>>live from Las Vegas. It's the Cube covering Splunk dot com. 19. Brought to You by spunk. >>Hey, welcome back. Everyone's live Cube coverage in Las Vegas. That's plunks dot com. 2019 thistles their annual customer conference, where they unleash all the new technologies, announce all the new things. Everyone's here. It's the 10th anniversary of Splunk dot com cubes. Seventh year we've been covering slung been quite the journey from scrappy, startup going public growth phase. Now market leader on Outside has to come to success from the products and the engineering. And, of course, the people in the field that that served customers. And we're here with Susan St Leger, who's the president of worldwide field operations. Thanks for coming back to see you. >>Thank you, John. It's exciting to be here. >>So in the keynote, bringing data to every outcome is really the theme. Um, you seem to got a spring to your step here. You excited this year? What an amazing successful show because you got a platform. But the proof is out there. You got that ecosystem. You got people building APS on top of it. It's kind of all coming together this year, >>It sure is experience. It's it's it's just it's a huge leap forward, and I think so. Much of it is a vision of data to everything. And if you think about it, we talk about. We want to bring data to every question, every problem in every action. And the biggest thing you're going to see that you did see in the show is it's no longer just about the Splunk index. We're going to help you get you get value out of data wherever it lives. >>You had some big news on acquisition front Signal FX. Big chunk of change for that company. Private hot category. Observe ability, which really taste is out. That next 20 mile stare in the marketplace, which is cloud native. >>That's a >>cloud Service is, which comes together in the platform with logging coming together. >>Yeah, so exciting Way looked hard at that entire market, and signal FX was definitely the right answer. They operated a scale similar to us. They know how to how to operate it that scale, and so they're gonna be able to serve our customers well. And our view of the world is it's going to be hybrid for a very long time. But they serve that new cloud native world better than anybody else. It's It's when you do monitoring the cloud native world. It's really interesting to think about it. It's all made up of Micro service is right. So thousands of Micro Service's hundreds, thousands of Micro Service's and so in traditional monitoring, it's always you're tryingto monitor things you know could go wrong. In a microt service landscape, you don't know everything that could possibly go wrong. And so it's a level of complexity that's just very different. And so it's all about instrument ing, so that when something does go wrong, you can solve it. >>You guys have a very loyal based customer base, and that's again testament success. But the product has changed, and the value problems is emerging even further with data. That's a big theme. Data to everywhere, everything and security has come up on the radar a few years ago, here, the show. But this almost is a full blown security show at this point, because security center of everything you can't ignore it's become a centerpiece of everything data, the access to the diversity, How is that impacting the field because you're not. I mean, I guess you're a security company enabler and solve security problems. Date is a big part of it. Sure, I was at shaping your operations, >>So I think the thing to understand is correct. We're not just a security company, but we are number one in the security Magic quadrant. We're number one in both I. D. C and Gardner, and so that's important. But what happens is all the data the equal act for security can also be used for all these other use cases. So, generally speaking, whatever you're collecting for security is also valuable for I t operations, and it's also valuable for many other use cases. So I'll give you an example. Dominoes, which is a great customer of ours. They're gone 65% of their orders now come in digitally, okay? And so they monitor the entire intend customer experience. But they monitor it not only from a nightie operations perspective. That same data that they used righty operations also tells them you know what's being ordered, what special orders are being made and they use that data for promotions based upon volume and traffic and timing. they actually create promotion. So now you're talking about the same data that he collected for security night operations you can actually use for promotions, which is marketing is >>not a lot of operating leverage in data. You're getting out this. The old model was is a database. Make a queer. You get a report. Little time problem there. But now you have. Well, that other date is over there in another database. Who runs that data? So the world has certainly changes now, data needs to be addressable. This seems to be a big theme here on undercurrent. I know data to everywhere is kind of global theme, but don't diverse data feeds a I cracked and address ability allows for application access. >>Correct. So we look at the entire data landscape and say, we want to help you get data value out of your data wherever it lives. And it's right now, we've changed to the point where we are operating on data in motion, which is with data stream processor, which is hugely beneficial. You mentioned you know, a I m l way actually do something so unique from an ML perspective because we're actually doing the ml on the live streaming so, so much more valuable than doing it in batch mode. And so the ability to create those ML models by working on live data is super powerful. >>Good announcement. So you guys had the data processor. You have the search fabric, >>data fabric search, >>real time and acceleration our themes there. I want to get your thoughts on your new pricing options. Yes. Why now? What's that mean for customers? >>So if we want to bring data to everything, we have to allow them to actually get all the data right? So we needed to give them more flexible models and more alternative models. So for some people and just motto is very comfortable. But what they want it was more flexibility. So if you look at our new traunch pricing are predictable pricing, there's a couple of things that we've done with it. Number one is from 125 gig all the way up to unlimited. We'll show your predictable pricing so you don't have to guess. Well, if I move from 20 terabytes 2 50 what's that gonna cost me? We're gonna tell you, and you're gonna know and so That's one. The second thing is you don't have to land on the exact ingest. So before, if you bought a terabyte, you got a terabyte. Right now there's a traunch from 1 to 2 terabytes. There's a trunk from 2 to 5 terabytes. And so it gives the customers flexibility so that they don't have to worry about it coming back to buy more right away. >>So that's kind of cloud by as you go variable pricing. Exactly. I want your thoughts on some of the sales motions and position and you guys have out in the field. Visa VI. The industry has seen a lot of success and say Observe ability. For instance, Southern to Rick and Kartik About this. Yes, you guys are an enterprise software cloud and on premises provider you Enterprise sales motion. >>Yes, >>there's a lot of other competition up there that sells for the SNB. They're like tools. What's the difference between an offering that might look like Splunk but may be targeting the SNB? Small means business and one that needs to be full blown enterprise. >>Yeah, so I think the first and foremost most of the offerings that we see land in S and B. They have scale issues over time, I and so what we look at it and say is and they're mostly point products, right? So you can you can clutter up your environment with a bunch of point products, doing all these different things and try and stitch them together. Or you can go with this fun clock for him. So which allows you thio perform all of the same operations, whether B I t Security or Data Analytics in general. But it really isn't. It's about having the platform. >>You guys, what reduced the steps it takes to implement our What's the value? I guess. Here's Here's the thing. What's the pitch? So I'm on Enterprise. I'm like, Okay, I kept Dad. I got a lot of potential things going on platform. I need to make my data work for me any day to be everywhere. I au g Enterprise Cloud. What's the Splunk pitch? >>So our pitches were bringing dated everything, and first and foremost it's important. Understand why? Because we believe at the heart of every problem is a data problem. And we're not just talking t and security. As you know, you saw so many examples. I think you talk to his own haven earlier this week. Right? Wildfires is a data problem New York Presbyterian is using using us for opioid crisis. Right? That's a data problem. So everything's a data problem. What you want is a platform that can operate against that data and remove the barriers between data and action. And that's really what we're focused on. >>He mentions own haven that was part of Splunk Ventures Fund. You have a social impact fund? Yes, what's the motivation line that is just for social good? Is there a business reason behind it or both? >>What's this? So we actually have to social focuses. One is long for good, and that is non profit. What we announced this, what we announced a couple weeks ago that we reiterated yesterday was the spunk, social impact funds, a splint venture social impact fund, and this is to invest in for profit companies using data for social good. And the whole reason is that we look at it and so we say we're a platform. If you're a platform, you want to build out the ecosystem, right? And so the Splunk Innovation Fund splint Ventures Innovation Fund is to invest in new technology focused on that that brings value out of data. And on the other side, it's the spunk. Social impact. Thio get data companies that are taking data and creating such a >>Splunk for good as Splunk employees or a separate nonprofit. And >>it's not a separate nonprofit entity, but it is what we what we invest in. Okay. >>Oh, investing in >>investing in non for profit. Exactly like when we talked about the Global Emancipation Network right, which uses Splunk to fight human trafficking. That's on the nonprofit side. >>So take me through. This is a really hot area we've been covering for good because all roads I want now is for bad. Mark Zuckerberg's testifying from the Congress this morning kind of weird to watch that, actually, but there's a lot of good use cases. Tech tech can be shaped for good. A lot of companies are starting and getting off the ground for good things, but they're kind of like SMB, but they want the Splunk benefit. How do they engage with spunk if I'm gonna do ah social impact thing say cube for good? I got all this Tech. How do I engage punk? I wanted, but I don't know what to do. Have access to tools? How do I buy or engage with Splunk? >>Yes, start parties. Fund managers is making sure it's not just money, right? It's money, its access to talent. It's access to our product. And it's, you know, help with actually thinking through what they're trying to achieve, so it really is the entire focus. It's not just about the tech, Thea. Other thing I would say is you saw that we put out a Splunk investigate, and you also saw us talking about spunk, business slow and mission control. Those air now all built on a native SAS platform. And so the ability for our ecosystem now to go build on a native son platform is going to be incredibly powerful. >>So you expect more accelerated opportunities that all right, what's your favorite customer success stories? I know it's hard to pick your favorites, like picking a favorite child may be filled with the categories. Most ambitious class clown class favorite me. What's the ones you would call a really strong, >>so hit on a couple of my lover Domino story and the other one that I love, that I touched on. But I want to expand on because I think it's an amazing story. Is New York Presbyterian on using the Yes See you sprung for traditional security for private patient privacy. They also use it for medical devices. But here's the thing they use it for to help the opioid crisis. And you're like, How is opioid crisis a data problem? What they do is they actually correlate all the data that so doctors are prescribing the opioids who they're prescribing them to a number of prescriptions being building their pharmacy and then the inventory of opioids. Because they actually have sensors on all the cabinets where they get the opioids, they correlate all the data, and they make sure that if they understand if opioids being stolen from the hospital, because what people don't understand is that the opioid a lot of big part of the opioid crisis starts with hospitals to say of such a big volume of opioids. And so that, to me, is just I guess I love it because it's a great customer success story. But it's also again, it's so much fun doing good problem. >>A lot of deaths. I gotta ask you around your favorite moments here dot com, and you're a lot of conversations in your customer conversations this year. Let's do a little Splunk of the Cube right now can take the patterns, all the data, your meetings. What's the top patterns that are emerging? What are some of the top conversation themes that just keep popping up with customer? Specifically, >>I think the biggest thing is that they have seen more innovation unleash this year than they have ever seen in one year from Splunk. The other thing is that we've gone far outside of our traditional spunk index right and that the portfolio has grown so much and that we're allowing them to operate and get value out of the data wherever it lives. So data in motion and then you saw in data fabric search. We'll let you query not only the Splunk indices, but also H D. F s and s three buckets and more buckets to come. So more sinks if you will. So, really, what we're trying to do is say, we're just going to be your date a platform to help you get value >>Susan, you're a great leader and slung. Congratulations on your success again. They continue to grow every year. Splunk defies the critics. Now you're a market leader. Culture is a big part of this. What is your plans this year To take it to the next level? You're president of field worldwide, field operations, global business landscape. What are some of your goals and objectives on culture >>and the culture? So thank you, Jon. First of all, for your comments and were so committed to our culture, I think you know, as you grow so quickly, it takes a real effort to stay focused on culture way, have an incredible diversity and inclusion program. Onda We do way. It's a business imperative for us. Every single leader has diversity, diversity, inclusion, focuses and targets. And so I think that's a huge part of our culture. And the reason I say that, John, I don't know if you've ever heard about a 1,000,000 data points. Did anybody ever way Always talk about, you know in different different settings will share a couple of our 1,000,000 data points. What we want to make sure is a culture is that way. >>We >>have our employees showing up with their authentic self and because you do your best work when you can show up is your authentic self. And so we have people share a handful of their 1,000,000 data points at all different times throughout the year to get to know each other as individuals, as human beings and really understand what matters to each other. And I love that 1,000,000 data points culture, and I got that. We truly live it. And again it's It's about authenticity. And so I think that's what makes us incredibly special. >>And inclusion helps that trust >>fund elaboration, yes, and also just add to that. We're very proud of the fact that we made the fortune list this year for best places to work for women. So it shows that our focus, you know, we started. We started revealing our metrics just about two years ago, and we've had significant improvement way. Believe that what you focus on what you measure is what you improve. So we started measuring and improving it, and this year we made the list for a fortune that's called walking. It is Congratulations. Thank you. We're very excited about >>awesome on global expansion. I'm assuming is on the radar. Well, >>always, especially at this point. We're ready to double down and some of the tier one mark. It's a lovely for sure >>wasn't saying. Legend. President of worldwide field operations here inside the Cube. Where day to slung dot com 10th anniversary of their customer conference Our seventh year covering Splunk Amazing Ride They continue to ride the big wave. Thats a Q bring you all the data on insights here. I'm John Ferrier. Thanks for watching.

live from Orlando Florida it's the cube covered conf 18 got to you by Splunk welcome back to our land Oh everybody I'm Dave Volante with my co-hosts two minima and you're watching the cube the leader in live tech coverage we're brought here by Splunk toises Splunk off 18 hashtag spunk conf 18 Susan st. Leger is here she's the president of worldwide field operations at Splunk Susan thanks for coming on the cube thanks so much for having me today so you're welcome so we've been reporting actually this is our seventh year we've been watching the evolution of Splunk going from sort of hardcore IT OPSEC ops now really evolving in doing some of the things that when everybody talked about big data back in the day and spunk really didn't they talked about doing all these things that actually they're using Splunk for now so it's really interesting to see that this has been a big tailwind for you guys but anyway big week for you guys how do you feel I feel incredible we had you know we've it announced more innovations today just today then we have probably in the last three years combined we have another big set of innovations to announce tomorrow and you know just as an indicator of that I think you heard Tim today our CTO say on stage we to date have 282 patents and we are one of the world leaders in terms of the number of patents that we have and we have 500 pending right so if you think about 282 since the inception of the company and 500 pending it's a pretty exciting time for spunk people talk about that flywheel we were talking stew and I were talking earlier about some of the financial metrics and you know you have a lot of a large deal seven-figure deals which which you guys pointed out on your call let's see that's the outcome of having happy customers it's not like you turn to engineer that you just serving customers and that's what what they do I talk about how Splunk next is really bringing you into new areas yeah so spike next is so exciting there's really three three major pillars if you will design principles to spunk next one is to help our customers access data wherever it lives another one is to get actionable outcomes from the data and the third one is to allow unleash the power spunk to more users so there really the three pillars and if you think about maybe how we got there we have all of these people within IT and security that are the experts on Splunk the swing ninjas ful and their being they see the power of spunk and how it can help all these other departments and so they're being pulled in to help those other departments and they're basically saying Splunk help us help our business partners make it easier to get there to help them unleash the power spunk for them so they don't necessarily need us for all of their needs and so that's really what's what next is all about it's about making it again access data easier actionable outcomes and then more users and so we're really excited about it so talk about those new users I mean obviously the ITA ops they're your peeps so are they sort of advocating to you into the line of business or are you probably being dragged into the line of business what's that dynamic like yeah it's definitely we're customer success first and we're listening to our customers and they're asking us to take them that should go there with them right there being pulled that they know that what we what we say with our customers what are what our deepest customers understand about us is everybody needs funk it's just not everyone knows it yet and I said they're teaching their business why they need it and so it's really a powerful thing and so we're partnering with them to say how do we help them create business applications more which you'll see tomorrow in our announcements to help their business users you know one of the things that strikes us if we were talking it was the DevOps gentleman when you look at the companies that are successful with so-called digital transformation they have data at the core and they have sort of I guess I don't want to say a single data model but it's not a data model of stovepipes and that's what he described and essentially if I understand the power of Splunk just in talking to some of your customers it's really that singular data model that everybody can collaborate on with get advice from each other across the organization so not this sort of stovepipe model it seems like a fundamental linchpin of digital transformation even though you guys haven't been using that overusing that term thank you sort of a sign of smug you didn't use the big data term when big data was all hot now you use it same thing with digital transformation you're a fundamental it would seem to me to a lot of companies digital transformation that's exactly if you think about we started nineteen security but the reason for that is they were the first ones to truly do digital transformation right those are just the two the two organizations that started but exactly the way that they did it now all the other business units are trying to do it and that same exact platform that same exact platform that we use there's no reason we can't use it for those other areas those other functions but but if we want to go there faster we have to make it easier to use spunk and that's what you're seeing with spunk next you know I look at my career the last couple of decades we've been talking about oh well there's going to we're gonna leverage data and there's go where we want to be predictive on the models but that the latest wave of kind of AI ml and deep learning what I heard what you're talking about and in the Splunk next maybe you could talk a little bit about why it's real now and why we're actually going to be able to do more with our data to be able to extract the value out of it and really enable businesses sure so I think machine learning is that is at the heart of it and you know we we actually do two things from a machine learning perspective number one is within each of our market groups so IT security IT operations we have data scientists that work to build models within our applications so we build our own models and then we're hugely transparent with our customers about what those models are so they can tweak them if they like but we pre build those so that they have them in each of those applications so that's number one and and that's part of the actionable outcomes right ml helps drive actionable outcomes so much faster the second aspect is the ML TK right which is we give the our customers in ml TK so they can you know build their own algorithms and leverage everything all of the models that are out there as well so I think that two-fold approach really helps us accelerate the insights that we give to our customers Susan how are you evolving your go-to-market model as you think about Splunk next and just think about more line of business interactions so what are you doing on the go-to-market side yeah so the go to market when you think about reaching all of those other verticals if you will right it's very much going to be about the ecosystem all right so it's it's going to be about the solution provider ecosystem about the ISV ecosystem about the big the si is both boutique and the global s is to help us really Drive Splunk into all the verticals and meet their needs and so that will be one of the big things that you see we will obviously still have our horizontal focus across IT and security but we are really understanding what are the use cases within financial services what are the use cases within healthcare that can be repeated thousands of times and if you saw some of the announcements today in particular the data stream processor which allows you to act on data in motion with millisecond response that now puts you as close to real-time as anything we've ever seen in the data landscape and that's going to open up just a series of use cases that nobody ever thought of using spoil for so I wonder what you're hearing from customers when they talk about how do they manage that that pace of change out there I really like I walked around the show floor stuff I've been hearing lots people talking about you know containers and we had one of the your customers talking about how kubernetes fits into what they're doing seems like it really is a sweet spot for spunk that you can deal with all of these different types of information and it makes it even more important for customers to come to you yeah as you heard from Doug today in our keynote our CEO and the keynote it is a messy world right and part of the message just because it's a digital explosion and it's not going to get any slower it's just going to continue to get faster and I know you met with some of our customers earlier today and if'n carnival if you think about the landscape of NIF right I mean their mission is to protect the arsenal of nuclear weapons for the country right to make them more efficient to make them safer and if you think about all of it they not only have traditional IT operations and security they have to worry about but they have this landscape of lasers and all these sensors everywhere and that and when you look at that that's the messy data landscape and I think that's where Splunk is so uniquely positioned because of our approach you can operate on data in motion or at rest and because there is no structuring upfront I would I want to come back to what you said about real-time because that you know I oh I've said this now for a couple years but never used to use the term when Big Data was at its the peak of what does a gardener call it the hype cycle you guys didn't use that term and and so when you think about the use cases and in the Big Data world you've been hearing about real time forever now you're talking about it enterprise data warehouse you know cheaper EDW is fraud detection better analytics for the line of business obviously security and IT ops these are some of the use cases that we used to hear about in Big Data you're doing like all these now and sort of your platform can be used in all of these sort of traditional Big Data use cases am i understanding that problem 100% understanding it properly you know Splunk has again really evolved and if you think about again some of the announcements today think about date of fabric search right rather than saying you have to put everything into one instance or everything into one place right we're saying we will let you operate across your entire landscape and do your searches at scale and you know spunk was already the fastest at searching across your global enterprise to start with and when we were two to three times faster than anybody who compete it with us and now we improve that today by fourteen hundred percent I don't I don't even know where like you just look at again it ties back to the innovations and what's being done in our developer community within our engineering and team in those traditional use cases that I talked about in big data it was it was kind of an open source mess really complex zookeeper is the big joke right and always you know hive and pig and you know HBase and blah blah blah and we're practitioners of a lot of that stuff that's it's very complex essentially you've got a platform that now can be used the same platform that you're using in your traditional base that you're bringing to the line of business correct okay right it's the same exact platform we are definitely putting the power of Splunk in in the users hand so by doing things like mobile use on mobile and AR today and again I wish I could talk about what's coming tomorrow but let's just say our business users are going to be pretty blown away by what they're going to see tomorrow in our announcements yeah so I mean I'm presuming these are these are modern it's modern software micro services API base so if I want to bring in those open source tool tools I can in fact what you'll actually see when you understand more about the architecture is we're actually leveraging a lot of open-source and what we do so you know capabilities a spark and flink and but what we're doing is we're masking the complex the complexity of those from the user so instead of you having to do your own spark environment your own flink environment and you know having to figure out Kafka on your own and how you subscribe to what we're giving you all that we're we're masking all that for you and giving you the power of leveraging those tools so this becomes increasingly important my opinion especially as you start bringing in things like AI and machine learning and deep learning because that's going to be adopted both within a platform like use as yours but outside as well so you have to be able to bring in innovations from others but at the same time to simplify it and reduce that complexity you've got to infuse AI into your own platform and that's exactly what you're doing it's exactly what we're doing it's in our platform it's in our applications and then we provide the toolkit the SDK if you will so users can take it to another level all right so you've got 16,000 customers today if I understand the vision of SPARC next you're looking to get an order of magnitude more customers that you of it as addressable market talk to us about the changes that need to happen in the field is it just you're hitting an inflection point you've got those you know evangelists out there and I you know I see the capes and the fezzes all over the show so how is your field get ready to reach that broader audience yeah I think that's a great question again once again it will I'll tell you what we're doing internally but it's also about the ecosystem right in order to go broader it has to be about this this Splunk ecosystem and on the technology side we're opening the aperture right it's micro services it's ap eyes it's cloud there's there's so much available for that ecosystem and then from a go-to-market perspective it's really about understanding where the use cases are that can be repeated thousands of times right that the the the big problems that each of those verticals are trying to solve as opposed to