(light music) (airy white noise rumbling) >> Hey everyone, welcome back to Las Vegas. It's theCUBE. We're here, day four of our coverage of AWS re:Invent 22. There's been about, we've heard, north of 55,000 folks here in person. We're seeing only a fraction of that but it's packed in the expo center. We're at the Venetian Expo, Lisa Martin, Dave Vellante. Dave, we've had such great conversations as we always do on theCUBE. With the AWS ecosystem, we're going to be talking with another partner on that ecosystem and what they're doing to innovate together next. >> Well, we know security is the number one topic on IT practitioners, mine, CIOs, CISOs. We also know that they don't have the bench strength, that's why they look to manage service providers, manage service security providers. It's a growing topic, we've talked about it. We talked about it at re:Inforce earlier this year. I think it was July, actually, and August, believe it or not, not everybody was at the Cape. It was pretty well attended conference and that's their security focus conference, exclusive on security. But there's a lot of security here too. >> Lot of security, we're going to be talking about that next. We have two guests from Capgemini joining us. Mike Wasielewski, the head of cloud security, and NextGen secure architectures, welcome Mike. Anne Saunders also joins us, the Director of Cybersecurity Technology Partnerships at Capgemini, welcome Anne. >> Thank you. >> Dave: Hey guys. >> So, day four of the show, how you feeling? >> Anne: Pretty good. >> Mike: It's a long show. >> It is a long, and it's still jamming in here. Normally on the last day, it dwindles down. Not here. >> No, the foot traffic around the booth and around the totality of this expo floor has been amazing, I think. >> It really has. Anne, I want to start with you. Capgemini making some moves in the waves in the cloud and cloud security spaces. Talk to us about what Cap's got going on there. >> Well, we actually have a variety of things going on. Very much partner driven. The SOC Essentials offering that Mike's going to talk about shortly is the kind of the starter offer where we're going to build from and build out from. SOC Essentials is definitely critical for establishing that foundation. A lot of good stuff coming along with partners. Since I manage the partners, I'm kind of keen on who we get involved with and how we work with them to build out value and focus on our overall cloud security strategy. Mike, you want to talk about SOC Essentials? >> Yeah, well, no, I mean, I think at Capgemini, we really say cybersecurity is part of our DNA and so as we look at what we do in the cloud, you'll find that security has always been an underpinning to a lot of what we deliver, whether it's on the DevSecOps services, migration services, stuff like that. But what we're really trying to do is be intentional about how we approach the security piece of the cloud in different ways, right? Traditional infrastructure, you mentioned the totality of security vendors here and at re:Inforce. We're really seeing that you have to approach it differently. So we're bringing together the right partners. We're using what's part of our DNA to really be able to drive the next generation of security inside those clouds for our clients and customers. So as Anne was talking about, we have a new service called the Capgemini Cloud SOC Essentials, and we've really brought our partners to bear, in this case Trend Micro, really bringing a lot of their intelligence and building off of what they do so that we can help customers. Services can be pretty expensive, right, when you go for the high end, or if you have to try to run one yourself, there's a lot of time, I think you mentioned earlier, right, the people's benches. It's really hard to have a really good cybersecurity people in those smaller businesses. So what we're trying to do is we're really trying to help companies, whether you're the really big buyers of the world or some of the smaller ones, right? We want to be able to give you the visibility and ability to deliver to your customers securely. So that's how we're approaching security now and we're cloud SOC Essentials, the new thing that we're announcing while we were here is really driving out of. >> When I came out of re:Invent, when you do these events, you get this Kool-Aid injection and after a while you're like hm, what did I learn? And one of the things that struck me in talking to people is you've got the shared responsibility model that the cloud has sort of created and I know there's complexities across cloud but let's just keep it at cloud generically for a moment. And then you've got the CISO, the AppDev, AppSecDev group is being asked to do a lot. They're kind of being dragged into security that's really not their wheelhouse and then you've got audit which is like the last line of defense. And so one of the things that struck me at re:Inforce is like, okay, Amazon, great job for their portion of the shared responsibility model but I didn't hear a lot in terms of making the CISO's life easier and I'm guessing that's where you guys come in. I wonder if you could talk about that trend, that conceptual layers that I just laid out and where you guys fit. >> Mike: Sure, so I think first and foremost, I always go back to a quote from, I think it's attributed to Peter Drucker, whether that's right or wrong, who knows? But culture eats strategy for breakfast, right? And I think what we've seen in our conversations with whether you're talking to the CISO, the application team, the AppDev team, wherever throughout the organization, we really see that culture is what's going to drive success or failure of security in the org, and so what we do is we really do bring that totality of perspective. We're not just cloud, not just security, not just AppDev. We can really bring across the totality of the Capgemini estate. So that when we go, and you're right, a CISO says, I'm having a hard time getting the app people to deliver what I need. If you just come from a security perspective, you're right, that's what's going to happen. So what we try to do is so, we've got a great DevSecOps service, for example in the cloud where we do that. We bring all the perspectives together, how do we align KPIs? That's a big problem, I think, for what you're seeing, making CISO's lives easier, is about making sure that the app team KPIs are aligned with the CISO's but also the CISO's KPIs are aligned with the app teams. And by doing that, we have had really great success in a number of organizations by giving them the tools then and the people on our side to be able to make those alignments at the business level, to drive the right business outcome, to drive the right security outcome, the right application outcome. That's where I think we've really come to play. >> Absolutely, and I will say from a partnering perspective, what's key in supporting that strategy is we will learn from our partners, we lean on our partners to understand what the trends they're seeing and where they're having an impact with regards to supporting the CISO and supporting the overall security strategy within a company. I mean, they're on the cutting edge. We do a lot to track their technology roadmaps. We do a lot to track how they build their buyer personas and what issues they're dealing with and what issues they're prepared to deal with regards to where they're investing and who's investing in them. A lot of strategy around which partner to bring in and support, how we're going to address the challenges, the CISO and the IT teams are having to kind of support that overall. Security is a part of everything, DNA kind of strategy. >> Yeah, do you have a favorite example, Anne, of a partner that came in with Capgemini, helped a customer really be able to do what Capgemini is doing and that is, have cybersecurity be actually part of their DNA when there's so many challenges, the skills gap. Any favorite example that really you think articulates how you're able to enable organizations to achieve just that? >> Anne: Well, actually the SOC Essentials offering that we're rolling out is a prime example of that. I mean, we work very, very closely with Trend on all fronts with regards to developing it. It's one of those completely collaborative from day one to going to the customer and that it's almost that seamless connectivity and just partnering at such a strategic level is a great example of how it's done right, and when it's done right, how successful it can be. >> Dave: Why Trend Micro? Because I mean, I'm sure you've seen, I think that's Optiv, has the eye test with all the tools and you talk to CISOs, they're like really trying to consolidate those tools. So I presume there's a portfolio play there, but tell us, tell the audience a little bit more about why Trend Micro and I mean your branding with them, why those guys? >> Well, it goes towards the technology, of course, and all the development they've done and their position within AWS and how they address assuring security for our clients who are moving onto and running their estates on AWS. There's such a long heritage with regards to their technology platform and what they've developed, that deep experience, that kind of the strength of the technology because of the longevity they've had and where they sit within their domain. I try to call partners out by their domain and their area of expertise is part of the reason, I mean. >> Yeah, I think another big part of it is Gartner is expecting, I think they published this out in the next three years, we expect to see another consolidation both inside of the enterprises as well as, I look back a couple years, when Palo Alto went on a very nice spending spree, right? And put together a lot of really great companies that built their Prisma platform. So what I think one of the reasons we picked Trend in this particular case is as we look forward for our customers and our clients, not just having point solutions, right? This isn't just about endpoint protection, this isn't just about security posture management. This is really who can take the totality of the customer's problems and deliver on the right outcomes from a single platform, and so when we look at companies like Trend, like Palo, some of the bigger partners for us, that's where we try to focus. They're definitely best in breed and we bring those to our customers too for certain things. But as we look to the future, I think really finding those partners that are going to be able to solve a swath of problems at the right price point for their customers, that is where I think we see the industry moving. >> Dave: And maybe be around as an independent company. Was that a factor as well? I mean, you see Thoma Bravo buying up all his hiring companies and right, so, and maybe they're trying to create something that could be competitive, but you're saying Trend Micros there, so. >> Well I think as Anne mentioned, the 30 year heritage, I think, of Trend Micro really driving this and I've done work with them in various past things. There's also a big part of just the people you like, the people that are good to work with, that are really trying to be customer obsessed, going back right, at an AWS event, the ones that get the cloud tend to be able to follow those Amazon LPs as well, right, just kind of naturally, and so I think when you look at the Trend Micros of the world, that's where that kind of cloud native piece comes out and I like working with that. >> In this environment, the macro environment, lets talk a bit, earning season, it's really mixed. I mean you're seeing some really good earnings, some mixed earnings, some good earnings with cautious guidance. So nobody really (indistinct), and it was for a period time there was a thinking that security was non-discretionary and it's clearly non-discretionary, but the CISO, she or he, doesn't have unlimited budgets, right? So what are you seeing in terms of how are customers dealing with this challenging macro environment? Is it through tools consolidation? Is that a play that's going on? What are you seeing in the customer base? >> Anne: I see ways, and we're working through this right now where we're actually weaving cybersecurity in at the very beginning of how we're designing offers across our entire offer portfolio, not just the cybersecurity business. So taking that approach in the long run will help contain costs and our hope, and we're already seeing it, is it's actually helping change the perception that security's that cost center and that final obstacle you have to get over and it's going to throw your margins off and all that sort of stuff. >> Dave: I like that, its at least is like a security cover charge. You're not getting in unless we do the security thing. >> Exactly, a security cover charge, that's what you should call it. >> Yeah. >> Like it. >> Another piece though, you mentioned earlier about making CISO's life easier, right? And I think, as Anne did a really absolutely true about building it in, not to the security stack but application developers, they want visibility they want observability, they want to do it right. They want CI/CD pipeline that can give them confidence in their security. So should the CISO have a budget issue, right? And they can't necessarily afford, but the application team as they're looking at what products they want to purchase, can I get a SaaS or a DaaS, right? The static or dynamic application security testing in my product up front and if the app team buys into that methodology, the CISO convinces them, yes, this is important. Now I've got two budgets to pull from, and in the end I end up with a cheaper, a lower cost of a service. So I think that's another way that we see with like DevSecOps and a few other services, that building in on day one that you mentioned. >> Lisa: Yeah. >> Getting both teams involved. >> Dave: That's interesting, Mike, because that's the alignment that you were talking about earlier in the KPIs and you're not a tech vendor saying, buy my product, you guys have deep consultancy backgrounds. >> Anne: And the customer appreciates that. >> Yeah. >> Anne: They see us as looking out for their best interest when we're trying to support them and help them and bringing it to the table at the very beginning as something that is there and we're conscientious of, just helps them in the long run and I think, they're seeing that, they appreciate that. >> Dave: Yeah, you can bring best practice around measurements, alignment, business process, stuff like that. Maybe even some industry expertise which you're not typically going to get from a product company. >> Well, one thing you just mentioned that I love talking about with Capgemini is the industry expertise, right? So when you look at systems integrators, there are a lot of really, really good ones. To say otherwise would be foolish. But Capgemini with our acquisition of Altran, a couple years ago, I think think it was, right? How many other GSIs or SIs are actually building silicon for IoT chips? So IoT's huge right now, the intelligent industry moving forward is going to drive a lot of those business outcomes that people are looking for. Who else can say we've built an autonomous vehicle, Capgemini can. Who can say that we've built the IoT devices from the ground up? We know not just how to integrate them into AWS, into the IoT services in the cloud, but to build and have that secure development for the firmware and all and that's where I think our customers really look to us as being those industry experts and being able to bring that totality of our business to bear for what they need to do to achieve their objectives to deliver to their customer. >> Dave: That's interesting. I mean, using silicon as a differentiator to drive a lot of business outcomes and security. >> Mike: Absolutely. >> I mean you see what Amazon's doing in silicon, Look at Apple. Look at what Tesla's doing with silicon. >> Dave: That's where you're seeing a lot of people start focusing 'cause not everybody can do it. >> Yeah. >> It's hard. >> Right. >> It's hard. >> And you'll see some interesting announcements from us and some interesting information and trends that we'll be driving because of where we're placed and what we have going around security and intelligent industry overall. We have a lot of investment going on there right now and again, from the partner perspective, it's an ecosystem of key partners that collectively work together to kind of create a seamless security posture for an intelligent industry initiative with these companies that we're working with. >> So last question, probably toughest question, and that's to give us a 30 second like elevator pitch or a billboard and I'm going to ask you, Anne, specifically about the SOC Essentials program powered by Trend Micro. Why should organizations look to that? >> Organizations should move to it or work with us on it because we have the expertise, we have the width and breadth to help them fill the gaps, be those eyes, be that team, the police behind it all, so to speak, and be the team behind them to make sure we're giving them the right information they need to actually act effectively on maintaining their security posture. >> Nice and then last question for you, Mike is that billboard, why should organizations in any industry work with Capgemini to help become an intelligent industrial player. >> Mike: Sure, so if you look at our board up top, right, we've got our tagline that says, "get the future you want." And that's what you're going to get with Capgemini. It's not just about selling a service, it's not just about what partners' right in reselling. We don't want that to be why you come to us. You, as a company have a vision and we will help you achieve that vision in a way that nobody else can because of our depth, because of the breadth that we have that's very hard to replicate. >> Awesome guys, that was great answers. Mike, Anne, thank you for spending some time with Dave and me on the program today talking about what's new with Capgemini. We'll be following this space. >> All right, thank you very much. >> For our guests and for Dave Vellante, I'm Lisa Martin, you're watching theCUBE, the leader in live enterprise and emerging tech coverage. (gentle light music)

>> Boston, Massachusetts. It's the queue covering W T G transformed 2019 Accio by Winslow Technology Group. >> Hi, I'm Stew Minutemen And this is the Cubes. Third year at W. T. G. Transform 2019 which is the Window Technologies Group, Their user conference. Longtime compelling customer, of course. Compelling, bought by Del del Body M. C. So it's now the deli emcee user event and to help me kick off a day of content where we're gonna be talking. Toa some of the W T G executive some of their customers and some of their partners is first time guests on the program. Joe Batista, Who's a Creek and easy chief creative, apologised at Del Technologies. Joe Appreciate you making it all the way in from the suburbs to come here to downtown Boston in the shadow of Fenway. >> It was a long haul this morning with no traffic of 5 30 35 minutes in. >> Yeah, a Zeiss safe for the people. Adele. It's about the distant from Boston Towe where we live as it is to go from Austin to Round Rock. So >> there we go, >> you know, similar types of things. So I have to start create apologised. A song. You know, I did a little bit of reading and, you know, having watched Aquino, it's, you know, sparking that creativity. So I love the idea of it. You've had this title for quite a while since before you Riddell just give us a little bit about background of you know what you do, and you know why you're qualified to do it. >> Well, it was quite a fight. It's a fun brand, but literally. It sits at the nexus of business and technology, and my job's simply is to help it re image the business, because now every company's a technology company. So what does that look like? So I get involved Also, it's a really cool problems, opportunities that customers are facing by re imaging it >> well, it's funny that you say re image, because when I did my history, the oldest thing I found some article from the nineties talking about somebody from Polaroid that that title and I was actually talking to some of the young people in the office there, like everybody's using Polaroids. There's these days, it's cool. It's true. They're doing it. So what's old is new again. You know everything come back together. So luckily, you know our industry. I mean, nothing changes, right? You know, it's the same now as it was 10 years ago, 100 years ago. You know, I'll just go into the factory and pumping things out now. >> Still, you know, I've been a referee a long time, and in the old days we had swim lengths, right? You know, you thought about certain vendors. They were in swim lanes. Now, today, with the influx of cash, as I was talking about, and the level of it of even innovation cycle time and how the industry's become more fragmented with lots of products, the complexity index has increased exponentially, and the velocity around that complexity is even more accelerate. So, no, it hasn't gotten easier. It's gotten more difficult. >> Yeah, fascinating. Actually. I just heard a segment on our national public radio station here in Boston talking about that. One of the biggest changes and how people think over the last few decades is we're better at recognizing patterns. Used to be, we could be an expert on something and do our thing, you know. We know the old trope is well, you know. My grandfather, you know, worked at a company for 30 years and did his same thing today. Things are changing constantly. You know, we didn't have, you know, the power of a supercomputer in our pocket, you know, 10 years ago, you know, let alone even older. So, you know, this is a user conference. So you know what air they did do. I mean, if if I understand, if I'm, you know, making a decision today for my business. And oftentimes that decision is something I need to live with for a while. How do I make sure that I'm making the right decisions That's going to keep me, you know, you know, keeping up with the competition and keeping my business moving forward as things constantly change. >> Yeah. So there is no easy answer to that question. There's a couple of thoughts and hasn't said in the presentation. You gotta look at these vectors that impacted trajectory of the thinking. And I love the Peter Drucker coat. Right. If he using yesterday's logic probably gonna get in trouble, you have to rethink the logic. In the example I gave you was the high jumper and how we did high jumping before and after 1960. So? So the question becomes one of those vectors, and I went through some of those vectors to help people think about, Okay, I do. My analysis on technology, that's all good. And, uh, tell technology you got a huge portfolio of technology. But how do you think about the perimeter? About how those things change over depreciation cycle. So is trying to add a little bit more color in there, thought processes. And I got a lot of post questions afterwards and a lot of engagements. So it seemed to resonate with the field. And I'll tell you what. The thing that they like the most was the business conversation off. They're like, you know, we don't do that enough. >> Yeah, right. I mean, you know, when we look at the successful companies today, it is not, You know, we've been talking for years, you know? Does it matter? Is it just a cost center? And it needs to be if it isn't helping the business drive forward and responding to what the business needs, uh, you know, could be replaced. That's where we got. Shadow it. It's It can't be the nowhere the slow needs to be. When the business says we need to go, you know, get on board and drive. I love one of the analogies you used is, you know, in this world of complexity, there's so many things out there, You know, when I've worked with, you know, enterprises and small cos you look at their environments and it's like, Oh my God, it's this Hedorah genius mess, you know? How do we standardize things? How do we make things easier? You had a fun little analogy talking about space. Maybe, maybe. >> Okay, that was good. I always try to use visuals as much as possible. So high, high, high light with challenges. So the challenge was, Oh, actually have it in my pocket. So they pulled this out and basically what it is. If you look at the international space shuttle, that's the only thing that they need to fix anything on Specialist 7/16 inch socket or the millimeter version of it. I can't read. Excited my glasses on to fix anything. So imagine if I had one tool to fix anything that's Nirvana. That's not reality. I have to fatigue. So I need to get to that simplicity. Its glasses law remember, every 25% increase in function shin is 100% increase in complexity. And that's public enemy number one for us. >> All right, so So you hopped on board the Dell family relatively recently, when most people think, Adele, it's well, you know, Delpy sees, you know, talk to my you know, my parents. They're like they know Dell computers. They've used them forever. You're talking most people, you know, Del servant. Like you talked a lot about your presentation software is eating the world. Give give us how you know where Del fits in that software was eating the world picture. >> Well, what I can tell you, though, is I was absolutely amazed when I did my due diligence about all the innovation that happens in this company. Phenomenal not only about the hardware but the soft. And I think actually, Jeff said it best. I think we have more software engineers now that we have heart hardware engineers. So the pivots there, we're pivoting our talent, the software, but it's the innovation that's in this company. And I think I kind of rattled off a couple of statistics by how much we spend the quantity of I p that we have. And I think customers are amazed at that innovation. But the supercharger on is okay. How does the innovation apply to the business mechanics of the company? And what value do you extract from it? And that's where the whole language and conversation usually happens with us. I will tell you, though, I'm really excited that Del Technologies kind of doubling down on business outcomes. They're really trying to change the culture and helping customers understand what the technology >> means. Yeah, one of things that struck me. I've been to this event now for a couple of years, and, you know, there's a lot of product discussion here, you know, when you get down to the channel, it's like, Okay, great. You know, I'm doing a server refresh. I'm looking to things like hyper convert, you know? What am I doing in my network? You know, when you up level things a little bit, You know, when I went to del World, it's like, you know, we hear about the venture, you know, activity that's happening around and things like coyote coming down the pipe. But How does that trickle down to the customers? That talking event here? It's great to talk about innovation, but, you know, I got to run my business. You know what? You know. Where does Del fit in that picture >> for you? Got it? Well, it's a custom you got to do both, right? So this has got to be a shift, because now I have to think differently, right? I know how to do feature analysis and benefit analysis of a point in time product, but what's the periphery of activities that inspecting, impacting that decision? Does that architectures scale? What are the economics around that? So you need to think about all those things. And I think it's just a journey for not only us as a vendor, but also for customers as well. >> Okay, so you're relatively new in today. L I want to ask you You gave a great quote in your presentation from from Jack Welch. Er said if the rate of change outside the company is greater than inside the company, the end is near. >> I would say the post. >> So, you know, explain to us the pace of change inside of del technologies. >> Well, you know, that's That's a That's a big question. I mean, piece of change varies by organization by business unit I really can't comment on your individual business units, but I will say, though there's a definite desire toe. Understand? We're customers interested. He is there. So what's the customer trying to dio? And then how do we satisfy the customer request? It's a matter of fact. I don't know if you know this and it was amazing because that's what the customer the other day, you know, Stevie Awards. Which a customer satisfaction, which we double down on customer satisfaction. We have a customer chief customer officer was Karen, and we just won 15 Stevie Awards, which is about customer satisfaction. So I think there's a slow shift, but there's a real focus on customer Central City. For us, the velocity will get there. But if you put the customer at the center like we do, that's a winning strategy. >> Yeah, well, yeah, we know Karen Kim does quite well, you know, culture and working with customers. You know, quite dio you talked about the portfolio of companies and l We know Del Bhumi quite well. We've done their event in the team were well, and you know, VM wears no slouch in the industry. I've had one of the pleasures of my careers. You know, I started working with him. Where when they were, like, 100 person company. No, watch them grow and pack. El Singer, I think was just named like the number one number two, you know, CEO work for employees by employees from glass doors. So, you know No, no slouch on the the venture family. So congratulations, toe Dale family on all that. >> Thank you very much are exciting. >> Joe Batista. Thank you so much for joining me here at the W T. G. Transformed 2019. Pleasure to catch up with you. Appreciate the opportunity. All right, so we're here with customers, the executives, and digging into all the industry trends. Of course. Check out the cute dot net for where we will be. And, uh, I think it was always for watching the cube

>> Narrator: Live from Orlando, Florida. It's theCUBE. Covering Accelerate 19. Brought to you by Fortinet. >> Welcome back to theCUBE. We are live in Orlando, Florida for Fortinet Accelerate 2019. I'm Lisa Martin with my co-host for the day Peter Burroughs. Peter and I are pleased to welcome back to theCUBE Patrice Perche, the senior executive vice president of world wide sales and support from Fortinet. Patrice, it's a pleasure to have you, fresh from the Keynote Stage, here on theCUBE program. >> Yeah, well thank you for inviting me here. It's a great opportunity. >> So lots of people this morning and an energy infused keynote starting from all this loud music that I loved and helped wake me up, so thanks to your events team for that. About 4000 attendees, >> Patrice: Yeah. >> From 40 countries. You have a ton of partners here. You can hear a lot of the noise behind us. I'd like you to share with our audience the connection that you made in your keynote about what you guys shared last Accelerate 2018 and the connection to the world economic forum from just a few months ago. >> Okay well, the last year we definitely in fact exposed our strategy in term of the product, in term of the go to market and of course how we can increase in fact the value proposition to our customer, it was all about the fabric and the eco-system that we build around the fabric. So we have been of course since now 12 months working hard on expanding and growing what I call the Phase two of the fabric and when Cain went to Davos which the World Economic Forum is held early in January. And when we got all this, I would say a vision from Klaus Schwab which is the Chairman and Founder of the World Economic Forum. Explaining that the false revolution that we are all going through. The Cyber Security its a massive, I would say problem for them and it will be a key point for the future because they will enable, in fact, most of those take technology and use it that we will go for this revolution, so >> Peter: It's intrinsic. >> It's intrinsic, they call it guardian. So it really is something that if we can't, fix this problem, it's all about digital trust. So none of the user, you, myself we will not trust maybe voting system, or you cannot trust. We know that everything is going digital. And they expressed the need for of course the education, because you need to educate and you need to, increase the skilled people especially with cyber-security as we have a huge shortfall about 1.5million, some say even two million for next year. They need also need to work as an eco-system. So, for them the eco-system is really to see public, private collaboration but also Government, technologies, companies like us. And that's in fact the purpose of Davos. To bring all this different, in fact groups and be able to talk and share and define some line for the future. And for us of-course, the concept of the eco-system is all about building around in fact this major problem that we are facing, has all the traits. In fact, a collective approach where everybody can add value. We as a vendor we build technology, we build a lot of value but we can't be with each of the customers. So and we want to build a partnership not only with the partner but also with the customer because cyber security is a real time problem. So when something happen, you need to jump, and you need to make sure that all the line is set and then everybody can work together to fix the problem. So this eco-system really resonates value well for us after we was talking last year at the Accelerate. And the last I will say pillar for the Economic Forum is about of course Education and clearly I was mentioning one of the Engineers from the SERP which is nuclear agency in Europe Januity she said that 3D, of course the problem is that with these robots we will have a lot of jobs that will be omitted. So they talk about 800 million. So it's a massive number but it sees more than an opportunity to up-skill people. So the education is really helping of course especially the young generation to go and to up-skill it and especially on the cyber security. Because, as everything is going digital, we have to secure everything so it's really, these pieces will grow much beyond that what we think today. Those three pillars are the Education, the Eco-system and of course the Technology. And the good news is that Ken was representing in fact cyber security at the Davos, so it was also a great moment for us to see in fact pushing 14 at that stage of level of discussion so. Those three pillars: Education, Technology and Eco-system of course fit very well with our strategy that we build and that's why I decided to share a bit this morning. It's not everybody going to such a place. And what really resonate well in term of the strategy and the vision we are in fact pursuing so that's what we are doing. >> So I want to build on something that you said to do so I want to paraphrase. Peter who is much smarter than I am, Peter Drucker who observed many years ago there's a difference in strategies between what he called: value in exchange was his presumption that what I am selling is valuable. And value in utility which is a presumption that the value stems from how the something is used. And that notion of partnership that Fortinet has put in place with its customers so they can get value in utility is so crucially important. And you talked a bit this morning about you know the different levels of customization, and how you are going to allow customers to engage you and apply technology to suit their business. Could you take a bit about that especially based on your experience in the field? >> Yeah so as I mention I think we, it has been also our sales strategy from day one. So we always consider that in order to succeed, we need to work through the partners and through the people that are very close to our customer. And as technology evolves of course it's a real challenge to keep them at the level. Even for