[Music] everyone welcome to this cube conversation here in Palo Alto California I'm John Fourier co-host of the cube were in the cube studios our next guest is Adam Weinstein who's the CEO of a company called cursor so introducing curse it's hot startup growing in the data analytics space doing something unique very innovative around changing the game on data data catalogs but more importantly how data is being used and consumed and also kind of revitalized so Adam welcome to the cube conversation thanks for joining us thanks for having me excited to be here so you guys are a young startup you're in a really good wave right now it's the cloud data the changing nature of data take him into explain what cursor does what's the company what's the focus how big you raise money start the update yeah yeah so I'll give you a quick background on me that sort of leads into that right so spent most of my career as an analyst I might say right so working with data living in data good the bad the ugly right and spent last couple years prior to this at LinkedIn working an analytics team there and one of the challenges we had as an organization was you know finding what was where and who worked on what so when you had literally a thousand people across the company of 10% of the business touching data on a daily basis one thing we struggled with was knowing you know who was working on what what was where what was accurate what was maybe outdated data was getting created it insane velocity was talking earlier little we were creating a trillion events a day inside the business and so you know as an analytics practitioner if you all it became increasingly difficult to get to a quick answer there was no search to go and say okay I want to look for this question as I've been asked before and if so where's the data so you know there was this new space called data cataloging at the time that seemed interesting with the cataloging was really only looking at how do we create like a yellow pages of data not necessarily how do you put it in the workflow of a person that's then taking that and acting on it and then you know feeding that insight that they may have created back into that sort of cataloging feel right so it's all an opportunity to create something new that really supported an analyst and really was you know mindful of how their day-to-day what job existed and you know that was that was cursor right what's the role of the analyst now because one of the things that's challenging the industry was this idea of and you just go back five years data science is the next big thing there are more open jobs in data science than there are people but then this also trend came on around humanizing data science and not requiring you to know hardcourt C++ or Python or having all this wrangling environments doing all this provisioning of stuff to get started to his idea of okay can we level up that and also can he make it easier almost like using Excel yeah I thought of the kind of the trend what's your thought on the current state of the data analyst role no I think that there is a lot of analytics work that maybe five years ago you know was being done and and there was no automation around it and in the next five years it'll get it wouldn't say automated away but I'll be at heavily automated away called 80% of the workload but that 20% use or 20% of data that it's really difficult to understand and may not be able to you know get an answer out of it automatically that that's not you know that needs people and someone that understands the business that's technical enough to go dive into the data and even though that may not be the hundred percent that existed before the amount of like effort that's needed to decipher it I think is is maybe even greater than it used to be because the rate of data getting created is so much greater to is the demand for more solutions how about cursor how big are you guys who's on the team what's the product is it SAS as a software sir give a quick overview now great so we're small or seven person team right now I started the company a little over a year and a half ago you know the idea was to get a solution to market that was lightweight enough that someone could come and download it and try it very quickly without having to go through a long enterprise sale cycle they could get something on their computer literally stand it up in five minutes start putting in a data and having it you know identify and help with their day-to-day job the team is is volunteering - me right so you know there's that we have folks from Salesforce where you know I came from a company called ExactTarget the tails for spot Pandora thumbtack were basically tried to bring people together that if all you know seen companies scale and data scale and and you know bring those insights alongside them so first generation data scale yet the classic you know web scale build it out on open source grow it have things break rebuild it yeah I mean we levered some open source I think you for us right now how do we get something that unique to market as quickly as possible right so there's things that we can use that that are out there that are that are available that are you know especially if they're you know standardized right we'll make use of them but other times well we've built quite a bit of stuff on our own and our solution lives you can't live in the cloud it can also live on premise and actually see a lot of customers deploy it in a hybrid manner so they may have this sort of collaboration layer live in the cloud but it's pointing at data that's both cloud-based and on-prem and even though that data may get migrated to the cloud over the next several years a lot of large enterprises are still so are you guys going to market by selling a product as freemium what's the and is it software they download on-premise is it SAS in the cloud you talk about the go to market and how people engage with the product no it's heavily SAS in the cloud right so I think sort of companies that are in a heavily regulated industry that really haven't yet figured out that cloud model you know our products SAS delivered there is a client that lives on the users local machine and the reason that exists is just for security purposes because data is still often behind the firewall so like you can ask your security guy hey poke a hole in the firewall for this company I've never heard of or you can have a tool that lives on the machine that sort of brokers that in a fall way you guys are flexible we're flexible right you don't necessarily need that right if you deploy it in your own infrastructure obviously there's there's no need to then have that client it can it can handle things so why curse or what are the market drivers for you guys what's driving your business yeah we saw this need errors I felt this needed very acutely LinkedIn which is you know with analysts are getting you know hundreds or thousands of questions as a team on a daily or weekly basis if they're within a large organization how do you address some meaningful portion of those with automation so if a questions been asked before and you've got you know great solutions like a tableau or a look or a thought spot or a power bi like you've got tons of reporting solutions around the business but there's no place to go and say hey where's the answer to this question which one of those is it in is it a Salesforce report is a tableau dashboard and and so you'd ask your friendly analysts who'd be happy to help but like that's taking them away from doing things that are new and so I I kind of became that switchboard unfortunately and so I saw an opportunity to create a solution that would sort of want to meet me and that's that's really obviously index all the questions kind of see what the frequency was the behavior you have the analytics kind of packaging it up in the catalog yeah and taking it a step further I think what are the topics how do you map topics and understand okay there's a fire in Aisle seven and that fire happens to be churn and it's q3 and why is fire on turn and how do we dig into the data behind turn and get some water they made an insight surround it and then you know but yes certainly the step one is being able to direct people on the right to the right place once you get beyond that doe to understanding what our company's data is and what the sort of you know size and shape and characteristics of it are you can actually take it a step further and you know really sort of recommend things which is what we want the alternatives I'm not having like a data catalog and a cursor is to go ask your resident analyst or hope that someone posted something on slack and then you search through slow I mean all kinds of I mean really not up not a viable no it's a hodgepodge of solutions right so one of the things we saw in this is interesting having been at LinkedIn is that you know more and more teams around organizations are hiring analyst talent they may not call it analyst I might call like a citizen data scientist they might call it a researcher they might even call it an engineer like a data engineer a lot of overlapping skills and what the real need is is like someone to be on that team that knows their data inside and out but yet can help answer like you said sort of the ad hoc question that comes up you know every day and and so for that like you know if they can use her sword answer 80% of those or you know as many as possible right we've got it's interesting I do see the same kind of knee-jerk reaction when LinkedIn and and other clients that have a similar profile where they have a lot of data I certainly see that when they get hired what's the kind of what's the marching orders go jump into the data and figure it out is there I mean because this is kind of an evolving new position that's growing very very fast what are they directed to do I mean what's this what's the job responsible it's a great question so I think one of the challenges is how do you onboard people when when there is no place to start right like it's okay here the hundred places we store data go figure it out with Lauren on your own we had built a little bit of a training and onboarding every college they really have start as a PowerPoint deck and then it expanded into some code and some additional training but you know there is no solution for that