>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. >>Welcome back to the Bellagio in Las Vegas. Everybody, this is Dave Vellante and we're here day two of UI path forward three. The third North American event is the cubes, second year covering UI path. The rocket ship that is UI path. PDC is here, he's the vice president of AI at UI path and Rudy Coon who is the chief marketing officer and co founder of process gold UI path. Just announced this week, the acquisition of process gold. So Rudy, congratulations and you may as well PD. Thank you. So that's cool. Um, process gold is focused on process mining. You guys may or may not know about them, but really maybe, maybe you cofounded the company. Why did you co-found you and your founders process gold and tell us a little bit about the problems that you're solving. Yeah, right. You know, um, many years ago I started my career with IBM and I used to be a business consultant. >>And typically if you try to implement any kind of technology like RPA, but back then we didn't have the LPA. But if you try to figure out what the real process and the company are and you ask people, please tell me how does the process where it looks like. Usually people cannot tell you. They say yes we have a documentation but it's outdated the moment you print it. So the idea was um, actually I came across process mining more than 10 years ago and I met the guy in, at the university of and he had this bright idea to reconstruct business processes solely based on digital footprints from any kind of it system. I mean, think about it. You, you use SAP, you use any kind of other it systems and you take the data that is left behind after the execution or the support of a process. >>You take it, you push the magic button and you see what the process really is, like an extra races and from business processes. But we, we saw that in the demo at the a analyst event. I thought it was like magic. I mean I think it's actually, I think of a small company like ours easement even though the number of processes we have and the relative complexity and by the way, half the time people aren't following them and but you were able to visualize them. So. So first of all, why did you acquire process gold? What was the thinking there? So you know, just to pop one level up the stack, what exactly are we trying to do as a company? And you are about as we are building this whole new set of platform capabilities, right? We used to have product lines in studio, orchestra and robot, but now when we look at the whole customer journey and all the elements that need to be there in that customer journey, we essentially have to weld something, what I call the operating system called a self improving enterprise. >>And what that means is that our three elements you need to combine. You need to have a measurement system in place, which can quantify the ROI of your automations. Of course you need a really solid RPA platform like ours to do the automation itself, you have to be able to bring in pieces for doing complex stuff, cognitive stuff using AI. And then you need a scientific way of planning those automations using tools like process board because you have to do process mining. Once you complete this, watch your cycle, you can keep doing more and more of the automation. Essentially you're feeding the beast of efficiency in your organizations. So essentially the way this worked, we can't do, don't, don't have the means to do the demo here, but you essentially pointed your system at a process and it visually showed me the steps and laid them out and in great detail. >>Um, and I said, wow, that's like magic. Um, but this stuff actually works. You got no real customers using this if you do. Yeah. Okay. >> So you know, we worked for companies like, like portion Germany, maybe you have heard about them. They, they build cars and they are using process code for part of the production process. Today in today's world, every process, no matter how offensive is a physical process like production or purchasing or whatever it's used or it's supported by it and at least a lot of data behind. And this is exactly that, the goldmine for us. So we extract this data and again, you know, we have a lot of algorithms in the, in the software. It's, it's sort of magic as it is a lot of mathematics, which is magic for me. But um, it works. Yeah, just take the data, you pushed a button and just see the process with all the details. >>As you mentioned, like stupid times, bottlenecks, compliance issues and this three, the, the, the source, you know, if he wants to see the process, you can then decide is it, is this process now suitable for automation or maybe should we first optimize the process and then vote for automation. And this is key for, for RPA. >> Well, I think, you know, I'm talking a lot of customers this week and last year offline as well. A lot of times we'll tell us the mistakes they made is they'll, they'll automate a crappy process. Yup. This presumably allows me to sort of highlight the shine a light on some of the weaknesses and the weak links in the chain. >> So process optimization is a big deal, right? Both in the pre automation phase and in the post