us, internally, we used to understand and be always at the top level about the new technology that we are putting in place. We imagine that, just for the employees, it's a challenge so we do a lot of training. But then for the partner it's another challenge. So, I think we have been always trying to help them to of course evolving on this expertise, but we don't see that the cloud of course there is a certain I will say a trend about Okay lets go Direct because we don't. The cloud can allow to sell direct to the customer so you don't need those channels. So there's no value for them. We don't see that in the cyber security because it's a much more complex environment and I think that is why we have been successful. We even see some of our competitor, they tempted to go on this direction. I think it's maybe one of the challenges we'll face in the future. So for us they key message I was trying to give this morning to the partner is really that we count on them, it's a partnership, it's very important and of course when we adapt our partner program we want to learn from them to make sure that the three pillars of this program will fit well with their, in fact, view because of course we are from a vendor in certain perspective, they have of course a different perspective on their side. What I was mentioning the goal to market because we see that some are very specialized on the cloud, some are specialized on the premise. We have to define what is the go to market here. What kind of expertise there are in fact having because as you can see we have very broad product of frame cover, you know from almost everything, so from OT, IT, even imbedded. So, we are working with partners that are, as an example, on the connected car. That's for maybe the next two years where we will secure those car. They are not the typical or traditional partner that you see on the networking business so, we try to adapt in fact our engagement with them and make sure that in fact we build a value proposition that can fit in fact for the customer requirement. So it's really about be very close and try to have a bit of a-la-carte kind of approach. And not try to enforce a very historical view that we had in fact, to be honest. But okay, you have like three tiers, depending of level of business and then you sign. So it's really moving away from that. >> I want to stay in this notion of partners because I think it's so crucially important. You talked about the skills that they have the capabilities that they have, but your partners in general are amongst the companies that have to learn the most about cyber security. Because they are the ones that are trying to match technology to the outcomes the customers have. That leads to a question about your education programs. I got to believe that there is, that even as you're trying to educate your enterprise customers, you're also really investing in your enterprise, in upgrading and up scaling your partnership. Talk a bit about the relationship of education and Eco-system from a very practical standpoint. >> Yeah so it's a very good point because of course we need to help them to evolve and as an example we have seen traditional IT partner, interested to evolve on this OT security but they didn't have too much skill so it was new for them. So we see the purpose of building this NSC training vocation course which we have eight level, which started in 2015 and we have about now almost 200 000 certified engineers. A very large portion of those Engineers come from partners. So, on this program, in fact the origin of the program, depending on where you want to play. If it's cloud, you will have to go for NSC4. If you want to go beyond a OT, it can be NSC5. We build in fact this expertise and we ask them also to of course follow those course so the engagement with us, the requirement will be also that they have the right certified Engineer. Depending on level of engagement they want to work with us. So we build this course which is a lot of work and we have a lot of, a big team to make this happen. We have to refresh constantly because it's evolving almost every day. But as for this, the great value, you may have seen it pass on the networking sites. Cisco made a bit similar approach, which was very successful. I think we went like three years ago on thinking about this and that's what we are achieving right now and we are in fact the most I will say advanced in such a report and I think it's all NSC certification is becoming a bit stand out in the market. Both from end-user but the partner. And even going as we was mentioning, we are also working with the Academics to build in fact and train in fact new Engineers that will come in the market in the next two to three years. So we help them on, it's not pure about product, it's really about cyber security expertise that we have and of course we help them on understanding a bit how the Fortinet value can deploy on the customer, so that aspect and we try to target of course young I will say people going for university but also veterans who we had program to bring those veterans because they're also looking we are talking about up skilling. That's a perfect example on bringing a change to them. And I think it's high level, maybe it's a bit, you know think that we have a high potential. But we want of course to help on resolving the overall challenge to be unemployed. I can tell you that if you invest time and you get a certification on cyber security you will never have any problems with a job. So that's a bit the overall idea we have behind this education and certification. And truly the partner, I will say, evolution in terms of their expertise, it's based about this NSC. >> Alright Patrice as we kind of get towards the end here, let's talk about outcomes. Peter mentioned that word, I know that when I was looking at my notes here that in Q4 of 2018, service providers, and managed security service providers represented 11 of the top 25 deals. You guys also closed a massive seven figure deal in Europe. Let's talk about outcomes that Fortinet and your partner eco-system are helping businesses achieve at the business level. Not just in terms of obviously improving security but are you helping businesses generate new revenue streams, skip to new products on market faster, identify attacks and become pro active? What is one of those really key outcomes that you are proud of? >> I think the, and I was part of the presentation last year. We all, I would say on this digital transformation journey. Whole company, even us. We're evolving with much more tools, much automation. So I think every sector, whether it's public or private company, has to go for this evolution. The biggest challenge is all about digital so again the blocking point is about security. So last year we explained about how we can help with positioning our platform in the security fabric to run to this obstacle and that was the purpose of the security transformation that we was talking last year. And I know it's some, even complete as relayed a message, it was interesting that it was part of all. So I think it was really trying to unlock this digital transformation that add business benefit because at the end this whole, those company will evolve in the future, generate more profit, be more efficient, leverage, I would say, the data that they are collecting from almost everywhere. From customer but also the sensor, and transform this to a more business intelligence and then I will say generate in fact future revenue and future dollars from that. That has been a bit the idea behind. So we definitely help on evolving and going through this digital transformation journey. I think we had few example of course as one of our customers, they deploying world wide on their gas-station. A better customer journey. Typically of course you calm, you try to make your gas and you want to be connected. And they try to increase of course by upselling a lot of things. So of course you have your go for the coffee machine, you can even buy many goods. We have been deploying you know, with secure access so they have secure access they go for the internet so that's where we play with segmentation. But our wireless which is fully connected also to the Fortigate and the analytic tools allow them to do business intelligence in term of where people are moving inside you know the shops. And then, you know, redesign and rethink about okay, how they move here. So that's, that allow them to accelerate even more business or decide that maybe this spot doesn't work well, so they push that to the side and they evolve. That's depicted the value of all this intelligence and we can grasp from the data that we collect to transform to a business value. >> So I want to make one comment before we close here and that is that I don't know the degree to which people really understand the relationship between secure networking and digital business. Data is an interesting asset. It could be shared, it could be copied, it could be easily corrupted. In many respects over the next five years we believe that people will recognize that network security is the basis for privatizing data. It is what you do to turn data into an asset. I don't think people have made that connection, to the degree that they need to. >> No I agree, I agree because maybe the mindset they think about network, they think about wired. In fact we are talking about 5G, we are talking about Wireless so the data is that what we want to protect because we don't want that people stole your personal information or even company. >> It's more than protect. You want to create the asset. >> Yes, we integrate the asset. And then of course when we talk about network it's no longer wires, of course it's much more virtual I would say networks. And that's the misinterpretation and why they feel okay the network is moving away. No, it's even more in the future. And as Cain mentioned early this morning: I think the edge will become much more important in future because the compute power that we are having now on every device and even. That will in fact allow to of course generate much more data. Yeah and you need to protect. You notice when you need to go and to consolidate this into the cloud. So it's really, the age will become a very important aspect. But this will be a hybrid and that's what we feel as Fortinet we've been building in fact the very comprehensive offer and to the partner and to our customer. We just want, in fact to give them the time to move at their pace. But they have everything ready for today. That's a bit the concept. >> Well if only we had more time Patrice, we could keep going and going. Thank you so much for sharing some time on the program today talking about your GTM, what you are doing to educate partners and customers and this tremendous potential that Fortinet is attacking. We appreciate your time. >> Thank you very much, I appreciate it too. >> We want to thank you for watching for Peter Burroughs, I'm Lisa Martin. You're watching theCUBE. (upbeat music)

>> Hi I'm Peter Burris. Welcome to another Wikibon Action Item. (upbeat music) We're once again broadcasting from theCube's beautiful Palo Alto, California studio. I'm joined here in the studio by George Gilbert and David Floyer. And then remotely, we have Jim Kobielus, David Vellante, Neil Raden and Ralph Finos. Hi guys. >> Hey. >> Hi >> How you all doing? >> This is a great, great group of people to talk about the topic we're going to talk about, guys. We're going to talk about the notion of de-risking digital business. Now, the reason why this becomes interesting is, the Wikibon perspective for quite some time has been that the difference between business and digital business is the role that data assets play in a digital business. Now, if you think about what that means. Every business institutionalizes its work around what it regards as its most important assets. A bottling company, for example, organizes around the bottling plant. A financial services company organizes around the regulatory impacts or limitations on how they share information and what is regarded as fair use of data and other resources, and assets. The same thing exists in a digital business. There's a difference between, say, Sears and Walmart. Walmart mades use of data differently than Sears. And that specific assets that are employed and had a significant impact on how the retail business was structured. Along comes Amazon, which is even deeper in the use of data as a basis for how it conducts its business and Amazon is institutionalizing work in quite different ways and has been incredibly successful. We could go on and on and on with a number of different examples of this, and we'll get into that. But what it means ultimately is that the tie between data and what is regarded as valuable in the business is becoming increasingly clear, even if it's not perfect. And so traditional approaches to de-risking data, through backup and restore, now needs to be re-thought so that it's not just de-risking the data, it's de-risking the data assets. And, since those data assets are so central to the business operations of many of these digital businesses, what it means to de-risk the whole business. So, David Vellante, give us a starting point. How should folks think about this different approach to envisioning business? And digital business, and the notion of risk? >> Okay thanks Peter, I mean I agree with a lot of what you just said and I want to pick up on that. I see the future of digital business as really built around data sort of agreeing with you, building on what you just said. Really where organizations are putting data at the core and increasingly I believe that organizations that have traditionally relied on human expertise as the primary differentiator, will be disrupted by companies where data is the fundamental value driver and I think there are some examples of that and I'm sure we'll talk about it. And in this new world humans have expertise that leverage the organization's data model and create value from that data with augmented machine intelligence. I'm not crazy about the term artificial intelligence. And you hear a lot about data-driven companies and I think such companies are going to have a technology foundation that is increasingly described as autonomous, aware, anticipatory, and importantly in the context of today's discussion, self-healing. So able to withstand failures and recover very quickly. So de-risking a digital business is going to require new ways of thinking about data protection and security and privacy. Specifically as it relates to data protection, I think it's going to be a fundamental component of the so-called data-driven company's technology fabric. This can be designed into applications, into data stores, into file systems, into middleware, and into infrastructure, as code. And many technology companies are going to try to attack this problem from a lot of different angles. Trying to infuse machine intelligence into the hardware, software and automated processes. And the premise is that meaty companies will architect their technology foundations, not as a set of remote cloud services that they're calling, but rather as a ubiquitous set of functional capabilities that largely mimic a range of human activities. Including storing, backing up, and virtually instantaneous recovery from failure. >> So let me build on that. So what you're kind of saying if I can summarize, and we'll get into whether or not it's human expertise or some other approach or notion of business. But you're saying that increasingly patterns in the data are going to have absolute consequential impacts on how a business ultimately behaves. We got that right? >> Yeah absolutely. And how you construct that data model, and provide access to the data model, is going to be a fundamental determinant of success. >> Neil Raden, does that mean that people are no longer important? >> Well no, no I wouldn't say that at all. I'm talking with the head of a medical school a couple of weeks ago, and he said something that really resonated. He said that there're as many doctors who graduated at the bottom of their class as the top of their class. And I think that's true of organizations too. You know what, 20 years ago I had the privilege of interviewing Peter Drucker for an hour and he foresaw this, 20 years ago, he said that people who run companies have traditionally had IT departments that provided operational data but they needed to start to figure out how to get value from that data and not only get value from that data but get value from data outside the company, not just internal data. So he kind of saw this big data thing happening 20 years ago. Unfortunately, he had a prejudice for senior executives. You know, he never really thought about any other people in an organization except the highest people. And I think what we're talking about here is really the whole organization. I think that, I have some concerns about the ability of organizations to really implement this without a lot of fumbles. I mean it's fine to talk about the five digital giants but there's a lot of companies out there that, you know the bar isn't really that high for them to stay in business. And they just seem to get along. And I think if we're going to de-risk we really need to help companies understand the whole process of transformation, not just the technology. >> Well, take us through it. What is this process of transformation? That includes the role of technology but is bigger than the role of technology. >> Well, it's like anything else, right. There has to be communication, there has to be some element of control, there has to be a lot of flexibility and most importantly I think there has to be acceptability by the people who are going to be affected by it, that is the right thing to do. And I would say you start with assumptions, I call it assumption analysis, in other words let's all get together and figure out what our assumptions are, and see if we can't line em up. Typically IT is not good at this. So I think it's going to require the help of a lot of practitioners who can guide them. >> So Dave Vellante, reconcile one point that you made I want to come back to this notion of how we're moving from businesses built on expertise and people to businesses built on expertise resident as patterns in the data, or data models. Why is it that the most valuable companies in the world seem to be the ones that have the most real hardcore data scientists. Isn't that expertise and people? >> Yeah it is, and I think it's worth pointing out. Look, the stock market is volatile, but right now the top-five companies: Apple, Amazon, Google, Facebook and Microsoft, in terms of market cap, account for about $3.5 trillion and there's a big distance between them, and they've clearly surpassed the big banks and the oil companies. Now again, that could change, but I believe that it's because they are data-driven. So called data-driven. Does that mean they don't need humans? No, but human expertise surrounds the data as opposed to most companies, human expertise is at the center and the data lives in silos and I think it's very hard to protect data, and leverage data, that lives in silos. >> Yes, so here's where I'll take exception to that, Dave. And I want to get everybody to build on top of this just very quickly. I think that human expertise has surrounded, in other businesses, the buildings. Or, the bottling plant. Or, the wealth management. Or, the platoon. So I think that the organization of assets has always been the determining factor of how a business behaves and we institutionalized work, in other words where we put people, based on the business' understanding of assets. Do you disagree with that? Is that, are we wrong in that regard? I think data scientists are an example of reinstitutionalizing work around a very core asset in this case, data. >> Yeah, you're saying that the most valuable asset is shifting from some of those physical assets, the bottling plant et cetera, to data. >> Yeah we are, we are. Absolutely. Alright, David Foyer. >> Neil: I'd like to come in. >> Panelist: I agree with that too. >> Okay, go ahead Neil. >> I'd like to give an example from the news. Cigna's acquisition of Express Scripts for $67 billion. Who the hell is Cigna, right? Connecticut General is just a sleepy life insurance company and INA was a second-tier property and casualty company. They merged a long time ago, they got into health insurance and suddenly, who's Express Scripts? I mean that's a company that nobody ever even heard of. They're a pharmacy benefit manager, what is that? They're an information management company, period. That's all they do. >> David Foyer, what does this mean from a technology standpoint? >> So I wanted to to emphasize one thing that evolution has always taught us. That you have to be able to come from where you are. You have to be able to evolve from where you are and take the assets that you have. And the assets that people have are their current systems of records, other things like that. They must be able to evolve into the future to better utilize what those systems are. And the other thing I would like to say-- >> Let me give you an example just to interrupt you, because this is a very important point. One of the primary reasons why the telecommunications companies, whom so many people believed, analysts believed, had this fundamental advantage, because so much information's flowing through them is when you're writing assets off for 30 years, that kind of locks you into an operational mode, doesn't it? >> Exactly. And the other thing I want to emphasize is that the most important thing is sources of data not the data itself. So for example, real-time data is very very important. So what is your source of your real-time data? If you've given that away to Google or your IOT vendor you have made a fundamental strategic mistake. So understanding the sources of data, making sure that you have access to that data, is going to enable you to be able to build the sort of processes and data digitalization. >> So let's turn that concept into kind of a Geoffrey Moore kind of strategy bromide. At the end of the day you look at your value proposition and then what activities are central to that value proposition and what data is thrown off by those activities and what data's required by those activities. >> Right, both internal-- >> We got that right? >> Yeah. Both internal and external data. What are those sources that you require? Yes, that's exactly right. And then you need to put together a plan which takes you from where you are, as the sources of data and then focuses on how you can use that data to either improve revenue or to reduce costs, or a combination of those two things, as a series of specific exercises. And in particular, using that data to automate in real-time as much as possible. That to me is the fundamental requirement to actually be able to do this and make money from it. If you look at every example, it's all real-time. It's real-time bidding at Google, it's real-time allocation of resources by Uber. That is where people need to focus on. So it's those steps, practical steps, that organizations need to take that I think we should be giving a lot of focus on. >> You mention Uber. David Vellante, we're just not talking about the, once again, talking about the Uberization of things, are we? Or is that what we mean here? So, what we'll do is we'll turn the conversation very quickly over to you George. And there are existing today a number of different domains where we're starting to see a new emphasis on how we start pricing some of this risk. Because when we think about de-risking as it relates to data give us an example of one. >> Well we were talking earlier, in financial services risk itself is priced just the way time is priced in terms of what premium you'll pay in terms of interest rates. But there's also something that's softer that's come into much more widely-held consciousness recently which is reputational risk. Which is different from operational risk. Reputational risk is about, are you a trusted steward for data? Some of that could be personal information and a use case that's very prominent now with the European GDPR regulation is, you know, if I ask you as a consumer or an individual to erase my data, can you say with extreme confidence that you have? That's just one example. >> Well I'll give you a specific number on that. We've mentioned it here on Action Item before. I had a conversation with a Chief Privacy Officer a few months ago who told me that they had priced out what the fines to Equifax would have been had the problem occurred after GDPR fines were enacted. It was $160 billion, was the estimate. There's not a lot of companies on the planet that could deal with $160 billion liability. Like that. >> Okay, so we have a price now that might have been kind of, sort of mushy before. And the notion of trust hasn't really changed over time what's changed is the technical implementations that support it. And in the old world with systems of record we basically collected from our operational applications as much data as we could put it in the data warehouse and it's data marked satellites. And we try to govern it within that perimeter. But now we know that data basically originates and goes just about anywhere. There's no well-defined perimeter. It's much more porous, far more distributed. You might think of it as a distributed data fabric and the only way you can be a trusted steward of that is if you now, across the silos, without trying to centralize all the data that's in silos or across them, you can enforce, who's allowed to access it, what they're allowed to do, audit who's done what to what type of data, when and where? And then there's a variety of approaches. Just to pick two, one is where it's discovery-oriented to figure out what's going on with the data estate. Using machine learning this is, Alation is an example. And then there's another example, which is where you try and get everyone to plug into what's essentially a new system catalog. That's not in a in a deviant mesh but that acts like the fabric for your data fabric, deviant mesh. >> That's an example of another, one of the properties of looking at coming at this. But when we think, Dave Vellante coming back to you for a second. When we think about the conversation there's been a lot of presumption or a lot of bromide. Analysts like to talk about, don't get Uberized. We're not just talking about getting Uberized. We're talking about something a little bit different aren't we? >> Well yeah, absolutely. I think Uber's going to get Uberized, personally. But I think there's a lot of evidence, I mentioned the big five, but if you look at Spotify, Waze, AirbnB, yes Uber, yes Twitter, Netflix, Bitcoin is an example, 23andme. These are all examples of companies that, I'll go back to what I said before, are putting data at the core and building humans expertise around that core to leverage that expertise. And I think it's easy to sit back, for some companies to sit back and say, "Well I'm going to wait and see what happens." But to me anyway, there's a big gap between kind of the haves and the have-nots. And I think that, that gap is around applying machine intelligence to data and applying cloud economics. Zero marginal economics and API economy. An always-on sort of mentality, et cetera et cetera. And that's what the economy, in my view anyway, is going to look like in the future. >> So let me put out a challenge, Jim I'm going to come to you in a second, very quickly on some of the things that start looking like data assets. But today, when we talk about data protection we're talking about simply a whole bunch of applications and a whole bunch of devices. Just spinning that data off, so we have it at a third site. And then we can, and it takes to someone in real-time, and then if there's a catastrophe or we have, you know, large or small, being able to restore it often in hours or days. So we're talking about an improvement on RPO and RTO but when we talk about data assets, and I'm going to come to you in a second with that David Floyer, but when we talk about data assets, we're talking about, not only the data, the bits. We're talking about the relationships and the organization, and the metadata, as being a key element of that. So David, I'm sorry Jim Kobielus, just really quickly, thirty seconds. Models, what do they look like? What are the new nature of some of these assets look like? >> Well the new nature of these assets are the machine learning models that are driving so many business processes right now. And so really the core assets there are the data obviously from which they are developed, and also from which they are trained. But also very much the knowledge of the data scientists and engineers who build and tune this stuff. And so really, what you need to do is, you need to protect that knowledge and grow that knowledge base of data science professionals in your organization, in a way that builds on it. And hopefully you keep the smartest people in house. And they can encode more of their knowledge in automated programs to manage the entire pipeline of development. >> We're not talking about files. We're not even talking about databases, are we David Floyer? We're talking about something different. Algorithms and models are today's technology's really really set up to do a good job of protecting the full organization of those data assets. >> I would say that they're not even being thought about yet. And going back on what Jim was saying, Those data scientists are the only people who understand that in the same way as in the year 2000, the COBOL programmers were the only people who understood what was going on inside those applications. And we as an industry have to allow organizations to be able to protect the assets inside their applications and use AI if you like to actually understand what is in those applications and how are they working? And I think that's an incredibly important de-risking is ensuring that you're not dependent on a few experts who could leave at any moment, in the same way as COBOL programmers could have left. >> But it's not just the data, and it's not just the metadata, it really is the data structure. >> It is the model. Just the whole way that this has been put together and the reason why. And the ability to continue to upgrade that and change that over time. So those assets are incredibly important but at the moment there is no way that you can, there isn't technology available for you to actually protect those assets. >> So if I combine what you just said with what Neil Raden was talking about, David Vallante's put forward a good vision of what's required. Neil Raden's made the observation that this is going to be much more than technology. There's a lot of change, not change management at a low level inside the IT, but business change and the technology companies also have to step up and be able to support this. We're seeing this, we're seeing a number of different vendor types start to enter into this space. Certainly storage guys, Dylon Sears talking about doing a better job of data protection we're seeing middleware companies, TIBCO and DISCO, talk about doing this differently. We're seeing file systems, Scality, WekaIO talk about doing this differently. Backup and restore companies, Veeam, Veritas. I mean, everybody's looking at this and they're all coming at it. Just really quickly David, where's the inside track at this point? >> For me it is so much whitespace as to be unbelievable. >> So nobody has an inside track yet. >> Nobody has an inside track. Just to start with a few things. It's clear that you should keep data where it is. The cost of moving data around an organization from inside to out, is crazy. >> So companies that keep data in place, or technologies to keep data in place, are going to have an advantage. >> Much, much, much greater advantage. Sure, there must be backups somewhere. But you need to keep the working copies of data where they are because it's the real-time access, usually that's important. So if it originates in the cloud, keep it in the cloud. If it originates in a data-provider, on another cloud, that's where you should keep it. If it originates on your premise, keep it where it originated. >> Unless you need to combine it. But that's a new origination point. >> Then you're taking subsets of that data and then combining that up for itself. So that would be my first point. So organizations are going to need to put together what George was talking about, this metadata of all the data, how it interconnects, how it's being used. The flow of data through the organization, it's amazing to me that when you go to an IT shop they cannot define for you how the data flows through that data center or that organization. That's the requirement that you have to have and AI is going to be part of that solution, of looking at all of the applications and the data and telling you where it's going and how it's working together. >> So the second thing would be companies that are able to build or conceive of networks as data. Will also have an advantage. And I think I'd add a third one. Companies that demonstrate perennial observations, a real understanding of the unbelievable change that's required you can't just say, oh Facebook wants this therefore everybody's going to want it. There's going to be a lot of push marketing that goes on at the technology side. Alright so let's get to some Action Items. David Vellante, I'll start with you. Action Item. >> Well the future's going to be one where systems see, they talk, they sense, they recognize, they control, they optimize. It may be tempting to say, you know what I'm going to wait, I'm going to sit back and wait to figure out how I'm going to close that machine intelligence gap. I think that's a mistake. I think you have to start now, and you have to start with your data model. >> George Gilbert, Action Item. >> I think you have to keep in mind the guardrails related to governance, and trust, when you're building applications on the new data fabric. And you can take the approach of a platform-oriented one where you're plugging into an API, like Apache Atlas, that Hortonworks is driving, or a discovery-oriented one as David was talking about which would be something like Alation, using machine learning. But if, let's say the use case starts out as an IOT, edge analytics and cloud inferencing, that data science pipeline itself has to now be part of this fabric. Including the output of the design time. Meaning the models themselves, so they can be managed. >> Excellent. Jim Kobielus, you've been pretty quiet but I know you've got a lot to offer. Action Item, Jim. >> I'll be very brief. What you need to do is protect your data science knowledge base. That's the way to de-risk this entire process. And that involves more than just a data catalog. You need a data science expertise registry within your distributed value chain. And you need to manage that as a very human asset that needs to grow. That is your number one asset going forward. >> Ralph Finos, you've also been pretty quiet. Action Item, Ralph. >> Yeah, I think you've got to be careful about what you're trying to get done. Whether it's, it depends on your industry, whether it's finance or whether it's the entertainment business, there are different requirements about data in those different environments. And you need to be cautious about that and you need leadership on the executive business side of things. The last thing in the world you want to do is depend on data scientists to figure this stuff out. >> And I'll give you the second to last answer or Action Item. Neil Raden, Action Item. >> I think there's been a lot of progress lately in creating tools for data scientists to be more efficient and they need to be, because the big digital giants are draining them from other companies. So that's very encouraging. But in general I think becoming a data-driven, a digital transformation company for most companies, is a big job and I think they need to it in piece parts because if they try to do it all at once they're going to be in trouble. >> Alright, so that's great conversation guys. Oh, David Floyer, Action Item. David's looking at me saying, ah what about me? David Floyer, Action Item. >> (laughing) So my Action Item comes from an Irish proverb. Which if you ask for directions they will always answer you, "I wouldn't start from here." So the Action Item that I have is, if somebody is coming in saying you have to re-do all of your applications and re-write them from scratch, and start in a completely different direction, that is going to be a 20-year job and you're not going to ever get it done. So you have to start from what you have. The digital assets that you have, and you have to focus on improving those with additional applications, additional data using that as the foundation for how you build that business with a clear long-term view. And if you look at some of the examples that were given early, particularly in the insurance industries, that's what they did. >> Thank you very much guys. So, let's do an overall Action Item. We've been talking today about the challenges of de-risking digital business which ties directly to the overall understanding of the role of data assets play in businesses and the technology's ability to move from just protecting data, restoring data, to actually restoring the relationships in the data, the structures of the data and very importantly the models that are resident in the data. This is going to be a significant journey. There's clear evidence that this is driving a new valuation within the business. Folks talk about data as the new oil. We don't necessarily see things that way because data, quite frankly, is a very very different kind of asset. The cost could be shared because it doesn't suffer the same limits on scarcity. So as a consequence, what has to happen is, you have to start with where you are. What is your current value proposition? And what data do you have in support of that value proposition? And then whiteboard it, clean slate it and say, what data would we like to have in support of the activities that we perform? Figure out what those gaps are. Find ways to get access to that data through piecemeal, piece-part investments. That provide a roadmap of priorities looking forward. Out of that will come a better understanding of the fundamental data assets that are being created. New models of how you engage customers. New models of how operations works in the shop floor. New models of how financial services are being employed and utilized. And use that as a basis for then starting to put forward plans for bringing technologies in, that are capable of not just supporting the data and protecting the data but protecting the overall organization of data in the form of these models, in the form of these relationships, so that the business can, as it creates these, as it throws off these new assets, treat them as the special resource that the business requires. Once that is in place, we'll start seeing businesses more successfully reorganize, reinstitutionalize the work around data, and it won't just be the big technology companies who have, who people call digital native, that are well down this path. I want to thank George Gilbert, David Floyer here in the studio with me. David Vellante, Ralph Finos, Neil Raden and Jim Kobelius on the phone. Thanks very much guys. Great conversation. And that's been another Wikibon Action Item. (upbeat music)