the one corner use case that you know you could you could solve for one customer and that was actually one of the things we found is when we did analysis we used to do case studies on Big Data number one use case that always came back was custom because nothing was repeatable and that's how we were seeing you know a little bit more industry specific issues I was at soft ignite last week and you know Microsoft is going deep on verticals to get specific as to you know for IOT and AI how they can get specific in those environments I agreed I think again one of the things that so unique about Splunk platform is because it is the same platform that's at the underlying aspect that serves all of those use cases we have the ability in my opinion to do it in a way that's far less custom than anybody else and so we've seen the ecosystem evolve as well again six seven years ago it was kind of a tiny technology ecosystem and last year in DC we saw it really starting to expand now you walk around here you see you know some big booths from some of the SI partners that's critical because that's global scale deep deep industry expertise but also board level relationships absolutely that's another part of the the go-to markets Splunk becomes more strategic this is a massive Tam expansion that where we are potentially that we're witnessing with Splunk how do you see those conversations changing are you personally involved in more of those boardroom discussions definitely personally involved in your spot on to say that that's what's happening and I think a perfect example is you talk to Carnival today right we didn't typically have a lot of CEOs at the Splunk conference right now we have CEOs coming to the spunk conference right because it is at that level of strategic to our customers and so when you think about Carnival and yes they're using it for the traditional IT ops and security use cases but they're also using it for their customer experience and who would ever think you know ten years ago or even five years ago of Splunk as a customer experience platform but really what's at the heart of customer experience it's data so speaking of the CEO of Carnival Arnold Donald it's kind of an interesting name and and so he he stood up in the States today talking about diversity doubling down on diversity as an african-american you know you frankly in our industry you don't see a lot of african-americans CEOs you don't see a ton of women CEOs you don't see the son of women with with president in their title so he he made a really kind of interesting statement where he said something to the effect of forty years ago when I started in the business I didn't work with a lot of people like me and I thought that was a very powerful statement and he also said essentially look at if we're diverse we're gonna beat you every time your thoughts as an executive and in tech and a woman in tech so first of all i 100% agree with him and i can actually go back to my start i was a computer scientist at NSA so i didn't see a lot of people who looked like me and so from that perspective I know exactly where he's coming from and I am I'll tell you at Splunk we have a huge investment in diversity and not because it's a checkbox but because we believe in exactly what he says it's a competitive edge when you get people who think differently because you came from a different background because you're a different ethnicity because you were educated differently whatever it is whether it's gender whether it's ethnicity whether it's just a different approach to thinking all differentiation puts a different lens and and that way you don't get stove you don't have stovepipe thinking and I what I love about our culture at spunk is that we we call it a high growth mindset and if you're not intellectually curious and you don't want to think beyond the boundaries then it's probably not a good fit for you and a big part of that is having a diverse environment we do a lot of spunk to drive that we actually posted our gender diversity statistics last year because we believe if you don't measure it you're never going to improve it and it was a big step right to say we want to publish it we want to hold herself accountable and we've done a really nice job of moving it a little over 1% in one year which for our population is pretty big but we're doing really unique things like we have all job descriptions are now analyzed there's actually a scientific analysis that can be done to make sure that the job description does not bias whether men are women whether men alone or whether it's you know gender neutral so that that's exciting obviously we have a big women in technology program and we have a high potential focus on our top women as well what's interesting about your story Susan and we spent a lot of time on the cube talking about diversity generally in women in tech specifically we support a lot of WI t and we always talk him frequently we're talking about women and engineering roles or computer science roles and how they they oftentimes even when they graduate with that degree they don't come into tech and what strikes me about your path is your technical and yet now you've become this business executive so and I would imagine that having that background that technical background only helped in terms of especially in this industry so there are paths beyond just the technical role one hundred percent it first of all it's a huge advantage I believe it's the core reason why I am where I am today because I have the technical aptitude and while I enjoyed the business side of it as much and I love the sales side and the marketing side and all of the above the truth of the matter is at my core I think it's that intellectual curiosity that came out of my technical background that kept me going and really made me very I took risks right and if you look at my career it's much more of a jungle gym than a ladder and the way you know I always give advice to young people who generally it's young women who ask but oh sometimes it's the young men as well which is like how did you get to where you are how do I plan that how do I get and the truth of the matter is you can't if you try and plan it it's probably not going to work out the exactly the way you plan and so my advice is to make sure that you every time you're going to make a move your ask yourself what am I going to learn Who am I going to learn from and what is it going to add to my experience that I can materially you know say is going to help me on a path to where I ultimately want to be but I think if you try and figure it out and plan a perfect ladder I also think that when you try and do a ladder you don't have what I call pivots which is looking at things from different lenses right so me having been on the engineering side on the sales side on the services side of things it gives me a different lens and understanding the entire experience of our customers as well as the internals of an organization and I think that people who pivot generally are people who are intellectually curious and have intellectual capacity to learn new things and that's what I look for when I hire people I love that you took a nonlinear progression to the path that you're in now and it's speaking of you know the the technical I think if you're in this business you better like tech or what are you doing in this business but the more you understand technology the more you can connect the dots between how technology is impacting business and then how it can be applied in new ways so well congratulations on your careers you got a long way to go and thanks so much for coming on the queue so much David I really appreciate it thank you okay keep it right - everybody stew and I'll be back with our next guest we're live from Splunk Don Capcom 18 you're watching the cube [Music]

>>Okay, welcome back everyone to the Cube's live coverage here in Monaco for the MoCo crypto summit. I'm John fur. You're host of the cube. We got a great guest Dustin plant Boltz who is a crypto advisor, but also the crypto editor for Forbes Monaco here. Seeing the official event, the AAL event of the Monaco crypto summit in Monaco, your coverage area for Forbes, your MCing. Welcome to the >>Cube. Thank you for having me. And it's, it's always fun when I get to have an event in our backyard, cuz I get to hear what others know. And to me I'm very curious. Yeah. Always >>Learning. So you're on the MC on the stage here, you know, queue in the program online great program. So it's innovative event, inaugural event, great name by the way. Crypto summit and mono crypto >>Summit. Yeah, the MoCo crypto summit. >>That sounds like I want to attend every year. >>You're you're more than welcome to attend next year. >>Well, I hope so. Either way. I'm at the Al event with you. So gimme the take on what's on stage. What's been the program, like what's your observations going on here at the event today? >>So what we're starting to see globally is this digitization of things and the people that are part of the innovation side. And so that's what we've been able to see this morning. We're we're now at the break is what sort of companies are out there, the good ones and what are they building? Is this innovation? Is it even innovative and figuring out how they're gonna do it and the roadmaps to getting there from the metaverses to NFTs and even to decentralized finance. >>Yeah, it's the number one question I get is what's legit. What's not legit. And then you're starting to see the, the, the wheat and the shaft separating here and you know, something called crypto winter. But I don't see it. I mean, I see correction for some of the bad things going on in terms of not having the right underpinning infrastructure, the creative ideas are amazing. We're also seeing like digital bits and other platforms kind of coming together to enable the creators and, and the NFT side for instance has been huge. What has been your observation on that enablement? Because you have two schools of thoughts. You have the total nerds we're up and down building everything. Then you have artists and creators, whether it's music, tech apps building, they don't necessarily want to get 'em to the covers. They don't want to deal with all that. Yeah. Have you seen, what's your, what's your take on that? >>So I I'm seeing that a lot of these major brands, you know, they they're striving for excellence. You know, they're being more careful of who they partner with and the types of companies and you know, they, they look at it from reality and a little tough love to figure out should they align their brand. So what we're seeing here is is that there is so much inertia moving forward. That we're just at the beginning of this thing. Yeah. McKinsey recently said that the ecosystem will be over $30 trillion. So when you recognize that we are so early and it's those right now, or some might say are the risk takers. But to me there, aren't taking risk. They're being a part of making history. >>Yeah. You get the pioneers and you get the financial. So as they come together, how do you see the market? Cause what I've noticed with crypto and here in, in this, this market is international. One lot of international finance us is kind of lag behind. You got all kinds of rules, but you got the, the combination of the, the future billionaires. Sure. Okay. The pioneers and then the financeers yeah. Coming the money, the money and the power coming together. What's your reporting show you that's going on right now? What should people know about on how this is evolving? What they shouldn't >>Expect? Well, so you have a group that wants to become cryers they're seeing these individuals globally. They're making lots and lots of money, but what they don't realize is that not everybody is gonna have that outcome, but looking at the technology aspect of it and how it's going to improve a system that many can agree is collectively broken legacy just can't move beyond. It was never designed to you'll see people take shots at certain card companies and I go, but you recognize they developed the assembly line. And so I'm seeing that the smart money they got in long ago, believe it or not. And those now they're looking out for their errors are the ones that saying, I will not have an excuse when my, my grandkids or my, my nieces or my nephews, when they come and ask, where were you when the greatest transformational shift in human history, from both education to jobs, to careers and even wealth was being shifted to a digital world, why were you on the sideline waiting? And so I think what we're gonna see is this tsunami coming, and it's gonna start with one big player and then two and five, you go, go alone. You go far, go together. You go further. And that's what we're seeing is that this collective is moving forward >>And the community, we just had Beth Kaiser on, I've known Beth for many, many years. And she's what she's her journey has done. She's had a great mission and then gets she's a data scientist and came to Analytica. Now she's doing work with Ukraine and the rallying support around it has been impressive. And it's a community vibe, but the community's not just like sympathetic they're hands on together to your point. >>Yeah. It, but it also takes courage. I mean, you look at Britney Kaiser and what she had, and to me, courage is not, not having fear. Courage is not allowing the fear to stop. You, you know, recently asked my executive coach, who's 85 and I'm turning 39. This question of, do you let fear stop you? How do you decide? And he said, you know, you can either let, you can either ride the dragon. And I said, or let the dragon chase you. And Brittany has been one of these that made a decision to do what was right. And it came down to integrity. Yeah. >>So what are you have to these days what's going on in your world? >>What is going on in my world? So I moderate events all over and I connect and I like to ask people questions. So I'm gonna ask you, I'm gonna turn at the interviewer on the >>Interview. It's good. Natural. >>What are you learning? >>I mean, I'm learning, I mean today or this week or this month or this year. Well, I was just talking with Brittany about this. The security world is converging cloud technology, cloud computing. That revolution has just been amazing. Amazon posted their earnings yesterday. They blew it away as far as I'm concerned. So they kind of show there's no tech recession. I've learned that this recession, that we're so called in is the first downturn in tech where there's been cloud players as hyperscalers as an economic engine. Okay. So from a, from a business perspective, Amazon web services, Microsoft Azure now Google cloud, Alibaba's now in, in international version. This is the first time at downturns ever happened with cloud computing as an economic engine. And so therefore what I'm seeing is the digital transformation that's happening across the world for enterprises and entrepreneurs is not stopping. >>It's actually accelerating. So although the GDPs down in inflation is down, you're seeing a massive shift continuing to accelerate, spending and transformation with cloud technologies and decentralized. So you can almost see it kind of in the, this event and other events, even some of the bigger events, the best smartest people are working on it. The applications in all the categories are transforming. If cloud is step one, decentralized gonna be step two. So I see that kind of bridge going from cloud computing, cloud native to decentralized native. And I think a D DAPP market's gonna just explode. I think NFTs are just scratched on the surface. I think that's kind of, I won't say gimmicky, but I think no, but you're right, much more of a much more of a, an illustration that there's more coming. >>There is a lot more coming because people are seeing that there's more to an NFT than an ugly luck and J you know, ugly and JP image that there's, that there's data in there. And that your avatar will be stored as just that as an NFT. And I learned today from go of sing, that decentralization is, is the key to innovation. And I agree with that statement. Holy. >>Yeah. I mean, I think access to stuff is gonna be multidimensional. Like you think about the NFT as, as an ID, whether it's him or UN unstoppable domains is that company just got financing another round where the billion dollars, their concept is like, Hey, one NFT is your access for all of your potential identities in context. >>And isn't that exciting that we're now gonna be at this stage where you travel with you. Yeah. Instead of someone else traveling with you, you get to decide who you will be. And to me, everything you're doing in this world, this reality is now becoming part of your digital asset as a whole. >>I remember when I started my podcasting company in 20 2004, early pioneers, Evan Williams was there with Odo and you had, you know, the blogging revolution going on that whole democratization wave actually didn't happen right then. But all the people that were involved in that web two oh, kind of CRAs was all about democratization. It's kind of happening now. I mean, 15, 20 years later at web services is transformed cloud the democratization for own your own data, putting users in control. And I think in the middle of that, the Facebook's the world, the world garden data, you know, manipulation kind of took it off track a little bit. So I think now I'm, I psych to see that it's back on track to where it was. I mean, Facebook made billions of dollars. Now you got LinkedIn. I mean, LinkedIn's great for your resume, but it's also become a wall's garden with no data export. >>Yeah. And then >>No APIs keep >>Changing. Think about this. That if you wanna apply for a job, just change something quickly. Yeah. Ah, now you're the senior VP. Yeah. Before you were, you're an office manager >>Like to see the immutable block change, >>You don't get to see when did the record change. Yeah. >>Reputation data. You're a digital exhaust people gonna wanna reign that in. And I think the user in charge message that Brit Kaiser was talks about is hugely a mess under, under, under amplified concept. Digital assets are key, but the data ownership is something that I think is, is >>Powerful. So I'm gonna be launching a brand new company in and around September called cryptos. And it's a crypto career center. Think of it like the, the crypto for LinkedIn, that it's an aggregator becoming the industry standard for education, becoming the industry standard for crypto ships, with partners like ledger and moon pay and Casper labs. >>Look at this, we got an exclusive scoop on the cube. This >>Is the first time I will tell you this the first time in, in an environment like this. Yeah. That I'm excited to, I'm excited to talk about, right. Because it's time to be part of the change. Yeah, exactly. You know, as a father, I look at, I know where it's headed in the world of business. I know in the world of this, that we're gonna call the internet of connected things. Yeah. That it's gonna require you to have a certain talent skill or a certain certification. And to me, it's important to have an industry that supports one >>Staff and also, and also history on misinformation, smear campaigns can happen and ruin a career >>Overnight. Can you imagine that one little thing and because the internet never forgets. Yeah. It stays around indefinitely. >>The truth has to come out. Dustin. Great to have you on the queue. Thank you so much. Final question. What have you learned in there is MC what's your takeaway real quick? >>What I've learned is I never tire of learning. Thank you again, to learn more. Dustin plan.com. >>All right. Thanks for coming. Thank you. Cube coverage here at Monaco. I'm Shawn furry. We'll back with more coverage after this short break.

the cloud has dramatically changed the way providers think about delivering database technologies not only has cloud first become a mandate for many if not most but customers are demanding more capabilities from their technology vendors examples include a substantially similar experience for cloud and on-prem workloads increased automation and a never-ending quest for more secure platforms broadly there are two prevailing models that have emerged one is to provide highly specialized database products that focus on optimizing for a specific workload signature the other end of the spectrum combines technologies in a converge platform to satisfy satisfy the needs of a much broader set of use cases and with me to get a perspective on these and other issues is andy mendelson is the executive vice president of oracle the world's leading database company andy leads database server technologies hello andy thanks for coming on hey dave glad to be here okay so we saw the recent announcements this is kind of your baby around next generation autonomous data warehouse maybe you could take us through the path you took from the original cloud data warehouses to where we are today yeah when we uh we first brought autonomous database out uh we were basically a second generation technology at that point you know we decided that what customers wanted was to the other you know the push of a button provision the really powerful oracle database technology that they've been using for years and um we did that with autonomous database and beyond that we provided a very unique capability that around self-tuning self-driving of the database which is something the first generation vendors didn't provide and this this is really important because customers today are you know developers and data analysts you know you know at the push of a button build out their their data warehouses but you know they're not experts in tuning and so what we thought was really important is that customers get great performance out of the box and that's one of the really unique things about autonomous data warehouse autonomous database in particular and then this latest generation that we just came out with also answers the questions we got from you know the data analysts and developers they said you know it's really great that i can press a button and provision this very powerful data warehouse infrastructure or database infrastructure from oracle but you know if i'm an analyst i want data you know so it's still hard for me to go and you know get data from various data sources transform them clean them up and get them to a way a place where i can start querying the data now i still need data engineers to help me do that and so we've done in the new release we said okay we want to give data analysts and data engineer data scientists developers is a true self-service experience where they can do their job completely without bringing in any you know any any engineers from their i.t organization and so that's what this new version is all about yeah awesome i mean look years ago you guys identified the i.t labor problem and you've been focused on r d and putting it in your r d to solve that problem for customers so we're really starting to see that hit now now gartner recently did some analysis they ranked and rated them some of the more popular cloud databases and oracle did very well i mean particularly particularly in operational categories i mean an operational side and the mission critical stuff you smoked everybody we had mark stamer and david floyer on and our big takeaways were that you're you're again dominating in that mission critical workloads that that that dominance continues but your approach of converging functionality really differs from some others that we saw i mean obviously when you get high ratings from gartner you're pretty stoked about that but what do you think contributed to those rankings and what are you finding specifically in customer interactions yeah so gardner does a lot of its analysis based on talking to customers finding out how their product these products that sound great on paper actually work in practice and i think that's one of the places where oracle database technology really shines it's it's uh it solves real-world problems um it's been doing it for a long time and as we've moved that technology into the cloud you know that continues you know the differentiation we've built up over the years really stands out you know you look at like amazon's databases they generally take some open source technology that isn't that new it could be 30 years old 25 years old and they put it up on the cloud and they say oh it's cloud native it's great but but in fact it's the same old you know technology that that doesn't really compete you know decade behind oracle's database technology so i think the gartner analysis really showed that sort of thing quite clearly yeah so let's talk about that a little bit because obviously i've learned a lot you know one of the things i've learned over the last many years of following this business a lot of ways to skin a cat and cloud database vendors if you think about you mentioned aws you know look at snowflake kind of right tool for the right job approach they're going to say that their specialty databases they're focused uh are better than your converged approach which they make you know think of as a you know swiss army knife what's your take on that yeah well the converged approach is something of course we've been working on for a long time so the the idea is pretty simple you know think about your smartphone you know if you can think back you know over 10 years ago used to have you know a camcorder and a a camera and a messaging device and also a dump phone device that all those different devices got converged into what we now call the smartphone why did the smartphone win it's just simply much more productive for you to carry one device around that that is actually best to breed in all the different categories instead of lots of separate devices and that's what we're doing with converge database over the years you know we've been able to build out technologies that are really good at transaction breasts at analytics for data warehousing now we're working on you know json technologies graph technologies the other vendors basically can't do this i mean it's much easier to build a specialty database that does one thing to build out a converged database that does end things really well and that's what we've been doing for years and again it's it's based on technology that uh you've invested in for quite a long time um and it's something that i think uh customers and developers and analyze analysts find to be a much more productive way of doing their jobs it's very unique and not common at all to see a technology that's been around as long as oracle database to see that sort of morph into a more modern platform i mean you mentioned aws uses leverages open source a lot you know snowflake would say okay hey we are born in the cloud and they are i think google bigquery would be another good example but but but that notion of boy i want to get your take on this born in the cloud those folks would say well we're superior to oracle's because you know they started you know decades ago not necessarily you know native cloud services uh how have you been able to address that i know you know cloud first is kind of the buzzword but but how have you you made that sort of transparent to users or or irrelevant to users because you are cloud first maybe you could talk about how you've able to achieve that and convince us that you actually really are cloud native now you know one of the things we we sort of like pointing out is that um oracle very uniquely has had this scale out technology for running all kinds of workloads not just analytic workloads which is what you see out in the cloud there but we can also scale out transaction processing workloads now that was another one of the reasons we do so well in for example the gardner analysis for trans operational workloads and that technology is really valuable as we went to cloud it lets us do some really unique things and the most obvious unique thing we we have is something we like to call you know you know cloud native you know instant elasticity and so with our technology if you want to provision a share you know some number of amount of compute to run your workloads you can provision exactly what you need you know if you need 17 cpus to get your job done you do 17 cpus when you provision your autonomous database our competitors who claim to be born in the cloud like snowflake and amazon they still use this this archaic way of provisioning uh servers based on shapes you know snowflake you know says what which shape cluster do you want you want 16 you want 32 you want 64. no it goes up by a power of 2 which means if you compare that to what oracle does you you have to provision up to like twice as much cpu than you really need so if you really need 17 they make you provision 32. if you really need 33 they make your provision 64. so this is not a cloud native experience at all it's an archaic way of doing things and and we like to point out with our instant elasticity you know we can go from 17 to 18 to 19 you know whatever you want plus we have something called auto scale so you can set your baseline to be 17 let's say but we will automatically based on your workload scale you up to three times that so in this case be 51 and because of that true elasticity we have we are really the only ones that can deliver true pay as you go kind of you know just pay for what you need kind of capability which is certainly what amazon was talking about when they first called their cloud elastic but it turns out for database services these guys still do this archaic thing with shapes so that's a really good example of where we're quite better than the other guys and it's much more cloud native than the other guys i want to follow up on that uh just stay here for a second because you're basically saying we have we have better granularity than the so-called cloud native guys now you mentioned snowflake right you got you got the shapes you got to you got to choose which shape you want and it sounds like it sounds like redshift the same and of course i know the way in which amazon separates compute from storage is largely a tiering exercise so it's not as as is as smooth as you might expect but nonetheless it's it's good how is it that you were you were able to achieve this with a database that was you know born you know many decades ago is it i mean what is it in from a technical standpoint an r d standpoint that you were able to do i mean did you design that in in the 1980s how did you how did you get here yeah well um it's a combination of interesting technologies so autonomous database you know it has the oracle database software that software is running on a very powerful optimized infrastructure for database based on the exadata technology that we've had on prem for many years we brought that to the cloud and that technology is a scale-out infrastructure that supports