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.

. >>Okay, welcome back. This is live in Las Vegas. This is the end of day one. This is our wrap up segment of the cube at Splunk conference dot conference 2013. I'm John furrier with Dave Alante, my cohost and Jeff Kelly making an appearance in this segment has been scouring for stories, talking to all the folks, talking to the CEO, talking to all the people on the team, customers scouring the web. Guys, welcome to the wrap up. Thank you John. John guys, I gotta I gotta say I'm really impressed with what Splunk's done here. Um, and with post IPO you kind of see what people are made of when they have to do transitional things day. We know we do and I've seen companies pivot, turn on a dime. You guys certainly have helped companies, you know, get into that, into the, into the thermal growth and um, but here a companies succeeding, um, they hit a rocket ship growth. >>They go public. A lot of challenges could be distraction, but certainly, uh, my impression is no distraction here. Splunk certainly is hitting cruising altitude only getting better and stronger. Certainly the customer acquisition numbers as strong and their partner ecosystem is great. Their keynote and fan based or customers are loyal. All in all, Dave, I've got to say, you know Splunk's looking really good. Yeah, John. I mean I think you see a lot of different models. This is too broad models. I guess in the, in the it business one is the safe bet. It's, it's IBM, it's, it's HP, it's, it's EMC, it's Oracle, it's Cisco. I mean you're going to do business with those companies because you know they're going to deliver a product and they're going to stand behind it and they're going to service you and then you got the 10 X value proposition companies, that's companies like Tableau service now Workday, Splunk, these are the companies that are really transforming their irreverence. >>Steve Cohen said disruptive, they're disruptive so they got a little mojo going and I'm gone. But at the same time, customers are willing to take a chance because the value proposition is so compelling and so transformative to their business and they can't get that from their traditional it suppliers despite what the traditional it suppliers are telling them. So I love that kind of mojo at a, at a, at an event like this. Jeff Kelly, I want to go to you for a second. Let's talk about what you're finding us. Show us who you are on the, on the, uh, we had a crowd chat today w you know, preparing them for Hadoop world and big data in New York city. A quick programming note. Um, we, the Q will be in New York city for strata conference. Had duper world covering that in con in concert to the big data New York city event going on as well that week. >>Um, but you're out, you did a chat this morning about big data with Hadoop ecosystem. A lot of had doopy we had cloud era MRR Dhalla on, they have a relationship also with Hortonworks. Um, what did you find out there? What stories did you dig in? What observations did you find? Well, very much like a, the last show we were at a Tableau's customer conference. It's a really excited, uh, customer base here. These, these customers, uh, you know, are, are clapping and cheering during the keynote. It's something you don't necessarily see more than excited. They're giddy, right? I mean, right. They're there, they're getting yapping, they're hooting or hollering, right. And, and there's really a sense of community around the, around the customer base. They love to trade stories. They love to trade best practices. The hackathon, last night I was at, uh, you know, just rooms filled off the, off the corridors here at the, uh, the cosmopolitan. >>They were there till 11 o'clock at night. They were in there, you know, they had, uh, some, some, some, some TV going at, I saw a rerun of Alf playing on the big screen for some reason. I guess that's a popular with the group here. But anyways, these guys were up there all night. You know, they're coding the drinking beers, they're having a good time. Uh, they really enjoy this. You didn't, it's not something you see at eight. At one of the, a larger events, some of the mega vendors we see. Um, you know, the other thing, you know, Mike coming into this Splunk I think was really early on, uh, recognizing the, the value that providing applications that allow you to really manipulate and understand data. Really they saw the value of that very early. Obviously that's, they base their whole premise of their organization on that. >>Oh, they have re, you know, kind of written this wave, uh, of big data, all things big data. And they're one of the few companies out there that are actually selling and providing applications that allow people and make sense of, um, in this case, machine generated data, but they're expanding to other data types. Um, the key for them I think going forward is to continue innovating. You know, they've kinda got that lead, uh, I think because they were the, one of the first out of the gate to recognize the value in this. They gotta keep innovating. And I think you saw with the announcements today, clearly they are, uh, the cloud, uh, option that they unveiled today was very popular. Um, and it's going to help them, especially against