>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen, Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Las Vegas, Lisa Martin. With the cue, we're live Day one of our duel set coverage of Del Technologies World twenty nineteen student a menace here with me, and we're welcoming back a couple of alumni. But for the first time together on our set, we've got Terry Pellegrino, the BP of high performance computing at Delhi Emcee and Nick, who grew VP of Data Analytics and Cyber Securities just at MasterCard. Did I get that right? All right, good. So, guys, thanks for joining Suited me this afternoon, by the way. So we will start with you High performance computing. Talk about that a lot. I know you've been on the Cube talking about HPC in the Innovation lab down in in Austin, high performance computing, generating a ton of data really requiring a I. We talk a lot of it II in machine learning, but let's look at it in the context of all this data. Personal data data from that word, you know, it turns out do with mastercard, for example How are you guys working together? Dell Technologies and MasterCard to ensure that this data is protected. It secure as regulations come up as fraud, is a huge, expensive >> issue. Well, I think make way worked together to really well worry about the data being secure, but also privacy being a key item that we worry about every day you get a lot of data coming through, and if we let customer information or any kind of information out there, it can be really detrimental. So we've really spent a lot of time not only helping manage and worked through the data through the infrastructure and the solutions that we've put together for. For Nick, who also partnered with the consortium project that got started Mosaic Crown to try to focus even more on data privacy on Mosaic Crown is is really interesting because it's getting together and making sure that the way we keep that privacy through the entire life cycle of the data that we have the right tools tio have other folks understand that critical point. That's that's how we got all the brains working together. So it's not just Delon DMC with daily emcee and MasterCard It's also ASAP We have use of Milan, you're sort of bergamot and we'Ll solve the only three c and all together back in January decided to get together and out of Nick's idea. Think about how we could put together with all those tools and processes to help everybody have more private data. Other. >> I think this was your idea. >> I can't say it was my idea. The European Union itself with what? The advent of Judy parent privacy. Their biggest concern was we don't want people to stop sharing. Data began with artificial intelligence. The great things that we do with it from the security, you know, carrying diseases all the way through, making sure transactions are safe and secure. Look, we don't want people to stop our organizations to stop sharing that data because they have fear of the regulations. How do we create a date on market? So the U has something called Horizon twenty twenty on one of their initiatives. Wass Way wanted to understand what a framework for data market would look like where organizations can share that data with confidence that they're complying to all the regulations there, doing the anonymous ization of that data, and the framework itself allows someone to say, I could do analysis without worrying that if it's surfacing personally identifiable information or potentially financial information, but I can share it so that it can progress the market data economy. So as a result of that, what we did is we put the guilt. I said, This is a really good idea for us. Went to the partners at del. That's it, guys, this is something we should consider doing now. Organization always been looking at privacy, and as a result, we've done a very good job of putting that consortium together. >> So, Nick, we've talked with you on the Cuba quite a few times about security. >> Can you just give >> us? You know, you talked about that opportunity of a I We don't want people to stop giving data in. There was concerned with GPR that Oh, wait, I need you to stop collecting information because I'm going to get sued out of existence. If it happened, how do we balance that? You know, data is the new oil I need, you know, keep not flowing and oh, my God. I'm going to get hacked. I'm going to get sued. I'm going to have the regulation, You know, people's personal information. I'm goingto walk down the grocery store and they're going to be taking it from me. How do we balance that? >> Well, the nice part is, since State is the new oil, well, we considered it is artificial intelligences that refinery for that oil. So, for our perspective, is the opportunity to say we can use a eye to help. Somebody says, Hey, I don't want you to share my data information. I want to be private, but I can use a I d. S. Okay, let's filter those out so I can use a I'd actually sit on top of that. I can sit down and say, Okay, how do I keep that person's safe, secure and only share the necessary data that will solve the problem again, using artificial intelligence through different types of data classifications, whoever secure that data with different methods of data security, how we secure those types of things come into play. And again, there's also people say, I don't ever want my data to be we identified so we can use different methods to do complete anonymous ation. >> How do you do that when there are devices that are listening constantly, what Walmart's doing? Everybody that has those devices at home with the lady's name. I won't say it. I know it activates it. How How do you draw the line with ensuring that those folks that don't want certain things shared if they're in the island Walmart talking about something that they don't want shared? How do you facilitate that? >> Well, part of that is okay. At a certain point, when it comes to privacy, you've gotta have a little bit of parenting. Just because you have that information doesn't mean you need to use that information. So that's where we as humans have to come into play and start thinking about what is the data that we're collecting And how should we use that information on that person and who is walking through a store? And we say we are listening to what their conversations are? Well, I don't need to identify that you or you. I just didn't know what is the top talking about? Maybe that's the case, but again, you have to make that decision again. It's about being a parent at this point. That's the ethical part of data which we've discussed on this program before. Alright, >> so teary. Talkto us some about the underlying architecture that's going to drive all of this. You know, we we love the shift. For years ago, it was like storing my data. You know, Now we're talking about how do we extract the value of the data? We know data's moving a lot, So you know what's changing And I talk every infrastructure company I talked to, it's like, Oh, well, we've got the best ai ai, you know, x, whatever. So you know what kind of things should custom be looking for To be able to say, Oh, this is something, really. It's about scale. It's about, you know, really focused on my data. Yeah, absolutely. Well, I will say first, the end of underlying infrastructure. We have our set of products that have security intrinsic in the way they're designed. I really worry about ki management for software we have silicon based would have trust throughout a lot of our portfolio. We also think about secure supply chain, even thinking through security race. If you lose your hard drive on, we can go and make sure that the data is not removed. So that's on the security front. On the privacy side, as a corporation, William C. Is very careful about the data that we have access to on. Then you think about a HBC. So being in charge of H. P. C for Cordelia emcee way actually are part of how the data gets created, gets transferred, gets generated, curated and then stored. Of course, storage s O. What we want to make sure is our customers feel like where that one company that can help them through their journey for their data. And as you heard Michael this morning during keynote, >> uh, getting that value out of the data because it's really where that little transformation is going to get everybody to the next level. But right now there's a lot of data. Has Nick stated this data has more personal information at times? Andan i'll add one more thing way. Want to really make sure that innovation is not stifled and the way we get there is to make sure >> that the data sets are as broad as possible, and today it's very difficult to share data. Sets mean that there are parts of the industry there are so worried about data that they will not even get it anywhere else than their own data center and locked behind closed doors. But if you think about all the data scientists, they're craving more data. And the way we can get there is with what make it talked about is making sure that the data that is collected is free of personal information and can still be qualified for some analysis and letting all the data scientists out there to get a lot of value out of it. >> So HBC can help make the data scientist job simpler or simplify evaluating this innumerable amun of data. >> Correct. So what in the days you had an Excel spreadsheet and wanted to run and put the table on it, you could do that on a laptop for end up tablet. When you start thinking about finding a black hole in the galaxy, you can do that on tablet. So you're gonna have to use several computers in a cluster with the right storage of the right interconnect. And that's why it's easy comes in place. >> I mean, if I man a tactical level, what you'LL see with HBC computing is when someone's in the moment, right? You want to be able to recognize that person has given me the right to communicate to them or has not given me the right to communicate to them, even though they're trying to do something that could be a transaction. The ability to