(upbeat techno music) >> From Mountain View California, it's the Cube covering the 15th Annual Grow Awards. Brought to you by ACG SV. >> Hi, Lisa Martin with the Cube on the ground at the Computer History Museum for the 15th annual ACG SV Awards. And in Mountain View California excited to welcome to the Cube for the first time, John Hennessy, the chairman of Alphabet and the co-founder of the Knight-Hennessy Scholars Program at Stanford. JOHN, it's truly a pleasure to have you on the Cube today. >> Well delighted to be here, Lisa. >> So I was doing some research on you. And I see Marc Andreessen has called you the godfather of Silicon Valley. >> Marc very generous (loughs) >> so I thought I was pretty cool I'm going to sit down with the godfather tonight. (loughs) >> I have not done that yet. So you are keynoting the 15th Annual ACG SV Awards tonight. Talk to us a little bit about the takeaways that the audience is going to hear from you tonight. >> Well, they're going to hear some things about leadership the importance of leadership, obviously the importance of innovation. We're in the middle of Silicon Valley innovation is a big thing. And the role that technology plays in our lives and how we should be thinking about that, and how do we ensure the technology is something that serves the public good. >> Definitely. So there's about I think over 230 attendees expected tonight over 100 sea levels, the ACG SV Is has been it's it's much more than a networking organization. there's a lot of opportunities for collaboration for community. Tell me a little bit about your experience with that from a collaboration standpoint? >> Well, I think collaboration is a critical ingredient. I mean, for so many years, you look at the collaboration is gone. Just take between between the universities, my own Stanford and Silicon Valley and how that collaboration has developed over time and lead the founding of great companies, but also collaboration within the valley. This is the place to be a technology person in the whole world it's the best place partly because of this collaboration, and this innovative spirit that really is a core part of what we are as a place. >> I agree. The innovative spirit is one of the things that I enjoy, about not only being in technology, but also living in Silicon Valley. You can't go to a Starbucks without hearing a conversation or many conversations about new startups or cloud technology. So the innovative spirit is pervasive here. And it's also one that I find in an in an environment like ASG SV. You just hear a lot of inspiring stories and I was doing some research on them in the last 18 months. Five CEO positions have been seated and materialized through ACG SV. Number of venture deals initiated several board positions. So a lot of opportunity in this group here tonight. >> Right, well I think that's important because so much of the leadership has got to come by recruiting new young people. And with the increase in concerned about diversity and our leadership core and our boards, I think building that network out and trying to stretch it a little bit from the from perhaps the old boys network of an earlier time in the Valley is absolutely crucial. >> Couldn't agree more. So let's now talk a little bit about the Knight-Hennessy Scholars Program at Stanford. Tell us a little bit about it. When was it founded? >> So we are we are in our very first year, actually, this year, our first year of scholars, we founded it in 2016. The motivation was, I think, an increasing gap we perceived in terms of the need for great leadership and what was available. And it was in government. It was in the nonprofit world, it was in the for profit world. So I being a lifelong educator said, What can we do about this? Let's try to recruit and develop a core of younger people who show that they're committed to the greater good and who are excellent, who are innovative, who are creative, and prepare them for leadership roles in the future. >> So you're looking for are these undergraduate students? >> They are graduate students, so they've completed their undergraduate, it's a little hard to tell when somebody's coming out of high school, what their civic commitment is, what their ability to lead is. But coming out of coming out of undergraduate experience, and often a few years of work experience, we can tell a lot more about whether somebody has the potential to be a future leader. >> So you said, found it just in 2016. And one of the things I saw that was very interesting is projecting in the next 50 years, there's going to be 5000 Knight-Hennessy scholars at various stages of their careers and government organizations, NGOs, as you mentioned, so looking out 50 years you have a strong vision there, but really expect this organization to be able to make a lasting impact. >> That's what our goal is lasting impact over decades, because people who go into leadership positions often take a decade or two to rise to that position. But that's what our investment is our investment is in the in the future. And when I went to Phil Knight who's my co-founder and donor, might lead donor to the program, he was enthusiastic. His view was that we had a we had a major gap in leadership. And we needed to begin training, we need to do multiple things. We need to do things like we're doing tonight. But we also need to think about that next younger generation is up and coming. >> Some terms of inspiring the next generation of innovative diversity thinkers. Talk to me about some of the things that this program is aimed at, in addition to just, you know, some of the knowledge about leadership, but really helping them understand this diverse nature in which we now all find ourselves living. >> So one of the things we do is we try to bring in leaders from all different walks of life to meet and have a conversation with our scholars. This morning, we had the UN High Commissioner for Human Rights in town, Michelle Bachelet, and she sat down and talked about how she thought about her role as addressing human rights, how to move things forward in very complex situations we face around the world with collapse of many governments and many human rights violations. And how do you how do you make that forward progress with a difficult problem? So that kind of exposure to leaders who are grappling with really difficult problems is a critical part of our program. >> And they're really seeing and experiencing real world situations? >> Absolutely. They're seeing them up close as they're really occurring. They see the challenges we had, we had Governor Brown and just before he went out of office here in California, to talk about criminal justice reform a major issue in California and around the country. And how do we make progress on that on that particular challenge? >> So you mentioned a couple of other leaders who the students I've had the opportunity to learn from and engage with, but you yourself are quite the established leader. You went to Stanford as a professor in 1977. You are a President Emeritus you were president of Stanford from 2000 to 2016. So these students also get the opportunity to learn from all that you have experienced as it as a professor of Computer Science, as well as in one of your current roles as chairman of Alphabet. Talk to us a little bit about just the massive changes that you have seen, not just in Silicon Valley, but in technology and innovation over the last 40 plus years. >> Well, it is simply amazing. When I arrived at Stanford, there was no internet. The ARPANET was in its young days, email was something that a bunch of engineers and scientists use to communicate, nobody else did. I still remember going and seeing the first demonstration of what would become Yahoo. Well, while David Filo and Jerry Yang had it set up in their office. And the thing that immediately convinced me Lisa was they showed me that their favorite Pizza Parlor would now allow orders to go online. And when I saw that I said, the World Wide Web is not just about a bunch of scientists and engineers exchanging information. It's going to change our lives and it did. And we've seen wave after wave that with Google and Facebook, social media rise. And now the rise of AI I mean this this is a transformative technology as big as anything I think we've ever seen. In terms of its potential impact. >> It is AI is so transformative. I was I was in Hawaii recently on vacation and Barracuda Networks was actually advertising about AI in Hawaii and I thought that's interesting that the people that are coming to to Hawaii on vacation, presumably, people have you know, many generations who now have AI as a common household word may not understand the massive implications and opportunities that it provides. But it is becoming pervasive at every event we're at at the Cube and there's a lot of opportunity there. It's it's a very exciting subject. Last question for you. You mentioned that this that the Knight-Hennessy Scholars Program is really aimed towards graduate students. What is your advice to those BB stem kids in high school right now who are watching this saying, oh, John, what, what? How do you advise me to be able to eventually get into a program like this? >> Well, I think it begins by really finding your passion, finding something you're really dedicated to pushing yourself challenging yourself, showing that you can do great things with it. And then thinking about the bigger role you want to have with technology. In the after all, technology is not an end in itself. It's a tool to make human lives better and that's the sort of person we're looking for in the knight-Hennessy Scholars Program, >> Best advice you've ever gotten. >> Best advice ever gotten is remember that leadership is about service to the people in the institution you lead. >> It's fantastic not about about yourself but really about service to those. >> About service to others >> JOHN, it's been a pleasure having you on the Cube tonight we wish you the best of luck in your keynote at the 15th annual ACG SV Awards and we thank you for your time. >> Thank you, Lisa. I've enjoyed it. Lisa Martin, you're watching the Cube on the ground. Thanks for watching. (upbeat tech music)

>> (Announcer) From Mountain View, California it's the Cube. Covering the 15th Annual Grow! Awards. Brought to you by ACG SV. >> I'm Lisa Martin with the Cube, on the ground at the Computer History Museum in Mountain View, California for the 15th Annual Grow! Awards for the Association of Corporate Growth Silicon Valley, ACG SV. That's a mouthful. I'm here with one of the ACG SV board members, Zac Mutrux, the District Sales Manager at Insperity. Hey, Zack, it's great to have you on the Cube. >> Thank you so much, Lisa, I'm pleased to be here. >> So before we talk about what you're doing here at the 15th Annual Grow! Awards, tell our audience about Insperity. I was reading, I love taglines. >> Yes. >> And I see on your homepage, Insperity is obsessed with delivery HR mastery. Wow. >> Oh yeah. >> Obsessed and mastery. Those two words just jumped out. Tell us a little bit about what you guys do. >> Impressive, isn't it? Well, we actually just adjusted our tagline to HR that makes a difference. And that's really what it's all about. We feel like companies that are growing, if they're going to make it from good to the best, it has everything to do with the people. Attracting the best people and keeping them, developing them over time, and that's exactly what we do with our clients. >> So Insperity has been in business since 1986, and if I think of today's modern workforce, highly mobile, distributed, there's the whole on-demand industry. You guys have seen a tremendous amount of change that now can be massively influenced, and your customers can, using technology. Give me a little bit of that historical perspective on Insperity's inception and today's workforce, and how you're helping them attract and retain the best talent. >> Oh, absolutely. Well, when the company started it was in a maybe a 200 square foot room with one telephone between the two co-founders. There's no such thing as email. So, absolutely, there's been immense technological changes and there continues to be. I think that's one of the things that has been responsible for Insperity's success is its adoption of technology. Today we are as much a technology company as we are an employee benefits company, or an HR consulting company. It's really about creating a positive experience for the employees. That's part of being a competitive employer. >> Well it has to be a positive experience, right? For your customers. Because acquiring great talent is one thing, retaining them is another. And I want to kind of pivot off the retention there for a second. As the District Sales Manager, I was asking you before we went live, tell me maybe one of your favorite stories, and you said, "Wow". One of the great things, you guys are coming off great growth and FY18 revenue growth. One of the great things that Insperity has been really successful at is customer retention. And that's hard. You're proud of this. Tell us about that statistic that you mentioned, and how it is that Insperity is evolving and innovating over the last few decades to keep that retention number as phenomenal as it is. >> Well, Insperity's been named one of the most admired corporations in the country, actually, five years in a row by Fortune magazine. And that's the kind of press that you can't buy. One of the accolades that I'm most proud of is that in the past year our own employees named us one of the top 100 companies to work for in the United States. Which is, I think, the proof that we really know what we're doing with our clients. Because there are a lot of different companies out there, various competitors, and almost none of them are on that list. So, it's living our values and expressing through our service team, our extraordinary service team, that, I think, keeps our clients coming back to us year after year. About 85% renew. That's been consistent. A high level of client retention for the past three years. Even more extraordinary is that we've been growing both top line and bottom line revenue at the same time. So there's just a testament to our leadership, to our co-founder and CEO, Paul Sarvadi, and to the best of team-- >> But it sounds like it's a lot of symbiotic relationships between the internal retention at Insperity that is maybe leading through to your customers seeing, hey, there's not a high turnover here. These people are doing, they love what they're doing. They're working for a good company. So there's probably a lot of symbiotic behaviors. >> Well, that's exactly right. I think you really hit the nail of the head. It's about culture. It's a culture that starts from the top with leadership, and it filters down throughout the organization. And we're not looking to do business with every single company. We're looking to do business with the companies that believe the things that we believe. That is, companies that have high levels of commitment, trust, communication. They do better financially then companies that don't have those things. >> And along those lines, mentioning just before we wrap here, we are at the 15th Annual ACG SV Awards tonight, where they're honoring two award winners. The Outstanding Growth Award winner is Arista Networks. And the Emerging Growth winner is Adesto Technologies. I'm excited to talk to them later. But I wanted to get a little bit of perspective on you've been involved as a board member of ACSG since last year. Tell me a little bit about what makes ACG SV worthy of your time. >> Oh, absolutely. That's a great question. It's just an extraordinary community, I think, of the top leaders in Silicon Valley come together. The monthly Key Notes add a lot of value. It's an intimate setting and there's real conversations that are taking place on topics that are relevant to today's professionals. So for me to be able to engage and hopefully add some value as a board member is privilege. >> And you can hear probably a lot of those conversations going on right behind Zac and me tonight. Zac, it's been a pleasure to have you on the Cube. Thank you so much for giving us some of your time. >> Oh, right, thank you, Lisa. >> For the Cube, I'm Lisa Martin on the ground. Thanks for watching. (pop electronic music)

>> From Mountainview, California, it's The Cube, covering the 15th annual Grow! Awards. Brought to you by ACG SV. >> Hi, Lisa Martin on the ground with The Cube at the 15th annual ACG SV Grow! Awards. At the Computer History Museum in Mountainview, can you hear the buzz of 230 plus attendees behind me? I'm very pleased to welcome one of the ACG SV board members Dr. Shampa Banerjee, technology executive, and chief product officer at Eros Digital. Shampa, thank you so much for giving us some of your time this evening. >> Thank you, it's a pleasure. >> So lots of great, innovative, and inspiring conversations, no doubt, going on behind us. >> I'm trying to listen to it. >> Yeah, so talk to us a little bit about Eros Digital, who you are, what you do. >> So Eros International is the largest studio in India. It produces or distributes around 60 to 70 percent of the films made in India, Bollywood films. So I run the streaming platform, the Netflix for Bollywood, that's what I call it. >> The Netflix for Bollywood, I love it. Now, tell us more about that. >> So, you know, it's a streaming platform, a lot of the titles are from what we produce. A lot of the titles we lease from other production houses, and that is the entire technology platform, and then how do you get to the, we connect the consumers, rather, to the entertainment, right? So we like to help them discover, we help them indulge in the whole experience, and then as they keep coming to us more and more, we personalize the experience for them, so that's really what we give them. >> You know, personalization is so key. We expect it right in our lives, and whatever it is that we're doing, we're engaging with an Amazon or a Netflix or at Eros for example, we kind of now expect that. We're sort of demanding consumers, right? We expect them to know what I want, just what I want, don't give me any things that I don't want, so is that one of the things that you've seen, maybe surprising in your career, is this increasing demand for personalization? >> Absolutely, because, you know, there's so much content out there, so much information, and unless there's a filtering mechanism that makes sense for you, people don't want to, you know, it's very hard for them, so they want you to do the work for them. It's entertainment, right? So absolutely. Everyone kind of expects it. It's not said. It's not explicit, but that's the expectation. >> And obviously, with the goal of delighting and retaining those customers, you as the chief product officer have to listen and react to that. >> I spent, I'll tell you a short story. I spent once a month going through all the customers' comments in different platforms, right? And one of the stories I read was this 17-year-old French gal in Paris, she loves watching Bollywood because she was suffering from leukemia and after she gets a treatment, she comes home, she wants to watch something that makes her happy, and we had some issues with that subtitles, and she was having a problem watching our movies and she begged "Please bring them back". And I ran out of my office, went to my team, and I said, "Guys, this is who we wake up for every day. We give her joy, we give her pleasure." So to me, that's how listening to the customers to me is primary, to me they are my biggest stakeholder, and I've told the CEO and founder that, look at the end of the day, I leave and argue with you if it doesn't serve my customers. That's what I believe, listening to the customers, listening to them, understanding, of course, we do a lot of data collection and we look at what we are doing and the patterns, and based on that we make modifications, we test different things to see what makes sense, what's working, and what's not working, because people don't always tell you, and even if you ask them, they're shy to tell you. But then you can see what they're doing, and that's an indicator. >> Well that makes you feel really good, seeing and hearing and feeling the impact that you're making, and speaking of impact, you have been, in the last minute or so that we have, you've been on the board of ACG SV for about the last five years. We're here tonight to honor Arista as the Outstanding Growth Award winner and (mumbles) Technology as the Emerging Growth winner, but really quickly, what makes ACG SV worth your time? >> So ACG honestly is a fantastic organization and you know, living in the Bay Area, there are many organizations, there are many events that are always going on, you know. ACG has been a place where I've seen it's a very, very, very, very diverse organization, of course I still wish there were more females, you know, but it's a very diverse organization, people of all ages, people from different walks of life, from different kinds of companies, you know, and people are very, very collaborative and help each other to do business. I've become personal friends with many of them, but the main thing is, you know, you come here, if you're new to the Valley especially, whether as a company or as an individual, this is one of the best places to come to because it's not too large, it's not too small, it has the right number of people, and it helps you quickly on board. They'll introduce you to people, introduce you to events, they give you what you need to kind of get started. So to me it's like, when I joined, I joined before I was on the board, almost, I don't know, seven or eight years ago, and I've seen this whole thing transform and it's just an excellent, supportive, the people are very open-minded, great ideas, and it's just an excellent organization, love it. So it's worth my time, you know, to take the extra hours, and I would love to see it get even bigger and more diverse and more interesting. >> Well it sounds like, I love how you kind of described ACG SV as being that Goldilocks type of organization, not too big, not too small, just right, but we thank you so much. I wish we had more time to talk, as a female in technology, but we'll have to have you back at the studio on The Cube! >> Thank you so much. >> Thank you so much for your time. For The Cube, I am Lisa Martin. Thanks for watching. (music)

>> from Mountain View, California It's the Cube covering the fifteenth annual Grow Awards. Brought to you by A C. G S V. >> Hi, Lisa Martin on the ground with the Cube at the Computer History Museum for the fifteenth annual TGS Grow Awards. Can you hear the collaboration of the innovation going on behind me? Very excited to welcome to the Cube, one of tonight's award winners from a Jaso Technologies, Norby, Jericho B and the President and CEO of Modesto. Congratulations on the Emerging Growth Award that adjuster has been honored with tonight. >> Thank you very much. We're very honored to be here. So you've been at >> the helm of a desert for a long time. I'd like our audience to hear a little bit from you about whom destiny is what you do. What makes you different. >> Perfect. So we are at a technology company on our products are used primarily in Internet of things, applications across many, many segments. Most off our businesses within the industrial segment on our customers use our products to actually build a Iot solutions for their end markets. Our products include semiconductor chips that are used at the edge of Coyote EJ gateway devices that connects the local networks to the more broad networks on. Basically, we enable our customers to take data from the physical world and send it up into the clouds >> to you guys. Our have had a great great trajectory, obviously being recognized by the emerging growth winner from a C. G S B. Tell me a little bit about it was looking at some information from you guys and on twenty eighteen, You guys did a great job of executing on your strategic initiatives to really make twenty eighteen a transformative year couple of acquisitions to us about the last year, in particular in the group that you have seen the momento and you're bringing into twenty nineteen. >> Correct? Correct. So we started. We enter twenty eighteen as a provider up application specific memory devices for I ot however, we realize that for our customers to take true benefit off the technologies we provide, we need to be a more holistic supplier of solutions. So as a result, we went through a whole process off looking at other technologies that can complement what we have in a very similar way, with strategic focus in the markets that we were focused, and as a result, we made two acquisitions in past summer that ended up its expanding our market opportunity, broadening our reach within existing customer and significantly expanding our offering portfolio to foreign markets. >> Negroes have a really strong position with tear one customers in the industrial sector. You mentioned that expecting Don't be a little bit more than about your leadership here in what makes these large industrial cheer. One players say Augusto is for us, >> right? So before I asked her that let me talk a little bit about the difference between industrial I ot and Consumer >> Riley's Yes, >> So if you think about consumer, I ot, it's what grabs headlines. It's the fitness trackers, the latest home smart thermostats, and the smartwatch is on so forth. The's are new markets. Volumes are girl very fast, but if next year and new shiny object is created, it's easy for the consumers to replace. They basically buy the new one. Repent replaced the old. One interesting thing about industrial I ot is that industrial I ot has this fragmented legacy systems that today run in their businesses. So if you look at the building we're in Today there is a fired and safety system that runs there's H Vac system that runs the business. There's a security systems, and this could have been installed here decades ago. There are billions of connected things in that industrial network today, but the data is unable to go up into the cloud. Where come cloud providers? Aye, aye. Providers can actually take the data on provide benefits to the business owners. We understand the language of industrial I ot very well because off our roots in that space. And we also understand this universe very well because of our roots being in Silicon Valley. So for industrial customers to benefit from this transformation, it's very important to be able to understand the OT world operational technology world of old days on the IT world that we're very familiar with. So with addition off these acquisitions that we've done this summer very well, positions with the building blocks that way can put together on offer differentiated solutions to our customers? >> Well, no, but it's been a pleasure having you on the queue. But the fifteenth annual acey GSP grow words. Congratulations to adjust of your whole team for the emerging growth award. And we look forward to seeing what happens this year in the space with you. Thank >> you. Thank you very much. Thank you. >> Lisa. Martin, you're watching the Cube. Thanks for watching.

