>> Live from Midtown Manhattan, it's the CUBE. Covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Fifth year of coverage of our own event Big Data NYC where we cover all the action in New York City. For this week in big data, in conjunction with Strata Data which was originally Hadoop World in 2010. We've been covering it for eight years. It became Strata Conference, Strata Hadoop, now called Strata Data. Will probably called Strata AI tomorrow. Who knows, but certainly the trends are going in that direction. I'm John Furrier, your co-host. Our next guest here in New York City is Sergei Rabotai, who is the Head of Business Development at InData Labs from Belarus. In town, doing some biz dev in the big data ecosystem. Welcome to theCUBE. >> Yeah. Good morning. >> Great to have you. So, obviously Belarus is becoming known as the Silicon Valley of Eastern Europe. A lot of great talent. We're seeing that really explode. A lot of great stuff going on globally, even though there's a lot of stuff, you know GDPR and all these other things happening. It's clearly a global economy with tech. Silicon Valley still is magical. I live there in Palo Alto but you're starting to see peering points within these ecosystems of entrepreneurship and now big companies are taking advantage of it as well. What do you guys do? I mean you're in the middle of that. What is InData Labs do in context of all this? >> Well, InData Labs is a full stack data science company. Which means that we provide professional services for data strategy, big data engineering and the data science. So, yeah, like you just said, we are based - my team is based in Minsk, Belarus. We are about 40 people strong at the moment. And in our recent years we have been very successful starting this business and we have been getting customers from all over the world, including United States, Great Britain, and European Union. The company was launched about four years ago and very important thing, that it was launched by two tech leaders who come from very data-driven industries. Our CEO, Ilya Kirillov, has been running several EdTech companies for many years. Our second founder, Marat Karpeko, has been holding C-Level positions in one of the most successful gaming companies in the world. >> John: So they know data. They're data guys. >> Yeah they're data guys. They know data from different aspects and that brings synergy to our business. >> You guys bring that expertise now into professional services for us. Give me an example of some of the things someone might want to call you up on, because the thing we're hearing here in New York City this week is look, we need more data sciences and they got to be more productive. They're spending way too much time wrangling and doing stuff that they shouldn't be doing. In the old days, sysadmins were built to let people be productive and they ran the infrastructure. That's not what data scientists should be doing. They're the users. There's a level of setting things up and then there's a level of provisioning, it's actually data assets, but then the data scientists just want to do their job. How do you help companies do that? >> Well I would probably, if I take all of our activities, I would split them into two big parts. First of all, we are helping big companies, who already have a lot of data. We help them in managing this data more effectively. We help them with predictive analytics. We help them with, helping them build the churn prediction and user segmentation solutions. We have been recently involved into several natural language processing projects. In one of our successful key studies we helped one of the largest gaming companies to automate their customer feedback processing. So, like, a couple years ago they were working manually with their customer feedback and we built them a tool that allows them to instantly get the sentiment of what the user says. It's kind of like a voice of a customer, which means they can be more effective in developing new things for their games. So, we-- >> So what would someone engage? I'm just trying to peg a order of magnitude of the levels of engagements you do. Startups come in? Is it big companies? What kind of size scoped work do you do? >> So I would say at the moment we work with startups, but it's a bit of a different approach than we have with big or well-established companies. When startups typically approach us with asking to help them implement some brand new technologies like neural networks or deep learning. So they want to be effective from the start. They want to use the cutting edge technology to be more attractive, to provide a better value on the market and just to be effective and to be a successful business from the start. The other part, the well-established companies, who already have the data but they understand that so far their data might not be used that effectively as it should have been used. Therefore, they approach us with a request to help them to get more insights out of the data. Let's say, implement some machine learning that can help them. >> How about larger companies? What kind of projects do you work for them? >> It could be a typical project like churn prediction, that is very actual for the companies who have got a lot of customer data. Then it could be companies from such industries like betting industry, where churn is a very big issue. And, the same probably applies to companies who do trading. >> So is scale one of the things you differentiate around? It sounds like your founders have an EdTech background obviously must be a larger, large data set. Is your profile of engagements large scale? Is it ... I'm just trying to get a handle of if someone's watching who, what is the kind of engagements people should be calling you for? Give us an example of that. >> Like, let's say there is a company who has got a lot of customer data, has got some products and they have a problem of churn, or they have a problem of segmenting their customers so they can later address the specific segments of the customers with the right offers at the right time and through the right marketing channel. Then it could be customers or requests where natural text processing is required where we have to automate some understanding of the written or spoken text. Then I should say that we have been getting recently some requests where computer vision skills are required. I think the first stage of AI being really