>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Welcome back to Boston. Everybody watching the Cube, the leader and on the ground tech coverage money was David wanted here with my co host. A student of John for is also in the house. This is active FiOS data driven 19 conference. They're second year, John. Her stick is here is the co founder and CEO of on shape John. Thanks for coming in the Cube. Great to have you great to be here. So love the cofounder. I always ask your father. Why did you start the company? Well, we found it on shape because >> we saw an opportunity to improve how every product on Earth gets developed. Let people who develop products do it faster, B'more, innovative, and do it through a new generation software platform based in the cloud. That's our vision for on shape, That's why. Okay, >> so that's great. You start with the widened. The what is just new generation software capabilities to build the great products visualized actually create >> way took the power of cloud web and mobile and used it to re implement a lot of the classic tools for product development. Three d cad Data management Workflow Bill of Materials. He's may not mean anything to you, but they mean a lot to product developers, and we believe by by moving in the cloud by rethinking them for the cloud we can give people capabilities they've never had before. >> John, bring us in tight a little bit. So you know, I think I've heard a lot the last few years. It's like, Well, I could just do everything a simulation computer simulation. We can have all these models. They could make their three D printings changing the way I build prototypes. So what's kind of state of the state and in your fields? So >> the state of the Art R field is to model product in three dimensions in the computer before you build it for lots of reasons. For simulation for three D printing, you have to have a CAD model to do it, to see how it'll look, how parts fit together, how much it will cost. Really, every product today is built twice. First, it's built in the computer in three dimensions, is a digital model, then it's built in the real world, and what we're trying to do is make those three D modeling and data management collaboration tools to take them to a whole nother level to turbo charge it, if you will, so that teams can can work together even if they're distribute around the world. They work faster. They don't have to pay a tax to install and Karen feed for these systems. You're very complicated, a whole bunch of other benefits. So we talk about the cloud model >> you're talking about a sass model, a subscription model of different customer experience, all of the above, all of the above. Yeah, it's definitely a sass model we do on Ly SAS Way >> hosted and, uh, Amazon. Eight of us were all in with Amazon. It's a it's a subscription model, and we provide a much better, much more modern, better, more productive experience for the user CIA disrupting the traditional >> cad business. Is that Is that right? I mean more than cat cat Plus because there's no such thing as a cad company anymore. We're essentially disrupting the systems that we built because I've been in this business 30 38 years now. I've been doing this. I feel like I'm about half done. Really, really talking about >> your career. Way to start out. Well, I grew up in Chicago. I went to M I t and majored in mechanical engineering and knew howto program computers. And I go to get an internship in 1981 and they say computers, mechanical injury. You need to work on CAD. And I haven't stopped since, you know, because Because we're not done, you know, still still working here. You would >> have me, right? You can't let your weight go dynamic way before we get off on the M I t. Thing you were part of, you know, quite well known group. And Emmet tell us a little bit >> about what you're talking about. The American society of Mechanical Engineer >> has may I was actually an officer and and as any I know your great great events, but the number 21 comes to >> mind you're talking about the MIT blackjack team? Yes, I was, ah, player on the MIT blackjack team, and it's the team featured in movies, TV shows and all that. Yeah, very exciting thing to be doing while I was working at the cath lab is a grad student, you know, doing pursuing my legitimate career. There is also also, uh, playing blackjack. Okay, so you got to add some color to that. So where is the goal of the M I T. Blackjack team? What did you guys do? The goal of the M I t blackjack team was honestly, to make money using legal means of skill to Teo obtain an edge playing blackjack. And that's what we did using. Guess what? The theme of data which ties into this data driven conference and what active Eo is doing. I wish we had some of the data tools of today. I wish we had those 30 years ago. We could have We could have done even more, but it really was to win money through skill. Okay, so So you you weren't wired. Is that right? I mean, it was all sort of No, at the time, you could not use a computer in the casino. Legally, it was illegal to use a computer, so we didn't use it. We use the computer to train ourselves to analyze data. To give a systems is very common. But in the casino itself, we were just operating with good old, you know, good. This computer. Okay. And this computer would what you would you would you would count cards you would try to predict using your yeah, count cards and predict in card. Very good observation there. Card counting is really essentially prediction. In a sense, it's knowing when the