automation phase. Once you automated a process, you need to know what are the bad things that are happening there, what are the blockers, what are the nonconforming steps that you're taking? >>So that's in the post automation but also in the pre automation phase where you haven't even decided what exactly are you going to automate. It's really hard to quantify what are the high ROI processes, right? I can go in our bottle, automate something which is not useful at all for the users, right. And so we want our users to a wide making those mistakes. And that's why we are exposing these powerful, powerful set of tools where you can use all these tools to easily document your processes, manage your processes, use process mining to look deeper into how our people and the different entities in your organizations working together. You know, historically if you look at stuff like all of in all of human history, there have been certain processes, but as computers came on and stuff, you look at it on in, in scifi movies, everyone has always, as Rudy says, the X way for the enterprise. >>You always wanted to have this Uber system that can understand everything that we are doing and tell us, you know, how can we improve stuff? Or what can we do better? Because as a species that fuels our evolution. And so this is, it's, it's, it's fundamental to a lot of things that people do in every day and almost in every action that they did. >> So the in the secret sauce is math, right? So again, please, the secret sauce. Yeah, it's math, but you've got to have some kind of discovery engine as well. I mean this is, it's a system. So maybe can you give us a little bit more idea as to what's under the covers? Well, you know, it all starts with data and the data we need in the beginning, it's very, very simple. We need only three different attributes. The first attribute is what we call the case ID. >>So the case ID is a unique identifier for a case and it depends on the process. If we talk, for example, a very simple invoice approved process in the case that it would be the invoice number. When we talk about claims management or with a claims number or a purchase number, whatever the second attribute we need is the timestamp. And every time we find the timestamp in a system like SAP or lock file or database, this time subsume a timestamp actually represents some sort of activity. So we need a case ID, timestamp and activity and solely based on this data we can already show you how the process looks like. And then we enrich this data with other attributes like let's say supplier or invoice amount to give you some more ideas and some statistics. So this is the data we need. We, you know, we transformed this data, we access directly the database. >>So there is no, there's no need to extract the data. We directly access to data and we transform it and then it will be represented in our application. So you get rid of full transparency of what's going on. So when you were a consultant, you mentioned you're a consultant at IBM, you would sit down with a pen and paper and talk to people about what they did. Maybe time and motion studies and studies, you know, you know, this process mapping workshops, everybody comes out and just allows it. So you sit together with people in the room and at the end of the day you have more processes than you have people there. And everybody's telling you a different story and you know exactly that. Not everything is totally true. So a lot of gray area. Yeah. And the maps that you had to build and people simply don't know what the processes are. >>It's not that they don't want to tell you, they simply don't know. Or as I said before, different people have different processes and they don't follow those. There's no standard to follow. She's pretty, what's the vision for how, how process gold fits into UI path. So as a problem was talking about in his keynote, and Daniel talked about this too, um, a lot of our customers came to us, uh, to automate the processes that they already know about for the processes that they don't know about. We have this whole set of tools, the Explorer set of rules that we are releasing. Process world is a part of that. But essentially now you don't need to know what processes to automate. You can use an automated set of tools to do that process scored, as Rudy was talking about, can go in and look at these log files, uh, ordered logs that are generated by your systems of record. >>Um, and then be able to visualize, optimize our process. But the technologies are really complimentary because these guys, uh, used to work in the backend systems. That's why, you know, that's where most of the process mining works works in the back end looking at the audit logs, but you have as has, you know, we have really strong background in understanding the gooey in the front end, uh, understanding of apps, controls and the control flows that the users have using our computer vision technology. When you combine these technologies, there's a magical effect that happens. Like if your backend does not contain the audit, log off some actions that people are taking in the front end. Let's say it's a small application which does not generate