(upbeat music) >> Hey, welcome back everybody Jeff Frick here with theCUBE. We're in our Palo Alto studios, the conference season is getting ready to ramp up, it hasn't really hit full speed yet, so, it gives us the opportunity to have CUBE Conversations, and we're really excited to have our next guest, we haven't had him on for quite a while, George Mathew. He's the chairman and CEO of Kespry. George great to see you. >> Jeff, great to be here. Thanks for having me. >> So, you used to be big time in the data analytics world we used to see you at all the big data shows, and now you've made the move to autonomous flying machines. >> I did, I did, and there's a very strong relationship between the two, right? When you look at the lot worth that I was doing in the horizontal data analytic space, there was really a need to be able to accumulate data and process and understand that, and make better decisions off of it. Well, when you look at the industrial world that Kespry serves today, the ability to drive a full, complete application, where sensor based data is now being processed in our cloud infrastructure, and packaged up as complete applications, is exactly the market that we're focused on. >> So, George also a lot of big words. Let's talk about the fun words. >> Sure. >> You have drones, you have cool industrial drones. >> That's right. >> So, but what you've done is different than some the more popular drones that people know, some of the big names. You guy are really kind of single purpose, industrial only, totally integrated solution, sold as a service. >> Is that accurate? >> That's right. When you look at the drone space today, it's a big market. Its actually a 100 billion dollar market overall for drones. just in the commercial aspect of the drone space, it's a 15, 16 billion dollar market. Industrial use cases are proliferating everywhere. Kespry actually started in the mining aggregate space, where we were able to take our industrial grade drone, be able to do volumetric stock pile measurement to a level of accuracy that was literally down to one, two percent forecast accuracy, because we can now take imagery and convert that to super accurate three dimensional models of a mine site, of a query, and be able to make better decisions on how much inventory you had on that work site. >> Now, let's dive into that a little bit, cus most people when they think of drones, they think of, aerial photography at their wedding, and sweeping shots at the beach of their Maui vacation. But the industrial applications are real, and these are huge pieces of real estate that you're operating over. Huge masses of material, and men, and machines. So, the impacts, of small incremental impacts in being able to measure, and make decisions on that, have huge financial impact. >> So, what's amazing with drone tech that's available today, think about it as the new sensor network Jeff, so it's not just the fact that we can take images off a drone. It's the fact that we can take those images, and combine that with additional sensor based input. One of the key elements that Kespry introduced into the market, is taking imagery, and being able to augment the ability to have precision GPS along with that images. So, you can now have images that are processed in our cloud that are converted into full three dimensional models, and each one of those models are hyper accurate within three centimeters of real space. So, when you want to apply that for a full topological assessment of what a construction site looks like. If you wanted to measure the amount of volumetric stock pile of material that might be on 250 acres, you can fly a drone overhead in 30 minutes, be able to collect all that sensor based input, and process that in the cloud and have very accurate answers in terms of what's happening on an industrial work site without the danger and the challenges of manually collecting that information. >> Cus how did they do it before? >> Yup >> What was state of the art three years ago? >> The status quo in the market was being able to collect that data using a GPS backpack or laser guided precision equipment, but you still needed to have someone manually be able to bring that equipment to the work site. Often times, the data that you were collecting, you know, on a volumetric measurement of a stock pile, might be 20, 30, 40 points of measurement. When you're flying a drone overhead, and converting the imagery into a point cloud, you're creating five, six hundred thousand points of measurement. >> Right. >> And so the accuracy of what you're able to now accomplish with a level of safety, is unprecedented. >> Well, it's interesting, one of the Kespry tag lines is no joysticks, which I think is kind of funny. >> That's right. >> But the fact that it's really an automated system. You're selling us solutions. I'm teasing you about having fun with drones and flying with vacation, but that's not what it is. Basically it's a platform in which to deploy sensors. Which could be visual sensors, could be infrared sensors, could be GPS, could be all kinds of stuff, so it really opens up a huge opportunity to put different types of payloads, for different use cases into use. >> That's right, when you think about where Kespry's differentiation in the market is. We've introduced that capability to have different payloads, and be able to fuse those sensors together in a meaningful way, and combine that with a fully autonomous solution for flight control. So, now you don't have to have specialist piloting skills to be able to collect that information. The sensor based input is fused in a way where we