(upbeat music) >> Live from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Okay, welcome back everyone to theCUBE NYC. This is theCUBE's live coverage covering CubeNYC Strata Hadoop Strata Data Conference. All things data happen here in New York this week. I'm John Furrier with Peter Burris. Our next guest is Basil Faruqui lead solutions marketing manager digital business automation within BMC returns, he was here last year with us and also Big Data SV, which has been renamed CubeNYC, Cube SV because it's not just big data anymore. We're hearing words like multi cloud, Istio, all those Kubernetes. Data now is so important, it's now up and down the stack, impacting everyone, we talked about this last year with Control M, how you guys are automating in a hurry. The four pillars of pipelining data. The setup days are over; welcome to theCUBE. >> Well thank you and it's great to be back on theCUBE. And yeah, what you said is exactly right, so you know, big data has really, I think now been distilled down to data. Everybody understands data is big, and it's important, and it is really you know, it's quite a cliche, but to a larger degree, data is the new oil, as some people say. And I think what you said earlier is important in that we've been very fortunate to be able to not only follow the journey of our customers but be a part of it. So about six years ago, some of the early adopters of Hadoop came to us and said that look, we use your products for traditional data warehousing on the ERP side for orchestration workloads. We're about to take some of these projects on Hadoop into production and really feel that the Hadoop ecosystem is lacking enterprise-grade workflow orchestration tools. So we partnered with them and some of the earliest goals they wanted to achieve was build a data lake, provide richer and wider data sets to the end users to be able to do some dashboarding, customer 360, and things of that nature. Very quickly, in about five years time, we have seen a lot of these projects mature from how do I build a data lake to now applying cutting-edge ML and AI and cloud is a major enabler of that. You know, it's really, as we were talking about earlier, it's really taking away excuses for not being able to scale quickly from an infrastructure perspective. Now you're talking about is it Hadoop or is it S3 or is it Azure Blob Storage, is it Snowflake? And from a control-end perspective, we're very platform and technology agnostic, so some of our customers who had started with Hadoop as a platform, they are now looking at other technologies like Snowflake, so one of our customers describes it as kind of the spine or a power strip of orchestration where regardless of what technology you have, you can just plug and play in and not worry about how do I rewire the orchestration workflows because control end is taking care of it. >> Well you probably always will have to worry about that to some degree. But I think where you're going, and this is where I'm going to test with you, is that as analytics, as data is increasingly recognized as a strategic asset, as analytics increasingly recognizes the way that you create value out of those data assets, and as a business becomes increasingly dependent upon the output of analytics to make decisions and ultimately through AI to act differently in markets, you are embedding these capabilities or these technologies deeper into business. They have to become capabilities. They have to become dependable. They have to become reliable, predictable, cost, performance, all these other things. That suggests that ultimately, the historical approach of focusing on the technology and trying to apply it to a periodic or series of data science problems has to become a little bit more mature so it actually becomes a strategic capability. So the business can say we're operating on this, but the technologies to take that underlying data science technology to turn into business operations that's where a lot of the net work has to happen. Is that what you guys are focused on? >> Yeah, absolutely, and I think one of the big differences that we're seeing in general in the industry is that this time around, the pull of how do you enable technology to drive the business is really coming from the line of business, versus starting on the technology side of the house and then coming to the business and saying hey we've got some cool technologies that can probably help you, it's really line of business now saying no, I need better analytics so I can drive new business models for my company, right? So the need for speed is greater than ever because the pull is from the line of business side. And this is another area where we are unique is that, you know, Control M has been designed in a way where it's not just a set of solutions or tools for the technical guys. Now, the line of business is getting closer and closer, you know, it's blending into the technical side as well. They have a very, very keen interest in understanding are the dashboards going to be refreshed on time? Are we going to be able to get all the right promotional offers at the right time? I mean, we're here at NYC Strata, there's a lot of real-time promotion happening here. The line of business has direct interest in the delivery and the timing of all of this, so we have always had multiple interfaces to Control M where a business user who has an interest in understanding are the promotional offers going to happen at the right time and is that on schedule? They have a mobile app for them to do that. A developer who's building up complex, multi-application platform, they have an API and a programmatic interface to do that. Operations that has to monitor all of this has rich dashboards to be able to do that. That's one of the areas that has been key for our success over the last couple decades, and we're seeing that translate very well into the big data place. >> So I just want to go under the hood for a minute because I love that answer. And I'd like to pivot off what Peter said, tying it back to the business, okay, that's awesome. And I want to learn a little bit more about this because we talked about this last year and I kind of am seeing it now. Kubernetes and all this orchestration is about workloads. You guys nailed the workflow issue, complex workflows. Because if you look at it, if you're adding line of business into the equation, that's just complexity in and of itself. As more workflows exist within its own line of business, whether it's recommendations and offers and workflow issues, more lines of business in there is complex for even IT to deal with, so you guys have nailed that. How does that work? Do you plug it in and the lines of businesses have their own developers, so the people who work with the workflows engage how? >> So that's a good question, with sort of orchestration and automation now becoming very, very generic, it's kind of important to classify where we play. So there's a lot of tools that do release and build automation. There's a lot of tools that'll do infrastructure automation and orchestration. All of this infrastructure and release management process is done ultimately to run applications on top of it, and the workflows of the application need orchestration and that's the layer that we play in. And if you think about how does the end user, the business and consumer interact with all of this technology is through applications, k? So the orchestration of the workflow's inside the applications, whether you start all the way from an ERP or a CRM and then you land into a data lake and then do an ML model, and then out come the recommendations analytics, that's the layer we are automating today. Obviously, all of this-- >> By the way, the technical complexity for the user's in the app. >> Correct, so the line of business obviously has a lot more control, you're seeing roles like chief digital officers emerge, you're seeing CTOs that have mandates like okay you're going to be responsible for all applications that are facing customer facing where the CIO is going to take care of everything that's inward facing. It's not a settled structure or science involved. >> It's evolving fast. >> It's evolving fast. But what's clear is that line of business has a lot more interest and influence in driving these technology projects and it's important that technologies evolve in a way where line of business can not only understand but take advantage of that. >> So I think it's a great question, John, and I want to build on that and then ask you something. So the way we look at the world is we say the first fifty years of computing were known process, unknown technology. The next fifty years are going to be unknown process, known technology. It's all going to look like a cloud. But think about what that means. Known process, unknown technology, Control M and related types of technologies tended to focus on how you put in place predictable workflows in the technology layer. And now, unknown process, known technology, driven by the line of business, now we're talking about controlling process flows that are being created, bespoke, strategic, differentiating doing business. >> Well, dynamic, too, I mean, dynamic. >> Highly dynamic, and those workflows in many respects, those technologies, piecing applications and services together, become the process that differentiates the business. Again, you're still focused on the infrastructure a bit, but you've moved it up. Is that right? >> Yeah, that's exactly right. We see our goal as abstracting the complexity of the underlying application data and infrastructure. So, I mean, it's quite amazing-- >> So it could be easily reconfigured to a business's needs. >> Exactly, so whether you're on Hadoop and now you're thinking about moving to Snowflake or tomorrow something else that comes up, the orchestration or the workflow, you know, that's as a business as a product that's our goal is to continue to evolve quickly and in a manner that we continue to abstract the complexity so from-- >> So I've got to ask you, we've been having a lot of conversations around Hadoop versus Kubernetes on multi cloud, so as cloud has certainly come in and changed the game, there's no debate on that. How it changes is debatable, but we know that multiple clouds is going to be the modus operandus for customers. >> Correct. >> So I got a lot of data and now I've got pipelining complexities and workflows are going to get even more complex, potentially. How do you see the impact of the cloud, how are you guys looking at that, and what are some customer use cases that you see for you guys? >> So the, what I mentioned earlier, that being platform and technology agnostic is actually one of the unique differentiating factors for us, so whether you are an AWS or an Azure or a Google or On-Prem or still on a mainframe, a lot of, we're in New York, a lot of the banks, insurance companies here still do some of the most critical processing on the mainframe. The ability to abstract all of that whether it's cloud or legacy solutions is one of our key enablers for our customers, and I'll give you an example. So Malwarebytes is one of our customers and they've been using Control M for several years. Primarily the entire structure is built on AWS, but they are now utilizing Google cloud for some of their recommendation analysis on sentiment analysis because their goal is to pick the best of breed technology for the problem they're looking to solve. >> Service, the best breed service is in the cloud. >> The best breed service is in the cloud to solve the business problem. So from Control M's perspective, transcending from AWS to Google cloud is completely abstracted for them, so runs Google tomorrow it's Azure, they decide to build a private cloud, they will be able to extend the same workflow orchestration. >> But you can build these workflows across whatever set of services are available. >> Correct, and you bring up an important point. It's not only being able to build the workflows across platforms but being able to define dependencies and track the dependencies across all of this, because none of this is happening in silos. If you want to use Google's API to do the recommendations, well, you've got to feed it the data, and the data's pipeline, like we talked about last time, data ingestion, data storage, data processing, and analytics have very, very intricate dependencies, and these solutions should be able to manage not only the building of the workflow but the dependencies as well. >> But you're defining those elements as fundamental building blocks through a control model >> Correct. >> That allows you to treat the higher level services as reliable, consistent, capabilities. >> Correct, and the other thing I would like to add here is not only just build complex multiplatform, multiapplication workflows, but never lose focus of the business service of the business process there, so you can tie all of this to a business service and then, these things are complex, there are problems, let's say there's an ETL job that fails somewhere upstream, Control M will immediately be able to predict the impact and be able to tell you this means the recommendation engine will not be able to make the recommendations. Now, the staff that's going to work under mediation understands the business impact versus looking at a screen where there's 500 jobs and one of them has failed. What does that really mean? >> Set priorities and focal points and everything else. >> Right. >> So I just want to wrap up by asking you how your talk went at Strata Hadoop Data Conference. What were you talking about, what was the core message? Was it Control M, was it customer presentations? What was the focus? >> So the focus of yesterday's talk was actually, you know, one of the things is academic talk is great, but it's important to, you know, show how things work in real life. The session was focused on a real-use case from a customer. Navistar, they have IOT data-driven pipelines where they are predicting failures of parts inside trucks and buses that they manufacture, you know, reducing vehicle downtime. So we wanted to simulate a demo like that, so that's exactly what we did. It was very well received. In real-time, we spun up EMR environment in AWS, automatically provision control of infrastructure there, we applied spark and machine learning algorithms to the data and out came the recommendation at the end was that, you know, here are the vehicles that are-- >> Fix their brakes. (laughing) >> Exactly, so it was very, very well received. >> I mean, there's a real-world example, there's real money to be saved, maintenance, scheduling, potential liability, accidents. >> Liability is a huge issue for a lot of manufacturers. >> And Navistar has been at the leading edge of how to apply technologies in that business. >> They really have been a poster child for visual transformation. >> They sure have. >> Here's a company that's been around for 100 plus years and when we talk to them they tell us that we have every technology under the sun that has come since the mainframe, and for them to be transforming and leading in this way, we're very fortunate to be part of their journey. >> Well we'd love to talk more about some of these customer use cases. Other people love about theCUBE, we want to do more of them, share those examples, people love to see proof in real-world examples, not just talk so appreciate it sharing. >> Absolutely. >> Thanks for sharing, thanks for the insights. We're here Cube live in New York City, part of CubeNYC, we're getting all the data, sharing that with you. I'm John Furrier with Peter Burris. Stay with us for more day two coverage after this short break. (upbeat music)

>> Live from New York, it's the Cube (funky music) covering the Cube New York City 2018 brought to you by SiliconANGLE Media and its ecosystem partners. (funky music) >> Everyone welcome back to theCUBE live in New York City for Cube NYC. This is our live broadcast - two days of coverage around the big data world, AI, the future of Cloud analytics. I'm John Furrier, my cohost Peter Burris. Our next guest is Paul Appleby, CEO Kinetica. Thanks for coming back to theCUBE - good to see you. >> Great to be back again and great to visit in New York City - it's incredible to be here on this really important week. >> Last time we chatted was in our big data Silicon Valley event, which is going to be renamed Cube SV, because it's not just data anymore; there's a lot of Cloud involved, a lot of new infrastructure. But analytics has certainly changed. What's your perspective now in New York as you're in here hearing all the stories around the show and you talk to customers - what's the update from your perspective? Because certainly we're hearing a lot of Cloud this year - Cloud, multi Cloud, analytics, and eyeing infrastructure, proof in the pudding, that kind of thing. >> I'm going to come back to the Cloud thing because I think that's really important. We have shifted to this sort of hybrid multi Cloud world, and that's our future - there is no doubt about it, and that's right across all spectre of computing, not just as it relates to data. But I think this evolution of data has continued this journey that we've all been on from whatever you want to call it - systems or record - to the world of big data where we're trying to gain insights out of this massive oceans of data. But we're in a world today where we're leveraging the power of analytics and intelligence, AI machine learning, to make fundamental decisions that drive some action. Now that action may be to a human to make a decision to interact more effectively with a customer, or it could be to a machine to automate some process. And we're seeing this fundamental shift towards a focus on that problem, and associated with that, we're leveraging the power of Cloud, AI, ML, and all the rest of it. >> And the human role in all this has been talked about. I've seen in the US in the political landscape, data for good, we see Facebook up there being basically litigated publicly in front of the Senate around the role of data and the elections. People are talking in the industry about the role of humans with machines is super important. This is now coming back as a front and center issue of hey, machines do great intelligence, but what about the human piece? What's your view on the human interaction component, whether it's the curation piece, the role of the citizen analyst, or whatever we're calling it these days, and what machines do to supplement that? >> Really good question - I've spent a lot of time thinking about this. I've had the incredible privilege of being able to attend the World Economic Forum for the last five years, and this particular topic of how Robotics Automation Artificial Intelligence machine learning is impacting economies, societies, and ultimately the nature of work has been a really big thread there for a number of years. I've formed a fundamental view: first of all, any technology can be used for good purposes and bad purposes, and it's - >> It always is. >> And it always is, and it's incumbent upon society and government to apply the appropriate levels of regulation, and for corporations to obviously behave the right way, but setting aside those topics - because we could spend hours talking about those alone - there is a fundamental issue, and this is this kind of conversation about what a lot of people like to describe as the fourth industrial revolution. I've spent a lot of time, because you hear people bandy that around - what do they really mean, and what are we really talking about? I've looked at every point in time where there's been an industrial revolution - there's been a fundamental shift of work that was done by humans that's now done by machines. There's been a societal uproar, and there're being new forms of work created, and society's evolved. What I look at today is yes, there's a responsibility and a regular treaside to this, but there's also a responsibility in business and society to prepare our workers and our kids for new forms of work, cause that's what I really think we should be thinking about - what are the new forms of work that are actually unlocked by these technologies, rather than what are the roles that are displaced by this steam powered engine. (laughs softly) >> Well, Paul, we totally agree with you. There's one other step in this process. It kind of anticipates each of these revolutions, and that is there is a process of new classes of asset formation. Mhm. So if you go back to when we put new power trains inside row houses to facilitate the industrial revolution in the early 1800s, and you could say the same thing about transportation, and what the trains did and whatnot. There's always this process of new asset formation that presaged some of these changes. Today it's data - data's an asset cause businesses ultimately institutionalize, or re institutionalize, their work around what they regard as valuable. Now, when we start talking about machines telling other machines what to do, or providing options or paring off options for humans so they have clear sets of things that they can take on, speed becomes a crucial issue, right? At the end of the day, all of this is going to come back to how fast can you process data? Talk to us a little bit about how that dynamic and what you guys are doing to make it possible is impacting business choices. >> Two really important things to unpack there, and one I think I'd love to touch on later, which is data as an asset class and how corporations should treat data. You talk about speed, and I want to talk about speed in the context of perishability, because the truth is if you're going to drive these incredible insights, whether it's related to a cyber threat, or a terrorist threat, or an opportunity to expand your relationship with a customer, or to make