that are the, once you combine these two data points, this is one of the first in the industry on the wonderful kind system that can look across all the different spectrum of applications and be able to understand the processes at a deeper level. >>Technically when you make an acquisition, you obviously looking at the technology and how it's going to integrate, how challenging will it be for you to integrate? What have you done any sort of, when you did the due diligence, you know, a lot of companies are really dogmatic about integration. Others frankly aren't that let's buy the company up by another one. What's your philosophy? It >>was kind of a match made in heaven. I remember the first time I talked to Rudy on the phone and uh, you know, are at the end of the day our philosophies aligned like almost a hundred percent because at the end of the day process goal and UI bad is all about that customer obsession, delivering the value to our customers. And the values are saying we want our customers to get out of this mundane tasks to automate the tasks as optimally as possible. And so both the companies, the, the, the outcomes aligned pretty well. Now the mechanics of the integration, um, I think both do. Both the companies are, these aren't you know, dot com era companies where you know, somebody came over the an idea and did this take Rudy and the team had been working in this area for 10 years. They have organization knowledge, they have the expertise and so does you have adults. >>And so we will take what I'm, what I call a loosely coupled approach where we can choose common customers, we can choose comments that are features that we are going to work on and that's how we will integrate. But again, the focus of all this is to deliver the value to our customers. Not think about the mechanics of what the integration would look like. I think one of the most exciting things that I'm hearing is this notion of the processes that are not known. Um, because so many processes today are unknown, especially as we go into this new digital world. We used to know what processes we want to automate your point, some technology at it. Okay great. We're going to automate now with this digital disruption that's going on. You actually may have no idea. You may be making processes up on the fly, so you need a way to identify those processes quickly and then those ones that are driving our ROI. >>Um, I'm interested in your thoughts on AI and ROI and how to measure that, how those things fit together. So, you know, AI, this is I think the biggest problem in the AI right now. There's a lot of hype in this space. We are tracking close to 3000 different AI startups in the world and uh, nobody can actually put a number to the revenues or the valuation, the real valuation because of this ROI quantification problem, right? Um, let's say I have a company, we'd say, Oh, we are the best in class. And understanding faces short, how is it going to be useful to an enterprise if you cannot measure what well you official recognition system is adding to your enterprise, it's not good enough for the business people. Because at the end of the day, my, I can have the world's brightest PhDs telling me I have the state of the art model in the world, which does law, but in fact cannot translate it into business value. >>It doesn't really work. And so that's why ROI quantification is so in parking and you have to make sure you align them econometrics of the AI, uh, measures and the business KPIs so that if, for example, so your data science team should be able to know what metrics they have to improve in order to get a better ROI for the business. So you have to align those two things. And that is part of research that is not really prevalent in academic circles. Interesting. I mean, you've seen some narrow successes in I'll call AI, you know, things like a infrastructure optimization. Okay, great. Makes sense. What I'm hearing from you is identify the KPIs that are going to drive your voice of the customer defines value first to take away, identify what those KPIs are. And this every business has thousands of KPIs, but there's really like three or four that matter, right? >>So identify those top ones and then you're saying measure on a continuous basis how your system affects those metrics. So in economics this is called the treatment effect. Uh, so for example, if you water my term sales and marketing processes, the KPIs that matter to you is what is your conversion rate from when the leads hit your system to when the revenue is realized or what is the total revenue that you're making? Right? As you said, there's only two or three top level gave you as that really matter. And now if for example you put an AI system in place that treats your leads differently, you should see an increase and uptick in revenue. And so that's what I mean by the Ottawa quantification. So if you instrumented the system properly, put it in the right quantification measurement system in place and have the auto optimization mechanism, that's how things should work. >>You know, with with cross mining we can even add additional KPIs to the picture KPIs you usually don't have because if you ask a company, nobody