>> live from Boston, Massachusetts. It's the Cube covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day Volante and Stew Minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. Stillman and I have pleased to have Jean Kolker on a Cuba lem. Uh, he's IBM vice president and chief data officer of the Global Technology Services division. And Seth Dobrin who's the Director of Digital Strategies. That Monsanto. You may have seen them in the news lately. Gentlemen. Welcome to the Cube, Jean. Welcome back. Good to see you guys again. Thanks. Thank you. So let's start with the customer. Seth, Let's, uh, tell us about what you're doing here, and then we'll get into your role. >> Yes. So, you know, the CDO summit has been going on for a couple of years now, and I've been lucky enoughto be participating for a couple of a year and 1/2 or so, Um, and you know, really, the nice thing about the summit is is the interaction with piers, um, and the interaction and networking with people who are facing similar challenges from a similar perspective. >> Yes, kind of a relatively new Roland topic, one that's evolved, Gene. We talked about this before, but now you've come from industry into, ah, non regulated environment. Now what's happened like >> so I think the deal is that way. We're developing some approaches, and we get in some successes in regulated environment. Right? And now I feel with And we were being client off IBM for years, right? Using their technology's approaches. Right? So and now I feel it's time for me personally to move on something different and tried to serve our power. I mean, IBM clients respected off in this striking from healthcare, but their approaches, you know, and what IBM can do for clients go across the different industries, right? And doing it. That skill that's very beneficial, I think, for >> clients. So Monsanto obviously guys do a lot of stuff in the physical world. Yeah, you're the head of digital strategy. So what does that entail? What is Monte Santo doing for digital? >> Yes, so, you know, for as head of digital strategies for Monsanto, really? My role is to number one. Help Monsanto internally reposition itself so that we behave and act like a digital companies, so leveraging data and analytics and also the cultural shifts associated with being more digital, which is that whole kind like you start out this conversation with the whole customer first approach. So what is the real impact toe? What we're doing to our customers on driving that and then based on on those things, how can we create new business opportunities for us as a company? Um, and how can we even create new adjacent markets or new revenues in adjacent areas based on technologies and things we already have existing within the company? >> It was the scope of analytics, customer engagement of digital experiences, all of the above, so that the scope is >> really looking at our portfolio across the gamut on DH, seeing how we can better serve our customers and society leveraging what we're doing today. So it's really leveraging the re use factor of the whole digital concept. Right? So we have analytics for geospatial, right? Big part of agriculture is geospatial. Are there other adjacent areas that we could apply some of that technology? Some of that learning? Can we monetize those data? We monetize the the outputs of those models based on that, Or is there just a whole new way of doing business as a company? Because we're in this digital era >> this way? Talked about a lot of the companies that have CEOs today are highly regulated. What are you learning from them? What's what's different? Kind of a new organization. You know, it might be an opportunity for you that they don't have. And, you know, do you have a CDO yet or is that something you're planning on having? >> Yes, So we don't have a CDO We do have someone acts as an essential. he's a defacto CEO, he has all of the data organizations on his team. Um, it's very recent for Monsanto, Um, and and so I think, you know, in terms of from the regular, what can we learn from, you know, there there are. It's about half financial people have non financial people, are half heavily regulated industries, and I think, you know, on the surface you would. You would think that, you know, there was not a lot of overlap, but I think the level of rigor that needs to go into governance in a financial institution that same thought process. Khun really be used as a way Teo really enable Maur R and D. Mohr you know, growth centered companies to be able to use data more broadly and so thinking of governance not as as a roadblock or inhibitor, but really thinking about governance is an enabler. How does it enable us to be more agile as it enable us to beam or innovative? Right? If if people in the company there's data that people could get access to by unknown process of known condition, right, good, bad, ugly. As long as people know they can do things more quickly because the data is there, it's available. It's curated. And if they shouldn't have access it under their current situation, what do they need to do to be able to access that data? Right. So if I would need If I'm a data scientist and I want to access data about my customers, what can I can't? What can and can't I do with that data? Number one doesn't have to be DEA Nana Mayes, right? Or if I want to access in, it's current form. What steps do I need to go through? What types of approval do I need to do to do to access that data? So it's really about removing roadblocks through governance instead of putting him in place. >> Gina, I'm curious. You know, we've been digging into you know, IBM has a very multifaceted role here. You know how much of this is platforms? How much of it is? You know, education and services. How much of it is, you know, being part of the data that your your customers you're using? >> Uh so I think actually, that different approaches to this issues. My take is basically we need Teo. I think that with even cognitive here, right and data is new natural resource worldwide, right? So data service, cognitive za za service. I think this is where you know IBM is coming from. And the BM is, you know, tradition. It was not like that, but it's under a lot of transformation as we speak. A lot of new people coming in a lot off innovation happening as we speak along. This line's off new times because cognitive with something, really you right, and it's just getting started. Data's a service is really new. It's just getting started. So there's a lot to do. And I think my role specifically global technology services is you know, ah, largest by having your union that IBM, you're 30 plus 1,000,000,000 answered You okay? And we support a lot of different industries basically going across all different types of industries how to transition from offerings to new business offerings, service, integrated services. I think that's the key for us. >> Just curious, you know? Where's Monsanto with kind of the adoption of cognitive, You know what? Where are you in that journey? >> Um, so we are actually a fairly advanced in the journey In terms of using analytics. I wouldn't say that we're using cognitive per se. Um, we do use a lot of machine learning. We have some applications that on the back end run on a I So some form of artificial or formal artificial intelligence, that machine learning. Um, we haven't really gotten into what, you know, what? IBM defined his cognitive in terms of systems that you can interact with in a natural, normal course of doing voice on DH that you spend a whole lot of time constantly teaching. But we do use like I said, artificial intelligence. >> Jean I'm interested in the organizational aspects. So we have Inderpal on before. He's the global CDO, your divisional CDO you've got a matrix into your leadership within the Global Services division as well as into the chief date officer for all of IBM. Okay, Sounds sounds reasonable. He laid out for us a really excellent sort of set of a framework, if you will. This is interval. Yeah, I understand your data strategy. Identify your data store says, make those data sources trusted. And then those air sequential activities. And in parallel, uh, you have to partner with line of business. And then you got to get into the human resource planning and development piece that has to start right away. So that's the framework. Sensible framework. A lot of thought, I'm sure, went into it and a lot of depth and meaning behind it. How does that framework translate into the division? Is it's sort of a plug and play and or is there their divisional goals that are create dissonance? Can you >> basically, you know, I'm only 100 plus days in my journey with an IBM right? But I can feel that the global technology services is transforming itself into integrated services business. Okay, so it's thiss framework you just described is very applicable to this, right? So basically what we're trying to do, we're trying to become I mean, it was the case before for many industries, for many of our clients. But we I want to transform ourselves into trusted broker. So what they need to do and this framework help is helping tremendously, because again, there's things we can do in concert, you know, one after another, right to control other and things we can do in parallel. So we trying those things to be put on the agenda for our global technology services, okay. And and this is new for them in some respects. But some respects it's kind of what they were doing before, but with new emphasis on data's A service cognitive as a service, you know, major thing for one of the major things for global technology services delivery. So cognitive delivery. That's kind of new type off business offerings which we need to work on how to make it truly, you know, once a sense, you know, automated another sense, you know, cognitive and deliver to our clients some you value and on value compared to what was done up until recently. What >> do you mean by cognitive delivery? Explained that. >> Yeah, so basically in in plain English. So what's right now happening? Usually when you have a large systems  computer IT system, which are basically supporting lot of in this is a lot of organizations corporations, right? You know, it's really done like this. So it's people run technology assistant, okay? And you know what Of decisions off course being made by people, But some of the decisions can be, you know, simple decisions. Right? Decisions, which can be automated, can standardize, normalize can be done now by technology, okay and people going to be used for more complex decisions, right? It's basically you're going toe. It turned from people around technology assisted toa technology to technology around people assisted. OK, that's very different. Very proposition, right? So, again, it's not about eliminating jobs, it's very different. It's taken off, you know, routine and automata ble part off the business right to technology and given options and, you know, basically options to choose for more complex decision making to people. That's kind of I would say approach. >> It's about scale and the scale to, of course, IBM. When when Gerstner made the decision, Tio so organized as a services company, IBM came became a global leader, if not the global leader but a services business. Hard to scale. You could scare with bodies, and the bigger it gets, the more complicated it gets, the more expensive it gets. So you saying, If I understand correctly, the IBM is using cognitive and software essentially to scale its services business where possible, assisted by humans. >> So that's exactly the deal. So and this is very different. Very proposition, toe say, compared what was happening recently or earlier? Always. You know other. You know, players. We're not building your shiny and much more powerful and cognitive, you know, empowered mouse trap. No, we're trying to become trusted broker, OK, and how to do that at scale. That's an open, interesting question, but we think that this transition from you know people around technology assisted Teo technology around people assisted. That's the way to go. >> So what does that mean to you? How does that resonate? >> Yeah, you know, I think it brings up a good point actually, you know, if you think of the whole litany of the scope of of analytics, you have everything from kind of describing what happened in the past All that to cognitive. Um, and I think you need to I understand the power of each of those and what they shouldn't should be used for. A lot of people talk. You talk. People talk a lot about predictive analytics, right? And when you hear predictive analytics, that's really where you start doing things that fully automate processes that really enable you to replace decisions that people make right, I think. But those air mohr transactional type decisions, right? More binary type decisions. As you get into things where you can apply binary or I'm sorry, you can apply cognitive. You're moving away from those mohr binary decisions. There's more transactional decisions, and you're moving mohr towards a situation where, yes, the system, the silicon brain right, is giving you some advice on the types of decisions that you should make, based on the amount of information that it could absorb that you can't even fathom absorbing. But they're still needs really some human judgment involved, right? Some some understanding of the contacts outside of what? The computer, Khun Gay. And I think that's really where something like cognitive comes in. And so you talk about, you know, in this in this move to have, you know, computer run, human assisted right. There's a whole lot of descriptive and predictive and even prescriptive analytics that are going on before you get to that cognitive decision but enables the people to make more value added decisions, right? So really enabling the people to truly add value toe. What the data and the analytics have said instead of thinking about it, is replacing people because you're never going to replace you. Never gonna replace people. You know, I think I've heard people at some of these conferences talking about, Well, no cognitive and a I is going to get rid of data scientist. I don't I don't buy that. I think it's really gonna enable data scientist to do more valuable, more incredible things >> than they could do today way. Talked about this a lot to do. I mean, machines, through the course of history, have always replaced human tasks, right, and it's all about you know, what's next for the human and I mean, you know, with physical labor, you know, driving stakes or whatever it is. You know, we've seen that. But now, for the first time ever, you're seeing cognitive, cognitive assisted, you know, functions come into play and it's it's new. It's a new innovation curve. It's not Moore's law anymore. That's driving innovation. It's how we interact with systems and cognitive systems one >> tonight. And I think, you know, I think you hit on a good point there when you said in driving innovation, you know, I've run, you know, large scale, automated process is where the goal was to reduce the number of people involved. And those were like you said, physical task that people are doing we're talking about here is replacing intellectual tasks, right or not replacing but freeing up the intellectual capacity that is going into solving intellectual tasks to enable that capacity to focus on more innovative things, right? We can teach a computer, Teo, explain ah, an area to us or give us some advice on something. I don't know that in the next 10 years, we're gonna be able to teach a computer to innovate, and we can free up the smart minds today that are focusing on How do we make a decision? Two. How do we be more innovative in leveraging this decision and applying this decision? That's a huge win, and it's not about replacing that person. It's