intelligent was the speech recognition and I think nowadays we manage to reach to the level of what we earlier saw in fantastic movies or sci-fi movies. Computer vision is going to be the next leap in all that AI buzz we're having at the moment. >> So you solve, the problem that you solve for customers is data problems. If they're swimming in a lot of data, you can help them. >> Sergei: Yep. >> If they actually want to make that data do things that are cutting edge, you guys can help them. >> Sergei: Yeah. That's-- >> Alright, so here's a question for you. I mean, Belarus has obviously got good things going on. I've heard the press that you guys have been getting, the whole area, and you guys in particular. So I'm a buyer, one of the questions I might ask is "Hey Sergei, how do I know that you'll keep that talent because the churn is always a big problem. I've dealt with outsourcing before and in the US it's hard to keep talent but I've heard there's a churn." How do you guys keep the talent in the country? How do you keep talent on the projects? Is there certain economic rules over there? What's happening in Belarus? Give us the economical. >> Yeah, so, basically what you're saying. The churn problem has always been known for companies who have their development teams in Asian regions. That's a known problem because I have a lot of meetings with clients in the UK and the US, potential prospects, I would say. So they say it is a problem for them. With Belarus, I don't think we have that because from what I know, we have an average churn of under 10 percent. That's the figures across the industry. In smaller companies, the churn is even less and there are specific reasons for that. First of all, that due to Belarusian mentality, we always try to keep to a job that we're having. Yeah? So we do not-- >> John: That's a cultural thing. >> That's just the cultural thing. We do not ... >> You honor, you honor a code, if you will. >> Yeah. >> Okay. >> So, that's one of the things. Another thing is that Belarusian IT industry is very small. We have, I would say, no more than 40 thousand people being involved in different IT companies. The community is very small, so if somebody is hopping jobs from one job to another, it is going to be known and this person is not likely to have like, a good career. >> So job hoppers is kind of like a code of community, honor. Silicon Valley works that way too, by the way. >> Yeah. >> You get identified, that's who you are. >> Yeah. And so nowadays-- >> Economic tax breaks going on over there? What's the government to get involved? >> One of the key things is, the special tax and legal regulations that Belarus has got at the moment. I can definitely say that there is no country in the world that has got the same tax preferences, and the same support from the government. If a Belarusian company, IT company, becomes a part of Belarusian High Tech Park it means the company becomes automatically exempt from BET tax, corporate income tax. The employees of that company having the reliefs on their income, personal income tax rate, and there are a lot more reliefs that make the talent stay in the country. Having this relief for the IT business allows the companies to provide better working conditions for the employees and stop the people from migrating to other parts of the world. That's what we have. >> Sort of created an environment where there's not a lot of migration out of the area. The tech community kind of does it's own policing of behavior for innovation. >> Yeah but I think before those initiatives were adopted there was a certain percentage of people migrating but I think that nowadays even if it happens, yes, you're right, it's not that substantial. >> Great. Tell us ... Great overview of the company and congratulations, it's a good opportunity for folks watching to explore new areas of talent, especially ones that have the work ethic and knowledge you guys have over there. New York here, there's codes here too. Get the job done. Be on time. What's your experience like in New York here? What's your goal this week? What's some of the meetings you're having? Share with the folks kind of your game plan for Big Data NYC. >> Well, yeah, I've really enjoyed my stay here. It, so far, has been a very enjoyable experience. From the business perspective, I had over 10 meetings with the prospective customers. And we are likely to have follow-ups coming in the next couple of weeks. I can definitely say there is a great demand for professional services. You can see that if you go to whichever center you can see there's a lot of jobs being posted on the job boards. It means that there is lack of knowledge here in the US, yeah? One more important thing that I wanted to share with you from my personal observations that USA, UK and maybe Nordic countries, they have very, very strong background for creating the business ideas but Eastern Europe or Eastern European countries and Belarus in particular, they are very strong in actually implementing those ideas. >> Building them. >> Yes, building them. I think we have lots of synergies and we can ... we can ... >> John: Great. >> We can work together. I also got some meetings with our existing customers here in the US and so far we had good experiences. I can see that New York is moving fast. I travel a lot. I've been to over 40 countries in the previous five years and I just ... New York is different. >> It's fun. >> Different. Even different from many other cities in the US. >> Lot of banks are here. Lot of business in New York. New York is a great town. Love New York City. It's one of my favorites. Love coming here as I grew up right across the river in New Jersey. >> Yeah. But, great town, obviously California, Palo Alto, >> Yeah. >> Is a little more softer in terms of weather, but they have a culture there too. Sounds a lot like what's going on in Belarus, so congratulations. If we get some business for you, should we give them theCUBE discount, tell them John sent you and you get 10 percent off? Alright? >> Alright, yes. Sounds great. We can make it a good deal. (laughter) >> Tell them John sent you, you get 10% off. No I'm only kidding because it's services. Congratulations. Final question. What's the number one thing that people are buying for service from you guys? Number one thing. What's the most requested service you provide? >> The most requested services ... First of all, many