(upbeat music) >> Announcer: Live from San Jose, it's theCUBE presenting Big Data Silicon Valley brought to you by SiliconANGLE Media and its ecosystem partner. >> What am I going to spend time, next 15, 20 minutes or so, talking about. I'm going to answer three things. Our research has gone deep into where are we now in the big data community. I'm sorry, where is the big data community going, number one. Number two is how are we going to get there and number three, what do the numbers say about where we are? So those are the three things. Now, since when we want to get out of here, I'm going to fly through some of these slides but again there's a lot of opportunity for additional conversation because we're all about having conversations with the community. So let's start here. The first thing to know, when we think about where this is all going is it has to be bound. It's inextricably bound up with digital transformation. Well, what is digital transformation? We've done a lot of research on this. This is Peter Drucker who famously said many years ago, that the purpose of a business is to create and keep a customer. That's what a business is. Now what's the difference between a business and a digital business? What's the business between Sears Roebuck, or what's the difference between Sears Roebuck and Amazon? It's data. A digital business uses data as an asset to create and keep customers. It infuses data and operations differently to create more automation. It infuses data and engagement differently to catalyze superior customer experiences. It reformats and restructures its concept of value proposition and product to move from a product to a services orientation. The role of data is the centerpiece of digital business transformation and in many respects that is where we're going, is an understanding and appreciation of that. Now, we think there's going to be a number of strategic capabilities that will have to be built out to make that possible. First off, we have to start thinking about what it means to put data to work. The whole notion of an asset is an asset is something that can be applied to a productive activity. Data can be applied to a productive activity. Now, there's a lot of very interesting implications that we won't get into now, but essentially if we're going to treat data as an asset and think about how we could put more data to work, we're going to focus on three core strategic capabilities about how to make that possible. One, we need to build a capability for collecting and capturing data. That's a lot of what IoT is about. It's a lot of what mobile computing is about. There's going to be a lot of implications around how to ethically and properly do some of those things but a lot of that investment is about finding better and superior ways to capture data. Two, once we are able to capture that data, we have to turn it into value. That in many respects is the essence of big data. How we turn data into data assets, in the form of models, in the form of insights, in the form of any number of other approaches to thinking about how we're going to appropriate value out of data. But it's not just enough to create value out of it and have it sit there as potential value. We have to turn it into kinetic value, to actually do the work with it and that is the last piece. We have to build new capabilities for how we're going to apply data to perform work better, to enact based on data. Now, we've got a concept we're researching now that we call systems of agency, which is the idea that there's going to be a lot of new approaches, new systems with a lot of intelligence and a lot of data that act on behalf of the brand. I'm not going to spend a lot of time going into this but remember that word because I will come back to it. Systems of agency is about how you're going to apply data to perform work with automation, augmentation, and actuation on behalf of your brand. Now, all this is going to happen against the backdrop of cloud optimization. I'll explain what we mean by that right now. Very importantly, increasingly how you create value out of data, how you create future options on the value of your data is going to drive your technology choices. For the first 10 years of the cloud, the presumption is all data was going to go to the cloud. We think that a better way of thinking about it is how is the cloud experience going to come to the data. We've done a lot of research on the cost of data movement and both in terms of the actual out-of-pocket costs but also the potential uncertainty, the transaction costs, etc, associated with data movement. And that's going to be one of the fundamental pieces or elements of how we think about the future of big data and how digital business works, is what we think about data movement. I'll come to that in a bit. But our proposition is increasingly, we're going to see architectural approaches that focus on how we're going to move the cloud experience to the data. We've got this notion of true private cloud which is effectively the idea of the cloud experience on or near premise. That doesn't diminish the role that the cloud's going to play on industry or doesn't say that Amazon and AWS and Microsoft Azure and all the other options are not important. They're crucially important but it means we have to start thinking architecturally about how we're going to create value of data out of data and recognize that means that it, we have to start envisioning how our organization and infrastructure is going to be set up so that we can use data where it needs to be or where it's most valuable and often that's close to the action. So if we think then about that very quickly because it's a backdrop for everything, increasingly we're going to start talking about the idea of where's the workload going to go? Where's workload the dog going to be against this kind of backdrop of the divorce of infrastructure? We believe that and our research pretty strongly shows that a lot of workloads are going to go to true private cloud but a lot of big data is moving into the cloud. This is a prediction we made a few years ago and it's clearly happening and it's underway and we'll get into what some of the implications are. So again, when we say that a lot of the big data elements, a lot of the process of creating value out of data is going to move into the cloud. That doesn't mean that all the systems of agency that build or rely on that data, the inference engines, etc, are also in a public cloud. A lot of them are going to be distributed out to the edge, out to where the action needs to be because of latency and other types of issues. This is a fundamental proposition and I know I'm going fast but hopefully I'm being clear. All right, so let's now get to the second part. This is kind of where the industry's going. Data is an asset. Invest in strategic business capabilities to appreciate, to create those data assets and appreciate the value of those assets and utilize the cloud intelligently to generate and ensure increasing returns. So the next question is well, how will we get there? Now. Right now, not too far from here, Neil Raden for example, was on the show floor yesterday. Neil made the observation that, as he wandered around, he only heard the word big data two or three times. The concept of big data is not dead. Whether the term is or is not is somebody else's decision. Our perspective, very simply, is that the notion is bifurcating. And it's bifurcating because we see different strategic imperatives happening at two different levels. On the one hand, we see infrastructure convergence. The idea that increasingly we have to think about how we're going to bring and federated data together, both from a systems and a data management standpoint. And on the other hand, we're going to see infrastructure or application specialization. That's going to have an enormous implication over next few years, if only because there just aren't enough people in the world that understand how to create value out of data. And there's going to be a lot of effort made over the next few years to find new ways to go from that one expertise group to billions of people, billions of devices, and those are the two dominant considerations in the industry right now. How can we converge data physically, logically, and on the other hand, how can we liberate more of the smarts associated with this very, very powerful approach so that more people get access to the capacities and the capabilities and the assets that are being generated by that process. Now, we've done at Wikibon, probably I don't know, 18, 20, 23 predictions overall on the role that or on the changes being wrought by digital business. Here I'm going to focus on four of them that are central to our big data research. We have many more but I'm just going to focus on four. The first one, when we think about infrastructure convergence we worry about hardware. Here's a prediction about what we think is going to happen with hardware and our observation is we believe pretty strongly that future systems are going to be built on the concept of how do you increase the value of data assets. The technologies are all in place. Simpler parts that it more successfully bind specifically through all its storage and network are going to play together. Why, because increasingly that's the fundamental constraint. How do I make data available to other machines, actors, sources of change, sources of process within the business. Now, we envision or we are watching before our very eyes, new technologies that allow us to take these simple piece parts and weave them together in very powerful fabrics or grids, what we call UniGrid. So that there is almost no latency between data that exists within one of these, call it a molecule, and anywhere else in that grid or lattice. Now again, these are not systems that are going to be here in five years. All the piece parts are here today and there are companies that are actually delivering them. So if you take a look at what Micron has done with Mellanox and other players, that's an example of one of these true private cloud oriented machines in place. The bottom line though is that there is a lot of room left in hardware. A lot of room. This is what cloud suppliers are building and are going to build but increasingly as we think about true private cloud, enterprises are going to look at this as well. So future systems for improving data assets. The capacity of this type of a system with low latency amongst any source of data means that we can now think about data not as... Not as a set of sources that have to be each individually, each having some control over its own data and sinks woven together by middleware and applications but literally as networks of data. As we start to think about distributing data and distributing control and authority associated with that data more broadly across systems, we now have to think about what does it mean to create networks of data? Because that, in many respects, is how these assets are going to be forged. I haven't even mentioned the role that security is going to play in all of this by the way but fundamentally that's how it's likely to play out. We'll have a lot of different sources but from a business standpoint, we're going to think about how those sources come together into a persistent network that can be acted upon by the business. One of the primary drivers of this is what's going on at the edge. Marc Andreessen famously said that software is eating the world, well our observation is great but if software's eating the world, it's eating it at the edge. That's where it's happening. Secondly, that this notion of agency zones. I said I'm going to bring that word up again, how systems act on behalf of a brand or act on behalf of an institution or business is very, very crucial because the time necessary to do the analysis, perform the intelligence, and then take action is a real constraint on how we do things. And our expectation is that we're going to see what we call an agency zone or a hub zone or cloud zone defined by latency and how we architect data to get the data that's necessary to perform that piece of work into the zone where it's required. Now, the implications of this is none of this is going to happen if we don't use AI and related technologies to increasingly automate how we handle infrastructure. And technologies like blockchain have the potential to provide a interesting way of imagining how these networks of data actually get structured. It's not going to solve everything. There's some people that think the blockchain is kind of everything that's necessary but it will be a way of describing a network of data. So we see those technologies on the ascension. But what does it mean for DBMS? In the old way, in the old world, the old way of thinking, the database manager was the control point for data. In the new world these networks of data are going to exist beyond a single DBMS and in fact, over time, that concept of federated data actually has a potential to become real. When we have these networks of data, we're going to need people to act upon them and that's essentially a lot of what the data scientist is going to be doing. Identifying the outcome, identifying the data that's required, and weaving that data through the construction and management, manipulation of pipelines, to ensure that the data as an asset can persist for the purposes of solving a near-term problem or over whatever duration is required to solve a longer term problem. Data scientists remain very important but we're going to see, as a consequence of improvements in tooling capable of doing these things, an increasing recognition that there's a difference between a data scientist and a data scientist. There's going to be a lot of folks that participate in the process of manipulating, maintaining, managing these networks of data to create these business outcomes but we're going to see specialization in those ranks as the tooling is more targeted to specific types of activities. So the data scientist is going to become or will remain an important job, going to lose a little bit of its luster because it's going to become clear what it means. So some data scientists will probably become more, let's call them data network administrators or networks of data administrators. And very importantly as I said earlier, there's just not enough of these people on the planet and so increasingly when we think about again, digital business and the idea of creating data assets. A central challenge is going to be how to create the data or how to turn all the data that can be captured into assets that can be applied to a lot of different uses. There's going to be two fundamental changes to the way we are currently conceiving of the big data world on the horizon. One is well, it's pretty clear that Hadoop can only go so far. Hadoop is a great tool for certain types of activities and certain numbers of individuals. So Hadoop solves problems for an important but relatively limited subset of the world. Some of the new data science platforms that we just talked about, that I just talked about, they're going to help with a degree of specialization that hasn't been available before in the data world, will certainly also help but it also will only take it so far. The real way that we see the work that we're doing, the work that the big data community is performing, turned into sources of value that extend into virtually every single corner of humankind is going to be through these cloud services that are being built and increasingly through packaged applications. A lot of computer science, it still exists between what I just said and when this actually happens. But in many respects, that's the challenge of the vendor ecosystem. How to reconstruct the idea of packaged software, which has historically been built around operations and transaction processing, with a known data model and an unknown or the known process and some technology challenges. How do we reapply that to a world where we now are thinking about, well we don't know exactly what the process is because the data tells us at the moment that the actions going to be taking place. It's a very different way of thinking about application development. A very different way of thinking about what's important in IT and very different way of thinking about how business is going to be constructed and how strategy's going to be established. Packaged applications are going to be crucially important. So in the last few minutes here, what are the numbers? So this is kind of the basis for our analysis. Digital business, role of data is an asset, having an enormous impact in how we think about hardware, how do we think about database management or data management, how we think about the people involved in this, and ultimately how we think about how we're going to deliver all this value out to the world. And the numbers are starting to reflect that. So why don't you think about four numbers as I go through the two or three slides. Hundred and three billion, 68%, 11%, and 2017. So of all the numbers that you will see, those are four of the most important numbers. So let's start by looking at the total market place. This is the growth of the hardware, software, and services pieces of the big data universe. Now we have a fair amount of additional research that breaks all these down into tighter segments, especially in software side. But the key number here is we're talking about big numbers. 103 billion over the course of next 10 years and let's be clear that 103 billion dollars actually has a dramatic amplification on the rest of the computing industry because a lot of the pricing models associated with, especially the software, are tied back to open source which has its own issues. And very importantly, the fact that the services business is going to go through an enormous amount of change over the next five years as service companies better understand how to deliver some of these big data rich applications. The second point to note here is that it was in 2017 that the software market surpassed the hardware market in big data. Again, for first number of years we focused on buying the hardware and the system software associated with that and the software became something that we hope to discover. So I was having a conversation here in theCUBE with the CEO of Transwarp which is a very interesting Chinese big data company and I asked what's the difference between how you do things in China and how we do things in the US? He said well, in the US you guys focus on proof of concept. You spend an enormous amount of time asking, does the hardware work? Does the database software work? Does the data management software work? In China we focus on the outcome. That's what we focus on. Here you have to placate the IT organization to make sure that everybody in IT is comfortable with what's about to happen. In China, were focused on the business people. This is the first year that software is bigger than hardware and it's only going to get bigger and bigger over time. It doesn't mean again, that hardware is dead or hardware is not important. It's going to remain very important but it does mean that the centerpiece of the locus of the industry is moving. Now, when we think about what the market shares look like, it's a very fragmented market. 60%, 68% of the market is still other. This is a highly immature market that's going to go through a number of changes over the next few years. Partly catalyzed by that notion of infrastructure convergence. So in four years our expectation is that, that 68% is going to start going down pretty fast as we see greater consolidation in how some of these numbers come together. Now IBM is the biggest one on the basis of the fact that they operate in all these different segments. They operating the hardware, software, and services segment but especially because they're very strong within the services business. The last one I want to point your attention to is this one. I mentioned earlier on, that our expectation is that the market increasingly is going to move to a packaged application orientation or packaged services orientation as a way of delivering expertise about big data to customers. Splunk is the leading software player right now. Why, because that's the perspective that they've taken. Now, perhaps we're a limited subset. It's perhaps for a limited subset of individuals or markets or of sectors but it takes a packaged application, weaves these technologies together, and applies them to an outcome. And we think this presages more of that kind of activity over the course of the next few years. Oracle, kind of different approach and we'll see how that plays out over the course of the next five years as well. Okay, so that's where the numbers are. Again, a lot more numbers, a lot of people you can talk to. Let me give you some action items. First one, if data was a core asset, how would IT, how would your business be different? Stop and think about that. If it wasn't your buildings that were the asset, it wasn't the machines that were the asset, it wasn't your people by themselves who were the asset, but data was the asset. How would you reinstitutionalize work? That's what every business is starting to ask, even if they don't ask it in the same way. And our advice is, then do it because that's the future of business. Not that data is the only asset but data is a recognized central asset and that's going to have enormous impacts on a lot of things. The second point I want to leave you with, tens of billions of users and I'm including people and devices, are dependent on thousands of data scientists that's an impedance mismatch that cannot be sustained. Packaged apps and these cloud services are going to be the way to bridge that gap. I'd love to tell you that it's all going to be about tools, that we're going to have hundreds of thousands or millions or tens of millions or hundreds of millions of data scientists suddenly emerge out of the woodwork. It's not going to happen. The third thing is we think that big businesses, enterprises, have to master what we call the big inflection. The big tech inflection. The first 50 years were about known process and unknown technology. How do I take an accounting package and do I put on a mainframe or a mini computer a client/server or do I do it on the web? Unknown technology. Well increasingly today, all of us have a pretty good idea what the base technology is going to be. Does anybody doubt it's going to be the cloud? We got a pretty good idea what the base technology is going to be. What we don't know is what are the new problems that we can attack, that we can address with data rich approaches to thinking about how we turn those systems into actors on behalf of our business and customers. So I'm a couple minutes over, I apologize. I want to make sure everybody can get over to the keynotes if you want to. Feel free to stay, theCUBE's going to be live at 9:30. If I got that right. So it's actually pretty exciting if anybody wants to see how it works, feel free to stay. Georgia's here, Neil's here, I'm here. I mentioned Greg Terrio, Dave Volante, John Greco, I think I saw Sam Kahane back in the corner. Any questions, come and ask us, we'll be more than happy. Thank you very much for, oh David Volante. >> David: I have a question. >> Yes. >> David: Do you have time? >> Yep. >> David: So you talk about data as a core asset, that if you look at the top five companies by market cap in the US, Google, Amazon, Facebook, etc. They're data companies, they got data at the core which is kind of what your first bullet here describes. How do you see traditional companies closing that gap where humans, buildings, etc at the core as we enter this machine intelligence era, what's your advice to the traditional companies on how they close that gap? >> All right. So the question was, the most valuable companies in the world are companies that are well down the path of treating data as an asset. How does everybody else get going? Our observation is you go back to what's the value proposition? What actions are most important? what's data is necessary to perform those actions? Can changing the way the data is orchestrated and organized and put together inform or change the cost of performing that work by changing the cost transactions? Can you increase a new service along the same lines and then architect your infrastructure and your business to make sure that the data is near the action in time for the action to be absolute genius to your customer. So it's a relatively simple thought process. That's how Amazon thought, Apple increasingly thinks like that, where they design the experience and they think what data is necessary to deliver that experience. That's a simple approach but it works. Yes, sir. >> Audience Member: With the slide that you had a few slides ago, the market share, the big spenders, and you mentioned that, you asked the question do any of us doubt that cloud is the future? I'm with Snowflake, I don't see many of those large vendors in the cloud and I was wondering if you could speak to what are you seeing in terms of emerging vendors in that space. >> What a great question. So the question was, when you look at the companies that are catalyzing a lot of the change, you don't see a lot of the big companies being at the leadership. And someone from Snowflake just said, well who's going to lead it? That's a big question that has a lot of implications but at this point time it's very clear that the big companies are suffering a bit from the old, from the old, trying to remember what the... RCA syndrome. I think Clay Christensen talked about this. You know, the innovators dilemma. So RCA actually is one of the first creators. They created the transistor and they held a lot of original patents on it. They put that incredible new technology, back in the forties and fifties, under the control of the people who ran the vacuum tube business. When was the last time anybody bought RCA stock? The same problem is existing today. Now, how is that going to play out? Are we going to see a lot of, as we've always seen, a lot of new vendors emerge out of this industry, grow into big vendors with IPO related exits to try to scale their business? Or are we going to see a whole bunch of gobbling up? That's what I'm not clear on but it's pretty clear at this point in time that a lot of the technology, a lot of the science, is being done in smaller places. The moderating feature of that is the services side. Because there's limited groupings of expertise that the companies that today are able to attract that expertise. The Googles, the Facebooks, the AWSs, etc, the Amazons. Are doing so in support of a particular service. IBM and others are trying to attract that talent so they can apply it to customer problems. We'll see over the next few years whether the IBMs and the Accentures and the big service providers are able to attract the kind of talent necessary to diffuse that knowledge into the industry faster. So it's the rate at which that the idea of internet scale computing, the idea of big data being applied to business problems, can diffuse into the marketplace through services. If it can diffuse faster that will have both an accelerating impact for smaller vendors, as it has in the past. But it may also again, have a moderating impact because a lot of that expertise that comes out of IBM, IBM is going to find ways to drive in the product faster than it ever has before. So it's a complicated answer but that's our thinking at this point time. >> Dave: Can I add to that? >> Yeah. (audience member speaking faintly) >> I think that's true now but I think the real question, not to not to argue with Dave but this is part of what we do. The real question is how is that knowledge going to diffuse into the enterprise broadly? Because Airbnb, I doubt is going to get into the business of providing services. (audience member speaking faintly) So I think that the whole concept of community, partnership, ecosystem is going to remain very important as it always has and we'll see how fast those service companies that are dedicated to diffusing knowledge, diffusing knowledge into customer problems actually occurs. Our expectation is that as the tooling gets better, we will see more people be able to present themselves truly as capable of doing this and that will accelerate the process. But the next few years are going to be really turbulent and we'll see which way it actually ends up going. (audience member speaking faintly) >> Audience Member: So I'm with IBM. So I can tell you 100% for sure that we are, I hired literally 50 data scientists in the last three months to go out and do exactly what you're saying. Sit down with clients and help them figure out how to do data science in the enterprise. And so we are in fact scaling it, we're getting people that have done this at Google, Facebook. Not a whole lot of those 'cause we want to do it with people that have actually done it in legacy fortune 500 Companies, right? Because there's a little bit difference there. >> So. >> Audience Member: So we are doing exactly what you said and Microsoft is doing the same thing, Amazon is actually doing the same thing too, Domino Data Lab. >> They don't like they're like talking about it too much but they're doing it. >> Audience Member: But all the big players from the data science platform game are doing this at a different scale. >> Exactly. >> Audience Member: IBM is doing it on a much bigger scale than anyone else. >> And that will have an impact on ultimately how the market gets structured and who the winners end up being. >> Audience Member: To add too, a lot of people thought that, you mentioned the Red Hat of big data, a lot of people thought Cloudera was going to be the Red Hat of big data and if you look at what's happened to their business. (background noise drowns out other sounds) They're getting surrounded by the cloud. We look at like how can we get closer to companies like AWS? That was like a wild card that wasn't expected. >> Yeah but look, at the end of the day Red Hat isn't even the Red Hat of open source. So the bottom line is the thing to focus on is how is this knowledge going to diffuse. That's the thing to focus on. And there's a lot of different ways, some of its going to diffuse through tools. If it diffuses through tools, it increases the likelihood that we'll have more people capable of doing this in IBM and others can hire more. That Citibank can hire more. That's an important participant, that's an important play. So you have something to say about that but it also says we're going to see more of the packaged applications emerge because that facilitates the diffusion. This is not, we haven't figured out, I don't know exactly, nobody knows exactly the exact shape it's going to take. But that's the centerpiece of our big data researches. How is that diffusion process going to happen, accelerate, and what's the resulting structure going to look like? And ultimately how are enterprises going to create value with whatever results. Yes, sir. (audience member asks question faintly) So the recap question is you see more people coming in and promising the moon but being incapable of delivering because they are, partly because the technology is uncertain and for other reasons. So here's our approach. Or here's our observation. We actually did a fair amount of research on this. When you take a look at what we call a approach to doing big data that's optimized for the costs of procurement i.e. let's get the simplest combination of infrastructure, the simplest combination of open-source software, the simplest contracting, to create that proof of concept that you can stand things up very quickly if you have enough expertise but you can create that proof of concept but the process of turning that into actually a production system extends dramatically. And that's one of the reasons why the Clouderas did not take over the universe. There are other reasons. As George Gilbert's research has pointed out, that Cloudera is spending 53, 55 % of their money right now just integrating all the stuff that they bought into the distribution five years ago. Which is a real great recipe for creating customer value. The bottom line though is that if we focus on the time to value in production, we end up taking a different path. We don't focus as much on whether the hardware is going to work and the network is going to work and the storage can be integrated and how it's going to impact the database and what that's going to mean to our Oracle license pool and all the other things that people tend to think about if they're focused on the technology. And so as a consequence, you get better time to value if you focus on bringing the domain expertise, working with the right partner, working with the appropriate approach, to go from what's the value proposition, what actions are associated with a value proposition, what's stated in that area to perform those actions, how can I take transaction costs out of performing those actions, where's the data need to be, what infrastructure do I require? So we have to focus on a time to value not the time to procure. And that's not what a lot of professional IT oriented people are doing because many of them, I hate say it, but many of them still acquire new technology with the promise to helping the business but having a stronger focus on what it's going to mean to their careers. All right, I want to be really respectful to everybody's time. The keynotes start in about five minutes which means you just got time. If you want to stay, feel free to stay. We'll be here, we'll be happy to talk but I think that's pretty much going to close our presentation broadcast. Thank you very much for being an attentive audience and I hope you found this useful. (upbeat music)

