 Okay, we're back. This is Dave Vellante with Jeff Kelly. This is the Tableau Customer Conference and we're live with theCUBE. We're really going wall to wall today and tomorrow. We're here interviewing customers and partners and executives from Tableau. Ray Wang's coming on later. My colleague, John Furrier, is back in Silicon Valley crunching the data from this weekend's crowd spots. We did a bunch on the NFL and the NCAAs. And so we're just unpacking the innovations of Tableau. We're talking to practitioners. Bruce Boston is here. He's a practitioner, he loves data. He's worked for, so many companies. Bruce, I can't even imagine. And we're excited to have you here. So thanks for coming on theCUBE. Thank you. Thank you. Yeah, so we just heard the big announcement. We're excited, we're all Mac users here. So we get to now finally go play with Tableau. I see you have a Mac, so you must be excited too. Absolutely. So talk about how you have used data throughout your career and we'll talk a little bit more about visualization and the impact that this had. Sure, so for me, data is about finding the truth, right? There's what we know, there's what we think, and there's what is, right? And data is those clues that we use to find out what is. And you can do anything with data. I mean, Christian had a whole bunch of great examples of how scientists use it or everyday people use it or how you can use it for a bike program in your local city, right? And data, you just give me some data and you'll see, I crunch it, I crunch it all over the place. So you can't help but invoke the Benjamin Disraeli quote, lies, damn lies and statistics. And I've heard the data doesn't lie, so what's the story there? The data doesn't lie, people do. So I actually flip it around. I say it's very difficult to lie to a statistician, statistician, right? So yes, if you want to know who can tell you the truth, the statisticians know when data is lying to you and when it's not lying to you. You give a statistician the data and they're going to be able to say not, you got to crunch it this way, not you got to crunch it that way, not ask this question and you'll be able to figure that out. So the statistician like, oh, Nate Silva's going to be here tomorrow. Absolutely. You can't lie to him. You can't lie to him. I don't care how many TV shows you have or how many things you try to do, you can't lie to Nate. But he will say, but on the other hand, he'll say that based on the methodology that I'm using, the data set that I'm using, there's an eight, 7.5% probability that this is going to occur, right? Well, so. And that's not a lie. No, no, that's not a lie. But that's a straight fact. And so the lack of data or the inconsistency of data or the fact that life is complicated, sure, it creates this vacuum that we call the unknown. But when you're trying to explain the unknown, we don't call that a lie. I mean, that's just the unknown. It's just a no, right? We can speculate about it, we can ask about it, we can wonder what's there and we can make our best predictions. But we don't think someone's lying when they are predicting or suggesting what's in that dark void. So Bruce, how has visualization shed light on that dark void? Sure, so I go back to what computers do well and what humans do well, right? So what does a human do well? Humans, we do intent, right? We do desire and we do satisfaction, right? You can't ask a computer, what are you trying to do, right? You can't ask a computer what do you want? And you can't ask a computer when have we succeeded, right? Those are questions that humans ask, right? And the problem that humans have versus the computers, the computers can do computations that we can't even imagine. They can go beyond anything that we could hope for and they have the ability to crunch numbers like you've never seen. But the problem that we have is I can't talk to a computer. Make me coffee, doesn't do it yet, right? It has to be a language between myself and the computer, between the machine and my mind, right? Between what I want and what I need. And that communication goes two ways. And the me to the computer is me typing things in and me moving a mouse and me moving everything that you saw up there because that's intuitively how I speak to machines. And for the machine to speak back to me, that's the visualization. The machine says, how about a picture like this? How about these colors? How about if I parse it out like this? What if I move this over here instead of moving that over there? Do you see something? And I say, yes, I see something. And the computer says, fine, tell me what that is and tell me what the next question is. So that's the visualization. The visualization is how the computer speaks back to me. So it feels like in the last 10 years. I mean, you remember in the early part of the 2000's Harvard Business Review, many, many other publications would come out with basically how great CEOs operate on gut instinct. Yep. And it was sort of presumed at that time that that was sort of status quo that wasn't going to change. In the last 10 years, that's changed quite dramatically. Not that gut instinct still doesn't matter. In fact, we heard Christian talk about instinct being critical to decision-making. It's just that the amount of data that we have