>>Live from Seattle, Washington. It's the queue at Tableau conference 2014 brought to you by headline sponsor Tableau.. >>Here are your hosts, John furrier and Jeff Kelly. >>Okay, welcome back. And when we hear live in Seattle, Washington for the cube, this is our flagship program. We go out to the events, expect to see with the noise. I'm John furrier, my coach Jeff Kelly, analysts that we bond.org and we'd love to go talk to the senior leaders of the companies that are hosting the event, the Tablo data 14 conference and Kelly, right EVP of sales for Tableau software. Welcome to the cube. >>Thank you. Thank you for having me. >>So, uh, you're under the, you're in the pressure cooker seat. So sales is everything, right? You know, you guys are a public company and you have to perform. Performance is happy customers, they pay you money, you collect the cash, you put it in the bank and invested into your business and do it again and again. Um, you've done very well as a company. You guys have been great. So I got to ask you, um, about where Chad blow is today. Share with the folks a little bit of the history. Um, you know, we've been big fans of the company actually. We are, uh, you know, me personally being an entrepreneur, I love when companies get built by the founders and don't have to raise money to start the company. They get critical mass and take the extra growth capital. And you guys have done that. You've been in real big success story is an entrepreneurial venture. So share the culture and kind of where you guys are now and with the customer base, the culture. >>Oh, that's a lot of questions all in one. Uh, well thank you for having me. It's a pleasure being here. You know, you asked about what it's been like on this whole journey and a lot of the people that were here at the beginning, we're all still here, right? So I was the first salesperson at Tableau. I joined a month before we started version one. And I've seen how things have changed and evolved. And the truth of the matter is we have a lot more people. We have more customers, but the culture of the company has stayed really sound from the beginning. We were a bunch of people who were very, very passionate about this mission to help people see and understand data. And that's still our mission today. So from the day I started to now, it's all been focused on empowering people to answer their questions more. And so the culture of the people that started were very passionate, really excited about the mission, really a group of company builders who wanted to roll up their sleeves and go make things happen. And yes, we're a bigger company now. Now we're a public company, but we're still just barely, barely scratching the surface. I mean, they're 55 million companies out there in the world. We have 20,000 customers. So we have a long, long way to go. >>I love that you're a senior lead as a company. You've been there as the first is awesome. So I've got to ask you, I mean there's always a moment in time where you go, Oh, will we make it? Or that moment where you going? We've the flywheels going. Could you share just some color around because startups are very hard. Think they're easy all yet. Anyone can do that. So share with a moment where you go, Oh my God, it's gonna be tough shipping where they're shipping a product or hiring or personnel or, and an aha moment where you said, Oh my God, we're doing it. Well, >>when, when you're in this company building mode, it's just you put your head down and you go and you're just go, go, go. And it's always about going and finding the next customer, making sure that customer is excited, ecstatic, hiring more people on the team, making sure that culture is still vibing. And we really just took the focus of doing things one day at a time and treating each customer like their goals. And that's still what we do. Our customers are our lifeblood, right? And that's what's keeping us going. So there were certain times at during during the whole journey, I mean, I remember 2009 when the economy was slowing down. Tableau actually still grew at a really healthy clip, but it was harder. But there was really no time that I felt, Oh, this is a huge uphill battle. I, it was an uphill battle all the time. >>We're still kind of the underdogs, right, where there's tons of customers to help. We haven't helped tons of them yet. And it's just doing things to make sure that we're building good products, empowering people to you go, wow, we're really doing this well. Did you take a break and pause and say, Hey, we're doing it, we're making it. Well, you know, I think one of the moments that really resonated for me is we worked so long to say is Tao, is Tablo gonna make it just keep doing what we're doing and believe in what we're doing. Believe in that mission. And for a long time it was, can we make it to be a public company? Can we ever get to that moment? And I remember the day, it was May 17th last year, 2013 when we were on the floor of the New York stock exchange. And we had brought tons of customers. I mean not customers. We had a lot of employees. So we had over a hundred employees filling out the floor. And in that moment when we had the management team and Christian was ringing the bell, just looking out at all these people who had helped us build Tableau and get to that day. I think that was a moment of real. A lot of pride. And it's funny talking about it right now because where I just came from is gesturing in the bell again at the, at the closing bell. So >>cause that's a lot of those steps are very hard. I mean Jeff and I talked to special all the time. We'll get a big pile of money from the VCs. Four or five guys. >>Well we didn't get a big pile of, >>I know, I just, why I was thinking why it's such a great story because the pilot money could complicate it. Being hungry actually is motivating. So, and then having that customer product successes is a great testimony. So we, I mean I think you guys are a great testimonial to successful startups. Thank you. So let's dig into the sales strategy a little bit. So as you've grown up Tableau, when you started off you really, this is you know, this very nimble underdog. You were kind of going in there with really disrupting the old guard BI players. A lot of, more of a kind of I think a desktop focus, a single user kind of focus. You've expanded, you've got enterprise licenses, now you've got cloud, now you've got mobile. How has the sales strategy evolved over that time period to, to adopt or to adjust to these new, uh, Kevin, the new ways of reaching your customer? >>Well, you know, our model is actually really quite simple. I'll go back to what I had talked about before. We help people see and understand data. So everything about what we're trying to do is to help people to be able to answer their own questions and to empower them with flexibility and agility and self service. And as we add additional products, it's really just extending the number of people that we can help. Some people want to work in the cloud, so Tableau online's better. Some people want to do it on their desktop so they're doing it more with tablet, desktop, some people out in the server and so as long as our salespeople are are looking for what is the best way that I can help this customer to be able to be more self sufficient in answering their own question and then we really hear what's the customer's use case. >>Then to answer that we have different products that actually fit that in. So in terms of how our sales strategy is working, the sales strategy is the same as it always is so we don't really focus on what to do with this product line versus that product line or this product line or small customers versus big customers. It's really all in this landed expand, let the customer buy as big or as little as they want to get started. We'll work with them very closely to make them successful and then as they're successful, they'll come back to buy more. And we have all these different ways that they can buy software and types of software that they can buy to be able to address their needs of self service agility and answering their own questions. >>The buyer, the profile of the buyer changed at all. So I know obviously Tableau is all about the end user, the person who's interacting with the software interact with the data as you'd like to focus on. But as you move to larger accounts, larger enterprises, are you still dealing directly with that user when you sell? Are you dealing with essential it more often? Right, right. >>And I guess that was kind of my question. You evolve to that, you know, I think that's a great, it's a great question because if I were to roll back the clock to almost 10 years ago when I was starting, we were, we were actually interacting mostly with the business user. So the end user and over time we're interacting with the C level, the C suite, we're interacting with the VP of it, we're interacting with the business users. And actually we're, we're working with both groups a lot. So what happened early on was we'd start with the business and over time as they bought more and more and more, they would bring us into it. And now actually we're seeing a shift that sometimes it's the it and the C suite that's coming to us and they're saying, Hey, we want to be able to empower our user community answered their own questions, but we need to be able to do that in a more secure governed control type of way. >>And is there a way that we can balance with Tableau? So we see it happening in both. I think one of the interesting changes that we're seeing is there is a cultural shift that's going on right now and companies are now starting to realize that the way that the past is very different than the wave of the future. So the wave of the past was if you had a question, you threw it over the fence to this central group that was report writers and these report writers knew how to code and they were very, very specialized. And the user that had the question, they had absolutely no idea how to operate those systems well. Now that companies are saying as data's coming in at such a fast clip, it just takes too long. They have to empower people to be able to answer their own questions, otherwise they end up being at a standstill. And so as we start having more discussions with the enterprise in the C suite, those folks who are in it and the CIO who realize, Hey, there's a shift that's going on and we need to be doing things in the way of where the world is going, not the way that we've done it in the past. It makes that conversation quite a bit easier. And so now we're seeing more and more conversations that are along those lines of how are we going to keep our organization to be competitive going into the. >>So I've got to ask you about the international expansion. We were talking earlier with your