okay we're back this is Dave Volante with Jeff Kelly we're with Ricky bond on organ this is the cubes silicon angles flagship product we go out to the events we extract the signal from the noise we bring you the tech athletes who are really changing the industry and we have one here today christiane sabo is the CEO the leader the spiritual leader of of this conference and of Tablo Kristin welcome to the cube thanks for having me yeah it's our pleasure great keynote the other day I just got back from Italy so I'm full of superlatives right it really was magnificent I was inspired I think the whole audience was inspired by your enthusiasm and what struck me is I'm a big fan of simon Sinek who says that people don't buy what you do they buy why you do it and your whole speech was about why you're here everybody can talk about their you know differentiators they can talk about what they sell you talked about why you're here was awesome so congratulations I appreciate that yeah so um so why did you start then you and your colleagues tableau well it's how below really started with a series of breakthrough research innovations that was this seed there are three co-founders of tableau myself dr. crystal T and professor Pat Hanrahan and those two are brilliant inventors and designers and researchers and the real hero of the tableau story and the company formed when they met on entrepreneur and a customer I had spent several years as a data analyst when I first came out of college and I understood the problems making sense of data and so when I encountered the research advancements they had made I saw a vision of the future a much better world that could bring the power of data to a vastly larger number of people yeah and it's really that simple isn't it and and so you gave some fantastic examples them in the way in which penicillin you know was discovered you know happenstance and many many others so those things inspire you to to create this innovation or was it the other way around you've created this innovation and said let's look around and see what others have done well I think the thing that we're really excited about is simply put as making databases and spreadsheets easy for people to use I can talk to someone who knows nothing about business intelligence technology or databases or anything but if I say hey do you have any spreadsheets or data files or databases you you just feel like it could it could get in there and answer some questions and put it all together and see the big picture and maybe find a thing or two everyone not everyone has been in that situation if nothing else with the spreadsheet full of stuff like your readership or the linkage the look the the traffic flow on on the cube website everyone can relate to that idea of geez why can't I just have a google for databases and that's what tableau is doing right right so you've kind of got this it's really not a war it's just two front two vectors you know sometimes I did I did tweet out they have a two-front war yeah what'd you call it the traditional BI business I love how you slow down your kids and you do that and then Excel but the point I made on Twitter in 140 characters was you it will be longer here I'm a little long-winded sometimes on the cube but you've got really entrenched you know bi usage and you've got Excel which is ubiquitous so it sounds easy to compete with those it's not it's really not you have to have a 10x plus value problem solutely talked about that a little bit well I think the most important thing we're doing is we're bringing the power of data and analytics to a much broader population of people so the reason the answer that way is that if you look at these traditional solutions that you described they have names like and these are the product brand names forget who owns them but the product brand names people are used to hearing when it comes to enterprise bi technology our names like Business Objects and Cognos and MicroStrategy and Oracle Oh bi and big heavy complicated develop intensive platforms and surprise surprise they're not in the hands of very many people they're just too complicated and development heavy to use so when we go into the worlds even the world's biggest companies this was a shocker for us even when we go into the world's most sophisticated fortune 500 companies and the most cutting-edge industries with the top-notch people most of the people in their organization aren't using those platforms because of theirs their complication and expense and development pull and so usually what we end up doing is just bringing the power of easy analytics and dashboards and visualization and easy QA with data to people who have nothing other than maybe a spreadsheet on their desk so in that sense it's actually a little easier than it sounds well you know I have to tell you I just have a cio consultancy and back in the day and we used to go in and do application portfolio analysis and we would look at the applications and we always advise the CIOs that the value of an application is a function of its use how much is being adopted and the impact of that use you know productivity of the users right and you'd always find that this is the dss system the decision support system like you said there were maybe 3 to 15 users yeah and an organization of tens of thousands of people yeah if they were very productive so imagine if you can you can permeate the other you know hundreds of thousands of users that are out there do you see that kind of impact that productivity impact as the potential for your marketplace absolutely I you know the person who I think said it best was the CEO of Cisco John Chambers and I'll paraphrase him here but he has this great thing he said which is he said you know if I can get each of the people on my team consulting data say oh I don't know twice per day before making a decision and they do the same thing with their people and their people and so you know that's a million decisions a month you did the math better made than my competition I don't want people waiting around for top top management to consult some data before making a decision I want all of our people all the time Consulting data before making a decision and that's the real the real spirit of this new age of BI for too long it's been in the hands of a high priesthood of people who know how to operate these complicated convoluted enterprise bi systems and the revolution is here people are fed up with it they're taking power into their hands and they're driving their organizations forward with the power of data thanks to the magic of an easy-to-use suite like tableau well it's a perfect storm right because everybody wants to be a data-driven organization absolutely data-driven if you don't have the tools to be able to visualize the data absolutely so Jeff if you want to jump in well Christian so in your keynote you talked for the majority of the keynote about human intuition and the human element talk a little bit about that because when we hear about in the press these days about big data it's oh well the the volume of data will tell you what the answer is you don't need much of the human element talk about why you think the human element is so important to data-driven decision-making and how you incorporate that into your design philosophy when you're building the product and you're you know adding new features how does the human element play in that scenario yeah I mean it's funny dated the data driven moniker is coming these days and we're tableaus a big big believer in the power of data we use our tools internally but of course no one really wants to be data driven if you drive your company completely based on data say hello to the cliff wall you will drive it off a cliff you really want people intelligent domain experts using a combination of act and intuition and instinct to make data informed decisions to make great decisions along the way so although pure mining has some role in the scheme of analytics frankly it's a minor role what we really need to do is make analytic software that as I said yesterday is like a bicycle for our minds this was the great Steve Jobs quote about computers that their best are like bicycles for our mind effortless machines that just make us go so much faster than any other species with no more effort expended right that's the spirit of computers when they're at our best Google Google is effortless to use and makes my brain a thousand times smarter than it is right unfortunately over an analytic software we've never seen software that does tap in business intelligence software there's so much development weight and complexity and expense and slow rollout schedules that were never able to get that augmentation of the brain that can help lead to better decisions so at tableau in terms of design we value our product requirements documents say things like intuition and feel and design and instinct and user experience they're focused on the journey of working with data not just some magic algorithm that's gonna spit out some answer that tells you what to do yeah I mean I've often wondered where that bi business would be that traditional decision support business if it weren't for sarbanes-oxley I mean it gave it a new life right because you had to have a single version of the truth that was mandated by by the government here we had Bruce Boston on yesterday who works over eight for a company that shall not be named but anyway he was talking about okay Bruce in case you're watching we're sticking to our promise but he was talking about intent desire and satisfaction things those are three things intent desire and satisfaction that machines can't do like the point being you just you know it was the old bromide you can't take the humans in the last mile yeah I guess yeah do you see that ever changing no I mean I think you know I I went to a friend a friend of mine I just haven't seen in a while a friend of mine once said he was an he was an artificial intelligence expert had Emilie's PhD in a professorship in AI and once I naively asked him I said so do we have artificial intelligence do we have it or not and we've been talking about for decades like is it here and he said you're asking the wrong question the question is how smart our computers right so I just think we're analytics is going is we want to make our computers smarter and smarter and smarter there'll be no one day we're sudden when we flip a switch over and the computer now makes the decision so in that sense the answer to your question is I keep I see things going is there is it going now but underneath the covers of human human based decision making it are going to be fantastic advancements and the technology to support good decision making to help people do things like feel and and and chase findings and shift perspectives on a problem and actually be creative using data I think there's I think it's gonna be a great decade ahead ahead of us so I think part of the challenge Christian in doing that and making that that that evolution is we've you know in the way I come the economy and and a lot of jobs work over the last century is you know you're you're a cog in a wheel your this is how you do your job you go you do it the same way every day and it's more of that kind of almost assembly line type of thinking and now