 Hi everybody, we're back. This is Dave Vellante with Jeff Kelly. We're with Wikibon.org and this is theCUBE. We go out to the events, we extract the signal from the noise, we find the best guests. We bring you their knowledge and share with you our community. I'm at Dave Vellante, he's at Jeffrey F. Kelly. You can tweet us, hashtag for the conferences, pound TCC 13, Tableau Customer Conference, TCC 13. And we're here in DC, we're live all day today and tomorrow, today's mostly the customer day. We're unpacking case studies, talking to Tableau customers, their user base, to find out how they're using Tableau, how visualization is impacting their use of data, how it's driving value for their organizations. Simon John is here. He's the business analytics director at LinkedIn. LinkedIn, as everybody knows, is a tremendous company growing like crazy, one of the stars of Silicon Valley. It is a data company. Simon, welcome to theCUBE. Thank you. So tell us a little bit about your role at LinkedIn and how you're using data. Great. My role at LinkedIn is, LinkedIn is one of the most data-driven companies in the world. My job is how to use data to empower internal employees to make them more productive, efficient, and more successful. So, and we cover, currently we cover sales team, marketing team, product team, engineering, customer service, and operations. And we use the data to help them to more productive. Okay, so you're a data guy, but you've not always been, I guess, not been directly a data guy. You were a neurosurgeon, right? Yes. So talk about how a neurosurgeon becomes a data geek. How, why, how did that all come about? Great. That's about 10 years ago. I was working in a cancer hospital in my native country, China. And I think I operate about in a 350, different type of brain operations. And I work in the brain tumor center. But my passion at that moment was about the computer. Internet, even video games, I like to play computers a lot. I think that's what my passion about at that moment. And a neurosurgeon career is a great career, by the way. And I met a lot of amazing super-intelligent doctors, but I just feel it's not designed for me, right? And I like to do something I really enjoy. That's the reason I left the hospital and I started looking for career paths in IT, computer, and in the internet area. So I came here, starting my MBA degree. I was very lucky to find a intern position to do SAP-related programming. At that moment, I started to find out this amazing database system, a lot of really amazing logic, connect all the dots together. For example, you click a button here, let's say 100 miles somewhere in the facility, they will see the transaction happening and then they can be more efficient. Yeah. Okay, so you became a data scientist essentially, right? Yes. And now that's your role. We call you a data scientist, right? That's you consider yourself, I presume, a data scientist, right? Yes, I was a data scientist. Yeah, so, okay, so now you're at LinkedIn. Tell us about how you use visualization to, you know, let's say unlock the power of data. Sounds cliche, but it's true, right? If you can't visualize it, sometimes it's too hard to understand, you can't act on it. So talk a little bit about how you use visualization in the context of data. Absolutely. I tell LinkedIn, so we believe data needs to be explained, right? You know, a lot of people talk about the big data. Actually, our mission at LinkedIn is how to convert this, you know, petabytes, gigabytes, you know, terabytes data into very quick and small insights. We want to convert this, you know, petabyte data into kilobytes and then deliver the message in a very quick, easy and scalable format. And let's say, you know, there's a painting from a monad called, you know, Haystack, right? So the concept is same, you know. At LinkedIn, we believe, you know, analysts should, you know, build a report like you make a painting, you know, like an artist. So how you visualize it, you know, and make it extremely simple, easy, and straightforward and make people can understand, you know, in one second, you know. My job is not creating fancy but complex, you know, charts for our audience, you know. My job is, I want to visualize the data in very, very easy and simple format. Everyone really, they can get the idea, you know, quickly. And then they can use the insight to drive business results. So, and that's our core concept of LinkedIn data visualization, you know, particularly internally. So the driver was to simplify things, make it scalable, make it more consumable. So talk about how you do that. I presume we used Tableau to do that, but what was life like kind of before and after? Take us through the sort of the history of how you've been using visualization. Sure, and my first year at LinkedIn, I was a data scientist and the first year, you know, I closed, I think, about 500 different type of requests, including building models, delivery reports, you know, helping marketer, salespeople to analyze their campaigns, closed deals. What we found is, you know, the way people access data, actually it was not that easy, right? You know, big data, most of the time means the slow, processing, complex data structure. It's not a very simple and quick. So before, you know, sometimes people need to wait for, let's say 20 hours, hours, you know, actually sometimes by days, you know, to get insights. We found out, you know, that's not scalable. That's not, you know, empower a lot of people to access insight. What we did is we started shopping, you know, looking for solutions. And then about two years ago, and I came to the, you know, the same in the TCC 2011, I started exploring how Tableau can help us. I figured the concept, you know, Tableau is amazingly in a match with our LinkedIn core principle, right? I talked about how an artist is drawing a painting. Tableau is using Tableau as a canvas, right? Analytics can use color visualization to express themselves, to deliver insightful data message. Start from there, we build a system by using Tableau, visualize our amazing LinkedIn data into very precise, easy to be understood, and a quick message to empower almost every single employee in the company. Okay, so I want to follow up on that metaphor if I may, Jeff, and then let