can tell you how many different variations of the process you actually have. And with process mining we can exactly measure how many variations there are. So if you are up to streamlining to simplifying the process to speed it up, we can actually tell you if your optimization effort is successful or not because we can show you how the number of very our variations is going down over time. Even if we, you know, we can also measure the, the success of RPA implementation. So it really pros we use process code and pro money not only for identification of processes but also for the monitoring of processes after an successful RPA implementation. I can see so many use cases for this. >>I mean it's like my mind is just racing. I mean sales guys in one region and sales gals in the other region doing things differently. You've got different country management doing things differently. If I understand you correctly, you can identify the differences in those processes, document them, visualize them and identify the ones that are actually optimized or help people optimize and then standardized across the organization to drive those metrics that matter. It's very powerful. It is really powerful. You know, as I said, we are living in the golden age of this system that can self-improve your companies. I mean this, this was the Holy grail of all of computer science work with technologies like process score with RPA, with AI. I think we are at that inflection point where we can realize that. So we got to go. But I'll, I'll give you guys sort of the last, last word, each of you. >>So actually first of all, Rudy question, how large can you tell me how large the process gold team is? How many people? We have grown with 60 people. 60 equals zero. We are based, our headquarter is in the, is in the, in from the Netherlands. Um, so this is where we are very close to university. This is where our developers basically are located. And uh, I'm based in Frankfurt in Germany, but for now, let's see what the future will be. So what's a home run for you with this marriage? The home run, you know, since we are in Las Vegas, I was wondering if you hit the jet park Jack photo, if we hit the jackpot. But I actually think of the customers, our customers get the Jaguar because this combination of, of your technology, of our technology, this is really, you know, good answer. So that as I was gonna ask you the same question PD is, I can't even tell you, um, almost every one of the UI path customers has expressed interest in process glow, right? >>Because right now we have a portfolio of products, but the interest that we are getting in process board with the process mining offerings is unparalleled. So Rudy is right. Our customers are the ones which are driving this inhibition and the integration. And I'll be able to actually acquire this solution. I forget, I have my notes with relatively near term, right? Yes. We are gonna make it available to our customers as soon as possible. Awesome guys, congratulations. Really great to have you on the cube. Thank you. All right, and thank you everybody for watching. We'll be back with our next guest right after this short break. You're watching the cube alive from the Bellagio UI path forward three. We were right back.

>> Announcer: Live, from Las Vegas, it's the Cube. Covering I.B.M. Think 2018. Brought to you by I.B.M. >> Welcome back to the Cube. We are live at I.B.M. Think 2018, our inaugural event. I'm Lisa Martin with Dave Vellante. We're joined by another Vegas veteran, as we all are. First time guest to the Cube, Bret Greenstein, the V.P. of Watson I.o.T. Offerings. Bret, welcome to the Cube. >> Thank you very much, exciting to be here. >> This is the inaugural Think 2018 event. >> Yes. >> 40,000 plus attendees, expected over 10 keynotes, lots of cool stuff. Speaking of cool stuff, I.o.T. What is happening in I.o.T. this year? >> Yeah, so we've been here in Vegas several times over the last several years talking about the Internet of Things, but what's really pivoted, what's really changed, is people talking about applied I.o.T. How are they using it to get business outcomes. Something different happening. And I think when we all started with the Internet of Things we talked a lot about, connecting stuff and devices. But really, it was always about the data and the effect that data had on changing business, changing user engagement, changing outcomes. And so here, on stage, you're going to see people talking about how their businesses have been changed, how their customers are changing as a result of I.o.T. >> Yeah so, I've always felt like I.o.T. is the intersection of devices, data, and machine intelligence. >> Bret: Yeah. >> How are those sort of three things coming together and what's the data model look like? >> Data model is every type of data. I think what people really didn't expect was it wasn't just machine data coming off sensors, temperatures, vibrations. It's all this unstructured data coming in from connected things that are everywhere in our lives. So sensors with cameras for example, being able to see. It's not just recorded images, but it's information. Tons of information that you need A.I. systems and