(bright upbeat music) >> Hello, everyone, and welcome to this #CUBEConversation here in Palo Alto, California. I'm John Furrier, host of "theCUBE." We have the co-founder and CTO of Armis here, Nadir Izrael. Thanks for coming on. Appreciate it. Armis is hot company, RSA, we just happened. Last week, a lot of action going on. Thanks for coming on. >> Thank you for having me. Sure. >> I love CTOs and co-founders. One, you have the entrepreneurial DNA, also technical in a space with cyber security, that is the hottest most important area. It's always been important, but now more than ever, as the service areas are everywhere, tons of attacks, global threats. You got national security at every level, and you got personal liberties for privacy, and other things going on for average citizens. So, important topic. Talk about Armis? Why did you guys start this company? What was the motivation? Give a quick commercial what you guys do, and then we'll get into some of the questions around, who you guys are targeting. >> Sure, so yeah, I couldn't agree more about the importance of cybersecurity, especially I think in these days. And given some of the geopolitical changes happening right now, more than ever, I would say that if we go back 6.5 years or so, when Armis was founded, we at the time talked to dozens of different CIOs, CSOs, it managers. And every single one of them told us the same thing. And this was at least to me surprising at the time. We have no idea what we have. We have no idea what the assets that are connected to our network, or our environment are. At the time, when we started Armis, we thought this was simply, let's call it the other devices. IOT, OT, all kinds of different buzzwords that were kind of flying around at the time, and really that's, what we should focus on. But with time, what we understood, it's actually a problem of scale. Organizations are growing massively. The diversity of different assets they have to deal with is incredible. And if 6.5 or 7 years ago, it was all about just growth of actual physical devices, these days it's virtual, it's containerized, it's cloud-based. It's actually quite insane. And organizations find themselves really quickly dealing with billions of assets within their environment, but no real way to see, account for them, and be able to manage them. That's what Armis is here to solve. It's here to bring back visibility and order into the mix. It's here to bring a complete map of everything within the organization, and the ability to manage different security processes on top of that. And it couldn't have come, I think at a better time for organizations, because the ability to manage these days, the attack surface of an organization, understand where are different weak spots, what way to invest in? They start and end with a complete asset map, and that's really what we're here to solve. >> As I look at your story and understand what you guys are doing, certainly, a lot of great momentum at RSA. But also digging under the hood, you guys really crack the code with on the scale side as well. And also it's lockstep with the environment. If you look at the trends that we've been covering on "theCUBE," system on chip, you're seeing a lot of Silicon action going on, on all the hyperscalers. You're starting to see, again, you mentioned IOT devices and OT, IP enabled processors. I mean, that's basically you can run multi-threaded applications on a light bulb, basically. So, you have these new things going on that are just popping in into the environment. Just people are hanging them on the network. So, anything on the network is risk and that's happening massively, so I see that. But also you guys have this contextualization capability, scope the problem statement for us? How hard is it to do this? Because you got tons of challenges. What's the scale of the problem that you guys have been solving? 'Cause it's not easy. I mean, it's not network management, not just doing auto discovery, there's a lot of secret sauce there, scope the problem? >> Okay, so first of all, just to get a measure of how difficult this is, organizations have been trying to solve this for the better part of the last two decades. I think even when the problem was way smaller, they've still been struggling with being able to do this. It's an age old problem, that for the most part, I got to say that when I describe the problem the way that I did, usually, what the reaction from clients are, "Yes, I'd love for you to solve that." "I just heard this pitch from like five other vendors and I've yet to solve this problem. So, how do you do it?" So, as I kind of scope this, it's also a measure of just basically, how do you go about solving a complex situation where, to kind of list out some of the bold claims here in what I said. Number one, it's the ability to just fingerprint and be able to understand what your assets are. Secondly, being able to do it with very dirty data, if you will. I would say, in many cases, solutions that exist today, basically tell clients, or tell the users, were as good as the data that you provide us. And because the data isn't very good, the results aren't very good. Armis aspires to do something more than that. It aspires to create a logically perfect map of your assets despite being hindered by incomplete and basically wrong data, many times. And third, the ability to infer things about the environment where no source data even exists. So, to all of that, really Armis' approach is pretty straightforward, and it relies on something that we call our collective intelligence. We basically use the power and scale of these masses to our advantage, and not just as a shortcoming. What I mean by that, is Armis today tracks overall, over 2 billion assets worldwide. That's an astounding number. And it thanks to the size of some of the organization that we work with. Armis proudly serves today, for instance, over 35 of Fortune 100. Some of those environments, let me tell you, are huge. So, what Armis basically does, is really simple. It uses thousands, tens of thousands, hundreds of thousands sometimes, of instances of the same device and same assets to basically figure out what it is. Figure out how to fingerprint it best. Figure out how to marry conflicting data sources about it and figure out what's the right host name? What's the right IP address? What are all the different details that you should know about it? And be able to basically find the most minimalist fingerprints for different attributes of an asset in a changing environment. It's something that works really, really well. It's something that we honestly, may have applied to this problem, but it's not something that we fully invented. It's been used effectively to solve other problems as well. For instance, if you think about any kind of mapping software. And I use that analogy a lot. But if you think about mapping software, I happened to work for Google in the past, and specifically on Google Map. So, I know quite a bit about how to solve similar problems. But I can tell you that you think about something like a mapping software, it takes very dirty, incomplete data from lots of different sources, and creates not a pixel perfect map, but a logically perfect map for the use cases you need it to be. And that's exactly what Armis strives to do. Build the Google Maps, if you will, of your organization, or the kind of real time map of everything, and be able to supply that or project that for different business processes. >> Yeah, I love the approach, and I love that search analogy. Discover is a big part of mapping as you know, and reasoning in there with the metadata you have and the dirty data is critical. And by the way, we love bold statements on "theCUBE," because as long as you can back 'em up, then we'll dig into that. But let's back up some of those bold claims. Okay, you have a lot of devices, you've got the collective intelligence. How do you manage the real time nature of devices changing in real time? 'Cause if you do fingerprint on it, and you got some characteristics of the assets in the map, what happens in real time? How fast are you guys managing that? What's the process for that? >> So, very quickly, I think another quick analogy I like to use, because I think it orients people around kind of how Armis operates, is imagine that Armis is kind of like a Shazam for assets. We take different attributes coming from your environment, and we match it up, that collective intelligence to figure out what that asset is. So, we recognize an asset based off of its behavioral fingerprint, or based off of different attributes, figure out what it is. Now, if you take something that recognizes tunes on the radio or anything like that, it's built pretty similarly. Once you have access to different sources. Once we see real environments that introduce new devices or new assets, Armis is immediately learning. It's immediately taking those different queues, those different attributes and learning from them. And to your point, even if something changes its behavioral fingerprint. For instance, it gets updated, a new patch rolls out, something that changes a meaningful aspect of how that asset operates, Armis sees so many environments, and so much these days that it reacts in almost real time to the introduction of these new things. A patch rolls out, it starts changing multiple devices and multiple different environments around the world, Armis is already learning and adapting this model for the new type of asset and device out there. It works very quickly, and it's part of the effectiveness of being able to operate at the scale that we do. >> Well, Nadir, you guys got a great opportunity there at Armis. And as co-founder, you must be pretty pumped, actually working hard, stay up to date, and got a great, great opportunity there. How was RSA this year? And what's your take on the landscape? Because you're kind of in this, I call the new category of lockstep with an environment. Obviously, there's no perimeter, everyone knows that. Service area is the whole internet, basically, distributed computing paradigms and understanding things like discovery and mapping data that you guys are doing. And it's a data problem as well. It's a lot of problems that you guys are solving. But the industry's got some old beggars, as I still hear endpoint protection, zero trust. I hear trust, if you're talking about supply chain, software supply chain, S bombs, you mentioned in a previous interview. You got software supply chain issues with open source, 'cause everything's open source now on infrastructure, so that's happening. How do you manage all that? I mean, is it zero trust or is it trust? 'Cause as you hear, I hear you talking about Armis, it's like, you got to have trusted components in there and you got to trust the data. So, that's not zero trust, that's trust. So, where zero trust and trust solve? What's your take on that? How do you resolve? What's your reaction to that? >> Usually, I wait for someone else to bring up the zero trust buzzword before I touch on that. So, because to your point, it's such an overused buzzword. But let me try and tackle that for a second. First of all, I think that Armis treats assets in a way as, let's call it the vessels of everything. And what I mean by that, is that at a very atomic aspect, assets are the atoms of the environment. They're the vessels of everything. They're the vessels of vulnerabilities. There's the vessels of actual attacks. Like something, some asset needs to exist for something to happen. And every aspect of trust or zero trust, or anything like that applies to basically assets. Now, to your point, Armis, ironically, or like a lot of security tools, I think it assists greatly or even manages a zero trust policy within the environment. It provides the asset intelligence into the mix of how to manage an effective zero trust policy. But in essence, you need to trust Armis, right? I mean, Armis is a critical function now within your environment. And there has to be a degree of trust, but I would say, trust but verified. And that's something that I think the security industry as a whole is evolving into quite a bit, especially post events like solar, winds, or other things that happened in recent years. Armis is a SaaS platform. And in being a SaaS platform, there is an inherent aspect of trust and risk that you take on as a security organization. I think anyone who says differently, is either lying or mistaken. I mean, there are no foolproof, a 100% systems out there. But to mitigate some of that risk, we adhere to a very strict risk in security policy on our end. What that means, is we're incredibly transparent about every aspect of our own environment. We publish to our clients our latest penetration test reports. We publish our security controls and policies. We're very transparent about the different aspects we're involve in our own environment. We give our clients access to our own internal security organization, our own CSO, to be able to provide them with all the security controls they need. And we take a very least privileged approach in how we deploy Armis within an environment. No need for extra permissions. Everything read-only unless there is an explicit reason to do else... I think differently within the environment. And something that we take very seriously, is also anything that we deploy within the environment, should be walled off, except for whatever lease privilege that we need. On top of that, I'd add one more thing that adds, I think a lot of peace of mind to our clients. We are FeRAMP ready, and soon to be certified, We work with DOD clients within the U.S kind of DOD apparatus. And I think that this gives a lot of peace of mind to our clients, even commercial clients, because they know that we need to adhere to hundreds of different security controls that are monitored and government by U.S federal agencies. And that I think gives a lot of extra security measures, a lot of knowledge that this risk is being mitigated and controlled, and governed by different agencies. >> Good stuff there. Also at RSA, you kind of saw people come back together face-to-face, which is great. A lot of kind of similar, everyone kind of knows each other in the security business, but it's getting bigger. What was the big takeaways from you for the folks watching here that didn't get to go to RSA this year? What was the most important stories that came out of RSA this year? Just generally across the industry, from your perspective that people should pay attention to? >> First of all, I think that people were just really happy to get back together. I think it was a really fun RSA. I think that people had a lot of energy and excitement, and they love just walking around. I am obviously, somewhat biased here, but I will say, I've heard from other people too, that our event there, and the formal party that was there was by far the kind of the the talk of the show. And we were fortunate to do that with Sentinel One. with Torque who are both great partners of ours, and, of course, Insight partners. I think a lot of the themes that have come up during RSA, are really around some of the things that we already talked about, visibility as a driver for business processes. The understanding of where do assets and tax surfaces, and things like that play in. But also, I think that everything was, in light of macroeconomics and geopolitics that are kind of happening in the background, that no one can really avoid that. On the one hand, if we look at macroeconomics, obviously, markets are going through quite a shake up right now. And especially, when you talk about tech, the one thing that was really, really evident though, is it's cybersecurity is, I think market-wise just faring way better than others because the demand is absolutely there. I think that no one has slowed down one bit on buying and arming themselves, I'd say, with defensive solutions for cybersecurity. And the reason, is that the threats are there. I mean, we're all very, very much aware of that. And even in situations where companies are spending less on other things, they're definitely spending on cybersecurity, because the toll on the industry is going up significantly