>> From "theCUBE" studios in Palo Alto, in Boston, bringing you data driven insights from "theCUBE" and ETR. This is "Breaking Analysis" with Dave Vellante. >> There are very few political issues that get bipartisan support these days, nevermind consensus spanning geopolitical boundaries. But whether we're talking across the aisle or over the pond, there seems to be common agreement that the power of big tech firms should be regulated. But the government's track record when it comes to antitrust aimed at big tech is actually really mixed, mixed at best. History has shown that market forces rather than public policy have been much more effective at curbing monopoly power in the technology industry. Hello, and welcome to this week's "Wikibon CUBE" insights powered by ETR. In this "Breaking Analysis" we welcome in frequent "CUBE" contributor Dave Moschella, author and senior fellow at the Information Technology and Innovation Foundation. Dave, welcome, good to see you again. >> Hey, thanks Dave, good to be here. >> So you just recently published an article, we're going to bring it up here and I'll read the title, "Theory Aside, Antitrust Advocates Should Keep Their "Big Tech" Ambitions Narrow". And in this post you argue that big sweeping changes like breaking apart companies to moderate monopoly power in the tech industry have been ineffective compared to market forces, but you're not saying government shouldn't be involved rather you're suggesting that more targeted measures combined with market forces are the right answer. Can you maybe explain a little bit more the premise behind your research and some of your conclusions? >> Sure, and first let's go back to that title, when I said, theory aside, that is referring to a huge debate that's going on in global antitrust circles these days about whether antitrust should follow the traditional path of being invoked when there's real harm, demonstrable harm to consumers or a new theory that says that any sort of vast monopoly power inevitably will be bad for competition and consumers at some point, so your best to intervene now to avoid harms later. And that school, which was a very minor part of the antitrust world for many, many years is now quite ascendant and the debate goes on doesn't matter which side of that you're on the questions sort of there well, all right, well, if you're going to do something to take on big tech and clearly many politicians, regulators are sort of issuing to do something, what would you actually do? And what are the odds that that'll do more good than harm? And that was really the origins of the piece and trying to take a historical view of that. >> Yeah, I learned a new word, thank you. Neo-brandzian had to look it up, but basically you're saying that traditionally it was proving consumer harm versus being proactive about the possibility or likelihood of consumer harm. >> Correct, and that's a really big shift that a lot of traditional antitrust people strongly object to, but is now sort of the trendy and more send and view. >> Got it, okay, let's look a little deeper into the history of tech monopolies and government action and see what we can learn from that. We put together this slide that we can reference. It shows the three historical targets in the tech business and now the new ones. In 1969, the DOJ went after IBM, Big Blue and it's 13 years later, dropped its suit. And then in 1984 the government broke Ma Bell apart and in the late 1990s, went after Microsoft, I think it was 1998 in the Wintel monopoly. And recently in an interview with tech journalist, Kara Swisher, the FTC chair Lena Khan claimed that the government played a major role in moderating the power of tech giants historically. And I think she even specifically referenced Microsoft or maybe Kara did and basically said the industry and consumers from the dominance of companies like Microsoft. So Dave, let's briefly talk about and Kara by the way, didn't really challenge that, she kind of let it slide. But let's talk about each of these and test this concept a bit. Were the government actions in these instances necessary? What were the outcomes and the consequences? Maybe you could start with IBM and AT&T. >> Yeah, it's a big topic and there's a lot there and a lot of history, but I might just sort of introduce by saying for whatever reasons antitrust has been part of the entire information technology industry history from mainframe to the current period and that slide sort of gives you that. And the reasons for that are I think once that we sort of know the economies of scale, network effects, lock in safe choices, lot of things that explain it, but the good bit about that is we actually have so much history of this and we can at least see what's happened in the past and when you look at IBM and AT&T they both were massive antitrust cases. The one against IBM was dropped and it was dropped in as you say, in 1980. Well, what was going on in at that time, IBM was sort of considered invincible and unbeatable, but it was 1981 that the personal computer came around and within just a couple of years the world could see that the computing paradigm had change from main frames and minis to PCs lines client server and what have you. So IBM in just a couple of years went from being unbeatable, you can't compete with them, we have to break up with them to being incredibly vulnerable and in trouble and never fully recovered and is sort of a shell of what it once was. And so the market took care of that and no action was really necessary just by everybody thinking there was. The case of AT&T, they did act and they broke up the company and I would say, first question is, was that necessary? Well, lots of countries didn't do that and the reality is 1980 breaking it up into long distance and regional may have made some sense, but by the 1990 it was pretty clear that the telecom world was going to change dramatically from long distance and fixed wires services to internet services, data services, wireless services and all of these things that we're going to restructure the industry anyways. But AT& T one to me is very interesting because of the unintended consequences. And I would say that the main unintended consequence of that was America's competitiveness in telecommunications took a huge hit. And today, to this day telecommunications is dominated by European, Chinese and other firms. And the big American sort of players of the time AT&T which Western Electric became Lucent, Lucent is now owned by Nokia and is really out of it completely and most notably and compellingly Bell Labs, the Bell Labs once the world's most prominent research institution now also a shell of itself and as it was part of Lucent is also now owned by the Finnish company Nokia. So that restructuring greatly damaged America's core strength in telecommunications hardware and research and one can argue we've never recovered right through this 5IG today. So it's a very good example of the market taking care of, the big problem, but meddling leading to some unintended consequences that have hurt the American competitiveness and as we'll talk about, probably later, you can see some of that going on again today and in the past with Microsoft and Intel. >> Right, yeah, Bell Labs was an American gem, kind of like Xerox PARC and basically gone now. You mentioned Intel and Microsoft, Microsoft and Intel. As many people know, some young people don't, IBM unwillingly handed its monopoly to Intel and Microsoft by outsourcing the micro processor and operating system, respectively. Those two companies ended up with IBM ironically, agreeing to take OS2 which was its proprietary operating system and giving Intel, Microsoft Windows not realizing that its ability to dominate a new disruptive market like PCs and operating systems had been vaporized to your earlier point by the new Wintel ecosystem. Now Dave, the government wanted to break Microsoft apart and split its OS business from its application software, in the case of Intel, Intel only had one business. You pointed out microprocessors so it couldn't bust it up, but take us through the history here and the consequences of each. >> Well, the Microsoft one is sort of a classic because the antitrust case which was raging in the sort of mid nineties and 1998 when it finally ended, those were the very, once again, everybody said, Bill Gates was unstoppable, no one could compete with Microsoft they'd buy them, destroy them, predatory pricing, whatever they were accusing of the attacks on Netscape all these sort of things. But those the very years where it was becoming clear first that Microsoft basically missed the early big years of the internet and then again, later missed all the early years of the mobile phone business going back to BlackBerrys and pilots and all those sorts of things. So here we are the government making the case that this company is unstoppable and you can't compete with them the very moment they're entirely on the defensive. And therefore wasn't surprising that that suit eventually was dropped with some minor concessions about Microsoft making it a little bit easier for third parties to work with them and treating people a little bit more, even handling perfectly good things that they did. But again, the more market took care of the problem far more than the antitrust activities did. The Intel one is also interesting cause it's sort of like the AT& T one. On the one hand antitrust actions made Intel much more likely and in fact, required to work with AMD enough to keep that company in business and having AMD lowered prices for consumers certainly probably sped up innovation in the personal computer business and appeared to have a lot of benefits for those early years. But when you look at it from a longer point of view and particularly when look at it again from a global point of view you see that, wow, they not so clear because that very presence of AMD meant that there's a lot more pressure on Intel in terms of its pricing, its profitability, its flexibility and its volumes. All the things that have made it harder for them to A, compete with chips made in Taiwan, let alone build them in the United States and therefore that long term effect of essentially requiring Intel to allow AMD to exist has undermined Intel's position globally and arguably has undermined America's position in the long run. And certainly Intel today is far more vulnerable to an ARM and Invidia to other specialized chips to China, to Taiwan all of these things are going on out there, they're less capable of resisting that than they would've been otherwise. So, you thought we had some real benefits with AMD and lower prices for consumers, but the long term unintended consequences are arguably pretty bad. >> Yeah, that's why we recently wrote in Intel two "Strategic To Fail", we'll see, Okay. now we come to 2022 and there are five companies with anti-trust targets on their backs. Although Microsoft seems to be the least susceptible to US government ironically intervention at this this point, but maybe not and we show "The Cincos Comas Club" in a homage to Russ Hanneman of the show "Silicon Valley" Apple, Microsoft, Google, and Amazon all with trillion dollar plus valuations. But meta briefly crossed that threshold like Mr. Hanneman lost a comma and is now well under that market cap probably around five or 600 million, sorry, billion. But under serious fire nonetheless Dave, people often don't realize the immense monopoly power that IBM had which relatively speaking when measured its percent of industry revenue or profit dwarf that of any company in tech ever, but the industry is much smaller then, no internet, no cloud. Does it call for a different approach this time around? How should we think about these five companies their market power, the implications of government action and maybe what you suggested more narrow action versus broad sweeping changes. >> Yeah, and there's a lot there. I mean, if you go back to the old days IBM had what, 70% of the computer business globally and AT&T had 90% or so of the American telecom market. So market shares that today's players can only dream of. Intel and Microsoft had 90% of the personal computer market. And then you look at today the big five and as wealthy and as incredibly successful as they've been, you sort of have almost the argument that's wrong on the face of it. How can five companies all of which compete with each other to at least some degree, how can they all be monopolies? And the reality is they're not monopolies, they're all oligopolies that are very powerful firms, but none of them have an outright monopoly on anything. There are competitors in all the spaces that they're in and increasing and probably increasingly so. And so, yeah, I think people conflate the extraordinary success of the companies with this belief that therefore they are monopolist and I think they're far less so than those in the past. >> Great, all right, I want to do a quick drill down to cloud computing, it's a key component of digital business infrastructure in his book, "Seeing Digital", Dave Moschella coined a term the matrix or the key which is really referred to the key technology platforms on which people are going to build digital businesses. Dave, we joke you should have called it the metaverse you were way ahead of your time. But I want to look at this ETR chart, we show spending momentum or net score on the vertical access market share or pervasiveness in the dataset on the horizontal axis. We show this view a lot, we put a dotted line at the 40% mark which indicates highly elevated spending. And you can sort of see Microsoft in the upper right, it's so far up to the right it's hidden behind the January 22 and AWS is right there. Those two dominate the cloud far ahead of the pack including Google Cloud. Microsoft and to a lesser extent AWS they dominate in a lot of other businesses, productivity, collaboration, database, security, video conferencing. MarTech with LinkedIn PC software et cetera, et cetera, Googles or alphabets of business of course is ads and we don't have similar spending data on Apple and Facebook, but we know these companies dominate their respective business. But just to give you a sense of the magnitude of these companies, here's some financial data that's worth looking at briefly. The table ranks companies by market cap in trillions that's the second column and everyone in the club, but meta and each has revenue well over a hundred billion dollars, Amazon approaching half a trillion dollars in revenue. The operating income and cash positions are just mind boggling and the cash equivalents are comparable or well above the revenues of highly successful tech companies like Cisco, Dell, HPE, Oracle, and Salesforce. They're extremely profitable from an operating income standpoint with the clear exception of Amazon and we'll come back to that in a moment and we show the revenue multiples in the last column, Apple, Microsoft, and Google, just insane. Dave, there are other equally important metrics, CapX is one which kind of sets the stage for future scale and there are other measures. >> Yeah, including our research and development where those companies are spending hundreds of billions of dollars over the years. And I think it's easy to look at those numbers and just say, this doesn't seem right, how can any companies have so much and spend so much? But if you think of what they're actually doing, those companies are building out the digital infrastructure of essentially the entire world. And I remember once meeting some folks at Google, and they said, beyond AI, beyond Search, beyond Android, beyond all the specific things we do, the biggest thing we're actually doing is building a physical infrastructure that can deliver search results on any topic in microseconds and the physical capacity they built costs those sorts of money. And when people start saying, well, we should have lots and lots of smaller companies well, that sounds good, yeah, it's all right, but where are those companies going to get the money to build out what needs to be built out? And every country in the world is trying to build out its digital infrastructure and some are going to do it much better than others. >> I want to just come back to that chart on Amazon for a bit, notice their comparatively tiny operating profit as a percentage of revenue, Amazon is like Bezos giant lifestyle business, it's really never been that profitable like most retail. However, there's one other financial data point around Amazon's business that we want to share and this chart here shows Amazon's operating profit in the blue bars and AWS's in the orange. And the gray line is the percentage of Amazon's overall operating profit that comes from AWS. That's the right most access, so last quarter we were well over a hundred percent underscoring the power of AWS and the horrendous margins in retail. But AWS is essentially funding Amazon's entrance into new markets, whether it's grocery or movies, Bezos moves into space. Dave, a while back you collaborated with us and we asked our audience, what could disrupt Amazon? And we came up with your detailed help, a number of scenarios as shown here. And we asked the audience to rate the likelihood of each scenario in terms of its likelihood of disrupting Amazon with a 10 being highly likely on average the score was six with complacency, arrogance, blindness, you know, self-inflicted wounds really taking the top spot with 6.5. So Dave is breaking up Amazon the right formula in your view, why or why not? >> Yeah, there's a couple of things there. The first is sort of the irony that when people in the sort of regulatory world talk about the power of Amazon, they almost always talk about their power in consumer markets, whether it's books or retail or impact on malls or main street shops or whatever and as you say that they make very little money doing that. The interest people almost never look at the big cloud battle between Amazon, Microsoft and lesser extent Google, Alibaba others, even though that's where they're by far highest market share and pricing power and all those things are. So the regulatory focus is sort of weird, but you know, the consumer stuff obviously gets more appeal to the general public. But that survey you referred to me was interesting because one of the challenges I sort of sent myself I was like okay, well, if I'm going to say that IBM case, AT&T case, Microsoft's case in all those situations the market was the one that actually minimized the power of those firms and therefore the antitrust stuff wasn't really necessary. Well, how true is that going to be again, just cause it's been true in the past doesn't mean it's true now. So what are the possible scenarios over the 2020s that might make it all happen again? And so each of those were sort of questions that we put out to others, but the ones that to me by far are the most likely I mean, they have the traditional one of company cultures sort of getting fat and happy and all, that's always the case, but the more specific ones, first of all by far I think is China. You know, Amazon retail is a low margin business. It would be vulnerable if it didn't have the cloud profits behind it, but imagine a year from now two years from now trade tensions with China get worse and Christmas comes along and China just says, well, you know, American consumers if you want that new exercise bike or that new shoes or clothing, well, anything that we make well, actually that's not available on Amazon right now, but you can get that from Alibaba. And maybe in America that's a little more farfetched, but in many countries all over the world it's not farfetched at all. And so the retail divisions vulnerability to China just seems pretty obvious. Another possible disruption, Amazon has spent billions and billions with their warehouses and their robots and their automated inventory systems and all the efficiencies that they've done there, but you could argue that maybe someday that's not really necessary that you have Search which finds where a good is made and a logistical system that picks that up and delivers it to customers and why do you need all those warehouses anyways? So those are probably the two top one, but there are others. I mean, a lot of retailers as they get stronger online, maybe they start pulling back some of the premium products from Amazon and Amazon takes their cut of whatever 30% or so people might want to keep more of that in house. You see some of that going on today. So the idea that the Amazon is in vulnerable disruption is probably is wrong and as part of the work that I'm doing, as part of stuff that I do with Dave and SiliconANGLE is how's that true for the others too? What are the scenarios for Google or Apple or Microsoft and the scenarios are all there. And so, will these companies be disrupted as they have in the past? Well, you can't say for sure, but the scenarios are certainly plausible and I certainly wouldn't bet against it and that's what history tells us. And it could easily happen once again and therefore, the antitrust should at least be cautionary and humble and realize that maybe they don't need to act as much as they think. >> Yeah, now, one of the things that you mentioned in your piece was felt like narrow remedies, were more logical. So you're not arguing for totally Les Affaire you're pushing for remedies that are more targeted in scope. And while the EU just yesterday announced new rules to limit the power of tech companies and we showed the article, some comments here the regulators they took the social media to announce a victory and they had a press conference. I know you watched that it was sort of a back slapping fest. The comments however, that we've sort of listed here are mixed, some people applauded, but we saw many comments that were, hey, this is a horrible idea, this was rushed together. And these are going to result as you say in unintended consequences, but this is serious stuff they're talking about applying would appear to be to your point or your prescription more narrowly defined restrictions although a lot of them to any company with a market cap of more than 75 billion Euro or turnover of more than 77.5 billion Euro which is a lot of companies and imposing huge penalties for violations up to 20% of annual revenue for repeat offenders, wow. So again, you've taken a brief look at these developments, you watched the press conference, what do you make of this? This is an application of more narrow restrictions, but in your quick assessment did they get it right? >> Yeah, let's break that down a little bit, start a little bit of history again and then get to Europe because although big sweeping breakups of the type that were proposed for IBM, Microsoft and all weren't necessary that doesn't mean that the government didn't do some useful things because they did. In the case of IBM government forces in Europe and America basically required IBM to make it easier for companies to make peripherals type drives, disc drives, printers that worked with IBM mainframes. They made them un-bundle their software pricing that made it easier for database companies and others to sell their of products. With AT&T it was the government that required AT&T to actually allow other phones to connect to the network, something they argued at the time would destroy security or whatever that it was the government that required them to allow MCI the long distance carrier to connect to the AT network for local deliveries. And with that Microsoft and Intel the government required them to at least treat their suppliers more even handly in terms of pricing and policies and support and such things. So the lessons out there is the big stuff wasn't really necessary, but the little stuff actually helped a lot and I think you can see the scenarios and argue in the piece that there's little stuff that can be done today in all the cases for the big five, there are things that you might want to consider the companies aren't saints they take advantage of their power, they use it in ways that sometimes can be reigned in and make for better off overall. And so that's how it brings us to the European piece of it. And to me, the European piece is much more the bad scenario of doing too much than the wiser course of trying to be narrow and specific. What they've basically done is they have a whole long list of narrow things that they're all trying to do at once. So they want Amazon not to be able to share data about its selling partners and they want Apple to open up their app store and they don't want people Google to be able to share data across its different services, Android, Search, Mail or whatever. And they don't want Facebook to be able to, they want to force Facebook to open up to other messaging services. And they want to do all these things for all the big companies all of which are American, and they want to do all that starting next year. And to me that looks like a scenario of a lot of difficult problems done quickly all of which might have some value if done really, really well, but all of which have all kinds of risks for the unintended consequence we've talked before and therefore they seem to me being too much too soon and the sort of problems we've seen in the past and frankly to really say that, I mean, the Europeans would never have done this to the companies if they're European firms, they're doing this because they're all American firms and the sort of frustration of Americans dominance of the European tech industry has always been there going back to IBM, Microsoft, Intel, and all of them. But it's particularly strong now because the tech business is so big. And so I think the politics of this at a time where we're supposedly all this great unity of America and NATO and Europe in regards to Ukraine, having the Europeans essentially go after the most important American industry brings in the geopolitics in I think an unavoidable way. And I would think the story is going to get pretty tense over the next year or so and as you say, the Europeans think that they're taking massive actions, they think they're doing the right thing. They think this is the natural follow on to the GDPR stuff and even a bigger version of that and they think they have more to come and they see themselves as the people taming big tech not just within Europe, but for the world and absent any other rules that they may pull that off. I mean, GDPR has indeed spread despite all of its flaws. So the European thing which it doesn't necessarily get huge attention here in America is certainly getting attention around the world and I would think it would get more, even more going forward. >> And the caution there is US public policy makers, maybe they can provide, they will provide a tailwind maybe it's a blind spot for them and it could be a template like you say, just like GDPR. Okay, Dave, we got to leave it there. Thanks for coming on the program today, always appreciate your insight and your views, thank you. >> Hey, thanks a lot, Dave. >> All right, don't forget these episodes are all available as podcast, wherever you listen. All you got to do is search, "Breaking Analysis Podcast". Check out ETR website, etr.ai. We publish every week on wikibon.com and siliconangle.com. And you can email me david.vellante@siliconangle.com or DM me @davevellante. Comment on my LinkedIn post. This is Dave Vellante for Dave Michelle for "theCUBE Insights" powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (slow tempo music)