about freeing their time up to do more valuable things. >> Yes, sure. So, for example, from my previous experience writing healthcare So physicians, right now you know, basically, it's basically impossible for human individuals, right to keep up with spaced of changes and innovations happening in health care and and by medical areas. Right? So in a few years it looks like there was some numbers that estimate that in three days you're going to, you know, have much more information for several years produced during three days. What was done by several years prior to that point. So it's basically becomes inhuman to keep up with all these innovations, right? Because of that decision is going to be not, you know, optimal decisions. So what we'd like to be doing right toe empower individuals make this decision more, you know, correctly, it was alternatives, right? That's about empowering people. It's not about just taken, which is can be done through this process is all this information and get in the routine stuff out of their plate, which is completely full. >> There was a stat. I think it was last year at IBM Insight. Exact numbers, but it's something like a physician would have to read 1,500 periodic ALS a week just to keep up with the new data innovations. I mean, that's virtually impossible. That something that you're obviously pointing, pointing Watson that, I mean, But there are mundane examples, right? So you go to the airport now, you don't need a person that the agent to give you. Ah, boarding pass. It's on your phone already. You get there. Okay, so that's that's That's a mundane example we're talking about set significantly more complicated things. And so what's The gate is the gate. Creativity is it is an education, you know, because these are step functions in value creation. >> You know, I think that's ah, what? The gate is a question I haven't really thought too much about. You know, when I approach it, you know the thinking Mohr from you know, not so much. What's the gate? But where? Where can this ad the most value um So maybe maybe I have thought about it. And the gate is value, um, and and its value both in terms of, you know, like the physician example where, you know, physicians, looking at images. And I mean, I don't even know what the error rate is when someone evaluates and memory or something. And I probably don't want Oh, right. So, getting some advice there, the value may not be monetary, but to me, it's a lot more than monetary, right. If I'm a patient on DH, there's a lot of examples like that. And other places, you know, that are in various industries. That I think that's that's the gate >> is why the value you just hit on you because you are a heat seeking value missile inside of your organisation. What? So what skill sets do you have? Where did you come from? That you have this capability? Was your experience, your education, your fortitude, >> While the answer's yes, tell all of them. Um, you know, I'm a scientist by training my backgrounds in statistical genetics. Um, and I've kind of worked through the business. I came up through the RND organization with him on Santo over the last. Almost exactly 10 years now, Andi, I've had lots of opportunities to leverage. Um, you know, Data and analytics have changed how the company operates on. I'm lucky because I'm in a company right now. That is extremely science driven, right? Monsanto is a science based company. And so being in a company like that, you don't face to your question about financial industry. I don't think you face the same barriers and Monsanto about using data and analytics in the same way you may in a financial types that you've got company >> within my experience. 50% of diagnosis being proven incorrect. Okay, so 50% 05 0/2 summation. You go to your physician twice. Once you on average, you get in wrong diagnosis. We don't know which one, by the way. Definitely need some someone. Garrett A cz Individuals as humans, we do need some help. Us cognitive, and it goes across different industries. Right, technologist? So if your server is down, you know you shouldn't worry about it because there is like system, you know, Abbas system enough, right? So think about how you can do that scale, and then, you know start imagined future, which going to be very empowering. >> So I used to get a second opinion, and now the opinion comprises thousands, millions, maybe tens of millions of opinions. Is that right? >> It's a try exactly and scale ofthe data accumulation, which you're going to help us to solve. This problem is enormous. So we need to keep up with that scale, you know, and do it properly exactly for business. Very proposition. >> Let's talk about the role of the CDO and where you see that evolving how it relates to the role of the CIA. We've had this conversation frequently, but is I'm wondering if the narratives changing right? Because it was. It's been fuzzy when we first met a couple years ago that that was still a hot topic. When I first started covering this. This this topic, it was really fuzzy. Has it come in two more clarity lately in terms of the role of the CDO versus the CIA over the CTO, its chief digital officer, we starting to see these roles? Are they more than just sort of buzzwords or grey? You know, areas. >> I think there's some clarity happening already. So, for example, there is much more acceptance for cheap date. Office of Chief Analytics Officer Teo, Chief Digital officer. Right, in addition to CEO. So basically station similar to what was with Serious 20 plus years ago and CEO Row in one sentence from my viewpoint would be How you going using leverage in it. Empower your business. Very proposition with CDO is the same was data how using data leverage and data, your date and your client's data. You, Khun, bring new value to your clients and businesses. That's kind ofthe I would say differential >> last word, you know, And you think you know I'm not a CDO. But if you think about the concept of establishing a role like that, I think I think the name is great because that what it demonstrates is support from leadership, that this is important. And I think even if you don't have the name in the organization like it, like in Monsanto, you know, we still have that executive management level support to the data and analytics, our first class citizens and their important, and we're going to run our business that way. I think that's really what's important is are you able to build the culture that enable you to leverage the maximum capability Data and analytics. That's really what matters. >> All right, We'll leave it there. Seth Gene, thank you very much for coming that you really appreciate your time. Thank you. Alright. Keep it right there, Buddy Stew and I'll be back. This is the IBM Chief Data Officer Summit. We're live from Boston right back.

>>I'm really glad to have you with us today, john, I know you stepped out of vacation so thanks very much for joining us. >>No, it's great to be joining you from Hawaii and I love the partnership with H. P. E. And the way you're reinventing an industry, >>well, you've always excelled john at catching market transitions and there are so many transitions and paradigm shifts happening in the market and text specifically right now as you see, companies rush to accelerate their transformation. What do you see as the keys to success? >>Well, I, I think you're seeing actually an acceleration following the covid challenges that always faced and I wasn't sure that would happen. It's, it's probably at three times the paces before. There was a discussion point about how quickly the companies need to go digital. Uh, that's no longer discussion point. Almost all companies are moving with tremendous speed on digital and its ability as the cloud moves to the edge with compute and security uh, at the edge and how you deliver these services to where the majority of applications uh reside are going to determine. I think the future of the next generation company leadership and it's the area that Neil we're working together on in many, many ways. So I think it's about innovation. It's about the cloud moving to the edge and an architectural play with silicon to speed up that innovation. >>Yes, we certainly see the our customers of all sizes trying to accelerate what's next and get that digital transformation moving even faster as a result of the environment the world living in. And we're finding that workload focus is really key customers and all kinds of different scales are having to adapt and support the remote workforces with beady eye. And as you say, john they're having to deal with the deployment of workloads at the edge, with so much data getting generated at the edge and being acted upon on the edge. The analytics and the infrastructure to manage that as these processes get digitized and automated is so important for so many workflows. We really believe that the choice of infrastructure partner that underpins those transformations really matters. A partner that can help create the financial capacity that can help optimize your environments and enable our customers to focus on supporting their business are all super key to success. And you mentioned that in the last year there's been a lot of rapid course correction for all of us, a demand for velocity and the ability to deployed resources. That scale is more and more needed, maybe more than ever. What are you hearing customers looking for as they are rolling out their digital transformation efforts? >>Well, I think they're being realistic that they're going to have to move a lot faster than before and they're also realistic on core versus context. Their their their core capability is not the technology themselves, it's how to deploy it and there were looking for partners that can help bring them there together, but there can also innovate. And very often the leaders who might have been a leader in a prior generation may not be on this next move. Hence the opportunity for HP and startups like Monsanto to work together as the cloud moves to the edge and perhaps really balanced or even challenge some of the big, big incumbents in this category as well as partners uniquely with our joint customers on how do we achieve their business goals? Tell me a little bit more about how you move from this being a technology position in for a J e to literally helping your customers achieve their outcomes they want and and how are you changing hb in that way? >>Well, I think when you consider these transformations the infrastructure that you choose to underpin, it is incredibly critical. Our customers need a software defined management plane that enables them to automate so much of their infrastructure. They need to be able to take faster action where the data is and to do all of this in a cloud like experience where they can deliver their infrastructure as code anywhere from exa scale through the enterprise data center to the edge. And really critically, they have to be able to do this securely, which becomes an ever increasing challenge and doing it at the right economics relative to the alternatives. And part of the right economics, of course includes adopting the best practices from web scale architectures and bringing them to the heart of the enterprise. And in our partnership with Pensando, we're working to enable these new ideas of Web scale architecture and fleet management for the enterprise at scale. >>You know, what is fun is HP has an unusual talent from the very beginning Silicon Valley of working together with others and creating a win win innovation approach. If you watch what your team has been able to do. And I want to say this for everybody listening, you work with startups better than any other company I've seen in terms of how you do win win together and pennsylvania is just the example of that. Uh this startup, which by the way, is the ninth time I have done with this team, a new generation of products and we're designing that together with H. P. E. In terms of as the cloud moves to the edge, how do we get the leverage out of that and produce results for your customers on this? Uh, to give the audience appeal for it. You're talking with Manzano alone in terms of the efficiency versus an amazon amazon web services of an order of magnitude. I'm not talking 100% grader, I'm talking 10 X grader and things went through, Put number of connections, you do the jitter capability, etcetera. And it talks how to companies uniquely who believe in innovation and trust with each other and have very similar cultures can work uniquely together on it. How do you bring that to life with an H. B? How do you get your company to really say that's harvest the advantages of your ecosystem in your advantages of startups? >>Well, you say more and more companies are faced with these challenges of hitting the right economics for the infrastructure. And we see many enterprises of various sizes trying to come to terms with infrastructures that look a lot more like a service provider that require that software defined management plane and the automation to deploy at scale. And with the world we're doing with Pensando, the benefits that we bring in terms of the observe ability and the telemetry and the encryption and the distributed network functions. But also a security architecture that enables that efficiency on the individual nodes is just so key to building a competitive architecture moving forwards for an on prem private cloud or internal service provider operation. And we're really excited about the work we've done to bring that technology across our portfolio and bring that to our customers so that they can achieve those kind of economics and capabilities and go focus on their own transformations rather than building and running the infrastructure themselves. Artisanal e and having to deal with integrating all of that great technology themselves >>makes tremendous sense. You know, Neil you and I work on a board together etcetera. I've watched your summarization skills and I always like to ask a question after you do a quick summary like this, what are the three or four takeaways we would like for the audience to get out of our conversation? >>Well, that's a great question. Thanks john we believe that customers need a trusted partner to work through these digital transformations that are facing them and confront the challenge of the time that the covid crisis has taken away. As you set out front, every organizations having to transform and transform more quickly and more digitally. I'm working with a trusted partner with the expertise that only comes from decades of experience is a key enabler for that, a partner with the ability to create the financial capacity to transform the workload expertise to get more from the infrastructure and optimize the environment so that you can focus on your own business, a partner that can deliver the systems and the security and the automation that makes it easily deployable and manageable anywhere you need them at any scale, whether the edge, the enterprise data center or all the way up to exa scale in high performance computing and can do that all as a service as we can at H P E through H PE Green Lake enabling our customers most critical workloads. It's critical that all of that is underpinned by an A I powered, digitally enabled service experience so that our customers can get on with their transformation and running their business instead of dealing with their infrastructure. And really only H PE can provide this combination of capabilities and we're excited and committed to helping our customers accelerate what's next for their businesses >>Neil. It's fun. I love being your partner and your wingman or values and cultures are so similar. Thanks for letting me be a part of this discussion today. >>Thanks for being with us, john, it was great avenue here. >>Oh, his friends were like.