colleague Dave Martin, um, and also move at the HP big data event. And I had also had a conversation with Dave, CEO firearm, huge international. He says, John, my big growth happened. He's public company. You got you guys, he says international huge growth opportunity for us. So you have a Tam, then you have 55 million customers. You have one of those unique products at all customers need. So that's good. Check growth is on the horizon. How are you going to attack that new territory? I mean international and to grow, I mean channel strategy, indirect big part of it. I mean you guys are enabling people to create value. That seems to be the formula for a great indirect strategy. You've built a successful direct sales force graduations, but that's can take time. >>Yeah. Well you know, our model for international international is a huge opportunity for us. So we are putting a lot of resources and time into expanding internationally. We have our headquarters over in AMEA, we have headquarters over an APAC. We're now just w we opened up offices in Japan and in Germany we opened up operations in India. We are opening up another, a bigger office in, in Australia and even in Latin America, Brazil and Mexico. There's a fair amount going on now as we're going to market. It actually is pretty similar, so we're building direct sales force in all of those regions. But international, as you start doing more international, the channel becomes even increasingly important and it is, we're focusing a lot of time and energy on the channel here in the States. But in places like AMEA and certain locations over an APAC and and certainly in Latin America there is just the way of doing business tends to be more around the channel. >>Equalization has always been a nice thing of having in country operations. So that's always been kind of the international playbook. But with data I can be complicated. So having people in country, in a channel delivering value, is that the preferred way you guys, is that what you're saying? Is that, is that kind of? >>You know what I th th well the interesting part about Tableau is as we talked about, it's agnostic. Anyone can use it. And so when we go into a new country, there's two ways that we can go in. We can go on with our directing and we can go in with empowering our channel. And we actually have customers in over a hundred countries throughout the world, right? And we have partners operating in a large number of those. So our partners often are the ones that are the local feet on the street. They're going and they're having the conversations and, and they're providing the local support in the language and in the culture that it is now. When we actually open up offices in those different regions, we try to be very aligned, not only just putting our salespeople in, but having our entire company all lined up behind it. So we have our sales team, we have our marketing, we have our product. So when we go into Japan, for instance, we want to be able to have the website in Japanese. We want to be able to have the product localized in Japanese, we want to be able to have support staff that can help. And, and then of course having the partner ecosystem where the partners are able to help us make those customers all realistic. >>Flip yet in the U S I mean, as you guys get the channel going, has there been some channel conflict on order orders and who owns the accounts? >>Yeah, well you know what, our channel, we were developing a lot in the channel, but we're still pretty early in the, in our channel development and we're spending a lot of time to make sure that our channel is really successful as well as our, as well as our customers being successful. And the truth of the matter is we can't, we can't go and help all the people that we want to help without embracing the channel. And they're system integrators that they're in there and they're doing huge multi-year projects and we're working closely with them. And when we talk about the channel, we're working with resellers but also OEM and technology partners and system integrators. So lots and lots of channel activity going on. >>Yeah, I think you just touched on, well I think is one of the going to be one of the challenges for Tableau is that you can't, as you expand so fast, you can't keep your finger or your pulse on the customer quite as quite as closely as maybe you'd like. You've got to, you've got to count on the channel to do some of that. So that, and Tableau is of course known for being very customer focused. I mean the show here, you know, the crowds are cheering and Christian as he's giving his keynote and different visualizations are being demoed on stage and the crowds standing on their feet, you know, to keep that kind of customer focus as you expand. I think it's a challenge. It sounds like you really got to focus on those relationships with your partners and your OEM partners, et cetera. So they kind of understand that the Tableau approach is that, yeah, >>I I, I totally agree. Actually. I think you can even see at the show today, if you go down to that partner expo hall, there are so many partners, you're way more partners than we've ever had before. And when I was checking in with them, even yesterday where the show hadn't even started, they're getting a huge number of leads that are coming in and they're, there's so many opportunities for us to work together with our partners. In fact, this year, not only did we build of being really growing our partner sales team, but we had a whole series of partner summits this year and we traveled around the world. We had one in AMEA, one in APAC, one here in the States of being able to really train and enable our partners not only how to sell Tableau, but to work with them in a conversation of what's the best way that we can engage with them and make them really successful. So when we think about our ecosystem, it's not just about our customers, it's now about our customers and about our partners. And we're all part of the Tableau >>here. So obviously one of the things that you guys have done, you do a great job because you're such walking testimonials as customers. Um, what channel partners do you have as customers and that are top references now that you're showcasing and what end users are you showcasing here at this event? Can you name names and? >>Yeah, well I think you can, you can actually go downstairs and look in the partners of who we are and we're doing Watson, lots of, uh, partner with, with whether it's Vertica or with Alteryx or with data, uh, where we're doing joint sales and a lot of those, a lot of the that you'll see here, they're using Tableau internally in a pretty big way. And then in terms of customers, and we have showcases all over the place. I think we have a hundred customer speakers that are here. So there are there hospitals, we have Barnes, Jewish and Seattle children's who are talking about how they're using Tableau actually in the operating rooms and with nurses. And to be able to help save lives. We have education institutions who are using Tableau for how they can teach better in school, how the teachers can have their administration going. Uh, and we also have a number of corporate customers who are helping with that as well. >>So one of the things that we always talk about when we talk about startups, you guys want to start certainly, but company building is a great team. You guys are on that next generation of building out. Um, you always get the question, um, high touch sales, indirect low cost, our automated self-service if you're, you know, kind of a platform, um, inside sales is a great strategy for expanding out growth. Um, but it's hard. Um, do you guys have an inside sales organization? You, are you building it out? Is that a big part of your increase in your customer service? Cause a lot of you got great fans. Loyalties, high products is good. So are you building out? >>Yeah. You know, we actually, we got predominantly with inside sales, so we started with inside sales and then enterprise sales came later. And with our inside sales, we still have a very, very robust inside sales. We have kind of both models, some customers prefer to be interacted with field, face to face. And so we have field folks that are all over, uh, in our, all our major regions and we have a lot of inside folks. And the same is true when we look at how we're going to support them. So we have technical folks and services folks in training folks that will go out and meet the customer on their site, help to enable them setting up center of excellence, all that. And then we have a large number of that is that is done remotely. The benefit we have at Tableau is actually tablets, pretty easy to use. >>And so we don't always have to sit down and do it beside them. So how about sales compensation, if you will? Not with numbers, but like, I mean culturally is it, is it, we're hiring you killed like in the early days of Cisco sales guys were making zillions of dollars. Um, there's Tableau have, um, the kind of product pricing mix where you guys have a lot of like huge compensation, uh, rewards. So how does that work? You know, what we focus on having our salespeople be really excited about working here, having it be a very good as you know, right. I mean, compensation drives behavior. How do you guys, we have a lot of salespeople that have been here for a very long period of time. So we have a huge opportunity and we focus on the opportunity to help more customers and then the opportunity to have a really good career progression path. >>You know? Yes. I'm not going to answer your question, but you can keep on top a little bit about the competitive landscape. So, and again, maybe you know, because you've been with Tableau since the beginning, how has it evolved again, when you guys started, you were very much the disruptor going in. Yeah. Let's name some names, the disruptor, SAP business objects. You had Cognos, Hyperion, you guys are going in there and say, no, that's the old way. This is the new way. Um, since then you've now that some of those old players are started, they're focusing now on you know, being very self service, kind of emulating a lot of the things top load yet now you've got also kind of even newer companies, newer startups out there that are coming, even some are maybe mobile focused or cloud focused. What's the competitive landscape look like for you and from a sales perspective, again, how do you adapt as you got to come in from, you know, from the, from the new guys, you've got to come in from the old guard, you guys are targeted. >>When you're this successful you're always going to be a target. What it's like from your perspective. You know what, one of the things that we actually really focused on at Tableau, cause we talk about this a lot internally with our team is we