can process that in our cloud infrastructure. We add a series of artificial intelligence machine learning algorithms to augment what's coming off of these sensors, and then package them as industrial grade applications. Good examples: inventory management in the mining aggregate space. Being able to do full earth works topological assessment in construction projects. Being able to do claims management for what the dimensionality, and their current state of a roof might be after a weather event has occurred. To be able to understand the number of missing shingles. The amount of hail damage that's occurred, and so all of these applications are packaged in an end to end manner, so that, you as a decision maker, and you as a user, don't have to be, you know, basically, playing with broken toys, to be able to get very clean answers in terms of what's happening in physical space. >> The roof story is so fascinating to me, 'cause people just think "oh it's a roof," they have no idea to really think through the impact of roofing in commercial real estate, and in industrial real estate. You know, roofs are where buildings fail, and so roofs, roof inspections is a really really important piece of title processes, and operational processes, so to be able now to automate that. It's classic right, automated, data driven, software driven, processes, really is a game changer versus having to send somebody up on a roof to physically inspect, I mean the accuracy's got to just be ore's of magnitude better. >> So, a few facts there, right. First of all, it's a multi billion dollar industry. You won't believe that just hail alone as far as damage that occurs on an annualized basis, is a 2.4 billion dollar challenge. It's also, the third most-- >> Is that in the U.S only? >> In the US, it's the third most occupationally hazardous job in the country, where people fall off roofs all the time when they're doing this kind of inspection. So, when you're able to now apply a drone to fly over that roof autonomously, collect that data, do the dimensional analysis, as well as being able to create the hail damage model, or the missing shingle model. You're now effectively enabling that claim process, for instance for the insurance carrier to adjudicate a claim to effectively happen within hours, right, after you know, you're on site. What we're seeing today in the market, is, if you're effectively looking at a claims assessment process, a claims adjuster would usually take about a day to cover three homes. With the use of a Kespry drone, we're seeing that same claims adjuster cover three homes in an hour. It's a massive productivity gain for this industrial use case. >> So, that brings up another topic. We've gone to a couple commercial drone shows and obviously it's a cool space, it's a fun space, but it's also really important space. I just think back to the end of World War I, when suddenly there were these things called airplanes, and the military trying to figure out, what do we do with this new asset, and those people maybe don't know that the Air Force was actually, the Army Air Force at the beginning. They didn't think that they needed a different branch, with different tactics, strategy, training, governance, et cetera. So, as we look at kind of, commercial drones entering into the business space, and I'm sure you've seen it, in some of these aggregate examples, construction. How having an air force, as a company, as a resource, you know, air deployed assets is such a big game changer. It's going to people a long time to figure out how to use it beyond the obvious in the short term, but it's a completely different tool, to apply to your business problems. >> This is why we consider this a whole new category of aerial intelligence, right. When you think about the capabilities that we're going to be able to deliver, as far as very accurate views of physical space, and being able to digitize it, to be able to model it, to be able to predict the material assets that are on a work site, and understand what the future value is, what the challenges might be for a maintenance cycle, to be able to understand the level and extent of damage, the anomaly detection, these are all incredible use cases that are opening up as we speak. I remember when I was on the show years ago, and we talked about the data analytics space, and particularly the self service aspect that I was pretty involved in, we used to talk about it being in the early innings of a ball game. Well, in the aerial intelligence market, we were literally in the first inning of the ball game. Like it is just getting off the ground, and when you think about the regulatory frameworks that are effectively in place, even as of 2016. The commercial operations in the United States have just opened up. You're now able to legitimately fly below 400 feet of air space. Maintaining the drone with a visual line of sight where a human operator is involved, that has actually passed the part 107 pilots exam. So, it's a framework. It's a start, but there's so much more expansion opportunities that occur when we're flying over people, when we're de-conflicting the air space, when we have the ability to do night flights, when we have the ability to be able to literally have that drone fly, without having a human operator controlling it, and understanding the visual line of sight where the drone is operating. So, these are all going to happen in the next several years, and completely open up the aerial intelligence market accordingly. >> It's fascinating, and of course the other thing that you're doing, which all good companies do, and all good