customers they understand that they have got a lot of data. They want to do something with their data. But before you actually do some implementation you have to do a lot of discovery or preparatory work. I would say, no matter how we end up with a customer, this stage is basically ... The idea of that stage is to identify the ways data science can be implemented and can provide benefits to the business. That's the most important. I think that, like, 95 percent of the customers they approach us with this thing in the first place. And based on the results of that preparatory stage we can then advise the customers. What can they do? Or how they can actually benefit from the existing data? Or what other things they should collect in order to make their business more effective. >> Sergei, thanks for coming on. Belarus has got a lot of builders there. Check 'em out. >> Thanks a lot. >> Builders are critical in this new world. Lots of them with clout, a lot of great opportunities. A lot of builders in Belarus. This is theCUBE, bringing you all the action from New York City. More after this short break. We'll be right back. (theme music) (no audio) >> Hi, I'm John Furrier, the co-founder of SiliconANGLE Media and co-host of theCUBE. I've been in the tech ...

>>From Miami beach, Florida. It's the queue covering a cryonics global cyber summit 2019 brought to you by Acronis. >>Okay. Welcome back everyone. This is the cubes coverage here at the Chronis global cyber summit 2019 I'm John furrier, host to the cube. We're Miami beach at the Fontainebleau hotel with a second day. Excited to have this next guest on Christian Garcia, senior vice president of finance and administration at the chauffeur housing ShipIt housing Institute of technology. Did they get it right? Almost right. housing. welcome back. Welcome to the cube. Good to see you. Good to see you. Thanks for having me here. This is a really cool story because you guys are doing something very entrepreneurial, right, with education, right. Okay. Inspired by the founder of a Chronis. Exactly as well. He's got. He's made a lot of money in his day, so he's doing some good things with it. Um, but this is an interesting opportunity for you to take a minute to explain what this Institute stands for. >>It's sit for short. >> Yeah, so sat actually as a name Schaffhausen Institute of technology. So we are actually starting up a university in Schaffhausen in Schaffhausen. These a beautiful tiny CD in Switzerland, 30 minutes or 30 minutes from the Zurich airport, which is the biggest airport in Switzerland, uh, close to Germany at the border with Germany. And uh, so that's kind of your, in the center of Europe and that's where we plan to have our main campus. Now let me tell you this story. How about the vision about target, his vision on these, on this project? Um, he, he said that, you know, uh, he needs to have skills in 10 to 15 years time that nowadays at the institutions that do not do not, do not bring, um, there is the need of computer scientists that are not enough computer scientists and we are having emergent technologies and these is something that provides us with tremendous opportunities, which we cannot even imagine nowadays what type of opportunities and to be on the forefront there. >>That's why we want to found these are, we have founded the Schaffhausen Institute of technology. >> Chef housing is a technology just for share. The day was just two months ago, couple months ago. It was two months ago where we, where we have started up the legal structure and now we are really laying the foundation. We have to find some that are kind of secured for for the next 12 to 18 months. And um, we are, you know, defining the strategic advisory board. We are setting up the curriculum for our students. And so it's everything up and running and to be defined. So risk is right at the creation present at creation. We are talking about this as a, this is the origination story. Exactly. Of the shelf house in Institute of technology. Exactly. What's the vision? >>I mean obviously getting skills for jobs that are our century, our time that's having been teaching in universities and before I get back. But is it about being open and what's the vision is just Switzerland is going to be global. Can you just share, what do you guys are thinking? >>Sure, absolutely. So basically what we are trying to do is to design a curriculum in um, computer science and physics because we think that computer science or present the software in physics represents the hardware. And these two things need to be combined in a entrepreneurial mindset or with an entrepreneurial mindset, which means that we also want to foster the transformation process and the anti entrepreneurship. Now, let me go back to the software path. Uh, our curriculum will cover, um, software engineering, cybersecurity. That's why we are here today. Uh, the curriculum we also cover, um, on the physics part. On the hardware part, we'll cover, uh, quantum technologies, uh, quantum physics and also new materials. Um, and these will be kind of the foundation that will build the curriculum for students, computer scientists to have physics and physics to have computer science in their curriculum so that at some point in time they can come together and to research together. >>This is the digital transformation that we're talking about. The, the intersection and the confluence of physical reality. A world that we live in, whether it's a baseball game or a soccer match to the digital culture, they're not mutually exclusive anymore and they're together. And then the impact is profound. I can only imagine. IOT, industrial, IOT, airplanes, cars, electricity, electronic batteries, all these things, correct. It's software and digital. And physical material. Exactly that you guys are thinking. >>Exactly. Exactly that and actually also considering the industry, talking to the industry, talking to chief information technology officers around the world to understand what they need are and what type of they believe of skills are needed in in 10 to 15 years time. And that's what we want to build up now to get >>well you guys car gotta go, you gotta go faster because there's jobs now. There's thousands of jobs right now in cybersecurity. There's thousands and thousands of jobs for provision and cloud computing. Amazon educate. We talked to them all the time. They just can't get the word