say Hey, I have I know that this person's or this device is operating here is this and they have given me these permissions. You've got to do that in real time, and that's what you're looking for. HBC competing to do. That's what you're saying. I need my G p you to process in that way, and I need that cpt kind of meat it from the courts. The edges say Yep, you can't communicate. No, you can't. Here's where your permissions like. So, >> Nick, what should we >> be looking for? Coming out of this consortium is people are watching around the industry. You know what, what, what >> what expect for us? The consortium's about people understand that they can trust that they're data's being used properly, wisely, and it's being used in the way it was intended to be used so again, part of the framework is what do you expect to do with the data so that the person understands what their data is being used for the analysis being done? So they have full disclosure. So the goal here is you can trust your data's being used. The way was intended. You could trust that. It's in a secure manner. You can trust that your privacy is still in place. That's what we want this construction to create that framework to allow people to have that trust and confidence. And we want the organization to be able to not, you know, to be able to actually to share that information to again move that date economy forward. >> That trust is Nirvana. Well, Nick Terry, thank you so much for joining suing me on the cue this afternoon. Fascinating conversation about HPC data security and privacy. We can't wait to hear what's in store next for this consortium. So you're gonna have to come back. Thank >> you. We'LL be back. Excellent. Thanks so much. >> Our pleasure. First Minutemen, I'm Lisa Martin. You're watching us live from Las Vegas. The keeps coverage of day one of del technology World twenty nineteen. Thanks for watching

(bright orchestral music) >> I'm Peter Burris and welcome to another Cube Conversation from our beautiful studios here in Palo Alto, California. Not a great show today. First off, being joined by my colleague at SiliconANGLE Wikibon, Dave Vellante. >> Peter >> But the real star of the show, Nick Curcuru with MasterCard. Welcome to The Cube Nick. >> Thanks for having me. >> So Nick, MasterCard, 165 million transactions an hour. A financial juggernaut. Blockchain, interesting technology, a lot of applications. How are they going to come together? >> Well, the biggest thing that we look at when we look at those two technologies: our world which is the network and you look at blockchain, is they're the challenge. And I think we have the opportunity to actually meet the challenge and those challenges are speed, transparency of the transaction itself, and actually even trying to reduce the cost of those transactions, especially when you talk cross border. You know when you're going from country to country right now blockchain has a big cost in order to let that happen. The other component is that transparency. I need to know who I am dealing with on the other side and create an auditable trail to understand how that transaction is going through, and again this is something that we do within our core business and again, we're trying to make that meet and then work on the speed. Again, one of the things we pride ourselves are on that 165 million transactions per hour, making it a smooth flow, making it seamless, making it frictionless, that we can do. So again, can we do the same now with blockchain. You know, and for us, we're experimenting now with our B2B, but we hopefully will be able to move that right into individuals as well, to the consumer level. >> So, we're a decade into when Satoshi, whoever he or she was created Bitcoin. >> Or them. >> Or them and yeah, was it the Russians? People are asking that question, so who knows? But, of course a lot of people have been facing negative comments in the press, et cetera. What was your motivation for exploring blockchain, starting to experiment with it? Take us through that if you would. >> Well you know part of what we started to see is that it started to gain traction. That was the biggest thing, and as you start to take a look at more and more people that started to use that technology, it's one of those items that in the beginning we're like okay it's nice it's a hobby right as it started to come out. But as you started to see some more heavyweights come into the place to use it and actually utilize what that technology can provide, we're like, there is something here. Again, MasterCard, our CEO has been very good to say, we need to always be thinking outside of our core. What else do we have to be able to include to allow our MasterCard stakeholders, our banks, our issuers, and everyone, the opportunities that we can continuously expand. So our CEO has been really good about that. And when blockchain started to gain some momentum, he goes, we need to actually take a look, so our guys in the labs, our smart people that sit there in O'Fallon and New York City started to explore how do we take what we know, apply it here to help with that particular way a transaction is being done, and then, can we really allow ourselves and blockchain to grow? So, that's pretty much where we started. Again, it was a little hobby, we started to see it pick up momentum, and about three years ago we were like, there is something here. We need to actually begin to think about how we can interact with this form of payment. >> So what are you actually doing? Are you experimenting, kicking the tires, trying to figure out the use cases? >> That's actually everything that we're doing. Right now, we've actually got a few patents that have just come out, which is very good for what we are trying to accomplish. Right now, we're in the B2B space because that's what we're watching mostly is being used right now is in that business to business space. So we're out there piloting. We actually have set up a whole bunch of APIs to allow people to actually put the blockchain inside, whether it's a mobile device that you want to use, or within the Internet of things. So we have developed a set of APIs that we have got out there that we are allowing our different people within B2B to use, to experiment, to start to say, hey give us feedback on how are they operating. Is it seamless, is it frictionless, are we reducing that operational time, making it efficient, reducing those costs. So that's what we're beginning to roll out. And again, our goal is, if we can do it in B2B, how do we finally get it to the consumer? Because again, that's going to be a big part of what people are going to want to do, to be able to do those transactions amongst themselves. >> When you think about things like AML and Know Your Customer KYC, do you see blockchain as having a role there or does it sort of accentuate your need to understand different ways to know your customer and fight money laundering. >> Well that's actually a big part of it. That's the whole thing we talk about being able to authorization and authentication. So there is a big thing, again, when you deal with blockchain, people, you got the wire in transit right? And there are people trying to skim off that, trying to find a way to get into your bank account, basically, because that's really what you're exposing because you're making a payment. So the question for us is okay, again, that's a core competency of ours is data in motion and securing the transaction while it's in motion before that. So for us, when you start to take a look at the way we can do the authorization and authentication becomes a big deal. And our core competency is to do that, to make sure that you can't have anti-money laundering, to make sure that you can't have fraud existing because we can verify it's you who is transacting with Dave, that you are the two people transacting, just like we do with a card, right? And when you do the pin, chip, we know it's you. Even with our new products like new data with biometrics, we know it's you. We can validate and verify and authenticate it's you. That's where we think we can provide tremendous value with the blockchain. >> So blockchain is kind of a hot new technology, but there's got to be more than just the fact that it's a hot new technology. Give us some examples of some use cases that you're envisioning that will be made possible and will be sustained with the blockchain approach. >> A lot of it is actually, if you take a look at the supply chain, the ability to make sure that when I need goods and services, not only, I don't have to wait for it. I think actually one of the best stories that we heard when it came down to the blockchain is how, actually the Defense Department has used it. So for example, if you can imagine, on an aircraft carrier, there's a plane that went down, right? That needed a part. Or I think it was a helicopter, sorry. And it needed a part. Well the question was it's in the middle of the Pacific Ocean. So how do you get the part there? Well if you go through the normal channels, to get that helicopter up and running, it's going to take you two to three months to get it there. But using blockchain, because it's anonymous and you have some privacy within it, being able to say, can you send me the specs? This particular ship had a metal 3D printer on it. So not only were they able to send the specs via blockchain in an anonymous manner so no one else could pick it up, they could actually put it on the ship. They could actually create the part, and what's really kind of cool is they actually put a flaw. They put a scratch across the part itself so that you knew the guys who sent it are the guys that you are getting it from and no one else picked it up along the way. So that's one way to be able to do it, to actually create the parts that you need when you need them in a secure manner. The other part, if you believe it or not, I was just at a sports conference, and the other thing was is can I actually use blockchain to transfer my tickets? So