[Music] hi I'm Peter Burroughs welcome to another cube conversation from our beautiful Palo Alto studios today we're talking with Dan Rogers who's the chief marketing officer of ServiceNow good to say pietà Dan thanks very much for being here so Dan you as a CMO we're gonna spend some time talking about what the CMO does with the CMO doz now at ServiceNow and but give us a little bit of your background who are you where'd you came from how'd you get to where you are sometimes I joke that I was born in the clouds I come from the north of England it does rain there a lot but professionally I spent all my time in cloud companies so Salesforce Amazon Web Services and now of course so is now and what is ServiceNow do give us a little bit of background how is ServiceNow doing where is it going how our customers working with you well I think the way to answer that is by saying every company is undergoing a digital transformation and as they undergo digital transformation they realize that all the great stuff that they have in people's personal lives great user experiences great service experiences they want that at work as well so ServiceNow really brings those great experiences to work we have a platform which is called the now platform now platform basically is a set of services that deliver great user experiences the ability to request things easily help me fix my X helped me get a common answer to a question around say an employee question and then great service experiences so we create great workflow underneath so that all of those activities orchestrated across the organization and then great service intelligence so that over time were predicting things and recommending things just like you have with your consumer services today bringing all of that to the enterprise so let's talk a little bit about the CMO role because ultimately the there's you mention digital transformation and there's been for quite some time predictions made by various folks that you know the CMO is going to spend more money on technology than the IT manager well that clearly hasn't happened but that does not mean that the CMOS role and the marketing function hasn't changed as a consequence of technology how has technology how has a data orientation how has speed and alignment with data and how the organization operates it serves now on others change the CMO job changed marking I think in both a b2c context it's a very rich data environment a lot of that's happening through the web so you have instant data data you can make you know changes on the fly do a/b testing dialing your forms improve your completion rates dialing your conversions the same is also true in B to B and B to B a lot of what marketers are doing is providing the pipeline to the sales team and that has a funnel mindset a discipline around how much is converting at each stage why is it converting what's not converting row the leads going which leads are the most effective and where should we ultimately spend differently to help get those leads into meetings and on to our sales teams so they can execute against the opportunities now it used to be the b2b he was characterized by what Peter Drucker would have called value in exchange that you would sell a product and the product imbued the value of the company and that was up to the customer to figure out how to get value out of it we now seem to be moving to a value in utility model where instead of selling products were increasingly selling outcomes or increasingly it's actually taking the form of services serves now is at the vanguard of that change tell us a little bit about how that notion of value in exchange to value in utility is changing your job in quite frankly changing service now so yeah I'll actually take us right back to the founding of our company in 2004 our company was founded by Fred ludie and it was founded on a simple idea that we were going to make work better for people and what we would do therefore is listen to our customers about the problems that they had and design solutions with them for the to get them to answers so in my world that means that I'm not just going to describe the speeds and feeds of the products in fact I'm going to dial in to the solutions that our customers want to talk to us about and the business outcomes that they need there are seven solutions that we go to market where they'll just briefly tell you a little bit about those the first one is modernize IT Service Management customers are asking us we have a legacy IT service management infrastructure how get help desk from IT help us to modernize that we know we can do better than our antiquated process that's what you started that's where we started thank you and then you know we've migrated in IT to a much richer conversation around help you eliminate service outages how can we predict anomalies before they happen in your IT environment and then I want to run IT like a business I know you're gonna be talking to our CIO later in the series a lot of what modern CIOs are thinking about is looking at all the projects across the companies how can I support those with IT to transform the organization those are our IT conversations we have conversations happening in HR and they want to consumer eyes the employee experience and then customer service how can I improve customer satisfaction by resolving those underlying issues faster in security operations how can I resolve vulnerabilities and incidents faster and finally we open up our our whole platform to allow anyone to build applications that are intelligent and smart take advantage of all those platform capabilities around great user experience those are the seven solutions that would go to market with and our customers care about those outcomes against those seven solutions so increasingly the marketing organization is talking in the language of business value to what extent are our customers doing those seven things what business value of they had have they increased IT productivity by 20% have they resolved those security incidents 45% faster and we're talking in that language and we're helping customers accelerate their time to get to those outcomes increasingly the modern marketer I think is stepping into that role not just get the leads get them to our sales team but really thinking about the whole way through getting those customers to this end outcomes yeah I want to talk to you about that a little bit but let me take a quick Waypoint here that you mentioned earlier the biggest sea world the market has always been familiar with the role the data could play within our organization simply because in most b2c circumstances you have a lot of customers that are doing that value in exchange you know I'm buying dumb a lot of people are buying go but one of the things that's interesting about the b2b world especially as we move to this notion of value in utility this solution the ongoing service provisioning is we don't have a lot of customers with limited engagement we have perhaps fewer customers but with a lot of engagement because now it's at a service level and that creates new forms of data new types of data a much richer set of insights and what customers are doing how are you using that to inform marketing do a better job of serving customers do a better job of service sales do a better job of serving Cheryl yeah and it's a question I love and you know I'll interpret the questions how do we get customer insight how do we make sure that our marketing is customer centric and not generic we have a few feelers for that you talk about a data obviously from a web perspective we have really good fidelity on where customers are going what they're interacting with what demos they're doing what the conversion rates of those are we also have a lot of physical world interactions so my organization runs the EBC it's policy yeah executive briefing center drive so it's probably the executive briefing center we have hundreds of customers joining us we actually survey them and ask them what's top of mind we begin every one of our ABCs for the section called voice of customer where we hear from them what's most important for them as our product teams come and have those discussions they're gleaning from those customers what are they most want to talk about what are they most want to hear about and because all of that data is captured on a platform that she becomes rich and actionable for the rest of my product marketing organization that's a set of customer insights our knowledge event so we have an annual user conference called knowledge this year knowledge 18 will actually have around 18,000 registrants so you know these are become small little intimate a huge huge event but what's very unique about our event is 95% of the sessions are designed by and delivered by customers this isn't a marketing event this is a peer group event of customers teaching customers telling customers what they've learned sharing their experiences so when we do a we do a call for content for knowledge we're really building our agenda based on exactly what the data is telling us what our customers want to hear about what do they want to say again that's really from marketing perspective just such rich ground for us to learn exactly what they care about we have customer feelers of course you know through all of our our activities that we're doing in the field in fact not a single field activity that my team does is without a customer so every time we're getting that rich insight you know to the point which I'd say we are a customer centric marketing organization is there any other way well some would say that there there might be but they're probably gonna get eaten by ServiceNow over the course of the next few years but let me really tie this back because again historically marketers have been asked to get engaged customers generate leads that funnel you know get us that original group that's going to want to talk to us and marketers have sometimes taken some very annoying approaches to make this happen one of the things that our research shows is that increasingly the sustained engagement requires that marketing also has to be a source of value to customers you mentioned the community approach at your big conference and the fact that you're providing content providing information that the customers will find valuable do you subscribe to that notion that marketing should be a source of value to customers in addition to others what do you think yeah absolutely I think if you have this limited mindset that somehow you're getting a lead and leaders victory I think it's game over you talked about community I'll just build on that real quick ServiceNow as a very active community itself online with 150,000 community members my team run the community we literally provide advice to the community that's one of the most joyful things that we can do similarly my relationship with sales isn't you know her over the lead we're working with the sales team to understand how they want to develop those accounts what are the accounts need from them and that really influences my marketing plan so I see us definitely as part of value exchange with customers so we believe pretty strongly also that the marketing function because of this orientation towards outcome because of the you know a services increasingly a services approach an ongoing sense of value and the fact that you have this rich opportunity to capture data has to take a more broader whole lifecycle role in customer engagement that doesn't mean that sales is less important which is I think a mistake that many of maids at OU fewer sales people and I think that sales gets more focused that much more important more of a problem-solving function for customers but talk to me a little bit about this idea of marketing becoming more a part of the entire customer journey and not just that discover and evaluate phase first of all do you agree with me and second of all how's it playing out for your team well I'd say you know one of the amazing things about a subscription business and you know we're in a subscription businesses customers get to vote with their feet every month Venus is a subscription the great news is service now our neural rates are over 97 percent which is you know yeah well in a lot of other businesses they talk about 85 and b2b they talk about 85% being good but 97% is almost be to see like churn numbers there is only one way to get that and that is the entire company needs to be focused on customer success the way we think about how we develop products through our sales team is engaged in their marketing teams engage is around customer success so I think it's almost like if you don't have that hat on and the executive seat you never get to get those numbers so my role half three quarters is customer success ultimately that's what I'm doing and you may start to see a lot more of how we go to market you know really having a lot more of that success mindset I'm looking forward to knowledge 18 I think you'll see a very different orientation from us at that conference you'll see things like success clinics things like office hours and a whole bunch of other best practices that we're going to be sharing with our customers and that helping customers get to value quicker is very much something I care deeply about and that's really a big orientation for my team so you mentioned if they don't have the hat on then it's not going to work that says something about culture and says something about the type of people that you hire and bring in service now is growing very very rapidly give us a couple of key things if we had a group of marketers here and you said the one thing you need in the culture beyond just customer centric but the one thing you need is this and then one thing you need about when you look for people what's the one thing you need in the marketing culture you know it's such a fast-moving space I'd actually say me this combination of innovation and execution execution is clear that means you do have a relationship with a product team your relationship the sales team your relationship with your customers and they have needs and those things need executing on but also because it's such a fast moving environment the nature of the job is changing the nature of the toolset is changing what our customers need which is ultimately driving it is changing very fast you have to have this sense of innovation this idea you know Jeff Bezos of Amazon talks about it this idea of day one so it's really day one for how you do those traditional things in marketing because they're not being done in the same way everyone needs to come with that day one mindset you learn you go and we can execute that so a culture performance and nonetheless is porous and open to change people what kind of people what kind of things are you looking for when when you sit down an interview potential service now marketing employee and of course we have those different functions so there's functional skills sort of harder skills but again I'd probably say the same thing it's that ability to innovate because a lot of what we're doing hasn't been done before or it's not done well and we want to do it better we want to reimagine and reinvent so that idea of dynamism and flexibility and then this underlying execution is can you get it done we want to be an organization that commits to things and gets them done so in the thing that's the combination of those two things and then those functional disciplines of course we've got product marketing we have digital marketers we have some of the you know folks we're qualifying the leads we call those ad hours they'll have a very different functional disciplines and then some of those underlying values I think so you and I are having the same conversation in twenty eight twenty three what is the one thing that you're doing more of in 2023 than you're doing today what is the one thing you're doing less of in 2023 than you're doing today you know I'm going to use the customers the North Star on that as well I think will be even more intimate with our customers in 2023 that's how I'm grounded some organizations grounded that's how my company's grounded I don't think we can go far enough on that they're spending more time with them looking at the data more engaging with sales more to understand what's working what's not working ensuring they get to value that's really as possible being so speed the value time to value and increase the level of value that serves and I was able to provide yeah okay what's one thing you're doing loss of talking to me that's great this is a great question I want to give it to giver the right you know the right mindset you know I think so much is going to change I think the way we go about what we do is going to change fundamentally I think the way we think about events is going to be different I think away we think about meetings is going to be different the way we engage is going to be different it's going to be all driven by that North Star of the customer so I can't even imagine what it's going to look like and that's why it's such an exciting profession it really is more or less how about outbound more or less outbound I think that will look just different I think we'll be doing outbound I think I'll have a different flavor and that's one of the things I love about my job that's why I get up every day because it's all going to be different what we're doing now is entirely different than it was two years ago but it's super exciting so reflecting what you said about the culture that you want the people that you hire you yourself are performing great growth in service now while at the same time being very porous very flexible to change and anticipating expecting it that's it paid off all right Dan Rogers thank you very much for come on on in Dan Rogers service now and his great cube conversation Dan again thanks very much for coming here and we look forward to I'm Peter burns from our Palo Alto studios and we look forward to having another cube conversation with you [Music] you

(upbeat music) >> Hi, welcome to this year's Annual Wikibon Predictions. This is our 2018 version. Last year, we had a very successful webinar describing what we thought was going to happen in 2017 and beyond and we've assembled a team to do the same thing again this year. I'm very excited to be joined by the folks listed here on the screen. My name is Peter Burris. But with me is David Floyer, Jim Kobielus is remote. George Gilbert's here in our Pal Alto studio with me. Neil Raden is remote. David Vellante is here in the studio with me. And Stuart Miniman is back in our Marlboro office. So thank you analysts for attending and we look forward to a great teleconference today. Now what we're going to do over the course of the next 45 minutes or so is we're going to hit about 13 of the 22 predictions that we have for the coming year. So if you have additional questions, I want to reinforce this, if you have additional questions or things that don't get answered, if you're a client, give us a call. Reach out to us. We'll leave you with the contact information at the end of the session. But to start things off we just want to make sure that everybody understands where we're coming from. And let you know who is Wikibon. So Wikibon is a company that starts with the idea of what's important as to research communities. Communities are where the action is. Community is where the change is happening. And community is where the trends are being established. And so we use digital technologies like theCUbE, CrowdChat and others to really ensure that we are surfacing the best ideas that are in a community and making them available to our clients so that they can succeed successfully, they can be more successful in their endeavors. When we do that, our focus has always been on a very simple premise. And that is that we're moving to an era of digital business. For many people, digital business can mean virtually anything. For us it means something very specific. To us, the difference between business and digital business is data. A digital business uses data to differentially create and keep a customer. So borrowing from what Peter Drucker said if the goal of business is to create customers and keep and sustain customers, the goal of digital business is to use data to do that. And that's going to inform an enormous number of conversations and an enormous number of decisions and strategies over the next few years. We specifically believe that all businesses are going to have establish what we regard as the five core digital business capabilities. First, they're going to have to put in place concrete approaches to turning more data into work. It's not enough to just accrete data, to capture data or to move data around. You have to be very purposeful and planful in how you establish the means by which you turn that data into work so that you can create and keep more customers. Secondly, it's absolutely essential that we build kind of the three core technology issues here, technology capabilities of effectively doing a better job of capturing data and IoT and people, or internet of things and people, mobile computing for example, is going to be a crucial feature of that. You have to then once you capture that data, turn it into value. And we think this is the essence of what big data and in many respects AI is going to be all about. And then once you have the possibility, kind of the potential energy of that data in place, then you have to turn it into kinetic energy and generate work in your business through what we call systems of agency. Now, all of this is made possible by this significant transformation that happens to be conterminous with this transition to digital business. And that is the emergence of the cloud. The technology industry has always been defined by the problems it was able to solve, catalyzed by the characteristics of the technology that made it possible to solve them. And cloud is crucial to almost all of the new types of problems that we're going to solve. So these are the five digital business capabilities that we're going to talk about, where we're going to have our predictions. Let's start first and foremost with this notion of turn more data into work. So our first prediction relates to how data governance is likely to change in a global basis. If we believe that we need to turn more data into work well, businesses haven't generally adopted many of the principles associated with those practices. They haven't optimized to do that better. They haven't elevated those concepts within the business as broadly and successfully as they have or as they should. We think that's going to change in part by the emergence of GDPR or the General Data Protection Regulation. It's going to go in full effect in May 2018. A lot has been written about it. A lot has been talked about. But our core issues ultimately are is that the dictates associated with GDPR are going to elevate the conversation on a global basis. And it mandates something that's now called the data protection officer. We're going to talk about that in a second David Vellante. But if is going to have real teeth. So we were talking with one chief privacy officer not too long ago who suggested that had the Equifax breach occurred under the rules of GDPR that the actual finds that would have been levied would have been in excess of 160 billion dollars which is a little bit more than the zero dollars that has been fined thus far. Now we've seen new bills introduced in Congress but ultimately our observation and our conversations with a lot of data chief privacy officers or data protection officers is that in the B2B world, GDPR is going to strongly influence not just our businesses behavior regarding data in Europe but on a global basis. Now that has an enormous implication David Vellante because it certainly suggest this notion of a data protection officer is something now we've got another potential chief here. How do we think that's going to organize itself over the course of the next few years? >> Well thank you Peter. There are a lot of chiefs (laughs) in the house and sometimes it gets confusing as the CIO, there's the CDO and that's either chief digital officer or chief data officer. There's the CSO, could be strategy, sometimes that could be security. There's the CPO, is that privacy or product. As he says, it gets confusing sometimes. On theCUbE we talked to all of these roles so we wanted to try to add some clarity to that. First thing we want to say is that the CIO, the chief information officer, that role is not going away. A lot of people predict that, we think that's nonsense. They will continue to have a critical role. Digital transformations are the priority in organizations. And so the chief digital officer is evolving from more than just a strategy role to much more of an operation role. Generally speaking, these chiefs tend to report in our observation to the chief operating officer, president COO. And we see the chief digital officer as increasing operational responsibility aligning with the COO and getting incremental responsibility that's more operational in nature. So the prediction really is that the chief digital officer is going to emerge as a charismatic leader amongst these chiefs. And by 2022, nearly 50% of organizations will position the chief digital officer in a more prominent role than the CIO, the CISO, the CDO and the CPO. Those will still be critical roles. The CIO will be an enabler. The chief information security officer has a huge role obviously to play especially in terms of making security a teams sport and not just falling on IT's shoulders or the security team's shoulders. The chief data officer who really emerged from a records and data management role in many cases, particularly within regulated industries will still be responsible for that data architecture and data access working very closely with the emerging chief privacy officer and maybe even the chief data protection officer. Those roles will be pretty closely aligned. So again, these roles remain critical but the chief digital officer we see as increasing in prominence. >> Great, thank you very much David. So when we think about these two activities, what we're really describing is over the course of the next few years, we strongly believe that data will be regarded more as an asset within business and we'll see resources devoted to it and we'll see certainly management devoted to it. Now, that leads to the next set of questions as data becomes an asset, the pressure to acquire data becomes that much more acute. We believe strongly that IoT has an enormous implication longer term as a basis for thinking about how data gets acquired. Now, operational technology has been in place for a long time. We're not limiting ourselves just operational technology when we talk about this. We're really talking about the full range of devices that are going to provide and extend information and digital services out to consumers, out to the Edge, out to a number of other places. So let's start here. Over the course of the next few years, the Edge analytics are going to be an increasingly important feature overall of how technology decisions get made, how technology or digital business gets conceived and even ultimately how business gets defined. Now David Floyer's done a significant amount of work in this domain and we've provided that key finding on the right hand side. And what it shows is that if you take a look at an Edge based application, a stylized Edge based application and you presume that all the data moves back to an centralized cloud, you're going to increase your costs dramatically over a three year period. Now that moderates the idea or moderates the need ultimately for providing an approach to bringing greater autonomy, greater intelligence down to the Edge itself and we think that ultimately IoT and Edge analytics become increasingly synonymous. The challenge though is that as we evolve, while this has a pressure to keep more of the data at the Edge, that ultimately a lot of the data exhaust can someday become regarded as valuable data. And so as a consequence of that, there's still a countervailing impression to try to still move all data not at the moment of automation but for modeling and integration purposes, back to some other location. The thing that's going to determine that is going to be rate at which the cost of moving the data around go down. And our expectation is over the next few years when we think about the implications of some of the big cloud suppliers, Amazon, Google, others, that are building out significant networks to facilitate their business services may in fact have a greater impact on the common carriers or as great an impact on the common carriers as they have on any server or other infrastructure company. So our prediction over the next few years is watch what Amazon, watch what Google do as they try to drive costs down inside their networks because that will have an impact how much data moves from the Edge back to the cloud. It won't have an impact necessarily on the need for automation at the Edge because latency doesn't change but it will have a cost impact. Now that leads to a second consideration and the second consideration is ultimately that when we talk about greater autonomy at the Edge we need to think about how that's going to play out. Jim Kobielus. >> Jim: Hey thanks a lot Peter. Yeah, so what we're seeing at Wikibon is that more and more of the AI applications, more of the AI application development involves AI and more and more of the AI involves deployment of those models, deep learning machine learning and so forth to the Edges of the internet of things and people. And much of that AI will be operating autonomously with little or no round-tripping back to the cloud. What that's causing, in fact, we're seeing really about a quarter of the AI development projects (static interference with web-conference) as Edge deployment. What that involves is that more and more of that AI will be, those applications will be bespoke. They'll be one of a kind, or unique or an unprecedented application and what that means is that, you know, there's a lot of different deployment scenarios within which organizations will need to use new forms of learning to be able to ready that data, those AI applications to do their jobs effectively albeit to predictions of real time, guiding of an autonomous vehicle and so forth. Reinforcement learning is the core of what many of these kinds of projects, especially those that involve robotics. So really software is hitting the world and you know the biggest parts are being taken out of the Edge, much of that is AI, much of that autonomous, where there is no need or less need for real time latency in need of adaptive components, AI infused components where as they can learn by doing. From environmental variables, they can adapt their own algorithms to take the right actions. So, they'll have far reaching impacts on application development in 2018. For the developer, the new developer really is a data scientist at heart. They're going to have to tap into a new range of sources of data especially Edge sourced data from the senors on those devices. They're going to need to do commitment training and testing especially reinforcement learning which doesn't involve trained data so much as it involves being able to build an algorithm that can learn to maximum what's called accumulative reward function and if you do the training there adaptly in real time at the Edge and so forth and so on. So really, much of this will be bespoke in the sense that every Edge device increasingly will have its own set of parameters and its own set of objective functions which will need to be optimized. So that's one of the leading edge forces, trends, in development that we see in the coming year. Back to you Peter. >> Excellent Jim, thank you very much. The next question here how are you going to create value from data? So once you've, we've gone through a couple trends and we have multiple others about what's going to happen at the Edge. But as we think about how we're going to create value from data, Neil Raden. >> Neil: You know, the problem is that data science emerged rapidly out of sort of a perfect storm of big data and cloud computing and so forth. And people who had been involved in quantitative methods you know rapidly glommed onto the title because it was, lets face it, it was very glamorous and paid very well. But there weren't really good best practices. So what we have in data science is a pretty wide field of things that are called data science. My opinion is that the true data scientists are people who are scientists and are involved in developing new or improving algorithms as opposed to prepping data and applying models. So the whole field really kind of generated very quickly, in really, just in a few years. To me I called it generation zero which is more like data prep and model management all done manually. And it wasn't really sustainable in most organizations because for obvious reasons. So generation one, then some vendors stepped up with tool kits or benchmarks or whatever for data scientists and made it a little better. And generation two is what we're going to see in 2018, is the need for data scientists to no longer prep data or at least not spend very much time with it. And not to do model management because the software will not only manage the progression of the models but even recommend them and generate them and select the data and so forth. So it's in for a very big change and I think what you're going to see is that the ranks of data scientists are going to sort of bifurcate to old style, let me sit down and write some spaghetti code in R or Java or something and those that use these advanced tool kits to really get the work done. >> That's great Neil and of course, when we start talking about getting the work done, we are becoming increasingly dependent upon tools, aren't we George? But the tool marketplace for data science, for big data, has been somewhat fragmented and fractured. And hasn't necessarily focused on solving the problems of the data scientists. But in many respects focusing the problems that the tools themselves have. What's going to happen in the coming year when we start thinking about Neil's prescription