>>from around the globe. It's the Cube with digital coverage of biz ops Manifesto unveiled. Brought to you by Biz Ops Coalition. Hey, Welcome back, everybody. Jeffrey here with the Cube. We're coming to you from our Palo Alto studios. And welcome back to this event. Is the biz Opps Manifesto unveiling? So the biz Opps manifesto and the biz Opps coalition have been around for a little while, But today's the big day. That's kind of the big public unveiling are excited to have some of the foundational people that put their put their name on the dotted line, if you will, to support this initiative to talk about why that initiative is so important. And so the next guest, we're excited to have his doctor, Mick Kirsten. He is the founder and CEO of Task Top. Make great to see you coming in from Vancouver, Canada, I think. Right. >>Yes. Great to be here, Jeff. Thank you. Absolutely. >>I hope your air is a little better out there. I know you had some of the worst air of all of us a couple a couple of weeks back, so hopefully things air, uh, getting a little better. And we get those fires under control? >>Yeah, Things have cleared up now, so yeah, it's good. It's good to be close to the U. S. And it's gonna have the Arabic clean as well. >>Absolutely. So let's let's jump into it. So you you've just been an innovation guy forever Starting way back in the day and Xerox Park. I was so excited to do an event at Xerox Park for the first time last year. I mean that that to me represents along with Bell Labs and and some other, you know, kind of foundational innovation and technology centers. That's got to be one of the greatest one. So I just wonder if you could share some perspective of getting your start there at Xerox Parc. You know, some of the lessons you learn and what you've been ableto kind of carry forward from those days. >>Yeah, I was fortunate. Joined Xerox Park in the computer science lab there at a very early point in my career, and to be working on open source programming languages. So back then, and the computer science lab where some of the inventions around programming around software development names such as Object of programming and ah, lot of what we had around really modern programming levels construct. Those were the teams that had the fortune of working with and really our goal waas. And of course, there's a Z. You know, this, uh, there's just this DNA of innovation and excitement and innovation in the water. And really, it was the model that was all about changing the way that we work was looking at for how we could make it 10 times easier to write. Code like this is back in 99 we were looking at new ways of expressing especially business concerns, especially ways of enabling people who are who want to innovate for their business, to express those concerns in code and make that 10 times easier than what that would take. So we created a new open source programming language, and we saw some benefits, but not quite quite what we expected. I then went and actually joined Charles Stephanie that former chief actor Microsoft, who is responsible for I actually got a Microsoft word as a out of Xerox Parc and into Microsoft and into the hands of Bill Gates and the company I was behind the whole office suite and his vision and the one I was trying to execute with working for him was to, you know, make Power point like a programming language, make everything completely visual. And I realized none of this was really working, that there was something else fundamentally wrong that programming languages or new ways of building software like Let's try to do with Charles around intentional programming. That was not enough. >>That was not enough. So you know, the agile movement got started about 20 years ago, and we've seen the rise of Dev ops and really this kind of embracing of of, of sprints And, you know, getting away from M. R. D s and P. R. D s and these massive definitions of what we're gonna build and long billed cycles to this iterative process. And that's been going on for a little while. So what was still wrong? What was still missing? Why the Biz Ops Coalition? Why the biz ops manifesto? >>Yeah, so I basically think we nailed some of the things that the programming language levels of teams can have. Effective languages deployed softened the club easily now right and at the kind of process and collaboration and planning level agile two decades decades ago was formed. We were adopting all the all the teams I was involved with on. It's really become a solved problem. So agile tools, agile teams actually of planning are now very mature and the whole challenges when organizations try to scale that. And so what I realized is that the way that Agile was scaling across teams and really scaling from the Technology Party organization to the business was just completely flawed. The agile teams had one set of doing things. One set of metrics, one set of tools and the way that the business was working was planning was investing in technology was just completely disconnected and using a a whole different set of measures. It's pretty interesting because I think it's >>pretty clear from the software development teams in terms of what they're trying to deliver, because they've got a feature set right and they've got bugs and it's easy. It's easy to see what they deliver, but it sounds like what you're really honing in on is is disconnect on the business side in terms of, you know, is it the right investment you know. Are we getting the right business? R o I on this investment? Was that the right feature? Should we be building another feature or shall we building a completely different products? That so it sounds like it's really a core piece of this is to get the right measurement tools, the right measurement data sets so that you can make the right decisions in terms of what you're investing, you know, limited resource is you can't Nobody has unlimited resources and ultimately have to decide what to do, which means you're also deciding what not to dio. It sounds like that's a really big piece of this of this whole effort. >>Yeah, Jeff, that's exactly it. Which is the way that the adult measures their own way of working is very different from the way that you measure business outcomes. The business outcomes are in terms of how happy your customers are. Are you innovating fast enough to keep up with the pace of, ah, rapidly changing economy, rapidly changing market and those are those are all around the customer. And so what? I learned on this long journey of supporting many organizations transformations and having them trying to apply those principles vigilant develops that those are not enough. Those measures technical practices, those measures, technical excellence of bringing code to the market. They don't actually measure business outcomes. And so I realized that really was much more around having these entwined flow metrics that are customer centric and business centric and market centric where we needed to go. So I want to shift gears >>a little bit and talk about your book because you're also a best selling author project a product, and and you you brought up this concept in your book called The Flow Framework. And it's really interesting to me because I know, you know, flow on one hand is kind of a workflow in the process flow, and you know that's how things get done and and embrace the flow. On the other hand, you know, everyone now in a little higher level, existential way is trying to get into the flow right into the workflow and, you know not be interrupted and get into a state where you're kind of your highest productivity, you know, kind of your highest comfort. Which floor you talking about in your book, or is it a little bit of both. >>That's a great question, is it's not what I gotta ask very often, cause me, it's It's absolutely both. So the thing that we want to get that we've learned how toe and, uh, master individual flow, that there's this beautiful book by me Holly teachings mentality. There's a beautiful Ted talk about him as well, about how we can take control of our own flow. So my question with the book with project surprise, How can we bring that to entire teams and really entire organizations? How come we have everyone contributing to a customer outcome? And this is really what if you go to the bazaar manifesto? It says, I focus on Out comes on using data to drive, whether we're delivering those outcomes rather than a focus on proxy metrics such as How quickly did we implement this feature? And now it's really how much value did the customs of the future and how quickly did we learn? And how quickly did you use that data to drive to that next outcome? Really, that with companies like Netflix on, like Amazon, have mastered, how do we get that every large organization, every idea, organization and make everyone be a softer innovator. So it's to bring that on the concept of flow to these entering value streams. And the fascinating thing is, we've actually seen the data. We've been able to study a lot of value streams. We see when flow increases, when organizations deliver value to a customer faster developers actually become more happy. So things like that implying that promotes course rise. And we've got empirical data for this. So that beautiful thing to me is that we've actually been able thio, combine these two things and and see the results in the data that you increased flow to the customer, your development or more happy. I >>love it. I love it, right, because we're all more. We're all happier when we're in the flow and we're all more productive winner in the flow. So I that is a great melding of two concepts. But let's jump into the into the manifesto itself a little bit. And you know, I love that you know, that took this approach really of having kind of four key values, and he gets 12 key principles and I just want to read a couple these values because when you read them, it sounds pretty brain dead, right? Of course. Right. Of course, you should focus on business outcomes. Of course, you should have trust and collaboration. Of course, you should have data based decision making processes and not just intuition or, you know, whoever is the loudest person in the room on toe, learn and respond and pivot. But >>what's the >>value of actually just putting them on a piece of paper? Because again, this is not this. These are all good positive things, right? When when somebody reads these to you or tells you these or sticks it on the wall? Of course. But unfortunately, of course, isn't always enough. >>No, I think what's happened is some of these core principles originally from the agile manifested two decades ago. The whole Dev ops movement of the last decade off flow feedback and continue learning has been key. But a lot of organizations, especially the ones undergoing transformations, have actually gone a very different way, right? The way that they measure value in technology innovation is through costs For many organizations, the way that they actually are looking at at their moving to cloud is actually is a reduction in costs, whereas the right way of looking at moving the cloud is how much more quickly can we get to the value to the customer? How quickly can we learn from that? And how could quickly can we drive the next business outcome? So, really, the key thing is to move away from those old ways of doing things that funding projects and call centers to actually funding and investing in outcomes and measuring outcomes through these flow metrics, which in the end are your fast feedback for how quickly you're innovating for your customer. So these things do seem, you know, very obvious when you look at them. But the key thing is what you need to stop doing. To focus on these, you need to actually have accurate real time data off how much value your phone to the customer every week, every month, every quarter. And if you don't have that, your decisions are not given on data. If you don't know what your bottle like, it's. And this is something that in the decades of manufacturing car manufacturers, other manufacturers master. They always know where the bottom back in their production processes you ask, uh, random. See, I all want a global 500 company where the bottleneck is, and you won't get it there. Answer. Because there's not that level of understanding. So have to actually follow these principles. You need to know exactly where you follow like is because that's what's making your developers miserable and frustrated on having them context, which on thrash So it. The approach here is important, and we have to stop doing these other things right. >>There's so much. They're a pack. I love it, you know, especially the cloud conversation, because so many people look at it wrong as a cost saving device as opposed to an innovation driver, and they get stuck, they get stuck in the literal. And, you know, I think the same thing always about Moore's law, right? You know, there's a lot of interesting riel tech around Moore's law and the increasing power of microprocessors. But the real power, I think in Moore's laws, is the attitudinal change in terms of working in a world where you know that you've got all this power and what will you build and design? E think it's funny to your your comment on the flow in the bottleneck, right? Because because we know manufacturing assumes you fix one bottleneck. You move to your next one, right, You always move to your next point of failure. So if you're not fixing those things, you know you're not. You're not increasing that speed down the line unless you can identify where that bottleneck is, or no matter how Maney improvements you make to the rest of the process, it's still going to get hung up on that one spot. >>That's exactly, and you also make it sound so simple. But again, if you don't have the data driven visibility of where the bottleneck is. And but these bottlenecks are just as you said, if it's just lack, um, all right, so we need to understand is the bottleneck, because our security use air taking too long and stopping us from getting like the customer. If it's that automate that process and then you move on to the next bottleneck, which might actually be that deploy yourself through the clouds is taking too long. But if you don't take that approach of going flow first rather than again the sort of way cost production first you have taken approach of customer centric city, and you only focus on optimizing cost. Your costs will increase and your flow will slow down. And this is just one, these fascinating things. Whereas if you focus on getting back to the customer and reducing your cycles on getting value your flow time from six months to two weeks or 21 week or two event as we see with tech giants, you actually could both lower your costs and get much more value. Of course, get that learning going. So I think I've I've seen all these cloud deployments and modernizations happen that delivered almost no value because there was such a big ball next up front in the process. And actually the hosting and the AP testing was not even possible with all of those inefficiencies. So that's why going flow first rather than costs. First, there are projects versus Sochi. >>I love that and and and and it begs, repeating to that right within a subscription economy. You know you're on the hook to deliver value every single month because they're paying you every single month. So if you're not on top of how you delivering value, you're going to get sideways because it's not like, you know, they pay a big down payment and a small maintenance fee every month. But once you're in a subscription relationship, you know you have to constantly be delivering value and upgrading that value because you're constantly taking money from the customers. It's it's such a different kind of relationship, that kind of the classic, you know, Big Bang with the maintenance agreement on the back end really important. >>Yeah, and I think in terms of industry ship, that's it. That's what catalyzed this industry shift is in this SAS that subscription economy. If you're not delivering more and more value to your customers, someone else's and they're winning the business, not you. So one way we know is that divide their customers with great user experiences. Well, that really is based on how many features you delivered or how much. How about how many quality improvements or scaler performance improvements you delivered? So the problem is, and this is what the business manifesto was was the forefront of touch on is, if you can't measure how much value delivered to a customer, what are you measuring? You just back again measuring costs, and that's not a measure of value. So we have to shift quickly away from measuring costs to measuring value to survive in in the subscription economy. Mick, >>we could go for days and days and days. I want to shift gears a little bit into data and and a data driven, um, decision making a data driven organization. Because right day has been talked about for a long time. The huge big data mean with with Hadoop over over several years and data warehouses and data lakes and data, oceans and data swamps and you go on and on, it's not that easy to do right. And at the same time, the proliferation of data is growing exponentially were just around the corner from from I, O. T and five G. So now the accumulation of data at machine scale again this is gonna overwhelm, and one of the really interesting principles that I wanted to call out and get your take right is today's organizations generate mawr data than humans can process. So informed decisions must be augmented by machine learning and artificial intelligence. I wonder if you can again, you've got some great historical perspective reflect on how hard it is to get the right data to get the data in the right context and then to deliver to the decision makers and then trust the decision makers to actually make the data and move that down. You know, it's kind of this democratization process into more and more people and more and more frontline jobs, making more and more of these little decisions every day. >>Yeah, and Jeff, I think the front part of what you said are where the promises of big data have completely fallen on their face into these swamps. As you mentioned, because if you don't have the data and the right format, you can connect, collected that the right way, you're not. Model it that way the right way. You can't use human or machine learning on it effectively. And there have been the number of data, warehouses and a typical enterprise organization, and the sheer investment is tremendous. But the amount of intelligence being extracted from those is a very big problem. So the key thing that I've known this is that if you can model your value streams so you actually understand how you're innovating, how you're measuring the delivery value and how long that takes. What is your time to value through these metrics? Like for the time you can actually use both. You know the intelligence that you've got around the table and push that balance as it the assay, far as you can to the organization. But you can actually start using that those models to understand, find patterns and detect bottlenecks that might be surprising, Right? Well, you can detect interesting bottle next one you shift to work from home. We detected all sorts of interesting bottlenecks in our own organization that we're not intuitive to me that had to do with more senior people being overloaded and creating bottlenecks where they didn't exist. Whereas we thought we were actually organization. That was very good at working from home because of our open source route. So the data is highly complex. Software Valley streams are extremely complicated, and the only way to really get the proper analysts and data is to model it properly and then to leverage these machine learning and AI techniques that we have. But that front, part of what you said, is where organizations are just extremely immature in what I've seen, where they've got data from all the tools, but not modeled in the right way. >>Well, all right, so before I let you go, you know? So you get a business leader he buys in. He reads the manifesto. He signs on the dotted line. He says, Mick, how do I get started? I want to be more aligned with With the development teams, you know, I'm in a very competitive space. We need to be putting out new software features and engage with our customers. I want to be more data driven. How do I get started? Well, you know, what's the biggest inhibitor for most people to get started and get some early winds, which we know is always the key to success in any kind of a new initiative, >>right? So I think you can reach out to us through the website. Uh, on the is a manifesto, but the key thing is just it's exactly what you said, Jeff. It's to get started and get the key wins. So take a probably value stream. That's mission critical. It could be your new mobile Web experiences, or or part of your cloud modernization platform where your analysts pipeline. But take that and actually apply these principles to it and measure the entire inflow of value. Make sure you have a volumetric that everyone is on the same page on, right. The people on the development teams that people in leadership all the way up to the CEO and one of the where I encourage you to start is actually that enter and flow time, right? That is the number one metric. That is how you measure whether you're getting the benefit of your cloud modernization. That is the one metric that even Cockcroft when people I respect tremendously put in his cloud for CEOs Metric 11 way to measure innovation. So basically, take these principles, deployed them on one product value stream measure into and flow time on. Then you'll actually you well on your path to transforming and to applying the concepts of agile and develops all the way to the business to the way in your operating model. >>Well, Mick, really great tips, really fun to catch up. I look forward to a time when we can actually sit across the table and and get into this, because I just I just love the perspective. And, you know, you're very fortunate to have that foundational, that foundational base coming from Xerox parc. And it's, you know, it's a very magical place with a magical history. So the to incorporate that and to continue to spread that wealth, you know, good for you through the book and through your company. So thanks for sharing your insight with us today. >>Thanks so much for having me, Jeff. Absolutely. >>Alright. And go to the biz ops manifesto dot org's Read it. Check it out. If you want to sign it, sign it. They'd love to have you do it. Stay with us for continuing coverage of the unveiling of the business manifesto on the Cube. I'm Jeffrey. Thanks for watching. See you next time.

