabilities that they're building of their own. Imagine the power of it being able to tie those two together. I just think that that's extraordinarily powerful. >>What about Gross? This is a company that is growing like gangbusters. Can you give us a road map? What you can expect from Coach? >>Look, I've never seen growth like this. I mean, I joined specifically to look at a lot of the cloud, and the file on Object service is and, you know, obviously have a background in backup data protection as well. I haven't seen growth like this since my old days when I was a nice guy. Started in, like, Isil on back in the, you know, way, way old days, this is This is you know, I can't give you exact numbers, but I'll tell you, it's way in the triple digits. And I mean and it's extraordinarily fast to see from an an azure perspective. We're seeing, you know, close to triple digit growth as Well, so I love it. I mean, I'm just extraordinarily excited. All right, >>on the product side, Give us a little bit of a look forward as to what we should be expecting from cohesive. >>Absolutely so from a look forward perspective. As I said, we protect a lot of on premise workloads, and now and we protect, obviously, as your work clothes as well. So we protect observe e ems. But as we think about some of the azure native service is like sequel in other service is that air kind of built native within a azure. We'll extend our application to be able to actually do that as well will extend kind of the ease of use and the deployment models to make it easier for customers to go on, deploy and manage. It really seems like a seamless single pane of glass, right? So when you're looking at Cory City, you should think of it as even if it's in the cloud or if it's on premise. It looks the same to you, which is great. If I want to do search and index, I can do it across the cloud, and I can do it across the on Prem so that integration is really what ties it together makes it extraordinarily interesting. >>Finally, this is this is not your first ignite. I'm interested to hear your impressions of this conference, what you're hearing from customers. What your conversations that you're having. >>You know, it's a lot of fun. I've been walking around the partner booths over here to see, like, you know, who could we partner with? That's more of those data management service is because we don't think of ourselves again. You know, we started kind of in the backup space. We have an extraordinarily scalable storage infrastructure. I was blown away by the capabilities of the file. An object. I mean, I was as a foul guy for a long time. It was unbelievable. But when you start to add those data management capabilities on top of that so that people could either, you know, again, either your point, make sure that they can detect threats and vulnerabilities are you find what they're looking for or be able to run analytics, for example, right on the box. I mean, I've been asked to do that for so long, and it's finally happening. It's like It's a dream >>come true, Jerry. Now >>everything you ever wanted software defined bringing the applications to the data. It's just like, if I could ever say like, Hey, if I could take all of the things that I always wanted a previous companies that put him together it's cohesive. I'm looking around here and I'm seeing a lot of great technology that we can go and integrate with >>Great. Well, David, No, I Thank you so much for coming on the Cube. >>Thank you very much. I appreciate it. >>I'm Rebecca Knight, First Amendment. You are watching the Cube.

(upbeat music) >> Live, from Las Vegas, it's theCUBE, covering UiPath Forward Americas 2019. Brought to you by UiPath. >> Welcome back to Las Vegas, everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events and we extract the signal from the noise. This is day two of UiPath Forward III, the third North American conference that UiPath-- The rocket ship that is UiPath. Clemmie Malley is here. She's the Enterprise RPA Center of Excellence Lead at NextEra Energy. Welcome. Great to have you. And Mariesa Coughanour, who is the Managing Principal of Intelligent Automation and Technology at Cognizant. Nice to see you guys. >> Nice seeing you. >> Nice to see you. >> Thanks for coming on. How's the show going for you? >> It's been great so far. >> Yes. >> It's been awesome. >> Have you been to multiple... >> This is my third. >> Yep. >> Really? Okay, great. How does this compare? >> It has changed significantly in three years, so. It was very small in New York in 2017 and even last year grew, but now it's a two-year event taking over. >> Yeah, last year Miami was-- >> I don't know. >> It was nice. >> Definitely smaller than this, but it was happening. Kind of hip vibe. We're here in Vegas, everybody loves to be in Vegas. CUBE comes to Vegas a lot. So tell me more about your role at NextEra Energy. But let's start with the company. You guys are multi billion, many, many, tens of billions, probably close to $20 billion energy firm. Really dynamic industry. >> Yeah, so NextEra Energy is actually an awesome company, right? So we're the world's largest in clean renewable energy. So with wind and solar, really, and we also have Florida Power and Light, which is one of the child companies to NextEra as a parent, which is headquartered out of Florida. So it's usually the regulated side of power in the state of Florida. >> We know those guys. We've actually done some work with Florida Power and Light. Cool people down there. And we heard, one of the keynotes today, Craig LeClaire, was saying, "Yeah, the Center of Excellence, "that's actually maybe asking too much." But there are a lot of folks here that are sort of involved in a COE and that's kind of your role. But I was surprised to hear him say that. I don't know if you were in the keynote this morning, but was it a challenge to get a Center of Excellence? What is that all about? >> So I think there's a little bit of caution around doing it initially. People are very aggressive. And we actually learned from this story. So when we started, it was more about showing value, building as many automations as possible. We didn't really care about having a COE. The COE just happened to form. >> Okay. >> Because we found out we needed some level of governance and control around what we were doing. But now that I look back on it, it's really instrumental to making sure we have the success. So whether you do a hybrid development to automation, which you can have citizen development, or you're fully centralized, I think having the strong COE to have that core governance model and control and process is important. >> Mariesa, so your title is not, there's not RPA in your title, right? RPA is too narrow, right? >> Yeah. >> In your business you're trying to help transform companies, it's all about automation. But maybe explain a little bit about your practice and your role. >> Sure, so Cognizant's been on the automation journey now for three years. We started back in 2014 and right out the gate it was all about intelligent automation, just not RPA. Because we knew to be able to do end-to-end solutions you would need multiple technologies to really get the job done and get the outcomes they wanted. So we sit now, over 2,500 folks at our practice, going out, working cross-industry, cross-regions to be able to work with people like Clemmie to put in their program. And we've even added some stuff recently. A lot of it actually inspired by NextEra. And we have an advisory team now. And our whole job is to go in and help people unstuck their programs, for lack of a better way to say it. Help them think about, how do you put that foundation? Get a little bit stronger and actually enable scale, and putting in all this technology to get outcomes? Versus just focusing on just the pure play RPA, which a lot of people struggle to gain the benefits from. >> So Clemmie, what leads you to the decision to bring in an outside firm like Cognizant? What's that discussion like internally? >> So, I'll just give you a little bit of backstory, because I think that's interesting, as well. When we started playing with RPA in late 2016, early 2017, we knew that we wanted to do a lot of things in-house, but in order to have a flex model and really develop automations across the company, we needed to have a partner. And we wanted them to focus more on delivery, so developing, and then partner with us to give us some best practices, things that we could do better. When we founded the COE we knew what we wanted to do. So we actually had two other partners before we went with Cognizant, and that was a huge challenge for us. We found we were reworking a lot of the code that they gave us. They weren't there to be our partners. They wanted to come and actually do the work for us, instead of enabling us to be successful. And we actually said, "We don't want a partner." And then Cognizant came in and they actually were like, "Let's give you somebody." So we wanted somebody around delivery, because we said, "Okay, now that we centralize, "we have a good foundation, a good model, "we're going to need to focus on scale. "So how do we do that? "We need a flex model." So Cognizant came in and they said, "Well, we're going to offer you a delivery lead "to help focus on making sure "you get the automations out the door." Well, Mariesa actually showed up, which was one of the best hidden surprises that we received. And she really just came in, learned the company, learned our culture, and was able to say, "Okay, here's some guidance. "What can you instill? "What can you bring?" Tracking, and