that as the tools improve what's going to happen to the tools. >> Okay so, the big thing that we see supporting what Neil's talking about, what Neil was talking about is partly a symptom of a product issue and a go to market issue where the produce issue was we had a lot of best of breed products that were all designed to fit together. That in the broader big data space, that's the same issue that we faced with more narrowly with ArpiM Hadoop where you know, where we were trying to fit together a bunch of open source packages that had an admin and developer burden. More broadly, what Neil is talking about is sort of a richer end to end tools that handle both everything from the ingest all to the way to the operationalization and feedback of the models. But part of what has to go on here is that with open source, these open source tools the price point and the functional footprints that many of the vendors are supporting right now can't feed an enterprise sales force. Everyone talks with their open source business models about land and expand and inside sales. But the problem is once you want to go to wide deployment in an enterprise, you still need someone negotiating commercial terms at a senior level. You still need the technical people fitting the tools into a broader architecture. And most of the vendors that we have who are open source vendors today, don't have either the product breadth or the deal size to support traditional enterprise software. An account team would typically a million and a half to two million quota every year so we see consolidation and the consolidation again driven by the need for simplicity for the admins and the developers and for business model reasons to support enterprise sales force. >> All right, so what we're going to see happen in the course of the coming year is a lot of specialization and recognition of what is data science, what are the practices, how is it going to work, supported by an increasing quality of tools and a lot of tool vendors are going to be left behind. Now the third kind of notion here for those core technology capabilities is we still have to enact based on data. The good new is that big data is starting to show some returns in part because of some of the things that AI and other technologies are capable of doing. But we have to move beyond just creating the potential for, we have to turn that into work and that's what we mean ultimately by this notion of systems of agency. The idea that data driven applications will increasingly be act on behalf of a brand, on behalf of a company and building those systems out is going to be crucial. It's going to have a whole new set of disciplines and expertise required. So when we think about what's going to be required, it always starts with this notion of AI. A lot of folks are presuming however, that AI is going to be relatively easy to build or relatively easy to put together. We have a different opinion George. What do we think is going to happen as these next few years unfold related to AI adoption in large enterprises? >> Okay so, let's go back to the lessons we learned from sort of the big data, the raw, you know, let's put a data link in place which was sort of the top of everyone's agenda for several years. The expectation was it was going to cure cancer, taste like chocolate and cost a dollar. And uh. (laughing) It didn't quite work out that way. Partly because we had a burden on the administrator again of so many tools that weren't all designed to fit together, even though they were distributed together. And then the data scientists, the guys who had to take all this data that wasn't carefully curated yet. And turn that into advanced analytics and machine learning models. We have many of the same problems now with tool sets that are becoming more integrated but at lower levels. This is partly what Neil Raden was just talking about. What we have to recognize is something that we see all along, I mean since the beginning of (laughs) corporate computing. We have different levels of extraction and you know at the very bottom, when you're dealing with things like Tensorflow or MXNet, that's not for mainstream enterprises. That's for you know, the big sophisticated tech companies who are building new algorithms on those frameworks. There's a level above that where you're using like a spark cluster in the machine learning built into that. That's slightly more accessible but when we talk about mainstream enterprises taking advantage of AI, the low hanging fruit is for them to use the pre-trained models that the public cloud vendors have created with all the consumer data on speech, image recognition, natural language processing. And then some of those capabilities can be further combined into applications like managing a contact center and we'll see more from like Amazon, like recommendation engines, fulfillment optimization, pricing optimization. >> So our expectation ultimately George is that we're going to see a lot of this, a lot of AI adoption happen through existing applications because the vendors that are capable of acquiring a talent, taking or experimenting, creating value, software vendors are going to be where a lot of the talent ends up. So Neil, we have an example of that. Give us an example of what we think is going to happen in 2018 when we start thinking about exploiting AI and applications. >> Neil: I think that it's fairly clear to be the application of what's called advanced analytics and data science and even machine learning. But really, it's rapidly becoming a commonplace in organizations not just at the bottom of the triangle here. But I like the example of SalesForce.com. What they've done with Einstein, is they've made machine learning and I guess you can say, AI applications available to their customer base and why is that a good thing? Because their customer base already has a giant database of clean data that they can use. So you're going to see a huge number of applications being built with Einstein against Salesforce.com data. But there's another thing to consider and that is a long time ago Salesforce.com built connectors to a zillion times of external data. So, if you're a SalesForce.com customer using Einstein, you're going to be able to use those advanced tools without knowing anything about how to train a machine learning model and start to build those things. And I think that they're going to lead the industry in that sense. That's going to push their revenue next year to, I don't know, 11 billion dollars or 12 billion dollars. >> Great, thanks Neil. All right so when we think about further evidence of this and further impacts, we ultimately have to consider some of the challenges associated with how we're going to create application value continually from these tools. And that leads to the idea that one of the cobblers children, it's going to gain or benefit from AI will in fact be the developer organization. Jim, what's our prediction for how auto-programming impacts development? >> Jim: Thank you very much Peter. Yeah, automation, wow. Auto-programming like I said is the epitome of enterprise application development for us going forward. People know it as co-generation but that really understates the control of auto-programming as it's evolving. Within 2018, what we're going to see is that machine learning driven co-generation approach of becoming the forefront of innovation. We're seeing a lot of activity in the industry in which applications use ML to drive the productivity of developers for all kinds of applications. We're also seeing a fair amount of what's called RPA, robotic process automation. And really, how they differ is that ML will deliver or will drive co-generation, from what I call the inside out meaning, creating reams of code that are geared to optimize a particular application scenario. This is RPA which really takes over the outside in approach which is essentially, it's the evolution of screen scraping that it's able to infer the underlined code needed for applications of various sorts from the external artifacts, the screens and from sort of the flow of interactions and clips and so forth for a given application. We're going to see that ML and RPA will compliment each other in the next generation of auto-programming capabilities. And so, you know, really application development tedium is really the enemy of, one of the enemies of productivity (static interference with web-conference). This is a lot of work, very detailed painstaking work. And what they need is to be better, more nuanced and more adaptive auto-programming tools to be able to build the code at the pace that's absolutely necessary for this new environment of cloud computing. So really AI-related technologies can be applied and are being applied to application development productivity challenges of all sorts. AI is fundamental to RPA as well. We're seeing a fair number of the vendors in that stage incorporate ML driven OCR and natural language processing and screen scraping and so forth into their core tools to be able to quickly build up the logic albeit to drive sort of the verbiage outside in automation of fairly complex orchestration scenario. In 2018, we'll see more of these technologies come together. But you know, they're not a silver bullet. 'Cause fundamentally and for organizations that are considering going deeply down into auto-programming they're going to have to factor AI into their overall plans. They need to get knowledgeable about AI. They're going to need to bring more AI specialists into their core development teams to be able to select from the growing range of tools that are out there, RPA and ML driven auto-programming. Overall, really what we're seeing is that the AI, the data scientists, who's been the fundamental developer of AI, they're coming into the core of development tools and skills in organizations. And they're going to be fundamental to this whole trend in 2018 and beyond. If AI gets proven out in auto-programming, these developers will then be able to evangelize the core utility of the this technology, AI. In a variety of other backend but critically important investments that organizations will be making in 2018 and beyond. Especially in IT operations and in management, AI is big in that area as well. Back to you there, Peter. >> Yeah, we'll come to that a little bit later in the presentation Jim, that's a crucial point but the other thing we want to note here regarding ultimately how folks will create value out of these technologies is to consider the simple question of okay, how much will developers need to know about infrastructure? And one of the big things we see happening is this notion of serverless. And here we've called it serverless, developer more. Jim, why don't you take us through why we think serverless is going to have a significant impact on the industry, at least certainly from a developer perspective and developer productivity perspective. >> Jim: Yeah, thanks. Serverless is really having an impact already and has for the last several years now. Now, everybody, many are familiar in the developer world, AWS Lambda which is really the ground breaking public cloud service that incorporates the serverless capabilities which essentially is an extraction layer that enables developers to build stateless code that executes in a cloud environment without having to worry about and to build microservices, we don't have to worry about underlined management of containers and virtual machines and so forth. So in many ways, you know, serverless is a simplification strategy for developers. They don't have to worry about the underlying plumbing. They can worry, they need to worry about the code, of course. What are called Lambda functions or functional methods and so forth. Now functional programming has been around for quite a while but now it's coming to the form in this new era of serverless environment. What we'll see in 2018 is that we're predicting is that more than 50% of lean microservices employees, in the public cloud will be deployed in serverless environments. There's AWS and Microsoft has the Azure function. IMB has their own. Google has their own. There's a variety of private, there's a variety of multiple service cloud code bases for private deployment of serverless environments that we're seeing evolving and beginning to deploy in 2018. They all involve functional programming which really, along, you know, when coupled with serverless clouds, enables greater scale and speed in terms of development. And it's very agile friendly in the sense that you can quickly Github a functionally programmed serverless microservice in a hurry without having to manage state and so forth. It's very DevOps friendly. In the very real sense it's a lot faster than having to build and manage and tune. You know, containers and DM's and so forth. So it can enable a more real time and rapid and iterative development pipeline going forward in cloud computing. And really fundamentally what serverless is doing is it's pushing more of these Lamba functions to the Edge, to the Edges. If you're at an AWS Green event last week or the week before, but you notice AWS is putting a big push on putting Lambda functions at the Edge and devices for the IoT as we're going to see in 2018. Pretty much the entire cloud arena. Everybody will push more of the serverless, functional programming to the Edge devices. It's just a simplification strategy. And that actually is a powerful tool for speeding up some of the development metabolism. >> All right, so Jim let me jump in here and say that we've now introduced the, some of these benefits and really highlighted the role that the cloud is going to play. So, let's turn our attention to this question of cloud optimization. And Stu, I'm going to ask you to start us off by talking about what we mean by true private cloud and ultimately our prediction for private cloud. Do we have, why don't you take us through what we think is going to happen in this world of true private cloud? >> Stuart: Sure Peter, thanks a lot. So when Wikibon, when we launched the true private cloud terminology which was about two weeks ago next week, two years ago next week, it was in some ways coming together of a lot of trends similar to things that you know, George, Neil and James have been talking about. So, it is nothing new to say that we needed to simplify the IT stack. We all know, you know the tried and true discussion of you know, way too much of the budget is spent kind of keeping lights on. What we'd like to say is kind of running the business. If you squint through this beautiful chart that we have on here, a big piece of this is operational staffing is where we need to be able to make a significant change. And what we've been really excited and what led us to this initial market segment and what we're continuing to see good growth on is the move from traditional, really siloed infrastructure to you want to have, you know, infrastructure where it is software based. You want IT to really be able to focus on the application services that they're running. And what our focus for the this for the 2018 is of course it's the central point, it's the data that matters here. The whole reason we've infrastructured this to be able to run applications and one of the things that is a key determiner as to where and what I use is the data and how can I not only store that data but actually gain value from that data. Something we've talked about time and again and that is a major determining factor as to am I building this in a public cloud or am I doing it in you know my core. Is it something that is going to live on the Edge. So that's what we were saying here with the true private cloud is not only are we going to simplify our environment and therefore it's really the operational model that we talked about. So we often say the line, cloud is not a destination. But it's an operational model. So a true private cloud giving me some of the you know, feel and management type of capability that I had had in the public cloud. It's, as I said, not just virtualization. It's much more than that. But how can I start getting services and one of the extensions is true private cloud does not live in isolation. When we have kind of a core public cloud and Edge deployments, I need to think about the operational models. Where data lives, what processing happens need to be as environments, and what data we'll need to move between them and of course there's fundamental laws of physics that we need to consider in that. So, the prediction of course is that we know how much gear and focus has been on the traditional data center. And true private cloud helps that transformation to modernization and the big focus is many of these applications we've been talking about and uses of data sets are starting to come into these true private cloud environments. So, you know, we've had discussions. There's Spark, there's modern databases. Many of these, there's going to be many reasons why they might live in the private cloud environment. And therefore that's something that we're going to see tremendous growth and a lot of focus. And we're seeing a new wave of companies that are focusing on this to deliver solutions that will do more than just a step function for infrastructure or get us outside of our silos. But really helps us deliver on those cloud native applications where we pull in things like what Jim was talking about with serverless and the like. >> All right, so Stu, what that suggests ultimately is that data is going to dictate that everything's not going to end up in the private or in the public cloud or centralized public clouds because of latency costs, data governance and IP protection reasons. And there will be some others. At bare minimum, that means that we're going to have it in most large enterprises as least a couple of clouds. Talk to us about what this impact of multi cloud is going to look like over the course of the next few years. >> Stuart: Yeah, critical point there Peter. Because, right, unfortunately, we don't have one solution. There's nobody that we run into that say, oh, you know, I just do a single you know, one environment. You know it would be great if we only had one application to worry about. But as you've done this lovely diagram here, we all use lots of SaaS and increasingly, you know, Oracle, Microsoft, SalesForce, you know, all pushing everybody to multiple SaaS environments that has major impacts on my security and where my data lives. Public clouds, no doubt is growing at leaps and bounds. And many customers are choosing applications to live in different places. So just as in data centers, I would kind of look at it from an application standpoint and build up what I need. Often, there's you know, Amazon doing phenomenal. But you know, maybe there's things that I'm doing with Azure. Maybe there's things that's I'm doing with Google or others as well as my service providers for locality, for you know, specialized services, that there's reasons why people are doing it. And what customers would love is an operational model that can actually span between those. So we are very early in trying to attack this multi cloud environment. There's everything from licensing to security to you know, just operationally how do I manage those. And a piece of them that we were touching on in this prediction year, is Kubernetes actually can be a key enabler for that cloud native environment. As Jim talked about the serverless, what we'd really like is our developer to be able to focus on building their application and not think as much about the underlined infrastructure whether that be you know, racket servers that I built myself or public cloud infrastructures. So we really want to think more it's at the data and application level. It's SaaS and pass is the model and Kubernetes holds the promise to solve a piece of this puzzle. Now Kubernetes is not by no means a silver bullet for everything that we need. But it absolutely, it is doing very well. Our team was at the Linux, the CNCF show at KubeCon last week and there is you know, broad adoption from over 40 of the leading providers including Amazon is now a piece. Even SalesForce signed up to the CNCF. So Kubernetes is allowing me to be able to manage multi cloud workflows and therefore the prediction we have here Peter is that 50% of developing teams will be building, sustaining multi cloud with Kubernetes as a foundational component of that. >> That's excellent Stu. But when we think about it, the hardware of technology especially because of the opportunities associated with true private cloud, the hardware technologies are also going to evolve. There will be enough money here to sustain that investment. David Floyer, we do see another architecture on the horizon where for certain classes of workloads, we will be able to collapse and replicate many of these things in an economical, practical way on premise. We call that UniGrid, NVME is, over fabric is a crucial feature of UniGrid. >> Absolutely. So, NVMe takes, sorry NVMe over fabric or NVMe-oF takes NVMe which is out there as storage and turns it into a system framework. It's a major change in system architecture. We call this UniGrid. And it's going to be a focus of our research in 2018. Vendors are already out there. This is the fastest movement from early standards into products themselves. You can see on the chart that IMB have come out with NVMe over fabrics with the 900 storage connected to the power. Nine systems. NetApp have the EF750. A lot of other companies are there. Meta-Lox is out there looking for networks, for high speed networks. Acceler has a major part of the storage software. So and it's going to be used in particular with things like AI. So what are the drivers and benefits of this architecture? The key is that data is the bottleneck for application. We've talked about data. The amount of data is key to making applications more effective and higher value. So NVMe and NVMe over fabrics allows data to be accessed in microseconds as opposed to milliseconds. And it allows gigabytes of data per second as opposed to megabytes of data per second. And it also allows thousands of processes to access all of the data in very very low latencies. And that gives us amazing parallelism. So what's is about is disaggregation of storage and network and processes. There are some huge benefits from that. Not least of which is you save about 50% of the processor you get back because you don't have to do storage and networking on it. And you save from stranded storage. You save from stranded processor and networking capabilities. So it's overall, it's going to be cheaper. But more importantly, it makes it a basis for delivering systems of intelligence. And systems of intelligence are bringing together systems of record, the traditional systems, not rewriting them but attaching them to real time analytics, real time AI and being able to blend those two systems together because you've got all of that additional data you can bring to bare on a particular problem. So systems themselves have reached pretty well the limit of human management. So, one of the great benefits of UniGrid is to have a single metadata lab from all of that data, all of those processes. >> Peter: All those infrastructure elements. >> All those infrastructure elements. >> Peter: And application. >> And applications themselves. So what that leads to is a huge potential to improve automation of the data center and the application of AI to operations, operational AI. >> So George, it sounds like it's going to be one of the key potential areas where we'll see AI be practically adopted within business. What do we think is going to happen here as we think about the role that AI is going to play in IT operations management? >> Well if we go back to the analogy with big data that we thought was going to you know, cure cancer, taste like chocolate, cost a dollar, and it turned out that the application, the most wide spread application of big data was to offload ETL from expensive data warehouses. And what we expect is the first widespread application of AI embedded in applications for horizontal use where Neil mentioned SalesForce and the ability to use Einstein as SalesForce data and connected data. Now because the applications we're building are so complex that as Stu mentioned you know, we have this operational model with a true private cloud. It's actually not just the legacy stuff that's sucking up all the admin overhead. It's the complexity of the new applications and the stringency of the SLA's, means that we would have to turn millions of people into admins, the old you know, when the telephone networks started, everyone's going to have to be an operator. The only way we can get past this is if we sort of apply machine learning to IT Ops and application performance management. The key here is that the models can learn how the infrastructure is laid out and how it operates. And it can also learn about how all the application services and middleware works, behaving independently and with each other and how they tie with the infrastructure. The reason that's important is because all of a sudden you can get very high fidelity root cause analysis. In the old management technology, if you had an underlined problem, you'd have a whole sort of storm of alerts, because there was no reliable way to really triangulate on the or triage the root cause. Now, what's critical is if you have high fidelity root cause analysis, you can have really precise recommendations for remediation or automated remediation which is something that people will get comfortable with over time, that's not going to happen right away. But this is critical. And this is also the first large scale application of not just machine learning but machine data and so this topology of collecting widely desperate machine data and then applying models and then reconfiguring the software, it's training wheels for IoT apps where you're going to have it far more distributed and actuating devices instead of software. >> That's great, George. So let me sum up and then we'll take some questions. So very quickly, the action items that we have out of this overall session and again, we have another 15 or so predictions that we didn't get to today. But one is, as we said, digital business is the use of data assets to compete. And so ultimately, this notion is starting to diffuse rapidly. We're seeing it on theCUbE. We're seeing it on the the CrowdChats. We're seeing it in the increase of our customers. Ultimately, we believe that the users need to start preparing for even more business scrutiny over their technology management. For example, something very simple and David Floyer, you and I have talked about this extensively in our weekly action item research meeting, the idea of backing up and restoring a system is no longer in a digital business world. It's not just backing up and restoring a system or an application, we're talking about restoring the entire business. That's going to require greater business scrutiny over technology management. It's going to lead to new organizational structures. New challenges of adopting systems, et cetera. But, ultimately, our observations is that data is going to indicate technology directions across the board whether we talk about how businesses evolve or the roles that technology takes in business or we talk about the key business capability, digital business capabilities, of capturing data, turning it into value and then turning into work. Or whether we talk about how we think about cloud architecture and which organizations of cloud resources we're going to utilize. It all comes back to the role that data's going to play in helping us drive decisions. The last action item we want to put here before we get to the questions is clients, if we don't get to your question right now, contact us. Send us an inquiry. Support@silicongangle.freshdesk.com. And we'll respond to you as fast as we can over the course of the next day, two days, to try to answer your question. All right, David Vellante, you've been collecting some questions here. Why don't we see if we can take a couple of them before we close out. >> Yeah, we got about five or six minutes in the chat room, Jim Kobielus has been awesome helping out and so there's a lot of detailed answer there. The first, there's some questions and comments. The first one was, are there too many chiefs? And I guess, yeah. There's some title inflation. I guess my comment there would be titles are cheap, results aren't. So if you're creating chief X officers just for the, to check a box, you're probably wasting money. So you've got to give them clear roles. But I think each of these chiefs has clear roles to the extent that they are you know empowered. Another comment came up which is we don't want you know, Hadoop spaghetti soup all over again. Well true that. Are we at risk of having Hadoop spaghetti soup as the centricity of big data moves from Hadoop to AI and ML and deep learning? >> Well, my answer is we are at risk of that but that there's customer pressure and vendor economic pressure to start consolidating. And we'll also see, what we didn't see in the ArpiM big data era, with cloud vendors, they're just going to start making it easier to use some of the key services together. That's just natural. >> And I'll speak for Neil on this one too, very quickly, that the idea ultimately is as the discipline starts to mature, we won't have people that probably aren't really capable of doing some of this data science stuff, running around and buying a tool to try to supplement their knowledge and their experience. So, that's going to be another factor that I think ultimately leads to clarity in how we utilize these tools as we move into an AI oriented world. >> Okay, Jim is on mute so if you wouldn't mind unmuting him. There was a question, is ML a more informative way of describing AI? Jim, when you and I were in our Boston studio, I sort of asked a similar question. AI is sort of the uber category. Machine learning is math. Deep learning is a more sophisticated math. You have a detailed answer in the chat. But maybe you can give a brief summary. >> Jim: Sure, sure. I don't want too pedantic here but deep learning is essentially, it's a lot more hierarchical deeper stacks of neural network of layers to be able to infer high level extractions from data, you know face recognitions, sentiment analysis and so forth. Machine learning is the broader phenomenon. That's simply along a different and part various approaches for distilling patterns, correlations and algorithms from the data itself. What we've seen in the last week, five, six tenure, let's say, is that all of the neural network approaches for AI have come to the forefront. And in fact, the core often market place and the state of the art. AI is an ancient paradigm that's older than probably you or me that began and for the longest time was rules based system, expert systems. Those haven't gone away. The new era of AI we see as a combination of both statical approaches as well as rules based approaches, and possibly even orchestration based approaches like graph models or building broader context or AI for a variety of applications especially distributed Edge application. >> Okay, thank you and then another question slash comment, AI like graphics in 1985, we move from a separate category to a core part of all apps. AI infused apps, again, Jim, you have a very detailed answer in the chat room but maybe you can give the summary version. >> Jim: Well quickly now, the most disruptive applications we see across the world, enterprise, consumer and so forth, the advantage involves AI. You know at the heart of its machine learning, that's neural networking. I wouldn't say that every single application is doing AI. But the ones that are really blazing the trail in terms of changing the fabric of our lives very much, most of them have AI at their heart. That will continue as the state of the art of AI continues to advance. So really, one of the things we've been saying in our research at Wikibon `is that the data scientists or those skills and tools are the nucleus of the next generation application developer, really in every sphere of our lives. >> Great, quick comment is we will be sending out these slides to all participants. We'll be posting these slides. So thank you Kip for that question. >> And very importantly Dave, over the course of the next few days, most of our predictions docs will be posted up on Wikibon and we'll do a summary of everything that we've talked about here. >> So now the questions are coming through fast and furious. But let me just try to rapid fire here 'cause we only got about a minute left. True private cloud definition. Just say this, we have a detailed definition that we can share but essentially it's substantially mimicking the public cloud experience on PRIM. The way we like to say it is, bringing the cloud operating model to your data versus trying to force fit your business into the cloud. So we've got detailed definitions there that frankly are evolving. about PaaS, there's a question about PaaS. I think we have a prediction in one of our, you know, appendices predictions but maybe a quick word on PaaS. >> Yeah, very quick word on PaaS is that there's been an enormous amount of effort put on the idea of the PaaS marketplace. Cloud Foundry, others suggested that there would be a PaaS market that would evolve because you want to be able to effectively have mobility and migration and portability for this large cloud application. We're not seeing that happen necessarily but what we are seeing is that developers are increasingly becoming a force in dictating and driving cloud decision making and developers will start biasing their choices to the platforms that demonstrate that they have the best developer experience. So whether we call it PaaS, whether we call it something else. Providing the best developer experience is going to be really important to the future of the cloud market place. >> Okay great and then George, George O, George Gilbert, you'll follow up with George O with that other question we need some clarification on. There's a question, really David, I think it's for you. Will persistent dims emerge first on public clouds? >> Almost certainly. But public clouds are where everything is going first. And when we talked about UniGrid, that's where it's going first. And then, the NVMe over fabrics, that architecture is going to be in public clouds. And it has the same sort of benefits there. And NV dims will again develop pretty rapidly as a part of the NVMe over fabrics. >> Okay, we're out of time. We'll look through the chat and follow up with any other questions. Peter, back to you. >> Great, thanks very much Dave. So once again, we want to thank you everybody here that has participated in the webinar today. I apologize for, I feel like Hans Solo and saying it wasn't my fault. But having said that, none the less, I apologize Neil Raden and everybody who had to deal with us finding and unmuting people but we hope you got a lot out of today's conversation. Look for those additional pieces of research on Wikibon, that pertain to the specific predictions on each of these different things that we're talking about. And by all means, Support@silicongangle.freshdesk.com, if you have an additional question but we will follow up with as many as we can from those significant list that's starting to queue up. So thank you very much. This closes out our webinar. We appreciate your time. We look forward to working with you more in 2018. (upbeat music)

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

