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

>> Narrator: Live, from San Jose, it's theCUBE. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners (mellow electronic music) >> Welcome back to theCUBE, on day two of our coverage of our event, Big Data SV. I'm Lisa Martin, my co-host is Peter Burris. We are the down the street from the Strata Data Conference, we've had a great day yesterday, and great morning already, really learning and peeling back the layers of big data, challenges, opportunities, next generation, we're welcoming back to theCUBE an alumni, the CMO of Kinetica, Dan Raskin. Hey Dan, welcome back to theCUBE. >> Thank you, thank you for having me. >> So, I'm a messaging girl, look at your website, the insight engine for the extreme data economy. Tell us about the extreme data economy, and what is that, what does it mean for your customers? >> Yeah, so it's a great question, and, from our perspective, we sit, we're here at Strata, and you see all the different vendors kind of talking about what's going on, and there's a little bit of word spaghetti out there that makes it really hard for customers to think about how big data is affecting them today, right? And so, what we're actually looking at is the idea of, the world's changed. That, big data from five years ago, doesn't necessarily address all the use cases today. If you think about what customers are going through, you have more users, devices, and things coming on, there's more data coming back than ever before, and it's not just about creating the data driven business, and building these massive data lakes that turn into data swamps, it's really about how do you create the data-powered business. So when we're using that term, we're really trying to call out that the world's changed, that, in order for businesses to compete in this new world, they have to think about to take data and create CoreIP that differentiates, how do I use it to affect the omnichannel, how do I use it to deal with new things in the realm of banking and Fintech, how do I use it to protect myself against disruption in telco, and so, the extreme data economy is really this idea that you have business in motion, more things coming online ever before, how do I create a data strategy, where data is infused in my business, and creates CoreIP that helps me maintain category leadership or grow. >> So as you think about that challenge, there's a number of technologies that come into play. Not least of which is the industry, while it's always to a degree been driven by what hardware can do, that's moderated a bit over time, but today, in many respects, a lot of what is possible is made possible, by what hardware can do, and what hardware's going to be able to do. We've been using similar AI algorithms for a long time. But we didn't have the power to use them! We had access to data, but we didn't have the power to acquire and bring it in. So how is the relationship between your software, and your platform, and some of the new hardware that's becoming available, starting to play out in a way of creating value for customers? >> Right, so, if you think about this in terms of this extreme data concept, and you think about it in terms of a couple of things, one, streaming data, just massive amounts of streaming data coming in. Billions of rows that people want to take and translate into value. >> And that data coming from-- >> It's coming from users, devices, things, interacting with all the different assets, more edge devices that are coming online, and the Wild West essentially. You look at the world of IoT and it's absolutely insane, with the number of protocols, and device data that's coming back to a company, and then you think about how do you actually translate this into real-time insight. Not near real-time, where it's taking seconds, but true millisecond response times where you can infuse this into your business, and one of our whole premises about Kinetica is the idea of this massive parallel compute. So the idea of not using CPUs anymore, to actually drive the powering behind your intelligence, but leveraging GPUs, and if you think about this, a CPU has 64 cores, 64 parallel things that you can do at a time, a GPU can have up to 6,000 cores, 6,000 parallel things, so it's kind of like lizard brain verse modern brain. How do you actually create this next generation brain that has all these neural networks, for processing the data, in a way that you couldn't. And then on top of that, you're using not just the technology of GPUs, you're trying to operationalize it. So how do you actually bring the data scientist, the BI folks, the business folks all together to actually create a unified operational process, and the underlying piece is the Kinetica engine and the GPU used to do this, but the power is really in the use cases of what you can do with it, and how you actually affect different industries. >> So can you elaborate a little bit more on the use cases, in this kind of game changing environment? >> Yeah, so there's a couple of common use cases that we're seeing, one that affects every enterprise is the idea of breaking down silos of business units, and creating the customer 360 view. How do I actually take all these disparate data feeds, bring them into an engine where I can