entrepreneurs do, is build on the shoulders of others. So you're leveraging cloud, you're leveraging A.I., you're using the flight controls, you're using mobile applications, you're using all these bits and pieces of infrastructure, and you've packaged it up to deliver it as a service, which is fantastic. >> This is one of the fundamentals tenants for Kespry, even as of our founding in 2013. We knew that there was a lot of broken toys in the market, because if you had to take a consumer grade solution, be able to roll your own software, to be able to look at the way you collect that data on a manual basis, to be able to process that information, and get to results without having this connectivity involved with the entire end to end experience, we knew that a lot of companies could not succeed in their aerial intelligence offerings. And this is why Kespry believed that a full end to end solution, the way we built it, was better for the industrial markets that we serve, and so far so good. This past week we actually announced, just in the mining aggregate space alone, we have over 170 customers, and-- >> 170? >> Correct. Just in mining aggregate. >> How long has Kespry been around? >> We've been in business since we were founded in 2013. We started commercial operations in 2015. >> Wow. >> Amazingly, we covered over 10,400 just, mining query work sites, just in those last two and a half years that we've been in commercial operation. So, this is something that has really exponentialized, just in that market, and we're seeing similar adoptions starting to take off in the insurance roofing space, as well the construction markets. >> It's so funny. I just consider, you're an autonomous vehicle. You're just one that flies, not, that drives on the road, but, there's so much going on on the commercial side that people don't see, you know? They see the Lambo cars driving around the neighborhood, and we read about what's going on with Tesla, but on the agg side, on the commercial side, with John Deere, and these huge mining trucks, that many of them are already autonomous. This stuff is really moving very very quickly on the commercial side. >> If you think about the digital transformation of industrial work. This is a one trillion dollar market opportunity over the next several decades, and the ability to sense physical assets, and be able to make better decisions using drone tech, using other sensor based information. This is transforming the nature of industrial work, right? This is, in my view, the beginning of the fourth industrial age, and in that regard, we see this as something that's not just, like I said, you know, the first few innings of a ball game. We're going to see this evolve for decades, as we move forward. And drones are effectively a critical piece of that infrastructure evolving. >> Yeah, just in delivery. Just sensor delivery is basically what it is. Place it in places that people maybe shouldn't go, don't want to go, that dangerous to go, it makes a ton of sense. >> And then being able to blend that with the other sensors that might be on the ground, that might be in other places, that you can fuse that information together to get better understanding of physical space. >> Yeah, I love it. I love the solution approach, right. Nobody ever buys a new platform, but it sure is great to build a platform underneath a terrific application, that then you can expand after you knock it out of the park with that first application. >> And that's exactly the approach that we're going after >> All right. Well Mat, hopefully it won't be a, we looked it up before. Last time you were on was like 2014, so hopefully-- >> It's been a while. >> It won't be so long before we see you next, and thanks for stopping by. >> Thanks for having me on board, Jeff. >> All right, he's George Mathew. I'm Jeff Frick, You're watching theCUBE. Thanks for watching, I'll see you next time. (upbeat music)

>>The cube at big data SV 2014 is brought to you by headline sponsors. When disco we make Hadoop invincible and Aptean accelerating big data, 2.0, >>Okay. We're back here, live in Silicon valley. This is big data. It has to be, this is Silicon England, Wiki bonds, the cube coverage of big data in Silicon valley and all around the world covering the strata conference. All the latest news analysis here in Silicon valley, the cube was our flagship program about the events extract the signal from noise. I'm John furrier, the founders of looking angle. So my co-host and co-founder of Wiki bond.org, Dave Volante, uh, George Matthew CEO, altruist on the cube again, back from big data NYC just a few months ago. Um, our two events, um, welcome back. Great to be here. So, um, what fruit is dropped into the blend or the change, the colors of the big data space this this time. So we were in new Yorkers. We saw what happened there. A lot of talk about financial services, you know, big business, Silicon valley Kool-Aid is more about innovation. Partnerships are being formed, channel expansion. Obviously the market's hot growth is still basing. Valuations are high. What's your take on the current state of the market? >>Yeah. Great question. So John, when we see this market today, I remember even a few years ago when I first visited the cave, particularly when it came to a deep world and strata a few years back, it was amazing that we talked about this early innings of a ballgame, right? We said it was like, man, we're probably in the second or third inning of this ball game. And what has progressed particularly this last few years has been how much the actual productionization, the actual industrialization