Dealing with Scale and Security: How MasterCard is Mastering the Game
You are now listening to the big data. This is our podcast for the trends, technology people making Vic dated. Hey, everybody. This is Corey Menton with the big data beard. And we are recording at Dell technologies world in Las Vegas. And we've spent some money. And so the money we've been spending has been powered by my favorite credit card in the world MasterCard, and I am so excited to have our good friend, Nick Kuku VP of data analytics, and cyber security for MasterCard. Joining us today, Nick, how're you doing, buddy? It's been a spectacular day here at Deltec -nology where wild conference. It is something I can't tell you that the Elian where booth out there already to play games. I know it's so little they're planning, what's the rocket league Iraq, it, what's it called? I don't know. I just on better than you. No summer and salt, like we'll do miss a favor. Why don't you tell a little bit about yourself? Ano- problem, you want to one of the greatest things is I'm a data guy at heart. Always has been from my days when I was a cast member at the Walt Disney company all the way through now at MasterCard. It's all about what the data that I get to play with, you know, creating a bigger data set so that always has intrigued me and I keep get gets better and better bigger and bigger data sets more powerful machines actually process that data and the insights that is what really gets me excited in the morning when I wake up, so it's been a great ride these last five years. I gotta tell you very cool. So we've had some time at Disney, but now you're a MasterCard and master guards here at the conference. You guys are giving some presentations, introduce at a high level, obviously, I think we can make the jump on my MasterCard about data. But, like, tell me what massacre thinks in terms of data analytics while you're talking. Well, first Ashley Cory what I want be let everyone they walkway at one thing from this is MasterCard's not a credit card company. We don't issue the credit cards. Rashly technology company, rerun the network that actually powers those carts. So we'll do everything from not just the card itself. But we're in the connected car all the way to allowing you to order, grocery three refrigerator, our technologies being embedded everywhere. So as a technology company, we take a look at how we can make things easier. But yet continue to do it in a safe and secure manner. Our prime directive is all about safety security, and now actually bringing in privacy to make sure that comes into play. But the first and foremost, it's always about a safe transaction secure transaction so that we can create that trust between us and the person actually holds that card just tell the audience how many transactions MasterCard do a day of the week, a year, it's fashion article. Well, this is the exciting part of what I get to do. So I'm gonna give you some sense. We actually were processed about seventy four billion transactions every year. Corean vegas. And the amazing part is, it's not going to be out of the room to say in the next two three years that can double two hundred and fifty billion as we take a look at it. And that's the data set I get to us. I mean, I got two point five billion cardholders out there that are using MasterCards goes across twenty three thousand financial institutions, fifty million merchants two hundred ten countries and territories globally, and the insane part is actually this though soda four billion transactions when they're lined up in the queue. And by the way, if you, if you being a Disney guy, being acute guy Little's law, always about Little's law in the queue itself, we actually got to try to process, those transactions in about one hundred and thirty. Milliseconds. Milliseconds milliseconds. Timescale the most people are really comfortable with. And when you thought about feud had been here, ten years ago, we'd be looking going. We're just happy with seconds. Yeah. And now the team is, is looking at saying, we want to be milliseconds, and how do we even get better at it better performance better scale, more scale, and it's just as an old guy just boggles my mind. Right. So it's been a great, right? I got tell you very cool. So when I think about credit card transaction data as obviously technology which I love the thought that data to me though, is that is, is so personal like the way people use their credit cards. It's like it's their life, and I can only imagine the kinds of crazy that you have, but the responsibility that you have. So why is it that, like, when I think about MasterCard is a data company technology company that I've heard you talk like you're really passionate about the security in the stressing helping understand why that's so critical. Well, that versus the trust factor, first and foremost. And one of the ways we get that trust back to. Believe it or not is we anonymous all of our data. So I don't know if you're holding that card. I don't know the individual holding it. I just know the card and what's being done with the com. So that's, that's how we start to work through trust. That's how we start to be able to say we can take some of our insights, and help understand, you know, from high macroeconomics to even regional economic down even to some zip codes. What's going on to improve, but the most important thing is the trust factor. That's why safety securities air. We want you to have that card, and understand we're doing everything to fight fraud, so that car to safe with you, and more importantly, if we do that. Right. And you trust it that allows us to go into, and we call it inclusive growth to allow other people to join that are generally on banked, which are about two billion start to join the economy. So there's, there's a larger mission for us. But the first one in any type of things you guys know is first and foremost, get the primary directive