start capturing the outcomes that she's mentioned. And I know that was a little bit more, but it's been quite a journey. >> No, it's really good, back up. So Mariesa, I'm hearing from Clemmie that you were willing to teach these guys how to fish, as opposed to just perpetual, hourly, daily rate billing. >> Yep. And that's really what our belief is. We can go in, and yes, we can augment, from resourcing perspective, help them deliver, develop, support everything, which we do. And we work with Clemmie and others to do that. But what's really important to get to scale was how do we teach them how to go do this? Because if you're going to really embed this type of automation culture and mindset, you have to teach people how to do it. It's not about just leaning on me. I needed to help Clemmie. I need to help her team, and also their leadership and their employees. On how do you identify opportunities, and how then do you make these things actually work and run? >> So you really understand the organization. Clemmie was saying you learned the culture. >> Yeah. >> So you're not just a salesperson going in and hanging out in theCUBE. So you're kind of an extension, really, of the staff. So, either of you, if you can explain to me sort of, where RPA fits into this broader vision. That would really be helpful. >> Sure, so maybe I can kick a little bit off from what I'm seeing from clients like Clemmie, and also other customers. So what you'll find is RPA tends to be like this gateway. It's the stepping stone to all things automation. Because folks in the business, they really understand it. It's rule-based, right? It's a game of Simon Says, in some ways, when you first get this going. And then after that, it's enabling the other technology and looking at, "Look, if I want to go end-to-end, "what do I need to get the job done? "What do I need around data intake? "How do I have the right framework "to pick the right OCR tool, "or put analytics on, "or machine learning?" Because there's so much out there today and you need to have the stuff that's right-fit to come in. And so it's really about looking at what's that company strategy? And then looking at this as a tool set. And how to use these tools to go and get the job done. And that's what we were doing a lot with Clemmie and team when we sat down. They have a steering committee that's chaired by their CIO, Chief Accounting Officer, and senior leaders from every business unit across their enterprise. >> So you mentioned scaling. >> Yep. >> We heard today in the predictions segment that we're going to move from snowflake to snowball. And so I would think for scaling it's important to identify reusable components. And so how have you, how has that played out for you? And how's the scaling going? >> Yeah, so that's been one really cool component that we've built out in the COE. So I had my team actually vote on a name and we said, "We want to go after reusable components." They decided to call them Microbots. So it's a cool little term that we coined. >> That's cool. >> And our CIO and CAO actually talk about them frequently. "How are our Microbots? "How many do we have? "What are they doing?" So it's pretty catchy. But what it's really enabled us is to build these reusable snippets of code that are specific to how we perform as a company that we can plug and play and reduce our cycle time. So we've actually reduced our cycle time by over 50%. And reusable components is one of the major key components. >> So how do you share those components? Are they available in some kind of internal marketplace? And how do you train people to actually know what to apply where? >> Right. So because we're centralized, it's a little bit easier, right? We have a stored repository, where they're available. We document them-- >> And it's the COE-- Sorry to interrupt. It's the COE's responsibility, and-- >> Exactly. So the COE has it. We're actually working with Cognizant right now to figure out how can we document those further, right? And UiPath. There's a lot of cool tools that were introduced this week. So I think we're definitely going to be leveraging from them. But the ability to really show what they are, make them available, and we're doing all of that internally right now. Probably a little manual. So it'll be great to have that available. >> So Amazon has this cool concept they call working backwards documents. I don't know if you ever heard this. But what they do is they basically write the press release, thinking five years in advance. This is how they started AWS, they actually wrote. This is what we want, and then they work backwards from there. So my question is around engineering outcomes. Can you engineer outcomes, and is that how you were thinking about this? Or is it just too many unknown parts of the process that you can't predict? >> So I think one of the things that we did was we did think about, "What do we want to achieve with this?" So one of the big programs that Clemmie and the team have is also around accelerate. And their key initiatives to drive, whether it's improve customer experience, more efficiencies of certain processes across the company. And so we looked at that first, and said, "Okay, how do we enable that?" That's a top strategy driven by their CEO. And even when we prioritize all the work, we actually build a model for them. So that it's objective. So if any opportunities that come in align to those key outcomes that the company's striving for, they can prioritize first to be worked on. I actually also think this is where this is all going. Everyone focuses today on these automation COEs and automation teams, but what you will see, and this is happening at NextEra, and all the places we're starting to see this scale, is you end up with this outcomes management office. This is a core nucleus of a team that is automation, there's IT at the table, there's this lean quality mindset at the table, and they're actually looking at opportunities and saying, "All right, this one's yours. "This one's yours and then I'll pick up from you." And it's driving, then, the right outcomes for the organization versus just saying, "I have a hammer, I'm going to go find a nail," which sometimes happens. >> Right, oh, for sure. And it may be a fine nail to hit, but it might not be the most strategic-- >> Exactly. >> Or the most valuable. So what are some examples of areas that you're most excited about? Where you've applied automation and have given a business outcome that's been successful? >> Yeah, so we are an energy company. And we've had a lot of really awesome brainstorming sessions that we've held with UiPath and Cognizant. And a couple of key ones that have come out of it, really around storm season is big for us in the state of Florida. And making sure that our critical infrastructure is available. So our nursing homes, our hospitals, and so on. So we've actually built automations that help us to ping and make sure that they're available, so that we can stay proactive, right? There's also a cool use-case around, really, the intelligent automations space. So our linemen in their trucks are saying, "Hey, we spend a lot of time having to log on the computer, "log our tickets, "and then we have to turn our computers off, "drive to the next site, "and we're not able to restore as much power "or resolve issues as quickly as possible." So we said, "How can we enable them?" Speech recognition, where they can talk to it, it can log a ticket for them on their behalf. So it's pretty exciting. >> So that's kind of an interesting example. Where RPA, in and of itself's not going to solve that problem, right, but speech recognition-- >> It's a combination. >> So you got to bring in other technology, so using, what, some NLP capability, or? >> Yeah, so that's one we're currently working on. But yes, you would need some type of cognitive speech recognition, and. >> So you sort of playing around with that in R&D right now? The speech [Mumbles]. >> Yeah. >> Which, as you know, is not perfect, right? >> It is not. >> Talk to us. We know about it all. Because we transcribe every word that's said on theCUBE. And so, there's some good ones and there's some not so good ones. And they're getting better, though. They're getting better. And that's going to be kind of commodity shortly. You really need just good enough, right? I mean, is that true? Or do you need near perfect? >> So I think there's a happy medium. It depends on what you're trying to do. In this case we're logging tickets, so there might be some variability that you can have. But I will say, so NextEra is really focused on energy, but they're also trying to set themselves apart. So they're trying to focus on innovation, as well. So this is a lot of the areas that they're focusing on: the machine learning, and the processing, and we even have chat bots that they're coining and branding internally, so it's pretty exciting. >> So NextEra is, are you entirely new energy? Is that right? No fossil fuels, or? >> So it's all clean energy, yes. Across the enterprise. >> Awesome. How's that going? Obviously you guys are very successful, but, I mean, what's kind of happening in the energy business today? You're sort of seeing a resurgence in oil, right, but? >> Yeah, so I think we had a really good boom. A couple years ago there were a lot of tax credits that we were able to grow that side of our company. And it enabled us to really pivot to be the clean energy that we are. >> I mean, that's key, right? I mean, United States, we want to lead in clean energy. And I'm not sure we are. I mean, like you say, there was tax