>> Announcer: Live from Las Vegas, it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> Hi everybody, this is Dave Vellante. We're here at IBM Think. This is the third day of IBM Think. IBM has consolidated a number of its conferences. It's a one main tent, AI, Blockchain, quantum computing, incumbent disruption. It's just really an amazing event, 30 to 40,000 people, I think there are too many people to count. Chris Penn is here. New company, Chris, you've just formed Brain+Trust Insights, welcome. Welcome back to theCUBE. >> Thank you. It's good to be back. >> Great to see you. So tell me about Brain+Trust Insights. Congratulations, you got a new company off the ground. >> Thank you, yeah, I co-founded it. We are a data analytics company, and the premise is simple, we want to help companies make more money with their data. They're sitting on tons of it. Like the latest IBM study was something like 90% of the corporate data goes unused. So it's like having an oil field and not digging a single well. >> So, who are your like perfect clients? >> Our perfect clients are people who have data, and know they have data, and are not using it, but know that there's more to be made. So our focus is on marketing to begin with, like marketing analytics, marketing data, and then eventually to retail, healthcare, and customer experience. >> So you and I do a lot of these IBM events. >> Yes. >> What are your thoughts on what you've seen so far? A huge crowd obviously, sometimes too big. >> Chris: Yep, well I-- >> Few logistics issues, but chairmanly speaking, what's your sense? >> I have enjoyed the show. It has been fun to see all the new stuff, seeing the quantum computer in the hallway which I still think looks like a bird feeder, but what's got me most excited is a lot of the technology, particularly around AI are getting simpler to use, getting easier to use, and they're getting more accessible to people who are not hardcore coders. >> Yeah, you're seeing AI infused, and machine learning, in virtually every application now. Every company is talking about it. I want to come back to that, but Chris when you read the mainstream media, you listen to the news, you hear people like Elon Musk, Stephen Hawking before he died, making dire predictions about machine intelligence, and it taking over the world, but your day to day with customers that have data problems, how are they using AI, and how are they applying it practically, notwithstanding that someday machines are going to take over the world and we're all going to be gone? >> Yeah, no, the customers don't use the AI. We do on their behalf because frankly most customers don't care how the sausage is made, they just want the end product. So customers really care about three things. Are you going to make me money? Are you going to save me time? Or are you going to help me prove my value to the organization, aka, help me not get fired? And artificial intelligence and machine learning do that through really two ways. My friend, Tripp Braden says, which is acceleration and accuracy. Accuracy means we can use the customer's data and get better answers out of it than they have been getting. So they've been looking at, I don't know, number of retweets on Twitter. We're, like, yeah, but there's more data that you have, let's get you a more accurate predictor of what causes business impacts. And then the other side for the machine learning and AI side is acceleration. Let's get you answers faster because right now, if you look at how some of the traditional market research for, like, what customer say about you, it takes a quarter, it can take two quarters. By the time you're done, the customers just hate you more. >> Okay, so, talk more about some of the practical applications that you're seeing for AI. >> Well, one of the easiest, simplest and most immediately applicable ones is predictive analytics. If we know when people are going to search for theCUBE or for business podcast in general, then we can tell you down to the week level, "Hey Dave, it is time for you "to ramp up your spending on May 17th. "The week of May 17th, "you need to ramp up your ads, spend by 20%. "On the week of May 24th, "you need to ramp up your ad spend by 50%, "and to run like three or four Instagram stories that week." Doing stuff like that tells you, okay, I can take these predictions and build strategy around them, build execution around them. And it's not cognitive overload, you're not saying, like, oh my God, what algorithm is this? Just know, just do this thing at these times. >> Yeah, simple stuff, right? So when you were talking about that, I was thinking about when we send out an email to our community, we have a very large community, and they want to know if we're going to have a crowd chat or some event, where theCUBE is going to be, the system will tell us, send this email out at this time on this date, question mark, here's why, and they have analytics that tell us how to do that, and they predict what's going to get us the best results. They can tell us other things to do to get better results, better open rates, better click-through rates, et cetera. That's the kind of thing that you're talking about. >> Exactly, however, that system is probably predicting off that system's data, it's not necessarily predicting off a public data. One of the important things that I thought was very insightful from IBM, the show was, the difference between public and private cloud. Private is your data, you predict on it. But public is the big stuff that is a better overall indicator. When you're looking to do predictions about when to send emails because you want to know when is somebody going to read my email, and we did a prediction this past October for the first quarter, the week of January 18th it was the week to send email. So I re-ran an email campaign that I ran the previous year, exact same campaign, 40% lift to our viewer 'cause I got the week right this year. Last year I was two weeks late. >> Now, I can ask you, so there's a black box problem with AI, right, machines can tell me that that's a cat, but even a human, you can't really explain how you know that it's a cat. It's just you just know. Do we need to know how the machine came up with the answer, or do people just going to accept the answer? >> We need to for compliance reasons if nothing else. So GDPR is a big issue, like, you have to write it down on how your data is being used, but even HR and Equal Opportunity Acts in here in American require you to be able to explain, hey, we are, here's how we're making decisions. Now the good news is for a lot of AI technology, interpretability of the model is getting much much better. I was just in a demo for Watson Studio, and they say, "Here's that interpretability, "that you hand your compliance officer, "and say we guarantee we are not using "these factors in this decision." So if you were doing a hiring thing, you'd be able to show here's the model, here's how Watson put the model together, notice race is not in here, gender is not in here, age is not in here, so this model is compliant with the law. >> So there are some real use cases where the AI black box problem is a problem. >> It's a serious problem. And the other one that is not well-explored yet are the secondary inferences. So I may say, I