visualize concepts about my customer and the environment that they're living in, and provide more insight? So if you think about things like Whole Foods and Amazon merging together, you now have this power of, how do I actually bridge the digital and physical world to create a better omnichannel experience for the user, how do I think about things in terms of what preferences they have, personalization, how to actually pair that with sensor data to affect how they actually navigate in a Whole Foods store more efficiently, and that's affecting every industry, you could take that to banking as well and think about the banking omminchannel, and ATMs, and the digital bank, and all these Fintech upstarts that are working to disrupt them. A great example for us is the United States Postal Service, where we're actually looking at all the data, the environmental data, around the US Postal Service, we're able to visualize it in real-time, we're able to affect the logistics of how they actually navigate through their routes, we're able to look things like postal workers separating out of their zones, and potentially kicking off alerts around that, so effectively making the business more efficient. But, we've moved into this world where we always used to talk about brick and mortar going to cloud, we're now in this world where the true value is how you bridge the digital and physical world, and create more transformative experiences, and that's what we want to do with data. So it could be logistics, it could be omnichannel, it could be security, you name it. It affects every single industry that we're talking about. >> So I got two questions, what is Kinetica's contribution to that, and then, very importantly, as a CMO, how are you thinking about making sure that the value that people are creating, or can create with Kinetica, gets more broadly diffused into an ecosystem. >> Yeah, so the power that we're bringing is the idea of how to operationalize this in a way where again, you're using your data to create value, so, having a single engine where you're collecting all of this data, massive volumes of data, terabytes upon terabytes of data, enabling it where you can query the data, with millisecond response times, and visualize it, with millisecond response times, run machine learning algorithms against it to augment it, you still have that human ability to look at massive sets of data, and do ad hoc discovery, but can run machining learning algorithms against that and complement it with machine learning. And then the operational piece of bringing the data scientists into the same platform that the business is using, so you don't have data recency issues, is a really powerful mix. The other piece I would just add is the whole piece around data discovery, you can't really call it big data if, in order to analyze the data, you have to downsize and downsample to look at a subset of data. It's all about looking at the entire set. So that's where we really bring value. >> So, to summarize very quickly, you are providing a platform that can run very, very fast, in a parallel system, and memories in these parallel systems, so that large amounts of data can be acted upon. >> That's right. >> Now, so, the next question is, there's not going to be a billion people that are going to use your tool to do things, how are you going to work with an ecosystem and partners to get the value that you're able to create with this data, out into the engine enterprise. >> It's a great question, and probably the biggest challenge that I have, which is, how do you get above the word spaghetti, and just get into education around this. And so I think the key is getting into examples, of how it's affecting the industry. So don't talk about the technology, and streaming from Kafka into a GPU-powered engine, talk about the impact to the business in terms of what it brings in terms of the omnichannel. You look at something like Japan in the 2020 Olympics, and you think about that in terms of telco, and how are the mobile providers going to be able to take all the data of what people are doing, and to related that to ad-tech, to relate that to customer insight, to relate that to new business models of how they could sell the data, that's the world of education we have to focus on, is talk about the transformative value it brings from the customer perspective, the outside-in as opposed to the inside-out. >> On that educational perspective, as a CMO, I'm sure you meet with a lot of customers, do you find that you might be in this role of trying to help bridge the gaps between different roles in an organization, where there's data silos, and there's probably still some territorial culture going on? What are you finding in terms of Kinetica's ability to really help educate and maybe bring more stakeholders, not just to the table, but kind of build a foundation of collaboration? >> Yeah, it's a really interesting question because I think it means, not just for Kinetica, but all vendors in the space, have to get out of their comfort zone, and just stop talking speeds and feeds and scale, and in fact, when we were looking at how to tell our story, we did an analysis of where most companies were talking, and they were focusing a lot more on the technical aspirations that developers