year by year, which really ties into also the geopolitics. One of the themes that I've heard significantly, is all the buzz around different initiatives coming from both U.S federal agencies, as well as different governing bodies around anything, from things like shields up in critical infrastructure, all the way to different governance aspects of the TSA. Or even the SCC on different companies with regards to what are they doing on cyber? If some of the initiatives coming from the SCC on public companies come out the way that they are right now, cyber security companies will elevate... Well, sorry, companies in general, would actually elevate cyber security to board level discussions on a regular basis. And everyone wants to be ready to answer effectively, different questions there. And then on top of all of that, I think we're all very aware of, I think, and not to be too doom and gloom here, but the geopolitical aspect of things. It's very clear that we could be facing a very significant and very different cyber warfare aspect than anything that we've seen before in the coming months and years. I think that one of the things you could hear a lot of companies and clients talk about, is the fact that it used to be that you could say, "Look, if a nation state is out to get me, then a nation state is out to get me, and they're going to get me. And I am out to protect myself from common criminals, or cybersecurity criminals, or things like that." But it's no longer the case. I mean, you very well might be attacked by a nation state, and it's no longer something that you can afford to just say, "Yeah, we'll just deal with that if that happens." I think some of the attacks on critical infrastructure in particular have proven to us all, that this is a very, very important topic to deal with. And companies are paying a lot of attention to what can give them visibility and control over their extended attack surface, and anything in between. >> Well, we've been certainly ringing the bell for years. I've been a hawk on this for many, many years, saying we're at cyber war, well below everyone else. So, we've been pounding our fist on the table saying, it's not just a national security issue. Finally, they're waking up and kind of figuring out countermeasures. But private companies don't have their own, they should have their own militia basically. So, what's the role of government and all this? So, all this is about competency and actually understanding what's going on. So, the whole red line, lowering that red line, the adversaries have been operating onside our infrastructure for years. So, the industrial IOT side has been aware of this for years, now it's being streamed, right? So, what do we do? Is the government going to come in and help, and bring some cyber militia to companies to protect their business? I mean, if troops dropped on our shores, I'm sure the government would react, right? So, where is that red line, Nadir? Where do you see the gap being filled? Certainly, people will defend their companies, they have assets obviously. And then, you critical infrastructure on the industrial side is super important, that's the national security issue. What do we do? What's the action here? >> That is such a difficult question. Such a good question I think to tackle, I think, there are similarities and there are differences, right? On the one hand, we do and should expect the government to do more. I think it should do more in policy making. I mean, really, really work to streamline and work much faster on that. And it would do good to all of us because I think that ultimately, policy can mean that the third party vendors that we use are more secure, and in turn, our own organizations are more secure in how they operate. But also, they hold our organizations accountable. And in doing so, consumers who use different services feel safer as well because basically, companies are mandated to protect data, to protect themselves, and do everything else. On the other hand, I'd say that government's support on this is difficult. I think the better way to look at this, is imagine for a second, no troops landing on our kind of shores, if you will. But imagine instead, a situation where Americans are spread all over the world and expect the government to protect them in any country, or in any situation they're at. I think that depicts maybe a little better, how infrastructure looks like today. If you look at multinational companies, they have offices everywhere. They have assets spread out everywhere. They have people working from everywhere around the world. It's become an attack surface, that I think you said this earlier, or in a different interview as well. There's no more perimeter to speak of. There are no more borders to this virtual country, if you will. And so, on the one hand, we do expect our government to do a lot. But on the other hand, we also need to take responsibility as companies, and as vendors, and as suppliers of services, we need to take accountability and take responsibility for the assets that we deploy and put in place. And we should have a very security conscious mind in doing this. >> Yeah. >> So, I think tricky government policy aspect to tackle. I think the government should be doing more, but on the other hand, we should absolutely be pointing internally at where can we do better as companies? >> And the asset understanding the context of what's critical asset too, can impact how you protect it, defend it, and ensure it, or manage it. I mean, this is what people want. It's a data problem in flight, at rest, and in action. So, Armis, you guys are doing a great job there. Congratulations, Nadir on the venture, on your success. I love the product, love the approach. I think it scales nicely with the industry where it's going. So, especially with the intelligent edge booming, and it's just so much happening, you guys are in the middle of it. Thanks for coming on "theCUBE." Appreciate it. >> Thank you so much. As I like to say, it takes a village, and there's so many people in the company who make this happen. I'm just the one who gets to take credit for it. So, I appreciate the time today and the conversation. And thank you for having me. >> Well, we'll check in with you. You guys are right there with us, and we'll be in covering you guys pretty deeply. Thanks for coming on. Appreciate it. Okay, it's #CUBEConversation here in Palo Alto. I'm John Furrier. Thanks for watching. Clear. (bright upbeat music)

>>mhm >>Hello welcome to this cube conversation here in Palo alto California and john for with the cube we had a great conversation around the rise of the cloud and the massive opportunities and challenges around analytics data ai suggestion. Air ceo of thought spot is here with me for conversation. Great to see you. Welcome back to the cube. How are you? >>Well john it is so good to be back. I wish that we could do one of those massive set up that you have and do this face to face but zoom is not bad. >>You guys are doing very well. We have been covering you guys been covering the progress um great technology enabled business. You're on the wave of this cloud analytics you're seeing, we've seen massive changes and structural changes for the better. It's a tailwind for anyone in the cloud data business. And you also on the backdrop of all that the Covid and now the covid is looking at coming out of covid with growth strategies. People are building modern or modernizing their infrastructure and data is not just a department, it's everywhere. You guys are in the middle of this. Take us through what's the update on thought spot. What are you guys doing? What do you see the market right now? Honestly, delta variants coming coming strong but we think will be out of this soon. Where where are >>we look I think it all starts with the users like you said the consumers are demanding more and more from the business they are interacting with. You're no longer happy with being served like uh I'm gonna put you all in a bucket and then Delaware services to you. Everyone's like look look at me, I have likes and dislikes that is probably going to be different from someone that you think are similar to me. So unless you get to know me and deliver bespoke services to me, I'm gonna go somewhere else who does that And the call that the way you do that is through the data that I'm giving to you. So the worst thing you can do is to take my data and still treat me like an average and numbers and what's happening with the cloud is that it is now possible and it wasn't okay. So I grew up in India where newspapers will always have stock market summary on like one full page full of takers and prices and the way it used to work is that you wake up in the morning you look at the newspaper, I don't know if you have had the same thing and then you call your broker is based on in place of that. Can you imagine doing that now? I mean the information is at your fingertips. Hurricane IDa either is actually going to enter in Louisiana somewhere. What good is it? Yesterday morning state on this morning state if I'm trying to make a decision on whether I should pack my stuff and move away or you know finding to from home depot supply chain manager. I shouldn't figure out what should I be doing for Louisiana in the next two days, this is all about the information that's available to you. If you plan to use it and deliver better services for your consumer cloud makes it possible. >>You know, it's interesting you mentioned that the old way things were it seems so slow, then you got the 15 minute quotes, then there's now a real time. Everything has to be real time. And clearly there's two major things happening at the same time which makes exciting the business model and the competitive advantages for leaders and business to use data is critical but also on the developer side where apps are being developed if you don't have the data access, the machine learning won't work well. So as machine learning becomes really courted driving ai this modern analytics cloud product that you guys announced brings to bear kind of two major lifts the developer app modernization as well as competitive advantage for the companies that need to deploy this. So you guys have announced this modern approach analytics cloud, so to speak. What are some of the challenges that companies are having? Because you gotta, if you hit both of those you're gonna right a lot of value. What are some of the challenges for people who want to do this modern cloud? >>I think the challenge is basically all inside in the company. If you ask companies why are they failing to modernize? They will point to what's inside, it's not outside the technology is there the stack is the vendors are there, It is sometimes lack of courage at the leadership level which is a huge problem. I'll give an example. Uh, we have recently announced what we call thoughts part everywhere, which is our way of looking at how to modernize and bring the data inside that you're looking forward to where you are because Lord knows we all have enough apps on our Octa or a single sign on. The last thing you need is one more how no matter how good it is, they don't want to log into yet under their tool, whether it's thought spot or not. But the insights that you are talking about needs to be there when you need. And the difference is uh, the fundamental approach of data analytics was built on embedded model. You know what we are proposing is what we call data apps. So the difference between data apps and the typical dashboard being embedded into your analytics model is sort of like think of it. Uh newspapers telephones and the gap in between. So there is newspapers radio that is walkie talkie and telephone. They're all different and newspapers get printed and it comes to you and you read in the morning, you can talk back to it, you can drag and drop, you can change it right walkie talkies on the other hand, you know, you could have one conversation then come back to that. Whereas phone, you can have true direction conversation? They're all different if you think of embedding it is sort of like the newspaper, the information that you can't talk back. So somebody resembling something that came out monday, you're going to a board meeting on Wednesday and you look at that and make decisions. That is not enough in the new world, you just can't do that. It's not about what a lot of tools can actually answer what the real magic the real value for customers are unlocked when you ask three subsequent questions and answer them and they will come down to when you hear what you have to know. So what? Right and then what if and then the last is what next Imagine you can answer those three questions every business person every time no matter how powerful the dashboard is, they will always have the next question. What? So what? Okay the business customers are turning so what is it good, is it bad? Is it normal or the next question is like now what what do I do with it two, the ability to take all these three questions so what and what a fun. Now what? That requires true interactivity, you know, start with an intent and with an action and that is what we are actually proposing with the data apps which is only possible if you're sitting on top of a snowflake or red shift kind of really powerful and massive cloud data warehouse where the data comes and moves with agility. >>So how has this cloud data model rewritten the rules of business? Because what you're bringing up is essentially now full interactivity really getting in, getting questions that are iterating and building on context to each other. But with all this massive cloud data, people are really excited by this. How is it changing business than the rules of business? >>Yeah. So think about, I mean topical things like there is a hurricane able to enter, hit the cost of the United States. It's a moving target. No one knows exactly where it is going to be. There is only 15 models from here. 10, 10 models from Europe that's going to predict which way it's going to take every millimeter change in that map is going to have significant consequences for lives and resources and money. Right. This is true for every business. What cloud does this? Uh you have your proprietary data for example, let's say you're a bank and you have proprietary data, you're launching a new product And the propriety data was 2025 extremely valuable. But what what's not proprietary but what is available to you? Which could make that data so much more relevant if you layer them on top census data, this was a census here. The census data is updated. Do you not want that vaccination leader? We clearly know that purchasing power parity will vary based on vaccinations and county by county. But is that enough? You need to have street by street is county data enough. If you're going to open startup, Mr Starbucks? No, you probably want to know much more granular data. You wanna know traffic. Is the traffic picking up business usually an office space where people are not coming to office or is it more of a shopping mall where people are still showing all of these data is out there for you? What cloud is making it possible? Unlike the old era where you know, your data is an SFP oracle or carry later in your data center, it's available for you with a matter of clicks. What thought sport modern analytics. Cloud is a simple thing. We are the front end to bring all of this data and make sense of it. You can sit on top of any cloud data and then interact with a complete sort of freedom without compromising on security, compliance or relevance. And what happens is the analysts, the people who are responsible for bringing the data and then making sure that it is secure and delivered. They are no longer doing incremental in chart updates and dashboard updates. What they're doing is solving business problems, business people there freely interacting and making bigger decisions. That actually adds value to their consumers. This is what your customers are looking for, your users are looking for and if you're not doing it, your competitor will do that. So this is why cloud is not a choice for you. It's not an option for you. It is the only way and if you fail to take