 Live from Austin, Texas, it's theCUBE. Covering Dell EMC World 2016, brought to you by Dell EMC. Now, here are your hosts, Dave Vellante and Stu Miniman. Welcome back, y'all, to Austin, Texas. I knew I couldn't say that with a straight face. This is Dell EMC World. This is theCUBE, the worldwide leader in live tech coverage. Jeered Brink is here as the CFO of Pachanga Casinos. We're going to talk about a big data story and a big data journey. Jeered, welcome to theCUBE. Thanks for coming on. Thank you, it's great to be here. So we were talking off camera. This is the second time your organization's been on, but I didn't know much about it. So maybe you could tell our audience a little bit about your operation, the casino and what you guys are all about. We are a Native American-owned casino in Temecula, California, which is approximately an hour north of San Diego. A lot of people don't know that there are casinos in California, but there are very, very large casinos in California, and Pachanga is one of the biggest. We have approximately 4,000 slot machines and over 150 table games. The casinos themselves, the casino itself is bigger than any casino footprint in Las Vegas, more slot machines than any casino in Las Vegas to give a comparison. It's just fully integrated resort, currently under construction to double the size of our hotel, build a much larger, more inclusive spa, and just resort-style swimming pools just really make us just even that much better than we currently are, so. And you mentioned your very technology-driven, technology-oriented organization. You're a CFO. Yes. A lot of times, CFO likes to say no. Tell us about the technology ethos of Pachanga. Well, we're very, very technology-oriented, always on the cutting edge. We know that you have to spend money on your IT infrastructure to be successful in this space because our business, our industry is very data-driven, and you need the technology to drive the data and to market it properly. Can you unpack a little bit of for us? I mean, we've said so many times you're on theCUBE. Data is the new oil, so. I'm sure you can't show too much without giving away some of the state secrets, but where are you finding new business value from data? How does it help you run your business more efficiently, get close to your customers? Well, I think the biggest change since we've integrated our data lake is the transformation and trying to be predictive in our analytics and not reactive. We've always had a lot of data at our disposal because like the grocery store, people in a casino get a club card, get a membership. It's tiered based on value, and we accumulate a lot of data on individual customers, and we've always marketed to those customers in groups, and what we're trying to do is get more proactive in marketing to the individual and to predict if we do this, how they might react and how they might behave. So that's where we see the value in our future analytics. So when you say you're data-driven, when we hear that, we have a little mental model we use, so I'd like to test it with you. And in our model, there's like five things that you have to do, we think, to be data-driven. So maybe you can help us evolve it, debunk it, add to what we're missing. Three of the things are sort of sequential that you have to do, and two are sort of ongoing. And the two that are ongoing, you got to get the right skills, data orientation, people who are comfortable with data, and you've got to have a partnership with the line of business because the line of business ultimately understands what data matters to them. The other three things that are sequential, one is you got to understand how data can support the monetization strategy of the company. The second is you've got to understand the sources of data, whether it's internal or external, and the third is got to trust those data sources. Do those sound like reasonable parameters? Did you have to go through a process that's similar? Are there things that are missing there? No, those are very good points. First, the consistency of data is very important. We, in the past, before we integrated this solution and before we got into this data lake environment, we had many, many sources of data and we could have a marketing analyst come up with a ratio or a number and we could have a finance analyst come up with a completely different number and it's because the sources weren't the same. So part of the benefit now is that there's one source for our data and it's consistency. So the data lake has provided a lot of consistency. So sources? Sources, yeah. The integrity. The integrity. We absolutely consistently test that data. And you understand how it feeds the monetization strategy of the business. Obviously the most important part. We're a for-profit business and want to make money and it's, in our business, we have competitors around us. We have other casinos and in the casino business, it's about getting the share of our customer's entertainment wallet. A customer might have their entertainment budget. We're competing with other entertainment outlets in addition to casinos, but as it goes with other casinos, our objective is to, and another point, our customers aren't necessarily monogamous. They'll go and they'll try different casinos. They'll try to see what offers other casinos. We'll give them and our objective is to get as many visits if a player's going to make 10 visits a month to a casino for entertainment purposes and we currently get six on average. If we can get six and a half, we can get seven. That's how we're going to be more successful. That's how we're going to make more profits for the tribe and for our ownership group. Chir, you talked about being able to be more predictive with your data. Can you talk a little bit about kind of the timeliness of how you can act on data? You know, the term real term gets thrown around a lot. You know, what does that mean? How real time do you need to be on your data? Well, we're much, much