you know thousands of cpus and then we use our multi-tenant technology which is a way of sharing large infrastructures amongst amongst separate uh clients and we divide it up dynamically on the fly so if there's thousands of cpus you know this guy wants 20 and this one wants 30 we we divide it up and give them exactly what they need and if they want to grow we just take some extra cpus that are in reserve and we give it to them instantly and so that's a very different way of doing things and that's been a shape based approach where you know what what snowflake and amazon do under the covers they give you a real physical server you know or a cluster and that's how they provision if you want to grow they give you another big physical cluster which takes a long time to get the data populated to get it get it working we just have that one infrastructure that we're sharing among lots of users and we just give you a little extra capacity we don't it doesn't it's done instantly there's no need for data to be moved to populate the new clusters that you know snowflake or amazon are provisioning for you so it's a very different way of doing things and you're able to do that because of the tight integration between you mentioned exadata tight integration between the hardware and software we got david floyer calls it the iphone of enterprise sometimes sometimes you get some grief for that but it's it's not a bad metaphor but is that really the sort of secret well the big secret under the covers is this you know exudated technology our real application cluster scale out technologies our multi-tenant technologies so these are things we've been working on for a long time and they are very mature very powerful technologies and they really provide very unique benefits in a cloud world where people want things to happen instantly and they want to work well for any kind of workload um you know that's that's why we call we talk about being converged we can do mixed workloads you can do transactions and analytics all in the same data the other guys can't do that you know they're really good at like you said a narrow workload like i can do analytics or i can do graph you know i can do json but they can't really do the combination which is what real world applications are like they're not pure one thing versus enough right thank you for that so one of the questions people want to know is can oracle attract you know new customers that aren't existing oracle customers so maybe you could talk about that and you know why should uh somebody who's not an existing oracle customer think about using autonomous database yeah that's a that's a really good question you know oracle if you look at our customer base has a lot of really large enterprises you know the biggest banks and the biggest telcos you know they run oracle they run their businesses on oracle and these guys are sort of the most conservative of the bunch out there and they are moving to cloud at a somewhat slower rate than the than the smaller companies and so if you look at who's using autonomous database now it's actually the smaller companies you know the same type of people that first decided amazon was an interesting cloud 10 years ago they're also using our technologies and it's for the same reason they're finding you know they don't have large it organizations they don't have large numbers of engineers to engineer their infrastructure and that's why cloud is so attractive to them and autonomous database on top of cloud is really attractive as well because you know information is the lifeblood of every organization and if they can empower their analysts to get their job done without lots of help from it organizations they're going to do it and you know that's really what's made autonomous database really interesting you know the whole self-driving nature is very attractive to the smaller shops that don't have a lot of sophisticated um i.t expertise all right let's talk about developers you guys are the stewards of the java community so obviously you know big probably you know the biggest most popular programming language out there but when i think of developers i think of guys in hoodies pounding away but when i think of oracle developers i might think of maybe an app dev team inside of maybe some of those large customers that you talked about but why would developers and or analysts be interested in in using oracle as opposed to some some of those more focused narrow use databases that we were talking about earlier yeah so if you're a developer um you want to get your job done as fast as possible and so having a database that gives you the most productive application development experience is important to you and so you know i was talking we've been talking about converged database off and on so if i'm a developer i have a given job to do a converged database that lets me do a combination of analytics and and transactions and do a little json and little graph all in one is a much more productive place to go because if i if i i don't have something like that then i'm stuck taking my my application and breaking it up into pieces you know this piece i'm going to run on say aurora on amazon and this piece i have to run on the graph database and here's some json i got to run that on some document database and then i have to move the data around the data gets sort of fragmented between these databases and i have to do all this data you know integration and and whatever with a converged database i have a much simpler world where i can just use one technology stack i can get my job done and then i'm future proof against change you know requirements change all the time so you build the initial version of the application and your users say you know that this is not what i want i want some something else and it turns out that something else often is why i want analytics and you use something like a you know a document stored technology that has really poor analytic capabilities and then so you have to take that data and you have to move it to another database and so with with our converged approach you don't have to do that you know you're already in a place where everything works everything that you need you can possibly need in the future is going to be there as well and so for developers i i think you know converged is the right way to go plus for people who are what we call citizen developers you know like the data analysts that they cuddle they write a little code occasionally but they're really after getting value of the data we have this really fabulous no code loco tool called apex and apex is again a very mature technology it's been around for years and it lets somebody who's just a data analyst he knows a little sql but doesn't want to write code get their job done really fast and we've published some benchmark on our website showing you know basically you can get the job done 20 to 40 times faster using a no co loco tool like apex versus something like you know just writing cutting lots of traditional code i'm glad you brought up apex we recently interviewed one of your former colleagues amit xavery and all he would talk about is low code no code and then in the apex announcement you said something to the effect of coding should be the exception not the rule did you mean that what do you mean by that yeah so apex is a tool that people use with our our database technology for building what we call data driven applications so if you got a bunch of data and you want to get some value out of it you want to build maybe dashboards or more sophisticated reports apex is an incredible tool for doing that and it's it's modern you know it builds applications that look great on your smartphone and it automatically you know renders that same user interface on a bigger device like a laptop desktop device as well and uh it's very it's one of these things that uh the people that use it just go bonkers with it it's a viral technology they get really excited about how productive they they've been using it and they tell all their friends and i think we decided uh i guess about a year ago when we came up with this apex service that you know we really want to start going bigger on the marketing around it because it's very unique nobody else has anything quite like it and it's it again it just adds value to the whole developer productivity story around an oracle database so uh that's why we have the apex service now and we also have apex available with every oracle database on the cloud god i want to i want to ask you about some of the features around 21c there are a lot of them you announced earlier this year maybe you could tease out some of the top things that we should be paying attention to in 21c yeah sure um so one of the ways to look at 21c is we're we're continuing down this path of a converged database and so one of the the marquee features in 21c is something we call blockchain tables so what is blockchain well blockchain was this technology that's under the covers behind bitcoin you know it's a way of creating a tamper-proof data store um that was used by the original bitcoin algorithms well developers actually like having tamper proof data objects and databases too um you know and so what we decided to do was say well if i create a sql table in an oracle database what if there's a new option that just says i want that table implemented using blockchain technology to make the table tamper proof and fully audited etc and so we just did that and so in 21c you can now get a basically another feature of the converged database that says uh you know give me a sql table i can do everything i can query it i can insert rows into it but it's it's tamper proof i can't ever update it i can't delete rows from it amazon did the their usual thing they took again some open source technology and they said hey we got this great thing called quantum ledger database and it does blockchain tables but but if you want to do blockchain tables in any of their other databases you're out of luck they don't have it you have to go move the data into this new thing and it's again one of their it's again showing sort of the problem with their their proprietary this proprietary approach of having specialty databases versus just having one conversion that does it all so that's the blockchain cable feature uh we did a bunch of other things um the one i i think is worth mentioning the most is is support for persistent memory so a lot of people out there haven't noticed this this very interesting technology that intel shipped a couple years ago called optane data center memory and what it is it's basically a hybrid of flash memory which is persistent memory and standard dram which is not persistent means you can't store a database in dram um and so with this persistent memory you can basically have a database stored persistently in memory all the time and so it's a very innovative new technology from a database standpoint it's a very disruptive technology to the database market because now you can have an in-memory database basic period all the time 24 7. and so 21c is the first database out there that has native support for this new kind of persistent memory technology and we think it's it's really important so we're actually making it available as uh to our 19c customers as well and uh you know that's another technology i'd call out that we think is very unique we're way ahead of the game there and we're going to continue investing moving forward in that space as well yeah so that layer in between dram and and persistent flash that's that's a great innovation and good game changing from a from a performance and actually the way you write applications but i gotta i gotta ask you i and all the analysts were wrong with juan recently juan loyza and and to listen to that introduction of blockchain and everybody wants to know is safra going to start putting bitcoin on the oracle balance sheet i'm about to get that leap yeah that's a good question who knows yeah i can't comment on speculation ah that would be interesting okay last question then we got to go uh look oracle the narrative on oracle is you're expensive and you're mean you know it's hard to do business with do you care are you doing things to maybe change that perception in the cloud yeah i think we've made a very conscious decision that as we move to the cloud we're offering a totally new business model on the club that is a a cloud-native model you pay for what you use um you have everyday low prices you don't have to negotiate with some salesman for for months to get get a good price um so yeah we really like the message to get out there that those of you who think you know what oracle's all about um you know i and how it might be to work with oracle on in from your on premises days um you should really check out how oracle is now on the cloud we have this autonomous database technology really easy to use really simple any analysts can help get value out of the data without any help from any other engineers it's very unique it's it's uh it's the same technology you're used to but now it's delivered in a way that's much easier to consume and much lower cost and so yeah you should definitely take a look at what we've got out there on the cloud and it's all free to try out we got this free tier you can provision free vms free databases um free apex whatever you want and uh try it out and see what you think well thanks for that i was kidding about me and a lot of a lot of friends at oracle some relatives as well and thanks andy for coming on thecube today it's really great to talk to you yeah it's my pleasure and thanks for watching this is dave vellante we'll see you next time you

>>Hello, everyone. This is not appropriate to lapse of America. I'm going to talk about the motivation. For zero knowledge goes back to the heart off, winding down identity, ownership, community and control. Much of photography exists today to support control communications among individuals in the one world. We also consider devices as extensions of individuals and corporations as communities. Here's hoping you're not fit in this picture. What defines the boundary off an individual is the ability to hold a secret with maybe, it says, attached to the ownership. Off some ethic, we want the ability to use the secret to prove ownership of this asset. However, giving up the secret itself essentially announced ownership since then, anybody else can do the same. Dear Knowledge gives us tools to prove ownership without revealing the secret. The notion of proving ownership off a digital object without revealing it sounds very paradoxical outside the model off. So it gives us a surprise when this motion was formalized and constructed by Goldwasser Miccoli and back off in the late eighties, we'll focus on the non interactive >>version of Siri, a knowledge our music in the >>stock, which was first developed by blow Tillman and Peggy, where the general it can span multiple rounds of communications music only allows a single message to be trusted. No, let's get into some technical details for musics. The objective of for music is to show that an object X, which you can think off as the public footprint, often asset, belonging clan and the language without revealing its witness. W, which you can think off as the Future Analytics team consists off three algorithms, video proof and very. The key generation process is executed by a trusted third party and the very opposite, resulting in a common >>random string, or steers, which is made public. The >>true vendor produces a proof by based on the CIA's X and the very fine with the checks. The proof against X and accepts or rejects music off course has to satisfy some properties. We needed to be correct, which basically says that when everyone follows the protocol correctly on, so we can expect, we need to be thought, which says that a false statement cannot be proven. The channel is a trickier properly to form this. How do we capture the intuition behind saying that the proof there is no knowledge of the witness. One way to capture that is to imagine their tools is the real world where the proof is calculated. Using the witness on there's a simulation worth where the proof is calculated without a witness. To make this possible, the simulator may have some extra information about the CIA's, which is independent off the objectives. The property then requires that it is not possible to effectively distinguish these words Now. It is especially challenging to construct music's compared to encryption signature schemes, in particular in signature schemes. The analog off the Hoover can use a secret, and in any case, the analog off the very fire can use a secret. But in is it's none of the crew layer and the verifier can hold a secret. Yeah, in this talk, I'm going to focus on linear subspace languages. This class is the basis of hardness. >>Assumptions like GH and deliver >>on has proved extremely useful in crypto constructions. This is how we express DD it and dealing as linear software. We will use additive notation on express the spirit logs as the near group actions on coop elements. You think the syntax we can write down Deitch on dealing Jupiter's very naturally a zoo witness sector times a constant electric so we can view the language as being penetrated by a constant language. Metrics really was hard by many groups in our instructions. What does it mean? S while uh, Standard group allows traditions and explain it off by in your group also allows one modification In such groups, we can state various in yourself facing elections. The DDN is the simplest one. It assumes that sampling a one dimensional space is indistinguishable from something full professional. The decisional linear assumption assumes the theme from tours is three dimensional spaces generalizing the sequence of Presumptions. The scaling the resumption asks to distinguish between gay damaged examples and full it and >>examples from a K plus one national space. >>Right, So I came up with a breakthrough. Is the construction in Europe 2008 in particular? There? Music for many years Off Spaces was the first efficient >>construction based on idiots and gear. Structurally, >>it consisted of two parts Our commitment to the witness Andre question proof part and going how the witness actually corresponds to the object. The number of elements in the proof is linear in the number >>of witnesses on the number of elements in the object. >>The question remains to build even shorter visits. The Sierras itself seemed to provide some scoop Rosa Russo fix. See how that works for an entire class of languages? Maybe there's a way to increase proof efficiency on the cost of having had Taylor Sierra's for each year. This is what motivates quality and after six, where we let the solace depend on the language itself. In particular, we didn't require the discrete logs of the language constants to generate this, Yes, but we did require this constant student generated from witness sample distributions. This still turns out to be sufficient for many applications. The construction achieved a perfect knowledge, which was universally in the sense that the simulator was independent. However, soundness is competition. So here's how the construction differed from roots high at a very high level, the language constants are embedded into the CIA s in such a way that the object functions as it's only so we end up not needing any separate commitment in the perfect sense. Our particular construction also needed fewer elements in the question proof, as there On the flip side, the CIA's blows up quadratic instead of constant. Let's get into the detail construction, which is actually present with this script. Let the language apparently trace by Giovanni tricks with the witness changing over time, we sat down and matrices >>D and B with appropriate damages. >>Then we construct the public series into what C. S. D is meant to be used. By the way. On it is constructed by >>multiplying the language matrix with D and being worse, Sierra's V is the part that is meant to be used by the very fair, and it is constructed using details be on be embedded in teaching. >>Now let's say you're asked to computer proof for a candidate X with fitness number we computed simply as a product of the witness with CSP. The verification of the truth is simply taking with the pairing off the candidate and the proof with the Sierras. Seeming threats is equal to zero. If you look carefully. Sierra's V essentially embedded in G to the kernel of the Matrix, owned by the language metrics here and so to speak. This is what is responsible for the correctness. The zero knowledge property is also straightforward, >>given the trapdoor matrices, D and B. Now, >>when corrected journalism relatively simple to prove proving illnesses strictly The central observation is that, given CSP, there is still enough entropy. >>India and me to >>random I seriously in particular Sierra's we Can we expand it to have an additional component with a random sample from the kernel allows it. This transformation is purely statistical. No, we essentially invented idiots are killing their talent in the era of kernel part in this transform sitting within show that an alleged proof on a bad candidate and we used to distinguish whether a subspace sample was used for a full space >>sample was used at the challenge. The need >>to have the kernel of the language in this city. That's the technical >>reason why we need the language to come from a witness. Sample. >>Uh, let's give a simple illustration >>of the system on a standard Diffie Hellman, which g one with the hardness assumption being idiot. >>So the language is defined by G one elements small D, E and F, with pupils off the phone due to the W. After that ugly, the CIA is is generated as follows example D and >>B from random on Compute Sierra speak as due to the day after the being verse and Sierra's V as G to do to do the big on day two of the video. The >>proof of the pupil >>detail that I do after the bill is computed using W. As Sierra Speed race to the party. I know that this is just a single element in the group. The verification is done by bearing the Cooper and the proof with the Sierras VMS and then checking in quality. The >>similar can easily compute the proof using trapdoors demand without knowing that what we are expecting. People leave a Peter's die and reduce the roof size, the constant under a given independent of the number of witnesses and object dimensions. Finally, at Cryptocurrency 14 we optimize the proof toe, one group >>element under the idiots. In both the works, the theorists was reduced to linear sites. The >>number of bearings needed for ratification was also industry in years. This is the crypto Ford in construction in action, the construction skeleton remains more or less the famous VR turkey. But the core observation was that many of the Sierras elements could were anomaly. Comite. While still >>maintaining some of this, these extra random items are depicted in red in this side. >>This round of combination of the Sierras elements resulted in a reduction of boat, Bruce says, as also the number of clearings required for education in Europe in 2015 kills, and we came up with a beautiful >>interpretation of skill sets based on the concept of small predictive hash functions. >>This slide is oversimplified but illustrated, wanting, uh, this system has four collecting >>puzzle pieces. The goodness of the language metrics okay again and a key Haider when >>the hidden version of the key is given publicly in the Sears. Now, when we have a good object, the pieces fit together nicely into detectable. However, when we have a bad object, the pieces no longer fit and it becomes >>infeasible to come up with convincing. Zero knowledge is demonstrable by giving the key to the simulator on observing that the key is independent of the language metrics. >>Through the years, we have extended >>enhanced not mind to be six system, especially with our collaborators, Masayuki Abby Koko Jr. Born on U. >>N. Based on your visits, we were able to construct very efficient, identity based encryption structure, resulting signatures >>public verifiable CCS, secure encryption, nine signatures, group signatures, authorities, key extremes and so on. >>It has also been gratifying to see the community make leaps and bounces ideas and also use queuing visits in practical limits. Before finishing off, I wanted to talk to you a little bit about >>some exciting activities going on Hyper ledger, which is relevant for photographers. Hyper >>Leisure is an open source community for enterprise. Great. It's hosted by the minute formation on enjoys participation from numerous industry groups. Uh, so difficult funded to efforts in Africa, we have versa, which is poised to be the crypto home for all. Blocking it and practice a platform for prospecting transactions are part of the legs on the slide here, >>we would love participation from entity inference. So >>that was a brief history of your analytics. Thanks for giving me the opportunity. And thanks for listening

(upbeat electronic music) >> Narrator: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation! >> Hi, I'm Stu Miniman, your host for Cloud Native Insight. When we launched this series, one of the things we wanted to talk about was that we're not just using cloud as a destination, but really enabling new ways of thinking, being able to use the innovations underneath the cloud, and that if you use services in the cloud, that you're not necessarily locked into a solution or can't move forward. And that's why I'm really excited to help welcome to the program, I have the co-founders of Vendia. First we have Dr. Tim Wagner, he is the co-founder and CEO of the company, as well as generally known in the industry as the father of Serverless from the AWS Lambda, and his co-founder, Shruthi Rao, she is the chief business officer at Vendia, also came from AWS where she worked on blockchain solutions. Tim, Shruthi, thanks so much for joining us. >> Thanks for having us in here, Stu. Great to join the show. >> All right, so Shruthi, actually if we could start with you because before we get into Vendia, coming out of stealth, you know, really interesting technology space, you and Tim both learned a lot from working with customers in your previous jobs, why don't we start from you. Block chain of course had a lot of learnings, a lot of things that people don't understand about what it is and what it isn't, so give us a little bit about what you've learned and how that lead towards what you and Tim and the team are doing with Vendia. >> Yeah, absolutely, Stu! One, the most important thing that we've all heard of was this great gravitational pull towards blockchain in 2018 and 2019. Well, I was one of the founders and early adopters of blockchain from Bitcoin and Ethereum space, all the way back from 2011 and onwards. And at AWS I started the Amazon Managed Blockchain and launched Quantum Ledger Database, two services in the block chain category. What I learned there was, no surprise, there was a gold rush to blockchain from many customers. We, I personally talked to over 1,092 customers when I ran Amazon Managed Blockchain for the last two years. And I found that customers were looking at solving this dispersed data problem. Most of my customers had invested in IoT and edge devices, and these devices were gathering massive amounts of data, and on the flip side they also had invested quite a bit of effort in AI and ML and analytics to crunch this data, give them intelligence. But guess what, this data existed in multiple parties, in multiple clouds, in multiple technology stacks, and they needed a mechanism to get this data from wherever they were into one place so they could the AI, ML, analytics investment, and they wanted all of this to be done in real time, and to gravitated towards blockchain. But blockchain had quite a bit of limitations, it was not scalable, it didn't work with the existing stack that you had. It forced enterprises to adopt this new technology and entirely new type of infrastructure. It didn't work cross-cloud unless you hired expensive consultants or did it yourself, and required these specialized developers. For all of these reasons, we've seen many POCs, majority of POCs just dying on the vine and not ever reaching the production potential. So, that is when I realized that what the problem to be solved was not a trust problem, the problem was dispersed data in multiple clouds and multiple stacks problem. Sometimes multiple parties, even, problem. And that's when Tim and I started talking about, about how can we bring all of the nascent qualities of Lambda and Serverless and use all of the features of blockchain and build something together? And he has an interesting story on his own, right. >> Yeah. Yeah, Shruthi, if I could, I'd like to get a little bit of that. So, first of all for our audience, if you're watching this on the minute, probably want to hit pause, you know, go search Tim, go watch a video, read his Medium post, about the past, present, and future of Serverless. But Tim, I'm excited. You and I have talked in the past, but finally getting you on theCUBE program. >> Yeah! >> You know, I've looked through my career, and my background is infrastructure, and the role of infrastructure we know is always just to support the applications and the data that run business, that's what is important! Even when you talk about cloud, it is the applications, you know, the code, and the data that are important. So, it's not that, you know, okay I've got near infinite compute capacity, it's the new things that I can do with it. That's a comment I heard in one of your sessions. You talked about one of the most fascinating things about Serverless was just the new creativity that it inspired people to do, and I loved it wasn't just unlocking developers to say, okay I have new ways to write things, but even people that weren't traditional coders, like lots of people in marketing that were like, "I can start with this and build something new." So, I guess the question I have for you is, you know we had this idea of Platform as a Service, or even when things like containers launched, it was, we were trying to get close to that atomic unit of the application, and often it was talked about, well, do I want it for portability? Is it for ease of use? So, you've been wrangling and looking at this (Tim laughing) from a lot of different ways. So, is that as a starting point, you know, what did you see the last few years with Lambda, and you know, help connect this up to where Shruthi just left off her bit of the story. >> Absolutely. You know, the great story, the great success of the cloud is this elimination of undifferentiated heavy lifting, you know, from getting rid of having to build out a data center, to all the complexity of managing hardware. And that first wave of cloud adoption