 Live from New York, extracting the signal from the noise. It's theCUBE, covering RapidMiner Wisdom 2016, brought to you by RapidMiner. Now, your hosts, Dave Vellante and Jeff Brick. We're back at RapidMiner Wisdom. This is theCUBE, we go out to the events, we extract the signal from the noise. Paul Lillford is here, he's the Senior Director of Technology and Market Intelligence at Tableau, one of our favorite companies. Great to see you again, how you been? It's great to see you again, Dave. I was pleasantly surprised when I bumped into you. Did I? I heard this morning, Tableau is one of our sponsors. I wonder if Paul's here, is it fantastic? So give us the update, what's new? You got a new role, Tableau keeps cranking. I mean, we just love the momentum. Yeah, we continue to move ahead. We're north of 32,000 customers as a company now and we continue to grow and move ahead and we're still investing heavily in the product so we still put 26% of our top lines back into product. We'll actually spend more this year than we've spent in the previous 11 or 12 and so it's exciting times, right? The spaces are getting more crowded. I think we're seeing the democratization movement move across all spaces. So it's happening in integration, it's happening in advanced analytics, it's happening in data warehousing. Across the board, I think we're seeing a pivot to kind of the business driven self-service and powered kind of state across all of these technologies which is a really interesting time. I mean, your CEO, Kristen Chevaux, sums it up so well about the slow, traditional BI world, obviously the entrenched spreadsheet world and you guys have this land and expand strategy that's something you've been operating in that model for years now and it's worked great. A lot of people sort of waiting for that momentum to attenuate, it hasn't. You know, why, what do you attribute that to? How are you sort of staying ahead? So I think the biggest single thing, Dave, is our community, right? What makes Tableau different is if you're looking at feature function things, people make choices on that all the time, right? But we have a passionate community of customers that goes out there and uses our technology. We're not shelfware, right? And the land and expand really plays nicely into this. If you think about the old traditional side, you bought something and you were maybe used or maybe wasn't used or was forced upon you. And Tableau isn't forced upon you, right? People buy it because they have a pain and a need and it expands because that need is met and they need more, right? And there's something really beautiful in the model. At the same time, from an enterprise perspective where we've invested really heavily over the last three, four years in ensuring that the top-down kind of approach for selling other than the organic also works well, right? And so when those meet in the middle, as I think when we all hit a saturation point, that's extremely interesting. Well, the other thing too was, you know, back in the day used to be to come out of school with the cobalt skills and then it was SPSS and now kids coming out, we are intern this summer, it's like Tableau skills. Oh yeah, I'm going to apply Tableau to the data set. Music to our ears, R, you're hearing the same thing with R. So it's becoming sort of a standard. Maybe not a verb yet, maybe it is a verb, I don't know, but maybe talk about that a little bit. We're hoping we become a verb at some point but I actually saw something interesting on this. I've seen a few cases of students using Tableau to be their resume. So you do an interactive resume, imagine that thought five years ago, right? That you would show up and say, I'm not going to give you a piece of paper to look at and tell you what I did. I'm going to show you my history and I'm going to show you what I do. And in the process of that, you're going to see what I can do in action, right? And it's a really cool idea. So we're starting to see that, I think our academic program helps a lot because we allow Tableau to be used for academic purposes for free. That really actually helps and it's the same reason why I think you see R scaling across as everyone's learning R in college now. And I think that new workforce coming out is going to really be the tipping point for data driven. I mean, you know, when we have these conversations, like what's the future of email? We're going to have email in 15 years and so you think about the entrenched culture of how we do analytics today. Can you talk a little bit about, I mean, your TAM is like infinite. Well, maybe it's bounded by the IT TAM. Wow, two, three trillion. I mean, it's just crazy. So talk about the headwinds and the cultural headwinds and how you're addressing those. Yeah, so that's actually really near and dear to what I'm doing right now, Dave, is, you know, it's great that we have an awesome product but culturally, how do organizations really embrace empowered analytics? I don't even like the word self-service when I say it because I can use traditional tool to make a cherry-picked self-service vehicle, right? And that's very different than a blank screen analytic idea where a user starts with a thought, they start asking questions and they iterate from that. And so changing this culture in organizations, I think is actually the biggest blocker to data driven in general. And it's unlearning the 30 years of muscle memory we've got for the old waterfall approach, the old ways of doing it and really breaking free of it. You know, Tableau, with all our success, we believe we're only very early in the stages of this opportunity, right? We have 30,000 plus customers, but we believe there are 50 million more to