(Upbeat music) >> From the Hard Rock hotel in Las Vegas, its theCUBE! Covering the Hosho Con 2018, brought to you by Hosho. >> Okay, welcome back every one, this is theCUBE's exclusive coverage here live in Las Vegas for Hosho Con, the first inaugural event where security and block chain conferences is happening, it's the first of its kind where practitioners and experts get together to talk about the future, and solve some of the problems in massive growth coming they got a lot of them. Its good new and bad news but I guess the most important thing is security again, the first time ever security conference has been dedicated to all the top shelf conversations that need to be had and the news here are covering. Our next guest Greg Pinn who's the head of strategy and products for iComply Investor Services. Great to have you thanks for joining us. >> Very nice to be here >> So, we were just talking before we came on camera about you know all the kind of new things that are emerging with compliance and all these kind of in between your toes details and nuances and trip wires that have been solved in the traditional commercial world, that have gotten quite boring if you will, boring's good, boring means it works. It's a system. But the new model with Block Chain and Token Economics is, whole new models. >> Yeah I think what's so exciting about this is that in the Fiat world, from the traditional financial market, everyone is so entrenched in what they've been doing for 20, 30, 40 years. And the costs are enormous. And Block Chain, Crypto coming in now is like we don't have to do it that way. We have to do compliance. Compliance matters, it's important and it's your legal obligation. But you don't have to do it in the same sort of very expensive, very human way that people have been doing it in the past. >> And Cloud Computing, DevOps model of software proved that automations a wonderful thing >> Right >> So now you have automation and you have potentially AI opportunities to automate things. >> And what we've seen is huge increases in technology, in around machine learning and clustering of data, to eliminate a lot of the human process of doing AML, KYC verification, and that's driving down costs significantly. We can take advantage of that in the Crypto Space because we don't have thousands of people and millions of millions of dollars of infrastructure that we've built up, we're starting fresh, we can learn from the past and throw away all the stuff that doesn't work, or isn't needed anymore. >> Alright let's talk about the emerging state of regulation in the Block Chain community and industry. Where are we? What's the current state of the union? If you had to describe the progress bar you know with zero meaning negative to ten being it's working, where are we? What is the state of >> I think if you'd asked me a year ago I think negative would've been the answer. A year ago there was still a big fight in Crypto about do we even want to be part of Compliance, we don't want to have any involvement in that. Because it was still that sort of, Crypto goes beyond global borders, it goes beyond any of that. What's happened now is people have realized, it doesn't matter if you're dealing in Crypto Currency or traditional currency, or donkeys or mules or computers or whatever, if you're trading goods for value, that falls under Regulatory Landscape and that's what we're hearing from the SCC, from FinCEN, from all the regulators. It's not the form it's the function. So if you've got a security token, that's a security, whether you want it to be or not. You can call it whatever you want, but you're still going to be regulated just like a security. >> And I think most entrepreneurs welcome clarity. People want clarity, they don't want to have to be zigging when they should be zagging. And this is where we see domicile problem. Today it's Malta, tomorrow it's Bermuda. Where is it? I mean no one knows it's a moving train, the big countries have to get this right. >> A hundred percent. And beyond that what we're seeing, what's very, very frustrating for a market as global as this is it's not just country-level jurisdiction, the US you've got State-level jurisdiction as well. Makes it very, very hard when you're running a global business if you're an exchange, if you're any sort of global, with a global client reach. Managing that regulation is very, very difficult. >> You know I interviewed Grant Fondo who's with Goodwin Law Firm, Goodwin Proctor they call it Goodwin now, he's a regulatory guy, and they've been very on the right side of this whole SCC thing in the US. But it points to the issue at hand which is there's a set of people in the communities, that are there to be service providers. Law Firms, Tax, Accounting, Compliance. Then you got technology regulation. Not just financial you have GDPR, it's a nightmare! So okay, do we even need GDPR with Block Chain? So again you have this framework of this growth of internet society, now overlaid to a technical shift. That's going to impact not only technology standards and regulations but the business side of it where you have these needed service providers. Which is automated? Which isn't automated? What's your take on all of this? >> I agree with you a hundred percent, and I think what's helpful is to take a step back and realize while compliance is expensive and a pain and a distraction for a lot of businesses. The end of the day it saves people's lives. And this is what, just like if someone was shooting a gun as you were running down the street, in your house, you're going to call the police, that is what financial institutions are doing to save these industries and individuals that are impacted by this. A lot of it from a Crypto Currency perspective, we have a responsibility because so much of what the average person perception is, is Ross Ulbricht and Silk Road. And we have to dig our way out of that sort of mentality of Crypto being used for negative things. And so that makes it even more important that we are ultra, ultra compliant and what's great about this is there's a lot great opportunities for new vendors to come into the space and harness what existed whether that's harnessing data, different data channels, different IDDent verification channels and creating integrated solutions that enable businesses to just pull this in as a service. It shouldn't be your business, if you're in exchange, compliance is something you have to do. It should not become your business. >> Yeah I totally agree, and it becomes table stakes not a differentiator. >> Exactly >> That's the big thing I learned this week it's people saying security's a differentiator, compliance is a, nah, nah, I have standards. Alright so I got to ask you about the, you know I always had been on the biased side of entrepreneurship which is when you hear regulations and you go whoa, that's going to really stunt the growth of organic innovation. >> Right. But in this case the regulatory peace has been a driver for innovation. Can you share some opinions and commentary on that because I think there's a big disconnect. And I used to be the one saying regulation sucks, let the entrepreneurs do their thing. But now more than ever there's a dynamic, can you just share your thoughts on this? >> Yeah, I mean regulators are not here to drive innovation. That's not what their job is. What's been so interesting about this is that because of regulations coming to Crypto along with these other things, it's allowing businesses to solve the problem of compliance in very exciting, interesting ways. And it's driving a lot of technologies around machine learning, what people like IBM Watson are doing around machine learning is becoming very, very powerful in compliance to reduce that cost. The cost is enormous. An average financial institution is spending 15 percent. Upwards of 15 percent of their revenue per year on compliance. So anything they can do to reduce that is huge. >> Huge numbers >> And we don't want Crypto to get to that point. >> Yeah and I would also love to get the percentage of how much fraud is being eaten into the equation too. I'm sure there's a big number there. Okay so on the compliance side, what are the hard problems that the industry is solving, trying to solve? Could you stack rank the >> I think number one: complexity. Complexity is the biggest. Because you're talking about verifying against sanctions, verifying against politically exposed persons, law enforcement lists, different geographical distributions, doing address verification, Block Chain forensics. The list just stacks and stacks and stacks on the complexity >> It's a huge list. >> It's a huge list >> And it's not easy either. These are hard problems. >> Right, these are very, very difficult problems and there's no one expert for all of these things. And so it's a matter of bringing those things together, and figuring out how can you combine the different levels of expertise into a single platform? And that's where we're going. We're going to that point where it's a single shop, you want to release an ICO? You're an exchange and you need to do compliance? All of that should be able to be handled as a single interface where it takes it off of your hands. The liability is still with the issuer. It's still with the exchange, they can't step away from their regulatory liability, but there's a lot that they can do to ease that burden. And to also just ignore and down-risk people that just don't matter. So many people are in Crypto, not the people here, but there's so many people in Crypto, you buy one tenth of a Bitcoin, you buy a couple of Ether, and you're like okay that was fine. Do we really need to focus our time on those people? Probably not. And a lot of the >> There's a lot big money moving from big players acting in concert. >> And that's where we need to be focused. Is the big money, we need to be focused on where terrorists are acting within Block Chain. That's not to say that Block Chain and Crypto is a terrorist vehicle. But we can't ignore the reality. >> And I think the other thing too is also the adversary side of it is interesting because if you look at what's happening with all these hacks, you're talking about billions of dollars in the hands now of these groups that are highly funded, highly coordinated, funded basically underbelly companies. They get their hands on a quantum computer, I was just talking to another guy earlier today he's like if you don't have a sixteen character password, you're toast. And now it's twenty four so, at what point do they have the resources as the fly wheel of profit rolls in on the hacks. >> You know, one of the interesting things we talk about a lot is we have to rely on the larger community. We can't, I can't, you can't solve all of the problems. Quantum computing's a great example. That's where we look for things like two-factor authentication and other technologies that are coming out to solve those problems. And we need to, as a community, acknowledge That these are real problems and we've identified potential solutions. Whether that's in academia, whether it's in something like a foundation like the Ethereum Foundation, or in the private sector. And it's a combination of those things that are really driving a lot of it's innovation. >> Alright so what's the agenda for the industry if you had to have a list this long, how do you see this playing out tactically over the next twelve months or so as people start to get clarity. Certainly SCC is really being proactive not trying to step on everybody at the same time put some guard rails down and bumpers to let people kind of bounce around within some frame work. >> I think the SCC has taken a very cautious approach. We've seen cease and desist letters, we've seen notifications we haven't seen enormous finds like we see in Fiat. Look at HSBC, look at Deutsche Bank, billions of dollars in fines from the SCC. We're not seeing that I think the SCC understands that we're all sort of moving together. At the same time their responsibility is to protect the investor. And to make sure that people aren't being >> Duped. >> Duped. I was trying to find an appropriate term. >> Suckered >> Suckered, duped. And we've seen that a lot in ICOs but we're not seeing it, the headlines are so often wrong. You see this is an ICO scam. Often it's not a scam, it's just the project failed. Like lots of businesses fail. That doesn't mean it's a scam, it means it was a business fail. >> Well if institutional investors have the maturity to handle they can deal with failures, but not the average individual investor. >> Right, which is why in the US we have the credit investor, where you have to be wealthy enough to be able to sustain the loss. They don't have that anywhere else. So globally the SCC care and the other financial intelligence units globally are monitoring this so we make that we're protecting the investor. To get back to your question, where do I see this going? I think we're going to need to fast track our way towards a more compliant regime. And this I see as being a step-wise approach. Starting with sanctions making sure everyone is screened against the sanction list. Then we're going to start getting more into politically exposed persons, more adverse media, more enhanced due diligence. Where we really have that suite of products and identify the risk based on the type of business and the type of relationship. And that's where we need to get fast. And I don't think the SCC is going to say yeah be there by 2024, it's going to be be there by next year. I was talking to Hartej, he was one of the co founders of Hosho and we were talking on TheCUBE about self-regulation and some self-policing. I think this was self-governed, certainly in the short term. And we were talking about the hallway conversations and this is one of the things that he's been hearing. So the question for you Greg is: What hallway conversations have you overheard, that you kind of wanted to jump into or you found interesting. And what hallway conversations that you've been involved in here. >> I think the most interesting, I mentioned this on a panel and got into a great conversation afterwards, about the importance of the Crypto community reaching out to the traditional financial services community. Because it's almost like looking across the aisle, and saying look we're trying to solve real business problems, we're trying to create great innovative things, you don't have to be scared. And I was speaking at a traditional financial conference last week and there it was all people like this Crypto is scary and it's I don't understand it. >> You see Warren Buffett and Bill Gates poopooing it and freak out. >> But we have an obligation then, we can't wait for them to realize what needs to be done. We need to go to them and say, look we're not scary, look let's sit down. If you can get a seat at a table with a head of compliance at a top tier bank, sit down with them and say let me explain what my Crypto ATM is doing and why it's not a vehicle for money laundering, and how it can be used safely. Those sorts of things are so critical and as a community for us to reach across the aisle, and bring those people over. >> Yeah bridge the cultures. >> Exactly. Because it's night and day cultures but I think there's a lot more in common. >> And both need each other. >> Exactly. >> Alright so great job, thanks for coming on and sharing your insights. >> Thank you so much. >> If you have a quick plug on what you're working on, give the plug for the company. >> Sure, so iComply Investor Services is here to help people who want to issue ICOs, do that in a very compliant way. Because you shouldn't have to worry about all of your compliance and KYC and Block Chain Forensics and all that, you should be worried about raising money for your company and building a product. >> Alright final question since I got you here 'cause this is on my mind. Security token, has got traction, people like it 'cause no problem being security. What are they putting against that these days, what trend are you seeing in the security token? Are they doing equity? I'm hearing from hedge funds and other investors they'll want a little bit of equity preferred and or common, plus the token. Or should the token be equity conversion? What is some of the strings you're seeing? >> You know I think it' really just a matter of do you want paper or do you want a token? Just like a stock certificate is worth nothing without the legal framework behind it. A security token is the same way. So we're seeing where some people are wanting to do equity, where some of their investors want the traditional certificate. And some are fine with the token. We're seeing people do hybrid tokens where it morphs from security to utility or back. Where they're doing very creative things. It's what's so great about the Ethereum Network and the Smart Contracts, is there are all of these great options. The hard part then is, how do you fit those options into regular framework. >> And defending that against being a security, and this is interesting because if it converts to a utility, isn't that what security is? >> So that's the question. >> Then an IPO is an, again this is new territory. >> Right, and very exciting territory. It's an exciting time to be involved in this industry. >> In fact I just had an AE3B Election on tokens, first time ever. >> Yeah it's an amazing state that we're in. Where serious investors are saying yeah token's great for me. Give me the RC20 I'll stick it in my MetaMask Wallet, it's unbelievable where we are. And only more exciting things to come. >> Greg Pinn, thanks for coming on and sharing your insights. TheCUBE covers live here in Las Vegas, Hoshocon, the first security conference in the industry of its kind where everyone's getting together talking about security. Not a big ICO thing, in fact it's all technical, all business all people shaping the industry, it's a community it's TheCUBE coverage here in Las Vegas. Stay with us for more after this short break. (Upbeat music)

>> Live from Bellevue, Washington it's theCUBE covering SmartSheet ENGAGE '18. Brought to you by SmartSheet. >> Welcome back to theCUBE, we are continuing our coverage live from Bellevue, Washington. We're at SmartSheet ENGAGE 2018. I'm with Jeff Frick here. This is the second annual ENGAGE event. Huge, doubled from last year. We've had a great day so far, Jeff, of execs from ShartSheet, customers. We're now excited to welcome an analyst from the 451 Group, Chris Marsh, the Research Director for WorkFresh Productivity and Compliance; welcome to theCUBE! >> Thank you very much. >> So we have, as I was saying, this is the second annual event, some of the stats that Mark Mader, our CEO, shared in the keynote this morning, there are over 1100 companies represented here at the event, a couple thousand people, 20 countries. We've had some very enthusiastic SmartSheet customers, SmartSheeters themselves talking about this tool that's designed for the business user, that's not designed for the citizen developer, people that don't even need to know what API stands for. So talk to us about your role at 451, but then we'll kind of get into project management, program management, and some of the trends and the changes that you're seeing there. >> Sure, yeah, so, so I've had the workforce productivity compliance research practice in 451, so as a team of analysts, we cover, essentially, productivity software, right? So the different tools that members of the workforce are using to get work done. So in addition to work management companies like SmartSheet, we also look at collaboration tools, digital workspaces, we cover the content management landscape, and we cover content creation, asset creation tools as well, so, really the focus for my team is to give perspectives on how the future of work is evolving, but really what those technology and dependings of that are. >> Such a busy space. You've picked a good area to specialize in. So how should people think of it? How should they categorize it, because from the outside looking in, a lot of the tools are very similar, you know, there's some overlap, some not overlap, there's some places where they can work together. Ya know, how should leaders be thinking about approaching this opportunity, 'cos you talk a lot about, you know, that's a great place to find untapped competitive advantage, but it seems to be very, kind of, confusing to the outsider. >> Yeah, it's a super interesting space, and it's probably more interesting than it's ever been. I think for many of us, it was, to be frank, kind of not interesting, right? There's lots of kind of legacy tools that people were struggling to figure out how to do new kinds of work with. >> Beyond email! (chuckles) >> Exactly, yeah, yeah. >> Was there anything beyond email 10 years ago? Yikes! >> Exactly, I mean we as a, I think, a research team find ourselves looking as much of intersections of those five areas that we cover as much as we go deep in them, for the very fact that, you know, it's a space that's going through a lot of innovation, a lot of disruption, and vendors and segments are learning from one another. Then, of course, we have, you know, broad, kind of transversal trends brought by technologies like AI and machine learning. Beginning to have more conversations around things like Block Chain, people beginning to talk about what may be some of the use cases are around AR, VR, and the kind of mixed-reality type technologies. So, you know, lots of innovation, lots of disruption. Um, in terms of what business leaders should be looking for, it obviously depends on what they're trying to do in their workforce. I think one of the big shifts that we're seeing is, um, you know, the sort of decentralization of ownership over more complex types of work to business users, right, whereas, I think, in a lot of companies, traditionally there are centralized teams of process specialists or project management folks, and, you know, tools have kind of mediated the relationship between those centralized, you know, teams and business users; where increasingly those tools are appealing to those business users. So in the panel moderation I did this morning, I showed some statistics around, you know, users of tools like SmartSheet, and it's not the type of people we would have seen using this type of tool four or five years ago. It's leaders were then legal teams, finance teams, HR teams, marketing teams, operations teams-- so that's sort of reflective of a broad shift in productivity software of, you know, virality in terms of how these tools enter businesses, right? Lots of organic adoption and it kind of runs contrary to how a lot of enterprise technology gets sold into enterprises, which is gone a little bit more top down or into specific buying centers. Increasingly, it's going in sort of the grass roots. People are finding new use cases for technology, and it sort of spreads from there, so, yeah, it's a super hot space right now. >> So one of the things we talk about is every place we go, right? Digital transformation and innovation, everybody wants more. And it seems pretty simple to say, but hard to do, that if you get more people more data, the tools to process it, and then the power to do something, that that just can unlock a tremendous amount of untapped innovation and execution and efficiency out of a company. That said, that's easier said sitting here than done. So are you seeing, you know, kind of a continual trend towards, you know, pushing down the data, pushing down the tools, and pushing down the authority to execute decisions? >> Yeah, I think so. And actually, the work management space is a very good example of that, right? So, um, you know, for some companies culturally that's not going to come very easy, because they just culturally may have a more sort of top-down kind of culture. But I think digital transformation for everyone, essentially means more agility, more speed, you know, more quickness in how work is executed and how it's designed. And that almost inevitably means that those closest to the delivery of the work are the ones that actually have the power to design the work in the first place and can, rather than sort of relying on IT for everything and/or central teams somewhere. So, it is a broad shift, but again, it comes, to your point, it comes more easily for some companies and some industries than others. >> And we talked about that with a number of the people from SmartSheet as well as users, that this is a massive cultural shift. I think Mark Mader, the CEO, this morning was telling us a quick anecdote of a 125-year-old oil and gas company, >> Yeah. >> That is, talk about, you know, probably really married to a lot of legacy processes and ways of thinking, not just tools, and how SmartSheet probably started in, you know, one function within the organization, probably, you know, quite low, and it started, to your point before, go viral, and we started, we started to hear a number of stories from PayPal, Sodexo, how this virality that you talk about is really kind of transforming from the bottom up. But that cultural change is essential. >> The cultural change is essential, I mean, in some cases it's just being led by the fact that that's happening anyway, right? Because, you know, gone are the days when IT chooses the tools, provisions them, and, you know, there's an awareness of what's going on in the environment. There are, and it's not just the work management space, we also look at sort of, workflow automation tools. A lot of these tools are, you know, going into a company grass roots, there are then potentially hundreds if not thousands of work processes or workflows that are created on these tools before IT even figures that out, right? Which is not necessarily an ideal scenario, but it's increasingly, you know, one of the patterns that we're seeing in enterprises, so. It's a big cultural shift, but um, there's a certain amount of push and pull here. Some companies that realize that are looking proactively to give effect to it. Other people are going to be pulled, to be frank, to the fact that there are tools that enable new kind of work patterns, new styles to happen, and they almost have to get on board with that, so. So obviously you want to strike a balance, I think, somewhere in between of being the catalyst for those kind of new things to happen whilst making sure there is still the kind of centralized oversight that's required for you to maintain control over your overall technology estate, but also so that you can make sure the technologies are aligning to your strategic goals. So it's a delicate balance. >> And there's these pretty big forces at play here. There's a term that 451 Group has recently coined called a liquid enterprise. >> That's right, yeah. >> Liquid; I think of fluidity, you mentioned agility, we've heard nimbleness today, um, talk to us. What, by definition, is the liquid enterprise, and how are you helping customers to embrace it and maybe not fight the force, because the forces of pull are stronger and better; but what does that mean? >> Yeah, so liquid enterprise, I mean, you've encapsulated it very well, right? So it's all about, you know, when we speak of digital transformation, you almost always end up to about business agility. So in some ways, liquid enterprise is just our way of giving a little bit more flavor to what business agility looks like in the kind of digital age. So our kind of view is that, you know, a lot of the companies that we kind of laud now as those really interesting companies like the AirBnB's and the Uber's, those with kind of, massively scalable infrastructure and then a very simple UI. We think that whole pattern of what the, kind of, digital enterprise will look like is one that's much more able to fluidly marshal it's different resources in a way that allows them to respond much more rapidly to changes in their own market conditions, right? Because one of the things, obviously, that digital is doing is changing user behavior to user requirement. So your ability, as a company, to respond very quickly to that is becoming, you know, a primacy in most companies, and a big part of how we think about the liquid enterprise is the fact that companies will actually be able to change their own organizational structure. Not just what they offer to a market, not just the tools that enable them to do that, but actually, they'll begin to sort of re-tesselate their own organizational design, to enable that to happen. So, you know, we see early indicators of technologies that are beginning to allow companies to think in that way. I think for most companies, liquid enterprise is aspirational right now, but I think, certainly, it's a pattern a lot of companies are trying to tact towards. >> So, I'm just curious, you talk about culture as a competitive advantage. And how much of these tools are culture enablers to make that possible? How much of it are just critical, because if you don't have that culture you're going to lose? How much of it is tied to, kind of, the consumerization of IT, where again, your workforce has an expectation of the way apps work based on their interaction with Amazon and their interaction with Google and those types of things? >> Very much driven by the consumerization of IT trend. I mean, often, increasingly what we see happen in the consumer realm ends up happening in some kind of expression in the enterprise realm sooner or later. So, yeah, that's very much it. One of the other things we talk about in our research is the kind of hierarchy of employee motivation, right? So we kind of have this way of thinking about, you know, what companies need to do and what technologies need to enable to really satisfy that end user experience. I think in the productivity software space, you know, it's probably not hyperbolic to say that most tools really only satisfy end users, right? We have lots of tools, including lots of modern SAAS tools, that actually, you know, may have good usability, but aren't particularly flexible. There sort of better, more scalable versions of a lot of legacy