you're in Palo Alton. I got 9ers tickets. I'm a season ticket holder, and what I want to be able to do is send you my tickets, but you need to know it's me who has the tickets, not a fraudster, right, that's going up there saying I got two tickets for sale or whatever it may be. So I can use blockchain in an anonymous transaction You send me the funds, you know it's me, and I can send you the tickets because I am a verified, valid ticket holder. So there is another case where it is consumer to consumer. >> But coming back to the B2B examples, there are a lot of circumstances when a business realizes that entering into a transaction is signaling an enormous amount of information other than just the part that they're getting or the business activity that they're performing, and so it has the potential to be a great technology to dramatically focus the characteristics of the transaction just on the transaction and keep all the other signaling that might otherwise be picked up on out of the equation. Is that right? >> Yeah, that's absolutely correct. The other part is it creates that efficiency in that transaction itself. We're always worried about can you reduce paperwork? We did that, that's the 80's and 90's, right? And then it became into now we got these electronic transfers. But what blockchain is allowing you to do is almost in real time to be able to order those goods and services and get them delivered when you need them and be able to run those transactions. That's a big part to it. Now we're getting faster and better at what we're doing. We're not letting antiquated processes and procedures really bog us down. And again, the blockchain allows you to do that, allows an easier transfer of cash amongst the providers, a lower cost in many cases on that transfer when you're talking about the funds, more of the ability to actually interact with the consumer itself, especially if you've got artificial intelligence, because one of the other use cases in the supply chain is the auto-ordering. Right, so this thing is learning, it's understanding what's coming off the shelves, what's going on the shelves, where it needs to be. Can I actually that to help me distribute my products amongst my warehouses, amongst my stores? Blockchain is doing that. It's automating that and allows those transactions, both I need this and you sent it to me as well as actually going through and making the financial transaction happen. >> So you guys must be having some mind-melting conversations inside your company. (laughing) When you think about the examples that you gave those transactions, I presume, the ticket transaction, doesn't require a trusted third party to validate that transaction because the technology of blockchain is doing that and then yet, but MasterCard is a trusted third party. So how are you thinking about, this might change your business? You've still got amazing assets. You've got a brand, you've got a network, you've got your partnerships, you've got the relationships that you have with the suppliers and customers and consumers, et cetera. So how do you think about that notion of when you talk to the world of crypto. Oh let's find where there's a trusted third party and we can disintermediate that. So what do you think all this means for the future of financial services and companies like MasterCard? >> Well, you know for us it's not the ability to say that one is going to... for a lot of folks, their complaint is, what we hear is, blockchain is going to take over everything. Cryptocurrency is going to... no it's how you actually have to live within that, because you're going to have to have multiple ways to do that. So that's how we feel we can make that help those folks in the transition. So that trusted third party, okay you can have five trusted third parties take care of your credit cards, your debit cards, your blockchain, your cryptocurrency. Our goal is, just come to us. Let's get you that solution. We can help embed that API. We can give you some flexibility. We can give you the reach of being able to have you know 22,000 banks and issuers worldwide at your disposal if you need that. So again, that's where we see ourselves really playing a good role, and that's how it's going to change our business. >> But it's, related to that, it's we can bring the scale, we can bring your operational certainty, we can bring you all the things because at the end of the day, it's still a computer, right, and it has to stay up and it has to be auditable and it has to be backed up and that's something that there's not a lot of companies that know how to operate at the kind of scale you guys do. >> Technology platform is critical. >> Absolutely >> Yes, absolutely. And again, that's when you look at quadruple and quint- types of redundancy, not just primary and secondary. I mean we are running four or five types of redundancy to make sure those networks are up and running. >> So Nick, I got a question because one of the things that I find interesting about all this and I know that you and I have talked about this, Dave, is that a blockchain presumes that there's some sort of contract in the middle of all this, but the processes of running contracts are complex. The design of the blockchain is crucial ultimately to the behavior and the success of the blockchain. Not a lot of tools to do that. How do you think the future of blockchain design is going to evolve so that issues like scale, technological, operational certainty, et cetera, come into play? >> Well, it's almost, as you take a look at it, it's almost the way that you have to be interacting today. So you've got the edge where the transaction is happening right and you've got the core part of the business where you're using that machine learning, the artificial intelligence to help you make better decisions. And then of course, you've got the deep learning. So as you look at those technologies, it's how you're handling within that contract, where things need to be done. Right, so again, if you're looking at how we supply a shelf, well that's not going to be done potentially at the edge. That's potentially in your core. It could be part of deep learning, but then how do you bring it to the edge to make that transaction go through to make that part of blockchain? So as you think about the contracts, something that's real important with blockchain is picking the right partner to go to market with because, again, you're looking at those technologies you want to make sure are in place. >> So, you're adding to a notion of scale and operational certainty, the expertise associated with how do you design these things well so that they can be put in an operation and you don't have to, you know, the immutability issue doesn't come and bite you in the butt in six months. >> Yeah, absolutely. So again, what you're looking for is, what we always look for are those people that have the right ability for scale, have the global experience that we really need, because again, when you think about it, you're in a global economy, so you're really looking to see how those people interact and can they do it. You're looking for that partner. You're not looking for the guy who's got the coolest, latest technology. Those are always fine to know about, but again, you're always worried about scale at this point. You're looking at flexibility. You know, how do I, how can I be flexible in the way I'm making those contracts and those contracts always change. It's not like there's a template, all right? Almost with blockchain, it's almost individual companies and B2B are coming back with their own types of contracts. >> Sure. >> And that's the part that you also have to have make sure is available to you, both from a technology standpoint and being able to you know actually operationalize it. >> Peter, at the top, talked about the transaction volumes being you know limited, you were talking about Bitcoin transaction volumes. Obviously, in the near term anyway, limits some of the use cases, but I wonder how you guys are thinking about solving that problem. Do you see that as MasterCard's role or is that, is Google, a Google-like company going to solve that? Is it going to be a partnership? How do you see that shaking out? >> It is going to be, it's a collaborative partnership, so again, we have conversations with people like, the Googles of the world, the Microsofts, the Dells, and people like that. It's a collaboration now. So just like four years ago. Remember Hadoop's community? >> Yeah. >> So we see it, there is a blockchain community because we are all seeing the same issues, but what's nice is, because of the experience that we're having through being part of a community, we're helping each other solve those particular problems. Because again, Google sees a different part of blockchain. Right, we see a different part of blockchain. And when you start to bring those resources together and you start talking to them and the Microsofts and the Dells and even the Amazons of the world. When you start putting everybody into a room, we're frenemies at that point. Because we're all trying to solve the same problem. We all have different interests within the major issue, but if we can do it together, tide rises all boats, right? >> The best innovations are combinatorial. >> Correct. >> Taking a lot of folks with expertise and mature technology and bringing it together and creating something new not just because you're creating something new but because you have the social reach to actually have it happen in the marketplace. >> Absolutely. >> Nick Curcuru, MasterCard, thanks very much for being on The Cube and talking about blockchain. >> Appreciate it. >> Thank you for having me, thanks guys. (orchestral music fading out)