>> Announcer: From around the globe, it's theCUBE. Covering the UPGRADE 2020, the NTT Research Summit. Presented by NTT research. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. Welcome back to our ongoing coverage of UPGRADE 2020. It's the NTT Research Labs Summit, and it's all about upgrading reality. Heavy duty basic research around a bunch of very smart topics. And we're really excited to have our next guest to kind of dive in. I promise you, it'll be the deepest conversation you have today, unless you watch a few more of these segments. So our first guest we're welcoming back Kazuhiro Gomi He's the president and CEO of NTT research, Kaza great to see you. >> Good to see you. And joining him is Yoshi Yamamoto. He is a fellow for NTT Research and also the director of the Physics and Informatics Lab. Yoshi, great to meet you as well. >> Nice to meet you. >> So I was teasing the crew earlier, Yoshi, when I was doing some background work on you and I pulled up your Wikipedia page and I was like, okay guys, read this thing and tell me what a, what Yoshi does. You that have been knee deep in quantum computing and all of the supporting things around quantum heavy duty kind of next gen computing. I wonder if you can kind of share a little bit, you know, your mission running this labs and really thinking so far in advance of what we, you know, kind of experience and what we work with today and this new kind of basic research. >> NTT started the research on quantum computing back in 1986 87. So it is already more than 30 years. So, the company invested in this field. We have accumulated a lot of sort of our ideas, knowledge, technology in this field. And probably, it is the right time to establish the connection, close connection to US academia. And in this way, we will jointly sort of advance our research capabilities towards the future. The goal is still, I think, a long way to go. But by collaborating with American universities, and students we can accelerate NTT effort in this area. >> So, you've been moving, you've been working on quantum for 30 years. I had no idea that that research has been going on for such a very long time. We hear about it in the news and we hear about it a place like IBM and iSensor has a neat little demo that they have in the new sales force period. What, what is, what makes quantum so exciting and the potential to work so hard for so long? And what is it going to eventually open up for us when we get it to commercial availability? >> The honest answer to that question is we don't know yet. Still, I think after 30 years I think of hard working on quantum Physics and Computing. Still we don't know clean applications are even, I think we feel that the current, all the current efforts, are not necessarily, I think, practical from the engineering viewpoint. So, it is still a long way to go. But the reason why NTT has been continuously working on the subject is basically the very, sort of bottom or fundamental side of the present day communication and the computing technology. There is always a quantum principle and it is very important for us to understand the quantum principles and quantum limit for communication and computing first of all. And if we are lucky, maybe we can make a breakthrough for the next generation communication and computing technology based on quantum principles. >> Right. >> But the second, is really I think just a guess, and hope, researcher's hope and nothing very solid yet. >> Right? Well, Kazu I want to go, go to you cause it really highlights the difference between, you know, kind of basic hardcore fundamental research versus building new applications or building new products or building new, you know, things that are going to be, you know, commercially viable and you can build an ROI and you can figure out what the customers are going to buy. It really reflects that this is very different. This is very, very basic with very, very long lead times and very difficult execution. So when, you know, for NTT to spend that money and invest that time and people for long, long periods of time with not necessarily a clean ROI at the end, that really, it's really an interesting statement in terms of this investment and thinking about something big like upgrading reality. >> Yeah, so that's what this, yeah, exactly that you talked about what the basic research is, and from NTT perspective, yeah, we feel like we, as Dr. Yamamoto, he just mentioned that we've been investing into 30 plus years of a time in this field and, you know, and we, well, I can talk about why this is important. And some of them is that, you know, that the current computer that everybody uses, we are certainly, well, there might be some more areas of improvement, but we will someday in, I don't know, four years, five years, 10 years down the road, there might be some big roadblock in terms of more capacity, more powers and stuff. We may run into some issues. So we need to be prepared for those kinds of things. So, yes we are in a way of fortunate that we are, we have a great team to, and a special and an expertise in this field. And, you know, we have, we can spend some resource towards that. So why not? We should just do that in preparation for that big, big wall so to speak. I guess we are expecting to kind of run into, five, 10 years down the road. So let's just looking into it, invest some resources into it. So that's where we are, we're here. And again, I I'm, from my perspective, we are very fortunate that we have all the resources that we can do. >> It's great. Right, as they give it to you. Dr. Yamamoto, I wonder if you can share what it's like in terms of the industry and academic working together. You look at the presentations that are happening here at the event. All the great academic institutions are very well represented, very deep papers. You at NTT, you spend some time at Stanford, talk about how it is working between this joint development with great academic institutions, as well as the great company. >> Traditionally in the United States, there has been always two complementary opportunities for training next generation scientists and engineers. One opportunity is junior faculty position or possible position in academia, where main emphasis is education. The other opportunity is junior researcher position in industrial lab where apparently the focus emphasis is research. And eventually we need two types of intellectual leaders from two different career paths. When they sort of work together, with a strong educational background and a strong research background, maybe we can make wonderful breakthrough I think. So it is very important to sort of connect between two institutions. However, in the recent past, particularly after Better Lab disappeared, basic research activity in industrial lab decreases substantially. And we hope MTT research can contribute to the building of fundamental science in industry side. And for that purpose cross collaboration with research Universities are very important. So the first task we have been working so far, is to build up this industry academia connection. >> Huge compliment NTT to continue to fund the basic research. Cause as you said, there's a lot of companies that were in it before and are not in it any more. And when you often read the history of, of, of computing and a lot of different things, you know, it goes back to a lot of times, some basic, some basic research. And just for everyone to know what we're talking about, I want to read a couple of, of sessions that you could attend and learn within Dr. Yamamoto space. So it's Coherent nonlinear dynamics combinatorial optimization. That's just one session. I love it. Physics successfully implements Lagrange multiplier optimization. I love it. Photonics accelerators for machine learning. I mean, it's so it's so interesting to read basic research titles because, you know, it's like a micro-focus of a subset. It's not quantum computing, it's all these little smaller pieces of the quantum computing stack. And then obviously very deep and rich. Deep dives into those, those topics. And so, again, Kazu, this is the first one that's going to run after the day, the first physics lab. But then you've got the crypto cryptography and information security lab, as well as the medical and health information lab. You started with physics and informatics. Is that the, is that the history? Is that the favorite child you can lead that day off on day two of the event. >> We did throw a straw and Dr. Yamamoto won it Just kidding (all laugh) >> (indistinct), right? It's always fair. >> But certainly this quantum, Well, all the topics certainly are focuses that the basic research, that's definitely a commonality. But I think the quantum physics is in a way kind of very symbolic to kind of show that the, what the basic research is. And many people has a many ideas associated with the term basic research. But I think that the quantum physics is certainly one of the strong candidates that many people may think of. So well, and I think this is definitely a good place to start for this session, from my perspective. >> Right. >> Well, and it almost feels like that's kind of the foundational even for the other sessions, right? So you talk about medical or you talk about cryptography in information, still at the end of the day, there's going to be compute happening to drive those processes. Whether it's looking at, at, at medical slides or trying to do diagnosis, or trying to run a bunch of analysis against huge data sets, which then goes back to, you know, ultimately algorithms and ultimately compute, and this opening up of this entirely different set of, of horsepower. But Dr. Yamamoto, I'm just curious, how did you get started down this path of, of this crazy 30 year journey on quantum computing. >> The first quantum algorithm was invented by David Deutsch back in 1985. These particular algorithm turned out later the complete failure, not useful at all. And he spent seven years, actually, to fix loophole and invented the first successful algorithm that was 1992. Even though the first algorithm was a complete failure, that paper actually created a lot of excitement among the young scientists at NTT Basic Research Lab, immediately after the paper appeared. And 1987 is actually, I think, one year later. So this paper appeared. And we, sort of agreed that maybe one of the interesting future direction is quantum information processing. And that's how it started. It's it's spontaneous sort of activity, I think among young scientists of late twenties and early thirties at the time. >> And what do you think Dr. Yamamoto that people should think about? If, if, if again, if we're at a, at a cocktail party, not with not with a bunch of, of people that, that intimately know the topic, how do you explain it to them? How, how should they think about this great opportunity around quantum that's kept you engaged for decades and decades and decades. >> The quantum is everywhere. Namely, I think this world I think is fundamentally based on and created from quantum substrate. At the very bottom of our, sort of world, consist of electrons and photons and atoms and those fundamental particles sort of behave according to quantum rule. And which is a very different from classical reality, namely the world where we are living every day. The relevant question which is also interesting is how our classical world or classical reality surfaces from the general or universal quantum substrate where our intuition never works. And that sort of a fundamental question actually opens the possibility I think by utilizing quantum principle or quantum classical sort of crossover principle, we can revolutionize the current limitation in communication and computation. That's basically the start point. We start from quantum substrate. Under classical world the surface is on top of quantum substrate exceptional case. And we build the, sort of communication and computing machine in these exceptional sort of world. But equally dig into quantum substrate, new opportunities is open for us. That's somewhat the fundamental question. >> That's great. >> Well, I'm not, yeah, we can't get too deep cause you'll lose me, you'll lose me long before, before you get to the bottom of the, of the story, but, you know, I really appreciate it. And of course back to you this is your guys' first event. It's a really bold statement, right? Upgrade reality. I just wonder if, when you look at the, at the registrant's and you look at the participation and what do you kind of anticipate, how much of the anticipation is, is kind of people in the business, you know, kind of celebrating and, and kind of catching up to the latest research and how much of it is going to be really inspirational for those next, you know, early 20 somethings who are looking to grab, you know, an exciting field to hitch their wagon to, and to come away after this, to say, wow, this is something that really hooked me and I want to get down and really kind of advance this technology a little bit, further advance this research a little bit further. >> So yeah, for, from my point of view for this event, I'm expecting, there are quite wide range of people. I'm, I'm hoping that are interested in to this event. Like you mentioned that those are the, you know, the business people who wants to know what NTT does, and then what, you know, the wider spectrum of NTT does. And then, and also, especially like today's events and onwards, very specific to each topic. And we go into very deep dive. And, and so to, to this session, especially in a lot of participants from the academia's world, for each, each subject, including students, and then some other, basically students and professors and teachers and all those people as well. So, so that's are my expectations. And then from that program arrangement perspective, that's always something in my mind that how do we address those different kind of segments of the people. And we all welcoming, by the way, for those people. So to me to, so yesterday was the general sessions where I'm kind of expecting more that the business, and then perhaps some other more and more general people who're just curious what NTT is doing. And so instead of going too much details, but just to give you the ideas that the what's that our vision is and also, you know, a little bit of fla flavor is a good word or not, but give you some ideas of what we are trying to do. And then the better from here for the next three days, obviously for the academic people, and then those who are the experts in each field, probably day one is not quite deep enough. Not quite addressing what they want to know. So day two, three, four are the days that designed for that kind of requirements and expectations. >> Right? And, and are most of the presentations built on academic research, that's been submitted to journals and other formal, you know, peer review and peer publication types of activities. So this is all very formal, very professional, and very, probably accessible to people that know where to find this information. >> Mmh. >> Yeah, it's great. >> Yeah. >> Well, I, I have learned a ton about NTT and a ton about this crazy basic research that you guys are doing, and a ton about the fact that I need to go back to school if I ever want to learn any of this stuff, because it's, it's a fascinating tale and it's it's great to know as we've seen these other basic research companies, not necessarily academic but companies kind of go away. We mentioned Xerox PARC and Bell Labs that you guys have really picked up that mantle. Not necessarily picked it up, you're already doing it yourselves. but really continuing to carry that mantle so that we can make these fundamental, basic building block breakthroughs to take us to the next generation. And as you say, upgrade the future. So again, congratulations. Thanks for sharing this story and good luck with all those presentations. >> Thank you very much. >> Thank you. >> Thank you. Alright, Yoshi, Kazu I'm Jeff, NTT UPGRADE 2020. We're going to upgrade the feature. Thanks for watching. See you next time. (soft music)

>> Narrator: From around the globe, it's theCUBE, covering the Upgrade 2020, the NTT Research Summit presented by NTT Research. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in Palo Alto studio for our ongoing coverage of the Upgrade 2020, it's the NTT Research conference. It's our first year covering the event, it's actually the first year for the event inaugural, a year for the events, we're really, really excited to get into this. It's basic research that drives a whole lot of innovation, and we're really excited to have our next guest. He is Kazuhiro Gomi, he is the President and CEO of NTT Research. Kazu, great to see you. >> Hi, good to see you. >> Yeah, so let's jump into it. So this event, like many events was originally scheduled I think for March at Berkeley, clearly COVID came along and you guys had to make some changes. I wonder if you can just share a little bit about your thinking in terms of having this event, getting this great information out, but having to do it in a digital way and kind of rethinking the conference strategy. >> Sure, yeah. So NTT Research, we started our operations about a year ago, July, 2019. and I always wanted to show the world that to give a update of what we have done in the areas of basic and fundamental research. So we plan to do that in March, as you mentioned, however, that the rest of it to some extent history, we needed to cancel the event and then decided to do this time of the year through virtual. Something we learned, however, not everything is bad, by doing this virtual we can certainly reach out to so many peoples around the globe at the same time. So we're taking, I think, trying to get the best out of it. >> Right, right, so you've got a terrific lineup. So let's jump into a little bit. So first thing just about NTT Research, we're all familiar, if you've been around for a little while about Bell Labs, we're fortunate to have Xerox PARC up the street here in Palo Alto, these are kind of famous institutions doing basic research. People probably aren't as familiar at least in the states around NTT basic research. But when you think about real bottom line basic research and how it contributes ultimately, it gets into products, and solutions, and health care, and all kinds of places. How should people think about basic research and its role in ultimately coming to market in products, and services, and all different things. But you're getting way down into the weeds into the really, really basic hardcore technology. >> Sure, yeah, so let me just from my perspective, define the basic research versus some other research and development. For us that the basic research means that we don't necessarily have any like a product roadmap or commercialization roadmap, we just want to look at the fundamental core technology of all things. And from the timescale perspective obviously, not that we're not looking at something new, thing, next year, next six months, that kind of thing. We are looking at five years or sometimes longer than that, potentially 10 years down the road. But you mentioned about the Bell Lab and Xerox PARC. Yeah, well, they used to be such organizations in the United States, however, well, arguably those days have kind of gone, but so that's what's going on in the United States. In Japan, NTT has have done quite a bit of basic research over the years. And so we wanted to, I think because that a lot of the cases that we can talk about the end of the Moore's laws and then the, we are kind of scary time for that. The energy consumptions on ITs We need to make some huge, big, fundamental change has to happen to sustain our long-term development of the ideas and basically for the sake of human beings. >> Right, right. >> So NTT sees that and also we've been doing quite a bit of basic research in Japan. So we recognize this is a time that the let's expand this activities and then by doing, as a part of doing so is open up the research lab in Silicon Valley, where certainly we can really work better, work easier to with that the global talents in this field. So that's how we started this endeavor, like I said, last year. And so far, it's a tremendous progress that we have made, so that's where we are. >> That's great, so just a little bit more specific. So you guys are broken down into three labs as I understand, you've got the Physics, the PHI, which is Physics and Informatics, the CIS lab Cryptography and Information Security, and the MEI lab Medical and Health Informatics, and the conference has really laid out along those same tracks, really day one is a whole lot of stuff, or excuse me, they do to run the Physics and Informatics day. The next day is really Cryptography and Information Security, and then the Medical and Health Informatics. So those are super interesting but very diverse kind of buckets of fundamental research. And you guys are attacking all three of those pillars. >> Yup, so day one, general session, is that we cover the whole, all the topics. And but just that whole general topics. I think some people, those who want to understand what NTT research is all about, joining day one will be a great day to be, to understand more holistic what we are doing. However, given the type of research topic that we are tackling, we need the deep dive conversations, very specific to each topic by the specialist and the experts in each field. Therefore we have a day two, three, and four for a specific topics that we're going to talk about. So that's a configuration of this conference. >> Right, right, and I love. I just have to read a few of the session breakout titles 'cause I think they're just amazing and I always love learning new vocabulary words. Coherent nonlinear dynamics and combinatorial optimization language multipliers, indistinguishability obfuscation from well-founded assumptions, fully deniable communications and computation. I mean, a brief history of the quasi-adaptive NIZKs, which I don't even know what that stands for. (Gomi laughing) Really some interesting topics. But the other thing that jumps out when you go through the sessions is the representation of universities and really the topflight university. So you've got people coming from MIT, CalTech, Stanford, Notre Dame, Michigan, the list goes on and on. Talk to us about the role of academic institutions and how NTT works in conjunction with academic institutions, and how at this basic research level kind of the commercial academic interests align and come together, and work together to really move this basic research down the road. >> Sure, so the working with academic, especially at the top-notch universities are crucial for us. Obviously, that's where the experts in each field of the basic research doing their super activities and we definitely need to get connected, and then we need to accelerate our activities and together with the entities researchers. So that has been kind of one of the number one priority for us to jumpstart and get some going. So as you mentioned, Jeff, that we have a lineup of professors and researchers from each top-notch universities joining to this event and talking at a generous, looking at different sessions. So I'm sure that those who are listening in to those sessions, you will learn well what's going on from the NTT's mind or NTT researchers mind to tackle each problem. But at the same time you will get to hear that top level researchers and professors in each field. So I believe this is going to be a kind of unique, certainly session that to understand what's it's like in a research field of quantum computing, encryptions, and then medical informatics of the world. >> Right. >> So that's, I am sure it's going to be a pretty great lineups. >> Oh, absolutely, a lot of information exchange. And I'm not going to ask you to pick your favorite child 'cause that would be unfair, but what I am going to do is I noticed too that you also write for the Forbes Technology Council members. So you're publishing on Forbes, and one of the articles that you publish relatively recently was about biological digital twins. And this is a topic that I'm really interested in. We used to do a lot of stuff with GE and there was always a lot of conversation about digital twins, for turbines, and motors, and kind of all this big, heavy industrial equipment so that you could get ahead of the curve in terms of anticipating maintenance and basically kind of run simulations of its lifetime. Need concept, now, and that's applied to people in biology, whether that's your heart or maybe it's a bigger system, your cardiovascular system, or the person as a whole. I mean, that just opens up so much interesting opportunities in terms of modeling people and being able to run simulations. If they do things different, I would presume, eat different, walk a little bit more, exercise a little bit more. And you wrote about it, I wonder if you could share kind of your excitement about the potential for digital twins in the medical space. >> Sure, so I think that the benefit is very clear for a lot of people, I would hope that the ones, basically, the computer system can simulate or emulate your own body, not just a generic human body, it's the body for Kazu Gomi at the age of whatever. (Jeff laughing) And so if you get that precise simulation of your body you can do a lot of things. Oh, you, meaning I think a medical professional can do a lot of thing. You can predict what's going to happen to my body in the next year, six months, whatever. Or if I'm feeling sick or whatever the reasons and then the doctor wants to prescribe a few different medicines, but you can really test it out a different kind of medicines, not to you, but to the twin, medical twin then obviously is safer to do some kind of specific medicines or whatever. So anyway, those are the kind of visions that we have. And I have to admit that there's a lot of things, technically we have to overcome, and it will take a lot of years to get there. But I think it's a pretty good goal to define, so we said we did it and I talked with a couple of different experts and I am definitely more convinced that this is a very nice goal to set. However, well, just talking about the goal, just talking about those kinds of futuristic thing, you may just end up with a science fiction. So we need to be more specific, so we have the very researchers are breaking down into different pieces, how to get there, again, it's going to be a pretty long journey, but we're starting from that, they're try to get the digital twin for the cardiovascular system, so basically the create your own heart. Again, the important part is that this model of my heart is very similar to your heart, Jeff, but it's not identical it is somehow different. >> Right, right. >> So we are looking on it and there are certainly some, we're not the only one thinking something like this, there are definitely like-minded researchers in the world. So we are gathered together with those folks and then come up with the exchanging the ideas and coming up with that, the plans, and ideas, that's where we are. But like you said, this is really a exciting goal and exciting project. >> Right, and I like the fact that you consistently in all the background material that I picked up preparing for this today, this focus on tech for good and tech for helping the human species do better down the road. In another topic, in other blog post, you talked about and specifically what are 15 amazing technologies contributing to the greater good and you highlighted cryptography. So there's a lot of interesting conversations around encryption and depending kind of commercialization of quantum computing and how that can break all the existing kind of encryption. And there's going to be this whole renaissance in cryptography, why did you pick that amongst the entire pallet of technologies you can pick from, what's special about cryptography for helping people in the future? >> Okay, so encryption, I think most of the people, just when you hear the study of the encryption, you may think what the goal of these researchers or researches, you may