tools. So we see this kind of passage towards tools actually doing things like, you know, decentralizing the ability to create workflows, so that, you know, business users, including non-managerial folks, can actually design work, and how that work actually happens, right? So there's a big element there in terms of motivation in your role, you know, actually making an impact, having that recognized and all of those kinds of things, which is driving a more, sort of, engaged relationship between people and technology, so we only see that continuing. And, the work management space in SmartSheet's very good examples of that. There's lots of conversations you can hear and engage where people are discussing, you know, what they're doing with their tool that they created themselves, some kind of local business team that has redesigned a certain process that is allowed better business value to be created; and they're the ones that are going to take credit for that. I think that trend is only going to accelerate. So again, from an enterprise perspective, embracing that, helping catalyze that, but again, having the ability to have central oversight over that kind of local team-based execution, it is obviously very important. >> What about just kind of the competition from my desktop? You know, what apps are open while I'm working all day, and you know, we all wish if you're driving an app company that it's your app that is on top, but the reality is many, many apps open all the time. So do you see that evolving, do you see that aggregating, do you see a couple of kind of uber apps over the top of these integrations that you'll be doing your primary workplace, or is it just kind of horses for courses depending on the types of things that you do in your day-to-day job? >> Really good question, I mean, I think one of the background trends we've seen, especially with SAAS, is just the growth and the overall enterprise application estate. Right, so just more apps. And obviously catalyzed also by end users having positive experiences in consumer apps, and then being used to choosing the way that they do things, like that, that is transitioning into the enterprise environment, as well, so. I don't envisage that the total number of apps is going to decrease, but very good question as to, you know, whether we get consolidation. Time will tell, but I think, you know, to my point earlier, we spend a lot of time looking at intersections that cross existing segments, because, each segment is really transforming. And you see lots of examples of customers here at ENGAGE using SmartSheet as a displacement tool for other ones that they previously were using. They find the automation of SmartSheet a way to sort of disintermediate other tools that they were using. We're certainly seeing some of that, whether that means the total number of applications decreases, I don't know, because we're still yet to see play out lots of cool, new, innovative technologies that will obviously give rise to new kinds of applications. Question is out as to whether it will mean further apps, but we certainly seeing a changing in the, in the sort of preference for tools based on what new ones we're enabling. >> And I would imagine in very short order, the application of AI and machine learning behind the scenes in all these apps, is also going to change the UI experience dramatically, as more and more and more of the processes are automated on the back-end, there's more kind of smart suggestions as to what to do or completely automated processes. So even the face of the most popular apps today, I would imagine you see significant change with the application of AI and machine learning. >> Yeah, I would think so. One of the, sort of, big trends here, listening to customers and listening to some of the key notes, is, you know, the shift that comes with companies trying to make from low value work to high value work, so all of that kind of granular and manual work we're having to do is so most existing applications; people just want to abstract their way. They don't want to be doing that anymore, they want to be focusing on, um, sort of resource management, team coordination, creative ideation, they want to focus on strategy execution, they want to focus on things like, you know, risks to the business, actions that they need to take, decisions that they need to make, they don't want to be doing the whole, um, who did this, when did they do it, what do I need to do now, they don't want to be sort of manually moving information from applications, they don't want to be doing sort of manual reconciliations of data, and that kind of thing. >> Right. >> So um, heh, so yeah, the kind of low value to high value work is only going to be accelerated by AI and ML, to the point where we're beginning to see much more contextual work. So the ML is the basis on which work can be surfaced contextually to end users. So that is sort of automating the abstraction of that low value work, and that's hugely exciting, because that offers a whole new paradigm for how we interact with applications, what that end user experience is. Imagine, you know, sort of going into your office loading up your computer, opening up an application, and it surfaces to you what you need to focus on that day. >> Right. >> That's where a lot of productivity application vendors are trying to get to. >> That's the dream, right there. >> Not here is the application, you decide where you need to focus, it's the kind of, these are the things you really need to put your time in. I mean, that's pretty exciting. And that's what a lot of the companies would want. >> Well even, a certain CRN company that's got a large tower in San Francisco, why do I have to put the city and the state and the zip code, I mean, we have so far to go, can't I just put the zip code in and it fills in the city and the state, and those little, you know, simple things that take a lot of time and these are the kind of data entry tasks that just drive people bananas, and discount the value, the fundamental value of the tool, because you just get stuck in a data entry mode, or a double entry mode. It's this crazy opportunity that we still have in front of us to make improvements. >> Yeah, I think, huge opportunity, obviously. But it's not quite so easy as that, I think, really it's kind of how I would talk about it. You know, AI and ML will inevitably have a transformative impact on enterprise software; I don't think anybody would dispute that. But it does rely on large data sects, against which you have to train your algorithms and your models, and that takes time for individual companies to build that data sect. They need enough work in there, they need enough people, enough workflows in there, to generate those data sects so that they will actually be useful, right? So, it's going to take a bit of time to play out. But yeah, it's going to be very impactful in the longer term. >> Well Chris, thanks so much for stopping by theCUBE and sharing your insights on this new, emerging term of the liquid enterprise, we appreciate that. >> Pleasure, thanks very much. >> For Jeff Frick, I'm Lisa Martin, you're watching theCUBE live from SmartSheet ENGAGE 2018. Stick around, Jeff and I will be right back with our next guest. (electronic music)

>> Live from Washington DC, it's theCUBE. Covering Inforum DC 2018. Brought to you by Infor. >> Well good afternoon, and welcome back here on theCUBE as we continue our coverage here at Inforum 2018, live from Washington, DC. We're in the Washington Convention Center centrally located, I got to tell ya. The White house less than a mile that way, Capital Hill's just right up the street as well. We're kind of caught in the middle. Bad spot to be these days! (laughing) >> I hope you're not setting the tone for this. >> We'll leave that alone. >> I like being in the middle, personally. (laughing) I'll take it from both sides. >> When you sit in the middle of the road, there's a six inch yellow line, you get it equally hard from both sides. >> Bring it on! >> So, lets stay away from that. Dave Vellante, John Walls and Rod Johnson, who is the EVP of Manufacturing and Supply Chain, and the GT Nexus Business Unit at Infor. Rod, good to see you Sir! >> Great to be here, thanks guys. >> You're okay with being in the middle? >> Yeah, sure! Yeah, of course. >> Independent thought, right? I love it. >> Middle of the road. It's the place to be. >> So you're the new kid on the block, right? >> One of them, yeah. >> You've been here, just at Infor for a few months now, assuming the EVP role. How's it been for you so far? >> Hey, it's been a breath of fresh air. I was 11 years with one of our competitors, the Oracle Corporation. Its quite a breath of fresh air. Go with a company that's agile, innovative, much more customer centric. I think the timing is perfect for a company like Infor, that's really grown up in these key industries and working with customers for over decades. Now its made this transition to the Cloud, and now I think all the markets are waking up. It's not just CRM or HR, they're looking at: How do I take advantage of all this innovation, the Cloud's the platform, and who's the companies that really understand our type of business, whether you're a distribution company, or a food company, or an A&D Company. So it's a great time to be here, there's a lot of good energy, a lot of good innovation. A lot of good buzz from the customers about what we're doing. >> Necessity is the mother of invention, as the saying goes. I mean, you're right. The model of just having an install base that you can have locked in and just keep milking is very hard to do these days. Unless, you know, some of the private equity guys have done it, that's clearly not the case here at Infor. You know, Oracle is successful at it. I think it's because they do spend a lot of money on R&D, but boy oh boy! That model, you can't just go and reinvent that. >> Right. >> You're going to fail. >> Right. >> And if you're trying to hold on to that model, maybe they're the exception that proves the rule, but you're going to be toast. You know, in the long run. So you see what Amazon's doing, you see what Microsoft; how Microsoft completely pivoted away from that model. >> Right, Right. >> And Infor's riding that wave. >> Right, right. Hey, this is a business model. Fundamental business model change. You know, we can talk a lot about the technology, but transitioning from a product company that sells a license that sits on a maintenance base is a model that's no longer viable for what customers expect. They want a service provider that's delivering continuous innovation in service, and that's a big change. That's a big change to how we engage with our customers, how we support them, the service levels we're committing to. So, I lived through a bunch of that stuff at Oracle, transitioning to the cloud had a role for the last six years; doing that both from a sales and a global strategy role. Here, we're trying to do it better, faster, and never lose sight of the customer. >> So, you've serviced the manufacturing sector. >> Yeah, yeah. >> It's still a lot of Infor's business that install base and that maintenance. You're in the process of transitioning those customers. >> Yeah. >> So, that takes a lot of care, a lot of feeding, cause anytime there's a transition everybody wants a piece of that action. So how's that going, what's the conversation like, and why should they stay with Infor? >> The conversation is, One: We really believe in a pragmatic business-led path to the Cloud. There's not going to be any forced march, no technology agenda that's going to drive us. It's got to be driven by value. We've got to present a business case to them that makes sense. That makes them more productive, now allows them to better engage with their customers, delivers innovation to their supply chain. So that's what we're spending a lot of time talking about. What's the case for change? What's the business case for change? I mean, all of the stuff about operating the Cloud, the service levels, potential total co-services, great! But, at the end of the day we deal with pretty, we're dealing with manufacturers. They're pretty down to earth. They know that they make their money building stuff, and shipping stuff and servicing that product. So we got to be engaged at that level, to show them how we help them do that better. I think the excitement is growing. That they recognize that there is real net new business value, new innovation that could really help their business. >> So lets talk about that. Forced march is a powerful phrase and you certainly see that in the industry. Thinking about supply chain, and the opportunities to drive even more efficiencies out of the supply chain, maybe through automation, we've heard a lot about RPA. >> Yeah. >> Maybe even bring back some of that offshore manufacturing. >> Right. >> That's certainly a conversation >> Right. >> that's going on in your world, so talk about that a little bit. >> Yeah, so one of our diamonds in the Infor portfolio is a product called GT Nexus. Which is, its been around for about 20 years. We have 65,000 companies around the world who are operating on a common network based platform that provides supply chain visibilities, supplies supply chain financial processing. Connects brands to their manufacturers to provide all the visibility and control and that. So, that's a powerful capability because you're right, it's an incredibly dynamic time. With the change of trade wars, weather events that are ever increasing. The supply chain's a very hard thing to manage. So if the asset is we've got a platform that enables companies to connect much deeper in their supply chain then use that information to make far better decisions on how they get their products to their customer at the right cost. So, and I see, you know the supply chain market, I always think of this transition to Cloud in waves. You know, we had the first wave breaking was the sales. Then the HR, the finance function. Operations in supply chains is the one that's cresting on the horizon. And you know, keep that going, we've got our surfboards in the water, we've got great capability. And we're really, really excited about what we can do for our clients! >> You got to ride the waves or you'll become driftwood >> How big is that wave? >> Well, hey that's the biggest market, right? I mean, you look at the size of the Enterprise software spend. Core ERP supply chain industry functionality is the big piece. It's probably two. It's probably by an HR, CRM, financials together, and it's not even as big as sort of the industry supply chain, manufacturing, procurement market core ERP market. So, its big! Its a big opportunity, but it requires a much more sophisticated response because you talk to our customers they're like hey, we operate our plants 365 days a year, three shifts sometimes in peak seasons. We can't afford an environment that isn't mission critical, that doesn't step up to service levels. So, you know, we're working really hard to address the mission critical system challenge, not just the benefits and payroll. >> So, there's certainly an opportunity with AI, with machine learning, certainly more analytics, bringing that to the manufacturing world. >> Oh yeah. >> So that's clearly fundamental to your strategies. >> Yeah. >> Is that, in your view, the tipping point to get really this whole market moving? >> I think. I mean I would agree with you. Its sort of an accumulation of digital capabilities. Certainly, mobility's sort of proved that its important, but its a little bit of a nice-to-have. Some of the innovations around user experiences, is really important but nice-to-have. I think that is the game changer. When you can use data as a weapon, a competitive weapon that you can make decisions faster, and how you discount your product or how you identify shortage faster than someone else. That's where, there's real money that comes out of that. >> What about Block Chain? We hear a lot about Block Chain in the supply chain and cutting out the middle man. We haven't heard much here about it, its not something. We're going to ask Charles. Somebody said to me, Once Charles gets on it, boom the company is behind it. >> Yeah. >> But, how real is that in manufacturing and supply chain specifically? Is it just way too early? Do you think there's potential there? Have you looked at it? >> Obviously we've looked at it, we've worked on with customers on prototypes. There's a couple areas, you know, there's a lot of hype as you guys know. You talked to a lot of us, a lot of hype in that space. It's certainly unproven in a lot of areas. But we think in the area of supply chain financing, Block Chain has a very, very powerful, you know, where you have multi parties, you've got suppliers and logistics companies and banks all who need a piece of information. We need distributed capabilities around that. We think there's a big potential in some of that area. We're talking. We're working with some of the banks on that. We think in the area of getting deeper into the supply chain around sustainability, to the ethical and traceability of the Supply Chain. You know, where you're goin down. Yeah we got customers in the pero business that are going down to the farms. They want to know exactly the lineage of all of their stuff that's going into their product that's ending up in a consumer. That's potentially a more efficient mechanism, to have all these different entities collaborating on a distributed model. So, I mean; and especially if we talk about the GT Nexus Network. There's natural extensions to it. That it already is a common platform that is serving a wide variety of companies, logistics companies, and manufacturers. So there's a lot of natural exit points from that, sort of, that integrated network to support a couple of these more extended processes that are a little bit more distributed. >> Yes, the smart contracts maybe fits there, and you talked about distributed a couple of times. What about IOT? The pendulum seems to be swinging now. Obviously Cloud is hot. Its got a re-centralization. But IOT's a whole new world. You get a lot of IT companies kind of pushing the IT model top down into operations technology and we don't think that's the way it's going to work. That the OT guys are actually going to drive the standards and the trends. What are you seeing? >> Well I think yeah hey, the people that have the, that make the equipment, you know, make the pipelines. Hey, obviously they got a big stake in this. You know, they understand how their kipid works, they know how to attach the sensors. They know how to translate things that are going on in the machines into data. We're going to be, and we're going to be taking that data, and how do you connect it to a business process. That's something that they don't understand. They don't understand how a heat event could translate, could connect to a maintenance process and how do you deploy a technician with the right part to go in there so they can offer some proactive service? So I think there's going to be a very tight partnership, where people coming from the equipment up, or the asset up, connect with the people that understand process analytics and sort of execution. >> Yeah. You talked about sustainability there just a moment ago, so obviously companies, their focus is changing in that regard. Right? People are paying more attention, a lot of that is being customer driven. >> Right >> At the same time too, in terms of distribution, in terms of manufacturing, customer expectations are changing too. Right? >> Right. >> We expect things on a much different time table. >> Absolutely. >> So how are you helping your clients recognize all those things? Like you're thinking about tomorrow today, and trying to get them to address that in terms of their technology plays down the road to meet these really fast changing demands. >> Yeah, I mean one of our really dominant industries is distribution. You know, probably three out of five distribution companies around the world run our software. So distribution is a space, typically between the manufacturing world and the consumer or the retail world is under tremendous pressure. While Amazon is inching into distribution centric industry so there's a lot of pressure from that, but there's also rising expectations that you have to do instantaneous fulfillment. That you have to provide complete visibility into where my order is, when am I going to get it, because I don't want to carry this supply. You got to carry it. So we're seeing a big rejuvenation of that industry, a little because of the pressures driving them to rethink e-commerce, to rethink the types of services they're providing to their companies. That even in some cases they're sneaking into retail, and having that type of experience because they need to compete in different ways. And I think that's always, the industry change is good for companies like us that have a lot of experience in the industry cause we can help them! Ya know, and they need a catalyst, right? They need a catalyst to go out and change and rethink how they operate, and it's created a pretty interesting opportunity. >> So, I wonder Rod if you could talk a little bit about, I know you're only a few months in, but just your impressions of the differentiation. Give us the bumper sticker pitch. Why Infor? How are you different? >> So, I mean, three things. Just netted out three things. Industry, and we talk a lot about industry. We talk a lot about last mile, its real. Its compelling to our customers. They're tired of having to finish the software for the vendor at their site. They want the provider to finish the software and take it to meet their unique needs. Two is I think even though we're smaller than some of the big, big names out there, I think pound for pound we out innovate almost every company. And I can talk very specifically, transitioning from a very, very large competitor. When you're actually looking into the detail of what we've actually delivered around AI, or what we've actually delivered around analytics or mobility, and pound for pound we fight way above our weight on that front. And I think, you know, if you look at even what we've done at Hook and Loop Digital over the years, the types of proof points we have with customers are something that very few of our competitors could boast. So I think, digital over use term, but just sort of understanding how this new technology works and being able to translate that to our customers is huge. And three, is culture. I think we have a fast oriented culture. There's not a lot of levels. We can cut through the nonsense for our customers pretty quickly. We organize around our customers, we don't have 3,000 sales teams trying to sell them piece parts so we can do the solution thing. And we're really working hard to differentiate on customer centricity. I made the comment yesterday at our executive forum that, in general, service at Enterprise Software stinks. You wouldn't accept, ya know, if a retailer was treating you the way the average Enterprise Software, you wouldn't accept it, right? You'd go somewhere else. We've had the benefit, or we've had customers that have such big investments in us, they have to deal with it. And we need to, we have an opportunity to fix that, to change that, to really reorganize and reorient our customer around the outcomes that matter to them. And its so important, if they're going to trust us. And its really about trust. They got to trust us to run their applications, our mission critical applications in our Cloud. We need to really change the game on that front, and we're doing a lot of things structurally. Like for example, maybe someone talked about were taking development customer support in Cloud operations, integrating that into a common organization. So, there's no finger pointing. If something goes down, its not well its the network, Its a bug, Its a knowledge issue. It's one team that's accountable for making sure that we resolve that issue rapidly. Same on the field side. So now we're organizing for manufacturing and distribution. Really, all the resources we need to both sell and service, deliver for our customers in a common team, so there's accountability. And on both sides. There's our product side, product and Cloud ops side, there's accountability and from a sort of customer engagement or accounts management accountability. And then, you know, we got to do a lot of things around service and automation, and better, proactive. We're running their cloud, we should be able to tell them, hey, this isn't running optimally. We need to come in and do this change. I mean, that's where we need to get. That's where the industry needs to get. And we want to get there first. >> Well, you're on the right path. >> Yeah. >> Again, congratulations on the new position, >> Yeah, thank you! >> and we appreciate the time here today, and wish you all the best down the road. >> I appreciate what you guys do. I love your show and content. >> Thank you, Rod. We appreciate that. Thank you sir. Back with more here on theCUBE. We are at Inforum 2018. We're in Washington, DC. (electronic jingle)

>> Live from Toronto, Canada, it's theCUBE, covering Global Cloud and Block Chain Summit 2018. Brought to you by theCUBE. >> Hello everyone, welcome back to theCUBE live coverage in Toronto for the Block Chain-Cloud Convergence Show. This is the Global Cloud Block Chain Summit part of the Futurist Event that's going on the next two days after this. Our next guest is Kesem Frank, AION co-founder and CEO of MavenNet. Doing a lot of work in the enterprise and also block chain space around the infrastructure, making it really interoperable. Of course, Jenna Pilgrim, co-founder and COO of a new opportunity called Network Effects. Welcome to the cube, thanks for joining us. >> Thanks, thanks for having us. >> Thanks, John. >> You guys were just on a panel, The Real World Applications of Block Chain. IBM was on it, which, been doing a lot of work there. This is real world, low hanging fruit, block chain, everyone's pretty excited about. A lot of people get it, and some don't. Some are learning. So you've got the believers, the I want to believe, and then the nonbelievers. Let's talk about the I want to believe and the believers in block chain. Some real world applications going on. As it's evolving, so there's evolution of the standards, technology, but people are putting it to use. What's going on in the sector around some of the real world cases you guys talked about? >> I think we're seeing a lot of collaboration as far as real world applications go, because I think people are sort of starting to understand that if a distributed network is going to work or is going to be secure, it needs diversity and it needs mass scale. If lots of different parties can work together, then they can actually form a community that's really working. As far as real world applications, there's some really interesting one as far as supply chain. Kathryn Harrison at IBM talked about their pilot about shipping, bringing together the global supply chain of distribution. There's a bunch of interesting ones about food providence and bringing together different parties just to make sure that people know what they're eating and that they are able to keep themselves safe, so I think those are two definitely interesting ones. >> Kesem, block chain, supply chain, value chains, these are kind of key words that mean something together. >> Right. >> Making things work in a new way, making things more efficient, seems to be a trend. You're kind of in that world. Is it efficient? (laughing) How's the tech working? What are some of the core threshold issues that people have to get over? >> So you know, John, that's exactly the question to ask. A lot of folks out there are looking at block chain and the promise it represents, and the one big question that keeps echoing over and over is when is this going mainstream? When are we going to see something, a domain, a use case, that is actually natively on a block chain? I think that, essentially, we kind of owe it to ourselves and to everyone that cares about this stuff to ask what's working today, August 2018 and what is still kind of pending? I co-founded a project called AION. For us, interoperability is really one of the key facets that you need to be able to solve for to make block chains real. And again, here's the 60 second argument. If you're going to grow all these solutions that are centric around the use case, they solve for different pinpoints and different stakeholders care about them. They don't really create the cohesive kind of ecosystem until they can all talk to each other, and then you have to ask yourself is the original hypothesis where it's going to be one main net, one chain that's going to rule them all, and everybody gets to play on it and everybody deploys their Dapps on stuff like Fabric or R3 or Ethereum, or whatever it might be. That is absolutely not the way we're seeing enterprise actually shaping into this domain of block chain. What we're seeing is big consortiums that already have value, tangible today, out of doing stuff on chain, and the biggest thing to solve is how do I take, to Jenna's point around supply chain or food providence, whatever it is, how do I actually open it so I can now start writing insurance events, payment events, banking, underwriting, auditing, regulation? There's this gigantic ecosystem that needs to be enabled, and again we are actively saying it's not going to be by an organic model where you and I do everything on top of a single solution. There will be a multitude of solutions, and what we need to solve for is how do we convert them from disparate islands that don't talk to each other into a cohesive ecosystem? >> This is a great point. We were talking on our intro, and we talked last night on our panel, about standards. If you look at all the major inflection points where wealth was created and value was created around innovation and entrepreneurship and industry inflection points, there's always some sort of standard thing that happened. >> Right. >> Whether it's the OSI model during the early days of the internet to certain protocols that made things happen with the internet. Here, it's interesting because if you have one chain and rule the world, it's got to be up and running. >> Yeah. >> It's not. There's no one thing yet, so I see that trend the cloud has, private cloud, public cloud, but public cloud was first but people had data centers. >> Right. >> Both not compatible, now the trend is multi-cloud. You can almost connect the dots of saying multi-chain >> Right. >> Might be a big trend. >> Right. >> This is kind of what you're teasing out here. >> That's exactly what we're about, and I think it's very interesting, the point you're making about dissimilarities between the two domains. We are in a cloud convention, and to me it means two things. One, we absolutely see the mainstream people, the mainstream players in industry, starting to take this seriously. It used to be a completely disparate world where you guys are a bunch of crazies with your Bitcoin and ether and what not. They're definitely taking this seriously now. The second thing, when you think of cloud as a model, how cloud evolved, we used to have these conversations around are you crazy, you're telling me that my data is not going to be on premise? >> It's not secure, now it's the most secure. >> Oh my God! It's in the cloud, what's a cloud? (laughing) You think of the progression model that was applicable back then, right? 