incentives and credits that sort of drove a lot of innovation, but am I correct? You see countries outside the U.S., really, maybe leaning in harder. I mean, obviously we got NextEra, but. >> I mean, I think there's definitely competition out there. We're focused on trying to be, maybe not the best, but compete with the best. We're also trying to focus on what's next, right? So be proactive, and grow the company in a multitude of ways. Maybe even outside the energy sector, just to make sure that we can compete. But really what we're focused on is the clean renewables, so. >> That's awesome. I mean, as a country we need this, and it's great to have organizations like yours. Mariesa, I'll give you the final word. Kind of, the landscape of automation. What inning are we in? Baseball analogy. Or how far can this thing go? And what's your sort of, as you pull out the binoculars, maybe not the telescope, but the binoculars, where do you see it going? >> I think there's a lot of runway left. So if you look at a lot of the research out there today, I heard today, 10% was quoted by one person. I heard 13% quoted from HFS around where are we at on scale from an RPA perspective? And that's just RPA. >> Yeah. >> So that means there's still so much out there to still go and look at and be able to make an impact. But if you look, there's also a lot of runway on this intelligent automation. And that's where, I think, we have to shift the focus. You're seeing it now, at these conferences. That you're starting to see people talk about, "How do I integrate? "How do I actually think about connecting the dots "to get bigger and broader outcomes for an organization?" and I think that's where we're going to shift to, is talking about how do we bring together multiple technologies to be able to go and get these end-to-end solutions for customers? And ultimately go, what we were talking a little bit about before, on outcome-focused for an organization. Not talking about just, "How do I go do AI? "How do I go put a bot in?" But, "I want to choose this outcome for my customer. "I need to grow the top line. "I'm getting this feedback." Or even internally, "I want to get more efficient so I can deliver." And focus there, and then what we'll do is find the right tools to be able to move all that forward. >> It's interesting. We're out of time, but you think about, it's somewhat surprising when people hear what you just said, Mariesa, because people think, "Wow, we've had all this technology for 50 years. "Haven't we automated everything?" Well, Daniel Dines, last night, put forth the premise that all this technology's actually creating inefficiencies and somewhat creating the problem. So technology's kind of got us into the problem. We'll see if technology can get us out. All right? Thanks, you guys, for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you for having us. >> You're welcome. >> Thanks. >> All right, keep it right there, everybody. We'll be right back with our next guest right after this short break. UiPath Forward III from Las Vegas. You're watching theCUBE. (electronic music)

produced from the cube studios this is strong by science in-depth conversations about science based training sports performance and all things health and wellness here's your host max smart [Music] [Applause] [Music] all right thank you guys tune in today I have the one and only Dean of big data the man the myth the legend bill Schwarz oh also my dad is the CTO of Hitachi van Tara and IOC in analytics he has a very interesting background because he is the well he's known as the Dean of big data but also the king of the court and all things basketball related when it comes to our household and unlike most people in the data world and I want to say most as an umbrella term but a some big bill has an illustrious sports career playing at Coe College the Harvard of the Midwest my alma mater as well but I think having that background of not just being computer science but where you have multiple disciplines involved when it comes to your jazz career you had basketball career you have obviously the career Iran now all that plays a huge role in being able to interpret and take multiple domains and put it into one so thank you for being here dad yeah thanks max that's a great introduction I rep reciate that no it's it's wonderful to have you and for our listeners who are not aware bill is referring him is Bill like my dad but I call my dad the whole time is gonna drive me crazy bill has a mind that thinks not like most so he he sees things he thinks about it not just in terms of the single I guess trajectory that could be taken but the multiple domains that can go so both vertically and horizontally and when we talk about data data is something so commonly brought up in sports so commonly drop in performance and athletic development big data is probably one of the biggest guess catchphrases or hot words or sayings that people have nowadays but doesn't always have a lot of meaning to it because a lot of times we get the word big data and then we don't have action out of big data and bill specialty is not just big data but it's giving action out of big data with that going forward I think a lot of this talk to be talking about how to utilize Big Data how do you guys data in general how to organize it how to put yourself in a situation to get actionable insights and so just to start it off Becky talked a little bit on your background some of the things you've done and how you develop the insights that you have thanks max I have kind of a very nos a deep background but I've been doing data analytics a long time and I was very fortunate one of those you know Forrest Gump moments in life where in the late 1980s I was involved in a project at Procter & Gamble I ran the project where we brought in Walmart's point of sales data for the first time into a what we would now call a data warehouse and for many of this became the launching point of the data warehouse bi marketplace and we can trace the effect the origins of many of the BI players to that project at Procter & Gamble in 87 and 88 and I spent a big chunk of my life just a big believer in business intelligence and data warehousing and trying to amass data together and trying to use that data to report on what's going on and writing insights and I did that for 20 25 years of my life until as you probably remember max I was recruited out Business Objects where I was the vice president of analytic applications I was recruited out of there by Yahoo and Yahoo had a very interesting problem which is they needed to build analytics for their advertisers to help those advertisers to optimize or spend across the Yahoo ad network and what I learned there in fact what I unlearned there was that everything that I had learned about bi and data warehouse and how you constructed data warehouses how you were so schema centric how everything was evolved around tabular data at Yahoo there was an entirely different approach the of my first introduction to Hadoop and the concept of a data Lake that was my first real introduction into data science and how to do predictive analytics and prescriptive analytics and in fact it was it was such a huge change for me that I was I was asked to come back to the TD WI data world Institute right was teaching for many years and I was asked to do a keynote after being at Yahoo for a year or so to share sort of what were the observations what did I learn and I remember I stood up there in front of about 600 people and I started my presentation by saying everything I've taught you the past 20 years is wrong and it was well I didn't get invited back for 10 years so that probably tells you something but it was really about unlearning a lot about what I had learned before and probably max one of the things that was most one of the aha moments for me was bi was very focused on understanding the questions that people were trying to ask an answer davus science is about us to understand the decisions they're trying to take action on questions by their very nature our informative but decisions are actionable and so what we did at Yahoo in order to really drive the help our advertisers optimize your spend across the Yahoo ad network is we focus on identifying the decisions the media planners and buyers and the campaign managers had to make around running a campaign know what what how much money to allocate to what sides how much how many conversions do I want how many impressions do I want so all the decisions we built predictive analytics around so that we can deliver prescriptive actions to these two classes of stakeholders the media planners and buyers and the campaign managers who had no aspirations about being analysts they're trying to be the best digital marketing executives or you know or people they could possibly be they didn't want to be analysts so and that sort of leads me to where I am today and my my teaching my books my blogs everything I do is very much around how do we take data and analytics and help organizations become more effective so everything I've done since then the books I've written the teaching I do with University of San Francisco and next week at the National University of Ireland and Galway and all the clients I work with is really how do we take data and analytics and help organizations become more effective at driving the decisions that optimize their business and their operational models it's really about decisions and how do we leverage data and analytics to drive those decisions so what would how would you define the difference between a question that someone's