go, right? And so this opportunity, we're on the very tip of it. And as this cultural adoption idea starts to change, can you get away from the idea of the centralized control team who creates work for others to consume? And that's kind of an unhuman thing when we think about it. It's unnatural to human nature to have someone hand you something and say, trust what I did and go crazy with it. And that's the method that the old waterfall approach and the older tools of the past. Now in fairness to those tools, compute power wasn't there, right? What we do on our phones today has completely changed the expectation of every employee in every organization. You walk into work and it's hard, but in your personal life, if you wanna ask a question and get an answer, it's instantaneous, right? Take the Google example. If I'm out there and I Google something and I like it, I accept it. If I don't like it, I re-Google until I find something that I agree with and then I go, okay, I like that, right? And then I go into work and I have to find a guy like me to build a database so you can ask a question. And it's asinine, right? It just does not work. And this is what Christian has pointed out for years and it's actually one of the main premises behind Tableau is you can't remove the subject expert from their own data, right? And so as this cultural shift starts to take and as organizations truly adopt democratization of data, we'll see a big shift, but it's a huge undertaking. And I do really feel like Tableau is a fundamental part of that cultural shift. And when you go to a Tableau event and I've noticed that I mean Tableau, Splunk, ServiceNow and some others, there is a cultural upheaval going on within these organizations. We talked about email. I mean, I'm not sure if tapping with our thumbs is going to be the mode of interaction for devices in the future. So what gives you confidence that Tableau will be part of that new world? Yeah, so I think the fact that we're a customer company first and foremost, so we listen there and then we also are dropping 26 back into the product to innovate. And at our scale, typically you're down at the single digits or low double digits, a percentage of what you put back. And kind of Christian and Chris's and Pat's vision for this still permeates Tableau, right? From my customer days with Tableau to now, the feeling, yeah, we're bigger, but kind of the environment is very consistent, right? And from day one, when I was a customer, I asked Christian, you know, when are you going to get out of this, right? What are you, you know, that's my fear, what's going to happen? And he's been 100% consistent in what he says about the goals of Tableau and we're not where we want to be yet, right? And so that gives me the most confidence that we have leadership that gets it, that's committed to it, and that's investing in our people and our products the way we do. Well, that's spending on innovation. I mean, it might not always make Wall Street happy. I mean, you could turn down that knob and drive profits quite easily, actually. You could be substantially more profitable, but you're going for a bigger prize. That's right. That's the vision of the company. That's right. And I think that's what gives me the confidence there because we could do what everyone does at our point, right, or we could even be a little less extreme than we are at our point. But you know, that isn't Tableau, right? And the beauty of our company is we haven't followed the trend of what everyone else does, and the disruptive nature of what we do is part of our core, right? It's so important to have such a driven founder. I mean, the interview that we did with Christian in 2013 in Washington D.C. is still one of my favorites. The guy is just so passionate about the mission and really bringing this enablement to a much broader set of people. So, but when you see it in the companies and the adoption, is it this top up, the kid that comes in with the Tableau resume and those types of people that are kind of bringing it to the old 30 year old intuition guy or is he kind of wake up when they say, okay, I got to go with this new program where the results are so much faster, the results are so much more impactful, you know, so much quicker. What's happening in the real world? So I think it's a little bit of both of that, but I actually do think that the existing workforce, based on the consumerization of technology, has a changed expectation, right? And I gave the example of Google, but think about it in a retail world. If you want to know if you're getting the best price, you get instant gratification. You can find out are you getting the best price and make your decision. And this is kind of everyone's expectation working in, regardless of your, you know, older like we are. Or the younger base that's in Tableau coming out of college. The same thing, I think, drives everyone this hunger to be data driven. And our society is a very data driven society naturally, right? The irony is we talk about doing it in organizations, but we make data choices every day in our personal life and we don't even think about them anymore, right? You think about the rise of the cell phone and what the smartphone has done to people's just extension of their brain, right? Maybe in good ways, maybe in bad ways. But you know, I do love that about Tableau, that we're an extension of the human brain, we're not trying to replace it, right? And there's something beautiful in that that we're trying to extend capability, not replace people because people drive organizations. You know, you hear data is the new oil. I hate that expression. I believe people are organizations' biggest asset. And I like to