now to drive that instinct, to inform that instinct, has changed. I wonder if you could talk about that a little bit. I mean, the old bromide and big data, you can't take the humans, the humans are the last mile, right? Yeah, yeah, yeah. Talk about that a little bit. So here's what I think. I think there's different parts of the problem. So I like chess. I like the game of chess. And I don't play it as well as the professionals do because it takes a lot of time and I don't want to spend the time on the game. But here's how the game looks to me, okay? There are opening moves and then there's a whole bunch of unknown that they have to deal with. And there's the end of the game where both players go, or at least one of them before the other one says, I know how this ends up, okay? And he knows what the answer to the end of the game is. And the other one might say, uh-oh. And the other one says, uh-oh, right? But that area where we have the unknown, that area what you get are aggressive players, defensive players, classic players, creative players. You have all these adjectives that we would use for CEOs, for managers and everybody else, right? But what you see is the reason they have to go some sort of strategy of personality is because that's, they're dealing with the unknown. As soon as the game moves into, I know how Checkmate works, I know how this thing ends up, okay? There are no more aggressive moves or defensive moves or classic moves or fantastical moves. They're all just the right move. And so it's the same with data. Data takes us closer to the future. It takes us closer to the right move than that void that we've always had to deal with in the past, yeah? So, okay, so talk a little bit more about how you have used data in your own experiences and your various work environments and what's excited you. Right, and so it's almost always that same thing. I have a basic theory which is, so I worked with Toyota for a while. Toyota has a concept of Genshin Gimba. And Genshin Gimba says basically, the ivory tower is wrong, right? The ivory tower is wrong. And why is the ivory tower wrong? Because the ivory tower is too far away from the problem, right? So where do you gotta go? You gotta go to the problem and you gotta see what the problem is. You have to talk with the people on the front line. You have to figure out what their intuition is telling them and how they think the problem ought to be saved, right? They don't have the resources to the front line to solve problems, but they have the intuition that you need. Data in the conversation of data allows the people on the front line to speak to the ivory tower and allows the people at the ivory tower to be able to take the input of the front line and turn it into something meaningful so the resources get to where the resources need to be. And almost every single time in my career, I've done exactly that. I go to the front line and say, what's your intuition telling you? Right, and Toyota was, you know, how do you think we're going to fix this so that we can get this up and running, right? And that wasn't actually Toyota. We worked for a company that, we were doing a special show car for them. We were trying to get the show car up and running. But you talk to the people on the front line, you get their intuition, and then you collect the data points. What kind of data points would I need to show exactly what you're saying? Well, you need this kind of data point, that kind of data point, this kind of data point, that kind of data point. You can pull those data points together to build the picture that convinces the people that need to be convinced. So your method is you gather information of the front lines about the premise. Yes. And then you either help support or refute that premise. Absolutely. Have you been more successful in supporting or refuting? And what are some of the outcomes have been? It's almost always, I mean, you're almost always never going to start where the answer ends. Again, they're going back to chess. You can't predict how the checkmate's going to happen. Right, you can start to predict the principles. Look at this, if you put the night in the middle, it does better than the night on the side. Right, if you put the bishops on the side, they do better than bishops in the middle. If it's the kings on black, you better not use a white bishop, right? And so the principles and the intuition kind of tell you what kind of pieces you're going to use for that in game. But how you actually do that, no, nobody actually knows that. But if I know what the pieces in the game are and I know how they move and I know what they are, because I talk to the people at the front line and I have the data to know when they're right and when they're wrong, then when it goes into time to play the game, yes, I checkmate more often than anybody else in the room. So let's just kind of take a slightly different view in this so-called big data world that they often talk about, well, it's not really testing hypotheses anymore with data. You don't come to the data with a preconceived notion, you go to the data and the data tells you what the answer is. You almost don't know what the question is. Does that, it sounds a little bit different than the approach that you were describing. Is there a room for both