remaining cards to be dealt are favorable to the player. That's the goal card counting and other systems we used. We had some proprietary systems to that were very, very not very well known. But it was all about knowing when you had an edge and when you did betting a lot of money and when you didn't betting less double doubling down on high probability situations, so on, So did that proceed Or did that catalyze like, you know, four decks, eight decks, 12 12 decks or if they were already multiple decks. So I don't think we drove them to have more decks. But we did our team. Really. Some of the systems are team Pioneer did drive some changes in the game, which are somewhat subtle. I could get into it, you know, I don't know how much time we have that they were minor changes that our team drove. The multiple decks were already are already well established. By the time my team came up, how did you guys do you know it was your record? I like to say we won millions of dollars during the time I was associated with the team and pretty pretty consistently won. We didn't win every day or every weekend, but we'd run a project for, say, six months at a time. We called it a bank kind of like a fund, if you will, into no six months periods we never lost. We always won something, sometimes quite a bit, where it was part of your data model understanding of certain casinos where there's certain casinos that were more friendly to your methodology. Yes, certain casinos have either differences in rules or, more commonly, differences in what I just call conditions like, for instance, obviously there's a lot of people betting a lot of money. It's easier to blend in, and that's a good thing for us. It could be there there. Their aggressiveness about trying to find card counters right would vary from casino to casino, those kinds of factors and occasionally minor rule variations to help us out. So you're very welcome at because he knows is that well, I once that welcome, I've actually been been Bardet many facilities tell us about that. Well, you get, you get barred, you get usually quite politely asked toe leave by some big guy, sometimes a big person, but sometimes just just honestly, people who like you will just come over and say, Hey, John, we'd rather you not play blackjack here, you know that. You know, we only played in very upstanding professional kind of facilities, but still, the message was clear. You know, you're not welcome here in Las Vegas. They're allowed to bar you from the premises with no reason given in Las Vegas. It's just the law there in Atlantic City. That was not the law. But in Vegas they could bar you and just say you're not welcome. If you come back, we'll arrest you for trespassing. Yeah, And you really think you said everything you did was legal? You know, we kind of gaming the system, I guess through, you know, displaying well probabilities and playing well. But this interesting soothe casinos. Khun, rig the system, right? They could never lose, but the >> players has ever get a bet against the House. >> How did >> you did you at all apply that experience? Your affinity to data to you know, Let's fast forward to where you are now, so I think I learned a lot of lessons playing blackjack that apply to my career and design software tools. It's solid works my old company and now death. So System, who acquired solid words and nowt on shape I learned about data and rigor, could be very powerful tools to win. I learned that even when everyone you know will tell you you can't win, you still can win. You know that a lot of people told me Black Jack would never work. A lot of people told me solid works. We never worked. A lot of people told me on shape would be impossible to build. And you know, you learn that you can win even when other people tell you, Can't you learn that in the long run is a long time? People usually think of what you know, Black Jack. You have to play thousands of hands to really see the edge come out. So I've learned that in business sometimes. You know, sometimes you'll see something happened. You just say, Just stay the course. Everything's gonna work out, right? I've seen that happen. >> Well, they say in business oftentimes, if people tell you it's impossible, you're probably looking at a >> good thing to work on. Yeah. So what's made it? What? What? What was made it ostensibly impossible. How did you overcome that challenge? You mean, >> uh, on >> shape? Come on, Shake. A lot of people thought that that using cloud based tools to build all the product development tools people need would be impossible. Our software tools in product development were modeling three D objects to the precision of the real world. You know that a laptop computer, a wristwatch, a chair, it has to be perfect. It's an incredibly hard problem. We work with large amounts of data. We work with really complex mathematics, huge computing loads, huge graphic loads, interactive response times. All these things add up to people feeling Oh, well, that would never be possible in the cloud. But we believe the opposite is true. We believe we're going to show the world. And in the future, people say, you know We don't understand how you do it without the cloud because there's so much computing require. >> Yeah, right. It seems you know where we're heavy in the cloud space. And if you were talking about this 10 years ago, I could understand some skepticism in 10 2019. All of those things that you mentioned, if I could spin it up, I could do it faster. I can get the resources I need when I needed a good economics. But