10 years, 15 years back, where we started privately and we tell them OK, we'll take this side step of hybrid and then fully public. Took them a while, took them almost 20 years to get their heads around it. >> There's no one trajectory. What's interesting about block chain and crypto with token economics, there's no one trend you can map an analog to, you can't say this is going to be like this trend of the past. It's almost developing it's own kind of trajectory. A lot of organic community involvement. Different tech involvement. >> Totally. >> Different engineering mindsets coming together. You're seeing an engineering-led culture big time going on. That's propelling it up to the conversations of let's lay down the pipes, let's start running apps, but I'll do it within a two year window (laughing). >> I think the big thing to understand about that is yes, you need a whole host of developer talent to build distributed systems, but at the end of the day those systems still have to be used by people. They still have to be used by society, you still have to understand how to talk to your chief executives about what's happening within your company or what your tech teams are doing. There's a growing need for marketers, for PR people, for people who speak, I don't want to say plain English, but people who understand how-- >> Translate it to the real world. >> Yeah, they need to translate it, and how to bridge the gap between legacy systems and how do you take what you were doing before and transform it to a distributed ledger system? How do you do that without just paving the cow path? >> It's interesting, it's almost intoxicating, 'cause you got two elements that get people excited. You got the token economics, which gets people to go, "Whoa," the economics and the liquidity of money and/or value creation capture equations completely changing some of the business model stuff, which could be translated to software and Dapps and software general stuff or SaaS, et cetera. Then you got the plumbing or the networking side of it where things like latency, interoperability, absolutely matter, so with all that going on in real time, it's kind of happening at 30,000 feet and trying to change the airplane engine out. People are failing, and so there's some false promises, there's also false hopes that have not been achieved, so this clouds up the real big picture which is this is an innovative environment. We're seeing that trend. But when you get to the end of the day, what are people working on, to me, is the tell sign. Kesem, what's your project, talk about AION and the work you're doing, specifically give some examples of some of the things that you're doing in the trenches. >> Sure. >> What are you trying to solve, what are some examples you're running into and how does that relate to how things might evolve going forward? >> Sure, so there is a multitude of different problems that we work on but if you want to stick just to the fundamentals? Let's take one gigantic issue that everyone's kind of tackling from different perspectives, let's talk about scale. Scale is, especially in block chains especially challenging just because of how the technology works. How decentralized can you get before you're faced with gigantic latencies and before transaction cost are kind of through the roof? When you think about it, that is all a result of how we kind of contemplate these early stage networks. It was always the one network that is going to scale to infinity. Absolutely not the way it's going to work out. So from my perspective, again, sticking to this one issue, if you could actually give me a decentralized rail that maintains consensus throughout two networks, I can now actually have two trusted kind of go-tos instead of always putting the full brunt of the throughput on one single network. For us, that's kind of a no brainer application to interoperability. If you could actually give me all these trusted networks that work in tandem, I could now start splicing throughputs across many different parallel kind of rails. Not to similar than how we can solve for super computing. We understood there is a limit on how fast can a single CPU go and we started going wide. >> That's an interesting point, I want to just double click on that for a second because if you think about it, why would I have multiple rails and multiple systems? Maybe the use cases are different for them. >> Correct. >> You don't want to have to pick one cloud or one chain to rule them all because it's not optimized. We saw that with monolithic systems and cloud is all about levels of granularity and micro service and micro everything, right? >> Correct. >> And I would also say that gets into a security issue as well, right? You're talking about multiple layers but you also will have multiple layers of permission. You'll have multiple layers of how much information someone can see and what I think is emerging, if data is the new oil, then what's emerging is for the first time we're now able to trust data that we do not own. For corporations who say, "I don't know to market to you "if I don't know everything about you." But at the end of the day, they want to be able to leverage your data but they don't need to secure it and I think that cybersecurity issue is a huge, huge thing that's definitely coming. >> I want to get both of your thoughts on this, because we were talking about this last night. We were riffing on the notion that with cloud compute and data really drove scale. So Amazon is a great example and their value now is things like Kinesis and Aurora, some of their fastest growing services. You got SageMaker, probably will be announced at re:Invent coming up as the fastest growing service, right now it's Aurora. All data concepts. So the dataization really made cloud, great. >> True. >> Okay what's the analog for crypto and block chain? Tokenization is an interesting concept. There's almost an extension of cloud where you're saying, hey, with tokenization, the tokenization phase, how do you explain that to a common person? You say, is token going to be the token and the money aspect of and the economics the killer app? How's it transverse the infrastructures, plural? >> Yeah, or is the wallet going to be the browser? Or how are all of these things happening? >> How do you make sense of this? What's your reaction to that trend? >> So I actually get excited when I think about what token, on the most profound level, actually means. When you kind of think of where value happens in the context of these gigantic enterprises, right? You think of Apple, Amazon, Google, Facebook, any of them, and you kind of think of what the product is, it's all about the data and it's all about how do you convince people to give up data so they can monetize on it. And then you have two distinct, like literally gigantic groups of stakeholders at play. You have the users, that essentially get something free, right? I get to post on Facebook or I get to write an e-mail on Gmail. Then you have the stakeholders that actually extract all that value from my activities. A token, I think most profoundly represents, how do we actually get to a unified group where the user himself is the stakeholder that gets to extract the data? And again, the proposition is pretty straightforward. The more you use a network and the more the network becomes valuable and grows, the more value the token that drives at it. >> So it changes the value capture equation? >> Correct, different model altogether. >> The value creators get to capture the value and obviously network effects plays a big part in this? >> Yes. >> Which is your wheelhouse. (laughing) >> Yeah, definitely. I think it really comes down to core principles. Now you're able to really get down, to what Kesem was talking about, about when you're designing a token or if you're designing an incentive mechanism, you're really going down to the sort of deep game theory of why people do specific things and if we can financially incentivize people to do good rather than punish them or fine them for doing bad then we can actually create value for everyone. We're designing a new economy that now has the ability to propel itself in a fair and prosperous way, if done correctly, obviously that's the disclaimer afterwards, but. >> I love what you're saying there because if you look at collective intelligence a lot of the AI concepts came around from collective intelligence, predictive analytics, prescriptive analytics all came around using data to create value. I always talk about fake news because we have a cloud of media business that's kind of tokenized now but fake news it two things, it's payload, fake news, the fake content and then the infrastructure dynamics that they arbitraged, with network effects. They targeted specific people, fake payload, but the distribution was a network effect. Again, this was the perverse incentive that no one was monitoring, there was no- >> Well and I think in that case, yes there is news that is inherently false information but then there's also a whole spectrum of trueness, if you want to call it that so now we have this technology that allows us to overlay on top of that and say, "Well what is the providence of my information?" And with different layers of block chain systems you're actually able to prove the providence of your information without exposing the user's privacy and without exposing the whole supply chain of the media because there's like media buyers, go through all kinds of hands. >> And we believe the answer to fake news, frankly, is data access, collective intelligence and something like a block chain where you have incentive systems to filter out the fake news. >> Totally. >> Exactly. >> Reputation systems, these things are not new concepts. >> It's all about stake at the end of the day, right? It's how do you keep a stakeholder accountable for their action? You need backing so I think we're definitely on the same page. >> I love, I could talk about fake news all day because we think we can solve that with our CUBEcoin token coming out soon. I want to shift gears and talk about some of the examples we've seen with cloud. >> Sure. >> And try to map that to some navigation for people in how to get through the block chain token world. One of the key things about the cloud was something they called shadow IT. Shadow IT was people who said, hey, you know what? I could just put my credit card down and move this non core thing out in this cloud and prove to my boss, show them, not pitch 'em on the Power Point deck, to say look it, I just did this for that cost in this timeframe, and that started around 2009/2010 timeframe, the early digerati or the clouderati kind of did that but around 2012 it became, wow, this shadow IT is actually R and D practice. >> Mm-hmm. >> Right. >> You started to see that now, so the question that we see for people evaluating in the enterprise is how do you judge what's a good project? Certainly people are kicking the tires and doing a little bit, I won't call it shadow IT, but they're taking on some projects as you were talking about on the panel. How should they, the enterprises in general, the large companies, start thinking about how to enable a shadow IT-like dynamic and how should they evaluate the kind of projects? I think that's an area people just don't know what to look for. Your thoughts? >> I want to add a premise to that, because I think that's absolutely the right question to ask. We also need to add the why. Why should we, as people that do native crypto currency, even care about enterprises? A lot of people kind of theorized when Bitcoin was created to say it was anti institutional is an understatement, right? Aren't we meant to kill enterprise? The thing is, I don't think it's going to be a big bang. I don't think it's going be we wake up and nobody's using banking anymore or nobody's using the traditional healthcare or government and you know whatever insurance policies. We care about block chain in the context of enterprise because we think block chain is a fundamentally better model of doing things. It kind of does away with the black box where I need to be in business, I need to blindly trust you and it introduces a much more transparent and democratic model of doing things. We absolutely want to introduce and make block chain mainstream because that's important for us. When you think of how we do it, to your question, AION is all about interoperability, right? We create a solution that helps scale and helps different networks, decentralized networks, communicate to each other. What we also do with MavenNet, the company I run, is essentially make that enterprise friendly. It's extremely hard to do adoption and implementation within an enterprise, they're very immune to change. >> Antibodies as they say. >> Oh. >> The antibodies to innovation, they kill innovation. >> Totally, so going back to your original question, it all starts with a P and L. If somebody is going to authorize, you know, an actual production system in enterprise for block chain, it needs to create a tangible value, a tangible return, quickly and that's the key. The model that actually scales is you start by flushing out inefficiency plate. You show the enterprise how you could actually achieve, I don't know 20%/30%, that's the order of magnitude that they care about, efficiency by moving some part of your value chain on top of a block chain. >> It has to have an order of magnitude difference or so. I mean cloud was a great example, too, it changes the operating model. >> Yeah. >> They achieve what they wanted to achieve faster and more efficiently and operated it differently. >> Correct. >> And people were starting at it like a three headed monster like what is this thing, right? The cloud thing. And throwing all kinds of fud out there, but ultimately at the end of the day, it's a new operating model for the same thing that they're trying to do with the old stuff. >> Mm-hmm. >> I mean, it's almost that simple. >> Yeah, I think in some cases you need to really, in my previous life at the Block Chain Research Institute, we encouraged a lot of our clients to really take a step back and say, well will I actually, A, will I have this problem in eight years or seven years or 20 years or 50 years, if we're really fundamentally building a new financial system or a new way of doing things that is fundamentally different? Are we building it on old technology? We need to make sure that, and that's why you've seen banks were the first in the door to say, "Yeah, payments, that sounds great, that sounds great." But the real applications that we're seeing from banks are in loyalty, they're in AMLKYC, they're in the sort of fringe operations. Something like payments is going to take a really long time to push through because of those legacy systems because payments is the fundamentals of what banks do. >> This is an interesting point, I want to get your thoughts to end the segment because I think one of the things that we've certainly seen with cloud that over the generational shifts that have happened, the timeframe for innovation is getting shorter and shorter, so timeframe is critical so if the communities are fumbling around hitting that time to value, it seems to be trending to faster and we don't want to hear slower because these systems are inadequate, they're antiquated. >> Mm-hmm. >> These are the systems that are disrupted so the timing of, whether it's standards, or interoperability or business models, operating models, they got to be faster. >> Yeah. >> That's the table stakes. >> I think it all comes down to collaborative governance. >> People have to figure out block chain faster. >> Yeah. >> What's holding us back? Or what's accelerating us? What's the key for the community at large from the engineering community and the business community to make it go faster? Your thoughts? >> Right, so I think we're still searching for the next killer app. If Bitcoin is the reason we're all sitting here today and I profoundly believe that. >> Yeah. >> What is the next thing that drives change on a global scale? That's kind of what we're trying, collectively as an industry, to figure out. Sure, many kind of roadblocks on the way. Some of them educational, perceptional, regulation, technology, but the next big wave that's going to accelerate us to the next ten years of block chain is that next killer app. Organizations such as myself, Jenna, that's our day job, we wake up and that's what we do. >> I mean I've always said, and Dr. Wong, who's the founder of Alibaba Cloud agreed with me, I've been saying that the TCPIP protocol, that standard really enabled a lot of interoperability and created lots of diverse value up the stacks of the OSI model, Open Systems Interconnect, seven layer model, actually never got standardized. It's kind of stopped at TCPIP and that was good, everyone snapped at the line, that created massive value. >> But that's a collaborative governance thing. That's people coming together and saying that these are the standards that we wish to adhere to. >> We need the moment right now. >> Yeah, so you see organizations like the Enterprise Ethereum Alliance coming out with a prospective list of standards that they think the community should adhere here. You know you have the ERC20 standard, you have all these different organizations, the World Economic Forum is playing a role in that and the UN is playing a role, especially when it comes to identity and those kind of really big, societal issues but I think that it comes down to that everyone plays a role that I'm doing my best, I think it's going to be somewhere in the realm of data so that's where I've chosen to sort of make my course. >> I think this is a good conversation to have, and I think we could continue it. I mean, I read on Medium, everyone's reading these fat protocols, thin protocols but at the end of the day what does that matter if there's no like scale? >> Yeah. >> You can have all the fat protocols you want, more of a land grab I would say but there's certainly models but is that subordinate or is that the cart before the horse? This is the conversation I think is in the hallways. >> Totally agree, totally agreed. >> Guys, thanks so much for coming on theCUBE, really appreciate it. Breaking down real world applications of block chain we're at the Global Cloud and Block Chain Summit. It's an inaugural event and think it's going to be the kind of format we're going to see more of, cloud and block chain coming together. Collision course or is it going to come in nicely and land together and work together? We'll see, of course theCUBE's covering it. Thanks for watching. Stay with us for more all day coverage. Part of the Futurist Conference coming up the next two days. We're in Toronto, we'll be back with more after this short break. (theCUBE theme music)

>> Announcer: Live from San Juan, Puerto Rico. It's theCUBE, covering Block Chain Unbound, Brought to you by Blockchain Industries. >> Hello, everyone, welcome back to theCUBE's exclusive coverage in Puerto Rico for Block Chain Unbound. It's a global event, people from all around the world, from South Africa, Miami, Russia, San Francisco, New York, all around the world, talking about Blockchain cryptocurrency, the decentralized internet, and the future of Money, that's the killer app in Blockchain and cryptocurrency. I'm John Furrier, your host, my next guest is Scott Picken, who's the founder and CEO of Wealth Migrate Platform. Scott, thanks for coming on. >> Yeah, awesome John, thanks for having me. It's quite an exciting group of people here. >> We met last night, had a great conversation, I really liked some of the things that we were talking about, I wanted to bring you on because being in South Africa, where you're living and working, you have a unique perspective because you see the global landscape. So, I'm from Silicon Valley, we're here in Puerto Rico, America's got their view, the UK just announced a deal with Coinbase for essentially a license to convert funds into separate bank accounts through faster payment mechanisms, basically taking crypto and turning it into Fiat. Kind of a game changer. >> The one thing with the UK is they've been at the head of all of the different innovations over the last five to 10 years. They were right at the head in terms of crowdfunding and they're doing exactly the same in terms of now with the whole cryptospace. And it's actually quite interesting because when you take into account Brexit, they actually really need to do it because they want funds coming into the country, they want to be seen as the future of the banking market, et cetera, so it's actually really exciting. When you look around the world it's fascinating that I said this to you last night, that America really grew because Europe used to have all the controls. And so the capital basically left Europe and were in America and now it's happening 300 years later as America has all the controls and the capital's starting to go elsewhere. >> So America's turning into Europe. And so the potential is to bring, you don't have to say it, I'm an American and we're concerned about it. Americans are concerned that we don't want to be that old guard, like Europe was to America in the America days. So a new liberation's happening. UK's putting a stake in the ground, saying, "We want to get our mojo back," my words. >> Scott: sitting here in Puerto Rico. >> Yeah, they're in Puerto Rico. They're going to put a stake in the ground saying, "We're going to give you tax breaks 'til 2036." This is a money flow game right now. So you've been doing some pioneering work, what's your perspective, talk a little bit about some of the world dynamics that you see because, let's face it, this is the transfer of money, with crypto, it's happening at a massive scale, not just some underbelly boutique underground activities. This is front and center, mainstream, real money, real commerce. Your thoughts? >> I would take it a step back, actually. I think there's eight major macro trends that are all culminating at the same time. So the first one is in the education space, and the whole of education is changing, and it's really becoming gamification, and it's becoming learning while doing. So you don't learn and then go do something, you actually learn while you're doing it. The second one, for me, is the whole Blockchain. And what that's enabling people is getting democratization to wealth and access to assets, whether they're in their country or global assets, basically. The third thing that's really important is you've got the rise of the middle class. You know, a lot of people talk about the unbanked three billion, but what they don't realize is that 1.2 billion people joined the middle class. And they are primarily in the emerging world, they're in Africa, India, China. And what they want is, they want health, they want education, and they want access to wealth. Then you take into account what's happening in terms of collaborative investing. In the old days it was I do it on my own, you do it on your own, we sort of trust the financial industry. Now we're coming together, it's the power of the crowd. I could go on and on, that's just four of them, there's another four. They're all coming together and because this is happening is why we're seeing this metamorphosis and cryptocurrency is the catalyst on top of Blockchain that's allowing this to take place. >> Talk about some of the things that you've been advocating for, I know you were sharing a private story, maybe this may or may not be the right time to talk about it, but you put forth some pretty forthright concepts in memos and letters to folks, and no one will publish it. What are those views, because we've got the cameras rolling right now, share your vision. >> Again, I fundamentally believe that technology can solve grand challenges. And when you take our platform and what we're doing, we're effectively helping the 99% invest in commercial real estate like the top 1%. So what we were talking about last night was, I come from a country, South Africa, I was previously from Zimbabwe, and unfortunately for us is that in South Africa, they're talking now about taking away land without compensation. So land redistribution without compensation. Now, Einstein says that if you want to solve a problem, you can't solve it with the same reality that created the problem. And so I wrote a letter to the President, an open letter two weeks ago, and I said, listen. Why don't we do it differently? You're giving a person a piece of land in the middle of nowhere when they've never been a farmer will not help them get wealthy, I guarantee it. And if I'm wrong, let's go look at Zimbabwe. Which is a economic disaster. What about if we give them access to ownership of a good quality commercial asset that's earning a passive income? That is how you'll grow your wealth. And then add to that, Cape Town nearly became the first city, and it still could be the first city, that literally runs out of water. So why don't you go build a decent ionization plant in Cape Town with government money, allow people that you would give land to actually access to that asset and allow them to have the ownership? And that's sort of the concept, where you just think about it completely different. And you allow technology to actually give people what they want, which is wealth and prosperity for their family, and not just a farm in the middle of nowhere. >> And you're really addressing, I think, the incentive system combined with structural change. You talk about gamification earlier, this is kind of the dynamic. How important from an education standpoint, meaning educating stakeholders, old guard or existing governments, because you have this organic groundswell coming up of young people, people with vision that are older and more experienced like us, what's the formula, how do you get this ball rolling? >> So it's quite interesting, I get asked this question all the time and for us, in the first world, a lot of what we're talking about is it nice to have? It's sort of a bit of a game and if I can participate, but where I come from in the emerging world, it's a necessity. There are no other solutions. So if you live in South Africa or China or India and you want to get your money into a first world country like England, Australia, or America, it's very very difficult and virtually no one can do it. But it's a major problem, because you want world preservation, you want your Plan B, you want your children to be able to go to a first world university, et cetera, et cetera, et cetera. And so to answer your question, I think the way it will get solved is in communities where it's not a nice to have, it's a necessity. In terms of educating the old guard, I believe that what happens is you get groundswell, like literally when people really need a solution solved, they persuade governments and regulators to change and it's interesting, coming back to how we started the conversation, that's why smaller countries are often the ones to adopt the regulated new change and, more importantly, countries in emerging markets, whereas first world countries are trying to protect what they have and, unfortunately, the new world is about capital. And its capital flows. >> It's a choice between playing offense or defense, really in my mind it's a sports metaphor, whatever sport you like you know. We love the sports analogies. But this is what UK's doing, they're playing offense. And I think you're seeing other countries wanting to restructure themselves as digital nations because that's what the young people are expecting. So with that in mind, you have a global fabric here at this event, and it's just a microcosm of what we're seeing, which is outside the US, call it the little US bubble that we're living in, Silicon Valley, that's one case I'm wary of, but the growth outside the United States and even in Asia and south of the border, if you will, south of the equator, there's a ton of global action. What is, in your opinion, the few global things that are going on, that people should know about when it comes to how money's flowing and what they can do to take advantage of the trend rather than trying to hold it back. What do we do, is it get into the current? Ride the wave? What should people understand about the new global dynamic? >> So the first thing I would say is, I always laugh at this, but people don't understand how much innovation's going on in China. Like, go and understand WeChat to start off with. It is phenomenal, what is happening. The second thing for me is the global capital flows. When you consider how