we're you know we're shifting now we're really the to get ahead in your career you've got to be as good but at an artist you've got to create B you've got to make a difference is the challenge do you see a challenge there in terms of getting people to embrace this new kind of creativity and again how do you as a company and as a you know provider of data visualization technology help change some of those attitudes and make people kind of help people make that shift to more of less of a you know a cog in a larger organization to a creative force inside that organ well mostly I feel like we support what people natively want to do so there are there are some challenges but I mostly see opportunity there in category after category of human activity we're seeing people go from consumers to makers look at publishing from 20 years ago to now self-publishing come a few blogs and Twitter's Network exactly I mean we've gone from consumers to makers everyone's now a maker and we have an ecosystem of ideas that's so positive people naturally want to go that way I mean people's best days on the job are when they feel they're creating something and have that sense of achievement of having had an idea and seeing some progress their hands made on that idea so in a sense we're just fueling the natural human desire to have more participation with data to id8 with data to be more involved with data then they've been able to in the past and again like other industries what we're seeing in this category of technology which is the one I know we're going from this very waterfall cog in a wheel type process is something that's much more agile and collaborative and real-time and so it's hard to be creative and inspired when you're just a cog stuck in a long waterfall development process so it's mostly just opportunity and really we're just fueling the fire that I think is already there yeah you talked about that yesterday in your talk you gave a great FAA example the Mayan writing system example was fantastic so I just really loved that story you in your talk yesterday basically told the audience first of all you have very you know you have clarity of vision you seem to have certainty in your vision of passion for your vision but the same time you said you know sometimes data can be confusing and you're not really certain where it's going don't worry about that it's no it's okay you know I was like all will be answered eventually what but what about uncertainty you know in your minds as the you know chief executive of this organization as a leader in a new industry what things are uncertain to you what are the what are the potential blind spots for you that you worry about do you mean for tableau as a company for people working with data general resource for tableau as a company oh I see well I think there's always you know I got a trip through the spirit of the question but we're growing a company we're going a disruptive technology company and we want to embrace all the tall the technologies that exist around us right we want to help to foster day to day data-driven decision-making in all of its places in forms and it seems to me that virtually every breakthrough technology company has gone through one or two major Journal technology transformations or technology shocks to the industry that they never anticipated when they founded the company okay probably the most recent example is Facebook and mobile I mean even though even though mobile the mobile revolution was well in play when when Facebook was founded it really hadn't taken off and that was a blind Facebook was found in oh seven right and look what happened to them right after and here's that here's new was the company you can get it was founded in oh seven yeah right so most companies I mean look how many companies were sort of shocked by the internet or shocked by the iPod or shocked by the emergence of a tablet right or shocked by the social graph you know I think for us in tableaus journey if this was the spirit of the thought of the question we will have our own shocks happen the first was the tablet I mean when we founded tableau like the rest of the world we never would have anticipated that that a brilliant company would finally come along and crack the tablet opportunity wide open and before in a blink of an eye hundreds of millions of people are walking around with powerful multi-touch graphic devices in their I mean who would have guessed people wouldn't have guessed it no six let alone oh three know what and so luckily that's what that's I mean so this is the good kind of uncertainty we've been able to really rally around that there are our developers love to work on this area and today we have probably the most innovative mobile analytics offering on the market but it's one we never could have anticipated so I think the biggest things in terms of big categories of uncertainty that we'll see going forward are similar shocks like that and our success will be determined by how well we're able to adapt to those so why is it and how is it that you're able to respond so quickly as an organization to some of those tectonic shifts well I think the most important thing is having a really fleet-footed R&D team we have just an exceptional group of developers who we have largely not hired from business technology companies we have something very distributed going a tableau yeah one of the amazing things about R&D key our R&D team is when we decided to build just this amazing high-wattage cutting-edge R&D team and focus them on analytics and data we decided not to hire from other business intelligence companies because we didn't think those companies made great products so we've actually been hiring from places like Google and Facebook and Stanford and MIT and computer gaming companies if you look at the R&D engineers who work on gaming companies in terms of the graphic displays and the response times and the high dimensional data there are actually hundreds of times more sophisticated in their thinking and their engineering then some engineer who was working for an enterprise bi reporting company so this incredible horsepower this unique team of inspired zealots and high wattage engineers we have in our R&D team like Apple that's the key to being able to respond to these disruptive shocks every once in a while and rule and really sees them as an opportunity well they're fun to I mean think of something on the stage yesterday and yeah we're in fucky hats and very comfortable there's never been an R&D team like ours assembled in analytics it's been done in other industries right Google and Facebook famously but in analytics there's never been such an amazing team of engineers and Christian what struck me one of the things that struck me yesterday during your keynote or the second half of the keynote was bringing up the developers and talking about the specific features and functions you're gonna add to the product and hearing the crowd kind of erupt at different different announcements different features that you're adding and it's clear that you're very customer focused at this at tableau of you I mean you're responding to the the needs and the requests of your customers and I that's clearly evident again in the in the passion that these customers have for your for your product for your company how do you know first I'm happy how do you maintain that or how do you get get to that point in the first place where you're so customer focused and as you go forward being a public company now you're gonna get pressure from Wall Street and quarter results and all that that you know that comes with that kind of comes with the territory how do you remain that focused on the customer kind of as your you know you're going to be under a lot of pressure to grow and and you know drive revenue yeah I keep that focus well there's two things we do it's a it's always a challenge to stay really connected to your customers as you get big but it's what we pride ourselves on doing and there's two specific things we do to foster it the first is that we really try to focus the company and we try to make a positive aspect of the culture the idea of impact what is the impact of the work we're having and in fact a great example of how we foster that is we bring our entire support and R&D team to this conference no matter where it is we take we fly I mean in this case we literally flew the entire R&D team and product management team and whatnot across country and the time they get here face to face face to face with customers and hearing the customer stories and the victories and actually seeing the feedback you just described really inspires them it gives them specific ideas literally to go back and start working on but it also just gives them a sense of who comes first in a way that if you don't leave the office and you don't focus on that really doesn't materialize and the way you want it the second thing we do is we are we are big followers of I guess what's called the dog food philosophy of eat your own dog so drink your own champagne and so one of our core company values that tableau is we use our products facility a stated value of the company we use our products and into an every group at tableau in tests in bug regressions in development in sales and marketing and planning and finance and HR every sip marketing marketing is so much data these these every group uses tableau to run our own business and make decisions and what happens Matt what's really nice about a company because you know we're getting close to a thousand people now and so it's keeping the spirit you just described alive is really important it becomes quite challenging vectors leagues for it because when that's one of your values and that's the way the culture has been built every single person in the company is a customer everyone understands the customer's situation and the frustrations and the feature requests and knows how to support them when they meet them and can empathize with them when they're on the phone and is a tester automatically by virtue of using the product so we just try to focus on a few very authentic things to keep our connection with the customer as close as possible I'll say christen your company is a rising star we've been talking all this week of the similarities that we were talking off about the similarities with with ServiceNow just in terms of the passion within the customer base we're tracking companies like workday you know great companies that are that are that are being built new emerging disruptive companies we put you in that in that category and we're very excited for different reasons you know different different business altogether but but there are some similar dynamics that we're watching so as observers it's independent observers what kinds of things do you want us to be focused on watching you over the next 12 18 24 months what should we be paying attention to well I think the most important thing is tableau ultimately is a product company and we view ourselves very early in our product development lifecycle I think people who don't really understand tableau think it's a visualization company or a visualization tool I don't I don't really understand that when you talk about the vision a lot but okay sure we can visualization but there's just something much bigger I