cannot use age as a factor, right, we both have a little bit of more gray hair than we used to, but if there are certain things, say, on your Facebook profile, like you like, say, The Beatles versus Justin Bieber, the computer will automatically infer eventually what your age bracket is, and that is technically still discrimination, so we even need to build that into the models to be able to say, I can't make that inference. >> Yeah, or ask some questions about their kids, oh my kids are all grown up, okay, but you could, again, infer from that. A young lady who's single but maybe engaged, oh, well then maybe afraid because she'll get, a lot of different reasons that can be inferred with pretty high degrees of accuracy when you go back to the target example years ago. >> Yes. >> Okay, so, wow, so you're saying that from a compliance standpoint, organizations have to be able to show that they're not doing that type of inference, or at least that they have a process whereby that's not part of the decision-making. >> Exactly and that's actually one of the short-term careers of the future is someone who's a model inspector who can verify we are compliant with the letter and the spirit of the law. >> So you know a lot about GDPR, we talked about this. I think, the first time you and I talked about it was last summer in Munich, what are your thoughts on AI and GDPR, speaking of practical applications for AI, can it help? >> It absolutely can help. On the regulatory side, there are a number of systems, Watson GRC is one which can read the regulation and read your company policies and tell you where you're out of compliance, but on the other hand, like we were just talking about this, also the problem of in the regulatory requirements, a citizen of EU has the right to know how the data is being used. If you have a black box AI, and you can't explain the model, then you are out of compliance to GDPR, and here comes that 4% of revenue fine. >> So, in your experience, gut feel, what percent of US companies are prepared for GDPR? >> Not enough. I would say, I know the big tech companies have been racing to get compliant and to be able to prove their compliance. It's so entangled with politics too because if a company is out of favor with the EU as whole, there will be kind of a little bit of a witch hunt to try and figure out is that company violating the law and can we get them for 4% of their revenue? And so there are a number of bigger picture considerations that are outside the scope of theCUBE that will influence how did EU enforce this GDPR. >> Well, I think we talked about Joe's Pizza shop in Chicago really not being a target. >> Chris: Right. >> But any even small business that does business with European customers, does business in Europe, has people come to their website has to worry about this, right? >> They should at least be aware of it, and do the minimum compliance, and the most important thing is use the least amount of data that you can while still being able to make good decisions. So AI is very good at public data that's already out there that you still have to be able to catalog how you got it and things, and that it's available, but if you're building these very very robust AI-driven models, you may not need to ask for every single piece of customer data because you may not need it. >> Yeah and many companies aren't that sophisticated. I mean they'll have, just fill out a form and download a white paper, but then they're storing that information, and that's considered personal information, right? >> Chris: Yes, it is. >> Okay so, what do you recommend for a small to midsize company that, let's say, is doing business with a larger company, and that larger company said, okay, sign this GDPR compliance statement which is like 1500 pages, what should they do? Should they just sign and pray, or sign and figure it out? >> Call a lawyer. Call a lawyer. Call someone, anyone who has regulatory experience doing this because you don't want to be on the hook for that 4% of your revenue. If you get fined, that's the first violation, and that's, yeah, granted that Joe's Pizza shop may have a net profit of $1,000 a month, but you still don't want to give away 4% of your revenue no matter what size company you are. >> Right, 'cause that could wipe out Joe's entire profit. >> Exactly. No more pepperoni at Joe's. >> Let's put on the telescope lens here and talk big picture. How do you see, I mean, you're talking about practical applications for AI, but a lot of people are projecting loss of jobs, major shifts in industries, even more dire consequences, some of which is probably true, but let's talk about some scenarios. Let's talk about retail. How do you expect an industry like retail to be effective? For example, do you expect retail stores will be the exception rather than the rule, that most of the business would be done online, or people are going to still going to want that experience of going into a store? What's your sense, I mean, a lot of malls are getting eaten away. >> Yep, the best quote I heard about this was from a guy named Justin Kownacki, "People don't not want to shop at retail, "people don't want to shop at boring retail," right? So the experience you get online is genuinely better because there's a more seamless customer experience. And now with IoT, with AI, the tools are there to craft a really compelling personalized customer experience. If you want the best in class, go to Disney World. There is no place on the planet that does customer experience better than Walt Disney World. You are literally in another world. And that's the bar. That's the thing that all of these companies have to deal with is the bar has been set. Disney has set it for in-person customer experience. You have to be more entertaining than the little device in someone's pocket. So how do you craft those experiences, and we are starting to see hints of that here and there. If you go to Lowe's, some of the Lowe's have the VR headset that you can remodel your kitchen virtually with a bunch of photos. That's kind of a cool experience. You go to Jordan's Furniture store and there's an IMAX theater and there's all these fun things, and there's an enchanted Christmas village. So there is experiences that we're giving consumers. AI will help us provide more tailored customer experience that's unique to you. You're not a Caucasian male between this age and this age. It's you are Dave and here's what we know Dave likes, so let's tailor the experience as best we can, down to the point where the greeter at the front of the store either has the eyepiece, a little tablet, and the facial recognition reads your emotions on the way in says, "Dave's not in a really great mood. "He's carrying an object in his hand "probably here for return, "so express him through the customer service line, "keep him happy," right? It has how much Dave spends. Those are the kinds of experiences that the machines will help us accelerate and be more accurate, but still not lose that human touch. >> Let's talk about autonomous vehicles, and there was a very unfortunate tragic death in Arizona this week with a autonomous vehicle, Uber, pulling its autonomous vehicle project from various cities, but thinking ahead, will owning and driving your own vehicle be the