that back the other way is taking the world out of a cliff. >>Yeah, that's I love it. But I want to get this uh topic of thoughts about anywhere, but I want to just close out on this whole idea of modern cloud scale analytics. What technology under the hood do you guys see that customers should pay attention to with thought spot and in general because the scale there. So is it just machine learning? We hear data lakes, you know, you know different configurations of that. Machine learning is always thrown around like a buzzword. What new technology capability should every executive by your customer look for when it comes to really doing analytics, modern in the cloud >>analytics has to be near real time, Which means what two things speed at scale, make sure it's complex, it can deal with complexity in data structure. Data complexity is a huge problem. Now imagine doing that at scale and then delivering with performance. That means you have to rethink Look Tableau grew out of excellent worksheets that is the market leader, it is a $40 billion dollar market with the largest company having only a billion dollars in revenue. This is a massive place where the problems need to be solved differently. So the underlying technology to me are like I said, these three things, number one cannot handle the cloud scale, you will have hundreds of billions of rows of data that you brought. But when you talk about social media sentiment of customers, analysis of traffic and weather patterns, all of these publicly available valuable data. We're talking trillions of rows of data. So that is scale. Now imagine complexity. So financial sector for example, there is health care where you know some data is visible, some data is not visible, some some is public assumption not or you have to take credit data and let it on top of your marketing data. So it becomes more complex. And the last is when you answer ask a question, can you deliver with absolute confidence that you're giving the right answer With extremely high performance and to do that you have to rebuild the entire staff. You cannot take your, you know, stack that was built in 1990s and so now we can do search So search that is built for these three things with the machine learning and ai essentially helping at every step of the way so that you're not throwing all this inside directly to a human, throw it to a i engine and the ai engine curates what is relevant to you, showing it to you. And then based on your interaction with that inside, I improve my own logic so that the next interaction, the next situation is going to be significantly better. My point is you cannot take a triple a map and then try to act like this google maps. One is built presuming and zoom out and learn from you. The other one is built to give you rich information but doesn't talk back. So the staff has to be fundamentally rebuilt for the club. That's what he's doing. >>I love I love to buy direction. I love the interactivity. This topic of thought spot everywhere, which you mentioned at the beginning of this conversation, you mentioned data apps which by the way I love that concept. I want to do a drill down on that. Uh I saw data marketplace is coming somewhat working but I think it's going to get it better. I love that idea of an app um, and using as developers but you also mentioned embedded analytics. You made a comment about that. So I gotta ask you what's the difference between data apps and embedded analytics? >>Embedded analytics means that uh you know the dashboards that you love but the one that doesn't talk back to you is going to be available inside the app that you built for your other So if a supply chain app that was built by let's say accenture inside that you haven't had your dashboard without logging into tablet. Great. But what you do, what's the big deal? It is the same thing. My point is like I said every time a business user sees a chart. The questions are going to come up. The next 10 question is where the values on earth for example on Yelp imagine if you will piece about I'm hungry. I want to find a restaurant and it says go to this burrito place. It doesn't work like that. It's not good enough. The reason why yell towards is because I start with an intent. I'm hungry. Okay show me all restaurants. Okay I haven't had about it for a while. Let me see the photos. Let me read the reviews. Let me see if my friends have eaten, let me see some menu. Can I walk there? I do all of this but just what underneath it. There is a rich set of data that probably helped have their own secret source and reviews and then you have google map powering some of them. But I don't care all of that is coming together to deliver a seamless experience that satisfies my hunger. Which will be very different from if you use the same map at the same place you might go to an italian place. I go to bed right. That is the power of a data app in business people are still sitting with this. I am hungry. I gotta eat burrito. That's not how it should be in the new world. A business user should have the freedom to add exactly what the customers require looking for and solve that problem without delay. That means every application should be power and enriched with the data where you can interact and customized. That is not something that enterprise customers are actually used to and to do that you need like I said a I and search powering like the google map underneath it, but you need an app like a yelp like app, that's what we deliver. So for example, uh just last week we delivered a service now app on snowflake. You know, it just changes the game. You are thinking about customer cases. You're a large company, you have support coming from Philippines and India some places the quality is good. Some places bad dashboards are not good enough saying that okay, 17% of our customers are unhappy but we are good. That's not the world we live in. That is the tyranny of >>average, >>17% were unhappy. You got to solve for them. >>You mentioned snowflake and they had their earnings. David and I were commenting about how some of the analysts got it all wrong. And you bring up a really good point that kind of highlights the real trend. Not so much how many new customers they got. But there do what customers are doing more. Right? So, so what's happening is that you're starting to see with data apps, it does imply Softwares in there because it's it's application. So the software wrapping around data. This is interesting because people that are using the snowflakes of the world and thought spot your software and your platform, they're doing more with data. So it's not so much. I use snowflake, I use snowflake now I'm going to do more with it. That's the scale kicking. So this is an opportunity to look at that more equation. How do you talk >>with >>when you see that? Because that's the real thing is like, okay, that's I bought software as a service. But what's the more that's happening? What do you see >>that is such an important point? Even I haven't thought about it that john but you're absolutely right. That is sometimes people think of snowflake is taking care of it and no. Yeah, yes, Sarah later used to store once and zeros and they're moving it into club. That is not the point. Like I said, marketplace as an example when you are opening it up for for example, bringing the entire world's data with one click accessible to you securely. That is something you couldn't do on number two. You can have like 100 suppliers and all of a sudden you can now take a single copy of data and then make it available to all of them without actually creating multiple copies and control it differently. That's not something without cloudy, potentially could do. So things like that are fundamentally different. It is much more than like one plus one equals two. It is one plus one is 33. Like our view is that when you are re platform ng like that, you have to think from customer first. What does the customer do? The customer care that you meant from Entre into cloud or event from Teradata snowflake. No, they will care if their lives are better. Are they able to get better services are able to get it faster. That's what it is. So to me it is very simple. The destiny of an insight or data information is action, right? Imagine you're driving a car and if your car updates the gas tank every monday morning, imagine how you know, stressful your life will be for the whole week. I have to wait until next monday wanting to figure out what, whether I have enough gas or not, that's not the new world, that information is there, you need to have it real time and act on it. If you go through the Tesla you realize now that you know, I'm never worried about mileage because it is going to take me to the supercharger because it knows what I need to get to, it knows how long it is going to be, how bad the traffic is. It is synthesizing all of that to give me peace of mind. >>So this is a great >>conversation. That's a >>great question. It's a great conversation because it's really kind of brings in kind of what's happening, you see successful companies that are working with cloud scale and data like you're talking about, it's you get in there, you get the data, the data apps and all of a sudden you hit it, you hit the value equation and it's like almost like discovering oil all of a sudden you have a gusher and then people just see massive increase in value. It's not like the outcome, it's kind of there, you've got to kind of get in there and this is the scale piece and you see people having strategies to do that, they say okay we're gonna get in there, we're going to use the data to iterate but also watch the data learn where's that value, This is that more trend and and there's a successful of the developing. So I have to ask you when you, when you talk about people and culture, um that's not the way it used to be, used to be like okay I'm buying an outcome. I deployed some software mechanisms and at the end of the day there's some value there. Maybe I write it off maybe I, you know, overtime charges and some accounting thing. All changed the culture and the people in charge now are transforming the management techniques. What do you see as a successful mindset for a customer as they managed through these new paradigms and new new success formulas. >>I see a fork in leadership when it comes to courage. There are people with the spine and there are people without the spine and the ones with the spine are absolutely killing it. They are unafraid. They are not saying, look, I'm just going to stick with the incumbents that I've known for the last 20 years. Look, I used to drive a Toyota forever because I love the Toyota. And then you know after Nutanix IPO went to Lexus still Toyota because it's reliable. I don't, I'm not a huge card person. It works. But guess what? I knew they were missing Patrick and I care about the environment. I don't want to keep pushing hydrocarbons out there. It's not politics. I just don't like burning stuff into the earth atmosphere. So when Tesla came out, it's not like I love the quality I don't personally like alone mask, you know after that Thailand fiasco of cave rescue and all of that. But I can clearly see that Toyota is not going to catch up to Tesla in the next 10 years. And guess what? My loyalty is much more to doing the right thing for my family and to the world. And I switched this is what business leaders need to know. They can't simply say, well, tabloid as search to. They're not as good as thought sports. We'll just stick with them because they have done with us. That's what weak leaders do and customers suffer for that. What I see like the last two weeks ago when I was in new york. I met with them. A business leader for one of the largest banks in the world with 25,000 people reporting to him. The person walks into the room wearing shorts and t shirts uh, and was so full of energy and so full of excitement. I thought I'm going to learn from him and he was asking questions about how we do our business in bed and learning from me. I was humbled, I was flawed and I realized that's what a modern business leader looks like. Even if it is one of the largest and oldest banks in the world, that's the kind of people are making big difference and it doesn't matter how all the companies, how old their data is they have mainframes or not. I hear this excuses all the type of er, mainframes, we can't move, we have COBOL going on. And guess what? You keep talking about that and hear leaders like him are going to transform those companies And next thing you know, there are some of the most modern companies in the world. >>Well certainly they, we know that they don't have any innovation strategy or any kind of R and D or anything going on that could be caught flat footed in the companies that didn't have that going on, didn't have the spine or the, the, the vision to, to at least try the cloud before Covid when Covid hit, those companies are really either going out of business or they're hurting the people who were in the cloud really move their teams into the cloud quicker to take advantage of uh, the environment that they had to. So this became a skill issue. So, so this is a big deal. This is a big deal. And having the right skills are people skilled, it will be a, I both be running everything for them. What is your take on that? >>This is an important question. You can't just say you got to do more things or new things and not take care of all things. You know, there's only 89, 10 hours so you can work in their uh, analysts in the Atlantic species constantly if your analysts are sitting there and making incremental dashboards and reports change every day and then backlog is growing for 56 days and the users are unhappy because you're not getting answers and then you ask them to go to new things. It's just not going to be enough and you can hire your way out of it. You have to make sure that if you say that I have 20 100 x product already, I don't want 21st guess what? Sometimes to be five products, you need to probably go to 21 you got to do new things to actually take away the gunk off the old and in that context, the re skilling starts with unburdening, unburdening of menial task, unburned routine task. There is nothing more frustrating than making reports and dashboards that people don't even use And 90% of the time analysts, they're amazing experiences completely wasted when they're making incremental change to tabloid reports. I kind of believe thought spot and self service on top of cloud data takes away all of that without compromising security and then you invest the experienced people. Business experience is so critical. So don't just go and hire university students and say, okay, they'll go come and quote everything the experience that they have in knowing what the business is about and what it matters to their users, that domain experience and then uplevel them res kill them and then bring fresh energy to challenge that and then make sure there is a culture that allows that to happen. These three things. That's why I said leadership is not just about hiring event of firing another, it's about cultivating a culture and living that value by saying, look if I am wrong, call me, call me out in public because I want to show you how I deal with conflict. So this is I love this thing because when I see these large companies where they're making these massive changes so fast, it inspires you to say you know what if they can do it, anyone can do it. But then I also see if the top leadership is not aligned to that. They are just trying to retire without the stock tanking too much and let me just get through two more years. The entire company suffers. >>So that's great to chat with you got great energy, love your business, love the energy, love the focus. Um it's a new wave you're on. It's a big wave um and it's it's relevant, it's cool and relevant and it's the modern way and people have to have a spine to be successful if not for the faint of heart, but the rewards are there if you get this right. This is what I I love about this new environment. Um so I gotta ask you just to kind of close it out. How would you plug the company for the folks watching that might want to engage with you guys. What's the elevator pitch? What's the positioning? How would you describe thought spot in a bumper sticker or in a positioning statement. Take a minute to talk about that. >>Remember martin Anderson said that software is eating the world, I think it is now time to update that data is eating everything including software. If you don't have a way to turn data into bespoke action for your customers. Guess what? Your customers are gonna go somewhere where they that's happening right? You may not be in the data business but the data company is going to take your business. Thought spot is very simple. We want to be the friend tent for all cloud data when it comes to structured because that's where business value numbers is world satisfaction and dissatisfaction for reduces allying it is important to move data to action and thought Spot is the pioneer in doing that through search and I >>I really think you guys want something very powerful. Looking forward to chatting with you at the upcoming eight of a startup showcase. I think data is a developer mindset. It's an app, it's part of everything. It will. Everyone's a data company, everyone is a media company. Data is everything you guys are on something really big and people got a program it with it, make experiences whether it's simple scripts, point and click. That is a new kind of developer out there. You guys are tapping into it. Great stuff. Thank >>you for coming on. Thank you john it's good to talk to you. >>Okay. It's a cube conversation here in Palo alto California were remote. We're virtual. That's the cube virtual. I'm sean for your host. Thanks for watching. Mhm. Mhm

