 Live from Seattle, Washington, it's The Cube at Tableau Conference 2014. Brought to you by headline sponsor, Tableau. Here are your hosts, John Furrier and Jeff Kelly. Okay, welcome back everyone here. Live in Seattle, this is The Tableau Data 14 Conference. This is The Cube, this is our flagship program. We go out to the events, extract the signal from the noise. I'm John Furrier, the founder of SiliconANGLE, joining my co-host Jeff Kelly, who's the big data analyst at Wikibon, the leading analyst. Our next guest is Chris Bertel, Senior Vice President, Wholesale, Internet Solutions, Customer Insights now at Wells Fargo. So you do the insights, the analytics. Chris, welcome to The Cube. Thank you, thank you for having me. Great to have you. So we love getting the banks on because we could talk for hours about all the different algorithms, things that you do. At the end of the day is, people want their money protected. Yes. Right, so that's job one. Two, making it easy to do the banking. And three, making sure no one rips them off. So how are you guys doing big data? Cause I've noticed that there's some new techniques going on and just, what's the big picture? You guys collect everything. Yeah. And how do you make sense of it? So I work for the Wholesale Internet Solutions Group under a customer experience insights team. And so we're a customer insights and analytics group. And our customers are basically companies who need business banking. And the data that we collect is the online clickstream data of our customers coming on and doing banking. Now we have about 90 products on our business banking portal that cover 23 lines of business. So our customers are really unique. Our products are really complicated. And our data is really massive. We have about 50 billion web log records. And that's just the clickstream data, right? But then we're merging and we're marrying that with all kinds of other data, like call center data, qualitative data that we have to make sense of. So bringing in all this data requires just a really great infrastructure and a great team to support it. So everyone's worried about security. I do my online banking. I noticed that it saves my password. I'm like, hmm, uncheck that, clean the cookies. And I got multiple browser tabs open. We get timing things out, SSL, encryption there. But you got mobile, we have these diversity, you got the apps. How are you guys using the big data angle for security? I'm obviously assuming there's a lot of techniques. But can you share some insight in how you use that to help us get protected? Yeah, I mean, we're really working with Best in Class vendors. We have Best in Class software solutions. We're bringing in real time data. And we have, I have a great peer team who does fraud analytics. And it's really important to make sure that our customer's data is safe and protected. And then there's always that, you know, balance or pull. We've got customers, you know, we want to keep customers safe, but that means really having really great controls in place. And sometimes these controls can be a little, little painful with your tokens and everything, but we're really trying to push the envelope in researching biometrics and doing things like that that really will benefit our customers. So you guys have like a tube, I'm sure you have a tube back in. What's some of the technology you guys are using? So we're on Teradata. We are also using Aster. We're using SSRS. We have multi-dimensional cubes. And then of course, Tableau. So tell about Tableau. What is, how is that being deployed? Yeah, so we started using Tableau about a little over four years ago. When I started with the bank, we were a net new team and we were hired because we were about to embark on a redesign of our portal. And when you're redesigning the portal from the ground up, you need to know a little bit something about your customers. So I was hired to start this team, but I had no team, no data visualizations, no data mart and no budget more importantly. So this other team had Tableau and they gave me a few licenses to get started out and we went from there. So shadow IT and play there. So you kind of kicked in tires. Yes, I let everybody know. We banged and we borrowed at a reasonable interest rate. So Chris, tell us a little bit about the culture in the financial services world when it relates to doing big data and analytics. So on the one hand, I think of, when I think of financial services, you think kind of traditional, a little bit stodgy. On the other hand, some of the financial services folks that I've talked to are doing some of the most cutting edge, big data analytics and visualizations that I've seen. What's the approach or the philosophy around using data inside a financial services firm such as Wells Fargo? Yeah, I mean, I think the biggest issue is security is job number one, the security of our customers data and then making sure if we have a customer facing application that system never goes down. I mean, there's rightfully so from protocols or technology put in place to make sure that that never happens. But with analytics teams, I think that if you approach your partners and you make the right argument and bring them into your business and share with them what you're doing and how important it is and how those decisions can save the company money and can help you make better strategic decisions and help your customers that it ends up, you end up winning the argument at the end of the day. So we, our team in particular, because we have a web log data and it's so complex and it's such a hard thing to tackle, we've brought our partners in and we haven't had those issues on our team. They've been completely open to trying and testing out new things. It doesn't happen still at the speed we would like it to happen, but it does happen. But communicating stakeholder sounds like it's a really important part of your job. It's huge, I find that if you don't take the time to talk about what you're doing and the importance that you're just not going to be able to be successful within your company. So at every stage, with every partner, you have to do that. Yeah, and again, that's a theme we've heard at a number of different shows we've done, John and I and Dave are the co-hosts, is that you've got to be able to communicate both the reasoning behind the benefits the analysts are going to provide to the organization, because if you don't get that stakeholder buy-in usually the project isn't really going to go anywhere. Yeah, I mean our team does, we conduct road shows everywhere for every team. And what helps bring everybody in and makes everybody a partner is we have a dashboard for everybody. So we have a dashboard for product managers, for ethnography teams, for sales, for technology. Technology uses our dashboards for risk, for fraud, for marketing. So it's easier to make partners of people when you're serving their needs too. So what about the issue around, not necessarily