of this activity, particularly from a big data analytics standpoint has merged. And that's amazing, right? And in a short span, two, three years, we're talking about technologies and capabilities that were kind of considered things that you play with. And now these are things that are keeping the lights on and running, you know, major portions of how better decision-making and analytics are done inside of organizations. So I think that industrialization is a big shift forward. In fact, if you've listened to guys like Narendra Mulani who runs most of analytics at Accenture, he'll actually highlight that as one of the key elements of how not only the transformation is occurring among organizations, but even the people that are servicing a large companies today are going through this big shift. And we're right in the middle of it. >>We saw, you mentioned a censure. We look at CSC, but service mesh and the cloud side, you seeing the consulting firms really seeing build-out mandates, not just POC, like let's go and lock down now for the vendors. That means is people looking for reference accounts right now? So to me, I'm kind of seeing the tea leaves say, okay, who's going to knock down the reference accounts and what is that going to look like? You know, how do you go in and say, I'm going to tune up this database against SAP or this against that incumbent legacy vendor with this new scale-out, all these things are on in play. So we're seeing that, that focus of okay, tire kicking is over real growth, real, real referenceable deployments, not, not like a, you know, POC on steroids, like full on game-changing deployments. Do you see that? And, and if you do, what versions of that do you seeing happening and what ending of that is that like the first pitch of the sixth inning? Uh, w what do you, how would you benchmark that? >>Yeah, so I, I would say we're, we're definitely in the fourth or fifth inning of a non ballgame now. And, and there's innings. What we're seeing is I describe this as a new analytic stack that's emerged, right? And that started years ago when particularly the major Hadoop distro vendors started to rethink how data management was effectively being delivered. And once that data management layer started to be re thought, particularly in terms of, you know, what the schema was on read what the ability to do MPP and scale-out was in terms of how much cheaper it is to bring storage and compute closer to data. What's now coming above that stack is, you know, how do I blend data? How do I be able to give solutions to data analysts who can make better decisions off of what's being stored inside of that petabyte scale infrastructure? So we're seeing this new stack emerge where, you know, Cloudera Hortonworks map are kind of that underpinning underlying infrastructure where now our based analytics that revolution provides Altrix for data blending for analytic work, that's in the hands of data analysts, Tableau for visual analysis and dashboarding. Those are basically the solutions that are moving forward as a capability that are package and product. >>Is that the game-changing feature right now, do you think that integration of the stack, or is that the big, game-changer this sheet, >>That's the hardening that's happening as we speak right now, if you think about the industrialization of big data analytics that, you know, as I think of it as the fourth or fifth inning of the ballgame, that hardening that ability to take solutions that either, you know, the Accentures, the KPMGs, the Deloitte of the world deliver to their clients, but also how people build stuff internally, right? They have much better solutions that work out of the box, as opposed to fumbling with, you know, things that aren't, you know, stitched as well together because of the bailing wire and bubblegum that was involved for the last few years. >>I got it. I got to ask you, uh, one of the big trends you saw in certainly in the tech world, you mentioned stacks, and that's the success of Amazon, the cloud. You're seeing integrated stacks being a key part of the, kind of the, kind of the formation of you said hardening of the stack, but the word horizontally scalable is a term that's used in a lot of these open source environments, where you have commodity hardware, you have open source software. So, you know, everything it's horizontally scalable. Now, that's, that's very easy to envision, but thinking about the implementation in an enterprise or a large organization, horizontally scalable is not a no brainer. What's your take on that. And how does that hyperscale infrastructure mindset of scale-out scalable, which is a big benefit of the current infrastructure? How does that fit into, into the big day? >>Well, I think it fits extremely well, right? Because when you look at the capabilities of the last, as we describe it stack, we almost think of it as vertical hardware and software that's factually built up, but right now, for anyone who's building scale in this world, it's all about scale-out and really being able to build that stack on a horizontal basis. So if you look at examples of this, right, say for instance, what a cloud era recently announced with their enterprise hub. And so when you look at that capability of the enterprise data hub, a lot of it is about taking what yarn has become as a resource manager. What HDFS has been ACOM as a scale-out storage infrastructure, what the new plugin engines have merged beyond MapReduce as a capability for engines to come into a deep. And that is a very horizontal description of how you can do scale out, particularly for data