a critical decision in a motor vehicle in an autonomous operating mode, these things are about taking massive volumes of streaming data, running analytics in real time, and making decisions in real time. These are not about gleaning insights from historic pools or oceans of data; this is about making decisions that are fundamental to - >> Right now. >> The environment that you're in right now. You think about the autonomous car - great example of the industrial Internet, one we all love to talk about. The mechanical problems associated with autonomy have been solved, fundamentally sensors in cars, and the automated processes related to that. The decisioning engines - they need to be applied at scale in millions of vehicles in real time. That's an extreme data problem. The biggest problem solved there is data, and then over time, societal and regulatory change means that this is going to take some time before it comes to fruition. >> We were just saying - I think it was 100 Teslas generating 100 terabytes of data a day based on streams from its fleet of cars its customers have. >> We firmly believe that longer term, when you get to true autonomy, each car will probably generate around ten terabytes of data a day. That is an extremely complex problem to solve, because at the end of the day, this thinking that you're able to drive that data back to some centralized brain to be making those decisions for and on behalf of the cars is just fundamentally flawed. It has to happen in the car itself. >> Totally agree. >> This is putting super computers inside cars. >> Which is kind of happening - in fact, that 100 terabytes a day is in fact the data that does get back to Tesla. >> Yeah. >> As you said, there's probably 90% of the data is staying inside the car, which is unbelievable scale. >> So the question I wanted to ask you - you mentioned the industrial revolution, so every time there's a new revolution, there's an uproar, you mentioned. But there's also a step up of new capabilities, so if there's new work being developed, usually entrepreneur activity - weird entrepreneurs figured out that everyone says they're not weird anymore; it's great. But there's a step up of new capability that's built. Someone else says hey, the way we used to do databases and networks was great for moving one gig Ethernet on top of the rack; now you got 10 terabytes coming off a car or wireless spectrum. We got to rethink spectrum, or we got to rethink database. Let's use some of these GPUs - so a new step up of suppliers have to come in to support the new work. What's your vision on some of those things that are happening now - that you think people aren't yet seeing? What are some of those new step up functions? Is it on the database side, is it on the network, is it on the 5G - where's the action? >> Wow. Because who's going to support the Teslas? (Paul laughs) Who's going to support the new mobile revolution, the new iPhones the size of my two hands put together? What's your thoughts on that? >> The answer is all of the above. Let me talk about that and what I mean by that. Because you're looking at it from the technology perspective, I'd love to come back and talk about the human perspective as well, but from the technology perspective, of course leveraging power is going to be fundamental to this, because if you think about the types of use cases where you're going to have to be gigathreading queries against massive volumes of data, both static and streaming, you can't do that with historic technology, so that's going to be a critical part of it. The other part of it that we haven't mentioned a lot here but I think we should bring into it is if you think about these types of industrial Internet use cases, or IOT - even consumer Internet IOT related use cases - a lot of the decisioning has to occur out of the H. It cannot occur in a central facility, so it means actually putting the AI or ML engine inside the vehicle, or inside the cell phone tower, or inside the oil rig, and that is going to be a really big part of you know, shifting back to this very distributive model of machine lining in AI, which brings very complex questions in of how you drive governance - (John chuckles) >> And orchestration around employing Ai and ML models at massive scale, out to edge devices. >> Inferencing at the edge, certainly. It's going to be interesting to see what happens with training - we know that some of the original training will happen at the center, but some of that maintenance training? It's going to be interesting to see where that actually - it's probably going to be a split function, but you're going to need really high performing databases across the board, and I think that's one of the big answers, John, is that everybody says oh, it's all going to be in software. It's going to be a lot of hard word answers. >> Yep. >> Well the whole idea is just it's provocative to think about it and also intoxicating if you also want to go down that rabbit hole... If you think about that car, okay, if they're going to be doing century machine learning at the edge - okay, what data are you working off of? There's got to be some storage, and then what about real time data coming from other either horizontally scalable data sets. (laughs) So the question is, what do they have access to? Are they optimized for the decision making at that time? >> Mhm. >> Again, talk about the future of work - this is a big piece, but this is the human piece as well. >> Yeah. >> Are our kids going to be in a multi massive, multi player online game called Life? >> They are. >> They are now. They're on Fortnite, they're on Call of Duty, and all this gaming culture. >> But I think this is one of the interesting things, because there's a very strong correlation between information theory and thermodynamics. >> Mhm. >> They're the same exact - in physics, they are the identical algorithms and the identical equations. There's not a lot of difference, and you go back to the original revolution, you have a series of row houses, you put a power supply all the way down, you can run a bunch of looms. The big issue is entropy - how much heat are you generating? How do you get greater efficiency out of that single power supply? Same thing today: we're worried about the amount of cost, the amount of energy, the amount of administrative overhead associated with using data as an asset, and the faster the database, the more natural it is, the more easy it is to administer, the more easy it is to apply to a lot of different cases, the better. And it's going to be very, very interesting over the next few year to see how - Does database come in memory? Does database stay out over there? A lot of questions are going to be answered in the next couple years as we try to think about where these information transducers actually reside, and how they do their job. >> Yeah, and that's going to be driven yes, partially by the technology, but more importantly by the problems that we're solving. Here we are in New York City - you look at financial services. There are two massive factors in financial services going on what is the digital bank of the future look like, and how the banks interact with their customers, and how you get that true one-to-one engagement, which historically has been virtually impossible for companies that have millions or tens of millions of customers, so fundamental transformation of customer engagement driven by these advanced or excelerated analytics engines, and the pair of AI and ML, but then on the other side if you start looking at really incredibly important things for the banks like risk and spread, historically because of the volumes of data, it's been virtually impossible for them to present their employees with a true picture of those things. Now, with these accelerated technologies, you can take all the historic trading data, and all of the real time trading data, smash that together, and run real time analytics to make the right decisions in the moment of interaction with a customer, and that is incredibly powerful for both the customer, but also for the bank in mitigating risk, and they're the sorts of things we're doing with banks up and down the city here in New York, and of course, right around the world. >> So here's a question for you, so with that in mind - this is kind of more of a thought exercise - will banks even be around in 20 years? >> Wow. (laughs) >> I mean, you've got block chains saying we're going to have new crypto models here, if you take this Tesla with ten terabytes going out every second or whatever that number is. If that's the complex problem, banking should be really easy to solve. >> I think it's incumbent on boards in every industry, not just banking, to think about what existential threats exist, because there are incredibly powerful, successful companies that have gone out of existence because of fundamental shifts and buying behaviors or technologies - I think banks need to be concerned. >> Every industry needs to be concerned. >> Every industry needs to be concerned. >> At the end of the day, every board needs to better understand how they can reduce their assets specificities, right? How they can have their assets be more fungible and more applicable or appropriable to multiple different activities? Think about a future where data and digital assets are a dominant feature of business. Asset specificities go down; today their very definition of vertical industry is defined by the assets associated with bottling, the assets associated with flying, the assets associated with any number of other things. As aspect specialist needs to go down because of data, it changes even the definition of industry, let alone banking. >> Yeah, and auto industry's a great example. Will we own cars in the future? Will we confirm them as a service? >> Exactly. >> Car order manufacturers need to come to terms with that. The banks need to come to terms with the fact that the fundamental infrastructure for payments, whether it's domestic or global, will change. I mean, it is going to change. >> It's changing. It's changing. >> It has to change, and it's in the process of changing, and I'm not talking about crypto, you know, what form of digital currency exists in the future, we can argue about forever, but a fundamental underlying platform for real time exchange - that's just the future. Now, what does that mean for banks that rely heavily on payments as part of their core driver of profitability? Now that's a really important thing to come to terms with. >> Or going back to the point you made earlier. We may not have banks, but we have bankers. There's still going to be people who're providing advice in council, helping the folks understand what businesses to buy, what businesses to sell. So whatever industry they're in, we will still have the people that bring the extra taste to the data. >> Okay, we got to break it there, we've run out of time. Paul, love to chat further about future banking, all this other stuff, and also, as we live in a connected world, what does that mean? We're obviously connected to data; we certainly know there's gonnna be a ton of data. We're bringing that to you here, New York City, with Cube NYC. Stay with us for more coverage after the short break. (funky music)

Jeffrey Kier with the Qbert Computer History Museum in Mountain View California for the 14th annual association of corporate growth Silicon Valley grow Awards we've been here for a couple years now and it's a big event 300 people coming in to talk about really an ecosystem that helping other companies grow always great to be on the cube [Music] essentially what we are is an organization that's dedicated towards providing networking opportunities educational opportunities programming for c-level executives and other senior level executives at companies to help them develop their career and also grow their businesses tonight it's about tech as a force for good and I'm gonna talk about what I call the four superpowers today mobile unlimited reach cloud unlimited scale ai unlimited intelligence and IOT bridging from the digital to the physical world and how those four superpowers are reinforcing each other today very sophisticated population I mean it's just wonderful living in this seventy some people our biggest thing that we see is just the whole better together message that all of the resources from the strategically line businesses all working together to support the customers technology is evolving at a remarkable speed you know that's being driven largely by the availability of increased processing power less and less expensive faster and faster digital transformation IT transformation security transformation and work force transformation those are the big things for us this year it's great to be able to have a computer that really understands how to generate meaningful realistic text it's our opportunity to improve the quality of lives for every human on the planet as a result of those superpowers and really how it's our responsibility as a tech community to shape those superpowers for good there are issues created operationally day to day that we have to sort of always be on the watch for like you know readiness distance or these technologies it's the two sides of the same point always you can use it for good or you can use it for bad and unfortunately the bads within the news more than the good but there's so many exciting things going on in medicine health care oh yeah agriculture energy that the opportunities are almost endless not just the first world problems those of us here in the Silicon Valley see every day but really open our eyes to what's happening in other parts of the globe the need for water clean water water filtration clean air having access to information education so these are some things that are you know really personally dear to me in the last 50 years we've taken the extreme poverty rate from over 40 percent to less than 10 percent on the planet we've increased the length of life by almost 20 years these are stunning things and largely the result of the technological breakthroughs that we're doing that's the beauty of this right that's all of these things actually create opportunities you just have to stick with it and look at solutions and there's no shortage of really talented creative people to go address these opportunities and it's so fun to be involved in it right now the scale that we're able to now conduct business to be able to develop software to reach customers and truly write to change people's lives there are in many ways the technology halves and the technology have not absolutely and a lot of it is not just about making the product but then taking the product you've made and then implementing it in various use cases that really make a change from about in the world as I say today is the fastest day of tech evolution of your life it's also the slowest day of tech devolution of the rest of your life the rest of your life I'm Jeff Rick you're watching the cube from the a cts-v Awards thanks for watching [Music]

>> Announcer: Live from San Jose, it's theCUBE, presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and it's ecosystem partners. >> Welcome back to Big Data SV in San Jose. This theCUBE, the leader in live tech coverage. My name is Dave Vellante and this is day two of our wall-to-wall coverage. We've been here most of the week, had a great event last night, about 50 or 60 of our CUBE community members were here. We had a breakfast this morning where the Wikibon research team laid out it's big data forecast, the eighth big data forecast and report that we've put out, so check out that online. Jacques Nadeau is here. He is the CTO and co-founder of Dremio. Jacque, welcome to theCUBE, thanks for coming on. >> Thanks for having me here. >> So we were talking a little bit about what you guys do. Three year old company. Well, let me start. Why did you co-found Dremio? >> So, it was a very simple thing I saw, so, over the last ten years or so, we saw a regression in the ability for people to get at data, so you see all these really cool technologies that came out to store data. Data lakes, you know, SQL systems, all these different things that make developers very agile with data. But what we were also seeing was a regression in the ability for analysts and data consumers to get at that data because the systems weren't designed for analysts, they were designed for data producers and developers. And we said, you know what, there needs to be a way to solve this. We need to be able to empower people to be self-sufficient again at the data consumption layer. >> Okay, so you solved that problem how, you said, called it a self-service of a data platform. >> Yeah, yeah, so self-service data platform and the idea is pretty simple. It's that, no matter where the data is physically, people should be able to interact with a logical view of it. And so, we talk a little bit like it's Google Docs for your data. So people can go into the system, they can see the different data sets that are available to them, collaborate around those, create changes to those that they can then share with other people in the organization, always dealing with the logical layer and then, behind the scenes, we have physical capabilities to interact with all the different system we interact with. But that's something that business users shouldn't have to think as much about and so, if you think about how people interact with data today, it's very much about copies. So every time you want to do something, typically you're going to make a copy. I want to reshape the data, I make a copy. I want to make it go faster, I make a copy. And those copies are very, very difficult for people to manage and they could have mixed the business meaning of data with the physical, I'm making copies to make them faster or whatever. And so our perspective is that, if you can separate away the physical concerns from the logical, then business users have a much more, much more likelihood to be able to do something self-service. >> So you're essentially virtualizing my corpus of data, independent of location, is that right, I mean-- >> It's part of what we do, yeah. No, it's part of what we do. So, the way we look at it is, is kind of several different components to try to make something self-service. It starts with, yeah, virtualize or abstract away the details of the physical, right? But then, on top of that, expose a very, sort of a very user-friendly interface that allows people to sort of catalog and understand the different things, you know, search for things that they want to interact with, and then curate things, even if they're non-technical users, right? So the goal is that, if you talk to sort of even large internet companies in the Valley, it's very hard to even hire the amount of data engineering that you need to satisfy all the requests of your end-users of data. And so the, and so the goal of Dremio is basically to figure out different tools that can provide a non-technical experience for getting at the data. So that's sort of the start of it but then the second step is, once you've got access to this thing and people can collaborate and sort of deal with the data, then you've got these huge volumes of data, right? It's big data and so how do you make that go faster? And then we have some components that we deal with, sort of, speed and acceleration. >> So maybe talk about how people are leveraging this capability, this platform, what the business impact is, what have you seen there? >> So a lot of people have this problem, which is, they have data all over the place and they're trying to figure out "How do I expose this "to my end-users?" And those end-users might be analysts, they might be data scientists, they might be product managers that are trying to figure out how their product is working. And so, what they're doing today is they're typically trying to build systems internally that, to provide these capabilities. And so, for example, working with a large auto manufacturer. And they've got a big initiative where they're trying to make the data that they have, they have huge amounts of data across all sort of different parts of the organization and they're trying to make that available to different data consumers. Now, of course, there's a bunch of security concerns that you need to have around that, but they just want to make the data more accessible. And so, what they're doing is they're using Dremio to figure out ways to, basically, catalog all the data below, expose that to the different users, applying lots of different security rules around that, and then create a bunch of reflections, which make the things go faster as people are interacting with the things. >> Well, what about the governance factor? I mean, you heard this in the hadoop world years ago. "Ah, we're going to make, we're going to harden hadoop, "we're going to" and really, there was no governance and it became more and more important. How do you guys handle that? Do you partner with people? Is it up to the customer to figure that out? Do you provide that? >> It's several different things, right? It's a complex ecosystem, right? So it's a combination of things. You start with partnering with different systems to make sure that you integrate well with those things. So the different things that control some parts of credentials inside the systems all the way down to "What's the file system permissions?", right? "What are the permissions inside of something like Hive and the metastore there?" And then other systems on top of that, like Sentry or Ranger are also exposing different credentialing, right? And so we work hard to sort of integrate with those things. On top of that, Dremio also provides a full security model inside of the sort of virtual space that we work. And so people can control the permissions, the ability to access