sell, which is important, you still need to court the developer, you have community products that they can download, and kick the tires with, but we need to extend our dialogue, get out of our customer comfort zone, and start talking more to CIOs, CTOs, CDOs, and that's just reaching out to different avenues of communication, different ways of engaging. And so, I think that's kind of a core piece that I'm taking away from Strata, is we do a wonderful job of speaking to developers, we all need to get out of our comfort zone and talk to a broader set of folks, so business folks. >> Right, 'cause that opens up so many new potential products, new revenue streams, on the marketing side being able to really target your customer base audience, with relevant, timely offers, to be able to be more connected. >> Yeah, the worst scenario is talking to an enterprise around the wonders of a technology that they're super excited about, but they don't know the use case that they're trying to solve, start with the use case they're trying to solve, start with thinking about how this could affect their position in the market, and work on that, in partnership. We have to do that in collaboration with the customers. We can't just do that alone, it's about building a partnership and learning together around how you use data in a different way. >> So as you imagine, the investments that Kinetica is going to make over the next few years, with partners, with customers, what do you hope Kinetica will be in 2020? >> So, we want it to be that transformative engine for enterprises, we think we are delivering something that's quite unique in the world, and, you want to see this on a global basis, affecting our customer's value. I almost want to take us out of the story, and if I'm successful, you're going to hear wonderful enterprise companies across telco, banking, and other areas just telling their story, and we happen to be the engine behind it. >> So you're an ingredient in their success. >> Yes, a core ingredient in their success. >> So if we think about over the course of the next technology, set of technology waves, are they any particular applications that you think you're going to be stronger in? So I'll give you an example, do you envision that Kinetica can have a major play in how automation happens inside infrastructure, or how developers start seeing patterns in data, imagine how those assets get created. Where are some of the kind of practical, but not really, or rarely talked about applications that you might find yourselves becoming more of an ingredient because they themselves become ingredients to some of these other big use cases? >> There are a lot of commonalities that we're starting to see, and the interesting piece is the architecture that you implement tends to be the same, but the context of how you talk about it, and the impact it has tends to be different, so, I already mentioned the customer 360 view? First and foremost, break down silos across your organization, figure out how do you get your data into one place where you can run queries against it, you can visualize it, you can do machine learning analysis, that's a foundational element, and, I have a company in Asia called Lippo that is doing that in their space, where all of the sudden they're starting to glean things they didn't know about their customer before to create, doing that ad hoc discovery, so that's one area. The other piece is this use case of how do you actually operationalize data scientists, and machine learning, into your core business? So, that's another area that we focus on. There are simple entry points, things like Tableau Acceleration, where you put us underneath the existing BI infrastructure, and all of the sudden, you're a hundred times faster, and now your business folks can sit at the table, and make real-time business decisions, where in the past, if they clicked on certain things, they'd have to wait to get those results. Geospatial visualization's a no-brainer, the idea of taking environmental data, pairing it with your customer data, for example, and now learning about interactions. And I'd say the other piece is more innovation driven, where we would love sit down with different innovation groups in different verticals and talk with them about, how are you looking to monetize your data in the future, what are the new business models, how does things like voice interaction affect your data strategy, what are the different ways you want to engage with your data, so there's a lot of different realms we can go to. >> One of the things you said as we wrap up here, that I couldn't agree with more, is, the best value articulation I think a brand can have, period, is through the voice of their customer. And being able to be, and I think that's one of the things that Paul said yesterday is, defining Kinetica's success based on the success of your customers across industry, and I think really doesn't get more objective than a customer who has, not just from a developer perspective, maybe improved productivity, or workforce productivity, but actually moved the business forward, to a point where you're maybe bridging the gaps between the digital and physical, and actually enabling that business to be more profitable, open up new revenue streams because this