management. >>When we built a lot of the work that was announced at strata a few years ago, particularly around how the analytics architecture for Galerie, uh, emerged at Altryx. Now we have hundreds of, of apps, thousands of users in that infrastructure. And when we built that out was actually scaling out on Amazon where the worker nodes and the capability for us to manage workload was very horizontal built out. If you look at servers today of any layer of that stack, it is really about that horizontal. Scale-out less so about throwing more hardware, more, uh, you know, high-end infrastructure at it, but more about how commodity hardware can be leveraged and use up and down that stack very easily. So Georgia, >>I asked you a question, so why is analytics so hard for so many companies? Um, and you've been in this big data, we've been talking to you since the beginning, um, and when's it going to get easier? And what are you guys specifically doing? You know, >>So facilitate that. Sure. So a few things that we've seen to date is that a lot of the analytics work that many people do internal and external to organizations is very rote, hand driven coding, right? And I think that's been one of the biggest challenges because the two end points in analytics have been either you hard code stuff that you push into a, you know, a C plus plus or a Java function, and you push it into database, or you're doing lightweight analytics in Excel. And really there needs to be a middle ground where someone can do effective scale-out and have repeatability in what's been done and ease of use. And what's been done that you don't have to necessarily be a programmer and Java programmer in C plus plus to push an analytic function and database. And you certainly don't have to deal with the limitations of Excel today. >>And really that middle ground is what Altryx serves. We look at it as an opportunity for analysts to start work with a very repeatable re reasonable workflow of how they would build their initial constructs around an analytic function that they would want to deploy. And then the scale-out happens because all of the infrastructure works on that analyst behalf, whether that be the infrastructure on Hadoop, would that be the infrastructure of the scale out of how we would publish an analytic function? Would that be how the visualizations would occur inside of a product like Tableau? And so that, I think Dave is one of the biggest things that needs to shift over where you don't have the only options in front of you for analytics is either Excel or hard coding, a bunch of code in C plus plus, or Java and pushing it in database. Yeah. >>And you correct me if I'm wrong, but it seems to be building your partnerships and your ecosystem really around driving that solution and, and, and really driving a revolution in the way in which people think about analytics, >>Ease of use. The idea is that ultimately if you can't get data analysts to be able to not only create work, that they can actually self-describe deploy and deliver and deliver success inside of an organization. And scale that out at the petabyte scale information that exists inside of most organizations you fail. And that's the job of folks like ourselves to provide great software. >>Well, you mentioned Tableau, you guys have a strong partnership there, and Christian Chabot, I think has a good vision. And you talked about sort of, you know, the, the, the choices of the spectrum and neither are good. Can you talk a little bit more about that, that, that partnership and the relationship and what you guys are doing together? Yeah. >>Uh, I would say Tableau's our strongest and most strategic partner today. I mean, we were diamond sponsors of their conference. I think I was there at their conference when I was on the cube the time before, and they are diamond sponsors of our conference. So our customers and particular users are one in the same for Tablo. It really becomes a, an experience around how visual analysis and dashboard, and can be very easily delivered by data analysts. And we think of those same users, the same exact people that Tablo works with to be able to do data blending and advanced analytics. And so that's why the two software products, that's why the two companies, that's where our two customer bases are one in the same because of that integrated experience. So, you know, Tableau is basically replacing XL and that's the mission that thereafter. And we feel that anyone who wants to be able to do the first form of data blending, which I would think of as a V lookup in Excel, should look at Altryx as a solution for that one. >>So you mentioned your conference it's inspire, right? It >>Is inspiring was coming up in June, >>June. Yeah. Uh, how many years have you done inspire? >>Inspire is now in its fifth year. And you're gonna bring the >>Cube this year. Yeah. >>That would be great. You guys, yeah, that would be fun. >>You should do it. So talk about the conference a little bit. I don't know much about it, but I mean, I know of it. >>Yeah. It's very centered around business users, particularly data analysts and many organizations that cut across retail, financial services, communications, where companies like Walmart at and T sprint Verizon bring a lot of their underlying data problems, underlying analytic opportunities that they've wrestled with and bring a community together this year. We're expecting somewhere in the neighborhood of 550 600 folks attending. So largely to, uh, figure out how to bring this, this, uh, you know, game forward, really to build out this next rate analytic capability that's emerging