closer than we've ever been. So, it's important. What somebody did six months ago isn't as important as what they did today. So, we've definitely gotten there where we're much more real time. I think the other key to this whole process is the speed at which we can access the data and the speed at which we can react to the data and the speed at which we can make decisions going forward. And that's probably one of the, well, in my opinion, one of the biggest advantages to this whole solution. Yeah, so. There's some customers that we've talked to that said, I need to be fast enough to not lose that customer. You know, are you trying to be fast enough to get them for that next visit sooner? Or, you know, what is, what's some of the outcomes that you're hoping to have? You talked about getting there once more. Next visit and trying to perhaps plot what you might send them information-wise because they're sitting at a slot machine with a little window that we can communicate with them through. And if we can send them a message that's a call to action that entices them to stay longer or to take another step to stay rather than leave or to offer them a food offer or something that makes them think of us first the next time. I mean, it's those type of activities and we'll know what kind of food they like. We know what, which of our restaurants they like. You know, it's collecting all that data and pinging them on an individual basis if we know what they want. Or if you have somebody that's playing outside their norm, you know, we can send a host over quickly to make sure that there's a personal greeting and, you know, that personal encounter to differentiate ourselves. You know, the other point, Pichanga's already at the forefront. From an IT perspective, from a marketing perspective, we are already the market leader. We didn't do this to catch up. We did this to stay ahead. You know, we made this investment to stay ahead of the competition, to be number one and to continue to be number one and to ensure that we maintain that leader position. So let's talk about that data journey a little bit. You've been around for 20 years. So 20 years ago, the state of the art was, you shove everything into a UNIX box and a big data warehouse and buy a bunch of Oracle licenses. If you had any money left over, you might do some app development. So you were there back in those days. No, I've been there 10 years. The company who lived that, through that. And then, you know, Hadoop came, democratized data, et cetera, et cetera, et cetera. But take us through sort of your big data journey and that speed of access and how that all changed. Because back then, the data warehouse days, it was, we sometimes say it was like a snake swallowing a basketball. It just took a long time. Yeah. We started about two years ago with Bill Schmorezo from EMC doing a big data vision workshop. And we brought all of our different leadership group, our executive team in, and not just the executives, we brought in directors and managers and others and just threw ideas out and talked about our business and the folks from, we were on the EMC side now that it's all combined. But they asked questions and through this workshop, they helped us develop creative use case ideas and we picked one and developed it and proved it out. And, you know, from there, we developed a few more and we're still in our infancy. I mean, there's no end point to data science. This is just, we're just getting started. You know, the sky's the limit. And, but that's kind of how we got started was through the vision workshop and then we evolved, we made the investment in the hardware and everything else that we needed to do and we're still just getting started. So. And you've implemented this platform, this new platform that was just announced today, I think, right, the analytics insight module. So that's a, you guys are an early customer of that offering, correct? Yes. And you're in production with that now? Yes. Okay, so what's that experience been like? I mean, can you give us any indications of the outcome kind of before and after even at a high level? I think the biggest outcome is just the speed that I mentioned before, the ability to get at data quickly and to react quickly. I think that's probably been the biggest advantage so far. I'm not a data scientist, so I, you know. Nor am I. I don't get to play with this. They don't let me play with the boys, but what's really cool is that, you know, as a finance guy, if I have ideas, we will sit with the science team and, you know, our marketing and our IT folks and whoever else are slot guys or are table game guys and we'll throw out ideas and they'll listen and you can just see them smile and they say, oh, you know, yeah, we should look at that. We can do that and we can go here to get that data and they start churning and that's when I don't understand what the heck they're talking about, but you can see them implementing what we want to do and thinking about how they're going to get there. So that's interesting. And you can see the output. Yeah, and what's a typical back and forth on that? Is that you go talk to them and weeks later they come back with it or is it an hour's minutes? No, we try to meet monthly at least and they'll report back on ideas and suggestions. They'll do feasibility to see, you know, should this be a use case? Cause you can only have so many of those going at once if they're going to be effective. And, you know, if they determine it might be difficult or too difficult or it might not have a return, we might put that aside for later and focus on the ones where we think we're going to have a good return on investment quickly. And this dynamic that you described earlier about, you kind of, you're not a data scientist, but if you interact with them and you make requests, ideas, et cetera, that they're able to execute on, would they not have been able to execute on that? You know, previously, is this a new capability or just would have taken