right I think our internally we had this notion that you know somewhere between three and six months the person was ramped enough to begin to be productive it was like how we how do you measure ROI on a person when you hire them right and that was LinkedIn where I think we were pretty you know we were out here we you know we have you know quite a few nerds right like I think we're pretty good at organizing things relatively speaking I can't imagine what that's like in productivity just write some Python code spit out some Angela is that good enough look yeah I guess then or sink-or-swim kind of mentality and then you know to get someone else in there yeah and the nuance of the data has gotten just because everyone's mindset is record everything right like it becomes harder and harder to actually get a quick answer so gonna give an example like you know looking at data do you know if something's you know test data if it's you know fake data if it's you know if there's something you need to be mindful of like in e-commerce how do you account for returns how do you account for you know fraud how do you account for things that you know if you look at the data and say I just want to add up all my orders and get some total amount of receipts like you would think oh that's my sales for the day but then you forget like there are all these things that if you don't know the data really well that you miss out on and so yeah multiply that by you know large corporates what's the phrasing needle in a stack of needles I'm trying to find it like everything in there so I mean data structures data cleanliness yep these are huge issues huge and you know we will address every single one of them many think we're courser wants to sit is in between a lot of best-of-breed solutions right so we're not building a new Hadoop we think we do a great job of storing data whether you want to call it a lake or you know something beyond a lake right like you know there are plenty of data stores in an organization to do a great job at storing data you know on the opposite end of the spectrum like in terms of visualizing data are actually generating you know insights they're a great bi solution to the market but in between those two sort of you know ends of the spectrum there's a lot of work that gets done and that's what we want to live Adam talked about the innovation and the tech behind cursor and then just you know innovation in general the way you see it and the team sees it because you're on the Front Range of a new trend bleeding edge cutting edge whatever you want to call it certainly you're pushing the envelope yeah yeah what's the core tech for cursor sir where's the innovation lie has it all tie together sure so we have a you know a couple different deployment models but our most common one is we have a you know a cloud layer that enables collaboration so anytime a company is using our product you know metadata we don't ever look at company data that's one promise we've made because we want to work in regulated industries we want to be in places where there are high security environments but we never pushed actual data to the cloud but met about a company's data so you know what's the name of a column you know what's the name of a database who's used often have they used it what dashboard names are using all those kinds of things could push to our cloud you know we use a language called Kotlin which is a java derivative to write most of our back-end code mostly because a lot of legacy data stores or you know designed to interoperate with Java and then you know we have a client component that lives on a user's machine and that's what facilitates a lot of the day-to-day work and we do that just for security purposes because you know because most data is behind a firewall whether it's cloud based or not is you know it gets independent of that it's oftentimes not publicly accessible we can't expect our cloud will be able to get directly to it right whether or in WSG CP or arouser we can work with any of them you know we you know expected the company's security policies requires some sort of you know local connectivity and so that's you know that that client it's actually just a product called electron that wraps you know react front ends are very very common and you know paradigms you know we try to pick packages that we think have some staying power cuz you know every time the wind blows there's a new framework that's you know the latest and greatest so that's that's awesome I talked about the marketplace and customer interactions you have up so you guys are a year and a half into this or so what's the feedback what are you seeing what are you learning what are the key signals from the marketplace that you're seeing that's supporting your company the direction you're going share some anecdotes and data around what you're seeing and hearing so we launched the the first personal product it was last May and what we were trying to do was get something out there in the wild that anybody could try and get value out of without having to go through like it's a sort of long enterprise sale cycle so download it you can use it you can share it with the guy next to you think of like an Evernote or a Google Drive style approach to actually being able to do something and you know so that that had some great success rate when we went out with announcement we announced we'd you know fun with the company we roughly we got 1500 users in the first four months just that we're trying it it was across about four to five hundred companies of four ish five ish users a company and that will let us get a bunch of feedback which was great right some of it was hey we don't like this and other words hey double down here and the key thing that we learned was they're sort of three audiences that we're serving right one is that traditional analysts which you know hopefully that was the case cuz that's where I came from and that was the goal there's also two other audiences I didn't expect as much of one being software engineers and software engineers that you know constantly pulled into you know like you said find the needle on the pile of needles and they don't want that to be their day job but they do want to like do it once and then share it with the rest organization and they don't have a place to do that today so there's a poly there's a great great you know audience of softwares and then the last one is actually business leaders that are the ones asking the questions and they want to find a place that they can go to that you know will answer the majority of them and so the feedback we've gotten is that there's probably three skins of the product that we're gonna have to build ones for that analysts the second a little bit more technical for an engineer and the third is actually very business-friendly which is just you know you don't care about sequel code you don't want Python code you don't want any code at all you just want to know the reports here or if it's not ask Danny that's interesting so the feedback of the marketplace is kind of lays out the workflow stakeholders yeah you know the analysts got to do their job and doesn't want to be coding so they bring the coder and coders once the kid put gets pulled into the project so they're doing their thing and they certainly want to get back to their coding but get pulled in for business reasons the business wants a search and discover yeah kind of all kind of coming together that seems to be the stakeholders it's the stakeholders exactly right I mean I think it's it almost lines up probably engineer analyst business leader right like in the engineer oftentimes is the one that has to go build a pipeline if that's what's needed right and the analyst is the one that consumes from it and then business leaders the one that looks the report every morning and says hope that's bad and really what you're getting down to his classic software development kind of thinking of DevOps and cloud computing which is you don't what you want to automate repetitive tasks and you don't want one offs all right so engineer doesn't want to do one office of constant one-off pipelining yep yep know that you hit the nail on the head like I think you know it like the whole notion of like self-service bi or self-service data like it it's aspirational I think it will be forever right even as you get into AI and yes automated AI and in you know a certain percentage of problems will always be able to be automated but a certain percentage won't be right it was get more point about the reporting is it's only good as the data being reported so you might feel good he's looking at a dashboard with underlying data that sucks and you're like you're dead in the water that's that's a very true thing unfortunately we saw that you know not just did like every company feels that but I talk about the environment and customer base okay as as you worked at linkedin which i think is a very acute example because you know linkedin is one of those magical companies where they really hit the data equation really well obviously it's like a resume for recruiters and it turned into a social network and then they got a treasure trove of data all kinds of gesture data they got great metadata on profiles now they've got a feed so again it's like Facebook analyst this data and so the unknowns probably got came came piling in so it's great proxy for as enterprises and businesses start thinking about how to think about the tsunami of new kinds of data not just grow the data but like hey there's all kinds of new data mobile the touch point gesture day all those kinda stuffs coming together how should they think about setting up a plan so if I'm a customer say hey you know I got a date I got Cuban of you data I got consumption data all these new things and what do I do yeah how do I create a holistic architecture yep take advantage of the different data silos or data sets but yet not screw up the operations of those days yes we can't stop right what's your advice on that cuz it seems to be a core problem it is and one of the things I think I've come to believe is that you know companies will get together and they'll spend months or even years coming up with like an architecture of the future right and and I don't believe that you can come up you know and sit in a room no matter how many days it takes and come