live from austin texas it's the cube covering deli MC world 2016 brought to you by delhi MC now here are your hosts dave vellante and stu minimus welcome back to dell emc world at austin texas 2016 this is the cube the worldwide leader in live tech coverage dale skiffington is here sees the chief privacy officer at dell she's joined by nick koo koo koo roo was a vice president of big data practice at mastercard folks welcome to the cube thanks for coming on thank you having us very important topic a privacy security I like to talk to them as two sides of the same coin but Dale why don't you start it off tell us what you guys are talking about here at Delhi MC world thanks well oftentimes you're right privacy and security are two really different topics to talk about and Nick will cover a lot this afternoon about the importance of securing data in order to have a successful big data program but privacy is also a concern to our shareholders and stakeholders and that is privacy deals with what information do you collect what information how do you use that information and who to whom do you should with whom do you share it and that's a little different than securing the data and our regulators and our customers are getting increasingly concerned about those issues and so it requires some governance some thought to be put into those programs and that's what we're going to talk about today and it's interesting Nick because in 2006 when the Federal Rules of Civil Procedure enabled or required organizations to retain and produce electronic material it instantly became the notion that data was a liability and everybody wanted to understand okay when can i delete it when can I get rid of it and then when this big data mean occurred all of a sudden data becomes an asset in a big way even though it's always been an asset we know that but in a bigger way it was almost like a bit flip and it sort of changed the attitude is that a reasonable description and how did that affect how you approached privacy well part of it is is you're absolutely right he became an asset everyone started wanted to monetize the data that they were carrying because there were great nuggets that set inside that data so we started talking about security you know the original he's talked about personally identifiable information right and that's what everyone's at name address phone numbers you know you many email addresses but then it started to turn into as we started to bring other sources of data such as Facebook Twitter all that data that sits out there in social media together we started to realize other pieces of information needed to be secure as well so now you've broaden the way that you want to take look at security because all this unstructured data starts to come in where you can identify people through a picture a photograph through a twitter feed what you want to be able to say is how do I protect them as much as I protect someone's credit card or someone's personally identifiable name address and phone number tell what talk about your role at Adele it's interesting to have a chief privacy officer on a tail and now of course Delhi MC he opens up a whole new can of worms if I could say that yes so together with our chief information security officer who looks at the policies that procedures around securing data my team is responsible for the policies procedures and controls relating to the use of the data so you know in terms of the reason why our session today is called the ethical use of data is because the laws are lagging a little bit in terms of requiring certain things to be put in place about the use they're starting to develop but what each regulator has said in the US and Europe and elsewhere is they've given companies and technology companies a chance to put in good governance in place and they've asked the companies to put in internal review boards and an accountable responsible individuals in those organizations to make good decisions about the use of data and that's what a chief privacy officer helps the organization do develop the governance structure and help with the accountability of the use of decisions around using data so they obviously the big discussion going on like this inside of MasterCard and Nicki we're talking about everybody wants to monetize the data or figure out how data can help them monetize so how do you deal with that you know analytics and you know you guys talk about the creepy factor I always worried the Amazon knows more about me than I do you know what I'm out of something and I'm reordering and my patterns and and that's kind of creepy so how do you deal with that you know part of what we do and my side of the house is we anonymize the data in many cases for that type of analysis so we try to take that personally identifiable information out of the analysis so again I can we call it an autumn is a shin where we actually on the front end say I don't care who you are what I care about is your are your patterns and can I figure out what those patterns are to create affinities so by taking them out front end and anonymizing the data doing the analysis on it and then potentially at the back end our customers re identifying those people that we have anonymized on the front end that makes it a little bit better because it's no longer a creepy factor per se because when you work with someone like Dale and what the usage of that data is in many cases when you do that analysis it's doing it for the good of that person so that person either a gets a healthier lifestyle be gets to see the products and services that they want to see or want to be able to you know purchase or whatever so again for us it's been able to understand how we protect the individual as you look through the entire analysis string and that's what we do on the advisor size with our customers so that's cool but the chief marketing officer he or she lets you identify that individual you know the the customer of one you know that one-to-one personal interaction how do you square that circle well that's actually we work with the marketing team they always say that well we have a population of 5 million in our database and I want to look at all five minutes like yes you can look at all 5 million but anonymize them because most cases you're going to send us your data scientists and there's 20 or 30 data scientists that could be working on these five million to create your campaigns they don't need to know names phone numbers or addresses so secure the data so that you're not carrying identifiable information through the ecosystem only at the very end when you say out of that population of 5 million mr. marketer here's the half a million that have a high propensity to do what you're asking do is when you re identifier so at that particular point you haven't put 5 million people at risk you've actually put half a million people what you want them to do which is the propensity to purchase or the propensity to taking action so again at the end is when you re identify and say these are the number of these are the people we should be sending a mail or two or an email to or so an offer and that narrows the threat correct matrix if I use that term and and reduces the risk very much stuff to the consumer and obviously to the organization yeah and that's why when we work with people like our privacy officers it's what are you trying to do in the analysis so that we can understand that data usage because that becomes important with what the data is that's carried through the analysis phase you may not have to carry gender you may not have to carry ethnic background you may not have to carry and these other markers that could put someone as Anna you can identify someone with so if we can keep those out it's how you're using the data and the analysis at the end and to follow up on that you know so that's the what the privacy office does it works with the business when they are envisioning a particular use of data and application a product that's going to do some of these analytics we work with them to design that product to avoid some of these risks sometimes you can sometimes the answer is we absolutely need that personal information because that's the purpose of that particular project and in those cases then we look at did you have permission from the data subject to do what you want to do with the data and if not does the society good outweigh the risks and can you mitigate those risks in certain ways so that's the balancing act that we do and that's when we decide when it's past that creepy line or when it hasn't because my role within the company is to advocate for the data subject to make sure that their expectations are being met by Del I wonder if we can unpack another use case which is fraud detection which is advanced so rapidly in the last 10 years it used to be six months and you find maybe something happened you had a look at your own statements and now you're getting texts and very proactive but certainly a lot of information has to be accessible but it's very narrow in terms of the individual can you talk about that using yeah the one thing that we find from our customers are the people we work with when you talk about fraud people don't mind that you're watching because you're reducing their liability you're reducing someone from stealing that credit card from them or being able to run up charges so when you talk about protecting someone protecting someone's digitalpersona their wallet they're willing to give and take a little bit on what information they provide to you they don't mind that you know that Pam in austin texas today and then someone's trying to charge in you know guitar at the same day they understand that it's not a privacy issue but i want to ask you about the pendulum is kind of swung like I said it used to be it would take forever to find out if there was some kind of fraud and then it became like this flawed of false positives and and and it seems to be getting better and presumably it's because a big data analytics but I wonder if you could talk absolutely our fraud teams matter of fact at mastercard we work very hard to reduce the false positives because that creates a bad experience for both the user as well as the issue of that card right so what we try to do all the times you can continue to