was just phenomenally successful at that. But as you say, the real thing businesses wrestle with are applications, right? It's ultimately about the business solution, not the hardware and software on which it runs. So, the very first time I sat down with Andy Jassy to talk about what eventually become Lambda, you know, one of the things I said was, look, if we want to get 10x the number of people to come and, you know, and be in the cloud and be successful it has to be 10 times simpler than it is today. You know, if step one is hire an amazing team of distributed engineers to turn a server into a full tolerance, scalable, reliable business solution, now that's going to be fundamentally limiting. We have to find a way to put that in a box, give that capability, you know, to people, without having them go hire that and build that out in the first place. And so that kind of started this journey for, for compute, we're trying to solve the problem of making compute as easy to use as possible. You know, take some code, as you said, even if you're not a diehard programmer or backend engineer, maybe you're just a full-stack engineer who loves working on the front-end, but the backend isn't your focus, turn that into something that is as scalable, as robust, as secure as somebody who has spent their entire career working on that. And that was the promise of Serverless, you know, outside of the specifics of any one cloud. Now, the challenge of course when you talk to customers, you know, is that you always heard the same two considerations. One is, I love the idea of Lamdba, but it's AWS, maybe I have multiple departments or business partners, or need to kind of work on multiple clouds. The other challenge is fantastic for compute, what about data? You know, you've kind of left me with, you're giving me sort of half the solution, you've made my compute super easy to use, can you make my data equally easy to use? And so you know, obviously the part of the genesis of Vendia is going and tackling those pieces of this, giving all that promise and ease of use of Serverless, now with a model for replicated state and data, and one that can cross accounts, machines, departments, clouds, companies, as easily as it scales on a single cloud today. >> Okay, so you covered quite a bit of ground there Tim, if you could just unpack that a little bit, because you're talking about state, cutting across environments. What is it that Vendia is bringing, how does that tie into solutions like, you know, Lamdba as you mentioned, but other clouds or even potentially on premises solutions? So, what is, you know, the IP, the code, the solution that Vendia's offering? >> Happy to! So, let's start with the customer problem here. The thing that every enterprise, every company, frankly, wrestles with is in the modern world they're producing more data than ever, IMT, digital journeys, you know, mobile, edge devices. More data coming in than ever before, at the same time, more data getting consumed than ever before with deep analytics, supply chain optimization, AI, ML. So, even more consumers of ever more data. The challenge, of course, is that data isn't always inside a company's four walls. In fact, we've heard 80% or more of that data actually lives outside of a company's control. So, step one to doing something like AI, ML, isn't even just picking a product or selecting a technology, it's getting all of your data back together again, so that's the problem that we set out to solve with Vendia, and we realized that, you know, and kind of part of the genesis for the name here, you know, Vendia comes from Venn Diagram. So, part of that need to bring code and data together across companies, across tech stacks, means the ability to solve some of these long-standing challenges. And we looked at the two sort of big movements out there. Two of them, you know, we've obviously both been involved in, one of them was Serverless, which amazing ability to scale, but single account, single cloud, single company. The other one is blockchain and distributed ledgers, manages to run more across parties, across clouds, across tech stacks, but doesn't have a great mechanism for scalability, it's really a single box deployment model, and obviously there are a lot of limitations with that. So, our technology, and kind of our insight and breakthrough here was bringing those two things together by solving the problems in each of them with the best parts of the other. So, reimagine the blockchain as a cloud data implementation built entirely out of Serverless components that have all of the scale, the cost efficiencies, the high utilization, like all of the ease of deployment that something like Lambda has today, and at the same time, you know, bring state to Serverless. Give things like Lambda and the equivalent of other clouds a simple, easy, built-in model so that applications can have multicloud, multi-account state at all times, rather than turning that into a complicated DIY project. So, that was our insight here, you know and frankly where a lot of the interesting technology for us is in turning those centralized services, a centralized version of Serverless Compute or Serverless Database into a multi-account, multicloud experience. And so that's where we spent a lot of time and energy trying to build something that gives customers a great experience. >> Yeah, so I've got plenty of background in customers that, you know, have the "information silos", if you will, so we know, when the unstructured data, you know so much of it is not searchable, I can't leverage it. Shruthi, but maybe it might make sense, you know, what is, would you say some of the top things some of your early customers are saying? You know, I have this pain point, that's pointing me in your direction, what was leading them to you? And how does the solution help them solve that problem? >> Yeah, absolutely! One of our design partners, our lead design partners is this automotive company, they're a premier automotive company, they want, their end goal is to track car parts for warranty recall issues. So, they want to track every single part that goes into a particular car, so they're about 30 to 35,000 parts in each of these cars, and then all the way from manufacturing floor to when the car is sold, and when that particular part is replaced eventually, towards the end of the lifecycle of that part. So for this, they have put together a small test group of their partners, a couple of the parts manufacturers, they're second care partners, National Highway Safety Administration is part of this group, also a couple of dealers and service centers. Now, if you just look at this group of partners, you will see some of these parties have high technology, technology backgrounds, just like the auto manufacturers themselves or the part manufacturers. Low modality or low IT-competency partners such as the service centers, for them desktop PCs are literally the IT competency, and so does the service centers. Now, most of, majority of these are on multiple clouds. This particular auto customer is on AWS and manufactures on Azure, another one is on GCP. Now, they all have to share these large files between each other, making sure that there are some transparency and business rules applicable. For example, two partners who make the same parts or similar parts cannot see each other's data. Most of the participants cannot see the PII data that are not applicable, only the service center can see that. National Highway Safety Administration has read access, not write access. A lot of that needed to be done, and their alternatives before they started using Vendia was either use point-to-point APIs, which was very expensive, very cumbersome, it works for a finite small set of parties, it does not scale, as in when you add more participants into this particular network. And the second option for them was blockchain, which they did use, and used Hyperledger Fabric, they used Ethereum Private to see how this works, but the scalability, with Ethereum Private, it's about 14 to 15 transactions per second, with Hyperledger Fabric it taps out at 100, or 150 on a good day, transaction through, but it's not just useful. All of these are always-on systems, they're not Serverless, so just provisioning capacity, our customers said it took them two to three weeks per participant. So, it's just not a scalable solution. With Vendia, what we delivered to them was this virtual data lake, where the sources of this data are on multiple clouds, are on multiple accounts owned by multiple parties, but all of that data is shared on a virtual data lake with all of the permissions, with all of the logging, with all of the security, PII, and compliance. Now, this particular auto manufacturer and the National Highway Safety Administration can run their ML algorithms to gain intelligence off of it, and start to understand patterns, so when certain parts go bad, or what's the propensity of a certain manufacturing unit producing faulty parts, and so on, and so forth. This really shows you this concept of unstructured data being shared between parties that are not, you know, connected with each other, when there are data silos. But I'd love to follow this up with another example of, you know, the democratization, democratization is very important to Vendia. When Tim launched Lambda and founded the AWS Serverless movement as a whole, and at AWS, one thing, very important thing happened, it lowered the barrier to entry for a new wave of businesses that could just experiment, try out new things, if it failed, they scrap it, if it worked, they could scale it out. And that was possible because of the entry point, because of the paper used, and the architecture itself, and we are, our vision and mission for Vendia is that Vendia fuels the next generation of multi-party connected distributed applications. My second design partner is actually a non-profit that, in the animal welfare industry. Their mission is to maintain a no-kill for dogs and cats in the United States. And the number one reason for over populations of dogs and cats in the shelters is dogs lost, dogs and cats lost during natural disasters, like the hurricane season. And when that happens, and when, let's say your dogs get lost, and you want to find a dog, the ID or the chip-reading is not reliable, they want to search this through pictures. But we also know that if you look at a picture of a dog, four people can come up with four different breed names, and this particular non-profit has 2,500 plus partners across the U.S., and they're all low to no IT modalities, some of them have higher IT competency, and a huge turnover because of volunteer employees. So, what we did for them was came up with a mechanism where they could connect with all 2,500 of these participants very easily in a very cost-effective way and get all of the pictures of all of the dogs in all these repositories into one data lake so they can run some kind of a dog facial recognition algorithm on it and identify where my lost dog is in minutes as opposed to days it used to take before. So, you see a very large customer with very sophisticated IT competency use this, also a non-profit being able to use this. And they were both able to get to this outcome in days, not months or years, as, blockchain, but just under a few days, so we're very excited about that. >> Thank you so much for the examples. All right, Tim, before we get to the end, I wonder if you could take us under the hood a little bit here. My understanding, the solution that you talk about, it's universal apps, or what you call "unis" -- >> Tim: Unis? (laughs) >> I believe, so if I saw that right, give me a little bit of compare and contrast, if you will. Obviously there's been a lot of interest in what Kubernetes has been doing. We've been watching closely, you know there's connections between what Kubernetes is doing and Serverless with the Knative project. When I saw the first video talking about Vendia, you said, "We're serverless, and we're containerless underneath." So, help us understand, because at, you know, a super high level, some of the multicloud and making things very flexible sound very similar. So you know, how is Vendia different, and why do you feel your architecture helps solve this really challenging problem? >> Sure, sure, awesome! You know, look, one of the tenets that we had here was that things have to be as easy as possible for customers, and if you think about the way somebody walks up today to an existing database system, right? They say, "Look, I've got a schema, I know the shape of my data." And a few minutes later I can get a production database, now it's single user, single cloud, single consumer there, but it's a very fast, simple process that doesn't require having code, hiring a team, et cetera, and we wanted Vendia to work the same way. Somebody can walk up with a JSON schema, hand it to us, five minutes later they have a database, only now it's a multiparty database that's decentralized, so runs across multiple platforms, multiple clouds, you know, multiple technology stacks instead of being single user. So, that's kind of goal one, is like make that as easy to use as possible. The other key tenet though is we don't want to be the least common denominator of the cloud. One of the challenges with saying everyone's going to deploy their own servers, they're going to run all their own software, they're going to build, you know, they're all going to co-deploy a Kubernetes cluster, one of the challenges with that is that, as Shruthi was saying, first, anyone for whom that's a challenge, if you don't have a whole IT department wrapped around you that's a difficult proposition to get started on no matter how amazing that technology might be. The other challenge with it though is that it locks you out, sort of the universe of a lock-in process, right, is the lock-out process. It locks you out of some of the best and brightest things the public cloud providers have come up with, and we wanted to empower customers, you know, to pick the best degree. Maybe they want to go use IBM Watson, maybe they want to use a database on Google, and at the same time they want to ingest IoT on AWS, and they wanted all to work together, and want all of that to be seamless, not something where they have to recreate an experience over, and over, and over again on three different clouds. So, that was our goal here in producing this. What we designed as an architecture was decentralized data storage at the core of it. So, think about all the precepts you hear with blockchain, they're all there, they all just look different. So, we use a no SQL database to store data so that we can scale that easily. We still have a consensus algorithm, only now it's a high speed serverless and cloud function based mechanism. You know, instead of smart contracts, you write things in a cloud function like Lambda instead, so no more learning Solidity, now you can use any language you want. So, we changed how we think about that architecture, but many of those ideas about people, really excited about blockchain and its capabilities and the vision for the future are still alive and well, they've just been implemented in a way that's far more practical and effective for the enterprise. >> All right, so what environments can I use today for your solution, Shruthi talked about customers spanning across some of the cloud, so what's available kind of today, what's on the roadmap in the future? Will this include beyond, you know, maybe the top five or six hyper scalers? Can I do, does it just require Serverless underneath? So, will things that are in a customer's own data center eventually support that. >> Absolutely. So, what we're doing right now is having people sign up for our preview release, so in the next few weeks, we're going to start turning that on for early access to developers. That'll be, the early access program, will be multi-account, focused on AWS, and then end of summer, well be doing our GA release, which will be multicloud, so we'll actually be able to operate across multiple clouds, multiple cloud services, on different platforms. But even from day one, we'll have API support in there. So, if you got a service, could even be running on a mainframe, could be on-prem, if it's API based you can still interact with the data, and still get the benefits of the system. So, developers, please start signing up, you can go find more information on vendia.net, and we're really looking forward to getting some of that early feedback and hear more from the people that we're the most excited to have start building these projects. >> Excellent, what a great call to action to get the developers and users in there. Shruthi, if you could just give us the last bit, you know, the thing that's been fascinating, Tim, when I look at the Serverless movement, you know, I've talked to some amazing companies that were two or three people (Tim laughing) and out of their basement, and they created a business, and they're like, "Oh my gosh, I got VC funding, and it's usually sub $10,000,000. So, I look at your team, I'd heard, Tim, you're the primary coder on the team. (Tim laughing) And when it comes to the seed funding it's, you know, compared to many startups, it's a small number. So, Shruthi, give us a little bit if you could the speeds and feeds of the company, your funding, and any places that you're hiring. Yeah, we are definitely hiring, lets me start from there! (Tim laughing) We're hiring for developers, and we are also hiring for solution architects, so please go to vendia.net, we have all the roles listed there, we would love to hear from you! And the second one, funding, yes. Tim is our main developer and solutions architect here, and look, the Serverless movement really helped quite a few companies, including us, to build this, bring this to market in record speeds, and we're very thankful that Tim and AWS started taking the stands, you know back in 2014, 2013, to bring this to market and democratize this. I think when we brought this new concept to our investors, they saw what this could be. It's not an easy concept to understand in the first wave, but when you understand the problem space, you see that the opportunity is pretty endless. And I'll say this for our investors, on behalf of our investors, that they saw a real founder market-fit between us. We're literally the two people who have launched and ran businesses for both Serverless and blockchain at scale, so that's what they thought was very attractive to them, and then look, it's Tim and I, and we're looking to hire 8 to 10 folks, and I think we have gotten to a space where we're making a meaningful difference to the world, and we would love for more people to join us, join this movement and democratize this big dispersed data problem and solve for this. And help us create more meanings to the data that our customers and companies worldwide are creating. We're very excited, and we're very thankful for all of our investors to be deeply committed to us and having conviction on us. >> Well, Shruthi and Tim, first of all, congratulations -- >> Thank you, thank you. >> Absolutely looking forward to, you know, watching the progress going forward. Thanks so much for joining us. >> Thank you, Stu, thank you. >> Thanks, Stu! >> All right, and definitely tune in to our regular conversations on Cloud Native Insights. I'm your host Stu Miniman, and looking forward to hearing more about your Cloud Native Insights! (upbeat electronic music)

>>from around the globe. It's the queue with digital coverage of AWS Public sector online brought to you by Amazon Web services. Everyone welcome back to the Cube's virtual coverage of AWS Public sector online summit, which is also virtual. I'm John Furrier, host of the Cube, with a great interview. He remotely Jennifer Cronus, who's the general manager with the D. O. D. Account for Amazon Web services. Jennifer, welcome to the Cube, and great to have you over the phone. I know we couldn't get the remote video cause location, but glad to have you via your voice. Thanks for joining us. >>Well, thank you very much, John. Thanks for the opportunity here >>to the Department of Defense. Big part of the conversation over the past couple of years, One of many examples of the agencies modernizing. And here at the public sector summit virtual on line. One of your customers, the Navy with their air p is featured. Yes, this is really kind of encapsulate. It's kind of this modernization of the public sector. So tell us about what they're doing and their journey. >>Sure, Absolutely. So ah, maybe er P, which is Navy enterprise resource planning is the department of the Navy's financial system of record. It's built on S AP, and it provides financial acquisition and my management information to maybe commands and Navy leadership. Essentially keep the Navy running and to increase the effectiveness and the efficiency of baby support warfighter. It handles about $70 billion in financial transactions each year and has over 72,000 users across six Navy commands. Um, and they checked the number of users to double over the next five years. So essentially, you know, this program was in a situation where their on premises infrastructure was end of life. They were facing an expensive tech upgrade in 2019. They had infrastructure that was hard to steal and prone to system outages. Data Analytics for too slow to enable decision making, and users actually referred to it as a fragile system. And so, uh, the Navy made the decision last year to migrate the Europe E system to AWS Cloud along with S AP and S two to s AP National Security Services. So it's a great use case for a government organization modernizing in the cloud, and we're really happy to have them speaking at something this year. >>Now, was this a new move for the Navy to move to the cloud? Actually, has a lot of people are end life in their data center? Certainly seeing in public sector from education to modernize. So is this a new move for them? And what kind of information does this effect? I mean, ASAP is kind of like, Is it, like just financial data as an operational data? What is some of the What's the move about it Was that new? And what kind of data is impacted? >>Sure. Yeah, well, the Navy actually issued a Cloud First Policy in November of 2017. So they've been at it for a while, moving lots of different systems of different sizes and shapes to the cloud. But this migration really marked the first significant enterprise business system for the Navy to move to the actually the largest business system. My migrate to the cloud across D o D. Today to date. And so, essentially, what maybe Air P does is it modernizes and standardizes Navy business operation. So everything think about from time keeping to ordering missile and radar components for Navy weapon system. So it's really a comprehensive system. And, as I said, the migration to AWS govcloud marks the Navy's largest cloud migration to date. And so this essentially puts the movement and documentation of some $70 billion worth of parts of goods into one accessible space so the information can be shared, analyzed and protected more uniformly. And what's really exciting about this and you'll hear from the Navy at Summit is that they were actually able to complete this migration in just under 10 months, which was nearly half the time it was originally expected to take different sizing complexity. So it's a really, really great spring. >>That's huge numbers. I mean, they used to be years. Well, that was the minicomputer. I'm old enough to remember like, Oh, it's gonna be a two year process. Um, 10 months, pretty spectacular. I got to ask, What is some of the benefits that they're seeing in the cloud? Is that it? Has it changed the roles and responsibilities? What's what's some of the impact that they're seeing expecting to see quickly? >>Yeah, I'd say, you know, there's been a really big impact to the Navy across probably four different areas. One is in decision making. Also better customer experience improves security and then disaster recovery. So we just kind of dive into each of those a little bit. So, you know, moving the system to the cloud has really allowed the Navy make more timely and informed decisions, as well as to conduct advanced analytics that they weren't able to do as efficiently in the past. So as an example, pulling financial reports and using advanced analytics on their own from system used to take them around 20 hours. And now ah, maybe your API is able to all these ports in less than four hours, obviously allowing them to run the reports for frequently and more efficiently. And so this is obviously lead to an overall better customer experience enhance decision making, and they've also been able to deploy their first self service business intelligence capabilities. So to put the hat, you know, the capability, Ah, using these advanced analytics in the hands of the actual users, they've also experienced improve security. You know, we talk a lot about the security benefits of migrating to the cloud, but it's given them of the opportunity to increase their data protection because now there's only one based as a. We have data to protect instead of multiple across a whole host of your traditional computing hardware. And then finally, they've implemented a really true disaster recovery system by implementing a dual strategy by putting data in both our AWS about East and govcloud West. They were the first to the Navy to do those to provide them with true disaster become >>so full govcloud edge piece. So that brings up the question around. And I love all this tactical edge military kind of D o d. Thinking the agility makes total sense. Been following that for a couple of years now, is this business side of it that the business operations Or is there a tactical edge military component here both. Or is that next ahead for the Navy? >>Yeah. You know, I think there will ultimately both You know that the Navy's big challenge right now is audit readiness. So what they're focusing on next is migrating all of these financial systems into one General ledger for audit readiness, which has never been done before. I think you know, audit readiness press. The the D has really been problematic. So the next thing that they're focusing on in their journey is not only consolidating to one financial ledger, but also to bring on new users from working capital fund commands across the Navy into this one platform that is secure and stable, more fragile system that was previously in place. So we expect over time, once all of the systems migrate, that maybe your API is going to double in size, have more users, and the infrastructure is already going to be in place. Um, we are seeing use of all of the tactical edge abilities in other parts of the Navy. Really exciting programs for the Navy is making use of our snowball and snowball edge capabilities. And, uh, maybe your key that that this follows part of their migration. >>I saw snow cones out. There was no theme there. So the news Jassy tweeted. You know, it's interesting to see the progression, and you mentioned the audit readiness. The pattern of cloud is implementing the business model infrastructure as a service platform as a service and sass, and on the business side, you've got to get that foundational infrastructure audit, readiness, monitoring and then the platform, and then ultimately, the application so a really, you know, indicator that this is happening much faster. So congratulations. But I want to bring that back to now. The d o d. Generally, because this is the big surge infrastructure platform sas. Um, other sessions at the Public sector summit here on the D. O. D is the cybersecurity maturity model, which gets into this notion of base lining at foundation and build on top. What is this all about? The CME EMC. What does it mean? >>Yeah, well, I'll tell you, you know, I think the most people know that are U S defense industrial base of what we call the Dev has experienced and continues to experience an increasing number of cyber attacks. So every year, the loss of sensitive information and an election property across the United States, billions each year. And really, it's our national security. And there's many examples for weapons systems and sensitive information has been compromised. The F 35 Joint Strike Fighter C 17 the Empty Nine Reaper. All of these programs have unfortunately, experience some some loss of sensitive information. So to address this, the d o. D. Has put in place, but they all see em and see which is the Cybersecurity Maturity Models certification framework. It's a mouthful, which is really designed to ensure that they did the defense industrial base. And all of the contractors that are part of the Defense Supply Chain network are protecting federal contract information and controlled unclassified information, and that they have the appropriate levels of cyber security in place to protect against advanced, persistent, persistent threats. So in CMC, there are essentially five levels with various processes and practices in each level. And this is a morton not only to us as a company but also to all of our partners and customers. Because with new programs the defense, investor base and supply take, companies will be required to achieve a certain see MNC certification level based on the sensitivity of the programs data. So it's really important initiative for the for the Deal E. And it's really a great way for us to help >>Jennifer. Thanks so much for taking the time to come on the phone. I really appreciate it. I know there's so much going on the D o d Space force Final question real quick for a minute. Take a minute to just share what trends within the d o. D you're watching around this modernization. >>Yeah, well, it has been a really exciting time to be serving our customers in the D. And I would say there's a couple of things that we're really excited about. One is the move to tactical edge that you've talked about using out at the tactical edge. We're really excited about capabilities like the AWS Snowball Edge, which helped Navy Ear Key hybrid. So the cloud more quickly but also, as you mentioned, our AWS cone, which isn't even smaller military grades for edge computing and data transfer device that was just under £5 kids fitness entered mailbox or even a small backpacks. It's a really cool capability for our diode, the warfighters. Another thing. That's what we're really watching. Mostly it's DRDs adoption of artificial intelligence and machine learning. So you know, Dear D has really shown that it's pursuing deeper integration of AI and ML into mission critical and business systems for organizations like the Joint Artificial Intelligence. Enter the J and the Army AI task force to help accelerate the use of cloud based AI really improved war fighting abilities And then finally, what I'd say we're really excited about is the fact that D o. D is starting Teoh Bill. New mission critical systems in the cloud born in the cloud, so to speak. Systems and capabilities like a BMS in the airports. Just the Air Force Advanced data management system is being constructed and created as a born in the cloud systems. So we're really, really excited about those things and think that continued adoption at scale of cloud computing The idea is going to ensure that our military and our nation maintain our technological advantages, really deliver on mission critical systems. >>Jennifer, Thanks so much for sharing that insight. General General manager at Amazon Web services handling the Department of Defense Super important transformation efforts going on across the government modernization. Certainly the d o d. Leading the effort. Thank you for your time. This is the Cube's coverage here. I'm John Furrier, your host for AWS Public sector Summit online. It's a cube. Virtual. We're doing the remote interviews and getting all the content and share that with you. Thank you for watching. Yeah, Yeah, yeah, yeah, yeah