>> Welcome to our Palo Alto studio, all of you coming in over the airwaves. It's a wonderful opportunity today to talk about something very important with Computer Associates or, CA Tech, as they're now known. And I want to highlight one point about the slide title, the title they chose for the day, we chose for the day, Cloud and Hybrid IT Analytics for Digital Business. One of the most interesting things that you're going to hear about today is that it's going to keep coming back to business challenges and business problems. At the end of the day that's what the focus needs to be on. While we certainly do want to do more with the technology we have and drive greater effectiveness and utilization out of the technology that we use in our digital business, increasingly the ability to tie technology decisions to business outcomes is possible and all IT professionals must make that effort, as well as all IT vendors, if the community is going to be successful. Now what I'm going to talk about specifically is how cloud monitoring plays inside this drive to increase the effectiveness of business through digital technologies. And to do that, I'm going to talk about a few things. The first thing I'm going to talk about is what is a digital business and how does it impact strategic technology capabilities? Now the reason why this is so important is because there's an enormous amount of conversation in the industry about digital businesses, multi-channel for digital businesses, customer experience for digital businesses, some other attribute. And while those are all examples or potential benefits of digital business, at its core digital business is something else. We want to articulate what that is because it informs all decisions that we're going to make about a lot of different things. The second thing I'm going to talk about is this notion of advanced analytics and how advanced analytics are crucial to not only achieving the outcomes of digital business but also to sustain the effort in the transformation process. And as you might expect, if we're going to use analytics to improve our effectiveness, then we have to be in a position to gather the data that we need from the variety of resources necessary to succeed with a digital business strategy. Those are the three things I'm going to talk about but let's start with this first one. What is digital business and how does it impact technology capabilities? Now to do that, I want to show you something that we're quite proud of here at Wikibon SiliconANGLE because we're a research firm and a company that's dedicated to helping communities make better decision. The power of digital community is clear. It's a very, very important resource, overall, inside any business. And what we do is we have a tool that we call CrowdChat. And the purpose of CrowdChat is to bring together members of the community and surface the best insights they have about their undertakings. Now I'm not using this to just pitch what CrowdChat is, I really want to talk through how this is a representation of the power of digital community. I want to point you to a few things in this slide. First off, note that it's, very importantly, this was from a CrowdChat that we did on 31 January 2017 but the thing to note here is a couple of things. Now let's see if I can click through them here. Well the first thing to note is that it reached 3.4 million people linked to the technology decision making. Think about that. Wikibon SiliconANGLE is not a huge company. We're a very focused company that strongly emphasizes the role that technology can play in helping to make decisions and improve business outcomes. But this CrowdChat reached 3.4 million decision makers as part of our ongoing effort. And it clearly is an indication, ultimately, that today customers, in fact, are at the center of what goes on within digital business decision making. So customers are at the centers of these crucial market information flows. Now this is going to be something we come back to over and over and over. It used to be that folks who sold stuff were the primary centers of what happened with the information flows of the industry. But through social media, tools like CrowdChat and others, today customers are in a much better position overall to establish their voices and share their insights about what works and what doesn't work. In many respects, that is the core focus of digital business. So that leads us to this question of what is digital business. Now I am a fan of Peter Drucker. It's hard to argue with Peter Drucker and it's one of the reasons I start with Peter Drucker is because people don't typically argue with me when I start there. And Peter Drucker famously said many years ago that the purpose of a business is to create and keep a customer. Now you can go on about what about shareholder value, what about employees, and those are all true things. There's no question that that's also important. But the fundamental keeps coming back that if you don't have customers and you don't provide a great experience for those customers, you're not going to have a business. So what's the difference between digital business and business? The biggest difference between digital business and business and in fact how we properly define the concept of digital business is that digital businesses apply data to create and keep customers. That's the basis of digital business. It's how do you use your data assets to differentiate your business and especially to provide a superior experience, a superior value proposition, and superior outcomes for your customers. That is the core of digital business. If you're using data to differentiate how you engage customers, how you provide that experience for customers, and how you improve their outcomes, then you are more digital business than you were yesterday. If you use more data, you are more digital business than your competition. So this is a way of properly thinking about the role of digital business. And to summarize it slightly differently, what we strongly believe is that what decision makers have to do over the course of the next number of years is find ways to put their data to work. That is the fundamental goal of an IT professional today. And increasing, increasingly the goal of many business professionals. Find ways to apply data so that you can increase the work the firm does for customers. That's kind of the simple thread we're trying to pull here. Data, put to work, superior customer experience. Now at the centerpiece of this simple prescriptive is an enormous amount of complexity. A lot of decisions that have to be made because most businesses are not organized around their data. Most businesses don't institutionalize the way they engage customers or perform their work based on what their data assets can provide. Most businesses are built around the hardware, at least if you're an IT person, they're built around the hardware assets or maybe even the application assets. But increasingly it's become incumbent on CIOs and IT leaders to recognize that the central value of the business, at least that they work with, is the data and how that data performs work for the business. So that leads to the second question. Given the enormity of data in the future of digital business, we have to ask the question, "Well what role "is advanced analytics playing to keep us on track "as we thing about, ultimately, driving forward "for a digital business?" Now we draw this picture out to customers to try to explain the things that they'll have to do to become an increasingly digital business. And it starts with this idea that a digital business transformation requires investment in new capabilities, new business capabilities that foster the role that digital assets can play within the business that simplify making decisions about where to put people and how to institutionalize work and ultimately help sustain the value of the data within the business over time. And a way to think about it is that any digital business has to establish the capabilities to better capture data create catalysts from data. Now what do we mean by that? We mean basically that data is a catalyst for action. Data can actually be the source of value if you're a media company, for example. But in most businesses data is a catalyst, the next best action, a better prediction of superior forecast, a faster and simpler, and less expensive report for compliance purposes. Data is a catalyst. So we capture it and we translate it into a catalyst that then can actually guide action. That's the simple set of capabilities that we have to deploy here. Capturing data, turning it into the catalysts that then have consequential impacts in front of customers, provides superior experience and better business. Now if we try to map those prescriptions for business capabilities onto industry buzzwords, here's what we end with. Capture Data, well that's the centerpiece of what the industrial internet of things is about, or the internet of things is about, if we're talking mainly about small devices in a consumer world. Capturing data is essential and IIoT is going to be crucial to that effort as well as mobile computing and other types of things. We like to talk about it sometimes is the internet of things and people. Big data and analytics should be properly thought of as helping businesses turn those streams of information into models and insights that can lead to action. So that's what the whole purpose of what big data analytics is all about. It's not to just capture more data and store more data, it's about using that data that comes from a lot of different locations and turning it into catalysts, sources of value within the business. And the final one is branded customer experience. At the end of the day, what we're talking about is how we're going to use digital technology to better engage our customers, better engage our partners, better engage our markets, and better engage our employees. And increasingly, as customers demonstrate a preference for greater utilization of digital technology in their lives, the whole notion of a branded experience is going to be tied back to how well we provide these essential digital capabilities to our customers in our markets. So analytics plays an incredibly important role here because we've always been pretty good at capturing data and we've always, we're getting better I guess I should say, at utilizing insights from that data that could be gleaned on an episodic basis and turning that into some insight for a customer. Usually really smart people in sales or marketing or manufacturing or product management play that role. But what we're talking about is operationalizing, turning data into value for customers on a continuous ongoing basis. And Analytics is crucial for that and analytics also is crucial to ensure that we could stay on track as we effect these transformations and transitions. Now I want to draw your attention, obviously, to an important piece as we go forward here. And that is this notion how do we capture that data so that it is appropriately prepped and set up so that we can create value from analytics. And that's going to be the basis of the third point that I'm going to talk about. Why is hybrid cloud monitoring emerging as a crucial transformation tool? Now monitoring has been around for a long time. We've been monitoring individual assets to ensure we get greater efficiency and utilization. CA's been a master of that for 30, 35 years. Increasingly though, we need to think about how systems come together in a lot of different ways to increase what we call the plasticity of the infrastructure. The ability of the infrastructure to not only scale but to reconfigure itself in response to the crucial new work that digital businesses have to perform. So how's that going to play out? It's become very popular within the industry to talk about how data is going to move to the cloud. And that's certainly going to happen. There's going to be a lot of data that ends up in the cloud. But as we think about the realities of moving data, data is not just an ephemeral thing. Data has real physical characteristics, real legal implications. And ultimately intellectual property is increasingly rendered in the form of data. And so we have to be very careful how we think about data being moved across the enterprise into any number of different locations. It's one of the most strategic decisions that a board of directors is going to make. How do we handle and take care of our data assets? Now I want to focus just on one element of that. Hopefully provide a simple proof point to make this argument. And that is, if we looked at how data is generated, for example, in an Edge setting. Say we looked at the cost of moving data from a wind farm. A relatively small straightforward wind farm with a number of different sensors. What does it cost to move that data to the cloud? And that's provided here. If we think about the real costs of data, the cost of moving data from an Edge situation, even in a relatively simple example, back to the cloud can be dramatic. Hundreds of thousands of dollars. Limitations based on latencies, concerns about traversing borders that have legal jurisdictions, and obviously also, as I said, the intellectual property realities. But the bottom line here is that it shows that it's going to be much cheaper to process the data in place, process the data close to where the action needs to be taken, than to move it all to the cloud. And we think that's going to become a regular feature of how we think about setting up infrastructure in business in the future. Increasingly, it's not going to be about moving data to the cloud only, we're going to have additional options about moving cloud and cloud services to the data. Increasingly this is going to be the tact that businesses are going to take. It's find ways to move that sense of control, that notion of quality of service, and that flexibility in how we provision infrastructure so that the cloud experience comes to where the event needs to take place. That going forward will be the centerpiece of a lot of technology decision making. It doesn't mean we're not going to move data to the cloud it just means that we're going to be smart about when we do it, how we do it, and understanding when it makes more sense to move the cloud or the cloud set of services closer to the event so that we can process it in place. Now this is a really crucial concern because it suggests there's going to be a greater distribution of data and not a greater centralization of data. And you can probably see where I'm going with this. Greater distribution of data ultimately means that there's going to be a lot more things that require that we have to have visibility into their performance, visibility into how they work. If it was all going to be in one place then we could let someone else actually handle a lot of those questions about what's going on, how is it working. But as our businesses become more digital and our data assets become more central to how we provide customer experience, it means that the resources that we use to generate value out of those assets have to be managed and monitored appropriately. Now we have done a lot of work around this and what our research pretty strongly shows is that over the next 10 years, we're going to see three things happen. First off, we're going to see a lot of investment in public cloud options both in the form of SaaS as well as infrastructure as a service. So that will continue. There's no question that we're going to see some of the big public cloud suppliers become more important. But our expectation also, is we will see significant net new investment in what we call true private cloud. The idea of moving those cloud services on premise so that we can support local events that need high quality data and that kind of capability. The second thing I want to point out here is that while we do expect to see significant net new efficiencies and how we run all these resources, if we look at the cost of labor over the course of operational labor over the course of the next decade, we do expect to see the cost go down about around 7%. So we will see greater productivity in the world of IT labor. But it's not going to crash like many people predict. And one of the reasons it's not going to crash is because of the incredible net new reports of digital assets. But the third thing to note here is that we are not going to see the type of massive dumping of traditional infrastructure that many people predict. There's too many assets, too much value already in place in a lot of systems, and instead what we're going to see is a blending of all of these different capabilities in a rational way so that the business can achieve the digital outcomes that it seeks. The challenge, though, over the course of the next decade, however, is going to be to find ways, while we're going to have all these different resources, be a feature of our technology plan, be a feature of how we run our business. Historically we've tended to think about these in silos and the monitoring challenge that we put in place was to better generate efficiencies out of an individual asset. Well as we go forward, increasingly we need to think about how not one resource works, but how all these resources work. It's time for business to think about the internet not as something that's external, but as the basis for their computing. The internet is a computer. How we slice it up for our business is a statement about how we're going to build a set of distributive capabilities but weave them together so that we have a set of resources that can, in fact, reflect the business needs and support business requirements. And monitoring becomes crucial to that because as we move forward the goal needs to be to be able to enfranchise, federate a lot of these distributive resources into a working coherent statement of how computing serves our business. And that's going to require an approach that is much more focused on how things come together and how things can be bought into a coherent whole as opposed just the efficiency of any single tool or any single device. That's where digital business has to go, how can we bring all of these resources together into a coherent whole that supports our business needs. And that is the goal of the next generation of monitoring is to make that possible. Okay, so as we think about what we've talked about we basically made a couple of points here. The first when we talked about what is digital business, the first point that I made is data is the digital business asset. That's what we're trying to do here is use data to improve the effectiveness of the outcomes that we seek for customers. Digital business elevates IT but forces real and material changes. The second point that I made is how are advanced analytics helping. Well analytics turns business, or turns data into business catalysts that ultimately guide and shape customer experience. Crucial point. And the last point that I want to make is when we think about cloud monitoring remember that if we move forward in the digital world, as you make choices, your brand fails when your infrastructure fails. So as a consequence for those of you who are in the midst of thinking about the future role that monitoring is going to play in your world, choose your suppliers carefully. It's not about having a tool for a device, it's about thinking about how all of this can be, how monitoring can bring a lot of different resources into a coherent picture to ensure that your business is able to process, compute, store, and effect dramatic improvements to customer experience across the entire infrastructure asset. And the last thought that I'll leave you with is that CA Tech has been one of the companies of the vanguard of thinking about how this is going to work over the next decade in the industry.

>> Announcer: Live from Fisherman's Wharf in San Francisco, it's theCUBE, covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit Spring 2017. It's a mouthful, it's 170 people here, all high-level CXOs learning about data, and it's part of an ongoing series that IBM is doing around chief data officers and data, part of a big initiative with Cognitive and Watson, I'm sure you've heard all about it, Watson TV if nothing else, if not going to the shows, and we're really excited to have the drivers behind this activity with us today, also Peter Burris from Wikibon, chief strategy officer, but we've got Caitlin Lepech who's really driving this whole show. She is the Communications and Client Engagement Executive, IBM Global Chief Data Office. That's a mouthful, she's got a really big card. And Cortnie Abercrombie, who I'm thrilled to see you, seen her many, many times, I'm sure, at the MIT CDOIQ, so she's been playing in this space for a long time. She is a Cognitive and Analytics Offerings leader, IBM Global Business. So first off, welcome. >> Thank you, great to be here. >> Thanks, always a pleasure on theCUBE. It's so comfortable, I forget you guys aren't just buddies hanging out. >> Before we jump into it, let's talk about kind of what is this series? Because it's not World of Watson, it's not InterConnect, it's a much smaller, more intimate event, but you're having a series of them, and in the keynote is a lot of talk about what's coming next and what's coming in October, so I don't know. >> Let me let you start, because this was originally Cortnie's program. >> This was a long time ago. >> 2014. >> Yeah, 2014, the role was just starting, and I was tasked with can we identify and start to build relationships with this new line of business role that's cropping up everywhere. And at that time there were only 50 chief data officers worldwide. And so I-- >> Jeff: 50? In 2014. >> 50, and I can tell you that earnestly because I knew every single of them. >> More than that here today. >> I made it a point of my career over the last three years to get to know every single chief data officer as they took their jobs. I would literally, well, hopefully I'm not a chief data officer stalker, but I basically was calling them once I'd see them on LinkedIn, or if I saw a press announcement, I would call them up and say, "You've got a tough job. "Let me help connect you with each other "and share best practices." And before we knew, it became a whole summit. It became, there were so many always asking to be connected to each other, and how do we share best practices, and what do you guys know as IBM because you're always working with different clients on this stuff? >> And Cortnie and I first started working in 2014, we wrote IBM's first paper on chief data officers, and at the time, there was a lot of skepticism within our organization, why spend the time with data officers? There's other C-suite roles you may want to focus on instead. But we were saying just the rise of data, external data, unstructured data, lot of opportunity to rise in the role, and so, I think we're seeing it reflected in the numbers. Again, first summit three years ago, 30 participants. We have 170 data executives, clients joining us today and tomorrow. >> And six papers later, and we're goin' strong still. >> And six papers later. >> Exactly, exactly. >> Before we jump into the details, some of the really top-level stuff that, again, you talked about with John and David, MIT CDOIQ, in terms of reporting structure. Where do CDOs report? What exactly are they responsible for? You covered some of that earlier in the keynote, I wonder if you can review some of those findings. >> Yeah, that was amazing >> Sure, I can share that, and then, have Cortnie add. So, we find about a third report directly to the CEO, a third report through the CIO's office, sort of the traditional relationship with CIOs, and then, a third, and what we see growing quite a bit, are CXOs, so functional or business line function. Originally, traditionally it was really a spin-off of CIO, a lot of technical folks coming up, and we're seeing more and more the shift to business expertise, and the focus on making sure we're demonstrating the business impact these data programs are driving for our organization. >> Yeah, it kind of started more as a data governance type of role, and so, it was born out of IT to some degree because, but IT was having problems with getting the line of business leaders to come to the table, and we knew that there had to be a shift over to the business leaders to get them to come and share their domain expertise because as every chief data officer will tell you, you can't have lineage or know anything about all of this great data unless you have the experts who have been sitting there creating all of that data through their processes. And so, that's kind of how we came to have this line of business type of function. >> And Inderpal really talked about, in terms of the strategy, if you don't start from the business strategy-- >> Inderpal? >> Yeah, on the keynote. >> Peter: Yeah, yeah, yeah, yeah. >> You are really in big risk of the boiling the ocean problem. I mean, you can't just come at it from the data first. You really have to come at it from the business problem first. >> It was interesting, so Inderpal was one of our clients as a CEO three times prior to rejoining IBM a year ago, and so, Cortnie and I have known him-- >> Express Scripts, Cambia. >> Exactly, we've interviewed him, featured him in our research prior, too, so when he joined IBM in December a year ago, his first task was data strategy. And where we see a lot of our clients struggle is they make data strategy an 18-month, 24-month process, getting the strategy mapped out and implemented. And we say, "You don't have the time for it." You don't have 18 months to come to data, to come to a data strategy and get by and get it implemented. >> Nail something right away. >> Exactly. >> Get it in the door, start showing some results right away. You cannot wait, or your line of business people will just, you know. >> What is a data strategy? >> Sure, so I can say what we've done internally, and then, I know you've worked with a lot of clients on what they're building. For us internally, it started with the value proposition of the data office, and so, we got very clear on what that was, and it was the ability to take internal, external data, structured, unstructured, and pull that together. If I can summarize it, it's drive to cognitive business, and it's infusing cognition across all of our business processes internally. And then, we identified all of these use cases that'll help accelerate, and the catalyst that will get us there faster. And so, Client 360, product catalog, et cetera. We took data strategy, got buy-in at the highest levels at our organization, senior vice president level, and then, once we had that support and mandate from the top, went to the implementation piece. It was moving very quickly to specify, for us, it's about transforming to cognitive business. That then guides what's critical data and critical use cases for us. >> Before you answer, before you get into it, so is a data strategy a means to cognitive, or is it an end in itself? >> I would say it, to be most effective, it's a succinct, one-page description of how you're going to get to that end. And so, we always say-- >> Peter: Of cognitive? >> Exactly, for us, it's cognitive. So, we always ask very simple question, how is your company going to make money? Not today, what's its monetization strategy for the future? For us, it's coming to cognitive business. I have a lot of clients that say, "We're product-centric. "We want to become customer, client-centric. "That's our key piece there." So, it's that key at the highest level for us becoming a cognitive business. >> Well, and data strategies are as big or as small as you want them to be, quite frankly. They're better when they have a larger vision, but let's just face it, some companies have a crisis going on, and they need to know, what's my data strategy to get myself through this crisis and into the next step so that I don't become the person whose cheese moved overnight. Am I giving myself away? Do you all know the cheese, you know, Who Moved My Cheese? >> Every time the new iOS comes up, my wife's like-- >> I don't know if the younger people don't know that term, I don't think. >> Ah, but who cares about them? >> Who cares about the millenials? I do, I love the millenials. But yes, cheese, you don't want your cheese to move overnight. >> But the reason I ask the question, and the reason why I think it's important is because strategy is many things to many people, but anybody who has a view on strategy ultimately concludes that the strategic process is what's important. It's the process of creating consensus amongst planners, executives, financial people about what we're going to do. And so, the concept of a data strategy has to be, I presume, as crucial to getting the organization to build a consensus about the role the data's going to play in business. >> Absolutely. >> And that is the hardest. That is the hardest job. Everybody thinks of a data officer as being a technical, highly technical person, when in fact, the best thing you can be as a chief data officer is political, very, very adept at politics and understanding what drives the business forward and how to bring results that the CEO will get behind and that the C-suite table will get behind. >> And by politics here you mean influencing others to get on board and participate in this process? >> Even just understanding, sometimes leaders of business don't articulate very well in terms of data and analytics, what is it that they actually need to accomplish to get to their end goal, and you find them kind of stammering when it comes to, "Well, I don't really know "how you as Inderpal Bhandari can help me, "but here's what I've got to do." And it's a crisis usually. "I've got to get this done, "and I've got to make these numbers by this date. "How can you help me do that?" And that's when the chief data officer kicks into gear and is very creative and actually brings a whole new mindset to the person to understand their business and really dive in and understand, "Okay, this is how "we're going to help you meet that sales number," or, "This is how we're going to help you "get the new revenue growth." >> In certain respects, there's a business strategy, and then, you have to resource the business strategy. And the data strategy then is how are we going to use data as a resource to achieve our business strategy? >> Cortnie: Yes. >> So, let me test something. The way that we at SiliconANGLE, Wikibon have defined digital business is that a business, a digital business uses data as an asset to differentially create and keep customers. >> Caitlin: Right. >> Does that work for you guys? >> Cortnie: Yeah, sure. >> It's focused on, and therefore, you can look at a business and say is it more or less digital based on how, whether it's more or less focused on data as an asset and as a resource that's going to differentiate how it's business behaves and what it does for customers. >> Cortnie: And it goes from the front office all the way to the back. >> Yes, because it's not just, but that's what, create and keep, I'm borrowing from Peter Drucker, right. Peter Drucker said the goal of business is to create and keep customers. >> Yeah, that's right. Absolutely, at the end of the day-- >> He included front end and back end. >> You got to make money and you got to have customers. >> Exactly. >> You got to have customers to make the money. >> So data becomes a de-differentiating asset in the digital business, and increasingly, digital is becoming the differentiating approach in all business. >> I would argue it's not the data, because everybody's drowning in data, it's how you use the data and how creative you can be to come up with the methods that you're going to employ. And I'll give you an example. Here's just an example that I've been using with retailers lately. I can look at all kinds of digital exhaust, that's what we call it these days. Let's say you have a personal digital shopping experience that you're creating for these new millenials, we'll go with that example, because shoppers, 'cause retailers really do need to get more millenials in the door. They're used to their Amazon.coms and their online shopping, so they're trying to get more of them in the door. When you start to combine all of that data that's underlying all of these cool things that you're doing, so personal shopping, thumbs up, thumb down, you like this dress, you like that cut, you like these heels? Yeah, yes, yes or no, yes or no. I'm getting all this rich data that I'm building with my app, 'cause you got to be opted in, no violating privacy here, but you're opting in all the way along, and we're building and building, and so, we even have, for us, we have this Metro Pulse retail asset that we use that actually has hyperlocal information. So, you could, knowing that millenials like, for example, food trucks, we all like food trucks, let's just face it, but millenials really love food trucks. You could even, if you are a retailer, you could even provide a fashion truck directly to their location outside their office equipped with things that you know they like because you've mined that digital exhaust that's coming off the personal digital shopping experience, and you've understood how they like to pair up what they've got, so you're doing a next best action type of thing where you're cross-selling, up-selling. And now, you bring it into the actual real world for them, and you take it straight to them. That's a new experience, that's a new millennial experience for retail. But it's how creative you are with all that data, 'cause you could have just sat there before and done nothing about that. You could have just looked at it and said, "Well, let's run some reports, "let's look at a dashboard." But unless you actually have someone creative enough, and usually it's a pairing of data scientist, chief data officers, digital officers all working together who come up with these great ideas, and it's all based, if you go back to what my example was, that example is how do I create a new experience that will get millenials through my doors, or at least get them buying from me in a different way. If you think about that was the goal, but how I combined it was data, a digital process, and then, I put it together in a brand new way to take action on it. That's how you get somewhere. >> Let me see if I can summarize very quickly. And again, just as an also test, 'cause this is the way we're looking at it as well, that there's human beings operate and businesses operate in an analog world, so the first test is to take analog data and turn it into digital data. IOT does that. >> Cortnie: Otherwise, there's not digital exhaust. >> Otherwise, there's no digital anything. >> Cortnie: That's right. >> And we call it IOT and P, Internet of Things and People, because of the people element is so crucial in this process. Then we have analytics, big data, that's taking those data streams and turning them into models that have suggestions and predictions about what might be the right way to go about doing things, and then there's these systems of action, or what we've been calling systems of enactment, but we're going to lose that battle, it's probably going to be called systems of action that then take and transduce the output of the model back into the real world, and that's going to be a combination of digital and physical. >> And robotic process automation. We won't even introduce that yet. >> Which is all great. >> But that's fun. >> That's going to be in October. >> But I really like the example that you gave of the fashion truck because people don't look at a truck and say, "Oh, that's digital business." >> Cortnie: Right, but it manifested in that. >> But it absolutely is digital business because the data allows you to bring a more personal experience >> Understand it, that's right. >> right there at that moment, and it's virtually impossible to even conceive of how you can make money doing that unless you're able to intercept that person with that ensemble in a way that makes both parties happy. >> And