up with something that's gonna be you know all things to all people like you're gonna basically need solutions that are nimble enough to be to be you know installed and get value very very quickly even if just a small amount of value and then grow with you over time so of course that's sort of the way we're set up right like you know you can come have a small team so take take on marketing operations D and they work with advertising data they're dealing with how do you get you know a lead and convert them into a sale they can use you know a product like cursor or I think any other good product in the marketplace should be you know you designed it this way where you you nibble on it you get some value and then you deploy it to other teams once you've learned how to how to best do that I think the like Big Bang approach of like hey this is our solution that's gonna you know work for everyone is really tough okay take an area we can get time to value quicker right and is it like a data Lake of model where you just kind of throw some data into one corpus or so we can have a base data doesn't actually live ever within cursor right we may you know if you're actually operating on it say you're an analyst you're writing some Python you're writing some sequel like yes I mean you for the sake of seeing in the UI it will temporarily be cached and encrypted there but we never actually store any company data we just point to it and when in in what we've built are these really intelligent connectors they can go mine what's there so if we're looking at a tableau instance we can say okay here all the dashboards there here all the code behind those dashboards here the table the data stores those dashboards are hitting here's are often they're consumed Oh every Monday morning at 9:00 a.m. 250 people in New York hit this dashboard and how do we learn from that and then hopefully make recommendations on it like what happens when data underlying a dashboard changes every Monday morning and all of a sudden it doesn't should that be a red flag somewhere that you know we should tell somebody that hey there's probably an issue with this so we're trying to really learn from things that are already there today as opposed to having you create new things what's next what's going on now how you going forward what's the key objectives for you guys yeah so I think there's two things really stage business like you can get sort of pulled into this hey we want to be a generic solution for everything what we found is that there probably a couple industries that are really they feel this problem really acutely and some of its financial services actually retail surprisingly just given you know dispersion of data inside retail so we've had pretty good success in both of those areas and I think our next step will be to actually probably formalize some you know sort of play books if you will and continue down that path and then integrations are that are the next thing right like we integrate with a bunch of stuff but we definitely won't agree with everything and there's you know an infinite amount of tools out there right so we want to continue to you know partner with companies that have you know Best of Breed solutions work with them to create deep integrations we're not trying to displace them what is trying to you know complement them and help drive you know the traffic to them that's looking for what's in there and so like that integration work is really never-ending why should the company keep up the old way to bring in the new way what's your what's your yeah I don't think they're actually having to give up the old way I think it's you know there are some things that you're gonna naturally be transitioning off of right there's there's always gonna be a bi solution that transitions from you know legacy to new whatever legacy may be defined as and as you're doing that there's there's there's this missing ingredient I feel like how do I track what's where when you could say that that was sort of solved by data catalog so I think the old data catalog is kind of dead and I think what's really happening is that you need something that works with you know where you are and every day whether you're an analyst a business leader or an engineer right and they can follow you along not disrupt you from your day-to-day workflow and also be intelligent about what's what what's where and that's sort of what we're trying to build well great to chat thanks for coming in spending the time talking about cursor congratulations on the venture thanks looking forward to seeing that be round coming soon yeah thanks for having you very much it's coming soon be round a round a round seed round and yeah it will definitely be on the on the near term horizon and Weinstein CEO cursor serial entrepreneur here inside the cube innovating around the data this is the new model this is what's going on it's the new wave that they're ride I'm John furry with the cube thanks for watching [Music]

>> Narrator: Live from the Silicon Valley, it's theCUBE. (bright music) Covering Google Cloud Next 17. >> Hi, and welcome to the second day of live coverage here of theCUBE covering Google Next 2017. We're at the heart of Silicon Valley here at our 4,500 square foot new studio in Palo Alto. We've got a team of reporters and analysts up in San Francisco checking out everything that's happening in Google. I was up there for the day two keynote, and happy to have with me is the first guest of the day, friend of theCUBE, Marc Farley, Vulcancast, guy that knows clouds, worked for one the big three in the past and going to help me break down some of what's going on in the marketplace. Mark, it's great to see you. >> Oh, it's really nice to be here, Stu, thanks for asking me on. >> Always happy to have you-- >> And what a lot of fun stuff to get into. >> Oh my god, yeah, this is what we love. We talked about, I wonder, Amazon Reinvent is like the Superbowl of the industry there. What's Google there if, you know-- >> Well, Google pulls a lot of resources for this. And they can put on a very impressive show. So if this is, if Invent is the Superbowl, then maybe this, maybe Next is the college championship game. I hate to call it college, but it's got that kind of draw, it's a big deal. >> Is is that, I don't want to say, arena football, it's the up and coming-- >> Oh, it's a lot better than that. Google really does some spectacular things at events. >> They're Google, come on, we all use Google, we all know Google, 10,000 people showed up, there's a lot of excitement. So what's your take of the show so far in Google's positioning in cloud? >> It's nothing like the introduction of Glass. And of course, Google Glass is a thing of the past, but I don't know if you remember when they introduced that, when they had the sky diver. Sky divers diving out of an airplane and then climbing up the outside of the building and all that, it was really spectacular. Nobody can ever reach that mark again, probably not even the Academy Awards. But you asked the second part of the question, what's Google position with cloud, I think that's going to be the big question moving forward. They are obviously committed to doing it, and they're bringing unique capabilities into cloud that you don't see from either Amazon or Microsoft. >> Yeah. I mean, coming into it, there's certain things that we've been hearing forever about Google, and especially when you talk about Google in the enterprise. Are they serious, is this just beta, are they going to put the money in? I thought Eric Schmidt did a real good job yesterday in the close day keynote, he's like, "Look, I've been telling Google to push hard "in the enterprise for 17 years. "Look, I signed a check for 30 billion dollars." >> 30 billion! >> Yeah, and I talked to some people, they're a little skeptical, and they're like, "Oh, you know, that's not like it all went to build "the cloud, some of it's for their infrastructure, "there's acquisitions, there's all these other things." But I think it was infrastructure related. Look, there shouldn't be a question that they're serious. And Diane Greene said, in a Q&A she had with the press, that thing about, we're going to tinker with something and then kill it, I want to smash that perception because there's certain things you can do in the consumer side that you cannot get away with on the enterprise side, and she knows that, they're putting a lot of effort to transform their support, transform the pricing, dig in with partners and channels. And some of it is, you know, they've gotten the strategy together, they've gotten the pieces together, we're moving things from beta to GA, and they're making good progress. I think they have addressed some of the misperceptions, that being said, everybody usually, it's like, "I've been hearing this for five years, "it's probably going to take me a couple of years "to really believe it." >> Yeah, but you know, the things is, for people that know Diane Greene and have watched VMware over the years, and then her being there at Google is a real commitment. And she's talking about commitment when she talks about that business. It's full pedal to the metal, this is a very serious, the things that's interesting about it, it's a lot more than infrastructure as a service. >> Yeah. >> The kinds of APIs and apps and everything that they're bringing, this is a lot more than just infrastructure, this is Google developed, Google, if you will, proprietary technology now that they're turning to the external world to use. And there's some really sophisticated stuff in there. >> Yes, so before we get into some of the competitive landscape, some of the things you were pretty impressed with, I think everybody was, the keynote this morning definitely went out much better, day one keynote, a little rocky. Didn't hear, the biggest applauses were around some of the International Women's Day, which is great that they do that, but it's nice when they're like, "Oh, here's some cool new tech," or they're like, oh, wow, this demo that they're doing, some really cool things and products that people want to get their hands on. So what jumped out at you at the keynote this morning? >> I'm trying to remember what it's called. The stuff from around personal identifiable information. >> Yeah, so that's what they call DLP or it's the Data Loss Prevention API. Thank