out fast enough that Hey, if you're unemployed there's no excuse for being unemployed. Write down there's so many new jobs. But because someone didn't go to the linear school and exactly know go step by step over the years and now you can level up very quickly. Exactly for certification. But you guys are taking a much more bigger idea around real kind of masters level. Is that what it is? Undergraduate masters level? What's the level of, actually we, we, we are starting >>out with this university and we have already students that are at our or with our partner universities currently in Singapore with NUS. And we then move to Karnak and Molly here in the U S um, in order to have it, we'll do a degree. So that's a unique opportunity to already start up with some presence, uh, in, in education. And uh, you ultimately, they will be then acquired. So we hope by, by, by, by the industry and the were terrific. Elon Musk is in there somewhere innovating with who knows what's next out there and he's around. And next Sergei is out there too. A exactly. Exactly. So just look at our, at our home page, look at the curriculum, which we are currently defining now. Eh, that would be, that would be great on sit.org take me through how it works. I know you're just starting, but as you guys look at the world, I mean, first of all, I can see, I can see the attractiveness of a dual degree. >>Yeah. Because most kids get bored in college. They're freelancing anyway. They're learning on their own. I get that. But I can S so I want, so as you guys start building it out, what's going on? What's going, how's it work? What are you guys doing? You're recruiting tickets through the, the factory of work that needs to get done, if you will. What's the workflows look like? What's happening right now? So currently, I mean, we are talking about the university because we, we have students and we will have students and we weren't to have the best talents, uh, globally available. And that's why we are building institution that attracts those talents. And these is kind of the first priority to have, do I have the talents to get the tens to get students come to, to, to sit? And obviously the second part is he said, well, talking to the CEOs and Tito was in to understand what are the needs in 10 to 15 years as an outcome of this digital transformation. >>I mean, the world is computerized. Uh, as you just mentioned before, there are not enough computer scientists currently available. So four out of five companies in Switzerland direction also globally are lacking. Uh, of computer scientists and they understand, you know, at what the digital transformation means. And that's something that we really try to understand as well to build it up the curriculum. What's the timeline of starting with students? Is you right away? Do you have a location? Is there a building, I mean, give us a timeline. When did classes start? When you start bringing people in? Is it happening now? I mean, absolutely. So, so actually currently we are, we are hunting at, at uh, at some campus locations, looking at some campus locations, each a thousand where our main campus will be, will be located. Um, at the, at the, at the same time we are really building buildings structure. >>So we are appointing the strategic advisory board will be, we twill direct, eh, the curriculum of the university. Um, and, and which is represented already by, uh, very, um, great scientists. One of them, the president of the strategic advisory board being professor Dr. Noble selloff, which is a Nobel prize winner. And which actually brings in that, that new ma new material, um, science in our physics curriculum. So that's another thing that we are currently trying to do to build up that governance appropriate components. And third element that we are looking at is also to attract uh, industries and companies that sponsor the students. And that's actually an attractive ecosystem that we are trying to build up to combine science education and also entrepreneurship in business. In order to foster that, which means that we are looking at the campus, we are setting up a research center and I'm talking about two or three years down the line, the research center and then also a tech park where we can commercialize the innovation that the science green Springs in. >>So all in all we really aim to have a closed ecosystem and self sustaining ecosystem. Hopefully that we are going to establish. It's a really big idea. Congratulations. It's bold. It's and it's relevant. Absolutely. So I got to ask you the question, how do you finance all this? Who's paying for it? So tell us how do we get funded? It's very important. Otherwise we pull in, start up with such a tremendous pace. Uh, actually the vision is, is from Sergei Velo self, uh, founder and CEO of Acronis. Um, he, he's, Hey has actually secured the initial founding of the institution and now really we need to have more partners on board in order to make this self sustaining education edge educational system system as sustainable as you are going to be tuition base or scholarship based. Have you guys thought about that? Um, in terms of students it would be tuition-based ah, that's a classical classical model or at least at least in Switzerland and obviously to get the industry sponsoring students in order to also down the line employee them later on. >>That would be the idea situation. Nice vision for Sergei and nice gesture. But you've got to look at what his business is doing. They created a category called cyber protection. Extending the benefit to him is more candidates know physics edge. So why not? This is a great vision. Absolutely the win-win. Absolutely. And we all believe in that the entire, um, you know, stand up team believe in that vision. That's where we are here and building up this institution. Well when you need to go global will be in Silicon Valley and waiting for you guys to come there and collaborate with us there. I hope. I hope that because we want to compliment each other. As I mentioned, computer scientists, our need is globally and obviously also in the Silicon Valley and why not? I think the collaboration aspect is going to be a big part of the growth as you guys get >>settled in on the the first use case in Shevon housing. Exactly. You know, and get that built out, but I think with digital technologies, I think there'll be a great collaboration, bring some good talent in as faculty and advisors and exactly get the flywheel going except congratulations. Thanks for coming on. The key, the education game is changing with modernization of a global impact of technology for good. You're seeing the landscape of innovation hit education. This is another great example of it. Super proud. The interview. Thanks for coming on and sharing the insights. The world continues to evolve. Of course, the cube is, they're watching every turn. I'm John Feria here in Miami beach for the Crohn's global cyber summit. 2019 deck with more coverage after this short break.