done. First safety security, Maria, 'bout that. And then you start to work towards that bigger mission, which is getting other people to join the digital economy. Interesting. So it is. I do find it interesting that as a technology company you're here at adult technologies world, right? And one of the things I know I've heard about MasterCard in the passes. You guys were pretty early adopters of some of the technologies early partners with think it was Cloudera and you've done some early work with Dell. Can you explain a little bit about the history of how you've built some of these data intensive platforms and, and how it's changed because of the world is different today than it was yesterday? I gotta tell you. We'll stay with the hit platform. We started with is not the platform today, and that's in the last five or six years. But I think one, interesting parts that makes massacred do what it does is. It's not just looking today. Yes. We are very concerned that those seventy four billion transactions get processed this year. But massacred has at four looking saying what I gotta be in five years. Where do I need to be? And we allow that what we where we need to be to actually dictate how architect and technology. That's why we were in early. Adopter, Duke or like there's the only way we're gonna start to get scale is to start to look at the new technology. And how we can how can we when it comes to mobile payments? How do we when it comes to the internet of things when someone's using their speed this up in an old legacy system? It's not going to be possible you're going to be in seconds, the jeopardy song will play whites. But when you talk about today, it's how do we get that scale? How do we actually bail scale and performance? And that's what's changed. And working with guys like Cloudera working with guys like Dell. We've seen that Ford looking. That's how we actually like to choose our partners. What are you seen in five years? What are you seeing in seven years? What are your customers looking at again, we're not worried worried about today, but we know we don't do something about five seven eight years befall behind in this, in this war against fraud. Right. You always have to say. One step ahead of the people that are committing frogs Davies, three or four steps. So looking five years ahead, obviously gives you that capability to day, though, like, how are you looking ahead to kind of new technologies new capabilities to stay once a head of behalf, or whatever it is, the we'll take one step back what where people are starting to realize is this business about the hackers and fraud? It's a business is a, it's a six trillion dollar business Noah estimates that we're getting out. Six trillion dollars is what these guys are going after and they're doing it in an agile manner, and they're using a I to try to stay one step ahead of us because I'm in the long I'm in the wrong line of work by, so it would be a good boy. I and it's amazing how adaptive they've become. So for us as we take a look what we've got to be able to do if you looked at what we were doing ten years ago. We're the public in about ten variables in regards to knowing it's you. It's really you doing the transaction. Action. Now, the advent of AI and bringing that in through our partners with eighteen such a we're not looking at potentially up to one hundred and fifty variables. So we keep going deeper and deeper and we're actually able to tear, though, so that the first ten you're gonna look at if like an alert you feel a little suspicious, you feel a little bit, you know machines, like, I don't sure about this. We drop down to the next level, and we start looking like it's a little bit deeper. Now. What do we know has this IP address ever been used before, you know, has this person shopped at this time of day before what's their patterns from that Russian say person has that card? That's our perspective shot at this time of day. And we look at other things like okay, what else do we need to be seen that we could say, I wanna make sure the transaction goes through? Right. Because it's also a bad experience, if I shut you down for sure, and it's legit here in Vegas. You know that becomes really important. If you need to get to your credit. Because of the tables. But that's you know we look at there's a double edged sword there. You want us to protect of soy. But then you also like I also wanted to go through when it's me. So again, for us being able to get to the hundred and fifty variables and started at that next layer, because we're even looking at passive biometrics so active biometrics is, you know, retinal scans, you'll see in the movies or your thumbprint or face, you know, selfie. Well, we're also using passive biometrics where local verification, the way you hold your mobile device can be distinctive to you the way you actually swipe distinctive to you actually combining those two becomes even more distinctive the way you tap, your cadence even to the point of, whether it's the mobile device or your keyboard, the pressure, you put on your keyboard, the way you use your mouse. All when you start to put them together becomes a Nikolay at individual. So now we can sit there. Because most of the fraud moving to the to the digital space internet of things because we've done a really good job at fraud in the stores. We're like, now, we gotta make sure it's not a bought who's trying to impersonate year. Exactly. Or someone. Who's hijacked? Your phone has cloned it, you know, you see some of these clone farms, where you've got ten thousand iphones or Samsung's that are literally there. They've clone your phone and they're trying to actually duplicate what you're doing to create the fraud. And now it's like how do we smart them will pass by mattress as one of those ways to do it that becomes seamless to you? Sure. Because at any point, you're always going to hold the phone, and I'm not asking you additional