mean you asked about people watching the company I think what's important to watch is that as I spoke about makino yesterday tableau believes what is called the business intelligence industry what's called the business analytics technology stack needs to be completely rewritten from scratch that's what we believe to do over it's a do-over it's based on technology from a prior hair prior era of computing there's been very little innovation the R&D investment ratios which you can look up online of the companies in this space are pathetically low and have been for decades and this industry needs a Google it needs an apple it's a Facebook an RD machine that is passionate and driven and is leveraging the most recent advances in computing to deliver products that people actually love using so that people start to enjoy doing analytics and have fun with it and make data-driven driven decision in a very in a very in a way that's just woven into their into their into their enjoyment and work style every every single day so the big series of product releases you're going to see from us over the next five years that's the thing to watch and we unveiled a few of them yesterday but trust me there's a lot more that's you a lot of applause christina is awesome you can see you know the passion that you're putting forth your great vision so congratulations in the progress you've made I know I know you're not done we'll be watching it thanks very much for coming to me I'm really a pleasure thank you all right keep right there everybody we're going wall to wall we got a break coming up next and then we'll be back this afternoon and this is Dave Volante with Jeff Kelly this is the cube we'll be right back

>> Announcer: Live from Orlando, Florida It's theCUBE covering .conf18. Brought to you by Splunk. >> Welcome back to theCUBE's coverage of Splunk .conf18. It's Florida week. I'm Stu Miniman, and my co-host for this week is Dave Vellante. Dave, I'm really excited. You've done this show a handful of times. It's our seventh year doing theCUBE here. It is my first time here. Thought I understood a few of the pieces and what's going on, but it's really been crystallizing to me. When we talk about on theCUBE, for the last couple of years, data is at the center of everything, and in the keynote this morning they talked about Splunkers are at the crossroads of data. I've talked to a bunch of practitioners here. People come to them to try to get access to data, and the vision that they've laid out this week for Splunk Next is how they can do a massive TAM expansion, try to get from the 16,000 users that they have today to 10x more. So, what's your take been on where we are today and what Splunk of the future looks like? >> Well so Stu, as you know, the keynotes are offsite, about a half hour away from the hotel where we're broadcasting, and there's like 8,000 buses that they're jamming customers in. It's a bit of a pain to get there, so logistically it's not ideal. So I thought the keynotes today, just remotely, we didn't hop in the bus because we had to miss a lot of the keynotes yesterday, to get back here. So we watched remotely today. It just felt like there wasn't as much energy in the room. And I think that's for a couple of reasons, and I'll get into that. But before I do, you're right. This is my fourth .conf, and I was struck by in the audience at how few people actually, it was probably less than a third of the audience, when they asked people to stand up, had been to four or more .confs. A ton of people, first year or second year. So, why is that relevant? It's relevant because these are new people. The core of Splunk's audience are security people and IT operations management people. And so with that many newbies, newbies, they're trying to learn about how they can get more value out of the tool. Today's announcements were all about line of business and industrial IOT. And frankly, a lot of people in the audience didn't directly care. Now, I'll explain why it's important, and why they actually do care and will care going forward. But the most important thing here is that we are witnessing a massive TAM expansion, total available market expansion, for Splunk. Splunk's a one point six, one point seven billion dollar company. They're going to blow through two billion. This is a playbook that we've seen before, out of the likes of particularly ServiceNow. I'm struck by the way in which Splunk is providing innovation for non-IT people. It's exactly the playbook that ServiceNow has used, and it works beautifully, and we'll get into some of that. >> So Dave, one of the things that really struck me, we had seven customers on the program yesterday, and the relationship between Splunk and the customers is a little different. You always hear, oh well, I love this technology. Lots of companies. You've been telling me how passionate you were. But really partnerships that you talk about, when you talked about, we had an insurance company from Toronto, and how they're thinking about how the security and risks that they look at, how that passes on to their customers. So many, it's not just people are using Splunk, but it's how it affects their business, how it affects their ultimate end users, and that value of data is something that we come back to again and again. >> So the classic Splunk user is somebody in IT, IT operations management, or the security knock. And they're hardcore data people, they're looking at screens all day and they love taking a bath in data. And Splunk has completely changed their lives, because rather than having to manually go through log files, Splunk has helped them organize that sort of messy data, as Doug Merritt said yesterday. Today, the whole conversation was about expanding into line of business and industrial IOT. These are process engineers, there weren't a lot of process engineers in the audience today. That's why I think not a lot of people were excited about it. I'm super excited about it because this is going to power, I've always been a bull on Splunk. This is going to power the next wave of growth at Splunk. Splunk is a company that got to the public markets without having to raise a ton of capital, unlike what you're seeing today. You're seeing hundreds of millions of dollars raised before these companies IPO. So, Splunk today in the keynotes, first of all, they had a lot of fun. I was laughing my you-know-what off at the auditions. I mean, I don't really, some of that stuff is kind of snarky, but I thought it was hilarious. What they did is, they said, well Doug Merritt wasn't a shoo-in to keynote at this, so we auditioned a bunch of people. So they came in, and people were singing, they were goofing, you know, hello, Las Vegas! We're not in Las Vegas, we're in Orlando this year. I thought it was really, really funny and well done. You know Stu, we see a lot of this stuff. >> Yeah, absolutely. Fun is definitely part of the culture here at Splunk, love that we talked about yesterday, the geeky t-shirts with all the jokes on that and everything. Absolutely so much going on. But, Dave there's something I knew coming in, and we've definitely heard it today in the keynotes, developers are such an audience that everybody is trying to go after, and you talk about kind of the traditional IT and security might not really be the developer audience, but absolutely, that's where Splunk is pushing towards. They announced the beta of the Splunk Developer Cloud, a number of other products that they've put in beta or are announcing. What's your take as to how they go beyond kind of the traditional Splunk user? >> Yeah so that's what I was saying. This is to me a classic case of, we saw this with ServiceNow, who's powering their way through five billion land and expand, something that Christian Chabot, former CEO of Tableau used to talk about. Where you come in and you get a foot in the door, and then it just spreads. You get in like a tick, and then it spreads to other parts of the business. So let's go through some of the announcements. Splunk Next, they built on top of that today. Splunk Business Flow, they showed, what I thought was an awesome demo. They had a business person, it was an artificial example of the game company. What was the name of the game company? >> Stu: Buttercup Sames. >> Buttercup Games. So they took a bunch of data, they ingested a bunch of data on the business workflow. And it was just that, it was just a big, giant flow of data. It looked like a huge search. So the business user was like, well what am I supposed to do with this? He then ingested that into Splunk Business Flow, and all of a sudden, you saw a flow chart of what all that data actually said in terms of where buyers were exiting the system, calling the call center, et cetera. And then they were able to make changes through this beautiful graphical user interface. So we'll come back to that, because one would be skeptical naturally as to, is it really that easy? They also announced Splunk for industrial IOT. So the thing I like about this, Stu, and we've seen a lot of IOT announcements in the past year from IT companies. What's happening is that IT companies are coming in with a top-down message to industrial IOT and OT, Operations Technology, professionals. We think that is not the right approach. It's going to be a bottoms-up approach, driven by the operations technology professionals, these process engineers. What Splunk is doing, and the brilliance of what Splunk is doing is they're starting with the data. We heard today, OEE. What's OEE? I haven't heard that term. It's called Overall Equipment Effectiveness. These aren't words that you hear from IT people. So, they're speaking a language of OT people, they're starting with the data, so what we have seen thus far is, frankly a lot of box companies saying, hey we're going to put a box at the edge. Or a lot of wireless companies saying, hey, we're going to connect the windmill. Or analytics companies saying, we're going to instrument the windmill. The engineers are going to decide how it gets instrumented, when it get instrumented, what standards are going to be used. Those are headwinds for a lot of the IT companies coming in over the top. What Splunk is doing is saying, we're going to start with the data coming off the machines. And we're going to speak your language, and we're going to bring you tooling you can use to analyze that operations data with a very specific use case, which is predictive maintenance. So instead of having to do a truck roll to see if the windmill is working properly, we're going to send you data, and you're going to have to roll the truck until the data says there's going to be a problem. So I really like that. Your thoughts on Splunk's IOT initiative versus some of the others we've seen? >> Yeah, Dave. That dynamic of IT versus OT, Splunk definitely came across as very credible. The customers we've talked to, the language that they use. You talk about increasing plan for performance and up time. How can they take that machine learning and apply it to the IOT space, it all makes a lot of sense. Once again, it's not Splunk pushing their product, it's, you're going to have more data from more different sources, and therefore it makes sense to be able to leverage the platform and take that value that you've been seeing with Splunk