Jeff Kelly jump in here. So the user is the artist in this example. Is that right? Yes, Tableau user, right? Okay, so they're not the end user necessarily. Not the end user. Okay, so they've got to be able to draw, and then understand textures and colors. Yeah, colors, yeah. Okay, so because the user may not be a good artist, maybe a user like me, I can't draw, so I would be a bad artist, but I know what I like. So I would now interface with an analyst. Is that right? Is that how it works organizationally? Yes, you know, our analysts, you know, work with all of our different teams. For example, salespeople, marketers, you know, PMs, program managers, and also operation, you know, related to folks. We understand what they need, you know, because they send us a lot of requests before, right? You know, almost every day. A lot of requests. We learn from what's being asked, and then we provide what truly they need. A lot of times people ask a lot, actually what they truly need, only one number, right? This is good or bad. You know, how can I close this deal? Which person should I contact too? And then we visualize that, you know, and provide, let's say, the art to them. They can appreciate, you know, the information, and then use it quickly to make a decision. And you use Tableau to build these dashboards and these paintings. Yes. And it makes people more productive. Can you share, like, any examples, or not examples, but any metrics on how much more productive, or even, you know? I cannot share exactly, you know, the usage within LinkedIn, you know, and, but a high level, I can say this. We've been 100 times more productive than before. Actually, the number is higher than that. Two words in 92, okay. Yeah, you know, 100 times more productive than before. Well, Simon, you touched on a lot of really interesting topics. You know, one thing that struck me was you mentioned, you know, it used to take hours or days to get some of these insights and some of these visualizations to your end users. Which, to you and your world, is a long time. I think about some of the other companies and attendees we've talked to who are in other industries, maybe not as data savvy, or that would be quite quick for them, where sometimes it takes days and weeks or even longer. But the other thing that you touched on and I'd like you to expand on a little bit was simplicity of the data visualization. We heard that from our previous guest from GE, also talking about keep things simple with data visualization. And as we cover this market, you know, we go to other events, we see data scientists and we see competitions. The most, and some of these data visualizations are very elaborate and they're very pretty, but it's, sometimes you're looking at them and you're saying, but it doesn't necessarily get across exactly. I don't completely understand what they're trying to get across. It looks beautiful, but they're not simple. Can you talk a little bit about that concept of simplicity in data visualization and why that's so important? Because of, you know, in nowadays market, people are so busy, right, you know? I saw a lot of charts, you know, even before LinkedIn career. Very fancy, but complex. I told the team, I said, hey, I'm a little professional. If I need to spend like five minutes to really understand, you know, the charts, there's a problem because of, you know, we want to deliver precise, accurate, easy message to the audience, you know, right away. So they can leverage this to make decisions. The point is, we don't want them to spend hours a day to enjoy, like I said, more nice painting. To understand what's the background story, right? We want them to have a quick and a busy insight and then drive value. So that's why, you know, I think, you know, easy and simple to understand is extremely important, right? Provide a fancy chart, it's not my mission, you know? And because the speed, we found out, you know, when we provide a very simple and straightforward insight, you know, audience adopts it in a very, very amazing way. You know, for example, in adoptions, you know, David just asked me, pre-post, right? Pre was, let's say, 10, you know, employees use it, right? After is thousands, right? You know, we see this in extremely, you know, hyper growth of adoption because it's useful. It's easy. They can explain, they need to, you know, get back to data scientists or analysts to understand what's this number mean? Why this color but not that, right? So that's why I think that simple is the most important in the factor of our success. You know, with your colleagues, not necessarily LinkedIn, but elsewhere, you're the world of data scientists, do you find that, is that a, you know, this concept of simplicity? Is this something that's lacking, do you think, in a lot of data scientists and others out there trying to maybe get a little bit too elaborate with the tools and the technology to allow you to create these visualizations? So, and I think that's, you know, draw back, you know, to our basic understanding about what is the data science, right, you know? My personal understanding about science, science must be used by a lot, a lot of people. To make sure science are applied, right, you know, empower everyone to be more productive and efficient, we have to keep that very simple. And that's why, you know, the data science, a lot of people think, oh, this is the science, right? This is like Einstein, you know, a formula in the notebook or somewhere in the library. No, the science, you know, should be existing everywhere, right, you know, make our life a lot of better and easy. That's my basic understanding of data science. Data science means, you know, we need to productionize this and then empower everyone. That's what I call science. That's why, you know, so there's some schools, people thinking about data science as fancy, you know, attractive, you know, cool, right, you know, to be honest, you know, from our perspective, I think, you know, the action, what people can do about it, you know, only being interested is not enough, you know. We need to provide a valuable information and make people take action and make decisions, yeah. Simon, you've said that if you focus enough time and thought on the hardest problems, you will ultimately solve them. I know