types of approaches or how do you view that? So I've never found a case where, again, the human role of intention, the human role of desire, and the human role of defining when you've succeeded, satisfaction, hasn't been part of the deal, never. I've never had a CEO come to me and say, I got this question, but it has nothing to do with what I want and it has nothing to do with what I'm trying to get done and it has nothing to do with making someone inside, outside, anywhere in the world happy. Never had that question before. So no, I don't think the role of human is lost in big data. I think it's there and it just, we spend too much time and this is what Christian's doing here. We spend too much time on the other pieces of the problem, which is how do we get the data, how do we crunch the data, how do we manipulate the data, how do we decide what's true and how do we decide what's false and all those things. Because we spend so much time there, so much of the focus of the message has been put there, that's not where it's at. At the end of the day, it's the human that's looking for something that they can't find and it's the human that's trying to get something that they don't have. So I love the, you saw Christian's keynote, I take it, right? You were in there. And you know, Simon Sinek, right? People don't buy what you do, they buy why you do it. I felt like his keynote and the great companies, Simon Sinek uses many, many examples. He uses Apple as an example and I felt like Christian's keynote talked to that, the why. He was really, he didn't talk about Tableau, what they're doing and their products and their features and all. He left that for the session. People loved that, by the way, he ended up. But I felt as though he was communicating why he and his colleagues started this company. I wonder what you thought of his keynote and what you think of Tableau in general. So I love it, right? I love it. Yes. So he always does really, really well and where he's going. So there are a couple of things that he's done today. So this is the sixth conference that they've had. Every single year it's the same thing. Here's all the milestones they ticked off and they all mean the same thing. It's going to be easier for people to solve problems next year than it is this year. I think he showed that, right? Yeah. And the message of going back and showing that data is ultimately about figuring out the problems of life is also something, I believe. Can I tell you a story? Yeah, please. So I go to Socrates. Can I go to Socrates? Absolutely. So two quotes from Socrates. All right. The first one where I think he gets it right and the second one where he gets it wrong. So let's start where I think where Socrates gets it right, okay? There is only one good, knowledge. And only one evil, ignorance. If you know the right answer and how to checkmate things, how to get what we want as humans, then there is only one good. It's the knowledge to get there. And ignorance, ignorance is what causes us to make the mistakes. Ignorance is what leads us to having to take random things which was the waste that the toil tell you about that we do in life, right? So that's where Socrates gets it wrong. So where does it get wrong, right? And why not? Why not disagree with Socrates? I disagree with everybody else. Yeah. Right. So Socrates says, the life, the unexamined life is not worth living, right? The unexamined life is not worth living. And I say, no, no, Socrates, you got that one wrong. Every life is worth the same. Every life is worth living, right? The unexamined life, examined life, all of that is worth living, right? But I think he has a point. And here's the point. So I'm gonna clean up what I think he was trying to say, right? Why not re-quote Socrates, might not say he's wrong and then say I can do it better, right? That's a proof upon Socrates. A proof on Alex, and here's what I think. Life is worth examining. There's one place we're gonna spend our time and we're gonna spend what we do, right? The hours and the day, let's examine life, right? I think that's what he was trying to say. And so yes, what Kristen was saying today, that we ought to look at the Petri dish, that we ought to live in chaos sometimes, that we ought to look at the stars and wonder what's up there, right? That we ought to do that day to day in everywhere that we are, absolutely. So since you invoke Socrates, I'm a Greek scholar, but you know, probably forgotten more than I'll ever know, but wasn't at Socrates we had a student to come to him and say, I have some information to tell you about another student, wasn't at Socrates? And he said, well, can you tell me with a certainty, is that information accurate? I said, well, I can't tell you. Can you tell me if certainly it's true? No, I can't tell you. Well, is it useful to me? He said, well, not really. Well, if it's not accurate, it's not true, it's not useful, then why are you telling me? How does that relate to data, right? So it's- No, no, he's absolutely true. So I go back, let's go back to where I think I started. Socrates is saying, if this is not something that someone is intending, that has some sort of intention in it, why do I care? If it doesn't have some sort of thing that I desire and Socrates didn't desire much, right? Why do I care? And