in more spaces. >> So the other thing that they announced was machine learning and natural language processing four dot oh. They had BMW up on the stage, talking about, that was really a good IOT example, but also predicting traffic patterns. If you think about Waze, you and I, well I especially, use Waze, I know that Waze is wrong. It's telling me I'm going to get there at four thirty, and I know traffic is building up in Boston, I'm not going to get there until ten to five, and Waze somehow doesn't know that. BMW had an example of using predictive analytics to predict what traffic flow is going to look like in the future so I thought that was pretty strong. >> And I loved in the BMW example, they've got it married with Alexa so the business person, sitting at their desk can say, hey Alexa, go ask Splunk something about my data, and get that result back. So pretty powerful example, really obvious to see how we get the value of data to the business user, even faster. >> Now the problem is, I'm going to mention some of the challenges I see in some of these initiatives. The problem with NLP is NLP sucks. Okay, it's not that good today, but it's going to get better. They used an example on stage with Alexa, it obviously worked, they had it rehearsed. It doesn't always work that way, so we know that. They also announced the Splunk Developer Cloud. They said it was three Fs: familiar, flexible, and fast. What I love about this is, this is big data, actually in action. Splunk, as I've been saying all week, they never use the term big data when big data was all on the hype cycle, they now use the term big data. Back when everybody was hyping big data, the big vacuum was applications. Pivotal came out, Paul Maritz had the vision, We're going to be the big data application development platform. Pivotal's done okay there, but it's not taking the world by storm. It's a public company, it had a decent IPO, but it's not like killing it. Splunk is now, maybe a little late to the game, a little later than Pivotal, or maybe even on IBM, but they key is, Splunk has the data. I keep coming back to the data. The data is the linchpin of all of this. Splunk also announced SplunkTV, that's nice, you're in the knock, and you got smart TV. Woo hoo! That's kind of cool. >> Yeah but Dave, on the Developer Cloud, this is a cloud native application, so it's fitting with that model for next generation apps, and where they're going to live, definitely makes a lot of sense. >> They talked about integrating Spark and TensorFlow, which is important obviously in that world. Stu, you in particular, John Ferrier as well, spent a lot of time, Jim Kabilis in the developer community. What's your take on what they announced? I know it was sort of high level, but you saw some demos, you heard their language. There were definitely some developers in the room. I would say, as a constituency, they sounded pretty excited. They were a relatively small number, maybe hundreds, not thousands. >> One of the feedback I heard from the community is being able to work with containers and dockers, something that people were looking for. They're delivering on that. We talked to one of the customers that is excited about using Kubernetes in this environment. So, absolutely, Splunk is reaching out to those communities, working with them. When we talked to the field executive yesterday, she talked about- >> Dave: Susan St. Ledger >> How Splunk is working with a lot of these open source communities. And so yeah, good progress. Good to see where Splunk's moving. Absolutely they listen to their customers. >> So, land and expand, Splunk does not use that term. It's my term that I stole from Christian Chabot and Tableau. Certainly we saw that with ServiceNow. We're seeing a very similar playbook. Workday, in many ways, is trying it as well, but Workday's going from HR into financials and ERP, which is a way more entrenched business. The thing I love about Splunk, is they're doing stuff that's new. Splunk was solving a problem that nobody else could solve before, whereas Workday and ServiceNow, as examples, were essentially replacing legacy systems. Workday was going after PeopleSoft. ServiceNow was going after BMC. Tableau, I guess was going after old, tired OBI. So they were sort of disruptive in that sense. Splunk was like, we can do stuff that nobody's been able to do before. >> Yeah Dave, the last thing that I want to cover in this analysis segment is, we talk about the data. It's the people interacting with it. We've been talking for years, there's not enough skills in data scientists. There's so many companies that we're going to be your platform for everything. Splunk is a platform company, but with a big ecosystem at the center of everything they do. It's the data, it's the data that's most important. They're not trying to say, this is the rigid structure. We talked about a lot yesterday, how Splunk is going to let you use the data where you want it, when you want it. How do you look at what Splunk does, the Splunkers out there, all the people coming to them? Compare and contrast against the data scientists. >> Well this is definitely one of the big challenges. To me, the role of a Splunker, they're IT operations people, they're people in the security knock, and Splunk is a tool for them, to make them more productive, and they've fallen in love with it. You've seen the guys running around with the fez, and that's pretty cool. They've created a whole new class of skill sets in the organization. I see the data scientists as, again, becoming a Splunker and using the tools. Splunk are giving the data scientists tools, that they perhaps didn't have before, and giving them a way to collaborate. I'll come back to that a little bit. If I go through the announcements, I see some challenges here, Stu. Splunk next for the LLB. Is it really as easy as Splunk has shown? As time will tell, we're going to have to just talk to people and see how quickly it gets adopted. Can Splunk democratize data for the line of business? Well on the IOT side, it's all about the operations technology professionals. How does Splunk reach those people? It's got to reach them through partnerships and the ecosystem. It's not going to do a belly to belly direct sales, or it's not going to be able to scale. We heard that from Susan St. Ledger yesterday. She didn't get into IOT because it hadn't been announced yet, but she hinted at that. So that's going to be a big thing. The OT standards, how is Splunk going to adopt those. The other thing is, a lot of the operations technology data is analog. There's a headwind there, which is the pace at which the engineers are going to digitize. Splunk really can't control that in a big way. But, there's a lot of machine data and that's where they're focusing. I think that's really smart of Splunk. The other thing, generally, and I don't know the answer to this Stu, is how does Splunk get transaction data into the system? They may very well may do it, but we heard yesterday, data is messy. There is no such thing as unstructured data. We've heard that before. Well there's certainly a thing as structured data, and it's in databases, and it's in transaction systems. I've always felt like this is one of IBM's advantages, as they got the mainframe data. Bringing transaction data and analytic data together, in real time, is very important, whether it's to put an offer in front of the customer before you lose that customer, to provide better customer service. Those transaction systems and that data are critical. I just don't know the answer to how much of that is getting into the Splunk system. And again, as I said before, is it really that easy as Spark and TensorFlow integration enough? It sounds like the developers will be able to handle it. NLP will evolve, we talked about that as a headwind. Those are some of the challenges I see, but I don't think they're insurmountable at all. I think Splunk is in a really good position, if not the best position to take advantage of this. Why? Because digital transformation is all about data, and Splunk is data. They're all about data. They don't have to go find the data, obviously they have to ingest the data, but the data's there. If you're a Splunker, you have access to that data. All the data? Not necessarily, but you can bring that through their API platforms, but a lot of the data that you need is already there. That's a huge, huge advantage for Splunk. >> Well, Dave, this is one of the best conferences I've been at, with data at the core. It's been so great to talk to the customers. We really appreciate the partnership of Splunk. Splunk events team, grown this from seven years ago, when we started a 600 person show, to almost 10,000 now. So for those of you that don't know, there's so much that goes on behind the scenes to make something like this go off. Really appreciate the partnership and the sponsorship that allows us to help us document this, bring it out to our communities. The analysis segments that we do, we actually bring in podcast form. Go to iTunes or Spotify, your favorite podcast player, look for theCUBE insights. Of course go to theCUBE.net for the video. SiliconANGLE.com for all of the news. Wikibon.com for the research, and always feel free to reach out with us, if you've got questions, or want to know what shows we're going to be in next. For my cohost, Dave Vellante who is Dvellante on Twitter. I'm Stu Miniman, at stu on Twitter, and thanks so much for watching theCUBE. (techno music)

(upbeat electronic music) >> Announcer: Live from Las Vegas, it's theCUBE, covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. (crowd chattering) >> Hello everybody and welcome to theCUBE's live coverage of ServiceNow. We are here in Las Vegas, Nevada at The Venetian. I'm your host, Rebecca Knight. Co-hosting with Dave Vellante and Jeff Frick. It's great to be here with you-- >> Hey, Rebecca. >> doing the show. >> Busy week. >> Very busy week and we are only-- >> Busy month. (laughs) >> And it's only day one. So we just heard John Donahoe who is the new CEO, he's been CEO for a year, he was at eBay for a decade. He got up on stage and he said, "When I came "to this job I could barely spell IT." But I want to talk to you first, Dave, and say how's John doing, how's the company doing? What's your take on this? >> Well, the company's doing great. It's the fastest growing software company over a billion dollars. It's got consistent growth. 