 It's theCUBE, covering HPE Big Data Conference 2016. Now, here are your hosts, Dave Vellante and Paul Gillan. Welcome back to the Boston Waterfront, everybody. This is theCUBE, SiliconANGLE's special presentation of the HPE Big Data Conference. Hashtag, seize the data. Keenan Rice is here, he's the Vice President of Alliances and Channels at Looker. Keenan, good to see you. Thanks for coming on. Thanks for having me, guys. So, we're talking about Vertica, the momentum in the marketplace. You see a lot of stuff happening in Big Data, not all of it's good, but seems like Vertica's pretty solid, growth continues. A lot of customer momentum, what's your take? Yeah, I really couldn't agree with you more. I think Vertica's really solidly positioned themselves in the market as kind of the best Big Data software that can be deployed anywhere, right? It can be deployed on-prem, hyper-converged, hybrid, cloud, whatever, right? So, we're seeing that across our customer base at Looker as well, petabyte scale workloads going anywhere they want to deploy with all the kind of best and breed analytic stuff. So, Vertica, a big emphasis on cloud. You guys are kind of born in the cloud. Yup. You're ethos. What's happening there? You know, a lot of people were saying, no, everything's going to be on-prem and then all of a sudden, wow, the customer conversations have changed, haven't they? Yeah, I mean, I think with any new change, you got to get over your fears, right? So, whether they're regulatory fears, whether they're just general control fears, right? And we've really seen that shift tremendously from every industry and our customer base from healthcare to retail to obviously born-in-the-web companies staying in the cloud. And they're going there and they're going to continue to go there and probably have faster rate than they did in the past, too. So, this is a big show for you guys. You guys are a major sponsor. You won the Hackathon last year. Two years running, actually. Oh, that's right, two years in a row. So, give us the update on Looker. Yeah, you know, we've continued to just keep growing really, really fast. Last year, about this time, we were about just over 100 employees, about 250 customers. Just starting to go international, now we're about 250 employees, over 700 customers. Big office now in London, probably looking to expand into APAC. Did a good $40 million round of funding with Kleiner back in February. So, yeah, we're a petal to the metal here. Yes, we've raised almost, what, close to 100 million now, right? Yeah, 98. So that helps you with the international expense. Your software, you think of this capital-efficient business, but to compete, you got to go channels, you got to go global, you got marketing expense against the big guys, so. Yeah, yeah. So, I mean, obviously, raising that money, you know, definitely helps us put fuel on the fire and then that SaaS business model really helps us, you know, perpetuate and make that money go a lot further. Got a lot of players in this market. You got Tableau, you got Burst, you got, you guys, why do customers bring in you against those others? Yeah, it's, you know, if I would look back four years ago and say, do I want to enter a space as a startup into completely hyper-competitive space, I probably wouldn't choose this. Get into the BI business, yeah. Probably not go back to the BI business, right? But I think we fundamentally had the right time, right place with the right idea, right? Which was companies like Vertica, the cloud, all these things, you know, the big data trend, the idea of storing as much data as possible, getting it all together and figuring out what to do with it later. That opportunity is what we kind of wrote on, right? So we built the company from the ground up to be cloud-native, 100% in database, leverage the growth and innovation that's happening in the data storage layer and be that thing on top of it to make the organization be able to describe their data, have a single source of the truth and then let everybody else find it, access it, do their own data discovery, embed it into portals, integrate it, do whatever they would like to do with the data. So the in-database architecture, the born-of-the-web thing really, really kind of helped us solidify against the other players in the market. And the gap between Excel and Oracle or SaaS is just so enormous. Is it fair to say you guys are with Vertica, tucking in there, really solving a lot of problems that can't be solved with Excel and too expensive to solve with those other solutions and too cumbersome? It's exactly right. I mean, I think the Vertica Looker solution is that next-gen business intelligence platform, right? You know, the first wave might've been the tear data micro-strategy, right? And I think the next wave, you know, really looks like the Vertica Looker world, right? Pedabyte scale, transform data query time, let everybody analyze it and do whatever they want to do with the data. We see the Watson platforms are changing people's thoughts about the role of machine learning in doing these kinds of analytics. Are you seeing other