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum, World 2019 brought to you by IBM Wear and its ecosystem partners. >> Welcome back. This is The Cube. Live at VM World, 2019 times. Too many men with my co host Justin Warren, David Dante, John Ferrier. John Wall's John Troyer. Like a certain founding fonder, Alexander Hamilton, the Cube is going non stop coverage for the three days Habito. Welcome back to our program. Two of our Cube alumni's Lynn Lucas, who is the chief marketing officer of Cohee City. And she's brought along John White, who is the chief innovation officer at Expedient. Thank you both for joining us. >> Happy to be here. Happy 10 year anniversary to the cute >> Well, thank you so much. And you know, so many people we've known over the years. Actually, the first time I met John wait was I believe in this whole, you know, at VM World, you know, talk about what's going on. Eso You know, I always love talking to the service riders because it's been going through just a massive transformation, you know, all along on you, John, you've got a different title Since the last time I interviewed you, You been involved in the strategy You and I have gone toe cloud shows together and some of the other things they're so bring us up to speed as to, you know, expedient here at the show. And, uh, what brings you here with cohesive? >> Yes. Oh, yeah, thanks to your right. But I think it's probably five or six years ago. Maybe I asked too. You said, Hey, what do you know about the service provider community? Cause I wanted to kind of educate you because it was something that's been fairly new and expedience been in that industry for a long time. We were mainly in infrastructure of the service provider, man. The service providers are rounding on all that I t. Stuff that people need and ah, this VM world is a huge one for us. Last year we launched Enterprise Cloud, which is a product based on VM. Where's full softer to find stack And that was something we wanted to go to market with to give an alternative people, When you say Hey, I need to go to the cloud and you realize that okay. I really can't just take my APS and lift and shift and go there. There's another place for you that's a little a little more familiar. And as we see 20,000 people know VM were obviously that air here. So that's usually what what's usually happening in the Enterprise. So we're talking with those folks, and so we would launch that platform. We thought about everything differently. We were running virtualization since, 06 of seven, and we wanted to change everything back up platforms as well as needing things like scale out now, as were necessity at that point. So this VM worlds are coming out because we had, ah, release that last year. And now we have a lot of good customers to talk about on that platform. 12 months later. >> Yeah, well, Lynn, first of all, congratulations because, you know, I know John sits on customer counsels for some of these events. I've dug into a bunch of the networking pieces with him and actually was, you know, we spent a bunch of years. We went to a bunch of shows together and he was looking at the some of the various vendors and a bunch of the new startups and cohesive E is the one that you know really stepped up provided the solution that he was looking for. So it's been interesting to hear service fighters. You were usually the first ones that companies were talking to. But bring brings the cohesive story there. >> Yeah, and so were a super pleased and honored to have expedient working with Cohee City. And John has been instrumental in really providing a lot of direction to us on what his needs are and how to make the product even better for for him and the service provider community, it's a huge part of our go to market strategy. We believe that with the massive growth in the interest in hybrid and Justus John was saying, There's so many customers that really aren't equipped to deal with How do I move to ah hybrid cloud strategy, whether that before compliance reasons, whether that be geographical reasons under you've seen them all. And so this is something where we feel really thrilled to have the, um, where's cloud partner of the Year working with us and to help serve customers with the expedient enterprise Cloud platform. >> Congratulations on that team. And right when we first met five years ago Public cloud for VM wear And for most of the service bodies, it was the enemy, all right. It's like, Oh, my gosh. There, you know, you went toe Amazon reinvent and create a little bit of partnership. So give us the update on hybrid. What that means and how solutions like, Oh, he city, you know, help you provide services to your customer. >> Yeah, it's funny you mentioned. I mean, that's you would ask me, What are you doing here? What are you doing here? Because it was going out doing this research, seeing where the market was on containers on adoption of Claude practices. So we wanted to make sure that we were very open with all the different solutions that are out there. And that's been our strategy from the from the start. So building enterprise cloud was one thing we need to do to come to market with a platform like everybody else. But multi cloud is really where we have focused. And it's funny. I mean, when I when I first started coming to the M world, it was very product centric, you know, you had this product, you could do X with it. And here's the return that you would get at it. And when you're coming to the emerald now it's more about platforms. And that's really what I found most interesting with Cohee City when I first met Mohit, probably two and 1/2 3 years ago, was that he was focusing on the platform of data management. And that's really what the problem was. It wasn't now, specifically, it wasn't data protection. Specifically, it was What all can you do with that with that data management? And we're You know, we're spending a ton of time with Cohee City on. We were building multi Tennessee with them to them, be able to support that. And that's what we're delivering on now. So it's it's it's the scale out now's it's ah, data protection. And then we're taking those service is and then putting him in eight of us in azure or wherever it might be because it doesn't matter to us, because long term we want to care about the long term I t care and feeding and focus on that as our value prop instead of just actually which silo >> it lives in. Yeah, and right from the very beginning, I remember speaking to Kohei City very, very early on when it just sort of come out of stealth. And it was baked into into the product the idea of data management that it was going to be much more, much more than just data protection. So, what are you seeing now that we have a lot of years of product development has clearly gone into it since we first looked at it then. But what are you seeing? Customers using the platform for here in 2019? >> Well, we started a little bit more unique than most other customers. I think we talked about this, you know, throughout CO e city. And we actually started on a scale out now's platform s. So we have one of our clients homes dot com who? They're with us this week because they have a really interesting you. No need for this type of enterprise. Cloud there with us and they're talking about all the different benefits they received out. And they started actually with on the file side of things homes dot com, real estate, online real estate. So I think you know about how many images and how many fouls you have out there. We have 2.6 billion images right now running on the cohesive platform. That was 2.6 billion and a 30% annual growth rate. Yes. So the numbers are crazy. You can't put those on any other traditional now, as that's out there. Uh, so we used a he city first to get started there, and then the backups really were the icing on the cake. So last October, we built We started going out the Nats platform to handle those images. And if you actually go to homes dot com right now, it's being served fully out Enterprise cloud from a container and virtual machine layer and then on the back end from Cohee City. And they were using that to protect it as well. >> And I think that if I add on to that is really a testament to the, you know, the foundation that mo it built, which is a true distributed file system, Google like, in that sense. And I think correct me if I'm wrong, John. But you know, you then also saw while that benefit of a platform approach and not another silo for the backup and having co he city help solve the challenges for both files as well as data protection and then maybe one day in the future. Looking at some of the things that we're doing now that we're doing more security on running APS and things like that on the platform as that may be an extension for you, >> Yeah, that's that's definitely a big focus of our effort, the global d duplication that you get with CO he city. When you add all those files in all the different customers, we have all the different virtue machines. Ah, the ratios were hitting or just insane. And it's something we decided as a service provider that we we said, OK, this technology, we actually want to give that benefit back to the customer. And so when somebody buys data stores from us on the data production, they buy what they're actually consuming on the disk. So you could have 100 terabytes in all of your V EMS. If you only need one terabyte, that's all you're buying from us. And that's a lot of the power of that platform that we get with cohesive. >> Yeah. So, John, wanna help? They want you to help us understand the nuance of something. We're platform? Yeah. Has a little bit of new Monsanto. Little bit, a little loaded thing. GM was the platform. Cohesive is a platform. You use both of them. So just help us understand how cohesive Ian Veum wear and all those things that they go together. They're not, you know, competing against each other in as your architectural Or are they? They're >> not competing there two layers, In my opinion, where you have your primary stores really living in and the M R and secondary storage is everything else on Cohee City. Ah, what? The nice thing is, they did a lot of cool things to kind of marry the two together. Um, one of the one of the tools that we're using Aesthetic ahi city is called Instant on or instant Restore a Virtue machines so we can actually spin up a virtual machine almost instantaneously. It lives on the Coast City platform. Once it's rehydrated, then does a storage the emotion automatically into the V, m or environment. So we're able to do migration or if we had, You know, we have a bad ransomware attack and we need to restore 100 V EMS within a few minutes. We can instantly bring those back up in the cohesive platform and then move them to a production virtual environment once it's done. And that's something that we weren't able to do with our existing vendor. And that was something we needed to actually go and focus on because being in the healthcare space being in the compliance space, that's that's a big problem for us. >> Yeah, just add, I think that, um Vienna, where is clearly one of our most important partners. The very first area that mo it developed was data protection, for I am where, I would say, well north of 70 75% of our customers are protecting their Veum, where environments We have a very large customer that's protecting over 18,000 V EMS on Cohee City. So with the certifications that we have with V. C. D. And with the integrations, as you've mentioned with of you realize it's it's really it's a partnership. Andi think we're adding a lot of value to the customers that are building on V. M. where is >> very complimentary for sure. >> Yeah, it's been interesting that to see how customers choosing to go with a platform like like expedient because, as you mentioned earlier, stewed like five years ago, Cloud was the enemy. And but we're being told that on a public card is gonna take everything, and it's just gonna own all of the environment where is now? In 2019 we found that the story is actually much more complicated than that. Some of us probably believe that at the time I put my hand up is one of those that customers actually needed to live in multiple places. It's not a story of or it's a story of end, so you do need to be ableto have something which can work well with others on the same way we've got. We've got co, he's ity and V M. Where is it? Well, they're not really competing with each other. They work better together and particularly as customers scale, we find my any any kind of enterprise customer is header a genius, so you have to have solutions that give them options and that work together well with you have to play nicely with others. >> I think that's exactly right. And part of what we've done is built a software to find solution and to also give John expedient flexibility. How do you want to deploy for your customers? The solution. Is it in the, uh, the hyper scaler? Is it Hello, somewhere? Is that your own cloud? And so that's part of the advantage. I think all in one solution that then you can give your customers some flexibility as well as to how they want to consume the service as well. >> Absolutely meaning the flexibility And you mentioned software only, and are, you know, software or suffer to find that was something that was big for us. When we're looking for partnerships, we have a standardized hardware build that we want to use. And that was all built on Del. And it was something that, you know, we were able to work with obesity to get that standardized so we can continue to roll out. Excuse that we were most comfortable with. And they could just have the software layer on top. >> Yeah. So you've managed to do this successfully. You're going really well. Yeah. What's next? >> Well, um you know we have, you know, So in we ordered in October of last year. Right now we have six petabytes online enrolling. So that's that's great. You know, that's that's good to see. That's going to continue to grow at a pretty rapid rate. Data is something that obviously, we all know is never gonna shrink on. We're gonna continue to grow that with new customer acquisitions, and that's everything where we want to continue to go with this, Uh, this product specifically is on the data management side. The things that they're doing in Helios to start to get understanding and awareness in value out of that data that's sitting there is really, really important and exciting for us in the future. We deal what tonic compliance PC idea says hip. And we want to make sure that the data that we're storing inside of there will be compliant by those. So being able to write an application to see if the credit card numbers in a file or in a database by using the cozy platform that we brought us value. Same thing goes with a lot of the ransomware protection they're doing. So if you see a foul that gets encrypted, then I know. Okay, I have a problem. I better go look at that and give me a time stamped, Actually. Go on, restore from instead of actually trying toe, you know, pick around. And hopefully I find it when it before it was encrypted. So we're really excited about those opportunities is the future and seeing what data management can just bring to it. >> Well, Lynn, always a pleasure to catch up with you. Thank you so much for joining us again. And John, my friend, this is your six time on the program, actually gonna have a celebration in New England in a little over unveil for, ah, six s o right in New England. So for Justin Warren, I'm stupid him in. We love talking about sports here, and, uh, yeah, the Cuban way have the Niners and the Patriots for the team there. But as always, Thank you for watching the cue