some of the more nimble startups. It's funny, it's, Splunk is now kind of a kind of a big established company in a sense in this large, in this big data world, there are companies like om Bogley and Sumo logic who are coming at Splunk doing similar things, but doing it from a cloud perspective, well sponsored down. >>Got an answer for that. Why would I want to ask you guys about that? Because you know, John, Jeremy Burton, we, you know, made, we were there when cloud met big data and so people have been putting those two together. But you take a company like Splunk and a couple of like Tableau, not big cloud plays. What about that cloud meets big data? Is that, is that a misconception on the industry's part or not? Or is it a fundamental requirement that cloud meets big data? I think it's a fundamental requirement as you know, we were, you know, close to EMC when they put that together and we had the first cloud mobile social editorial. You guys had the first real research around those three pillars. Um, and big data just became a, came out of social and cloud and since the cloud era, you know, pun intended with Cloudera, the company, um, but you know, Dave, we saw this from day one. >>This is a fundamental economic wealth creating inflection point, meaning new companies, new brands going to emerge that are going to change the game and this is where all the chips are on the table and you're seeing the incumbent vendors like EMC changed their game and go cloud meets big data and go in there. And EMC, I give Ian, Jeremy Burton a lot of credit. He saw the work we were doing. He saw the marketplace, he came fresh into EMC and said cloud and big data. Those are the two pillars. He bet the ranch on that and the beds coming home. Jeremy is making more money than any, even not a CMO anymore. He's the executive vice president doing great just on the stock options. He made a good bet that's playing out who's also a great executive with some product shops. Absolutely. Table stakes in my opinion. >>Um, that the application market is going to be enabled by that. So, Jeff, Kelly, so I've got to ask you, there are forces that you mentioned you've got open source. Uh, you've got some new players that are or have seen the opportunity that Splunk has created, the, they're going to have to Splunk. So, so what's your prediction here? I mean, you've got, you've got a public company now, they've got more resources. They're clearly a leader in the, in the business, but you got other companies coming after him. Not only start us, you know, we were at, um, we were at HP, uh, the, the Vertica user group, they were talking about, you know, their Splunk killer. Uh, you hear it all the time. Oh, we can do that. We can do that. What does that all mean for Splunk? Well, the good news for Splunk is they're, they're, they're ahead of everybody in this game because they've been doing this for longer. >>Uh, you know, they, they, they have a, a more generally accepted among the customers, uh, you know, a better application for VMware, for instance. So they're actually ahead of a lot of these other vendors, VMware itself trying to claim Oh yeah, it'd be where it says, well now we've got a tool for monitoring that's just as good as Splunk. Well, you know, if you talk to some of the people using the Splunk app for VM ware, they'll disagree with that. So bottom line is, you know, this is a little bit simplified, but people really like the Splunk user interface in the application. It's very easy to use and that's something that you can't necessarily replicate. So, you know, it'll take, it'll take some time for some of these players to catch up. But you know, back to the point John was making this whole idea of cloud and big data and you're asking, you know, is that really, is that really the, the, the two mega trends here? >>And I think absolutely when we start talking about, uh, industrial internet, internet of things, whatever term you wanna use, we're, we're years away from that really being a, a reality I think in terms of it's an interconnected world, but clearly the two key enabling technologies are going to be big data, making sense of all those connected devices and cloud being able to connect them in a way that that makes sense. Um, where you can't do that in an on premise situation if you've got isolated data centers. Now the other thing, this company who started in 2005, it's yet another Silicon Valley success story. John, I mean it's just Silicon Valley is just running the table. What's your take on the Valley action going on here? I think Silicon Valley is going to continue to do well and, and um, and rule the road here and on IPOs and success. >>Silicon Valley is the ecosystem that drives a lot of wellness to wall street of startups. However, there are, there are a lot of successes outside of Silicon Valley. This is just another string of, of successes. Um, but Dave, this is an absolute poster child in my opinion, of a venture that could have gone the wrong way. I mean, Splunk was not a shining star when it got funded. It took two visionary venture capitalists, Nick and David Hornick, Nick from, uh, he'd know the ignition and uh, David Hornik from August capital made the bet. They bet on technical founders, they bet on the right