trying to answer versus a decision but they're trying to be better informed on so here's what I'd put it I call it the Sam test I am and that is it strategic is it actionable is it material and so you can ask questions that are provocative but you might not fast questions that are strategic to the problems you're trying to solve you may not be able to ask questions that are actionable in a sense you know what to do and you don't necessarily ask questions that are material in the sense that the value of that question is greater than the cost of answering that question right and so if I think about the Sam test when I apply it to data science and decisions when I start mining the data so I know what decisions are most important I'm going through a process to identify to validate the value and prioritize those decisions right I understand what decisions are most important now when I start to dig through the data all this structured unstructured data across a number different data sources I'm looking for I'm trying to codify patterns and relationships buried in that data and I'm applying the Sam test is that against those insights is it strategic to the problem I'm trying to solve can I actually act on it and is it material in the sense that it's it's it's more valuable to act than it is to create the action around it so that's the to me that big difference is by their very nature decisions are actually trying to make a decision I'm going to take an action questions by their nature are informative interesting they could be very provocative you know questions have an important role but ultimately questions do not necessarily lead to actions so if I'm a a sport coach I'm writing a professional basketball team some of the decisions I'm trying to make are I'm deciding on what program best develops my players what metrics will help me decide who the best prospect is is that the right way of looking at it yeah so we did an exercise at at USF too to have the students go through an exercise - what question what decisions does Steve Kerr need to make over the next two games he's playing right and we go through an exercise of the identifying especially in game decisions exercise routes oh no how often are you gonna play somebody no how long are they gonna play what are the right combinations what are the kind of offensive plays that you're gonna try to run so there's a know a bunch of decisions that Steve Kerr is coach of the Warriors for example needs to make in the game to not only try to win the game but to also minimize wear and tear on his players and by the way that's a really good point to think about the decisions good decisions are always a conflict of other ideas right win the game while minimizing wear and tear on my players right there's there are there are all the important decisions in life have two three or four different variables that may not be exactly the same which is by this is where data science comes in the data science is going to look across those three or four very other metrics against what you're going to measure success and try to figure out what's the right balance of those given the situation I'm in so if going back to the decision about about playing time well think about all the data you might want to look at in order to optimize that so when's the next game how far are they in this in this in the season where do they currently sit ranking wise how many minutes per game has player X been playing looking over the past few years what's there you know what's their maximum point so there's there's a there's not a lot of decisions that people are trying to make and by the way the beauty of the decisions is the decisions really haven't changed in years right what's changed is not the decisions it's the answers and the answers have changed because we have this great bound of data available to us in game performance health data you know all DNA data all kinds of other data and then we have all these great advanced analytic techniques now neural networks and unstructured supervised machine learning on right all this great technology now that can help us to uncover those relationships and patterns that are buried in the data that we can use to help individualize those decisions one last point there the point there to me at the end when when people talk about Big Data they get fixated on the big part the volume part it's not the volume of big data that I'm going to monetize it's the granularity and what I mean by that is I now have the ability to build very detailed profiles going back to our basketball example I can build a very detailed performance profile on every one of my players so for every one of the players on the Warriors team I can build a very detailed profile it the details out you know what's their optimal playing time you know how much time should they spend before a break on the feet on the on the on the court right what are the right combinations of players in order to generate the most offense or the best defense I can build these very detailed individual profiles and then I can start mission together to find the right combination so when we talk about big it's not the volume it's interesting it's the granularity gotcha and what's interesting from my world is so when you're dealing with marketing and business a lot of that when you're developing whether it be a company that you're trying to find more out about your customers or your startup trying to learn about what product you should develop there's tons of unknowns and a lot of big data from my understanding it can help you better understand some patterns within customers how to market you know in your book you talk about oh we need to increase sales at Chipotle because we understand X Y & Z our current around us now in the sports science world we have our friend called science and science has helped us early identify certain metrics that are very important and correlated to different physiological outcomes so it almost gives us a shortcut because in the big data world especially when you're dealing with the data that you guys are dealing with and trying to understand customer decisions each customer is individual and you're trying to compile all together to find patterns no one's doing science on that right it's not like a lab work where someone is understanding muscle protein synthesis and the amount of nutrients you need to recover from it so in my position I have all these pillars that maybe exist already where I can begin my search there's still a bunch of unknowns with that kind of environment do you take a different approach or do you still go with the I guess large encompassing and collect everything you can and siphon after maybe I'm totally wrong I'll let you take it away no that's it's a it's a good question and what's interesting about that max is that the human body is governed by a series of laws we'll say in each me see ology and the things you've talked about physics they have laws humans as buyers you know shoppers travelers we have propensity x' we don't have laws right I have a propensity that I'm gonna try to fly United because I get easier upgrades but I might fly you know Southwest because of schedule or convenience right I have propensity x' I don't have laws so you have laws that work to your advantage what's interesting about laws that they start going into the world of IOT and this concept called digital twins they're governed by laws of physics I have a compressor or a chiller or an engine and it's got a bunch of components in it that have been engineered together and I can actually apply the laws I can actually run simulations against my digital twins to understand exactly when is something likely to break what's the remaining useful life in that product what's the severity of the the maintenance I need to do on that so the human body unlike the human psyche is governed by laws human behaviors are really hard right and we move the las vegas is built on the fact that human behaviors are so flawed but body mate but bat body physics like the physics that run these devices you can actually build models and one simulation to figure out exactly how you know what's the wear and tear and what's the extensibility of what you can operate in gotcha yeah so that's when from our world you start looking at subsystems and you say okay this is your muscular system this is your autonomic nervous system this is your central nervous system these are ways that we can begin to measure it and then we can wrote a blog on this that's a stress response model where you understand these systems and their inferences for the most part and then you apply a stress and you see how the body responds and even you determine okay well if I know the body I can only respond in a certain number of ways it's either compensatory it's gonna be you know returning to baseline and by the mal adaptation but there's only so many ways when you look at a cell at the individual level that that cell can actually respond and it's the aggregation of all these cellular responses that end up and manifest in a change in a subsystem and that subsystem can be measured inferential II through certain technology that we have but I also think at the same time we make a huge leap and that leap is the word inference right we're making an assumption and sometimes those assumptions are very dangerous and they lead to because that assumptions unknown and we're wrong on it then we kind of sway and missed a little bit on our whole projection so I like the idea of looking at patterns and look at the probabilistic nature of it and I'm actually kind of