if this isn't making me happier, why do I care? So yes, absolutely. Socrates is saying exactly that, that unless it links to something that we care about, we shouldn't care. See, Jeff, the cube goes to all places. I did not think we were going to go there today, but I'm going to throw a little bit of cold water on this conversation, just a touch. I thought Christian's keynote was fantastic. I thought it was very big picture. It was talking about some really important issues. But then I take a step back and I think of, okay, I'm a BI analyst at a company. I got to get my job done. Yeah, yeah. Some of that core just day to day is not very exciting stuff that you got to do. You still got to do the reporting. You still got to, you know, you still got to get the dashboards out. That might not be, you're not solving great problems like Charles Darwin or Socrates or whoever. How do we bring this back down to earth for practitioners watching who are saying, well, this sounds fantastic, but I've got a job to do tomorrow. And how can I actually do my job better every day? And then, you know, maybe it still strives some of these larger, you know, be a better worker, a better person with some of these bigger issues we're talking about. But still, I got to get my job done. How do you apply some of these notions inside an organization when you're trying to, you know, you're trying to get the train's run on time and you're trying to get the things moving. So it depends on where the bottleneck is and I always go to the bottleneck. So if the bottleneck is getting the job done, you got to get tools that get the job done faster, right? If the bottleneck is you don't have the resources, then you're going to have to figure out how to get the resources, right? And if the bottleneck is you don't have support from the organization, you're going to have to figure out how to get support. But some of those problems are something that Christian's going to be able to solve. Some of those problems are something that we're going to have to solve ourselves, right? And, you know, he is going to make it easier and easier for me to justify using a tool to do some really amazing things. I mean, I can talk about features that I think that I'm going to use over the next year. Yeah, I mean, what are some of the things that Tableau is focused on that you think are going to make your life easier, going to make the way you do your job better? Besides calendar control, because we know you love it. Yeah, calendar control is really sweet. So the big three that I took out of there was the storytelling. You know, again, we can transform data with some code. We can't transform people with code, right? And so I think you take them down the path of a story. You say, here's where I started and here's the key points along the path to where I got to my conclusion. You know, I used to get in trouble with my geometry teacher. He would say, you're skipping steps in your proofs, right? You've jumped from here to there without showing me how you got there, right? And storytelling basically says, I have seven steps in my proof. Here's each of the steps. How do I get to where I am? And there you go, there's storytelling. Web authoring, and what you can do on an iPad today, is where you go into self-service. We're not gonna be able to crunch all the problems for everybody. We're arrogant if we think we can crunch all the problems that everybody needs. We're arrogant if we think we know what they need. We're arrogant if we think that they can't do it for themselves, right? Web authoring, and what you showed us today is, yes, we can come up with data sets that are clean, that are good, that are curated, right? And we can give them to the people that need to have that data to crunch and solve the problems that they need to do. And we can get out of the business of crunching the data for everybody, and that's gonna make my job as a data cruncher that much easier, right? And then the final one was well, having it on the Mac, right? So having it on the Mac, really, I think what he's trying to push there is, the data's going to follow you. And the analogy of you having to go home to get your email or having to go to work to get your work email, and now you can do it on iPad while you're on the plane, now you can do it at your desktop when you're at desktop, now you can do it on your home computer or at your home computer. Those three are pretty powerful to me, right? Yeah, so let's talk a little bit more about, you mentioned kind of the storytelling, but maybe taking a little bit of a step back, just in terms of how you approach visualization. I mean, we talked today with a few practitioners about who both kind of independently said, keep it simple, and my sort of antidote was that we've seen as we've taken theCUBE around to different big data events and analytic events, sometimes you'll see competitions for the best data visualization, and a lot of times there's some really busy visualizations that look very pretty, but I couldn't tell you what they're trying to get across. Do you agree with that idea that simplicity is really one of the most critical elements in data visualization? How do you approach that? So I'm a chess player, right? So chess players don't want to know how many thousands of steps you