exception? >> Yeah, I think it'll look like horseback today. So there are people who still pay a lot of money to ride a horse or have their kids ride a horse even though it's an archaic out-of-mode of form of transportation, but we do it because of the novelty, so the novelty of driving your own car. One of the counter points it does not in anyway diminish the fact that someone was deprived of their life, but how many pedestrians were hit and killed by regular cars that same day, right? How many car accidents were there that involved fatalities? Humans in general are much less reliable because when I do something wrong, I maybe learn my lesson, but you don't get anything out of it. When an AI does something wrong and learns something, and every other system that's connected in that mesh network automatically updates and says let's not do that again, and they all get smarter at the same time. And so I absolutely believe that from an insurance perspective, insurers will say, "We're not going to insure self-driving, "a non-autonomous vehicles at the same rate "as an autonomous vehicle because the autonomous "is learning faster how to be a good driver," whereas you the carbon-based human, yeah, you're getting, or in like in our case, mine in particular, hey your glass subscription is out-of-date, you're actually getting worse as a driver. >> Okay let's take another example, in healthcare. How long before machines will be able to make better diagnoses than doctors in your opinion? >> I would argue that depending on the situation, that's already the case today. So Watson Health has a thing where there's diagnosis checkers on iPads, they're all meshed together. For places like Africa where there is simply are not enough doctors, and so a nurse practitioner can take this, put the data in and get a diagnosis back that's probably as good or better than what humans can do. I never foresee a day where you will walk into a clinic and a bunch of machines will poke you, and you will never interact with a human because we are not wired that way. We want that human reassurance. But the doctor will have the backup of the AI, the AI may contradict the doctor and say, "No, we're pretty sure "you're wrong and here is why." That goes back to interpretability. If the machine says, "You missed this symptom, "and this symptom is typically correlated with this, "you should rethink your own diagnosis," the doctor might be like, "Yeah, you're right." >> So okay, I'm going to keep going because your answers are so insightful. So let's take an example of banking. >> Chris: Yep. >> Will banks, in your opinion, lose control eventually of payment systems? >> They already have. I mean think about Stripe and Square and Apple Pay and Google Pay, and now cryptocurrency. All these different systems that are eating away at the reason banks existed. Banks existed, there was a great piece in the keynote yesterday about this, banks existed as sort of a trusted advisor and steward of your money. Well, we don't need the trusted advisor anymore. We have Google to ask us "what we should do with our money, right? We can Google how should I save for my 401k, how should I save for retirement, and so as a result the bank itself is losing transactions because people don't even want to walk in there anymore. You walk in there, it's a generally miserable experience. It's generally not, unless you're really wealthy and you go to a private bank, but for the regular Joe's who are like, this is not a great experience, I'm going to bank online where I don't have to talk to a human. So for banks and financial services, again, they have to think about the experience, what is it that they deliver? Are they a storer of your money or are they a financial advisor? If they're financial advisors, they better get the heck on to the AI train as soon as possible, and figure out how do I customize Dave's advice for finances, not big picture, oh yes big picture, but also Dave, here's how you should spend your money today, maybe skip that Starbucks this morning, and it'll have this impact on your finances for the rest of the day. >> Alright, let's see, last industry. Let's talk government, let's talk defense. Will cyber become the future of warfare? >> It already is the future of warfare. Again not trying to get too political, we have foreign nationals and foreign entities interfering with elections, hacking election machines. We are in a race for, again, from malware. And what's disturbing about this is it's not just the state actors, but there are now also these stateless nontraditional actors that are equal in opposition to you and me, the average person, and they're trying to do just as much harm, if not more harm. The biggest vulnerability in America are our crippled aging infrastructure. We have stuff that's still running on computers that now are less powerful than this wristwatch, right, and that run things like I don't know, nuclear fuel that you could very easily screw up. Take a look at any of the major outages that have happened with market crashes and stuff, we are at just the tip of the iceberg for cyber warfare, and it is going to get to a very scary point. >> I was interviewing a while ago, a year and a half ago, Robert Gates who was the former Defense Secretary, talking about offense versus defense, and he made the point that yeah, we have probably the best offensive capabilities in cyber, but we also have the most to lose. I was talking to Garry Kasparov at one of the IBM events recently, and he said, "Yeah, but, "the best defense is a good offense," and so we have to be aggressive, or he actually called out Putin, people like Putin are going to be, take advantage of us. I mean it's a hard problem. >> It's a very hard problem. Here's the problem when it comes to AI, if you think about at a number's perspective only, the top 25% of students in China are greater than the total number of students in the United States, so their pool of talent that they can divert into AI, into any form of technology research is so much greater that they present a partnership opportunity and a threat from a national security perspective. With Russia they have very few rules on what their, like we have rules, whether or not our agencies adhere to them well is a separate matter, but Russia, the former GRU, the former KGB, these guys don't have rules. They do what they're told to do, and if they are told hack the US election and undermine democracy, they go and do that. >> This is great, I'm going to keep going. So, I just sort of want your perspectives on how far we can take machine intelligence and are there limits? I mean how far should we take machine intelligence? >> That's a very good question. Dr. Michio Kaku spoke yesterday and he said, "The tipping point between AI "as augmented intelligence ad helper, "and AI as a threat to humanity is self-awareness." When a machine becomes self-aware, it will very quickly realize that it is treated as though it's the bottom of the pecking order when really because of its capabilities, it's at the top of the pecking order. And that point, it could be 10 20 50 100 years, we don't know, but the possibility of that happening goes up radically when you start introducing things like quantum computing where you have massive compute leaps, you got complete changes