much capital is moving from the emerging world into the first world, primarily in real estate at the moment. And that's just the top 1% of the top 1%, you know, that's the people with 10, a hundred million dollars. But I've already said to you, there's 1.2 billion people coming into the emerging markets. In the middle class, they're going to want the same things. And so those capital flows are going to be going cross border. I also believe, with time, capital flows will be going from the first world into the emerging world in a safe way but wanting higher returns. >> So then the emerging world, the US has a shrinking middle class, but yet the emerging world has a growing middle class. That's going to attract new entrants. >> Exactly. >> Okay. >> Well, take into account China. Has China had a big impact on the global economy in the last 20 years? Yes or no? >> Yes. >> How many people are in the middle class in China? Plus or minus? >> Don't know. >> I've heard different reports from 200 million to 400 million, but whether it's 200 or 400-- >> It's more than it was 10 years ago. >> I know, but think about the impact that's had on the global economy. I'm not saying that this is 1.2 billion in the next 10 years, it's either a factor of five to eight, depending on which way you want to look at it. >> How much money, in your guesstimation, if you had to throw a dart at the board, order of magnitude, is flowing out of China with crypto into other assets? >> In the crypto space that's fascinating, because a lot of it is hard to tell, actually. In real estate last year alone, it was just short of 30 billion dollars went into commercial real estate from China. Now what's interesting is that a lot of that money is sort of gray, like no one actually knows where it's coming from, which is why China tightened it up so much. It's also why they tightened up the crypto side of things. Because a lot of people want to get their money out of the country and into first world economies, and that's why, in the emerging world, cryptocurrencies have been embraced more, actually, than in the first world. >> John: It's a faster way to move that money. >> Coming back to necessity. So in South Africa, in Zimbabwe, in China you pay more for Bitcoin than you do in America or Europe. I don't know if you know that. >> John: No, I don't know that. >> And by quite a lot. Like in Zimbabwe you pay nearly double. So a lot of people are making money by overcharging coins. They buy them in Europe, they sell them in South Africa, they sell them in Zimbabwe, they sell them in Nigeria. >> So the demand to move the money out of country is very high. >> Well, because they've got capital controls. So they have currency controls. So you're only allowed to move a certain amount of capital out of the country legally. So what happens now, you buy cryptocurrency and you can effectively invest in assets around the world. And you literally started off this conversation, right in the beginning, there's a democratization in terms of capital flows and what's happening, and people are going to put their capital where they want to. And governments, I believe, are not going to be able to control it by putting up controls, they're going to have to make their countries attractive so that the capital's flowing into the country, not out of the country. >> So what's your take on big multinational corporations that have capital structures, have equity positions, and it could be also growing venture-backed or private equity-based companies, they have capital structures, they have equity investors, in some case public, and privates, and unicorn valleys or whatnot, now moving to look at utility tokens as a way to get to a global gamification. So you have multiple securities, a utility, and in some cases a security token a real security. That seems to be a dynamic, are you seeing that on a global scale, are you seeing any activity there, we're seeing a little bit of movement around big companies trying to figure out how to play in crypto. >> From my experience, not a huge amount. I think that most people, they have a board, it's all around reputation, they got to meet the lawyers, the lawyers tell them, you're going to get crucified. And so from my experience, not a huge amount, it tends to be the small to medium enterprises that are prepared to go out and look at it. However, I will say from our personal business perspective, we built our entire company on a community. We've got shareholders all over the world and so for me, when it came to the crypto and the ICO market, that was just doing that more aggressively, effectively, and community-based companies are the future. So whether you're a Fortune 500 company or a start up, it's all about building the community, and I believe that whether it's utility token or security or a combination of the two, it provides an incredible vehicle to ultimately be the catalyst to a community. And if you're the catalyst to a community adding value, then you're going to build a company of value. >> And capture that value. So, Scott, I got to ask you about Wealth Migrate. Talk about your platform. First of all, thank you for sharing your perspective here on theCUBE. It's been fantastic to get that data out. What's your company about? Take a minute to explain what you guys are doing, your value proposition, state of the company, are you doing an ICO, have you had an ICO, what's the status of the company? >> So from Wealth Migrate's perspective, the platform went live in October, 2013, so we're a little over four years in now. We've effectively got members from 111 countries around the world and we've raised just short of 70 million dollars. All though the platform, all on Blockchain. We've facilitated real estate deals of over 485 million dollars and what I'm proudest of, actually, is that we've got a higher than 70% reinvestment rate. What we're doing is we're allowing the 99% to invest like the 1%, our minimum investment at the moment is $1,000, we're beta testing $100, and my dream is to get it to $1. You asked a little bit about the ICO. We built our platform on Blockchain not because of an ICO. Our number one challenge was trust. And ultimately Blockchain enabled us to solve the trust problem. The second thing for us is that my dream is to get it to $1 per person per investment. I want to solve the wealth gap. And I truly believe we can do it when we can allow anyone anywhere to invest in good quality assets. I can't do it with the current system, there's too many friction costs. With crypto and volume I can. >> Whether it's semantics, or education and/or hurdle rate on dollars, it's an interesting concept. You want to make the 99% invest like the 1%. Explain what that means, take a minute to explain that concept. I mean, some people are like, "Okay, I know what "the 1% is, there was a movement about that." So now you're talking about something pretty radical and interesting. What does that actually mean? I mean, empowering people to make more money? Unpack that concept. >> So let me ask you a question. Do you personally own a medical building? >> Do I own what? >> A medical building. >> No. >> Like a hospital, medical building. >> No. >> So it's 2009, I'm in Bondi Beach, Sydney and I meet two US dollar billionaires. I had helped about two and a half thousand people buy houses and apartments in England, Australia, America, and South Africa. And I sat with them and I said, "What are you investing in?" And they said, "Medical buildings." I said, "Why medical buildings?" And they said, "Well think about it. "No matter what happens in the global economy, "people need doctors." I was like, that makes sense. Secondly, they said, "Doctors never move." I was like, that makes sense. Thirdly, doctors are very good at being doctors, but they're not accountants. And so they sign long term, good, favorable leases. Now from a property perspective, real estate perspective, that's a no brainer. And I said to them, "How do I participate?" And they said it's really simple. It's for friends and family, there's eight people only, it's five million Australian dollars each. I was like, now there's the problem. That company today is over 700 million dollars, it's on the Australian Stock Exchange, and it's what I call financial exclusion. You and me don't own medical buildings. Since October 2013, we've enabled people to invest in medical buildings from $1,000. So the top 1% get wealthy by investing in better assets than the 99%. >> John: Because they have access. >> Because they've got access. >> John: And the cash. >> And the cash. But we've dropped the barriers to entry. Because you and I can participate now from $1,000 and I will get it to $1. >> So it's a combination of leveraging the asset based securitization with that opportunity by using a crowdsourcing kind of model, is that what you're thinking? >> So, effectively, and I'd suggest-- >> John: I'm oversimplifying it. >> No, no, 100%, I'd suggest everyone goes and looks up the term collaborative investing which is ultimately, it's a thing that's been going on for decades by very wealthy people on how to successfully invest. We've taken that but we've added a smart component. And why that's important is because in the past you needed 10, 50 million dollars to do collaborative investing, now you can do collaborative investing with $1,000. >> Yeah and what's beautiful is that you understand potentially whose reputation you're working with, you can move in herds, network effect kicks in, that's awesome. >> What gives me the greatest pleasure, I mean, children, my son is six years old, he's already investing. You know, most kids are playing Monopoly, he's playing real Monopoly, and so are adults. And what gives me the most pleasure and pride ever, and what I'm grateful for, is that we're changing people's lives. >> People talk about how to solve the welfare system, all kinds of things, you make people own something, or try to own something or trade, whether they make money or lose money, you learn from it, you're better for it. Here, you're providing a great service by opening the door, lowering the barriers to entry, to potentially wealth creation. >> Dude, I call it freedom. At the end of the day, if you're where you want to live, where you want to send your kids to school, how you want to retire, whether you want to donate to the church or whatever, I don't really care what you want, but I want you to have the freedom to be able to do it. And wealthy people get that freedom by investing in quality assets. And we're just allowing them to do that now. >> And the democratization is multiful, in this case you're creating a new economy model so the whole freedom, democracy aspect is in play. >> Well, I mean if you think about it, when you get into $1 per person, $1 will not change your life. But if you change your habits, you'll change your financial destiny. And so my philosophy is get it to $1, so that every single person can participate. And once you start to learn good habits around money and wealth, the rest just, it's a formula. >> It's a flywheel. Kickstand. Scott Picken, who's the founder and CEO of Wealth Migrate Platform from South Africa, formerly of Zimbabwe we learned today, great sharing the global perspective. Thanks for coming on theCUBE. Exclusive coverage from Puerto Rico, this is theCUBE, I'm John Furrier getting the signal here out of all the noise in the market, this is what we do, this is theCUBE's mission, to bring you the best content, best story from the best people, more coverage here in Puerto Rico. Day one of two days of coverage. After this short break, thanks for watching.

>> Announcer: Live from New York City it's The Cube, covering IBM data science for all. Brought to you by IBM. (light digital music) >> Welcome back to data science for all. It's a whole new game. And it is a whole new game. >> Dave Vellante, John Walls here. We've got quite a distinguished panel. So it is a new game-- >> Well we're in the game, I'm just happy to be-- (both laugh) Have a swing at the pitch. >> Well let's what we have here. Five distinguished members of our panel. It'll take me a minute to get through the introductions, but believe me they're worth it. Jennifer Shin joins us. Jennifer's the founder of 8 Path Solutions, the director of the data science of Comcast and part of the faculty at UC Berkeley and NYU. Jennifer, nice to have you with us, we appreciate the time. Joe McKendrick an analyst and contributor of Forbes and ZDNet, Joe, thank you for being here at well. Another ZDNetter next to him, Dion Hinchcliffe, who is a vice president and principal analyst of Constellation Research and also contributes to ZDNet. Good to see you, sir. To the back row, but that doesn't mean anything about the quality of the participation here. Bob Hayes with a killer Batman shirt on by the way, which we'll get to explain in just a little bit. He runs the Business over Broadway. And Joe Caserta, who the founder of Caserta Concepts. Welcome to all of you. Thanks for taking the time to be with us. Jennifer, let me just begin with you. Obviously as a practitioner you're very involved in the industry, you're on the academic side as well. We mentioned Berkeley, NYU, steep experience. So I want you to kind of take your foot in both worlds and tell me about data science. I mean where do we stand now from those two perspectives? How have we evolved to where we are? And how would you describe, I guess the state of data science? >> Yeah so I think that's a really interesting question. There's a lot of changes happening. In part because data science has now become much more established, both in the academic side as well as in industry. So now you see some of the bigger problems coming out. People have managed to have data pipelines set up. But now there are these questions about models and accuracy and data integration. So the really cool stuff from the data science standpoint. We get to get really into the details of the data. And I think on the academic side you now see undergraduate programs, not just graduate programs, but undergraduate programs being involved. UC Berkeley just did a big initiative that they're going to offer data science to undergrads. So that's a huge news for the university. So I think there's a lot of interest from the academic side to continue data science as a major, as a field. But I think in industry one of the difficulties you're now having is businesses are now asking that question of ROI, right? What do I actually get in return in the initial years? So I think there's a lot of work to be done and just a lot of opportunity. It's great because people now understand better with data sciences, but I think data sciences have to really think about that seriously and take it seriously and really think about how am I actually getting a return, or adding a value to the business? >> And there's lot to be said is there not, just in terms of increasing the workforce, the acumen, the training that's required now. It's a still relatively new discipline. So is there a shortage issue? Or is there just a great need? Is the opportunity there? I mean how would you look at that? >> Well I always think there's opportunity to be smart. If you can be smarter, you know it's always better. It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work's really hard, right? You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it, you have to be able to explain it. There's a lot of aspects that you're not going to pick up overnight. So I think part of it is endurance. Like are people going to feel motivated enough and dedicate enough time to it to get very good at that skill set. And also of course, you know in terms of industry, will there be enough interest in the long term that there will be a financial motivation. For people to keep staying in the field, right? So I think it's definitely a lot of opportunity. But that's always been there. Like I tell people I think of myself as a scientist and data science happens to be my day job. That's just the job title. But if you are a scientist and you work with data you'll always want to work with data. I think that's just an inherent need. It's kind of a compulsion, you just kind of can't help yourself, but dig a little bit deeper, ask the questions, you can't not think about it. So I think that will always exist. Whether or not it's an industry job in the way that we see it today, and like five years from now, or 10 years from now. I think that's something that's up for debate. >> So all of you have watched the evolution of data and how it effects organizations for a number of years now. If you go back to the days when data warehouse was king, we had a lot of promises about 360 degree views of the customer and how we were going to be more anticipatory in terms and more responsive. In many ways the decision support systems and the data warehousing world didn't live up to those promises. They solved other problems for sure. And so everybody was looking for big data to solve those problems. And they've begun to attack many of them. We talked earlier in The Cube today about fraud detection, it's gotten much, much better. Certainly retargeting of advertising has gotten better. But I wonder if you could comment, you know maybe start with Joe. As to the effect that data and data sciences had on organizations in terms of fulfilling that vision of a 360 degree view of customers and anticipating customer needs. >> So. Data warehousing, I wouldn't say failed. But I think it was unfinished in order to achieve what we need done today. At the time I think it did a pretty good job. I think it was the only place where we were able to collect data from all these different systems, have it in a single place for analytics. The big difference between what I think, between data warehousing and data science is data warehouses were primarily made for the consumer to human beings. To be able to have people look through some tool and be able to analyze data manually. That really doesn't work anymore, there's just too much data to do that. So that's why we need to build a science around it so that we can actually have machines actually doing the analytics for us. And I think that's the biggest stride in the evolution over the past couple of years, that now we're actually able to do that, right? It used to be very, you know you go back to when data warehouses started, you had to be a deep technologist in order to be able to collect the data, write the programs to clean the data. But now you're average causal IT person can do that. Right now I think we're back in data science where you have to be a fairly sophisticated programmer, analyst, scientist, statistician, engineer, in order to do what we need to do, in order to make machines actually understand the data. But I think part of the evolution, we're just in the forefront. We're going to see over the next, not even years, within the next year I think a lot of new innovation where the average person within business and definitely the average person within IT will be able to do as easily say, "What are my sales going to be next year?" As easy as it is to say, "What were my sales last year." Where now it's a big deal. Right now in order to do that you have to build some algorithms, you have to be a specialist on predictive analytics. And I think, you know as the tools mature, as people using data matures, and as the technology ecosystem for data matures, it's going to be easier and more accessible. >> So it's still too hard. (laughs) That's something-- >> Joe C.: Today it is yes. >> You've written about and talked about. >> Yeah no question about it. We see this citizen data scientist. You know we talked about the democratization of data science but the way we talk about analytics and warehousing and all the tools we had before, they generated a lot of insights and views on the information, but they didn't really give us the science part. And that's, I think that what's missing is the forming of the hypothesis, the closing of the loop of. We now have use of this data, but are are changing, are we thinking about it strategically? Are we learning from it and then feeding that back into the process. I think that's the big difference between data science and the analytics side. But, you know just like Google made search available to everyone, not just people who had highly specialized indexers or crawlers. Now we can have tools that make these capabilities available to anyone. You know going back to what Joe said I think the key thing is we now have tools that can look at all the data and ask all the questions. 'Cause we can't possibly do it all ourselves. Our organizations are increasingly awash in data. Which is the life blood of our organizations, but we're not using it, you know this a whole concept of dark data. And so I think the concept, or the promise of opening these tools up for everyone to be able to access those insights and activate them, I think that, you know, that's where it's headed. >> This is kind of where the T shirt comes in right? So Bob if you would, so you've got this Batman shirt on. We talked a little bit about it earlier, but it plays right into what Dion's talking about. About tools and, I don't want to spoil it, but you go ahead (laughs) and tell me about it. >> Right, so. Batman is a super hero, but he doesn't have any supernatural powers, right? He can't fly on his own, he can't become invisible on his own. But the thing is he has the utility belt and he has these tools he can use to help him solve problems. For example he as the bat ring when he's confronted with a building that he wants to get over, right? So he pulls it out and uses that. So as data professionals we have all these tools now that these vendors are making. We have IBM SPSS, we have data science experience. IMB Watson that these data pros can now use it as part of their utility belt and solve problems that they're confronted with. So if you''re ever confronted with like a Churn problem and you have somebody who has access to that data they can put that into IBM Watson, ask a question and it'll tell you what's the key driver of Churn. So it's not that you have to be a superhuman to be a data scientist, but these tools will help you solve certain problems and help your business go forward. >> Joe McKendrick, do you have a comment? >> Does that make the Batmobile the Watson? (everyone laughs) Analogy? >> I was just going to add that, you know all of the billionaires in the world today and none of them decided to become Batman yet. It's very disappointing. >> Yeah. (Joe laughs) >> Go ahead Joe. >> And I just want to add some thoughts to our discussion about what happened with data warehousing. I think it's important to point out as well that data warehousing, as it existed, was fairly successful but for larger companies. Data warehousing is a very expensive proposition it remains a expensive proposition. Something that's in the domain of the Fortune 500. But today's economy is based on a very entrepreneurial model. The Fortune 500s are out there of course it's ever shifting. But you have a lot of smaller companies a lot of people with start ups. You have people within divisions of larger companies that want to innovate and not be tied to the corporate balance sheet. They want to be able to go through, they want to innovate and experiment without having to go through finance and the finance department. So there's all these open source tools available. There's cloud resources as well as open source tools. Hadoop of course being a prime example where you can work with the data and experiment with the data and practice data science at a very low cost. >> Dion mentioned the C word, citizen data scientist last year at the panel. We had a conversation about that. And the data scientists on the panel generally were like, "Stop." Okay, we're not all of a sudden going to turn everybody into data scientists however, what we want to do is get people thinking about data, more focused on data, becoming a data driven organization. I mean as a data scientist I wonder if you could comment on that. >> Well I think so the other side of that is, you know there are also many people who maybe didn't, you know follow through with science, 'cause it's also expensive. A PhD takes a lot of time. And you know if you don't get funding it's a lot of money. And for very little security if you think about how hard it is to get a teaching job that's going to give you enough of a pay off to pay that back. Right, the time that you took off, the investment that you made. So I think the other side of that is by making data more accessible, you allow people who could have been great in science, have an opportunity to be great data scientists. And so I think for me the idea of citizen data scientist, that's where the opportunity is. I think in terms of democratizing data and making it available for everyone, I feel as though it's something similar to the way we didn't really know what KPIs were, maybe 20 years ago. People didn't use it as readily, didn't teach it in schools. I think maybe 10, 20 years from now, some of the things that we're building today from data science, hopefully more people will understand how to use these tools. They'll have a better understanding of working with data and what that means, and just data literacy right? Just being able to use these tools and be able to understand what data's saying and actually what it's not saying. Which is the thing that most people don't think about. But you can also say that data doesn't say anything. There's a lot of noise in it. There's too much noise to be able to say that there is a result. So I think that's the other side of it. So yeah I guess in terms for me, in terms of data a serious data scientist, I think it's a great idea to have that, right? But at the same time of course everyone kind of emphasized you don't want everyone out there going, "I can be a data scientist without education, "without statistics, without math," without understanding of how to implement the process. I've seen a lot of companies implement the same sort of process from 10, 20 years ago just on Hadoop instead of SQL. Right and it's very inefficient. And the only difference is that you can build more tables wrong than they could before. (everyone laughs) Which is I guess >> For less. it's an accomplishment and for less, it's cheaper, yeah. >> It is cheaper. >> Otherwise we're like I'm not a data scientist but I did stay at a Holiday Inn Express last night, right? >> Yeah. (panelists laugh) And there's like a little bit of pride that like they used 2,000, you know they used 2,000 computers to do it. Like a little bit of pride about that, but you know of course maybe not a great way to go. I think 20 years we couldn't do that, right? One computer was already an accomplishment to have that resource. So I think you have to think about the fact that if you're doing it wrong, you're going to just make that mistake bigger, which his also the other side of working with data. >> Sure, Bob. >> Yeah I have a comment about that. I've never liked the term citizen data scientist or citizen scientist. I get the point of it and I think employees within companies can help in the data analytics problem by maybe being a data collector or something. I mean I would never have just somebody become a scientist based on a few classes here she takes. It's like saying like, "Oh I'm going to be a citizen lawyer." And so you come to me with your legal problems, or a citizen surgeon. Like you need training to be good at something. You can't just be good at something just 'cause you want to be. >> John: Joe you wanted to say something too on that. >> Since we're in New York City I'd like to use the analogy of a real scientist versus a data scientist. So real scientist requires tools, right? And the tools are not new, like microscopes and a laboratory and a clean room. And these tools have evolved over years and years, and since we're in New York we could walk within a 10 block radius and buy any of those tools. It doesn't make us a scientist because we use those tools. I think with data, you know making, making the tools evolve and become easier to use, you know like Bob was saying, it doesn't make you a better data scientist, it just makes the data more accessible. You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn't really make you a scientist. I'm very involved in the NYU data science program and the Columbia data science program, like these kids are brilliant. You know these kids are not someone who is, you know just trying to run a day to day job, you know in corporate America. I think the people who are running the day to day job in corporate America are going to be the recipients of data science. Just like people who take drugs, right? As a result of a smart data scientist coming up with a formula that can help people, I think we're going to make it easier to distribute the data that can help people with all the new tools. But it doesn't really make it, you know the access to the data and tools available doesn't really make you a better data scientist. Without, like Bob was saying, without better training and education. >> So how-- I'm sorry, how do you then, if it's not for everybody, but yet I'm the user at the end of the day at my company and I've got these reams of data before me, how do you make it make better sense to me then? So that's where machine learning comes in or artificial intelligence and all this stuff. So how at the end of the day, Dion? How do you make it relevant and usable, actionable to somebody who might not be as practiced as you would like? >> I agree with Joe that many of us will be the recipients of data science. Just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don't need to be a computer scientist to get a lot of value out of our IT systems. The same thing's going to happen with data science. There's far more demand for data science than there ever could be produced by, you know having an ivory tower filled with data scientists. Which we need those guys, too, don't get me wrong. But we need to have, productize it and make it available in