can take to get to where you want to be. They're always looking for, okay, I know how to get there in eight, can I get there in seven, right? I know how to get there in seven, can I get there in six? Nope, I can't get there in six. Fine, the minimum number of steps and the right number of steps is seven, and then yes, taking out all the fluff. What's distracting from what I'm trying to say? My step seven is all about this to that, and not about this other, well, let me get that other stuff off, right? And just get down to the seven steps that you need to prove your point. So absolutely, yeah. So chess is this closed system in a sense. Yeah. And yeah, that's an infinite intellectual playground, for sure. But what gives you confidence, or do you have confidence that this open system of this world that we live in, everybody talks about data being the new source of competitive advantage, and we're here cheerleading visualization, which is a great thing. theCUBE is a big data cheerleader. What gives you confidence, and do you have confidence that data is the new source of competitive advantage? You will actually realize that vision, in not just clicking on ads and getting people to buy things, but whether it's healthcare or sociopolitical issues, et cetera, do you subscribe to that vision and do you have confidence that we'll get there in our lifetimes? So will we get there? Yes, we'll get there. Is data the competitive advantage? No. Crunching data? Nope. What I think is probably at the core of where we're going to end up getting, and being and wanting to be, is the intuition of the people in your organization. What you don't have in the database is going to decide what answers and what solves those problems. I've never seen the computer really come to a good conclusion. I've seen a lot of people look at a Petrie dish with mold in it and say, that mold is surviving. Right? And say, that's what's unique about that. And what data is doing is data is getting to the right people, so the right people with the intuition to say, that mold is surviving have all the information they need to make that observation. Well, so you're touching on a point there about, there's this concern and you read the Economist, Harvard Business Review. Think, are these articles about, are data and analytics and automation going to remove the human element? No. We're going to lose jobs and we're going to be an automated society. We're going to deliver all this value through data and automation, but kind of paradoxically, it's not actually going to, that value is not going to translate into a higher standard of living for most people. Do you disagree with that? Absolutely. And why? Explain on that a little if you would. So if you look at the technology that's available to even the poorest of the poor in today, right? The technology that's available to the poorest of the poor is amazing compared to what was available to even the rich a thousand years ago, right? I don't think that trend subsides. As for us being automated, you can, oh, there we go. Technology, right? It's not getting in my way. It's not making my life better here. As far as technology automating our lives, it doesn't automate how I think. Maybe it's smarter at knowing that this has lower cholesterol and lower cholesterol is going to lead to a happier Thanksgiving with my kids because I'm going to be able to indulge in a little more turkey if I save up during the summer. But this doesn't say that the calculation is wrong. It's helping me and showing me how to make calculations because it's right. When Google Maps picks a path that I've never taken between my home and work, because there's three accidents today, they're not getting that calculation wrong. They're getting it right, right? And yes, the first time that Google reroutes me to my work, for any other reason, they got the calculation right and that gets out on the internet? No, no, that doesn't work for Google, right? As soon as they do it because of advertisements, soon they do some sort of deal they had with a local guy or as soon as they do it because of some other reason, other than they have the right answer, okay, they go away. So no, I'm not afraid of the manipulation of data. I'm not afraid, the peer review is through the roof. And no, no, I'm not afraid of the jobs. What we need in this country, so this political, personal, yeah? Yeah, go for it. Yeah, why not, why not? Is to solve more problems, right? And I do believe that when you make a nice park, everybody gets to enjoy it. When you have a nice beach, everybody gets to enjoy it. When you have good schools with good facilities and everybody gets to enjoy it. When you have budgets that aren't wasting money, everybody gets to enjoy it. When you have good roads and good airplanes and safe this and safe that, do we all get to enjoy it? Absolutely, we all get to enjoy it. Bruce Boston, awesome, we love it. Can't take the humans out of the equation. Gut feel, intuition, intent, desire, and known outcomes that are satisfying. Yeah, yeah. Great stuff, really appreciate you coming on. My pleasure. Pleasure to meet you. Yeah, you too. All right, keep it right there, everybody. Anel is up next for Manpower. We're back. Jeff Kelly and Dave Vellante live from the Tableau Customer Conference. This is theCUBE.