do learning machine learning the artificial intelligence how to reduce that as you also look at people's patterns is this person a professional traveler or always traveling so that goes into the algorithm which are take a look at a false positive around fraud do they buy these types of goods with their credit cards so going you start to look at the protection and you start to add those rules into it and you start to actually reduce it it's all about learning it's not just one and done those algorithms have to be constantly updated in real time in some cases so that you're constantly in a learning phase you're building models and iterating those models and that's always a challenge but I'd love to talk about that if we have time but but I wanted to ask you Dale talk about deep learning Michael was talking a lot about machine learning and deep learning and part of his visionary discussion this morning what's the role of transparency how do you guide your constituents in terms of transparency what are the guidelines how transparent when to be trans Aaron yeah that's a great question and you know transparency was where the privacy profession lived 10 years ago it was all about giving the consumers notice about why you're collecting the data and using it consistent with that notice and being very visible with privacy statements and you know there's lots of laws around that now where you have to give specific notices the problem with big data is the power of it is using the data in ways that you didn't envision when you collected the data and that is the dilemma for privacy and big data and that's where the privacy community is trying to develop some tools for organizations to do a balancing act of okay the consumer didn't know that when they gave you that data it was going to be used for this purpose but they're not it's good its tangential to that use so that would be an acceptable use but if it's going to so surprised the consumer that you're using the data for you really need to go back and get reap Reaper missioned and in some countries it's an opt-in permission I'm going to mix Pam law spam and do not call laws seem trivial doesn't it you were mentioning off camera that I think it's your CISO is participates in public policy through the Obama administration is that as that was it you say so it's part of our DNA is security and securing the data our CEOs made a tremendous commitment to make sure that we can apply our best practices into and help the community understand how to make sure the data is secure because that's a digital persona we consider ourselves to be stewards of data not owners of data someone has entrusted us with that we want to make sure that we're constantly contributing back how to make sure it's secure and used right as we take a look at that how about regional nuances local laws haha describe sort of what you're seeing there how you address those complexities yeah so a good example is the new European regulation that's going into effect may of 2018 that has a new specific requirement about profiling automated decision that's used for marketing purposes you have to have an opt-in for using that data companies are going to struggle with how to implement that but nonetheless it's a new law and that law has four percent of annual revenue as a potential penalty Wow so it may get this straight you have to opt-in to be automated profiled automated profiling where it's going to be used for certain types of purposes decisions and you know what they're really trying to avoid is the things that the Obama administration came out with a big data report as well discrimination decisions that are made about insurance and credit etc that are automated decisions and then marketing decisions on those you know with that data the law now requires very specific opt-in and and transparency boy that's going to be tricky yeah the other thing for us is which was just described as working with people is the ability to tag that data as it's being brought in so as you think a big day that ingestion that tagging of that data and carrying the metadata what types of data needs to be tagged what types of data you have to be watching out for was it an opt-in versus an opt-out all that adds into understanding the power of what big data can do to protect both the individual and the company from being able to do something wrong with information so the nice part is with big data you can do that so again we're working with our customers and with the privacy officers understand how you do your data classifications what data needs to be tagged and then to be able to follow that full lineage through the entire ecosystem and obviously that has to be done at the point of creation correct otherwise it's it's not going to scale and and technology helps you solve that problem and that's been a challenge for years but it's a day where that actually works now yeah there's a lot of great partners and we're here at you know Dell world WMC world and they're here as well to help on that ingestion of data as it's coming in to start to tag it and to start to index and catalog it if that's the power of what big data can help you with because before you had to do it individually now you can actually use the tools you can use AI to actually understand about that information coming in to do that tagging to create that lineage it's very very important and very powerful especially as we start looking at what's coming down the road till you get involved in in helping guide solutions is that sir we have a process that is called the privacy impact assessment process and it's in the life cycle development of our products and services so much like the security reviews that are done when we when we commercialize a product we now are interjecting ourselves with a privacy review so if that project or product development or application is intending to use big data analytics as part of it we will we will help guide the business whether they need to build in opt-in consents what it is that what do they want to do with the product and what kinds of things are from a compliance perspective there do they need to build in so that we are at the table with our business partners all right we got a rep and Nick I'll give you the last word to mean so festive as the big data analytics I'll call you a visionary you know what's the future hold where's your focus in the next you know near the midterm you know under stay right with the ethics world and and probably always tell people what we're asking now is just because you have the data doesn't mean you have to use the data just because you have that information you've got to become a parent and start to be able to put some parameters around how that data is use so people in the privacy world you need to bring them to the table so again just because you have it doesn't mean you should be using it and now it's better to be a parent then just let people run crazy right Nick Goodell thanks very much for coming too i love this conversation is fascinating thank you for working do all right keep right to everybody will be back this is dell emc world from Austin Texas this is the cube right back

(bright orchestra music) >> Hi, I'm Peter Burris. Welcome to a special digital community event brought to you by Wikibon and theCUBE. Sponsored by Dell EMC. Today we're gonna spend quite some time talking about some of the trends in the relationship between hardware and AI. Specifically, we're seeing a number of companies doing some masterful work incorporating new technologies to simplify the infrastructure required to take full advantage of AI options and possibilities. Now at the end of this conversation, series of conversations, we're gonna run a CrowdChat, which will be your opportunity to engage your peers and engage thought leaders from Dell EMC and from Wikibon SiliconANGLE and have a broader conversation about what does it mean to be better at doing AI, more successful, improving time to value, et cetera. So wait 'til the very end for that. Alright, let's get it kicked off. Tom Burns is my first guest. And he is the Senior Vice President and General Manager of Networking Solutions at Dell EMC. Tom, it's great to have you back again. Welcome back to theCUBE. >> Thank you very much. It's great to be here. >> So Tom, this is gonna be a very, very exciting conversation we're gonna have. And it's gonna be about AI. So when you go out and talk to customers specifically, what are you hearing then as they describe their needs, their wants, their aspirations as they pertain to AI? >> Yeah, Pete, we've always been looking at this as this whole digital transformation. Some studies say that about 70% of enterprises today are looking how to take advantage of the digital transformation that's occurring. In fact, you're probably familiar with the Dell 2030 Survey, where we went out and talked to about 400 different companies of very different sizes. And they're looking at all these connected devices and edge computing and all the various changes that are happening from a technology standpoint, and certainly AI is one of the hottest areas. There's a report I think that was co-sponsored by ServiceNow. Over 62% of the CIO's and the Fortune 500 are looking at AI as far as managing their business in the future. And it's really about user outcomes. It's about how do they improve their businesses, their operations, their processes, their decision-making using the capability of compute coming down from a class perspective and the number of connected devices exploding bringing more and more data to their companies that they can use, analyze, and put to use cases that really make a difference in their business. >> But they make a difference in their business, but they're also often these use cases are a lot more complex. They're not, we have this little bromide that we use that the first 50 years of computing were about known process, unknown technology. We're now entering into an era where we know a little bit more about the technology. It's gonna be