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi buddy welcome back to the cube this is Dave Volante and you're watching a special presentation data ops enacted made possible by IBM you know what's what's happening is the innovation engine in the IT economy is really shifted used to be Moore's Law today it's applying machine intelligence and AI to data really scaling that and operationalizing that new knowledge the challenges that is not so easy to operationalize AI and infuse it into the data pipeline but what we're doing in this program is bringing in practitioners who have actually had a great deal of success in doing just that and I'm really excited to have it Kumal a Himalayan Manali is here she's the executive head of data management or personal and business banking at Standard Bank of South Africa the tomb of length thanks so much for coming in the queue thank you for having me Dave you're very welcome and first of all how you holding up with this this bovid situation how are things in Johannesburg um things in Johannesburg are fine we've been on lockdown now I think it's day 33 if I'm not mistaken lost count and but we're really grateful for the swift action of government we we only I mean we have less than 4,000 places in the country and infection rate is is really slow so we've really I think been able to find the curve and we're grateful for being able to be protected in this way so all working from home or learning the new normal and we're all in this together that's great to hear why don't you tell us a little bit about your your role you're a data person we're really going to get into it but here with us you know how you spend your time okay well I head up a date operations function and a data management function which really is the foundational part of the data value chain that then allows other parts of the organization to monetize data and liberate it as as as the use cases apply we monetize it ourselves as well but really we're an enterprise wide organization that ensures that data quality is managed data is governed that we have the effective practices applied to the entire lineage of the data ownership and curation is in place and everything else from a regulatory as well as opportunity perspective then is able to be leveraged upon so historically you know data has been viewed as sort of this expense it's it's big it's growing it needs to be managed deleted after a certain amount of time and then you know ten years ago of the Big Data move data became an asset you had a lot of shadow I people going off and doing things that maybe didn't comply to the corporate ethics probably drove here here you're a part of the organization crazy but talk about that how what has changed but they in the last you know five years or so just in terms of how people approach data oh I mean you know the story I tell my colleague who are all bankers obviously is the fact that the banker in 1989 had to mainly just know debits credits and be able to look someone in the eye and know whether or not they'd be a credit risk or not you know if we lend you money and you pay it back the the banker of the late 90s had to then contend with the emergence of technologies that made their lives easier and allowed for automation and processes to run much more smoothly um in the early two-thousands I would say that digitization was a big focus and in fact my previous role was head of digital banking and at the time we thought digital was the panacea it is the be-all and end-all it's the thing that's gonna make organizations edit lo and behold we realized that once you've gotten all your digital platforms ready they are just the plate or the pipe and nothing is flowing through it and there's no food on the face if data is not the main photo really um it's always been an asset I think organizations just never consciously knew that data was that okay so so what sounds like once you've made that sort of initial digital transformation you really had to work it and what we're hearing from a lot of practitioners like self as challenges related to that involve different parts of the organization different skill sets of challenges and sort of getting everybody to work together on the same page it's better but maybe you could take us back to sort of when you started on this initiative around data Ops what was that like what were some of the challenges that you faced and how'd you get through them okay first and foremost Dave organizations used to believe that data was I t's problem and that's probably why you you then saw the emergence of things like chatter IP but when you really acknowledge that data is an essay just like money is an asset then you you have to then take accountability for it just the same way as you would any other asset in the organization and you will not abdicate its management to a separate function that's not cold to the business and oftentimes IT are seen as a support or an enabling but not quite the main show in most organizations right so what we we then did is first emphasize that data is a business capability the business function it presides in business makes to product management makes to marketing makes to everything else that the business needs for data management also has to be for to every role in every function to different degrees and varying bearing offense and when you take accountability as an owner of a business unit you also take accountability for the data in the systems that support the business unit for us that was the first picture um and convincing my colleagues that data was their problem and not something that we had to worry about they just kind of leave us to to it was was also a journey but that was kind of the first step into it in terms of getting the data operations journey going um you had to first acknowledge please carry on no you just had to first acknowledge that it's something you must take accountability of as a banker not just need to a different part of the organization that's a real cultural mindset you know in the game of rock-paper-scissors you know culture kinda beats everything doesn't it it's almost like a yep a trump card and so so the businesses embrace that but but what did you do to support that is there has to be trust in the data that it has to be a timeliness and so maybe you could take us through how you achieve those objectives and maybe some other objectives that business the man so the one thing I didn't mention Dave is that obviously they didn't embrace it in the beginning it wasn't a it wasn't there oh yeah that make sense they do that type of conversation um what what he had was a few very strategic people with the right mindset that I could partner with that understood the case for data management and while we had that as as an in we developed a framework for a fully matured data operations capability in the organization and what that would look like in a target date scenario and then what you do is you wait for a good crisis so we had a little bit of a challenge in that our local regulator found us a little bit wanting in terms of our date of college and from that perspective it then brought the case for data quality management so now there's a burning platform you have an appetite for people to partner with you and say okay we need this to comply to help us out and when they start seeing their opt-in action do they then buy into into the concept so sometimes you need to just wait for a good Christ and leverage it and only do that which the organization will appreciate at that time you don't have to go Big Bang data quality management was the use case at the time five years ago so we focused all our energy on that and after that it gave us leeway and license really bring to maturity all the other capabilities at the business might not well understand as well so when that crisis hit of thinking about people process in technology you probably had to turn some knobs in each of those areas can you talk about that so from a technology perspective that that's when we partnered with with IBM to implement information analyzer for us in terms of making sure that then we could profile the data effectively what was important for us is to to make strides in terms of showing the organization progress but also being able to give them access to self-service tools that will give them insight into their data from a technology perspective that was kind of I think the the genesis of of us implementing and the IBM suite in earnest from a data management perspective people wise we really then also began a data stewardship journey in which we implemented business unit stewards of data I don't like using the word steward because in my organization it's taken lightly almost like a part-time occupation so we converted them we call them data managers and and the analogy I would give is every department with a P&L any department worth its salt has a FDA or financial director and if money is important to you you have somebody helping you take accountability and execute on your responsibilities in managing that that money so if data is equally important as an asset you will have a leader a manager helping you execute on your data ownership accountability and that was the people journey so firstly I had kind of soldiers planted in each department which were data managers that would then continue building the culture maturing the data practices as as applicable to each business unit use cases so what was important is that every manager in every business unit to the Data Manager focus their energy on making that business unit happy by ensuring that they data was of the right compliance level and the right quality the right best practices from a process and management perspective and was governed and then in terms of process really it's about spreading through the entire ecosystem data management as a practice and can be quite lonely um in the sense that unless the whole business of an organization is managing data they worried about doing what they do to make money and most people in most business units will be the only unicorn relative to everybody else who does what they do and so for us it was important to have a community of practice a process where all the data managers across business as well as the technology parts and the specialists who were data management professionals coming together and making sure that we we work together on on specific you say so I wonder if I can ask you so the the industry sort of likes to market this notion of of DevOps applied to data and data op have you applied that type of mindset approach agile of continuous improvement is I'm trying to understand how much is marketing and how much actually applicable in the real world can you share well you know when I was reflecting on this before this interview I realized that our very first use case of data officers probably when we implemented information analyzer in our business unit simply because it was the first time that IT and business as well as data professionals came together to spec the use case and then we would literally in an agile fashion with a multidisciplinary team come together to make sure that we got the outcomes that we required I mean for you to to firstly get a data quality management paradigm where we moved from 6% quality at some point from our client data now we're sitting at 99 percent and that 1% literally is just the timing issue to get from from 6 to 99 you have to make sure that the entire value chain is engaged so our business partners will the fundamental determinant of the business rules apply in terms of what does quality mean what are the criteria of quality and then what we do is translate that into what we put in the catalog and ensure that the profiling rules that we run are against those business rules that were defined at first so you'd have upfront determination of the outcome with business and then the team would go into an agile cycle of maybe two-week sprints where we develop certain things have stand-ups come together and then the output would be - boarded in a prototype in a fashion where business then gets to go double check that out so that was the first iterate and I would say we've become much more mature at it and we've got many more use cases now and there's actually one that it's quite exciting that we we recently achieved over the end of of 2019 into the beginning of this year so what we did was they I'm worried about the sunlight I mean through the window you look creative to me like sunset in South Africa we've been on the we've been on CubeSat sometimes it's so bright we have to put on sunglasses but so the most recent one which was in in mates 2019 coming in too early this year we we had long kind of achieved the the compliance and regulatory burning platform issues and now we are in a place of I think opportunity and luxury where we can now find use cases that are pertinent to business execution and business productivity um the one that comes to mind is we're a hundred and fifty eight years old as an organization right so so this Bank was born before technology it was also born in the days of light no no no integration because every branch was a standalone entity you'd have these big ledges that transactions were documented in and I think once every six months or so these Ledger's would be taken by horse-drawn carriage to a central place to get go reconcile between branches and paper but the point is if that is your legacy the initial kind of ERP implementations would have been focused on process efficiency based on old ways of accounting for transactions and allocating information so it was not optimized for the 21st century our architecture had has had huge legacy burden on it and so going into a place where you can be agile with data is something that we constantly working toward so we get to a place where we have hundreds of branches across the country and all of them obviously telling to client servicing clients as usual and and not being able for any person needing sales teams or executional teams they were not able in a short space of time to see the impact of the tactic from a database fee from a reporting history and we were in a place where in some cases based on how our Ledger's roll up and the reconciliation between various systems and accounts work it would take you six weeks to verify whether your technique were effective or not because to actually see the revenue hitting our our general ledger and our balance sheet might take that long that is an ineffective way to operate in a such a competitive environment so what you had our frontline sales agents literally manually documenting the sales that they had made but not being able to verify whether that or not is bringing revenue until six weeks later so what we did then is we sat down and defined all the requirements were reporting perspective and the objective was moved from six weeks latency to 24 hours um and even 24 hours is not perfect our ideal would be that bite rows of day you're able to see what you've done for that day but that's the next the next epoch that will go through however um we literally had the frontline teams defining what they'd want to see in a dashboard the business teams defining what the business rules behind the quality and the definitions would be and then we had an entire I'm analytics team and the data management team working around sourcing the data optimising and curating it and making sure that the latency had done that's I think only our latest use case for data art um and now we're in a place where people can look at a dashboard it's a cubed self-service they can learn at any time I see the sales they've made which is very important right now at the time of covert nineteen from a form of productivity and executional competitiveness those are two great use cases of women lying so the first one you know going from data quality 6% the 99% I mean 6% is all you do is spend time arguing about the data bills profanity and then 99% you're there and you said it's just basically a timing issue use latency in the timing and then the second one is is instead of paving the cow path with an outdated you know ledger Barret data process week you've now compressed that down to 24 hours you want to get the end of day so you've built in the agility into your data pipeline I'm going to ask you then so when gdpr hit were you able to very quickly leverage this capability and and apply and then maybe other of compliance edik as well well actually you know what we just now was post TDP our us um and and we got GDP all right about three years ago but literally all we got right was reporting for risk and compliance purposes they use cases that we have now are really around business opportunity lists so the risk so we prioritize compliance report a long time it but we're able to do real-time reporting from a single transaction perspective I'm suspicious transactions etc I'm two hours in Bank and our governor so from that perspective that was what was prioritize in the beginning which was the initial crisis so what you found is an entire engine geared towards making sure that data quality was correct for reporting and regulatory purposes but really that is not the be-all and end-all of it and if that's all we did I believe we really would not have succeeded or could have stayed dead we succeeded because Dana monetization is actually the penis' t the leveraging of data for business opportunity is is actually then what tells you whether you've got the right culture or not you're just doing it to comply then it means the hearts and minds of the rest of the business still aren't in the data game I love this story because it's me it's nirvana for so many years we've been pouring money to mitigate risk and you have no choice do it you know the general council signs off on it the the CFO but grudgingly signs off on it but it's got to be done but for years decades we've been waiting to use these these risk initiatives to actually drive business value you know it kind of happened with enterprise data warehouse but it was too slow it was complicated and it certainly didn't happen with with email archiving that was just sort of a tech balk it sounds like you know we're at that point today and I want to ask you I mean like you know you we talking earlier about you know the crisis gonna perpetuated this this cultural shift and you took advantage of that so we're out who we the the mother nature dealt up a crisis like we've never seen before how do you see your data infrastructure your data pipeline your data ops what kind of opportunities do you see in front of you today as a result of ovid 19 well I mean because of of the quality of kind data that we had now we were able to very quickly respond to to pivot nineteen in in our context where the government put us on lockdown relatively early in in the curve or in the cycle of infection and what it meant is it brought a little bit of a shock to the economy because small businesses all of a sudden didn't have a source of revenue or potentially three to six weeks and based on the data quality work that we did before it was actually relatively easy to be agile enough to do the things that we did so within the first weekend of of lockdown in South Africa we were the first bank to proactively and automatically offer small businesses and student and students with loans on our books a instant three month payment holiday assuming they were in good standing and we did that upfront though it was actually an opt-out process rather than you had to fall in and arrange for that to happen and I don't believe we would have been able to do that if our data quality was not with um we have since made many more initiatives to try and keep the economy going to try and keep our clients in in a state of of liquidity and so you know data quality at that point and that Dharma is critical to knowing who you're talking to who needs what and in which solutions would best be fitted towards various segments I think the second component is um you know working from home now brings an entirely different normal right so so if we had not been able to provide productivity dashboard and and and sales and dashboards to to management and all all the users that require it we would not be able to then validate or say what our productivity levels are now that people are working from home I mean we still have essential services workers that physically go into work but a lot of our relationship bankers are operating from home and that face the baseline and the foundation that we said productivity packing for various methods being able to be reported on in a short space of time has been really beneficial the next opportunity for us is we've been really good at doing this for the normal operational and front line and type of workers but knowledge workers have also know not necessarily been big productivity reporters historically they kind of get an output then the output might be six weeks down the line um but in a place where teams now are not locate co-located and work needs to flow in an edge of passion we need to start using the same foundation and and and data pipeline that we've laid down as a foundation for the reporting of knowledge work and agile team type of metric so in terms of developing new functionality and solutions there's a flow in a multidisciplinary team and how do those solutions get architected in a way where data assists in the flow of information so solutions can be optimally developed well it sounds like you're able to map a metric but business lines care about you know into these dashboards you usually the sort of data mapping approach if you will which makes it much more relevant for the business as you said before they own the data that's got to be a huge business benefit just in terms of again we talked about cultural we talked about speed but but the business impact of being able to do that it has to be pretty substantial it really really is um and and the use cases really are endless because every department finds their own opportunity to utilize in terms of their also I think the accountability factor has has significantly increased because as the owner of a specific domain of data you know that you're not only accountable to yourself and your own operation but people downstream to you as a product and in an outcome depend on you to ensure that the quality of the data you produces is of a high nature so so curation of data is a very important thing and business is really starting to understand that so you know the cards Department knows that they are the owners of card data right and you know the vehicle asset Department knows that they are the owners of vehicle they are linked to a client profile and all of that creates an ecosystem around the plan I mean when you come to a bank you you don't want to be known as a number and you don't want to be known just for one product you want to be known across everything that you do with that with that organization but most banks are not structured that way they still are product houses and product systems on which your data reside and if those don't act in concert then we come across extremely schizophrenic as if we don't know our clients and so that's very very important stupid like I can go on for an hour talking about this topic but unfortunately we're we're out of time thank you so much for sharing your deep knowledge and your story it's really an inspiring one and congratulations on all your success and I guess I'll leave it with you know what's next you gave us you know a glimpse of some of the things you wanted to do pressing some of the the elapsed times and the time cycle but but where do you see this going in the next you know kind of mid term and longer term currently I mean obviously AI is is a big is a big opportunity for all organizations and and you don't get automation of anything right if the foundations are not in place so you believe that this is a great foundation for anything AI to be applied in terms of the use cases that we can find the second one is really providing an API economy where certain data product can be shared with third parties I think that probably where we want to take things as well we are really utilizing external third-party data sources I'm in our data quality management suite to ensure validity of client identity and and and residents and things of that nature but going forward because been picked and banks and other organizations are probably going to partner to to be more competitive going forward we need to be able to provide data product that can then be leveraged by external parties and vice-versa to be like thanks again great having you thank you very much Dave appreciate the opportunity thank you for watching everybody that we go we are digging in the data ops we've got practitioners we've got influencers we've got experts we're going in the crowd chat it's the crowd chat net flash data ops but keep it right there way back but more coverage this is Dave Volante for the cube [Music] you

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cute conversation everybody welcome back to the cube this is Dave Volante and you're watching a special presentation data ops enacted made possible by IBM you know what's what's happening is the innovation engine in the IT economy is really shifted used to be Moore's Law today it's applying machine intelligence and AI to data really scaling that and operationalizing that new knowledge the challenges that is it's not so easy to operationalize AI and infuse it into the data pipeline but what we're doing in this program is bringing in practitioners who have actually had a great deal of success in doing just that and I'm really excited to have it Kumal a the tumor lang Manali is here she's the executive head of data management or personal and business banking at Standard Bank of South Africa the tumor length thanks so much for coming in the cube thank you for having me Dave you're very welcome and first of all how you holding up with this this bovid situation how are things in Johannesburg um things in Johannesburg are fine and we've been on lockdown now I think it's day 33 if I'm not mistaken lost count and but we're really grateful for the swift action of government we only I mean we have less than 4,000 places in the country and infection rate is is really slow so we've really I think been able to flatten the curve and we're grateful for being able to be protected in this way so we're all working from home or learning the new normal and we're all in this together that's great to hear why don't you tell us a little bit about your your role you're a data person we're really going to get in with here with us you know how you spend your time okay well I hit up a date operations function in a data management function which really is the foundational part of the data value chain that then allows other parts of the organization to monetize data and leverage it as as the use cases apply we monetize it ourselves as well but really we're an enterprise wide organization that ensures that data quality is managed data is governed that we have the effective practices applied to the entire lineage of the data ownership and curation is in place and everything else from a regulatory as well as opportunity perspective then is able to be leveraged upon so historically you know data has been viewed as sort of this expense it's it's big it's growing it needs to be managed deleted after a certain amount of time and then you know ten years ago the Big Data move data became an asset you had a lot of shadow ID people going off and doing things that maybe didn't comply to the corporate ethics probably drove here here you're a part of the organization crazy but talk about that how what has changed but they in the last you know five years or so just in terms of how people approach data oh I mean you know the story I tell my colleague who are all bankers obviously is the fact that um the banker in 1989 had to mainly just know debits credit and be able to look someone in the eye and know whether or not they'd be a credit risk or not you know if we lend you money and you pay it back um the the banker of the late 90s had to then contend with the emergence of technologies that made their lives easier and allowed for automation and processes to run much more smoothly um in the early two-thousands I would say that digitization was a big focus and in fact my previous role was head of digital banking and at the time we thought digital was the panacea it is the be-all and end-all is the thing that's gonna make organizations edit lo and behold we realized that once you've gotten all your digital platforms ready they are just the plate or the pipe and nothing is flowing through it and there's no food on the plate if data is not the main so really um it's always been an acid I think organizations just never consciously knew that data was there okay so so it sounds like once you've made that sort of initial digital transformation you really had to work it and what we're hearing from a lot of practitioners like toughest challenges related to that involve different parts of the organization different skill sets of challenges and sort of getting everybody to work together on the same page it's better but maybe you could take us back to sort of when you started on this initiative around data ops what was that like what were some of the challenges that you faced and how'd you get through them first and foremost Dave organizations used to believe that data was I t's problem and that's probably why you you then saw the emergence of things like shadow IP but when you really acknowledge that data is and si just like money is an asset then you you have to then take accountability for it just the same way as you would any other asset in the organization and you will not add the a its