wouldn't that be cheaper than having big, huge retail stores? Someone's going to take me up on that. Retailers are going to take me up on this, I'm telling you. >> But I think the other part is-- >> Right next to the taco truck. >> There could be other trucks in that, a much cleaner truck, and this and that. But one thing, Cortnie, you talk about and you got to still have a hypothesis, I think of the early false promises of big data and Hadoop, just that you throw all this stuff in, and the answer just comes out. That just isn't the way. You've got to be creative, and you have to have a hypothesis to test, and I'm just curious from your experience, how ready are people to take in the external data sources and the unstructured data sources and start to incorporate that in with the proprietary data, 'cause that's a really important piece of the puzzle? It's very different now. >> I think they're ready to do it, it depends on who in the business you are working with. Digital offices, marketing offices, merchandising offices, medical offices, they're very interested in how can we do this, but they don't know what they need. They need guidance from a data officer or a data science head, or something like this, because it's all about the creativity of what can I bring together to actually reach that patient diagnostic, that whatever the case may be, the right fashion truck mix, or whatever. Taco Tuesday. >> So, does somebody from the chief data office, if you will, you know, get assigned to, you're assigned to marketing and you're assigned to finance, and you're assigned to sales. >> I have somebody assigned to us. >> To put this in-- >> Caitlin: Exactly, exactly. >> To put this in kind of a common or more modern parlance, there's a design element. You have to have use case design, and what are we going, how are we going to get better at designing use cases so we can go off and explore the role that data is going to play, how we're going to combine it with other things, and to your point, and it's a great point, how that turns into a new business activity. >> And if I can connect two points there, the single biggest question I get from clients is how do you prioritize your use cases. >> Oh, gosh, yeah. >> How can you help me select where I'm going to have the biggest impact? And it goes, I think my thing's falling again. (laughing) >> Jeff: It's nice and quiet in here. >> Okay, good. It goes back to what you were saying about data strategy. We say what's your data strategy? What's your overarching mission of the organization? For us, it's becoming cognitive business, so for us, it's selecting projects where we can infuse cognition the quickest way, so Client 360, for example. We'll often say what's your strategy, and that guides your prioritization. That's the question we get the most, what use case do I select? Where am I going to have the most impact for the business, and that's where you have to work with close partnership with the business. >> But is it the most impact, which just sounds scary, and you could get in analysis paralysis, or where can I show some impact the easiest or the fastest? >> You're going to delineate both, right? >> Exactly. >> Inderpal's got his shortlist, and he's got his long list. Here's the long term that we need to be focused on to make sure that we are becoming holistically a cognitive company so that we can be flexible and agile in this marketplace and respond to all kinds of different situations, whether they're HR and we need more skills and talent, 'cause let's face it, we're a technology company who's rapidly evolving to fit with the marketplace, or whether it's just good old-fashioned we need more consultants. Whatever the case may be. >> Always, always. >> Yes! >> I worked my business in. >> More consultants! >> Alright, we could go, we could go and go and go, but we're running out of time, we had a full slate. >> Caitlin: We just started. >> I know. >> I agree, we're just starting this convers, I started a whole other conversation to him. We haven't even hit the robotics yet. >> We need to keep going, guys. >> Get control. >> Cortnie: Less coffee for us. >> What do people think about when they think about this series? What should they look forward to, what's the next one for the people that didn't make it here today, where should they go on the calendar and book in their calendars? >> So, I'll speak to the summits first. It's great, we do Spring in San Francisco. We'll come back, reconvene in Boston in fall, so that'll be September, October frame. I'm seeing two other trends, which I'm quite excited about, we're also looking at more industry-specific CDO summits. So, for those of our friends that are in government sectors, we'll be in June 6th and 7th at a government CDO summit in D.C., so we're starting to see more of the industry-specific, as well as global, so we just ran our first in Rio, Brazil for that area. We're working on a South Africa summit. >> Cortnie: I know, right. >> We actually have a CDO here with us that traveled from South Africa from a bank to see our summit here and hoping to take some of that back. >> We have several from Peru and Mexico and Chile, so yeah. >> We'll continue to do our two flagship North America-based summits, but I'm seeing a lot of growth out in our geographies, which is fantastic. >> And it was interesting, too, in your keynote talking about people's request for more networking time. You know, it is really a sharing of best practices amongst peers, and that cannot be overstated. >> Well, it's community. A community is building. >> It really is. >> It's a family, it really is. >> We joke, this is a reunion. >> We all come in and hug, I don't know if you noticed, but we're all hugging each other. >> Everybody likes to hug their own team. It's a CUBE thing, too. >> It's like therapy. It's like data therapy, that's what it is. >> Alright, well, Caitlin, Cortnie, again, thanks for having us, congratulations on a great event, and I'm sure it's going to be a super productive day. >> Thank you so much. Pleasure. >> Thanks. >> Jeff Frick with Peter Burris, you're watchin' theCUBE from the IBM Chief Data Officer Summit Spring 2017 San Francisco, thanks for watching. (electronic keyboard music)

>> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Data Privacy Day at Twitter's World Headquarters in downtown San Francisco. Full-day event, a lot of seminars and sessions talking about the issue of privacy. Even though Scott McNealy in 1999 said, "Privacy's dead, get over it," everyone here would beg to differ; and it's a really important topic. We're excited to have Michelle Dennedy. She's the Chief Privacy Officer from Cisco. Welcome, Michelle. >> Indeed, thank you. And when Scott said that, I was his Chief Privacy Officer. >> Oh you were? >> I'm well acquainted with my young friend Scott's feelings on the subject. >> It's pretty interesting, 'cause that was eight years before the iPhone, so a completely different world than actually one of the prior guests we were talking about privacy is an issue in the Harvard Business Review from 125 years ago. So this is not new. >> Absolutely. >> So how have things changed? I mean that's a great perspective that you were there. What was he kind of thinking about and really what are the privacy challenges now compared to 1999? >> So different. Such a different world. I mean fascinating that when that statement was made the discussion was a press conference where we were introducing Connectivity. It was an offshoot of Java, and it basically allowed you to send from your personal computer a wireless message to your printer so that a document could come out (gasp). >> That's what it was? >> Yeah. >> Wireless printing? >> Wireless printing. And really it was gyro technology, so anything wirelessly could start talking to each other in an internet of things world. >> Right. >> So, good news bad news. The world has exploded from there, obviously; but the base premise of, can I be mobile, can I live in a world of connectivity, and still have control over my story, who I am, where I am, what I'm doing? And it was really a reframing moment of when you say privacy is dead, if what you mean by that is secrecy and hiding away and not being connected to the world around you, I may agree with you. However, privacy as a functional definition of how we define ourselves, how we live in a culture, what we can expect in terms of morality, ethics, respect, and security, alive and well, baby. Alive and well. >> (laughs) No shortage of opportunity to keep you busy. We talked to a lot of people who go to a lot of tech conferences. I have to say I don't know that we've ever talked to a Chief Privacy Officer. >> You're missing out. >> I know, so not you get to define the role, I love it. So what are your priorities as Chief Priority Officer? What are you keeping an eye on day to day as well as what are your more strategic objectives? >> It's a great question. So the rise of the Chief Privacy Officer, actually Scott was a big help in that and gave me exactly the right amount of rope to hang myself with. The way I look at it is, probably the simplest analogy is, should you have a Chief Financial Officer? >> Yeah. >> I would guess yeah, right? That didn't exist about 100 years ago. We just kind of loped along, and whoever had the biggest bag of money at the end was deemed to be successful. Where if somebody else who had no money left at the end but bought another store, you would have no way of measuring that. So the Chief Privacy Officer is that person for your digital currency. I look at the pros and the cons, the profit and the loss, of data and the data footprint for our company and for all the people to whom we sell. We think about, what are those control mechanisms for data? So think of me as your data financial officer. >> Right, right. But the data in and of itself is just stagnant, right? It's really just the data in the context of all these other applications. How it's used, where it's used, when it's used, what it's combined with, that really starts to trip into areas of value as well as potential problems. >> I feel like we scripted this before, but we didn't. >> Jeff: We did not script it, we don't script the-- >> So if I took out a rectangle out of my wallet, and it had a number on it, and it was green, what would you say that thing probably is? >> Probably Andrew Jackson on the front. >> Yeah, probably Andrew Jackson. What is that? >> A 20 dollar bill. >> Why is that a 20 dollar bill? >> Because we agree that you're going to give it to me and it has that much value, and thankfully the guy at Starbucks will give me 20 bucks worth of coffee for it. >> (laughs) Exactly. Well which could be a cup the way we're going. >> Which could be a cup. >> But that's exactly right. So is that 20 dollar bill stagnant? Yes. That 20 dollar bill just sitting on the table between us is nothing. I could burn it up, I could put it in my pocket and lose it and never see it again. I could flush it down the toilet. That's how we used to treat our data. If you recognize instead the story that we share about that piece of currency, we happen to be in a place where it's really easy to alienate that currency. I could go downstairs here and spend it. If I was in Beijing I probably would have to go and convert it into a different currency, and we'd tell a story about that conversion because our standards interface is different. Data is exactly the same way. The story that we share together today is a valuable story because we're communicating out, we're here for a purpose. >> Right. >> We're making friends. I'm liking you because you're asking me all these great questions that I would have fed you had I been able to feed you questions. >> Jeff: (laughs) But it's only that context, it's only that communicability that brings it value. We now assume as a populous that paper currency is valuable. It's just paper. It's only as good as the story that enlivens it. So now we're looking at smaller, smaller Microdata transactions of how am I tweeting out information to people who follow me? >> Jeff: Right, right. >> How do I share that with your following public, and does that give me a greater opportunity to educate people about security and privacy? Does that allow my company to sell more of my goods and services because we're building ethics and privacy into the fabric of our networks? I would say that's as valuable or more valuable than that Andrew Jackson. >> So it's interesting 'cause you talk about building privacy into the products. We often hear about building security into the products, right? Because the old way of security of building a bigger wall doesn't work any more and you really have to bake it in at all steps of the application: development, the data layer, the database, et cetera, et cetera. When you look at privacy versus security, and especially 'cause Cisco's sitting on, I mean you guys are sitting on the pipes, everything is running through your machines. >> That's right. >> How do you separate the two, how do you prioritize, and how do you make sure the privacy discussion is certainly part of that gets the right amount of relevance within the context of the security conversation? >> It's a glib answer that's much more complicated, but the security is really in many instances the what. I can really secure almost any batch of data. It can be complete gobbley gook zeroes and ones. It could be something really critical. It could be my medical records. The privacy and the data about what that context is, that's the why. I don't see them as one or the other at all. I see security and security not as not a technology but a series of verb things that you actually physically, people process technologies. That enactment should be addressed to a why. So it's kind of Peter Drucker's management of you manage what you measure. That was like incendiary advice when it first came out. Well I wanted to say that you secure what you treasure. So if you treasure a digital interaction with your employees, your customers, and your community, you should probably secure that. >> Right. But it seems like there's a little bit of a disconnect about maybe what should be treasured and what is the value with folks that have grown up. Let's pick on the young kids, not really thinking through or having the time or knowing an impact of a negative event in terms of just clicking and accepting the EULA and using that application on their phone. They just look at in a different way. Is that valid? How do they change that behavior? How do you look at this new generation, and there's this sea of data which is far larger than it used to be coming off all these devices, internet of things, obviously. People are things too. The mobile devices with all that geolocation data, and the sensor data, and then oh by the way it's all going to be in our cars and everything else shortly. How's that landscape changing and challenging you in new ways, and what are you doing about it? >> The speed and dynamics are astronomical. How do you count the stars, right? >> Jeff: (laughs) >> And should you? Isn't that kind of a waste of time? >> Jeff: Right, right. >> It used to be that knowledge, when I was a kid, was knowing what was in A to Z of the Encyclopedia Britannica. Now facts are cheap. Facts used to be expensive. You had to take time and commit to them, and physically find them, and be smart enough to read, and on, and on, and on. The dumbest kid is smarter than I was with my Encyclopedia Britannica because we have search engines. Now their commodity is how do I critically think? How do I make my brand and make my way? How do I ride and surf on a wave of untold quantities of information to create a quality brand for myself? So the young people are actually in a much better position than, I'll still count us as young. >> Jeff: Yeah, Uh huh. >> But maybe less young. >> Less young, less young than we were yesterday. >> We are digital natives, but I think I am hugely optimistic that the kids coming up are really starting to understand the power of brand: personal brand, family brand, cultural brand. And they're feeling very activist about the whole thing. >> Yeah, which is interesting 'cause that was never a factor when there was no personal brand, right? You were part of-- >> No way. >> whatever entity that you were in. >> Well, you were in a clique. >> Right. >> Right? You identified as when I was home I was the third out of four kids. I was a Roman Catholic girl in the Midwest. I was a total dork with a bowl haircut. Now kids can curate who and what and how they are over the network. Young professionals can connect with people with experience. Or they can decide, I get this all the time on Twitter actually. How did you become a Chief Privacy Officer? I'm really interested in taking a pivot in my career. And I love talking to those people 'cause they always educate me, and I hope that I give them a little bit of value too. >> Right, right. Michelle, we could go on for on and on and on. But, unfortunately, I think you got to go cover a session. So we're going to let you go. >> Thank you. >> Michelle Dennedy, thanks for taking a few minutes of your time. >> Thank you, and don't miss another Data Privacy Day. >> I will not. We'll be back next year as well. I'm Jeff Frick. You're watching theCUBE. See you next time.

>> Announcer: Live from New York, it's theCUBE covering Big Data New York City 2016. Brought to you by headline sponsors Cisco, IBM, Nvidia, and our ecosystem sponsors. Now, here are your hosts, Dave Vellante and Peter Burris. >> We're back. Goutham Belliappa is here. He's with Capgemini. He's the Big Data Integration and Analytics Leader at Capgemini. Welcome to theCUBE. >> Thank you. Happy to be here with you. >> So a lot going on this week at Big Data. You guys have one of the top SI's consultants in the world. What are you seeing as far as the transformation of organizations to become data driven? What are some of the drivers that you're seeing out there? >> It's a good question. So a couple of years ago, we started on this journey with Cloudera about four years ago. When we started this journey on LinkedIn, you saw the poster that said, "Big Data is like teenage sex - everybody talks about it, nobody does it." Right? The reality shifted considerably. So while the technology's evolved considerably over the last four years, the most important thing is most of our clients are feeling pressure from the disruptors in Silicon Valley. You see the AirBnb's and the Amazon's and the Google apply pressure's on traditional industries that didn't exist before. For example, a lot of our auto clients don't believe auto clients are the biggest threat. They believe Apple, and Google, and Amazon are the biggest threat. Right? Because what our clients are afraid of, the incumbents, the traditional companies are afraid of, is they don't want to become a commodity manufacturer of components for a software company. They don't want, for example, GM manufacturing a part that Apple is putting the wrapper on, selling and making the margin on. So, more and more tech is driving the industry to where GE made the announcement they no longer want to be known as an engine manufacturer, they want to be an IT company. >> Peter: Or a financial services firm. >> Or a financial services firm. And you see the same thing in pharma as well. We see the pharma companies don't want to be known as manufacturers of med devices, they want to own the service industry. Move up the value chain and secure the revenue stream. So that's what's changing the industry as a whole and then Big Data Central to the strategy of data-enabled transformation. >> So it's like the death, what was the article we saw yesterday? Who wrote that? "The Death of Tech". It was Rob Thomas, right? The death of tech companies is now the rebirth of... all companies are tech companies. >> All companies are tech companies and that's the future of all companies: to be a tech company and move from selling commodities to selling services and having a vested interest in the outcome that the clients receive at the end of the day. >> Yeah, I once wrote a piece many year ago that suggested that we would see more non-tech companies generate SAS and Cloud applications than tech companies themselves. And while it's still hasn't come true there's evidence on the horizon that it very well likely will be a major feature of how companies engage their customers through their own version of SAS or deploying their own Clouds for their own ecosystem. And you can go back, thirty years, thirty-five years and look at MAP/TOP for example and the promise of what it meant to define and deploy standards that could integrate whole industries around data. Hasn't happened, but we can see it actually happening on the horizon. What industry? I mean, you're still looking at things through an industry lenses, right? Where do you see it happening before it's happening elsewhere? >> So, the first place it happens naturally is tech because they're closest to it, right? To give you the classic example, I can go anywhere and buy an Office license today. I have to subscribe to Office, right? So, what it's done to Microsoft, it's changed the fundamentals of the balance sheet from selling perpetual licenses, getting revenue once and then having the prospect of not having a customer later, to selling it over a sustained period of time. So moving from one-time revenue hits to perpetual revenue. So tech is where it's starting off. And even in tech, we're actually pushing the boundaries by working some of our providers like Cloudera and some of the other providers out there to move from a perpetual license model to as-a-service model. So what this enables people like us to do is to offer as-a-service to our customers because our customers need to offer as-a-service to their end users as well, right? I gave you the example of GE because it's public knowledge. They want to move up the spectrum of not selling an engine but leasing an engine to an airplane manufacturer and then owning the services revenue on it, right? So when Delta, let's say, that's leasing the engine is no longer owning a commodity, they're becoming asset light, right? The companies like GE and other companies when they become tech, they need to become asset light as well, which means not being burdened by land, labor, and capital but, as they get paid for outcome, they want to pay for outcome as well. >> Somebody's got to own the asset eventually. This is not a game of musical chairs where the asset-owning music keeps playing and then it stops and somebody's got all the assets. >> Ghoutham: Exactly. >> So how do you see... the global sense of how organization, how is this going to get institutionalized? Are we just going to have a few companies with enormous assets and everybody else running software? How do you think it's going to play out? >> Good question. So Jeff Bezos was at a manufacturing company outside of Arland recently and he pointed at and antique generator sitting next to the plane and said, 'Back in the day, everybody had 'a generator sitting next to the 'company producing electricity.' But today we have a big distribution plan and we get it off the grid, right? So to your point, yes, we see the scale and the price reduction coming from a few companies owning those pieces of assets. For example, it's almost impossible to compete with the Amazon's and Google's of the world today because at the scale that they receive. And the customers get the benefit of that. Similarly, you'll see the software, right? So software, you see the software companies owning the assets and title and leasing it back to the customer. So to your point, yes, we're moving to a model where it's more scalable and the price efficiencies of them, they're passed on to the end consumer. >> Peter: So historically, in a more asset-oriented company, historically, if you take a look, for example, at Porter. Porter's competitive strategy. So Porter would say, 'Pick your industry' where an industry is a way of categorizing companies with similarly procured and deployed assets. Automobile had a collection of assets and hotelery had a collection of assets. So pick your industry based on your knowledge and what kind of returns you're likely to get. Pick your position in that industry and then decide what games you're going to play using the five-factor analysis you did. But it was all tied back to assets. So if the world's getting less asset-oriented, hard asset-oriented >> Ghoutham: Hard assets >> What does that do to competitive strategy? >> Good point. So the hard assets are getting commoditized. The value comes in what you can build on top of the hard assets, which is your IP, right? So the soft assets of IP and software is where the value's going to be. So there's a lot of pressure on hard-asset companies. You see many companies getting at the server market because they can't compete with the Amazon's and the Google's. They can wide-label and manufacture all their stuff. The differentiation is going to come in the software. That's the reason companies like GE and the other pharma companies and automobile companies want to become tech companies, because that's where the margin is, that's where the differentiation is. It's no longer in the tangible, hard-assets but it's in what you can do with them. >> Dave: Well, and it says data's going to be one of those differentiators. >> Yeah, yeah. >> And a big asset so what... Everybody in theory has to become data-driven, maybe in fact has to be- >> Data is their asset, is their differentiator. >> You've pointed out many times all this digitization is data. >> Peter: Well, yeah. >> Digital equals data. >> So our basic proposition is that increasingly the whole notion of being a digital business is about how you differentially use data to create and sustain customers. So let me build on that for a second and say that there's this term in economics known as "asset specificity" which essentially is the degree to which an asset is applied to a single or limited numbers of uses. Programmability reduces asset specificity so if we go back to the airline engine example, GE added programmability to an airplane engine and was able to turn it into a service. Uber was able to add programmability to a bunch of consumer cars and was able to turn it into a ride sharing capability. What does that say about the future of an industry-oriented approach to conducting business if I am now able to reconfigure my asset base very quickly and the industry's based on how my assets are reconfigured. What does that say about the future of industry? >> Ghoutham: So, in my opinion, I don't think the future of industry is going to change because you still going to have a specialization based on the domain you're selling to and the expertise that you have. >> Peter: So it's customer-focused industry definitions not asset-based industry definition. >> Ghoutham: The hard assets or going to get commoditized and get moved out to a few specialty players. But the differentiation is going to be on how you serve the customers and the type of customer that you serve. >> Dave: So what are the head winds you're seeing in terms of customers getting to this data nirvana? What are the challenges that they're facing? >> So, Peter Drucker. There's an attribute of Peter Drucker, regardless of who said it, 'Culture eats strategy for breakfast.' We work with retailers all the time who understand that they face an existential threat from Amazon, however their culture prevents them from being like Amazon. It prevents them from experimenting. It prevents them from failing fast. It prevents them from acting together. For example, a lot of customers want to have an OmniChannel strategy. It's a seamless commerce strategy but then they have a silo for the stores they have a silo for the call centers, they have a silo for the web, but they don't act together. So culture is one of the biggest barriers we see in enabling that journey. Tech, we know that tech works. Two years ago we're doing technical POC's. Today, we're not anymore. We know that tech works, right? So get over it. So it's a culture and the attitude and the ability to change how you go to market that's to me the biggest challenge. >> Peter: But isn't there also finance? Because hard assets still are associated with a rate of amortization, depreciation, and utilization. There's expertise and what not built up around that, and this becomes especially critical when you start thinking about the impedance mismatch between agile development and budgeting, for example. So how do you anticipate that not only culture has to change, but also the way we think about finance? Or is financing disciplines end up being a part of the culture? >> Ghoutham: So you're absolutely right. So, financing discipline has to be part of the culture. To give you an abstract example, back in the day when we did a data warehouse or a data project, we'd do a huge, let's say for lack of an argument, 10 million dollar project. Today we're doing 40, 50, 50k, 100k projects. So Agile has gone from fixed scope where you laid out a two-year project with an end in mind and by the time you achieve that end the requirements have changed and the business has moved on, to achieving small objectives. So we're consuming it in chunks. You're going from fixed scope to fixed budget. So I've got a certain allocation that I need to use and I prioritize it on a regular basis on how I want to consume that basis that I have. >> So it's almost a subscription? Are you going in basically almost subscription-basis? Going to a customer and saying, here's the outcome. We will achieve that outcome over a period of time. You'll sign up to achieve that outcome over a 12-month period and will consume that budget in 12-month increments? >> First and second, in any given period, you can re-prioritize the outcome that you want to achieve. During the journey for 12 months, if you realize something new, you have the flexibility to change. Let me take out this chunk of work and do something else so I have the flexibility. >> Peter: So you can redefine the outcomes? >> Yes. >> It's almost like, I don't know if you'd call it this, I'd be interested to know what you guys call it, but it's almost like a subscription-to-outcome business model. >> Ghoutham: Exactly. >> Dave: Service is a service. >> Ghoutham: We call it sprint as a service. >> Service is a service. >> We call it sprint as a service is our defined model of how to go to market around that is we know two sprints ahead what we're going to deliver. Everything else is indicative, right? Because not everything we do has to succeed. That's a mindset change that our customers need to realize. We believe the biggest reason clients fail is because failure is not an option. They put so much behind it, when they fail, it's catastrophic. >> Peter: Because careers fail- >> Yes >> Peter: And not the project fails. >> Exactly. >> Dave: You're not saying "failure equals fire" mentality. If that's the culture, then people refuse to fail and they end up failing. >> Until it's catastrophic. >> (Dave laughing) >> So I was having a conversation last week at Oracle OpenWorld when theCUBE was here, great show, and had a really good conversation with a competitor of yours who talked about how they were going to use machine-learning in the contracting process by sweeping up all kinds of data and that would help them actually define the characteristics of what they were going to deliver. How much work was going to take, how much labor, what other resources? And they were able to get rid of the 500 thousand to five million dollar part of the assessment or the assessment part of a deal, drive it down to 50 thousand dollars or less and in the process come up with contracts who are much more customer-friendly. What other types of changes are happening in the services business as we do a better job of packaging intellectual property whether it's this "service as a service" or "service subscription" or whatever you mentioned or even thinking about machine learning being applied to the contracting process. >> Dave: "Sprint as a service" >> That's correct. Sorry. Thank you. >> You've asked a number of questions so first thing >> I did. >> Let me talk about machine learning and human task automation. So one of the biggest things we're doing today is learning to understand and automate human tasks. One of the biggest things we've seen, supply chain companies for example, is they don't have enough planners, right? So you hire a bunch of planners. You have different variations and skills. So we're taking the top 5% of planners, automating what everybody else does and letting them handle exceptions. And workforce automation, in many of those areas, we're beginning to automate human tasks and letting the human handle exceptions that a machine cannot handle. So machine learning has becoming fundamental in everything, and not just contract negotiation, but actually enabling companies to scale in areas where they could never scale because they never had enough people to do it. We're not just doing it externally to our clients. One of the things we're doing internally is we don't have an Big Data developers so we're beginning to use machine learning to automate a lot of tasks that developers will do. Industrialize a lot of it so we can scale in our delivery approach as well. >> Peter: Excellent. >> Come back to this event. You guys are here, you're on the floor. We've been talking all week about, you know, Hadoop is kind of yesterday's news. >> Ghoutham: Yes, yes. >> What are you guys seeing? You got a big chunk of customers that said alright, we're going to invest in Hadoop. We have the skill sets. And then a big chunk of... I'm not going there. And now they're sort of looking at new ways. Whether it's Cloud, whether it's Spark. >> Peter: And a big chunk of customers will say I do want to go there, but I'm having problems getting there. >> Yeah, right. And I got some serious challenges. So what are you seeing there, and how is CapGemini helping them? >> So we did an analysis with Forrester and one thing we'll say that 100% of our clients are going to Hadoop. It's not 95%. So everybody's going to Hadoop in one way, shape, or form. Whether you go with the traditional distribution, go with an Amazon as your whatever, everybody's going to Hadoop in some way, shape, or form. To address the reluctance, we spoke about the Uberization of the industry, which is you have a contract, which is an outcome-based contract. So we go to our clients who have fears about moving to Hadoop and say, 'We'll take the risk'. Let's write an outcome-based contract to move you guys into the noob because you know you need to go there. You're afraid to go there so we'll take the risk, we'll shift the risk over to us and we'll move you onto Hadoop. The last piece is industrialization. So back two years ago, we designed code for every little thing that we needed to do. Today, we've automated a lot of our code generation from existing systems, from knowledge we've gained, including machine learning to we're able to mechanize a lot of the code. Frankly, we did it because we had a developer shortage. So we started industrializing a lot of our IPN, our assets, and our learnings, but this is also helping our customers move on to the new world. It's improved the quality of a delivery. It's improved the velocity of a delivery. It's reduced the price where we're much more competitive. To give you an example in the BPO space back in the day we did labor arbitrage. But more and more, like with our clients who use manual auditing, we're using machine learning to automate a lot of that. And that more than pays for the cost of Hadoop. So to answer your specific question, gone are the days of 'Hey, I want to get into Hadoop.' The question is what business value can I achieve? How fast can I achieve it, and if you're afraid, can I take the risk for you? >> And that business value, historically, if I can use that term on such a nascent industry, Has been... the ROI's been a Reduction on Investment. >> Ghoutham: Correct. I'm going to lower the cost of my enterprise data warehouse. >> Ghoutham: That was two years ago. >> Okay so what is it today? >> Today, it is 'How can I reduce your marketing span? 'How can I optimize your marketing span? 'How can I improve the accuracy 'of your supply chain planning?' So it's more in terms of directly delivering business value versus the cost reduction. Many of our clients say the cost reduction is irrelevant. Frankly, because the business case is so huge. To give you an example of one of our supply chain clients, their fill-rate for orders is 60% which means they're a big manufacturer, they're only to fill 60% of the orders that come through. That's because they're not able to plan where to deploy product and so on and so forth. So if you increase it by 5%, it's a 300 million dollar annual business case. My two million dollar data warehouse optimization, it's irrelevant. It's peanuts in a 300 million dollar annual business case. It's things like that that's helping machine learning and Hadoop evolve in the ecosystem. The cost-reduction play was just a way to slide the infrastructure in. You can do a lot more with it. >> And when you're selling to the CIO's and business leaders, that resonates. >> Ghoutham: Yeah. Absolutely. >> Great. We'll have to leave it there. Thanks very much for coming to theCUBE, Ghou. >> Ghoutham: My pleasure. My pleasure. >> Alright keep it right there everybody. We'll be back with our next guest. This is theCUBE. We're live at Big Data NYC. Be right back. (techno music)