cloud-like, but we don't know what the processes are, because we're engaging directly with customers or partners in much more complex domains. That suggests a lot of things. How are customers dealing with that new level of complexity and where are they looking to simplify? >> You actually nailed it on the head. What's happening in our customers' environment is they're hiring these data scientists to really look at this data. And instead of looking at analyzing the data that's being connected, that's being analyzed and connected, they're spending more time worried about the infrastructure and building the components and looking about allocations of capacity in order to make these data scientists productive. And really, what we're trying to do is help them get through that particular hurdle. So you have the data scientists that are frustrated, because they're waiting for the IT Department to help them set up and scale the capacity that they need and infrastructure that they need in order to do their job. And then you got the IT Departments that are very frustrated, because they don't know how to manage all this infrastructure. So the question around do I go to the cloud? Do I remain on-prem? All of this is things that our companies, our customers, are continuing to be challenged with. >> Now, the ideal would be that you can have a cloud experience but have the data reside where it most naturally resides, given physics, given the cost, given bandwidth limitations, given regulatory regimes, et cetera. So how are you at Dell EMC helping to provide that sense of an experience based on what the work load is and where the data resides, as opposed to some other set of infrastructure choices? >> Well, that's the exciting part is that we're getting ready to announce a new solution called the Ready Solutions for AI. And what we've been doing is working with our customers over the last several years looking at these challenges around infrastructure, the data analytics, the connected devices, but giving them an experience that's real-time. Not letting them worry about how am I gonna set this up or management and so forth. So we're introducing the Ready Solutions for AI, which really focuses on three things. One is simplify the AI process. The second thing is to ensure that we give them deep and real-time analytics. And lastly, provide them the level of expertise that they need in a partner in order to make those tools useful and that information useful to their business. >> Now we want to not only provide AI to the business, but we also wanna start utilizing some of these advanced technologies directly into the infrastructure elements themselves to make it more simple. Is that a big feature of what the ready system for AI is? >> Absolutely, as I said, one of the key value propositions is around making AI simple. We are experts at building infrastructure. We have IP around compute, storage, networking, infinity band. The things that are capable of putting this infrastructure together. So we have tested that based upon customers' input, using traditional data analytics, libraries, and tool sets that the data scientists are gonna use, already pre-tested and certified. And then we're bringing this to them in a way which allows them through a service provisioning portal to basically set up and get to work much faster. The previous tools that were available out there, some from our competition. There were 15, 20, 25 different steps just to log on, just to get enough automation or enough capability in order to get the information that they need. The infrastructure allocated for this big data analytics through this service portal we've actually gotten it down to around five clicks with a very user-friendly GUI, no CLI required. And basically, again, interacting with the tools that they're used to immediately right out of the gate like in stage three. And then getting them to work in stage four and stage five so that they're not worried about the infrastructure, not worried about capacity, or is it gonna work. They basically are one, two, three, four clicks away, and they're up and working on the analytics that everyone wants them to work on. And heaven knows, these guys are not cheap. >> So you're talking about the data scientists. So presumably when you're saying they're not worried about all those things, they're also not worried about when the IT Department can get around to doing it. So this gives them the opportunity to self-provision. Have I got that right? >> That's correct. They don't need the IT to come in and set up the network to do the CLI for the provisioning, to make sure that there is enough VM's or workloads that are properly scheduled in order to give them the capacity that they need. They basically are set with a preset platform. Again, let's think about what Dell EMC is really working towards and that's becoming the infrastructure provider. We believe that the silos, the service storage, and networking are becoming eliminated, that companies want a platform that they can enable those capabilities. So you're absolutely right. The part about the simplicity or simplifying the AI process is really giving the data scientists the tools they need to provision the infrastructure they need very quickly. >> And so that means that the AI or rather the IT group can actually start acting more like a DevOps organization as opposed to a specialist in one or another technology. >> Correct, but we've also given them the capability by giving the usual automation and configuration tools that they're used to coming from some of our software partners, such as Cloudera. So in other words, you still want the IT Department involved, making sure that the infrastructure is meeting the requirements of the users. They're giving them what they want, but we're simplifying the tools and processes around the IT standpoint as well. >> Now we've done a lot of research into what's happening in the big data now is likely to happen in the AI world. And a lot of the problems that companies had with big data was they conflated or they confused the objectives, the outcome of a big data project, with just getting the infrastructure to work. And they walked away often, because they failed to get the infrastructure to work. So it sounds though what you're doing is you're trying to take the infrastructure out of the equation while at the same time going back to the customer and saying, "Wherever you want this job "to run or this workload to run, you're gonna get the same "experience irregardless." >> Correct, but we're gonna get an improved experience as well. Because of the products that we've put together in this particular solution, combined with our compute, our scale-out mass solution from a storage perspective, our partnership with Mellon Oshman infinity band or ethernet switch capability. We're gonna give them deeper insights and faster insights. The performance and scalability of this particular platform is tremendous. We believe in certain benchmark studies based upon the Reznik 50 benchmark. We've performed anywhere between two and half to almost three times faster than the competition. In addition from a storage standpoint, all of these workloads, all of the various characteristics that happen, you need a ton of IOPS. >> Yeah. >> And there's no one in the industry that has the IOP performance that we have with our All-Flash Isilon product. The capabilities that we have there we believe are somewhere around nine times the competition. Again, the scale-out performance while simplifying the overall architecture. >> Tom Burns, Senior Vice President of Networking and Solutions at Dell EMC. Thanks for being on theCUBE. >> Thank you very much. >> So there's some great points there about this new class of technology that dramatically simplifies how hardware can be deployed to improve the overall productivity and performance of AI solutions. But let's take a look at a product demo. >> Every week, more customers are telling us they know AI is possible for them, but they don't know where to start. Much of the recent progress in AI has been fueled by open source software. So it's tempting to think that do-it-yourself is the right way to go. Get some how-to references from the web and start building out your own distributive deep-learning platform. But it takes a lot of time and effort to create an enterprise-class AI platform with automation for deployment, management, and monitoring. There is no easy solution for that. Until now. Instead of putting the burden of do-it-yourself on your already limited staff, consider Dell EMC Ready Solutions for AI. Ready Solutions are complete software and hardware stacks pre-tested and validated with the most popular open source AI frameworks and libraries. Our professional services with proven AI expertise will have the solution up and running in days and ready for data scientists to start working in weeks. Data scientists will find the Dell EMC data science provisioning portal a welcome change for managing their own hardware and software environments. The portal lets data scientists acquire hardware resources from the cluster and customize their software environment with packages and libraries tested for compatibility with all dependencies. Data scientists choose between JupyterHub notebooks for interactive work, as well as terminal sessions for large-scale neural networks. These neural networks run across a high-performance cluster of power-edge servers with scalable Intel processors and scale-out Isilon storage that delivers up to 18 times the throughput of its closest all-flash competitor. IT pros will experience that AI is simplified as Bright Cluster Manager monitors your cluster for configuration drift down to the server BIOS using exclusive integration with Dell EMC's open manage API's for power-edge. This solution provides comprehensive metrics along with automatic health checks that keep an eye on the cluster and will alert you when there's trouble. Ready Solutions for AI are the only platforms that keep both