>>from Miami Beach, Florida It's the Q covering a Cronus Global Cyber Summit 2019. Brought to you by a Cronus. >>Welcome back with Cubes Coverage here for two days at the Cronus Global Cyber Sum of 2019. I'm John Courier, Post keeper in Miami Beach at the Fontainebleau Hotel, and I am here with the CEO and chairman of Cronus SP Sergei, known as SP. >>Yeah, joining that, that's fine. It's fine. >>So your inaugural event of the Global Cyber Summit What you're what you're feeling so far like it's >>very good to have exceeded the expectations. In terms of a dangerous with high quality audience. Everything is organized quite well. It's our first event of a kind. It's a first marks a transformation of the company from being data protection company to be decided protection company from the application company to be a platform. >>Talk about the vision and we're how you got here. Because again, the market's changing cloud computing, Internet of things, more threats than ever before data seems to be at the center of all this. >>Don't think about the team in terms of data, will look at 18 terms of foreclose, so workload could be day to put the application for the system. We also look at the team, not from the standpoint large passed him a small fast mark, but from the standpoint off and point, like your computer right here on the table or a mobile device from step with authority, which is a large that the center of a gentle price. Or it is a cloud like Amazon, like Google, like Microsoft. And from the standpoint of something in the middle, which we call EJ, and it's growing very rapidly, that's a small data center. That more door is that a small office that's also specialized vacations, like practically my hospital, like a railway station like restaurants. Like any retail location where you actually have specialized computers. Detective Lee servers running the infrastructure, for example. Every Starbucks location is actually 12 and those computers edge and then point. Need protection, need complete protection. And our mission is to provide a complete protection from the standpoint of safety, accessibility, privacy, authenticity of security that something which will go for any of us. >>You know, I think your divisions right on. In fact, when you think about data protection, my observation is it was because of disruption and operations. Somehow an event happened. Hurricane flood the operation of destructive. They gotta roll back and get the snapshots and bring it back. But security is now causing a disruption. I think you guys are honing in on with disruptions coming from a security vector way. Official mechanisms have to change a little bit. That seems a bit your success here with. >>I think we look at this holistically way, don't see really different, so safe its accessibility, privacy of authenticity and security. A love. This vectors are a problem, you know, perhaps authenticity. He's not yet visible as march, and privacy is new, So privacy is not the bad guys. You know, it's a good guys, guys. It's maybe yours. Employees. Maybe your partner. Or maybe maybe it's your customer. One. You don't want to see the information about somebody else and so alone. This is a threat, and you really don't want your infrastructure to be damage to your business to yourself. Unintentional damage. If you want to break something, you better break it, wasting your decision and you better be able to roll back so you know it comes from data protection, but it goes to security and privacy and authenticity. All of this together is important for defensive your idea. Infrastructure is functional and old times controlled by you. >>In your opinion, has ransomware provided a wake up call to I t around this area? Because that seems to be a theme. A lot with Ransomware. People realize that they're stuck highlights >>a problem somewhere is an interesting trend. I wouldn't really be happy about Ransomware around somewhere is a scene. So we help people to be protected against run somewhere. But that doesn't mean we like Ransomware. So yeah, >>extortion. Not really. Well, like, yeah, you're the one being extorted. >>Nice. But it's one of the wake up calls in reality again. It comes from all the directions. I think Ransomware is just very, very easy to understand. >>People can see and understand it. Explain You mentioned s a P A s. What does that explain that acronym? What does it mean? What's the vision behind >>Sabba says is safe Accessibility, privacy else intensity and security combined in a single product. That's what it means. It means that you know, don't lose in using everything is accessible at all times with the right people have access and you can control the access. Nothing is mortified in such ways that you don't know it was modified and no bad guys can break into your tea or into your date or NT applications >>you mentioned. The platform platforms are well known concept and computer science and certainly the Internet. You've seen great successes with platforms, enable something. How would you describe the enablement that comes from Cronus platform, Cyber platform. >>I think it comes back to what you start at the waist. There is a lot of new friends and part of this new Frances. The world for a while maybe 20 years ago looked like the world which is consolidating. And you can one vendor which provide solutions to watch majority of problems. Which was Michael, right? So you remember 1999. It looked like pretty much everybody is gonna use windows. Mark is not going to be there. Microsoft was making some inroads in Mobile was in C and so on and so forth. Well, now the water is consolidating. You have thousands of different types of workers. You have different systems. You have different applications. You have different cloud applications. You need to protect them in a very different way. That's another thing you need