a lot longer? I think like most businesses, it can be difficult to run projects like that because in a lot of places, those different areas are a little segmented. I mean, in prior, you know, my background's Las Vegas casinos and in prior operations, the different business units aren't necessarily, you know, in consensus. They're not necessarily in agreement on how we should do things. And what this has done is it provided one source, you know, one data source for us to go analyze. And we also, through the division workshop, we built consensus. We showed value. We helped our staff and our leadership group understand why there can be value in this and how it can be better for the company, how it can be better for all of us to increase profitability. So I think that that was probably the biggest aha moment is that we figured we could work together and it kind of broke down some, I don't want to use the word silos because that implies that we weren't united. But it definitely broke down what some smaller barriers that were still in place and you know, we have marketing and IT and finance and slots and table games, all these different divisions, getting together, working together because I think everybody sees the value in our data and understands that the sky's the limit in what we can do, so. I'm curious, in the keynote this morning, Michael Dell was talking about the internet of everything and we censor everything and all the people that are there. I have to think there's got to be some applicability in your world. I'm not sure if that's something that you guys have looked at, tackled it or you can talk about yet. Does IoT kind of fit into what you guys were looking at yet? Yeah, I think it fits into everything we do. So, yeah, it's definitely applicable. Can we talk? I don't know if I answered your... Anything that's exciting you about the potential of what you can use with those kind of technologies? Yeah, it's just future analytics, future predictability. It just adds more data sources to what you're doing. Exactly. It's another data source, it's not, you know. I could track people down and know that, okay, maybe I can redesign my floor based on what they're doing. Right, and consistency is the other great part to it, so. Roll the CFO. Do you, does IT report to you? No. Okay, so what's the reporting structure? CIO, do you have a CIO? Yes. Reports to what, the CIO? The general manager. To the general manager. And you report to? The same person. The same person, okay. So does the CMO, all three of us do. The C... The chief marketing officer. The CMO? Yeah. Really? Yeah. Okay, it makes sense, right? Because you're such a marketing-driven organization in that sense. Okay, and do you have a chief data officer? No. Oh, okay, is this something you guys talk about? It's under the CIO. He basically controls the data. Okay. The governance and everything there is spearheaded by IT. And we have an analytics team, you know, a leadership committee that oversees that the entire process. And you and the CIO are essentially peers, right? Yes. Is that right? Correct. So that's interesting dynamic, because I was saying before, oftentimes the CFO just red lines things and many organizations, although that's, I mean, it's sort of a bad rap. You're stereotyping me here. It is. I mean, it is a poor stereotype. And I think in general data becoming an asset has sort of changed that mindset. People, organizations that believe data will give them competitive advantage and understand that they have to invest. At the same time, they don't want to invest in non-differentiated, you know, plumbing. Right. So how do you guys sort of manage that? And do you get involved as the CFO in sort of how IT spends its money on non-differentiated heavy lifting versus value producing activity? Do you- Absolutely. Are you a real hard ass about that? Absolutely. Absolutely. I'm hard on our IT guys because their favorite word or the favorite phrase is end of life. You know, they come looking for money, everything's always, well, it's end of life. We need to replace it. Well, okay. How do we get more life? How do we resuscitate? How do we stretch it out? And my favorite return response there is, well, what do less successful casinos do that don't have the bankroll or the capital budgets that we have? I mean, we continuously invest in infrastructure. We're very, very proactive in investing. And he and I work very well together, but it, you know, end of life to me, yeah, one of my favorite phrases that I hear from them, but having said that, I think that we're, as an organization, are very progressive on spending on infrastructure, on spending on things that are going to make us better and that are going to make us money. Ultimately, it's about profitability. Right, and end of life generally means there's something else out there that's going to run faster and better, which, if you're in the technology business, that's guaranteed certainty. Yeah, I shouldn't be saying that at a technology conference. No, but you know what I'm saying, I mean, you're always going to run into that. So the question then is, okay, how does running faster, running better, you know, either save me money or making money? Right. Make that case and if they can make it, you sign and if they can convince you. Sometimes it might not make more money, but you have to do it to keep the doors open too. You know, I mean, if your systems break down, your customers aren't going to come back. Right, so that's that loss avoidance factor. So you got a factor in that, there's really three equations, right? The making money, you know, saving money or risk reduction. Really the sort of three ends in the pyramid. Right? Yeah. All right, here we go, listen. Thanks very much for coming to theCUBE and telling us your story. It's been great. Great. Thank you for having me. Good to meet you. All right, keep it right there, everybody. Stu and I will be back. This is Dell EMC World 2016. We're live from Austin. Y'all come back now.