data center professionals and data scientists productive and getting along. IT operations are simplified and that produces a more consistent experience for everyone. Data scientists get a customizable, high-performance, deep-learning service experience that can eliminate monthly charges spent on public cloud while keeping your data under your control. (upbeat guitar music) >> It's always great to see the product videos, but Tom Burns mentioned something earlier. He talked about the expansive expertise that Dell EMC has and bringing together advanced hardware and advanced software into more simple solutions that can liberate business value for customers, especially around AI. And so to really test that out, we sent Jeff Frick, who's the general manager and host of theCUBE down to the bowels of Dell EMC's operations in Austin, Texas. Jeff went and visited the Dell EMC HPC and AI Innovation Lab and met with Garima Kochhar, who's a tactical staff Senior Principal Engineer. Let's hear what Jeff learned. >> We're excited to have with us our next guest. She's Garima Kochhar. She's on the tactical staff and the Senior Principal Engineer at Dell EMC. Welcome. >> Thank you. >> From your perspective what kinda changing in the landscape from high-performance computing, which has been around for a long time, into more of the AI and machine learning and deep learning and stuff we hear about much more in business context today? >> High-performance computing has applicability across a broad range industries. So not just national labs and supercomputers, but commercial space as well. And our lab, we've done a lot of that work in the last several years. And then the deep learning algorithms, those have also been around for decades. But what we are finding right now is that the algorithms and the hardware, the technologies available, have hit that perfect point, along with industries' interest with the amount of data we have to make it more, what we would call, mainstream. >> So you can build an optimum solution, but ultimately you wanna build industry solutions. And then even subset of that, you invite customers in to optimize for what their particular workflow or their particular business case which may not match the perfect benchmark spec at all, right? >> That's exactly right. And so that's the reason this lab is set up for customer access, because we do the standard benchmarking. But you want to see what is my experience with this, how does my code work? And it allows us to learn from our customers, of course. And it allows them to get comfortable with their technologies, to work directly with the engineers and the experts so that we can be their true partners and trusted advisors and help them advance their research, their science, their business goals. >> Right. So you guys built the whole rack out, right? Not just the fun shiny new toys. >> Yeah, you're right. So typically, when something fails, it fails spectacularly. Right, so I'm you've heard horror stories where there was equipment on the dock and it wouldn't fit in the elevator or things like that, right? So there are lots of other teams that handle, of course Dell's really good at this, the logistics piece of it, but even within the lab. When you walk around the lab, you'll see our racks are set up with power meters. So we do power measurements. Whatever best practices in tuning we come up with, we feed that into our factories. So if you buy a solution, say targeted for HPC, it will come with different BIOS tuning options than a regular, say Oracle, database workload. We have this integration into our software deployment methods. So when you have racks and racks of equipment or one rack of equipment or maybe even three servers, and you're doing an installation, all the pieces are baked-in already and everything is easy, seamless, easy to operate. So our idea is... The more that we can do in building integrated solutions that are simple to use and performant, the less time our customers and their technical computing and IT Departments have to spend worrying about the equipment and they can focus on their unique and specific use case. >> Right, you guys have a services arm as well. >> Well, we're an engineering lab, which is why it's really messy, right? Like if you look at the racks, if you look at the work we do, we're a working lab. We're an engineering lab. We're a product development lab. And of course, we have a support arm. We have a services arm. And sometimes we're working with new technologies. We conduct training in the lab for our services and support people, but we're an engineering organization. And so when customers come into the lab and work with us, they work with it from an engineering point of view not from a pre-sales point of view or a services point of view. >> Right, kinda what's the benefit of having the experience in this broader set of applications as you can apply it to some of the newer, more exciting things around AI, machine learning, deep learning? >> Right, so the fact that we are a shared lab, right? Like the bulk of this lab is High Performance Computing and AI, but there's lots of other technologies and solutions we work on over here. And there's other labs in the building that we have colleagues in as well. The first thing is that the technology building blocks for several of these solutions are similar, right? So when you're looking at storage arrays, when you're looking at Linux kernels, when you're looking at network cards, or solid state drives, or NVMe, several of the building block technolgies are similar. And so when we find interoperability issues, which you would think that there would never be any problems, you throw all these things together, they always work like-- >> (laughs) Of course (laughs). >> Right, so when you sometimes, rarely find an interoperability issue, that issue can affect multiple solutions. And so we share those best practices, because we engineers sit next to each other and we discuss things with each other. We're part of the larger organization. Similarly, when you find tuning options and nuances and parameters for performance or for energy efficiency, those also apply across different domains. So while you might think of Oracle as something that it's been done for years, with every iteration of technology there's new learning and that applies broadly across anybody using enterprise infrastructure. >> Right, what gets you excited? What are some of the things that you see, like, "I'm so excited that we can now apply "this horsepower to some of these problems out there?" >> Right, so that's a really good point, right? Because most of the time when you're trying to describe what you do, it's hard to make everybody understand. Well, not what you're doing, right? But sometimes with deep technology it's hard to explain what's the actual value of this. And so a lot of work we're doing in terms of excess scale, it's to grow like the... Human body of knowledge forward, to grow the science happening in each country moving that forward. And that's kind of, at the higher end when you talk about national labs and defense and everybody understands that needs to be done. But when you find that your social media is doing some face recognition, everybody experiences that and everybody sees that. And when you're trying to describe the, we're all talking about driverless cars or we're all talking about, "Oh, it took me so long, "because I had this insurance claim and then I had "to get an appointment with the appraisor "and they had to come in." I mean, those are actual real-world use cases where some of these technologies are going to apply. So even industries where you didn't think of them as being leading-edge on the technical forefront in terms of IT infrastructure and digital transformation, in every one of these places you're going to have an impact of what you do. >> Right. >> Whether it's drug discovery, right? Or whether it's next-generation gene sequencing or whether it's designing the next car, like pick your favorite car, or when you're flying in an aircraft the engineers who were designing the engine and the blades and the rotors for that craft were using technologies that you worked with. And so now it's everywhere, everywhere you go. We talked about 5G and IoT and edge computing. >> Right. >> I mean, we all work on this collectively. >> Right. >> So it's our world. >> Right. Okay, so last question before I let you go. Just being, having the resources to bear, in terms of being in your position, to do the work when you've got the massive resources now behind you. You have Dell, the merger of EMC, all the subset brands, Isilon, so many brands. How does that help you do your job better? What does that let you do here in this lab that probably a lot of other people can't do? >> Yeah, exactly. So when you're building complex solutions, there's no one company that makes every single piece of it, but the tighter that things work together the better that they work together. And that's directly through all the technologies that we have in the Dell technologies umbrella and with Dell EMC. And that's because of our super close relationships with our partners that allows us to build these solutions that are painless for our customers and our users. And so that's the advantage we bring. >> Alright. >> This lab and our company. >> Alright, Garima. Well, thank you for taking a few minutes. Your passion shines through. (laughs) >> Thank you. >> I really liked hearing about what Dell EMC's doing in their innovation labs down at Austin, Texas, but it all comes together for the customer. And so the last segment that we wanna bring you here is a great segment. Nick Curcuru, who's the Vice President of Big Data Analytics at Mastercard is here to talk about how some of these technologies are coming together to speed value and realize the potential of AI at Mastercard. Nick, welcome to theCUBE. >> Thank you for letting me be here. >> So Mastercard, tell us a little bit about what's going on at Mastercard. >> There's a lot that's