goodness for my Evernote here, which I believe runs on Google cloud, keeping up to date, so I'm-- >> Data loss prevention shouldn't be so hard to remember. >> And by the way, you said proprietary stuff. One thing about Google is, that Data Loss Prevention, it's an API, they want to make it easy to get in, a lot of what they do is open source. They feel that that's one of their differentiations, is to be, we always used to say on the infrastructure side, it's like everybody's pumping their chest. Who's more open than everybody else? Google. Lots of cool stuff, everything from the TensorFlow and Kubernetes that's coming out, where some of us are like, "Okay, how will they actually make money on some of this, "will it be services?" But yeah, Data Loss Prevention API, which was a really cool demo. It's like, okay, here's a credit card, the video kind of takes it and it redacts the number. It can redact social security numbers, it's got that kind of machine learning AI with the video and all those things built in to try to help security encrypt and protect what you're doing. >> It's mind boggling. You think about, they do the facial recognition, but they're doing content recognition also. And you could have a string of numbers there that might not be a phone number, it might not be a social security number, and the question is, what DLP flagged that to, who knows, it doesn't really matter. What matters is that they can actually do this. And as a storage person, you're getting involved, and compliance and risk and mitigation, all these kinds of things over the years. And it's hard for software to go in and scan a lot of data to just look for text. Not images of numbers on a photograph, but just text in a document, whether it's a Word file or something. And you say, "Oh, it's not so hard," but when you try to do that at scale, it's really hard at scale. And that's the thing that I really wonder about DLP, are they going to be able to do this at large scale? And you have to think that that is part of the consideration for them, because they are large scale. And if they can do that, Stu, that is going to be wildly impressive. >> Marc, everything that Google does tends to be built for scale, so you would think they could do that. And I'd think about all the breaches, it was usually, "Oh, oops, we didn't realize we had this information, "didn't know where it was," or things like that. So if Google can help address that, they're looking at some of those core security issues they talked about, they've got a second form factor authentication with a little USB tab that can go into your computer, end to end encryption if you've got Android and Chrome devices, so a lot of good sounding things on encryption and security. >> One of the other things they announced, I don't know if this was part of the same thinking, but they talk about 64 core servers, and they talk about, or VMs, I should say, 64 core VMs, and they're talking about getting the latest and greatest from Intel. What is it, Skylink, Sky-- >> Stu: Skylake. >> Skylake, yeah, thanks. >> They had Raejeanne actually up on stage, Raejeanne Skillern, Cube alumn, know her well, was happy to see her up on stage showing off what they're doing. Not only just the chipset, but Intel's digging in, doing development on Kubernetes, doing development on TensorFlow to help with really performance. And we've seen Intel do this, they did this with virtualization with the extensions that they did, they're doing it with containers. Intel gets involved in these software pieces and makes sure that the chipset's going to be optimized, and great to see them working with Google on it. >> My guess is they're going to be using a lot of cycles for these security things also. The security is really hard, it's front and center in our lives these days, and just everything. I think Google's making a really interesting play, they take their own internal technology, this security technology that they've been using, and they know it's compute heavy. The whole thing about DLP, it's extremely compute heavy to do this stuff. Okay, let's get the biggest, fastest technology we can to make it work, and then maybe it can all seem seamless. I'm really impressed with how they've figured out to take the assets that they have in different places, like from YouTube. These other things that you would think, is YouTube really an enterprise app? No, but there's technology in YouTube that you can use for enterprise cloud services. Very smart, I give them a lot of credit for looking broadly throughout their organization which, in a lot of respects, traditionally has been a consumer oriented experience, and they're taking some of these technologies now and making it available to enterprise. It's really, really hard. >> Absolutely. They did a bunch of enhancements on the G Suite product line. It felt at times a little bit, it's like, okay, wait, I've got the cloud and I've got the applications. There are places that they come together, places that data and security flow between them, but it still feels like a couple of different parts, and how they put together the portfolio, but building a whole solution for the enterprise. We see similar things from Microsoft, not as much from Amazon. I'm curious what your take is as to how Google stacks up against Microsoft who, disclaimer, you did work for one time on the infrastructure side. >> Yeah, that's a whole interesting thing. Google really wants to try to figure out how to get enterprises that run on Microsoft technology moving to Google cloud, and I think it's going to be very tough for them. Satya Nadella and Microsoft are very serious about making a seamless experience for end users and administrators and everybody along managing the systems and using their systems. Okay, can Google replicate that? Maybe on the user side they can, but certainly not on the administration side. And there are hooks between the land-based technology and the cloud-based technology that Microsoft's been working on for years. Question is, can Google come close to replicating those kinds of things, and on Microsoft's side, do customers get enough value, is there enough magic there to make that automation of a hybrid IT experience valuable to their customers. I just have to think though that there's no way Google's going to be able to beat Microsoft at hybrid IT for Microsoft apps. I just don't believe it. >> Yeah, it's interesting. I think one of the not so secret weapons that Google has there is what they're doing with Kubernetes. They've gotten Kubernetes in all the public clouds, it's getting into a lot of on premises environment. Everything from we were at the KubeCon conference in Seattle a couple of months ago. I hear DockerCon and OpenStacks Summit are going to have strong Kubernetes discussions there, and it's growing, it's got a lot of buzz, and that kind of portability and mobility of workload has been something that, especially as guys that have storage background, we have a little bit of skepticism because physics and the size of data and that whole data gravity thing. But that being said, if I can write applications and have ways to be able to do similar things across multiple environments, that gives Google a way to spread their wings beyond what they can do in their Google cloud. So I'm curious what you think about containers, Kubernetes, serverless type activity that they're doing. >> I think within the Google cloud, they'll be able to leverage that technology pretty effectively. I don't think it's going to be very effective, though, in enterprise data centers. I think the OpenStack stuff's been a really hard road, and it's a long time coming, I don't know if they'll ever get there. So then you've got a company like Microsoft that is working really hard on the same thing. It's not clear to me what Microsoft's orchestrate is going to be, but they're going to have one. >> Are you bullish on Asure Stack that's coming out later this year? >> No, not really. >> Okay. >> I think Asure Stack's a step in the right direction, and Microsoft absolutely has to have it, not so much for Google, but for AWS, to compete with AWS. I think it's a good idea, but it's such a constrained system at this point. It's going to take a while to see what it is. You're going to have HPE and Lenovo and Cisco, all have, and Dell, all having the same basic thing. And so you ask yourself, what is the motivation for any of these companies to really knock it out of the park when Microsoft is nailing everybody's feet to the floor on what the options are to offer this? And I understand Microsoft wanting to play it safe and saying, "We want to be able to support this thing, "make sure that, when customers install it, "they don't have problems with it." And Microsoft always wants to foist the support burden onto somebody else anyway, we've all been working for Microsoft our whole lives. >> It was the old Dilbert cartoon, as soon as you open that software, you're all of a sudden Microsoft's pool boy. >> (laughs) I love that, yeah. Asure Stack's going to be pretty constrained, and they keep pushing it further out. So what's the reality of this? And Asure Pack right now is a zombie, everybody's waiting for Asure Stack, but Asure Stack keeps moving out and Asure Stack's going to be small and constrained. This stuff is hard. There's a reason why it's taking everybody a long time to get it out, there's a reason why OpenStack hasn't had the adoption that people first expected, there's going to be a reason why I think Asure Stack does not have the adoption that Microsoft hoped for either. It's going to be an interesting thing to watch over what will play out over the next five or six years. >> Yeah, but for myself, I've seen this story play out a few times on the infrastructure side. I remember the original precursor, the Vblock with Acadia and the go-to-market. VMware, when they did the VSAN stuff, the generation one of Evo really went nowhere, and they had to go, a lot of times it takes 18 to 24 months to sort out some of those basic pricing, packaging, partnering, positioning type things, and even though Asure Stack's been coming for a while, I want to say TP3 is like here, and we're talking about it, and it's going to GA this summer, but it's once we really start getting this customer environment, people start selling it, that we're going to find out