recently change my view a little bit from my room first I talked about this I was much more hardwired and laws but I think it's a law but maybe a law with some level of variation or standard deviation and it we have guardrails instead so that's kind of how I think about it personally is that something that you say that's on the right track for that or how would you approach it yeah actually there's a lot of similarities max so your description of the human body made up of subsystems when we talk to organizations about things like smart cities or smart malls or smart hospitals a smart city is comprised of a it's made up of a series of subsystems right I've got subsystems regarding water and wastewater traffic safety you know local development things like this look there's a bunch of subsystems that make a city work and each of those subsystems is comprised of a series of decisions or clusters of decisions with equal use cases around what you're trying to optimize so if I'm trying to improve traffic flow if one of my subsystems is practically flow there are a bunch of use cases there about where do I do maintenance where do I expand the roads you know where do I put HOV lanes right so and so you start taking apart the smart city into the subsystems and then know the subsystems are comprised of use cases that puts you into really good position now here's something we did recently with a client who is trying to think about building the theme park of the future and how do we make certain that we really have a holistic view of the use cases that I need to go after it's really easy to identify the use cases within your own four walls but digital transformation in particular happens outside the four walls of an organization and so what we what we're doing is a process where we're building journey maps for all their key stakeholders so you've got a journey map for a customer you have a journey map for operations you have a journey map for partners and such so you you build these journey maps and you start thinking about for example I'm a theme park and at some point in time my guest / customer is going to have a pity they want to go do something you want to go on vacation at that point in time that theme park is competing against not only all the other theme parks but it's competing against major league baseball who's got things it's competing against you know going to the beach in Sanibel Island just hanging around right there they're competing at that point and if they only start engaging the customer when the customers actually contacted them they must a huge part of the market they made you miss a huge chance to influence that person's agenda and so one of the things that think about I don't know how this applies to your space max but as we started thinking about smart entities we use design thinking and customer journey match there's a way to make certain that we're not fooling ourselves by only looking within the four walls of our organization that we're knocking those walls down making them very forest and we're looking at what happens before somebody engages it with us and even afterwards so again going back to the theme park example once they leave the theme park they're probably posting on social media what kind of fun they had or fun they didn't have they're probably making plans for next year they're talking to friends and other things so there's there's a bunch of stuff we're gonna call it afterglow that happens after event that you want to make certain that you're in part of influencing that so again I don't know how when you combined the data science of use cases and decisions with design thinking of journey Maps what that might mean to do that your business but for us in thinking about smart cities it's opened up all kinds of possibilities and most importantly for our customers it's opened up all kinds of new areas where they can create new sources of value so anyone listening to this need to understand that when the word client or customer is used it can be substituted for athlete and what I think is really important is that when we hear you talk about your the the amount of infrastructure you do for an idea when you approach a situation is something that sports science for in my opinion especially across multiple domains it's truly lacking what happens is we get a piece of technology and someone says go do science while you're taking the approach of let's actually think out what we're doing beforehand let's determine our key performance indicators let's understand maybe the journey that this piece of technology is going to take with the athlete or how the athletes going to interact with this piece of technology throughout their four years if you're in the private sector right that afterglow effect might be something that you refer to as a client retention and their ability to come back over and over and spread your own word for you if you're in the sector with student athletes maybe it's those athletes talking highly about your program to help with recruiting and understanding that developing athletes is going to help you know make that college more enticing to go to or that program or that organization but what really stood out was the fact that you have this infrastructure built beforehand and the example I give I spoke with a good number of organizations and teams about data utilization is that if if you're to all of a sudden be dropped in the middle of the woods and someone says go build a cabin now how was it a giant forest I could use as much wood as I want I could just keep chopping down trees until I had something that had with a shelter of some sort right even I could probably do that well if someone said you know what you have three trees to cut down to make a cabin you could become very efficient and you're going to think about each chop in each piece of wood and how it's going to be used and your interaction with that wood and conjunction with that woods interaction with yourself and so when we start looking at athlete development and we're looking at client retention or we're looking at general health and wellness it's not just oh this is a great idea right we want to make the world's greatest theme park and we want to make the world's greatest training facility but what infrastructure and steps you need to take and you said stakeholders so what individuals am i working with am I talking with the physical therapist am i talking with the athletic trainer am I talking with the skill coach how does the skill coach want the data presented to them maybe that's different than how the athletic trainer is going to have a day to present it to them maybe the sport coach doesn't want to see the data unless something a red flag comes up so now you have all these different entities just like how you're talking about developing this customer journey throughout the theme park and making sure that they have a you know an experience that's memorable and causes an afterglow and really gives that experience meaning how can we now take data and apply it in the same way so we get the most value like you said on the granular aspect of data and really turn that into something valuable max you said something really important and one of the things that let me share one of many horror stories that that that comes up in my daily life which is somebody walking up to me and saying hey I got a client here's their data you know go do some science on it like well well what the heck right so when we created this thing called the hypothesis development canvas our sales teams hate it or do the time our data science teams love it because we do all this pre work we just say we make sure we understand the problem we're going after the decision they're trying to make the KPI is it's what you're going to measure success in progress what are they the operational and financial business benefits what are the data sources we want to consider here's something by the way that's it's important that maybe I wish Boeing would have thought more about which is what are the costs of false positives and false negatives right do you really understand where your risks points are and the reason why false positive and false negatives are really important in data science because data size is making predictions and by virtue of making predictions we are never 100% certain that's right or not predictions hath me built on I'm good enough well when is good enough good enough and a lot of that determination as to when is good enough good enough is really around the cost of false positives and false negatives think about a professional athlete like the false the you know the ramifications of overtraining professional athlete like a Kevin Durant or Steph Curry and they're out for the playoffs as huge financial implications them personally and for the organization so you really need to make sure you understand exactly what's the cost of being wrong and so this hypothesis development canvas is we do a lot of this work before we ever put science to the data that yeah it's it's something that's lacking across not just sports science but many fields and what I mean by that is especially you referred to the hypothesis canvas it's a piece of paper that provides a common language right it's you can sit it out before and for listeners who aren't aware a hypothesis canvas is something bill has worked and developed with his team and it's about 13 different squares and boxes and you can manipulate it based on your own profession and what you're diving into but essentially it goes through the infrastructure that you need to have setup in order for this hypothesis or idea or decision to actually be worth a damn and what I mean by that is that so many times and I hate this but I'm gonna go in a little bit of a rant and I apologize that people think oh I get an idea and they think Thomas Edison all son just had an idea and he made a light bulb Thomas Edison's famous for saying you know I did you know make a light bulb I learned was a 9000 ways to not make a light bulb and what I mean by that is he set an environment that allowed for failure and allowed for learning but what happens often people think oh I have an idea they think the idea comes not just you know in a flash because it always doesn't it might come from some research but they also believe that it comes with legs and it comes with the infrastructure supported around it that's kind of the same way that I see a lot of the data aspect going in regards to our field is that we did an idea we immediately implement and we hope it works as opposed to set up a learning environment that allows you to go okay here's what I think might happen here's my hypothesis here's I'm going to apply it and now if I fail because I have the infrastructure pre mapped out I can look at my infrastructure and say you know what that support beam or that individual box itself was the weak link and we made a mistake here but we can go back and fix it