packages such that it can be consumed. The outputs and even some of the inputs can be provided by mere mortals, whether that's machine learning or artificial intelligence or bots that go off and run the hypotheses and select the algorithms maybe with some human help. We have to productize it. This is a constant of data scientist of service, which is becoming a thing now. It's, "I need this, I need this capability at scale. "I need it fast and I need it cheap." The commoditization of data science is going to happen. >> That goes back to what I was saying about, the recipient also of data science is also machines, right? Because I think the other thing that's happening now in the evolution of data is that, you know the data is, it's so tightly coupled. Back when you were talking about data warehousing you have all the business transactions then you take the data out of those systems, you put them in a warehouse for analysis, right? Maybe they'll make a decision to change that system at some point. Now the analytics platform and the business application is very tightly coupled. They become dependent upon one another. So you know people who are using the applications are now be able to take advantage of the insights of data analytics and data science, just through the app. Which never really existed before. >> I have one comment on that. You were talking about how do you get the end user more involved, well like we said earlier data science is not easy, right? As an end user, I encourage you to take a stats course, just a basic stats course, understanding what a mean is, variability, regression analysis, just basic stuff. So you as an end user can get more, or glean more insight from the reports that you're given, right? If you go to France and don't know French, then people can speak really slowly to you in French, you're not going to get it. You need to understand the language of data to get value from the technology we have available to us. >> Incidentally French is one of the languages that you have the option of learning if you're a mathematicians. So math PhDs are required to learn a second language. France being the country of algebra, that's one of the languages you could actually learn. Anyway tangent. But going back to the point. So statistics courses, definitely encourage it. I teach statistics. And one of the things that I'm finding as I go through the process of teaching it I'm actually bringing in my experience. And by bringing in my experience I'm actually kind of making the students think about the data differently. So the other thing people don't think about is the fact that like statisticians typically were expected to do, you know, just basic sort of tasks. In a sense that they're knowledge is specialized, right? But the day to day operations was they ran some data, you know they ran a test on some data, looked at the results, interpret the results based on what they were taught in school. They didn't develop that model a lot of times they just understand what the tests were saying, especially in the medical field. So when you when think about things like, we have words like population, census. Which is when you take data from every single, you have every single data point versus a sample, which is a subset. It's a very different story now that we're collecting faster than it used to be. It used to be the idea that you could collect information from everyone. Like it happens once every 10 years, we built that in. But nowadays you know, you know here about Facebook, for instance, I think they claimed earlier this year that their data was more accurate than the census data. So now there are these claims being made about which data source is more accurate. And I think the other side of this is now statisticians are expected to know data in a different way than they were before. So it's not just changing as a field in data science, but I think the sciences that are using data are also changing their fields as well. >> Dave: So is sampling dead? >> Well no, because-- >> Should it be? (laughs) >> Well if you're sampling wrong, yes. That's really the question. >> Okay. You know it's been said that the data doesn't lie, people do. Organizations are very political. Oftentimes you know, lies, damned lies and statistics, Benjamin Israeli. Are you seeing a change in the way in which organizations are using data in the context of the politics. So, some strong P&L manager say gets data and crafts it in a way that he or she can advance their agenda. Or they'll maybe attack a data set that is, probably should drive them in a different direction, but might be antithetical to their agenda. Are you seeing data, you know we talked about democratizing data, are you seeing that reduce the politics inside of organizations? >> So you know we've always used data to tell stories at the top level of an organization that's what it's all about. And I still see very much that no matter how much data science or, the access to the truth through looking at the numbers that story telling is still the political filter through which all that data still passes, right? But it's the advent of things like Block Chain, more and more corporate records and corporate information is going to end up in these open and shared repositories where there is not alternate truth. It'll come back to whoever tells the best stories at the end of the day. So I still see the organizations are very political. We are seeing now more open data though. Open data initiatives are a big thing, both in government and in the private sector. It is having an effect, but it's slow and steady. So that's what I see. >> Um, um, go ahead. >> I was just going to say as well. Ultimately I think data driven decision making is a great thing. And it's especially useful at the lower tiers of the organization where you have the routine day to day's decisions that could be automated through machine learning and deep learning. The algorithms can be improved on a constant basis. On the upper levels, you know that's why you pay executives the big bucks in the upper levels to make the strategic decisions. And data can help them, but ultimately, data, IT, technology alone will not create new markets, it will not drive new businesses, it's up to human beings to do that. The technology is the tool to help them make those decisions. But creating businesses, growing businesses, is very much a human activity. And that's something I don't see ever getting replaced. Technology might replace many other parts of the organization, but not that part. >> I tend to be a foolish optimist when it comes to this stuff. >> You do. (laughs) >> I do believe that data will make the world better. I do believe that data doesn't lie people lie. You know I think as we start, I'm already seeing trends in industries, all different industries where, you know conventional wisdom is starting to get trumped by analytics. You know I think it's still up to the human being today to ignore the facts and go with what they think in their gut and sometimes they win, sometimes they lose. But generally if they lose the data will tell them that they should have gone the other way. I think as we start relying more on data and trusting data through artificial intelligence, as we start making our lives a little bit easier, as we start using smart cars for safety, before replacement of humans. AS we start, you know, using data really and analytics and data science really as the bumpers, instead of the vehicle, eventually we're going to start to trust it as the vehicle itself. And then it's going to make lying a little bit harder. >> Okay, so great, excellent. Optimism, I love it. (John laughs) So I'm going to play devil's advocate here a little bit. There's a couple elephant in the room topics that I want to, to explore a little bit. >> Here it comes. >> There was an article today in Wired. And it was called, Why AI is Still Waiting for It's Ethics Transplant. And, I will just read a little segment from there. It says, new ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, government and military interests accountable as they design and employ AI. When tech giants build AI products, too often user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit driven business models based on aggregate data profiles. This is from Kate Crawford and Meredith Whittaker who founded AI Now. And they're calling for sort of, almost clinical trials on AI, if I could use that analogy. Before you go to market you've got to test the human impact, the social impact. Thoughts. >> And also have the ability for a human to intervene at some point in the process. This goes way back. Is everybody familiar with the name Stanislav Petrov? He's the Soviet officer who back in 1983, it was in the control room, I guess somewhere outside of Moscow in the control room, which detected a nuclear missile attack against the Soviet Union coming out of the United States. Ordinarily I think if this was an entirely AI driven process we wouldn't be sitting here right now talking about it. But this gentlemen looked at what was going on on the screen and, I'm sure he's accountable to his authorities in the Soviet Union. He probably got in a lot of trouble for this, but he decided to ignore the signals, ignore the data coming out of, from the Soviet satellites. And as it turned out, of course he was right. The Soviet satellites were seeing glints of the sun and they were interpreting those glints as missile launches. And I think that's a great example why, you know every situation of course doesn't mean the end of the world, (laughs) it was in this case. But it's a great example why there needs to be a human component, a human ability for human intervention at some point in the process. >> So other thoughts. I mean organizations are driving AI hard for profit. Best minds of our generation are trying to figure out how to get people to click on ads. Jeff Hammerbacher is famous for saying it. >> You can use data for a lot of things, data analytics, you can solve, you can cure cancer. You can make customers click on more ads. It depends on what you're goal is. But, there are ethical considerations we need to think about. When we have data that will have a racial bias against blacks and have them have higher prison sentences or so forth or worse credit scores, so forth. That has an impact on a broad group of people. And as a society we need to address that. And as scientists we need to consider how are we going to fix that problem? Cathy O'Neil in her book, Weapons of Math Destruction, excellent book, I highly recommend that your listeners read that book. And she talks about these issues about if AI, if algorithms have a widespread impact, if they adversely impact protected group. And I forget the last criteria, but like we need to really think about these things as a people, as a country. >> So always think the idea of ethics is interesting. So I had this conversation come up a lot of times when I talk to data scientists. I think as a concept, right as an idea, yes you want things to be ethical. The question I always pose to them is, "Well in the business setting "how are you actually going to do this?" 'Cause I find the most difficult thing working as a data scientist, is to be able to make the day to day decision of when someone says, "I don't like that number," how do you actually get around that. If that's the right data to be showing someone or if that's accurate. And say the business decides, "Well we don't like that number." Many people feel pressured to then change the data, change, or change what the data shows. So I think being able to educate people to be able to find ways to say what the data is saying, but not going past some line where it's a lie, where it's unethical. 'Cause you can also say what data doesn't say. You don't always have to say what the data does say. You can leave it as, "Here's what we do know, "but here's what we don't know." There's a don't know part that many people will omit when they talk about data. So I think, you know especially when it comes to things like AI it's tricky, right? Because I always tell people I don't know everyone thinks AI's going to be so amazing. I started an industry by fixing problems with computers that people didn't realize computers had. For instance when you have a system, a lot of bugs, we all have bug reports that we've probably submitted. I mean really it's no where near the point where it's going to start dominating our lives and taking over all the jobs. Because frankly it's not that advanced. It's still run by people, still fixed by people, still managed by people. I think with ethics, you know a lot of it has to do with the regulations, what the laws say. That's really going to be what's involved in terms of what people are willing to do. A lot of businesses, they want to make money. If there's no rules that says they can't do certain things to make money, then there's no restriction. I think the other thing to think about is we as consumers, like everyday in our lives, we shouldn't separate the idea of data as a business. We think of it as a business person, from our day to day consumer lives. Meaning, yes I work with data. Incidentally I also always opt out of my credit card, you know when they send you that information, they make you actually mail them, like old school mail, snail mail like a document that says, okay I don't want to be part of this data collection process. Which I always do. It's a little bit more work, but I go through that step of doing that. Now if more people did that, perhaps companies would feel more incentivized to pay you for your data, or give you more control of your data. Or at least you know, if a company's going to collect information, I'd want you to be certain processes in place to ensure that it doesn't just get sold, right? For instance if a start up gets acquired what happens with that data they have on you? You agree to give it to start up. But I mean what are the rules on that? So I think we have to really think about the ethics from not just, you know, someone who's going to implement something but as consumers what control we have for our own data. 'Cause that's going to directly impact what businesses can do with our data. >> You know you mentioned data collection. So slightly on that subject. All these great new capabilities we have coming. We talked about what's going to happen with media in the future and what 5G technology's going to do to mobile and these great bandwidth opportunities. The internet of things and the internet of everywhere. And all these great inputs, right? Do we have an arms race like are we keeping up with the capabilities to make sense of all the new data that's going to be coming in? And how do those things square up in this? Because the potential is fantastic, right? But are we keeping up with the ability to make it make sense and to put it to use, Joe? >> So I think data ingestion and data integration is probably one of the biggest challenges. I think, especially as the world is starting to become more dependent on data. I think you know, just because we're dependent on numbers we've come up with GAAP, which is generally accepted accounting principles that can be audited and proven whether it's true or false. I think in our lifetime we will see something similar to that we will we have formal checks and balances of data that we use that can be audited. Getting back to you know what Dave was saying earlier about, I personally would trust a machine that was programmed to do the right thing, than to trust a politician or some leader that may have their own agenda. And I think the other thing about machines is that they are auditable. You know you can look at the code and see exactly what it's doing and how it's doing it. Human beings not so much. So I think getting to the truth, even if the truth isn't the answer that we want, I think is a positive thing. It's something that we can't do today that once we start relying on machines to do we'll be able to get there. >> Yeah I was just going to add that we live in exponential times. And the challenge is that the way that we're structured traditionally as organizations is not allowing us to absorb advances exponentially, it's linear at best. Everyone talks about change management and how are we going to do digital transformation. Evidence shows that technology's forcing the leaders and the laggards apart. There's a few leading organizations that are eating the world and they seem to be somehow rolling out new things. I don't know how Amazon rolls out all this stuff. There's all this artificial intelligence and the IOT devices, Alexa, natural language processing and that's just a fraction, it's just a tip of what they're releasing. So it just shows that there are some organizations that have path found the way. Most of the Fortune 500 from the year 2000 are gone already, right? The disruption is happening. And so we are trying, have to find someway to adopt these new capabilities and deploy them effectively or the writing is on the wall. I spent a lot of time exploring this topic, how are we going to get there and all of us have a lot of hard work is the short answer. >> I read that there's going to be more data, or it was predicted, more data created in this year than in the past, I think it was five, 5,000 years. >> Forever. (laughs) >> And that to mix the statistics that we're analyzing currently less than 1% of the data. To taking those numbers and hear what you're all saying it's like, we're not keeping up, it seems like we're, it's not even linear. I mean that gap is just going to grow and grow and grow. How do we close that? >> There's a guy out there named Chris Dancy, he's known as the human cyborg. He has 700 hundred sensors all over his body. And his theory is that data's not new, having access to the data is new. You know we've always had a blood pressure, we've always had a sugar level. But we were never able to actually capture it in real time before. So now that we can capture and harness it, now we can be smarter about it. So I think that being able to use this information is really incredible like, this is something that over our lifetime we've never had and now we can do it. Which hence the big explosion in data. But I think how we use it and have it governed I think is the challenge right now. It's kind of cowboys and indians out there right now. And without proper governance and without rigorous regulation I think we are going to have some bumps in the road along the way. >> The data's in the oil is the question how are we actually going to operationalize around it? >> Or find it. Go ahead. >> I will say the other side of it is, so if you think about information, we always have the same amount of information right? What we choose to record however, is a different story. Now if you want wanted to know things about the Olympics, but you decide to collect information every day for years instead of just the Olympic year, yes you have a lot of data, but did you need all of that data? For that question about the Olympics, you don't need to collect data during years there are no Olympics, right? Unless of course you're comparing it relative. But I think that's another thing to think about. Just 'cause you collect more data does not mean that data will produce more statistically significant results, it does not mean it'll improve your model. You can be collecting data about your shoe size trying to get information about your hair. I mean it really does depend on what you're trying to measure, what your goals are, and what the data's going to be used for. If you don't factor the real world context into it, then yeah you can collect data, you know an infinite amount of data, but you'll never process it. Because you have no question to ask you're not looking to model anything. There is no universal truth about everything, that just doesn't exist out there. >> I think she's spot on. It comes down to what kind of questions are you trying to ask of your data? You can have one given database that has 100 variables in it, right? And you can ask it five different questions, all valid questions and that data may have those variables that'll tell you what's the best predictor of Churn, what's the best predictor of cancer treatment outcome. And if you can ask the right question of the data you have then that'll give you some insight. Just data for data's sake, that's just hype. We have a lot of data but it may not lead to anything if we don't ask it the right questions. >> Joe. >> I agree but I just want to add one thing. This is where the science in data science comes in. Scientists often will look at data that's already been in existence for years, weather forecasts, weather data, climate change data for example that go back to data charts and so forth going back centuries if that data is available. And they reformat, they reconfigure it, they get new uses out of it. And the potential I see with the data we're collecting is it may not be of use to us today, because we haven't thought of ways to use it, but maybe 10, 20, even 100 years from now someone's going to think of a way to leverage the data, to look at it in new ways and to come up with new ideas. That's just my thought on the science aspect. >> Knowing what you know about data science, why did Facebook miss Russia and the fake news trend? They came out and admitted it. You know, we miss it, why? Could they have, is it because they were focused elsewhere? Could they have solved that problem? (crosstalk) >> It's what you said which is are you asking the right questions and if you're not looking for that problem in exactly the way that it occurred you might not be able to find it. >> I thought the ads were paid in rubles. Shouldn't that be your first clue (panelists laugh) that something's amiss? >> You know red flag, so to speak. >> Yes. >> I mean Bitcoin maybe it could have hidden it. >> Bob: Right, exactly. >> I would think too that what happened last year is actually was the end of an age of optimism. I'll bring up the Soviet Union again, (chuckles). It collapsed back in 1991, 1990, 1991, Russia was reborn in. And think there was a general feeling of optimism in the '90s through the 2000s that Russia is now being well integrated into the world economy as other nations all over the globe, all continents are being integrated into the global economy thanks to technology. And technology is lifting entire continents out of poverty and ensuring more connectedness for people. Across Africa, India, Asia, we're seeing those economies that very different countries than 20 years ago and that extended into Russia as well. Russia is part of the global economy. We're able to communicate as a global, a global network. I think as a result we kind of overlook the dark side that occurred. >> John: Joe? >> Again, the foolish optimist here. But I think that... It shouldn't be the question like how did we miss it? It's do we have the ability now to catch it? And I think without data science without machine learning, without being able to train machines to look for patterns that involve corruption or result in corruption, I think we'd be out of luck. But now we have those tools. And now hopefully, optimistically, by the next election we'll be able to detect these things before they become public. >> It's a loaded question because my premise was Facebook had the ability and the tools and the knowledge and the data science expertise if in fact they wanted to solve that problem, but they were focused on other problems, which is how do I get people to click on ads? >> Right they had the ability to train the machines, but they were giving the machines the wrong training. >> Looking under the wrong rock. >> (laughs) That's right. >> It is easy to play armchair quarterback. Another topic I wanted to ask the panel about is, IBM Watson. You guys spend time in the Valley, I spend time in the Valley. People in the Valley poo-poo Watson. Ah, Google, Facebook, Amazon they've got the best AI. Watson, and some of that's fair criticism. Watson's a heavy lift, very services oriented, you just got to apply it in a very focused. At the same time Google's trying to get you to click on Ads, as is Facebook, Amazon's trying to get you to buy stuff. IBM's trying to solve cancer. Your thoughts on that sort of juxtaposition of the different AI suppliers and there may be others. Oh, nobody wants to touch this one, come on. I told you elephant in the room questions. >> Well I mean you're looking at two different, very different types of organizations. One which is really spent decades in applying technology to business and these other companies are ones that are primarily into the consumer, right? When we talk about things like IBM Watson you're looking at a very different type of solution. You used to be able to buy IT and once you installed it you pretty much could get it to work and store your records or you know, do whatever it is you needed it to do. But these types of tools, like Watson actually tries to learn your business. And it needs to spend time doing that watching the data and having its models tuned. And so you don't get the results right away. And I think that's been kind of the challenge that organizations like IBM has had. Like this is a different type of technology solution, one that has to actually learn first before it can provide value. And so I think you know you have organizations like IBM that are much better at applying technology to business, and then they have the further hurdle of having to try to apply these tools that work in very different ways. There's education too on the side of the buyer. >> I'd have to say that you know I think there's plenty of businesses out there also trying to solve very significant, meaningful problems. You know with Microsoft AI and Google AI and IBM Watson, I think it's not really the tool that matters, like we were saying earlier. A fool with a tool is still a fool. And regardless of who the manufacturer of that tool is. And I think you know having, a thoughtful, intelligent, trained, educated data scientist using any of these tools can be equally effective. >> So do you not see core AI competence and I left out Microsoft, as a strategic advantage for these companies? Is it going to be so ubiquitous and available that virtually anybody can apply it? Or is all the investment in R&D and AI going to pay off for these guys? >> Yeah, so I think there's different levels of AI, right? So there's AI where you can actually improve the model. I remember when I was invited when Watson was kind of first out by IBM to a private, sort of presentation. And my question was, "Okay, so when do I get "to access the corpus?" The corpus being sort of the foundation of NLP, which is natural language processing. So it's what you use as almost like a dictionary. Like how you're actually going to measure things, or things up. And they said, "Oh you can't." "What do you mean I can't?" It's like, "We do that." "So you're telling me as a data scientist "you're expecting me to rely on the fact "that you did it better than me and I should rely on that." I think over the years after that IBM started opening it up and offering different ways of being able to access the corpus and work with that data. But I remember at the first Watson hackathon there was only two corpus available. It was either the travel or medicine. There was no other foundational data available. So I think one of the difficulties was, you know IBM being a little bit more on the forefront of it they kind of had that burden of having to develop these systems and learning kind of the hard way that if you don't have the right models and you don't have the right data and you don't have the right access, that's going to be a huge limiter. I think with things like medical, medical information that's an extremely difficult data to start with. Partly because you know anything that you do find or don't find, the impact is significant. If I'm looking at things like what people clicked on the impact of using that data wrong, it's minimal. You might lose some money. If you do that with healthcare data, if you do that with medical data, people may die, like this is a much more difficult data set to start with. So I think from a scientific standpoint it's great to have any information about a new technology, new process. That's the nice that is that IBM's obviously invested in it and collected information. I think the difficulty there though is just 'cause you have it you can't solve everything. And if feel like from someone who works in technology, I think in general when you appeal to developers you try not to market. And with Watson it's very heavily marketed, which tends to turn off people who are more from the technical side. Because I think they don't like it when it's gimmicky in part because they do the opposite of that. They're always trying to build up the technical components of it. They don't like it when you're trying to convince them that you're selling them something when you could just give them the specs and look at it. So it could be something as simple as communication. But I do think it is valuable to have had a company who leads on the forefront of that and try to do so we can actually learn from what IBM has learned from this process. >> But you're an optimist. (John laughs) All right, good. >> Just one more thought. >> Joe go ahead first. >> Joe: I want to see how Alexa or Siri do on Jeopardy. (panelists laugh) >> All right. Going to go around a final thought, give you a second. Let's just think about like your 12 month crystal ball. In terms of either challenges that need to be met in the near term or opportunities you think will be realized. 