think that you want to make your encryption more robust and more difficult to break. That you can probably imagine that's the type of research that we are doing. >> Jeff: Right. >> And yes, yes, we are doing that, but that's not the only direction that we are working on. Our researchers are working on different kinds of encryptions and basically encryptions controls that you can just reveal, say part of the data being encrypted, or depending upon that kind of attribute of whoever has the key, the information being revealed are slightly different. Those kinds of encryption, well, it's kind of hard to explain verbally, but functional encryption they call is becoming a reality. And I believe those inherit data itself has that protection mechanism, and also controlling who has access to the information is one of the keys to address the current status. Current status, what I mean by that is, that they're more connected world we are going to have, and more information are created through IOT and all that kind of stuff, more sensors out there, I think. So it is great on the one side that we can do a lot of things, but at the same time there's a tons of concerns from the perspective of privacy, and securities, and stuff, and then how to make those things happen together while addressing the concern and the leverage or the benefit you can create super complex accessing systems. But those things, I hate to say that there are some inherently bringing in some vulnerabilities and break at some point, which we don't want to see. >> Right. >> So I think having those securities and privacy mechanism in that the file itself is I think that one of the key to address those issues, again, get the benefit of that they're connected in this, and then while maintaining the privacy and security for the future. >> Right. >> So and then that's, in the end will be the better for everyone and a better society. So I couldn't pick other (Gomi and Jeff laughing) technology but I felt like this is easier for me to explain to a lot of people. So that's mainly the reasons that I went back launching. >> Well, you keep publishing, so I'm sure you'll work your way through most of the technologies over a period of time, but it's really good to hear there's a lot of talk about security not enough about privacy. There's usually the regs and the compliance laws lag, what's kind of happening in the marketplace. So it's good to hear that's really a piece of the conversation because without the privacy the other stuff is not as attractive. And we're seeing all types of issues that are coming up and the regs are catching up. So privacy is a super important piece. But the other thing that is so neat is to be exposed not being an academic, not being in this basic research every day, but have the opportunity to really hear at this level of detail, the amount of work that's being done by big brain smart people to move these basic technologies along, we deal often in kind of higher level applications versus the stuff that's really going on under the cover. So really a great opportunity to learn more and hear from, and probably understand some, understand not all about some of these great, kind of baseline technologies, really good stuff. >> Yup. >> Yeah, so thank-you for inviting us for the first one. And we'll be excited to sit in on some sessions and I'm going to learn. What's that one phrase that I got to learn? The N-I-K-Z-T. NIZKs. (laughs) >> NIZKs. (laughs) >> Yeah, NIZKs, the brief history of quasi-adaptive NI. >> Oh, all right, yeah, yeah. (Gomi and Jeff laughing) >> All right, Kazuhiro, I give you the final word- >> You will find out, yeah. >> You've been working on this thing for over a year, I'm sure you're excited to finally kind of let it out to the world, I wonder if you have any final thoughts you want to share before we send people back off to their sessions. >> Well, let's see, I'm sure if you're watching this video, you are almost there for that actual summit. It's about to start and so hope you enjoy the summit and in a physical, well, I mentioned about the benefit of this virtual, we can reach out to many people, but obviously there's also a flip side of the coin as well. With a physical, we can get more spontaneous conversations and more in-depth discussion, certainly we can do it, perhaps not today. It's more difficult to do it, but yeah, I encourage you to, I think I encouraged my researchers NTT side as well to basic communicate with all of you potentially and hopefully then to have more in-depth, meaningful conversations just starting from here. So just feel comfortable, perhaps just feel comfortable to reach out to me and then all the other NTT folks. And then now, also that the researchers from other organizations, I'm sure they're looking for this type of interactions moving forward as well, yeah. >> Terrific, well, thank-you for that open invitation and you heard it everybody, reach out, and touch base, and communicate, and engage. And it's not quite the same as being physical in the halls, but that you can talk to a whole lot more people. So Kazu, again, thanks for inviting us. Congratulations on the event and really glad to be here covering it. >> Yeah, thank-you very much, Jeff, appreciate it. >> All right, thank-you. He's Kazu, I'm Jeff, we are at the Upgrade 2020, the NTT Research Summit. Thanks for watching, we'll see you next time. (upbeat music)

>> Announcer: Live from San Francisco, it's theCUBE covering RSA Conference 2020, San Francisco. Brought to you by SiliconANGLE Media. >> Welcome back everybody, Jeff Frick with theCUBE. We're at RSA 2020. It's day four, it's Thursday. This is a crazy long conference, 40,000 people. Even with the challenges presented by coronavirus, and there's a lot of weird stuff going on, the team pulled it together, they went forward. And even though there was drops out here and there, I think all in all, most people will tell you, it's been a pretty successful conference. And we're excited to be joined by really one of the top level sponsors here, that's still here and still doing good things. It's Vittorio Viare... Viarengo, sorry, the new interim CMO of McAfee. >> Yeah. >> Vittorio, I just call you Vittorio all the time. I never look past your first name. Great to see you. >> Likewise. It's always a pleasure to be here with an institution of Silicon Valley-- >> Oh thank you, thank you. So interim CMO, I always think of like interim football coaches that they get pulled in halfway through the season, so the good news is you kind of got the job and all the responsibilities. The bad news is, you still have that interim thing, but you don't care, you just go to work, right? >> Now whenever you have an interim job, you have to just do the job and then that's the best way to operate. >> Yeah, so again, I couldn't help but go back and look at that conversation that we had at Xerox Parc, which is interesting. That's pretty foundational, everything that happens in Silicon Valley, and so many discoveries up there. And you touched on some really key themes in the way you manage your teams, but I think they're really much more valuable, and worth bringing back up again. And the context was using scrum as a way to manage people, but more importantly, what you said is it forced you as a leader to set first priorities and have great communication; and to continually do that on this two week pace, to keep everybody moving down the road. I think that is so powerful and so lacking unfortunately, in a lot of organizations today. >> Yeah, look, I think that when you hire smart people, if you just make sure that they understand what their priorities are, and then remove the obstacle and get out of the way, magical things happen. And I give you example that is very close to your heart. When I took over a great team at Skyhigh, that got bought by McAfee, they had content marketing down to a science, but they were lacking videos. So I brought that in. I said, "Guys, people watch videos, "people engage with videos, "we need to start telling the story through videos." And I started pushing, pushing, pushing, and then I pulled back, and these guys took it to a whole new level. And then they're doing videos, they're very creative, they are crisp. And I'm like, "Yeah, my job is done." >> It is really wild how video has become such an important way for education. I mean it used to be... I remember the first time I ever saw an engineer use Google to answer a question on writing code. I had never seen that before. I'm not a coder. Wow, I thought it was just for finding my local store or whatever. And now to see what really... I think YouTube has pushed people to expect that the answer to any question should be in a video. >> So, yesterday literally, somebody from a company I don't even know stopped me and said, "I watch you to videos on container. "Thank you very much." I was like, "What, you?" And the genesis of that was the sales people ask me, "Hey, we're selling container security and all that," but I don't even understand what containers are. Okay, sure. So I shot a video and I'm the CMO, I was the vice president. I think you have to put your face on your content. It doesn't matter how senior you are, you're not in a corner office, you're down there with the team. So I got into the studio, based on my background at VMware, I knew virtual machine, and I said, "Okay, how do you explain this "to somebody who's not technical?" And next thing you know, it makes its way out there, not just to our sales force, but to the market at large. That's fantastic. >> Right, and let me ask you to follow up on that because it seems like the world is very divergent as to those who kind of want their face, and more their personality to be part of their business culture and their business messaging, and those that don't. And you know, as part of our process, we always are looking at people's LinkedIn, and looking at people's Twitter. I get when people don't have Twitter, but it really surprises me when professionals, senior professionals within the industry aren't on LinkedIn. And is just like, wow! That is such a different kind of world. >> LinkedIn right now is... and I'm stealing this from Gary on the Chuck, as a big believer in this. LinkedIn right now is like Facebook 10 years ago. You get amazing organic distribution, and it's a crime not to use it. And the other thing is if you don't use it, how are you going to inspire your team to do the right thing? Modern marketing is all about organic distribution with a great content. If you're not doing it yourself... I grew up in a bakery. I used to look at my mom, we have a big bakery. We had eight people working, and I said, "Ma, why are you workin' so hard? "Your first day, last hour?" And she said, "Look, you cannot ask your people, "to work harder than you do." That was an amazing lesson. So it's not just about working hard, and harder than your team, it's about are you walking the walk? Are you doing the content? Are you doing the modern marketing things that work today, if you expect your people to also do it? >> Yeah, it's just funny 'cause, when we talk to them, I'm like, "If you don't even have a LinkedIn account, "we shouldn't even be talking to you "because you just won't get what we do. "You won't see the value, you won't understand it "and if you're not engaging at least "a little bit in the world then..." And then you look at people say like Michael Dell, I'll pick on or Pat Gelsinger who use social media, and put their personalities out there. And I think it's, people want to know who these people are, they want to do business with people that they they like, right? >> Absolutely. You know what's the worst to me? I can tell when an executive as somebody else manages their account, I can tell from a mile away. That's the other thing. You have to be genuine. You have to be who you are on your social and all your communication because people resonate with that, right? >> Right. All right, so what are you doing now? You got your new title, you've got some new power, you've got a great brand, leading brand in the industry, been around for a while, what are some of your new priorities? What's some of the energy that you're bringing in and where you want to to go with this thing? >> Well, my biggest priority right now is to get the brand and our marketing to catch up with what the products and the customers are already which is, Cloud, Cloud, Cloud. So when we spun off from Intel two years ago, we had this amazing heritage in the endpoint security. And then we bought Skyhigh, and Skyhigh was transformational for us because it became the foundation for us to move to become a cloud-first organization. And is in the process of becoming a cloud-first organization, and creating a business that is growing really fast. We also brought along the endpoint, which now is all delivered from the Cloud, to the cloud-first open unified approach, which is exciting. >> And we see Edge is just an extension of endpoints, I would assume. It just changes the game. >> Yeah, so if you think about today modern work gets done with the backend in the Cloud, and accessing those backends from the device, right? >> Right. >> And so, our strategy is to secure data where modern work gets done, and it's in the device, in the Cloud, and on the edge. Because data moves in and out of the Cloud, and that's kind of the edge of the Cloud. That's what we launched this week at RSA we launched Unified Cloud Edge, which is our kind of a, Gartner call's it SaaS-y, so that we are kind of the security. We believe we have the most complete and unified security part of the SaaS-y world. >> Okay, I just laugh at Gartner and the trough of disillusion men and Jeff and I always go back to a Mars law. Mar does not get enough credit for a Mars law. We've got a lot of laws, but Mars law, we tend to overestimate in the short term, the impact of these technologies, and they completely underestimate really the long tail of this technology improvements, and we see it here. So let's shift gears a little bit. When you have your customers coming in here, and they walk into RSA for the first time, how do you tell people to navigate this crazy show and the 5,000 vendors and the more kind of solutions and spin vocabulary, then is probably save for anyone to consume over three days? >> Look, security is tough because you look around and say, "You have six, 700 vendors here." It's hard to stand out from the crowd. So what I tell our customers is use this as a way to meet with your strategic vendors in the booth upstairs. That's where you conduct business and all that. And I walk around to see from the ground up, send your more junior team out there to see what's happening because some of these smaller companies that are out here will be the big transformational companies or the future like Skyhigh was three four years ago, and now we're part of McAfee, and leading the charge there. >> Yeah, just how do you find the diamond in the rough, right? >> Yeah. >> 'Cause there's just so much. But it's still the little guys that are often on the leading edge and the bleeding edge, of the innovation so you want to know what's going on so that you're kind of walking into the back corners of the floor as well. >> That's why I am lifelong learner, so I go around to see what people do from a marketing perspective because, the last thing I want to do, I want to become obsolete. (Jeff laughs) And the way you don't become obsolete is to see what the new kids on the block do and steal their ideas, steal their tactics take them to the next level. >> Right, so I want to ask you a sensitive question about the conference itself and the coronavirus thing and we all saw what happened in Mobile World Congress. I guess it just got announced today that Facebook pulled F8, their developer conference. We're in the conference business. You go to a lot of conferences. Did you have some thought process? There were some big sponsors that pulled out of this thing. How did you guys kind of approach the situation? >> It's a tough one. >> It's a really tough one. >> It's a very tough one 'cause last thing you want to do is to put your employees and your customers at risk. But the way we looked at it was there were zero cases of coronavirus in San Francisco. And we saw what the rest of the industry was doing, and we made the call to come here, give good advice to our employees, wash their hands, and usual and this too will pass. >> Yeah, yeah. Well Vittorio, it's always great to catch up with you. >> Likewise. >> I just loved the energy, and congratulations. I know you'll do good things, and I wouldn't be at all surprised if that interim title fades away like we see with most great coaches. >> Good. >> So thanks for stopping by. >> My pleasure. >> All right, he's Vittorio, I'm Jeff. You're watching theCUBE, we're at RSA 2020 in San Francisco. Thanks for watching, we'll see you next time. (upbeat music)

[Music] okay welcome back and ready Jeff from here with the cube we're at the conference board event it's called the sixth annual innovation master classes here at Xerox PARC I'm really excited to be the arc spark I've never been here I lived like a stone's throw away and as you know if you're any type of a student of history this is where so many other really the core fundamental foundational technologies were developed what a long time ago mice GUI a lot of fun stuff but that's but now we're talking about today we're talking about helping companies be better at innovation a series of fantastic presentations that were excited to have our first guest he's Vittorio via Rango and he is the VP of cloud security for McAfee just coming off your your presentation so great to see you likewise I'm excited to be here about DevOps and how that that world has really changed in the software development world to get away from waterfall you talking about kind of applying the same principles not just for software development but in marketing and your role as a marketer how did you come to that kind of conclusion that this was probably a better way to get things done yeah well I have an interesting background when I used to run engineer engineering and product management and then I'm moving to the dark side to marketing and and I used successfully use Chrome in building products and if you look at scrum and agile methodologies at the end of the day their methodology methodologies to get things done in a world that changes often and that applies to any functions and so I said why not doing in marketing and so I've been doing in marketing now for six years but you juxtapose that you know it's now December 6th I believe so everyone with the whole room gotta get a good laugh out of them is in the throes of their annual business planning coming off their QPR's as they wrapped up 2018 so you know there is kind of an annual process and there is an annual budget so how did you you know find a convenient way to marry the two things together I think that everything is frantically pretending to know what's gonna happen next year and building plans they go out 12 months that never pan out right now unless you do is something that is the same thing over and over again then you can but if you're doing innovation by definition you don't know what's gonna happen so I think a better approach is to align around the goals and then take that goes decentralize the execution of that goes to the function and then in my case in marketing I take those goals that are applicable to me and I break it down using scrum and I do cycles of two weeks I tell the people I feel the the backlog with all the top initiatives that I think we should do and then when we get into a sprint I say okay what is the most important what are the most important priorities for the next two weeks right I tell the team and then the team tells me what we need to do to achieve those goals in every two weeks I'm in front of them talking about priorities and then reviewing how we move the needles to achieve the goals right so a lot of people hit there's plenty of stuff out there for people that aren't familiar with how scrum works and how about this process so we won't get on that but what I want to talk about is some of the the secondary benefits that maybe people don't understand it there's only looking at kind of the process of these two-week sprints but you you highlight it on a whole bunch of kind of side benefits that come as a result of this process number one being you know constantly reinforcing your priorities which are the company's priorities to your team every two weeks that's a pretty amazing communication flow yeah look every when people think about agile they obsess about the stand-up meeting every day and other people that are obsessed with that they don't get a job what agile is is about constant communication about the priorities letting the team innovate and tell you what to do and then being able every two weeks to adjust to changes so instead of executing against initiatives and plans that you build a year before that may not be relevant based on the market changes you're actually dealing with the reality measuring how you're progressing against the goals and then make changes as as you go and it gives an amazing platform for even junior people in a team to step up you know sometimes in a hierarchical structure you have somebody junior really good that is boxed in in the corner with scrum I come up with the priorities if somebody just out of college says I'll take that okay go ahead do it and then if they deliver good for them good for you right another you touched on so many good topics we could go on and on and on another one you talked about is really the giving up of time you know you try to manage kind of the interruptions for the team you try to be that kind of traffic cop if you will to enable them to use I think you said the target is 75% of the time during those two weeks is actually getting work done and 25% of the time is managing the minutia that we have to manage every day I think that's a really important concept because I think a lot of times it's it's easy it's easy to do the minutia yes it's in front of your face super important role for for a manager look when was the last time you you like being interrupted right and and if you are using your intellect to design to to sell to do whatever you know activity requires using your brain context switches is really expensive and so the ideal scrum is that you plan these two weeks so you don't have to like spend a lot of time thinking about three six months out just let's think about the next two weeks and then during those two weeks you never ever ever change the priorities and so that allows engineers or professionals to stay focused on what they're trying to do and get it done right right another piece that I thought was pretty interesting is is you've got the two weeks sprints and you've got your two weeks priorities and you now have an ability to switch if you need to based on market pressures competitive pressures whatever but how do you continue to tie that back to those goals how do you how do you make sure that you don't lose sight of the fact that maybe didn't have an annual plan because we know that's gonna change but you're still making sure you're driving towards kind of the general direction of where you're trying to go so the way I do it every two weeks we look at all our top goals and we look at how closer we are to achieving those goals and of course I map those goals I split them by quarter and then by weeks so that you at all times you know if you're achieving your goals or not and because of the two weeks interval if the cattle sales in my case comes as you know they they always have big priorities that has to happen tomorrow and yesterday usually I go to them and say hey here's the list of things I'm gonna deliver my team is gonna deliver to you in an axe in average next week right and is what this emergency you're talking about more important than this in most cases the answer is No if the answer is yes then the question is can that wait a week and then you have the full attention of my entire team and so that way you keep doing what you do in the scrum principle you always ship so you always work on things you can actually ship during those two weeks and then you can take the whole team in okay let's now please the head of sales and and I can go ahead with that you know the other thing is because we look at the goals every two weeks I can also look at the other sale say oh you know you won't really want to run this program in pick your region you know South America where we have no we don't have any goals of growth in that area this year so you can also use the constant communication constant interlocking goals to say you know maybe you shouldn't do it right so last thing Victoria just to get your insight is you've been doing this for years you know what's what's the greatest benefit of managing a team this this way that most people just don't get and we talked about the frequency of communications you talked about the frequency of being able to change course you know what is it that people are still kind of doing it the old line way or missed to me scrum forces you as a leader to focus on the two most important things that I think any leader should you know take care of one Chris priorities and communication I think those are the roots of how many companies get in trouble when they don't have clear priorities and all levels and they don't communicate those priorities and there is all there they're achieving and I think scrum really forces you every two weeks to be there on the treadmill with the team and and the third thing I think is to empower the team to size and tell you what to do and how to do it and not you telling them what to do you tell them without the priorities let them tell you what is the best way to achieve the goals it's such a great such a great lesson right be a leader not it not let let your people do what you hired him to do yeah because even more and more to me if you're hiring great people if you're managing them what are you gonna do if you alright people that are better than you if you're manage them what are you gonna do you're going to by definition so let them tell you what how to do give them a direction and get out of the way alright Vittorio thanks for for taking a few minutes and really really enjoyed your talk today all right we're at the innovation masterclass at Xerox PARC you're watching the Q see you next time thanks for watching [Music]