what it is and what it isn't. >> It's interesting. You know how important that technology is to Microsoft. It's, in many respects, Satya's baby. And it's so important to them, and at the same time, it's not there, it's not coming, it's going to be constrained. >> So Marc, unfortunately, you and I could talk all day about stuff like this, and we've had many times, at conferences, that we spend a long time. I want to give you just the final word. Wrap up the intro for today on what's happening at Google Next and what's interesting you in the industry. >> Well, I think the big thing here is that Google is showing that they put their foot down and they're not letting up. They're serious about this business, they made this commitment. And we sort of talk and we give lip service, a little bit, to the big three, we got Asure, we got Amazon, and then there's Google. I think every year it's Google does more, and they're proving themselves as a more capable cloud service provider. They're showing the integration with HANA is really interesting, SAP, I should say, not HANA but SAP. They're going after big applications, they've got big customers. Every year that they do this, it's more of an arrival. And I think, in two years time, that idea of the big three is actually going to be big three. It's not going to be two plus one. And that is going to accelerate more of the movement into cloud faster than ever, because the options that Google is offering are different than the others, these are all different clouds with different strengths. Of the three of them, Google, I have to say, has the most, if you will, computer science behind it. It's not that Microsoft doesn't have it, but Google is going to have a lot more capability and machine learning than I think what you're going to see out of Amazon ever. They are just going to take off and run with that, and Microsoft is going to have to figure out how they're going to try to catch up or how they're going to parley what they have in machine learning. It's not that they haven't made an investment in it, but it's not like Google has made investment in it. Google's been making investment in it over the years to support their consumer applications on Google. And now that stuff is coming, like I said before, the stuff is coming into the enterprise. I think there is a shift now, and we sort of wonder, is machine learning going to happen, when it's going to happen? It's going to happen, and it's going to come from Google. >> All right, well, great way to end the opening segment here. Thank you so much, Marc Farley, for joining us. We've got a full day of coverage here from our 4,500 square foot studio in the heart of Silicon Valley. You're watching theCUBE. (bright music)

>> Announcer: Live, from Silicon Valley, it's the Cube. Covering Google Cloud Next 17. (ambient music) >> Okay, welcome back everyone. We are here live in Palo Alto for a special two days of coverage of Google Next 2017 events in San Francisco. Sold out, 10,000 plus people. Yeah, really, an amazing turn of events. Amazon Web Services Reinvent had 36,000, Google's nipping at their heels, although different, we're going to break down the differences with Google versus Amazon because they're really two different things and again, this is Cube coverage here in Palo Alto studio, getting reaction. Sponsored by Intel, thanks, Intel, for allowing us to continue the wall-to-wall coverage of the key events in the tech industry. Our next guest is Val Bercovici who's the boardmember of the Cloud Native Compute Foundation, boardmember. >> That's right. >> Welcome back, you were here last week from Mobile World Congress, great to see you. Silicon contributor, what your reaction to the Google keynote, Google news? Not a lot of news, we saw the SAP, that was the biggest news and the rest were showcasing customers, most of the customers were G Suite customers. >> Yeah, exactly. So, I would say my first reaction is bit of a rough keynote, you know, there's definitely not as quit as much polish as Microsoft had in their heyday and of course, Amazon nowadays in the Cloud era. But what's interesting to me is there's the whole battle around empathy right now. So, the next gen developers and the Clouderati talk about user empathy and that means understanding the workflow of the user and getting the user to consume more of your stuff, you know, Snapchat gets user empathy for the millennial generation but anybody else. Facebook as well. So, you see Google, we emphasize, even the Google Twitter account, it emphasizes developer productivity and they have pretty strong developer empathy. But what AWS has, Amazon with AWS is enterprise empathy, right, they really understand how to package themselves and make themselves more consumable right now for a lot of mainstream enterprises, they've been doing this for three, four years at their Reinvent events now. Whereas Google is just catching up. They've got great developer empathy but they're just catching up on enterprise empathy. Those are the main differences I see. >> Yeah, I think that's an important point, Val, great, great point, I think Amazon certainly has, and I wrote this in my blog post this morning, getting a lot of reaction from that, actually, and some things I want to drill down on the network and security side. Some Google folks DMing me we're going to do that. But really, Amazon's lead is way out front on this. But the rest, you know, call 'em IBM, not in any particular, IBM, Oracle, Google, SAP, others, put Salesforces, we're talking Sass and Adobe, they're all in this kind of pack. It's like a NASCAR, you know, pack and you don't know who's going to slimshot around and get out there. But they all have their own unique use cases, they're using their own products to differentiate. We're hearing Google and again, this is a red flag for me because it kind of smells like they're hiding the ball. G Suite, I get the workplace productivity is a Cloud app, but that's not pure Cloud conversations, if you look at the Gartner, Gartner's recent, last report which I had a chance to get a peek at, there's no mention of Sassifications, Google G Suite's not in there, so the way Cloud is strictly defined doesn't even include Sass. >> Yeah. >> If you're going to include Sass, then you got to include Salesforce in that conversation or Adobe or others. >> Exactly. >> So, this is kind of an optical illusion in my mind. And I think that's something that points to Google's lack of traction on customers in the enterprise. >> This is where behind the scenes, Kubernetes, is so important and why I'm involved with the the CNCF. If anything, the first wave of Clouded option particularly by enterprise was centered around the VM model. And you know, infrastructure's a service based on VMs, Amazon, AWS is the king of that. What we're seeing right now is developers in particular that are developing the next generation of apps, most of them are already on our phones and our tablets and our houses and stuff, which is, you know, all these Echo-style devices. That is a container-based architecture that these next gen applications are based on. And so, Kubernetes, in my mind, is really nothing more than Google's attempt to create as much of a container-based ecosystem at scale so that the natural home for container-based apps will be GCP as opposed to AWS. That's the real long term play in why Google's investing so heavily in Kubernetes. >> Is that counterintuitive? Is that a good thing? I mean, it sounds like they're trying to change the goalpost, if you will, to change the game because we had Joe Arnold on, the founder of Swiftstack and you know, ultimately, you know, Clouds are Clouds and inter-Clouding and multi-Cloud is important. Does Kubernete actually help the industry? Or is that more Google specific in your mind? >> I think it will help the industry but the industry itself is moving so rapidly, we're seeing server-less right now and functions of service, and so, I think the landscape is shifting away from what we would think of as either VM or container-based infrastructure service towards having the right abstractions. What I'm seeing is that, really, even the most innovative enterprises today don't really care about their per minute or per hour cost for a cycle of computer, a byte of, you know, network transferred or stored. They care about big table, big quarry, the natural language processing, visual search, and a whole category of these AI based applications that they want to base their own new revenue-generating products and services based on. So, it's abstraction now as a new battlefield. AWS brings that cult of modularity to it, they're delivering a lot of cool services that are very high level Lambda centered based on really cool modularity, whereas Google's doing it, which is very, very elegant abstraction. It's at the developer level, at the technical level, that's what the landscape is at right now. >> Are you happy with Google's approach because I think Google actually doesn't want to be compared to AWS in a way. I mean, from what I can see from the keynote... >> Only by revenue. (laughs) >> Well, certainly, they're going to win that by throwing G Suite on it but, I mean, this is, again, a philosophy game, right? I mean, Andy Jassy is very customer focused, but they don't have their own Sass app, except for Amazon which they don't count on the Cloud. So, their success is all about customers, building on Amazon. Google actually has its own customer and they actually include that in, as does Microsoft with Office 365. >> Yeah, that's the irony, is if we go back to enterprise empathy I think it's Microsoft has that legacy of understanding the enterprise better than all the others. And they're beginning to leverage that, we're definitely seeing, as you're sliding comfortably to a number two position behind AWS, but it really does come back to, you know, are you going to lead with a propeller head lead in technology which Google clearly has, they've got some of the most superior technology, we were rattling off some the speeds and feeds that one of their product managers shared with you this morning. They've had amazing technology, that's unquestioned. But they do have also is this reputation of almost flying in rarefied air when it comes to enterprises. >> What do you mean by that? >> What I mean by that is that most enterprise IT