35-40% growth each quarter, year over year. It's growing sequentially, it's throwing off, it's free cash flow is actually growing faster than it's revenue, which is quite impressive. Company's got a 29 billion dollar market cap. Couple years ago ServiceNow, when Frank Slootman was running the company said, we're going to put the stake in the ground and we're going to be a four billion dollar company, I think this company's going to do four billion dollars in its sleep. I think the next milestone is how they get to 10 billion. And beyond that, how they get to 15 billion, how they take their market value from where it is today in the high 20's, low 30's, up to 100 billion. This company wants to be the next great enterprise software company. Basically automating manual tasks you wouldn't think there's that many manual left, but when you think about whether it's scheduling meetings, or scheduling travel or keeping track of medical leave, and all this other stuff that's manual, they want to automate that process. >> Right, exactly, that's what he talked, the tagline this year and really for the brand identity is making more work work better for people. He said that people are at the heart of this brand. Jeff, does this strike you as a new idea? Is this going to work for ServiceNow? >> It's not really a new idea but their kind of changing their shift. It's interesting when we saw Frank Slootman on he was always, the IT guys are my homies, right? He was very specifically focused on going after IT. And Fred's great kind of early intro was, remember the copier room with all the colored pieces of paper. (Rebecca laughs) Vacation requests, new laptop request, etc. How does he make that automated. And more importantly how does he let the people responsible for that be able to code and build a workflow. So I think the vision is consistent, they're obviously expanding beyond just, the IT are my homies, 'cause it's still ultimately workflow. And I think at the end of the day it's competition for how do you work. What screen or what app is on your screen as you go through your day to day workflow. And they're obviously trying to grab more of those processes so that you're doing them inside of ServiceNow versus one of the many other applications that you might be trying to do. >> Just to follow up on that, when Jeff and I first started covering this show it was 2013, less than 5% of ServiceNow's business was outside of the IT department. Today it's about 35% is outside the IT department. So they have their strategy of, they call it, land and expand. Christian Chabot from Tableau I think was the first I heard use that term. These guys are executing on that. Starting with IT and then moving into HR, moving into maybe facilities, moving into marketing, other parts of the organization, customer service management, security, I don't know if they count that as IT, but cohort businesses. So if you look at their financials their up-selling is phenomenal. Huge percentage of their business comes from existing customers. If you look at the anatomy of a typical ServiceNow customer, they might start with a 50 or 75 thousand dollar deal. That quickly jumps to a multi-hundred thousand dollar deal, then up to a multi-million dollar deal. And then up into the high eight figures. So it's really a tremendous story and the reason is, and Jeff you and I have talked about this a lot, is because when Fred Luddy started the company he developed a platform. He took that platform to the venture capital community and they said well what do you do with this? He said you can do anything with it. They said, yeah, get out. So he said all right I'm going to write an app. He worked at Peregrine so he wrote and IT service management app. And when ServiceNow went public, I remember Gartner Group came out and said, eh, it's a tiny little market, help desk is a dying market, flat, billion dollar TAM. Well this company's TAM, it's almost immeasurable. I mean it's, the TAM is literally in the half a trillion dollars in my view. I mean it's enormous. >> It's workflow, right, so again it's just that competition for the screen. And as everyone goes from their specialty and tries to expand, right? Sales force is trying to expand more into marketing. You've got Zendesk and other kinds of help desk platforms that are trying to get into more workflow. What they were smart is they went into IT 'cause IT controls the applications that are in shop. And so to use that as a basis, and IT touches whether it's an HR process where I need to get the person a new laptop. Or it's facilities where I need to open up a new building or etc., IT touches it all. So a really interesting way to try to grab that screen and application space via the IT systems. >> And that's where John Donahoe comes is. As you said Jeff, Frank Slootman, Data Domain, EMC, you know, IT guy. And now John Donahoe, not an IT guy, came from the consumer world, he's trying to take the ServiceNow brand into the C suite. So we have him on a little later, we're going to talk to him about sort of how he's doing that. But this is a company that's transforming, they're constantly transforming. Really trying to become a brand name, the next great enterprise software company. >> I think another thing that really came out in the keynote and also just on the main stage this morning is this idea of change is not just about the technology. In fact, the technology is the easy part. One of the things he kept saying, and he brought up other people and customers and partners to talk about his too, is that it really is a culture shift. And it really is about a different way of leading. It's a different way of bringing in the right kind of talent who are not just these IT guys, let's be honest. >> Right. >> But they are data scientists, they are creative people, they integrate design thinking into the way they do their jobs, with this over-arching goal of how do I make the employee experience better and how do I make the candidate experience better too. Because that's another part of this. It's not just the people who are already working for you. In the period where there is a war for talent-- >> Jeff: Right, right. >> you also have to be thinking about okay, how do the people that we want to get-- >> Jeff: Right. >> What's their experience like when we're trying to attract them. >> So question for you, Rebecca, 'cause you cover this space-- >> Rebecca: I do, yes. >> a lot, right, and you write for MIT and-- >> Rebecca: HBR. >> HBR and the new way to work and the good, I'm trying to remember-- >> Rebecca: It's called Best Practices, yeah. >> book that you did, that interview. So as it is competition for talent, how did it strike you? 'Cause at the end of the day that's really what it's all about. How do you get and retain the best people when there just aren't enough people for all the jobs that are out there. >> It's interesting because I do feel as though, obviously, you want to be able to enjoy your workday and that's what Andrew Wilson at Accenture was talking about, really it's about having fun. And it's about having it be a great experience. At the same time I do think the human part of work is so essential. As we've talked about before, you don't quit jobs you quit bosses. And it really is about who is your manager and who is the person who is leading this change. >> Jeff: Right. And how are they interacting with employees and with you personally. >> But should it be fun, I mean, they're still paying you to show up. (Rebecca laughs) >> And I think sometimes we get confused. Clearly the mundane still takes-- >> Yes. >> a ridiculously too high percentage-- >> Rebecca: True. >> of time to do the routine, where there's this automation opportunity. But the other piece is the purpose piece and they brought up purpose early on in the keynote, right? >> Rebecca: Yes. >> People want to work for purpose driven organizations and the millennial workers have said they want to be involved in that. It's not just about shareholders and stakeholders and customers. So there is a bigger calling that they need to deliver on to attract and maintain the best people. >> A couple words about the show. So we do a lot of shows. This is a legit 18,000 person show, we're at the Sands Convention Center. It's crowded, the line at the Starbucks coffee the morning-- >> Rebecca: (laughs) Around the block. >> was about 60 to 65 deep, I mean that's a lot of people waiting for coffee. The other thing I want to stress is the ecosystem. When Jeff and I first started this show the ecosystem was very thin, Jeff, as you recall, and that's one of the things we said is watch the ecosystem as an indicator of progress. Well the ecosystem's exploding. You've seen acquisitions where companies like CXC and Accenture have got into the business big time. You see E&Y, Deloitte coming in as big partners now of ServiceNow and as we've often joked, the system integrators like to eat at the trough. So there's a lot of business going on in this ecosystem. >> Right, and that was part of the keynote too. The software's the easy part. It's are you investing in the change management for your people, are you investing in best practices. And if you're not then you're probably wasting some of your money. >> Great. Well it's going to be a great show, this is just segment one, we've got a lot of great guests so I'm excited to get going with both of you. >> Jeff: All right. >> Dave: All-righty. >> I'm Rebecca Knight for Dave Allante and Jeff Frick, we will have more from ServiceNow Knowledge18 coming up just after this. (electronic music)

>> Announcer: Live from the Javits Center in New York City, it's theCUBE! Covering Inforum 2017. Brought to you by Inforum. >> Welcome to day one of theCUBE's coverage of Inforum here at the Javits Center in New York City. I'm your host, Rebecca Knight, along with my co-host, Dave Vellante. We are also joined by Jim Kobielus, who is the lead analyst for artificial intelligence at Wikibon. Thanks so much. It's exciting to be here, day one. >> Yeah, good to see you again, Rebecca. Really, our first time, we really worked a little bit at Red Hat Summit. >> Exactly, first time on the desk together. >> It's our very first time. I first met you a little while ago, and already you're an old friend. >> This is the third time we've done Inforum. The first time we did it was in New Orleans, and then Infor decided to skip a year. And then, last year, they decided to have it in the middle of July, which is kind of a strange time to have a show, but there are a lot of people here. I don't know what the number is, but it looks like several thousand, maybe as many as 4000 to 5000. I don't know what you saw. >> Rebecca: No, no, I feel like this is a big show. >> Jim: Heck, for July? For any month, actually. >> Exactly, particularly at a time where we're having a lot of rail issues, issues at LaGuardia too, so it's exciting. >> theCUBE first met Infor at the second Amazon re:Invent. I remember the folks at Amazon told us, "We really have an exciting SAS company. "It's the largest privately-held SAS company in the world." We were thinking, is that SAS? And they said, "No, no, it's a company called Infor." We said, "Who the heck is Infor?" And then we had Pam Murphy on. That's when we first were introduced to the company, and then, of course, we were invited to come to New Orleans. At the time, the questions around Infor were, who is Infor? What are they all about? And then it became, okay, we started to understand the strategy a little bit. For those of you who don't familiar with Infor, their strategy from early on was to really focus on the micro-verticals. We've talked about that a little bit. Just a quick bit of history. Charles Phillips, former president of Oracle, orchestrator of the M&A at Oracle, PeopleSoft, Siebel and many others, left, started Infor to roll up, gold-funded by Golden Gate Capital and other private equity, substantial base of Lawson Software customers, and then, many, many other acquisitions. Today, fast forward, you got a basically almost $3 billion company with a ton of debt, about $5 billion in debt, notwithstanding the Koch brothers' investment, which is almost $2.5 billion, which