that's what the clouds built for, as opposed to having to build out. You know, all of these resource is yourself. So what >> was the what was the big technical challenge? Was it was it? Was it latent? See, was it was tooling. So performance is one of the big technical challenges, As you'd imagine, You know, we deliver with on shape we deliver a full set of tools, including CAD formal release management with work flow. If that makes sense to you. Building materials, configurations, industrial grade used by professional companies, thousands of companies around the world. We do that all in a Web browser on any Mac Windows machine. Chromebook Lennox's computer iPad. I look atyou. I mean, we're using. We run on all these devices where the on ly tools in our industry that will run on all these devices and we do that kind of magic. There's nothing install. I could go and run on shape right here in your browser. You don't need a 40 pound laptop, so no, you don't need a 40 pound laptop you don't need. You don't need to install anything. It runs like the way we took our inspiration from tools like I Work Day and Sales Force and Zen Desk and Nets. Sweet. It's just we have to do three D graphics and heavy duty released management. All these complexities that they didn't necessarily have to do. The other thing that was hard was not only a technical challenge like that, but way had to rethink how workflow would happen, how the tools could be better. We didn't just take the old tools and throw him up in a cloud window, we said, How could we make a better way of doing workflow, release management and collaboration than it's ever been done before? So we had to rethink the user experience in the paradigms of the systems. Well, you know, a lot of talk about the edge and if it's relevant for your business. But there's a lot of concerns about the cloud being able to support the edge. But just listening to you, John, it's It's like, Well, everybody says it's impossible. Maybe it's not impossible, but maybe you can solve the speed of light problem. Any thoughts on that? Well, I think all cloud solutions use edge to some degree. Like if you look at any of the systems. I just mentioned sales for us workday, Google Maps. They're using these devices. I mean, it's it's important that you have a good client device. You have better experience. They don't just do everything in the cloud. They say There, there. To me, they're like a carefully orchestrated symphony that says We'll do these things in the core of the cloud, these things near the engineer, the user, and then these things will do right in the client device. So when you're moving around your Google map or when you're looking this big report and sales force you're using the client to this is what are we have some amazing people on her team, like R. We have the fellow who was CTO of Blade Logic. Robbie Ready. And he explains these concepts to make John Russo from Hey came to us from Verizon. These are people who know about big systems, and they helped me understand how we would distribute these workloads. So there's there's no such thing is something that runs completely in the cloud. It has to send something down. So, uh, talk aboutthe company where you're at, you guys have done several raises. You've got thousands of customers. You maybe want to add a couple of zeros to that over time is what's the aspirations? Yeah, correct. We have 1000. The good news is we have thousands of customer cos designing everything you could imagine. Some things never would everything from drones two. We have a company doing nuclear counter terrorism equipment. Amazing stuff. Way have people doing special purpose electric vehicles. We have toys way, have furniture, everything you'd imagined. So that's very gratifying. You us. But thousands of companies is still a small part of the world. This is a $10,000,000,000 a year market with $100,000,000,000 in market cap and literally millions of users. So we have great aspirations to grow our number of users and to grow our tool set capability. So let's talk to him for a second. So $10,000,000,000 current tam are there. Jason sees emerging with all these things, like three D printing and machine intelligence, that that actually could significantly increase the tam when you break out your binoculars or even your telescope. Yes, there are. Jason sees their increasing the tam through. Like you say, new areas drive us So So obviously someone is doing more additive manufacturing. More generative design. They're goingto have more use for tools like ours. Cos the other thing that I observed, if I can add one, it's my own observations. I think design is becoming a greater component of GDP, if you will, like if you look at how much goods in the world are driven by design value versus a decade or two or when I was a child, you know, I just see this is incredible amount, like products are distinguished by design more and more, and so I think that we'll see growth also through through the growth in design as an element of GDP on >> Jonah. I love that observation actually felt like, you know, my tradition. Engineering education. Yeah, didn't get much. A lot of design thing. It wasn't until I was in industry for years. That had a lot of exposure to that. And it's something that we've seen huge explosion last 10 years. And if you talk about automation versus people, it's like the people that designed that creativity is what's going to drive into the >> absolutely, You know, we just surveyed almost 1000 professionals product development leaders. Honestly, I