foundation of collaboration has been established. >> I think that's a great way to think about it-- >> Which is good, 'cause he's your CEO. >> (laughs) Yes, that sustains my job. But the other piece is, I almost get embarrassed talking about Kinetica, I don't want to be the car salesman, or the vacuum salesman, that sprinkles dirt on the floor and then vacuums it up, I'd rather us kind of fade to the behind the scenes power where our customers are out there telling wonderful stories that have an impact on how people live in this world. To me, that's the best marketing you can do, is real stories, real value. >> Couldn't agree more. Well Dan, thanks so much for stopping by, sharing what things that Kinetica is doing, some of the things you're hearing, and how you're working to really build this foundation of collaboration and enablement within your customers across industries. We look forward to hearing the kind of cool stuff that happens with Kinetica, throughout the rest of the year, and again, thanks for stopping by and sharing your insights. >> Thank you for having me. >> I want to thank you for watching theCUBE, I'm Lisa Martin with my co-host Peter Burris, we are at Big Data SV, our second day of coverage, at a cool place called the Forager Tasting Room, in downtown San Jose, stop by, check us out, and have a chance to talk with some of our amazing analysts on all things big data. Stick around though, we'll be right back with our next guest after a short break. (mellow electronic music)

>> Announcer: From San Jose, it's theCUBE. (upbeat music) Presenting Big Data, Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to theCUBE. We are live on our first day of coverage of our event, Big Data SV. This is our tenth Big Data event. We've done five here in Silicon Valley. We also do them in New York City in the fall. We have a great day of coverage. We're next to where the Startup Data conference is going on at Forger Tasting Room and Eatery. Come on down, be part of our audience. We also have a great party tonight where you can network with some of our experts and analysts. And tomorrow morning, we've got a breakfast briefing. I'm Lisa Martin with my co-host, Peter Burris, and we're excited to welcome to theCUBE for the first time the CEO of Kinetica, Paul Appleby. Hey Paul, welcome. >> Hey, thanks, it's great to be here. >> We're excited to have you here, and I saw something marketer, and terms, I grasp onto them. Kinetica is the insight engine for the extreme data economy. What is the extreme data economy, and what are you guys doing to drive insight from it? >> Wow, how do I put that in a snapshot? Let me share with you my thoughts on this because the fundamental principals around data have changed. You know, in the past, our businesses are really validated around data. We reported out how our business performed. We reported to our regulators. Over time, we drove insights from our data. But today, in this kind of extreme data world, in this world of digital business, our businesses need to be powered by data. >> So what are the, let me task this on you, so one of the ways that we think about it is that data has become an asset. >> Paul: Oh yeah. >> It's become an asset. But now, the business has to care for, has to define it, care for it, feed it, continue to invest in it, find new ways of using it. Is that kind of what you're suggesting companies to think about? >> Absolutely what we're saying. I mean, if you think about what Angela Merkel said at the World Economic Forum earlier this year, that she saw data as the raw material of the 21st century. And talking about about Germany fundamentally shifting from being an engineering, manufacturing centric economy to a data centric economy. So this is not just about data powering our businesses, this is about data powering our economies. >> So let me build on that if I may because I think it gets to what, in many respects Kinetica's Core Value proposition is. And that is, is that data is a different type of an asset. Most assets are characterized by, you apply it here, or you apply it there. You can't apply it in both places at the same time. And it's one of the misnomers of the notion of data as fuels. Because fuel is still an asset that has certain specificities, you can't apply it to multiple places. >> Absolutely. >> But data, you can, which means that you can copy it, you can share it. You can combine it in interesting ways. But that means that the ... to use data as an asset, especially given the velocity and the volume that we're talking about, you need new types of technologies that are capable of sustaining the quality of that data while making it possible to share it to all the different applications. Have I got that right? And what does Kinetica do in that regard? >> You absolutely nailed it because what you talked about is a shift from predictability associated with data, to unpredictability. We actually don't know the use cases that we're going to leverage for our data moving forward, but we understand how valuable an asset it is. And I'll give you two examples of that. There's a company here, based in the Bay Area, a really cool company called Liquid Robotics. And they build these autonomous aquatic robots. And they've carried a vast array of senses and now we're collecting