>> Announcer: Live from Miami Beach, Florida, it's theCUBE covering UiPath Forward Americas, brought to you by UiPath. (upbeat music) >> Welcome back to Miami Beach, everybody. I'm Dave Vellante with Stu Miniman. This is UiPathForward Americas. We're talking about robotic process automation. We're seeing the ascendancy of a new marketplace. You're watching theCUBE, the leader in live tech coverage. Let's see, let's get into it. So Param Kahlon is here. He's the UiPath's Chief Product Officer. Welcome, so we're going to get into some of the product stuff. We haven't really dug down deep today, so that's great. >> Thank you. >> Jairo Quiros is here. He's the Vice President of Global Shared Services, an RPA COE, center of excellence, leader at Equifax. Welcome, thanks for coming on theCUBE. >> Thank you, thank you. >> Jairo, let's start with you. Tell us about your role. I love the title. (Jairo laughs) You got automation in your title. Do people embrace you when they see you coming or run? >> No, no, no. Actually, that's very interesting. I've been with the company for 20 years now, so I'm responsible to lead Global Shared Services all across from business operations, financing, accounting, you name it, IT security, right? So, coming along with automation has been quite a journey for us. First of all, we love the product so thank you, Param for everything you guys do at the service, as well. But truly, automation, what it means to us is pushing our workforce to do stuff that is of more valued added to our customers, removing the but out of the human which is critical to us, so no fear of buts anymore. And it's been two years. >> The product's at the tip of the iceberg, I'm hearing. There's a whole lot of other stuff beneath it, culture, obviously process, mindset. >> Jairo: Yeah, correct. >> We will get into some of that. But Param, tell us about your role as Chief Product Officer. You make it all happen. (Param laughs) >> I'm responsible for making sure we can listen to what our customers want, what the market wants, translate that into requirements, and deliver that in the form of products. That's all I do, it's very simple. >> You're a translator. >> We translate it, transform it into requirements that can be given to the product team, their development team that can go write software for it. >> Kind of like that AI layer in UiPath that translates all this data into something that's actionable, right? >> Param: Absolutely. >> Jairo, you were saying you liked the product before. I mean, our personal experience is we could actually download it and play with it, and we're not ultra technical, some of our guys are. What do you like about the product? >> Well, I think many things. I mean, first of all, I think it's very easy to use, right? So, it's built for execution, right? For instance, in our case, we're having a lot of junior engineers coming on board. So we go out to colleges and recruit people that are passionate about process. So what UiPath offer us is a way for them to entry our operation and actually perform tasks and do, and realize results pretty easily. So then, they can see the work being done and appreciate it. >> So who are the users in your organization? Is it a spectrum? You got the sort of RPA developers and then you got business users, as well? Describe that. >> Well, it's a combination, right? So we built the COE over the past couple years. It's inclusive of not only configurators, but also analysts and people that can understand the business. So when you look at through the process, start thinking about how do you design for automation? So this tool allows a very comprehensive very easy to use and we see they make progress release after release, so it's very exciting. >> Alright, Param, why don't you walk us through the announcements that you made? What's new to the platform? Some enhancement to the community. >> Yeah, so we've done some really key announcements in this event today. The first one that we're very excited about is UiPath Go, which is our marketplace that enables broad innovation across our entire ecosystem of customers and partners. We can create on a platform or we can put it in a marketplace and then everybody else can easily access the innovation that's available there. We also released 2018.3 which is the third release we've done this year, but probably the most comprehensive release that we've done 'til date in the history of Enterprise Automation. So we're very excited about launching that release today, as well. And third, we've announced a $20 million fund that will fund our partners that will co-innovate together with us in bringing out new RPA capabilities, new machine learning and AI capabilities into the marketplace. Those are three key announcements. >> What are the-- >> I'm just-- Sorry, but from my understanding, you run on a quarterly cadence for the release of the primary product, correct? >> We're in a quarterly cadence, yes. >> What are the critical aspects of the new release? >> So, there's a few things we've done in the main release. One of the first things we've done is we've allowed for re-usability of the software. So if you're using a lot of components, if you've built a way to automate a certain process, it could be as simple as, here's how I log into a application, a financial application. The rest of the people in my organization don't have to go reinvent that thing themselves. They can reuse the component, the way I've built it, so they can be reused to process every single aspect of the customer, as well. We've made it very easy for our customers to upgrade to new versions of the software, as we're releasing very rapidly, we want to make sure that the upgrades are easy, but the upgrades are also seamless as in they don't affect any of the existing processes that are running in production. So we support version management and package management so we make it easier for people to manage that. There's some other capabilities that we've done. We've supported internationalization of the platform, so now customers in Japan can use our product in Japanese, customers can use it in Spanish, they can use it in Deutsche, German, so we've allowed that in this release, as well. Another cool thing we've done is allowing humans to provide input to what the robots need to do by putting a form that they can use to provide input to them, so it can provide a better symbiosis of humans working together with robots to achieve more processes and more automation in the ecosystems. There's a lot of stuff, this is some of the highlights. >> So what do you think? I mean, what of those, what of that compendium is of interest to you? >> I think, you know, I've been a member for a year now, from, of their customer advisory board, so they truly listen to what we need to say, right? Because the robotic aspect of it is critical, but there's so many other aspects, such as the analytics. So, understanding the business outcome, right? What's the bot producing? Not necessarily the bot that's up and running, but really, what's the impact to the business? I think that's part of the feedback that we've been given in UiPath, they're really working hard on that. The other aspect which is important also is how do you move forward from simple RPA to more complex automations? So, the human in the loop approach to things is important. We call that those small black boxes, you know people with 20 years of experience, they understand how to make decisions but those aren't documented, right? So, now we're giving the opportunity for that human to become part of the process, right? So that is very powerful to us. >> So one of the aspects we've been looking at, the marketplace seems interesting. I'm wondering if you've had a chance to look at that, are there things that you would consider using, and anything that you might even consider contributing in the future? >> I think so. I think this is a whole movement, it's a community today, so no matter where you are, developers, they love it. My guys are telling me, "When is this out?" Because, you know, they have I mean, they're so much hungry to get stuff done and to share what they can do, it makes a difference not only for our company, but for the world, right? So it means something. >> That's interesting. Your