other systems to interpret. So we're able to take all that data, structured data, numeric stuff coming off of devices and sensors, but images and sound and vibration. Even emotional content in people's dialogue. All of that is relevant to the Internet of Things. >> What's the conversation like with customers? For example, when we say, what physical assets do we have that we can instrument. >> Bret: Right. >> Parking meters or whatever, okay. >> Bret: Right. >> What physical assets don't we have that we should have? How can we leverage our existing data? What's the conversation like in terms of transformations that are going on? >> I think the conversations have shifted a lot. Over the couple years people were talking about we want to connect our thing, whatever the thing is, whether it's an elevator or car or whatever. We want to connect it, what does that mean? And that's shifted very quickly to customers who are coming in talking about information data and insights and they want to know, what should I do to get more of those insights? So I'm seeing customers now with Chief Data Officers or heads of digital transformation. Totally new roles that didn't exist before. And they're coming in with a data centric view. They're saying, we're going to be a digital business. We need to understand all of these live data about our customers and our things and our business process. Help us do that. And so that's much more than just instrumenting the individual devices now. And I find that conversation is really, really focused on the value of the data. >> What about the industry impact in this context? Do you see, does I.B.M.'s perspective, is I.o.T., it's certainly transformative. >> Bret: Right. >> But is it disruptive or it is sort of the guys with infrastructure are going to evolve to it? Is it more evolutionary, is it more disruptive? How do you see it? >> I think there's room for both. Obviously traditional players are going to instrument their business process. They're bringing in connected cars and all that. But you could also look at those same industries and say there's new players emerging who are coming in with software defined products that are digital by design. And they can come in and suddenly become leaders in their field. I don't think people would've expected companies like Tesla to be so disruptive in automotive, but coming in as electric changes the game without having to build on a hundred years of mechanical design. You're building on some new principles. And now we see some new players coming in to automotive who've never built cars at all before. Like Dyson for example, that recently announced they were working on electric cars. So I think a digital platform, a digital way of thinking, also creates opportunities for new entrance in every market. >> I think automobiles is a great example because it's an industry that hasn't been largely disrupted. But then you use an example of Tesla which is extremely innovative, you could actually pretend disruptions coming out. And you see whole ecosystems form around that. >> Right, right. And I think what was so powerful about the effect they had was it's a software defined product. The software in it is upgraded constantly. Sometimes you buy the car, the next day you get a new feature you didn't even expect. And this is the way we've come to appreciate, experience through mobile and everything else. Software that continues to improve products that get more valuable over time. Not less valuable over time. >> So let's talk about Watson and I.o.T. I'd also love to maybe take a slice on how I.B.M. is helping customers that maybe have been around maybe the flip side of a Tesla. They've been around a long time. How are they leveraging Watson and I.o.T. to transform their businesses? So kind of start with, what's new with Watson and I.o.T. >> Sure, so I mentioned before that there's a whole part of many data types now that previously were very hard to interpret through traditional analytics. But A.I. and machine learning give you the ability to absorb and consume some of that data. Unstructured sound, images, video, vibration, all of that stuff is now able to become part of a business process. So even traditional companies that have been around a long time can start to look at the data coming off of cameras, visual inspection in manufacturing, sound and voice for example. We work with Jefferson Hospital where they brought Watson into patients rooms so you could ask questions like visiting hours, or set the temperature. Put the patients in control of their experience in a hospital. That takes a traditional experience, like a hospital recovery room, and turns it into something A.I. driven, I.o.T. powered and puts the patient at the center. So very big changes can occur when you do that. >> How far do you see us being able to take A.I. in this whole world of I.o.T.? How far should we take it? >> I think we have to start become more appreciative of the power of machine learning to drive outcomes that are not as easily prescribed with code. So all of us, all of our business processes, all of our businesses will be enhanced with A.I. And we shouldn't look at that in any other way as a better tool to