think we haven't published our results yet, So you're getting it. We're about to publish it online, and we found that top of mind is designed process improvements over any particular technology. Be a machine learning, You know, the machine learning is a school for the product development. How did it manufacturers a tool to develop new products, but ultimately they have to have a great process to be competitive in today's very competitive markets. Well, you've seen the effect of the impact that Apple has had on DH sort of awakening people to know the value of grace. Desire absolutely have to go back to the Sony Walkman. You know what happened when I first saw one, right? That's very interesting design. And then, you know, Dark Ages compared to today, you know, I hate to say it. Not a shot at Sony with Sony Wass was the apple? Yeah, era. And what happened? Did they drop the ball on manufacturing? Was it cost to shoot? No. They lost the design leadership poll position. They lost that ability to create a world in pox. Now it's apple. And it's not just apple. You've got Tesla who has lit up the world with exciting design. You've got Dyson. You know, you've got a lot of companies that air saying, you know, it's all about designing those cos it's not that they're cheaper products, certainly rethinking things, pushing. Yeah, the way you feel when you use these products, the senses. So >> that's what the brand experience is becoming. All right. All right, John, thanks >> so much for coming on. The Cuban sharing your experiences with our audience. Well, thank you for having me. It's been a pleasure, really? Our pleasure. All right, Keep right. Everybody stupid demand. A volonte, John Furry. We've been back active, eo active data driven 19 from Boston. You're watching the Cube. Thanks

>> No. Yeah. Wait. >> Hi, everyone, and welcome to the big data. Deep Dive with the Cube on AMC TV. I'm Richard Schlessinger, and I'm here with tech industry entrepreneur and wicked bond analyst Dave Volonte and Silicon Angle CEO and editor in chief John Furrier. For this last segment in our show, we're talking about the future of big data and there aren't two better guys to talk about that you and glad that you guys were here. Let me sort of tee up the this conversation a little bit with a video that we did. Because the results of big data leveraging are only as good as the data itself. There has to be trust that the data is true and accurate and as unbiased as possible. So AMC TV addressed that issue, and we're just trying to sort of keep the dialogue going with this spot. >> We live in a world that is in a constant state of transformation, political natural transformation that has many faces, many consequences. A world overflowing with information with the potential to improve the lives of millions with prospects of nations with generations in the balance way are awakening to the power of big data way trust and together transform our future. >> So, Gentlemen Trust, without that, where are we and how big of an issue is that in the world of big data? Well, you know, the old saying garbage in garbage out in the old days, the single version of the truth was what you were after with data warehousing. And people say that we're further away from a single version of the truth. Now with all this data. But the reality is with big data and these new algorithms you, khun algorithmic Lee, weed out the false positives, get rid of the bad data and mathematically get to the good data a lot faster than you could before. Without a lot of processes around it. The machines can do it for you. So, John, while we were watching that video, you murmured something about how this is the biggest issue. This is cutting edge stuff. This is what I mean. >> Trust, trust issues and trust the trust equation. Right now it is still unknown. It's evolving fast. You see it with social networks, Stevens go viral on the internet and and we live in a system now with mobility and cloud things. Air scaling infinitely, you know, these days and so good day two scales, big and bad data scales being so whether it's a rumor on you here and this is viral or the data data, trust is the most important issue, and sometimes big data can be creepy. So a. This really, really important area. People are watching it on DH. Trust is the most important thing. >> But, you know, you have to earn trust, and we're still sort of at the beginning of this thing. So what has to happen to make sure that you know you don't get the garbage in, so you get the garbage. >> It's iterative and and we're seeing a lot of pilot projects. And then those pilot projects get reworked, and then they spawn into new projects. And so it's an evolution. And as I've said many, many times, it's very early we've talked about, were just barely scratching the surface here. >> It's evolving, too, and the nature of the data is needs to be questioned as well. So what kind of data? For instance, if you don't authorize your data to be viewed, there's all kinds of technical issues around. >> That's one side of it, But the other side of it, I mean, they're bad people out there who would try to influence, Uh, you know what? Whatever conclusions were being drawn by big data programs, >> especially when you think about big data sources. So companies start with their internal data, and they know that pretty well. They know where the warts are. They know how to manipulate. It's when they start bringing in outside data that this gets a lot fuzzier. >> Yeah, it's a problem. And security talk to a guy not long ago who thought that