in Cambridge Massachusetts it's the cube covering the MIT chief data officer and information quality symposium now here are your hosts Stu miniman and George Gilbert you're watching the cube SiliconANGLE media's flagship program we go out to lots of technology shows and symposiums like this one here help extract the signal from the noise I'm Stu miniman droid joined by George Gilbert from the Wikibon research team and really thrilled to have on the program the keynote speaker from this MIT event Tom Davenport whose pressure at babson author of some books including a new one that just came out and thank you so much for joining us my pleasure great to be here all right so uh you know so many things your morning keynote that I know George and I want to dig into I guess I'll start with you talk about the you know for eras of you called it data today used to be formation from the information sorry but you said you started with when it was three eras of analytics and now you've came to information so I'm just curious we you know we get caught up sometimes on semantics but is there a reason why you switch from you know analytics to information now well I'm not sure it's a permanent switch I just did it for this occasion but you know I I think that it's important for even people who aren't who don't have as their job doing something with analytics to realize that analytics or how we turn data into information so kind of on a whim I change it from four errors of analytics 24 hours of information to kind of broaden it out in a sense and make people realize that the whole world is changing it's not just about analytics ya know I it resonated with me because you know in the tech industry so much we get caught up on the latest tool George will be talking about how Hadoop is moving to spark and you know right if we step back and look from a longitudinal view you know data is something's been around for a long time but as as you said from Peter Drucker's quote when we endow that with relevance and purpose you know that that's when we get information so yeah and that's why I got interested in analytics a year ago or so it was because we weren't thinking enough about how we endowed data with relevance and purpose turning it into knowledge and knowledge management was one of those ways and I did that for a long time but the people who were doing stuff with analytics weren't really thinking about any of the human mechanisms for adding value to to data so that moved me in analytics direction okay so so Tommy you've been at this event before you know you you've taught in written and you know written books about this about this whole space so willing I'm old no no its you got a great perspective okay so bring us what's exciting you these days what are some of our big challenges and big opportunities that we're facing as kind of kind of humanity and in an industry yeah well I think for me the most exciting thing is they're all these areas where there's just too much data and too much analysis for humans to to do it anymore you know when I first started working with analytics the idea was some human analysts would have a hypothesis about how to do that about what's going on in the data and you'd gather some data and test that hypothesis and so on it could take weeks if not months and now you know we need me to make decisions in milliseconds on way too much data for a human to absorb even in areas like health care we have 400 different types of cancer hundreds of genes that might be related to cancer hundreds of drugs to administer you know we have these decisions have to be made by technology now and so very interesting to think about what's the remaining human role how do we make sure those decisions are good how do we review them and understand them all sorts of fascinating new issues I think along those lines come you know in at a primitive level in the Big Data realm the tools are kind of still emerging and we want to keep track of every time someone's touched it or transformed it but when you talk about something as serious as cancer and let's say we're modeling how we decide to or how we get to a diagnosis do we need a similar mechanism so that it's not either/or either the doctor or you know some sort of machine machine learning model or cognitive model some waited for the model to say here's how I arrived at that conclusion and then for the doctor to say you know to the patient here's my thinking along those lines yeah I mean I think one can like or just like Watson it was being used for a lot of these I mean Watson's being used for a lot of these oncology oriented projects and the good thing about Watson in that context is it does kind of presume a human asking a question in the first place and then a human deciding whether to take the answer the answers in most cases still have confidence intervals you know confidence levels associated with them so and in health care it's great that we have this electronic medical record where the physicians decision of their clinicians decision about how to treat that patient is recorded in a lot of other areas of business we don't really have that kind of system of record to say you know what what decision did we make and why do we make it and so on so in a way I think health care despite being very backward in a lot of areas is kind of better off than then a lot of areas of business the other thing I often say about healthcare is if they're treating you badly and you die at least there will be a meeting about it in a healthcare institution in business you know we screw up a decision we push it under the rug nobody ever nobody ever considered it what about 30 years ago I think it was with Porter's second book you know and the concept of the value chain and sort of remaking the the understanding of strategy and you're talking about the you know the AP AP I economy and and the data flows within that can you help tie your concept you know the data flows the data value chain and the api's that connect them with the porters value chain across companies well it's an interesting idea I think you know companies are just starting to realize that we are in this API economy you don't have to do it all yourself the smart ones have without kind of modeling it in any systematic way like the porter value chain have said you know we we need to have other people linking to our information through api's google is fairly smart i think in saying will even allow that for free for a while and if it looks like there's money to be made in what start charging for access to those api so you know building the access and then thinking about the the revenue from it is one of the new principles of this approach but i haven't seen its i think would be a great idea for paper to say how do we translate the sort of value chain ideas a michael porter which were i don't know 30 years ago into something for the api oriented world that we live in today which you think would you think that might be appropriate for the sort of platform economics model of thinking that's emerging that's an interesting question i mean the platform people are quite interested in inner organizational connections i don't hear them as talking as much about you know the new rules of the api economy it's more about how to two sided and multi-sided platforms work and so on Michael Porter was a sort of industrial economist a lot of those platform people are economists so from that sense it's the same kind of overall thinking but lots of opportunity there to exploit I think so tell me what want to bring it back to kind of the chief data officer when one of the main themes of the symposium here I really like you talked about kind of there needs to be a balance of offense and defense because so much at least in the last couple of years we've been covering this you know governance and seems to be kind of a central piece of it but it's such an exciting subject it's exciting subject but you know you you put that purely in defense on and you know we get excited the companies that are you know building new products you know either you know saving or making more money with with data Kenny can you talk a little bit about kind of as you see how this chief data officer needs to be how that fits into your kind of four arrows yeah yeah well I don't know if I mentioned it in my talk but I went back and confirmed my suspicions that the sama Phi odd was the world's first chief data officer at Yahoo and I looked at what Osama did at Yahoo and it was very much data product and offense or unity established yahoo research labs you know not everything worked out well at Yahoo in retrospect but I think they were going in the direction of what interesting data products can can we create and so I think we saw a lot of kind of what I call to point o companies in the in the big data area in Silicon Valley sing it's not just about internal decisions from data it's what can we provide to customers in terms of data not just access but things that really provide value that means data plus analytics so you know linkedin they attribute about half of their membership to the people you may know data product and everybody else as a people you may know now well we these companies haven't been that systematic about how you build them and how do you know which one to actually take the market and so on but I think now more and more companies even big industrial companies are realizing that this is a distinct possibility and we oughta we ought to look externally with our data for opportunities as much as supporting internal and I guess for you talk to you know companies like Yahoo some of the big web companies the whole you know Big Data meme has been about allowing you know tools and processes to get to a broader you know piece of the economy you know the counterbalance that a little bit you know large public clouds and services you know how much can you know a broad spectrum of companies out there you know get the skill set and really take advantage of these tools versus you know or is it going to be something that I'm going to still going to need to go to some outside chores for some of this well you know I think it's all being democratized fairly rapidly and I read yesterday the first time the quote nobody ever got fired for choosing amazon web services that's a lot cheaper than the previous company in that role which was IBM where you had to build up all these internal capabilities so I think the human side is being democratized they're over a hundred company over 100 universities in the US alone that have analytics oriented degree programs so i think there's plenty of opportunity for existing companies to do this it's just a matter of awareness on the part of the management team I think that's what's lacking in most cases they're not watching your shows i guess and i along the lines of the you know going back 30 years we had a preference actually a precedent where the pc software sort of just exploded onto the scene and it was i want control over my information not just spreadsheets you know creating my documents but then at the same time aighty did not have those guardrails to you know help help people from falling off you know their bikes and getting injured what are the what tools and technologies do we have for both audiences today so that we don't repeat that mistake ya know it's a very interesting question and I think you know spreadsheets were great you know the ultimate democratization tool but depending on which study you believe 22 eighty percent of them had errors in them and there was some pretty bad decisions that were made sometimes with them so we now have the tools so that we could tell people you know that spreadsheet is not going to calculate the right value or you should not be using a pie chart for that visual display I think vendors need to start building in those guardrails as you put it to say here's how you use this product effectively in addition to just accomplishing your basic task but you wouldn't see those guardrails extending all the way back because of data that's being provisioned for the users well I think ultimately if we got to the point of having better control over our data to saying you should not be using that data element it's not you know the right one for representing you know customer address or something along those lines we're not there yet and the vast majority of companies I've seen a few that have kind of experimented with data watermarks or something to say yes this is the one that you're allowed to to use has been certified as the right one for that purpose but we need to do a lot more in that regard yeah all right so Tommy you've got a new book that came out earlier this year only humans need apply winners and losers in the age of smart machines so ask you the same question we asked eric donaldson and Auntie McAfee when they wrote the second Machine Age you know are we all out of job soon well I think big day and I have become a little more optimistic as we look in some depth at at the data I mean one there are a lot of jobs evolving working with these technologies and you know it's just somebody was telling me the other day that is that I was doing a radio interview from my book and the guy was hung who said you know I've made a big transition into podcasting he said but the vast majority of people in radio have not been able to make that transition so if you're willing to kind of go with the flow learn about new technologies how they work I think there are plenty of opportunities the other thing to think about is that these transitions tend to be rather slow I mean we had about in the United States in 1980 about half a million bank tellers since then we've had ATMs online banking etc give so many bank tellers we have in 2016 about half a million it's rather shocking i think i don't know exactly what they're all doing but we're pretty slow in making these transitions so i think those of us sitting here today or even watching her probably okay we'll see some job loss on the margins but anybody who's willing to keep up with new technologies and add value to the smart machines that come into the workplace i think is likely to be okay okay do you have any advice for people that either are looking at becoming you know chief data officers well yeah as I as you said balanced offense and defense defense is a very tricky area to inhabit as a CDO because you if you succeed and you prevent you know breaches and privacy problems and security issues and so on nobody gives you necessarily any credit for it or even knows that it's helps of your work that you were successful and if you fail it's obviously very visible and bad for your career too so I think you need to supplement defense with offense activities are analytics adding valued information digitization data products etc and then I think it's very important that you make nice with all the other data oriented c-level executives you know you may not want to report to the CIO or if you have a cheap analytics officer or chief information security officer chief digitization officer chief digital officer you gotta present a united front to your organization and figure out what's the division of labor who's going to do what in too many of these organizations some of these people aren't even talking to each other and it's crazy really and very confusing to the to the rest of the organization about who's doing what yeah do you see the CDO role but you know five years from now being a standalone you know peace in the organization and you know any guidance on where that should sit is structurally compared to say the CIO yeah I don't you know I I've said that ideally you'd have a CIO or somebody who all of these things reported to who could kind of represent all these different interests of the rest of the organization that doesn't mean that a CDO shouldn't engage with the rest of the business I think CIO should be very engaged with the rest of the business but i think this uncontrolled proliferation has not been a good thing it does mean that information and data are really important to organization so we need multiple people to address it but they need to be coordinated somehow in a smart CEO would say you guys get your act together and figure out sort of who does what tell me a structure I think multiple different things can work you can have it inside of IT outside of IT but you can at least be collaborating okay last question I've got is you talked about these errors and you know that they're not you know not one dies in the next one comes and you talked about you know we know how slow you know people especially are to change so what happened to the company that are still sitting in the 10 or 20 era as we see more 30 and 40 companies come yeah well it's not a good place to be in general and I think what we've seen is this in many industries the sophisticated companies with regard to IT are the ones that get more and more market share the the late adopters end up ultimately going out of business I mean you think about in retail who's still around Walmart was the most aggressive company in terms of Technology Walmart is the world's largest company in moving packages around the world FedEx was initially very aggressive with IT UPS said we better get busy and they did it to not too much left of anybody else sending packages around the world so I think in every industry ultimately the ones that embrace these ideas tend to be the ones who who prosper all right well Tom Davenport really appreciate this morning's keynote and sharing with our audience everything that's happening in the space will be back with lots more coverage here from the MIT CDO IQ symposium you're watching the q hi this is christopher

>> Narrator: From San Jose, in the heart of Silicon Valley, it's The Cube, covering Big Data SV 2016. Now your hosts, John Furrier and Peter Burris. >> Okay, welcome back everyone. We are here live in Silicon Valley for Big Data Week, Big Data SV Strata Hadoop. This is The Cube, SiliconANGLE's flagship program. We go out to the events and extract the signals from the noise. I'm John Furrier, my co-host is Peter Burris. Our next guest is Ritika Gunnar, VP of Data and Analytics at IBM and David Richards is the CEO of WANdisco. Welcome to The Cube, welcome back. >> Thank you. >> It's a pleasure to be here. >> So, okay, IBM and WANdisco, why are you guys here? What are you guys talking about? Obviously, partnership. What's the story? >> So, you know what WANdisco does, right? Data replication, active-active replication of data. For the past twelve months, we've been realigning our products to a market that we could see rapidly evolving. So if you had asked me twelve months ago what we did, we were talking about replicating just Hadoop, but we think the market is going to be a lot more than that. I think Mike Olson famously said that this Hadoop was going to disappear and he was kind of right because the ecosystem is evolving to be a much greater stack that involves applications, cloud, completely heterogeneous storage environment, and as that happens the partnerships that we would need have to move on from just being, you know, the sort of Hadoop-specific distribution vendors to actually something that can deliver a complete solution to the marketplace. And very clearly, IBM has a massive advantage in the number of people, the services, ecosystem, infrastructure, in order to deliver a complete solution to customers, so that's really why we're here. >> If you could talk about the stack comment, because this is something that we're seeing. Mike Olson's kind of being political when he says make it invisible, but the reality is there is more to big data than Hadoop. There's a lot of other stuff going on. Call it stack, call it ecosystem. A lot of great things are growing, we just had Gaurav on from SnapLogic said, "everyone's winning." I mean, I just love that's totally true, but it's not just Hadoop. >> It's about Alldata and it's about all insight on that data. So when you think about Alldata, Alldata is a very powerful thing. If you look at what clients have been trying to do thus far, they've actually been confined to the data that may be in their operational systems. With the advent of Hadoop, they're starting to bring in some structured and unstructured data, but with the advent of IOT systems, systems of engagement, systems of records and trying to make sense of all of that, Alldata is a pretty powerful thing. When I think of Alldata, I think of three things. I think of data that is not only on premises, which is where a lot of data resides today, but data that's in the cloud, where data is being generated today and where a majority of the growth is. When I think of Alldata, I think of structured data, that is in your traditional operational systems, unstructured and semi-structured data from IOT systems et cetera, and when I think of Alldata, I think of not just data that's on premises for a lot of our clients, but actually external data. Data where we can correlate data with, for example, an acquisition that we just did within IBM with The Weather Company or augmenting with partnerships like Twitter, et cetera, to be able to extract insight from not just the data that resides within the walls of your organization, but external data as well. >> The old expression is if you want to go fast, do it alone, if you want to go deeper and broader and more comprehensive, do it as a team. >> That's right. >> That expression can be applied to data. And you look at The Weather data, you think, hmmm, that's an outlier type acquisition, but when you think about the diversity of data, that becomes a really big deal. And the question I want to ask you guys is, and Ritika, we'll start with you, there's always a few pressure points we've seen in big data. When that pressure is relieved, you've seen growth, and one was big data analytics kind of stalled a little bit, the winds kind of shifted, eye of the storm, whatever you want to call it, then cloud comes in. Cloud is kind of enabling that to go faster. Now, a new pressure point that we're seeing is go faster with digital transformation. So Alldata kind of brings us to all digital. And I know IBM is all about digitizing everything and that's kind of the vision. So you now have the pressure of I want all digital, I need data driven at the center of it, and I've got the cloud resource, so kind of the perfect storm. What's your thoughts on that? Do you see that similar picture? And then does that put the pressure on, say, WANdisco, say hey, I need replication, so now you're under the hood? Is that kind of where this is coming together? >> Absolutely. When I think about it, it's about giving trusted data and insights to everyone within the organization, at the speed in which they need it. So when you think about that last comment of, "At the speed in which they need it," that is the pressure point of what it means to have a digitally transformed business. That means being able to make insights and decisions immediately and when we look at what our objective is from an IBM perspective, it's to be able to enable our clients to be able to generate those immediate insights, to be able to transform their business models and to be able to provide the tooling and the skills necessary, whether we have it organically, inorganically, or through partnerships, like with WANdisco to be able to do that. And so with WANdisco, we believe we really wanted to be able to activate where that data resides. When I talk about Alldata and activation of that data, WANdisco provided to us complementary capabilities to be able to activate that data where it resides with a lot of the capabilities that they're providing through their fusion. So, being able to have and enable our end-users to have that digitally infused set of reactive type of applications is absolutely something... >> It's like David, we talk about, and maybe I'm oversimplifying your value proposition, but I always look at WANdisco as kind of the five nines of data, right? You guys make stuff work, and that's the theme here this year, people just want it to work, right? They don't want to have it down, right? >> Yeah, we're seeing, certainly, an uptick in understanding about what high availability, what continuous availability means in the context of Hadoop, and I'm sure we'll be announcing some pretty big deals moving forward. But we've only just got going with IBM. I would, the market should expect a number of announcements moving forward as we get going with this, but here's the very interesting question associated with cloud. And just to give you a couple of quick examples, we are seeing an increasing number of Global 1,000 companies, Fortune 100 companies move to cloud. And that's really important. If you would have asked me 12 months ago, how is the market going to shape up, I'd have said, well, most CIO's want to move to cloud. It's already happening. So, FINRA, the major financial regulator in the United States is moving to cloud, publicly announced it. The FCA in the UK publicly announced they are moving 100% to cloud. So this creates kind of a microcosm of a problem that we solve, which is how do you move transactional data from on-premise to cloud and create a sort of hybrid environment. Because with the migration, you have to build a hybrid cloud in order to do that anyway. So, if it's just archive systems, you can package it on a disk drive and post it, right? If we're talking about transactional data, i.e, stuff that you want to use, so for example, a big travel company can't stop booking flights while they move their data into the cloud, right? They would take six months to move petabyte scale data into cloud. We solve that problem. We enable companies to move transactional data from on-premise into cloud, without any interruption to services. >> So not six months? >> No, not six months. >> Six hours? >> And you can keep on using the data while it is in transit. So we've been looking for a really simplistic problem, right, to explain this really complex algorithm that we've got that you know does this active-active replication stuff. That's it, right? It's so simple, and nobody else can do it. >> So no downtime, no disruption to their business? >> No, and you can use the cloud or you can use the on-prem applications while the data is in transit. >> So when you say all cloud, now we're on a theme, Alldata, all digital, all cloud, there's a nuance there because most, and we had Gaurav from SnapLogic talk about it, there's always going to be an on-prem component. I mean, probably not going to see 100% everyone move to the cloud, public cloud, but cloud, you mean hybrid cloud essentially, with some on-prem component. I'm sure you guys see that with Bluemix as well, that you've got some dabbling in the public cloud, but ultimately, it's one resource pool. That's essentially what you're saying. >> Yeah, exactly. >> And I think it's really important. One of the things that's very attractive e about the WANdisco solution is that it does provide that hybridness from the on-premises to cloud and that being able to activate that data where it resides, but being able to do that in a heterogeneous fashion. Architectures are very different in the cloud than they are on premises. When you look at it, your data like may be as simple as Swift object store or as S3, and you may be using elements of Hadoop in there, but the architectures are changing. So the notion of being able to handle hybrid solutions both on-premises and cloud with the heterogeneous capability in a non-invasive way that provides continuous data is something that is not easily achieved, but it's something that every enterprise needs to take into account. >> So Ritika, talk about the why the WANdisco partnership, and specifically, what are some of the conversations you have with customers? Because, obviously there's, it sounds like, the need to go faster and have some of this replication active-active and kind of, five nines if you will, of making stuff not go down or non-disruptive operations or whatever the buzzword is, but you know, what's the motivation from your standpoint? Because IBM is very customer-centric. What are some of the conversations and then how does WANdisco fit into those conversations? >> So when you look at the top three use cases that most clients use for even Hadoop environments or just what's going on in the market today, the top three use cases are you know, can I build a logical data warehouse? Can I build areas for discovery or analytical discovery? Can I build areas to be able to have data archiving? And those top three solutions in a hybrid heterogeneous environment, you need to be able to have active-active access to the data where that data resides. And therefore, we believe, from an IBM perspective, that we want to be able to provide the best of breed regardless of where that resides. And so we believe from a WANdisco perspective, that WANdisco has those capabilities that are very complementary to what we need for that broader skills and tooling ecosystem and hence why we have formed this partnership. >> Unbelievably, in the market, we're also seeing and it feels like the Hadoop market's just got going, but we're seeing migrations from distributions like Cloudera into cloud. So you know, those sort of lab environments, the small clusters that were being set up. I know this is slightly controversial, and I'll probably get darts thrown at me by Mike Olson, but we are seeing pretty large-scale migration from those sort of labs that were set up initially. And as they progress, and as it becomes mission-critical, they're going to go to companies like IBM, really, aren't they, in order to scale up their infrastructure? They're going to move the data into cloud to get hyperscale. For some of these cases that Ritika was just talking about so we are seeing a lot of those migrations. >> So basically, Hadoop, there's some silo deployments of POC's that need to be integrated in. Is that what you're referring to? I mean, why would someone do that? They would say okay, probably integration costs, probably other solutions, data. >> If you do a roll-your-own approach, where you go and get some open-source software, you've got to go and buy servers, you've got to go and train staff. We've just seen one of our customers, a big bank, two years later get servers. Two years to get servers, to get server infrastructure. That's a pretty big barrier, a practical barrier to entry. Versus, you know, I can throw something up in Bluemix in 30 minutes. >> David, you bring up a good point, and I want to just expand on that because you have a unique history. We know each other, we go way back. You were on The Cube when, I think we first started seven years ago at Hadoop World. You've seen the evolution and heck, you had your own distribution at one point. So you know, you've successfully navigated the waters of this ecosystem and you had gray IP and then you kind of found your swim lanes and you guys are doing great, but I want to get your perspective on this because you mentioned Cloudera. You've seen how it's evolving as it goes mainstream, as you know, Peter says, "The big guys are coming in and with power." I mean, IBM's got a huge spark investment and it's not just you know, lip service, they're actually donating a ton of code and actually building stuff so, you've got an evolutionary change happening within the industry. What's your take on the upstarts like Cloudera and Hortonworks and the Dishrow game? Because that now becomes an interesting dynamic because it has to integrate well. >> I think there will always be a market for the distribution of opensource software. As that sort of, that layer in the stack, you know, certainly Cloudera, Hortonworks, et cetera, are doing a pretty decent job of providing a distribution. The Hadoop marketplace, and Ritika laid this on pretty thick as well, is not Hadoop. Hadoop is a component of it, but in cloud we talk about object store technology, we talk about Swift, we talk about S3. We talk about Spark, which can be run stand-alone, you don't necessarily need Hadoop underneath it. So the marketplace is being stretched to such a point that if you were to look at the percentage of the revenue that's generated from Hadoop, it's probably less than one percent. I talked 12 months ago with you about the whale season, the whales are coming. >> Yeah, they're here. >> And they're here right now, I mean... >> (laughs) They're mating out in the water, deals are getting done. >> I'm not going to deal with that visual right now, but you're quite right. And I love the Peter Drucker quote which is, "Strategy is a commodity, execution is an art." We're now moving into the execution phase. You need a big company in order to do that. You can't be a five hundred or a thousand person... >> Is Cloudera holding onto dogma with Hadoop or do they realize that the ecosystem is building around them? >> I think they do because they're focused on the application layer, but there's a lot of competition in the application layer. There's a little company called IBM, there's a little company called Microsoft and the little company called Amazon that are kind of focused on that as well, so that's a pretty competitive environment and your ability to execute is really determined by the size of the organization to be quite frank. >> Awesome, well, so we have Hadoop Summit coming up in Dublin. We're going to be in Ireland next month for Hadoop Summit with more and more coverage there. Guys, thanks for the insight. Congratulations on the relationship and again, WANdisco, we know you guys and know what you guys have done. This seems like a prime time for you right now. And IBM, we just covered you guys at InterConnect. Great event. Love The Weather Company data, as a weather geek, but also the Apple announcement was really significant. Having Apple up on stage with IBM, I think that is really, really compelling. And that was just not a Barney deal, that was real. And the fact that Apple was on stage was a real testament to the direction you guys are going, so congratulations. This is The Cube, bringing you all the action, here live in Silicon Valley here for Big Data Week, BigData SV, and Strata Hadoop. We'll be right back with more after this short break.