data. And of course, that's hugely powerful to oil and gas exploration, to research, to shipping companies, etc. etc. etc. Even homeland security applications. But what they did, they were selling the robots, and what they realized over time is that the value of their business wasn't the robots. It was the data. And that one piece of data has a totally different meaning to a shipping company than it does to a fisheries companies. But they could sell that exact same piece of data to multiple companies. Now, of course, their business has grown on in Scaldon. I think they were acquired by Bowing. But what you're talking about is exactly where Kinetica sits. It's an engine that allows you to deal with the unpredictability of data. Not only the sources of data, but the uses of data, and enables you to do that in real time. >> So Kinetica's technology was actually developed to meet some intelligence needs of the US Army. My dad was a former army ranger airborne. So tell us a little bit about that and kind of the genesis of the technology. >> Yeah, it's a fascinating use case if you think about it, where we're all concerned, globally, about cyber threat. We're all concerned about terrorist threats. But how do you identity terrorist threats in real time? And the only way to do that is to actually consume vast amount of data, whether it's drone footage, or traffic cameras. Whether it's mobile phone data or social data. but the ability to stream all of those sources of data and conduct analytics on that in real time was, really, the genesis of this business. It was a research project with the army and the NSA that was aimed at identifying terrorist threats in real time. >> But at the same time, you not only have to be able to stream all the data in and do analytics on it, you also have to have interfaces and understandable approaches to acquiring the data, because I have a background, some background in that as well, to then be able to target the threat. So you have to be able to get the data in and analyze it, but also get it out to where it needs to be so an action can be taken. >> Yeah, and there are two big issues there. One issue is the inter-offer ability of the platform and the ability for you to not only consume data in real time from multiple sources, but to push that out to a variety of platforms in real time. That's one thing. The other thing is to understand that in this world that we're talking about today, there are multiple personas that want to consume that data, and many of them are not data scientists. They're not IT people, they're business people. They could be executives, or they could be field operatives in the case of intelligence. So you need to be able to push this data out in real time onto platforms that they consume, whether it's via mobile devices or any other device for that matter. >> But you also have to be able to build applications on it, right? >> Yeah, absolutely. >> So how does Kinetica facilitate that process? Because it looks more like a database, which is, which is, it's more than that, but it satisfies some of those conventions so developers have an afinity for it. >> Absolutely, so in the first instance, we provide tools ourselves for people to consume that data and to leverage the power of that data in real time in an incredibly visual way with a geospatial platform. But we also create the ability for a, to interface with really commonly used tools, because the whole idea, if you think about providing some sort of ubiquitous access to the platform, the easiest way to do that is to provide that through tools that people are used to using, whether that's something like Tablo, for example, or Esri, if you want to talk about geospatial data. So the first instance, it's actually providing access, in real time, through platforms that people are used to using. And then, of course, by building our technology in a really, really open framework with a broadly published set of APIs, we're able to support, not only the ability for our customers to build applications on that platform, and it could well be applications associated with autonomous vehicles. It could well be applications associated with Smart City. We're doing some incredible things with some of the bigger cities on the planet and leveraging the power of big data to optimize transportation, for example, in the city of London. It's those sorts of things that we're able to do with the platform. So it's not just about a database platform or an insights engine for dealing with these complex, vast amounts of data, but also the tools that allow you to visualize and utilize that data. >> Turn that data into an action. >> Yeah, because the data is useless until you're doing something with it. And that's really, if you think about the promise of things like smart grid. Collecting all of that data from all of those smart sensors is absolutely useless until you take an action that is meaningful for a consumer or meaningful in terms of the generational consumption of power. >> So Paul, as the CEO, when you're talking to customers, we talk about chief data officer, chief information officer, chief information security officer, there's a lot, data scientist engineers, there's just so many stakeholders that need access to the data. As businesses transform, there's