or edit any object inside of Dremio based on user roles and LDAP and those kinds of things. So it's, it's kind of multiple layers that have to be working together. >> And tell me more about the company. So founded three years ago, I think a couple of raises, >> Yep >> who's backing you? >> Yeah, yeah, yeah, so we founded just under three years ago. We had great initial investors, in Red Point and Lightspeed, so two great initial investors and we raised about 15 million on that round. And then we actually just closed a B round in January of this year and we added Norwest to the portfolio there. >> Awesome, so you're now in the mode of, I mean, they always say, you know, software is such a capital-efficient business but you see software companies raising, you know, 900 million dollars and so, presumably, that's to compete, to go to market and, you know, differentiate with your messaging and branding. Is that sort of what the, the phase that you're in now? You kind of developed a product, it's technically sound, it's proven in the marketspace and now you're scaling the, the go-to-market, is that right? >> That's exactly right. So, so we've had a lot of early successes, a lot of Fortune 100 companies using Dremio today. For example, we're working with TransUnion. We're working with Intel. We actually have a great relationship with OVH, which is the third-largest hosting company in the world, so a lot of great, Daimler is another one. So working with a lot of great companies, seeing sort of great early success with the product with those companies, and really looking to say "Hey, we're out here." We've got a booth for the first time at Strata here and we're sort of letting people know about, sort of, a better way, or easier way, for people to deal with data >> Yeah. >> A happier way. >> I mean, it's a crowded space, right? There's a lot of tools out there, a lot of companies. I'm interested in how you sort of differentiate. Obviously simplification is a part of that, the breadth of your capabilities. But maybe, in your words, you could share with me how you differentiate from the competition and how you break out from the noise. >> Yeah, yeah, yeah, so it's, you're absolutely right, it's a very crowded space. Everybody's using the same words and that makes it very hard for people to understand what's going on. And so, what we've found is very simple is that typically we will actually, the first meeting we deal with a customer, within the first 10 minutes we'll demo the product. Because so many technologies are technologies, not, they're not products and so you have to figure out how to use the product. You've got to figure out how you would customize it for your certain use-case. And what we've found with our product is, by making it very, very simple, people start, the light goes on in a very short amount of time and so, we also do things on our website so that you can see, in a couple of minutes, or even less than that, little animations that sort of give you a sense of what it's about. But really, it's just "Hey, this is a product "which is about", there's this light bulb that goes on, it's great. And you figure this out over the course of working with different customers, right? But there's this light bulb that goes on for people that are so confused by all the things that are going on and if we can just sit down with them, show them the product for a few minutes, all of a sudden they're like "Wait a minute, "I can use this", right? So you're frequently talking to buyers that are not the most technical parts of the organization initially, and so most of the technologies they look at are technologies that are very difficult to understand and they have to look to others to try to even understand how it would fit into their architecture. With Dremio, we have customers that can, that have installed it and gotten up, and within an hour or two, started to see real value. And that sort of excitement happens even in the demo, with most people. >> So you kind of have this bifurcated market. Since the big data meme, everybody says they're data-driven and you've got a bifurcated market in that, you've got the companies that are data-driven and you've got companies who say they're data-driven but really aren't. Who are your customers? Are they in both? Are they predominantly in the data-driven side? Are they predominantly in the trying to be data-driven? >> Well, I would say that they all would say that they're data-driven. >> Yeah, everyone, who's going to say "Well, we're not data-driven." >> Yeah, yeah, yeah. So I would say >> We're dead. >> I would say that everybody has data and they've got some ways that they're using it well and other places where they feel like they're not using it as well as they should. And so, I mean, the reason that we exist is to make it so it's easier for people to get value out of data, and so, if they were getting all the value they think they could get out of data, then we probably wouldn't exist and they would be fully data-driven. So I think that everybody, it's a journey and people are responding well to us, in part, because we're helping them down that journey. >> Well, the reason I asked that question is that we go to a lot of shows and everybody likes to throw out the digital transformation buzzword and then use Uber and Airbnb as an example, but if you dig deeper, you see that data is at the core of those companies and they're now beginning to apply machine intelligence and they're leveraging all this data that they've built up, this data architecture that they built up over the last five or 10 years. And then you've got this set of companies where all the data lives in silos and I can see you guys being able to help them. At the same time, I can see you helping the disruptors, so how do you see that? I mean, in terms of your role, in terms of affecting either digital transformations or digital disruptions. >> Well, I'd say that in either case, so we believe in a very sort of simple thing, which is that, so going back to what I said at the beginning, which is just that I see this regression in terms of data access, right? And so what happens is that, if you have a tightly-coupled system between two layers, then it becomes very difficult for people to sort of accommodate two different sets of needs. And so, the change over the last 10 years was the rise of the developer as the primary person for controlling data and that brought a huge amount of great things to it but analysis was not one of them. And there's tools that try to make that better but that's really the problem. And so our belief is very simple, which is that a new tier needs to be introduced between the consumers and the, and the producers of data. And that, and so that tier may interact with different systems, it may be more complex or whatever, for certain organizations, but the tier is necessary in all organizations because the analysts shouldn't be shaken around every time the developers change how they're doing data. >> Great. John Furrier has a saying that "Data is the new development kit", you know. He said that, I don't know, eight years ago and it's really kind of turned out to be the case. Jacques Nadeau, thanks very much for coming on theCUBE. Really appreciate your time. >> Yeah. >> Great to meet you. Good luck and keep us informed, please. >> Yes, thanks so much for your time, I've enjoyed it. >> You're welcome. Alright, thanks for watching everybody. This is theCUBE. We're live from Big Data SV. We'll be right back. (bright music)

(royal music) >> Announcer: John Cleese. >> There's a lot of people out there who have no idea what they're doing, but they have absolutely no idea that they have no idea what they're doing. Those are the ones with the confidence and stupidity who finish up in power. That's why the planet doesn't work. >> Announcer: Knowledgeable, insightful, and a true gentleman. >> The guy at the counter recognized me and said... Are you listening? >> John Furrier: Yes, I'm tweeting away. >> No, you're not. >> I tweet, I'm tweeting away. >> He is kind of rude that way. >> You're on your (bleep) keyboard. >> Announcer: John Cleese joins the Cube alumni. Welcome, John. >> John Cleese: Have you got any phone calls you need to answer? >> John Furrier: Hold on, let me check. >> Announcer: Live from San Jose, it's the Cube, presenting Big Data Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. (busy music) >> Hey, welcome back to the Cube's continuing coverage of our event, Big Data SV. I'm Lisa Martin with my co-host, George Gilbert. We are down the street from the Strata Data Conference. This is our second day, and we've been talking all things big data, cloud data science. We're now excited to be joined by the CEO of a company called Data Science, Ian Swanson. Ian, welcome to the Cube. >> Thanks so much for having me. I mean, it's been a awesome two days so far, and it's great to wrap up my trip here on the show. >> Yeah, so, tell us a little bit about your company, Data Science, what do you guys do? What are some of the key opportunities for you guys in the enterprise market? >> Yeah, absolutely. My company's called datascience.com, and what we do is we offer an enterprise data science platform where data scientists get to use all they tools they love in all the languages, all the libraries, leveraging everything that is open source to build models and put models in production. Then we also provide IT the ability to be able to manage this massive stack of tools that data scientists require, and it all boils down to one thing, and that is, companies need to use the data that they've been storing for years. It's about, how do you put that data into action. We give the tools to data scientists to get that data into action. >> Let's drill down on that a bit. For a while, we thought if we just put all our data in this schema-on-read repository, that would be nirvana. But it wasn't all that transparent, and we recognized we have to sort of go in and structure it somewhat, help us take the next couple steps. >> Ian: Yeah, the journey. >> From this partially curated data sets to something that turns into a model that is actionable. >> That's actually been the theme in the show here at the Strata Data Conference. If we went back years ago, it was, how do we store data. Then it was, how do we not just store and manage, but how do we transform it and get it into a shape that we can actually use it. The theme of this year is how do we get it to that next step, the next step of putting it into action. To layer onto that, data scientists need to access data, yes, but then they need to be able to collaborate, work together, apply many different techniques, machine learning, AI, deep learning, these are all techniques of a data scientist to be able to build a model. But then there's that next step, and the next is, hey, I built this model, how do I actually get it in production? How does it actually get used? Here's the shocking thing. I was at an event where there's 500 data scientists in the audience, and I said, "Stand up if you worked on a model for more than nine months "and it never went into production." 90% of the audience stood up. That's the last mile that we're all still working on, and what's exciting is, we can make it possible today. >> Wanting to drill down into the sort of, it sounds like there's a lot of choice in the tools. But typically, to do a pipeline, you either need well established APIs that everyone understands and plugs together with, or you need an end to end sort of single vendor solution that becomes the sort of collaboration backbone. How are you organized, how are you built? >> This might be self-serving, but datascience.com, we have enterprise data science platform, we recommend a unified platform for data science. Now, that unified platform needs to be highly configurable. You need to make it so that that workbench, you can use any tool that you want. Some data scientists might want to use a hammer, others want to be able to use a screwdriver over here. The power is how configurable, how extensible it is, how open source you can adopt everything. The amazing trends that we've seen have been proprietary solutions going back decades, to now, the rise of open source. Every day, dozens if not hundreds of new machine learning libraries are being released every single day. We've got to give those capabilities to data scientists and make them scale. >> OK, so the, and I think it's pretty easy to see how you would have incorporate new machine learning libraries into a pipeline. But then there's also the tools for data preparation, and for like feature extraction and feature engineering, you might even have some tools that help you with figuring out which algorithm to select. What holds all that together? >> Yeah, so orchestrating the enterprise data science stack is the hardest challenge right now. There has to be a company like us that is the glue, that is not just, do these solutions work together, but also, how do they collaborate, what is that workflow? What are those steps in that process? There's one thing that you might have left out, and that is, model deployment, model interpretation, model management. >> George: That's the black art, yeah. >> That's where this whole thing is going next. That was the exciting thing that I heard in terms of all these discussion with business leaders throughout the last two days is model deployment, model management. >> If I can kind of take this to maybe shift the conversation a little bit to the target audience. Talked a lot about data scientists and needing to enable them. I'm curious about, we just talked with, a couple of guests ago, about the chief data officer. How, you work with enterprises, how common is the chief data officer role today? What are some of the challenges they've got that datascience.com can help them to eliminate? >> Yeah, the CIO and the chief data officer, we have CIOs that have been selecting tools for companies to use, and now the chief data officer is sitting down with the CEO and saying, "How do we actually drive business results?" We work very closely with both of those personas. But on the CDO side, it's really helping them educate their teams on the possibilities of what could be realized with the data at hand, and making sure that IT is enabling the data scientists with the right tools. We supply the tools, but we also like to go in there with our customers and help coach, help educate what is possible, and that helps with the CDO's mission. >> A question along that front. We've been talking about sort of empowering the data scientist, and really, from one end of the modeling life cycle all the way to the end or the deployment, which is currently the hardest part and least well supported. But we also have tons of companies that don't have data science trained people, or who are only modestly familiar. Where do, what do we do with them? How do we get those companies into the mainstream in terms of deploying this? >> I think whether you're a small company or a big company, digital transformation is the mandate. Digital transformation is not just, how do I make a taxi company become Uber, or how do I make a speaker company become Sonos, the smart speaker, it's how do I exploit all the sources of my data to get better and improved operational processes, new business models, increased revenue, reduced operation costs. You could start small, and so we work with plenty of smaller companies. They'll hire a couple data scientists, and they're able to do small quick wins. You don't have to go sit in the basement for a year having something that is the thing, the unicorn in the business, it's small quick wins. Now we, my company, we believe in writing code, trained, educated, data scientists. There are solutions out there that you throw data at, you push a button, it gets an output. It's this magic black box. There's risk in that. Model interpretation, what are the features it's scoring on, there's risk, but those companies are seeing some level of success. We firmly believe, though, in hiring a data science team that is trained, you can start small, two or three, and get some very quick wins. >> I was going to say, those quick wins are essential for survivability, like digital transformation is essential, but it's also, I mean, to survival at a minimum, right? >> Ian: Yes. >> Those quick wins are presumably transformative to an enterprise being able to sustain, and then eventually, or ideally, be able to take market share from their competition. >> That is key for the CDO. The CDO is there pitching what is possible, he's pitching, she's pitching the dream. In order to be able to help visualize what that dream and the outcome could be, we always say, start small, quick wins, then from there, you can build. What you don't want to do is go nine months working on something and you don't know if there's going to be outcome. A lot of data science is trial and error. This is science, we're testing hypotheses. There's not always an outcome that's to be there, so small quick wins is something we highly recommend. >> A question, one of the things that we see more and more is the idea that actionable insights are perishable, and that latency matters. In fact, you have a budget for latency, almost, like in that short amount of time, the more sort of features that you can dynamically feed into a model to get a score, are you seeing more of that? How are the use cases that you're seeing, how's that pattern unfolding? >> Yeah, so we're seeing more streaming data use cases. We work with some of the biggest technology companies in the world, so IoT, connected services, streaming real time decisions that are happening. But then, also, there are so many use cases around org that could be marketing, finance, HR related, not just tech related. On the marketing side, imagine if you're customer service, and somebody calls you, and you know instantly the lifetime value of that customer, and it kicks off a totally new talk track, maybe get escalated immediately to a new supervisor, because that supervisor can handle this top tier customer. These are decisions that can happen real time leveraging machine learning models, and these are things that, again, are small quick wins, but massive, massive impact. It's about decision process now. That's digital transformation. >> OK. Are you seeing patterns in terms of how much horsepower customers are budgeting for the training process, creating the model? Because we know it's very compute intensive, like, even Intel, some people call it, like, high performance compute, like a supercomputer type workload. How much should people be budgeting? Because we don't see any guidelines or rules of thumb for this. >> I still think the boundaries are being worked out. There's a lot of great work that Nvidia's doing with GPU, we're able to do things faster on compute power. But even if we just start from the basics, if you go and talk to a data scientist at a massive company where they have a team of over 1,000 data scientists, and you say to do this analysis, how do you spin up your compute power? Well, I go walk over to IT and I knock on the door, and I say, "Set up this machine, set up this cluster." That's ridiculous. A product like ours is able to instantly give them the compute power, scale it elastically with our cloud service partners or work with on-prem solutions to be able to say, get the power that you need to get the results in the time that's needed, quick, fast. In terms of the boundaries of the budget, that's still being defined. But at the end of the day, we are seeing return on investment, and that's what's key. >> Are you seeing a movement towards a greater scope of integration for the data science tool chain? Or is it that at the high end, where you have companies with 1,000 data scientists, they know how to deal with specialized components, whereas, when there's perhaps less of, a smaller pool of expertise, the desire for end to end integration is greater. >> I think there's this kind of thought that is not necessarily right, and that is, if you have a bigger data science team, you're more sophisticated. We actually see the same sophistication level of 1,000 person data science team, in many cases, to a 20 person data science team, and sometimes inverse, I mean, it's kind of crazy. But it's, how do we make sure that we give them the tools so they can drive value. Tools need to include collaboration and workflow, not just hammers and nails, but how do we work together, how do we scale knowledge, how do we get it in the hands of the line of business so they can use the results. It's that that is key. >> That's great, Ian. I also like that you really kind of articulated start small, quick ins can make massive impact. We want to thank you so much for stopping by the Cube and sharing that, and what you guys are doing at Data Science to help enterprises really take advantage of the value that data can really deliver. >> Thanks so much for having datascience.com on, really appreciate it. >> Lisa: Absolutely. George, thank you for being my co-host. >> You're always welcome. >> We want to thank you for watching the Cube. I'm Lisa Martin with George Gilbert, and we are at our event Big Data SV on day two. Stick around, we'll be right back with our next guest after a short break. (busy music)