organizations, even the progressive ones, have a hard time relating to Google technology. It's too far out there, it's too advanced, in some cases, they just can't understand it. They've never been trained in college courses on it or even post-grad courses on it. MBA is older than three years old, don't even reference the Cloud. So, there's a lot of training, a lot of knowledge that has to be, you know, conducted on the enterprise side. AWS is packaged, that technology there is the modularity in such a way that's more consumable. Not perfect, but more consumable than any other Cloud render and that's why, with an early head start, they've got the biggest enterprise traction today. >> Yeah, I mean, and I'm really bullish on Google, I love the company, I've been following them since '98, a lot of friends here at Palo Alto, a lot of Googlers living in my neighborhood, they're all around us. Larry Page, seen him around town. Great, great company and very, always been kind of like an academic, speed of academic. Very strong, technically, and that is, clearly, they're playing that card, "We have the technology." So, I would just say that, to counter that argument would be if Google, I'm Google, I'm on the team, the guy in green and you know, lookit, what I want to do is, we want to be the intel for the Cloud. So, the hard and top is we don't really care if people are trained, should be so easy to use, training doesn't matter. So, I mean, that's really more of an arrogant approach, but I don't think Google's being arrogant in the Cloud. I think that ship has sailed, I think Google has kind of been humbled in the sense, in recognizing that the enterprise is hard, they're checking the boxes. They have a partner program. >> Yeah, you're right, I mean, if you take a look at their customers today, you've got Spotify, and Snap, and Evernote, and you know, Pokemon Go and Niantic, all of the leading edge technology companies that have gone mainstream that are, you know, startup oriented Snap, of course. They're on Google Cloud. But that's not enough, you know, the enterprise, I did a seminar just last week promoting Container World with Jim Forge from ADP. The enterprise is not homogeneous, the enterprise is complicated. The L word legacy is all over, what they have to budget and plan for. So, the enterprise is just a lot more complicated than Google will acknowledge right now. And I believe if they were to humanize some of their advanced technology and package it and price it in such a way that AWS, you know, where they're seeing success, they'll accelerate their inevitable sort of leap to being one of those top three contenders. >> So, I'm just reading some of my, I'm putting together because for the Google folks, I'm going to interview them, just prepping for this, but just networking alone, isolating Cloud resources. That's hard, right? So, you know, virtual network in the Cloud, Google's got the virtual network. You get multiple IP addresses, for instance, ability to move network interfaces and IPs between instances, and AS networking support. Network traffic logging, virtual network peering, manage NAT gateways, subnet level filtering, IP V stick support, use any CIDR including RC 1918. Multiple network interface instances, I mean, this is complicated! (laughs) It's not easy so, you know, I think the strategy's going to be interesting to see how, does Google go into the point to point solution set, or they just say, "This is what we got, take it or leave it," and try to change the game? >> That's where they've been up until now and I don't think it's working because they have very formidable competitors that are not standing still. So, I think they're going to have to keep upping their game, again, not in terms of better technology but in terms of better packaging, better accessibility to their technology. Better trust, if you will, overseas. Cloud is a global game, it's not US only. And trust is so critical, there's a lot of skepticism in Europe today with the latest Wikileaks announcements, or Asia Today around. Any American based Cloud provider truly being able to isolate and protect my citizen's data, you know, within my borders. >> I think Google Cloud has one fatal flaw that I, looking at all the data, is that and the analysis that we've been looking at with Bookie Bontine and our research is that there's one thing that jumps out at me. I mean, the rest are all, I look at as, you know, Google's got such great technologies, they can move up fast, they can scale up to code. But the one thing that's interesting is their architecture, the way they handle their architecture is they can't let customers dictate data where data's stored. That is a huge issue for them. And if, to your point, if a user in Germany is using an app and it's got to stay in Germany. >> This is back to the empathy disconnect, right? As an abstraction layer for a developer, what I want is exactly what Google offers. I don't want to care as a developer where the bits and bytes are stored, I want this consistent, uniform API, I want to do cool stuff with the data. The operation side, particularly within legal parameters, regulatory parameters, you know, all sorts of other costs and quality assurance parameters, they really care about where that data is stored, and that's where having more enterprise empathy, and their thinking, and their offerings, and their pricing, and their packaging will leapfrog Google to where they want to be today. >> Val Bercovici, great analysis, I mean, I would totally agree just to lock that in, their developer empathy is so strong. And their operational one needs to be, they got a blind spot there where they got to work on that. And this is interesting because people who don't know Google are very strong operations, it's not like they don't have any ops chops. (Val laughs) They're absolutely in the five nines, they are awesome operations. But they've been operations for themselves. >> Exactly. >> So, that's the distinction you're getting at, right? >> Absolutely. >> Okay, so the next question I got to ask you is back to the developer empathy, 'cause I think it's a really big opportunity for Google. So, pointing out the fatal flaw in my opinions in the data locality thing. But I think the opportunity for Google to change the game, using the developer community opportunity because you mentioned the Kubernetes. There is a huge, open source, I don't want to say transformation but an evolution to the next generation, you're starting to see machine learning and AI start to tease out the leverage of not just data now. Data's become so massive now, you have data sets. That can be addressable and be treated like software programs. So, data as code becomes a new dynamic with AI. So, with AI, with open source, you're seeing a lot of activity, CNCF, the Cloud Native Compute Foundation, folks should check that out, that's an amazing group, analytics foundation. This is an awesome opportunity for Google to use Kubernetes as saying, "Hey, we will make orchestration of application workloads." >> Absolutely. >> This is something, Amazon's been great with open source, but they don't get a lot of love... >> Amazon has a blind spot on containers, let's not, you know, let's not call, you know, let's call it the speed of speed, let's not, you know, beat around the bush, they do have a blind spot around containers. It is something they strategically have to get a hold of, they've got some really interesting proprietary offerings. But it's not a natural home for a Docker workflow, it's not a natural home for a Kubernetes workflow yet. And it's something they have to work on and AI as a use case could not be more pertinent to business today because it's that quote, you know, "The future is here "but unevenly distributed." That's exactly where AI is today, the businesses that are figuring it out are really leaping ahead of their competitors. >> We're getting some great tweets, my phone's blowing up. Val, you've got great commentary. I want to bring up, so, I've been kind of over the top with the comment that I've been making. It's maybe mischaracterized but I'll say it again. There seems to be a Cold War going on inside the communities between, as Kubernetes have done, we've seen doc, or we've seen Docker Containers be so successful in this service list, server list vision, which is absolutely where Cloud Native needs to be in that notion of, you know, separating out fiscal gear and addressability, making it completely transparent, full dev ops, if you will. To who's going to own the orchestration and where does it sit on the stack? And with Kubernetes, to me, is interesting is that it tugs at some sacred cows in the container world. >> Yes. >> And it opens up the notion of multi-Cloud. I mean, assume latency can be solved at some point, but... >> It's actually core religion, what impressed me about he whole Kubernetes community, and community is its greatest strength, by the way, is the fact that they had a religion on multi-Cloud from day one. It wasn't about, "We'll add it later "'cause we know it's important," it's about portability and you know, even Docker lent that to the community. Portability is just a number one priority and now portability, at scale, across multiple Clouds, dynamically orchestrated, not through, you know, potential for human error, human interventions we saw last week. That the secret sauce there to stay. >> I think not only is, a Cold War is a negative connotation, but I think it's an opportunity to be sitting in the sun, if you will, on the beach with a pina colada because if you take the Kubernetes trend that's got developer empathy with portability, that speaks to what developers want, I want to have the ability to write code, ship it up to the network, and have it integrate in nicely and seamlessly so, you know, things can self-work and do all that. And AI can help in all those things. Connecting with operational challenges. So, what is, in your mind, that intersection? Because let's just say that Kubernetes is going to develop a nice trajectory which it has now and continues to be a nice way to galvanize a community around orchestration, portability, etc. Where does that intersect with some of the challenges and needs for operational effectiveness and efficiency? >> So, the dirtiest secret in that world is data gravity, rigtht? It's all well and