big data could be used to protect big data, that you could use big data techniques to detect anomalies in data that's coming into the system, which is poetic if nothing else, that guys think data has told me that that that's totally happened. It's a good solution. I want to move on because way really want to talk about how this stuff is going to be used. Assuming that these trust issues can be solved on and you know, the best minds in the world are working on this issue to try to figure out how to best, you know, leverage the data, we all produce, which has been measured at five exabytes every two days. You know, somebody made an analogy with, like something. If a bite was a paper clip and you stretched five exabytes worth of paper clips, they would go to the moon or whatever. Anyway, it's a lot of bike. It's a lot of actually, I think that's a lot of fun and back way too many times one hundred thousand times I lost track of my paper. But anyway, the best minds are trying to figure out, you know, howto, you know, maximize that the value that data. And they're doing that not far from here where we sit. Uh, Emmett in a place called C Sale, which was just recently set up, See Sail stands for the computer signs, an artificial intelligence lab. So we went there not long ago. It's just, you know, down the Mass. Pike was an easy trip, and this is what we found. It's fascinating >> Everybody's obviously talking about big data all the time, and you hear it gets used to mean all different types of things. So he thinks we're trying to do in the big data. Is he? Still program is to understand what are the different types of big data that exists in the world? And how do we help people to understand what different problems or fall under the the overall umbrella of big data? She sells the largest interdepartmental laboratory and mitt, so there's about one hundred principal investigators. So that's faculty and sort of senior research scientists. About nine hundred students who are involved, >> basically with big data, almost anything to do with it has to be in a much larger scale than we're used to, and the way it changes that equation is you have to You have to have the hardware and software to do the things you're used to doing. You have to meet them of comedy's a larger size a much larger size >> of times. When people talk about big data, they, I mean, not so much the volume of the data, but that the data, for example, is too complex for their existing data. Processing system to be able to deal with it. So it's I've got information from Social network from Twitter. I've got your information from a person's mobile phone. Maybe I've got information about retail records. Transactions hole Very diverse set of things that need to be combined together. What this clear? It says this is If you added this, credit it to your query, you would remove the dots that you selected. That's part of what we're trying to do here. And big data is he sail on. Our big data effort in general at MIT is toe build a set of software tools that allow people to take all these different data sets, combine them together, asked questions and run algorithms on top of them that allowed him to extracting sight. >> I'm working with it was dragged by NASA, but the purpose of my work right now is Tio Tio. Take data sets within Davis's, and instead of carrying them for table results, you query them, get visualizations. So instead of looking at large sets of numbers and text him or not, you get a picture and gave the motivation Behind that is that humans are really good into pretty pictures. They're not so that interpreting huge tables with big data, that's a really big issue. So this will have scientists tio visualize their data sets more quickly so they can start exploring And, uh, just looking at it faster, because with big data, it's a challenge to be able to visualize an exploiter data. >> I'm here just to proclaim what you already know, which is that the hour of big data has arrived in Massachusetts, and >> it's a very, very exciting time. So Governor Patrick was here just a few weeks ago to announce the Mass Big Data Initiative. And really, I think what he recognizes and is partly what we recognize here is that there's a expertise in the state of Massachusetts in areas that are related to big data, partly because of companies like AMC, as well as a number of other companies in this sort of database analytic space, CMC is a partner in our big data detail, initiatives and big data and See Sale is industry focused initiative that brings companies together to work with Emmet T. Think about it. Big data problems help to understand what big data means for the companies and also to allow the companies to give feedback. Tow us about one of the most important problems for them to be working on and potentially expose our students and give access to these companies to our students. >> I think the future will tell us, and that's hard to say right now, because way haven't done a lot of thinking, and I was interpreting and Big Data Way haven't reached our potential yet, and I just there's just so many things that we can't see right now. >> So one of the things that people tell us that are involved in big data is they have trouble finding the skill sets the data. Science can pick capability and capacity. And so seeing videos like this one of them, it is a new breed of students coming out there. They're growing up in this big data world, and that's critical to keep the big data pipeline flowing. And Jon, you and I have spent a lot of time in the East Coast