>> Live from San Francisco, California, it's the theCUBE, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Welcome back to San Francisco everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM Chief Data Officer Summit, 10th anniversary. Seth Dobrin is here, he's the Vice President and Chief Data Officer of the IBM Analytics Group. Seth, always a pleasure to have you on. Good to see you again. >> Yeah, thanks for having me back Dave. >> You're very welcome. So I love these events you get a chance to interact with chief data officers, guys like yourself. We've been talking a lot today about IBM's internal transformation, how IBM itself is operationalizing AI and maybe we can talk about that, but I'm most interested in how you're pointing that at customers. What have you learned from your internal experiences and what are you bringing to customers? >> Yeah, so, you know, I was hired at IBM to lead part of our internal transformation, so I spent a lot of time doing that. >> Right. >> I've also, you know, when I came over to IBM I had just left Monsanto where I led part of their transformation. So I spent the better part of the first year or so at IBM not only focusing on our internal efforts, but helping our clients transform. And out of that I found that many of our clients needed help and guidance on how to do this. And so I started a team we call, The Data Science an AI Elite Team, and really what we do is we sit down with clients, we share not only our experience, but the methodology that we use internally at IBM so leveraging things like design thinking, DevOps, Agile, and how you implement that in the context of data science and AI. >> I've got a question, so Monsanto, obviously completely different business than IBM-- >> Yeah. >> But when we talk about digital transformation and then talk about the difference between a business and a digital business, it comes down to the data. And you've seen a lot of examples where you see companies traversing industries which never used to happen before. You know, Apple getting into music, there are many, many examples, and the theory is, well, it's 'cause it's data. So when you think about your experiences of a completely different industry bringing now the expertise to IBM, were there similarities that you're able to draw upon, or was it a completely different experience? >> No, I think there's tons of similarities which is, which is part of why I was excited about this and I think IBM was excited to have me. >> Because the chances for success were quite high in your mind? >> Yeah, yeah, because the chance for success were quite high, and also, you know, if you think about it there's on the, how you implement, how you execute, the differences are really cultural more than they're anything to do with the business, right? So it's, the whole role of a Chief Data Officer, or Chief Digital Officer, or a Chief Analytics Officer, is to drive fundamental change in the business, right? So it's how do you manage that cultural change, how do you build bridges, how do you make people, how do you make people a little uncomfortable, but at the same time get them excited about how to leverage things like data, and analytics, and AI, to change how they do business. And really this concept of a digital transformation is about moving away from traditional products and services, more towards outcome-based services and not selling things, but selling, as a Service, right? And it's the same whether it's IBM, you know, moving away from fully transactional to Cloud and subscription-based offerings. Or it's a bank reimagining how they interact with their customers, or it's oil and gas company, or it's a company like Monsanto really thinking about how do we provide outcomes. >> But how do you make sure that every, as a Service, is not a snowflake and it can scale so that you can actually, you know, make it a business? >> So underneath the, as a Service, is a few things. One is, data, one is, machine learning and AI, the other is really understanding your customer, right, because truly digital companies do everything through the eyes of their customer and so every company has many, many versions of their customer until they go through an exercise of creating a single version, right, a customer or a Client 360, if you will, and we went through that exercise at IBM. And those are all very consistent things, right? They're all pieces that kind of happen the same way in every company regardless of the industry and then you get into understanding what the desires of your customer are to do business with you differently. >> So you were talking before about the Chief Digital Officer, a Chief Data Officer, Chief Analytics Officer, as a change agent making people feel a little bit uncomfortable, explore that a little bit what's that, asking them questions that intuitively they, they know they need to have the answer to, but they don't through data? What did you mean by that? >> Yeah so here's the conversations that usually happen, right? You go and you talk to you peers in the organization and you start having conversations with them about what decisions are they trying to make, right? And you're the Chief Data Officer, you're responsible for that, and inevitably the conversation goes something like this, and I'm going to paraphrase. Give me the data I need to support my preconceived notions. >> (laughing) Yeah. >> Right? >> Right. >> And that's what they want to (voice covers voice). >> Here's the answer give me the data that-- >> That's right. So I want a Dashboard that helps me support this. And the uncomfortableness comes in a couple of things in that. It's getting them to let go of that and allow the data to provide some inkling of things that they didn't know were going on, that's one piece. The other is, then you start leveraging machine learning, or AI, to actually help start driving some decisions, so limiting the scope from infinity down to two or three things and surfacing those two or three things and telling people in your business your choices are one of these three things, right? That starts to make people feel uncomfortable and really is a challenge for that cultural change getting people used to trusting the machine, or in some instances even, trusting the machine to make the decision for you, or part of the decision for you. >> That's got to be one of the biggest cultural challenges because you've got somebody who's, let's say they run a big business, it's a profitable business, it's the engine of cashflow at the company, and you're saying, well, that's not what the data says. And you're, say okay, here's a future path-- >> Yeah. >> For success, but it's going to be disruptive, there's going to be a change and I can see people not wanting to go there. >> Yeah, and if you look at, to the point about, even businesses that are making the most money, or parts of a business that are making the most money, if you look at what the business journals say you start leveraging data and AI, you get double-digit increases in your productivity, in your, you know, in differentiation from your competitors. That happens inside of businesses too. So the conversation even with the most profitable parts of the business, or highly, contributing the most revenue is really what we could do better, right? You could get better margins on this revenue you're driving, you could, you know, that's the whole point is to get better leveraging data and AI to increase your margins, increase your revenue, all through data and AI. And then things like moving to, as a Service, from single point to transaction, that's a whole different business model and that leads from once every two or three or five years, getting revenue, to you get revenue every month, right? That's highly profitable for companies because you don't have to go in and send your sales force in every time to sell something, they buy something once, and they continue to pay as long as you keep 'em happy. >> But I can see that scaring people because if the incentives don't shift to go from a, you know, pay all up front, right, there's so many parts of the organization that have to align with that in order for that culture to actually occur. So can you give some examples of how you've, I mean obviously you ran through that at IBM, you saw-- >> Yeah. >> I'm sure a lot of that, got a lot of learnings and then took that to clients. Maybe some examples of client successes that you've had, or even not so successes that you've learned from. >> Yeah, so in terms of client success, I think many of our clients are just beginning this journey, certainly the ones I work with are beginning their journey so it's hard for me to say, client X has successfully done this. But I can certainly talk about how we've gone in, and some of the use cases we've done-- >> Great. >> With certain clients to think about how they transformed their business. So maybe the biggest bang for the buck one is in the oil and gas industry. So ExxonMobile was on stage with me at, Think, talking about-- >> Great. >> Some of the work that we've done with them in their upstream business, right? So every time they drop a well it costs them not thousands of dollars, but hundreds of millions of dollars. And in the oil and gas industry you're talking massive data, right, tens or hundreds of petabytes of data that constantly changes. And no one in that industry really had a data platform that could handle this dynamically. And it takes them months to get, to even start to be able to make a decision. So they really want us to help them figure out, well, how do we build a data platform on this massive scale that enables us to be able to make decisions more rapidly? And so the aim was really to cut this down from 90 days to less than a month. And through leveraging some of our tools, as well as some open-source technology, and teaching them new ways of working, we were able to lay down this foundation. Now this is before, we haven't even started thinking about helping them with AI, oil and gas industry has been doing this type of thing for decades, but they really were struggling with this platform. So that's a big success where, at least for the pilot, which was a small subset of their fields, we were able to help them reduce that timeframe by a lot to be able to start making a decision. >> So an example of a decision might be where to drill next? >> That's exactly the decision they're trying to make. >> Because for years, in that industry, it was boop, oh, no oil, boop, oh, no oil. >> Yeah, well. >> And they got more sophisticated, they started to use data, but I think what you're saying is, the time it took for that analysis was quite long. >> So the time it took to even overlay things like seismic data, topography data, what's happened in wells, and core as they've drilled around that, was really protracted just to pull the data together, right? And then once they got the data together there were some really, really smart people looking at it going, well, my experience says here, and it was driven by the data, but it was not driven by an algorithm. >> A little bit of art. >> True, a lot of art, right, and it still is. So now they want some AI, or some machine learning, to help guide those geophysicists to help determine where, based on the data, they should be dropping wells. And these are hundred million and billion dollar decisions they're making so it's really about how do we help them. >> And that's just one example, I mean-- >> Yeah. >> Every industry has it's own use cases, or-- >> Yeah, and so that's on the front end, right, about the data foundation, and then if you go to a company that was really advanced in leveraging analytics, or machine learning, JPMorgan Chase, in their, they have a division, and also they were on stage with me at, Think, that they had, basically everything is driven by a model, so they give traders a series of models and they make decisions. And now they need to monitor those models, those hundreds of models they have for misuse of those models, right? And so they needed to build a series of models to manage, to monitor their models. >> Right. >> And this was a tremendous deep-learning use case and they had just bought a power AI box from us so they wanted to start leveraging GPUs. And we really helped them figure out how do you navigate and what's the difference between building a model leveraging GPUs, compared to CPUs? How do you use it to accelerate the output, and again, this was really a cost-avoidance play because if people misuse these models they can get in a lot of trouble. But they also need to make these decisions very quickly because a trader goes to make a trade they need to make a decision, was this used properly or not before that trade is kicked off and milliseconds make a difference in the stock market so they needed a model. And one of the things about, you know, when you start leveraging GPUs and deep learning is sometimes you need these GPUs to do training and sometimes you need 'em to do training and scoring. And this was a case where you need to also build a pipeline that can leverage the GPUs for scoring as well which is actually quite complicated and not as straight forward as you might think. In near real time, in real time. >> Pretty close to real time. >> You can't get much more real time then those things, potentially to stop a trade before it occurs to protect the firm. >> Yeah. >> Right, or RELug it. >> Yeah, and don't quote, I think this is right, I think they actually don't do trades until it's confirmed and so-- >> Right. >> Or that's the desire as to not (voice covers voice). >> Well, and then now you're in a competitive situation where, you know. >> Yeah, I mean people put these trading floors as close to the stock exchange as they can-- >> Physically. >> Physically to (voice covers voice)-- >> To the speed of light right? >> Right, so every millisecond counts. >> Yeah, read Flash Boys-- >> Right, yeah. >> So, what's the biggest challenge you're finding, both at IBM and in your clients, in terms of operationalizing AI. Is it technology? Is it culture? Is it process? Is it-- >> Yeah, so culture is always hard, but I think as we start getting to really think about integrating AI and data into our operations, right? As you look at what software development did with this whole concept of DevOps, right, and really rapidly iterating, but getting things into a production-ready pipeline, looking at continuous integration, continuous development, what does that mean for data and AI? And these concept of DataOps and AIOps, right? And I think DataOps is very similar to DevOps in that things don't change that rapidly, right? You build your data pipeline, you build your data assets, you integrate them. They may change on the weeks, or months timeframe, but they're not changing on the hours, or days timeframe. As you get into some of these AI models some of them need to be retrained within a day, right, because the data changes, they fall out of parameters, or the parameters are very narrow and you need to keep 'em in there, what does that mean? How do you integrate this for your, into your CI/CD pipeline? How do you know when you need to do regression testing on the whole thing again? Does your data science and AI pipeline even allow for you to integrate into your current CI/CD pipeline? So this is actually an IBM-wide effort that my team is leading to start thinking about, how do we incorporate what we're doing into people's CI/CD pipeline so we can enable AIOps, if you will, or MLOps, and really, really IBM is the only company that's positioned to do that for so many reasons. One is, we're the only one with an end-to-end toolchain. So we do everything from data, feature development, feature engineering, generating models, whether selecting models, whether it's auto AI, or hand coding or visual modeling into things like trust and transparency. And so we're the only one with that entire toolchain. Secondly, we've got IBM research, we've got decades of industry experience, we've got our IBM Services Organization, all of us have been tackling with this with large enterprises so we're uniquely positioned to really be able to tackle this in a very enterprised-grade manner. >> Well, and the leverage that you can get within IBM and for your customers. >> And leveraging our clients, right? >> It's off the charts. >> We have six clients that are our most advanced clients that are working with us on this so it's not just us in a box, it's us with our clients working on this. >> So what are you hoping to have happen today? We're just about to get started with the keynotes. >> Yeah. >> We're going to take a break and then come back after the keynotes and we've got some great guests, but what are you hoping to get out of today? >> Yeah, so I've been with IBM for 2 1/2 years and I, and this is my eighth CEO Summit, so I've been to many more of these than I've been at IBM. And I went to these religiously before I joined IBM really for two reasons. One, there's no sales pitch, right, it's not a trade show. The second is it's the only place where I get the opportunity to listen to my peers and really have open and candid conversations about the challenges they're facing and how they're addressing them and really giving me insights into what other industries are doing and being able to benchmark me and my organization against the leading edge of what's going on in this space. >> I love it and that's why I love coming to these events. It's practitioners talking to practitioners. Seth Dobrin thanks so much for coming to theCUBE. >> Yeah, thanks always, Dave. >> Always a pleasure. All right, keep it right there everybody we'll be right back right after this short break. You're watching, theCUBE, live from San Francisco. Be right back.