fine to have workload portability across, you know, multiple instances and a cluster across multiple Clouds, so to speak. But data has weight, data has mass and gravity, and it's very hard to move particularly at scale. Kubernetes only in the last few releases with a furious pace in evolution, one four, one five, has a notion of provisioning persistent volumes, this thing they affectionately called pet sets that are not a stateful sets, I love that name. >> Cattle. >> Exactly. (laughs) So, Google is waking up and Kubernetes, I should say, in particular is waking up to the whole notion of managing data is really that last mile problem of Cloud portability and operational maturity. And planning around data gravity and overcoming where you can data gravity through meta-operational procedures is where this thing is going to really take off. >> I think that's where Google, I like Google's messaging, I like their posture on machine learning AI, I think that's key. But Amazon has been doing AI, they've got machine learning as a service, they've had Kineses for a while. In fact, Redshift and Kineses were their fastest growing services before Aurora became the big thing that they had. So, I think, you know, they're interested in the jets, with the trucks, and the snowmobile stuff. So I think certainly, Amazon's been doing that data and then rolling in as some sort of AI. >> And they've been humanizing it better, right? I can relate to some of Amazon's offering and sometimes I have it in the house. You know, so, the packaging and just the consumerability of these Amazon services today is ahead of where Google is and Google arguably has the superior technology. >> Yeah, and I think, you know, I was laying out my analysis of Google versus Amazon but I think it's not fair to try to compare them too much because Google is just making their opening moves on the chessboard. Because they had Diane Green, got to give her credit, she's really starting behind. And that's been talked about but they are serious, they're going to get there. The question is what does an enterprise need to do? So, your advice to enterprise would be what? Stick with the use cases that are either Google specific apps or Cloud Native, where do you go, how do you...? >> I would say to remember the lock-in days of the Linux vendors and even Microsoft in their heyday and definitely think multi-Cloud, you know, Cloud first is fine. But think, we need data first in a Cloud before I think a particular Cloud first. Always keep your options open, seek the highest levels of abstraction, particularly as you're innovating early on and fast failing in the Cloud. Don't go low right away, go low later on when you're operationalizing and scaled and looking to squeeze efficiencies out of a new product or service. >> Don't go low, you mean don't go low in the stack? >> Don't go low in the stack, exactly. Start very high in the stack. >> What would be an example? >> Lambda, you know, taking advantage of, if we bring in Kineses, IOT workflows, all sorts of sensor data coming in from the Edge. Don't code that for efficiency day one and switch to Kafka or something else that's more sophisticated, but keep it really high level as events triggering off, whether it's the IOTICK in the sensor inputs or whether it's S3 events, Dynamo, DB events. Write your functions that are very, very high level. >> Yeah. >> Get the workflows right. Pay a bit more money up front, pay premium for the fast... >> Well, there's also Bootstraps and the Training Channel Digimation, so, with Google, pick some things that are known out there. But you mentioned IOT and one of the things I was kind of disappointed in the keynote today, there wasn't much talk about IOT. You're not seeing IOT in the Google story. >> That may come up in tomorrow's keynote, it may come up tomorrow in a more technical context. But you're right, it's an area both Agar and AWS have a monster of a lead right now, as they've had really good SDKs out there to be able to create workflows without even being an expert in some of the devices that you know, you might own and maintain. >> Google's got some differentiation, they've got something, I'll highlight one that I like that I think is really compelling. Tensor flow. Tensor flow as got a lot of great traction and then Intel is writing chips with their Skylake product that actually runs much faster silicon... >> What was that, Nvidia? You know, it's a GPU game as much as a CPU game when it comes to machine learning. And it's just... >> What does that mean for you? I mean, that's exciting, you smile on that, I get geeked out on that because if you think about that, if you can have a relationship between the silicon and software, what does it mean from an impact standpoint? Do you think that's going to be a good accelerant for the game? >> Massive accelerant, you know, and this is where we get into sort of more rarefied air with Elon Musk's quote around the fact we'll need universal income for society. There a lot of static tasks that are automated today. There's more and more dynamic tasks now that these AI algorithms, through machine learning, can be trained to conduct in a very intelligent manner. So, more and more task based work all over the world, including in a robotic context but also call centers, stock brokerage, for example, it's been demonstrated that AI ML algorithms are superior to humans nine times out of ten in terms of recommending stocks. So, there's a lot of white collars, while it's blue collared work that just going to be augmented and then eliminated with these technologies and the fact that you have major players, economies at scales such as Intel and Nvidia and so forth accelerating that, making it affordable, fast, low power in certain edge context. That's, you know, really good for the industry. >> So, day one of two days of coverage here with Google, just thoughts real quick on what Google needs to do to really conquer the enterprise and really be credible, viable, successful, number two, or leader in the enterprise? >> I'm a big fan, you know, I've had personal experiences with fast following as opposed to leading and innovating sometimes in terms of getting market traction. I think they should unabashedly, unashamedly examine what Microsoft or what Amazon are doing right in the Cloud. Because you know, simple things like conducting a bit more of a smooth keynote, Google doesn't seem to have mastered it yet, right now in the Cloud space. And it's not rocket science, but shamelessly copying what works, shamelessly copying the packaging and the humanization that some of the advanced technologies that Amazon and Microsoft have done in particular. And then applying their technical superiority, you know, their uptime availability advantages, their faster networks, their strong consistency which is a big deal for developers across their regions. Emphasizing their strengths after they package and make their technology more consumable. As opposed to leading where the tech specs. >> And you have a lot of experience in the enterprise, table stakes out there that are pretty obvious that they need to check the boxes on, and would be what? >> A very good question, I would say, first and foremost, you really have to focus on more, you know, transparent pricing. Think something that is a whole black art in terms of optimizing your AWS usage in this industry that's formed around that. I think Google has and they enact blogs advertising a lot of advantages they have in the granularity, in the efficiency of their auto scaling up and down. But businesses don't really map that, they don't think of that first even though it can save them millions of dollars as they do move to Cloud first approaches. >> Yeah and I think Google got to shake that academic arrogance, in a way, that they've had a reputation for. Not that that's a bad thing, I'll give you an example, I love the fact that Google leads a lot of price performance on many levels in the Cloud, yet their SLAs are kind of wonky here and there. So, it's like, okay, enterprises like SLAs. You got to nail that. And then maybe keep their price a little high here, it can make more money, but... So, you were saying, is that enterprise might not get the fact that it's such a good deal. >> It's like enterprise sales 101, you talk about, you know, the operational benefits but you also talk about financial benefits and business benefits. Catching into those three contexts in terms of their technical superiority would do them a world of good as they seek more and more enterprise opportunities. >> Alright, Val Bercovici, CTO, also CTO, and also on the board of the Cloud Native Compute Foundation known as CNCF, a newly formed organization, part of the Linux Foundation. Really looking at the orchestration, looking at the containers, looking at Kubernetes, looking at a whole new world of app enablement. Val, thanks for the company, great to see you. Turning out to be guest contributor here on the Cube studio, appreciate his time. This is the Cube, two days of live coverage. Hope to have someone from Google on the security and network side coming in and calling in, we're going to try to set that up, a lot of conversations happening around that. Lot of great stuff happening at Google Next, we've got all the wall-to-wall coverage, reporters on the ground in San Francisco as well as analysts. And of course, in studio reaction here in Palo Alto. We'll be right back. (ambient music)