platforms emerge that as machine learning libraries are now open source from several of the major players? Is that becoming a major factor in how you continue to develop your product? You know, I mean, I think the continuous, yeah, I think the continuous trend in the kind of operationalization of machine learning and things like this is actually only showcasing how much more powerful our architecture is in this new world. So we have a tight relationship with IBM and we do some really interesting stuff with Watson, with machine learning and that kind of thing. It's really cool because data is not just the relational business data now, right? It's the sentiment analysis. It's the sensor data. It's all these things that when you start applying machine learning algorithms, you enrich that data. You write that back down to the database. Now you can actually, you know, give tremendously more rich context analytics for a normal business user, right? Talk about your business a little bit because you're on a growth trajectory. You're seeing a lot of companies in the general big data space struggling a little bit. So on platform, you know, get taken up by workday and that was sort of a had to happen move. You guys have a different story, different narrative. Tell it. Yeah, you know, we're heads down, kind of trying to build the best, you know, next-gen business intelligence platform. You know, whether we're going to do that as an individual company, you know, for the foreseeable future, whether we're going to do that as a department. You know, we're really just trying to build that next-gen BI tool and really help our customers succeed with data and it's really telling, I think, in the customer acquisition, for sure, right? Well over, you know, 100 plus customers quarter over quarter coming to the Looker platform. Usage across each customer base, something I think is like 60% of the employees in the organizations usually get access to Looker. They start using Looker on a daily basis. It's almost as prevalent as email in a lot of these organizations, right? You have email on one screen, you have Looker on the other screen. So I think the fundamental usage, the change of philosophy in the organization around really becoming data-driven is driving our growth, right? And we're just going to continue to help our customers be successful with that and become more data-driven in that sense. Well, and we're really kind of six years into the bromide of becoming data-driven and now it's the better part of a decade. So it's actually happening. It's realizing, for sure, 100%. Visualization's a big part of that. Being able to interact with your existing database, SQL is something you guys bet on early, right? SQL, yeah, retro hip. Yeah, right. It used to be kind of boring at parties, but now it's all the rage. Yeah, I mean, it was kind of, it was very interesting. I think timing was definitely fortuitous to us, but our founders, they fundamentally believed wherever the big data world was going to go, SQL is always going to be the lingua franca for analytics, right? And we really saw a minor shift to see if you could do it without SQL. And then it realized it all came back to SQL as the lingua franca for analytics. Are you seeing new industries emerge or interest growing in any particular industries faster than others now? Man, data, it's been shocking. I mean, obviously, we had a big, big initial growth with born-of-the-web companies, right, with Uber's and Etsy's and all these companies that are really born data-driven, born-of-the-web and all this, but we've started to see it from financial services, healthcare, nonprofits, banking, et cetera. So it's kind of, it's across everything. Kim, you talked about essentially getting a foothold in an organization and then the number of, the usage explodes. What are the roles that you're seeing in terms of adoption for your solutions? Yeah, so we target going into the organization with the BI team, right, in the IT organization. So also retro-hip for the world of SaaS, right? They always try to disintermediate. We're like, no, let's make you more powerful. Let's give you the platform to then let everyone else succeed on top of this, right? So- Very service now-like, as it works, right? Very much, very much, very much. And it works extremely well, right? Because those are the folks that have the technical aptitude to really empower the organization. As long as you give them the platform to do that and empower everyone and not make them the gatekeepers of the information. So that's kind of, that's been a philosophy of ours for the development of the product. And then we see it across to all departments. Marketing is usually a huge user of data, right? They're doing really sophisticated analysis like customer journey stuff, sentiment, digital marketing, campaign attribution, all this kind of really cool stuff. You see finance, you know, really diving deeper into the numbers than they may have in the past with just looking at reports or spreadsheets. You know, they're really getting into the details at petabyte scale data, which is a pretty cool change