(energetic music) >> Hey, welcome back, get ready. Jeff Frick here with theCUBE. We're at our Palo Alto Studios for a CUBEConversation, get a little bit of a break from the conference madness which is in full force right now. And we're excited to have our next guest, he's Mike Tuchen, the CEO of Talend coming off a really good quarter. Mike, great to see you. >> Thank you, Jeff. >> You guys are on fire! >> You know, it's a great time to be in the data business right now. (Jeff laughs) >> So give us a little update, what's going on recently? You've got a big show coming up, I imagine there's lots of announcements that are going to come out that you probably can't tell us about at the show, but go ahead and give a plug. It's coming up really soon and let's get into it. >> Yeah, exactly. So just in a couple weeks Talend Connect in London on the 15th and 16th and Talend Connect in Paris on the 17th and 18th. And Talend Connect is our user conference, so we'll have hundreds of people there, a lot of partners there, we'll roll out a whole bunch of new product announcements and talk about a lot of the great stuff that our customers are doing with Talend. >> So you've got an interesting way to kind of package up what you guys do in a really simple way and that's, you said before we turned on the cameras, the first mile, you know there's always so much conversation about the last miles, not necessarily in data but you know, in getting cable to your home and broadband and this, that, and the other but you talked about the first mile. Arguably, that's a lot more important than the last mile. >> Well you can't even get started on anything else until you solve the first mile problem and that's what we do. And the problem is, right now, every single customer in the world is waking up to the power of data and they need to be data-driven. They know it can make a huge difference in their business, and competitively the market leaders are all incredibly data-driven and if companies aren't equally data-driven then they fall behind. And so there's an incredible surge of interest in data-driven, becoming data-driven right now. The challenge that everyone faces is in order to get started down that path, your data is locked up in a lot of different places, it's dirty, it's inconsistent. And until you bring it together, clean it up, and make it consistent you can't do anything with it. That's the first mile. That's what we do. >> So how does it change now? I mean there's obviously been EDL and data cleansing issues for a very, very long time. So when you look at some of the trends, the growth of public cloud, obviously the explosion of data. Now you guys are taking a little bit different approach than kind of the historical method so how do you do it differently and why is it so important? >> From our perspective, we made a bet about five years ago when I joined, that the entire landscape, the IT landscape was being reinvented from the ground up. Not just the data world, the data world for sure, but the entire IT landscape was being reinvented. And that meant you had to solve the problem differently. And so from our perspective, there's four or five big trends that are completely reshaping the IT landscape. Number one of course is the move to the cloud. You've talked about it just a second ago but we're probably 10 years into a 20 or 30 year shift to the cloud and it's actually accelerating right now. We're now seeing not just early adopters but mainstream companies are now making a big bet on the cloud and deciding that's where they're going to be for the foreseeable future. We're seeing the move to more and more self service, where rather than having an IT team solve all your data problems, they're seeing data analysts and data scientists are solving the problems themselves. So creating a world where all of those different roles can play together in a team sport kind of way is an important way. It's moving to more and more real time, right? Everything back 10, 20 years ago used to be done in batch. So at the end of the day, or end of the week, or end of the month you collect a whole bunch of stuff and package it together and crank it through. But think about today's applications, right? The expectation is it's done in real time. If you make a deposit in a bank, you expect to look up the bank balance and see it right there. You don't expect to see it there the next day. >> Right, right. >> You expect your apps to be immediately responsive, that's real time, right? It's now this ubiquitous expectation. And that means that data integration needs to follow that. Tightly connected with that is the move to machine learning. Companies now don't want to do all of the analytics and insight generation with a whole bunch of people looking at data. 'Cause machines can do that a whole lot better, right? Machines are really, really good at finding patterns. And so those are some of the big trends that we see that are completely reshaping the landscape. So clearly, data integration today is just very different than where it was five or 10 years ago. >> It's so funny, we go to a lot of shows and there's always a lot of conversations about innovation and how do you innovate? And to me, one of the really simple answers, not necessarily simple to implement, is you give more people in the organization more access to more data and the tools to manipulate it and then ultimately, hopefully, to make decisions you know, based on that output. So it is kind of unlocking it, it is giving more people that access you talked about. Self service and cloud and really pushing that out and the other funny thing, you talked about real time, is you used to make decisions based on a sample of things that happened in the past. Now with the capacity of the machines, the complete, basically infinite capacity from an individual company point of view of a cloud application, now hopefully, I'm making decisions on all the data while it's happening. Completely different way. >> Yes, yes. And as a matter of fact, the outliers sometimes are really an important part of the data. And so looking at not just where does most the data fall, but why are the outliers there? What do they mean, right? In a fraud detection case the outliers are the frauds, usually, right? So it's an important part of the data and looking at the entire data set allows you to find that. If you're looking at a sample, you'll miss it. >> So as we look forward to machine learning, kind of the last part of your four key drivers, that's a big impact on the way these things work. My favorite little example on machine learning in AI is the new Google Gmail, on that little tiny response that it suggests that on your reply. Which seems relatively straightforward, right? Thanks, I'll get right back to you, you know they're relatively short usually. But the amount of machine learning and artificial intelligence and data analysis that goes into the generation of my three responses versus your three response options back to me is pretty phenomenal. And you guys are now going to be able to bake that into all types of general type processes. >> That's right, and that's right. You know, you described a really cool consumer scenario around e-mail, but there's a bunch of commercial scenarios around things like predictive maintenance. GE, with its big gas turbines. If that thing goes offline at the wrong time it can be real expensive. Because then you have customers who are out of service and it turns out it takes hours to spin up a new gas turbine that might be sitting idle. But if you can do it in a maintenance window it's just not a big deal at all. And so if they can predict when parts are about to fail, that's a savings of literally billions of dollars across their install base. We have one of the major car companies did a really cool analysis around predicting potential recalls based on, in manufacturing, as tools were starting to go out of alignment. And what they could do was start to track and say, if it gets more than this far out of alignment the odds of a recall go up dramatically, and so now's the time to intervene and readjust that tool because a recall is a very, very expensive thing. If you can fix it upfront in the tool you're saving millions of dollars. >> Right. >> Fascinating examples of real world industrial scenarios using machinery. >> Right, and disconnected kind of data sets that actually are tied together in hindsight but probably the person who's responsible for keeping that machine up and running isn't really thinking about the impact to the company if there's a recall on that particular model of car. >> Yeah, exactly. Who would have known that the tolerance, you know, acceptable tolerance was exactly this, right? How would you set that in advance? But it turns out when you actually start running the correlations and throw some learning algorithms at it, you can really start pinpointing it and say, for this tool, it's this, for this other tool it might be something else. >> So the other kind of big trend that you did mention in this explosion of data is using so many more data sets. Going beyond the data that you own, that you generate, that you create, and pulling in a lot of this external data whether it's weather data, whether it's social sentiment data. There's so many data repositories now that you can integrate in with that proprietary data to then drive kind of a secret sauce algorithm that gives you that competitive advantage. You see more and more of that and I think you mentioned kind of the sloppy, crazy variability in all these data sets as you're trying to pull them into these systems. >> That's right, that's right. And we're seeing a bunch of customers doing that. There was an interesting scenario of, we have a customer that does soil testing for farmers with a neat, little device, kind of an IoT scenario, they plug it in, it does the soil test, sends it up to the cloud. Now it correlates that soil with the weather patterns in that area to say, here is the seeding and fertilizing regimen that we should be using for this plot of land. Right? Really cool scenario. >> Well, I'll tell you even a crazier version. I talked to a guy that ran a drone company with the sensors that did a similar type of thing. They run the drone and they analyze the field. And I had to ask him, I'm like, "Come on, I mean people have been sampling fields forever, "this can't be new, right?" And then it feeds back to their little Monsanto engine or whatever that tells you what to do. He goes, "Yeah, but here's what's different, Jeff. "Again, we used to take a sample. "We would take sample points on that field "and we would make a decision based on that sample." He goes, "Now I can track literally every single plant." >> That's cool. >> "Every single plant with the consistency of this "drone coverage and now I can micro, micro, micro "the application of water, the application of hydrogen," or whatever they give, the herbicides, et cetera. Pretty amazing. >> Yeah, and what we're seeing now is that the tractor companies are doing that on a, as you say, on a per seed basis as they're driving through the field based on samples that have been taken, based on drone surveys of what's there, and based on the weather patterns. I mean it's really cool what we're doing in terms of precision farming right now. >> Right. So I'll just take that kind of one step further. The other trend that's coming down the pike which is big and not going to have less data but a lot more is IoT. So from where you're sitting you've been in this business a while, as you look at kind of this next generation of explosion of all this additional machine-generated data, what type of future do you see? How is that going to play? What kind of opportunities is that going to open up? There's a whole nother, multiple orders of magnitude of data coming soon. >> Yeah, no, so IoT is clearly a... It multiplies the amount of data by literally an order of magnitude of, and many of the streams are real time in nature and the absolute requirement then is that you're doing some sort of machine learning to take advantage of it. To me, you can take almost any industry and talk about a potential machine learning scenario in the industry. My favorite one right now is cars, right? This was, you know, it's now, it's in real life. It's not a future thing. If you're driving a Tesla right now, your car is actually starting to fix itself sometimes. Literally, I got a call one time as I was driving down the road, they say, "Hey, we've detected this fault in your car "and if it's okay with you we're going to reset it "right now and it'll be fine." And I was like, "What was the problem?" They're like, "Don't worry about it." Well, that's pretty cool, right? When was the last time-- >> Did they at least ask you to pull over first? (Jeff and Mike laugh) >> But no, the whole idea of having a car that's self-diagnosing and fixing itself is really cool. That's a game changer, I think. >> On so many ways, I mean not only that but you generalize that to a much broader audience. I mean it used to be you made your product, you sent it to your distributor, and you maybe had some assumptions of how it's used, how it's not used. Are people using the features that you created? Are they not using them? Are they using them they way you thought? And now with this connected feedback loop, the ability for manufacturers to know how people are using their tools even beyond just the prescriptive maintenance is a phenomenal impact. >> Yes, and in that particular scenario for those kind of smart devices, not just the one-way feedback loop, but closing the loop and the in field update ability is you know, you combine those two, and wow! It's a whole new world. >> Right, I guess software really is eating the world. I guess you had it right way back when. All right, Mike, well thanks for stopping by. Good luck on your event across the pond here in a couple weeks and great to catch up. >> All right, thank you, Jeff. >> All right, he's Mike, I'm Jeff. You're watching theCUBE. It's a CUBEConversation in our Palo Alto Studios. Thanks for watching and we'll see you next time. (energetic music)