to integrate a lot about. You cannot do it all. So we opened our applications and our black from certain parties. Was event like this toe actually build on top of the platform to provide the functionality, which we don't >>You say that word system a few times, and I think this is interesting platform validation systems Thinking is like an operating system. It's a lot of consequences and systems The old system that seems that systems thinking is back in in the front lines of I t and technology because you got a cloud you got on premises, you got I ot way networks. It's a system, and so realistically thinking about it's interesting. Do you think people are getting their are you get the right thing to do? I think like a system >>wear simple people in a Cronus. We look at the world and we don't see anything but data by zeros and ones way don't look it everywhere, and I don't see anything but more clothes and these workers they could be in the cloud that could be on prayer. Music would be a partner location. It could be on your mobile device. It could be the whole device apart with. And we also see the world in terms of partners. And from our point of view, you know, it's it's was that people realize that, you know, people have idea needs to work on their partners to help him. So if I did, that work can do, innit? They cannot call their friends. They can communicate is a relative word possible head of the world. And so what we provide is a protection to make sure that it works a full time, no matter what is a possible challenge. >>That's me. Thank you for taking the time to answer some questions. I want to get one final question to you. News today Opening AP Eyes up Trading Developer network and a portal New New things. What's your message to the folks that want developing on your platform? What's the guiding principles with what's the simple value proposition of why I'm a developer? I wouldn't want to work on The Cronus is Global Platform >>so way might look relatively small. We're only 1.5000 people and we're only several $100 million. They were growing very rapidly. We have 6000 partners who can sell your products, and this number is going. Read it after you have 30,007 years. And so you have also a lot of data on the management. Five exabytes of data on the management and this amount of it is growing very rapidly. If you build applications for protection of this data, this number of workloads, this number off partners to sell it, you can sell your products successfully. Ultimately, for developers, it's It's about doing something which makes money and doing something which makes sense. And with our partner network, with our workload and they reach, they get to make sense and they get to make money. >>And it's a hot area. Cyber protection of a new Category Emerging out of the old data protection If you had to describe someone, the old waivers of the New way data protection the old way. Cyber Protection New Way. What's the difference between the two? >>Well, the difference is that includes security, privacy management, know sadistic management in one package. The difference is that it's designed to work in the world which is in parenting secure. It designed to work in the world where if you connect a network, you don't trust this network. And so if you have a cyber protection application cyber protection car where it has to be protected itself, that's >>thank you. Come on. Cue and taking the time out of your busy schedule to talk to us. Thank you. Very welcome. Appreciate it to give coverage here in Miami Beach across Global Cyber 7 2019 I'm John. Four year. Thanks for watching two days of coverage here. Be right back.

>> And we're back, this is George Gilbert from Wikibon, and I'm here with Aman Naimat at Demandbase, the pioneers in the next gen AI generation of CRM. So Aman, let's continue where we left off. So we're talking about natural language processing, and I think most people are familiar with it more on the B to C technology, where the big internet providers have sort of accumulated a lot of voice data and have learned how to process it and convert it into text. So tell us how B to B NLP is different, to use a lot of acronyms. In other words, how you're using it to build up a map of relationships between businesses. >> Right, yeah, we call it the demand graph. So it's an interesting question, because firstly, it turns out that, while very different, B to B is also, the language is quite boring. It doesn't evolve as fast as consumer concepts. And so it makes the problem much more approachable from a language understanding point of view. So natural language processing or natural language understanding is all about how machines can understand and store and take action on language. So while we were working on this four or five years ago, and that's my background as well, it turned out the problem was simpler, because human language is very rich, and natural language processing converting voice to text is trivial compared to understanding meaning of things and words, which is much more difficult. Or even the sense of the word, apparently in English each word has six meanings, right? We call them word senses. So the problem was only simpler because B to B language doesn't tend to evolve as fast as regular language, because terms stick in an industry. The challenge with B to B and why it was different is that each industry or sub-industry has a very specific language and jargon and acronyms. So to really understand that industry, you need to come from that industry. So if you go back to the CRM example of what happened 10, 20 years ago, you would have a sales person that would come from that industry if you wanted to sell into it. And that still happens in some traditional companies, right? So the idea was to be able to replicate the knowledge that they would have as if they came from that industry. So it's the language, the vocabularies, and then ultimately have a way of storing and taking action on it. It's very analogous to what Google had done with Knowledge Graph. >> Alright, so two questions I guess. First is, it sounds almost like a translation problem, in the sense that you have some base language primitives, like partner, supplier, competitor, customer. But that the language in each industry is different, and so you have to map those down to those sort of primitives. So tell us the process. You don't have on staff people who translate from