that I've seen shift. Supply chain, inventory management, even IT teams actually using it to manage infrastructure and monitoring and this kind of thing. So, I mean, it's really going across to every department. Let's talk a little bit more about competition. You seem to find a good swim lane relative to some of the cool upstarts. Yep. I guess Oracle, you don't kind of, you worry about because they're so big, but it's Oracle, right? They get their red stack, okay. Microsoft's doing some interesting things. You know, you got Power BI coming in and you got this new cloud ethos. What do you make of Microsoft as a competitor? Yeah, no, they've been extremely successful at the revamp of Power BI. Power BI is definitely riding on the success of Azure as well, right? So, you know, we're top tier partners with Azure. We see tremendously large and innovative workloads going to Azure as well. So, that platform is really matured quite nicely. With Power BI, I mean, I think, you know, they've really focused on the self-service visualization aspect, right? So, them, Tableau, AWS, QuickSite, you know, Click, et cetera. You know, they're all really competing and they've started to really define out that self-service, you know, individual or small team workload, right? We've continued to kind of stay in the, you want to take a little bit of a larger investment. You want to, you know, actually do this organizationally. You want to deploy it to everybody. You want to kind of take that traditional BI approach and really modernize it with your next-gen data infrastructure stack and still give everybody self-service. But do it on this larger platform. That's kind of the swim lane we've really established. That's a transformative sell in many regards. So, what's that narrative like? Who are you typically selling to in the IT organization and how are you making that transformative business case? Yeah, so it's selling into the BI and the analytics teams. You know, and it's really the whole idea of empowerment. It's don't, you know, your business users are hungry for information. Your previous tools are becoming the gatekeeper. You know, we call this a lot of times the data bread line. You know, you're making your business users stand in line and beg for information. So, get out of the way. Allow everybody to have access to free market goods, right? And that's the organization's data. Okay, so you're taking on a big training task there, though, as well. And BI organizations have traditionally done a lot of the work themselves for users. You're talking about democratizing this. What kind of a task are your customers taking on when they seek to democratize information like this? Yeah, you know, it's going to look like a spectrum. Right? We have a very large and growing younger workforce that is very data savvy, kind of from the beginning, right? Data and computers and things like that are part of their DNA, right? And then we have other parts of the workforce that might not be as data savvy and pick it up really quickly, right? So we have a really interesting curriculum and our customers adapt to it really easily where the entry point might be a dashboard. And they might only click on this link in their internet every single day, go to the dashboard, click on a few data points, drill into some details, maybe change the filters. And that might be it, but that's the access of information that they need from the BI team, right? And then others might need to build their own dashboards, to go build their own reports, to do more ad hoc discovery. And so then they easily can just click through and go into it, or we set up what our BI teams call Explorer Sessions, and they help them learn the functionality and the kind of how do you ask questions of data, the curiosity driven kind of approach. It's a really kind of easy curriculum, we help with them. And then you get analysts and business users and stuff that start going really crazy and integrating it to R, or integrating it into other business applications and really starting to do advanced workflows with the UI. So this is starting to come into focus for me anyway, is Microsoft cheap and cheerful, okay, that's cool. Tableau's Ascendancy- You said it, not me. But Tableau's Ascendancy was built upon denigrating your peeps, the BI teams, basically saying slow and cumbersome and you don't want to hang out with these, you're going and empowering that BI team, saying transform very service now like, and which is extremely powerful because they're in a position to permeate the organization, support it. If you make them heroes, good things can happen. So I didn't understand that before about Looker. That's- The BI team, those are our peeps for sure. That's who we are at Looker, right? And we want to make sure that they're successful. So you get advocates that are selling internally to the extent that you can make them successful. Come on, data's hard. It's hard. These guys are not just dead weight. They're extremely, extremely smart, right? So how do you leverage them across the organization? Oh, you're absolutely right. I mean, and they're dying for new