>> Announcer: Live from San Jose, it's The Cube, presenting Big Data, Silicon Valley. Brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to The Cube. Our continuing coverage of our event, Big Data SV continues. I am Lisa Martin joined by Peter Burris. We're in downtown San Jose at a really cool place called Forager Tasting and Eatery. Come down, hang out with us today as we have continued conversations around all things big data, everything in between. This is our second day here and we're excited to welcome to The Cube the CMO of VANTIQ, Blaine Mathieu. Blaine, great to meet you, great to have you on the program. >> Great to be here, thanks for inviting me. >> So, VANTIQ, you guys are up the street in Walnut Creek. What do you guys do, what are you about, what makes VANTIQ different? >> Well, in a nutshell, VANTIQ is a so called high productivity application development platform to allow developers to build, deploy, and manage so called event driven real time applications, the kind of applications that are critical for driving many of the digital transformation initiatives that enterprises are trying to get on top of these days. >> Digital trasformation, it's a term that can mean so many different things, but today, it's essential for companies to be able to compete, especially enterprise companies with newer companies that are more agile, more modern. But if we peel apart digital transformation, there's so many elements that are essential. How do you guys help companies, enterprises, say, evolve their application architectures that might currently not be able to support an actual transformation to a digital business? >> Well, I think that's a great question, thank you. I think the key to digital trasformation is really a lot around the concept of real time, okay. The reason Uber is disrupting or has disrupted the taxi industry is the old way of doing it was somebody called a taxi and then they waited 30 minutes for a taxi to show up and then they told the taxi where to go and hopefully they got there. Whereas, Uber, turned that into a real time business, right? You called, you pinged something on your phone. They knew your location. They knew the location of the driver. They matched those up, brought 'em together in real time. Already knew where to bring you to and ensured you had the right route and that location. All of this data flowing, all of these actions have been taken in real time. The same thing applies to a disruptor like Netflix, okay? In the old days, Blockbuster used to send you, you know, a leaflet in the mail telling you what the new movies are. Maybe it was personalized for you. Probably not. No, Netflix knows who you are instantly, gives you that information, again, in real time based on what you've done in the past and is able to give you, deliver the movie also, in real time pretty well. Every disruptor you look at around digital transformation is bringing a business or a process that was done slowly and impersonally to make it happen in real time. Unfortunately, enterprise applications and the architectures, as you said a second ago, that are being used in most applications today weren't designed to enable these real time use cases. A great example is sales force. So, a sales force is a pretty standard, what you'd call a request application. So, you make a request, a person, generally, makes a request of the system, system goes into a database, queries that database, find information and then returns it back to the user. And that whole process could take, you know, significant amounts of time, especially if the right data isn't in the database at the time and you have to go request it or find it or create it. A new type of application needs to be created that's not fundamentally database centric, but it's able to take these real time data streams coming in from devices, from people, from enterprise systems, process them in real time and then take an action. >> So, let's pretend I'm a CEO. >> Yeah. >> One of the key things you said, and I want you to explain it better, is event. What is event? What is an event and how does that translate into a digital business decision? >> This notion of complex event processing CEP has been around in technology for a long time and yet, it surprises me still a lot of folks we talk to, CEOs, have never heard of the concept. And, it's very simple really. An event is just something that happens in the context of business. That's as complex and as simple as it is. An event could be a machine increases in temperature by one degree, a car moves from one location to another location. It could be an enterprise system, like an ERP system, you know, approves a PO. It could be a person pressing a button on a mobile device. All of those, or it could be an IOT device putting off a signal about the state of a machine. Increasingly, we're getting a lot of events coming from IOT devices. So, really, any particular interesting business situation or a change in a situation that happens is an event And increasingly driven, as you know, by IOT, by augmented reality, by AI and machine learning, by autonomous vehicles, by all these new real time technologies are spinning off more and more events, streams of these events coming off in rapid fashion and we have to be able to do something about them. >> Let me take a crack at it and you tell me if I've got this right. That, historically, applications have been defined in terms of processes and so, in many respects, there was a very concrete, discreet, well established program, set of steps that were performed and then the transaction took place. And event, it seems to me is, yeah, we generally described it, but it changes in response to the data. >> Right, right. >> So, an event is kind of like an outside in driven by data. >> Right, right. >> System response, whereas, your traditional transaction processing is an inside out driven by a sequence of programmed steps, and that decision might have been made six years ago. So, the event is what's happening right now informed by data versus a transaction, traditional transaction is much more, what did we decide to do six years ago and it just gets sustained. Have I got that right? >> That's right. Absolutely right or six hours ago or even six minutes ago, which might seem wow, six minutes, that's pretty good, but take a use case for a field service agent trying to fix a machine or an air conditioner on top of a building. In today's world now, that air conditioner has hundreds of sensors that are putting off data about the state of that air conditioner in real time. A service tech has the ability to, while the machine is still putting off that data, be able to make repairs and changes and fixes, again, in the moment, see how that is changing the data coming off the machine, and then, continue to make the appropriate repairs in collaboration with a smart system or an application that's helping them. >> That's how identifying patterns about what the problem is, versus some of the old ways was where we had recipe of, you know, steps that you went through in the call center. >> Right, right. And the customer is getting more and more frustrated. >> They got their clipboard out and had the 52 steps they followed to see oh that didn't work, now the next step. No, data can help us do that much more efficiently and effectively if we're able to process it in real time. >> So, in many respects, what we're really talking about is an application world or a world looking forward where the applications, which historically have been very siloed, process driven, to a world where the application function is much more networked together and the application, the output of one application is having a significant impact through data on the performance of an application somewhere else. That seems like it's got the potential to be an extremely complex fabric. (laughing) So, do I wait until I figure all that out (laughing) and then I start building it? Or do I, I mean, how do I do it? Do I start small and create and grow into it? What's the best way for people to start working on this? >> Well, you're absolutely right. Building these complex, geeking out a little bit, you know, asynchronous, non-blocking, so called reactive applications, that's the concept that we've been using in computer science for some time, is very hard, frankly. Okay, it's much easier to build computing systems that process things step one, step, two, step three, in order, but if you have to build a system that is able to take real time inputs or changes at any point in the process at any time and go in a different direction, it's very complex. And, computer scientists have been writing applications like this for decades. It's possible to do, but that isn't possible to do at the speed that companies now want to transform themselves, right? By the time you spec out an application and spend two years writing it, your business competitors have already disrupted you. The requirements have already changed. You need to be much more rapid and agile. And so, the secret sauce to this whole thing is to be able to write these transformative applications or create them, not even write is actually the wrong word to use, to be able to create them. >> Generate them. >> Yeah, generate them in a way which is very fast, does not require a guru level developer and reactive Java or some super low level code that you'd have to use to otherwise do it, so that you can literally have business people help design the applications, conceptually build them almost in real time, get them out into the market, and then be able to modify them as you need to, you know, on the fly. >> If I can build on that for just one second. So, it used to be we had this thing called computer assisted software engineer. >> (laughs) Right, right. >> We were going to operate this very very high level language. It's kind of-- But then, we would use code and build a code and the two of them were separated and so the minute that we deployed, somebody would go off and maintain and the whole thing would break. >> Right, right. >> Do you have that problem? >> No, well, that's exactly right. So, the old, you know, the old, the previous way of doing it was about really modeling an application, maybe visually, drag and drop, but then fundamentally, you created a bunch of code and then your job, as you said after, was to maintain and deploy and manage. >> Try to sustain some connection back up to that beautiful visual model. >> And you probably didn't because that was too much. That was too much work, so forget about the model after that. Instead, what we're able to do these days is to build the applications visually, you know, really for the most part with either super low code or, in many cases, no code because we have the ability to abstract away a lot of the complexity, a lot of the complex code that you'd have to write, we can represent that, okay, with these logical abstractions, create the applications themselves, and then continue to maintain, add to, modify the application using the exact same structure. You're not now stuck on, now you're stuck with 20,000 lines of code that you have to, that you have to edit. You're continuing to run and maintain the application just the way you built it, okay. We've now got to the place in computer science where we can actually do these things. We couldn't do them, you know, 20 years ago with case, but we can absolutely do them now. >> So, I'm hearing from a customer internal perspective a lot of operational efficiencies that VANTIQ can drive. Let's look now from a customer's perspective. What are the business impacts you're able to make? You mentioned the word reactive a minute ago when you were talking about applications, but do you have an example where you've, VANTIQ, has enabled a customer, a business, to be more, to be proactive and be able to identify through, you know, complex event processing, what their customers are doing to be able to deliver relevant messages and really drive revenue, drive profit? >> Right, right. So many, you know, so many great examples. And, I mentioned field service a few minutes ago. I've got a lot of clients in that doing this real time field service using these event processing applications. One that I want to bring up right now is one of the largest global shoe manufacturers, actually, that's a client of VANTIQ. I, unfortunately, can't say the name right now 'cause they want to keep what they're doing under wraps, but we all definitely know the company. And they're using this to manage the security, primarily, around their real time global supply chain. So, they've got a big challenge with companies in different countries redirecting shipments of their shoes, selling them on the gray market, at different prices than what are allowed in different regions of the world. And so, through both sensorizing the packages, the barcode scanning, the enterprise systems bringing all that data together in real time, they can literally tell in the moment is something is be-- If a package is redirected to the wrong region or if literally a shoe or a box of shoes is being sold where it shouldn't be sold at the wrong price. They used to get a monthly report on the activities and then they would go and investigate what happened last month. Now, their fraud detection manager is literally sitting there getting this in real time, saying, oh, Singapore sold a pallet of shoes that they should not have been able to sell five minute ago. Call up the guy in Singapore and have him go down and see what's going on and fix that issue. That's pretty powerful when you think about it. >> Definitely, so like reduction in fraud or increase in fraud detection. Sounds like, too, there's a potential for a significant amount of cost savings to the business, not just meeting the external customer needs, but from a, from a cost perspective reduction. Not just some probably TCO, but in operational expenses. >> For sure, although, I would say most of the digital transformation initiatives, when we talk to CEOs and CIOs, they're not focused as much on cost savings, as they're focused on A, avoiding being disrupted by the next interesting startup, B, creating new lines of business, new revenue streams, finding out a way to do something differently dramatically better than they're currently doing it. It's not only about optimizing or squeezing some cost out of their current application. This thing that we are talking about, I guess you could say it's an improvement on their current process, but really, it's actually something they just weren't even really doing before. Just a total different way of doing fraud detection and managing their global supply chain that they just fundamentally weren't even doing. And now, of course, they're looking at many other use cases across the company, not just in supply chain, but, you know, smart manufacturing, so many use cases. Your point about savings, though, there's, you know, what value does the application itself bring? Then, there's the question of what does it cost to build and maintain and deploy the application itself, right? And, again, with these new visual development tools, they're not modeling tools, you're literally developing the application visually. You know, I've been in so many scenarios where we talked to large enterprises. You know, we talk about what we're doing, like we talk about right now, and they say, okay, we'd love to do a POC, proof of concept. We want to allocate six months for this POC, like normally you would probably do for building most enterprise applications. And, we inevitably say, well, how about Friday? How about we have the POC done by Friday? And, you know, we get the Germans laugh, you know, laugh uncomfortably and we go away and deliver the POC by Friday because of how much different it is to build applications this way versus writing low level Java or C-sharp code and sticking together a bunch of technologies and tools 'cause we abstract all that away. And, you know, the eyes drop open and the mouth drops open and it's incredible what modern technology can do to radically change how software is being developed. >> Wow, big impact in a short period of time. That's always a nice thing to be able to deliver. >> It is, it is to-- It's great to be able to surprise people like that. >> Exactly, exactly. Well, Blaine, thank you so much for stopping by, sharing what VANTIQ is doing to help companies be disruptive and for sharing those great customer examples. We appreciate your time. >> You're welcome. Appreciate the time. >> And for my co-host, Peter Burris, I'm Lisa Martin. You're watching The Cube's continuing coverage of our event, Big Data SV Live from San Jose, down the street from the Strata Data Conference. Stick around, we'll be right back with our next guest after a short breal. (techy music)

>> Announcer: Live from San Jose it's The Cube presenting Big Data, Silicon Valley brought to you by SiliconANGLE Media and its ecosystem partners. >> Good morning. Welcome to The Cube. We are on day two of our coverage our event Big Data SV. I'm Lisa Martin with my cohost Dave Vellante. We're down the street from the Strata Data Conference. This is The Cube's tenth big data event and we had a great day yesterday learning a lot from myriad guests on very different nuances of big data journey where things are going. We're excited to welcome back to The Cube an alumni, Octavian Tanase, the Senior Vice President of Data ONTAP fron Net App. Octavian, welcome back to The Cube. >> Glad to be here. >> So you've been at the Strata Data Conference for the last couple of days. From a big data perspective, what are some of the things that you're hearing, in terms of from a customer's perspective on what's working, what challenges, opportunities? I'm very excited to be here and learn about the innovation of our partners in the industry and share with our partners and our customers what we're doing to enable them to drive more value out of that data. The reality is that data has become the 21st Century gold or oil that powers the business and everybody's looking to apply new techniques, a lot of times machine learning, deep learning, to draw more value of the data, make better decisions and compete in the marketplace. Octavian, you've been at NetApp now eight years and I've been watching NetApp, as we were talking about offline, for decades and I've seen the ebb and flow and this company has transformed many, many times. The latest, obviously cloud came in, flash came into play and then you're also going through a major transition in the customer based to clustered ONTAP. You seemed to negotiate that. NetApp is back, thriving, stock's up. What's happening at NetApp? What's the culture like these days? Give us the update. >> I think we've been very fortunate to have a CEO like George Kurian, who has been really focused on helping us do basically fewer things better, really focus on our core business, simplify our operations and continue to innovate and this is probably the area that I'm most excited about. It's always good to make sure that you accelerate the business, make it simpler for your customers and your partners to do business with you, but what you have to do is innovate. We are a product company. We are passionate about innovation. I believe that we are innovating with more pace than many of the startups in the space so that's probably the most exciting thing that has been part of our transformation. >> So let's talk about big data. Back in the day if you had a big data problem you would buy a big Unix box, maybe buy some Oracle licenses, try to put all your data into that box and that became your data warehouse. The brilliance of Hadoop was hey we can leave the data where it is. There's too much data to put into the box so we're going to bring five megabytes to code to a petabyte of data. And the other piece of it is CFOs loved it, because we're going to reduce the cost of our expensive data warehouse and we're going to buy off the shelf components: white box, servers and off the shelf disk drives. We're going to put that together and life will be good. Well as things matured, the old client-server days, it got very expensive, you needed enterprise grade. So where does NetApp fit into that equation, because originally big storage companies like NetApp, they weren't part of the equation? Has that changed? >> Absolutely. One of the things that has enabled that transformation, that change is we made a deliberate decision to focus on software defined and making sure that the ONTAP operating system is available wherever data is being created: on the edge in an IoT device, in the traditional data center or in the cloud. So we are in the unique position to enable analytics, big data, wherever those applications reside. One of the things that we've recently done is we've partnered with IDC and what the study, what the analysis has shown is that deploying in analytics, a Hadoop or NoSQL type of solution on top of NetApp is half the cost of DAS. So when you consider the cost of servers, the licenses that you're going to have to pay for, these commercial implementations of Hadoop as well as the storage and the data infrastructure, you are much better off choosing NetApp than a white box type of solution. >> Let's unpack that a little bit, because if I infer correctly from what you said normally you would say the operational costs are going to be dramatically lower, it's easier to manage a professional system like a NetApp ONTAP, it's integrated, great software, but am I hearing you correctly, you're saying the acquisition costs are actually less than if I'm buying white box? A lot of people are going to be skeptical about that, say Octavian no way, it's cheaper to buy white box stuff. Defend that statement. >> Absolutely. If you're looking at the whole solution that includes the server and the storage, what NetApp enables you to do if you're running the solution on top of ONTAP you reduce the need for so many servers. If you reduce that number you also reduce the licensing cost. Moreover, if you actually look at the core value proposition of the storage layer there, DAS typically makes three copies of the data. We don't. We are very greedy and we're making sure that you're using shared storage and we are applying a bunch of storage efficiency techniques to further compress, compact that data for world class storage efficiency. >> So cost efficiency is obviously a great benefit