every industry. >> I mean that was the whole, writing logical rules or expressions for language, which use conventional good old fashioned AI. >> You mean this was the rules-based knowledge engineering? >> That's right. And that clearly did not succeed, because it is impossible to do it. >> The old quip which was, one researcher said, "Every time I fired a rules engineer, "my accuracy score would go up." (chuckles) >> That's right, and now the problem is because language is evolving, and the context is so different. So even pharmaceutical companies in the US or in the Bay Area would use different language than pharma in Europe or in Switzerland. And so it's just impossible to be able to quantify the variations. >> George: To do it manually. >> To do it manually, it's impossible. It's certainly not possible for a small startup. And we did try having it be generated. In the early days we used to have crowdsource workers validate the machine. But it turned out that they couldn't do it either, because they didn't understand the pharmaceutical language either, right? So in the end, the only way to do that was to have some sort of model and some seed data to be able to validate it, or to hire experts and to have small samples of data to validate. So going back to the graph, right, it turns out that when we have seen sophisticated AI work, you know, towards complex problems, so for example predicting your next connection on LinkedIn, or your next friend, or what ads should you see on Facebook, they have used network-based data, social graph data, or in the case of Google, it's the Knowledge Graph, of how things are connected. And somehow machine learning and AI systems based on network data tend to be more powerful and more intuitive than other types of models. >> So OK, when you say model, help us with an example of, you're representing a business and who it's connected to and its place in the world. >> So the demand graph is basically as Demandbase, who are our customers, who are their partners, who are their suppliers, who are their competitors. And utilizing that network of companies in a manner that we have network of friends on LinkedIn or Facebook. And it turns out that businesses are extremely social in nature. In fact, we found out that the connections between companies have more signal, and are more predictive of acquisition or predicting the next customer, than even the Facebook social graph. So it's much easier to utilize the business graph, the B to B business graph, to predict the next customer, than to say, predict your next friend on Facebook. >> OK, so that's a perfect analogy. So tell us about the raw material you churn through on the web, and then how you learn what that terminology might be. You've boot-strapped a little bit, now you have all this data, and you have to make sense out of new terms, and then you build this graph of who this business is related to. >> That's right, and the hardest part is to be able to handle rumors and to be able to handle jokes, like, "Isn't it time for Microsoft to just buy Salesforce?" Question mark, smiley face. You know, so it's a challenging problem. But we were lucky that business language and business press is definitely more boring than, you know, people talking about movies. >> George: Or Reddit. >> Or Reddit, right. So the way we work is we process the entire business internet, or the entire internet. And initially we used to crawl it ourselves, but soon realized that Common Crawl, which is an open source foundation that has crawled the internet and put at least a large chunk of it, and that really enabled us to stop the crawling. And we read the entire internet and look at, ultimately we're interested in businesses, 'cause that's the world we are, in business, B to B marketing and B to B sales. We look at wherever there's a company mentioned or a business person or business title mentioned, and then ignore everything else. 'Cause if it doesn't have a company or a business person, we don't care. Right, so, or a business product. So we read the entire internet, and try to then infer that this is, Amazon is mentioned in it, then we figure out, is it Amazon the company, or is it Amazon the river? So that's problem number one. So we call it the entity linking problem. And then we try to understand and piece together the various expressions of relationships between companies expressed in text. It could be a press release, it could be a competitive analysis, it could be announcement of a new product. It could be a supply chain relationship. It could be a rumor. And then it also turns out the internet's very noisy, so we look at corroboration across multiple disparate sources-- >> George: Interesting, to decide-- >> Is it true? >> George: To signal is it real. >> Right, yeah, 'cause there's a lot of fake news out there. (George laughs) So we look at corroboration and the sources to be able to infer if we can have confidence in this. >> I can imagine this could be applied to-- >> A lot of other problems. >> Political issues. So OK, you've got all these sources, give us some specific examples of feeds, of sources, and then help us understand. 'Cause I don't think we've heard a lot about the notion of boot-strapping, and it sounds like you're generalizing, which is not something that most of us are familiar with who have a surface-level familiarity with machine learning. >> I think there was a lot of research like, not to credit Google too much, but... Boot-strapping methods were used by Sergei I think was the first person, and then he gave up 'cause they founded Google and they moved on. And since then in 2003, 2004, there was a lot of research around this topic. You know, and it's in the genre of unsupervised machine learning models. And in the real world, because there's less labeled data, we tend to find that to be an extremely effective method, to learn language and obviously now with deep learning, it's also being utilized more, unsupervised methods. But the idea is really to, and this was around five years ago when we started building this graph, and I obviously don't know how the Google Knowledge Graph is built, but I can assume it's a similar technique. We don't tend to talk about how commercial products work that much. But the idea is basically to generalize models or learn from a small seed, so let's say I put in seed like Nike and Adidas, and say they compete, right? And then if you look at