challenges that aren't just fixing the enterprise data warehouse. They don't like to build reports. They're analytics in the cloud. You have to get the data in the cloud first. What progress are you seeing right now on customers actually moving large amounts of data to the cloud? Much more progress than I would have actually expected so quickly. We still see, we see a lot of the born, born in the web companies that have the petabyte scale that just kind of was born in the cloud, right? So that was quite easy for them, right? And then we start seeing a lot larger of the Fortune 500 companies moving to the cloud at a faster rate, right? They're still looking at multi workloads on-prem and cloud or some sort of hybrid in between, but they're getting terabytes and terabytes and even petabytes up there pretty quickly. And they're ascribing to the new philosophy, which is your next gen infrastructure is store everything, get it up there, and let's figure out what to do with it later, but don't get too cumbersome and burden yourself with doing multi-year structured data kind of approach like you would have done in the past, like not just shifting your data, but also shifting your data philosophy. Excellent. And Aiman O'Neill said this morning that people don't remember the exact quote, but copying data is a bad thing. Yeah, friends don't let friends copy data. Friends don't let friends copy data. Are you seeing, is that part of your philosophy is to work with the production data? For sure. Yeah, it could be a simple replication of the production. Well, obviously never connect to the production database, right? Unless you're doing some mixed workload kind of database itself, but yeah, copy the data over, augment it, write data to it that comes from any sources, business applications, sensors, internet of things, production databases, whatever, just get it all together there and leave it there, right? Let us leverage the power of the database by doing that transformation at query time rather than transforming it, copying it, transforming it, copying it, transforming it, and then all of a sudden, now you have data lineage problems, you have this data heritage, all these problems that are manufactured, right? It shouldn't actually exist. It sells a lot of storage. Yeah, it sure does. Yeah, it sells a lot of vertica, it sells a lot of storage, but those things are becoming cost effective. We see that that is a cost effective and a business effective approach at petabyte scale, so there isn't quite an argument to do the latter half these days. So what should we look for from Looker, going forward, what's on the horizon? So we have a big event that we're doing in October, so it's going to be our first conference, our first user event, so hopefully we'll have you guys there. Where is the house? It's going to be in New York, October 17th and 18th, or 18th and 19th, thank you. That's huge. Yeah, so hitting the critical mass, so that's really exciting. We have some really exciting initiatives that we're going to announce there. The next gen of Looker's going to be announced there. I can give you a little bit of a sneak peek. It's the idea of Looker being a data platform rather than just being a BI tool. So the idea of being a data platform is this is the central place for people to manage their data, to govern their data, to describe their data, and then allow the data to go into context for the end user. So whether that's our BI application, it could be our data apps initiative that we launched back in April, where it's pre-created templates of analytics, so whether that's customer journey analysis, or sales analytics, or customer success management tools. That's the data apps program, so that could be one thing. It could be our powered by Looker initiative, so building custom applications, or embedding into third-party applications. Or it could even be our data in context, what we call data everywhere, which is like using our Slack bot. So you might live in Slack and communicate a lot, and so you can actually be leveraging Looker in real time, asking questions of the data, getting that in context, embedding Looker into Salesforce, and actually looking at it pipeline analysis, and other sorts of trends in real time, in context of that account, of that opportunity, et cetera. So this data platform thing is what we think is pretty revolutionary to allow those BI teams to build data experiences for their customers. And that's the 18th, 17th, 18th, 19th in New York City. Where in New York City is it? It's going to be at the Mercantile Exchange. Oh, fantastic. Right, in fact, we're going to have an event there, and right around the same time. We'll warm it up for you. It's close to the Javits, right across the street from Pillars 37, where we have our big event every year. So it's a great location. You're going to love it. Fantastic. We're looking forward to seeing you guys there. All right, Keenan, thanks very much for coming to the queue. Really a pleasure seeing you. Thanks. All right, keep right there, everybody. Paul and I will be back with our next guest right after this short break.