think of data as the water that sustains us, right? And so when you think of that, I think there's a hunger and a human intuition that's out there where you want to create and gain insight and learn. And when you have a job, you don't do that. When you have a career, you actually think about how you grow and proceed and move. And I think Tableau helps give people a good career, right? I think we help passion them and they don't spend their time prepping to do their work. They do the work that they are passionate about and that is viral, right? It takes off and it gets people excited about it. I was struck this morning by the room full of data scientists laughing and giving examples of sort of dissonance between the data and the interpreted correlation. There were many, many examples of that. So how does Tableau help close that gap? What's the relationship with this community generally and rapid miners specifically? Well, so I have a bit of a personal stake in all of the partners. As you know, I ran our tech partners for the last three years or so and I've now moved roles, but rapid miner for me and the idea of data scientist is just like DBI's been democratized, I think you want to scale data science and organizations. The, what I would use to explain it is if you think about analytics to date, we look at 12% of the world's data that we can structure and that's what we call analytics. But very little of it is forward looking. Most of it is how well did we do or how poorly did we do. And it's limited, right? It's also limited by the structures that have been created for business people that may take valuable information out. So data science's role in that is massive, right? Finding the right elements, finding the things, then rules, you want to persist at scale, that's important and the beauty of the combination of our technologies if you think about the cycle of visual analytics we talk about. Now the business user can stay in this cycle, grab predictive elements that are much deeper than they ever used and use them in their analytics. And the data scientist is actually getting something really interesting out of that. They're getting a vetted requirement in real time from the business user about how they do it and they might actually learn the business driver in a different way than they thought through that process. So people complain about how complicated predictive analytics and machine learning solutions are, the time it takes, the effort they have to put into data cleansing. Does the Viz help accelerate that, I guess time to understanding where the gaps are or is there any way in which you can facilitate that closing or do you kind of have to wait till the data's clean before Tableau can do its magic? You definitely don't have to wait and actually what I'll give you, Dave, is my example as a customer, right? I ran a technical team in finance and we leveraged Tableau as a requirements gatherer. We were hired to be agile, so what was taking us two weeks, when I bought Tableau, I had 220 so active projects with a team of 28 and we weren't as agile as I was hired to be. It was what was taking us. 200 projects with a team of 28. And they were of all varying scales, but you looked at the team and you're like, well, how do we do this? And what was taking us two weeks was now taking three or four. Right, right. And so I bought Tableau out of a pain. I actually didn't buy it for the visual piece. I bought it because it was a common business face to all the assets we owned and I bought it for self-service, right? Those are the two reasons I really bought it. The visual thing was a really pleasant surprise in terms of what it does to the mind and how people work, but really I focused on I needed a common business interface that wasn't technical, that didn't require me or someone on my team to help you use it, that allowed you to go and show us your questions and so people would send us a vis packaged and we could just reverse engineer the requirement, apply it to our data stores and completely accelerate and change the way they leverage the data assets, the database assets. So basically here's the outcome. Yeah. And make it happen. That's right. That's right. And the beauty of that is, you know, you think about the requirements process. You get 40 people in a room and you say agree. Well lo and behold, they don't agree, right? You have to raise the level of the requirement to get consensus. And so every business unit is left short of what they really need at the end of the process, no matter what happens because it has to happen this way. And the old process did that. The beauty of Tableau is we go out there, you build it, you show it, and it's relevant to your business unit. And then I can abstract that as a technician and look at it at a corporate level and say what's common across five business units and what do I really wanna persist? And it's a real requirement. I didn't artificially pull it out of you or force you to tell me. You showed me what you were doing and I used it to actually support you better from an infrastructural perspective. And that same thing I think in the data science world is critical, right? How do you expand the power of the data scientist and get it used more relevantly in the everyday business decisions? Scale it, yeah. All right, Paul, we got to leave it there. Fantastic having you and Tableau back on theCUBE again. Tableau conference is in the fall this year in Austin, so go, it's one of the best events out there. So thanks again for coming on theCUBE. Thank you, always a joy, guys. Good to see you all. All right, keep right there, everybody. We'll be back at Rapid Minor Wisdom right after this. This is theCUBE, right back.