management to a separate function that's not code to the business and oftentimes IT are seen as a support for an enabling but not quite the main show in most organizations right so what we we then did is first emphasize that data is a business capability a business function it presides in business next to product management next to marketing makes to everything else that the business needs for data management also has to be for to every role in every function to different degrees and varying bearing events and when you take accountability as an owner of a business unit you also take accountability for the data in the systems that support the business unit for us that was the first picture um and convincing my colleagues that data was their problem and not something that we had to worry about and they just kind of leave us to - it was was also a journey but that was kind of the first step in - in terms of getting the data operations journey going um you had to first acknowledge please carry on no you just had to first acknowledge that it's something you must take accountability of as a banker not just need to a different part of the organization that's a real cultural mindset you know in the game of rock-paper-scissors you know culture kinda beats everything doesn't it it's almost like a yep a trump card and so so the businesses embrace that but but what did you do to support that is there has to be trust in the data that it has to be a timeliness and so maybe you could pick us through how you achieve those objectives and maybe some other objectives that business the man so the one thing I didn't mention Davis that obviously they didn't embrace it in the beginning it wasn't a it wasn't there oh yeah that make sense they do that type of conversation um what what he had was a few very strategic people with the right mindset that I could partner with that understood the case for data management and while we had that as as an in we developed a framework for a fully matured data operations capability in the organization and what that would look like in a target date scenario and then what you do is you wait for a good crisis so we had a little bit of a challenge in that our local regulator found us a little bit wanting in terms of our data quality and from that perspective it then brought the case for data quality management to the whole so now there's a burning platform you have an appetite for people to partner with you and say okay we need this to comply to help us out and when they start seeing their opt-in action do they stick then buy into into the concepts so sometimes you need to just wait for a good price and leverage it and only do that which the organization will appreciate at that time you don't have to go Big Bang data quality management was the use case at the time five years ago so we focused all our energy on that and after that it gave us leeway and license really bring to maturity or the other capabilities of the business might not well understand as well so when that crisis hit of thinking about people process in technology you probably had to turn some knobs in each of those areas can you talk about that so from a technology perspective that that when we partnered with with IBM to implement information analyzer for us in terms of making sure that then we could profile the data effectively what was important for us is to to make strides in terms of showing the organization progress but also being able to give them access to self-service tools that will give them insight into their data from a technology perspective that was kind of I think that the genesis of of us implementing and the IBM suite in earnest from a data management perspective people wise we really then um also began a data stewardship journey in which we implemented business unit stewards of data I don't like using the word steward because in my organization it's taken lightly it's almost like a part-time occupation so we converted them we call them data managers and and the analogy I would give is every department with a pl any department worth its salt has a FD or financial director and if money is important to you you have somebody helping you take accountability and execute on your responsibilities and managing that that money so if data is equally important as an asset you will have a leader a manager helping you execute on your data ownership accountability and that was the people journey so firstly I had kind of soldiers planted in each department which were data managers that would then continue building the culture maturing the data practices as as applicable to each business unit use cases so what was important is that every manager in every business unit to the Data Manager focus their energy on making that business unit happy by ensuring that their data was of the right compliance level and the right quality the right best practices from a process and management perspective and was governed through and then in terms of process really it's about spreading through the entire ecosystem data management as a practice and can be quite lonely in the sense that unless the core business of an organization is managing data they worried about doing what they do to make money and most people in most business units will be the only unicorn relative to everybody else who does what they do and so for us it was important to have a community of practice a process where all the data managers across business as well as the technology parts and the specialists who were data management professionals coming together and making sure that we we work together on on specific use so I wonder if I can ask you so the the industry sort of likes to market this notion of of DevOps applied to data and data op have you applied that type of mindset approach agile of continuous improvement is I'm trying to understand how much is marketing and how much actually applicable in the real world can you share well you know when I was reflecting on this before this interview I realized that our very first use case of data officers probably when we implemented information analyzer in our business unit simply because it was the first time that IT and business as well as data professionals came together to spec the use case and then we would literally in an agile fashion with a multidisciplinary team come together to make sure that we got the outcomes that we required I mean for you to to firstly get a data quality management paradigm where we moved from 6% quality at some point from our client data now we're sitting at 99 percent and that 1% literally is just the timing issue to get from from 6 to 99 you have to make sure that the entire value chain is engaged so our business partners were the fundamental determinant of the business rules apply in terms of what does quality mean what are the criteria of quality and then what we do is translate that into what we put in the catalog and ensure that the profiling rules that we run are against those business rules that were defined at first so you'd have upfront determination of the outcome with business and then the team would go into an agile cycle of maybe two-week sprints where we develop certain things have stand-ups come together and then the output would be - boarded in a prototype in a fashion where business then gets to go double check that out so that was the first iterate and I would say we've become much more mature at it and we've got many more use cases now and there's actually one that it's quite exciting that we we recently achieved over the end of 2019 into the beginning of this year so what we did was they've am worried about the sunlight coming through the window you look crazy to me like the sunset in South Africa we've been on the we've been on CubeSat sometimes it's so bright we have to put on sunglasses but so the most recent one which was in in late 2019 coming in too early this year we we had long kind of achieved the the compliance and the regulatory burning platform issues and now we are in a place of I think opportunity and luxury where we can now find use cases that are pertinent to business execution and business productivity the one that comes to mind is where a hundred and fifty eight years old as an organization right so so this Bank was born before technology it was also born in the days of light no no no integration because every branch was a standalone entity you'd have these big ledges that transactions were were documented in and I think once every six months or so these Ledger's would be taken by horse-drawn carriage to a central place to give go reconcile between branches and paper but the point is if that is your legacy the initial kind of ERP implementations would have been focused on process efficiency based on old ways of accounting for transactions and allocating information so it was not optimized for the 21st century our architecture had has had huge legacy burden on it and so going into a place where you can be agile with data is something that we're constantly working toward so we get to a place where we have hundreds of branches across the country and all of them obviously telling to client servicing clients as usual and and not being able for any person needing sales teams or executional teams they were not able in a short space of time to see the impact of the tactic from a data perspective um we were in a place where in some cases based on how our Ledger's roll up in the reconciliation between various systems and accounts work it would take you six weeks to verify whether your technique were effective or not because to actually see the revenue hitting our our general ledger and our balance sheet might take that long that is an ineffective way to operate in a such a competitive environment so what you had our frontline sales agents literally manually documenting the sales that they had made but not being able to verify whether that or not is bringing revenue until six weeks later so what we did then is we sat down and defined all the requirements from a reporting perspective and the objective was moved from six weeks latency to 24 hours um and even 24 hours is not perfect our ideal would be that bite rows of day you're able to see what you've done for that day but that's the next the next epoch that will go through however um we literally had the frontline teams defining what they'd want to see in a dashboard the business teams defining what the business rules behind the quality and the definitions would be and then we had an entire I'm analytics team and the data management team working around sourcing the data optimising and curating it and making sure that the latency had done that's I think only our latest use case for data art um and now we're in a place where people can look at a dashboard it's a cubed self-service they can Logan at any time I see the sales they've made which is very important right now and the time of overt nineteen from a from a productivity and executional competitiveness listing those are two great use cases of cooling so the first one you know going from data quality 6% the 99% I mean 6% is all you do is spend time arguing about the data stills probity and then 99% you're there and you said it's just basically a timing issue use latency in the timing and then the second one is is instead of paving the cow path with an outdated you know ledger Barratt data process week you've now compressed that down to 24 hours you want to get the end of day so you've built in the agility into your data pipeline I'm gonna ask you then so when GDP are hit were you able to very quickly leverage this capability and and imply and then maybe other of compliance edik as well Oh actually you know what we just now was post gdpr us um and and we got GDP all right about three years ago but literally all we got right was reporting for risk and compliance purposes the use cases that we have now are really around business opportunity lists so the risk so we prioritize compliance report a long time ago were able to do real-time reporting of a single transaction perspective I'm suspicious transactions etc I'm two hours in Bank and our governor so from that perspective that was what was prioritize in the beginning which was the initial crisis so what you found is an entire engine geared towards making sure that data quality was correct for reporting and regulatory purposes but really that is not the be-all and end-all of it and if that's all we did I believe we really would not have succeeded or could have stayed dead we succeeded because data monetization is actually the penisy the leveraging of data for business opportunity is is actually then what tells you whether you've got the right culture or not you're just doing it to comply then it means the hearts and minds of the rest of the business still aren't in the data game I love this story because it's me it's nirvana for so many years we've been pouring money to mitigate risk and you have no choice do it you know the general council signs off on it the the CFO but grudgingly signs off on it but it's got to be done but for years decades we've been waiting to use these these risk initiatives to actually drive business value you know kind of happened with enterprise data warehouse but it was too slow it was complicated it certainly didn't happen with with email archiving that was just sort of a tech balk it sounds like you know we're at that point today and I want to ask you to me like you know you we talking earlier about you know the crisis gonna perpetuated this this cultural shift and you took advantage of that so we're on the mother nature dealt up a crisis like we've never seen before how do you see your data infrastructure your data pipeline your data ops what kind of opportunities do you see in front of you today as a result of mobit nineteen well I mean because of of the quality of mind data that we had now we were able to very quickly respond to to pivot nineteen in in our context where the government and put us on lockdown relatively early in in the curve in disciple of infection and what it meant is it brought a little bit of a shock to the economy because small businesses all of a sudden didn't have a source of revenue for potentially three to six weeks and based on the data quality work that we did before it was actually relatively easy to be agile enough to do the things that we did so within the first weekend of of lockdown in South Africa we were the first bank to proactively and automatically offer small businesses and student um students with loans on our books a instant preman payment holiday assuming they were in good standing and we did that upfront though it was actually an up out process rather than you had to fall in and arrange for that to happen and I don't believe we would have been able to do that if our data quality was not with um we have since made many more initiatives to try and keep the economy going to try and keep our clients in in a state of of liquidity and so you know data quality at that point and that Dharma is critical to knowing who you're talking to who needs what and in which solutions would best be fitted towards various segments I think the second component is um you know working from home now brings an entirely different normal right so so if we have not been able to provide productivity dashboard and and sales and dashboards to to management and all all the users that require it we would not be able to then validate or say what our productivity levels are and other people are working from home I mean we still have essential services workers that physically go into work but a lot of our relationship bankers are operating from home and that face the baseline and the foundation that we said productivity packing for various metric being able to be reported on in a short space of time has been really beneficial the next opportunity for us is we've been really good at doing this for the normal operational and front line and type of workers but knowledge workers have also know not necessarily been big productivity reporters historically they kind of get an output then the output might be six weeks down the line um but in a place where teams now are not locate co-located and work needs to flow in an edge of passion we need to start using the same foundation and and and data pipeline that we've laid down as a foundation for the reporting of knowledge work and agile team type of metric so in terms of developing new functionality and solutions there's a flow in a multidisciplinary team and how do those solutions get architected in a way where data assists in the flow of information so solutions can be optimally developed well it sounds like you're able to map a metric the business lines care about you know into these dashboards you using the sort of data mapping approach if you will which makes it much more relevant for the business as you said before they own the data that's got to be a huge business benefit just in terms of again we talked about cultural we talked about speed but but the business impact of being able to do that it has to be pretty substantial it really really is um and and the use cases really are endless because every department finds their own opportunity to utilize in terms of their also I think the accountability factor has has significantly increased because as the owner of a specific domain of data you know that you're not only accountable to yourself and your own operation but people downstream to you as a product and and an outcome depend on you to ensure that the quality of the data you produces is of a high nature so so curation of data is a very important thing and business is really starting to understand that so you know the cards Department knows that they are the owners of card data right and you know the vehicle asset Department knows that they are the owners of vehicle they are linked to a client profile and all of that creates an ecosystem around the plan I mean when you come to a bank you you don't want to be known as a number and you don't want to be known just for one product you want to be known across everything that you do with that with that organization but most banks are not structured that way they still are product houses and product systems on which your data reside and if those don't act in concert then we come across extremely schizophrenic as if we don't know our clients and so that's very very important to me like I could go on for an hour talking about this topic but unfortunately we're out of time thank you so much for sharing your deep knowledge and your story it's really an inspiring one and congratulations on all your success and I guess I'll leave it with you know what's next you gave us you know a glimpse of some of the things you wanted to do pressing some of the the elapsed times and the time cycle but but where do you see this going in the next you know kind of mid term and longer term currently I mean obviously AI is is a big is a big opportunity for all organizations and and you don't get automation of anything right if the foundations are not in place so you believe that this is a great foundation for anything AI to to be applied in terms of the use cases that we can find the second one is really um providing an API economy where certain data product can be shared with third parties I think that probably where we want to take things as well we are ready utilizing external third-party data sources I'm in our data quality management suite to ensure validity of client identity and and and residents and things of that nature but going forward because been picked and banks and other organizations are probably going to partner to to be more competitive going forward we need to be able to provide data product that can then be leveraged by external parties and vice-versa the trooper like thanks again great having you thank you very much Dave appreciate the opportunity and thank you for watching everybody that we go we are digging in the data offs we've got practitioners we've got influencers we've got experts we're going in the crowd chat it's the crowd chat dot net flash data ops but keep it right there way back but more coverage this is Dave Volante for the cube [Music]

>> Announcer: Live, from San Francisco, celebrating 10 years of high-tech coverage, it's theCUBE. Covering VMworld 2019. Bought to you by VMware and its ecosystem partners. >> Welcome back to theCUBE's live coverage here at Vmworld 2019, San Francisco, California. We're in Moscone North Lobby. I'm John Furrier, with my co-host Dave Vellante. Dave, 10 years of covering VMworld. This is our 10th year. Pat, you've been on every year since 2010. We have photos. >> That's sort of scary. >> You had a goatee back then. (Pat laughs) We've heard your rap going way back. Welcome back, good to see you. >> Oh man, scary. You guys probably got some dirt on me. Boy, I better be careful. >> John: Pat Gelsinger, the CEO of VMware on theCUBE. Thanks for coming on this evening. >> Oh, always a pleasure to be on with you guys, love it. >> Don't end up as driftwood. Security is a do over. We're going to talk about all that. >> We're going to spend the entire segment just talking about Pat Gelsinger's predictions. We'll recycle some of them, but let's get into the core news here, VMworld. You've done such an amazing job. We've given you a lot of props on theCUBE over the years, but still continuing, even in the market climate that's swinging up and down right now, VMware still producing great results. The team is executing. Their transition since October 2016 when you kind of made that move, cloud is it, clear vision, a lot's been falling into place. Pivotal has dropped on your lap, and you got the engineering stuff coming out on top of vSphere and a bunch of other things. Great stuff, I mean, you must be geeking out. >> Well, thank you. At the US gymnastics finals, Simone Biles did a triple double. First time ever in competition. And I think of our last week as a triple double, right, two major acquisitions, an earnings call, and now VMworld and all the announcements as part of it. It's like wow. >> John: You stick the landing, you stick the landing. >> That's right, we did yesterday morning. We stuck the landing and Ray did that today as well. So super proud of the team in bringing these across the line. And I think certainly meeting with many of the customers and the partners here everybody's sort of going wow. And I was excited about VMware before I got here. Now I'm just euphoric, and it's really-- >> I'm told Ray did an exceptional job. I'm going to talk to him later today on theCUBE. Today in his keynote he was great. He repeated the messages over and over again, but he nailed the tech piece. I got to ask you, as the engine of VMware is continuing to be put together and expand it's like a new turbo engine gets pulled in here. There's a lot of really good engineering going on. What are you most excited about? How would you describe all the action going on? If someone says, "Pat, what's the underlying engine here?" What's being built? What's going to be the outcome of all this? >> Well, I think it sort of boils down to, right, these two phrases that you heard from me yesterday. We're going to engineer for good, the tech for good stuff, we're going to do good engineering. And doing both of those is just okay. And you sort of say, "Hmm, we got vSAN," right? We're not being able to optimize the performance because big blocks, little blocks, latency, buffer size, all this other kind of stuff, so now we're doing Magna, right? And when you see that demonstration there, it's like we're going to do it automatically for you to be a fine-grain optimizing your storage. Wow, that's pretty cool, and it's intelligence, right? It's sort of saying, "Wow, this is really cool." So let's go automatically produce an understanding of the underlying network, understand what's going on, give you the rules that we recommend, and allow you to simulate them, which is super cool, right? Within minutes, we will give the network engineer more understanding of what's really going on in our applications, and then allow them to see it in real time and then apply it. Every one of these, and it's just 10 or 15 tremendous engineers who are doing these little innovations that are fundamentally changing the industries that they're in, in addition to the big stuff. It's just thrilling. >> Dave did a survey before coming into VMworld with customers with a panel. 41% said they're not going to change their spending habits with VMware so creating the-- >> Dave: They said they're going to increase-- >> Increase. >> In the second half, only 7% said they're going to decrease. >> So great customer loyalty, and remember, VMware's moving so fast and transit. Customers aren't moving as fast as you guys are, and you've talked about that before. What are you hearing from customers as they look at it and say, "Wow, is it too much new stuff?" 'Cause they want to continue to operate, but they also want to enable the developer piece. Because remember, DevOps means dev and ops. You guys got the ops piece down. You're adding stuff to it. There's always concerns there making sure it's smooth and you guys work on that. The dev piece becomes super critical. That's where Amazon really shined with public cloud. So hybrid cloud's here. What is the DevOps equation for hybrid? I mean Kubernetes is a good start. Where do you see it going? >> Yeah, and that's really the center. To me, that is the most important news of VMworld this year is the entire Tanzu message, the coming together of Pivotal, the coming together of Pacific, coming together with Mission Control, so really leveraging VMware in the run layer, leveraging Pivotal in the build, and Heptio in the manage, right, and those coming together into Tanzu. I think that's the most important thing that we're doing. And I think for operators, which is really the center of our audience here at VMworld, they've always struggled with those crazy developers. They do this cool new stuff. It's not operational, it's not secure. But in bringing those together, the magic formula for that is Kubernetes. And that's why we're making these big bets. The move with Pivotal, obviously the Heptio guys, I mean Joe Beda and Craig, they're just the rock stars of that community because they really are solving in an industry-consensual standard way. That's really the magic of Kubernetes. This ain't a VMware thing, this is an industry thing. >> Is Kubernetes the technology enabler? I mean, TCP/IP was that in the old networking days. It enabled a lot of shifts in the industry. You were part of that wave. Is Kubernetes that disruptive enabler? >> Yeah, I really see it as one of those key transition points in the industry. And as I sort of joked, if my name was Scott, and we were 20 years ago, I'd be banging the table calling it Java. And Java defined enterprise software development for two decades. By the way, Scott's my neighbor. He's down the hill, so I look down on Mr. McNealy. I always sort of like that. (everybody laughs) >> He looks up to you. >> But it changed how people did enterprise software development for the last two decades. And Kubernetes has that same kind of transformative effect, but maybe even more important, it's not just development but also operations. And I think that's what we're uniquely bringing together with Project Pacific, really being able to bridge those two worlds together. And if we deliver on this, I think the next decade or two will be the center of innovation for us, how we bridge those two roles together and really give developers what they need and make it operator friendly out of the box, cross the history to the future. This is pretty powerful. >> So that does lead to the big question. You just mentioned developers. And when you look out the VMworld audience, it's not comprised of huge developers. I know you're thinking about this, so what's your plan to attract those developers? You're giving them platform now, and the technologies. but those builders, what are you going to do for them? Is it build community, more events, more training? What's the plan there? >> Yeah, and I'd say I think about it in a couple of different context. One is if we were here six years ago, and you would have asked me about open source, right? I mean, VMware's reputation in the open source community wasn't good, right? We hired Dirk, we started to build momentum, make contributions. One of the litmus tests for Joe and Craig on Heptio, 'cause remember, a lot of people could have bought Heptio. Because some was who's going to be the buyer, but also will they be a willing seller. And their litmus test was are you really serious about open source, right? Are you really committed to the open source, Kubernetes tree and development and cloud-native computing foundation? Are you really there? 