understand data in a way that's different than the way you interpret data. And so it wasn't long ago when big data just meant writing an algorithm across large volumes of data. And now we literally have algorithms whose job is to find patterns. Whose job is to understand data from training. And deliver an outcome that you couldn't have prescribed before. And so those type of problems, it just opens up a class of problems we can all solve now that we couldn't before. >> You're seeing a whole set of digital services emerge. The lingua franca is changing. It's sense, hear, see, respond. >> Bret: Right. >> Optimize. >> Right. >> Fix. (chuckles) >> And all that comes from comprehending. So having a system that can look. For example, I have a camera outside the window of my house and every once in a while I feed the images into Watson to see what it sees. When I first did it, it would say truck. But later, as we make Watson better, now it says FedEx truck or U.P.S. truck. It can read the writing, it can see the patterns. Every camera should know what it sees. Whether it's in a car or a home or somewhere else. Because it's much more valuable than just taking a picture and letting a human being interpret it later. So cameras should know what they see. Machines should know what they hear. Machines should tell us when they're about to break based on vibration or sound. And so this is possible with machine learning. >> So you're saying machines actually take on a whole new set of human-like activities. Digital twins is an example. >> Bret: Okay. >> What's your perspective on, let's start there, digital twins? >> Digital twins, for me, represents sort of the evolution of I.o.T. and that it's digitalizing things. And so, a thing that has no connectivity and very few sensors, is just a thing, it's just a box, it's a block. But as you start to put sensors on it and start to understand it's behavior, it's motion, it's vibration, it's location. Any of the mechanisms, the angels, all this stuff. Then you add a virtual representation of that thing. And if you can do that with all the things in your business, you can start to look for patterns. You can start to assess what's working and what's not working. So I think it just represents a true digitization of a business, of a class of objects in your business. >> Does I.o.T. make security a do-over in your opinion? >> No, but it certainly raises the bar. And so, when we all started connecting our computers to the internet, I remember everyone being panicked. It you put a disc in your machine, you might get a virus. Then we connected them to the internet, we all panicked, but the tools evolved and we start to get things that can help detect zero day problems. In the case of I.o.T. we've got these software defined products that are connected. That are inherently vulnerable cause they're in the real world. They can be touched by other things. So it raises the bar in the expectation of monitoring normal behavior for things. Monitoring all kinds of different threats and stuff, So companies like I.B.M. they focus so much on security and security services, we build that right into our platform so we can keep an eye on that. And also, when things occur, be able to push out new software that is protected. So for more updates, keeping the products live and current is a huge security protection. >> Bret, how would you describe the ecosystem. I.B.M.'s point of view on the ecosystem that you've got to form and catalog in order to succeed in I.o.T.? What does that look like? >> Yeah so, there are so many things for people to do in the world of I.o.T. That I.B.M. doesn't prescribe to do all of them, at all. There's certain things that we're really, really good at. We're certainly good at our cloud infrastructure and analytics and the platforms that enable this and deep industry knowledge. But the ability to apply that in businesses, to take on machine learning algorithms and make it work on the thousands of classes of machines in manufacturing, requires a huge partner ecosystem. So we work very openly on contributions to standards and open source. We certainly work with partners to build a lot of value around our stuff. So for example, on stage this week, we have several partners who are going to be up there. One of them is Harmen, who builds all kinds of things that's including info-tainment units in cars and the professional equipment that goes into hotels and buildings. So we work with them to build great innovative value together and they do things that they're experts in and we do what we're experts in. >> So, from an I.o.T. perspective, what are some of the cool things that are here at I.B.M. Think 2018, that those that are attending are going to get to see and feel and touch and smell? >> Well there are some things I can talk about, things that I can't. Tomorrow we have some very exciting announcements coming up. Going to talk a lot more about Watson and I.o.T. coming together, that's all I can say about that. You'll also see physical representations of things. There's a Jaguar Land Rover out here on the floor. To look at where we have contributed significantly to the engineering and the software development inside