can only control what we can control. We can control what our products are, we can control what our customer success is, we can control how we engage with our customers. And so we spend a lot of time just focusing on what it is that Tableau can do. And as we're now talking more about data discovery and agile and analytics and self-service, there's a lot of noise out there. A lot of other players who are saying that they can do the same thing and that they can do it as well. And our strategy is really, if you think you can use that, so why don't you go download their product and download our product and see how long it takes. And we actually encourage people to go out and test it out and try. And what we find is when someone is really interested in self service and helping people to answer their own questions, then the answer to them becomes really clear when it is an a question of we just want traditional old pixel perfect reporting you have. There are a lot of people that can play in that game. Uh, but we're finding the conversations changing quite a bit when they really want self-service. Then we actually feel like we're, we're pretty well positioned competitively. >>So are your lottery, your deals going up in, you know, competitive environments where you've got Tableau lined up against business objects against, I don't know. Good data against whoever. Is it a lot of that or do you have a lot of, you know, people who are trying the product love it and just say, Hey, we want to go with Tableau. >>You know, there's both, but the majority of our deals are actually when we're competing against the status quo, they actually aren't even looking at other business intelligence. They might have it in their company but it's not solving their need and their requirement. So a lot of people are just using what is already commissioned on their computer. Now there are situations where there is a competitive bake-off and we love competition. I mess with salespeople. Do we go and compete? Uh, but we're finding that the conversation is shifting and where we tend to really focus our time and energy is with those companies that are really looking for the new way. >>Kelly, you got to get the, I got to get the hook here, but I want to ask you two final questions. One is an easy one. What's it like working with Christian? >>It's great working with Christen. You know what? We've worked together all for so long and it's, it's really, we say it's like we're a family, right? We, we know each other, we know each other's families, we know each other's kids and it's pretty much the same as it was when I started almost 10 years ago. Nothing's really >>the second question. Share with the folks out there watching what is the culture of Tablo, if you could. Every culture has their own little weird tweak that makes them so unique. Intel, it's Moore's law. What's Tableau's cultural? >>Well, you have to go ask all the Tablo people if they think our culture is weird, probably not like a unique tweak that makes them so successful. The Moore's law was first called the weird, you know, people that work here are really, really passionate about what we do. We're passionate, we're mission focus and people have a lot of fun at what they do. They work hard and they play hard and it's, it's a very fun place to be. But we go fast. Yeah, certainly not weird, that's for sure. I didn't mean that, but I want a good way, a good thing. And it's usually the, it's the ones that the best deals are the ones that no one sees that doesn't look like it's going to be. And you guys were certainly a great winner of our hiring, so everyone in the world were hiring. We couldn't get the sales comp out of her, but we, you know, we tried our best, uh, Kelly, seriously, thanks for coming on cue. Really appreciate it. We know the journey you've been on has fantastic. It's a >>whirlwind now. You just got to go to the next leg of the journey, which is build a global 50 million customer business. Congratulations. Thank you for having me. We'll be right back with our next guest after this short break live in Seattle, Washington to the cube. Thank you.

okay we're back this is Dave Volante with Jeff Kelly we're with Ricky bond on organ this is the cubes silicon angles flagship product we go out to the events we extract the signal from the noise we bring you the tech athletes who are really changing the industry and we have one here today christiane sabo is the CEO the leader the spiritual leader of of this conference and of Tablo Kristin welcome to the cube thanks for having me yeah it's our pleasure great keynote the other day I just got back from Italy so I'm full of superlatives right it really was magnificent I was inspired I think the whole audience was inspired by your enthusiasm and what struck me is I'm a big fan of simon Sinek who says that people don't buy what you do they buy why you do it and your whole speech was about why you're here everybody can talk about their you know differentiators they can talk about what they sell you talked about why you're here was awesome so congratulations I appreciate that yeah so um so why did you start then you and your colleagues tableau well it's how below really started with a series of breakthrough research innovations that was this seed there are three co-founders of tableau myself dr. crystal T and professor Pat Hanrahan and those two are brilliant inventors and designers and researchers and the real hero of the tableau story and the company formed when they met on entrepreneur and a customer I had spent several years as a data analyst when I first came out of college and I understood the problems making sense of data and so when I encountered the research advancements they had made I saw a vision of the future a much better world that could bring the power of data to a vastly larger number of people yeah and it's really that simple isn't it and and so you gave some fantastic examples them in the way in which penicillin you know was discovered you know happenstance and many many others so those things inspire you to to create this innovation or was it the other way around you've created this innovation and said let's look around and see what others have done well I think the thing that we're really excited about is simply put as making databases and spreadsheets easy for people to use I can talk to someone who knows nothing about business intelligence technology or databases or anything but if I say hey do you have any spreadsheets or data files or databases you you just feel like it could it could get in there and answer some questions and put it all together and see the big picture and maybe find a thing or two everyone not everyone has been in that situation if nothing else with the spreadsheet full of stuff like your readership or the linkage the look the the traffic flow on on the cube website everyone can relate to that idea of geez why can't I just have a google for databases and that's what tableau is doing right right so you've kind of got this it's really not a war it's just two front two vectors you know sometimes I did I did tweet out they have a two-front war yeah what'd you call it the traditional BI business I love how you slow down your kids and you do that and then Excel but the point I made on Twitter in 140 characters was you it will be longer here I'm a little long-winded sometimes on the cube but you've got really entrenched you know bi usage and you've got Excel which is ubiquitous so it sounds easy to compete with those it's not it's really not you have to have a 10x plus value problem solutely talked about that a little bit well I think the most important thing we're doing is we're bringing the power of data and analytics to a much broader population of people so the reason the answer that way is that if you look at these traditional solutions that you described they have names like and these are the product brand names forget who owns them but the product brand names people are used to hearing when it comes to enterprise bi technology our names like Business Objects and Cognos and MicroStrategy and Oracle Oh bi and big heavy complicated develop intensive platforms and surprise surprise they're not in the hands of very many people they're just too complicated and development heavy to use so when we go into the worlds even the world's biggest companies this was a shocker for us even when we go into the world's most sophisticated fortune 500 companies and the most cutting-edge industries with the top-notch people most of the people in their organization aren't using those platforms because of theirs their complication and expense and development pull and so usually what we end up doing is just bringing the power of easy analytics and dashboards and visualization and easy QA with data to people who have nothing other than maybe a spreadsheet on their desk so in that sense it's actually a little easier than it sounds well you know I have to tell you I just have a cio consultancy and back in the day and we used to go in and do application portfolio analysis and we would look at the applications and we always advise the CIOs that the value of an application is a function of its use how much is being adopted and the impact of that use you know productivity of the users right and you'd always find that this is the dss system the decision support system like you said there were maybe 3 to 15 users yeah and an organization of tens of thousands of people yeah if they were very productive so imagine if you can you can permeate the other you know hundreds of thousands of users that are out there do you see that kind of impact that productivity impact as the potential for your marketplace absolutely I you know the person who I think said it best was the CEO of Cisco John Chambers and I'll paraphrase him here but he has this great thing he said which is he said you know if I can get each of the people on my team consulting data say oh I don't know twice per day before making a decision and they do the same thing with their people and their people and so you know that's a million decisions a month you did the math better made than my competition I don't want people waiting around for top top management to consult some data before making a decision I want all of our people all the time Consulting data before making a decision and that's the real the real spirit of this new age of BI for too long it's been in the hands of a high priesthood of people who know how to operate these complicated convoluted enterprise bi systems and the revolution is here people are fed up with it they're taking power into their hands and they're driving their organizations forward with the power of data thanks to the magic of an easy-to-use suite like tableau well it's a perfect storm right because everybody wants to be a data-driven organization