for any company when they're especially evolving, from a digital perspective. What are some of the business level benefits? You mentioned speed a minute ago. What is Data ONTAP and even ONTAP in the cloud enabling your enterprise customers to achieve at the business level, maybe from faster time to market, identifying with machine learning and AI new products? Give me an example of maybe a customer that you think really articulates the value that ONTAP in the cloud can deliver. >> One of the things that's really important is to have your data management capability, whatever the data is being produced so ONTAP being consumed either as a VM or a service ... I don't know if you've seen some of the partnerships that we have with AWS and Azure. We're able to offer the same rich data management capabilities, not only the traditional data center, but in the cloud. What that really enables customers to do is to simplify and have the same operating system, the same data management platform for the both the second platform traditional applications as well as for the third platform applications. I've seen a company like Adobe be very successful in deploying their infrastructure, their services not only on prem in their traditional data center, but using ONTAP Cloud. So we have more than about 1,500 customers right now that have adopted ONTAP in the AWS cloud. >> What are you seeing in terms of the adoption of flash and I'm particularly interested in the intersection of flash adoption and the developer angle, because we've seen, in certain instances, certain organizations are able to share data off of flash much more efficiently that you would be, for instance, of a spinning disk? Have you seen a developer impact in your customer base? >> Absolutely I think most of customers initially have adopted flash, because of high throughput and low latency. I think over time customers really understood and identified with the overall value proposition in cost of ownership in flash that it enables them to consolidate multiple workloads in a smaller footprint. So that enables you to then reduce the cost to operate that infrastructure and it really gives you a range of applications that you can deploy that you were never able to do that. Everybody's looking to do in place, in line analytics that now are possible, because of this fast media. Folks are looking to accelerate old applications in which they cannot invest anymore, but they just want to run faster. Flash also tends to be more reliable than traditional storage, so customers definitely appreciate that fewer things could go wrong so overall the value proposition of flash, it's all encompassing and we believe that in the near future flash will be the defacto standard in everybody's data center, whether it's on prem or in the cloud. >> How about backup and recovery in big data? We obviously, in the enterprise, very concerned about data protection. What's similar in big data? What's different and what's NetApp's angle on that? >> I think data protection and data security will never stop being important to our customers. Security's top of mind for everybody in the industry and it's a source of resume changing events, if you would, and they're typically not promotions. So we have invested a tremendous deal in certifications for HIPAA, for FIPS, we are enabling encryption, both at rest and in flight. We've done a lot of work to make sure that the encryption can happen in software layer, to make sure that we give the customers best storage class efficiency and what we're also leveraging is the innovation that ONTAP has done over many years to protect the data, replicate its snapshots, peering the data to the cloud. These are techniques that we're commonly using to reduce the cost of ownership, also protect the data the customers deploy. >> So security's still a hot topic and, like you said, it probably always will be, but it's a shared responsibility, right? So customers leveraging NetApps safe or on prem hybrid also using Azure or AWS, who's your target audience? If you're talking to the guys and gals that are still managing storage are you also having the CSO or the security guys come in, the gals, to understand we've got this appointment in Azure or AWS so we're going to bring in ONTAP to facilitate this? There's a shared responsibility of security. Who's at the table, from your perspective, in your customers that you need to help understand how they facilitate true security? >> It's definitely been a transformative event where more and more people in IQ organizations are involved in the decisions that are required to deploy the applications. There was a time when we would talk only to the storage admin. After a while we started talking to the application admin, the virtualization admin and now you're talking to the line of business who has that vested interest to make sure that they can harness the power of the data in their environment. So you have the CSO, you have the traditional infrastructure people, you have the app administration and you have the app owner, the business owner that are all at the table that are coming and looking to choose the best of breed solution for their data management. >> What are the conversations like with your CXO, executives? Everybody talks about digital transformation. It's kind of an overused term, but there's real substance when you actually peel the onion. What are you seeing as NetApp's role in effecting digital transformations within your customer base? >> I think we have a vision of how we can help enterprises take advantage of the digital transformation and adopt it. I think we have three tenants of that vision. Number one is we're helping customers harness the power of the cloud. Number two, we're looking to enable them to future proof their investments and build the next generation data center. And number three, nobody starts with a fresh slate so we're looking to help customers modernize their current infrastructure through storage. We have a lot of expertise in storage. We've helped, over time, customers time and again adopt disruptive technologies in nondisruptive ways. We're looking to adopt these technologies and trends on behalf of our customers and then help them use them in a seamless safe way. >> And continue their evolution to identify new revenue streams, new products, new opportunities and even probably give other lines of business access to this data that they need to understand is there value here, how can we harness it faster than our competitors, right? >> Absolutely. It's all about deriving value out of the data. I think earlier I called it the gold of the 21st Century. This is a trend that will continue. I believe there will be no enterprise or center that won't focus on using machine learning, deep learning, analytics to derive more value out of the data to find more customer touch points, to optimize their business to really compete in the marketplace. >> Data plus AI plus cloud economics are the new innovation drivers of the next 10, 20 years. >> Completely agree. >> Well Octavian thanks so much for spending time with us this morning sharing what's new at NetApp, some of the visions that you guys have and also some of the impact that you're making with customers. We look forward to having you back on the program in the near future. >> Thank you. Appreciate having the time. >> And for my cohost Dave Vellante I'm Lisa Martin. You're watching The Cube live on day two of coverage of our event, Big Data SV. We're at this really cool venue, Forager Tasting Room. Come down here, join us, get to hear all these great conversations. Stick around and we'll be right back with our next guest after a short break. (electronic music)

>> Announcer: Live from San Jose, it's theCUBE, presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partner. (upbeat electronic music) >> Welcome back to The Cube. I'm Lisa Martin with George Gilbert. We are live at our event, Big Data SV, in downtown San Jose down the street from the Strata Data Conference. We're joined by a new guest to theCUBE, Sastry Malladi, the CTO Of FogHorn. Sastry, welcome to theCUBE. >> Thank you, thank you, Lisa. >> So FogHorn, cool name, what do you guys do, who are you? Tell us all that good stuff. >> Sure. We are a startup based in Silicon Valley right here in Mountain View. We started about three years ago, three plus years ago. We provide edge computing intelligence software for edge computing or fog computing. That's how our company name got started is FogHorn. For our particularly, for our IoT industrial sector. All of the industrial guys, whether it's transportation, manufacturing, oil and gas, smart cities, smart buildings, any of those different sectors, they use our software to predict failure conditions in real time, or do condition monitoring, or predictive maintenance, any of those use cases and successfully save a lot of money. Obviously in the process, you know, we get paid for what we do. >> So Sastry... GE populized this concept of IIoT and the analytics and, sort of the new business outcomes you could build on it, like Power by the Hour instead of selling a jet engine. >> Sastry: That's right. But there's... Actually we keep on, and David Floor did some pioneering research on how we're going to have to do a lot of analytics on the edge for latency and bandwidth. What's the FogHorn secret sauce that others would have difficulty with on the edge analytics? >> Okay, that's a great question. Before I directly answer the question, if you don't mind, I'll actually even describe why that's even important to do that, right? So a lot of these industrial customers, if you look at, because we work with a lot of them, the amount of data that's produced from all of these different machines is terabytes to petabytes of data, it's real. And it's not just the traditional digital sensors but there are video, audio, acoustic sensors out there. The amount of data is humongous, right? It's not even practical to send all of that to a Cloud environment and do data processing, for many reasons. One is obviously the connectivity, bandwidth issues, and all of that. But the two most important things are cyber security. None of these customers actually want to connect these highly expensive machines to the internet. That's one. The second is the lack of real-time decision making. What they want to know, when there is a problem, they want to know before it's too late. We want to notify them it is a problem that is occurring so that have a chance to go fix it and optimize their asset that is in question. Now, existing solutions do not work in this constrained environment. That's why FogHorn had to invent that solution. >> And tell us, actually, just to be specific, how constrained an environment you can operate in. >> We can run in about less than 100 to 150 megabytes of memory, single-core to dual-core of CPU, whether it's an ARM processor, an x86 Intel-based processor, almost literally no storage because we're a real-time processing engine. Optionally, you could have some storage if you wanted to store some of the results locally there but that's the kind of environment we're talking about. Now, when I say 100 megabytes of memory, it's like a quarter of Raspberry Pi, right? And even in that environment we have customers that run dozens of machinery models, right? And we're not talking -- >> George: Like an ensemble. >> Like an anomaly detection, a regression, a random forest, or a clustering, or a gamut, some of those. Now, if we get into more deep learning models, like image processing and neural net and all of that, you obviously need a little bit more memory. But what we have shown, we could still run, one of our largest smart city buildings customer, elevator company, runs in a raspberry Pi on millions of elevators, right? Dozens of machinery algorithms on top of that, right? So that's the kind of size we're talking about. >> Let me just follow up with one question on the other thing you said, with, besides we have to do the low-latency locally. You said a lot of customers don't want to connect these brown field, I guess, operations technology machines to the internet, and physically, I mean there was physical separation for security. So it's like security, Bill Joy used to say "Security by obscurity." Here it's security by -- >> Physical separation, absolutely. Tell me about it. I was actually coming from, if you don't mind, last week I was in Saudi Arabia. One of the oil and gas plants where we deployed our software, you have to go to five levels of security even to get to there, It's a multibillion dollar plant and refining the gas and all of that. Completely offline, no connectivity to the internet, and we installed, in their existing small box, our software, connected to their live video cameras that are actually measuring the stuff, doing the processing and detecting the specific conditions that we're looking for. >> That's my question, which was if they want to be monitoring. So there's like one low level, really low hardware low level, the sensor feeds. But you could actually have a richer feed, which is video and audio, but how much of that, then, are you doing the, sort of, inferencing locally? Or even retraining, and I assume that since it's not the OT device, and it's something that's looking at it, you might be more able to send it back up the Cloud if you needed to do retraining? >> That's exactly right. So the way the model works is particularly for image processing because you need, it's a more complex process to train than create a model. You could create a model offline, like in a GPU box, an FPGA box and whatnot. Import and bring the model back into this small little device that's running in the plant, and now the live video data is coming in, the model is inferencing the specific thing. Now there are two ways to update and revise the model: incremental revision of the model, you could do that if you want, or you can send the results to a central location. Not internet, they do have local, in this example for example a PIDB, an OSS PIDB, or some other local service out there, where you have an opportunity to gather the results from each of these different locations and then consolidate and retrain the model, put the model back again. >> Okay, the one part that I didn't follow completely is... If the model is running ultimately on the device, again and perhaps not even on a CPU, but a programmable logic controller. >> It could, even though a programmable controller also typically have some shape of CPU there as well. These days, most of the PLCs, programmable controllers, have either an RM-based processor or an x86-based processor. We can run either one of those too. >> So, okay, assume you've got the model deployed down there, for the, you know, local inferencing. Now, some retraining is going to go on in the Cloud, where you have, you're pulling in the richer perspective from many different devices. How does that model get back out to the device if it doesn't have the connectivity between the device and the Cloud? >> Right, so if there's strictly no connectivity, so what happens is once the model is regenerated or retrained, they put a model in a USB stick, it's a low attack. USB stick, bring it to the PLC device and upload the model. >> George: Oh, so this is sort of how we destroyed the Iranian centrifuges. >> That's exactly right, exactly right. But you know, some other environments, even though it's not connectivity to the Cloud environment, per se, but the devices have the ability to connect to the Cloud. Optionally, they say, "Look, I'm the device "that's coming up, do you have an upgraded model for me?" Then it can pull the model. So in some of the environments it's super strict where there are absolutely no way to connect this device, you put it in a USB stick and bring the model back here. Other environments, device can query the Cloud but Cloud cannot connect to the device. This is a very popular model these days because, in other words imagine this, an elevator sitting in a building, somebody from the Cloud cannot reach the elevator, but an elevator can reach the Cloud when it wants to. >> George: Sort of like a jet engine, you don't want the Cloud to reach the jet engine. >> That's exactly right. The jet engine can reach the Cloud it if wants to, when it wants to, but the Cloud cannot reach the jet engine. That's how we can pull the model. >> So Sastry, as a CTO you meet with customers often. You mentioned you were in Saudi Arabia last week. I'd love to understand how you're leveraging and gaging with customers to really help drive the development of FogHorn, in terms of being differentiated in the market. What are those, kind of bi-directional, symbiotic customer relationships like? And how are they helping FogHorn? >> Right, that's actually a great question. We learn a lot from customers because we started a long time ago. We did an initial version of the product. As we begin to talk to the customers, particularly that's part of my job, where I go talk to many of these customers, they give us feedback. Well, my problem is really that I can't even do, I can't even give you connectivity to the Cloud, to upgrade the model. I can't even give you sample data. How do you do that modeling, right? And sometimes they say, "You know what, "We are not technical people, help us express the problem, "the outcome, give me tools "that help me express that outcome." So we created a bunch of what we call OT tools, operational technology tools. How we distinguish ourselves in this process, from the traditional Cloud-based vendor, the traditional data science and data analytics companies, is that they think in terms of computer scientists, computer programmers, and expressions. We think in terms of industrial operators, what can they express, what do they know? They don't really necessarily care about, when you tell them, "I've got an anomaly detection "data science machine algorithm", they're going to look at you like, "What are you talking about? "I don't understand what you're talking about", right? You need to tell them, "Look, this machine is failing." What are the conditions in which the machine is failing? How do you express that? And then we translate that requirement, or that into the underlying models, underlying Vel expressions, Vel or CPU expression language. So we learned a ton from user interface, capabilities, latency issues, connectivity issues, different protocols, a number of things that we learn from customers. >> So I'm curious with... More of the big data vendors are recognizing data in motion and data coming from devices. And some, like Hortonworks DataFlow NiFi has a MiNiFi component written in C plus plus, really low resource footprint. But I assume that that's really just a transport. It's almost like a collector and that it doesn't have the analytics built in -- >> That's exactly right, NiFi has the transport, it has the real-time transport capability for sure. What it does not have is this notion of that CEP concept. How do you combine all of the streams, everything is a time series data for us, right, from the devices. Whether it's coming from a device or whether it's coming from another static source out there. How do you express a pattern, a recognition pattern definition, across these streams? That's where our CPU comes in the picture. A lot of these seemingly similar software capabilities that people talk about, don't quite exactly have, either the streaming capability, or the CPU capability, or the real-time, or the low footprint. What we have is a combination of all of that. >> And you talked about how everything's time series to you. Is there a need to have, sort of an equivalent time series database up in some central location? So that when you subset, when you determine what relevant subset of data to move up to the Cloud, or you know, on-prem central location, does it need to be the same database? >> No, it doesn't need to be the same database. It's optional. In fact, we do ship a local time series database at the edge itself. If you have a little bit of a local storage, you can down sample, take the results, and store it locally, and many customers actually do that. Some others, because they have their existing environment, they have some Cloud storage, whether it's Microsoft, it doesn't matter what they use, we have connectors from our software to send these results into their existing environments. >> So, you had also said something interesting about your, sort of, tool set, as being optimized for operations technology. So this is really important because back when we had the Net-Heads and the Bell-Heads, you know it was a cultural clash and they had different technologies. >> Sastry: They sure did, yeah. >> Tell us more about how selling to operations, not just selling, but supporting operations technology is different from IT technology and where does that boundary live? >> Right, so typical IT environment, right, you start with the boss who is the decision maker, you work with them and they approve the project and you go and execute that. In an industrial, in an OT environment, it doesn't quite work like that. Even if the boss says, "Go ahead and go do this project", if the operator on the floor doesn't understand what you're talking about, because that person is in charge of operating that machine, it doesn't quite work like that. So you need to work bottom up as well, to convincing them that you are indeed actually solving their pain point. So the way we start, where rather than trying to tell them what capabilities we have as a product, or what we're trying to do, the first thing we ask is what is their pain point? "What's your problem? What is the problem "you're trying to solve?" Some customers say, "Well I've got yield, a lot of scrap. "Help me reduce my scrap. "Help me to operate my equipment better. "Help me predict these failure conditions "before