(upbeat music) >> Hey welcome back everybody. Jeff Frick here with theCube. We're at Xerox PARC in Palo Alto, one of the most historic pieces of ground really in the history of computer science. We're excited to be here for a special event. It's the Innovators Master Class put on by the Conference Board. Relatively small event, great content. We've been here all morning and we're excited to have our next guest, she's Maureen Rinkunas. She's the Innovations System Designer, Specialty Products Division for DowDuPont. Maureen, great to see you. >> It's great to be here. >> So you're, you're giving a panel in a little bit about really how do big companies work with little companies to basically be more innnovative, so what are some of the things that you're looking forward to, what are some of the lessons that you've learned, 'cause you've had a very varied experience, you've been in academia, you've been in industry, you've been kind of big company and little company. >> Yes, and I think, you know, you learn a lot from being able to look outside of your sphere. And so that's what I'm really excited about on this panel, we're going to be talking with startups and VCs and it's not surprising, people are really keeping an eye on what's happening in Silicon Valley but I think for large corporations, we have to go beyond that. We have to say, let's not just be observers, let's be active participants in the ecosystem. And so I think that by engaging with some of the startups and businesses on this panel, we're really going to get some pragmatic advice on how to do that in the best way possible. >> Yeah, you had some great statements, I've been doing some research on you, about some tricks to innovation and one of the great ones was, new hires as change agents. I wonder if you could dig into that a little bit because I think, you know, unfortunately new hires, especially at a big company, they don't have status, they don't have title, you know, they don't necessary have formal authority but there's a real opportunity for companies to take advantage of this fresh new outlook to help look at things in a slightly different way. >> Yeah, it's actually been great to be here at the conference for an event because I've talked with a lot of organizations that are bringing in this fresh view and especially in innovation centers where the proportion of people coming from outside the organization is sometimes as high as 80 percent of the team at that facility and so it's really great to have people who aren't carrying the baggage of how we always have done things. >> Right right. >> And they can push the limits a little bit which is sometimes what we need to, to really break out of our routines and I think as well, you know, bringing people in who have experience in startups, people who, perhaps, are coming from the venture world also offers that opportunity for people who have experienced working in that really fast-paced environment, they are very impatient, which is a good thing and I think really push teams to move faster. So it's great to be able to bring that, an element, into your team. >> Right. There was a great presentation earlier today about DevOps and, you know, agile software development and it's easy in software, you know, you can have a two week spread and get something out new. In the chemical world, right, there's lots of different axes of innovation but you guys, kind of by rule, have to move slower. These are much bigger investments in factory and plant, you know, there's ecological implications to all these things. So when you look at the innovation challenges and opportunities at a big company like DowDuPont, what are some of the easier paths to go down that you can, you can help to drive some of that innovative thought process and products? >> Well I think, you know, certainly we don't want to take any shortcuts with safety, and so you're absolutely right, that in some ways we can't move as quick as launching a new app to market, but we really do need to challenge ourselves to think about how we move as quickly as possible. One way to do that is to look at outside innnovations and so, I've just recently was working with a team and they had mapped out their development pipeline, they thought, oh this is 3 to 5 years in the making, and then we were able to connect them with a startup who cut about 4 years out of that and so, they are actually really excited, they're going to be partnering with that startup and moving forward with a customer in a very short timeframe. So, I think there are ways to make that window a much shorter timeline. >> Right. And then what about just the culture clash? I mean, just this example specifically, you've got people that had probably a very comfortable, maybe they thought it was aggressive, timeline that went out for 4 or 5 years, then you bring up this crazy aggressive startup who are doing things much quicker. Was it simply process? Was it a new technology innovation? Was it just a different kind of spin of the lens that they were able to reframe their problem differently? And then how do you get those two groups of people to work together effectively? >> Well you know, I think in the corporate space, there's a lot of this, well we don't care because it wasn't invented here, syndrome. We're very fortunate that at a leadership level at DuPont, there has been very much this perspective that we need to get beyond that, we need to collaborate with our customers, we need to move externally, and so, you know, that helps, having someone who champions looking outside for alternatives, but I think, too, it's helpful to have those change agents within, people who are really brave, people who aren't afraid to push back, often these are the people who are coming outside with the legacy, they're not worried about getting fired and they're pushing for what they know is right and that's moving fast and hopefully making some positive change. >> Right, and not breaking too many things, right? >> (laughs) >> We've kind of got away from the move fast and break things. So final question, you know, we're here at this Innovation Master Class, what are you looking to get out of this type of event? Have you been here before and you know, what types of things do you take away of kind of this small, intimate little affair? >> Yeah so this is my second time here and you know, after seeing what we've learned this morning and reflecting on what I learned last year, I think you always take things away that are really actionable, you know, the folks that come to these events are in the field, they are getting things done, and so you really have an opportunity to learn from people who have tested things, they've learned from those experiments, sometimes they've failed and we can learn from those failures too and so that's what I really appreciate about having this opportunity to be here. >> Well Maureen, thanks for taking a few minutes. Good luck on your panel this afternoon. I can't wait to, can't wait to watch. >> Great, thanks. >> Alright, she's Maureen, I'm Jeff, you're watching theCube. We are at the Innovation Master Class put on by the Conference Board at Xerox PARC. Thanks for watching. (upbeat music)

>> From Palo Alto, California, it's theCUBE, covering the Conference Board's Sixth Annual Innovation Master Class. (fast techno music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are at the Innovation Master Collab at Xerox PARC. It's put on by the Conference Board, a relatively small event, but really, a lot of high-caliber individuals giving really great presentations. And we're excited about our next guest, he kicked the whole thing off this morning, and we could go for hours. We won't go for hours, we'll go about 10 minutes. But Peter Coffee, he's the VP of Strategic Research for Salesforce. Been there a long time, but you were a media guy before that for many, many years? So Peter, great to see you. >> It's good to be with you, thanks. >> So, you talk about so many things. So many things in your opening statement, and I have a ton of notes. But let's just jump into it, I think. One of the big things is you know, the future happens faster than we expect it. And we as humans have a really hard time with exponential growth, because it's not built that way. That's the way things move. >> So how do you as a businessperson kind of deal with that reality? Because the issue is you're never going to be ready for when they come. >> Yeah, well, it's not just humans as individuals, but the institutions and processes we've built. If you look at the process of getting a college degree, it's really seriously misaligned with the timeframe of change. By the time you're a senior, half of the subject matter in your field may be new since your freshman year, and conversely four years after you've graduated, perhaps a third of what you were taught will no longer be considered to be current information. Someone at Motorola once said, "a batch process "no matter how much you accelerate it "doesn't become a continuous flow process". You have to rethink what does a continuous flow look like, and that's useful conversation to have getting back to your actual opening question. When we're talking with customers, we say what are your unvoiced assumptions about the manner in which you have succession of technology, succession of product, and so on? Can we try to see what it would look like if that were a continuous process and not a project process? Many of our partners will tell us that their most difficult conversations with their customers are about getting away from a project mentality, a succession of Big Bang changes, into a process in which transformation is a way of life and not a bold initiative that will take a big sigh of relief and congratulate yourself on having transformed. No, dude, you've gotten your running shoes tied now you can begin to run. But now the hard part begins. >> Right, and the sun comes up tomorrow and you start to run again. You talked on big shifts count on new abundance and use horsepower. >> George Gilder's phrase, "errors are punctuated "by a dramatic change from a scarcity "to an abundance" so for example, horsepower or bandwidth or intelligence. >> So now we're coming into the era of massive big data we are asymptotically approaching free compute, free storage, and free networking. So how do you get business leaders to kind of rethink in an era where they have basically infinite resources, and it always goes back, so what would you build then? Because we're heading that way even if we're not there today. >> A Jedi mind trick that I often use with them is to say, let's not talk about the next couple of quarters, I want you to imagine the next Winter Olympics. When they light the torch four years from now I want you to try to visualize the world you're pretty sure you'll be living in four years from now and work backwards from that and say well if we all agree that within four years that's going to get done, well there's some implications about things we should be doing now and some things that we should stop doing now if we know that four years from now, the world is going to look like this. It helps free your mind from the pressures of incremental improvement and meeting next quarterly goals. And instead saying, ya know, that's not going to be a thing in four years and we should stop getting better at doing something that's simply not going to be relevant in that short of a time. >> So hard though, right? Innovators still, I mean, that's the classic conundrum especially if it's something that you have paying customers and you're driving great revenue to, it's hard to face the music that that may not be so important down the path. >> The willingness to acknowledge that someone will disrupt you, so it might as well be you, you might as well disrupt yourself, the conversation was had with IBM back in the days of the IBM PC, that they thought that that might be a quarter of a million machines they would sell, but whatever you do, don't touch the bread and butter of the 3270 terminal business, right? And they did not ultimately succeed in visualizing the impact of what they had done. Ironically, because they didn't think it was that important, they opened all the technology, and so things like Microsoft becoming what it is and the fact that the bios was open and allowed the compatibles industry like Compact to emerge was a side effect of IBM failing to realize how big of a door they were opening for the world. You can start off a spinoff operation. At Salesforce we have a product line called Essentials which is specifically tasked with create versions of Salesforce that are packaged and priced and supported in a way that's suitable to that small business. And that way you can kind of uncouple from that Clayton Christensen innovators dilemma thing by acknowledging it's a separate piece of the business, it can be measured differently, rewarded differently, and it's going to convey itself maybe even through a genuinely different brand. This is an example that was used once with Disney which when it decided it wanted to get away from family and children's entertainment, and start making movies aimed at more adult audiences, fine, they created the Touchstone brand so they could do that without getting in the way of, or maybe even polluting, a brand that they spent so much time building. So branding is important. A brand is a set of promises, and if you want to make different promises to different people, have a different brand. >> Right, so I'm shifting gears 'cause you touched on so many great things. A really popular thing that's going on now is the conversion of products to services. And repackaging your product as a service. And you talked about the don't taze me bro story which has so many elements of fun and interesting but I thought the best part of it, though, was now they took it to the next step. And we're only a stones throw away from Tesla, a lot of innovation but I think one of the most kind of not reported on benefits of these connected devices and a feedback loop back to the manufacturer is how people are actually using these things, checking in from home, being able to do these updates. And you talk about how the TASER company now is doing all the services, it's not even a service, it's a process. I thought it's awesome. >> Taking a product and selling it at a subscription price does not turn it into a service, even though some people will say, well see now we're moving to a services model. If you're still delivering a product in a lumpy, change-it-every-couple-of-years way, you haven't really achieved that transformation. So you have to go back into more of a sense of I mean, look at the expectation people have of the apps on their smartphones, that they just get better all the time, that the update process is low-burden, low-complexity, low-risk, and you have to achieve that same fluidity of continuous improvement. So that's one of the differences. You can't just take the thing you sell, bill for it on a monthly subscription, and think that you achieved that transition. The thing that they folks who were once TASER and now are Axon, of which TASER is a sub-brand, they managed to elevate their view from the device in a police officer's hand to a process of which that device is a part. Which is the incident that begins, is concluded, results in a report, maybe results in a criminal prosecution, and they broadened the scope of the Axon services package to the point that now it is selling the proposition of increased peace officer productivity rather than merely the piece of hardware that's part of that. So being able to zoom out and really see the environment in which your product is used, and this relates to yet another idea which is that people are saying you got to think outside your box. It doesn't help if you get outside your box, but all of the people with whom you might want to collaborate are all still inside their boxes. And so you may actually have to invest in the transformation and interface development of partners or maybe even competitors, and isn't that a wild idea. Elon Musk at Tesla open sourced a lot of their technology with the specific goal of growing that whole ecosystem of charging stations and other things so Tesla could be a great success. And the comment that I once made is it doesn't help if you're a perfect drop of artisanal oil in a world of water. You have to make the world capable of interacting with you and supporting you if you really want to grow. Or else you're an oddity, you're Betamax, which might have been technically superior but by failing to really build the ecosystem around it, wound up losing big time to VHS for a while. I may have to explain to all of your viewers under the age of 30 what VHS and Betamax even mean. >> I was sellin' those, I could tell you the whole Panasonic factory optimization story, which is whole 'nother piece of that puzzle. So that's good, so I'm going to shift gears again. >> You have to look a big perspective, you have to be prepared to forget that your excellence is your product, and start thinking of that as just the kernel of what needs to be your real proposition which is the need you meet, the pain you address, the process of which you become an inseparable part instead of a substitutable chunk of hardware. >> Well and I think too it's embracing the ongoing relationship as part of the process, versus selling something to your distribution and off it goes you cash the check and you build another one. >> Well that's another aspect, we've got whole industries where there's been a waterfall model. Automobiles were a particular example. Where manufacturers wholesaled cars to distributors who gave them the small markup to dealers who owned the buyer customer. And dealers would be very hostile to manufacturers trying to get involved in that relationship. But now because of the connected vehicles the manufacturer may know things about the manner of use of the vehicle and about the preliminary engagement of the prospective buyer with the manufacturers website. And so improving that relationship from a futile model, or a waterfall model, into a collaborative model is really necessary if all these great digital aspects are to have any value. >> Right, right, right. And as a distribution of information that desire to get a level of knowledge is no longer the case, there's so much more. >> Well it's scary how easy it is to do it wrong. IDC just did a study about the use in retail banking of technology like apps and websites. Which that industry was congratulating itself on adopting in ways that reduce the cost of things like bank office hours. And yet J.D. Power has found that the result is that customers no longer see differentiation among banks, are less loyal, more easily seduced by $50 to open a new bank account with direct deposit. And so innovation's a vector, and if you aim it at cost reduction, you'll get one set of results. And if you aim it at customer satisfaction improvement, you'll innovate differently, and ultimately I think much more successfully. >> Right, right, so we're almost out of time here. I want to go down one more path with you which I love. You talked a lot about visualization, you brought up some old NOPs, really talked about context, right? In the right context, this particular visualization is of value. And there's a lot of conversation about visualization especially with big data. And something I've been looking for, and maybe you've got an answer is, is there a visualization of a billion data point dataset that I can actually look at the visualization and see something, and see the insight. 'Cause most of the ones we see that are examples, they're very beautiful and there's a lot of compound shapes going on, but to actually pinpoint an actionable something out of that array, often times I don't see, I wonder if you have any good examples that you've seen out there where you can actually use visualization to drive insight from a really, really big dataset. >> Well if a big data exercise produces a table of numbers, then someone's going to have to apply an awful lot of understanding to know which numbers look odd. But a billion points, to use your initial question, well what is that? That's an array that's 1,000 by 1,000 by 1,000. We look at 1,000 by 1,000 two-dimensional screens all the time, visualizing a three-dimensional 1,000 by 1,000 cube is something we could do. And if there is use of color, use of motion, superposition of one over another with highlighting of what's changed, what people need most is for their attention to be drawn to what's changing or what's out of a range. And so it's tremendously important that people who are presenting the output of a big data exercise go beyond the high-resolution snapshot, if you will, and construct at least some sense of A B. Back in the ancient days of astronomy, they had a thing called the Blink Camera which would put two pictures side-by-side and simply let you flip back-and-forth between the images, and the human eye turned out to be amazingly good. There could be thousands of stars in that picture, the one dot that's moving and represents some new object, the one dot that suddenly appears, the human brain is very good at doing that. And there's a misperception that the human eye's just a camera. The eye does a lot of pre-processing before it ever sends stuff to the brain. And understanding what human vision does, it impressed the heck out of me the first time I had a consultation on the big data program at a university where the faculty waiting to meet with me turned out to be from the schools of Computer Science, Mathematics, Business, and Visual Arts. And having people with a sense of visual understanding and human perception in the room is going to be that critical link between having data and having understanding of opportunity threat or change. And that's really where it has to go. So if you just ask yourself, how can I add an element of color, or motion, or something else that the human eye and brain have millennia of evolution to get good at detecting, do that. And you will produce something that changes behavior and doesn't just give people facts >> Right, right. Well, Peter, thank you for taking a few minutes. We could go on, and on, and on. >> Happy to do chapters two, three, and four any time you like, yeah. >> We'll do chapter two at the new tower downtown. >> Any old time, thanks so much. >> Thanks for stoppin' by. >> My pleasure. >> He's Peter, I'm Jeff, you're watching theCUBE. We're at the Master Innovation Class at Xerox PARC put on by the Conference Board. Thanks for watching. (fast techno music)

>> From Palo Alto, California, it's theCUBE, covering the conference boards sixth annual Innovation Master Class. >> Hey, welcome back everybody, Jeff with theCUBE, we're at the Innovation Master Class at Xerox Parc in Palo Alto. It's put on by the conference boards, a relatively small event. But a bunch of really high powered people, terrific presentations. If you ever get a chance to go, I suggest you check it out. We're happy to be here for our first time, we're here and one of the big themes on innovation is how do you innovate well as a big company. It's not easy to do, there's a lot of barriers in the way. We're excited to have an expert in the field, he's Alex Goryachev the senior director of innovation strategy and programs at Sisco. Great to see you. >> Thank you, I'm glad to be here. >> So you just gave a presentation on this topic so first off, give us a little overview of what your role is at Sisco and how it plays with innovation. >> So at Sisco, I'm lucky to lead two things. One is how do we work with the ecosystem, at our network of global innovation centers. And the second one is how do we capture best ideas from our employees. And most importantly, support them in making those ideas happen, turning them into products, or process improvements. >> Right, so Sisco's an interesting company, it's like intel and a lot of really dominant players in their field. Terrific market share, dominant for a long time. So it's really hard, that innovators dilemma is really written for companies like Sisco, so those innovation centers, how did those come about, how many of them are there, and what is the mission of the innovation centers? >> So the mission, if you think about innovation, it doesn't happen in San Jose or doesn't happen only in San Jose, it happens around the world. So when we think about the innovation centers, we've got around 12 of them around the globe. With a core mission of working with ecosystem players. Whenever that's start ups, customers, partners, academia, governments, and coming up with solutions that then we can deploy in a local market and potentially scale around the globe. >> So it's interesting, you lead with really working with the ecosystem partners, so their mission is more leveraged that greater ecosystem versus we need to come up with the great ideas inside of our four walls. >> Absolutely, because if you think about it, we have a lot of great ideas inside the four walls, but when we look at the specific problems that are you know, problems for Japan, may not necessarily be the same that they are for Australia. And what we really want to do, is be able to work on an issue of national relevancy and focus on the economic strengths and problems that are in the particular area, so that we can make a meaningful impact. >> Right, so one of the topics in one of the earlier presentations here, was how do big companies manage innovation centers, and we're here at Xerox Parc, this is probably one of the most historic innovation centers ever in computers industry. So how do you manage this kind of dichotomy between having them kind of set aside, the people at the innovation center in their own separate little location and still be innovative and kind of unbridled from some of the corporate tail winds I guess, would be head winds I should say. But also make them part of the bigger Sisco environment and still make em feel like they're included and that these things are important, not just to what they're working on and even their ecosystem, but are important to the whole Sisco. >> It's a great question, and I think that's where the corporate government comes in really well. Because at the end of the day with the innovation centers we don't want to boil the ocean right? We want to make sure that everybody wins. So when we think of creating products and solutions, we want to work with customers that have real problems and with start ups that can potentially close that gap and help us co develop a solution with them. So we're very focused on ar engineering priorities and be our specific country priorities and particular opportunities that exist in the country. For example, we have a center in Australia, right? And if you look at the Australian economy, a lot of it is with agriculture, right? So what we have in Australia is a concertia with other industry players in the region to focus on solving some problems for the agriculture. Which utilizes the internet of thinks technology. So that's one of the ways that we're connected to companies mission which is iot, one of the corporate missions. And at the same time we're solving the local problem, working with the ecosystem and creating something that can then be scaled around the world. >> Right, so the other part of your job that you mentioned is inside the four walls and trying to help foster the innovation that does come from your own internal people that are in line jobs, more regular jobs. So what are some of the initiatives that you have in place to identify and to surface and to ultimately support and maybe those grow into new products and divisions and whatever. What are some of the secrets you can share there. >> Well I think the secret is very simple. It's everyone, at the end of the day, everything in the company comes down to talent. People generally invest in talent, not necessarily in ideas. So, one is recognizing that the innovation is a mindset, and then the second thing is really focusing on empowering every single employee to innovate. And in practical terms, that means that we have to redefine innovation. It's not only about new product development, it's not only about top line grove, right? It could be about process improvements. It could be about other things that bring value to the company. Could be about corporate social responsibility, when you go in and listen and engage with employees across the entire company, you actually have far better ideas that touch all aspects of your business, and can produce a lasting impact. Not only in products but with sound process improvement as well. >> And how do you support that? How do you give people the encouragement to say listen, we're interested in your ideas or interested in your innovations across this broad swath of opportunities, like I said from product all the way to social responsibility or cleaning out the Guadalupe river, I'm sure there's all kinds of interesting things that you can point to. How do you make sure that's communicated, that this is a priority for us, the company, that we want to support you, our employee, in some of these opportunities. >> Well first of all, we're lucky to have the sponsorship of our CO Chuck Robbins, who really put this as one of his key priorities. The second one is because innovation is about talent first and product second, we're lucky to work with our chief people officer, Francine, and she's a sponsor for this as well. So we have an incredible opportunity to go and message this as a top corporate priority to our employee's year after year. But the other thing, which is the key, is for every single function in the company, we worked with them to define innovation ambition. So that when we got to employee's and say hey help us, give us your best ideas, we can go and guide them towards some of the Sisco's key priorities. So we connect them with strategy. Obviously at the end of the day, some of them will give us whatever ideas they're passionate about. And there are a lot of great things there as well. >> So Alex I'll give you the last word. We'll be at Sisco live in Barcelona, it's right around the corner, and Sisco live US, etc. This is a really small event. So for you as an attendee and also as a presenter what is this type of event here at the innovation master class mean to you, what are you hoping to get out of it, what do you get out of participating in these type of events? >> Well if I think about, the most important thing, again going back to Sisco, we believe that no single company can do this alone. The innovation program that I just talked about, they innovate everywhere, we put it for the entire world to use and I think just connecting with other fellow practitioners is very important. At the end of the day, innovation teams, they typically go against the grain. So a lot of this is group therapy, it's support. It's the human connection, but then we learn so much from each other, right? Because at the end of the day, we face the same challenges, we face the same problems together. So any industry concertia, we can make a meaningful difference for our companies and for our employee's. And by the way, if you're at Sisco live Barcelona, do stop by our booth, we have the innovation network booth, where we talk about the Sisco innovation centers, and the innovation programs that we run. >> Great, we'll do that. Well Alex thank you for taking a few minutes, and I guess we'll see you in Barcelona. >> Pleasure. >> Alright, he's Alex and I'm Jeff, and you're watching theCUBE, we're at the Innovation Master Class, put on by the conference board here at Xerox Parc in Palo Alto, thanks for watching. (upbeat techno music)