absolutely data-driven if you don't have the tools to be able to visualize the data absolutely so Jeff if you want to jump in well Christian so in your keynote you talked for the majority of the keynote about human intuition and the human element talk a little bit about that because when we hear about in the press these days about big data it's oh well the the volume of data will tell you what the answer is you don't need much of the human element talk about why you think the human element is so important to data-driven decision-making and how you incorporate that into your design philosophy when you're building the product and you're you know adding new features how does the human element play in that scenario yeah I mean it's funny dated the data driven moniker is coming these days and we're tableaus a big big believer in the power of data we use our tools internally but of course no one really wants to be data driven if you drive your company completely based on data say hello to the cliff wall you will drive it off a cliff you really want people intelligent domain experts using a combination of act and intuition and instinct to make data informed decisions to make great decisions along the way so although pure mining has some role in the scheme of analytics frankly it's a minor role what we really need to do is make analytic software that as I said yesterday is like a bicycle for our minds this was the great Steve Jobs quote about computers that their best are like bicycles for our mind effortless machines that just make us go so much faster than any other species with no more effort expended right that's the spirit of computers when they're at our best Google Google is effortless to use and makes my brain a thousand times smarter than it is right unfortunately over an analytic software we've never seen software that does tap in business intelligence software there's so much development weight and complexity and expense and slow rollout schedules that were never able to get that augmentation of the brain that can help lead to better decisions so at tableau in terms of design we value our product requirements documents say things like intuition and feel and design and instinct and user experience they're focused on the journey of working with data not just some magic algorithm that's gonna spit out some answer that tells you what to do yeah I mean I've often wondered where that bi business would be that traditional decision support business if it weren't for sarbanes-oxley I mean it gave it a new life right because you had to have a single version of the truth that was mandated by by the government here we had Bruce Boston on yesterday who works over eight for a company that shall not be named but anyway he was talking about okay Bruce in case you're watching we're sticking to our promise but he was talking about intent desire and satisfaction things those are three things intent desire and satisfaction that machines can't do like the point being you just you know it was the old bromide you can't take the humans in the last mile yeah I guess yeah do you see that ever changing no I mean I think you know I I went to a friend a friend of mine I just haven't seen in a while a friend of mine once said he was an he was an artificial intelligence expert had Emilie's PhD in a professorship in AI and once I naively asked him I said so do we have artificial intelligence do we have it or not and we've been talking about for decades like is it here and he said you're asking the wrong question the question is how smart our computers right so I just think we're analytics is going is we want to make our computers smarter and smarter and smarter there'll be no one day we're sudden when we flip a switch over and the computer now makes the decision so in that sense the answer to your question is I keep I see things going is there is it going now but underneath the covers of human human based decision making it are going to be fantastic advancements and the technology to support good decision making to help people do things like feel and and and chase findings and shift perspectives on a problem and actually be creative using data I think there's I think it's gonna be a great decade ahead ahead of us so I think part of the challenge Christian in doing that and making that that that evolution is we've you know in the way I come the economy and and a lot of jobs work over the last century is you know you're you're a cog in a wheel your this is how you do your job you go you do it the same way every day and it's more of that kind of almost assembly line type of thinking and now we're you know we're shifting now we're really the to get ahead in your career you've got to be as good but at an artist you've got to create B you've got to make a difference is the challenge do you see a challenge there in terms of getting people to embrace this new kind of creativity and again how do you as a company and as a you know provider of data visualization technology help change some of those attitudes and make people kind of help people make that shift to more of less of a you know a cog in a larger organization to a creative force inside that organ well mostly I feel like we support what people natively want to do so there are there are some challenges but I mostly see opportunity there in category after category of human activity we're seeing people go from consumers to makers look at publishing from 20 years ago to now self-publishing come a few blogs and Twitter's Network exactly I mean we've gone from consumers to makers everyone's now a maker and we have an ecosystem of ideas that's so positive people naturally want to go that way I mean people's best days on the job are when they feel they're creating something and have that sense of achievement of having had an idea and seeing some progress their hands made on that idea so in a sense we're just fueling the natural human desire to have more participation with data to id8 with data to be more involved with data then they've been able to in the past and again like other industries what we're seeing in this category of technology which is the one I know we're going from this very waterfall cog in a wheel type process is something that's much more agile and collaborative and real-time and so it's hard to be creative and inspired when you're just a cog stuck in a long waterfall development process so it's mostly just opportunity and really we're just fueling the fire that I think is already there yeah you talked about that yesterday in your talk you gave a great FAA example the Mayan writing system example was fantastic so I just really loved that story you in your talk yesterday basically told the audience first of all you have very you know you have clarity of vision you seem to have certainty in your vision of passion for your vision but the same time you said you know sometimes data can be confusing and you're not really certain where it's going don't worry about that it's no it's okay you know I was like all will be answered eventually what but what about uncertainty you know in your minds as the you know chief executive of this organization as a leader in a new industry what things are uncertain to you what are the what are the potential blind spots for you that you worry about do you mean for tableau as a company for people working with data general resource for tableau as a company oh I see well I think there's always you know I got a trip through the spirit of the question but we're growing a company we're going a disruptive technology company and we want to embrace all the tall the technologies that exist around us right we want to help to foster day to day data-driven decision-making in all of its places in forms and it seems to me that virtually every breakthrough technology company has gone through one or two major Journal technology transformations or technology shocks to the industry that they never anticipated when they founded the company okay probably the most recent example is Facebook and mobile I mean even though even though mobile the mobile revolution was well in play when when Facebook was founded it really hadn't taken off and that was a blind Facebook was found in oh seven right and look what happened to them right after and here's that here's new was the company you can get it was founded in oh seven yeah right so most companies I mean look how many companies were sort of shocked by the internet or shocked by the iPod or shocked by the emergence of a tablet right or shocked by the social graph you know I think for us in tableaus journey if this was the spirit of the thought of the question we will have our own shocks happen the first was the tablet I mean when we founded tableau like the rest of the world we never would have anticipated that that a brilliant company would finally come along and crack the tablet opportunity wide open and before in a blink of an eye hundreds of millions of people are walking around with powerful multi-touch graphic devices in their I mean who would have guessed people wouldn't have guessed it no six let alone oh three know what and so luckily that's what that's I mean so this is the good kind of uncertainty we've been able to really rally around that there are our developers love to work on this area and today we have probably the most innovative mobile analytics offering on the market but it's one we never could have anticipated so I think the biggest things in terms of big categories of uncertainty that we'll see going forward are similar shocks like that and our success will be determined by how well we're able to adapt to those so why is it and how is it that you're able to respond so quickly as an organization to some of those tectonic shifts well I think the most important thing is having a really fleet-footed R&D team we have just an exceptional group of developers who we have largely not hired from business technology companies we have something very distributed going a tableau yeah one of the amazing things about R&D key our R&D team is when we decided to build just this amazing high-wattage cutting-edge R&D team and focus them on analytics and data we decided not to hire from other business intelligence companies because we didn't think those companies made great products so we've actually been hiring from places like Google and Facebook and Stanford and MIT and computer gaming companies if you look at the R&D engineers who work on gaming companies in terms of the graphic displays and the response times and the high dimensional data there are actually hundreds of times more sophisticated in their thinking and their engineering then some engineer who was working for an enterprise bi reporting company so this incredible horsepower this unique team of inspired zealots and high wattage engineers we have in our R&D team like Apple that's