these kinds of products like J.L.R. So they're going to be up on stage talking about some of the things we're doing together. You'll hear A.B.B. here talking about some of the work we're doing around manufacturing techniques and helping manage wind turbines. So all kinds of really cool, industrial use cases. It's really exciting and I think working in I.o.T. is great because not only do you get to talk about the technology and the analytics and the data, but you actually get to see things. So it makes all of this feel very real when you walk up to and see a thing that's infused with I.o.T. and made better because of I.B.M. >> What inning are we in? >> What's that? >> What inning are we in? >> Oh it's still early, still early. Third inning still, mostly because so much of the market is still working to figure out how to take advantage of the data and the insights about this to transform their business. I think if you thought of the dot com era and how long it took for companies to emerge to be truly digital e-businesses, on demand businesses. The I.o.T. businesses, the A.I. driven businesses of the future, still very early. Some of them, you probably don't even know their names yet. But they're going to be the leaders that's coming. >> Do you think it'll happen faster because there is an internet? Or not so much because of the physical infrastructure that has to get built out? >> The infrastructure is actually not the gate at all. >> Dave: Okay. >> The real gate is the cultural difference of having people who are data driven, data thinkers. Having a leadership role in our clients. If you can think about it, mechanical things have dominated for a hundred years. Software engineers are still not even the most senior people in most of the companies that build physical things. But to have the data scientists, have the data leaders have a strong enough role to define business process. It's really the readiness and maturity of those data leaders. >> Yeah so the culture of a mechanical engineering culture that says "don't touch my things," >> Right. >> I'm not going to let a software engineer come in and mess with it because it works, it's secure, I trust it. >> Right. >> So that's the cultural one of the cultural dimensions. >> It's to look at what the data might mean. Just understand how your users use your things or if you want to understand what they're doing with those things somewhere else. Or even with the value of your insights of your users are and building entirely new ecosystems of the data of I.o.T. >> Alright, so we're in the third inning. We'll say the top of the third. >> Okay. >> But one of the things that you shared with us is that you're excited about is this is about applied I.o.T. To get business outcomes. >> Yes. >> Shared some examples that attendees of the event are going to hear from A.B.B., you mentioned, you mentioned the >> Bret: J.L.R. >> Land Rover that's here. Harman as well. And maybe some best practices for how to advise companies to get through some of those cultural hurdles, we'll say, to start embracing the opportunities that are within the I.o.T. space. >> I think the best thing people could do is to start to really, I'm going to say it again, put value on data science. It doesn't mean everyone has to be a data geek. But it does mean you have to have a certain value on the skills and the insights that come from a data driven business. What does it mean to make decisions in real time based on your customers? For a hundred years when companies shipped a washing machine it went into someone's house and sat for 10 years and they never heard from the person ever again until they bought another one 10 years later. But now when you ship a washing machine, you want people to connect it to the wi-fi. You want to know the features that are used. Suddenly as a manufacturer of things, you have to respect the data coming off those things because they inform you on how to design better. How to deliver better service and value. Which means those engineers who were the experts in washing machines, now have to be the experts in the data of washing machines and the data of their users. So, I would say, focus on the education, the recruitment, the enablement, the empowerment of people who are data centric by nature and who are looking for the transformation of a digital business from a physical business. >> Awesome, Bret thank you so much for stopping by the Cube and sharing your insights. >> You're very welcome. >> Good luck tomorrow with your presentations and we are going to be waiting on the edge of our seats for those lots of I.o.T. announcements. >> Very exciting. >> Very exciting. >> Okay. >> Alright you heard it here. >> Thank you so much. >> You can watch all of our good stuff on thecube.net live, of course, as we are now as well as the interviews that we've already done and those that we'll be doing for the next two days as our coverage continues of I.B.M. Think 2018. Also check out siliconangle.com our media site for all of your real time coverage of this event and others. For Dave Vellante and Bret, two Vegas Veterans, I'm Lisa Martin. Stick around, Dave and I are going to be right back after a short break. (upbeat music)