'Cause they were also looking do I want to be bought by you? Do I want to be part of the VMware family? And we passed the test. That's why Heptio's part of the team. Clearly, this has been central to Pivotal and their views. So we have to be open-source credible. We also have to be developer credible, and those two are tightly linked. And that's why we noted on stage Pivotal, particularly the Java community, is three-plus million developers. Bitnami is two million-ish developers. We now have high volume connections to the developer community, and you're going to see us show up in dramatically more profound ways at places like Kubicon and SpringOne is coming up, just start to be in the developer spaces. And ultimately, you got to do stuff that they care about. At the end of the day, winning developers has nothing to do with great marketing, even though that's important. You have to do great code, right, and bring them value to their development assignments. And we think with the assets that we're lining up, that's why we did Pivotal, Bitnami, Heptio, some of our organic things, Dirk's leadership here. I believe that a year or two from now VMware could be seen as the most developer and open source enterprise company in the industry. And that's the goal that I'm on. >> Well, I have an idea for you. Allocate 1,000 engineers to open source and start having them build new applications, new workloads, give it away to the open source community, and then sell your products and services to them. That would get you in fast. >> Well, by the way, we now have hundreds of engineers who are committed to open source, who their full-time job is open source contributions. So I'm not to 1,000 yet, but I'm now several hundred that their day job, night job, weekend job is open source contribution. So we're becoming very credible, and as you heard me say in the keynote, we are now top three contributor to Kubernetes. This is big, and some areas like the networking area we're clearly the leader in a number of the key networking open source technologies, and you'll see us do more of those kind of projects. >> One of the things you mentioned, I mean you mentioned about open source six years ago, you might have rolled your eyes, or you might not have had an opinion on it 'cause the timing of where VMware was. But one thing you've been banging the drum on since 2012 is hybrid cloud. And so you see certain things early. You see those waves. That's what you're known for, in my opinion. You're really good about it. You see blockchain as a great wave, but as a headline I'm reading on Fortune it says, "VMware CEO Pat Gelsinger, "Bitcoin is bad for humanity." >> Sold all my bitcoin (laughs). >> Okay, so now are you implying then, and blockchain is a lot of open source components there. It's evolving, you've a lot of blockchain projects. So is that an indictment on the unregulated currency market or is it the underlying infrastructure? And are you excited about blockchain as an underlying? Is it one of those hybrid cloud moments for you, or is it more of we'll see how it develops? What's your thoughts? And explain the bitcoin comment too. >> Yeah, the idea of distributed ledger technology, immutable distributed trust, I've said I think of that and blockchain as the underlying technology as almost like public private key encryption, right? If we go back 40 years before RSA or Vashumi and Ari, it's that important. This is breakthrough, innovative technology in how you do distributed secure trust. That's powerful, so we are huge believers, strongly committed to blockchain and distributed leverager technology. Now, why do I make my comments like I do on bitcoin? So bitcoin, as it's implemented, and implementation of blockchain and distributed ledger, I assert is bad. It's bad for two reasons. One is it's an environmental crisis, right? A single ledger, if you and I transacted a penny, right, I would consume enough energy to power your house for half a day. I mean, it's incredible, and I mean, that's why you have these crazy bitfarms being built and people finding GPUs. >> So you think from a sustainability standpoint. >> Absolutely. >> That's where you came from. >> Climate sustainability, right, this is a terrible implementation of blockchain. Secondly, the way it's also done as well in this totally unregulated environment, almost all of its uses are for illicit and criminal purposes. That's who's trading in bitcoin as well. So its purpose is almost all illicit, right, and it's environmental crisis. I say bad. Now, I'm not saying that blockchain is bad. I think this is revolutionizing. >> I want to make sure we clarify that because obviously unregulated outside the United States has been a big problem. We see it in the SEC crackdown, and results are-- >> Studies have shown over 95% of the use of bitcoin is criminal, so say bad. Let's go make it good, and that's what I mean these two phrases, do good engineering, and engineer for good. How do we make blockchain, and this is part of the reason, we had just announced on Sunday a partnership with Australian Stock Exchange and Data Asset, that they're leveraging the VMware distributed ledger technology, right, as part of their go-forward strategy for the stock exchange of Australia. Well, that's good, right? We're making it suitable for enterprises, meeting the regulatory requirements and-- >> John: Are you happy with the progress of where the blockchain is for you guys? >> Absolutely, and we're order-plus magnitude better in terms of performance and energy consumption. So yeah, and we're just getting started. >> And it's consensus-based, which is great. A quick question for you on multicloud. So hybrid cloud you said in 2012, I challenged you on it, and you've been banging the drum since 2012. It's a couple years into it, and hybrid cloud is pretty much standard. People see it, recognize it as the cloud 2.0. Multicloud is all the buzz and all the rage. I hear it everywhere. What does it actually mean is a different debate, so I want to get your thoughts on defining what multicloud is and is it going to have that same gestation period of the same kind of years? 'Cause if it's seven years to get or six years to get hybrid cloud mainstream, is multicloud going to have a similar trajectory? >> Yeah, so let's try to be very crisp with the definition. Multicloud is simply that. Customers using multiple clouds for different business purposes. And what we said is is that we're going to help them manage. That's the center point of cloud health, right? Help customers manage, cost optimize, secure in a multicloud environment where the underlying infrastructure is dissimilar, not compatible, right? And in that sense, you sort of say you can have consistent operations if we do our job well with cloud health, but you're not going to have consistent infrastructure, meaning I can't VMotion between these things, I can't have higher these things. So that's the multicloud. Now a proper subset of multicloud is hybrid cloud. And hybrid cloud is where you have both consistent operations and consistent infrastructure. And that's when we can do things like you saw on the demo today, right? We're running a VMware stack on Azure. We're moving Azure running workloads in real time, right, without stunning them, pausing them, to an Amazon VMC instead of moving workloads from Amazon VMC onto an Azure instance. That's the hybrid cloud, and that's the power at work, from private data centers to multiple different targets in the public cloud where you can be optimizing the location of work nodes based on the proper business requirements. And that might be governance. That might be performance. It might be latency. It might be the time of the day of the week when you have capacity available, right? And that's really what we're saying. Consistent operations and consistent infrastructure, proper subset of multicloud. >> I have a question on something you said yesterday. You said, "Strength lies in differences not similarities." True, I buy that. There's a number of difference between you and your preferred public cloud partner. AWS doesn't use the term multicloud. They say you shouldn't say security's not broken. And there are a number. You want to be the best infrastructure and developer software company. They want to be that platform. They want to be the security cloud, on and on and on. So I see this impending collision course, maybe not tomorrow, but what are your thoughts on the differences and the good or bad that does for the industry? >> Yeah, well, we appreciate Amazon, the investments that we're making. We've both bet big with each other, and they've been a great partner. And in fact, I'm going to talk to Andy before the end of the week, update some of the announcements and some of the things. Great partner, we have regular cadence of our activities with each other. And as we said, they're our preferred public cloud partner. And with it, it's preferred in two senses. It's a go to market and how we position that, but it's also an R&D statement, right? This is where we're doing a lot of core engineering, and that will flow into private cloud embodiments, flow into our other public cloud and our cloud-verified partners. But that's the point of the arrow in terms of the innovations, the go to market, and the R&D aspects of the partnership. And I expect we're going to be here five years from now and we're going to have this conversation, and I'm going to answer it exactly the same way. >> That'll be our CUBE's 15th anniversary, and so we'll be excited for that. It's our 10 year, so I want to last question put you on the spot, looking back over 10 years, pick the moments that you think were key inflection points. What were key notable good things that happened, bad things that happened, or things that didn't happen, right? And then going forward 10 years, you laid out a few of them with Kubernetes. Just past 10 years, could be CUBE memories, but in VMware's world, you were at EMC first, then became CEO, a lot's changed. Paul Maritz laid out the original vision. And where we are today, what's your key moments? >> Yeah, well, I think if you go all the way back, obviously, hey when the first WSX, right, people could run Linux and Windows on their client. Wow, right? The first VMotion, right, oh my gosh, and that sort of ushered in ESX. Obviously the transition from Diane to Paul, the public offering, boy, that was a pretty tumultuous time. And from Paul to Pat was very much we lay it out pretty much this any cloud vision, and that model, it was formative and we're sort of bringing it together. It was get rid of some assets, bring together, so sort of that transition was challenging for the company. But then we've started to sort of systematically say build from the core. What do we have? What do we need as we started to build these layers in the concentric circles? The Nicira acquisition, boom, that was the shot that changed the world of networking. And obviously, that doesn't change quickly, but we have a multibillion dollar networking business, Avi Networks, VeloCloud, we're building that set of assets. >> Software-defined data centers. The Core engine, that was a key point. >> Dave: That was a total game changer. >> You cannot build a software-defined data center if you don't address the networking. It's just that simple, and that's why I was so passionate about that. Obviously, the HCI move with vSAN. Joe Tucci was so pissed off at me, right? (everybody laugh) What are you doing? It's operative. It's part of the ingredients of the data center, Joe. I got to do it, wait. >> John: Just being a software company. >> Yeah, yeah, right, so that was a pretty tense moment. The period of the Dell EMC merger, a tough period, right, as well, and just where the company's going to go. And within a week, right, I'm going to be fired. I'm going to be spun out, right? I'm going to be the new CEO of Dell, right? I mean, it was going to be HP. >> John: All the rumor. >> Stock is 40, obviously the Amazon moment, when we did that partnership. vCloud Air, hey, we had the right idea. We didn't implement it properly, and then we did it right with the Amazon partnership, and that just changed the cloud industry. And I think we're going to look at today, this week, and the moves with Heptio, Kubernetes, Pivotal, those pieces coming together, and to this audience Project Pacific, right, it's just like okay, wow, everyone of them will become Kubernetes enabled. 20,000 selfies with Joe Beda, right, have now been ushered because it is that game changing, we believe. This is the biggest free architecture of the Core platform in a decade, so. >> My favorite quote from you was if you're not out on that next wave, you're driftwood. You said that on the QA, I forget which year it was. >> And mine's security's the do over. (Pat laughs) >> You're doing it over, you're doing it, Mr. Gelsinger. >> Next 10 years, what's the big wave everyone should be on? What's the wave that you identify? You've seen many waves, you've created waves, you've been part of waves. What's the wave for the next 10 years that people should pay attention to, that they need to be on? >> Well, if they're not on the networking wave, get on it, right? They got to be on this multicloud hybrid wave. Could it be louder? The Kubernetes one is the one, right? That's the one I'm going to put at the front of the list. And this move in security, I am just passionate about this, and as I've said to my team, if this is the last thing I do in my career is I want to change security. We just not are satisfying our customers. They shouldn't put more stuff on our platforms if they can't-- >> John: National defense issues, huge problems. >> It was just terrible. And I said if it kills me, right, I'm going to get this done. And they says, "It might kill you, Pat." >> Mount Kilimanjaro right there. Pat, thank you for all your commentary, and great look back 10 years. You've been one of our favorite guests coming on theCUBE, bringing A game, you're bringing the tech chops, the historian aspect, also you're running one of the most valuable open source companies in the cloud. (Pat and John laugh) >> Love you guys, thanks so much. >> Thanks, Pat. Pat Gelsinger here inside theCUBE. Our 10th year, VM's looking good off the tee right now, middle of the fairway, as they say, for the next 10 years. I'm John Furrier, Dave Vallante, thanks for watching. (upbeat music)

from the Hard Rock Hotel in Las Vegas it's the cube covering no joke on 2018 brought to you by osho everyone welcome back to our live coverage here in Las Vegas for Osho Khan's first industry security conference dedicated to security in the blockchain it's presented by ho show and also the industry it's an industry conference it's not necessarily a host show cause I'm John Ford's the cue for our coverage our next guest is Kenzie Crone and vice president of business development prime trust welcome to the cube thanks for joining us thanks for having me here so crowdsourcing and crowdfunding all this has been a big part of it I mean terrorists are funding through Bitcoin you've got all kinds of things going on in entrepreneurial spaces so it's clearly the money's flowing with with with crypto what do you guys do if we're getting into some of the things that we want to talk about what is prime trust to take a minute to explain your business business model value proposition absolutely so prime trust is a trust company so it's a regulated financial institution that holds funds between transactions between businesses you could also use prime trust to created a trust account for an individual as well so what our value is in this industry is that we hold crypto assets which very few qualified custodians like us exist to do that so that's a really important part of bringing in institutional funding because institutions are looking for qualified custodians as a regulated place to keep funds and they want to get into crypto so it's a it's a very important part of the puzzle so custody and custodial service has been a big topic here at O joke on controversial on the keynotes as well because you know the purists will say hey like Andreas why don't we need custody if it's working it's just it's the same old guard with new faces new business cards it's not really revolutionary and that's on one answer on the other inspection is there's so much growth in activity we've got a trusted partners to actually help us manage the risk and do these things so you have again two spectrums what's the story what should people understand about these two dynamics well what I think yeah what I think the key note you're talking about the the idea is we are just trading one type of banker for another type of banker right that's happening anyway so you are you're trading one type of financial system for another type of financial system the question is what does that look like and how can we be secure and safe in that space right personally I'm a big fan of anything that requires some kind of a license right and it's not because I think it's really fun to go through the bureaucratic process of getting a license or filling out paperwork but it's really because that once you have a license that license can be taken away from you if you misbehave right and that's really important so if you're following the laws that are set forth that are designed to protect people and then you break those laws then you're not you're not allowed to do that anymore right so that's what you get out of having regulation involved in this space is its protection and it's making sure that they're really by the way the regulation is happening anyway so that's another the regulation is happening anyway and that's why these very smart people who are managing billions of dollars are looking for that they're not saying oh cool you have a website that with technology that I don't understand you're telling me that you can safely hold something but there's no other protection there there's no liability you could just mount GOx me right and so there's got to be a way to get some sort of some sort of regulation in there and I know there's a lot of opinions in the space and obviously I'm very much on the side of regulation yeah and it also made some balance within the day those are polarized positions but I think the industry recognizes growth by recognizing the domicile problem of companies and governments so the question is you know really than a licenses legitimacy is people want legitimacy trust and growth yes at the same time but the other side says is hey you know who are those people making the laws so who's taking what away so again this is the ecosystem will solve these problems in my opinion and I believe that you know as much as I love the purist view and I think this architectural technical things that make that happen the end of the day is the self-governance of the community really is is what me happen here and so that's where the growth comes in because if real money is coming in to the sector you got to have parties that are trusted it's my opinion all right so what do you think about the conference here what's your take away so far I'll see its kind of diverse background you got you know people walking around with colorful costumes too you know buttoned up bankers and FBI agents and NSA agency folks yes we're in a really funny time in this space I think because you still have yet the Bitcoin garb and the like you know the flashing glasses and and then you've got people who spent 20 years on Wall Street and now they're in the space so I've seen that actually a lot lately in the last year at these conferences and it's very interesting I love when both sides can come in with an open mind to the other because you think there's something to be learned on both sides absolutely it's so for the people who have been in the traditional regulated space they are getting all this inspiration and the possibility of doing things differently the system that the financial system that we have now is one it's essentially you know a very old house that's just been added on to and built and there's corridors going into stairways that you know don't go anywhere right and that's that's something that needs to be fixed and and it is being fixed well Security's a driver in all this and I think one of the things I've observed you'd love to get your reaction to is you have the crypto world that's certainly changing a lot of in dynamics on the global scale you have a cyber security and then you have fin tech so you guys this is where everything I think is a melting pot which is interesting you have all these things happening but at the center of all this is security absolutely it's almost like we're all swimming out to the to the raft and whoever gets there first and wins a security model wins at all well I thought I think well I think this the conversations all threads through security so the cyber conversations we've had are like okay Cyrus security for individuals and nation-states crypto currency for protection and freedom and and you know in immutability Ledger's almost great supply-chain aspects and then you get the FinTech which is like hey people want to do business so you have the entire changeover on the financial services side all kind of happening yeah yeah I think that they're all gonna be contributing to a solution it's it's each one is going to learn we're really open-minded at prime trust we want to build and grow we know that this we're in the most embryonic stage of this and so we don't know exactly what's gonna come next or what's going to be down the road and we want to be informed by everybody that's around us at a place that makes sense do you have to work with with the industries so take me through I want to ask you a question about your job so we'll take me through the day in the life of what's going on in prime chess what are some of the things that you guys do customers and what are they asking for what's like what's the some of the issues you guys are solving what did some of the dynamics can you share some color around that sure so our main services are so we are a trust company so we do escrow services and we do compliance on all of the escrow that comes through our ICS and stos that come through so that's a ml and kyc that's really important what distinguishes us I think is a real a real game changer for our customers is that we're really a technology company and we have API stocks that allow for companies to build their businesses on top of integration so that they have customers coming in and making accounts on their their their website their dashboard their platform and that's all feeding directly and they're actually making an account so you're building your you're targeting folks saying hey we'll take care of the heavy lifting on kyc ma ml and all the stuff that needs to happens that's heavy lifting that's around DoDEA services custodial service all comes through you yes so it comes in we can hold it we can review it you're not having asset managers also holding funds which is a problem so you're not needing to touch the funds at all you can just you can just do you at you're trying to do in this space and we'll take care of that aspect that's entrepreneurial side that's the stos and the IC knows what's the alternative for the your customer build their own go with unknown shop of their other so what so if I if it's a great service sounds like a great service and takes a lot of pressure off the build out of a opportunity what's the alternative if someone doesn't go with you well there's a few I mean it's to hold your own funds right figure that out on your own in the case of many different types of funds and businesses their boards are not okay with that because it's it's too much risk and liability so in many cases the alternative is don't do it yet just keep watching and waiting and wanting to be in crypto but you can't yet so and when we're seeing that a lot that there's like a sigh of relief when we finally have this conversation and it turns out it's extremely easy to make an account with us and suddenly that major roadblock is just gone so that's what that's the career opportunity takes the risk off the table little bit and accelerates the opportunity when the sec bomb decrypt yesterday was reporting that the sec in the united states is actually going into IC OS and having them return their money because of of course they are like well of course they are that makes sense that's they were always going to do that just because they make a statement and slowly decide how to act because look last july is when they said we're going to do this and most of the crypto community said you can't because we really don't want you to and we are gonna tell ourselves all these excuses for why it's not possible for the US government to actually pursue this and why they won't really do it because they're dinosaurs and that's just not how the government works so the way the government does work is that they everything takes a long time and it's all thought through and there are a million different approval processes within the system and they don't tell you anything until they're really ready to stand by whatever same and they make so they leave you in the dark for eight months a year whatever well you guys have a good opportunity so I had to ask the question what's the business model how does someone engage with you guys sounds likely to go in and create an account is there a fee involved what's the fee can you share the engagement that somewhere would would engage with you young sure so they can visit our website which is prime trust com they can email me at Kinsey at prime trust pretty easy and we have different pricing for escort services versus custodial services and we actually pay interest on any Fiat that we held in custody and we charge a monthly basis point fee based on how much is in in custody with us and where's you guys located was the company located headquarters this here in Nevada in Las Vegas I'm based out of Los Angeles we've got some team members in San Francisco in New York as well that's awesome so it's a question how did you get into the space what's your story I got into the space I started out an equity crowdfunding so I was working with companies that were raising capital under A+ reg D and reg CF and I was in the trenches with them figuring out from like the very earliest days how what the laws were gonna look like you know launching companies the day the regulations came out barking into effect and then sort of working through that so it's been an adventure on that side and then my first experience in crypto was at an at a meet up in Santa Monica where companies were talking about raising 40 million dollars in ten seconds and that and they were also pitching in methods like I knew were not legal so it was it's kind of just dropping to me well one was how did you manage to get that many people to want to invest in you so quickly because it's a struggle for for many companies and then so that's amazing I want to learn more about that and then also did you know that there's a more legal way to do this and that you're putting yourself at a lot of risk so that made me really want to jump in and figure this out so you got totally intoxicated by the Wild West yeah there's a problem they gotta be solved in there it's kind of fun at the same time because you know all those those days are over thankfully so because you know it should be it should be more legitimize and it is getting there I think security tokens are a good sign that people are moving border security tokens at least in the u.s. the legal firms the service providers are starting to get hold up on some of the new things and that's good still expensive to run the run the process it's like own public almost as a start-up it's almost ridiculous and I kinda had the same view we're the gaps in your opinion so you now look at the crowdfunding which has been great you see all that stuff happening as essentially as a decentralized you know efficiency around disrupting venture capital and other fundraising which is great where are the gaps in your mind from a service provider standpoint from an ecosystem where's the to-do items what needs to get done faster where are the gaps I think everybody's building out their technology to make everything easier currently there's a lot that's done manually or just to manually and needs to be more automated and then I think there's also a lot of education on both sides that needs to be done that's that's I think a huge gap there's a tendency to create echo chambers and so you end up talking with people who just won't even consider the other side of it with the possibility for change in whichever area they're in and that is I think we are gonna see that come together but that tends to hold people back because you thanks for coming on and sharing your insights great to have you on the cube and good luck with prime trust thank you okay this is a cube live coverage here at hosts show con I'm John furrow your stay with us more live coverage after the short break