>> From Palo Alto, California, it's theCUBE. Covering the Conference Boards Sixth Annual Innovation Master Class. >> Hey, welcome back here, everybody. Jeff Frick here with theCUBE. We're at the Innovation Mater Class at Xerox PARC in Palo Alto. Really excited to be here, never been here, surprisingly, for all the shows we do just up the hill next to VMware, and Tesla. This is kind of the granddaddy of locations and innovation centers, it's been around forever. If you don't know the history, get a couple books, you'll learn it pretty fast. So we're excited to be here and our next guess is Antony Brydon, four-time founder and CEO, which is not easy to do. Again, check the math on that, most people are successful a couple times, hard to do it four times. And now he's the co-founder and CEO of Directly. So Antony, great to see you. >> It's good to be here. >> So, Directly, what is directly all about for people aren't familiar with the company? >> Most companies are excited to, and pursuing, the opportunity of automating up to 85% of their customer service. That's the ambition, and giving customers a delightful answer in their first experience. Most of those companies are falling down out of the gates because there are content gaps, and data gaps, and training gaps, and empathy gaps in the systems. So we build a CX automation platform and it puts experts at the heart of AI, letting these companies build networks of product experts and then rewarding those experts for creating content for AI systems, for training AI systems, for resolving customer questions. >> Right. So let's back up a step. So Zendesk is probably one we're all familiar with. You send in a customer service node, a lot of the times it comes back, customer service to Zendesk. >> Yes. >> But you're not building kind of a competitor of Zendesk, you're more of a partner, if I believe, for those types of applications, to help those apps do a better job. >> We are, we're a partner for Zendesk, we're a partner for Microsoft Dynamics, for Service Cloud and the like, and, essentially, are building the automation systems that make their AI systems work and work better. >> Right. >> Those are pure technology systems that often lack the data and the content to deliver AI at scale and quality, and that's where our platform and the human network, the experts in the mix, come into play. >> We could probably go for a long, long time on this topic. So what are some of the key things that make them not work now? Besides just the fact that it's kind of like the old dial-in systems. It's like, I just want to hit 0000. I just want to talk to a person. I have no confidence or faith that going through these other steps is going to get me the solution. Do you still see that on the online world as well? >> No, there are very clear gaps. There are four or five areas where systems are falling down. AI project mortality, as I refer to it. Very few companies have the structured data that systems need to work at scale. >> On the back, to feed the whole thing. >> That's right. Labeled, structured, organized data. So that doesn't exist. Many companies don't have the content. That's a second area. They may have enterprised knowledge bases, but they're five years old, they're seven years old, they're outdated, they're not accurate. Many companies don't have the signal. When a automated answer's delivered, they have to wait for a customer to rate it, and that tends to be really poor signal on whether that answer was good or not. And then last, many companies just don't have the teams to maintain these algorithms and constantly tune them. And that is where experts at the heart of a platform can come into play, by building a network of product experts who know the products inside and out. These could be Airbnb hosts for one of our customers, these could by Microsoft Excel users in the Microsoft example. Those experts can create that content, train the data, and actually resolve questions, filling those gaps, solving those problems. >> Right. I'm just curious, on the expert side, how many--? I don't know if there's best practices or if there's kind of certain buckets depending on the industry. Of those expert answers are generated by people inside the company versus a really kind of active, engaged community where you've got third-party experts that are happy to participate and help provide that info. >> Over 99% of the answers and the content is actually generated by the external network. >> 99%? >> 99%. You start with sources of enterprise knowledge, but it's a long, hard, arduous process to create those internal knowledge bases, and companies really struggle to keep up, it's Britannica. By the time you ship it it's outdated and you have to start all over again. The external expert networks work more like Wikipedia. Content constantly being organically created, the successful content is promoted, the unsuccessful content is demoted, and it's an evergreen cycle where it's constantly refreshing. Overwhelmingly external. >> Overwhelming. I mean, I could see where there's certain types of products. I was telling somebody else the other day about Harley-Davidson, one of the all-time great brands. People tattoo it on their body. Now, there aren't very many brands that people tattoo on their body. So easy to get people to talk about motorcycles or some of these types of things, but how do you do it for something that's really not that exciting? What are some of the tricks and incentives to engage that community? Or is there just always some little corps that you may or may not be aware of that are happy to jump in and so passionate about those types of products? >> There are definitely some companies where there's very little expertise and passion in the ecosystem around it. They're few and far between. If you find a product, if you find a company, you can find people that rely, love, and depend on that company. I gave some of the B to C examples, but we've also got networks for enterprise software companies, folks like SAP, folks like Autodesk. And those networks have experts that are developers, resellers, VARs, systems integrators, and the like. In the overwhelming majority of cases, the talent and the passion exists, you just have to have a simple platform to onboard and start tapping that talent and passion. >> So if I hear you right, you use kind of your Encyclopedia Britannica because that's what you have to start, to get the fly wheel moving, but as you start to collect inputs from third-party community, you can start to refine and get the better information back. And I ask specifically that way because you mentioned the human factors, and making people part of this thing, which is probably part of the problem with adoption, as I'd want confidence that there's some person behind this, even if the AI is smart. I'd want at least feel like there's some human-to-human contact when I reach out to this company. >> Yeah, that's critically important, because the empathy gap is real in almost all of the systems that are traditionally out there, which is when an automated answer's delivered, in a traditional system, it typically has a much lower CSAT than when it comes from a human being. What we found is when you have an expert author that content, when his or her face is shown next to the answer as it's presented to the user, and where he or she is there to back it up should that user still need more help, there you retain the human elements that personalize the contact, that humanize the experience, and immediately get big gains in CSAT. So It think that empathy piece is really important. >> Right. I wondered if you could share any specific examples of a customer that had an automated, kind of dumb system, I'll just use that word, compared to what they can do today, and some of the impacts when they put in some of the AI-powered systems like you guys support. >> So one of the first immediate impacts is often when we go in, a automated or unassisted system will be handling a very small percentage of the queries, and percentage of the customer questions coming in, and-- >> And people are going straight to zero, they're just like, I got to go to a person. >> Yeah, we're mostly in digital channels, so less phone, but yes, because the content there-- >> As an analogy, right. >> Because the content isn't there, it doesn't hit and resolve the question in that frequent a rate, or because the training and the signal isn't there, it's giving answers that are a little off-base. So the first and lowest hanging fruit is with a content library that's get created that can get 10, 50, 100 times broader that enterprise content pretty quickly. You're able to hit a much broader set of questions at a much higher rate. That's the first low-hanging fruit and kind of immediate impact. >> And is that helping them orchestrate, coordinate, collect data form this passionate ecosystem that's outside the four walls? Is that, essentially, what you're doing in that step? >> It essentially is. It is about companies having these ecosystems of these users, millions of hours of expertise in their head, millions of hours free time on their hands, and the ability to tap that in a systematic way. >> Wow. Shift gears a little bit, you are participating on a panel here at the event, talking about startups working with big companies and there's obviously a lot of challenges, starting with vendor viability issues, which is more kind of selling to big customers versus, necessarily, partnering with big companies. But what are some of the themes that you've seen that make that collaboration successful? Because, obviously, you've got different cultures, you got different kind of rates of the way things happen, you've got, beware the big company who eats you up in meetings all the time when you're a little start-up, they'll kill you accidentally just by scheduling so many meetings. What are some of the secrets of success that you're going to share here at the event? >> So we've got experience in that. Microsoft is a partner of ours, Microsoft Ventures is an investor. I think the single biggest key is an aligned vision and a complementary approach. The aligned vision where both the start-up and the partner are aiming for a similar point on the horizon. For example, the belief that automation can delight a very large set of customers by providing them a good, instant answer, but complementary approaches where the core skillsets of the companies round out each other and become less competitive. In this case, we've partnered with-- Microsoft is best in class AI platform and cognitive services, and we're able to tap and leverage that. We're also able to bring something unique to the equation by putting experts at the heart of it. So I think that architectural structure, in the first place, is a great example of kind of getting it right. >> Right. And your experience, that's been pretty easy to establish at the head-end of the process, so that you have kind of smooth sailing ahead? >> No, I don't think it's easy to establish at the head of the process, and I think that's where all of the good work and investment needs to happen. Upfront, on that kind of shared vision, and on that kind of complementary approach. And I think it is probably 20% building that together, but it's also 80% just finding it. The selection criteria by which a corporate partner picks a startup and the startup partner picks the corporate partner. I think just selecting right is the majority of the challenge, rather than trying to craft it kind of midstream. >> If it doesn't feel good at the beginning, it's probably not going to to work out. >> Right, it's about finding it. It's a little bit like the Venture analogy. Do they find great companies, or do they build great companies? Probably a little of both, but that finding that great company is a large part of the equation. >> Yeah, helps. So, Antony, finally get a last question. So, again, four successful startups. That does not happen very often with the same team. And look at your background, you're a psychology and philosophy major, not an engineer. So I'd just love to get kind of your thoughts about being a non-tech guy starting, running, and successfully exiting tech companies here in silicon valley. What's kind of the nice thing being from a slightly different background that you've used to really drive a number of successes? So I think the-- I think two things, I think one, coming from a non-tech and coming from a psych background has given us an appreciation of the human elements in these systems that tech alone can't do it. I'd say, personally, one of the impacts of being a non-tech founder in this valley is a heck of a lot of appreciation for what teams can do. And realizing that what teams can do is far more important than what individuals can do. And I say that because as a non-tech founder, there's literally nothing I could accomplish without being a part of a team. So that, I think, non-tech founders have that in spades. A harsh and frank realization that it's about team and they can't do anything on their own. >> Well, Antony, thanks for taking a minute out of your time. Good luck on the panel this afternoon and we'll keep an eye, watch the story unfold again. >> Yep, I appreciate it. Thanks very much. >> He's Antony, I'm Jeff, you're watching theCUBE. We're at the Master at the Master Innovation Class at Xerox PARC, thanks for watching.

>> From Palo Alto, California, it's theCUBE. Covering The Conference Board's 6th Annual Innovation Master Class. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at the Innovation Master Class that's put on by The Conference Board. We're here at Xerox PARC, one of the original innovation centers here in Silicon Valley. Tremendous history, if you don't know the history of Xerox PARC go get a book and do some reading. And we're excited to have our next guest because there's a lot of talk about tech but really not enough talk about people and where the people play in this whole thing. And as we're seeing more and more, especially in downtown San Francisco, an assumption of responsibility by tech companies to use some of the monies that they're making to invest back in the community. And one of the big problems in San Francisco if you've been there lately is homelessness. There's people all over the streets, there's tent cities and it's a problem. And it's great to have our next guest, who's actually doing something about it, small discrete steps, that are really changing people's lives, and I'm excited to have him. He's Kevin Adler, the founder and CEO of Miracle Messages. Kevin, great to meet you. >> Great to meet you too Jeff. >> So, before we did this, doing a little background, you knew I obviously stumbled across your TED Talk and it was a really compelling story so I wonder A, for the people, what is Miracle Messages all about, and then how did it start, how did you start this journey? >> Miracle Messages, we help people experiencing homelessness reconnect to their loved ones and in the process, help us as their neighbors reconnect with them. And we're really tackling what we've come to call the relational poverty on the streets. A lot of people that we walk by every day, Sure, they don't have housing, but their level of disconnection and isolation is mind boggling when you actually find out about it. So, I started it four years ago. I had an uncle who was homeless for about 30 years. Uncle Mark, and I never saw him as a homeless man. He was just a beloved uncle, remembered every birthday, guest of honor at Thanksgiving, Christmas. >> And he was in the neighborhood, he just didn't have a home? >> He was in Santa Cruz, he suffered from schizophrenia. And, when he was on his meds he was good and then he'd do something disruptive and get kicked out of a halfway house. And we wouldn't hear from him for six months or a year. >> Right. So, after he passed away, I was with my dad, and not far from here, visiting his grave site in Santa Cruz. And I was having a conversation with my dad of the significance of having a commemorative plot for Uncle Mark. I said, he meant something to us, this is his legacy. So that's nice, but I'm going to go back in the car, pull out my smartphone, and see status updates from every friend, acquaintance I've ever met, and I'm going to learn more about their stories on Facebook, with a quick scroll, than I will at the grave site of my Uncle Mark. So, I'm actually a Christian. I have a faith background, and I asked this question: "How would Jesus use a smartphone?" "How would Jesus use a GoPro camera?" Cause I didn't think it was going to be surfing pigs on surf boards. And I started a side project where homeless volunteers, like my Uncle Mark, wore GoPro cameras around their chests. And I invited them to narrate those experiences and I was shocked by what I saw. And I won't regale you with stories right now but I heard over and over again, people say "I never realized I was homeless when I lost my housing, "only when I lost my family and friends." >> Right. >> And that led me to say, if that's true, I can just walk down the street and go up to every person I see and say "Do you have any family or friends "you'd like to reconnect with?" And I did that in Market Street, San Francisco four years ago, met a man named Jeffrey, he hadn't seen his family in 22 years. Recorded a video on the spot to his niece and nephew, go home that night, posted the video in a Facebook group connected to his hometown, and within one hour the video was shared hundreds of times, makes the local news that night. Classmates start commenting, "Hey, "I went to high school with this guy, "I work in construction, does he need a job? "I work at the mayor's office does he need healthcare?" His sister gets tagged, we talk the next day. It turns out that Jeffrey had been a missing person for 12 years. And that's when I quit my job and started doing this work full time. >> Right, phenomenal. There's so many great aspects to this story. One of the ones that you talked about in your TED Talk that I found interesting was really just the psychology of people's reaction to homeless people in the streets. And the fact that once they become homeless in our minds that we really see through them. >> Totally. >> Which I guess is a defense mechanism to some point because, when there's just so many. And you brought up that it's not the condition that they don't have a place to sleep at night, but it's really that they become disassociated with everything. >> Yeah, so I mean, you're introduction to me, if you had said hey there's this guy, there's no TED talk, there's nothing else, he's a housed person, let's hear what he has to say. Like, what would I talk... That's what we do every single day with people experiencing homelessness. We define them by their lack of one physical need. And, sure, they need it, but it presumes that's all there is to being human. Not the higher order needs of belonging, love, self-actualization. And some of the research has found that the part of the brain that activates when we see a person, compared to an inanimate object, does not respond when we see a person who's experiencing homelessness. And in one experiment in New York, they had members of a person's very own family, mom and dad, dress up to look homeless on the streets. Not a single person recognized their own member of their own family as they walked by 'em. >> Yeah, it's crazy. It's such a big problem, and there's so many kind of little steps that people are trying to do. There's people that walk around with peanut butter and jelly sandwiches that we see on social media, and there's a couple guys that walk around with scissors and a comb and just give haircuts. These little tiny bits of humanization is probably the best way to describe it makes such a difference to these people. And I was amazed, your website... 80 percent of the people that get reconnected with their family, it's a positive reconnection. That is phenomenal because I would have imagined it's much less than that. >> Every time we reconnect someone, we're blown away at the lived examples of forgiveness, reconciliation. And every reunion, every message we record from a person experiencing homelessness, we have four, five messages from families reaching out to us saying, "Hey I haven't seen "my relative in 15 years, 20 years." The average time disconnect of our clients is 20 years. >> Right, wow. >> So what I've been doing now is, once you see it like this, you walk down the street, you see someone on the streets, you're like that's someone's son or daughter. That's someone's brother or sister. It's not to say that families sometimes aren't the problem. Half of the youth in San Francisco that are homeless, LGBTQ. But it's to say that everyone's someone's somebody that we shouldn't be this disconnected as people in this age of hyper-connectivity and let's have these courageous conversations to try to bring people back in to the fold. >> Right, so I'm just curious this great talk by Jeff Bezos at Amazon talking about some of the homeless situations in Seattle and he talks, there's a lot-- >> He's a wealthy guy, right? >> He's got a few bucks, yeah, just a few bucks. But he talks about there's different kind of classes of homelessness. We tend to think of them all as the same but he talks about young families that aren't necessarily the same as people that have some serious psychological problems and you talked about the youth. So, there's these sub-segments inside the homeless situations. Where do you find in what you offer you have the most success? What is the homeless sub population that you find reconnecting them with their history, their family, their loved ones, their friends has the most benefit, the most impact? >> That's a great question. Our sweet spot right now, we've done 175 reunions. >> And how many films have you put out? >> Films in terms of recording the messages? >> Yeah, to get the 175. >> 175 reunions, we have recorded just north of about 600 messages. And not all of 'em are video messages. So, we have a hotline, 1-800-MISS-YOU. Calls that number, we gather the information over the phone, we have paper for 'em. So 600 messages recorded, about 300, 350 delivered and then half of them lead to a reunion. The sweet spot, I'd say the average time disconnected of our clients is 20 years. And the average age is 50, and they tend to be individuals isolated by their homelessness. So, these are folks for decades who have had the shame, the embarrassment, might not have the highest level of digital literacy. Maybe outside of any other service provider. Not going to the shelter every night, not working with a case worker or social worker, and we say hey, we're not tryna' push anything on ya' but do you have any family or friends you'd like to reconnect with. That opens up a sense of possibility that was kind of dormant otherwise. But then we also go at the other end of the spectrum where we have folks who are maybe in an SRO, a single room occupancy, getting on their feet through a drug rehab program and now's the point that they're sayin' "Hey, I'm stably housed, I feel good, "I don't need anything from anyone. "Now's the time to rebuild that community "and that trust from loved ones." >> Kevin, it's such a great story. You're speaking here later today. >> I think so, I believe so. >> On site for good, which is good 'cause there's so much... There's a lot of negative tech press these days. So, great for you. How do people get involved if they want to contribute time, they want to contribute money, resources? Definitely get a plug in there. >> Now, or later? Right now, yeah, let 'em know. >> No time like the present. We have 1200 volunteer digital detectives. These are people who use social media for social good. Search for the loved ones online, find them, deliver the messages. So, people can join that, they can join us for a street walk or a dinner, where they go around offering miracle messages and if they're interested they can go to our website miraclemessages.org and then sign up to get involved. And we just released these T-shirts, pretty cool. Says, "Everyone is someone's somebody." I'm not a stylish man, but I wear that shirt and people are like "That's a great shirt." I'm like, wow, and this is a volunteer shirt? Okay cool, I'm in business. >> I hope you're putting one on before your thing later tonight. >> I have maybe an image of it, I should of. >> All right Kevin, again, congratulations to you and doing good work. >> Thanks brother, I appreciate it. >> I'm sure it's super fulfilling every single time you match somebody. >> It's great, yeah, check out our videos. >> All right he's Kevin, I'm Jeff. We're going to get teary if we don't get off the air soon so I'm going to let it go from here. We're at the Palo Alto Xerox PARC. Really the head, the beginning of the innovation in a lot of ways in the computer industry. The Conference Board, thanks for hosting us here at the Innovation Master Class. Thanks for watching, we'll see you next time. (bright ambient music)