the entire internet and look at all the expressions of how Nike and Adidas are expressed together in language, it could be, you know, "I think "Nike shoes are better than Adidas." >> Ah, so it's not just that you find an opinion that they're better than, but you find all the expressions that explain that they're different and they're competition. >> That's right. But we also find cases where somebody's saying, "I bought Nike and Adidas," or, "Nike and Adidas shoes are sold here." So we have to be able to be smart enough to discern when it's something else and not competition. >> OK, so you've told us how this graph gets built out. So the suppliers, the partners, the customers, the competitors, now you've got this foundation-- >> And people and products as well. >> OK, people, products. You've got this really rich foundation. Now you build and application on top of it. Tell us about CRM with that foundation. >> Yeah, I mean we have the demand graph, in which we tie in also things around basic data that you could find from graphics and intent that we've also built. But it also turns out that the knowledge graph itself, our initial intuition was that we'll just expose this to end users, and they'll be able to figure it out. But it was just too complicated. It really needed another level of machinery and AI on top to take advantage of the graph, and to be able to build prescriptive actions. And action could be, or to solve a business problem. A problem could be, I'm an IOT startup, I'm looking for manufacturing companies who will buy my product. Or it could be, I am a venture capital firm, I want to understand what other venture capital firms are investing in. Or, hey, I'm Tesla, and I'm looking for a new supplier for the new Tesla screen. Or you know, things of that nature. So then we apply and build specific models, more machine learning, or layers of machine learning, to then solve specific business problems. Like the reinforcement learning to understand next best action. >> And are these models associated with one of your customers? >> No, they're general purpose, they're packaged applications. >> OK, tell us more, so what was the base level technology that you started with in terms of the being able to manage a customer conversation, a marketing conversation, and then how did that get richer over time? >> Yeah, I mean we take our proprietary data sets that we've accumulated over the years and manufactured over the years, and then co-mingle with customer data, which we keep private, 'cause they own the data. And the technology is generic, but you're right, the model being generated by the machine is specific to every customer. So obviously the next best action model for a pharmaceutical company is based on doctors visiting, and is this person an oncologist, or what they're researching online. And that model is very different than a model for Demandbase for example, or Salesforce. >> Is it that the algorithm's different, or it's trained on different data? >> It's trained on different data. It's the same code, I mean we only have 20, 30 data scientists, so we're obviously not going to build custom code for... So the idea is it's the same model, but the same meta model is trained on different data. So public data, but also customers' private data. >> And how much does the customer, let's say your customer's Tesla, how much of it is them running some of their data through this boot-strapping process, versus how much of it is, your model is set up and it just automatically once you've boot-strapped it, it automatically starts learning from the interactions with the Tesla, with Tesla itself from all the different partners and customers? >> Right, I think you know, we have found, most startups are just learning over small data sets, which are customer-centric. What we have found is real magic happens when you take private data and combine it with large amounts of public data. So at Demandbase, we have massive amounts of public and proprietary data. And then we plug in, and we have to tell you that our client is Tesla, so it understands the localized graph, and knows the Tesla ecosystem, and that's based on public data sets and our proprietary data. Then we also bring in your private slice whenever possible. >> George: Private...? >> Slice of data. So we have code that can plug into your web site, and then start understanding interactions that your customers are having. And then based on that, we're able to train our models. As much as possible, we try to automate the data capture process, so in essence using a sensor or using a pixel on your web site, and then we take that private stream of data and include it in our graph and merge it in, and that's where we find... Our data by itself is not as powerful as our data mixed with your private data. >> So I guess one way to think about it would be, there's a skeletal graph, and that may be sounding too minimalistic, there's a graph. But let's say you take Tesla as the example, you tell them what data you need from them, and that trains the meta models, and then it fleshes out the graph of the Tesla ecosystem. >> Right, whatever data we couldn't get or infer, from the outside. And we have a lot of proprietary data, where we see online traffic, business traffic, what people are reading, who's interested in what, for hundreds of millions of people. We have developed that technology. So we know a lot without actually getting people's private slice. But you know, whenever possible, we want the maximum impact. >> So... >> It's actually simple, and let's divorce the words graphs for a second. It's really about, let's say that I know you, right, and there's some information you can tell me about you. But imagine if I google your name, and I read every document about you, every video you have produced, every blog you have written, then I have the best of both knowledge, right, your private data from maybe your social graph on Facebook, and then your public data. And then if I knew, you know... If I partnered with Forbes and they told me you logged in and read something on Forbes, then they'll get me that data, so now I really have a deep understanding of what you're interested in, who you are, what's your language, you know, what are you interested in. It's that, sort of simplified, but similar, at a much larger scale. >> Alright, let's take a pause at this point and then we'll come back with part three. >> Excellent.