questions or asking to pick out the number of what he called signs street signed executive chefs, you never get it, right. The first time you know, it takes not a robot definitely assigned. But now it's now it's like what we start using own. We have been. We're finding it becomes one friction list for the individual and a lot more people are like a little bit more. I get it. They don't they're gonna act by metrics. People are a little bit hesitant to share. I don't wanna take myself because it's going to be stored somewhere. Right. Right. You know, I don't want my thumbprints 'cause, you know, some. Could you became my thumper, assuring? But now with passive. It's like, okay, you got that next level incredible. And the only way you can do those things is when you when you actually for us, it's the ability to use those all within one hundred and thirty millisecond timeframe, that's crazy. And it is. I mean, again, it's you wouldn't think about this stuff for five six years ago. I didn't even know that the data points of how you held your film being collected. So all those unique data points. Now you have a challenge of normalization. How do you actually bring those into your, your data lake so to speak and did imagine becomes issue? So how are you actually affecting demand, so that you can apply all this data? That's I don't even know existed to your out rhythms. Well, that's actually that's what advent of AI and machine learning helped us with right? Or even after the I'll say this, even the way that we use meta data mandate is now extremely extremely important. That's how you track. That's the lineage that. Now that data's being used where it's being stored where it's being captured. So you take a look at it everyone else. You know, the Lynch pin of all that data turns into one data class, data profiling, what does that data data classification, but type of data do I have that's coming through. And then it turns into where that data goes, it's the lineage which the meta data. And the one thing that the bad guys do know is meta data can tell you what more than you can ever imagine and that to us. That's what we have to use to be to understand that foliage. So where that data lives, where it's been used in who has actually accessed to it that becomes another component to it because again, who has access because one of the hacker want he wants privileged access. So now you gotta monitor the access to that particular pieces of data, especially it's sensitive specialists mission critical. And you're like this is highly personal or this is really sensitive information, especially for hospitals. Right. So now how you. Mac and take care of it again, for us turns into the meditated gets crates the lineage. And also, by the way, you need the lineage. If you find bad guys that lineage, also helps create a case for law enforcement, so they can go back through. So your, your role has evolved, now it sounds, you know, obviously deriving value from data is a clearly, like it's a core part of a MasterCard is doing securing that data is incredibly important, but the threat landscape of how you're fraudsters has changed. Why is it important for organizations are thinking about bringing those two worlds of data and security together? Well, you know, the one thing is they're starting to go hand in hand. So for us data security is more than just governance. It's more than just compliance. It's more than just, you know what we what we, it's actually turning into stewardship of the data and that's two or ship has worth privacy starts to come in a bit. And it's now turned into the ethical use of data are. Are you being responsible because in many cases, whether it's MasterCard, whomever that date is someone's digital persona. It's digital identity and that is them and it's out there. It is it is. You can learn more from someone's data from it's out there than what they know about themselves in some cases. Sure. So now those two components come together and stewardship becomes important because that's what people are asking for us. Now what we've seen in the last eighteen months, people are asking. I gave you I did not give you permission to use my data in that way. What they're actually saying. I think you'd be mission to do that. Right. I think if permission to come into my house and use that information and do it. And that's one where people are taking a back. And the other part that I look at when it comes to those two pearls come together as people are also asking the question, what value, my getting out of this, because, again, you're driving data insult, insights, one of my jobs as data scientists used to try to figure out how to do the job. I might make you do something I want you to do. And now it's about the person's like, well, am I going to get value from it? And what we've seen in the abuses, someone's like I didn't give you permission to do it that way. And Secondly, this is a one way street, I get no value. But you do. And ticks me off, just like anyone else. I want some failure from this for sure. What are you going to give me back in return? If we don't prove that, to them that there's some value in response to sharing your information with me, they're gonna stop. Welcome to people are, like, we need this to stop GDP are California privacy acts. Exactly all the rest of the states here in the United States that are doing Australia's got their privacy rule. So does Canada more countries are trying to create trying to become parents. And that's what you're doing your parent of someone's data and got to be able to just because had the data doesn't mean you have to use the data because I have the right. Yeah. You know, our it doesn't make sense, even if even if you had the right to does it make sense. I mean I'm overweight. I know that, you know, do I wanna fitness center, Tacoma, puts a hey, Nick, we know your fat. They know I right? Nice. But they may say home, the message, it