looking at some of the big data cos it's almost a renaissance for Massachusetts in Cambridge and very exciting to see. Obviously, there's a lot going on the West Coast as well. Yeah, I mean, I'll say, I'm impressed with Emmett and around M I. T. In Cambridge is exploding with young, young new guns coming out of there. The new rock stars, if you will. But in California we're headquartered in Palo Alto. You know we in a chance that we go up close to Google Facebook and Jeff Hammer backer, who will show a video in a second that I interview with him and had dupe some. But he was the first guy a date at Facebook to build the data platform, which now has completely changed Facebook and made it what it is. He's also the co founder of Cloudera The Leader and Had Duke, which we've talked about, and he's the poster child, in my opinion of a data scientist. He's a math geek, but he understands the world problems. It's not just a tech thing. It's a bigger picture. I think that's key. I mean, he knows. He knows that you have to apply this stuff so and the passion that he has. This video from Jeff Hammer Bacher, cofounder of Cloud Ear, Watches Video. But and then the thing walk away is that big data is for everyone, and it's about having the passion. >> Wait. Wait. >> Palmer Bacher Data scientists from Cloudera Cofounder Hacking data Twitter handle Welcome to the Cube. >> Thank you. >> So you're known in the industry? I'LL see. Everyone knows you on Twitter. Young Cora heavily follow you there at Facebook. You built the data platform for Facebook. One of the guys mean guys. They're hacking the data over Facebook. Look what happened, right? I mean, the tsunami that Facebook has this amazing co founder Cloudera. You saw the vision on Rommedahl always quotes on the Cube. We've seen the future. No one knows it yet. That was a year and a half ago. Now everyone knows it. So do you feel about that? Is the co founder Cloudera forty million thousand? Funding validation again? More validation. How do you feel? >> Yeah, sure, it's exciting. I think of you as data volumes have grown and as the complexity of data that is collected, collected and analyzed as increase your novel software architectures have emerged on. I think what I'm most excited about is the fact that that software is open source and we're playing a key role in driving where that software is going. And, you know, I think what I'm most excited about. On top of that is the commodification of that software. You know, I'm tired of talking about the container in which you put your data. I think a lot of the creativity is happening in the data collection integration on preparation stage. Esso, I think. You know, there was ah tremendous focus over the past several decades on the modeling aspect of data way really increase the sophistication of our understanding, you know, classification and regression and optimization. And all off the hard court model and it gets done. And now we're seeing Okay, we've got these great tools to use at the end of the pipe. Eso Now, how do we get more data pushed through those those modeling algorithm? So there's a lot of innovative work. So we're thinking at the time how you make money at this or did you just say, Well, let's just go solve the problem and good things will happen. It was it was a lot more the ladder. You know, I didn't leave Facebook to start a company. I just left Facebook because I was ready to do something new. And I knew this was a huge movement and I felt that, you know, it was very gnashing and unfinished a software infrastructure. So when the opportunity Cloudera came along, I really jumped on it. And I've been absolutely blown away by the commercial success we've had s o. I didn't I certainly didn't set out with a master plan about how to extract value from this. My master plan has always been to really drive her duped into the background of enterprise infrastructure. I really wanted to be as obvious of a choice as Lennox and you See you, you're We've talked a lot at this conference and others about, you know, do moving from with fringe to the mainstream commercial enterprises. And all those guys are looking at night J. P. Morgan Chase. Today we're building competitive advantage. We're saving money, those guys, to have a master plan to make money. Does that change the dynamic of what you do on a day to day basis, or is that really exciting to you? Is an entrepreneur? Oh, yeah, for sure. It's exciting. And what we're trying to do is facilitate their master plan, right? Like we wanted way. Want to identify the commonalities and everyone's master plan and then commoditize it so they can avoid the undifferentiated heavy lifting that Jeff Bezos points out. You know where you know? No one should be required, Teo to invest tremendous amounts of money in their container anymore, right? They should really be identifying novel data sources, new algorithms to manipulate that data, the smartest people for using that data. And that's where they should be building their competitive advantage on. We really feel that, you know, we know where the market's going on. We're very confident, our product strategy. And I think over the next few years, you know, you guys are gonna be pretty excited about the stuff we're building, because I know that I'm personally very excited. And yet we're very excited about the competition because number one more people building open source software has never made me angry. >> Yeah, so So, you know, that's kind of market place. So, you know, we're talking about data science building and data science