it's too late." That's how the problem starts. Then we start inquiring them, "Okay, what kind of data "do you have, what kind of sensors do you have? "Typically, do you have information about under what circumstances you have seen failures "versus not seeing failures out there?" So in the process of inauguration we begin to understand how they might actually use our software and then we tell them, "Well, here, use your software, "our software, to predict that." And, sorry, I want 30 more seconds on that. The other thing is that, typically in an IT environment, because I came from that too, I've been in this position for 30 plus years, IT, UT and all of that, where we don't right away talk about CEP, or expressions, or analytics, and we don't talk about that. We talk about, look, you have these bunch of sensors, we have OT tools here, drag and drop your sensors, express the outcome that you're trying to look for, what is the outcome you're trying to look for, and then we drive behind the scenes what it means. Is it analytics, is it machine learning, is it something else, and what is it? So that's kind of how we approach the problem. Of course, if, sometimes you do surprisingly occasionally run into very technical people. From those people we can right away talk about, "Hey, you need these analytics, you need to use machinery, "you need to use expressions" and all of that. That's kind of how we operate. >> One thing, you know, that's becoming clearer is I think this widespread recognition that's data intensive and low latency work to be done near the edge. But what goes on in the Cloud is actually closer to simulation and high-performance compute, if you want to optimize a model. So not just train it, but maybe have something that's prescriptive that says, you know, here's the actionable information. As more of your data is video and audio, how do you turn that into something where you can simulate a model, that tells you the optimal answer? >> Right, so this is actually a good question. From our experience, there are models that require a lot of data, for example, video and audio. There are some other models that do not require a lot of data for training. I'll give you an example of what customer use cases that we have. There's one customer in a manufacturing domain, where they've been seeing a lot of finished goods failures, there's a lot of scrap and the problem then was, "Hey, predict the failures, "reduce my scrap, save the money", right? Because they've been seeing a lot of failures every single day, we did not need a lot of data to train and create a model to that. So, in fact, we just needed one hour's worth of data. We created a model, put the thing, we have reduced, completely eliminated their scrap. There are other kinds of models, other kinds of models of video, where we can't do that in the edge, so we're required for example, some video files or simulated audio files, take it to an offline model, create the model, and see whether it's accurately predicting based on the real-time video coming in or not. So it's a mix of what we're seeing between those two. >> Well Sastry, thank you so much for stopping by theCUBE and sharing what it is that you guys at FogHorn are doing, what you're hearing from customers, how you're working together with them to solve some of these pretty significant challenges. >> Absolutely, it's been a pleasure. Hopefully this was helpful, and yeah. >> Definitely, very educational. We want to thank you for watching theCUBE, I'm Lisa Martin with George Gilbert. We are live at our event, Big Data SV in downtown San Jose. Come stop by Forager Tasting Room, hang out with us, learn as much as we are about all the layers of big data digital transformation and the opportunities. Stick around, we will be back after a short break. (upbeat electronic music)

>> Voiceover: Live from San Jose, it's The Cube. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to The Cube, our continuing coverage of our event, Big Data SV. We're on day two of this event. I'm Lisa Martin, with George Gilbert. We've had a great day yesterday learning a lot and really peeling back the layers of big data, looking at it from different perspectives, from challenges to opportunities. Joining us next is one of our Cube alumni, Chris Selland, the VP of Strategic Alliances from Unifi Software. Chris, great to meet you, welcome back! >> Thank you Lisa, it's great to be here. I have to say, as a alumni and a many time speaker, this venue is spectacular. Congratulations on the growth of The Cube, and this is an awesome venue. I've been on The Cube a bunch of times and this is as nice as I've ever seen it, >> Yeah, this is pretty cool, >> Onward and upward. This place is great. Isn't it cool? >> It really is. This is our 10th Big Data event, we've been having five now in San Jose, do our fifth one in New York City in the fall, and it's always interesting because we get the chance, George and I, and the other hosts, to really look at what is going on from different perspectives in the industry of big data. So before we kind of dig into that, tell us a little bit about Unifi Software, what do you guys do, what is unique and differentiating about Unifi. >> Sure, yeah, so I joined Unifi a little over a year ago. You know, I was attracted to the company because it really, I think, is aligned with where the market is going, and Peter talked this morning, Peter Burris was talking this morning about networks of data. Unifi is fundamentally a data catalog and data preparation platform, kind of combined or unified together. So, you know, so people say, "What do you do?" We're a data catalog with integrated data preparation. And the idea behind that, to go to Peter's, you know, mention of networks of data, is that data is becoming more and more distributed in terms of where it is, where it lives, where it sits. This idea of we're going to put everything in the data warehouse, and then we're going to put everything in the data lake, well, in reality, some of the data's in the warehouse, some of the data's in the lake, some of the data's in SAS applications, some of the data's in blob storage. And where is all of that data, what is it, and what can I do with it, that's really the fundamental problem that we solve. And, by the way, solve it for business people, because it's not just data scientists anymore, it's really going out into the entire business community now, you know, marketing people, operations people, finance people, they need data to do their jobs. Their jobs are becoming more data driven, but they're not necessarily data people. They don't know what schemas are, or joins are, but they know, "I need better data "to be able to do my job more effectively." So that's really what we're helping with. So, Chris, this is, it's kind of interesting, if you distill, you know, the capability down to the catalog and the prep-- >> Chris: Yep. So that it's ready for a catalog, but that sort of thing is, it's like investment in infrastructure, in terms of like building the highway system, but there're going to be, you know, for those early highways, there's got to be roots that you, a reason to build them out. What are some of those early use cases that justifies the investment in data infrastructure? >> There absolutely are, I mean, and by the way, those roots don't go away, those roots, you know, just like cities, right? New roots get built on top of them. So we're very much, you know, about, there's still data sitting in mainframes and legacy systems and you know, that data is absolutely critical for many large organizations. We do a lot of working in banking and financial services, and healthcare. They're still-- >> George: Are there common use cases that they start with? >> A lot of times-- >> Like, either by industry or just cross-sectional? >> Well, it's interesting, because, you know, analysts like yourselves have tended to put data catalog, which is a relatively new term, although some other big analyst firm that's having another conference this week, they were telling us recently that, starts with a "G," right? They were telling us that data catalog is now the number one search term they're getting. But it's been, by many annals, also kind of lumped in, lumped in's the wrong word, but incorporated with data governance. So traditionally, governance, another word that starts with "G," it's been the term. So, we often, we're not a traditional data governance platform, per se, but cataloging data has to have a foundation of security in governance. You know, think about what's going on in the world right now, both in the court of law and the court of public opinion, things like GDPR, right? So GDPR sort of says any customer data you have needs to be managed a certain way, with a certain level of sensitivity, and then there's other capabilities you need to open up to customers, like the right to be forgotten, so that means I need to have really good control, first of all, knowledge of, control over, and governance over my customer data. I talked about all those business people before. Certainly marketers are a great example. Marketers want all the customer data they can get, right? But there's social security numbers, PII, who should be able to see and use what? Because, if this data is used inappropriately, then it can cause a lot of problems. So, IT kind of sits in a-- they want to enable the business, but at the same time, there's a lot of risk there. So, anyway, going back to your question, you know, the catalog market is kind of evolved out of the governance market with more of a focus on kind of, you know, enabling the business, but making sure that it's done in a secure and well-governed way. >> George: Guard rails. >> Yes, guard rails, exactly, good way to say it. So, yep, that's good, I said about 500 words, and you distilled it to about two, right? Perfect, yep. >> So, in terms of your role in strategic alliances, tell us a little about some of the partnerships that Unifi is forging, to help customers understand where all this data is, to your point earlier, the different lines of business that need it to drive, identify where's their value, and drive the business forward, can actually get it. >> Absolutely, well, certainly to your point, our customers are our partners, and we can talk about some of them. But also, strategic alliances, we work very closely with a number of, you know, larger technology companies, Microsoft is a good example. We were actually part of the Microsoft Accelerator Program, which I think they've now rebranded Microsoft for Startups, but we've really been given tremendous support by that team, and we're doing a lot of work to, kind of, we're to some degree cloud agnostic, we support AWS, we support Azure, we support Google Cloud, but we're doing a lot of our development also on the Azure cloud platform. But you know, customers use all of the above, so we need to support all of the above. So Microsoft's a very close partner of ours. Another, I'll be in two weeks, and we've got some interesting news pending, which unfortunately I can't get into today, but maybe in a couple weeks, with Adobe. We're working very closely with them on their marketing cloud, their experience cloud, which is what they call their enterprise marketing cloud, which obviously, big, big focus on customer data, and then we've been working with a number of organizations and the sort of professional services system integration. We've had a lot of success with a firm called Access Group. We announced the partnership with them about two weeks ago. They've been a great partner for us, as well. So, you know, it's all about an ecosystem. Making customers successful is about getting an ecosystem together, so it's a really exciting place to be. >> So, Chris, it's actually interesting, it sounds like there's sort of a two classic routes to market. One is essentially people building your solution into theirs, whether it's an application or, you know, >> Chris: An enabling layer. >> Yes. >> Chris: Yes. >> Even higher layer. But with corporate developers, you know, it's almost like we spent years experimenting with these data lakes. But they were a little too opaque. >> Chris: Yes. >> And you know, it's not just that you provide the guard rails, but you also provide, sort of some transparency-- >> Chris: Yes. >> Into that. Have you seen a greater success rate within organizations who curate their data lakes, as opposed to those who, you know, who don't? >> Yes, absolutely. I think Peter said it very well in his presentation this morning, as well. That, you know, generally when you see data lake, we associate it with Hadoop. There are use cases that Hadoop is very good for, but there are others where it might not be the best fit. Which, to the early point about networks of data and distributed data, so companies that have, or organizations that have approached Hadoop with a "let's use it what it's good for," as opposed to "let's just dump "everything in there and figure it out later," and there have been a lot of the latter, but the former have done, generally speaking, a lot better, and that's what you're seeing. And we actually use Hadoop as a part of our platform, at least for the data preparation and transformation side of what we do. We use it in its enabling technology, as well. >> You know, it's funny, actually, when you talk about, as Peter talked about, networks of data versus centralized repositories. Scott Gnau, CTO of Hortonworks, was on yesterday, and he was talking about how he had originally come from Teradata, and that they had tried to do work, that he had tried to push them in the direction of recognizing that not all the analytic data was going to be in Teradata, you know, but they had to look more broadly with Hadapt, and I forgot what the rest of, you know-- >> Chris: Right, Aster, and-- >> Aster, yeah. >> Chris: Yes, exactly, yep. >> But what was interesting is that Hortonworks was moving towards the "we believe "everything is going to be in the data lake," but now, with their data plane service, they're talking about, you know, "We have to give you visibility and access." You mediate access to data everywhere. >> Chris: Right. >> So maybe help, so for folks who aren't, like, all bought into Hortonworks, for example, how much, you know, explain how you work relative to data plane service. >> Well, you know, maybe I could step back and give you a more general answer, because I agree with that philosophically, right? That, as I think we've been talking about here, with the networks of data, that goes back to my prior statement that there's, you know, there's different types of data platforms that have different use cases, and different types of solutions should be built on top of them, so things are getting more distributed. I think that, you know, Hortonworks, like every company, has to make the investments that are, as we are, making their customers successful. So, using Hadoop, and Hortonworks is one of our supported Hadoop platforms, we do work with them on engagements, but you know, it's all about making customers successful, ultimately. It's not about a particular product, it's about, you know, which data belongs in which location, and for what use case and what purpose, and then at the same time, when we're taking all of these different data sets and data sources, and cataloging them and preparing them and creating our output, where should we put that and catalog that, so we can create kind of a continuous improvement cycle, as well, and for those types-- >> A flywheel. >> A flywheel, exactly, continuous improvement flywheel, and for those types of purposes, you know, that's actually great use case for, you know, Hortonworks, Hadoop. That's a lot of what we typically use it for. We can actually put the data any place our customers define, but that's very often what we do with it, and then, but doing it in a very structured and organized way. As opposed to, you know, a lot of the early Hadoop, and not specific to any particular distro that went bad, were, it was just like, "Let's just dump it all "into Hadoop because it's cheaper." You know, "Let's, 'cause it's cheaper than the warehouse, "so let's just put it all in there, "and we'll figure what to do with it later." That's bad, but if you're using it in a structured way, it can be extremely useful. At the same point, and at the same time, not everything's going to go there belongs there, if you're being thoughtful about it. So you're seeing a lot more thoughtfulness these days, which is good. Which is good for customers, and it's good for us in the vendor side. Us, Hortonworks, everybody, so. >> So is there, maybe you can tell us of the different approaches to, like, the advantage of integrating the data prep with the catalogized service, because as soon as you're done with data prep it's visible within the catalog. >> Chris: Absolutely, that's one, yep. >> When, let's say when people do derive additional views into the data, how are they doing that in a way that then gets also registered back in the catalog, for further discovery? >> Yeah, well, having the integrated data preparation which is a huge differentiator from us, there are a lot of data catalog products out there, but our huge differentiator, one of them, is the fact that we have integrated data preparation. We don't have to hand off to another product, so that, as you said, gives us the ability to then catalog our output and build that flywheel, that continuous improvement flywheel, and it also just basically simplifies things for customers, hence our name. So, you know, it really kind of starts there. I think I, the second part of your question I didn't really, rewind back on that for me, it was-- >> Go ahead. >> Well, I'm not sure I remember it, right now, either. >> We all need more coffee. >> Exactly, we all need more coffee. >> So I'll ask you this last question, then. >> Yes, please. >> What are, so here we are in March 2018, what are you looking forward to, in terms of momentum and evolution of Unifi this year? >> Well, a lot of it, and tying into my role, I mentioned we will be at Adobe Summit in two weeks, so if you're going to be at Adobe Summit, come see us there, some of the work that we're doing with our partner, some of the events we're doing with people like Microsoft and Access, but really it's also just customer success, I mean, we're seeing tremendous momentum on the customer side, working with our customers, working with our partners, and again, as I mentioned, we're seeing so much more thoughtfulness in the market, these days, and less talk about, you know, the speeds and feeds, and more around business solutions. That's really also where our professional services, system integration partners, many of whom I've been with this week, really help, because they're building out solutions. You know, GDPR is coming in May, right? And you're starting to really see a groundswell of, okay, you know, and that's not about, you know, speeds and feeds. That's ultimately about making sure that I'm compliant with, you know, this huge regulatory environment. And at the same time, the court of public opinion is just as important. You know, we want to make sure that we're doing the right thing with data. Spread it throughout organization, make ourselves successful and make our customers successful. So, it's a lot of fun. >> That's, fun is good. >> Exactly, fun is good. >> Well, we thank you so much, Chris, for stopping back by The Cube and sharing your insights, what you're hearing in the big data industry, and some of the momentum that you're looking forward to carrying throughout the year. >> It's always a pleasure, and you, too. So, love the venue. >> Lisa: All right. >> Thank you, Lisa, thank you, George. >> Absolutely. We want to thank you for watching The Cube. You're watching our coverage of our event, Big Data SV, hashtag BigDataSV, for George, I almost said George Martin. For George Gilbert. >> George: I wish. >> George R.R., yeah. You would not be here if you were George R.R. Martin. >> George: No, I wouldn't. >> That was a really long way to say thank you for watching. I'm Lisa Martin, for this George. Stick around, we'll be right back with our next guest. (techno music)