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Welcome back to Boston. Everybody watching the Cube, the leader and on the ground tech coverage money was David wanted here with my co host. A student of John for is also in the house. This is active FiOS data driven 19 conference. They're second year, John. Her stick is here is the co founder and CEO of on shape John. Thanks for coming in the Cube. Great to have you great to be here. So love the cofounder. I always ask your father. Why did you start the company? Well, we found it on shape because >> we saw an opportunity to improve how every product on Earth gets developed. Let people who develop products do it faster, B'more, innovative, and do it through a new generation software platform based in the cloud. That's our vision for on shape, That's why. Okay, >> so that's great. You start with the widened. The what is just new generation software capabilities to build the great products visualized actually create >> way took the power of cloud web and mobile and used it to re implement a lot of the classic tools for product development. Three d cad Data management Workflow Bill of Materials. He's may not mean anything to you, but they mean a lot to product developers, and we believe by by moving in the cloud by rethinking them for the cloud we can give people capabilities they've never had before. >> John, bring us in tight a little bit. So you know, I think I've heard a lot the last few years. It's like, Well, I could just do everything a simulation computer simulation. We can have all these models. They could make their three D printings changing the way I build prototypes. So what's kind of state of the state and in your fields? So >> the state of the Art R field is to model product in three dimensions in the computer before you build it for lots of reasons. For simulation for three D printing, you have to have a CAD model to do it, to see how it'll look, how parts fit together, how much it will cost. Really, every product today is built twice. First, it's built in the computer in three dimensions, is a digital model, then it's built in the real world, and what we're trying to do is make those three D modeling and data management collaboration tools to take them to a whole nother level to turbo charge it, if you will, so that teams can can work together even if they're distribute around the world. They work faster. They don't have to pay a tax to install and Karen feed for these systems. You're very complicated, a whole bunch of other benefits. So we talk about the cloud model >> you're talking about a sass model, a subscription model of different customer experience, all of the above, all of the above. Yeah, it's definitely a sass model we do on Ly SAS Way >> hosted and, uh, Amazon. Eight of us were all in with Amazon. It's a it's a subscription model, and we provide a much better, much more modern, better, more productive experience for the user CIA disrupting the traditional >> cad business. Is that Is that right? I mean more than cat cat Plus because there's no such thing as a cad company anymore. We're essentially disrupting the systems that we built because I've been in this business 30 38 years now. I've been doing this. I feel like I'm about half done. Really, really talking about >> your career. Way to start out. Well, I grew up in Chicago. I went to M I t and majored in mechanical engineering and knew howto program computers. And I go to get an internship in 1981 and they say computers, mechanical injury. You need to work on CAD. And I haven't stopped since, you know, because Because we're not done, you know, still still working here. You would >> have me, right? You can't let your weight go dynamic way before we get off on the M I t. Thing you were part of, you know, quite well known group. And Emmet tell us a little bit >> about what you're talking about. The American society of Mechanical Engineer >> has may I was actually an officer and and as any I know your great great events, but the number 21 comes to >> mind you're talking about the MIT blackjack team? Yes, I was, ah, player on the MIT blackjack team, and it's the team featured in movies, TV shows and all that. Yeah, very exciting thing to be doing while I was working at the cath lab is a grad student, you know, doing pursuing my legitimate career. There is also also, uh, playing blackjack. Okay, so you got to add some color to that. So where is the goal of the M I T. Blackjack team? What did you guys do? The goal of the M I t blackjack team was honestly, to make money using legal means of skill to Teo obtain an edge playing blackjack. And that's what we did using. Guess what? The theme of data which ties into this data driven conference and what active Eo is doing. I wish we had some of the data tools of today. I wish we had those 30 years ago. We could have We could have done even more, but it really was to win money through skill. Okay, so So you you weren't wired. Is that right? I mean, it was all sort of No, at the time, you could not use a computer in the casino. Legally, it was illegal to use a computer, so we didn't use it. We use the computer to train ourselves to analyze data. To give a systems is very common. But in the casino itself, we were just operating with good old, you know, good. This computer. Okay. And this computer would what you would you would you would count cards you would try to predict using your yeah, count cards and predict in card. Very good observation there. Card counting is really essentially prediction. In a sense, it's knowing when the remaining cards to be dealt are favorable to the player. That's the goal card counting and other systems we used. We had some proprietary systems to that were very, very not very well known. But it was all about knowing when you had an edge and when you did betting a lot of money and when you didn't betting less double doubling down on high probability situations, so on, So did that proceed Or did that catalyze like, you know, four decks, eight decks, 12 12 decks or if they were already multiple decks. So I don't think we drove them to have more decks. But we did our team. Really. Some of the systems are team Pioneer did drive some changes in the game, which are somewhat subtle. I could get into it, you know, I don't know how much time we have that they were minor changes that our team drove. The multiple decks were already are already well established. By the time my team came up, how did you guys do you know it was your record? I like to say we won millions of dollars during the time I was associated with the team and pretty pretty consistently won. We didn't win every day or every weekend, but we'd run a project for, say, six months at a time. We called it a bank kind of like a fund, if you will, into no six months periods we never lost. We always won something, sometimes quite a bit, where it was part of your data model understanding of certain casinos where there's certain casinos that were more friendly to your methodology. Yes, certain casinos have either differences in rules or, more commonly, differences in what I just call conditions like, for instance, obviously there's a lot of people betting a lot of money. It's easier to blend in, and that's a good thing for us. It could be there there. Their aggressiveness about trying to find card counters right would vary from casino to casino, those kinds of factors and occasionally minor rule variations to help us out. So you're very welcome at because he knows is that well, I once that welcome, I've actually been been Bardet many facilities tell us about that. Well, you get, you get barred, you get usually quite politely asked toe leave by some big guy, sometimes a big person, but sometimes just just honestly, people who like you will just come over and say, Hey, John, we'd rather you not play blackjack here, you know that. You know, we only played in very upstanding professional kind of facilities, but still, the message was clear. You know, you're not welcome here in Las Vegas. They're allowed to bar you from the premises with no reason given in Las Vegas. It's just the law there in Atlantic City. That was not the law. But in Vegas they could bar you and just say you're not welcome. If you come back, we'll arrest you for trespassing. Yeah, And you really think you said everything you did was legal? You know, we kind of gaming the system, I guess through, you know, displaying well probabilities and playing well. But this interesting soothe casinos. Khun, rig the system, right? They could never lose, but the >> players has ever get a bet against the House. >> How did >> you did you at all apply that experience? Your affinity to data to you know, Let's fast forward to where you are now, so I think I learned a lot of lessons playing blackjack that apply to my career and design software tools. It's solid works my old company and now death. So System, who acquired solid words and nowt on shape I learned about data and rigor, could be very powerful tools to win. I learned that even when everyone you know will tell you you can't win, you still can win. You know that a lot of people told me Black Jack would never work. A lot of people told me solid works. We never worked. A lot of people told me on shape would be impossible to build. And you know, you learn that you can win even when other people tell you, Can't you learn that in the long run is a long time? People usually think of what you know, Black Jack. You have to play thousands of hands to really see the edge come out. So I've learned that in business sometimes. You know, sometimes you'll see something happened. You just say, Just stay the course. Everything's gonna work out, right? I've seen that happen. >> Well, they say in business oftentimes, if people tell you it's impossible, you're probably looking at a >> good thing to work on. Yeah. So what's made it? What? What? What was made it ostensibly impossible. How did you overcome that challenge? You mean, >> uh, on >> shape? Come on, Shake. A lot of people thought that that using cloud based tools to build all the product development tools people need would be impossible. Our software tools in product development were modeling three D objects to the precision of the real world. You know that a laptop computer, a wristwatch, a chair, it has to be perfect. It's an incredibly hard problem. We work with large amounts of data. We work with really complex mathematics, huge computing loads, huge graphic loads, interactive response times. All these things add up to people feeling Oh, well, that would never be possible in the cloud. But we believe the opposite is true. We believe we're going to show the world. And in the future, people say, you know We don't understand how you do it without the cloud because there's so much computing require. >> Yeah, right. It seems you know where we're heavy in the cloud space. And if you were talking about this 10 years ago, I could understand some skepticism in 10 2019. All of those things that you mentioned, if I could spin it up, I could do it faster. I can get the resources I need when I needed a good economics. But that's what the clouds built for, as opposed to having to build out. You know, all of these resource is yourself. So what >> was the what was the big technical challenge? Was it was it? Was it latent? See, was it was tooling. So performance is one of the big technical challenges, As you'd imagine, You know, we deliver with on shape we deliver a full set of tools, including CAD formal release management with work flow. If that makes sense to you. Building materials, configurations, industrial grade used by professional companies, thousands of companies around the world. We do that all in a Web browser on any Mac Windows machine. Chromebook Lennox's computer iPad. I look atyou. I mean, we're using. We run on all these devices where the on ly tools in our industry that will run on all these devices and we do that kind of magic. There's nothing install. I could go and run on shape right here in your browser. You don't need a 40 pound laptop, so no, you don't need a 40 pound laptop you don't need. You don't need to install anything. It runs like the way we took our inspiration from tools like I Work Day and Sales Force and Zen Desk and Nets. Sweet. It's just we have to do three D graphics and heavy duty released management. All these complexities that they didn't necessarily have to do. The other thing that was hard was not only a technical challenge like that, but way had to rethink how workflow would happen, how the tools could be better. We didn't just take the old tools and throw him up in a cloud window, we said, How could we make a better way of doing workflow, release management and collaboration than it's ever been done before? So we had to rethink the user experience in the paradigms of the systems. Well, you know, a lot of talk about the edge and if it's relevant for your business. But there's a lot of concerns about the cloud being able to support the edge. But just listening to you, John, it's It's like, Well, everybody says it's impossible. Maybe it's not impossible, but maybe you can solve the speed of light problem. Any thoughts on that? Well, I think all cloud solutions use edge to some degree. Like if you look at any of the systems. I just mentioned sales for us workday, Google Maps. They're using these devices. I mean, it's it's important that you have a good client device. You have better experience. They don't just do everything in the cloud. They say There, there. To me, they're like a carefully orchestrated symphony that says We'll do these things in the core of the cloud, these things near the engineer, the user, and then these things will do right in the client device. So when you're moving around your Google map or when you're looking this big report and sales force you're using the client to this is what are we have some amazing people on her team, like R. We have the fellow who was CTO of Blade Logic. Robbie Ready. And he explains these concepts to make John Russo from Hey came to us from Verizon. These are people who know about big systems, and they helped me understand how we would distribute these workloads. So there's there's no such thing is something that runs completely in the cloud. It has to send something down. So, uh, talk aboutthe company where you're at, you guys have done several raises. You've got thousands of customers. You maybe want to add a couple of zeros to that over time is what's the aspirations? Yeah, correct. We have 1000. The good news is we have thousands of customer