>> Announcer: From the Corinium Chief Analytics Officer Conference, Spring, San Francisco, it's theCUBE. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at the Corinium Chief Analytics Officer Symposium or Summit in San Francisco at the Parc 55 Hotel. We came up here last year. It's a really small event, very intimate, but a lot of practitioners sharing best practices and we're excited to have a really data-driven company represented, see Scott Zoldi, Chief Analytics Officer from FICO, Scott, great to see you. >> It's great to be here, thanks Jim. >> Absolutely. So, before we jump into it, I was just kind of curious. One of the things that comes up all the time, when we do Chief Data Officer and there's this whole structuring of how do people integrate data organizationally? Does it report to the CIO, the CEO? So, how have you guys done it, where do you report into in the FICO? >> So at FICO, when we work with data, it's generally going up through our CIO, but as part of that we have both the Chief Analytics Officer and the Chief Technology Officer that are also part of that responsibility of ensuring that we organize the data correctly, we have the proper governance in place, right, and the proper sort of concerns around privacy and security in place. >> Right, so you guys have been in the data business forever, I mean, data is your business, so when you hear all this talk about digital transformation and becoming more data-driven as a company, how does that impact a company like FICO? You guys have been doing this forever. What kind of opportunities are there to take, kind of, analytics to the next level? >> For us, I think it's really exciting. So, you're right, we've been at it for 60 years, right? And analytics is at the core of our business, and operationalizing out the data and around bringing better analytics into play. And now there's this new term, you know, Operationalizing Analytics. And so as we look at digital, we look at all the different types of data that are available to decisions and all the computation power that we have available today, it's really exciting now, to see the types of decisions that can be made with all the data and different types of analytics that are available today. >> Right, so what are some of those nuanced decisions? 'Cause, you know, from the outside world looking in, we see, kind of binary decisions, you know either I get approved for the card or not, or I get the unfortunate, you know you card didn't get through, we had a fraud event, I got to call and tell them please turn my card back on. Seems very binary, so as you get beyond the really simple binary, what are some of the things that you guys have been able to do with the business, having a much more obviously nuanced and rich set of data from which to work? >> So one of the things that we focus on is really around having a profile of each and every customer so we can make a better behavioral decision. So we're trying to understand behavior, ultimately, and that behavior can be manifested in terms of making a fraud decision, or a credit decision. But it's really around personalized analytics, essentially like an analytics of one, that allows us to understand that customer very, very well to make a decision around, what is the next sort of opportunity from a business perspective, a retention perspective, or improving that customer experience. Right, and then how much is it is your driving, could you talk about the operationalizing this? So there's operationalizing it inside the computers and the machines that are making judgements, and scoring things, and passing out decisions, versus more the human factor, the human touch. How do you divide which goes where? And how do you prioritize so that more people get more data from which to work with and make decisions, versus just the ones that are driven inside of an algorithm, inside of a machine? >> Yeah, it's a great point, because a lot of times organizations want to apply analytics to the data they have, but they haven't given a thought to the entire operization of that. So we generally look at it in four parts. One is around data, what is the data we need to make a decision, 'cause decisions always come first, business decisions. Where is that data, how do we gather it and then make it available? Next stage, what are the analytics that we want to apply? And that involves the time that we need to make a decision and how to make that decision over time. And then comes the people part, right? What is the process to work with that score, record the use of, let's say, an analytic, what was the outcome, was it more positive or based on using that analytic, right? And incorporating that back to make a change to the business over time, make actions over time in terms of improving that process, and that's a continual sort of process that you have to have when you operationalize analytics. Otherwise, this could be a one-off sort of analytic adventure, but not part of the core business. >> Right, and you don't want that. Now what about the other data, you know third-party data that you've brought in that isn't kind of part your guys' core? Obviously you have a huge corpus of your own internal data and through your partner financial institutions, but have you started to pull in more kind of third-party data, social data, other types of things to help you build that behavioral model? >> It kind of depends on the business that we're in and the region that we're in. Some regions, for example, outside the United States they're taking much more advantage of social data and social media, and even mobile data to make, let's say, credit decisions. But we generally are finding that most organizations aren't even looking that up, they already have it housed appropriately and to the maximum extent, and so that's usually where our focus is. Right, so to shift gears about the inside, and there's an interesting term, explainable AI, I've never heard that phrase, so what exactly, when you guys talk about explainable AI, what does that mean? Yeah, so machine-learning is kind of a very, very hot topic today and it's one that is focused on development of machine-learning models that learn relationships in data. And it means that you can leverage algorithms to make decisions based on collecting all this information. Now, the challenge is that these algorithms are much more intelligent than a human being, they're superhuman, but generally they're very difficult to understand how they made the decision, and how they came up with a score. So, explainable AI is around deconstructing and analyzing that model so we can provide examples and reasons for why the model scored the way it did. And that's actually paramount, because today we need to provide explanations as part of regulatory concerns around the use of these models, and so it's a very core part of that fact that as we operationalize analytics, and we use things like machine-learning and artificial intelligence, that explainability, the ability to say why did this model score me this way, is at front and center so we can have that dialogue with a customer and they can understand the reasons, and maybe improve the outcome in the future. >> Right, and was that driven primarily by regulations or because it just makes sense to be able to pull back the onion? On the other hand, as you said, the way machines learn and the way machines operate is very different than the way humans calculate, so maybe, I don't know if there's just some stuff in there that's just not going to make sense to a person. So how do you kind of square that circle? >> So, for us our journey to explainable AI started in the early 90s, so it's always been core to our business because, as you say, it makes common sense that you need to be able to explain that score, and if you're going to have a conversation with the customer. You know, since that time, machine-learning's become much more mainstream. There's over 2,000 start-up companies today all trying to apply machine-learning and AI. >> Right. >> And that's where regulation is coming in, because in the early days we used explainable AI to make sure we understood what the model did, how to explain it to our governance teams, how to explain it to our customers, and the customers explain it to their clients, right? Today, it's around having regulation to make sure that machine-learning and artificial intelligence is used responsibly in business. >> Yeah, it's pretty amazing, and that's why I think we hear so much about augmented intelligence as opposed to artificial intelligence, there's nothing artificial about it. It's very different, but it really is trying to add to, you know, provide a little bit more data, a little bit more structure, more context to people that are trying to make decisions. >> And that's critically important because, you know, very often, the AI or machine-learning will make a decision differently than we will, so it can add some level of insight to us, but we always need that human factor in there to kind of validate the reasons, the explanations, and then make sure that we have that kind of human judgment that's running alongside. >> Right, right. So I can't believe I'm going to sit here and say that it's, whatever it is, May 15th today, the year's almost halfway over. But what are some of your priorities for the balance of the year, what are some of the things you are working on as you look forward? Obviously, FICO's a big data-driven company, you guys have a ton of data, you're in a ton of transactions so you've got kind of a front edge of this whole process. What are you looking at, what are some of your short-term priorities, mid-term priorities, as you move through the balance of the year and into next year? >> So number one is around explainable AI, right? And really helping organizations get that ability to explain their models. We're also focused very much around bringing more of the unsupervised analytic technologies to the market. So, very often when you build a model, you have a set of data and a set of outcomes, and you train that model, and you have a model that makes prediction. But more and more, we have parts of our businesses today that where unsupervised analytic models are much more important, in areas like-- >> What does that mean, unsupervised analytics models? >> So, essentially what it means is we're trying to look for patterns that are not normal, unlike any other customers. So if you think about a money launderer, there's going to be very few people that will behave like a money launderer, or an insider, or something along those lines. And so, by building really, really good models of predicting normal behavior any deviation or a mis-prediction from that model could point to something that's very abnormal, and something that should be investigated. And very often, we use those in areas of cyber-security crimes, blatant money laundering, insider fraud, in areas like that where you're not going to have a lot of outcome data, of data to train on, but you need to still make the decisions. >> Wow. Which is really hard for a computer, right? That's the opposite of the types of problems that they like. They like a lot of, a lot of, of revs. >> Correct, so that's why the focus is on understanding good behavior really, really well. And anything different than what it thinks is good could be potentially valuable. >> Alright, Scott, well keep track of all of our scores, we all depend on it. (laughs) >> Scott: We all do. >> Thanks for taking a few minutes out of your day. >> Scott: Appreciate it. >> Alright, he's Scott, I'm Jeff, you are watching theCUBE from San Francisco. Thanks for watching. (upbeat electronic music)

>> Announcer: From the Corinium Chief Analytics Officer Conference. Spring, San Francisco, it's theCUBE! >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Francisco at the Corinium Chief Analytics Officer Spring event 2018. About 100 people, really intimate, a lot of practitioners sharing best practices about how they got started, how are they really leveraging data and becoming digitally transformed, analytically driven, data driven. We're excited to have Vishal Morde. He's the VP of Data Science at Barclays, welcome. >> Glad to be here, yeah. >> Absolutely. So we were just talking about Philly, you're back in Delaware, and you actually had a session yesterday talking about Barclays journey. So I was wondering if you could share some of the highlights of that story with us. >> Absolutely, so I had a talk, I opened the conference with data science journey at Barclays. And, we have been on this journey for five years now where we transform our data and analytics practices and really harness the power of Big Data, Machine Learning, and advanced analytics. And the whole idea was to use this power of, newly found power that we have, to make the customer journey better. Better through predictive models, better through deeper and richer consumer insights and better through more personalized customer experience. So that is the sole bet. >> Now it's interesting because we think of financial services as being a data driven, organization already. You guys are way ahead Obviously Wall Street's trading on microseconds. What was different about this digital transformation than what you've been doing for the past? >> I think the key was, we do have all the data in the world. If you think about it, banks know everything about you, right? We have our demographic data, behaviors data. From very granular credit card transactions data, we have your attitudal data, but what we quickly found out that we did not have a strategy to use that data well. To improve our our productivity, profitability of a business and make the customer experience better. So what we did was step one was developing a comprehensive data strategy and that was all about organizing, democratizing, and monetizing our data assets. And step towards, then we went about the monetization part in a very disciplined way. We built a data science lab where we can quickly do a lot of rapid prototyping, look at any idea in machine learning data science, incubate it, validate it, and finally, it was ready for production. >> So I'm curious on that first stage, so you've got all this data, you've been collecting it forever, suddenly now you're going to take an organized approach to it. What'd you find in that first step when you actually tried to put a little synthesis and process around what you already had? >> Well the biggest challenge was, the data came from different sources. So we do have a lot of internal data assets, but we are in the business where we do have to get a lot of external data. Think about credit bureau's, right? Also we have a co-brand business, where we work with partners like Uber, imagine the kind of data we get from them, we have data from American Airlines. So our idea was to create a data governance structure of, we formed a Chief Data Office, the officer forum, we got all the people across our organization to understand the value of data. We are a data driven company as you said but, it took us a while to take that approach and importance of data, and then, data analytics need to be embedded in the organizational DNA, and that's what we're going to focus on first. Data awareness of importance of data, importance of governance as well, and then we could think about democratizing and monetizing, organization's the key for us. >> Right, right, well so how did you organize, how has the Chief Data Officer, what did he or she, who did he or she report to, how did you organize? >> Right, so it was directly reporting to our CEO. >> Jeff: Into the CEO, not into the CIO? >> Not into the CIO. We had a technology office, we do kind of, have a line-of-sight or adopted line with technology, and we made sure that that office has a lot of high-level organization buy-in, they are given budgets to make sure the data governance was in place, key was to get data ownership going. We were using a lot of data, but there was no data ownership. And that was the key, once we know that, who actually owned this data, then you can establish a governance framework, then you can establish how you use this data, and then, how to be monetized. >> So who owned it before you went through this exercise, just kind of, it was just kind of there? >> Yeah, there wasn't a clear ownership, and that's the key for us. Once you establish ownership, then it becomes an asset, we were not treating data as an asset, so there was a change in, kind of mindset, that we had to go through, that data is an asset, and it was used as a means to an end, rather than an asset. >> Right, well what about the conflict with the governance people, I'm sure there was a lot of wait, wait, wait, we just can't open this up to anybody, I'm sure it's a pretty interesting discussion because you have to open it up to more people, but you still have to obviously follow the regs. >> Right, and that's where there are a lot of interesting advancement in data science, where, in the area of data governance, there are new tools out there which lets you track who's actually accessing your data. Once we had that infrastructure, then you can start figuring out okay, how do we allow access, how do we actually proliferate that data across different levels of the organization? Because data needs to be in the hands of decision makers, no matter who they are, could be our CEO, to somebody who's taking our phone calls. So that democratization piece became so important, then we can think about how do you-- you can't directly jump into monetization phase before you get your, all the ducks in order. >> So what was the hardest part, the biggest challenge, of that first phase in organizing the data? >> Creating that 360 degree view on our customers, we had a lot of interesting internal data assets, but we were missing big pieces of the puzzles, where we're looking at, you're trying to create a 360 degree view on a customer, it does take a while to get that right, and that's where the data, setting up the data governance piece, setting up the CDO office, those are the more painful, more difficult challenges, but they lay the foundation for all the the work that we wanted to do, and it allowed to us to kind of think through more methodically about our problems and establish a foundation that we can now, we can take any idea and use it, and monetize it for you. >> So it's interesting you, you said you've been on this journey for five years, so, from zero to a hundred, where are you on your journey do you think? >> Right, I think we're just barely scratching the surface, (both laughing) - I knew you were going to say that >> Because I do feel that, the data science field itself is evolving, I look at data science as like ever-evolving, ever-mutating kind of beast, right? And we just started our journey, I think we are off to a good start, we have really good use-cases, we have starting using the data well, we have established importance of data, and now we are operationalized on the machine learning data science projects as well. So that's been great, but I do feel there's a lot of untapped potential in this, and I think it'll only get better. >> What about on the democratization, we just, in the keynote today there was a very large retailer, I think he said he had 50 PhDs on staff and 150 data centers this is a multi-billion dollar retailer. How do you guys deal with resource constraints of your own data science team versus PhDs, and trying to democratize the decision making out to a much broader set of people? >> So I think the way we've thought about this is think big, but start small. And what we did was, created a data science lab, so what it allowed is to kind of, and it was the cross-functional team of data scientists, data engineers, software developers kind of working together, and that is a primary group. And they were equally supported by your info-sec guys, or data governance folks, so, they're a good support group as well. And with that cross-functional team, now we are able to move from generating an idea, to incubating it, making sure it has a true commercial value and once we establish that, then we'll even move forward operationalization, so it was more surgical approach rather than spending millions and millions of dollars on something that we're not really sure about. So that did help us to manage a resource constraint now, only the successful concepts were actually taken through operationalization, and we before, we truly knew the bottom line impact, we could know that, here's what it means for us, and for consumers, so that's the approach that we took. >> So, we're going to leave it there, but I want to give you the last word, what advice would give for a peer, not in the financial services industry, they're not watching this. (both laugh) But you know, in terms of doing this journey, 'cause it's obviously, it's a big investment, you've been at it for five years, you're saying you barely are getting started, you're in financial services, which is at it's base, basically an information technology industry. What advice do you give your peers, how do they get started, what do they do in the dark days, what's the biggest challenge? >> Yeah, I feel like my strong belief is, data science is a team sport, right? A lot of people come and ask me: how do we find these unicorn data scientist, and my answer always being that, they don't exist, they're figments of imagination. So it's much better to take cross-functional team, with a complimentary kind of skill set, and get them work together, how do you fit different pieces of the puzzle together, will determine the success of the program. Rather than trying to go really big into something, so that's, the team sport is the key concept here, and if I can get the word out across, that'll be really valuable. >> Alright, well thanks for sharin' that, very useful piece of insight! >> Vishal: Absolutely! >> Alright thanks Vishal, I'm Jeff Frick, you are watching theCUBE, from the Corinium Chief Analytic Officer summit, San Francisco, 2018, at the Parc 55, thanks for watching! (bubbly music plays)

(techno music) >> Announcer: From the Corinium Chief Analytics Officer Conference Spring San Francisco. It's theCUBE. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at the Parc 55 Hotel at the Corinium Chief Analytics Officer Spring 2018 event, about 100 people, pretty intimate affair. A lot of practitioners here talking about the challenges of Big Data and the challenges of Analytics. We're really excited to have a very special Cube guest. I think he was the first guy to launch his company on theCUBE. It was Big Data New York City 2013. I remember it distinctly. It's Prakash Nanduri, the co-founder and CEO of Paxata. Great to see you. >> Great seeing you. Thank you for having me back. >> Absolutely. You know we got so much mileage out of that clip. We put it on all of our promotional materials. You going to launch your company? Launch your company on theCUBE. >> You know it seems just like yesterday but it's been a long ride and it's been a fantastic ride. >> So give us just a quick general update on the company, where you guys are now, how things are going. >> Things are going fantastic. We continue to grow. If you recall, when we launched, we launched the whole notion of democratization of information in the enterprise with self service data prep. We have gone onto now delivered real value to some of the largest brands in the world. We're very proud that 2017 was the year when massive amount of adoption of Paxata's adaptive information platform was taken across multiple industries, financial services, retail, CPG, high tech, in the OIT space. So, we just keep growing and it's the usual challenges of managing growth and managing, you know, the change in the company as you, as you grow from being a small start-up to know being a real company. >> Right, right. There's good problems and bad problems. Those are the good problems. >> Yes, yes. >> So, you know, we do so many shows and there's two big themes over and over and over like digital transformation which gets way over used and then innovation and how do you find a culture of innovation. In doing literally thousands of these interviews, to me it seems pretty simple. It is about democratization. If you give more people the data, more people the tools to work with the data, and more people the power to do something once they find something in the data, and open that up to a broader set of people, they're going to find innovations, simply the fact of doing it. But the reality is those three simple steps aren't necessarily very easy to execute. >> You're spot on, you're spot on. I like to say that when we talk about digital transformation the real focus should be on the deed . And it really centers around data and it centers around the whole notion of democratization, right? The challenge always in large enterprises is democratization without governance becomes chaos. And we always need to focus on democratization. We need to focus on data because as we all know data is the new oil, all of that, and governance becomes a critical piece too. But as you recall, when we launched Paxata, the entire vision from day one has been while the entire focus around digitization covers many things right? It covers people processes. It covers applications. It's a very large topic, the whole digital transformation of enterprise. But the core foundation to digital transformation, data democratization governance, but the key issue is the companies that are going to succeed are the companies that turn data into information that's relevant for every digital transformation effort. >> Right, right. >> Because if you do not turn raw data into information, you're just dealing with raw data which is not useful >> Jeff: Right >> And it will not be democratized. >> Jeff: Right >> Because the business will only consume the information that is contextual to their need, the information that's complete and the information that is clean. >> Right, right. >> So that's really what we're driving towards. >> And that's interesting 'cause the data, there's so many more sources of data, right? There's data that you control. There's structured data, unstructured data. You know, I used to joke, just the first question when you'd ask people "Where's your data?", half the time they couldn't even, they couldn't even get beyond that step. And that's before you start talking about cleaning it and making it ready and making it available. Before you even start to get into governance and rights and access so it's a really complicated puzzle to solve on the backend. >> I think it starts with first focusing on what are the business outcomes we are driving with digital transformation. When you double-click on digital transformation and then you start focusing on data and information, there's a few things that come to fore. First of all, how do I leverage information to improve productivity in my company? There's multiple areas, whether it is marketing or supply chain or whatever. The second notion is how do I ensure that I can actually transform the culture in my company and attract the brightest and the best by giving them the the environment where democratization of information is actually reality, where people feel like they're empowered to access data and turn it into information and then be able to do really interesting things. Because people are not interested on being subservient to somebody who gives them the data. They want to be saying "Give it to me. "I'm smart enough. "I know analytics. "I think analytically and I want to drive my career forward." So the second thing is the cultural aspect to it. And the last thing, which is really important is every company, regardless of whether you're making toothpicks or turbines, you are looking to monetize data. So it's about productivity. It's about cultural change and attracting of talent. And it's about monetization. And when it comes to monetization of data, you cannot be satisfied with only covering enterprise data which is sitting in my enterprise systems. You have to be able to focus on, oh, how can I leverage the IOT data that's being generated from my products or widgets. How can I generate social immobile? How can I consume that? How can I bring all of this together and get the most complete insight that I need for my decision-making process? >> Right. So, I'm just curious, how do you see it your customers? So this is the chief analytics officer, we go to chief data officer, I mean, there's all these chief something officers that want to get involved in data and marketing is much more involved with it. Forget about manufacturing. So when you see successful cultural change, what drives that? Who are the people that are successful and what is the secret to driving the cultural change that we are going to be data-driven, we are going to give you the tools, we are going to make the investment to turn data which historically was even arguably a liability 'cause it had to buy a bunch o' servers to stick it on, into that now being an asset that drives actionable outcomes? >> You know, recently I was having this exact discussion with the CEO of one of the largest financial institutions in the world. This gentleman is running a very large financial services firm, is dealing with all the potential disruption where they're seeing completely new type of PINTEC products coming in, the whole notion of blockchain et cetera coming in. Everything is changing. Everything looks very dramatic. And what we