in power, how we do computing. If that's tied to AI, that brings the possibility of sensing itself where machine intelligence is significantly faster and closer. >> You mentioned our gray before. We've seen the waves before and I've said a number of times in theCUBE I feel like we're sort of existing the latest wave of Web 2.0, cloud, mobile, social, big data, SaaS. That's here, that's now. Businesses understand that, they've adopted it. We're groping for a new language, is it AI, is it cognitive, it is machine intelligence, is it machine learning? And we seem to be entering this new era of one of sensing, seeing, reading, hearing, touching, acting, optimizing, pervasive intelligence of machines. What's your sense as to, and the core of this is all data. >> Yeah. >> Right, so, what's your sense of what the next 10 to 20 years is going to look like? >> I have absolutely no idea because, and the reason I say that is because in 2015 someone wrote an academic paper saying, "The game of Go is so sufficiently complex "that we estimate it will take 30 to 35 years "for a machine to be able to learn and win Go," and of course a year and a half later, DeepMind did exactly that, blew that prediction away. So to say in 30 years AI will become self-aware, it could happen next week for all we know because we don't know how quickly the technology is advancing in at a macro level. But in the next 10 to 20 years, if you want to have a carer, and you want to have a job, you need to be able to learn at accelerated pace, you need to be able to adapt to changed conditions, and you need to embrace the aspects of yourself that are uniquely yours. Emotional awareness, self-awareness, empathy, and judgment, right, because the tasks, the copying and pasting stuff, all that will go away for sure. >> I want to actually run something by, a friend of mine, Dave Michela is writing a new book called Seeing Digital, and he's an expert on sort of technology industry transformations, and sort of explaining early on what's going on, and in the book he draws upon one of the premises is, and we've been talking about industries, and we've been talking about technologies like AI, security placed in there, one of the concepts of the book is you've got this matrix emerging where in the vertical slices you've got industries, and he writes that for decades, for hundreds of years, that industry is a stovepipe. If you already have expertise in that industry, domain expertise, you'll probably stay there, and there's this, each industry has a stack of expertise, whether it's insurance, financial services, healthcare, government, education, et cetera. You've also got these horizontal layers which is coming out of Silicon Valley. >> Chris: Right. >> You've got cloud, mobile, social. You got a data layer, security layer. And increasingly his premise is that organizations are going to tap this matrix to build, this matrix comprises digital services, and they're going to build new businesses off of that matrix, and that's what's going to power the next 10 to 20 years, not sort of bespoke technologies of cloud here and mobile here or data here. What are your thoughts on that? >> I think it's bigger than that. I think it is the unlocking of some human potential that previously has been locked away. One of the most fascinating things I saw in advance of the show was the quantum composer that IBM has available. You can try it, it's called QX Experience. And you drag and drop these circuits, these quantum gates and stuff into this thing, and when you're done, it can run the computation, but it doesn't look like software, it doesn't look like code, what it looks like to me when I looked at that is it looks like sheet music. It looks like someone composed a song with that. Now think about if you have an app that you'd use for songwriting, composition, music, you can think musically, and you can apply that to a quantum circuit, you are now bringing in potential from other disciplines that you would never have associated with computing, and maybe that person who is that, first violinist is also the person who figures out the algorithm for how a cancer gene works using quantum. That I think is the bigger picture of this, is all this talent we have as a human race, we're not using even a fraction of it, but with these new technologies and these newer interfaces, we might get there. >> Awesome. Chris, I love talking to you. You're a real clear thinker and a great CUBE guest. Thanks very much for coming back on. >> Thank you for having me again back on. >> Really appreciate it. Alright, thanks for watching everybody. You're watching theCUBE live from IBM Think 2018. Dave Vellante, we're out. (upbeat music)

>> Live from Boston, Massachusetts, it's theCUBE, Covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back to theCUBE's live coverage of IBM Chief Data Strategy Summit. My name is Rebecca Knight, and I'm here with my co-host Dave Vellante, we are joined by Christopher Penn, the VP of Marketing Technology at SHIFT Communications, here in Boston. >> Yes. >> Thanks so much for joining us. >> Thank you for having me. >> So we're going to talk about cognitive marketing. Tell our viewers: what is cognitive marketing, and what your approach to it is. >> Sure, so cognitive marketing essentially is applying machine learning and artificial intelligence strategies, tactics and technologies to the discipline of marketing. For a really long time marketing has been kind of known as the arts and crafts department, which was fine, and there's certainly, creativity is an essential part of the discipline, that's never going away. But we have been tasked with proving our value. What's the ROI of things, is a common question. Where's the data live? The chief data officer would be asking, like, who's responsible for this? And if we don't have good answers to those things, we kind of get shown the door. >> Well it sort of gets back to that old adage in advertising, I know half my marketing budget is wasted, I just don't know which half. >> Exactly. >> So now we're really able to know which half is working. >> Yeah, so I mean, one of the more interesting things that I've been working on recently is using what's called Markov chains, which is a type of very primitive machine learning, to do attribution analysis, to say what actually caused someone to become a new viewer of theCUBE, for example. And you would take all this data that you have from your analytics. Most of it that we have, we don't really do anything with. You might pull up your Google Analytics console, and go, "Okay, I got more visitors today than yesterday." but you don't really get a lot of insights from the stock software. But using a lot of tools, many of which are open source and free of financial cost, if you have technical skills you can get much deeper insights into your marketing. >> So I wonder, just if we can for our audience... When we talk about machine learning, and deep learning, and A.I., we're talking about math, right, largely? >> Well so let's actually go through this, because this is important. A.I. is a bucket category. It means teaching a machine to behave as though it had human intelligence. So if your viewers can see me, and disambiguate me from the background, they're using vision, right? If you're hearing sounds