company's been around for a long time. You're not worried about, I mean, this open mindset is really intriguing to us, you're not worried about putting your IP in there? Or do you feel like, this open community, we're going to get back as much as we give? >> No, of course. Of course, there are controls in place, and of course, there'll be a protocol in place, but you know, at the end, you're making a difference in the world. So if someone wants to, for instance, have a mortgage because they're wanting to buy a house, you want to make it easy, right? At the end, that's the end goal. You know, for EquiFax and for all the institutions that are in the same sector. >> So from a product standpoint, we just have Craig LeClair on, he couldn't directly call out UiPath. It's not cool, right? I mean, he has to be independent. But, look, he wrote the report, UiPath went from third on the list to first on the list, out of I don't know, 10, 15 vendors. It's like the Gardiner magic quadrants, all these rating systems, right? We don't do 'em, but we read them because they're good, and they're informative. He said in there that last year's features have become this year's table stakes. And some of the things that are differentiating companies, and obviously UiPath won so I presume you have the differentiation ears. Analytics and governance. Those are two big areas, I see the heads nodding. Maybe you guys could each talk about that, Jairo let's start with you, why are those things important? You address the analytics, you kind of address governance, as well, but maybe you can summarize. >> I mean, we address governance as the get-go, and it's an evolution. So for instance, you know, really, truly when we're looking into RPA, it's not only so much about a tactical approach to a specific problem, but it's really turned into a strategy, right? So if you want to scale, you need to have the proper controls in place. So, these guys have done an amazing job integrating with tools such as Cyberart, for instance which is reall important for many companies. They're trying to secure their systems and make sure that the bots are operating on their very secure environment. >> So you guys not only you were in the place position, now you're in the lead. Now the pressure's really on. It's like the Red Sox, Stu. (laughs) So, how'd you get there? What is that enables that? Architecture? Mindset? Culture? You know, give us the insights there. >> Yeah, first of all, let's say we're super excited about being in the first place. I think it's really good, it's a really good testament to the hard work the team is putting in there, so we're super excited about that. We believe that our success and the product roadmap depends upon hearing a lot from customers and making sure that we're responding to their customers. So I think that's what we have done for the most part is ensuring that if there are things that our customers need, if there are things that our customers think our platform and technology is moving toward, we're actually doing the kinds of things that'll actually take us there. So a lot of the innovation that we've done on the platform has come from a direct result of engagement and working with customers and bringing their success into there. Specifically, the governance and analytics, those are very important aspects of what we're doing on a product. Most of our customers are very large corporations like Equifax, other corporations. They will not use our technology if we couldn't support the level of governance and compliance that they need from the ability to run those processes, especially when they're running autonomously without having a human look over what's happening. So that was a core part of what we've invested in. Analytics is also something that we've invested but we'll continue to make more investments there. We're now hearing from Equifax and other customers that people don't want to just get analytics that is responding to what the robots are doing but they want to understand what sort of business impact the robots are having on the corporation. So we want to build an analytics platform that is ingesting not just the robot workloads but bringing in information about line of business systems, as well, to be able to give the reports and perspectives that somebody can look at that and say the robots have done so much for me. Not just in terms of number of hours, but in terms of the business outcomes that I've achieved through the work the robots are executing. >> Jairo, I want to ask you about innovation at Equifax. We've observed many times in theCUBE that innovation in the tech industry used to march at the cadence of Moore's Law. Oh, new chip's out! We've got to do, we can now put better, faster data warehouse. You know, more storage, whatever it was. The innovation model is changing dramatically. And we've observed that it's a combination now, it seems, of data plus AI plus cloud, for scale. So, what do you think about that sort of innovation sandwich? Do you buy into it? How are you guys applying innovation in your business? >> I mean, I'll tell you I got a similar question the other day, you know. It's about, you know, I live in Costa Rica, right? So we surf all the time, right? So it's about riding, you know, the wave, right? So it's not about riding it, right? If you don't ride it, then you're going to drop, right? And then you're going to fall behind. >> Dave: You're going to be driftwood. >> So, yeah, innovation is there, you know. It's that demand for all companies. For us, innovating not only about how do we approach customers and consumers and we put them first in everything we do, but in how we operate internally. Creating a culture that drives automation, right? So giving time for people to think about stuff, you know, that makes a difference, right? I think that's how I can summarize innovation as of this moment. >> So, Stu had a question. >> So, if I understand this right now, we can blame the robots if our credit score isn't good enough now, right? (laughs) >> What do you think? Blame the robots, right? >> Blame the robots, always. >> Blame the innocent, as we say. Well, guys, thanks very much for coming to theCUBE. >> Param: Thank you. >> Param and Jairo, it was great to have you, appreciate it. >> Thank you again. >> Alright, keep it right there. Stu and I will be back with our next guest from UiPath Forward Americas. You're watching theCUBE. (upbeat music)

>> Narrator: Live from San Francisco, it's theCUBE covering Samsung Developer Conference 2017, brought to you by Samsung. >> Hello, everyone. Welcome back to theCUBE's live coverage, exclusive coverage of Samsung Developer Conference, SDC 2017. I'm John Furrier, the co-founder of SiliconANGLE Media. Next guest is Patrick Moorhead who is the president and principal analyst at Moor Insights and Strategy, friend of theCUBE. We see him everywhere we go. He's quoted in the Wall Street Journal, New York Times, all the top publications, and today, he was just on Power Lunch on CNBC. Here for our Power Cube segment, welcome to theCUBE. Good to see you again. >> Hey, thanks for being here, and I appreciate you putting up with me heckling you from outside of theCUBE. >> Always great to have you on. Hard hitting, you're one of the best analysts in the business. We know you work hard, we see you at all the events that we go to. I got to get your take, Samsung. Obviously now obviously you run in parallel, at some point on Amazon, obviously winning in the cloud. Samsung downplaying their cloud, but calling about smart things. I get that, the cloud is kind of fragmented, they're trying to hide the ball there, I get that. But they talk about IOT which you got to talk about cloud without IOT, what's your analysis of Samsung? >> Yeah so first off, Samsung is a collection of really really successful stovepiped companies, right? You have displays, you have semiconductors, you have mobile phones, you have all these different areas and they say a lot of times your strength is sometimes your weakness, and the divisions just don't talk a whole lot. But what they did, and this is the first time I've seen this in a long time, is they got on the same page and said you know, we have to work together because IOT and connected and intelligent connectedness can't be done in stovepipes, we can't all go do our thing. So they're agreeing on standards, they're doing some really good stuff. >> And obviously we know from the cloud game now go back to the enterprises, more consumer, backing in from the edge, obviously the edge being devices and other things, I get that. But now the horizontally scalable nature of the cloud is the holy grail, we've seen Amazon's success continue to boom, they do more compute than any other cloud out I think combined. Maybe outside Google with their internal cloud. That horizontal resource pool, serverless as example trend, IOT, you got to have, the stovepipes got to be decimated. However, you need specialism at the application level. >> That's exactly right, and a smartphone will act a little bit differently from a camera which would be different from a refrigerator as we saw, right? Samsung wants the new meeting area to be, well not the new meeting area, we all meet in the kitchen, but the connected meeting area. So they all act differently, so they have to have even though they're different devices they have to connect into that horizontal cloud to make it efficient enough and effective enough for good responsiveness. >> I like the message of smart things, I think that's phenomenal, and I like that 'cause it connects their things, which are