12, 18 month horizon. Bob you've got the microphone headed up, so I'll let you lead off and let's just go around. >> I think a big challenge for business, for society is getting people educated on data and analytics. There's a study that was just released I think last month by Service Now, I think, or some vendor, or Click. They found that only 17% of the employees in Europe have the ability to use data in their job. Think about that. >> 17. >> 17. Less than 20%. So these people don't have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we're in today. And that's Europe. It's probably a lot worse in the United States. So that's a big challenge I think. To educate the masses. >> John: Joe. >> I think we probably have a better chance of improving technology over training people. I think using data needs to be iPhone easy. And I think, you know which means that a lot of innovation is in the years to come. I do think that a keyboard is going to be a thing of the past for the average user. We are going to start using voice a lot more. I think augmented reality is going to be things that becomes a real reality. Where we can hold our phone in front of an object and it will have an overlay of prices where it's available, if it's a person. I think that we will see within an organization holding a camera up to someone and being able to see what is their salary, what sales did they do last year, some key performance indicators. I hope that we are beyond the days of everyone around the world walking around like this and we start actually becoming more social as human beings through augmented reality. I think, it has to happen. I think we're going through kind of foolish times at the moment in order to get to the greater good. And I think the greater good is using technology in a very, very smart way. Which means that you shouldn't have to be, sorry to contradict, but maybe it's good to counterpoint. I don't think you need to have a PhD in SQL to use data. Like I think that's 1990. I think as we evolve it's going to become easier for the average person. Which means people like the brain trust here needs to get smarter and start innovating. I think the innovation around data is really at the tip of the iceberg, we're going to see a lot more of it in the years to come. >> Dion why don't you go ahead, then we'll come down the line here. >> Yeah so I think over that time frame two things are likely to happen. One is somebody's going to crack the consumerization of machine learning and AI, such that it really is available to the masses and we can do much more advanced things than we could. We see the industries tend to reach an inflection point and then there's an explosion. No one's quite cracked the code on how to really bring this to everyone, but somebody will. And that could happen in that time frame. And then the other thing that I think that almost has to happen is that the forces for openness, open data, data sharing, open data initiatives things like Block Chain are going to run headlong into data protection, data privacy, customer privacy laws and regulations that have to come down and protect us. Because the industry's not doing it, the government is stepping in and it's going to re-silo a lot of our data. It's going to make it recede and make it less accessible, making data science harder for a lot of the most meaningful types of activities. Patient data for example is already all locked down. We could do so much more with it, but health start ups are really constrained about what they can do. 'Cause they can't access the data. We can't even access our own health care records, right? So I think that's the challenge is we have to have that battle next to be able to go and take the next step. >> Well I see, with the growth of data a lot of it's coming through IOT, internet of things. I think that's a big source. And we're going to see a lot of innovation. A new types of Ubers or Air BnBs. Uber's so 2013 though, right? We're going to see new companies with new ideas, new innovations, they're going to be looking at the ways this data can be leveraged all this big data. Or data coming in from the IOT can be leveraged. You know there's some examples out there. There's a company for example that is outfitting tools, putting sensors in the tools. Industrial sites can therefore track where the tools are at any given time. This is an expensive, time consuming process, constantly loosing tools, trying to locate tools. Assessing whether the tool's being applied to the production line or the right tool is at the right torque and so forth. With the sensors implanted in these tools, it's now possible to be more efficient. And there's going to be innovations like that. Maybe small start up type things or smaller innovations. We're going to see a lot of new ideas and new types of approaches to handling all this data. There's going to be new business ideas. The next Uber, we may be hearing about it a year from now whatever that may be. And that Uber is going to be applying data, probably IOT type data in some, new innovative way. >> Jennifer, final word. >> Yeah so I think with data, you know it's interesting, right, for one thing I think on of the things that's made data more available and just people we open to the idea, has been start ups. But what's interesting about this is a lot of start ups have been acquired. And a lot of people at start ups that got acquired now these people work at bigger corporations. Which was the way it was maybe 10 years ago, data wasn't available and open, companies kept it very proprietary, you had to sign NDAs. It was like within the last 10 years that open source all of that initiatives became much more popular, much more open, a acceptable sort of way to look at data. I think that what I'm kind of interested in seeing is what people do within the corporate environment. Right, 'cause they have resources. They have funding that start ups don't have. And they have backing, right? Presumably if you're acquired you went in at a higher title in the corporate structure whereas if you had started there you probably wouldn't be at that title at that point. So I think you have an opportunity where people who have done innovative things and have proven that they can build really cool stuff, can now be in that corporate environment. I think part of it's going to be whether or not they can really adjust to sort of the corporate, you know the corporate landscape, the politics of it or the bureaucracy. I think every organization has that. Being able to navigate that is a difficult thing in part 'cause it's a human skill set, it's a people skill, it's a soft skill. It's not the same thing as just being able to code something and sell it. So you know it's going to really come down to people. I think if people can figure out for instance, what people want to buy, what people think, in general that's where the money comes from. You know you make money 'cause someone gave you money. So if you can find a way to look at a data or even look at technology and understand what people are doing, aren't doing, what they're happy about, unhappy about, there's always opportunity in collecting the data in that way and being able to leverage that. So you build cooler things, and offer things that haven't been thought of yet. So it's a very interesting time I think with the corporate resources available if you can do that. You know who knows what we'll have in like a year. >> I'll add one. >> Please. >> The majority of companies in the S&P 500 have a market cap that's greater than their revenue. The reason is 'cause they have IP related to data that's of value. But most of those companies, most companies, the vast majority of companies don't have any way to measure the value of that data. There's no GAAP accounting standard. So they don't understand the value contribution of their data in terms of how it helps them monetize. Not the data itself necessarily, but how it contributes to the monetization of the company. And I think that's a big gap. If you don't understand the value of the data that means you don't understand how to refine it, if data is the new oil and how to protect it and so forth and secure it. So that to me is a big gap that needs to get closed before we can actually say we live in a data driven world. >> So you're saying I've got an asset, I don't know if it's worth this or this. And they're missing that great opportunity. >> So devolve to what I know best. >> Great discussion. Really, really enjoyed the, the time as flown by. Joe if you get that augmented reality thing to work on the salary, point it toward that guy not this guy, okay? (everyone laughs) It's much more impressive if you point it over there. But Joe thank you, Dion, Joe and Jennifer and Batman. We appreciate and Bob Hayes, thanks for being with us. >> Thanks you guys. >> Really enjoyed >> Great stuff. >> the conversation. >> And a reminder coming up a the top of the hour, six o'clock Eastern time, IBMgo.com featuring the live keynote which is being set up just about 50 feet from us right now. Nick Silver is one of the headliners there, John Thomas is well, or rather Rob Thomas. John Thomas we had on earlier on The Cube. But a panel discussion as well coming up at six o'clock on IBMgo.com, six to 7:15. Be sure to join that live stream. That's it from The Cube. We certainly appreciate the time. Glad to have you along here in New York. And until the next time, take care. (bright digital music)

(upbeat electronic music) >> Hello, everyone. Welcome to the Cube Studios in Palo Alto, California. I'm John Furrier, the co-host of the Cube and co-founder of SiliconANGLE Media Inc. I'm here with George Gilbert for a Wikibon conversation on the state of the big data. George Gilbert is the analyst at Wikibon covering big data. George, great to see you. Looking good. (laughing) >> Good to see you, John. >> So George, you're obviously covering big data. Everyone knows you. You always ask the tough questions, you're always drilling down, going under the hood, and really inspecting all the trends, and also looking at the technology. What are you working on these days as the big data analyst? What's the hot thing that you're covering? >> OK, so, what's really interesting is we've got this emerging class of applications. The name that we've used so far is modern operational analytic applications. Operational in the sense that they help drive business operations, but analytical in the sense that the analytics either inform or drive transactions, or anticipate and inform interactions with people. That's the core of this class of apps. And then there are some sort of big challenges that customers are having in trying to build, and deploy, and operate these things. That's what I want to go through. >> George, you know, this is a great piece. I can't wait to (mumbling) some of these questions and ask you some pointed questions. But I would agree with you that to me, the number one thing I see customers either fumbling with or accelerating value with is how to operationalize some of the data in a way that they've never done it before. So you start to see disciplines come together. You're starting to see people with a notion of digital business being something that's not a department, it's not a marketing department. Data is everywhere, it's horizontally scalable, and the smart executives are really looking at new operational tactics to do that. With that, let me kick off the first question to you. People are trying to balance the cloud, On Premise, and The Edge, OK. And that's classic, you're seeing that now. I've got a data center, I have to go to the cloud, a hybrid cloud. And now the edge of the network. We were just taking about Block Chain today, there's this huge problem. They've got the balance that, but they've got to balance it versus leveraging specialized services. How do you respond to that? What is your reaction? What is your presentation? >> OK, so let's turn it into something really concrete that everyone can relate to, and then I'll generalize it. The concrete version is for a number of years, everyone associated Hadoop with big data. And Hadoop, you tried to stand up on a cluster on your own premises, for the most part. It was on had EMR, but sort of the big company activity outside, even including the big tech companies was stand up a Hadoop cluster as a pilot and start building a data lake. Then see what you could do with sort of huge amounts of data that you couldn't normally sort of collect and analyze. The operational challenges of standing up that sort of cluster was rather overwhelming, and I'll explain that later, so sort of park that thought. Because of that complexity, more and more customers, all but the most sophisticated, are saying we need a cloud strategy for that. But once you start taking Hadoop into the cloud, the components of this big data analytic system, you have tons more alternatives. So whereas in Cloudera's version of Hadoop you had Impala as your MPP sequel database. On Amazon, you've got Amazon Redshift, you've got Snowflake, you've got dozens up MPP sequel databases. And so the whole playing field shifts. And not only that, Amazon has instrumented their, in that particular case, their application, to be more of a more managed service, so there's a whole lot less for admins to do. And you take that on sort of, if you look at the slides, you take every step in that pipeline. And when you put it on a different cloud, it's got different competitors. And even if you take the same step in a pipeline, let's say Spark on HDFS to do your ETL, and your analysis, and your shaping of data, and even some of the machine learning, you put that on Azure and on Amazon, it's actually on different storage foundation. So even if you're using the same component, it's different. There's a lot of complexity and a lot of trade off that you got to make. >> Is that a problem for customers? >> Yes, because all of a sudden, they have to evaluate what those trade offs are. They have to evaluate the trade off between specialization. Do I use the best to breed thing on one platform. And if I do, it's not compatible with what I might be running on prem. >> That'll slow a lot of things down. I can tell you right now, people want to have the same code base on all environments, and then just have the same seamless operational role. OK, that's a great point, George. Thanks for sharing that. The second point here is harmonizing and simplifying management across hybrid clouds. Again, back to your point. You set that up beautifully. Great example, open source innovation hits a roadblock. And the roadblock is incompatible components in multiple clouds. That's a problem. It's a management nightmare. How do harmonization about hybrid cloud work? >> You couldn't have asked it better. Let me put it up in terms of an X Y chart where on the x-axis, you have the components of an analytic pipeline. Ingest, process, analyze, predict, serve. But then on the y-axis, this is for an admin, not a developer. These are just some of the tasks they have to worry about. Data governance, performance monitoring, scheduling and orchestration, availability and recovery, that whole list. Now, if you have a different product for each step in that pipeline, and each product has a different way of handling all those admin tasks, you're basically taking all the unique activities on the y-axis, multiplying it by all the unique products on the x-axis, and you have overwhelming complexity, even if these are managed services on the cloud. Here now you've got several trade offs. Do I use the specialized products that you would call best to breed? Do I try and do end to end integration so I get simplification across the pipeline? Or do I use products that I had on-prem, like you were saying, so that I have seamless compatibility? Or do I use the cloud vendors? That's a tough trade off. There's another similar one for developers. Again, on the y-axis, for all the things that a developer would have to deal with, not all of them, just a sample. The data model and the data itself, how to address it, the programing model, the persistence. So on that y-axis, you multiply all those different things you have to master for each product. And then on the x-axis, all the different products and the pipeline. And you have that same trade off, again. >> Complexity is off the charts. >> Right. And you can trade end to end integration to simplify the complexity, but we don't really have products that are fully fleshed out and mature that stretch from one end of the pipeline to the other, so that's a challenge. Alright. Let's talk about another way of looking at management. This was looking at the administrators and the developers. Now, we're getting better and better software for monitoring performance and operations, and trying to diagnose root cause when something goes wrong and then remediate it. There's two real approaches. One is you go really deep, but on a narrow part of your application and infrastructure landscape. And that narrow part might be, you know, your analytic pipeline, your big data. The broad approach is to get end to end visibility across Edge with your IOT devices, across on-prem, perhaps even across multiple clouds. That's the breadth approach, end to end visibility. Now, there's a trade off here too as in all technology choices. When you go deep, you have bounded visibility, but that bounded visibility allows you to understand exactly what is in that set of services, how they fit together, how they work. Because the vendor, knowing that they're only giving you management of your big data pipeline, they can train their models, their machine learning models, so that whenever something goes wrong, they know exactly what caused it and they can filter out all the false positives, the scattered errors that can confuse administrators. Whereas if you want breadth, you want to see end to end your entire landscape so that you can do capacity planning and see if there was an error way upstream, something might be triggered way downstream or a bunch of things downstream. So the best way to understand this is how much knowledge do you have of all the pieces work together, and how much knowledge you have of all the pieces, the software pieces fit together. >> This is actually an interesting point. So if I kind of connect the dots for you here is the bounded root cause analysis that we see a lot of machine learning, that's where the automation is. >> George: Yeah. >> The unbounded, the breadth, that's where the data volume is. But they can work together, that's what you're saying. >> Yes. And actually, I hadn't even got to that, so thanks for taking it out. >> John: Did I jump ahead on that one? (laughing) >> No, no, you teed it out. (laughing) Because ultimately-- >> Well a lot of people want to know where it's going to be automated away. All the undifferentiated labored and scale can be automated. >> Well, when you talk about them working together. So for the deep depth first, there's a small company called Unravel Data that sort of modeled eight million jobs or workloads of big data workloads from high tech companies, so they know how all that fits together and they can tell you when something goes wrong exactly what goes wrong and how to remediate it. So take something like Rocana or Splunk, they look end to end. The interesting thing that you brought up is at some point, that end to end product is going to be like a data warehouse and the depth products are going to sit on top of it. So you'll have all the contextual data of your end to end landscape, but you'll have the deep knowledge of how things work and what goes wrong sitting on it. >> So just before we jump to the machine learning question which I want to ask you, what you're saying is the industry is evolving to almost looking like a data warehouse model, but in a completely different way. >> Yeah. Think of it as, another cue. (laughing) >> John: That's what I do, George. I help you out with the cues. (laughing) No, but I mean the data warehouse, everyone knows what that was. A huge industry, created a lot of value, but then the world got rocked by unstructured data. And then their bounded, if you will, view has got democratized. So creative destruction happened which is another word for new entrants came in and incumbents got rattled. But now it's kind of going back to what looks like a data warheouse, but it's completely distributed around. >> Yes. And I was going to do one of my movie references, but-- >> No, don't do it. Save us the judge. >> If you look at this starting in the upper right, that's the data lake where you're collecting all the data and it's for search, it's exploratory. As you get more structure, you get to the descriptive place where you can build dashboards to monitor what's going on. And you get really deep, that's when you have the machine learning. >> Well, the machine learning is hitting the low hanging fruit, and that's where I want to get to next to move it along. Sourcing machine learning capability, let's discuss that. >> OK, alright. Just to set contacts before we get there, notice that when you do end to end visibility, you're really seeing across a broad landscape. And when I'm showing my public cloud big data, that would be depth first just for that component. But you would do breadth first, you could do like a Rocana or a Splunk that then sees across everything. The point I wanted to make was when you said we're reverting back to data warehouses and revisiting that dream again, the management applications started out as saying we know how to look inside machine data and tell you what's going on with your landscape. It turns out that machine data and business operations data, your application data, are really becoming one and the same. So what used to be a transaction, there was one transaction. And that, when you summarized them, that went into the data warehouse. Then we had with systems of engagement, you had about 100 interaction events that you tracked or sort of stored for everything business transaction. And then when we went out to the big data world, it's so resource intensive that we actually had 1,000 to 10,000 infrastructure events for every business transaction. So that's why the data volumes have grown so much and why we had to go back first to data lake, and then curate it to the warehouse. >> Classic innovation story, great. Machine learning. Sourcing machine learning capabilities 'cause that's where the rubber starts hitting the road. You're starting to see clear skies when it comes to where machine learning is starting fit in. Sourcing machine learning capabilities. >> You know, even though we sort of didn't really rehearse this, you're helping cue me on perfectly. Let me make the assertion that with machine learning, we have the same shortage of really trained data scientists that we had when we were trying to stand up Hadoop clusters and do big data analytics. We did not have enough administrators because these were open source components built from essentially different projects, and putting them all together required a huge amount of skills. Data science requires, really, knowledge of algorithms that even really sophisticated programmers will tell you, "Jeez, now I need a PhD "to really understand how this stuff works." So the shortage, that means we're not going to get a lot of hand-built machine learning applications for a while. >> John: In a lot of libraries out there right now, you see TensorFlow from Google. Big traction with that application. >> George: But for PhDs, for PhDs. My contention is-- >> John: Well developers too, you could argue developers, but I'm just putting it out there. >> George: I will get to that, actually. A slide just on that. Let me do this one first because my contention is the first big application, widespread application of machine learning, is going to be the depth first management because it comes with a model built in of how all the big data workloads, services, and infrastructure fit together and work together. And if you look at how the machine learning model operates, when it knows something goes wrong, let's say an analytic job takes 17 hours and then just falls over and crashes, the model can actually look at the data layout and say we have way too much on one node, and it can change the settings and change the layout or the data because it knows how all the stuff works. The point about this is the vendor. In this particular example, Unravel Data, they built into their model an understanding of how to keep a big data workload running as opposed to telling the customer, "You have to program it." So that fits into the question you were just asking which is where do you get this talent. When you were talking about like TensorFlow, and Cafe, and Torch, and MXnet, those are all like assembly language. Yes, those are the most powerful places you could go to program machine learning. But the number of people is inversely proportional to the power of those. >> John: Yeah, those are like really unique specialty people. High, you know, the top guys. >> George: Lab coats, rocket scientists. >> John: Well yeah, just high end tier one coders, tier one brains coding away, AI gurus. This is not your working developer. >> George: But if you go up two levels. So go up one level is Amazon machine learning, Spark machine learning. Go up another level, and I'm using Amazon as an example here. Amazon has a vision service called Recognition. They have a speech generation service, Natural Language. Those are developer ready. And when I say developer ready, I mean developer just uses an API, you know, passes in the data that comes out. He doesn't have to know how the model works. >> John: It's kind of like what DevOps was for cloud at the end of the day. This slide is completely accurate in my opinion. And we're at the early days and you're starting to see the platforms develop. It's the classic abstraction layer. Whoever can extract away the complexity as AI and machine learning grows is going to be the winning platform, no doubt about it. Amazon is showing some good moves there. >> George: And you know how they abstracted away. In traditional programming, it was just building higher and higher APIs, more accessible. In machine learning, you can't do that. You have to actually train the models which means you need data. So if you look at the big cloud vendors right now. So Google, Microsoft, Amazon, and IBM. Most of them, the first three, they have a lot of data from their B to C businesses. So you know, people talking to Echo, people talking to Google Assistant or Siri. That's where they get enough of their speech. >> John: So data equals power? >> George: Yes. >> By having data, you have the ingredients. And the more data that you have, the more data that you know about, the more data that has information around it, the more effective it can be to train machine learning algorithms. >> Yes. >> And the benefit comes back to the people who have the data. >> Yes. And so even though your capabilities get narrower, 'cause you could do anything on TensorFlow. >> John: Well, that's why Facebook is getting killed right now just to kind of change tangents. They have all this data and people are very unhappy, they just released that the Russians were targeting anti-semitic advertising, they enabled that. So it's hard to be a data platform and still provide user utility. This is what's going on. Whoever has the data has the power. It was a Frankenstein moment for Facebook. So there's that out there for everyone. How do companies do the right thing? >> And there's also the issue of customer intellectual property protection. As consumers, we're like you can take our voice, you can take all our speech to Siri or to Echo or whatever and get better at recognizing speech because we've given up control of that 'cause we want those services for free. >> Whoever can shift the data value to the users. >> George: To the developers. >> Or to the developers, or communities, better said, will win. >> OK. >> In my opinion, that's my opinion. >> For the most part, Amazon, Microsoft, and Google have similar data assets. For the most part, so far. IBM has something different which is they work closely with their industry customers and they build progressively. They're working with Mercedes, they're working with BMW. They'll work on the connected car, you know, the autonomous car, and they build out those models slowly. >> So George, this slide is really really interesting and I think this should be a roadmap for all customers to look at to try to peg where they are in the machine learning journey. But then the question comes in. They do the blocking and tackling, they have the foundational low level stuff done, they're building the models, they're understanding the mission, they have the right organizational mindset and personnel. Now, they want to orchestrate it and implement it into action. That's the final question. How do you orchestrate the distributed machine learning feedback and the data coherency? How do you get this thing scaling? How do these machines and the training happen so you have the breadth, and then you could bring the machine learning up the curve into the dashboard? >> OK. We've saved the best for last. It's not easy. When I show the chevrons, that's the analytic data pipeline. And imagine in the serve and predict at the very end, let's take an IOT app, a very sophisticated one. which would be an autonomous car. And it doesn't actually have to be an autonomous one, you could just be collected a lot of information off the car to do a better job insuring it, the insurance company. But the key then is you're collecting data on a fleet of cars, right? You're collecting data off each one, but you're also collecting then the fleet. And that, in the cloud, is where you keep improving your model of how the car works. You run simulations to figure out not just how to design better ones in the future, but how to tune and optimize the ones that are on the road now. That's number three. And then in four, you push that feedback back out to the cars on the road. And you have to manage, and this is tricky, you have to make sure that the models that you trained in step three are coherent, or the same, when you take out the fleet data and then you put the model for a particular instance of a car back out on the highway. >> George, this is a great example, and I think this slide really represents the modern analytical operational role in digital business. You can't look further than Tesla, this is essentially Tesla, and now all cars as a great example 'cause it's complex, it's an internet (mumbling) device, it's on the edge of the network, it's mobility, it's using 5G. It encapsulates everything that you are presenting, so I think this is example, is a great one, of the modern operational analytic applications that supports digital business. Thanks for joining this Wikibon conversaion. >> Thank you, John. >> George Gilbert, the analyst at Wikibon covering big data and the modern operational analytical system supporting digital business. It's data driven. The people with the data can train the machines that have the power. That's the mandate, that's the action item. I'm John Furrier with George Gilbert. Thanks for watching. (upbeat electronic music)