product guys. It was in small tools and it was at the time it was, wasn't the trendy thing. This is pre big data. This is log files. They saw a problem, they saw a good team. Now this thing could've gone off the rails, right? >>If you look at today's market, this is what I worry about all this startup environment is that all the different funding dynamics, all of this crowd sourcing this, that you've got to have good investors. This is a great example of great investors back in their guys back on their team because this thing could have been off the rails in the fourth year. Okay. Product strategy, debate, board room dynamics, people not paying attention, uh, asleep at the switch as we say. And this is, this is an example of a company done right. They hit the growth curve, big data swooped in, they had a great product, happy customers and incrementally move the ball down the field. And finally, you know, scored the big long ball with the touchdown with big data. And I think, you know, it's classic. These are football analogies, you know, first down, first down, first down, and then big data comes down. >>They throw the ball in the end zone, touchdown home run. There it is. That's the IPO. That's the success story. There's a fine line between. Good and great here. Isn't there though? I mean, like you say, I mean who even Steven Cohen was saying, uh, uh, uh, not, not Steve Sorkin, sorry. Steven. I was saying that he didn't, could've never predicted, you know, where they'd be today, the IPO, et cetera. So there is a fine line. You could go, well, this is the thing, this is my point. If you look at Splunk, right? Dave, they could have, no one was buying their stuff initially. Right, and so except for some tech geeks, no one was kind of get it, but the recession hit and people weren't spending in 2008 that was a big surge and you saw the spending and Splunk became a great solution because for very little cash you can come in and create business value. >>That was a really, really important moment in the company's history, David, and what's also happened is they believed in their own product. You heard from the people here culture, they're Everett, they're disruptive, they use their own product and they focused on the customer. Those two things, good timing still is, you know, comes to people who are prepared. I mean it's not an, I mean, it's not enough to just have a big market. It's not enough to just have a lot of capital behind you. You need other ingredients obviously to succeed. I'm afraid the younger generation doesn't understand the startup world is you can't just magically put pixie desk and get the home run. You got some times really be in a good position as they say in basketball and be ready for the rebound off the rim. In this case it log file tool with good technology moves into the big data world and hello, they're got an enterprise customers. >>Part of, I think part of it is, look, you've got to admit, part of it is luck and timing. You've got to have that on your side. But they've also got a really good product and they're smart enough when that, when those opportunities present themselves to take them. I think they are. Again, timing is fantastic for them right now. We've been talking about the, uh, the year of the big data application and we're really still waiting for that. They are in a really good position right now to really take advantage of all the interest in, you know, SQL on Hadoop, interactive analytics on Hootsuite. Well guess what, they've got a product and hunk a cute name, but a good product that allows you to get right in there as a business user and start analyzing, searching data using a circular base. I gotta tell you it's a very good looking product and people are looking for this. >>People are like, well, how am I going to get all that value product? I'm going to get all that value out of Hadoop sense bugs in answer hunk. You got the naming convention, interesting names, but nevertheless they've got a, they've got a play right now in an area that's got a lot of interest and they've got, they've got the track record in the log data to actually show they've got, they know how to, they know what they're doing. I don't remember Mike Olson to cloud Hadoop worlds ago, announced the the application tsunami. That kind of never came the way they said. We said the analytics was a killer app. In the meantime, as the market kind of catches up, we still haven't seen that application framework, but yet still analytics is the killer app, right? It's definitely the killer app. I think. Well, the analytics for the masses is the, is the killer app and that's the Holy grail that everybody's going after. >>And I'm not, I'm not declaring Splunk is there. I don't think Splunk is there. I don't think anybody's there yet. You talk to a Tableau customers, you talk to Splunk customers, they're not there yet, but they're closer than the BI crowd ever was. They're certainly closer than the traditional BI players. And they, and then that's because they don't have that legacy architecture to deal with. But there's also a cultural issue. It's not just the technology of the products, it's getting business users to understand how to look at data and look at it as, as an asset and something that you can actually