consumer things, and people like 'em, like you said very successful stovepipes. The question that I ask here and I try to get the execs to talk about it but they weren't answering yet, and I think it's by design. They're not talking about the data. Because again at the end of the day what's different from Alibaba again last week when I was in China, they are very up front. We're all about data acquisition and using the data to fuel the user experience. >> Right. >> That has to traverse across stovepipes. So is Samsung baked in that area, they have things going on, what's your analysis of data traversal across, is Bixby 2.0 the answer? >> So companies have to take, particularly consumer companies related to the cloud, have to have one or two paths. The one that says, we're not going to mine personal data to either sell you products or run ads, so Facebook, AWS and even Google, that's their business model, and then the other side you have people like Apple who are only going to use the data to make the products and experiences better. I think, I'll just pontificate here, the reason you're not getting a straight answer is I don't think they know exactly what they want to do yet. Because look at the market cap of Facebook. Apple, and even Amazon is planning to start and expand their own ad network. So I just don't think they know yet. Now what I would recommend to them is- >> Or they might not have visibility on it product-wise. So there's knowing what to do, or how to do it, versus the product capability. >> Well they have access to a ton of data, so if you're using Samsung Mail, if you're using, they know every application gets deleted, usage models of those applications. So they know a lot more than I think people think. They have a lot more data than people probably give them credit for. >> So they're going to hide the ball, I think they said that they're buying more time, I would agree with you there. Alright, question on IOT. Do you think that hangs together, that strategy? Obviously security updates to chip-level, that's one thing, can they succeed with IOT in this emerging stovepipe collapse fabric that they're bringing out? >> So I need to do a little bit more research on the security and also their scalability. 'Cause if you're going to connect billions of devices you have to have scalability and we already saw what GE Predix did, right? They did an about-face and partnered up with AWS realizing they just couldn't handle the scale and the complexity. And the second thing is the security model and how things like RM Embed Cloud and the latest announcements from Intel which is how from a gateway perspective you secure this work. So I have to go do some research on this. >> And by the way it's a moving train, you mentioned the GE thing, great example, I mean let's take that example, I got to ask you about cloud, because let's talk about Amazon, Cloud Foundry. Cloud Foundry became this thing and Pivotal tried to take and shape it, now they're claiming huge success, some are questioning the numbers. They're claiming victory on one hand, and I hear record, record, record! But I just don't see any cloud on Cloud Foundry out there. >> Yeah and I think the reason is, PCF, Pivotal Cloud Foundry is a Fortune 500 thing. And if I compare Fortune 500 to startups and other people, there's not nearly as much activity in the Fortune 500 as there is with the startups and the cloud native companies. So I'm optimistic. >> So you're saying Pivotal Cloud is more Fortune 500, less cloud native? >> Exactly, exactly. >> How about Amazon, what's your take, I know you were on Power Lunch kind of, now you're on the Power Cube, our new segment that you just invented by being here. (laughing) What is the Amazon take, 'cause that Reinvent event's coming up, what's the preview? Obviously we're going to have some one on ones with Jassi and the team beforehand, theCUBE will be there with two sets to come on if you're going to be there I'd love to have you on. >> I'd love to. >> Again, what's the preview for AWS Reinvent? >> AWS right, they had a seven-year headstart on almost everybody and then Azure and GCP just recently jumped in, and if you notice over the past year they've been firing canons at each other. One vendor says hey, I do by the minute pricing, and then another one says, oh, I have the by-the-second pricing, right, and I'm going to accept VMWare, oh no I'm not doing VMWare, I'm doing SAP. So what you have now is a feature fest and a fistfight now. AWS is no longer the only man standing here. So what I'm expecting is they are going to come in and make the case that, okay, we still are the best choice not just for IAS but also for PAS, okay? Because they have a lot of competition. And also I think they're going to fill in gaps in some of the regional services where oh they don't have GPUs in a certain country. Oh, I don't have FPGAs over here. I think they're going to fill that in to look better against GCP and Azure. >> I know you cover Intel as well, I was just over there and saw some of the folks there, I saw some of the Linux Foundation folks, obviously you're seeing Intel be more a computing company, not a chip company anymore, they have that Five-G end to end UK Mind and Mobile World Congress, talked a little bit about Five-G. End-to-end is big message here at Samsung, how is Intel positioned in all this, what's your take on Intel? >> Yes so I think related to Intel, I think in some areas they're competitors, because they have their own gateway solutions, they don't have cloud solutions but they have the gateway solutions. Regarding to some of the endpoints, Intel has exited the small cork endpoints in watches, so I would say right now there's less overlap with Intel now. >> From Samsung perspective? >> Exactly, now on the back end it's more than likely there's a 99% chance that the back end doing the cloud processing is going to be Intel. >> If I'm Samsung, why wouldn't I want to partner within Samsung? 'Cause they make their own chips, is that the issue or is it more a...? >> No, I think Samsung up until this point hasn't taken a lot of responsibility for the cloud. So this is a first step, and I think it would make a good partnership. >> And Intel could get the home theater market, the home, how connected home is, but every CES going back 10 years has been a connected home theme. Finally they could get it here. >> That's right, and I have seen Intel get into things, a lot of Amazon's products with the cameras in the bedroom and in the bathroom, scary stuff. But Movidius, silicon that's doing object recognition, that is a place where I think they compete which frankly Samsung could develop the silicon but they just don't have it. Silicon doesn't have capability that a Movidius has. That can be used in any type of camera. >> Okay so final question I know we got to break here and I appreciate you coming on, making room for you, PowerCUBE segment here in San Francisco at SDC 2017. Ecosystem, we hear the host of SDC, Thomas Coe, come up and saying we're going to be honest and transparent to the community here at large in San Francisco and around the globe, kind of incurring that they've been kind of stovepiped and they're going to open up, they believe in open cloud, open IOT, and he talks about ecosystem, I'm not seeing a lot of ecosystem partners around here. What does Samsung need to do to, well first of all, what's your letter grade on the ecosystem and certainly they got an opportunity. What moves should they be making to build a robust healthy ecosystem, because we know you can't do it end to end without support in the white spaces. >> Yeah so I go to a lot of the developer conferences, whether it's Microsoft Build, Apple WWDC, and even the enterprise ones, and this is a smaller, low-key event and I think first and foremost, operating system drives a lot of the ecosystem. And other than Tizen they don't have an operating system. So what they're doing is they're working on the connectedness of it, which is a different kind of ecosystems, it's farther up in the stack, but I think what they can do is they have to be very clear and differentiated and I think back to our earlier, our first conversation, they're not going to mine the data, therefore they're the safe place for you, consumer and our smart things ecosystem, to put your data. And we're going to help you make money to do that, because I don't think Google is as interested in that and I don't think Amazon is as interested in that either. >> They were clear, they said permission-based and even if they don't know what their permission is offering we're going to take the conservative route and protect the data, but they still got to use the data. They got to get their cloud story together, if they want to do the data play, cloud has to be more clear at least in my mind. >> Well I think what they can do is they're sitting on and they will sit on a bigger treasure trove of data that can help their partners deliver better experiences and products, because if you're at the epicenter and you're at that smart things hub? You know everything that's going on in that home whether it's your stuff or your partner's stuff. >> Yeah and they got to be trusted, and they got to be transparent, okay. Patrick Moorhead from Moorhead Insights here on theCUBE, great analyst, follow him everywhere on Twitter, your Twitter handle is, let me just get the Twitter handle. >> It's @patrickmoorhead. >> Okay, @patrickmoorhead on Twitter. He travels the world, gets the data and so does theCUBE, traveling for you, this is John Furrier. More after this short break. (electronic beats)