for most organizations. And we think that that starts ultimately with data analysts. All right. We think that there are well over two and a half million data analysts that are underserved by the current big data tools that are in this space. And we've just been highly focused on targeting those users. And so far, it's been pretty good at us. >>It's moving, it's obviously moving to the casual user at some levels, but I ended up getting there not soon, but I want to, I want to ask you the role of the cloud and all this, because when you have underneath the hood is a lot of leverage. You mentioned integrates that's when to get your perspective on the data cloud, not data cloud is it's putting data in the cloud, but the role of cloud, the role of dev ops that intersection, but you're seeing dev ops, you know, fueling a lot of that growth, certainly under the hood. Now on the top of the stack, you have the, I guess, this middle layer for lack of a better description, I'm of use old, old metaphor developing. So that's the enablement piece. Ultimately the end game is fully turnkey, data science, personalization, all that's, that's the holy grail. We all know. So how do you see that collision with cloud and the big, the big data? >>Yeah. So cloud is basically become three things for a lot of folks in our space. One is what we talked about, which is scale up and scale out, uh, is something that is much more feasible when you can spin up and spin down infrastructure as needed, particularly on an elastic basis. And so many of us who built our solutions leverage Amazon being one of the most defacto solutions for cloud based deployment, that it just makes it easy to do the scale-out that's necessary. This is the second thing it actually enables us. Uh, and many of our friends and partners to do is to be able to bring a lower cost basis to how infrastructure stood up, right? Because at the end of the day, the challenge for the last generation of analytics and data warehousing that was in this space is your starting conversation is two to $3 million just in infrastructure alone before you even buy software and services. >>And so now if you can rent everything that's involved with the infrastructure and the software is actually working within days, hours of actually starting the effort, as opposed to a 14 month life cycle, it's really compressing the time to success and value that's involved. And so we see almost a similarity to how Salesforce really disrupted the market. 10 years ago, I happened to be at Salesforce when that disruption occurred and the analytics movement that is underway really impacted by cloud. And the ability to scale out in the cloud is really driving an economic basis. That's unheard of with that >>Developer market, that's robust, right? I mean, you have easy kind of turnkey development, right? Tapping >>It is right, because there's a robust, uh, economy that's surrounding the APIs that are now available for cloud services. So it's not even just at the starting point of infrastructure, but there's definite higher level services where all the way to software as industry, >>How much growth. And you'll see in those, in that, as that, that valley of wealth and opportunity that will be created from your costs, not only for the companies involved, but the company's customers, they have top line focus. And then the goal of the movement we've seen with analytics is you seeing the CIO kind of with less of a role, more of the CEO wants to the chief data officer wants most of the top line drivers to be app focused. So you seeing a big shift there. >>Yeah. I mean, one of the, one of the real proponents of the cloud is now the fact that there is an ability for a business analyst business users and the business line to make impacts on how decisions are done faster without the infrastructure underpinnings that were needed inside the four walls in our organization. So the decision maker and the buyer effectively has become to your point, the chief analytics officer, the chief marketing officer, right. Less so that the chief information officer of an organization. And so I think that that is accelerating in a tremendous, uh, pace, right? Because even if you look at the statistics that are out there today, the buying power of the CMO is now outstrip the buying power of the CIO, probably by 1.2 to 1.3 X. Right. And that used to be a whole different calculus that was in front of us before. So I would see that, uh, >>The faster, so yeah, so Natalie just kind of picked this out here real time. So you got it, which we all know, right. I went to the it world for a long time service, little catalog. Self-service, you know, Sarah's already architectures whatever you want to call it, evolve in modern era. That's good. But on the business side, there's still a need for this same kind of cataloguing of tooling platform analytics. So do you agree with that? I mean, do you see that kind of happening that way, where there's still some connection, but it's not a complete dependency. That's kind of what we're kind of rethinking real time you see that happen. >>Yeah. I think it's pretty spot on because when you look at what businesses are doing today, they're selecting software that enables them to be more self-reliant the reason why we have been growing as much among business analysts as we have is we deliver self-reliance software and in some way, uh, that's what tablet does. And so the, the winners in this space are going to be the ones that will really help users get to results faster for self-reliance. And that's, that's really what companies like Altrix Stanford today. >>So I want