says, hey, maybe want to be a little healthier. You that's a little better than. No, you're fat. You need to be in here and working out twice a day. I'm just listeners Nick is not fat work ler pitching, the dodgeball. Jim calling Ben Stiller saying that on the phone perfect. So it's, it's wild to me that I think that people forget that just because you can doesn't mean you should in so many cases, and I love your in that the, the I don't think how I don't think many of us are aware how the threat landscape has changed, as we've digitized experiences even think of this. So one of the reasons with any passive biometrics we, we actually had the opportunity to find out words that you frequently misspell was missed. Yeah. I spill just every time I don't know why put this and for the U, it's embarrassing and imagine now I can make that part of your verification instead of asking your mother. Made name where you went to high school where he went to elementary, where you what your first car was because guess what the hackers are going out on Facebook, and they're like I've matched. Exactly. I know your mother's maiden name. I know where you went to high school because you talked about your high school reunion. I know you talking about that wonderful shell. Right. What was your first car right? And then everybody responds with their first car managing that. Now, I know all that actually what, what was your first car? Are you a fraudster show? Tell you this. I'll tell anyone my first car was actually Chevy blazer, Mon was a Ford, f to fifty eight is a Texas boy. You got to be able to haul a lotta. Hey, I. That's very Boston of you. The thing is those people it. So with impassive by metrics, I'm asking to put a sense and say, hey, there's two or three words. I know you always misspell because I'm gonna put that in the sentence now type it out, and buy goodness, you should have one of those should be misspelled. If it's not guess what questionable which means I probably have about on the other side. So you get to the point where that's what you're trying to be able to do when it comes to that level of protection, but also going against the fraudsters who are out there mining that data, just like a marketing for sure person would do. Well, they've got a six trillion dollar market. You said they're going, there's money you definitely number. They have to hit every year. They do actually amazing because they do. So let's talk ransomware. So ransomware used to be I go after the fortune five hundred companies. Right. And I tried to get five million maybe ten million turn yourself back on. Well, what the fraudsters have done is like you know what they put some really good things in place around ransomware, and it takes me, generally forty eight to seventy two hours to try to extract or whatever. But if I can lock up someone's iphone until give me five bucks. And I do that a million times today, five million exactly with less hassle. And I have now the cash machine rolling. That's how they're adapting really, really. That's the business are like, what are we getting from the, the ransomware line? We're getting five million a day. The discussing thing about that is, that's the whole, like we so many companies have these edged to court cloud or edged took cloudy, AI, whatever. That's literally edge. Yes. Or for fraudsters like sick that they're using our technology like for good ideas and applying them to defraud us. And you've got to actually skiff getting front of him because also to them, that's not the individual. That's where I think we have the, the where disconnection their mind. It's just a number don't even know it's Corey, they don't know. They don't know it Sam, John. It's not the individual. And I think that's what I've enjoyed in my career. Whether as the Walt Disney. Whereas the SAS Institut or now MasterCard we do care about the person you're protecting a person. And when you had that mindset, you like, all right? We should be doing some the right thing. You know, it starts say, what is the right thing versus I gotta do this to meet my number, right? Exactly. And that's where you got to have that, that's where you need to be that parent. What's the right thing? So when you think about the, the next couple of years as maybe we move out of the hype cycle around a bit, and we get to more reality more broad adoption in the enterprise. What are those, like, what are the big barriers to enterprise success with a that, that, that I think we have to like overcome, and we have really have to, as, as broader technology companies? We have to help solve problems to me from a technology standpoint, the land architecture served us so well, these last actually probably two to three years. They really has come in its own. We're gonna have to get on. It really was a and that's because more of us are living in the streaming real time world land Arctic. It's all about speed layer and how it makes the bachelor right to the service layer. Well, we're living in so much of the speed layer now that were I'm seeing is the speed in the bachelor are becoming one for sure. Which means now we're moving to a cap architecture, which now means I have more demand on my em-empty data context in memory, I have more demand on my pre computations that I'm going to be making and as a result of that. We're at MasterCard or investigating. But also, I put what this conference is really good about is. How do we start to move the CPU into that streaming world because the CPU's about that's the power of neural networks? That's the power of actually deep learning. So deep learning right now sits in the bachelor. That's what is it's learning its processing, and then you say, hey, I've never seen this before. How do I apply? What I've learned before this. That's the cognitive learning. Well, when I get that in the real world in the streaming layer, I needed to be faster and the only way you're gonna do this with CPU power. Because if