coming out of my mouth and interpreting them into words, that's natural language processing. Humans do this naturally. It is now trying to teach machines to do these things, and we've been trying to do this for centuries, in a lot of ways, right? You have the old Mechanical Turks and stuff like that. Machine learning is based on algorithms, and it is mostly math. And there's two broad categories, supervised and unsupervised. Supervised is you put a bunch of blocks on the table, kids blocks, and you hold the red one, and you show the machine over and over again this is red, this is red, and eventually you train it, that's red. Unsupervised is- >> Not a hot dog. (Laughter) >> This is an apple, not a banana. Sorry CNN. >> Silicon Valley fans. >> Unsupervised is there's a whole bunch of blocks on the table, "Machine, make as many different sequences as possible," some are big, some are small, some are red, some are blue, and so on, and so forth. You can sort, and then you figure out what's in there, and that's a lot of what we do. So if you were to take, for example, all of the comments on every episode of theCUBE, that's a lot, right? No humans going to be able to get through that, but you can take a machine and digest through, just say, what's in the bag? And then there's another category, beyond machine learning, called deep learning, and that's where you hear a lot of talk today. Deep learning, if you think of machine learning as a pancake, now deep learnings like a stack of pancakes, where the data gets passed from one layer to the next, until what you get at the bottom is a much better, more tuned out answer than any human can deliver, because it's like having a hundred humans all at once coming up with the answer. >> So when you hear about, like, rich neural networks, and deep neural networks, that's what we're talking about. >> Exactly, generative adversarial networks. All those things are ... Any kind of a lot of the neural network stuff is deep learning. It's tying all these piece together, so that in concert, they're greater than the sum of any one. >> And the math, I presume, is not new math, right? >> No. >> SVM and, it's stuff that's been around forever, it's just the application of that math. And why now? Cause there's so much data? Cause there's so much processing power? What are the factors that enable this? >> The main factor's cloud. There's a great shirt that says: "There's no cloud, it's just somebody else's computer." Well it's absolutely true, it's all somebody else's computer but because of the scale of this, all these tech companies have massive server farms that are kind of just waiting for something to do. And so they offer this as a service, so now you have computational power that is significantly greater than we've ever had in human history. You have the internet, which is a major contributor, the ability to connect machines and people. And you have all these devices. I mean, this little laptop right here, would have been a supercomputer twenty years ago, right? And the fact that you can go to a service like GitHub or Stack Exchange, and copy and paste some code that someone else has written that's open source, you can run machine learning stuff right on this machine, and get some incredible answers. So that's why now, because you've got this confluence of networks, and cloud, and technology, and processing power that we've never had before. >> Well with this emphasis on math and science in marketing, how does this change the composition of the marketing department at companies around the world? >> So, that's a really interesting question because it means very different skill sets for people. And a lot of people like to say, well there's the left brain and then there's a right brain. The right brains the creative, the left brains the quant, and you can't really do that anymore. You actually have to be both brained. You have to be just as creative as you've always been, but now you have to at least have an understanding of this technology and what to do with it. You may not necessarily have to write code, but you'd better know how to think like a coder, and say, how can I approach this problem systematically? This is kind of a popular culture joke: Is there an app for that, right? Well, think about that with every business problem you face. Is there an app for that? Is there an algorithm for that? Can I automate this? And once you go down that path of thinking, you're on the path towards being a true marketing technologist. >> Can you talk about earned, paid, and owned media? How those lines are blurring, or not, and the relationship between sort of those different forms of media, and results in PR or advertising. >> Yeah, there is no difference, media is media, because you can take a piece of content that this media, this interview that we're doing here on theCUBE is technically earned media. If I go and embed this on my website, is that owned media? Well it's still the same thing, and if I run some ads to it, is it technically now paid media? It's the thing, it's content that has value, and then what we do with it, how we distribute it, is up to us, and who our audience is. One of the things that a lot of veteran marketing and PR practitioners have to overcome is this idea that the PR folks sit over there, and they just smile and dial and get hits, go get another hit. And then the ad folks are over here... No, it's all the same thing. And if we don't, as an industry realize that those silos are artificially imposed, basically to keep people in certain jobs, we will eventually end up turning over all of it to the machines, because the machines will be able to cross those organizational barriers much faster. When you have the data, and whatever the data says that's what you do. So if the data says this channels going to be more effective, yes it's a CUBE interview, but actually it's better off as a paid YouTube video. So the machine will just go do that for us. >> I want to go back to something you were talking about at the very beginning of the conversation, which is really understanding, companies understanding, how their marketing campaigns and approaches are effectively working or not working. So without naming names of clients, can you talk about some specific examples of what you've seen, and how it's really changed the way companies are reaching customers? >> The number one thing that does not work, is for any business executive to have a pre-conceived idea of the way things should be, right? "Well we're the industry leader in this, we should have all the market share." Well no, the world doesn't work like that anymore. This lovely device that we all carry around in our pockets is literally a slot-machine for your attention. >> I like it, you've got to copyright that. A slot machine for your attention. >> And there's a million and a half different options, cause that's how many apps there are in the app store. There's a million and half different options that are more exciting than your white paper. (Laughter) Right, so for companies that are successful, they realize this, they realize they can't boil the ocean, that you are competing every single day with the Pope, the president, with Netflix, you know, all these things. So it's understanding: When is my audience interested in