the key to being able to respond to these disruptive shocks every once in a while and rule and really sees them as an opportunity well they're fun to I mean think of something on the stage yesterday and yeah we're in fucky hats and very comfortable there's never been an R&D team like ours assembled in analytics it's been done in other industries right Google and Facebook famously but in analytics there's never been such an amazing team of engineers and Christian what struck me one of the things that struck me yesterday during your keynote or the second half of the keynote was bringing up the developers and talking about the specific features and functions you're gonna add to the product and hearing the crowd kind of erupt at different different announcements different features that you're adding and it's clear that you're very customer focused at this at tableau of you I mean you're responding to the the needs and the requests of your customers and I that's clearly evident again in the in the passion that these customers have for your for your product for your company how do you know first I'm happy how do you maintain that or how do you get get to that point in the first place where you're so customer focused and as you go forward being a public company now you're gonna get pressure from Wall Street and quarter results and all that that you know that comes with that kind of comes with the territory how do you remain that focused on the customer kind of as your you know you're going to be under a lot of pressure to grow and and you know drive revenue yeah I keep that focus well there's two things we do it's a it's always a challenge to stay really connected to your customers as you get big but it's what we pride ourselves on doing and there's two specific things we do to foster it the first is that we really try to focus the company and we try to make a positive aspect of the culture the idea of impact what is the impact of the work we're having and in fact a great example of how we foster that is we bring our entire support and R&D team to this conference no matter where it is we take we fly I mean in this case we literally flew the entire R&D team and product management team and whatnot across country and the time they get here face to face face to face with customers and hearing the customer stories and the victories and actually seeing the feedback you just described really inspires them it gives them specific ideas literally to go back and start working on but it also just gives them a sense of who comes first in a way that if you don't leave the office and you don't focus on that really doesn't materialize and the way you want it the second thing we do is we are we are big followers of I guess what's called the dog food philosophy of eat your own dog so drink your own champagne and so one of our core company values that tableau is we use our products facility a stated value of the company we use our products and into an every group at tableau in tests in bug regressions in development in sales and marketing and planning and finance and HR every sip marketing marketing is so much data these these every group uses tableau to run our own business and make decisions and what happens Matt what's really nice about a company because you know we're getting close to a thousand people now and so it's keeping the spirit you just described alive is really important it becomes quite challenging vectors leagues for it because when that's one of your values and that's the way the culture has been built every single person in the company is a customer everyone understands the customer's situation and the frustrations and the feature requests and knows how to support them when they meet them and can empathize with them when they're on the phone and is a tester automatically by virtue of using the product so we just try to focus on a few very authentic things to keep our connection with the customer as close as possible I'll say christen your company is a rising star we've been talking all this week of the similarities that we were talking off about the similarities with with ServiceNow just in terms of the passion within the customer base we're tracking companies like workday you know great companies that are that are that are being built new emerging disruptive companies we put you in that in that category and we're very excited for different reasons you know different different business altogether but but there are some similar dynamics that we're watching so as observers it's independent observers what kinds of things do you want us to be focused on watching you over the next 12 18 24 months what should we be paying attention to well I think the most important thing is tableau ultimately is a product company and we view ourselves very early in our product development lifecycle I think people who don't really understand tableau think it's a visualization company or a visualization tool I don't I don't really understand that when you talk about the vision a lot but okay sure we can visualization but there's just something much bigger I mean you asked about people watching the company I think what's important to watch is that as I spoke about makino yesterday tableau believes what is called the business intelligence industry what's called the business analytics technology stack needs to be completely rewritten from scratch that's what we believe to do over it's a do-over it's based on technology from a prior hair prior era of computing there's been very little innovation the R&D investment ratios which you can look up online of the companies in this space are pathetically low and have been for decades and this industry needs a Google it needs an apple it's a Facebook an RD machine that is passionate and driven and is leveraging the most recent advances in computing to deliver products that people actually love using so that people start to enjoy doing analytics and have fun with it and make data-driven driven decision in a very in a very in a way that's just woven into their into their into their enjoyment and work style every every single day so the big series of product releases you're going to see from us over the next five years that's the thing to watch and we unveiled a few of them yesterday but trust me there's a lot more that's you a lot of applause christina is awesome you can see you know the passion that you're putting forth your great vision so congratulations in the progress you've made I know I know you're not done we'll be watching it thanks very much for coming to me I'm really a pleasure thank you all right keep right there everybody we're going wall to wall we got a break coming up next and then we'll be back this afternoon and this is Dave Volante with Jeff Kelly this is the cube we'll be right back

>>Hi buddy, we're back. This is Dave Volante with the cube goes out to the shows. We extract the signal from the noise. Nate Silver's here. Nate, we've been saying that since 2010, rip you off. Hey Marcus feeder. Oh, you have that trademarks. Okay. So anyway, welcome to the cube. You man who needs no introduction, but in case you don't know Nate, uh, he's a very famous author, five 30 eight.com. Statistician influence, influential individual predictor of a lot of things including presidential elections. And uh, great to have you here. Great to be here. So we listened to your keynote this morning. We asked earlier if some of our audience, can you tweet it and you know, what would you ask Nate silver? So of course we got the predictable, how the red Sox going to do this year? Who's going to be in the world series? Are we going to attack Syria? >>Uh, will the fed E's or tightened? Of course we're down here. Who'd you vote for? Or they, you know, they all want to know. And of course, a lot of these questions you can't answer because it's too far out. But, uh, but anyway, again, welcome, welcome to the cube. Um, so I want to start by, uh, picking up on some of the themes in your keynote. Uh, you're here at the Tableau conference. Obviously it's all about about data. Uh, and you, your basic, one of your basic premises was that, um, people will misinterpret data, they'll just use data for their own own biases. You have been a controversial figure, right? A lot of people have accused you of, of bias. Um, how, what do you F how do you feel about that as a person who's, uh, you know, statistician, somebody who loves data? >>I think everyone has bias in the sense that we all have one relatively narrow perspective as compared to a big set of problems that we all are trying to analyze or solve or understand together. Um, you know, but I do think some of this actually comes down to, uh, not just bias, but kind of personal morality and ethics really. It seems weird to talk about it that way, but there are a lot of people involved in the political world who are operating to manipulate public opinion, um, and that don't really place a lot of value on the truth. Right. And I consider that kind of immoral. Um, but people like that I think don't really understand that someone else might act morally by actually just trying to discover the way the objective world is and trying to use science and research to, to uncover things. >>And so I think it's hard people to, because if they were in your shoes, they would try and manipulate the forecast and they would cheat and put their finger on their scale. They assume that anyone else would do the same thing cause they, they don't own any. Yeah. So will you, you've made some incredibly accurate predictions, uh, in the face of, of, of others that clearly had bias that, that, that, you know mispredicted um, so how did you feel when you got those, those attacks? Were you flabbergasted? Were you pissed? Were you hurt? I mean, all of the above having you move houses for, for you? I mean you get used to them with a lot of bullshit, right? You're not too surprised. Um, I guess it surprised me how, but how much the people who you know are pretty intelligent are willing to, to fool themselves and how specious arguments where meet and by the way, people are always constructing arguments for, for outcomes they happen to be rooting for. >>Right? It'd be one thing if you said, well I'm a Republican, but boy I think Obama's going to crush Romney electoral college or vice versa. But you should have an extra layer of scrutiny when you have a view that diverges from the consensus or what kind of the markets are saying. And by the way, you can go and they're betting Margaret's, you can go and you could have bet on the outcome of election bookies in the UK, other countries. Right. And they kind of had forecast similar to ours. We were actually putting their money where their mouth was. Agree that Obama was a. Not a lot, but a pretty heavy favorite route. Most of the last two months in the election. I wanted