teams. So first tell us Gerald feeling today to science about that. What you're doing that, Todd here, around data science on your team and your goals. And what is a data scientist? I mean, this is not, You know, it's a D B A for her. Do you know what you know, sheriff? Sure. So what's going on? >> Yeah, So, you know, to kind of reflect on the genesis of the term. You know, when we were building out the data team at Facebook, we kind of two classes of analysts. We had data analysts who are more traditional business intelligence. You know, building can reports, performing data, retrieval, queries, doing, you know, lightweight analytics. And then we had research scientists who are often phds and things like sociology or economics or psychology. And they were doing much more of the deep dive, longitudinal, complex modeling exercises. And I really wanted to combine those two things I didn't want to have. Those two folks be separate in the same way that we combined engineering and operations on our date infrastructure group. So I literally just took data analyst and research scientists and put them together and called it data scientist s O. So that's kind of the the origin of the title on then how that's translating what we do at Clyde era. So I've recently hired to folks into a a burgeoning data science group Cloudera. So the way we see the market evolving is that you know the infrastructure is going to be commoditized. Yes, mindset >> to really be a data scientists, and you know what is way should be thinking about it. And there's no real manual. Most people aboard that math skills, economic kinds of disciplines you mentioned. What should someone prepared themselves? How did they? How does someone wanna hire data scientist had, I think form? Yeah, kinds of things. >> Well, I tend to, you know, I played a lot of sports growing up, and there's this phrase of being a gym rat, which is someone who's always in the gym just practicing. Whatever support is that they love. And I find that most data scientists or sort of data rats, they're always there, always going out for having any data. So you're there's a genuine curiosity about seeing what's happening and data that you really can't teach. But in terms of the skills that are required, I didn't really find anyone background to be perfect. Eso actually put together a course at University California, Berkeley, and taught it this spring called Introduction to Data Science, and I'm teaching and teaching it again this coming spring, and they're actually gonna put it into the core curriculum. Uh, in the fall of next year for computer science. >> Right, Jack Harmer. Bakar. Thanks so much for that insight. Great epic talk here on the Cube. Another another epic conversations share with the world Live. Congratulations on the funding. Another forty months. It's great validation. Been congratulations for essentially being part of data science and finding that whole movement Facebook. And and now, with Amaar Awadallah and the team that cloud there, you contend a great job. So congratulations present on all the competition keeping you keeping a fast capitalism, right? Right. Thank >> you. But it's >> okay. It's great, isn't it? That with all these great minds working in this industry, they still can't. We're so early in this that they still can't really define what a data scientist is. Well, what does talk about an industry and its infancy? That's what's so exciting. Everyone has a different definition of what it is, and that that what that means is is that it's everyone I think. Data science represents the new everybody. It could be a housewife. It could be a homemaker to on eighth grader. It doesn't matter if you see an insight and you see something that could be solved. Date is out there, and I think that's the future. And Jeff Hamel could talked about spending all this time and technology with undifferentiated heavy lifting. And I'm excited that we are moving beyond that into essentially the human part of Big Data. And it's going to have a huge impact, as we talked about before on the productivity of organizations and potentially productivity of lives. I mean, look at what we've talked about this this afternoon. We've talked about predicting volcanoes. We've talked about, you know, the medical issues. We've talked about pretty much every aspect of life, and I guess that's really the message of this industry now is that the folks who were managing big data are looking too change pretty much every aspect of life. This is the biggest inflexion point in history of technology that I've ever seen in the sense that it truly affects everything and the data that's generated in the data that machine's generate the data that humans generate, data that forest generate things like everything is generating data. So this's a time where we can actually instrument it. So this is why this massive disruption, this area and disruption We should say the uninitiated is a good thing in this business. Well, creation, entrepreneurship, copies of being found it It's got a great opportunity. Well, I appreciate your time, I unfortunately I think that's going to wrap it up for our big date. A deep dive. John and Dave the Cube guys have been great. I really appreciate you showing up here and, you know, just lending your insights and expertise and all that on DH. I want to thank you the audience for joining us. So you should stay tuned for the ongoing conversation on the Cube and to emcee TV to be informed, inspired and hopefully engaged. I'm Richard Schlessinger. Thank you very much for joining us.