that's your philosophy. What hard problems are you working on that you can talk about that needs to be solved or just in general that you think need to be solved? In general, I think as an individual professional, the biggest challenge in my career is how to leverage science and make a lot of people access information be more productive, right. Before, my personal feeling is only a very small user group have the luxury can access the data inside because the whole flow is complex and slow. So the largest challenge is, you know, that the people that I support, they ask me, Simon, can you provide us, you know, insights? Let's say after I click the button, in one second, in two seconds, I can get what I need, you know. Before, it was days, weeks, hours, right. Right now, they ask for one second, two seconds, you know, no longer than three seconds, that's the requirements I had before. It was very, very challenging. I told them at that moment, I never seen anything like, you know, like this kind of fast. It's almost impossible. But, you know, in the world, there's nothing that's impossible. If people have passion and drive, then do it. And then finally, we make it happen. So how do you see that, the ability to democratizing data and visualization and extending it to a much wider group of users? How do you see that impacting? Obviously we know how it's impacting companies like LinkedIn and some of the web-scale companies, but how do you see that impacting larger industry, whether it's in more traditional industries like retail or manufacturing? You know, is it simply a productivity gain? Or do you see it could potentially change the way we do business and lead to new business models? I mean, what do you think the impact of all this democratizing data will have on the market? This is a great question. I personally believe, you know, I mean, basic, what is analytics, right? Analytics is about use data to learn history and then we predict the future, right? That's the basic concept of analytics. So only predicting future is not good enough. The way after we predict that it would happen and what we can do to change the history, to make history, I'm sorry, to change the future, to make the future better, that's the core mission of analytics. I can see data and the insight will change people's life in a dramatic way. This is not only for LinkedIn or in a social network company or internet company, but also everywhere, right? We will change how people to be more productive, right? Second, we will change the way people do business, you know, how to connect with other people. Third, you know, I think the information will help us to do better, right? Before people spend hours or days to collect information, make a decision. For example, let's say, I want to buy a house, right? I need to do all sorts of research. How about some day, some day, there is a system can tell us right away, this is your dream house, you know, which you can buy right now based on your budget, you know, with the school you need, you know, with the community, you will enjoy, let's say, in a second. You don't need to waste your time, you know, shopping around. People can save that time to enjoy their life. How about we do that? That's very basic information. And also we can use data to predict disease, right, you know, before I was a doctor. Let's say today, we use big data, you know, to understand why people get the answer. When we understand why, we can prevent it, you know, that will save millions of people's life. That's, I think, my passion about why data is impactful for human being and life, yeah. Yeah, it reminds me of my co-host, John Furrier and I have interviewed a couple of times, Jeff Hammabarker, who is the chief scientist at Cloudera, he was at Facebook. I don't know if you know Jeff, but interestingly, he's took an opposite path. He went from being a data scientist and now he's an MD at Mount Sinai in New York. But he's very famous for the quote, saying the best minds of my generation are trying to figure out how to get people to click on ads, you know, using data. But I think there's, even Jeff would admit, there's some socioeconomic benefit coming out of that, but, and you gave a number of good examples. Do you ever see yourself getting back into medicine? Why not, you know, why not? I think, you know, medicine and data analytics on the backend, they are very, very well connected, you know. Actually, analytics or medicine, we just try to decode, you know, the rule on the backend, right? Very basic but beautiful rules. I would say someday, you know, after I finish my, like I say, data journey, right, learn more and then know how to leverage data to change people's life, you know, someday we can apply all of this, you know, amazing, you know, methodologies and tools and solutions and back to the medical field and then empower, you know, to change patients' life, right, you know, yeah. Yeah, absolutely. I mean, I think there's so much potential in the healthcare industry. But of course we know with, you know, there are a lot of obstacles in the way as well with regulations and other things that need to be overcome. But really, there is so much potential. We're actually going to have a healthcare practitioner, Piedmont, come on later and talk a little bit about how they're using Tableau and data visualization in the healthcare field. But I think you're absolutely right. That's one area that really is ripe for innovation in that sense. Well, Simon, thanks very much for coming on theCUBE and sharing the LinkedIn story and your personal story. Fascinating and really appreciate it. Thank you for having me here. Thank you, David. Okay, so I mentioned, of course, John Furrier before. My regular co-host, Jeff Kelly sitting in. John is in Silicon Valley on personal commitment. Couldn't be here. Hi, John, I hope you're watching and shout out to you. Koi Gupta is up next. Koi is from Paychex. We're going to, again, today is customer day at the Tableau Customer Conference. It's all about the customers. We're going to hear from them. Koi is a practitioner at Paychex using data. So keep right there. I'm Dave Vellante with Jeff Kelly. This is theCUBE, SiliconANGLE's production of the Tableau Customer Conference. We're live in DC. We'll be right back after this word.