to ask you about prediction markets cause as you probably know, I mean the betting public are actually very efficient. Handicappers right over. >>So I'll throw a two to one shot is going to be to three to one is going to be a four to one, you know, more often than not. But what are your thoughts on, on prediction markets? I mean you just sort of betting markets, you'd just alluded it to them just recently or is that a, is that a good, well there a lot there then then I think the punditry right. I mean, you know, so with, with prediction markets you have a couple of issues. Number one is do you have enough, uh, liquidity, um, and my volume in the markets for them to be, uh, uh, optimal. Right. And I think the answer right now is maybe not exactly. And like these in trade type markets, knowing trade has been, has been shut down. In fact, it was pretty light trading volumes. It might've had people who stood to gain or lose, um, you know, thousands of dollars. >>Whereas in quote, unquote real markets, uh, the stakes are, are several orders of magnitude higher. If you look at what happened to, for example, just prices of common stocks a day after the election last year, um, oil and gas stocks lost billions of dollars of market capitalization after Romney lost. Uh, conversely, some, you know, green tech stocks or certain types of healthcare socks at benefit from Obamacare going into play gain hundreds of millions, billions of dollars in market capitalization. So real investors have to price in these political risks. Um, anyway, I would love to have see fully legal, uh, trading markets in the U S people can get bet kind of proper sums of money where you have, um, a lot of real capital going in and people can kind of hedge their economic risk a little bit more. But you know, they're, they're bigger and it's very hard to beat markets. They're not flawless. And there's a whole chapter in the book about how, you know, the minute you assume that markets are, are clairvoyant and perfect, then that's when they start to fail. >>Ironically enough. But they're very good. They're very tough to beat and they certainly provide a reality check in terms of providing people with, with real incentives to actually, you know, make a bet on, on their beliefs and people when they have financial incentives, uh, uh, to be accurate then a lot of bullshit. There's a tax on bullshit is one way. That's okay. I've got to ask him for anyway that you're still a baseball fan, right? Is that an in Detroit fan? Right. I'm a tiger. There's my bias. You remember the bird? It's too young to remember a little too. I, so I grew up, I was born in 78, so 84, the Kirk Gibson, Alan Trammell teams are kind of my, my earliest. So you definitely don't remember Mickey Lola cha. I used to be a big guy. That's right fan as well. But so, but Sony, right when Moneyball came out, we just were at the Vertica conference. >>We saw Billy being there and, and uh, when, when, when, when, when that book came out, I said Billy Bean's out of his mind for releasing all these secrets. And you alluded to in your talk today that other teams like the rays and like the red Sox have sort of started to adopt those techniques. At the same time, I feel like culturally when another one of your V and your Venn diagram, I don't want you vectors, uh, that, that Oakland's done a better job of that, that others may S they still culturally so pushing back, even the red Sox themselves, it can be argued, you know, went out and sort of violated the, the principles were of course Oakland A's can't cause they don't have a, have a, have a budget to do. So what's your take on Moneyball? Is the, is the strategy that he put forth sustainable or is it all going to be sort of level playing field eventually? >>I mean, you know, the strategy in terms of Oh fine guys that take a lot of walks, right? Um, I mean everyone realizes that now it's a fairly basic conclusion and it was kind of the sign of, of how far behind how many biases there were in the market for that, you know, use LBP instead of day. And I actually like, but that, that was arbitrage, you know, five or 10 years ago now, um, put butts in the seat, right? Man, if they win, I guess it does, but even the red Sox are winning and nobody goes to the games anymore. The red Sox, tons of empty seats, even for Yankees games. Well, it's, I mean they're also charging 200 bucks a ticket or something. you can get a ticket for 20, 30 bucks. But, but you know, but I, you know, I, I, I mean, first of all, the most emotional connection to baseball is that if your team is in pennant races, wins world series, right then that produces multimillion dollar increases in ticket sales and, and TV contracts down the road. >>So, um, in fact, you know, I think one thing is, is looking at the financial side, like modeling the martial impact of a win, but also kind of modeling. If you do kind of sign a free agent, then, uh, that signaling effect, how much does that matter for season ticket sales? So you could do some more kind of high finance stuff in baseball. But, but some of the low hanging fruit, I mean, you know, almost every team now has a Cisco analyst on their payroll or increasingly the distinctions aren't even as relevant anymore. Right? Where someone who's first in analytics is also listening to what the Scouts say. And you have organizations that you know, aren't making these kind of distinctions between stat heads and Scouts at all. They all kind of get along and it's all, you know, finding better ways, more responsible ways to, to analyze data. >>And basically you have the advantage of a very clear way of measure, measure success where, you know, do you win? That's the bottom line. Or do you make money or, or both. You can isolate guys Marshall contribution. I mean, you know, I am in the process now of hiring a bunch of uh, writers and editors and developers for five 38 right? So someone has a column and they do really well. How much of that is on the, the writer versus the ed or versus the brand of the site versus the guy at ESPN who promoted it or whatever else. Right. That's hard to say. But in baseball, everyone kind of takes their turn. It's very easy to measure each player's kind of marginal contribution to sort of balance and equilibrium and, and, and it's potentially achieved. But, and again, from your talk this morning modeling or volume of data doesn't Trump modeling, right? >>You need both. And you need culture. You need, you need, you know, you need volume of data, you need high quality data. You need, uh, a culture that actually has the right incentives align where you really do want to find a way to build a better product to make more money. Right? And again, they'll seem like, Oh, you know, how difficult should it be for a company to want to make more money and build better products. But, um, when you have large organizations, you have a lot of people who are, uh, who are thinking very short term or only about only about their P and L and not how the whole company as a whole is doing or have, you know, hangups or personality conflicts or, or whatever else. So, you know, a lot of success I think in business. Um, and certainly when it comes to use of analytics, it's just stripping away the things that, that get in the way from understanding and distract you. >>It's not some wave a magic wand and have some formula where you uncover all the secrets in the world. It's more like if you can strip away the noise there and you're going to have a much clearer understanding of, of what's really there. Uh, Nate, again, thanks so much for joining us. So kind of wanna expand on that a little bit. So when people think of Nate silver, sometimes they, you know, they think Nate silver analytics big data, but you're actually a S some of your positions are kind of, you take issue with some of the core notions of big data really around the, the, the importance of causality versus correlation. So, um, so we had Kenneth kookier on from, uh, the economist who wrote a book about big data a while back, the strata conference. And you know, he, in that book, they talk a lot about it really doesn't matter how valid anymore, if you know that your customers are gonna buy more products based on this dataset or this correlation that it doesn't really matter why. >>You just try to try to try to exploit that. Uh, but in your book you talk about, well and in the keynote today you talked about, well actually hypothesis testing coming in with some questions and actually looking for that causality is also important. Um, so, so what is your, what is your opinion of kind of, you know, all this hype around big data? Um, you know, you mentioned volume is important, but it's not the only thing. I mean, like, I mean, I'll tell you I'm, I'm kind of an empiricist about anything, right? So, you know, if it's true that merely finding a lot of correlations and kind of very high volume data sets will improve productivity. And how come we've had, you know, kind of such slow economic growth over the past 10 years, where is the tangible increase in patent growth or, or different measures of progress. >>And obviously there's a lot of noise in that data set as well. But you know, partly why both in the presentation today and in the book I kind of opened up with the, with the history is saying, you know, let's really look at the history of technology. It's a kind of fascinating, an understudied feel, the link between technology and progress and growth. But, um, it doesn't always go as planned. And I certainly don't think we've seen any kind of paradigm shift as far as, you know, technological, economic productivity in the world today. I mean, the thing to remember too is that, uh, uh, technology is always growing in and developing and that if you have roughly 3% economic growth per year exponential, that's a lot of growth, right? It's not even a straight line growth. It's like exponential growth. And to have 3% exponential growth compounding over how many years is a lot. >>So you're always going to have new technologies developing. Um, but what I, I'm suspicious that as people will say this one technology is, is a game changer relative to the whole history of civilization up until now. Um, and also, you know, again, a lot of technologies you look at kind of economic models where you have different factors or productivity. It's not usually an additive relationship. It's more a multiplicative relationships. So if you have a lot of data, but people who aren't very good at analyzing it, you have a lot of data but it's unstructured and unscrutinised you know, you're not going to get particularly good results by and large. Um, so I just want to talk a little bit about the, the kind of the, the cultural issue of adopting kind of analytics and, and becoming a data driven organization. And you talk a lot about, um, you