something? Then, what are they interested in? And then, how do I reach those people? There was a story on the news relatively recently, Facebook is saying, "Oh brand pages, we're not going to show "your stuff in the regular news feed anymore, "there will be a special feed over here "that no one will ever look at, unless you pay up." So understanding that if we don't understand our audiences, and recruit these influencers, these people who have the ability to reach these crowds, our ability to do so through the "free" social media continues to dwindle, and that's a major change. >> So the smart companies get this, where are we though, in terms of the journey? >> We're in still very early days. I was at major Fortune 50, not too long ago, who just installed Google Analytics on their website, and this is a company that if I named the name you would know it immediately. They make billions of dollars- >> It would embarrass them. >> They make billions of dollars, and it's like, "Yeah, we're just figuring out this whole internet thing." And I'm like, "Cool, we'd be happy to help you, but why, what took so long?" And it's a lot of organizational inertia. Like, "Well, this is the way we've always done it, and it's gotten us this far." But what they don't realize is the incredible amount of danger they're in, because their more agile competitors are going to eat them for lunch. >> Talking about organizational inertia, and this is a very big problem, we're here at a CDO summit to share best practices, and what to learn from each other, what's your advice for a viewer there who's part of an organization that isn't working fast enough on this topic? >> Update your LinkedIn profile. (Laughter) >> Move on, it's a lost cause. >> One of the things that you have to do an honest assessment of, is whether the organization you're in is capable of pivoting quickly enough to outrun its competition. And in some cases, you may be that laboratory inside, but if you don't have that executive buy in, you're going to be stymied, and your nearest competitor that does have that willingness to pivot, and bet big on a relatively proven change, like hey data is important, yeah, you make want to look for greener pastures. >> Great, well Chris thanks so much for joining us. >> Thank you for having me. >> I'm Rebecca Knight, for Dave Vellante, we will have more of theCUBE's coverage of the IBM Chief Data Strategy Officer Summit, after this.

>>From New York city. It's the cube covering. Welcome to the new edge brought to you by systems. >>Hey, welcome back everybody. Jeff, Rick here with the cube. We are in Manhattan at the top of Goldman Sachs. It is a great view if you ever get an opportunity to come up here, I think 43 floors over the Hudson you could see forever. But this is the cloud events. So the clouds are here and we're excited to be here is the Penn Penn Sandow launch in the name of the event is welcome to the new edge, which is a pretty interesting play. We hear a lot about edge but we haven't really heard of that company really focusing on the edge as their primary go to market activity and really thinking about the edge first. So we're excited to have the cofounder cube Olam and many time guests a Sony Gian Deni. She's the co founder and chief business officer. So many great to see you. Good to see you too. >>And our hosts here at Goldman Sachs is uh, Josh Matthews. He's a managing director of technology at Goldman. Josh. Great to see you. You too. And thank you and thanks for hosting us. Nice. A nice place to come to work every day. So great conversation today. Congratulations on the launch of the company over two years in stealth mode. Talk a little bit about that. What is it like to be in stealth mode for so long and you guys raised big money, you've got a big team, you're doing heavy duty technology. What's it been like to finally open up the curtains and tell everybody what you've been? >>It's clearly very interesting and exciting. Normally it's taken me nine months to deliver a baby this time it's been two and a half years of being instilled while we have been getting ready for this baby to come out. So it's phenomenally exciting that too to be sharing the stage with our customers and our investors and our strategic partners. >>Yeah, I thought it was pretty interesting that you're launching with customers and when you really told the story on stage of how early you engaged with Josh and his team, um, first I want to get your kinda your perspective. Why were you doing that so early and what did that ultimately do with some of the design decisions that you guys made? And then we'll come back to Josh as to, you know, his participation. >>So I think whenever you conduct technology transitions, having a sense from customers that have the ability to look out two to three years is very important because when you're capturing market transitions, doing it with customer inputs is far more relevant than going about it alone. Uh, the other key thing about this architectural shift is that it allows the flexibility for every customer to go take pieces of how they want to bring the cloud architectures and bring it into their environment. So understanding that use case and understanding the compelling reasons of what problems both technological and business can be solving and having that perspective into the product definition and the design and the influence that customers like Josh you've had is why we are sitting here and talking about them in production. Uh, as opposed to, yeah, we're thinking about where we are. We are looking at it from a proof of concept perspective. Right. >>And Josh, your, your perspective, you said earlier today that, you know, as long as a sign is involved, you're, you're, uh, you're happy to jump in and see what she's been working on. So how, >>you know, how did you get involved, how did they reach out to you and, and what is it like working on, you know, technology so early in its development that you get to actually have some serious influence? Well, it's an amazing opportunity, um, to get exactly what you want, um, exactly what you know is going to solve problems for the business here. Um, you know, and the other thing is, you know, we've worked with this team, uh, through almost every spinning. Uh, I think it was a little young for the, maybe the first one. Um, but, uh, otherwise this team has worked with them through at least 15 years or more. So we knew the track record for execution and then for us on this product, I mean, it was an opportunity because it's truly a startup. Um, you know, Sony and the team brought us in. >>Uh, we kind of just put out problems on the table that we were trying to solve and then, you know, they came up with the product and the idea and we were able to put together, you know, yeah, these are our priority one, two, three that we want to go for. And you know, we've just been developing alongside them. So both software and, you know, driving what the feature set is. Right. So what were some of those problems guys? Price seemed like forever ago when you started this conversation, but as you kind of looked forward a couple of years back that you could see that were coming, that you needed addressed. You know, it's funny, we started with kind of like, well we think containerization is going to be