drive. Timing's right for that. Absolutely. So I want to wrap up and ask you guys some follow up questions at the close, the segment out, first impressions of day one and what are you looking for for day two? Jeff, we'll start with you. >>I am first impressions. You know, like I said, very excited, uh, base of customers here and you know, 18,000, 1800, excuse me. Plus customers, 18,000. That'll be a few years. But, uh, nevertheless a good showing here. Uh, I think tomorrow, you know, on the cube, we're going to look for certainly some more customer stories. Um, you know, it's always interesting to hear from customers because they are on the front lines. They're using the product every day. So I expect to see a lot more of that. Um, and really tomorrow I think is going to be a lot about, a lot about uh, these customers networking with one another and I'm hoping to get out there. Let's add on the question to, uh, to you then, then to Dave. Same thing. What's the challenges for Splunk as well? I think the challenge for me is from, from my perspective is to continue and make the, make the cloud play real, continuing to invest in that, uh, and that product and that approach. >>Um, as we met, as I mentioned a minute ago, I think cloud and big data are critical to really leveraging industrial internet, the internet of things. And if Splunk wants to be a key player there, they've got to really fill out that portfolio of cloud based capabilities. I know you said David, go first. Sorry for me. For me, John, we heard from the executives today, very strong story. We heard very solid product lineup. It's very clear in talking to customers that there's, there's passion here, there's real traction. Um, it's substantive. To me. The big thing is ecosystem. I feel as though the ecosystem here at Splunk is, is, is good, but I feel like it's not been as deliberate as it can be. I think Splunk has a ways to go there. I think that is one of the leverage points that this company really has to focus on. >>Because like today we talked about earlier, 45% of Splunk sales goes through the channel. I think it's gotta be way, way, way higher than that. Now they're making great progress, but I think that they've got to have a goal of getting to 70% and that comes through the ecosystem. It's gonna take some time. It's going to take some investment. That's really where, to me, the big upside is for this company and my impression is I'm very impressed with Splunk. I'm very impressed with the ecosystem. I'm impressed by the rabid fan base of their customers who are proud of the private name getting exemplifies my point about startups having a great product focus products will win. Again, you know, the four P's of marketing, they teach you in marketing one Oh one one of those products. Um, but the challenge is, Dave, I would, I would agree with you. >>The ecosystem is a challenge. Good news is they have a great turnout here. Um, you're not, there's no lightweights out there, all heavyweights in terms of what they're doing with tech and their value proposition. So, you know, gray star for the ecosystem. So I think it's looking good off the tee to use the golf analogy, um, landing in the fairway. So, so that's one. My big, my big thing on the challenges for Splunk and that I'm watching is the cloud. I think moving to the cloud is not as easy as it appears, although that's the value proposition. So to move the DNA of the company with the pressure to drive revenue, luckily the market's kind of moving to them right now. So it might be a, a rising tide floats all boats. Moving to the cloud is very, very difficult. And I think that's gonna be a key challenge. >>We're going to keep watching them look at what SAP has challenged the cloud. They've had multiple restarts and misfires. Now they've seen them get their groove back with HANA. I think this could be a big challenge for Splunk and we're going to, I'm going to watch their cloud and that's going to be my focus then tomorrow. I would agree with that. I would just say on the ecosystem point, um, I, I think they would actually, I think they do have more work to do Dave, but I think they're in a really good position because some of the Hudu players, for instance, knees, Splunk, I think more than Splunk means to them right now. Okay. We're going to close down what the government is closing down right now. So, you know, that's, uh, that's, uh, we'll be back tomorrow because we work for free open source content, um, programming node. >>Next weekday we're gonna talk about big data and internet of things. I'll be interviewing the CEO of GE. Um, I'm really proud of you, John, for, uh, being selected out of the zillion people that they could choose. They chose you to, to host this panel. Yeah, that's fantastic. It might be my last, but we'll see. Moving some Q mojo to the GE event, industrial internet next week in Chicago. Minds and machines, another player to watch. Guys. Great day and great wrap up here. And that's day one. Wrap in the books tomorrow here when we go to the party tonight, find out what's going on here at, at, uh, inside the cube, inside a Splunk conference. Dot conference. 2013. I'm John furrier with Dave Alante and Jeff Kelly Wiki bond with back tomorrow. Goodnight. And, and join us tomorrow.