started talking about is the first thing as the CEO that we always focus on is do we have the right people? And do we have the people that are motivated and driven to basically go and disrupt and change? For those people, you need to be able to give them the right kind of tools, the right kind of environment to empower them. This doesn't start with lip service. It doesn't start about us saying "We're going to be on a digital transformation journey" but at the same time, your data is completely in silos. It's locked up. There is 15,000 checks and balances before I can even access a simple piece of data and third, even when I get access to it, it's too little, too late or it's garbage in, garbage out. And that's not the culture. So first, it needs to be CEO drive, top down. We are going to go through digital transformation which means we are going to go through a democratization effort which means we are going to look at data and information as an asset and that means we are not only going to be able to harness these assets, but we're also going to monetize these assets. How are we going to do it? It depends very much on the business you're in, the vertical industry you play in, and your strengths and weaknesses. So each company has to look at it from their perspective. There's no one size fits all for everyone. >> Jeff: Right. >> There are some companies that have fantastic cultures of empowerment and openness but they may not have the right innovation or the right kind of product innovation skills in place. So it's about looking at data across the board. First from your culture and your empowerment, second about democratization of information which is where a company like Paxata comes in, and third, along with democratization, you have to focus on governance because we are for-profit companies. We have a fiducial responsibility to our customers and our regulators and therefore we cannot have democratization without governance. >> Right, right >> And that's really what our biggest differentiation is. >> And then what about just in terms of the political play inside the company. You know, on one hand, used to be if you held the information, you had the power. And now that's changed really 'cause there's so much information. It's really, if you are the conduit of information to help people make better decisions, that's actually a better position to be. But I'm sure there's got to be some conflicts going through digital transformation where I, you know, I was the keeper of the kingdom and now you want to open that up. Conversely, it must just be transformational for the people on the front lines that finally get the data that they've been looking for to run the analysis that they want to rather than waiting for the weekly reports to come down from on high. >> You bet. You know what I like to say is that if you've been in a company for 10, 15 years and if you felt like a particular aspect, purely selfishly, you felt a particular aspect was job security, that is exactly what's going to likely make you lose your job today. What you thought 10 years ago was your job security, that's exactly what's going to make you lose your job today. So if you do not disrupt yourself, somebody else will. So it's either transform yourself or not. Now this whole notion of politics and you know, struggle within the company, it's been there for as long as, humans generally go towards entropy. So, if you have three humans, you have all sort of issues. >> Jeff: Right, right. >> The issue starts frankly with leadership. It starts with the CEO coming down and not only putting an edict down on how things will be done but actually walking the walk with talking the talk. If, as a CEO, you're not transparent, it you're not trusting your people, if you're not sharing information which could be confidential, but you mention that it's confidential but you have to keep this confidential. If you trust your people, you give them the ability to, I think it's a culture change thing. And the second thing is incentivisation. You have to be able to focus on giving people the ability to say "by sharing my data, "I actually become a hero." >> Right, right. >> By giving them the actual credit for actually delivering the data to achieve an outcome. And that takes a lot of work. But if you do not actually drive the cultural change, you will not drive the digital transformation and you will not drive the democratization of information. >> And have you seen people try to do it without making the commitment? Have you seen 'em pay the lip service, spend a few bucks, start a project but then ultimately they, they hamstring themselves 'cause they're not actually behind it? >> Look, I mean, there's many instances where companies start on digital transformation or they start jumping into cool terms like AI or machine-learning, and there's a small group of people who are kind of the elites that go in and do this. And they're given all the kind of attention et cetera. Two things happen. Because these people who are quote, unquote, the elite team, either they are smart but they're not able to scale across the organization or many times, they're so good, they leave. So that transformation doesn't really get democratized. So it is really important from day one to start a culture where you're not going to have a small group of exclusive data scientists. You can have those people but you need to have a broader democratization focus. So what I have seen is many of the siloed, small, tight, mini science projects end up failing. They fail because number one, either the business outcome is not clearly identified early on or two, it's not scalable across the enterprise. >> Jeff: Right. >> And a majority of these exercises fail because the whole information foundation that is taking raw data turning it into clean, complete, potential consumable information, to feed across the organization, not just for one siloed group, not just one data science team. But how do you do that across the company? That's what you need to think from day one. When you do these siloed things, these departmental things, a lot of times they can fail. Now, it's important to say "I will start with a couple of test cases" >> Jeff: Right, right. >> "But I'm going to expand it across "from the beginning to think through that." >> So I'm just curious, your perspective, is there some departments that are the ripest for being that leading edge of the digital transformation in terms of, they've got the data, they've got the right attitude, they're just a short step away. Where have you seen the great place to succeed when you're starting on kind of a smaller PLC, I don't know if you'd say PLC, project or department level? >> So, it's funny but you will hear this, it's not rocket science. Always they say, follow the money. So, in a business, there are three incentives, making more money, saving money, or staying out of jail. (laughs) >> Those are good. I don't know if I'd put them in that order but >> Exactly, and you know what? Depending on who are you are, you may have a different order but staying out of jail if pretty high on my list. >> Jeff: I'm with you on that one. >> So, what are the ambiants? Risk and compliance. Right? >> Jeff: Right, right. >> That's one of those things where you absolutely have to deliver. You absolutely have to do it. It's significantly high cost. It's very data and analytic centric and if you find a smart way to do it, you can dramatically reduce your cost. You can significantly increase your quality and you can significantly increase the volume of your insights and your reporting, thereby achieving all the risk and compliance requirements but doing it in a smarter way and a less expensive way. >> Right. >> That's where incentives have really been high. Second, in making money, it always comes down to sales and marketing and customer success. Those are the three things, sales, marketing, and customer success. So most of our customers who have been widely successful, are the ones who have basically been able to go and say "You know what? "It used to take us eight months "to be able to even figure out a customer list "for a particular region. "Now it takes us two days because of Paxata "and because of the data prep capabilities "and the governance aspects." That's the power that you can deliver today. And when you see one person who's a line of business person who says "Oh my God. "What used to take me eight months, "now it's done in half a day". Or "What use to take me 22 days to create a report, "is now done in 45 minutes." All of a sudden, you will not have a small kind of trickle down, you will have a tsunami of democratization with governance. That's what we've seen in our customers. >> Right, right. I love it. And this is just so classic too. I always like to joke, you know, back in the day, you would run your business based on reports from old data. Now we want to run your business with stuff you can actually take action on now. >> Exactly. I mean, this is public, Shameek Kundu, the chief data officer of Standard Chartered Bank and Michael Gorriz who's the global CIO of Standard Chartered Bank, they have embraced the notion that information democratization in the bank is a foundational element to the digital transformation of Standard Chartered. They are very forward thinking and they're looking at how do I democratize information for all our 87,500 employees while we maintain governance? And another major thing that they are looking at is they know that the data that they need to manipulate and turn into information is not sitting only on premise. >> Right, right. >> It's sitting across a multi-cloud world and that's why they've embraced the Paxata information platform to be their information fabric for a multi-cloud hybrid world. And this is where we see successes and we're seeing more and more of this, because it starts with the people. It starts with the line of business outcomes and then it starts with looking at it from scale. >> Alright, Prakash, well always great to catch up and enjoy really watching the success of the company grow since you launched it many moons ago in New York City >> yes Fantastic. Always a pleasure to come back here. Thank you so much. >> Alright. Thank you. He's Prakash, I'm Jeff Frick. You're watching theCUBE from downtown San Francisco. Thanks for watching. (techno music)

(techno music) >> Announcer: Live, from downtown San Francisco, it's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. >> Welcome back to San Francisco everybody, this is theCUBE, the leader in live tech coverage. We're here at the Parc 55 in San Francisco covering the IBM CDO Strategy Summit. I'm here with Joel Horwitz who's the Vice President of Digital Partnerships & Offerings at IBM. Good to see you again Joel. >> Thanks, great to be here, thanks for having me. >> So I was just, you're very welcome- It was just, let's see, was it last month, at Think? >> Yeah, it's hard to keep track, right. >> And we were talking about your new role- >> It's been a busy year. >> the importance of partnerships. One of the things I want to, well let's talk about your role, but I really want to get into, it's innovation. And we talked about this at Think, because it's so critical, in my opinion anyway, that you can attract partnerships, innovation partnerships, startups, established companies, et cetera. >> Joel: Yeah. >> To really help drive that innovation, it takes a team of people, IBM can't do it on its own. >> Yeah, I mean look, IBM is the leader in innovation, as we all know. We're the market leader for patents, that we put out each year, and how you get that technology in the hands of the real innovators, the developers, the longtail ISVs, our partners out there, that's the challenging part at times, and so what we've been up to is really looking at how we make it easier for partners to partner with IBM. How we make it easier for developers to work with IBM. So we have a number of areas that we've been adding, so for example, we've added a whole IBM Code portal, so if you go to developer.ibm.com/code you can actually see hundreds of code patterns that we've created to help really any client, any partner, get started using IBM's technology, and to innovate. >> Yeah, and that's critical, I mean you're right, because to me innovation is a combination of invention, which is what you guys do really, and then it's adoption, which is what your customers are all about. You come from the data science world. We're here at the Chief Data Officer Summit, what's the intersection between data science and CDOs? What are you seeing there? >> Yeah, so when I was here last, it was about two years ago in 2015, actually, maybe three years ago, man, time flies when you're having fun. >> Dave: Yeah, the Spark Summit- >> Yeah Spark Technology Center and the Spark Summit, and we were here, I was here at the Chief Data Officer Summit. And it was great, and at that time, I think a lot of the conversation was really not that different than what I'm seeing today. Which is, how do you manage all of your data assets? I think a big part of doing good data science, which is my kind of background, is really having a good understanding of what your data governance is, what your data catalog is, so, you know we introduced the Watson Studio at Think, and actually, what's nice about that, is it brings a lot of this together. So if you look in the market, in the data market, today, you know we used to segment it by a few things, like data gravity, data movement, data science, and data governance. And those are kind of the four themes that I continue to see. And so outside of IBM, I would contend that those are relatively separate kind of tools that are disconnected, in fact Dinesh Nirmal, who's our engineer on the analytic side, Head of Development there, he wrote a great blog just recently, about how you can have some great machine learning, you have some great data, but if you can't operationalize that, then really you can't put it to use. And so it's funny to me because we've been focused on this challenge, and IBM is making the right steps, in my, I'm obviously biased, but we're making some great strides toward unifying the, this tool chain. Which is data management, to data science, to operationalizing, you know, machine learning. So that's what we're starting to see with Watson Studio. >> Well, I always push Dinesh on this and like okay, you've got a collection of tools, but are you bringing those together? And he flat-out says no, we developed this, a lot of this from scratch. Yes, we bring in the best of the knowledge that we have there, but we're not trying to just cobble together a bunch of disparate tools with a UI layer. >> Right, right. >> It's really a fundamental foundation that you're trying to build. >> Well, what's really interesting about that, that piece, is that yeah, I think a lot of folks have cobbled together a UI layer, so we formed a partnership, coming back to the partnership view, with a company called Lightbend, who's based here in San Francisco, as well as in Europe, and the reason why we did that, wasn't just because of the fact that Reactive development, if you're not familiar with Reactive, it's essentially Scala, Akka, Play, this whole framework, that basically allows developers to write once, and it kind of scales up with demand. In fact, Verizon actually used our platform with Lightbend to launch the iPhone 10. And they show dramatic improvements. Now what's exciting about Lightbend, is the fact that application developers are developing with Reactive, but if you turn around, you'll also now be able to operationalize models with Reactive as well. Because it's basically a single platform to move between these two worlds. So what we've continued to see is data science kind of separate from the application world. Really kind of, AI and cloud as different universes. The reality is that for any enterprise, or any company, to really innovate, you have to find a way to bring those two worlds together, to get the most use out of it. >> Fourier always says "Data is the new development kit". He said this I think five or six years ago, and it's barely becoming true. You guys have tried to make an attempt, and have done a pretty good job, of trying to bring those worlds together in a single platform, what do you call it? The Watson Data Platform? >> Yeah, Watson Data Platform, now Watson Studio, and I think the other, so one side of it is, us trying to, not really trying, but us actually bringing together these disparate systems. I mean we are kind of a systems company, we're IT. But not only that, but bringing our trained algorithms, and our trained models to the developers. So for example, we also did a partnership with Unity, at the end of last year, that's now just reaching some pretty good growth, in terms of bringing the Watson SDK to game developers on the Unity platform. So again, it's this idea of bringing the game developer, the application developer, in closer contact with these trained models, and these trained algorithms. And that's where you're seeing incredible things happen. So for example, Star Trek Bridge Crew, which I don't know how many Trekkies we have here at the CDO Summit. >> A few over here probably. >> Yeah, a couple? They're using our SDK in Unity, to basically allow a gamer to use voice commands through the headset, through a VR headset, to talk to other players in the virtual game. So we're going to see more, I can't really disclose too much what we're doing there, but there's some cool stuff coming out of that partnership. >> Real immersive experience driving a lot of data. Now you're part of the Digital Business Group. I like the term digital business, because we talk about it all the time. Digital business, what's the difference between a digital business and a business? What's the, how they use data. >> Joel: Yeah. >> You're a data person, what does that mean? That you're part of the Digital Business Group? Is that an internal facing thing? An external facing thing? Both? >> It's really both. So our Chief Digital Officer, Bob Lord, he has a presentation that he'll give, where he starts out, and he goes, when I tell people I'm the Chief Digital Officer they usually think I just manage the website. You know, if I tell people I'm a Chief Data Officer, it means I manage our data, in governance over here. The reality is that I think these Chief Digital Officer, Chief Data Officer, they're really responsible for business transformation. And so, if you actually look at what we're doing, I think on both sides is we're using data, we're using marketing technology, martech, like Optimizely, like Segment, like some of these great partners of ours, to really look at how we can quickly A/B test, get user feedback, to look at how we actually test different offerings and market. And so really what we're doing is we're setting up a testing platform, to bring not only our traditional offers to market, like DB2, Mainframe, et cetera, but also bring new offers to market, like blockchain, and quantum, and others, and actually figure out how we get better product-market fit. What actually, one thing, one story that comes to mind, is if you've seen the movie Hidden Figures- >> Oh yeah. >> There's this scene where Kevin Costner, I know this is going to look not great for IBM, but I'm going to say it anyways, which is Kevin Costner has like a sledgehammer, and he's like trying to break down the wall to get the mainframe in the room. That's what it feels like sometimes, 'cause we create the best technology, but we forget sometimes about the last mile. You know like, we got to break down the wall. >> Where am I going to put it? >> You know, to get it in the room! So, honestly I think that's a lot of what we're doing. We're bridging that last mile, between these different audiences. So between developers, between ISVs, between commercial buyers. Like how do we actually make this technology, not just accessible to large enterprise, which are our main clients, but also to the other ecosystems, and other audiences out there. >> Well so that's interesting Joel, because as a potential partner of IBM, they want, obviously your go-to-market, your massive company, and great distribution channel. But at the same time, you want more than that. You know you want to have a closer, IBM always focuses on partnerships that have intrinsic value. So you talked about offerings, you talked about quantum, blockchain, off-camera talking about cloud containers. >> Joel: Yeah. >> I'd say cloud and containers may be a little closer than those others, but those others are going to take a lot of market development. So what are the offerings that you guys are bringing? How do they get into the hands of your partners? >> I mean, the commonality with all of these, all the emerging offerings, if you ask me, is the distributed nature of the offering. So if you look at blockchain, it's a distributed ledger. It's a distributed transaction chain that's secure. If you look at data, really and we can hark back to say, Hadoop, right before object storage, it's distributed storage, so it's not just storing on your hard drive locally, it's storing on a distributed network of servers that are all over the world and data centers. If you look at cloud, and containers, what you're really doing is not running your application on an individual server that can go down. You're using containers because you want to distribute that application over a large network of servers, so that if one server goes down, you're not going to be hosed. And so I think the fundamental shift that you're seeing is this distributed nature, which in essence is cloud. So I think cloud is just kind of a synonym, in my opinion, for distributed nature of our business. >> That's interesting and that brings up, you're right, cloud and Big Data/Hadoop, we don't talk about Hadoop much anymore, but it kind of got it all started, with that notion of leave the data where it is. And it's the same thing with cloud. You can't just stuff your business into the public cloud. You got to bring the cloud to your data. >> Joel: That's right. >> But that brings up a whole new set of challenges, which obviously, you're in a position just to help solve. Performance, latency, physics come into play. >> Physics is a rough one. It's kind of hard to avoid that one. >> I hear your best people are working on it though. Some other partnerships that you want to sort of, elucidate. >> Yeah, no, I mean we have some really great, so I think the key kind of partnership, I would say area, that I would allude to is, one of the things, and you kind of referenced this, is a lot of our partners, big or small, want to work with our top clients. So they want to work with our top banking clients. They want, 'cause these are, if you look at for example, MaRisk and what we're doing with them around blockchain, and frankly, talk about innovation, they're innovating containers for real, not virtual containers- >> And that's a joint venture right? >> Yeah, it is, and so it's exciting because, what we're bringing to market is, I also lead our startup programs, called the Global Entrepreneurship Program, and so what I'm focused on doing, and you'll probably see more to come this quarter, is how do we actually bridge that end-to-end? How do you, if you're startup or a small business, ultimately reach that kind of global business partner level? And so kind of bridging that, that end-to-end. So we're starting to bring out a number of different incentives for partners, like co-marketing, so I'll help startups when they're early, figure out product-market fit. We'll give you free credits to use our innovative technology, and we'll also bring you into a number of clients, to basically help you not burn all of your cash on creating your own marketing channel. God knows I did that when I was at a start-up. So I think we're doing a lot to kind of bridge that end-to-end, and help any partner kind of come in, and then grow with IBM. I think that's where we're headed. >> I think that's a critical part of your job. Because I mean, obviously IBM is known for its Global 2000, big enterprise presence, but startups, again, fuel that innovation fire. So being able to attract them, which you're proving you can, providing whatever it is, access, early access to cloud services, or like you say, these other offerings that you're producing, in addition to that go-to-market, 'cause it's funny, we always talk about how efficient, capital efficient, software is, but then you have these companies raising hundreds of millions of dollars, why? Because they got to do promotion, marketing, sales, you know, go-to-market. >> Yeah, it's really expensive. I mean, you look at most startups, like their biggest ticket item is usually marketing and sales. And building channels, and so yeah, if you're, you know we're talking to a number of partners who want to work with us because of the fact that, it's not just like, the direct kind of channel, it's also, as you kind of mentioned, there's other challenges that you have to overcome when you're working with a larger company. for example, security is a big one, GDPR compliance now, is a big one, and just making sure that things don't fall over, is a big one. And so a lot of partners work with us because ultimately, a number of the decision makers in these larger enterprises are going, well, I trust IBM, and if IBM says you're good, then I believe you. And so that's where we're kind of starting to pull partners in, and pull an ecosystem towards us. Because of the fact that we can take them through that level of certification. So we have a number of free online courses. So if you go to partners, excuse me, ibm.com/partners/learn there's a number of blockchain courses that you can learn today, and will actually give you a digital certificate, that's actually certified on our own blockchain, which we're actually a first of a kind to do that, which I think is pretty slick, and it's accredited at some of the universities. So I think that's where people are looking to IBM, and other leaders in this industry, is to help them become experts in their, in this technology, and especially in this emerging technology. >> I love that blockchain actually, because it's such a growing, and interesting, and innovative field. But it needs players like IBM, that can bring credibility, enterprise-grade, whether it's security, or just, as I say, credibility. 'Cause you know, this is, so much of negative connotations associated with blockchain and crypto, but companies like IBM coming to the table, enterprise companies, and building that ecosystem out is in my view, crucial. >> Yeah, no, it takes a village. I mean, there's a lot of folks, I mean that's a big reason why I came to IBM, three, four years ago, was because when I was in start-up land, I used to work for H20, I worked for Alpine Data Labs, Datameer, back in the Hadoop days, and what I realized was that, it's an opportunity cost. So you can't really drive true global innovation, transformation, in some of these bigger companies because there's only so much that you can really kind of bite off. And so you know at IBM it's been a really rewarding experience because we have done things like for example, we partnered with Girls Who Code, Treehouse, Udacity. So there's a number of early educators that we've partnered with, to bring code to, to bring technology to, that frankly, would never have access to some of this stuff. Some of this technology, if we didn't form these alliances, and if we didn't join these partnerships. So I'm very excited about the future of IBM, and I'm very excited about the future of what our partners are doing with IBM, because, geez, you know the cloud, and everything that we're doing to make this accessible, is bar none, I mean, it's great. >> I can tell you're excited. You know, spring in your step. Always a lot of energy Joel, really appreciate you coming onto theCUBE. >> Joel: My pleasure. >> Great to see you again. >> Yeah, thanks Dave. >> You're welcome. Alright keep it right there, everybody. We'll be back. We're at the IBM CDO Strategy Summit in San Francisco. You're watching theCUBE. (techno music) (touch-tone phone beeps)