to ask you a follow up on that CMOs CIO discussion. Um, so given that, that, that CMOs are spending a lot more where's the, who owns the data, is that, is we, we talk, well, I don't know if I asked you this before, but do you see the role of a chief data officer emerging? And is that individual, is that individual part of the marketing organization? Is it part of it? Is it a separate parallel role? What are you, >>One of the things I will tell you is that as I've seen chief analytics and chief data officers emerge, and that is a real category entitled real deal of folks that have real responsibilities in the organization, the one place that's not is in it, which is interesting to see, right? Because oftentimes those individuals are reporting straight to the CEO, uh, or they have very close access to line of business owners, general managers, or the heads of marketing, the heads of sales. So I seeing that shift where wherever that chief data officer is, whether that's reporting to CEOs or line of business managers or general managers of, of, you know, large strategic business units, it's not in the information office, it's not in the CEO's, uh, purview anymore. And that, uh, is kind of telling for how people are thinking about their data, right? Data is becoming much more of an asset and a weapon for how companies grow and build their scale less. So about something that we just have to deal with. >>Yeah. And it's clearly emerging that role in certain industry sectors, you know, clearly financial services, government and healthcare, but slowly, but we have been saying that, >>Yeah, it's going to cross the board. Right. And one of the reasons why I wrote the article at the end of last year, I literally titled it. Uh, analytics is eating the world, is this exact idea, right? Because, uh, you have this, this notion that you no longer are locked down with data and infrastructure kind of holding you back, right? This is now much more in the hands of people who are responsible for making better decisions inside their organizations, using data to drive those decisions. And it doesn't matter the size and shape of the data that it's coming in. >>Yeah. Data is like the F the food that just spilled all over it spilled out from the truck and analytics is on the Pac-Man eating out. Sorry. >>Okay. Final question in this segment is, um, summarize big data SV for us this year, from your perspective, knowing what's going on now, what's the big game changer. What should the folks know who are watching and should take note of which they pay attention to? What's the big story here at this moment. >>There's definite swim lanes that are being created as you can see. I mean, and, and now that the bigger distribution providers, particularly on the Hadoop side of the world have started to call out what they all stand for. Right. You can tell that map are, is definitely about creating a fast, slightly proprietary Hadoop distro for enterprise. You can tell that the folks at cloud era are focusing themselves on enterprise scale and really building out that hub for enterprise scale. And you can tell Horton works is basically embedding, enabling an open source for anyone to be able to take advantage of. And certainly, you know, the previous announcements and some of the recent ones give you an indicator of that. So I see the sense swimlanes forming in that layer. And now what is going to happen is that focus and attention is going to move away from how that layer has evolved into what I would think of as advanced analytics, being able to do the visual analysis and blending of information. That's where the next, uh, you know, battle war turf is going to be in particularly, uh, the strata space. So we're, we're really looking forward to that because it basically puts us in a great position as a company and a market leader in particularly advanced analytics to really serve customers in how this new battleground is emerging. >>Well, we really appreciate you taking the time. You're an awesome guest on the queue biopsy. You know, you have a company that you're running and a great team, and you come and share your great knowledge with our fans and an audience. Appreciate it. Uh, what's next for you this year in the company with some of your goals, let's just share that. >>Yeah. We have a few things that are, we mentioned a person inspired coming up in June. There's a big product release. Most of our product team is actually here and we have a release coming up at the beginning of Q2, which is Altryx nine oh. So that has quite a bit involved in it, including expansion of connectivity, uh, being able to go and introduce a fair degree of modeling capability so that the AR based modeling that we do scales out very well with revolution and Cloudera in mind, as well as being able to package into play analytic apps very quickly from those data analysts in mind. So it's, uh, it's a release. That's been almost a year in the works, and we're very much looking forward to a big launch at the beginning of Q2. >>George, thanks so much. You got inspire coming out. A lot of great success as a growing market, valuations are high, and the good news is this is just the beginning, call it mid innings in the industry, but in the customers, I call the top of the first lot of build-out real deployment, real budgets, real deal, big data. It's going to collide with cloud again, and I'm going to start a load, get a lot of innovation all happening right here. Big data SV all the big data Silicon valley coverage here at the cube. I'm Jennifer with Dave Alonzo. We'll be right back with our next guest. After the short break.