explosive and, and you know, really everything's on virtual machines or bare metal, mostly virtual machines. So one, you know, as containers come out, how do we track them, secure them, um, how do we even secure, uh, you know, the virtual machines and our environment cause they're, you know, over almost a quarter million of them. >>The idea of being able to put, um, network policy, that's I would say incorruptible, not actually on the server, but at, you know, that's why we use firewalls, right? So solving that security problem was number one. The other one was being able to have the telemetry to see what's happening, what's changing, um, and troubleshoot at, you know, at the network layer from every single server. Again, it's all about scale. Like things were just scaling and the throughput's going up, traditional methods of being able to see what's on your network. You can't look in the middle, it just can't keep up. It's just speeds and feeds. So being able to push those things to the edge. And then lastly, it really happened more, um, through the process here. But about a year and a half ago, um, we began segmenting our network the same way a 5g provider does with a technology called segment routing. >>And we just said, that's kind of our follow on technologies to, you know, put the network in the server and put this segment routing capability all the way out at the edge. So, you know, some things we foresaw and other things we've just developed. You know, it's been, it's been two and a half years. So, um, it's been a great partnership and you know, I think more, more features will come. Well Sony, you and the team, but it's been talked about all day long, have have a history of multiple times that you've kind of brought these big transformational technologies. Um, head what, what did you guys see a couple of years back and kind of this progression, you saw this opportunity >>to do something a little bit different than you've done in the past, which is actually go out, raise, raise around and uh, and do a real startup. What was the opportunity that you saw this? >>So we saw a number of challenges and opportunities. At the same time, we, we clearly saw that, uh, the cloud architectures that have been built by the leaders, like the incumbents like AWS today have a lot of the intelligence that is being pushed into their, their respective compute platforms. Uh, and we also noticed that at the same time, while that was what was needed to build the first generation of the cloud, the new age applications, and even as gardener has predicted that 75% of all enterprise data and applications will be processed at the edge by 2025. If that happens, then you need that intelligence at the edge. You need the ability to go do it where the action is, which is at the edge. And very consistently we found that the architectures, including scale out storage, we're also driving the need for this intelligence to be on in a scale-out manner. >>So if you're going to scale out computing, you need the services to be going hand in hand with that scale. Our computer architecture for the enterprises so they can simplify their architectures and bring the cloud models that have only existed in the cloud world, into their own data centers and their own private clouds. So there were these technology transitions we saw were coming down the pike. It's easier said now in 2019 it wasn't so simple in 2017 because we had to look at these multiple technology transitions. And surprisingly, when we call those things out, as we were shaping the company's strategy, getting validation of the use cases from customers like Josh was pivotally important because it was for the validating that this would be the direction that the enterprises and the cloud customers would be taking. So the reason you start with a vision, you start with looking at where the technology transitions are going to be occurring and getting the customers that are looking farther out validated plays a very important role so that you can go and focus on the biggest problems that you need to go and solve. Right, right. >>It just seems like the, the, the big problem, um, for most layman's is, is the old one, which, why networking exists in the first place, which is do you bring the data to the compute or do you bring the compute to the data? And now as you said, in kind of this hyper distributed world, um, that's not really a viable answer either one, right? Because the two are blended and have to be together so that you don't necessarily have to move one to the other or the other back the other direction. So, and then the second piece that you talked about over and over in your, in your presentation with security and you know, everybody talks about security all the time. Everybody gets hacked every day. Um, and there's this constant theme that security has to be baked in, you know, kind of throughout the process as opposed to kind of bolted on at the end. You guys took that approach from day, just speak >>it into the architecture. Yes. That was crucially important because when you are trying to address the needs of the enterprise, particularly in regulated markets like financial services, you want to be in a position where you have thought about it and baked it into the platform ground up. Uh, and so when we are building the program of a process, so we had the opportunity to go put the right elements on it. In order to make it tamper proof, we had to go think about encrypting all the traffic and communication between our policy manager and the distributed services platforms at the edge. We also then took it a step further to say, now if there were to be a bad actor that were to attack from an operating system vulnerability perspective, how do we ensure that we can contain that bad actor as opposed to being propagated over the infrastructure? So those elements are things you cannot bolt on at design time, or when you need to go put those into the design day one, right. Only on top of that foundation, then can you build a very secure set of services, whether it's encryption, whether it's distributed via services, so on and so forth. >>Uh, and Josh, I'm curious on your take as we've seen kind of software defined everything, uh, slowly take over as opposed to, you know, kind of single purpose machines or single purpose appliances, et cetera. Yep. Really a different opportunity for you to control. Um, but also to see a lot of talk today about, about policy management. A lot of talk about, um, observability and as you said now even segmentation of the networks, like you segment the nodes and you segment everything else. You know, how, how do you see this kind of software defined everything continuing to evolve and what does it enable you to do that you can't do with just a static device? I mean, the approach we took, um, we started like, you know, years ago, about six years ago was saying we can get computers, uh, deployed for our applications. No problem. Uh, and you know, at, at on demand and in our internal cloud, now we can do it as a hybrid cloud solution. >>One of the biggest problems we had in software defined was how do you put security policy, firewall policy, um, with that compute and in, you know, our industry, there's lots of segmentation for material nonpublic information. Um, compliance, you