going on with Mastercard, but I think the most exciting things that we're doing out of Mastercard right now is with artificial intelligence and how we're bringing the ability for artificial intelligence to really allow a seamless transition when someone's actually doing a transaction and also bringing a level of security to our customers and our banks and the people that use Mastercards. >> So AI to improve engagement, provide a better experience, but that's a pretty broad range of things. What specifically kinds of, when you think about how AI can be applied, what are you looking to? Especially early on. >> Well, let's actually take a look at our core business, which is being able to make sure that we can secure a payment, right? So at this particular point, people are used to, we're applying AI to biometrics. But not just a fingerprint or a facial recognition, but actually how you interact with your device. So you think of like the Internet of Things and you're sitting back saying, "I'm using, "I'm swiping my device, my mobile device, "or how I interact with a keyboard." Those are all key signatures. And we, with our company, new data that we've just acquired are taking that capability to create a profile and make that a part of your signature. So it's not just beyond a fingerprint. It's not just beyond a facial. It's actually how you're interacting so that we know it's you. >> So there's a lot of different potential sources of information that you can utilize, but AI is still a relatively young technology and practice. And one of the big issues for a lot of our clients is how do you get time to value? So take us through, if you would, a little bit about some of the challenges that Mastercard and anybody would face to try to get to that time to value. >> Well, what you're really seeing is looking for actually a good partner to be with when you're doing artificial intelligence, because again, at that particular point, you try to get to scale. For us, it's always about scale. How can we roll this across 220 countries? We're 165 million transactions per hour, right? So what we're looking for is a partner who also has that ability to scale. A partner who has the global presence, who's learning. So that's the first step. That's gonna help you with your time to value. The other part is actually sitting back and actually using those particular partners to bring their expertise that they're learning to combine with yours. It's no longer just silos. So when we talk about artificial intelligence, how can we be learning from each other? Those open source systems that are out there, how do we learn from that community? It's that community that allows you to get there. Again, those that are trying to do it on their own, trying to do it by themselves, they're not gonna get to the point where they need to be. In other words, in a six month time to value it's gonna take them years. We're trying to accelerate that, you say, "How can we get out of those algorithms operating for us "the way we need them to provide the experiences "that people want quickly." And that's with good partners. >> 165 million transactions per hour is only likely to go up over the course of the next few years. That creates an operational challenge. AI is associated with a probabilistic set of behaviors as opposed to categorical. Little bit more difficult to test, little bit more difficult to verify, how is the introduction of some of these AI technologies impacting the way you think about operations at Mastercard? >> Well, for the operations, it's actually when you take a look there's three components, right? There's right there on the edge. So when someone's interacting and actually doing the transaction, and then we'll look at it as we have a core. So that core sits there, right? Basically, that's where you're learning, right? And then there's actually, what we call, the deep learning component of it. So for us, it's how can we move what we need to have in the core and what we need to have on the edge? So the question for us always is we want that algorithm to be smart. So what three to four things do we need that algorithm to be looking for within that artificial intelligence needs to know that it then goes back into the core and retrieves something, whether that's your fingerprint, your biometrics, how you're interacting with that machine, to say, "Yes, that's you. "Yes, we want that transaction to go through." Or, "No, stop it before it even begins." It's that interaction and operational basis that we're always have a dynamic tension with, but it's how we get from the edge to the core. And it's understanding what we need it to do. So we're breaking apart what we have to have that intelligence to be able to create a decision for us. So that's how we're trying to manage it, as well as of course, the hardware that goes with it and the tools that we need in order to make that happen. >> When we get on the hardware just a little bit, so that historically different applications put pressure on different components within a stack. One of the observations that we've made is that the transition from spinning disk to flash allows companies like Mastercard to think about just persisting data to actually delivering data. >> Yeah. >> Much more rapidly. How does some of the, how does these AI technologies, what kinda new pressures do they put on storage? >> Well, they put a tremendous pressure, because that's actually again, the next tension or dynamics that you have to play with. So what do you wanna have on disk? What do you need flash to do? Again, if you look at some people, everyone's like, "Oh, flash will take over everything." It's like no, flash has, there's a reason for it to exist, and understanding what that reason is and understanding, "Hey, I need that to be able to do this "in sub-seconds, nanoseconds," I've heard the term before. That's what you're asking flash to do. When you want deep learning, that, I want it on disk. I want to be taking all those millions of billions of transactions that we're gonna see and learn from them. All the ways that people will be trying to attack me, right? The bad guys, how am I learning from everything that I'm having that can sit there on disk and let it continue to run, that's the deep learning. The flash is when I wanna create a seamless transaction with a customer, or a consumer, or from a business to business. I need to have that decision now. I need to know it is you who is trying to swipe or purchase something with my mobile device or through the, basically through the Internet. Or how am I actually even swiping or inserting, tipping my card in that particular machine at a merchant. That's we're looking at how we use flash. >> So you're looking at perhaps using older technologies or different classes technologies for some of the training elements, but really moving to flash for the interfacing piece where you gotta deliver the real-time effort right now. >> And that's the experience. And that's what you're looking for. And that's you're looking, you wanna be able to make sure you're making those distinctions. 'Cause again there's no longer one or the other. It's how they interact. And again, when you look at your partners, it's the question now is how are they interacting? Am I actually, has this been done at scale somewhere else? Can you help me understand how I need to deploy this so that I can reduce my time to value, which is very, very important to create that seamless, frictionless transaction we want our consumers to have. >> So Nick, you talked about how you wanna work with companies that demonstrate that they have expertise, because you can't do it on your own. Companies that are capable of providing the scale that you need to provide. So just as we talk about how AI is placing pressure on different parts of the technology stack, it's got also to be putting pressure on the traditional relationships you have with technology suppliers. What are you looking for in suppliers as you think about these new classes of applications? >> Well, the part is you're looking at, for us it's do you have that scale that we're looking at? Have you done this before, that global scale? Again, in many cases you can have five guys in a garage that can do great things, but where has it been tested? When we say tested, it's not just, "Hey, we did this "in a pilot." We're talking it's gotta be robust. So that's one thing that you're looking for. You're looking for also a partner we can bring, for us, additional information that we don't have ourselves, right? In many cases, when you look at that partner they're gonna bring something that they're almost like they are an adjunct part of your team. They are your bench strength. That's what we're looking for when we look at it. What expertise do you have that we may not? What are you seeing, especially on the technology front, that we're not privy to? What are those different chips that are coming out, the new ways we should be handling the storage, the new ways the applications are interacting with that? We want to know from you, because again, everyone's, there's a talent, competition for talent, and we're looking for a partner who has that talent and will bring it to us so that we don't have to search it. >> At scale. >> Yeah, especially at scale. >> Nick Curcuro, Mastercard. Thanks for being on theCUBE. >> Thank you for having me. >> So there you have a great example of what leading companies or what a leading company is doing to try to take full advantage of the possibilities of AI by utilizing infrastructure that gets the job done simpler, faster, and better. So let's imagine for a second how it might affect your life. Well, here's your opportunity. We're now gonna move into the CrowdChat part of the event, and this is your chance to ask peers questions, provide your insights, tell your war stories. Ultimately, to interact with thought leaders about what it means to get ready for AI. Once again, I'm Peter Burris, thank you for watching. Now let's jump into the CrowdChat.