cos designing everything you could imagine. Some things never would everything from drones two. We have a company doing nuclear counter terrorism equipment. Amazing stuff. Way have people doing special purpose electric vehicles. We have toys way, have furniture, everything you'd imagined. So that's very gratifying. You us. But thousands of companies is still a small part of the world. This is a $10,000,000,000 a year market with $100,000,000,000 in market cap and literally millions of users. So we have great aspirations to grow our number of users and to grow our tool set capability. So let's talk to him for a second. So $10,000,000,000 current tam are there. Jason sees emerging with all these things, like three D printing and machine intelligence, that that actually could significantly increase the tam when you break out your binoculars or even your telescope. Yes, there are. Jason sees their increasing the tam through. Like you say, new areas drive us So So obviously someone is doing more additive manufacturing. More generative design. They're goingto have more use for tools like ours. Cos the other thing that I observed, if I can add one, it's my own observations. I think design is becoming a greater component of GDP, if you will, like if you look at how much goods in the world are driven by design value versus a decade or two or when I was a child, you know, I just see this is incredible amount, like products are distinguished by design more and more, and so I think that we'll see growth also through through the growth in design as an element of GDP on >> Jonah. I love that observation actually felt like, you know, my tradition. Engineering education. Yeah, didn't get much. A lot of design thing. It wasn't until I was in industry for years. That had a lot of exposure to that. And it's something that we've seen huge explosion last 10 years. And if you talk about automation versus people, it's like the people that designed that creativity is what's going to drive into the >> absolutely, You know, we just surveyed almost 1000 professionals product development leaders. Honestly, I think we haven't published our results yet, So you're getting it. We're about to publish it online, and we found that top of mind is designed process improvements over any particular technology. Be a machine learning, You know, the machine learning is a school for the product development. How did it manufacturers a tool to develop new products, but ultimately they have to have a great process to be competitive in today's very competitive markets. Well, you've seen the effect of the impact that Apple has had on DH sort of awakening people to know the value of grace. Desire absolutely have to go back to the Sony Walkman. You know what happened when I first saw one, right? That's very interesting design. And then, you know, Dark Ages compared to today, you know, I hate to say it. Not a shot at Sony with Sony Wass was the apple? Yeah, era. And what happened? Did they drop the ball on manufacturing? Was it cost to shoot? No. They lost the design leadership poll position. They lost that ability to create a world in pox. Now it's apple. And it's not just apple. You've got Tesla who has lit up the world with exciting design. You've got Dyson. You know, you've got a lot of companies that air saying, you know, it's all about designing those cos it's not that they're cheaper products, certainly rethinking things, pushing. Yeah, the way you feel when you use these products, the senses. So >> that's what the brand experience is becoming. All right. All right, John, thanks >> so much for coming on. The Cuban sharing your experiences with our audience. Well, thank you for having me. It's been a pleasure, really? Our pleasure. All right, Keep right. Everybody stupid demand. A volonte, John Furry. We've been back active, eo active data driven 19 from Boston. You're watching the Cube. Thanks

>> Live from Mountain View, California, it's theCUBE covering DevNet Create 2019 brought to you by Cisco. >> Hello everyone, welcome back to theCUBE's live coverage here in Mountain View, California for the theCUBE's coverage of Cisco DevNet Create. It's a small, intimate event where we're bringing the cloud native creation world with the DevNet community within Cisco and of course building applications, programming networks, that's the theme. I'm John Furrier, your host, our next guest is Darryl Sladden, senior technical product manager at Cisco, 20 year veteran, built voice over IP systems. He's a coder, he's a builder, he's a creator. Great to see you, thanks for coming on. >> Thank you so much, I'm glad to be here. >> And you're a fan? >> I love being on theCUBE. Because-- >> And the trivia behind that? Share the context, you had a product, you built one? >> Yes, the first product management job at Cisco was building the Cisco Unified Border Element and of course, that became the Cube, so any time you mention Cube inside of Cisco, that's going to be my product. >> The renaissance within Cisco theCUBE is back and we're embedded in there. Of course we're breaking all the borders down, getting the data. Tell us what's going on in your world? Obviously you've seen a lot of waves. I mean voice over IP that you were involved in? >> Yeah. >> That took, that old PBX telephone-- >> Right. >> Got digital, created massive innovation. That's an inflection point moment. We're seeing a few of those big waves happening now. One of them's an architectural changes around IoT, Wi-fi 6, 5G, cloud computing all coming together. This is an interesting opportunity. What's your focus? Where do you fit into all that? >> Yeah, where I fit in is this is a massive change and one of the problem sets that hasn't been solved yet is how do I understand where I am indoors? There's been great solutions that have unlocked huge amount of value with the GPS system outdoors. You always know where you are, a lot of way to find out exactly the right, it always amazes me at how accurate they are at how long it's going to take me to get to the Computer Museum. But how do I know once I've got into the museum that theCUBE is in the upstairs, in the back corner? That's where we need to solve that problem and I think we're at the crux of that. >> Waze is a great example because one of the things I'm amazed by with Waze is how fast they report the incidents that are going on. People are so actively rapid of adding, inputting the data. You got data junkies adding it and there's been some side effects. The side streets are always clogged. (laughing) >> Police always know-- >> So in physical locations where Wi-fi 6 for instance comes out? >> Yeah. >> You're going to have new capabilities in bandwidth and throughput and coverage areas, these dense areas. It's going to create a navigation opportunity for either machines to machines, machines to humans, humans to machines, humans to humans, within a physical construct. >> Yeah. >> How do you see that evolving? Use cases? What's the pattern? >> Right. What I really see evolving is taking advantage of some of the capabilities that have already existed in wi-fi, meaning ranging from individual IPs but some of the new things that are coming with Wi-fi 6 is Wi-fi 6 creates a great baseline but there are new things where, 802.11mc for example, which is an extension of Wi-fi 6, has what's called fine timing measurement. I can now, with these super accurate chip sets, know the speed of light is about one nanosecond to go about three feet. If I have an accurate clock, now I can know how far I am from the APs. >> Yeah. >> And I can solve that in indoor locations. >> So a lot of physics involved? >> A lot of rates of physics involved. >> Alright, so what products are you working on now to make all this happen. Take us through some of the things that are out there that you've got your fingers on. >> Yeah, so what I'm working on is Cisco's new location platform, it's called Cisco DNA Spaces and so what we're focusing on is digitizing that indoor space. So people spend of their economic activity are indoors. Whether it's in a hotel, where they're selling the rooms, or a restaurant where they're selling food inside the spaces, but what goes on in that physical space? People don't have that same level of knowledge that you do on the web, right? When I go to a webpage and I shop for outdoor furniture? The next two weeks I'm followed by ads about outdoor furniture. But if I go to Home Depot and I spend an hour in the outdoor furniture aisle, they don't know about that. Now, it allows you to digitize that indoor space and provide that context for other types of applications. >> So the value, I mean I'm not saying, now they're going to know you actually shopped at Home Depot, now your ad go to Home Depot. (laughing) But the value is not so much in the advertising. It's really in the efficiencies around work, play, office. These are the things that are going to be impacted because, you know, take healthcare for instance? Manufacturing? How people do work? How services are delivered? Just like in the consumer side, we all relate to the iPhone days when oh my god, I can have GPS on a phone. Now I do a mash up on a Google Map. >> Right. >> Are you saying the same thing for buildings? You're going to import like architectural drawings? How do you get all of this built out? What's the playbook? >> Yeah. The playbook really will be starting at the larger buildings that will be put into Google Maps or put into other places where it can start to get really accurate indoor locations and then never losing things, right? Be able to know where you are indoors. Being able to always find your stuff, not only where you are but maybe I put a tag on some of my assets and I always know where they are? The idea of nurses becoming more efficient because they're going to know where that wheelchair is if I need to find a wheelchair to move a patient out of an office. All of these things just become a little bit more efficient but that just builds on a huge scale when that happens at scale. >> Darryl, talk about the impact of this because you built and deployed disruptive technology in the past. For the folks watching, whether it's an enterprise architect or CIO or CEO or facilities manager, whoever, what is the impact of these new location based services to their business? How should they be thinking about it, holistically? >> Yeah. >> What's your view? >> My real view is that you want to look at it from a platform, so you're not going to have one company. Even at Cisco, we're not going to solve every application but what you do want to do is build a platform that's extensible, right? We'll take in data from multiple sources, whether it APs or video cameras, other things, create a platform that normalizes that location, and then opens that up. So that's what happened as the mainframes transitioned to client server computing. Once you start breaking things up? That's really the value and so I think the CIOS and architects out there, shouldn't be looking at point products as much as understanding that a location platform will help them unlock the value moving forward. >> Talk about the data. How is the data traversing through this? Because obviously you mentioned connecting things like cameras and other things? It could be medical equipment, it could be anything. IoT's going to be a tsunami of opportunity, applications that are going to create a lot of opportunity. How should I think about the data flow? And the role of machine learning and data in all of this? Is that going to be a key part of this? >> Absolutely, the way that we're looking at it is there's kind of two groups. There's the ones that are all in on the cloud, and we are offering this as a software as a subscription service so you buy it on a subscription basis and you let Cisco deal with the problems. Of course with a regulated environment of access to the data and backing it up and restoring it and making sure it's well curated. Or you can decide, yeah I want to run it on premises. If you want it on prem you have to understand you're going to have to deal with those same problems of back up, the data will get really large as you start to collect more and more location and how are you going to best extract value from that data? So I think you really want to look at that this is something that's going to continue to expand and do I want to make that a core competence by running it myself? Or maybe turn that over to cloud service? >> So in terms of what's real and not real or what's coming and what's real today? So you mentioned there's some location services as a SAS. Talk about what's available now from your customer standpoint. >> Yeah. >> What can they get going on and what's coming around the corner? >> Yeah, so what they can get going on today is that location services, Cisco DNA Spaces. So if you go to ciscodnaspaces.com there's free trials available, it's a great sort of application. But more importantly, it provides you that initial start, right? What's coming is more and more applications will take advantage of that, right? We got a great one for things like student success, so that you know a student is inside of a classroom and then if he doesn't come to class for a couple days in a row? Oh maybe he needs counseling? Maybe his car broke down? You can start to do these really interesting student success applications as an example of a vertical. So the vertical applications are starting to really proliferate, but what's available today is the platform. >> So you see verticals really booming on this? >> Yeah. >> They're going to take advantage of it? Alright, so just kind of zoom out and put your industry hat on, not your Cisco hat. When you look at wi-fi and 5G or other technologies that are out there, what's the big movement? What moves the ball down the field the most? Is it going to be wi-fi and 5G? Because it seems like, you know, inch by inch, unified communication seemed stalled, now it's got an uplift with cloud, with data, more great user experiences. SD-WAN's been around for a long time and getting a resurgence. I mean campus networking had been around for a long, long time. >> I know. (chuckling) >> People go to stadiums, want to do Instagram and do videos. What's the big technology lever here? What's the big tailwind for location based in-building stuff? >> What I start to see for this is improving standards and improving accuracy, right? Until you get to that point where it's reliable and replaceable and I can really depend on it? It's all a niche product. I think that's been happening for literally the last eight years in this industry. Lots of niche examples of things that have been successful but it hasn't exploded, until you build that platform where I can absolutely, with reliability say, this device is at this point at this time? >> Yeah. >> Then you can start to really expand but that's really-- >> The timing and the through put, to your point earlier? >> Yeah. >> Okay, thoughts on DevNet, just to wrap up. What's here? Going on in the show here? DevNet Create, Susie did a good job of bringing communities together. A lot of co-creation, they're creating new things. This is a new application environment, programmable. What's your thoughts on DevNet? >> Yeah, I love being around some of the smartest people in the world here. (laughing) It's great. Humbling just to be able to talk to some of these guys. But I do think that really creates the community that teaches everything from little things, like I learned a quick, great new little API trick that I hadn't learned and maybe I taught some people some of the stuff that we're doing about streaming APIs. What I really like about this is all these small little interactions build something really good. >> Yeah. And you build API into all the products that's only going to create more enablement. >> Yeah. >> More creativity. The creativity's flowing big time. >> Right. >> Darryl, thanks for coming on. >> Well thank you so much. >> Great to see you. Thanks, a CUBE fan. >> Right. (laughing) >> Author of the product called The Cube at Cisco back in the day. I'm John Furrier, back with more live coverage after this short break. (light digital music)