you try to imitate a brain guess what, that machine has to imitate now and you the CPU, and they needed to come into that stream or that kept architect. How do you do that in the post Moore's law world or cebu's art, getting them much faster? You know that that's the challenge. That's the challenge that, that Deltec -nology spaces challenge until faces and show. AM. MD faces into sheep, but they keep adding layers. Right. And how do we continue to add those layers? And what does it helping us with because again, you know what we're only get so much out of that silicon now does that mean we have to add more notes more more machines to be hooked together, which now guess what? I don't I can't have any latency. Now they all have to work together. And now you're looking at how does that play out? And again for for us, we're like we're trying to figure out that problem now because we know in three years it's here and it could be even here sooner. But we know it's coming so in a Moore's world. Yes. Doubling is now also working on architecture, as well for usually always had a little time to catch up member European could catch up, and then we had, we could kind of catch up again. And then all of a sudden both kind of skip over big data. But now that we're in, we're like holy crap. That's exponential. That curve is a hockey stick for sure. One of the things that I when we think about architecture solving problems. I think the technology world is it's changing so fast. It's crazy. The number tools and tech and things that exists to empower machine learning are nuts. Always like to go back to a human issue, which is like, if we're going to build great a I we have to have lots and lots of data. But the sad fact is that so much of our data is human created, therefore it carries our human biases, how critical is like and are there things that you and the team MasterCard are doing to help fight that, that, you know, we can build great systems. We can do a lot of things. But at the end of the day were still putting data into these systems to train models to make predictions or stop things. How how critical is that concern over bias in our in our world bias is a big thing? We can't have bias, we're working with financial institutions. We have to eliminate it and some of the ways that you look to eliminate that as you can use artificial intelligence to help you. You understand it. There's bias there. So you can have a on top of AI, which we are doing in some cases. But what you also doing is instead of letting the machine term in the grid. Check the machine. So there's artificial intelligence. That's great. But the human intelligence is, is where you have to bring it in still superior again, I think of it, as you know, as a parent, I want to validate and verify. My kid is where he says he is. Right. Not just he's he he told me, I want to define validate the I which means some cases for us, you'll have two or three teams working on the same problem. How did they solve it? You know what, what variables were used here versus here versus what was chosen there? And then you start to take a look at those effort will have any consequences to it, and then you start to really think about how those variables feed into a model that's how you start to try to eliminate this much of the bias as you can. But if you have only one person doing the modeling, which I'd never suggest we always had to, and they would want to check the others work. That's how you. Start the realizes. Why was that important to you, because if you don't ask them, why it was important or wise that variable? You know, it's like, okay, I kinda get that. But that's, that's your opinion at that was the art of data science right, for sure. Now, you can use artificial intelligence and again, we started using decision trees and regression saying, this is why that variables really, really important. Now, does that favorable carrying inherent bias to it? That's why you know if you look at the way, we always do risk and credit scoring. You're gonna take out some of the most sensitive data. You don't want anything around race and gender excetera inside those because again, you just wanna go up here numbers for sure. So the question now is how do we make sure that we Rabl to do that? You know, because there's other things you can play into biases as well. You can have neighborhoods, you can have parts of the country, and you're like, how do I eliminate those so now you ask yourself, what are those variables going in? And then if I limit these, I can eliminate potentially that bias it could come with those so you have a crucial role in helping drive MasterCard forward in terms of continuing to not only use data well for providing benefit to your customers, your card holders. But also, securing it and making sure that you have the trust of those users long-term when you think about the next eighteen to twenty four months, what are the next things that you're excited about that. You're working on or that your team started to think about. Well, I think one of them is going to be the advent of the roll out of passive by metrics and being able to use that as another level of application that, that has tremendous potential across the board, not just with a credit card or financial transaction. But we're also talking access to data. We're talking even access to, you know, even buildings in some cases so that one has a tremendous. Essential to it. I think what also excites us is our, our investment within a I, we're being asked by other industries. Can you help us solve other problems really not just in the financial situtions? Can you help us solve other problems? One of them is, we actually just kicked off a project in Europe around a data market. So the European Union has we have a, consortium, we're taking part in, and it's about being able to actually sit back and create a data market? That is both compliant. That is both. We can anonymous data so