new business models that can come into development if, like you were saying, the data is evaluated and it's meaningful. What are the conversations that you're having, I guess I'm curious, maybe, which personas are the table (Paul laughs) when you're talking about the business values that this technology can deliver? >> Yeah, that's a really, really good question because the truth is, there are multiple personas at the table. Now, we, in the technology industry, are quite often guilty of only talking to the technology personas. But as I've traveled around the world, whether I'm meeting with the world's biggest banks, the world's biggest Telco's, the world's biggest auto manufacturers, the people we meet, more often than not, are the business leaders. And they're looking for ways to solve complex problems. How do you bring the connected card alive? How do you really bring it to life? One car traveling around the city for a full day generates a terabyte of data. So what does that really mean when we start to connect the billions of cars that are in the marketplace in the framework of connected car, and then, ultimately, in a world of autonomous vehicles? So, for us, we're trying to navigate an interesting path. We're dragging the narrative out of just a technology-based narrative speeds and feeds, algorithms, and APIs, into a narrative about, well what does it mean for the pharmaceutical industry, for example? Because when you talk to pharmaceutical executives, the holy grail for the pharma industry is, how do we bring new and compelling medicines to market faster? Because the biggest challenge for them is the cycle times to bring new drugs to market. So we're helping companies like GSK shorten the cycle times to bring drugs to market. So they're the kinds of conversations that we're having. It's really about how we're taking data to power a transformational initiative in retail banking, in retail, in Telco, in pharma, rather than a conversation about the role of technology. Now, we always needs to deal with the technologists. We need to deal with the data scientists and the IT executives, and that's an important part of the conversation. But you would have seen, in recent times, the conversation that we're trying to have is far more of a business conversation. >> So if I can build on that. So do you think, in your experience, and recognizing that you have a data management tool with some other tools that helps people use the data that gets into Kinetica, are we going to see the population of data scientists increase fast enough so our executives don't have to become familiar with this new way of thinking, or are executives going to actually adopt some of these new ways of thinking about the problem from a data risk perspective? I know which way I think. >> Paul: Wow, >> Which way do you think? >> It's a loaded question, but I think if we're going to be in a world where business is powered by data, where our strategy is driven by data, our investment decisions are driven by data, and the new areas of business that we explored to creat new paths to value are driven by data, we have to make data more accessible. And if what you need to get access to the data is a whole team of data scientists, it kind of creates a barrier. I'm not knocking data scientists, but it does create a barrier. >> It limits the aperture. >> Absolutely, because every company I talk to says, "Our biggest challenge is, we can't get access to the data scientists that we need." So a big part of our strategy from the get go was to actually build a platform with all of these personas in mind, so it is built on this standard principle, the common principles of a relational database, that you're built around anti-standard sequel. >> Peter: It's recognizable. >> And it's recognizable, and consistent with the kinds of tools that executives have been using throughout their careers. >> Last question, we've got about 30 seconds left. >> Paul: Oh, okay. >> No pressure. >> You have said Kinetica's plan is to measure the success of the business by your customers' success. >> Absolutely. >> Where are you on that? >> We've begun that journey. I won't say we're there yet. We announced three weeks ago that we created a customer success organization. We've put about 30% of the company's resources into that customer success organization, and that entire team is measured not on revenue, not on project delivered on time, but on value delivered to the customer. So we baseline where the customer is at. We agree what we're looking to achieve with each customer, and we're measuring that team entirely against the delivery of those benefits to the customer. So it's a journey. We're on that journey, but we're committed to it. >> Exciting. Well, Paul, thank you so much for stopping by theCUBE for the first time. You're now a CUBE alumni. >> Oh, thank you, I've had a lot of fun. >> And we want to thank you for watching theCUBE. I'm Lisa Martin, live in San Jose, with Peter Burris. We are at the Forger Tasting Room and Eatery. Super cool place. Come on down, hang out with us today. We've got a cocktail party tonight. Well, you're sure to learn lots of insights from our experts, and tomorrow morning. But stick around, we'll be right back with our next guest after a short break. (CUBE theme music)