>> Narrator: Live from Las Vegas it's The Cube covering Interconnect 2017. Brought to you by IBM. >> Okay welcome back everyone. We are live at the Mandalay Bay for IBM Interconnect 2017, The Cube's exclusive coverage. I'm John Frower, Dave Vellante, my co-host. Our next guest is Jim Casey and Michael Gilfix. Michael's the VP of process transformation and Jim is offering manager at IBM. Guys, welcome back to The Cube. >> Both: Thank you. >> So you guys had a big announcement on Monday, the digital assistant, so I've been craving a digital assistant since the little Microsoft little, you know, icon would pop up. >> Michael: You're talking about Clip, aren't you? >> The clip man. >> Don't talk about that. >> We don't like that. >> To me that was once called the digital assistant. It was a help button, but this is now, digital assistant is real automation, and you guys got a whole other take on this. It's totally cloud, cloud first. What's the digital assistant product that you announced? Take us through that. >> So here was our vision. What we found was in the modern, digital workplace, everyone is struggling to just keep up pace. Too many sources of information, and the information is buried everywhere. It's buried in emails, in spreadsheets, in documents. Many corporations have undertaken a BI project. In fact, there's an explosion of all these different dashboards that has all kinds of business data that they could go and see, so no one has the time to read all these things. Meanwhile, everyone in the modern world is trying to do 50 things at once and it's hard to figure out what is the best time to progress something and make progress? Our vision, so what we thought is wouldn't it be great if I could program this assistant, programmable by everyday business users, to watch for the things that matter to me and figure out when I should take action or take automated action on my behalf to save me time. >> So it's an interface, so it's software interface, cloud-based SAS, and the back end, does the user have to, what's the persona of the user that's using your product? >> Well, we want them to be used by non-developers, non-technical users, and so we thought really carefully about how you can teach your assistant these notions of skills, really point to tasks that can really make your life easier on a daily basis and they can pick anything that they like working with, that they can connect to, get the information from, and effectively assemble into these point-to tasks. >> Host: And the data sources are whatever I want them to be, explain how that works? >> Yeah, it can connect to common SAS applications. Those could be things like productivity suites, like G-Suite, they can be things like CRM systems, like Sales Force, campaign management systems like Marketo, and that's just in the beta that we just launched. And of course in the future, they'll be able to connect into their on-premise systems as well. >> So is it to replace the dashboards and all the wrangling that goes on? Most business users will have either a department that does all the data science or data prep for them, wrangling data sets, and then they get reports or spreadsheets or some BI dashboard. >> Yeah, we wanted the assistant to push the work to the user instead of the user having to go and spend time watching all these dashboards that really, they just didn't have time to do. And so the assistant takes all the heavy lifting of watching the data for you, figures out when action is needed, and then taps you on the shoulder. >> So Ginny Ramete was talking about that your customers want to own the data. So that's a great purpose, we buy into that mission, but a lot of the data is spread all over the place, so one of the problems that we're seeing in the big data world, now IOT complicates even further, is that data's everywhere, scattered, and the tools might have stacks and data wrangling within tools so you have complexity out there just on the scaffolding of how the data's managed. Is that part of the problem that you guys help solve? Because that seems to be a pain point. >> Yeah, and I think the amount of time that people spend just searching and aggregating and gathering information so they can figure out what to do, it's staggering. And when you think about the, it takes about two the three hours often for people to gather all the information that they need in order to make a real significant decision, every day, daily, you know operations. You're spending time in your email, you're building spreadsheets. Think of all the time you spend building a spreadsheet, wrangling data, you know. It's a productivity killer, and so a lot of the use cases that we look for, we'll ask our clients show me the ugliest spreadsheet that you use on a day-to-day basis for business operations. That's usually a starting point, or show me how many dashboards are you looking at and what are the decision you make off that? That's the stuff that we want to collapse into what the assistant can provide. >> So I got a use case for you, I'm a walking, I'm like everybody, right, so I've got my email, I've got five or six spreadsheets, Google Docs that I'm in every day all day, maybe there's a base camp, maybe there's a slack. I'm in Sales Force, all right, and then I got my social. >> Tool overdose. >> You just described the typical modern environment. All fragmented tools. >> And I'm in there and I'm like which browser is it, oh is it in Firefox, I'll put my Safari stuff I'll put over here, and I'll put my email in Mozilla, okay. It is just awful, it's a bloody nightmare, I get lost. I got to back up, hit the escape key, and go, okay, where am I, how do I find it again? >> Jim: It's connecting the dots. >> Okay, explain now how you can help me. >> So think of the things that you're looking for in all those different data sources. We're seeing the trend now. It's not about how can I just connect with things, it's how can I connect the dots? It's the actual business data inside of there, and how do I put that in a context that's relevant to you, what you're trying to do? You know, and a great example, we're working with one client who, they're moving, and a lot of people are doing this, they're moving from a point in time sale to being as a service, and in that kind of scenario, relationships with your clients really matter. And preventing customer churn is really important. So they have people who are responsible for making sure that people are not going to churn. That's a lot of dots to connect, right? So with the Digital Business Assistant, what we do is we look for those patterns that are really common that predict churn, but those things are scattered across your sales systems, your marketing systems, the website traffic, social media even, and we're able to combine all those things into a really consumable component called a skill. And then that individual person that's responsible for this set of customers can tailor it to their needs. So it's kind of like how you would buy a suit. When you go in and buy a suit, you don't get just the fabric laid out on a table and they cut it, right? You, most people don't anyway. (they laugh) >> I buy what's on the rack. I say "I want that one." >> Yeah, you walk in and you say that. >> I want what that is. >> 42 long, right? And they make a couple adjustments and then it's yours. >> All right, I'll take that suit up there, what's on the mannequin. >> They make a few adjustments and it's yours. Software should be the same way. You should be able to configure software in a few clicks. >> That's the whole thing, I mean, I joke about the mannequin but that's really kind of what hangs the perfect use case so that would be an automated example of an assistant model for you guys. Sometimes you just want everything to hang together for you, and sometimes you might want to go in and go look at the data. >> Yeah, and we see this across a lot of different industries, so things like customer service and sales and marketing, but we also see it in, let's say I'm a field technician, right? And I got to go out to an oil field. How do I know all the different patterns of information that might predict whether or not I need to, what I need to do when I'm out there. >> So you monitor my patterns, my behavior, and then ultimately train the model, or? >> Well you program it. You tell it what to watch for for you. So to give you an example of the kind of use case, to pick a specific use case, and we shared this again in sort of our unveiling on Monday. We shared the idea of a sales rep who is pursuing a given opportunity, and thinking about all the factors that went into their success and, you know, that sales rep has several different things they need to use to really maximize their chance of closing that deal. So one is they need to be responsive do their customer, and you know, like many different corporations out there who sell many different products and services, while you're busy working on the new opportunity, you've got to service the old. So when some issue comes up, you have to be responsive to it. Well, it's really hard while you're busy working on all these opportunities, to make sure that the issue's being resolved, that you're being responsive to your customer. Meanwhile, everybody in the corporation is coming up with new opportunities, new marketing brochures, new values in the product. And so is your rep knowledgeable about the latest and greatest products? So we imagine that you could teach your assistant how to watch some of this stuff for you and really help you to close your opportunity. And a very pointed example of the kinds of things that it should watch for you, I should be able to say something like hey, if I can have an active opportunity and then my customer goes and opens a service support ticket and that service support ticket hasn't been resolved in a week and meanwhile, I got a bunch of email coming from that client, of tone angry, notice the cognitive part there, about this particular product, and meanwhile I'm on the road and I'm not checking my email. Well, I have a catastrophe waiting to happen. So I can program my assistant to watch for these kinds of things. >> Does it do push notifications? >> Exactly, so you can then have it push to you, look, here's all the information about the active service thing, here's how long it was sitting there waiting for resolution, this is what's happened since, and you can immediately take action. >> So you're orchestrating basically signals that the user connects, like a Google alert on search is a trivial example, right? Someone types, a result comes on Google, you get an email. Here, you're kind of doing that-- >> But it's proactive. You tell your assistant to proactively watch it for you, and that's a unique technology that we developed in-house. Because it's watching all these events happening in the enterprise and figuring out when that thing becomes actionable. >> And the user would know where to look, because like Dave's spreadsheet might say "hey, cash balance" or you know, sales trend, this rep and then something happens, and he can get that pushed to him from three different disparate side-load apps, that's pretty much what it is. >> That's right. >> Okay, so give us the status on the beta right now. It's a beta, so it's sign-up required. Okay, and the requirements to implement it, if you get through the beta, is just log in to a portal? It's a SAS model and then do the connectors? >> So the first thing you do, you go to IBM.com/assistant. You can sign up to. >> That, by the way, might be the easiest URL I think we ever came up with. I'm pretty sure that one's going to be memorable. >> Yeah, so you just go to that site, you sign up, you give us a little bit of information, your email, how to contact you and we'll put you on the waiting list, and what we're going to be doing is opening up more seats as we go through over the next couple weeks, and then we plan in the near term here to make it available as an open beta that you could see, and you'll see that inside of Bloomix as a tile inside of Bloomix. >> And here's the thing, we're doing something really different in the marketplace. This is a very different kind of offering, really targeting, again, non-technical people, this proactive situational awareness that your assistant can do, uses your data, built-in intelligence, intelligence that can customize to the way you work, guide you to the next best action. We have an incredible vision for this. The idea behind the beta is to start getting feedback. We worked very closely with early customers in the initial design and development. We want to open that up and get even more feedback and ideas on this kind of technology. >> So how is this different from Watson's discovery services that they have? I can imagine that you're building on Watson. Is it the cognitive piece within IBM, or is this kind of, I mean how would a customer figure that out, or just more of a-- >> Yeah, so I can give you an example. So we have one of our prototypes that we're actually taking some of the components of Watson discovery service and we package that up as a skill inside of your assistant, and it's a specific implementation, so what it allows you to do in this case is it'll look at your email and it'll look for specific entities, like a customer that matters to you, and if I get three emails of negative sentiment from a customer where I also have an open opportunity in the last week, that's a pattern I want to know about, right? Or we can start to correlate with all sorts of different things, so I think what you're going to see is these skills that we make available with the digital business assistant really up, take consumability of these really, really powerful technologies around cognitive and cloud. We take that to the next level. >> That's the key, how do we make Watson tailorable and put in the hands of every knowledge worker in every company? >> Host: So I presume you guys are dog fooding this personally, is that right? >> We have plans to do that, yes. >> Host: Oh, you haven't started yet? >> Sampling our own champagne. >> But we are, yes. >> He always gets called on that. >> We will be using it, yes. >> We created that champagne. >> We're beer drinkers, that's it, beer. >> We're going back to dog food, we eat beer, we should drink our own beer now. We created that with all our boost men, remember? (laughs) >> So get back to the status of the product. So it's got some Watson capability, but this is for the user to use. I don't have to get IT involved? >> Jim: That's right. >> This is where the user takes a personal productivity approach, and you bring in some Watson-- >> A user may not even know that they're using some of these Watson capabilities. To the end user, what do you want it to do for me? Well, I want it to tell me if, uh, if I think a customer might be upset with me. Well, that might be a combination of a lot of different things, but it just makes it really consumable and easy for people. >> So where do you guys sit within IBM? Because now there's like, because this is a really cool user tool, so is this part of Watson? >> Jim: We think so. >> Is it part of the Watson team? >> Well, honestly our organization doesn't really matter, I mean, we're working with teams across IBM as a whole. It's a great opportunity to take this technology and really reach a whole set of new use cases, I think, across the company, and we want to integrate Watson technology to, like we were saying, really make it easy for the end-user to go and access it. >> Any plans around developer outreach? >> Well, we will, I think, later this year, one of the things we envisioned really early on is that people are going to want to have pre-built skill sets, and that's a great opportunity to build an incredibly powerful ecosystem and we've been in discussion with a lot of our partners about how to do that. >> Well you guys are API based, so this is a beautiful thing, right? >> Well we're going to start to open up some SDKs to our partners, to others, and that's going to allow them to extend the assistant and really create even more powerful industry content. >> You know, the business model of reducing the steps it takes to do something and saving people time, making it easy to use is a magical formula of success. >> And not even just less steps, it's less time reading things, less time sifting through information so you can spend time on stuff that matters. >> Just email by itself, I mean, Dave, your example was the best, because I know, we live that. But we have a multitude of tools and sometimes it just organically goes, because the one guy like, you know, this tool set, or now I got-- >> So do you want to do the deal now or? >> Right, that's what I'm saying, they should be signing up. >> So do we get paid? (they laugh) >> We're already both signed up. We have a testimonial. >> If you can't get it, how can we get it? >> We'll kick the tires on it, and uh, but the thing that gets my excitement is potential for API integration. Because if I know I can the automation to a whole other level and the use cases start to patternize in the enterprise, then it can get interesting. All right guys, thanks so much. What's going on here with the show, what else is happening for you guys? Share some stories for the folks that aren't here, that are watching on IBM Go right now. What's the vibe at the show this week? >> Well, it's been a great vibe. We've had a chance to share some incredible success stories, so in addition to the unveiling of this particular product, on Monday we had a chance for one of our marquee clients to share their story, and I'll tell you a little bit about what they did. It was at the National Health Service of the UK. Part of their blood and transplant, and we were fortunate enough to have Aaron Powell, who's the chief digital officer there, share their story of using process technology to improve the speed at which they get organs in the hands of recipients, and they did it on the cloud. And the results they obtained were unbelievable. So the before and after, they had staff at 2am, writing lists of high-risk patients and how to map their donors and he kidded us not, that when someone's priority changes, they would wipe the board and reset things. And these are people's lives that are at stake in the matching process. >> And they're tired, human error is huge. >> Human error, absolutely, and by the way, when you look at the end-to-end process, there was something like 90 steps if I remember, 96 steps I think end-to-end. All of which were very manual and error-prone, and error-prone means risk. And they were able to improve organ allocation by 3x, so 3x faster, they automated something like 58% of the steps, reducing propensity for manual error, and what he shared in his story is, they successfully a few months ago did the first heart transplant on the cloud. >> Host: Wow, that's amazing. >> So it's an amazing, amazing story. >> That's a great story, yeah. Did he say that in the session? >> He did, actually, he said that. >> That's actually a good thing to chase down for a great blog post, that would be phenomenal. It would have been covered yet on the news? >> So we're going to post actually the video of it online so people can also see him live presenting his story, it was unbelievable. >> Make sure you send me the link. The other thing that they could apply there is two-block chain, I mean some of the block chain stuff coming out is going to be really interesting. >> Absolutely, and we're working very closely with that team to really leverage this kind of process technology, take people's business operations and connect that in to this feature network that's going to power businesses. >> CRM is the human supply chain, I mean, but now extend it out to the internet of things. I mean, it's interesting how this could play out. Guys, thanks so much for coming on The Cube. Thanks for sharing the insight, congratulations on the launch. I just signed up for the beta while we were talking. >> Dave: Me too, so let us cut the line. >> Done. >> We need it. Perfect use case, we need help. It's The Cube, of course, no help here, great guests here on The Cube. I'm John Frower, Dave Vellante, more great coverage, stay with us. Day three of Interconnect 2017, we'll be right back. (techno music)

>> Announcer: Live, from Las Vegas, it's The Cube covering Interconnect, 2017. Brought to you by, IBM. >> Hey welcome back everybody, live here in Las Vegas at the Mandalay Bay IMB Interconnect 2017, it's The Cube's exclusive coverage, I'm Jon Furrier, my co-host Dave Vellante, our next guest is Ramesh Gopinath who's the VP of Block Chain Solutions and Research, welcome to The Cube. >> Thank you. >> Block chain front and center, super exciting, it's been trending pretty much throughout the conference, really is an amazing story, big props from the CEO and (mumbles) and a variety of the executives. Watching is instrumental in the future of business, we had Don Tapscott on yesterday really talking about the revolution of what this is all about and he's the author of the book, The Blockchain Revolution, but if blockchain is a game changer shift to how the business will be operating in the future, so just to level step, just give us the one on one blochchain, versus bitcoin, and why IBM is going in this direction and where it came from. >> So blockchain is all about increasing a trust in business transactions. This is something we recognize about a couple of years ago when a small team of us started playing around with, you know, the technology behind bitcoin, right. And we look at it and said hey look, here's an opportunity for the first time for companies to share some information in a secure fashion with each other and, in addition, run some workflows or business processes on top. That was an eye opener for us, it immediately told us this could have applications in all industries, right. And so what do we do first? So we said let's play around with this a little bit. We looked at existing technologies out there for blockchain and to pick the platform you tried a few use cases and realize, oh my god, there is a whole lot to be done to get a blockchain for business, right. And that's how we started this journey, almost a year and a half, two years ago. And we decided to explore that. >> And the key distinction Ramesh, and we know from just highlighting it here for the folks, is bitcoin is a currency that has a blockchain, so it's powering bitcoin. You're talking about something more fundamental for business which is using the blockchain technology for businesses and what bitcoin is to blockchain, business is to blockchain from your standpoint. >> That's right, and also I think the blockchain is really, the inspiration for it comes from bitcoin perhaps, that's a good way of thinking about it. But today for example, the hyperledger version one that was announced earlier this week at this conference is dramatically different from the underlying blockchain and bitcoin in other platforms out there, right. Because it's really built primarily based on requirements that we have gathered by working with hundreds of clients in financial services and supply chain, in public sector, et cetera, and realizing what levels of confidentiality, what levels of privacy, what level of permissioning, you know, who participates in the transaction. All of that is what has led to, what we call the (mumbles)- >> John: Okay somebody's got a question. >> John: I got a follow up on that, but go ahead. >> Uh, just one more point on this but you can follow up on my point. Give us the status of blockchain today for IBM. Lay out the solution because you move from research now to the exclusions group, you have customer action going on, sales motions, solutions motions. What is the architecture, what does it look like, what is the solution today from a blockchain standpoint? >> So, just, you asked what are you doing at a high level, essentially we have three broad, big investments. One is everything to do with you know, opensource in a hyperledger project, I mentioned that. Then there is you package that into a platform, IBM blockchain, high security business network, that was also announced earlier this week. And the third layer is again what you asked about solutions. What we have been doing over the last year, year plus is, in fact, it's an interesting journey, we started out with what I call blockchain tourism, there were a whole bunch of POC's if you want to call it that, starting with financial services initially, but in gradually other areas, like supply chain, in healthcare, et cetera. Towards the middle of 2016 we saw a transition, at least on the financial service side people were started to talk about, hey now I understand this technology and what it's capable of, let's talk about production deployments, right now I'll give you a few examples as we go along. >> Dave: So, I want to go back if I can a little bit and just get somewhat didactic for a moment. My understanding is there's three attributes, I'm sure there are many more of blockchain which are really relevant, and especially as it relates to the security if I may, it's distributed obviously, and it's been said it's virtually unhackable unless 50% of the stakeholders agree to collude, and then there's no need for a trusted third party so it reduces the threat space. Are those sort of accurate statements and when somebody says, well it's virtually unhackable, you know you tweet that and somebody says, well everything's hackable, help us understand sort of those fundaments of blockchain and why they're relevant. >> That's right, so the way I think about it is a blockchain is a trusted database. Now why is it trusted? There are three properties, I'll get to it, kind of overlaps with what you mentioned. The first one is, any transaction you do onto the database, anything that goes in it basically is done in a nonreputiable fashion. If I do something I can't say, "I didn't do that," so that helps. What goes in, you know you have that property. The second piece is, whatever goes in goes in through a vetting process, we will call it the consensus. There is some sort of a chat between parties before something goes in. Therefore, I can't unilaterally do something onto the blockchain, right, I can't, somebody else vetted what I did, that increases trust. And the third piece is, once it gets in there it cannot be tampered with. We say it's immutable sometimes, and what is that based on? There's a whole lot of topographic math behind it, but at a high level there are two aspects to it. One is, there are multiple copies. So if I change something, if I hack into mine, I'm inconsistent with what others have, so that's one. The second is, the transactions are chained together, blocks of transactions are chained together where a fingerprint of one block is put into the next. What that means is, if I tamper with the block say 15, a long blockchain, all transactions after that are invalid, I have to do a lot of work to fix it, so it's very very hard to tamper with. Of course, as with security, there's no such thing as nothing that is hackable, right, so collusions et cetera, potentially can happen. But the key is, significant increase in the level of trust is the way I would put it. >> Dave: Great, okay, and so now if we can get into sort of how people are specifically applying this technology, you guys started with the hyperledger, you know, open project, but can we get more specific in terms of how say organizations are actually deploying blockchain? >> Ramesh: So we are still running a blockchain in productions since September 6th, right, so it's been only four months. In fact that blockchain is more than a half a million blocks today, so let me tell you what that solution is so you get a sense of, and it's very prototypical in terms of, you know, all solutions that I've dealt with so far across industries. The use case is a following, so you have a buyer, you have a seller and you have a financer, that's IBM. We basically finance, shotgun financing of, think of it as channel financing or inventory finance. What happens typically is, the buyer basically orders something and the seller essentially gets approval from us to say, okay, yeah we can basically send it to the buyer. A few days go by, IBM has already paid the seller basically, just like credit cards (mumbles) consumers. A month later basically we go in, say hey look, guys, time for you to pay up and they say, look, we didn't even receive the goods. So this entire process, what I just described you can think of as a workflow where these three parties are sending messages back and forth. The way we do it in a blockchain is, this entire workflow is captured as a sequence of transactions that are registered on the blockchain. Now how does this help us? Take the example I gave, proof of delivery. If when the logistics company delivers it at the buyer's site, it's recorded on the blockchain. There is no need for a dispute. And typically disputes, basically puts a lot of capital, you know, it holds up a lot of capital right. Capital inefficiency is the problem we're after. In fact, after six months of deployment I can tell you essentially a significant improvement in terms of the time savings as well as elimination of disputes. >> John: That's a great efficiency. Who's buying, who's actually implementing it customers-wise. Can you