>>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.