>> Announcer: From the computer history museum, in the heart of Silicon Valley, it's The Cube. Covering food IT, Fork to Farm. Brought to you by Western Digital. >> Hey, welcome back to The Cube. From the food IT event, From Fork to Farm, yep, you heard that right, Fork to Farm. I'm Lisa Martin. Really excited to be joined by my next guests who are influencing the food chain with Big Data, Cloud, IoT and Blockchain in some very, very interesting ways. We have Rob O'Reilly, senior member and technical staff of Analog Devices. Welcome. >> Thank you. >> And we have Raja Ramachandran, the founder and CEO of Ripe.io. Welcome. >> Thank you Lisa. >> So I made that joke about the Fork to Farm because we think so often how trendy it is, farm to table, farm to mouth. And this has been a really interesting event for us to talk with so many different people and companies across the food chain that we often, I think, take for granted. So Rob, wanted to kind of start with you. Analog Devices has been around for 50 years. You serve a lot of markets. So how is, and maybe kind of tell me sort of the genesis, and I know you were involved in this, of Analog Devices evolving to start using Cloud, Big Data, IoT in the food and agriculture space. What was the opportunity that you saw light bulb moment? >> Yup. It's an interesting story. We started with a piece of technology, a sensor that we can connect. I was looking of an app to apply, 'cause it was a full sensor to the Cloud strategy I was working on. And through some conference attendees that I had met and from a fellow who's now our partner, we kind of put together a strategy of "Well we've got the sensor to the Cloud, "where would we apply this?" And we decided though a little bit of banter, tomatoes. And most of it was because, in New England specifically, there's a lot of, there's 7,000 farms in Massachusetts. >> Lisa: Wow. >> Not all of them produce tomatoes, but a lot of them do. So it was like having a test bed right in our backyard. And from that point it's grown to what it is now. >> And I hear that you don't like tomatoes. >> I really don't like tomatoes. >> Lisa: What about heirloom tomatoes? >> I don't like any tomatoes. >> Lisa: Mozzarella, little basil, no? >> No, no. (laughs) I don't mind pasta sauce so much, but that's just because it's all salt. >> Lisa: That's true. >> And sugar. But no, and I've managed to get through this entire project without anybody forcing me to eat a tomato, so. >> That's good, they're respectful. >> I'm proud of that. >> So I was joking earlier, we cover a lot of events across enterprise innovation, and we were at a Hadoop Dataworks events a couple weeks ago and one of the guests was talking about Big Data and how it's influencing shipping, and how shipping companies are leveraging Big Data to determine how often they should clean the ships to remove barnacles 'cause it slows them down. So the funny thing that popped into my mind from that show is, barnacles and Big Data? Never thought that. Today, the wow factor for me, the internet of tomatoes. What is the internet of tomatoes? >> The problem statement when we started was "Why do tomatoes taste like cardboard?" >> Lisa: He really doesn't like tomatoes! (laughs) >> And, you know, in order to go dig into that was let's collect data. So there's a variety of methods that we use to collect the data. We had to create all of this on our own, so we created our own apps for the phones, our own matchups for the web, our own gateways. We built our hardware, we 3-D printed all the housings, and two of us just went off and started to deploy so we could collect data. The second half of it was, "well, what is in the tomato? "and why does it taste the way it does?" So we started doing some chemistry analysis. So a bunch of refractometers and other instruments so we can see what the sugar levels were, what the acid levels were. We infused ourselves into the Boston Tomato Contest, which they have annually. So we showed up, we looked like the Rolling Stones. We showed up with cases of, trap cases of equipment. It took us about 11 and a half hours to test 113, I think it was, tomatoes, and then we compared those to the chefs' scorecards. And in the chef's scorecard, there wasn't just a taste profile, there was the looks and everything else. Well I found a few markers between what the chef's profile said was a good tasting tomato and what the chemistry said. So a year later we showed up with our optical solution and we managed to test 450 tomatoes. >> Wow. >> About 100 of those go to the slicing table, so we had information on 100 of them and we did the same thing. So it got to the point to where we at least had that reconciliation of "what's the farmer doing "and how does it taste?" And by bringing Raja and his group in, we're bringing a lot more of other Big Data, if you will. Other weather data, aerial drone data, you know, anything we could find in a telematic range that would affect the processing or whatever of the tomato. So that in a nutshell is the internet of tomatoes. >> And is this something that, you know, being able to aggregate Big Data from a variety of sources, something that you're planning to then take to, I heard you earlier in the talk, talking about kind of at the relationship building stage. Is this a dialogue that you're having yet with farms? You mentioned 7,000 farms in Massachusets. What's that kind of conversation like? >> Well that's a very interesting dynamic and I think, you know, that data point for the industry is you better go talk to the farmer. It's really been interesting, the hesitation from a farmer to talk to a semiconductor company was odd. But I wasn't John Deer, I wasn't Monsanto, so they were a little more open. And they understand, a lot of these farmers that I'm dealing with now are generational, you know they're fifth, sixth generation. They really haven't made significant change on their farm in 100 years. >> Probably nor do they have a lot of data that's automated, right? There's probably a lot of things that are in Excel. >> And a lot of it is, I mean beyond their first level of contact, say with a seed or a pesticide manufacturer, They have no idea what's going on in the rest of the world. Unlike, you know, a lot of the big, large farms that we see. But at the smaller region, they're regional. And we've still have Hatfield-McCoy type things going on in New England, where families don't talk to each other, they don't share information. So through one of our work groups, we actually invited two of them, and I felt like match maker. We were trying to just get these two to talk. And they did, and they both realized that they were spending way too much money on fertilizer, and they were both over watering. So, it's still Hatfield and McCoys but at least I think they wink at each other every once in a while. >> Right, I love that you bought that up. That was something that was talked about a number of times today is the lack of collaboration maybe that's still in the sort of competitive stage. So Raja, talk to us about Ripe.io. First of all, I think the name is fantastic, but Blockchain and food. What's the synergy? And what opportunity did you see coming from the financial services industry? >> So, you know one of the key points about what we felt brings all this together is creating a web of trust. And so in financial markets, insurance markets, healthcare markets, you know big institutional regulated markets, there's a lot of regulations that really bind together that notion of trust, because you have a way in which you could effectively call out foul. Now, so there's a center of gravity in each of those industries, whether it's a central bank, you know or a state regulator insurance, so the government in healthcare. Here, there's not. It's disparate. It's completely fragmented, yet somehow magically we all get food everyday, ane we're not dead you know. So from that perspective we just marvel at the fact that you're there. So, bringing Blockchain was a way to basically talk to the farmer, talk to the distributor, talk to the buyer, the producer, and all these different constituents, including certifiers, USDA, whomever it might be. And then also even health to health companies, right, so that you can relate it. So the idea is to basically take all of these desperate sets of data, because they don't necessarily collaborate in full, capture it in the way that we're working with ADI so that you can create a real story about where that food came from, how is it curated, how did it get transported, what's in it, you know, do I get it on time, is it ripe, is it tasty and so on, right? And so we looked at Blockchain as a technology, an enabling technology that quickly captures the data, allows each to preserve its own security about it, and then combine it so that you can achieve real outcomes. So you can automate things like, were you sustainable? Were you of quality? Did you meet these taste factors? Was it certified? That's what excited us. We though, this is a perfect place because you've got to feed 9,000,000,000 people and no one trusts their food, you know? >> Lisa: Right. >> So we felt this would be an excellent opportunity to deploy Blockchain. >> And it's interesting that you know, the transparency is one of the things that we hear from the consumers, you know. We want all these things. We want hormone free, cage free, et cetera. We want organic, we want to make sure it is organic, but we also want that transparency. I'm curious since you are talking to the farmers, the distributors and the consumers, what were some of the different requirements coming from each, and how do you blend that to really have that visibility or that traceability from seed to consumption? >> And it's a good point right, because there's all these competing factors where farmers want certain information done, they don't want the price to go to zero because it's so commoditized. The distributor, not entirely sure if they want anybody to know what they do is if they deliver it, they've done their job. The aggregator, a grocery store, a restaurant or whomever, are really feeling the pinch of demographic changes. Not only in America, but globally, you know about this notion that "I need to know more about my food". Millennials are doing it, look at Amazon and Whole Foods. >> Lisa: Yup. >> That is a tipping point of like where this is all going to go. So for us, what Blockchain does allows for each of those drivers to remain clean. And so in essence, what you can do is you take something called smart contracts, not a great word but basically these are codes in which you've got a checklist or if-then statements that you can say, "What does the farmer want?" "What is the distributor doing to get something there?" And of course the buyer. And so in that sense, we've talked a lot about a scorecard or this notion that you can basically highlight and show all of these different values, so that if the consumer is looking for, you know, I definitely want this in my lettuce, in my beets, in whatever it is, and I need to make this type of salad, how acidic should my tomatoes be? Well that's hard to count, like combine all that information. Since we're capturing that data set and validating it to make sure that they're true, then you actually enable that trust for that consumer. So the consumer may want a lot of information, the issue is will they pay for it? There's some evidence that they will. The second part is, you know, does the grocer have the ability to manage wide varietals in their shelf space, and so on. All the techniques that a grocer would go through, yet they want a clean supply chain. >> Lisa: Right. >> So you know, so like what're we're saying is that this is definitely not easy. And so we're taking it where the influencer of the entire chain is able to help drive it, in the meanwhile we're trying to help create a farmer community that creates a level of trust. Bind those together, we believe Blockchain and a lot of the technology that ADI is deploying helps achieve that. >> And it sounds like from a technology perspective, you're leveraging Blockchain, Big Data, aggregating that to help farmers, even consumers, grocers, retailers, become more data-driven businesses. >> Oh absolutely. I mean in one instance we've got, you know a customer that they're learning how Blockchain can be used to open up their markets and improve their existing customer service. So what they have are like data sets, you know Rob would definitely understand this, but basically you have data set on like what's best for apples, pears, avocados to ripen, you know. Now, they know it in their heads, right? But the issue is, they don't know when there's conditions that change. The grocery store says I want Braeburn apples to be 20% more crisper, well they actually have the answer but they don't know how to tie all that together. >> Lisa: Right. >> So this data-driven capability exposes automation, so that you can fulfill on that. Create new markets, 'cause if your growers don't have it you can go find it from elsewhere. And for the consumer, you're going to deliver that component on time. And so in that sense, you know these things are revealed as ways to, not only like lower cost you know, because in the end Blockchain has this sort of notion that it lowers costs. Like any technology, if you insert it, it typically adds costs. And I'm not saying that our Blockchain does, but the greater value is branding, preserving it, you know. A better economic consequence about it, a better customer satisfaction because I now have knowledge in transparency. >> Lisa: Right. >> So you can't value these things right, because I'm a millennial like all of a sudden I got all my information, well how did you value it? I just paid $60 at Whole Foods, or is it something else? >> Lisa: Right. >> So we think that there's whole new economic revitalization about the entire farming system and the food nag system, because if you show the transparency, you've got something. >> That's so interesting. Last question, and we're almost out of time, Rob you mentioned a lot of small farms in Massachusets. Where are those small farms in terms of readiness to look at technologies and the influence of Big Data? Is it still fairly early in those discussions, or is your market more the larger farms that ... >> I said it earlier, we're at the beginning of the beginning. I was actually shocked, excuse me, when I went out and started talking to them. I was under some assumption that a lot of this was already going on. And it turns out it's not, certainly at that level. So we were like new to these guys, and the fact that we had a technology that would help them was unique to them. The issue was, well how do you communicate with them? How would you sell that? What's the distribution channel? So through a lot of the workshops that we do with the farmers we ask the question, "If their is new technology and you want to go get it, "what do you do?" They google it. I said, "Okay, that's probably not the answer "I was looking for." (laughs) But no, the supporting infrastructure, the rest of the ecosystem they need to take advantage just isn't there yet. So a lot of that I think is slow for the adoption, but it's also kind of helped us because we're working on technologies. You know, timing is everything. So the fact that we've had time to catch up to what we thought was really needed, and then learned more from the farmer, well no, no this is really what they want. So we've been able to iterate. You know, we're a very small team. We've been able to fail miserably many, many times. But the good news is, when we're successful that's all people see. And the farmers are starting to see that, that hey, we're getting actionable data. You're telling me things that I kind of knew, 'cause they fly by the seat of their pants a lot. >> They want it validated, verified. >> Oh yeah, they're very frugal. >> Trustworthy, as you said Raja. >> There's a big push back to spend any money on anything at a farm. That's just the way it is, it's not anything unique. So when you show up now with some technology that could help them, they just want to make sure that you're spot on, you can predict what it is, and when they hand me the money they can start planning on the return on their investment. >> Well gentlemen, we want to thank you so much for sharing your insights, Blockchain of food, what ADI is doing in their 50th year. Sounds like the beginning is very exciting and we wish you the best of luck. I'm not going to hold my breath that you're going to like tomatoes but, you know. (laughs) We wish you the best of luck and enjoy the rest of today. We want to thank you for watching The Cube at the Food IT event, From Fork to Farm. I'm Lisa Martin, thanks for watching. (upbeat pop music)