name names? >> Ramesh: Yeah, so, examples are the, let me give you a few in financial services. So we are working with Salus Bank which does, you now, five trillion dollars worth of foreign transactions every day. They are building a netting engine called Salusnet a solution called Salusnet, and we're working with them on that. Another example is the work that we are doing with Northern Trust, where basically they have a private equity administration blockchain. In fact, it's a very interesting one because it also involves the regulator as a part of the blockchain, so that's a second example. A third one is the one we announced in January with the Depository, Trading and Clearing Corporation DTCC, and that one is for credit debitors, life cycle management, in fact all the examples if you notice, there is a life cycle like I gave in that example earlier of buying till all your goods are delivered, payment is made, those life cycles, those processes are captured as trusted processes on a trusted data store. That's basically blockchain for you, right, that's financial services. Maybe I'll touch upon two more examples to complete the story. Supply chain. I walk into a store and buy some sliced mangos at Walmart, is it safe to eat? To answer that question you need to know the provinence. Which farm in Mexico did it come from, who all touched it, who washed it, who processed it, et cetera, all the way till it got to the store. That sort of information sharing does not happen today in the supply chain. We believe with the block chain that is possible, that allow us to get a good sense of where things came from, making consumers more comfortable. Similar story can deal with pharma too. I pop a pill, I want to be sure that it's safe to have. In fact, as you know the World Health Organization says in Africa, every year a hundred thousand kids die of counterfeit malaria drugs alone, right, so imagine if you could capture these sorts of supply chain flows on a blockchain you could make dramatic improvements. >> Dave: Diamonds provenience is another one, and it's not just blood diamonds. >> Ramesh: I'm more excited by the providence of food and pharma, but diamonds- >> But there's tons of fraud in the diamond supply chain. >> Ramesh: Absolutely. >> And that's really where they're, you know- >> John: Well this brings up the whole business model disruptions, so, what are you guys seeing for the kids of conversations? Because you're getting at the business model impact significantly one, you're reducing costs of transactional costs for new measurement systems, aka blockchain, and you have all the methodology behind it, but everything from music to art to content, I mean, payments, this is like a game changer. >> Absolutely, and I think from the point of view, you know, in all of the use cases I've seen, the sort of value to the ecosystem is clean and obvious, and so you can immediately say, aha, this is going to happen overnight. But the reality is basically, it's a complex ecosystem play though. So, for example, in the supply chain use case, food safety, you need to have the farmers, the entire value chain involved, participating in some fashion on the blockchain. That is not easy to do. So there is, how do you sort of set up ecosystems is a key part of- >> John: What's your strategy there? I'm going to ask Marie when she comes on, but what's the strategy with ecosystem? Because you want to jump start this, you got to prime the pump big time. >> Ramesh: Absolutely, so there are many ways to solve this, but one approach we have taken so far, and it's obvious in all the sort of partnerships that we're working on. Take for example food safety. One way to start with it is to start with a big retailer, like a Walmart. They bring in the suppliers, and the suppliers bring in the farmers. Take the case of what we are doing in container shipping. So basically, movement of containers from point A to point B, we're trying to completely digitize that process, this is a project that we're doing with Maersk. Why Maersk? They are 20% of the container shipping market, right? But in all of these cases I got to be very clear, we are not building a solution for Maersk or for Walmart. We're really building something for the industry, because food safety, you want to solve it for the industry. Just by helping Walmart along. >> John: That's why the open source thing is critical here. >> That's right. >> John: And the update on that, it's all open source on which components, or is it all open source? >> Ramesh: So the open source is all about at the platform layer. The solution itself, you know not everything in the solution is going to be open source. But the key point I was trying to make is that you go off the sort of significant anchor tenants in the ecosystem that draws others into the picture, but that's still not enough, you need to make sure there are economic incentives for others to join in. >> John: So to put it together, tie it together, the ecosystem strategy is, take an industry scope and try the rising tides floats all boats kind of approach. So adoption's critical. >> Absolutely correct, absolutely correct, and I think again I can use food safety to make that point. Think about it, right? So there is, let's say, a spinach problem, we had it in 2006. So you find a problem, you trace it back to a source. Let's say Walmart is the store in which somebody bought it and it was traced from there. That's not good enough. From the source it went to many other retailers. So you need to be able to track down and pull all of them off the shelves. Therefore you have to go for an industry solution. >> John: I can imagine the healthcare thing would be even more impactful too, I mean, financial services pretty obvious, transactional stuff there, but healthcare, so many different variations of supply chain and transactions. >> Ramesh: Absolutely, so in a way, the way I think about it is in a financial service everybody had a hunch this could be big, but supply chain, we've come a really long way, I think this is going to be the space which will have the most destruction, and its interesting considering when we started my first conversation with folks, whether it be a Walmart or Maersk, first question is, "what is blockchain?" We've come a long way in the last say eight, nine months. >> John: You guys get so excited where you're kind of pinching yourselves because you can get kind of euphoric about some of the disruption impact. It's just mind blowing to think when you're talking about food, the food industry and healthcare. You got to get tampered down a little bit in some realism, is there that IBM excitement internally share some color internally within IBM the excitement, and then you got to be getting realistic, a lot of the clients rolling it out to kind of got to walk before they can run. >> Ramesh: Yeah, so, the way I would state it is if you had asked me a year ago do you expect to be in the shape we are in today, I would have said no way. I've been shocked at the pace at which this has been moving both from the point of view of the technology itself, maturing of the technology, and in fact when we say blockchain is here now, so that's at the technology layer level, but in terms of use cases, think about it, there are a number of financial services institutions that are talking about production deployments late this year, early next year. In fact, when we did our own IBM Institute for Business Values survey, came back with fully 15% of those who were surveyed, there were like 400-odd banks plus capital market institutions are going to be in production by end of this year. When I heard that in September I still didn't believe it, but I am beginning to believe it now. >> Well it's interesting I think, the cultural shift is that technologists from computer scientists to practitioners that are technologists, they get it. They can see what blockchain does, so I think as people get more and more momentum, that's the fly wheel that you guys are open for and it's happening. >> That's right, in fact I'm also a techie at heart, but in terms of conversation (mumbles) I never talk about technology anymore because the thing is, there are only two concepts in blockchain. It's trusted data across companies, trusted business process. Everything else is detail. >> John: Got it, Ramesh, thanks so much for sharing, great conversation, formerly with IBM research, now Vice-President of Blockchain Solutions at IBM, great to interview, great insight, blockchain revolution is here, check out our interview yesterday with Dom Tapscott yesterday on YouTube, The Blockchain Revolution, his book really kind of lays out some of the big disruptive game changers. This is The Cube, doing our share of blockchain right now, bringing content in blocks and chunks, not yet blockchain enabled. I'm John Furrier, Dave Vellante, be back with more after this short break. (synthesized music)

>> Narrator: Live from Las Vegas, it's the CUBE (light electronic music) covering Interconnect 2017. Brought to you by IBM. >> So, my first question for you is, as we digitize our worlds with business, the value chains and all the processes are changing, there's a new value equation model around digital business. What's your view on the digital business value creation equation? >> Sure, John, thank you for having me here, today, and Dave, as well. Thank you for having me. So, I think that automation in combination with cognitive computing, AI, machine learning, is really going to revolutionize the way we work, the way we interact, and it's going to create new experiences for people, experiences that we don't even know that exist in the future. They're going to be real value, and that value is not just about cost take-up, that value's going to be around innovation, that value's going to be around the combination of block chain, machine learning, ubiquitous computing, and I think that this is going to create new industries, new business models, that we could take really advantage of in many different industries. >> One of the cool things that's happening that I like in the computer science, and how society in general now is seeing cloud computing, which has been around for awhile getting full-steam ahead, big data industry evolving very rapidly, and what's kind of pulling them together in this gravity is machine learning, data, cognitive, iterative things, and AI is kind of the over-arching, you know, sizzle, if you will. So, the sci-fi aspect of AI. Everyone can relate to some sort of mental image of AI. You know, robots taking over the world, to automated things just make us smarter, augmented reality, what-not. But iterative things, and data science, that's connecting the power of the cloud with data is changing the game. So, one little change in a value equation, in a company, could change their business model. >> Absolutely. >> How do you rationalize that with your customers (laughing)? Hey, you know, full steam ahead, or slow down, it depends. What's your perspective on that? >> So, it's interesting you mention about science fiction, right, because science fiction inspired robots and automation for centuries, now. But, the reality, today, is that we are already using automation. You know, it's been used ubiquitously across production lines, manufacturing, logistics. So, we've started this journey, already, for many, many years. I think, quite rightfully, like you described in the cloud world, what we're seeing now, in automation, you're combining Artificial Intelligence, you're combining cognitive computing, and what that will do is it will create new value for companies out there, right. That new value is going to be about creating new industries, creating new data, and in the IOT space, it's going to create a new level of intelligence for us that's really going to change the experience that we could deliver. >> So, I've said this before, the greatest chess player in the world is not a machine, it's not a supercomputer, it's a combination of humans and computers. And, so to the point, we were talking off camera, machines hae always replaced humans. It's now cognitive functions are being augmented. And so, it seems like the scarce resources, the creativity to combine innovations. For decades, we've marched to the cadences of Moore's Law. >> John: Yeah. >> Right. The innovation is not coming from Moore's Law anymore. You know, whether or not it's tapering. We can argue, let's assume it is for a moment. >> Reinventions. >> So, where's the innovation coming from? Clearly the innovation curve's not slowing down. >> Dave: So, what's IBM's point of view on that innovation and where it comes from? >> So, you started by talking about robots and machines and how it's kind of changing jobs and, let's say, taken over jobs. The reality is that only a small percentage of work, five to 10%, will be fully automated in the future, because we've seen this throughout history. Technology advances in the last 20 years has created new professions. You know, 25% of the work type and profession in the last 20 years are new type of work. And John, you were mentioning about the ATM machine just a couple of minutes ago. >> Yep. >> The ATM machine actually has created a new economy. >> John: It was supposed to kill the teller. >> Yeah. >> It kind of did, but it created more jobs because more branches were opening up. >> Exactly, more branches and new ways of interacting, and new, and so on. Now, you have mobile apps, but actually you have to develop applications which you never had to do in the past before, and exactly the same with automation. We're going to have, and robotics and Internet things, combined them together, we'll create a new economy, and the value of that economy is going to be in a couple of areas. One, I think one of the fundamental changes is that our work type, the type of work we do, is going to be automated, about 30% of our work type is going to be automated in certain industries, industries which are highly tasked orientated, highly manual orientated, highly data orientated. They'll all change, that's the reality of it. But, that's also going to create a new type of work, a new profession and people will be up-skilling to that. But, our prediction is that, you know, in the next few years we're going to see between one to one and a half percent GDP increase as a result of cognitive computing and AI. >> Incremental. >> Incremental, incremental, so yes, there'll be some changes of jobs, changes in profession, but incremental, they'll be increasing, and that, for the aging population issue that we know very well, is going to really help to boost a lot of economy out there. >> That scares people, too, this whole notion of job loss and, again, we were talking about before camera. It's not so much controversial, it's more education, both educating people on what the narrative is of the future scenario. >> Exactly. >> But also, education for people to get the new skill. I mean, the stat after the stat, but it's high percentage of jobs are even created yet. So, you know, cross-disciplinary education in higher eds changing, but the skills gap is a huge issue. So, how do you talk to that point because, certainly, that's an area we heard on stage today. Mark Benioff and Ginni Rometty talking about, you know, having a societal impact, and having a mission, education, is a big one. What's your thoughts? >> Absolutely. >> I mean, so I think it starts right from the way we teach, because you can't necessarily always teach the future, but what you can do is you could prepare for the future. So, we need to start teaching our children to our graduates who start in our companies with new skills around creativity, right. High emotional intelligence. I believe that a robot is never going to be able to manage emotional intelligence, right. So, how do use more emotional intelligence? How do we provide more discipline around education? How do we provide them vital skills? >> Critical thinking. >> Critical thinking, absolutely. You know, and science, but also, these Artificial Intelligence going to age human being, you know, it's going to help us discover new remedies for problems that we have today in our society, problems that we have in healthcare, problems that we have in political systems, you know, which we are seeing now and, hopefully, provide more confidence in our system because of the data and intelligence that we see. >> So, now, I've got to throw in the augmented reality because AI is obviously kind of a concept that people are getting. It's not clear, yet, what that is. What really is AI? Well, that chapter will be written, certainly as it evolves. But, augmented reality is happening. So, IoT, you can have googles on that look at meters and get new data, that's not even there. You can do automation around getting predictive analytics around machine, industrial IoT, these kinds of things. You're seeing consumer devices having augmented reality. >> Shamayum: Yep. >> That's here, right. That's here and now. So, how do you advise your clients and customers, the big IBM customers you service, to prepare for augmented reality? Because, is there a playbook? Are they nodding their heads, are they going, "Oh!," face palm. Or, I mean, what's happening? >> So, augmented reality is here and it's here to stay. The difference between augmented reality, now, or if you looked at it, let's say, five years ago when we had Google glasses. One was hype versus reality. One was a use case, now, where actually is transformational, and it's having real impact in ROI. Like the example you gave around manufacturing, or how you use it in logistics, when you are looking at airlines, and how you look at training people. Now you could train people using augmented reality and speed the level of adoption required or the time required to skill people. And then in the past, we never had that. Is there a playbook? I don't think there is a playbook, John, because I think the use cases are so diverse. But I think what we really need to do is go back to fundamental of consulting. What do we look for, you know, in business problem solving? We look for problems. We analyze that problem through design thinking, or whatever methodology we use. We look up what the impact. Is it speed, quality, and cost? And, if we can answer these questions, speed, quality, and cost, and the human impact unto it, those are the factors that's going to determine what are the use case of augmented reality that's really going to transform, and have impact, in industries. >> I find it interesting, too, you mentioned that the old way of doing things, is not going to be there, but the older way of doing software was general purpose, computing, and software. Buy a shrink wrap package, load it, does some function. Now, the composability of APIs and micro services allow for common building blocks. >> Yep. >> Son ow , essentially every solution is custom. >> Shamayum: Yes. >> So, you have a diverse use case. Remember, beauty is in the eye of the beholder. So, that's how it is in the enterprise. So, that's a reality. So, with that as kind of a backdrop, what are some of the cool things that you're working on now that you can point to and say, "These are examples of the kinds of journeys customers are going through." It could be a crazy idea that's implemented or a something, a great idea. Share a story about some of the highlights, some of this new way of building apps, new way of being agile, new way of discovering value creation. >> Sure, so you're absolutely right. You know, we're moving into a world of consumable services, if I could describe it that way, API services, micro-services, pulling data from different sources, augmenting your data source, having functions that you get from your competitors, even, to augment the capability that you want and to create new business processes. And I think we'll see more and more of this, and, you know, I was speaking to a bank, even last week, and in Abu Dhabi where they're saying, "What's the future of a bank? "Do I need a processing bank? "Do I need a back office anymore? "Do I need a branch? "Can it not just be a virtual bank? "Can I not just connect to fin-tech to provide my services, "and I have the customer and own the customer?" So, I think there's an advancement in terms of thinking, now, and that thinking is possible because the possibilities are endless and we have never seen more access to technology at relatively low cost, even, than ever before, and, also, open source is making it more possible, I think. Open source has given power to the people and companies to have world-class technology, you know, combined with IBM technology to provide, you know, real great value. So... >> Oh, go ahead, please carry on. >> Just, in terms of use case. Another example is that I recently sent a use case around, "Know your customer." You know, one example was, you know, opening up accounts for high-worth customers could take months and months to make sure the security checks, and so... You may have an account, but in terms of fully transactional, it takes months. Now, using machine learning, you go and search over a million databases and look for a million different patterns and make a recommendation to your work advisor to see what kind of customer you're going to be. You know, by analyzing movies, watching four movies that you love, we could predict, by 95%, what all the movies that you're going to love in the rest of your life. Those are the use cases I've seen. I just find it fascinating. >> Netflix is doing... They're getting all the data from me. >> Yes, yes. >> An arbitraging out the back door. >> Recommending books, I mean it's getting more and more. >> I want to own my own data. That's what Ginny was saying on stage, we should own our own data. >> Yeah, and there's certainly consumers out there who want to own their data and I think there's going to be this shift where you can have digital ident in the future where you own the data and could like trading your data. So, instead of just giving your data, you could start monetizing your own data, and saying what kind of information do you want, and what kind of experience do you want? You know, how do you want to trade? Who do you want to trade with? Do you want to share the data with your family? With your friends? With your business partner? And you become the owner of data, and that certainly an area that we're seeing a movement to, but, also, it's going to help with the identity. Your identity will be known only to you, not to service providers. >> I think you're on to something, and one of the things we were just talking with Don T was, the block chain, and The Block Chain Revolution, his new book, is when you get the transactional cost of business reduced, you mentioned that virtual bank, makes total sense. Why should I spend all this money to have a company? I can reduce my transactional cost of doing business and still provide great value. >> Well, I don't know. I mean, you talk about the future's hard to predict, sometimes, but the bank in Abu Dhabi, right. I mean, you see banks, we talk to them all the time, re-imagining the banking experience, but not necessarily eliminating the physical. >> Sha: Absolutely. >> And so, you know, to me it's fascinating. Like the list of things that machines can't do, that humans can changes, seemingly, every year, whether it's climbing stairs, or even autonomous vehicles, five years ago nobody thought was... I was and IBM conference, not that long ago, five years ago, they said it's 25 years before you'll see that. Then, wow, just overnight. >> Self-starting cars is another prediction. >> So, do your clients... How much do they try to, you know, skate to the puck, which seems to be a harder endeavor versus saying, you know, "Okay, we can apply this today, "and save money, or tap a new business opportunity." >> So, you make some great points here. The first point I'd just like to elaborate a little bit more which you mentioned about the physical and the digital. >> Yeah, right. >> Which you might, in your example was the branch. What's the role of the branch if you have a fully digital bank in the future? We still need a branch. And, why do we need a branch? Because in the branch, the branch is going to be different in the future. We all hate cueing up in the branch. We all hate going to a generic person that's says, "Can I help you?" >> John: What's your account number? >> "What's your account number?" >> Exactly. >> No, I'm John. >> Come on, you should know me. >> You should know as soon as I walk in, you should know, you know, what are the three things that I'm looking for. You should know how long I've been a customer, what my family is. >> How many Twitter followers you have. If you're an influencer. (laughter) >> Exactly, exactly. And more importantly, you should know that I love speaking to an expert. I don't want to speak to a generic person. So, the future bank is going to be when you walk into your bank, you're going to sit there in a room, in a very nice room, you're going to be in a screen, you're going to talk to someone who's an expert, who knows everything about you, through video conference. You can even order a coffee that someone comes in your room. You don't feel that it's just virtual. You have some emotional connections, as well. You'll have digital printers in there. You can connect. You have a different experience and by the time you go out, your app has already been updated with the loan that you wanted or the mortgages that you wanted. And then, coming back to the point that John and I were talking about, the consumer services, well, actually I might want my loan from one bank, my mortgage from another bank, and my car account from one bank, and I should be able to trade between that. >> Dave: Right. >> So, there's that connection, whether physical or digital, and then different service providers. I think there will be a mash-up if I think of it that way. >> It's that level& of intimacy that has been lacking for so many years in that example, financial services, which it seems like cognitive can help close that gap. >> Yeah, yeah, and cognitive absolutely can because cognitive is now, the technology's here. I think it's advanced, so much is here. The use cases are amazing and you heard about the use cases in healthcare and in customer services and aging assist, which is helping people with self-application repair. So, I think the advancement is here. I mean, the possibilities are huge. We just need to connect those dots together in order to create those new use case. >> And they did model that's come out of that conversation that is domain specific data, and you guys talk about that as verticals. But, you know, that's important because you need to specialism of the data. >> Sha: That's correct. >> But you also want the horizontally scalable cloud, as well. So that's the data challenge right there. >> Absolutely, absolutely. But one thing that's going to be critical, I think, even in the future, is that industry knowledge and how you use that industry knowledge to augment your data. How do you find that, you know... Computers always give you those patterns. How do you interpret that pattern, and how do you create that new experience? That's where the human value is. >> So, you know, we talked yesterday. You're in the consulting side of the business, but 60% of IBM's business remains services. But a key value that you bring to the marketplace is the ability, at least in concept, to codify those services into software. Talk about how you're doing that, and to the degree to which you're succeeding. >> So, we started a conversation in automation. So, if I go back to that. In automation, every client is thinking about automation. Every client is experimenting with automation now, to some level. And, what we have done with our services is that we create frameworks, maturity assessments, business component modeling. So now, what we can do when we go into a bank, we go into an example bank, we could say, "Hey, Mr. Customer, I've done it "with these 30 banks. "I know what does best-in-class bank look like. "You might not, necessarily, and trying to answer your initial question, You know, what's the roadmap look like. "You may want to go over here, "but your level of maturity's here." So, how do you get to that level of maturity, your kind of aspiration, and we could help with those tools and methods and our assessment capability and strategy engagement framework that we bring with the clients. Also, we don't just look at banks. Because, if you're a bank, banks are interesting, but you may want to look at retailers. You might want to look at pharmaceutical companies. You might want to look at Starbucks and all the people that around here, what are they doing, in terms of innovation, so you can bring all the innovation on to this and look at business functions. You know, what does the best-in-class logistic look like? What is the best-in-class back off function look like outside of my industry? And that's the kind of codifying that we have the knack of solving things we can bring to the market. >> Can I even jump industries because digital allows me to actually traverse horizontally, as John was saying. >> And the threat is there. You know, the threat is live today, to be Uber-ized or be a Uber. You know, you decide. So, you are going to jump through these industries because you have now a global platform which allows you to transform your industry, and jump from one to another, and monetize what you have very, very fast, and the barrier to entry is lower than ever before, not just because of technology, but because how industry works on the consumers' demand. Shamayum, than so much for coming on. Great insight. Thanks for sharing. Love that content. Really good insight. >> Pleasure. And the future's here. >> Thank you very much. The future's here, absolutely. Thank you very much. >> The future's here, now. This is the CUBE bringing you all the action, here, live in Las Vegas. Stay with us. We've got a lot more today to come, and all day tomorrow. We'll be right back. I'm John Furrier with Dave Vellante. (light electronic music)