was to retire some of the equity that Golden Gate had, some of the owners, Charles and the three other owners took some money off the table, but the substantial amount of the investment goes into running the company. Here's what's interesting. Koch got a 2/3 stake in the company, but a 49% voting share, which implies a valuation of about, I want to say, just under four billion. Let's call it 3.7, 3.8 billion. For a $2 billion to $3 billion company, that's not a software company with 28% operating margin. That's not a huge valuation. So, we'll ask Charles Phillips about that, I mean, some of this wonky stuff in the financials, you know, we want to get through. I'm sure Infor doesn't want to talk too much about that. >> But it is true. It is, for a unicorn, for a privately-held company, this is one of them. This is up there with Uber and Airbnb, and it's a question that, why isn't it valued at more? >> My only assumption here is they went to Koch and said, "Okay, here's the deal. "We want $2 billion plus. "You only get 49%, only. "If you get 49% of the company in terms of voting rights, "we'll give you 2/3 in terms of ownership. "It's a sweetheart deal. "Of course, it's a lot of dough. "You get a board seat." Maybe two board seats, I can't remember. "And we'll pump this thing up, we'll build up the equity, "and we'll float it someday in the public markets, "and we'll all make a bunch of dough "and our shareholders will all be happy." That's the only thing I can assume, was this sort of conversation that went on. Well, again, we'll ask Charles Phillips, see if he answers that. But James, you sat in yesterday at the analyst event, you got sort of the history of the company, and the fire hose of information leading up to what was announced today, Coleman AI. What were your impressions as an analyst? >> Well, first of all, my first impression was a thought, a question. Is Infor with Coleman AI simply playing catch-up in a very, I call it a war of attrition in the ERP space. Really, it's four companies now. It's SAP, it's Microsoft, it's Oracle, and it's Infor duking it out. SAP, Microsoft and Oracle all have fairly strong AI capabilities and strategies and investments, and clearly they're infused, I was at Microsoft Build a few months ago. They're infusing those capabilities into all of their offerings. With Coleman, sounds impressive, thought it's just an early announcement, they've only begun to trickle it out to their vast suite. I want to get a sense, and probably later today we'll talk to Mr. Angove, Duncan Angove. I want to get a sense for how does, or does, Infor intend to differentiate their suite in this fiercely competitive ERP world? How will Coleman enable them to differentiate it? Right now it seems like everything they're announcing about Coleman is great in terms of digital assistance, conversational interface, everybody does this, too, now, with chatbots and so forth, in-line providing recommendations. Everybody's doing that. Essentially, everybody wants to go there. How are they going to stand apart with those capabilities, number one? Number two is just the timeline. They have this vast suite, and we just came from the keynote, where Charles and the other execs laid out in minute detail the micro-vertical applications. What is their timeline for rolling out those Coleman capabilities throughout the suite so customers can realize they have value? And is there a layered implementation? They talked about augmentation versus automation, and versus assistance. I'd like to see sort of a layer of capabilities in an architecture with a sense for how they're going to invest in each of those capabilities. For example, they talked about open source, like with TensorFlow, which is a new deep learning framework from Google Open Source. I just want to get a deep dive into where the investment funds that they're getting from Koch and others, especially from Koch, where that's going in terms of driving innovation going forward in their portfolio. I'm not cynical about it, I think they're doing some really interesting things. But I want some more meat on the bones of their strategy. >> Well, it's interesting, because I think Infor came into the show wanting to message innovation. They're not known as an innovative company. But you heard Charles Phillips up there talking, today he was talking about quantum computing, he was talking about the end of Moore's Law, he was obviously talking about AI. They named Coleman after Katherine Coleman Johnson. >> Here's my speculation. My speculation, of course, they recently completed the acquisition of Birst. Brad Peters did a really good discussion of Birst, the BI startup that's come along real fast. My sense, and I want to get confirmation, is that, possibly, Birst and Brad Peters and his team, will they drive the Coleman strategy going forward? It seems likely, 'cause Birst has some AI assets that Brad Peters brought us up to speed on yesterday. I want to get a sense for how Birst's AI and Coleman AI are going to come together into a convergence. >> But wouldn't they say that it's quote-unquote embedded, embedded AI? >> Jim: It'll be invisible, it has to be. >> You know, buried within the software suite? We saw, like you said, in gory detail the application portfolio that Infor had. I think one of the challenges the company has, it's like some of my staff meetings. Not everything is relevant to everybody. Very clearly, they have a lot of capabilities that most people aren't aware of. The question is, how much can they embed AI across those, and where are the use cases, and what's the value? And it's early days, right? >> Oh, yeah, very much. And you know, in some of those applications, probably many of them, the automation capabilities that they described for Coleman will be just as important as the human augmentation capabilities. In other words, micro-verticalize their AI in diverse ways going forward across their portfolio. In other words, one AI brush, broad brush of AI across every application probably won't make sense. The applications are quite different. >> I want to talk about the use cases, here. The selling points for these things are making the right decision all the time, more quickly. >> Jim: Productivity accelerators for knowledge workers, all that. >> And one of the other points that was made is that there are fewer arguments, because we are all looking at the same data, and we trust the data. Where do you see Birst and Coleman? Give me an example of where you can see this potentially transforming the industry? >> "We all trust data." Actually, we don't all trust data, because not all data is created the same. Birst comes into the portfolio not just to, really great visualizations and dashboarding and so forth, but they've got a well-built data management backend for data governance and so forth, to cleanse the data. 'Cause if you have dirty data, you can't derive high-quality decisions from the data. >> Rebecca: Excellent point, right. >> That's really my general take on where it's going. In terms of the Birst, I think the Birst acquisition will become pivotal in terms of them taking their data-driven functionality to the next level of consumability, 'cause Birst has done a really good job of making their capability consumable for the general knowledge worker audience. >> Well, a couple things. Actually, let me frame. Charles Phillips, I thought, did a good job framing the strategy. Sort of his strategy stack, if you will, starting with, at the bottom of the stack, the micro-verticals strategy, and then moving up the next layer was their decision to go all cloud, AWS Cloud. The third was the network. Infor made an acquisition of a company called GT Nexus, which is a commerce platform that has 18 years of commerce data and transaction data there. And the next layer was analytics, which is Birst, and I'll come back to that. And then the top layer is Coleman AI. The Birst piece is interesting, because we saw the ascendancy of Tableau and its land-and-expand strategy, and Christian Chabot, the CEO of Tableau, used to talk about, and they said this yesterday, the slow BI, you know, cubes, and the life cycle of actually getting an answer. By the time you get the answer, the market has changed. And that's what Tableau went after, and Tableau did very, very, well. But it turned out Tableau was largely a desktop tool. Wasn't available in the Cloud. It is now. And it had its limitations. It was basically a visualization tool. What Infor has done with Birst is they're positioning the old Cognos, which is now IBM, and the micro strategies of the world as the old guard. They're depositioning Tableau, and they didn't use that specific name, Tableau, but that's what they're talking about, Tableau and Click, as less than functional. Sort of spreadsheet plus. And they are now the rich, robust platform that both scales and has visualization, and has all the connections into the enterprise software world. So I thought it was interesting positioning. Would love to talk to some customers and see what that really looks like. But that, essentially, was the strategy stack that Charles Phillips laid out. I guess the last point I'd make as I come back to the decision to go AWS, you saw the application portfolio. Those are hardcore enterprise apps which everybody says don't live in the Cloud. Well, 55% of Infor's revenue is from the Cloud, so, clearly, it's not true. A lot of these apps are becoming cloud-enabled. >> Jim: Yeah, most of them. >> Most of them? >> Most of them are, yeah. BI, mode-predictive analytics, most AI. Machine learning is going in the Cloud. >> 'Cause Oracle's argument is, Oracle will be only one who can put those apps in the Cloud. >> 'Cause the data lives in the Cloud. It's trained on the data. >> Not all the data lives in the Cloud. >> It's like GT Nexus. That's EDI, that's rich EDI data, as they've indicated for training this new generation of neutral networks, machine learning and deep learning models continuously from fresh transaction data. You know that's where GT Nexus and e-commerce network fits into this overall strategy. It's a massive pile stream of data for mining. >> But, you know, SAP has struggled in the Cloud. SuccessFactors, obviously, is their SAS play. Most of their stuff remains on-prem. Oracle again claims they have the only end-to-end hybrid. You see Microsoft finally shipping Azure Stack, or at least claiming to soon be shipping Azure Stack. They've obviously got a strategy there with their productivity estate. But here you have Infor-- >> Don't forget IBM. They've got a very rich, high-rated portfolio. >> Well, you heard, I don't know if it was Charles, somebody took a swipe at IBM today, saying that the company's competitors have purchased all these companies, these SAS companies, and they don't have a way to really stitch them together. Well, that's not totally true. Bluemix is IBM's way. Although, that's been a heavy lift. We saw with Oracle Fusion, it took over a decade and they're still working on that. So, Infor, again, I want to talk to customers and find out, okay, how much of this claim that everything's seamless in the Cloud is actually true? I think, obviously, a large portion of the install base is still that legacy on-prem Lawson base that hasn't modernized. That's always, in my view, enforced big challenges. How do you get that base, leverage that install base to move, and then attract new customers? By all accounts, they're doing a pretty good job of it. >> I don't think what's going on, I don't think a lot of lift-and-shift is going on. Legacy Lawson customers are not moving in droves to the Cloud with their data and all that. There's not a massive lift-and-shift. It's all the new greenfield applications for these new use cases, in terms of predictive analytics. They're being born and living their entire lives in the Cloud. >> And a lot of HR, a lot of HCM, obviously, competing with Workday and Peoplesoft. That stuff's going into the Cloud. We're going to be unpacking this all day today, and tomorrow. Two days here of coverage. >> Indeed, yes indeed. >> Dave: Excited to be here. >> It's going to be a great show. Bruno Mars is performing the final day. >> Jim: Bruno Mars? >> I know, very-- >> You know a company's doing good, Infor, when they can pay for the likes of a Bruno Mars, who's still having mega hits on the radio. I wish I was staying long enough to catch that one. >> I know, indeed, indeed. Well, for Dave and Jim, I'm Rebecca Knight, and we'll be back with more from Inforum 2017 just after this. (fast techno music)