>> Announcer: Live, from San Jose, it's The Cube (light music) Presenting Big Data Silicon Valley. Brought to you by Silicon Angle Media, and its ecosystem partners. >> Hi, I'm Peter Burris and welcome back to Big Data SV, The Cube's, again, annual broadcast of what's happening in the big data marketplace here at, or adjacent to Strada here in San Jose. We've been broadcasting all day. We're going to be here tomorrow as well, over at the Forager eatery and place to come meander. So come on over. Spend some time with us. Now, we've had a number of great guests. Many of the thought leaders that are visiting here in San Jose today were on the big data marketplace. But I don't think any has traveled as far as our next guest. Yuanhao Sun is the ceo of Transwarp. Come all the way from Shanghai Yuanhao. It's once again great to see you on The Cube. Thank you very much for being here. >> Good to see you again. >> So Yuanhao, the Transwarp as a company has become extremely well known for great technology. There's a lot of reasons why that's the case, but you have some interesting updates on how the technology's being applied. Why don't you tell us what's going on? >> Okay, so, recently we announced the first order to the TPC-DS benchmark result. Our product, calling scepter, that is, SQL engine on top of Hadoop. We already add quite a lot of features, like dissre transactions, like a full SQL support. So that it can mimic, like oracle or the mutual, and also traditional database features so that we can pass the whole test. This single is also scalable, because it's distributed, scalable. So the large benchmark, like TPC-DS. It starts from 10 terabytes. SQL engine can pester without much trouble. >> So I know that there have been other firms that have claimed to pass TPCC-DS, but they haven't been audited. What does it mean to say you're audited? I'd presume that as a result, you've gone through some extremely stringent and specific tests to demonstrate that you can actually pass the entire suite. >> Yes, actually, there is a third party auditor. They already audit our test process and it results for the passed six, uh, five months. So it is fully audited. The reason why we can pass the test is because, actually, there's two major reasons for traditional databases. They are not scalable to the process large dataset. So they could not pass the test. For (mumbles) vendors, because the SQL engine, the features to reach enough to pass all the test. You know, there several steps in the benchmark, and the SQL queries, there are 99 queries, the syntax is not supported by all howve vendors yet. And also, the benchmark required to upload the data, after the queries, and then we run the queries for multiple concurrent users. That means you have to support disputed transactions. You have to make the upload data consistent. For howve vendors, the SQL engine on Hadoop. They haven't implemented the de-switch transaction capabilities. So that's why they failed to pass the benchmark. >> So I had the honor of traveling to Shanghai last year and going and speaking at your user conference and was quite impressed with the energy that was in the room as you announced a large number of new products. You've been very focused on taking what open source has to offer but adding significant value to it. As you said, you've done a lot with the SQL interfaces and various capabilities of SQL on top of Hadoop. Where is Transwarp going with its products today? How is it expanding? How is it being organizing? How is it being used? >> We group these products into three catalog, including big data, cloud, AI and the machine learning. So there are three categories. The big data, we upgrade the SQL engine, the stream engine, and we have a set of tools called adjustable studio to help people to streamline the big data operations. And the second part I lie is data cloud. We call it transwarp data cloud. So this product is going to be raised in early in May this year. So this product we build this product on top of common idiots. We provide how to buy the service, get a sense as service, air as a service to customers. A lot of people took credit multiple tenets. And they turned as isolated by network, storage, cpu. They free to create a clusters and speeding up on turning it off. So it can also scale hundreds of cost. So this is the, I think this is the first we implement, like, a network isolation and sweaty percendency in cobinets. So that it can support each day affairs and all how to components. And because it is elastic, just like car computing, but we run on bare model, people can consult the data, consult the applications in one place. Because all application and Hadoop components are conternalized, that means, we are talking images. We can spend up a very quickly and scale through a larger cluster. So this data cloud product is very interesting for large company, because they usually have a small IT team. But they have to provide a (mumbles), and a machine only capability to larger groups, like one found the people. So they need a convenient way to manage all these bigger clusters. And they have to isolate the resources. Even they need a bidding system. So this product is, we already have few big names in China, like China Post, Picture Channel, and Secret of Source Channel. So they are already applying this data cloud for their internal customers. >> And China has a, has a few people, so I presume that, you know, China Post for example, is probably a pretty big implementation. >> Yes so, they have a, but the IT team is, like less than 100 people, but they have to support thousands of users. So that's why they, you usually would deploy 100 cluster for each application, right, but today, for large organization, they have lots of applications. They hope to leverage big data capability, but a very small team, IT team, can also part of so many applications. So they need a convenient the way like a, just like when you put Hadoop on public cloud. We provide a product that allows you to provide a hardware service in private cloud on bare model machines. So this is the second product category. And the third is the machine learning and artificial intelligence. We provide a data sales platform, a machine learning tool, that is, interactive tools that allows people to create the machine only pipelines and models. We even implemented some automatic modeling capability that allow you to, to fisher in youring automatically or seeming automatically and to select the best items for you so that the machine learning can be, so everyone can be at Los Angeles. So they can use our tool to quickly create a models. And we also have some probuter models for different industry, like financial service, like banks, security companies, even iot. So we have different probuter machine only models for them. We just need to modify the template, then apply the machine only models to the applications very quickly. So that probably like a lesson, for example, for a bank customer, they just use it to deploy a model in one week. This is very quick for them. Otherwise, in the past, they have a company to build that application, to develop much models. They usually takes several months. Today it is much faster. So today we have three categories, particularly like cloud and machine learning. >> Peter Burris: Machine learning and AI. >> And so three products. >> And you've got some very, very big implementations. So you were talking about a couple of banks, but we were talking, before we came on, about some of the smart cities. >> Yuanhao Sun: Right. Kinds of things that you guys are doing at enormous scale. >> Yes, so we deploy our streaming productor for more than 300 cities in China. So this cluster is like connected together. So we use streaming capability to monitor the traffic and send the information from city to the central government. So all the, the sort of essential repoetry. So whenever illegal behavior on the road is detected, that information will be sent to the policeman, or the central repoetry within two second. Whenever you are seen by the camera in any place in China, their loads where we send out within two seconds. >> So the bad behavior is detected. It's identified as the location. The system also knows where the nearest police person is. And it sends a message and says, this car has performed something bad. >> Yeah and you should stop that car in the next station or in the next crossroad. Today there are tens of thousands policeman. They depends on this system for their daily work. >> Peter Burris: Interesting. >> So, just a question on, it sounds like one of your, sort of nearest competitors, in terms of, let's take the open source community, at least the APIs, and in their case open source, Waway. Have their been customers that tried to do a POC with you and with Waway, and said, well it took four months using the pure open source stuff, and it took, say, two weeks with your stack having, being much broader and deeper? Are any examples like that? >> There are quite a lot. We have more macro-share, like in financial services, we have about 100 bank users. So if we take all banks into account, for them they already use Hadoop. So we, our macro-share is above 60%. >> George Gilbert: 60. >> Yeah, in financial services. We usually do POC and, like run benchmarks. They are real workloads and usually it takes us three days or one week. They can found, we can speed up their workload very quickly. For Bank of China, they might go to their oracle workload to our platform. And they test our platform and the huave platform too. So the first thing is they cannot marry the whole oracle workload to open source Hadoop, because the missing features. We are able to support all this workloads with very minor modifications. So the modification takes only several hours. And we can finish the whole workload within two hours, but originally they take, usually take oracle more than one day, >> George Gilbert: Wow. >> more than ten hours to finish the workload. So it is very easy to see the benefits quickly. >> Now the you have a streaming product also with that same SQL interface. Are you going to see a migration of applications that used to be batch to more near real time or continuous, or will you see a whole new set of applications that weren't done before, because the latency wasn't appropriate? >> For streaming applications, real time cases they are mostly new applications, but if we are using storm api or spark streaming api, it is not so easy to develop your applications. And another issue is once you detect one new rule, you had to add those rules dynamically to your cluster. So to add to your printer, they do not have so many knowledge of writing scholar codes. They only know how to configure. Probably they are familiar with c-code. They just need to add one SQL statement to add a new rule. So that they can. >> In your system. >> Yeah, in our system. So it is much easier for them to program streaming applications. And for those customers who they don't have real time equations, they hope to do, like a real time data warehousing. They collect all this data from websites from their censors, like Petrol Channel, an oil company, the large oil company. They collect all the (mumbles) information directly to our streaming product. In the past, they just accredit to oracle and around the dashboard. So it only takes hours to see the results. But today, the application can be moved through our streaming product with only a few modifications, because they are all SQL statements. And this application becomes the real time. They can see the real time dashboard results in several seconds. >> So Yuanhao, you're number one in China. You're moving more aggressively to participate in the US market. What's the, last question, what's the biggest difference between being number one in China, the way that big data is being done in China versus the way you're encountering big data being done here, certainly in the US, for example? Is there a difference? >> I think there are some difference. Some a seem, katsumoto usually request a POC. But in China, they usually, I think they focus more on the results. They focus on what benefit they can gain from your product. So we have to prove them. So we have to hip them to my great application to see the benefits. I think in US, they focus more on technology than Chinese customers. >> Interesting, so they're more on technology here in the US, more in the outcome in China. Once again, Yuanhao Sun, from, ceo of Transwarp, thank you very much for being on The Cube. >> Thank you. And I'm Peter Burris with George Gilbert, my co-host, and we'll be back with more from big data SV, in San Jose. Come on over to the Forager, and spend some time with us. And we'll be back in a second. (light music)

>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and it's ecosystem partners. >> Welcome back to theCUBE's continuing coverage of our own event, Big Data SV. I'm Lisa Martin, with my cohost Dave Vellante. We're in downtown San Jose at this really cool place, Forager Eatery. Come by, check us out. We're here tomorrow as well. We're joined by, next, one of our CUBE alumni, Seth Dobrin, the Vice President and Chief Data Officer at IBM Analytics. Hey, Seth, welcome back to theCUBE. >> Hey, thanks for having again. Always fun being with you guys. >> Good to see you, Seth. >> Good to see you. >> Yeah, so last time you were chatting with Dave and company was about in the fall at the Chief Data Officers Summit. What's kind of new with you in IBM Analytics since then? >> Yeah, so the Chief Data Officers Summit, I was talking with one of the data governance people from TD Bank and we spent a lot of time talking about governance. Still doing a lot with governance, especially with GDPR coming up. But really started to ramp up my team to focus on data science, machine learning. How do you do data science in the enterprise? How is it different from doing a Kaggle competition, or someone getting their PhD or Masters in Data Science? >> Just quickly, who is your team composed of in IBM Analytics? >> So IBM Analytics represents, think of it as our software umbrella, so it's everything that's not pure cloud or Watson or services. So it's all of our software franchise. >> But in terms of roles and responsibilities, data scientists, analysts. What's the mixture of-- >> Yeah. So on my team I have a small group of people that do governance, and so they're really managing our GDPR readiness inside of IBM in our business unit. And then the rest of my team is really focused on this data science space. And so this is set up from the perspective of we have machine-learning engineers, we have predictive-analytics engineers, we have data engineers, and we have data journalists. And that's really focus on helping IBM and other companies do data science in the enterprise. >> So what's the dynamic amongst those roles that you just mentioned? Is it really a team sport? I mean, initially it was the data science on a pedestal. Have you been able to attack that problem? >> So I know a total of two people that can do that all themselves. So I think it absolutely is a team sport. And it really takes a data engineer or someone with deep expertise in there, that also understands machine-learning, to really build out the data assets, engineer the features appropriately, provide access to the model, and ultimately to what you're going to deploy, right? Because the way you do it as a research project or an activity is different than using it in real life, right? And so you need to make sure the data pipes are there. And when I look for people, I actually look for a differentiation between machine-learning engineers and optimization. I don't even post for data scientists because then you get a lot of data scientists, right? People who aren't really data scientists, and so if you're specific and ask for machine-learning engineers or decision optimization, OR-type people, you really get a whole different crowd in. But the interplay is really important because most machine-learning use cases you want to be able to give information about what you should do next. What's the next best action? And to do that, you need decision optimization. >> So in the early days of when we, I mean, data science has been around forever, right? We always hear that. But in the, sort of, more modern use of the term, you never heard much about machine learning. It was more like stats, math, some programming, data hacking, creativity. And then now, machine learning sounds fundamental. Is that a new skillset that the data scientists had to learn? Did they get them from other parts of the organization? >> I mean, when we talk about math and stats, what we call machine learning today has been what we've been doing since the first statistics for years, right? I mean, a lot of the same things we apply in what we call machine learning today I did during my PhD 20 years ago, right? It was just with a different perspective. And you applied those types of, they were more static, right? So I would build a model to predict something, and it was only for that. It really didn't apply it beyond, so it was very static. Now, when we're talking about machine learning, I want to understand Dave, right? And I want to be able to predict Dave's behavior in the future, and learn how you're changing your behavior over time, right? So one of the things that a lot of people don't realize, especially senior executives, is that machine learning creates a self-fulfilling prophecy. You're going to drive a behavior so your data is going to change, right? So your model needs to change. And so that's really the difference between what you think of as stats and what we think of as machine learning today. So what we were looking for years ago is all the same we just described it a little differently. >> So how fine is the line between a statistician and a data scientist? >> I think any good statistician can really become a data scientist. There's some issues around data engineering and things like that but if it's a team sport, I think any really good, pure mathematician or statistician could certainly become a data scientist. Or machine-learning engineer. Sorry. >> I'm interested in it from a skillset standpoint. You were saying how you're advertising to bring on these roles. I was at the Women in Data Science Conference with theCUBE just a couple of days ago, and we hear so much excitement about the role of data scientists. It's so horizontal. People have the opportunity to make impact in policy change, healthcare, etc. So the hard skills, the soft skills, mathematician, what are some of the other elements that you would look for or that companies, enterprises that need to learn how to embrace data science, should look for? Someone that's not just a mathematician but someone that has communication skills, collaboration, empathy, what are some of those, openness, to not lead data down a certain, what do you see as the right mix there of a data scientist? >> Yeah, so I think that's a really good point, right? It's not just the hard skills. When my team goes out, because part of what we do is we go out and sit with clients and teach them our philosophy on how you should integrate data science in the enterprise. A good part of that is sitting down and understanding the use case. And working with people to tease out, how do you get to this ultimate use case because any problem worth solving is not one model, any use case is not one model, it's many models. How do you work with the people in the business to understand, okay, what's the most important thing for us to deliver first? And it's almost a negotiation, right? Talking them back. Okay, we can't solve the whole problem. We need to break it down in discreet pieces. Even when we break it down into discreet pieces, there's going to be a series of sprints to deliver that. Right? And so having these soft skills to be able to tease that in a way, and really help people understand that their way of thinking about this may or may not be right. And doing that in a way that's not offensive. And there's a lot of really smart people that can say that, but they can come across at being offensive, so those soft skills are really important. >> I'm going to talk about GDPR in the time we have remaining. We talked about in the past, the clocks ticking, May the fines go into effect. The relationship between data science, machine learning, GDPR, is it going to help us solve this problem? This is a nightmare for people. And many organizations aren't ready. Your thoughts. >> Yeah, so I think there's some aspects that we've talked about before. How important it's going to be to apply machine learning to your data to get ready for GDPR. But I think there's some aspects that we haven't talked about before here, and that's around what impact does GDPR have on being able to do data science, and being able to implement data science. So one of the aspects of the GDPR is this concept of consent, right? So it really requires consent to be understandable and very explicit. And it allows people to be able to retract that consent at any time. And so what does that mean when you build a model that's trained on someone's data? If you haven't anonymized it properly, do I have to rebuild the model without their data? And then it also brings up some points around explainability. So you need to be able to explain your decision, how you used analytics, how you got to that decision, to someone if they request it. To an auditor if they request it. Traditional machine learning, that's not too much of a problem. You can look at the features and say these features, this contributed 20%, this contributed 50%. But as you get into things like deep learning, this concept of explainable or XAI becomes really, really important. And there were some talks earlier today at Strata about how you apply machine learning, traditional machine learning to interpret your deep learning or black box AI. So that's really going to be important, those two things, in terms of how they effect data science. >> Well, you mentioned the black box. I mean, do you think we'll ever resolve the black box challenge? Or is it really that people are just going to be comfortable that what happens inside the box, how you got to that decision is okay? >> So I'm inherently both cynical and optimistic. (chuckles) But I think there's a lot of things we looked at five years ago and we said there's no way we'll ever be able to do them that we can do today. And so while I don't know how we're going to get to be able to explain this black box as a XAI, I'm fairly confident that in five years, this won't even be a conversation anymore. >> Yeah, I kind of agree. I mean, somebody said to me the other day, well, it's really hard to explain how you know it's a dog. >> Seth: Right (chuckles). But you know it's a dog. >> But you know it's a dog. And so, we'll get over this. >> Yeah. >> I love that you just brought up dogs as we're ending. That's my favorite thing in the world, thank you. Yes, you knew that. Well, Seth, I wish we had more time, and thanks so much for stopping by theCUBE and sharing some of your insights. Look forward to the next update in the next few months from you. >> Yeah, thanks for having me. Good seeing you again. >> Pleasure. >> Nice meeting you. >> Likewise. We want to thank you for watching theCUBE live from our event Big Data SV down the street from the Strata Data Conference. I'm Lisa Martin, for Dave Vellante. Thanks for watching, stick around, we'll be rick back after a short break.