know, really what you do is, is setting, um, you know, try to predict the probabilities of something happening, not really predicting what's going to happen necessarily. >>And you talked to New York, you know, today about, you know, knowledging where, you know, you're not, you're not 100% sure acknowledging that this is, you know, this is our best estimate based on the data. Um, but of course in business, you know, a lot of people, a lot of, um, importance is put on kind of, you know, putting on that front that you're, you know, what you're talking about. It's, you know, you be confident, you go in, this is gonna happen. And, and sometimes that can actually move markets and move decision-making. Um, how do you balance that in a, in a business environment where, you know, you want to keep, be realistic, but you want to, you know, put forth a confident, uh, persona. Well, you know, I mean, first of all, everyone, I think the answer is that you have to, uh, uh, kind of take a long time to build the narrative correctly and kind of get back to the first principles. >>And so at five 38, it's kind of a case where you have a dialogue with the readers of the site every day, right? But it's not that you can solve in one conversation. If you come in to a boss who you never talked to you before, you have to present some PowerPoint and you're like, actually this initiative has a, you know, 57% chance of succeeding and the baseline is 50% and it's really good cause the upside's high, right? Like you know, that's going to be tricky if you don't have a good and open dialogue. And it's another barrier by the way to success is that uh, you know, none of this big data stuff is going to be a solution for companies that have poor corporate cultures where you have trouble communicating ideas where you don't everyone on the same page. Um, you know, you need buy in from, from all throughout the organization, which means both you need senior level people who, uh, who understand the value of analytics. >>You also need analysts or junior level people who understand what business problems the company is trying to solve, what organizational goals are. Um, so I mean, how do you communicate? It's tricky, you know, maybe if you can't communicate it, then you find another firm or go, uh, go trade stocks and, and uh, and short that company if you're not violating like insider trading rules of, of various kinds. Um, you know, I mean, the one thing that seems to work better is if you can, uh, depict things visually. People intuitively grasp uncertainty. If you kind of portray it to them in a graphic environment, especially with interactive graphics, uh, more than they might've just kind of put numbers on a page. You know, one thing we're thinking about doing with the new 580 ESPN, we're hiring a lot of designers and developers is in case where there is uncertainty, then you can press a button, kind of like a slot, Michigan and simulate and outcome many times, then it'll make sense to people. Right? And they do that already for, you know, NCAA tournament stuff or NFL playoffs. Um, but that can help. >>So Nate, I asked you my, my partner John furry, who's often or normally the cohost of this show, uh, just just tweeted me asking about crowd spotting. So he's got this notion that there's all this exhaust out there, the social exhaustive social data. How do you, or do you, or do you see the potential to use that exhaust that's thrown off from the connected consumer to actually make predictions? Um, so I'm >>a, I guess probably mildly pessimistic about this for the reason being that, uh, a lot of this data is very new and so we don't really have a way to kind of calibrate a model based on it. So you can look and say, well, you know, let's say Twitter during the Republican primaries in 2016 that, Oh, Paul Ryan is getting five times as much favorable Twitter sentiment as Rick Santorum or whatever among Republicans. But, but what's that mean? You know, to put something into a model, you have to have enough history generally, um, where you can translate X into Y by means of some function or some formula. And a lot of data is so new where you don't have enough history to do that. And the other thing too is that, um, um, the demographics of who is using social media is changing a lot. Where we are right now you come to conference like this and everyone has you know, all their different accounts but, but we're not quite there yet in terms of the broader population. >>Um, you have a lot of kind of thought leaders now a lot of, you know, kind of young, smart urban tech geeks and they're not necessarily as representative of the population as a whole. That will over time the data will become more valuable. But if you're kind of calibrating expectations based on the way that at Twitter or Facebook were used in 2013 to expect that to be reliable when you want a high degree of precision three years from now, even six months from now is, is I think a little optimistic. Some sentiment though, we would agree with that. I mean sentiment is this concept of how many people are talking about a thumbs up, thumbs down. But to the extent that you can get metadata and make it more stable, longer term, you would see potential there is, I mean, there are environments where the terrain is shifting so fast that by the time you know, the forecast that you'd be interested in, right? >>Like things have already changed enough where like it's hard to do, to make good forecast. Right? And I think one of the kind of fundamental themes here, one of my critiques is some of the, uh, of, uh, the more optimistic interpretations of big data is that fundamentally people are, are, most people want a shortcut, right? Most people are, are fairly lazy like labor. What's the hot stock? Yeah. Right. Um, and so I'm worried whenever people talk about, you know, biased interpretations of, of the data or information, right? Whenever people say, Oh, this is going to solve my problems, I don't have to work very hard. You know, not usually true. Even if you look at sports, even steroids, performance enhancing drugs, the guys who really get the benefits of the steroids, they have to work their butts off, right? And then you have a synergy which hell. >>So they are very free free meal tickets in life when they are going to be gobbled up in competitive environments. So you know, uh, bigger datasets, faster data sets are going to be very powerful for people who have the right expertise and the right partners. But, but it's not going to make, uh, you know anyone to be able to kind of quit their job and go on the beach and sip my ties. So ne what are you working on these days as it relates to data? What's exciting you? Um, so with the, with the move to ESPN, I'm thinking more about, uh, you know, working with them on sports type projects, which is something having mostly cover politics. The past four or five years I've, I've kind of a lot of pent up ideas. So you know, looking at things in basketball for example, you have a team of five players and solving the problem of, of who takes the shot, when is the guy taking a good shot? >>Cause the shot clock's running out. When does a guy stealing a better opportunity from, from one of his teammates. Question. We want to look at, um, you know, we have the world cup the summer, so soccer is an interest of mine and we worked in 2010 with ESPN on something called the soccer power index. So continuing to improve that and roll that out. Um, you know, obviously baseball is very analytics rich as well, but you know, my near term focus might be on some of these sports projects. Yeah. So that the, I have to ask you a followup on the, on the soccer question. Is that an individual level? Is that a team level of both? So what we do is kind of uh, uh, one problem you have with the national teams, the Italian national team or Brazilian or the U S team is that they shift their personnel a lot. >>So they'll use certain guys for unimportant friendly matches for training matches that weren't actually playing in Brazil next year. So the system soccer power next we developed for ESPN actually it looks at the rosters and tries to make inferences about who is the a team so to speak and how much quality improvement do you have with them versus versus, uh, guys that are playing only in the marginal and important games. Okay. So you're able to mix and match teams and sort of predict on your flow state also from club league play to make inferences about how the national teams will come together. Um, but soccer is a case where, where we're going into here where we had a lot more data than we used to. Basically you had goals and bookings, I mean, and yellow cards and red cards and now you've collected a lot more data on how guys are moving throughout the field and how many passes there are, how much territory they're covering, uh, tackles and everything else. So that's becoming a lot smarter. Excellent. All right, Nate, I know you've got to go. I really appreciate the time. Thanks for coming on. The cube was a pleasure to meet you. Great. Thank you guys. All right. Keep it right there, everybody. We'll be back with our next guest. Dave Volante and Jeff Kelly. We're live at the Tableau user conference. This is the cube.

>> Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Welcome back here at AWS re:Invent. Day three of our coverage here on theCUBE. We have been here since Tuesday, bringing you all kind of sights and insights from the show floor here. Some 40 guests that we've had on this set alone. Have a person that's actually four sets around here. There's a lot of content to capture. A lot of excitement in the air. And I'm John, that's Rebecca. I don't have to tell you that, you know that. We're joined by Ankit Khandelwal, who's the Senior Director of Engineering to Kyvos Insights. Good to see you, Ankit. >> Thank you, good to be here. >> And Ajay Anand, who's the Vice President of Products and Marketing at Kyvos as well. Thank you for joining us gentlemen. We appreciate the time. >> It's good to be back with you. >> All right, so share a little bit, just for folks at home who are watching and may not be familiar with Kyvos. I doubt there are many. (Rebecca laughing) But just in case, share with us a little bit, and with them, your core mission. >> Yeah, so what Kyvos does is we deliver the capability of doing instant business intelligence on data at a massive scale, either on-premises or in the cloud. So, one of the big problems people have is when they're trying to connect from their BI tools to huge amounts of data, it takes a long time for the data to come back into the tool. As they are dragging and dropping, they don't get that interactive response. So we solve that by building a BI consumption layer on top of the big data. And what that enables you to do is, you know, once we've pre-processed that data and built multi-dimensional cubes, then you can get that interactive response time, right. So the core technology is OLAP, which has been around for a long time. But what we do is we make OLAP scale to huge amounts of data and really take advantage of the capabilities of the cloud, or big clusters, and on-premises environments, and really scale out with the cloud. >> Can you give us some examples of who your customers are and the kind of specific problems you're solving for them? >> Sure, some of our customers have spoken publicly about us, so I can share what they said. Walgreens spoke about us at the Tableau Conference just a couple of weeks ago. And they're solving problems that they had never imagined they'd be able to solve before. Dealing with hundreds of billions of rows of data and getting instant responses. And these customers are building multi-dimensional cubes at a scale that's never been done before. 100 terabyte cubes. Walgreens is an example of that. Verizon has spoken about us at other conferences as well. >> Ankit, I'd like to know what your take is on, as we were just talking about, the volume that you're dealing with here. Like never before. How do you help your customers figure out what matters? What's important and what's not, because most, or I shouldn't say, much of what they generate really doesn't matter, and yet there are some valuable nuggets in there that they are still trying to extract and then analyze appropriately. So how do you help them with that job? >> Yeah, so you know what happens is organizations and enterprises keep getting more and more data. They take it to a data lake. Now, the data on the ground wasn't enough, and now you have other services which helps you get the data from even space. Andy announced that you can get data from satellite. So all this data. Now once that data reaches the data lake, the next challenge that comes to, or in front of a business user is, how do you really get the ROI out of it? Now when I say ROI, basically know I am talking about ROI of data. And the ROI of data actually improves, comes only when, the data goes in the hands of the business user. So that's the times Kyvos comes into the picture because you want your data and you want your business users to analyze it. It has to be super fast and that's what Kyvos does, number one, and number two, the business users want their data to see in a way that they want. So basically, Kyvos helps you to actually define a semantic layer, put a business view on top of your data. So that a business user actually sees the data the way they want. So those are the things that Kyvos provides and helps the business user to actually get the insights out of the data. >> So this week at AWS, you launched Version 5. Tell our viewers a little bit more about what Version 5 entails, some of the capacities. >> Right, so one big thing is the capability to do Elastic OLAP in the cloud. So the OLAP capability being able to really leverage the infrastructure cost-effectively, scale out to deal with big loads and scale it down as you're building these multi-dimensional cubes. So really being able to deal with the infrastructure cost-effectively and deal with massive amounts of data as you're building these cubes. So you can decide, I want to build a 100 terabyte cube and just spin up the right amount of infrastructure that you need to build that cube and then shrink it down. So that elastic capability both for cube building as well as querying. At Walgreens, they talk about dealing with hundreds and thousands of users both internal and external all connecting to this data using Tableau or some other BI tool, and being able to deliver that instant response to them. So having that elastic capability is the new capability we're offering. >> I think the point is, as Andy was talking about in his yesterday's keynote, if you can do it fast then why not do it fast? I think that's where cloud comes into the picture. That with our Kyvos 5 release, once you set up your Kyvos on the cloud, it could actually use that scalability or the elasticity of the cloud for its benefit and for the benefit of the customer. As the load increases, is that the complexity increases. We could actually scale out and deliver the performance that we promised to deliver. And then once the load actually reduces then we could again reduces the resources that we're consuming and that's how we actually reduce the cost that is borne by the customer. So essentially, that is again, you're now giving them better ROI on the hardware that they're investing on. >> So how do you pump the breaks a little bit on the speed? I mean, in terms of making sure that you're in control? Because speed's one thing, right, very important to have, but we need reliability, you need accuracy, latency is not as much of an issue, but how do you, pump the brakes might not be the right description, but how do you ensure that speed is not an inhibitor and it's actually a facilitator? >> There's a whole bunch of enterprise capabilities that we have to provide. Dealing with the resilience so that it's always available to their business users. Dealing with concurrency as you really scale out with the large numbers of users. Dealing with security, right. So as I mentioned, at Walgreens they've got external users as well as internal users, all accessing the same cube, and they all need to see only what they're allowed to see, right. So we maintain that security, right from the user to the data, and we keep track of who's allowed to see what and expose only that. So all of those capabilities are built into the product. >> And as an engineer, I can actually say that again I would take the code from Warner this morning that, hey, you really architecture it well. So architected the product right from the beginning to not only deliver the performance but also to be scalable, deliver performance at a scale. To be secure and then in order to be reliable, fault ordering. So those things are inherently built into the product but then putting a patch on top of the product. >> We're hearing so much at this conference that many enterprises have really had the ah-ha moment. I need to go to the cloud. The security, the governance, those concerns are really falling by the wayside. So what's next? I mean, now that we have so many companies migrating, where do we go from here? >> I think, what we are seeing is a lot of companies are still in the process of migrating. So they've had on-premises infrastructures. Now they're moving to a hybrid cloud and then moving to potentially everything in the cloud. So delivering a seamless experience to the business user is extremely important. Business users shouldn't have to care whether the data is on premises or in a hybrid cloud or in the cloud itself. They should get that same interactive response, the same familiar user interface, and that's what our BI layer provides. By delivering that consumption layer that sits the same way on premises as it was in the cloud. It's a completely seamless experience for the user. >> And I think the performance or the skills still presents a problem. The thing is, how can you make it easy to use for the user? How can I make it smarter? So I think that's where we are going towards with our latest releases, with Kyvos 5. We're bring certain capabilities into the product so that the user doesn't have to bother about how do you really create that semantic layer. The product is smart enough to tell there what should be included in there and what to leave out of it. So smartness is one area which we are moving towards so that we can help the business user to get the performance at a scale with a lot of ease of use. >> I assume you guys have been here for a day or two, correct? >> Yes. >> Right, you met with a lot of customers. I again would assume, right? >> Right. >> So what is your take-away going to be from those direct conversations you've had here in terms of what you take back to Kyvos and maybe start putting into practice? What are you hearing about, this is my next roadblock, this is my next barrier, this is what I'm going to come to you to help me fix. >> We heard Andy's talk this morning or was it maybe Warner. >> Yesterday, Warner this morning, yeah. >> So Warner's talk where they talk about, 95% of what goes into AWS comes from feedback from their customers, and that's true with us to a large extent. We learn from our customers, as they deploy these cubes and their environments, but what's important to them. What are the critical areas that we need to overcome. Really understanding their business use cases and making sure that we build that smartness into the product so we can see what kind of intelligence are they looking to gather, what kind of analysis are they looking to do. And then we use that to build the smartness into the cube. So that the user doesn't need to figure this out themselves. So that's one of the new capabilities that we are providing and we're continuing to work on, is to build more and more smartness into the product. So it helps the user go where they want to go. >> And I think as we go to cloud, specifically AWS, how can we really use the services required by the cloud and then how can we really provide a layer of extraction on top of what is already there, so that then it becomes really easy for the user to use whatever we are providing. >> Right. >> Great. Yeah, just, and I don't want to convolute this with things that I don't need and time and effort. It's all about money at the end of the day, right? Save me money, save me time. >> Well, it's not just saving money but really the topline benefit, right. So expanding the business opportunity. So, we've got a bank that's doing risk analysis as they look for new investments. It used to take them days to do that risk analysis before they could make a decision. Now they can do it in seconds. So their ability to make a decision much faster and react to market conditions, really opens the door for them for much greater business opportunity and revenue. So it's not just cost savings that's driving this. It's taking advantage of the opportunity. >> You bet. >> Because if the queries don't really come fast. Let's say you as a person sitting here and you fire a query and then it takes a lot of time, and you go back and then have a cup of coffee and then come back. Your chain of thought's actually broken. So you cannot explore from the data otherwise you could integrate it'll actually come within seconds. >> Gentlemen, thank you for being here with us. I hope the show's gone well for you. It sure does sound like it's been a success, and we look forward to seeing you down the road. >> Great. >> Thank you. >> Good to be here. >> Thanks. >> From Kyvos. >> Back with more in just a bit here on theCUBE. You're watching AWS re:Invent. (bright music)