>> Narrator: Live, from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017, brought to you by Databricks. Now, here are your hosts, David Vellante and George Gilbert. >> Back to Boston, everybody, where the city is bracing for a big snowstorm. Still euphoric over the Patriots' big win. Aaron Colcord is here, he's the director of engineering at FIS Global, and he's joined by Dave Favela, who's the director of BI at FIS Global. Gentlemen, welcome to theCUBE. It's good to see you. >> Yeah, thank you. >> Thank you very much. >> Thanks so much for coming on. So Dave, set it up. FIS Global, the company that does a ton of work in financial services that nobody's ever heard of. >> Yeah, absolutely, absolutely. Yeah, we serve and touch virtually every credit union or bank in the United States, and have services that extend globally, and that ranges anywhere from back office services to technology services that we provide by way of mobile banking or online banking. And so, we're a Fortune 500 company with a reach, like I said, throughout the nation and globally. >> So, you're a services company that provides, sort of, end-to-end capabilities for somebody who wants to start a bank, or upgrade their infrastructure? >> Absolutely, yeah. So, whether you're starting a bank or whether you're an existing bank looking to offer some type of technology, whether it's back-end processing services, mobile banking, bill pay, peer-to-peer payments, so, we are considered a FinTech company, and one of the largest FinTech companies there is. >> And Aaron, your role as the director of engineering, maybe talk about that a little bit. >> My role is primarily about the mobile data analytics, about creating a product that's able to not only be able to give the basic behavior of our mobile application, but be able to actually dig deeper and create interesting analytics, insights into the data, to give our customers understanding about not only the mobile application, but be able to even, as we're building right now, a use case for being able to take action on that data. >> So, I mean, mobile obviously is sweeping the banking industry by storm, I mean, banks have always been, basically, IT companies, when you think about it, a huge component of IT, but now mobile comes in and, maybe talk a little bit about, sort of the big drivers in the business, and how, you know, mobile is fitting in. >> Absolutely. So, first of all, you see a shift that's happening with the end user: you, David, as a user of mobile banking, right? You probably have gone to the branch maybe once in the last 90 days, but have logged into mobile banking 10 times. So, we've seen anywhere from an eight to nine time shift in usage and engagement on the digital channel, and what that means is, more interactions and more touch points that the bank is getting off of the consumer behavior. And so, what we're trying to do here is turn that into getting to know the customer profile better, so that they could better serve in this digital channel, where there's a lot more interactions occurring. >> Yeah, I mean, you look at the demographic, too. I mean, my kids don't even use cheques. Right, I mean, it's all, everything's done on mobile, Venmo, or whatever, the capabilities they have. So, what's the infrastructure behind that that enables it? I mean, it can't be what it used to be. I mean, probably back-end still is, but what else do you have to create to enable that? >> Well, it's been a tremendous amount of transformation on the back-ends over the last ten years, and particularly when we talk about how that interaction has changed, from becoming a more formal experience to becoming a more intimate experience through the mobile client. But, more specifically to the back-end, we have actually implemented Apache Spark as one of our platforms, to actually help transform and move the data faster. Mobile actually creates a tremendous amount of back-end activity, sometimes even more than what we were able to see in other channels. >> Yeah, and if you think about it, if you just kind of step back a little bit, this is about core banking, right, and as you speak to IT systems, and so, if you think about all the transactions that happen on the daily, whether you're in branch, at ATM, on a mobile device, it's processed through a core banking system, and so one of the challenges that, I think, this industry and FinTech is up against is that, you've got all these legacy old systems that have been built that can't compute all this data at a fast enough rate, and so for us, bringing in Aaron, this is about, how do you actually leverage new technology, and take the technical data of the old systems, data schemas and models, and marry the two to provide data, key data that's been generated. >> Dave: Without shutting down the business. >> Without shutting down the business. >> Because that's the hard part. >> Can you elaborate on that, because that's non-trivial. It used to be when banks merged, it could take years for the back-off of systems to come together. So now, let's say a bank comes to you, they have their, I don't want to say legacy systems, it's the systems they've built up over time, but they want the more modern capabilities. How do you marry the two? >> Would you take a first stab? >> Well, it is actually a very complicated process, because you always have to try to understand data itself, and how to put those two things together. More specifically on the mobile client, because of the way that we are able to think about how data can be transformed and transported, we came up with a very flexible mechanism to allow data to actually be interpreted on the fly, and processed, so that when you talk about two different banks, by transforming it into this type of format, we're able to kind of reinterpret it and process it. >> Would this be, could you think of this as a very, very smart stream processor that, where ETL would be at the most basic layer, and then you're adding meaning to the data so that it shows up to the mobile client in a way that coheres to the user model that the user is experiencing on their device? >> I think that's a really good way of putting it, yeah. I mean, there's a, we like to think of it, I call it a semantic layer, of how you, one, treat ETL as one process, and then you have a semantic layer that you basically transform the bottom bits, so to speak, into components that you can then assemble semantically so that it starts making sense to the end user. >> And to that point, you know, to your integration question, it is very challenging, because you're trying to marry the old with the new, and we'll tease the section for tomorrow in which Aaron will talk about that, but for us, at enterprise grade, it has to be done very cautiously, right? And we're under heavy regulation and compliance and security, and so, it's not about abandoning the old, right? It's trying to figure out, how do we take that, what's been in place and been stable, and then couple it with the new technology that we're introducing. >> Which is interesting conversation, the old versus new, and I look at your title, Dave, and it's got 'BI' in it. I remember I interviewed Christian Chabot, who was then CEO of Tableau, and he's like, "Old, slow, BI", okay, now you guys are here talking about Spark. Spark's all about real-time and speed and memory, and everything else. Talk about the transformation in your role as this industry has transformed. >> Yeah, absolutely, so, when we think about business intelligence and creating that intelligence layer, we elected the mobile channel, right? Because we're seeing that most inner activities happen there. So for us, an intelligent BI solution is not just, you know, data management and analytics platform. There has to be the fulfillment. You talk a lot about actioning on your data. So for us, it's, if we could actually create, you know, intelligence layer to analytics level, how can we feed marketing solutions with this intelligence to have the full circle and insights back? I believe, the gentlemen, they were talking about the RISE Lab in this morning session. >> Dave: The follow-on to AMP, basically. >> Yeah, exactly. So, there it was all about that feedback loop, right? And so, for us, when we think about BI, the whole loop is from data management to end-to-end marketing solutions, and then back, so that we can serve the mobile customer. >> Well, so, you know, the original promise of the data warehouse was this 365, what you just described, right? And being able to effect business outcomes, and that is now the promise of so-called big data, even though people don't really like that term anymore, so, my question is, is it same line, new bottle, or is it really transformational? Are we going to live up to that challenge this time around? As practitioners, I'd really love your input on that. >> I think I'd love to expand on that. >> Absolutely. >> Yeah, I mean, I don't think it's, I think it's a whole new bottle and a whole new wine. David here is from wine country, and, there's definitely the, data warehouse introduced the important concepts, of which is a tremendous foundation for us to stand on. You know, you always like to stand on the shoulders of giants. It introduced a concept, but in the case of marrying the new with the old, there's a tremendous extra third dimension, okay? So, we have a velocity dimension when we start talking about Apache Spark. We can accelerate it, make it go quick, and we can get that data. There's another aspect there when we start talking about, for example, hey, different banks have different types of way that they like to talk to it, so now we're kind of talking about, there's variation in people's data, and Apache Spark, actually, is able to give that capability to process data that is different than each other, and then being able to marry it, down the pipe, together. And then the additional, what I think is actually making it into a new wine is, when we start talking about data, the traditional mechanism, data warehousing, that 360 view of the customer, they were thinking more of