security or privacy, but just around almost, for lack of a better term, kind of the ethics of what you're going to do with customer data. In the larger, here in the big data world, data visualization world, obviously there's a lot of knowledge about analytics and what you can do with customer data, but in the wider public, there's still people realizing, wow, you're collecting my data, you're doing what with my data? Facebook had that kind of flap around some of the experimentation they were doing. How do you approach, here's, there are things we can do with customer data, but maybe there are certain things we just shouldn't do. How do you kind of make those decisions? Well, you know, the data we collect is all for the betterment of our customers. When we're redesigning the portal, we are wholly interested in making the right decisions. So we're blending quantitative data and we're actually talking to customers and we're balancing, what is that story telling us? Is what a customer saying really supported by the data, is that really what they want? And so we haven't had, I think our customers realize that, but you know, I want to turn that on its head a little bit because what we're hearing from our customers is a little bit something different. They're saying, hey, you collect all this data about us, why don't you share it back with us? We need that data, we need it for audit purposes, we need it to see if we can cut costs. We need it because our organization is so huge, we don't even know the employees within our company who use your product and we have to keep our company safe. We have to know who's moving money in our company and we have to, you know, maybe they have regulators, they have to answer to. So we're actually doing that right now. We take our Tableau dashboards, someone in sales or an executive can go visit a customer and they can take that, they can look up that customer's name and up will pop all that information about that customer, the employees, the products they're using, when they're coming in the day, the devices they're using, where they're logging in from what country. I mean, it's just amazing and it's really, customers are appreciative of that and they say, hey, you know, you really do know us and understand us. Well, so that's really, you really almost, that's a new line of business for you, really. You're becoming a data services company as well as a bank. Well, I think that data scientists, we often create insights and dashboards and analytics for internal partners to make decisions and we forget that there's really a flip side. There's, you know, what about sharing it with your customers? They would value that information too. So we're really doing that right now. Our executive, when he saw this dashboard for the first time, he said, you know, Chris, oh my God, this is what I've always wanted. Of course, then he paused and he said, all right, this isn't what I've always wanted, but for data, this is what I've always wanted. I was like, oh, I'm glad you cleared that out. It's a breath of fresh air when people see the data, that beautiful data laid out in an elegant way, right? So talk about the talent level now. We were talking before we started about the new skills. You can't just put an ad on Craigslist and say, give me a data scientist, or there's no discipline. You can't go to college, well, you can now, but like in the old days, there's no real college curriculum that's fully developed yet. What is the talent trend that you see for data scientists? Is it just math geeks? Is it more creative? They talk about art and science of data. What's your take on that? You know, I for a while worked at Eli Lilly and the total organization, the Salesforce operation group was brand new and they purposefully didn't hire a single person from pharmaceuticals. They hired people who worked for schools, universities. I had come from Kodak. There were folks from the travel industry and they had very diverse backgrounds. And it was the best team I'd ever worked for because everybody didn't have preconceived notions about what should work or what shouldn't work. They weren't in a rut. And so I've tried to take that with me along the way in my career. And I deliberately searched for people who do not have financial services backgrounds. And so, you know, finding, yeah. There's a new way of thinking. So basically that what you're saying, there's a new way of thinking, right? Exactly, exactly. And I think it makes teams stronger. You have new ideas and infusion of energy. What's the most amazing thing that you've done in your job with the data that you can talk about? I think it's that dashboard that we're now sharing with customers. I think it makes us look really smart and it looks like we're, you know, it shows customers we really care about what they're doing. We really understand what they're doing and we can have better conversations. When you walk into a customer's office and you say, I know, Joe, that, you know, you called in the last couple of weeks three times and here's what you called in about and let's talk about, you know. How to go. Yeah. To resolve it. Or do you need extra training for the team? When you have all that data perfectly in front of you before you go into a conversation, it's just a better conversation. The old expression gather the data before you do anything that's important, right? That comes into play here. Yeah, and it's complicated data, but, you know, Tableau makes it look really elegant and really easy. I got to ask you a vision question because yesterday we were talking about the whole iPhone 6 launch and the iWatch. What do you see happening in the future? Some, what's your vision? There's really no wrong answer here, but like, how are we going to get smarter with the data? How does the data get smarter? What do you see happening as more devices proliferate with like a watch or, you know, paying with a watch or tapping it or kind of Star Trek-like features in the iPhone 6? We're connected more. What are your vision around data? I think it makes everybody on my team have a stomach ache to think about that. We are always... Transfer funds now, talk to your watch. We are always chasing the data and the data is always changing. So when you're collecting weblog data, every new device that comes on, every operating system, every browser, every new technology, upgrade of your data infrastructure, we are having to be hands-on on our data, fixing it, you know, merging it, making sense of it. And it's a full-time job, but hey, that's job security. The data keeps coming. It's like the mail now. It's like that scene in Seinfeld, you know, the data just keeps coming. It keeps coming, it keeps changing. Chris, thanks for coming on theCUBE. We really appreciate it. Absolutely. It's great to see you here. This is the Tableau Conference. We were live in Seattle. I'm John Furrier with Jeff Kelly. We'll be right back with our next guest after the short break.