>> Narrator: From the Computer History Museum in the heart of Silicon Valley, it's theCUBE, covering food IT: Fork to Farm, brought to you by Western Digital. >> Hey, welcome back here and ready, Jeffrey Frick with theCUBE. We are in Silicon Valley at the Computer History Museum at a really unique event. It's food IT: Fork to farm, not the other way around, which you might think, "Hm, that doesn't make sense," but actually it does, really by the consumer-driven world that's hitting everything including the food and agriculture and we're really excited to have the guys running this show, representing The Mixing Bowl. Rob Trice is the founder and Michael Rose, partner, of The Mixing Bowl. Gentlemen, welcome. >> Thank you for having us. >> Thank you. >> So, first off, a little history on this event, it's the first time we've been here. I think you said there's about 350 people, really a broad spectrum: academe, technology, farmers, from New Zealand, I think was the one I heard from the furthest place. What's kind of the genesis of this show? >> So, my background is 15 years in mobile internet, telecom venture capital and my wife, actually, a couple of years ago, started running a cattle ranch out on the Pacific Coast and through that I saw how little technology was being used on the ranch and amongst local food producers. I came back to Silicon Valley and none of the big food or ag. players were here then, four years ago. Monsanto just had up a venture group, Unilever and Nestle had one person each here, but by and large, Silicon Valley's IT innovation ecosystem was not focused on food and agriculture. So I started The Mixing Bowl as a little bit more than just a Meetup group and we did it a couple of times and then somebody said, "You know, we should do a conference on this topic." So the first year we did it at Stanford with a partner of ours, and we thought might have 150 people come. We had over 300 people come and it was this kind of audience, kind of cross-section of technologists, food and agriculturalists. So that's when I said, "You know, I'm done with telecom. I want to go ride this food tech, ag. tech wave and see where the heck this comes to roost." So, it's been four years now and I'm pleased to be working not only with Michael, but then our colleagues Seana and Brita, and having a blast, learning a lot. >> Okay, so that's the conference. What about for The Mixing Bowl specifically, what is your charter as an organization? >> Well we've got three aspects of our business, so the first one is information sharing, so doing events like this. We do themed events, we did a water-tech for agricultural event down in Fresno. And then we also are contributing writers for Forbes. We also have an advisory business where we work with large corporates who are seeking innovation and trying to bring innovation to the food and ag. Sector, trying to bring technology and innovation. And then we have an investment side of our business, out of the brand Better Food Ventures. So we invest in the space as well, we have about 12 companies in our portfolio. >> That's interesting that you said there wasn't a lot of tech in ag. here and yet, we talked to Paul from Ford, we talked about their conference that they have at Salinas and of course, Sacramento Valley, San Fernando Valley, or not San Fernando Valley, San Joaquin Valley is a huge producer of food. So why do you think it was so late to come here? >> Well, I think that there have been other opportunities and I think that there's a misperception that agriculture doesn't need IT and I think what we've now realized is there's a huge opportunity, whether it is Internet of Things or looking at tracking and transparency, there's a lot of inefficiencies in our food production system and there also are a lot of societal challenges that we have. Everyone talks about feeding nine billion people by 2050, but then also we look at food safety, we look at what the consumer wants, which is why we're here today, talking about the fork to the farm. Consumers want change in food. They want different kinds of food. They want it delivered to them in different ways. All of these are opportunities for tech to be applied to food and agriculture. >> So we love being here. Go ahead, Michael. >> No, I was just going to say, I think it's like any other vertical in any other sector that starts to adopt technology over time. And even in the ag. sector, you've seen in the commodity crops in the Midwest with the automation that they adopted technology early but you've got other sectors, whether it's the specialty crops down in Salinas or people who are doing almonds, etc. Those people are starting to adopt technology, they're just a little further behind than you are with commodity crops. >> Right. It's funny, we interviewed the guy from Caterpillar a few weeks ago, and they are already running huge fleets of autonomous vehicles in mining. Obviously they have a lot of equipment involved in agriculture as well, so it seems kind of start and stop depending on the vendors that you're talking about. But one of the big themes we talk about, we go to a lot of platform shows, right? It's Cloud, it's edge, it's connectivity, it's big data, drones, I mean, as you look at some of these big classifications of technology that are now being applied in ag. are there any particular ones that kind of jump out as either the catalyst or the leading edge of adoption that's really helping drive this revolution? >> I guess, if you think about the fact that we're kind of looking at this staircase of adoption. One thing that we need to do is actually digitize information and that's one of the challenges that we have. Once we digitize, then we can start to manage operations based on that data, then we can start to optimize, and then we can automate. So it's a four-step staircase that we look at and I think in a lot of cases, even at restaurants, a lot of them are still placing orders via fax and telephone. We need to get off of that and start getting them to order online through online platforms and so forth. So, at any rate, one area that I'm particularly excited about is aerial imaging for agriculture because I think you are instantaneously, by just doing a flyover, providing farmers with more information than they've ever had. In some cases, I think you could actually argue, you're going from a data desert to a data flood. Now the challenge is moving up that staircase to go make sense of that data and then ultimately be able to give prescriptive machine-learning or artificial intelligence-based recommendations to that farmer on how to do a better job, whether that is increasing sustainability, maximizing yield, looking at pricing, any of those kind of things. >> Right, one of the things you hear real often in every industry, is kind of the old guy using intuition versus becoming really a data-driven organization. Are you seeing that classic conflict, or do people get it pretty quickly when you can provide the data to show them things that they could never really see before? >> I was going to say, one of the biggest challenges that's also dictating the market timing is the fact that average American farmer is about 65, so we now are having this turn as the kids are coming back who are tech-enabled back to the production point, back to the farm and starting to take over farms from their parents. And their parents, of course, have just been maybe a little slower to adopt new technology. So it's just a timing issue. I think the other thing is, there are all the different pieces, whether it's the sensors or whether it's the connectivity of data or whether it's the storage of data, there needs to be a solution and they need to be integrated. And so we see this on the farm, getting that data off and then getting it stored and then how to use it. But then you also see this in restaurants. In restaurants, you have all of the delivery services coming in, so a restaurant can have seven different delivery services picking up from the restaurant. And they have seven different iPads that they have to manage with their point of sales system and very few of them currently will integrate with a POS, right? >> Right. And I think whether it's in a restaurant or on a farm, this lack of integration, API integration, making it a usable solution as opposed to a number of features, is where we're probably going to see a lot more tech innovation. I think unfortunately what you're probably also going to see is a lot of consolidation because you've had venture capital-backed companies with solutions for food and agriculture that have their own proprietary solution, their own OS. And we know that, from other tech sectors, that's not a long-term viable strategy. Ultimately, the data will be free, it will open up, it will interconnect, and we just need to happen in food and in agriculture. >> And are they getting that? Because the classic farmer dilemma that you learn in economics 101 is they have a great crop, crap prices go in the toilet. They have a crappy crop, price is up but they don't have enough quantity to share and gaming the system, and who's going to plant what? Do they start to see the value of sharing some level of data aggregation for the benefit of all? >> I think there's a misperception out there that farmers won't share their data. The reality is they're willing to share their data, if it's providing some value to them. A lot of people want to charge these farmers for their data without any demonstrable benefit to using that data. And I think where you can find a solution, I think the farmers are, speaking generally here, I think the other thing is, farmers know, if you're not paying for the data, you probably are the product, right? And they're smart enough to figure that out, so they don't want people misusing their data for reasons that aren't clear to them. And they've had bad experiences with that in the past. >> It's not any different than any other sector. I mean, go back seven years ago when people said, "Well, we're going to mix your data up with somebody else's data, but it's not a problem, right? Zeros and ones, it's bits." And they were both like, "Nooo," and they got over it, right? >> Right, but the other thing I'll say is I think that the challenges are changing and this is not just standard commodity ups and downs, particularly if you look at here in California, the specialty crops. We have lost access to what has been a cheap labor pool historically and we need to automate. So now we need to go where northern Europe has already gone, in terms of automating production for specialty crops and then things like climate change are causing different crops to grow in different seasons and we need to be able to predict that, we need to take more of it indoors as a nice complement to outdoor growing. So there's a lot of different things that farmers are dealing with now that they really haven't had to deal with in the future. And I think the same is true on the restaurant side. >> Yeah, and the predictability of understanding what your needs are going to be is going to be so important here, particularly because we need to see more automation, both on the farm and production and the restaurants. I know a lot of people talk about being concerned about losing their jobs to automation or robotics, but the reality is, the National Restaurant Association says in the next 10 years, we have a shortage of 200,000 line cooks. >> Jeff: Just line cooks? >> Just line cooks, right. So when you see someone like Chowbotics who's here showing the automated customized salad maker, there's clearly a need in the market place for these kind of approaches. >> The other thing too is you touch on such big, global societal issues. Obviously we're in California here, water. We had a really wet winter, but you know, I'm looking for the water track, I mean that's got to be a huge piece of this whole thing. You have the environmental concern, again, in California, there's always the fight between the farmers that want the water in the rivers and the environmentalists who want to keep the salmon swimming upstream. These are not simple problems that have an obvious solution, and as I think somebody said in they keynote, there's no free trade-off. You've got to make decisions based on values and they're not simple problems. So you guys are right in the middle of a lot of big society changes. >> Yeah, and I think that's one of the things. This is not just a US or a California thing. Globally, things are changing. And whether it is China having more disposable income available to eat more meat and what the ramifications of that are versus other societies with more environmental challenges moving front and center to them, the labor challenge. There's a lot of different things that are happening globally and we don't really have that connectivity layer globally to share this innovation to find the right solutions and get them addressing these market challenges. >> Right. >> Yeah, I would say the thing is, it is complex, so they're going to be talking about tomato growth later on today, and the example somebody was giving is we went to precision watering instead of spray, well, when you go to drip irrigation, you actually have to pressurize an entire system so you actually use more energy. So we use less water but we burn more coal, more oil, whatever it may be, to pressurize the system. And then if it produces a product that has more