you can't really identify. So you feel safe and secure their and then we're also within GDP are and the compliancy of that at so that organizations can keep sharing their information to keep the date economy flowing we don't want people to hold back and all of a sudden we stymie innovation. So that's what we're looking forward in the next eighteen to twenty four months and probably the last thing. More tactically is how we are fighting AM L. So we've had the privilege of working with the. UK and their central Bank along with was eight or ten of the largest banks in the UK to start looking at how we can track the money flow and identify bad people and bad guys. And again, for us, it's like when you look at that we've done it in the UK, it's proven itself. How can we start doing that for other countries and other regions, so that we can start taking a look at how we can fight anti-money-laundering the flow of money the terrorism? Yeah. So you've shared MasterCards story at a lot of big conference this year conference Deltec world, whereas next place that you're gonna be talking about what you and master card technology company is doing. Well, you know, the nice part is, is, I'll be heading to Detroit. My hometown to talk about data privacy and how we MasterCard or approaching it and how we're looking at data privacy, and how other organizations need to be going. So I'm heading back to the great city of Detroit motorized raised on seven, miles, actually, I was born right there on seven mile morose. And be able to sit there and talk to folks around how you can ensure privacy I will be in Detroit. And it'll be great time. So I want to switch gears and I want to move to our rapid VAR sections. We wound a lot from Arcus about big data. But now get a bit personal in a segment to call. To call rapid fire. This rapid fire is brought to you by Disney data endless conference, which is taking place in Orlando. Florida on August twentieth through twenty first. The Disney data analytics conference will bring together over two thousand executives managers and analysts representing over two hundred and fifty companies universities, plus all the segments of the Walt Disney company. This is truly a great comfort to attend. We had a blast last year. Learn a whole lot and this year you can save twenty percent off your conference pass by using promo code big data beard, dash two thousand nineteen. We'll see you there. So what is the latest book? You've read that you would recommend to your listeners really what's point way of leadership love it as a captain. The army, if you have a song to play when you walk on stage all the mini conferences that you speak to what is your walk on song. So I'll tell you, I'll say the name of the band, you tell me the walk on, on ACDC back in black. Yeah. They go. A great Walker I blank, thank you. So. Up my CDC my since we asked this question we asked for the guests. And they're like, so what's your song in Britain was like he froze lights? I'll take I'll take back in black or I'll take thunderstruck strong hell's bells or something. All right. What piece of technology is making your life worse right now? Oh, what piece of technology is making my life worse right now? Wow. There's a lot of them. But no. It's funny that to work in tech, how much you get frustrated with tech it. So it's visceral for me. You know, you know that's actually a good one. You know what? It's still with the legacy our legacy rule systems are like Seattle. It's not a piece of technology. It's actually the processor around it. That's what usually caused me. This usually not the tech because also run a system. Thirty six and I'll absolutely love it to death because it runs like a tank, right? Or four hundred now usually the process, someone put inside of it. That's my go sees. His biggest personal money pit right now. Oh, it's going to be my daughter's college education. That's one but it's too. Vested. No, no. It'll be that and actually the at one, and we've got a we got a lay. We're putting. We'll stay with Alan. What show? Are you binging on right now? Really want the marvelous Mazel? Thinks it has caught my attention. Good. It's, it's funny. It's quick witted. It's beautiful shot in terms like visually fun to watch just say Amazon, it was on per. It was on per tell you what, not only did. I've been John it. When I went home to see my mother back in Detroit. I showed her the first episode. My mom who's eighty one years old. Sixteen hours later with both. Love that woke up the next morning, my mother's handy me back my phone. She's like house. Great. My. You slept. Come into right? So, by the way, there's a show video on YouTube of James corden. One of his employees was forced to watch all seven seasons of game of thrones. Nonstop, they locked in a room all delivery people over hang out. That is not a set like straight through binge-watching. Joe, I felt bad after the first I got a process game of thrones. You really do. Two years to process. He ended up almost like whenever conversation with my wife. What just happened? All right in the last one, where's the next interesting place that you're going to other than Detroit, which is going to Royal National Park in lake superior. Really go to a national park every year, we hike we head up to the mountains, but the summer going to, I'll Royal it's in between lake superior between Michigan provincial and Minnesota. The man will get been super fun to chat with you on the big beard podcast. I encourage folks to check out what MasterCard is doing. And honestly, follow you in the sessions because I've enjoyed listening to you at the at the strategy, and they I conferences in the past, and certainly pre should be in here with us adult technologies world pleasure. It's been it's tackler time. Thanks guys. Cheer slept here. Thanks for listening to the big data beard podcast. The music from this episode is by Andrew del. Check him out on itunes or Spotify.