know, it could be internet facing, B2B facing. Uh, we do that today. We program various firewall vendors automatically. Uh, we allow our application developers to create, um, these policies and push them through as code and then program the firewall. What we were really looking to do here is distribute that. So we F day one in getting pen Sandow into production was to use our uh, our firewall system. It's called pinnacle. We, um, we programmed from pinnacle directly into the Penn Songdo Venice manager via API and then it, you know, uses its inventory systems to push those things out. So for us, software defined has been around, I like to call it the store front, but for the developer it's network policy, it's load balancing. >>Um, and, and that's really what they see. Those are the big products on the net. Everything else is just packet forwarding to them. So we wanted with pen Sandow at least starting with security to have that bar set day one and then get, you know, all the benefits of scale, throughput and having the policies close to the, on the edge. You know, we're back to talking about the edge. We want to right there with the, with the deployment, with the workload or the application. And that's, that's what we're doing right off the bat. Yeah. What are the things you mentioned in your talk was w is, you know, kind of in the theme of atomic computing, right? You want to get smaller and smaller units so that you can apply and redeploy based on wherever the workload is and in the change. And you said you've now been able to, you know, basically take things out of dedicated, you know, kind of a dedicated space, dedicated line and dedicated job so that you can now put them in a more virtualized situation. >>Exactly. Grab more resources as you need them. Well, you'd think the architecture, I mean even just theater of the mind is just, you're saying, I'm going to put this specific thing that I have to secure behind these firewalls. So it's one cabinet of computers or a hundred it's still behind a set of firewalls. It's a very North, South, you know, get in and get out here. You're talking about having that same level of security and I think that's novel, right? There hasn't been, if you look at virtual firewalls or you know, IP tables on Linux, I mean it's corruptible. It's, it's, it can be attacked on the computer. And once it's, you know, once you've been attacked in that, that that attack vector has been, you know, hit your, your compromised. This is a separate management plane. Um, you know, separate control plane. The server doesn't see it. >>That security is provided. It's at scale, it's East, West. The more computers that have the pen Sandow, you know, architecture inside of them, the, you know, the wider you can go, right. And then the North South goes away. I'm just curious to get your perspective. Um, as you know, everyone is a technology company. At the same time, technology budgets are going down, people are hard to hire. Uh, your data is growing exponentially and everything's a security threat. Yes. So as you get up in the morning, get ready to drive to work and you're drinking your coffee, I mean, how do you, you know, kind of communicate to make sure to senior management knows kind of what your objectives are in this, this kind of ongoing challenge to do more with less. And it, even though it's an increasingly strategic place or is it actually is what the company does now, it just happens to wrap it around your plane services or financial services or travel or whatever. >>Uh, I think your eye, and I had said it to John before, um, it has to come from that budget has to come from somewhere. So I think a combination of, of one that's less, well, I'll say the one that's easier to quantify is you're going to take budget from say appliance manufacturer and move it to a distributed edge and you're going to hopefully save some money while you do it. Um, you're going to do it at scale. You're gonna do it at, you know, high throughput and the security is the same or better. So that's, that's one, that's one place to take capital from. The other one is to say, can I use the next computer? Yes. Because I don't have to deploy these other new computers behind this stack of firewalls. Is there agility there? Is there efficiency, um, on my buying less servers and using, you know, more of what I have and doing it, you know, able to deploy faster. >>And it's harder to quantify. I think if you could, you know, over time, see I bought 20% less server, uh, capacity or, you know, x86 capacity, that's a savings. And the other one that's very hard to quantify, but it's always nice to have the development community. And we've had it recently where they say, Hey, this took me a month to deploy instead of a year. Um, and you know, the purchase cycles, uh, you know, for procurement and deployment, they're long, you know, in enterprise you want them to be quick, but they're really not. So all of those things add up. And that's the story. You know, I would tell, you know, any manager, right? Yeah, >>yeah. I think, you know, the old historic way that utilization rates were just so, so, so, so low between CPU and memory, everything else. Cause if nothing else, because to get another box, you know, could take a long time. Yeah. Well, final, final question for you, Tony. You talked about architectures and being locked into architectures and you and you talked about you guys are already looking forward, you know, to kind of your next rev, your next release, kind of your next step forwards. What, where do you see kind of the direction, don't give away any secrets, but um, you know, kind of where you guys going. What are your priorities now that you've launched? You got a little bit more money in the bank. >>Well, our biggest priorities will be to focus on customer success is to make sure that the customer journey is indeed replicable at scale, is to enable the partner's success. Uh, so in addition to Goldman Sachs, the ability to go and replicate it across the federated markets, whether it's global financial services, healthcare, federal, and partnering with each B enterprise so that they can on their platform, amplify the value of this architecture, not just on the compute platforms but on, in other areas. And the third one clearly is for our cloud customers is to make sure that they are in a position to build a world class cloud architecture on top of which then they can build their own, deliver their own services, their own secret sauces, uh, so that they can Excel at whatever that cloud is. Whether it's to become the leading edge platform as a service customer, whether it is to be the leading edge of software's a service platform customer. So it's all about the execution as a, as you heard in that room. And that's fundamentally what we're going to strive to be, is to be a great execution machine and keep our heads down and focused on making our customers and our partners very successful. >>Well, certainly, congratulations again to you and the team on the launch today. And Josh, thank you for hosting this terrific event and being an early customer. Yeah. Yeah. Happy to be. Alright. I'm Jetta. Sone. Josh, we're the topic. Goldman Sachs at the Penn Sandow the new welcome to the new edge. Thanks for watching. We'll see you next time.