water content, you spend more energy drying it on the backend. So there's trade-offs. I would say the other thing that we found is really interesting is people ask us if we're social impact investors and we aren't but we have a social impact consideration about what we do, but pretty much everything that you see in this space right now from an innovative side is moving the ball forward, either it's better nutrition, it's less input, it's less chemicals, less water. So this innovation in food and ag. is just by its nature having a very positive impact. >> Right, two years ago, we called food IT macro to micro, and fundamentally what we believe at The Mixing Bowl is, as Michael said, at Better Food Ventures, we don't consider ourselves social impact investors, first and foremost, we want to keep financial grounding. However, I think at a core level, we all believe that harnessing IT to go address these societal challenges in food and agriculture is the biggest thing that we can make. So the reality is we're not going to be able to do much more with the chemical era, we've maximized the yield that we can get there. So now we are going to be looking at IT and how can we actually apply IT to these different challenges and I'm going to cough now. (Jeff laughs) (Rob coughs) >> Well, even something, people think IT and they think highly technical and they think of Cloud, they think of data connections, well look at food waste. The bulk of food waste that happens in our society happens at the home to the restaurant. So even if it's an iPhone app that's teaching our children how to deal with food waste in their home, it's a technical approach, it's hugely impactful. And it's those kind of touch points that will make a difference. >> Right, right. Well, Rob, Michael, thanks for inviting us, it's really fun to come to more of an application-centered show than an infrastructure show and see how the impact of Cloud and big data and sensors and IOT and drones and all of these things are having material impact on us day by day. So congratulations on the event and we'll let you go back to the keynote stage, they're waiting for you. >> Thank you. >> Thank you. >> All right, I'm Jeff Frick, you're watching theCUBE. We are at the Food IT show in Mountain View, California. We'll be right back with the next guest after this short break. Thanks for watching. (electronic music)

>> Announcer: From the Computer History Museum in the heart of Silicon Valley, it's the Cube, covering Food It, Fork to Farm. Brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick here at the Cube. We're in Silicon Valley at the Computer History Museum which celebrates history but we're talking about tech in the food and agricultural space. Here at the Food IT Convention, about 350 people, somebody came all the way from New Zealand, got food manufacturers. We've got tech people, we've got big companies, start-ups and we have a lot of represents from academe which is always excited to have them on, so our next guest is Dr. Andy Thulin, he's the Dean of the College of Agriculture, Food and Environmental Sciences at Cal Poly, San Luis Obispo, or SLO as we like to call them. Welcome. >> That's right. >> And all the way from Iowa, we have Dr. Wendy Wintersteen. She's the Dean of College of Agricultural and Life Sciences at Iowa State. Welcome. >> Thank you, it's great to be here. >> Absolutely, so first off, just kind of your impressions of this event? Small, intimate affair, one actually introduced everyone this morning, which I thought was a pretty interesting thing. Kind of your first impressions. >> It's a great environment. We have this mix of technology and a few production people here, but people thinking about the future. That's always an exciting place to be. >> Really, the environment, having the little set of exhibits, where people can go around, visit with entrepreneurs. It really, a great setting, I think for the discussion. >> So, Wendy, when you introduced your portion on the panel, you talked about the scale on which Iowa produces a lot of things. Pigs, and corns, and eggs, and chickens, and, so, you've been watchin' this space for a while. How do you see, from your perspective, kind of this technology wave, as it hits. Is it new, have we just not been payin' attention? Or is there something different now? >> Well, I think the speed of adoption, the speed of innovation is increasing, clearly. But, it's been a long time now that we've had power drive tractors so the farmers can sit and work on the technology in the cab related to their soil mapping, or yield monitors and the tractor's driving itself. So, we've had that sort of thing in Iowa for a long time and that continues to be improved upon, but that'd be just one example of what we're seeing. And, obviously, California has a huge agricultural presence, again, some people know, some people don't, the valley from top to bottom is something on the order of 500 miles of a whole lot of agriculture, so again, does this, do you see things changing? Is this more of the same? >> No, absolutely changing. I mean California produces some, a little over 400 different products. A lot of 'em, about a 100 of 'em, lead the country, in terms of marketplace. So, there's a lot of technology with the issues of water, lack thereof, or cleaning it up, or the labor challenges that we have for harvesting products. It's really turned into quite a challenge, so challenge drives innovation, you know, when you have your back against the wall, For example, in the strawberry fields I think, a year ago they had $800 million worth of labor to produce $2.4 million, billion dollars worth of strawberries. When you think about that, that's a lot of labor. When you can't get that labor in, you're drivin' by it, you got $300 million, wherever, they just weren't able to harvest it all 'cuz there was nobody to pick 'em. So, when you think about that, it's a billion dollars. It's a billion dollars that they couldn't get to. That drives innovation, so there's a lot of innovation goin' in these products. >> Pretty interesting, 'cuz, obviously, the water one jumps out, especially here in California, you know we had a really wet winter. The reservoirs are full. In fact, they're lettin' water out of the things. I would say we don't have a water problem, we have a water storage problem. This came up earlier today. The points of emphasis change, the points of pain change, and labor came up earlier. The number of people, the minimum wage laws, and the immigration stuff that's going on. Again, that's a real concern if you've got a billion dollars worth of strawberries sittin' in a field that you can't get to. >> Yeah, it's a real challenge. California faces a couple of shortages. We've got a water shortage, we've got a labor shortage, but we also have a talent shortage. We were talking this morning about the number of young people going to Ag colleges. It's up dramatically and we need all that talent and more. Everyone needs, all the grain industry, if you will, across the country, all the people that run these farms and ranches, and all, they're getting older. Who's coming back behind them? It's a technology driven industry today. It's not something that you can just go out and pick it up and start doing. It takes talent and science and technology to manage these operations. >> So, it's interesting. There's been science on kind of the genetic engineering if you will, genetically modified foods for a long time. Monsanto is always in the newspaper. But I asked something that's kind of funny, right, 'cuz we've been genetically modifying our food for a long time. Again, drive up and down I-5 and you'll see the funny looking walnut trees, that clearly didn't grow that way with a solid base on the bottom and a high-yield top. So, talk about attitudes, about this and people want it all. They want organic, but they also want it to look beautiful and perfect, be priced right and delivered from a local farmer. There's no simple solution to these problems. There's a lot of trade-offs that people have to make based on value so I wonder if you could talk about how that's evolving, Wendy, from your point of view. >> Well, certainly as we think about the products we produce in Iowa, we know that producers are willing to produce whatever the consumer would like. But they really want to be assured they have a market, so, right now in Iowa, we have cage-free eggs being produced, and those are being produced because there's a contract with a buyer, and, so I think producers are willing to adapt and address different opportunities in the big markets, different segments of that market, if they can see that profit opportunity that will allow them to continue in their business. From the producer's point of view, the subtheme of this show is Fork to Farm, as opposed to Farm to Fork which you think is the logical way, but it's come up and it's been discussed here quite a bit. It's the consumer, again, like they're doing in every business, is demanding what they want, they're willing to pay, and they're very specific in what they want. Was this like a sudden wave that hit from the producer point of view, or is this an opportunity? Is this a challenge? How is that kind of shifting market dynamics, impacting the producers? >> Well, I think it's all being driven by technology. We're talkin' this morning, years ago, it was the expert, you know, Wendy's of the world they had all the knowledge and then you had all the consumers listening to 'em and trusting 'em. Today, you have, as I call it, the mama tribe, or the soccer tribe, or that sort of thing, where they're listening to other parents, other mothers in that group, they're listening to the blogs, they're listening to their friends, that's driving the conversation and there's less science and technology behind it. They don't trust and the transparency thing comes up constantly. Technology has allowed this just wide open space where now they got so much information, how do they process that. What's real, what's not real, in terms of biotech, or is it this, or is it that? Is it wholesome, you know, all these factors. >> It's funny 'cuz you brought up the transparency earlier today as well, so people know what they're getting, they want to know, they really care. They just don't want to just get whatever generic ABC, like they used to. >> Right, and I think, again, there's a certain segment of the market that is very interested in that and companies are responding. I give the example of Nestles, and so, you get on their web page and you can see the ability to scan the code on a particular product and go and get a lot of information about that product back on the web page of that company. I think that for certain groups of consumers that's going to become even more important, and we have to be prepared to meet that demand. >> So, in terms of what's going on at your academic institutions, how is the environment changing because of technology, we've got these huge macro trends happening, right, cloud is a big thing, Edge Computing, which is obviously important, got to get the cloud to the edge (laughs) of the farm, sensors, big data, being able to collect all this data, I think somebody earlier said it went from no data to now a flood of data, how are you managing that? Better analytics and then, of course, there's fun stuff like drones and some of these other things that can now be applied. How's that workin' it's way into what you're doing in terms of training the next generation of entrepreneurs as well as the kind of traditional farmers in this space? >> Well, I think, first of all, we're seeing a lot more integration between what we do in engineering, and what we do in computer science, and what we do in agriculture and business. The overlap and the connection across those disciplines is occurring not just with our faculty but also with our students. We had a group of students at Iowa State before they graduated from the college, able to start a company called ScoutPro that was based on using technology to help farmers identify pests in the field, and that became a company using the technology to do that. Of course, that relied on software development, as well as clear understanding of agronomic and pest management strategy. I think those integrated approaches are occurring more and more. >> I think at Cal Poly it's, our motto has been for over a hundred years Learn by Doing, hands-on learning. That's key to us, as you have a lecture class, you have a lab that goes along with it so they're forced to. We have over 45 to 50 classes, enterprise classes, where you can come in and you can raise, let's say marigolds and then you can provide that whole value train, chain and sell it. You can raise broiler chicks every quarter, for 35 days you can raise 'em up, 7,000 birds and there's teams of students in these classes, they can do it, then they manage the whole process. A winery, for example, it's a bonded winery. They do the whole process. They know how to change the pumps and all that, so it's hands-on but you take that from there up to where those students go out into the industry. Our university just signed an agreement with Amazon for the cloud, so we're moving the whole complex, our IT, to the cloud through that organization. Is that right or wrong, I don't know, but we've got to do things faster, quicker, and just our infrastructure, would a cost us millions to do that, but that allowed the students, what is it, Apple is only, the iPhone is 10 years old tomorrow. Tomorrow. These kids, that's all they grew up with. So, we're constantly having to change our faculty, our leadership teams, constantly have to change to keep up and stay side-by-side with the technology, so it's changed our Center for Innovation and Entrepreneurship. Cal Poly has a partnership with the community, with the university, it started in College of Business and we have a whole floor of a building in downtown San Luis Obispo and across the street we've got 60 apartments for students that are involved in these start-ups to live there so they can walk across the street, get right engaged. So, we're trying to do everything we can, every university is trying to do everything they can to kind of keep this space flowing, and this enthusiasm with these young people. That's where the change is going to occur. >> Right, right. Exciting times. >> It is exciting. >> It is. >> Alright, well, unfortunately, we are out of time. So, we're going to have to leave it there, but I really want to thank you for stopping by and wish you both safe travels home. >> Thank you very much. >> Thank you. >> Dr. Thulin, Dr. Winterston, I'm Jeff Frick. You're watching the Cube. It's Food IT in Mountain View, California. Thanks for watching. We'll be right back after this short break. (electronic music)