>> Wait. Okay, We're back. Live here at Stanford University Alumni Center. What a great day. Stanford loved this place. A lot of brilliant minds here. And this is the Stanford Excel Seventeenth annual symposium called Excel Enterprise. That's the hashtag falls on Twitter here with Jeff Kelly. Silicon angles. Exclusive coverage is too cute. Our flagship program about the events extract the signal from the noise. And our next guest is Amanda Richardson, the head of product. That president. Welcome to the Cube. >> Thank you. Awesome. >> So really amazing event. I'LL see a lot of big minds here, and we're also live in San Francisco at the age of us somewhere all the developers air geeking out with Amazon and all the tools you the head of product President, tell us what is President. Tell us what you're doing here. >> So, President, a collaborative presentations will. So we look forward, Teo, helping our users create idea, share ideas and really have a platform for uh, putting. Their message is out there and better sharing with the audience is So we're here because we love excel. I'm here because I'm a GSP alum and any reason to get back to the farm is a good one, and we just think it's a great place to meet people piers and share ideas and hopefully learn from each other's mistakes. >> So what? You're the new business school president? >> So I got you a job before the new business school. It's pretty awesome, but I kind of want to go back. I was joking with my husband this morning. I think it may be time for a phD. >> Yeah, Sanford's Grace, but as the head of product, you can. You still get the geek out? >> Yeah, >> And look at also the market side. You gotta look into engineering also product. So in this whole enterprise two point Oh, thank you just never happened. It's still happening. It's like going and going. But now with cloud with mobile, it's all happening right? So I got that cloud mobile social thing going on. We've been covering. So knowing it's looking angle. What are you seeing now as the market drivers for those two forces cloud and mobile and social is all that coming together? >> Um, it is all coming together, and I think you know, we call it like the consumerism ation of enterprise. Right. So, um people have one phone, one device, one presence. I think five years ago you probably tried to keep your world separated between your enterprise, professional life and your personal life, and now it really all comes together. So you've gotta solve the problems for the enterprise users in the same way you solve problems for consumers, right? What are their big needs? What are their pain points? Where do they find value, focus on those areas and make it easy to use on DH? I think that's what's finally accelerating on bringing really cool, sexy problems to the enterprise users. You just bring a consumer approach. One >> of the biggest barriers that you see in that adopts House of consumer ization of consideration of the enterprise has been talked about for many, many years and finally was seeing a ray of hope. >> Yeah, wave and making the lives the end of the tunnel >> sunrise a face. So it's there, it's there. So one of the key drivers that are helping this go faster now versus years before Oh yes, next two years next year. >> Um so I think I think mobile is actually a great point, so you can't keep pieces like Evernote, Dropbox President out of your users hands. I mean, I remember being in meetings with manager meetings five, seven years ago, talking about how we're gonna ban Facebook, and that just seems quaint now because it's all in your phone and you can't tell people not to bring their phones to work. So I think Mobil's had a huge impact and getting more of these products and tools into the hands of the consumers and out of really this kind of big brother control type situation, Thie Other thing I think that's happening is just worlds are blending together and the availability of of tools on the Internet. It sounds silly to say, but, you know, you can remember five, ten years ago, you couldn't access your perhaps it's near p program. Or perhaps this even productivity tools from home. There was a time when we all had to remote in and yeah, I mean, I'm dating myself and showing why I color my hair. But it really is. You know, the world is changing, and I think, thank goodness for the Internet, thanking us for the Web and thank goodness for >> Mo. It's interesting you mention the dating yourselves first. You look fabulous, you know, you know world. I mean, I'm older, I know how old I am. And I just had the twenty seven year old kid on drop box. So you know, that's young. But, you know, it's a lot of senior folks now. This enterprise market is shifting from consumer. You seeing some of the leaders are those experienced managers because they've lived through the right client server that lived through the that first wave. So is that just because we're more, there's more people that know that market more amusing? You're seeing a lot more cos they're not not the twenty something. It's over thirty five over forty. >> Yeah, eso no comment on age. But Thea, I think what excites me about the space I can just talk about myself is you know, I have a consumer background. So it was super fun to be in consumer five ten years ago when you know Internet was taking off. He finally have a platform on which you have millions of users to test and learn and grow. And now that you can apply that to enterprise, I mean, I think it's new challenges but similar challenges. And I really think one of the more interesting things is that it's actually solving really compelling problems. One of the you know, um, I think there are a lot of opportunities out there around photo sharing and, um, Geo location and, you know, putting together your social graph. But you know, where I find passion and energy is actually providing value and solving problems and really being a key part of someone's someone's life, That's what gets me here. Hope that keeps others here. >> So let's talk about you're solving really interesting problem. What is the mean? What is your wife? What is president about? Why are you doing what you're doing? Is it simply, you know, we've talked. We hear a lot about the concerns around power point and right death by the PowerPoint slide and that kind of thing is that really, uh, the issue you've set out to overcome our tell us a little about what you do and why you do it. >> So, um, we certainly get compared to power point a lot, but where we, uh problem we really like to solve on on a more grand scale is that we believe ideas are best shared and best collaborated about. So if you think of ideas like jeans, they can come together. They can be built on each other. A great example of president uses. There is an organization in Syria rebel organization that used President to really be the platform to explain their ideas and what the issues really were in a quick, meaningful, impactful way. I think having a platform by which you can share ideas and better understand each other can apply Teo making the world a better place but can also apply Teo helping scientists share their information around the globe, building on ideas and I know even within president, we use the tool. Teo better communicate product road maps to engineering so that we can better align. I think it's all about communication. Helping ideas grow faster and helping the world to be a more understanding place. I mean, it's a little peace, love and happiness, but it it is why we get out of bed every morning. We really think because he's a great tool for people to be the platform for them to share their ideas. And >> so I'm actually president User. I've started using recently. Actually, I downloaded. You can see here about the kind of downloaded the desktop version, and I were working on the plane that was coming out here from Boston. But, you know, it's certainly a very interesting platform. It's very different from Power. Point certainly creates much, much more compelling type of way to present information. Uh, what are some of the design principles that's had a product? What are some of the things that you really kind of court your philosophy in terms of design, find it and and implementing our should say, creating the kind of user interface and the way people interact with information? >> Yes. So I'm really proud of President of the co founders have really doubled down, if you will, on our design effort. So we have a full time user research staff. We have a full time data research that we have a full time design staff, all three different roles, all three big teams. I'm really focused on understanding our users. So we saw for key user problems in terms of design principles, specifically that we focus on, we like to, uh, help users understand structure of their ideas. So one of the challenges of President. For those who come from a power point model is everything should be linear. And one of our principles is that not all ideas are linear. There may be areas where we consume into different pieces. So helping to communicate that that is particularly important for us and how to provide simple structure. Um, I think the other ideas, uh, helping to make it beautiful. We believe that words are better. What are? Excuse me? Pictures are better. Way to communicate in words. Um, you know, death by bullet point >> thing is a common affliction, >> eh? So how can you, uh, say something with a picture that would have taken a hundred words? And that's what we try to do. >> So you know what? Your problem is? Both kind of software service, but also down with stop version. Right? Mentioned. But you know what? The software service Mom, you're able, I assume, to collect data on the way people are using your product, right. How does kind of that type of information do you incorporate that into the design process and making changes to the product come to talk about how you used data analysis really >> product. Yeah. So, you know, I believe the role of product managers to understand the user intimately have a point of view on a strategy, but then early validate through data. So not to Pripyat. We do have data about your desktop PC, which is what >> I covered. Big data for what? You have no problem with that. >> So we focus a lot on one or user stew. Do what makes them successful way try toe. Have measurable outcomes for all of our initiatives, whether its user behavior or defining what a good presidents are really helping users to solve their problems. We use data tio, on the small level, optimized and on a big level really define an objective and a goal. So how can we really push things through the funnel to get to the user to their success point, which we measure is giving a presentation. >> So both find ten of tactical issues, but also a kind of inform your larger >> are big company KP eyes. They're all based on data. >> Okay, thanks for coming on The tears. We gotta break that. Their next guest coming in lining up all of the crowd's breaking up the Silicon Angles Exclusive coverage of Stanford Excel seventeen Annual symposium. Hashtag Excel Enterprise Where it sells Doing a lot of great enterprises is Cuba's looking angles. Coverage of Stanford Excel Symposium right back with our next guest after this short break.