data as in, I like to think of it as, let's count beans, right? Let's just come up with what how many people were doing X, how many were doing this? >> Dave: Accurate reporting, yeah. >> Exactly, and if you think about it, it was driving the business through the rear-view mirror, because all you had to do was base it off of the historical information, and that's how we're going to drive the business. We're going to look in the rear-view mirror, we're going to look at what's been going on, and then we're going to see what's going on. And I think the transformation here is taking technologies and being able to say, how do we put not only predictive analytics inside play, but how do we actually allow the customer to take control and actually move forward? And then, as well, expand those use cases for variation, use that same technology to look for, between the data points, are there more data points that can be actually derived and moved forward on? >> George, I loved that description. You have, in one of your reports, I remember, George had this picture of this boat, and he said, "Oh, imagine trying to drive the boat", and it was looking at the wake (laughs), you know, right? Rather than looking in the rear-view mirror. >> But in addition to that, yeah, it's like driving the rear-view mirror, but you also said something interesting about, sort of, I guess the words I used to use were anticipating and influencing the customer. >> Aaron: Exactly. >> Can you talk about how much of that is done offline, like scoring profiles, and how much of that is done in real-time with the customer? >> Go ahead. >> Well, a lot of it still is still being done offline, mostly because, you know, as trying to serve a bank, you have to also be able to serve their immediate needs. So, really, we're evolving to actually build that use case around the real-time. We actually do have the technology already in place. We built the POCs, we built the technology inside, we're being able to move real-time, and we're ready to go there. >> So, what will be the difference? Me as a consumer, how will that change my experience? >> I think that would probably be best for you. >> Yeah, well, just got to step back a little bit, too, because, you know, what we're representing here is the digital channel mobile analytics, right? But, there's other areas within FIS Global that handles real-time payments with real-time analytics, such as a credit card division, right? So, both are happening sort of in parallel right now. For us, from our perspective on the mobile and digital front, the experience and how that's going to change is that, if you were a bank, and as a bank or a credit union you're receiving this behavioral data from our product, you want to be able to offer up better services that meet your consumer profile, right? And so, from our standpoint, we're working with other teams within FIS Global via Spark and Cloud, to essentially get that holistic profile to offer up those services that are more targeted, that are, I think, more meaningful to the consumer when they're in the mobile banking application. >> So, does FIS provide that sort of data service, that behavioral service, sort of as a turnkey service, or as a service, or is that something that you sort of teach the bank or the credit union how to fish? >> That's a really good question. We stated our mission statement as helping these institutions, creating a culture of being data-driven, right? So, give them the taste of data in a way that, you know, democratizing data, if you will, as we talked about this morning. >> Dave: Yeah, that's right. >> That concept's really important to us, because with that comes, give FIS more data, right? Send them more data, or have them teach us how to manage all this data, to have a data science experience, where we can go in and play with the data to create our own sub-targeting, because our belief is that, you know, our clients know their customers the best, so we're here to serve them with tools to do that. >> So, I want to come back to the role of Spark. I mean, Hadoop was profound, right, I mean, shipped five megabytes of code, a petabyte a day, no doubt about it. But at the same time, it was a heavy lift. It still is a heavy lift. So talk about the role of Spark in terms of catalyzing that vision that we've been talking about. >> Oh, definitely. So, Apache Spark, when we talk in terms of big data, big data got started with Hadoop, and MapReduce was definitely an interesting concept, but Apache Spark really lifted and accelerates the entire vision of big data. When you look at, for example, MapReduce, you need to go get a team of trained engineers, who are typically going to work in a lower level language like Java, and they no longer focus in on what the business objectives are. They're focusing on the programming objectives, the requirements. With Spark, because it takes a more high-level abstraction of how we process data, it means that you're more focusing on, what's the actual business case? How are we actually abstracting the data? How are we moving data? But then it also gives you that same capability to go inside the actual APIs, get a little bit lower, to modify it for what's your specific needs. So, I think the true transformation with Apache Spark is basically allowing us, now, like for example, in the presentation this morning, it was, there's a lot of people who are using Scala. We use Scala, ourselves. There's now a lot of people who are using Python, and everybody's using SQL. How does SQL, something that has survived so robustly for almost 30, 40 years, still keep on coming back like a boomerang on us? And it's because a language composed of four simple keywords is just so easy to use, and so descriptive and declarative, that allows us to actually just concentrate on the business, and I think that's actually the acceleration that Apache Spark actually brings to the business, is being able to just focus in on what you're actually trying to do, and focus in on your objectives, and it actually lowers the actual, that same team of engineers that you're using for MapReduce now become extremely more productive. I mean, when I look at the number of lines of codes that we had to do to figure out machine learning and Hadoop, to the amount of lines that you have to do in Apache Spark, it's tremendously, it's like, five lines in Apache Spark, 30 in MapReduce, and the system just responds and gives it to you a hundred times faster. >> Why Spark, too? I mean, Spark, when we saw it two years ago, to your point of this tidal wave of data, we saw more mobile phone adoption, we saw those people that were on mobile banking using it more, logging in more, and then we're seeing the proliferation of devices, right, in IoT, so for us, these are all these interaction and data points that is a tsunami that's coming our way, so that's when we strategically elected to go Spark, so we could handle the volume and compute storage- >> And Aaron, what you just described is, all the attention used to be on just making it work, and now it's putting to work, is really- >> Aaron: Right, exactly. >> You're seeing that in your businesses. >> Quick question. Do you see, now that you have this, sort of, lower and lower latency analytics and ability to access more of the, what previously were data silos, do you see services that are possible that banks couldn't have thought of before, beyond just making different products recommended at the appropriate moment, are there new things that banks can offer? >> It's interesting. On one hand, you free up their time from an analysis standpoint, to where they could actually start to get out of the weeds to think about new products and services, so, from that component, yes. From the standpoint of seeing pattern recognition in the data, and seeing what it can do aside from target marketing, our products are actually often used by our product owners internally to understand, what are the consumers doing on the device, so that they could actually come up with better services to ultimately serve them, aside from marketing solutions. >> Notwithstanding your political affiliations, we won't go there, but there's certainly a mood of, and a trend toward, deregulation, that's presumably good news for the financial services industry. Can you comment on that, or, what's the narrative going on in your customer base? Are they excited about fewer regulations, or is that just all political nonsense? Any thoughts? >> Yeah (laughs), you know, on one hand, why people come to FIS is because we do adhere to a compliance and regulation standpoint, right? >> Dave: Complexity is your friend, then (laughs). >> Absolutely, right, so they can trust us in that regard, right? And so, from our vantage point, will it go away entirely? No, absolutely not, right. I think Cloud introduces a whole new layer of complexity, because how do you handle Cloud computing and NPI, and PII data in the Cloud, and our customers look to us to make sure that, first and foremost, security for the end consumer is in place, and so, but I think it's an interesting question, and one that you are seeing end users click through without even viewing agreements or whatnot, they just want to get to product, right? So, you know, will it go away, or do we see it going away? No, but ... >> You guys don't read all that text, do you? (laughing) >> No comment? >> Required, required to. >> You know, no matter where it goes with the politics, I think there's a theme over the last 10 years, and the 10 years before. Things are transforming, things are evolving in ways, and sometimes going extremely, extremely fast in ways that we don't, surely can't anticipate. I think, if we were to think about just a mobile application, or the mobile bank experience 10 years ago, all we wanted was just to be able to see just the bank balance, and now we're able to take that same application and not only see our bank balance, but be able to deposit our cheque, or even replace the card in our pocket completely, with the mobile app, and I think we're going to see the exact same types of transformations over the industry over the next 10 years. Whether or not it's more regulation or different regulation, I think it's going to still speak to the same services, which FIS is there to help deliver. >> Yeah, and you're right, there are going to be new regulations, because they'll evolve, maybe out with the old, in with the new, you see, and global regulations are on run book, and you've got your Cloud, there's data locality, and you know, it's never-ending. That's great for your business. Fantastic. >> It comes down to trust, ultimately, right? I mean, they still, our customers still go to banks and credit unions because they trust them with their data, if you will, or their online currency, in some regards. So, you know, that's not going to change. >> Right, yeah. Well, Aaron, Dave, thanks very much for coming to theCUBE, it was great to have you. >> Thanks so much for talking with us. >> Absolutely, good luck with everything. >> Alright, keep it right there, buddy. We'll be back with our next guest. This is theCUBE. We're live from Boston, Spark Summit East, #SparkSummit. Be right back. >> I remember, when I had such a fantastic batting practice-

>>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.