hey welcome back everybody Jeff Rick here with the cube we're at the mill pedis at an interesting event is called the auto tech council innovation in motion mapping and navigation event so a lot of talk about autonomous vehicles so it's a lot of elements to autonomous vehicles this is just one small piece of it it's about mapping and navigation and we're excited to have with us our first guest again and give us a background of this whole situation just Derick Curtin and he's the founder and chairman of the auto tech council so first up there welcome thank you very much good to be here absolutely so for the folks that aren't familiar what is the auto tech council autofit council is a sort of a club based in Silicon Valley where we have gathered together some of the industry's largest OMS om is mean car makers you know of like Rio de Gono from France and a variety of other ones they have offices here in Silicon Valley right and their job is to find innovation you find that Silicon Valley spark and take it back and get it into cars eventually and so what we are able to do is gather them up put them in a club and route a whole bunch of Silicon Valley startups and startups from other places to in front of them in a sort of parade and say these are some of the interesting technologies of the month so did they reach out for you did you see an opportunity because obviously they've all got the the Innovation Centers here we were at the Ford launch of their innovation center you see that the tagline is all around is there too now Palo Alto and up and down the peninsula so you know they're all here so was this something that they really needed an assist with that something opportunity saw or was it did it come from more the technology side to say we needed I have a new one to go talk to Raja Ford's well it's certainly true that they came on their own so they spotted Silicon Valley said this is now relevant to us where historically we were able to do our own R&D build our stuff in Detroit or in Japan or whatever the cases all of a sudden these Silicon Valley technologies are increasingly relevant to us and in fact disruptive to us we better get our finger on that pulse and they came here of their own at the time we were already running something called the telecom Council Silicon Valley where we're doing a similar thing for phone companies here so we had a structure in place that we needed to translate that into beyond modem industry and meet all those guys and say listen we can help you we're going to be a great tool in your toolkit to work the valley ok and then specifically what types of activities do you do with them to execute division you know it's interesting when we launched this about five years ago we're thinking well we have telecommunication back when we don't have the automotive skills but we have the organizational skills what turned out to be the cases they're not coming here the car bakers and the tier 1 vendors that sell to them they're not coming here to study break pad material science and things like that they're coming to Silicon Valley to find the same stuff the phone company two years ago it's lookin at least of you know how does Facebook work in a car out of all these sensors that we have in phones relate to automotive industry accelerometers are now much cheaper because of reaching economies of scale and phones so how do we use those more effectively hey GPS is you know reach scale economies how do we put more GPS in cars how do we provide mapping solutions all these things you'll set you'll see and sound very familiar right from that smartphone industry in fact the thing that disrupts them the thing that they're here for that brought them here and out of out of defensive need to be here is the fact that the smartphone itself was that disruptive factor inside the car right right so you have events like today so gives little story what's it today a today's event is called the mapping and navigation event what are people who are not here what's what's happening well so every now and then we pick a theme that's really relevant or interesting so today is mapping and navigation actually specifically today is high definition mapping and sensors and so there's been a battle in the automotive industry for the autonomous driving space hey what will control an autonomous car will it be using a map that's stored in memory onboard the car it knows what the world looked like when they mapped it six months ago say and it follows along a pre-programmed route inside of that world a 3d model world or is it a car more likely with the Tesla's current they're doing where it has a range of sensors on it and the sensors don't know anything about the world around the corner they only know what they're sensing right around them and they drive within that environment so there's two competing ways of modeling a 3d world around autonomous car and I think you know there was a battle looking backwards which one is going to win and I think the industry has come to terms with the fact the answer is both more everyday and so today we're talking about both and how to infuse those two and make better self-driving vehicles so for the outsider looking in right I'm sure they get wait the mapping wars are over you know Google Maps what else is there right but then I see we've got TomTom and meet a bunch of names that we've seen you know kind of pre pre Google Maps and you know shame on me I said the same thing when Google came out with a cert I'm like certain doors are over who's good with so so do well so Eddie's interesting there's a lot of different angles to this beyond just the Google map that you get on your phone well anything MapQuest what do you hear you moved on from MapQuest you print it out you're good together right well that's my little friends okay yeah some people written about some we're burning through paper listen the the upshot is that you've MapQuest is an interesting starting board probably first it's these maps folding maps we have in our car there's a best thing we have then we move to MapQuest era and $5,000 Sat Navs in some cars and then you might jump forward to where Google had kind of dominate they offered it for free kicked you know that was the disruptive factor one of the things where people use their smartphones in the car instead of paying $5,000 like car sat-nav and that was a long-running error that we have in very recent memory but the fact of the matter is when you talk about self-driving cars or autonomous vehicles now you need a much higher level of detail than TURN RIGHT in 400 feet right that's that's great for a human who's driving the car but for a computer driving the car you need to know turn right in 400.000 five feet and adjust one quarter inch to the left please so the level of detail requires much higher and so companies like TomTom like a variety of them that are making more high-level Maps Nokia's form a company called here is doing a good job and now a class of car makers lots of startups and there's crowdsource mapping out there as well and the idea is how do we get incredibly granular high detail maps that we can push into a car so that it has that reference of a 3d world that is extremely accurate and then the next problem is oh how do we keep those things up to date because when we Matt when when a car from this a Nokia here here's the company house drives down the street does a very high-level resolution map with all the equipment you see on some of these cars except for there was a construction zone when they mapped it and the construction zone is now gone right update these things so these are very important questions if you want to have to get the answers correct and in the car stored well for that credit self drive and once again we get back to something to mention just two minutes ago the answer is sensor fusion it's a map as a mix of high-level maps you've got in the car and what the sensors are telling you in real time so the sensors are now being used for what's going on right now and the maps are give me a high level of detail from six months ago and when this road was driven it's interesting back of the day right when we had to have the CD for your own board mapping Houston we had to keep that thing updated and you could actually get to the edge of the sea didn't work we were in the islands are they covering here too which feeds into this is kind of of the optical sensors because there's kind of the light our school of thought and then there's the the biopic cameras tripod and again the answers probably both yeah well good that's a you know that's there's all these beat little battles shaping up in the industry and that's one of them for sure which is lidar versus everything else lidar is the gold standard for building I keep saying a 3d model and that's basically you know a computer sees the world differently than your eye your eye look out a window we build a 3d model of what we're looking at how does computer do it so there's a variety of ways you can do it one is using lidar sensors which spin around biggest company in this space is called Bella died and been doing it for years for defense and aviation it's been around pointing laser lasers and waiting for the signal to come back so you basically use a reflected signal back and the time difference it takes to be billows back it builds a 3d model of the objects around that particular sensor that is the gold standard for precision the problem is it's also bloody expensive so the karmak is said that's really nice but I can't put for $8,000 sensors on each corner of a car and get it to market at some price that a consumers willing to pay so until every car has one and then you get the mobile phone aside yeah but economies of scale at eight thousand dollars we're looking at going that's a little stuff so there's a lot of startups now saying this we've got a new version of lighter that's solid-state it's not a spinning thing point it's actually a silicon chip with our MEMS and stuff on it they're doing this without the moving parts and we can drop the price down to two hundred dollars maybe a hundred dollars in the future and scale that starts being interesting that's four hundred dollars if you put it off all four corners of the car but there's also also other people saying listen cameras are cheap and readily available so you look at a company like Nvidia that has very fast GPUs saying listen our GPUs are able to suck in data from up to 12 cameras at a time and with those different stereoscopic views with different angle views we can build a 3d model from cheap cameras so there's competing ideas on how you build a model of the world and then those come to like Bosh saying well we're strong in car and written radar and we can actually refine our radar more and more and get 3d models from radar it's not the good resolution that lidar has which is a laser sense right so there's all these different sensors and I think there the answer is not all of them because cost comes into play below so a car maker has to choose well we're going to use cameras and radar we're gonna use lidar and high heaven so they're going to pick from all these different things that are used to build a high-definition 3d model of the world around the car cost effective and successful and robust can handle a few of the sensors being covered by snow hopefully and still provide a good idea of the world around them and safety and so they're going to fuse these together and then let their their autonomous driving intelligence right on top of that 3d model and drive the car right so it's interesting you brought Nvidia in what's really fun I think about the autonomous vehicle until driving cars and the advances is it really plays off the kind of Moore's laws impact on the three tillers of its compute right massive compute power to take the data from these sensors massive amounts of data whether it's in the pre-programmed map whether you're pulling it off the sensors you're pulling off a GPS lord knows where by for Wi-Fi waypoints I'm sure they're pulling all kinds of stuff and then of course you know storage you got to put that stuff the networking you gotta worry about latency is it on the edge is it not on the edge so this is really an interesting combination of technologies all bring to bear on how successful your car navigates that exit ramp you're spot-on and that's you're absolutely right and that's one of the reasons I'm really bullish on self-driving cars a lot more than in the general industry analyst is and you mentioned Moore's law and in videos taking advantage of that with a GPUs so let's wrap other than you should be into kind of big answer Big Data and more and more data yes that's a huge factor in cars not only are cars going to take advantage of more and more data high definition maps are way more data than the MapQuest Maps we printed out so that's a massive amount of data the car needs to use but then in the flipside the cars producing massive amounts of data I just talked about a whole range of sensors I talked lidar radar cameras etc that's producing data and then there's all the telemetric data how's the car running how's the engine performing all those things car makers want that data so there's massive amounts of data needing to flow both ways now you can do that at night over Wi-Fi cheaply you can do it over an LTE and we're looking at 5g regular standards being able to enable more transfer of data between the cars and the cloud so that's pretty important cloud data and then cloud analytics on top of that ok now that we've got all this data from the car what do we do with it we know for example that Tesla uses that data sucked out of cars to do their fleet driving their fleet learning so instead of teaching the cars how to drive I'm a programmer saying if you see this that they're they're taking the information out of the cars and saying what are the situation these cars are seen how did our autonomous circuitry suggest the car responds and how did the user override or control the car in that point and then they can compare human driving with their algorithms and tweak their algorithms based on all that fleet to driving so it's a master advantage in sucking data out of cars massive advantage of pushing data to cars and you know we're here at Kingston SanDisk right now today so storage is interesting as well storage in the car increasingly important through these big amount of data right and fast storage as well High Definition maps are beefy beefy maps so what do you do do you have that in the cloud and constantly stream it down to the car what if you drive through a tunnel or you go out of cellular signal so it makes sense to have that map data at least for the region you're in stored locally on the car in easily retrievable flash memory that's dropping in price as well alright so loop in the last thing about that was a loaded question by the way and I love it and this is the thing I love this is why I'm bullish and more crazier than anybody else about the self-driving car space you mentioned Moore's law I find Moore's law exciting used to not be relevant to the automotive industry they used to build except we talked about I talked briefly about brake pad technology material science like what kind of asbestos do we use and how do we I would dissipate the heat more quickly that's science physics important Rd does not take advantage of Moore's law so cars been moving along with laws of thermodynamics getting more miles per gallon great stuff out of Detroit out of Tokyo out of Europe out of Munich but Moore's law not entirely relevant all of a sudden since very recently Moore's law starting to apply to cars so they've always had ECU computers but they're getting more compute put in the car Tesla has the Nvidia processors built into the car many cars having stronger central compute systems put in okay so all of a sudden now Moore's law is making cars more able to do things that they we need them to do we're talking about autonomous vehicles couldn't happen without a huge central processing inside of cars so Moore's law applying now what it did before so cars will move quicker than we thought next important point is that there's other there's other expansion laws in technology if people look up these are the cool things kryder's law so kryder's law is a law about storage in the rapidly expanding performance of storage so for $8.00 and how many megabytes or gigabytes of storage you get well guess what turns out that's also exponential and your question talked about isn't dat important sure it is that's why we could put so much into the cloud and so much locally into the car huge kryder's law next one is Metcalfe's law Metcalfe's law has a lot of networking in it states basically in this roughest form the value of network is valued to the square of the number of nodes in the network so if I connect my car great that's that's awesome but who does it talk to nobody you connect your car now we can have two cars you can talk together and provide some amount of element of car to car communications and some some safety elements tell me the network is now connected I have a smart city all of a sudden the value keeps shooting up and up and up so all of these things are exponential factors and there all of a sudden at play in the automotive industry so anybody who looks back in the past and says well you know the pace of innovation here has been pretty steep it's been like this I expect in the future we'll carry on and in ten years we'll have self-driving cars you can't look back at the slope of the curve right and think that's a slope going forward especially with these exponential laws at play so the slope ahead is distinctly steeper in this deeper and you left out my favorite law which is a Mars law which is you know we underestimate in the short term or overestimate in the short term and underestimate in the long term that's all about it's all about the slope so there we could go on for probably like an hour and I know I could but you got a kill you got to go into your event so thanks for taking min out of your busy day really enjoyed the conversation and look forward to our next one my pleasure thanks all right Jeff Rick here with the Q we're at the Western Digital headquarters in Milpitas at the Auto Tech Council innovation in motion mapping and navigation event thanks for watching

>> (Announcer) Live from San Jose, in the heart of Silicon Valley, it's theCUBE covering DataWorks Summit 2017, brought to you by Hortonworks. >> Hi everybody, this is George Gilbert, Big Data and Analytics Analyst with Wikibon. We are wrapping up our show on theCUBE today at DataWorks 2017 in San Jose. It has been a very interesting day, and we have a special guest to help us do a survey of the wrap-up, George Chow from Simba. We used to call him Chief Technology Officer, now he's Technology Fellow, but when we was explaining the different in titles to me, I thought he said Technology Felon. (George Chow laughs) But he's since corrected me. >> Yes, very much so >> So George and I have been, we've been looking at both Spark Summit last week and DataWorks this week. What are some of the big advances that really caught your attention? >> What's caught my attention actually is how much manufacturing has really, I think, caught into the streaming data. I think last week was very notable that both Volkswagon and Audi actually had case studies for how they're using streaming data. And I think just before the break now, there was also a similar session from Ford, showcasing what they are doing around streaming data. >> And are they using the streaming analytics capabilities for autonomous driving, or is it other telemetry that they're analyzing? >> The, what is it, I think the Volkswagon study was production, because I still have to review the notes, but the one for Audi was actually quite interesting because it was for managing paint defect. >> (George Gilbert) For paint-- >> Paint defect. >> (George Gilbert) Oh. >> So what they were doing, they were essentially recording the environmental condition that they were painting the cars in, basically the entire pipeline-- >> To predict when there would be imperfections. >> (George Chow) Yes. >> Because paint is an extremely high-value sort of step in the assembly process. >> Yes, what they are trying to do is to essentially make a connection between downstream defect, like future defect, and somewhat trying to pinpoint the causes upstream. So the idea is that if they record all the environmental conditions early on, they could turn around and hopefully figure it out later on. >> Okay, this sounds really, really concrete. So what are some of the surprising environmental variables that they're tracking, and then what's the technology that they're using to build model and then anticipate if there's a problem? >> I think the surprising finding they said were actually, I think it was a humidity or fan speed, if I recall, at the time when the paint was being applied, because essentially, paint has to be... Paint is very sensitive to the condition that is being applied to the body. So my recollection is that one of the finding was that it was a narrow window during which the paint were, like, ideal, in terms of having the least amount of defect. >> So, had they built a digital twin style model, where it's like a digital replica of some aspects of the car, or was it more of a predictive model that had telemetry coming at it, and when it's an outside a certain bounds they know they're going to have defects downstream? >> I think they're still working on the predictive model, or actually the model is still being built, because they are essentially trying to build that model to figure out how they should be tuning the production pipeline. >> Got it, so this is sort of still in the development phase? >> (George Chow) Yeah, yeah >> And can you tell us, did they talk about the technologies that they're using? >> I remember the... It's a little hazy now because after a couple weeks of conference, so I don't remember the specifics because I was counting on the recordings to come out in a couples weeks' time. So I'll definitely share that. It's a case study to keep an eye on. >> So tell us, were there other ones where this use of real-time or near real-time data had some applications that we couldn't do before because we now can do things with very low latency? >> I think that's the one that I was looking forward to with Ford. That was the session just earlier, I think about an hour ago. The session actually consisted of a demo that was being done live, you know. It was being streamed to us where they were showcasing the data that was coming off a car that's been rigged up. >> So what data were they tracking and what were they trying to anticipate here? >> They didn't give enough detail, but it was basically data coming off of the CAN bus of the car, so if anybody is familiar with the-- >> Oh that's right, you're a car guru, and you and I compare, well our latest favorite is the Porche Macan >> Yes, yes. >> SUV, okay. >> But yeah, they were looking at streaming the performance data of the car as well as the location data. >> Okay, and... Oh, this sounds more like a test case, like can we get telemetry data that might be good for insurance or for... >> Well they've built out the system enough using the Lambda Architecture with Kafka, so they were actually consuming the data in real-time, and the demo was actually exactly seeing the data being ingested and being acted on. So in the case they were doing a simplistic visualization of just placing the car on the Google Map so you can basically follow the car around. >> Okay so, what was the technical components in the car, and then, how much data were they sending to some, or where was the data being sent to, or how much of the data? >> The data was actually sent, streamed, all the way into Ford's own data centers. So they were using NiFi with all the right proxy-- >> (George Gilbert) NiFi being from Hortonworks there. >> Yeah, yeah >> The Hortonworks data flow, okay >> Yeah, with all the appropriate proxys and firewall to bring it all the way into a secure environment. >> Wow >> So it was quite impressive from the point of view of, it was life data coming off of the 4G modem, well actually being uploaded through the 4G modem in the car. >> Wow, okay, did they say how much compute and storage they needed in the device, in this case the car? >> I think they were using a very lightweight platform. They were streaming apparently from the Raspberry Pi. >> (George Gilbert) Oh, interesting. >> But they were very guarded about what was inside the data center because, you know, for competitive reasons, they couldn't share much about how big or how large a scale they could operate at. >> Okay, so Simba has been doing ODBC and JDBC drivers to standard APIs, to databases for a long time. That was all about, that was an era where either it was interactive or batch. So, how is streaming, sort of big picture, going to change the way applications are built? >> Well, one way to think about streaming is that if you look at many of these APIs, into these systems, like Spark is a good example, where they're trying to harmonize streaming and batch, or rather, to take away the need to deal with it as a streaming system as opposed to a batch system, because it's obviously much easier to think about and reason about your system when it is traditional, like in the traditional batch model. So, the way that I see it also happening is that streaming systems will, you could say will adapt, will actually become easier to build, and everyone is trying to make it easier to build, so that you don't have to think about and reason about it as a streaming system. >> Okay, so this is really important. But they have to make a trade-off if they do it that way. So there's the desire for leveraging skill sets, which were all batch-oriented, and then, presumably SQL, which is a data manipulation everyone's comfortable with, but then, if you're doing it batch-oriented, you have a portion of time where you're not sure you have the final answer. And I assume if you were in a streaming-first solution, you would explicitly know whether you have all the data or don't, as opposed to late arriving stuff, that might come later. >> Yes, but what I'm referring to is actually the programming model. All I'm saying is that more and more people will want streaming applications, but more and more people need to develop it quickly, without having to build it in a very specialized fashion. So when you look at, let's say the example of Spark, when they focus on structured streaming, the whole idea is to make it possible for you to develop the app without having to write it from scratch. And the comment about SQL is actually exactly on point, because the idea is that you want to work with the data, you can say, not mindful, not with a lot of work to account for the fact that it is actually streaming data that could arrive out of order even, so the whole idea is that if you can build applications in a more consistent way, irrespective whether it's batch or streaming, you're better off. >> So, last week even though we didn't have a major release of Spark, we had like a point release, or a discussion about the 2.2 release, and that's of course very relevant for our big data ecosystem since Spark has become the compute engine for it. Explain the significance where the reaction time, the latency for Spark, went down from several hundred milliseconds to one millisecond or below. What are the implications for the programming model and for the applications you can build with it. >> Actually, hitting that new threshold, the millisecond, is actually a very important milestone because when you look at a typical scenario, let's say with AdTech where you're serving ads, you really only have, maybe, on the order about 100 or maybe 200 millisecond max to actually turn around. >> And that max includes a bunch of things, not just the calculation. >> Yeah, and that, let's say 100 milliseconds, includes transfer time, which means that in your real budget, you only have allowances for maybe, under 10 to 20 milliseconds to compute and do any work. So being able to actually have a system that delivers millisecond-level performance actually gives you ability to use Spark right now in that scenario. >> Okay, so in other words, now they can claim, even if it's not per event processing, they can claim that they can react so fast that it's as good as per event processing, is that fair to say? >> Yes, yes that's very fair. >> Okay, that's significant. So, what type... How would you see applications changing? We've only got another minute or two, but how do you see applications changing now that, Spark has been designed for people that have traditional, batch-oriented skills, but who can now learn how to do streaming, real-time applications without learning anything really new. How will that change what we see next year? >> Well I think we should be careful to not pigeonhole Spark as something built for batch, because I think the idea is that, you could say, the originators, of Spark know that it's all about the ease of development, and it's the ease of reasoning about your system. It's not the fact that the technology is built for batch, so the fact that you could use your knowledge and experience and an API that actually is familiar, should leverage it for something that you can build for streaming. That's the power, you could say. That's the strength of what the Spark project has taken on. >> Okay, we're going to have to end it on that note. There's so much more to go through. George, you will be back as a favorite guest on the show. There will be many more interviews to come. >> Thank you. >> With that, this is George Gilbert. We are DataWorks 2017 in San Jose. We had a great day today. We learned a lot from Rob Bearden and Rob Thomas up front about the IBM deal. We had Scott Gnau, CTO of Hortonworks on several times, and we've come away with an appreciation for a partnership now between IBM and Hortonworks that can take the two of them into a set of use cases that neither one on its own could really handle before. So today was a significant day. Tune in tomorrow, we have another great set of guests. Keynotes start at nine, and our guests will be on starting at 11. So with that, this is George Gilbert, signing out. Have a good night. (energetic, echoing chord and drum beat)

>> Announcer: Live from Munich, Germany, it's the CUBE, covering DataWorks Summit Europe 2017. Brought to you by Hortonworks. >> Hey welcome back everyone. We're here live in Munich Germany for DataWorks 2017 Summit, formerly know as Hadoop Summit, now called DataWorks. I'm John Furrier with the CUBE, my co-host Dave Vellante, here for two days of wall-to-wall coverage. Our next guest is Nadeem Gulzar, head of advanced Analytics at Danske Bank. Welcome to the CUBE. >> Thank you. >> You're a customer but also talking here at the event, bringing all your folks here. Your observation, I mean, Hadoop is not going away, certainly we see that. But now, as John Kreisa, who was MC'ing, was on earlier said, open up the aperture to analytics, is really where the action is. >> Nadeem: Absolutely. >> Your reaction to that. >> I completely agree, because again, Hadoop is basically just the basic infrastructure, right. Components build on components, and things like that. But, when you really utilize it, is when you add the advanced analytics frameworks. There are many out there. I'm not going to favor one over another. But the main thing is, you need that to really leverage Hadoop. And, at the same time, I think it's very important to realize how much power there actually is in this. For us at, in Danske Bank, getting Hadoop, getting the advanced analytics framework, has really proven quite a lot. It allowed us actually to dig into our core data, transaction data for instance, which we haven't been able to for decades. >> So take me through, because you guys are an interesting use case because you're advanced. You're gettin' at the data, which is cutting edge. But you're going through this transformation, and you have to because you're on the front lines. Take us inside the company, without giving away any trade secrets, and describe the environment. What's the current situation, and how is it evolving from an IT standpoint, and also from the relationship with the stakekholders in the business side. >> So again, we are a bank with 20,000 employees, so of course in a large organization you have silos, People feeling okay, this is my domain, this is my kingdom, don't touch it. Don't approach me, or you can approach me, talk to me, you have to convince me, otherwise don't talk to me at all. So we get that quite a lot, and to be honest, from my point of view, if we do not lift as a bank, we're not going to succeed. If I have success, if my organization of almost 60 people have success, that's good in itself, but we are not going to succeed as a bank. So for me, it's quite important that I go down and break down these barriers, and allow us to come in, tell the business units, tell them what sort of capabilities do we bring, and include them. That is actually the main key. I don't want to replace them or anything like that. >> So an organizational challenge is to get the mindset shifted. How 'about process gaps and product gaps? 'Cause I mean I almost see the sequence, kind of a group hug if you will, organizational mindset, kind of a reset or calibration. And then identify processes and then product gaps, seem to be the next transition. >> Absolutely, absolutely, and there are some gaps. Still, even though we have been on this journey for a considerable amount of time, there are still gaps, both in terms of processes and products. Because again, even though we have top management buy in, it doesn't go through all the way down to the middle layer. So we still struggle with this from time to time. >> How do you break down those barriers? What do you do, what's your strategy? >> I'm humble, to be honest. I go in, I tell them, listen you guys I have some capabilities that I can add to your capabilities. I want you to leverage me to make your life easier. I want to lift you as an organization. I don't care about myself, I want you to be better at what you're doing. >> So Nadeem, the money business and the technology business have always had a close relationship. It was like in 2010 after we came out of the downturn, it was like this other massive collision. You had begun experimenting with Cloud, the shift, CapEx to OpEx. The data thing hit in a big way, obviously mobile became real. So talk about the confluence of those technologies, specifically in the context of your big data journey. Where did you get started, and how did it evolve? >> So actually it fit in quite nicely because we were coming out of this down period, right, so there was extreme amount of focus on cost. So, of course at the time where we wanted to go into this journey, a lot of people were asking, okay how much does this cost, what's the big strategy, and so on. And how's the road map going to look like, and what's the cost of the road map? The thing is, if you buy some off the shelf commercial product, it's quite expensive. We can easily talk like half a billion, something like that, for a full end to end system. So with this, you were allowed, or we were allowed, to start up with relatively small funding, and I'm actually talking about just like a million dollars, roughly. And that actually allowed us a substantial boost in the capability department, in allowing us to show what kind of use cases we could build, and what kind of value we could bring to Danske Bank. >> So you started with understanding Hadoop? Is that right, was that the starting point? >> Yes, in a fairly small, very researched team set up. We did the initial research, we looked at, okay what could this bring? We did some initial, what we call, proof of value. So small, small, pilot projects, looking at, okay this is the data. We can leverage it in this way, this is the value we can bring. How much can we actually boost the business? So everything is directly linked to business value. So, for instance, one of the use cases was within customers, understanding customer behavior, directly linking it to marketing, do more targeted marketing, and at the end get more results in terms of increased sales. >> We just started a journey 2009, 2010, is that right? Or was it later? >> No, we started somewhat later. The initial research was in '14. >> In '14? Okay, alright, so '14 you sort of became familiar with Hadoop, and then I imagine, like many customers, you said okay, wow this stuff is complicated, but you were takin' it in small chunks, low risk. Let's get some value. Marketing is an obvious use case. I would imagine fraud is another obvious use case. So then, how did that evolve? I mean it's only a few years now, but I imagine you've evolved very quickly. >> Extremely quickly. Actually, within two months of the research, we actually saw a huge benefit in this area, and directly we went with the material to the senior members of the different boards we wanted to affect, and actually, you could call it luck. But, maybe we were just well prepared and convincing, so we actually directly got funding at that point in time. They said, listen, this is very promising. Here you go, start off with the initial, slightly larger projects, prove some value, and then come back to us. Initially they wanted us to do two things, look into the customer journey, or doing deeper customer behavior analytics, and the second was within risk. Doing things like, text mining, financial statements, getting some deeper into that, doing some web crawling on financial data such as Bloomberg, etcetera, and then pull it into the system. >> To inform your investments as a financial institution. From an architecture and infrastructure standpoint, we talked about starting at Hadoop. Has it evolved, how has it evolved? Where do you see it going? >> It has evolved quite a lot in the past couple of years. And again, to be honest, it's like every quarter something new is happening and we need to do some adjustments even to the core architecture. And with the introduction of HDB 3 hence later this year, I think we're going to see a massive change once again. Hortonworks already calls it a major change, or a major release. But actually, the things they are doing is extremely promising, so we want to take that step with them. But again, it's going to affect us. >> What's exciting about that to you? >> The thing that's very exciting is, we are now at like a balance point, where we have played quite a lot, we have released a couple of production grade solutions, but we have really not reached the full enterprise potential. So getting like into the real deep stuff with living under heavy SLA's, regulation stuff. All these kind of things is not in place yet, from my point of view. >> We talk a lot about, in the CUBE, and in our company, about these emergent work loads; you had batch, interactive, and the world went back to batch with Hadoop, and now you have this continuous workload, this streaming real-time workloads. How is that affecting your organization, generally, and specifically, you're thinking about architecture. How real is that and where do you see that in the future? >> It's the core, to be honest. Again, one of the main things we are trying to do is look into, so, gone are the days with heavy, heavy batches of data coming in. Because if you look at Weblocks for instance, so when customers interacts with our web, or our tablet solution, or mobile solution, the amount of data generated is humongous. So, no way on earth you can think about batches anymore. So it's more about streaming the data all the way in, doing real time analytics and then produce results. >> What would you say are your biggest, big data challenges, problems that you really want to attack and solve. >> So, what I really want to attack is, getting all sorts of data into the system. So, you can imagine, as a bank we have 2,000 plus systems. We have approximately 4,000 different points that delivers data. So getting all that mass into our data link, it's a huge task. We actually underestimated it. But now, we have seen we have to attack it and get it in because that is the gold. Data is the future gold. So we need to mine it in, we need to do analytics on top of it and produce value. >> And then once you get it in there, I'm sure you're anticipating that you want to make sure this doesn't go stale, doesn't become a swamp, doesn't get frozen. It's your job to talk about data oceans, which is really the long term vision I presume, right? >> And that is a key as well because with the GDPR for instance, we need to have full mapping and full control of all the data coming in. We need to be able to generate metadata, we need to have full data lineage. We need to know what, all the data where it came from, how it's interconnected, relations, all that. >> And that's what, two years away from implementation? Is that about right? >> It's going to take a while, of course. But again, the key thing is we make the framework so all the data coming in step by step, has that. >> Yeah, but so GDPR though, it goes into effect in '19, is that correct? >> It's actually May '18. >> May '18, oh, so it's much tighter time frame then I realized. >> John: You're under the gun. >> Nadeem: Yes. >> Okay, observation here at this event, obviously a lot of IOT, for you that's people. People and things are kind of the edge of the network. The intelligent edge is a big, big topic. Very dynamic. >> Nadeem: Extremely dynamic. >> A lot of things happening. Lot of opportunities for you to be this humble service provider to your constituents, but also your customers. How do you guys view that? What's the current landscape look like as you look outside the company and look at what's happening around you, the world. >> A lot of cool things are going on, to be honest. Especially in IOT, right? I mean, even though we are a core bank, still, there are a lot of sensors we can use. I talked a bit about, under the keynote, about ATM's, right? So, we're also looking at how can we utilize this technology? How can we enable our customers? If you look at our apps, they also generate extreme amounts of data, right? The mobile solution that we have, it gives away GPS location and things like that. And we want to include all that data in. At the end of the day, it's not for our gain, we are not always looking at making the next buck, right? It's also about being there for the customer, providing the services they need, making their banking life easier. >> And your ecosystem is evolving and rapidly adding new constituents to your network because, then you have the consumer with the phone, the mobile app alone, never mind the point of sale opportunity at the ATM. Now a digital, augmented reality experience could be enabled where you now have fintech suppliers, and potentially other suppliers in this now digital network that could be relational with you. >> Yes, and our job is to make sure that we leverage that. Acquiring a banking license is extremely difficult. But we have it, and what we need to do is to engage these fintechs, partners, even other banks, and say listen guys we invite you in. Utilize our services, utilize our framework, utilize our foundation and let's build something upon that. >> If you had to explain, Nadeem, this fintech start up trend because it is super hot, what is it? I mean how would you describe to someone who's not in the banking world. 'Cause most people would be scratching their head and say, isn't that banking? But, now this ecosystem is developing of new entrepreneurial activity and they're skyrocketing with success 'cause they have either a specialty focus, they do something extremely well. It may or may not be in a direct big space with a bank, but a white space. Use cases. So, is it good? Is it bad? Is it hype? What's the current state of the fintech situation? >> From my point of view, it's awesome. And the reason is, these guys are pushing us. Remember, we are a hundred fifty plus year old bank. And sometimes we do tend to just pat on our back and say, okay, this is going good, right? But, these guys are coming in, giving some competition, and we love it. >> Give me an example of a fintech capabilities. Randomly bring up some examples to highlight what fintech is. >> So what we've seen in, for instance the German market, is the fintechs coming in, utilizing some of the customer data, and then producing awesome new applications. Whether it is a new net bank, where a customer can interact with it, in a much, much more smoother way. Some of the banks tend to over clutter things, not make it simple. So things like, where you can put in, you can look at your transactions in a Google Map, for instance. You can see how much do you spend at this location. You can move around. >> You could literally follow the money, on a map. (laughing) >> So this is your home base, you go out here, you spend this amount of money, and maybe even add more on it. So, let's say you do your grocery shopping over here, but if I moved all my business from this company to this company, how much could I save? Imagine if you could just drag and drop it and see, okay, I could actually save a couple of thousand bucks, awesome. >> And machine learning is going to totally change the game with Augmented Intelligence. AI is called Artificial Intelligence, or Augmented Intelligence, depending upon your definition. This is a good thing for consumers. >> It is, it is. >> And thinking about disruption, what do you guys, what are your thoughts on blockchain? What is your research showing? You playing around with Hyperledger at all? >> Yes we are. And blockchain, it's also quite interesting. We're doing lots of research on that. What's it's shown actually is that this is a technology that we can also use. And we can also really utilize, even the security aspects of it. If you just take that, you could really implement that. >> The identity aspect, it's federating identity around fraud, another area you can innovate on. I'm bullish on blockchain, a lot of people are skeptical, but Dave knows I really, I love blockchain. Because it's not about Bitcoin per se, it's sort of the underlying opportunity. It just seems fascinating. Dave you know, I got to get on my soapbox, blockchain soapbox. >> We've never really looked at Bitcoin as just a currency, it's move of a technology platform, and I have always been fascinated with the security angle. Virtually unhackable, put that in quotes. No need for a third party to intermediate. So many positive fundamentals, now it's guys like you figuring out, okay the practitioner saying, here's how we're going to implement it and commercialize it. >> And actually it fits in quite well with things like GDPR. This is also about opening up, the same with PSD 2. Exposing the customer data, making it available for the general public. And ultimately the goal is, so you as a consumer, me as a consumer, we own our data. >> Nadeem, thank you so much for coming on the CUBE and sharing your practitioner situation, and your advice, as well as commentary. I'll give you the last word. As you and your team embark from DataWorks 2017 and head back to the ranch, so to speak, and bring back some stuff. What are you going to work on? What's the to do item? What are you going to sharpen the saw on and cut when you get back? >> So for us on the very, very short term, it's about taking our platform and our capabilities and move it into the real enterprise world. That is our first key milestone that we are going to go for. And, I'll tell you, we're going to go all in for that. Because, unless we do that, we're not able to really attack the core of banking, which requires this, right? Please remember that a consumer doing a transaction somewhere in the world, he cannot stand and wait for ages for something to be processed. It needs to be instantaneous. So, this is what we need to do. >> You think this event, you're armed up with product. >> Absolutely, absolutely. Lots of good insight we've gotten from this. Lots of potential, lots of networking guys and other companies that we can talk to about this. >> Also great recruiting, get some developers out there too, lot of great people. Congratulations on your success and thanks for sharing this great insight here on the CUBE, exposing the data to you live on the CUBE. Silicon Angle dot TV, I'm John Furrier, with Dave Vellante my co-host, more great coverage stay with us here live in Munich, Germany for DataWorks 2017 Summit. We'll be right back.