(energetic music) >> Live from Orlando, Florida, it's theCUBE covering .conf 18, brought to you by Splunk. >> We're back in Orlando, Splunk .conf 2018, I'm Dave Vellante, Stu Miniman, and this is theCUBE, the leader in live tech coverage. Doug Merritt is here, the CEO of Splunk, long time CUBE guest, great to see you again. >> Thank you, Dave, great to be here. >> So, loved the keynote yesterday and today. You guys have a lot of fun, I was laughing my you-know-what off at the auditions. They basically said, Doug wasn't a shoo in for the keynote, so they had these outtake auditions. They were really hilarious, you guys are a lot of fun. You got the great T-shirts, how do you feel? >> It's been a, my favorite time of year is .conf, both because there's usually so much that we're funneling to our customers at this time, but being here is just infectious, it's, and one of the things that always amazes me is it's almost impossible to tell who are the customers and who are the employees. That just, I think Devonia this morning said it's a family affair, and it's not just a family affair, it's that there's a shared passion, a shared, almost culture and value set, and there's, it just is a very inspiring and naturally flowing type of event and I know I'm biased because I'm the CEO of Splunk, but I don't, I just don't know of events that feel like our, like .conf does. There's a lot of great shows out there, but this has got a very unique feel to it. >> Well, we do a lot of shows, as you know, and I've always said, .conf, I think ServiceNow, does a great job obviously, re-invent the tableau shows. That energy is there, and the other thing is, we do, when we go to these shows, a lot of times, you'll look at the keynotes and say, are there any products being announced? You guys, that wasn't a problem here. You guys announced this -- >> Not this year. >> Bevy of products, I mean, it's clear the R and D is translating into stuff that people can consume, and obviously that you can sell, so that's huge. >> I'm really excited about the product roadmap right now, and it's, that was, when I got the job, almost three years ago, one of the key areas I leaned forward and the board was excited about it was, what, where or how are we going to take this product beyond the amazing index and search technology that we have? And this show, it takes a while to progress the roadmap to the point that you can get the type of volume that we have here, but this show was the first time that I felt that we had laid enough of the tracks, so you could see a much, much broader landscape of capabilities, and now it's a challenge of packaging and making sure our customers are successful with it, with the product that we just have, the products we've announced. >> Cloud caught a lot of companies and a lot of end user companies, flatfooted. You guys have embraced the cloud, not only with the AWS partnership, which we're going to talk about, but also the business model. You're successfully transitioning from a company with perpetual license model, to a ratable model, which is never easy. Wall Street is killing companies who try to do that. Why have you been successful doing that? You know, give us an update. >> Yeah, so five years ago, less than 20% of our contracts were, had any type of subscription orientation to it, whether it's a multi-year term or a cloud. We'd just launched our cloud four years ago. And we moved from there to we had told the street there would be 65% term in subscription by the end of this year and updated guidance at the end of the second quarter, which is just a month and change ago, that we've already hit the 75% mark that we were set in for next year, so it's been a pretty rapid progression and I think there're two elements that have helped us with that. One: cloud continues to catch fire and so the people's orientation on "Do I do something in the cloud?" four years ago they were much more nervous, so less nervous today. But data is growing at such a huge rate and people are still wrapping their heads around, "How do I take advantage of this data, how do I even begin to collect this data and then how do I take advantage of it?" And the elasticity that comes in the cloud and that comes with term contracts, we can flex out and flex back in, I think it's just a much more natural contracting motion than you bought this big, perpetual thing and pay maintenance on it, especially when someone is growing as fast as data is growing. >> Well and it requires you to communicate differently to the financial analysts. >> It does. >> Obviously, billings, you know, was an important metric. You've come up with some new metrics to help people understand the real health of the business. And one of the other metrics that strikes me, and you see this with some of the successful companies, I actually think Aneel Bhusri was sort of the modern version of this, is the number of seven figure deals. You're startin' to hit that, and it's not, the way he's phrased it was pretty good. It's not something you're trying to engineer, it's the outcome -- >> Yes. >> of having great, loyal customers, it's not something you try to micromanage. >> Right, and that's, just recently we dropped six figure deals, which, when I joined, you got this wonderful dynamic forecasting system that sits on top of sales for us, and so as head of sales, where I started, you're really paying attention to deals. I'd go down to a hundred thousand dollar deals that would track throughout the quarter. And now it's hard to get it down to the six figures 'cause we've got a big enough envelope of seven figure deals. So the business has changed pretty dramatically from where it was, but it is an outgrowth of our number one customer priority, which is, or number one corporate priority, which is customer success. 'Cause that investment by companies, when you get to a million dollars plus, in most cases that's a million annually, you better believe in and trust that vendor, 'cause that's no longer an easy, small departmental sale. You're usually at the CIO, CFO type level. So it's something that we're very honored by, that people trust us enough to get that footprint of Splunk to be that size and to feel like they're getting a value from Splunk to justify that purchase. >> Alright we'll get off the income statement, Stu, and you can read about all that stuff, and we're going to get into, we've got a lot of ground to cover with you, Doug. Jump in here, Stu. >> Yeah, so Doug, I've really enjoyed talking to some of your customers that, you know, most of them started on premises with you and now many of them, they're using Splunk cloud, it's really kind of a hybrid model, and it's been really interesting to watch the maturation of your partnership with Amazon, and being the leader in the cloud space. Give us a little bit of color as to what you're hearing from the customers, you said three, four years ago, you know, they were obviously a little bit more cautious around it, and bring us inside a little bit that partnership. >> Sure, so the first piece that, as part of Splunk, that I think is a little bit different than other vendors is because we are both a lower level infrastructural technology, right, data is, the way I frame what we do is there's these raw materials, which are all these different renditions of data around, and companies increasingly have to figure out how to gather together these different raw materials, put them together different ways, for the output that is driving their business. And we are the manufacturing parts provider that makes it easy for them to go and pick up any of these different compounds and then actually do what they want to do, which is make things happen with data. And that middle layer is really important and we have never taken a super strong stance either, we started on prem, but as we moved to cloud, we never took a strong stance saying everything should be in the cloud or everything should be on prem because data has gravity, there is physics to data. And it doesn't always make sense to move data around and it doesn't always make sense to keep data stagnant, so having that flexibility, being able to deploy your collection capability, whether it's ours or third party, your storage capability, and then your process and your search, what are you going to do with the data, anywhere that makes sense for a customer, I think, is important. And that's part of that hybrid story, is as people increasingly trust and interview us and other cloud vendors to build core apps and then house a lot of their data, we absolutely need to be there. And I think that momentum of the cloud is certainly as secure and, in many cases, more secure than my on prem footprint, and the velocity of invention that some like ABDS is driving allows me to be much more agile and effectively drive application development and leading edge capability, I think just has people continuing to trust the cloud service providers a little bit more. >> Yeah well, we're here in the pavilion, and seeing your ecosystem grow, we've been at re:Invent for about five years, that ecosystem is just so >> It's been amazing. >> massive and full, give us a little bit about the relationship with Amazon and how you look at that, how Amazon looks at a company like yours. >> Yeah, it's been, so one, whenever you're playing with a highly inventive and hugely successful company like Amazon, my orientation and what I convey back to the company is our job is to be more inventive, more agile, and continue to find value with our maniacal focus every day being the data landscape. Data is a service and outcomes is a service, so our job is run faster than Amazon. And I think that this show and our announcements help illustrate that our invention cycle is in high tilt gear and for what we do, we are leaning in in a really aggressive way to add that value. With that backdrop, Andy and I formed this partnership four years ago. He felt there's enough value in Splunk and we were a good enough partner and the way we consume their services that he would commission and quota their sales reps whenever a Splunk sale was done in the ADBS landscape, which I think has been really helpful for us, but we obviously are a huge customer of ADBS's and they become an increasingly large customer of ours and finally gave us approval with their three year renewal a quarter ago to publicly reference them as a sizeable customer for us. >> Oh, okay, congratulations on that. And something I've really, it's really crystallized for me: so many administrators out there, you look at their jobs, you know, what are they? It's like okay, I'm the security expert, I'm the network certified person. You're really, your users here, you know, they are the beacons of knowledge, they are the center of data, is really what they are. You know, Splunk's a tool, they're super excited about the product, but it's data at the center of what Splunk does and therefore, you're helping them in just such a critical aspect of what is happening in the industry today. >> Yeah, the key aspects of the keynote, of my keynote, were we are moving to a world where data is the product that people care about so the whole object is how do you make things happen with data and the people that can get that done increasingly are becoming the most valuable players on the field, so what infrastructure, what tooling, what capability exists that allows people from all departments, you know, we're very heavy within IT and security, but increasingly HR departments, finance departments, marketing departments, sales departments, manufacturing departments will not be successful without a really competent group of folks that understand how to make things happen with data and our job is to lower that bar so you don't have to go to Carnegie Mellon for four years and get a Masters in Computer Science and Data Science to be able to be that most valuable person on the field. >> I want to take a moment, I want to explain why I'm so bullish on Splunk. We had a conversation with Susan St. Ledger yesterday. Digital transformation is all about data. >> Yup. >> And you guys are all about data, there's the cliche which is "data is the new oil" and we've observed, well not really. I could put oil in my car, I can put oil in my house, I can't put it in both places, but data? I can use that same data in a lot of different use cases and that's exactly what you guys are doing now as you expand into line of business -- >> Yup. >> With Splunk Next. >> Yup. >> So you've announced that, you showed some cool demos today. I'd like you to talk about how you're going from your core peeps, the IT ops guys and the sec ops guys, and how, what your plan is to go to lines of business. More than just putting the data out there, you've come up with some new products that make it simpler, like business work flows, but what else are you doing from a go to market standpoint and a partnership standpoint, how do you see that playing out? >> Yeah, I think that the innovation on product, there are three key pillars that we're focusing on. Access data, any type of data, anywhere it lives. Make sure that we're driving actionable outcomes with that data, and acquisitions like Phantom and VictorOps have been a key pillar of that, but there's other things we're doing. And then, expand the capability of finding those outcomes to a much broader audience by lowering the bar. So the three key themes across the portfolio. But all of those are in service of the developers at a customer site, the developers in the ecosystem, to make it easier for them to actually craft a set of solutions that help a retailer, help a discrete manufacturer, help a hospital actually make things happen with data. 'Cause you could certainly start with a platform and build something specific for yourself but it's much easier if you start with a solution. And a lot of the emphasis we've been putting over the past two to three years is how do we up that platform game. And the many, many, 20 different product announcements that we rolled at this .conf and one of them that I'm also very excited about is our developer cloud where we've really enhanced the API layer that interacts with the different services that the entire Splunk portfolio represents. Not just the search and index pieces that people are familiar with but everything from orchestration to role based access to different types of visualization so a very broad API layer that's a well-mannered, restful set of APIs that allows third parties to much more crisply develop, excuse me, applications to compliment the 1800 apps that are already part of our Splunk base and right behind me is a developer pavilion where we've got the first hand full of early adopter OEM partners that are building their first sets of apps on top of that API framework. >> Dozens of them, it's actually worth walking around to see. Now, so that developer cloud is a lever, those developers are a lever for you to get into lines of business and build those relationships through the software, really, and through the apps. Same thing for IOT. >> Yup. >> Industrial IOT. Now, we've observed, and a lot of the IT companies that we see are trying to take a top down approach into IOT and we don't think it's going to work. It's, we talk about process engineers, it's operations technology people, they speak a different language. It's not going to be a top down, here, IT. >> A very different audience. >> It's going to be a bottoms up set of standards coming from the OT world. The brilliance of what you guys have, it's the data, you know, it's data coming off machines, data, you don't care. And so, you're in a good position to do a bottoms up in IOT and we heard some of that today. Now, there are some challenges. A lot of that data is still analog, okay, you can't really control that. A lot of the devices aren't instrumented, they're not connected, you can't control that. But once they become instrumented and connected and that analog data gets digitized, you're in a really good position, but then you got to build out the ecosystem as well. >> Yup. >> So talk about how you're addressing some of those challenges in industrial IOT. >> Yup, man, it's a great subject 'cause I think that the trying to rely on standards is the wrong approach. The velocity across this digital landscape is so high and my view over the past 30 years, I think it's only accelerated now, is there's going to be more and more varieties of data with different formats than there's ever been, and we've seen it in the past five years. Just look at the variety of services on top of AWS, which didn't even exist ten years ago, but and they now have hundreds of services and there is no organizing principle across those services as far as data definition. So it's a very chaotic data landscape and I don't think there's any way to manage it other than to embrace the chaos and work a little bit more bottoms up, you know, grab this data, don't worry about cleansing it, don't worry about structuring it, just make sure you have access to it and then make sure that you've got tools like Splunk that allow you to play with the data and try and find the patterns and the value inside of that data, which is where I think we're very uniquely suited as a technology set. Helping the ecosystem come to that realization is a key aspect of what we're doing. We're trying to attack it the same way we attacked the IT security piece which is pick a handful of verticals and really focus on the players, both the marquis anchor tenants, the BMWs, the Siemens', the Deutsche Bahn railroads of the world, as customers. And through that, get access to the key influencers and consultants and advisors to those industries and start to get that virtuous circle of "I actually have more data than I think I have." Even though there's some analog machines, there's so many different ways to attach to the signal that those machines are emitting and it may not be bi-directionally addressable, but at least you can see what's happening within those machines without a full manufacturing floor rip and replace. And everyone is excited about doing that. The advisors to the industry are excited, the industry themselves are excited. We had BMW on stage who walked through how they're using Splunk to help on everything from product design all the way through to predictive maintenance and feedback on the quality of the cars that they're rolling out. We've all heard stories that there's more lines of code in the Ford F150 and these other vehicles than there is within Facebook right now, so we all are dealing with rolling and sitting in building's and house's data centers. How do you make sure that you're able to pay attention what's happened within that data center? So I think that that is as big or bigger of an opportunity than what we've done with IT and security, it just has its own pace of understanding and adoption. >> Carnival Cruise Line, another one, Stu. We had those guys on today and they basically look, they have a lot of industrial equipment on those ships, so they're excited. >> Yeah, absolutely. Alright, so Doug, we started the beginning talking about the last couple years, how we measure Splunk has changed. Going to more subscription models, talk about how many customers you have. I look at developers, I look at IOT, whole different set of metrics. So if you look at Splunk Next, how do we measure you, going forward? What is success for your team and your customers going forward? >> Yeah, and the whole orientation around Splunk Next, as I'm sure Susan covered, it's not a product, it's a messaging framework. People are so used to Splunk being all about the collection of data within the index and searching in said index, and we're increasingly moving, we're complementing the index, the index is a incredibly unique piece of IP for us. But there's a lot of other modalities that can complement what that index does and Splunk Next represents all of our investments in next generation technologies that are helping in with everything from stream processing to distributed compute capability, next generation visualizations, et cetera. The metric that I care about over time is customer adoption and customer success. How many use cases are being deployed at different customers? How many companies, both customers and partners, are incorporating Splunk in what they do every day? You're getting OEM Splunk, making Splunk a backbone of their overall health and success. And ultimately that needs to translate into revenue, so revenue and bookings will always be a metric that we care about, but I think the leading indicators within theses different markets of rate of adoption of technology and, more importantly, the outcomes that they're driving as they adopt this technology, are going to be increasingly important. >> Yeah, I just have to tell you, when you talk about your customers not only excited, but it's a deeper partnership when you talk to insurance company out of Toronto that, like, they're talking to the people that they insure about, should they be using Splunk and how do they do that. It just, a much deeper, and you know, deeper than a partnership model for your customers. >> It's one of the things I love about this conference, is it's, we were talking about earlier, it's hard to tell the customers from the employees, like, there's a, there's a, this whole belief and purpose that everybody shares, which I adore about being here. But when you look at a sea of data, we've thought traditionally looked at the data we manufacture, typically data that's historic and at rest from our ERP systems. This next wave is certainly all the data that's happening within our organizations but increasingly it's all the data that's available in the world at large. And whether it's insurance or automotive or oil and gas, the services that I'm going to have to deliver to customers require me to farm data outside of my walls, data inside my walls, combine those two, to come up with unique value added services for my customers. So it's great to hear that, that our customers are on that journey 'cause that's where we all need to go to be successful. >> And there's a definitely alignment there. Doug, I know you're super busy, we got to go. Thanks so much for coming on theCUBE. Give you the last word, .conf 18 takeaways. >> (laughs) Unbelievable excitement and enthusiasm. A huge array of products that, I think, broaden the aperture of what Splunk does so dramatically that people are really trying to digest, "What should, how should I be thinking about Splunk moving forward?" And I'm, we started a whole series of transformations three years ago, and I'm really excited that they're all starting to land and I can't wait for the slow realization of the impact that our customers are counting on us to provide and that we'll increasingly be known for across the data landscape. >> Well and the landscape is messy and, as you said, the messiest part of that landscape is the data landscape. You guys are helping organize that, curate it. And hopefully we're helping curate some of the, from some of the noise and distracting to the signal to you on theCUBE. Doug, thanks so much for coming on theCUBE, great to see you again. >> Thank you Dave, thank you Stu, you guys do a great job. >> Thanks, we appreciate that. >> Thanks for being here with us. >> Alright, keep it right there, buddy. We'll be back with our next guest from .conf 18 from Orlando, we'll be right back. (digital music)

>> 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)

(techy music) >> Hi, everybody, this is Dave Vellante from theCUBE's Palo Alto studios, and welcome to this CUBE conversation. You know, theCUBE and SiliconANGLE/Wikibon have been documenting the evolution of data and storage over the last decade or so, and what we've seen is the simplification of storage. Going from hardware consolidation with conversion infrastructure and we saw hyper conversion infrastructure and sort of software-defined come on the stage, but now we're, you know, in the heart of the cloud era, and what we're seeing emerging is true cloud-like models for data services. So, we've asked Brian Biles and Sazzala Reddy from Datrium to come back into our CUBE studios and talk about this a little bit. Brian and Sazzala are both co-founders of Datrium. Brian is the chief product officer and Sazzala's the CTO. Gents, let's get into it, thanks for coming back on, and let's talk about that a little bit. So, your model, as we've talked about in the past, is a pure SAS model. You're accessing data services in a SAS-like, cloud-like experience, and people might say, "Well, isn't everything SAS today?" But in the storage world that's not the norm. Typically you would either install a box, you know, and that box might have a very rich set of software-defined services on top of it, but it's not really a cloud experience. We're starting to see certain models pop up. You're seeing some companies actually delivering that. You guys started there, that's your DNA, so let's talk about what you're doing and how that's different in the marketplace, Brian. >> Sure, you know, the way to maybe start the conversation is imagine that you're already, you know, embracing a multi-cloud, you know, plan in your IT organization, so you know, you might have a little Amazon, you might have some SAS. You know, software company, stuff going on, and you have some on-prem experience, and you want to make that as simple as possible-- >> You just described everybody. >> We want to make it as unified... (laughs) Yeah, as unified as you can, as simple as you can. You know, at that point you want to think about, you know, what is the highest leverage, simple thing to do on-prem that connects to that, you know, world of services in the cloud. How can you align that as closely as possible, so what Datrium is doing is trying to do that. We have, you know, our on-prem software is very Amazon-like, it has two layers, it operates in a very similar way, supporting many types of frameworks from VMware to Docker to Red Hat. What we've done with how to store, manipulate, mobilize data and orchestrate, you know, transitions between clouds is, it ends up feeling fundamentally different from other types of ways that you can deal with on-prem infrastructure. It's just much simpler, much more coordinated, and it allows more flexibility over time. So, Sazzal can maybe tell you about it. >> Okay, so but Nutanix, Sazzala, would say, "Okay, well we're cloud, we're creating "a cloud-like experience for on-prem," how are you guys different? >> I guess the fundamental difference is how we think of the problem. We want to say our goal is to run, protect applications in any cloud, because we cannot be in the business of building the infrastructure because that's an investment. There already are three players. What most customers want is to commoditize the cloud. They could care less if they're running on Amazon or Azure. In fact, they care that they're tied to one cloud vendor, so our goal is to make that cloud, commoditize the cloud, make it all seamless so they can move from one place to the other, whatever agreements you have. Tomorrow Google may give you credits, say, "You know what, I'll give you one year free, come on over." What you want is a one-click and move everything over to their stuff over the weekend. That's kind of where we are, that we want to provide that level of simplicity, run, protect your workloads in any cloud you want. So, Nutanix is, I think it looks like from at least from what we read from their press releases, is their cloud. They say one OS, one cloud, we are seeing any cloud. So, that's, I think we want to give that flexibility for people to not be locked in by any cloud vendor, that you can take advantage of it. You know, tomorrow Amazon may not be doing that well or tomorrow Amazon may be enemy to your business, so you want to click it and move it away to some other cloud-- >> Separating the data services from the underlying infrastructure. >> Yeah, that's right, so I think you have to separate the data services and the data management to the best, abstract it so it's so high level that then you don't care where it runs. It runs on-prem, it runs on Amazon, so it looks the same experience for you, that's what we're aiming for, that level of simplicity, but remember, to do these things you must run and protect. You can't just do run only, you must also protect because it's part of your data, you know, your IT philosophy that you must protect your data, you must have copies of it to guard against ransomware and other things, but compliance reasons, right? You want to manage your data, so it has to be a holistic view of the entire end-to-end lifecycle of your data. It cannot just be, "Run my apps here and there." >> How about Pure, how would you differentiate from Pure? Let's say pure wants to, say, OEM its stack to a cloud service provider, how is it different? >> So, the common denominator in cloud services is the workload, the instance, the VM. >> Mm-hmm. >> All of the coordination between clouds is going to be on that granularity. That's what we focus on, so, you know, we have a catalog to show relationships between VMs so that, you know, when we DR you can restart in a certain order or you can validate, you know, workload granularities, have policies at a workload granularity. That's how clouds', you know, behavior is sort of itemized today. If you buy separate parts, like you know, a SAN array, you have to buy something else to do that work. So, it's fundamentally limiting. You know, if you just take VMware because it's so well understood, you know, VMs are going to be put into a LUN as, you know, a file system of VMs. So, to transfer a LUN to a cloud, and then what do you do with it? You know, are... There's no instance to restart. So, you know, it doesn't, it just doesn't operate on the same granularity. >> Speaking different languages, essentially. >> Yeah, so you're either, you know, an ingredient to somebody who's building a cloud who's assembling lots of things to get to the level where Datrium is offering it today, or you could just be simple and... >> Ultimately are you a software company, are you a hardware company, right? That's the thing and the difference is how we are a software company. You have to think about it as a software scaling. You can scale and make it all scale quite well. >> So, let's talk about some of those services-- >> Yeah. >> Which are all software, so let's list some, and we've talked in the earlier segments about data reduction and... >> Right, so I think the company has built that background that we're going to enable the services one day, so the first service we enabled, and this, the beginning on the year, was backup and data archiving. So, it's a SAS platform, it's a multi-cloud services, this first one. Second one we are building right now, we're going to ship it pretty soon, it's something called Cloud Shift. It's a DR orchestration, app mobility orchestration kind of framework. You can just click, move your workloads anywhere you want, any cloud you want. It's a big piece of our next offering. The third offering we're going to be doing is how do you manage all these different data sets you have across multiple places you have, so we're going to offer that next. So, we also have something called Providence built into the system, like every object knows where it came from, where did it, like all these apps, they kind of know we have all the data, we kind of know where they came from, so that's the next one, we call it a Global Ledger, how do you keep track of all this stuff. And the fourth one is we have all this data now, we have all this metadata, how do we provide governance for the end user, because ultimately they do care about compliance, they do make sure, they want to make sure that they're not moving data to the wrong place, that they have made the SLAs, so that's the ultimately kind of like where are we going to, kind of that's a two-year road map-- >> Mm-hmm. >> Idea. >> Okay, so I've got mobility, discovery, there's analytics in here-- >> Analytics, yeah. >> You've got governance and compliance, obviously backup is something we talked about. >> Yep. >> Now, these are discreet services that I can acquire separately-- >> Yeah. >> Is it all included-- >> Well, in a SAN, yeah, you'd have to buy them separately. >> Yeah, okay, right, right, you do. >> In a cloud approach like ours, they're just automatic and always on, so you don't have to think about them. Global dedupe is an example, if we always have that on you can't turn it off, that helps it locally for cloning so you don't have to move data from server to server-- >> Mm-hmm. >> In a developer shop, for example. It's just, you just boot, you know, start it up and it all has access and it's very fast, or across clouds we don't send all the data when somebody says to move it. We look for the deltas between site A and site B and only send those in a compressed, encrypted way. So, having that stuff just be fundamental and always on means cloud mobility gets a lot easier and a lot faster. >> And I, backup's another good example. I don't need to go buy backup software from a backup software-- >> Or hardware. >> Vendor, or... (chuckles) >> Yeah, that's right. >> Or hardware, right, it's there. >> It's just standard. >> Yeah. >> It's self-protecting, so you know, when you think about cloud mobility it changes the way you think about the problems. For example, if you want to, you know, enable a context for automated DR from prem to cloud, there are a lot of risks in many of the current systems. I don't want to go through the whole, you know, problem set because it's bad and we're solving it in our own way, but just take the conversion problem. If you have to move from point A to point B, you know, 90% of the time if you convert a VMware VM to an Amazon instance it'll kind of work. Well, for DR that's not sufficient, so we're taking a much more sort of thoughtful and open approach to how we deal with, you know, stack providers. So, you know, we'll be able to... In the VMware case, for example, move things straight onto their cloud from our S3 data so that you don't have to convert, so it just always works. >> And I'm interfacing with your SAS, it looks the same where it's on-prem, whether it's in the cloud-- >> Yep. >> It's the same experience. >> We're hoping you have to do less work and less interaction because it's all built in, it all just works. >> Okay, so that, the vision is sets of discreet services separated from the underlying infrastructure-- >> Infrastructure. >> Able to call those services as needed, run on any cloud, on-prem-- >> Run, protect, any cloud. >> Full set of services. >> Right. >> Right. >> Integrated-- >> Right, and as time goes on all our sort of operating software and analytic software and governance, and so on, will actually be, you know, literally SAS in a cloud. That makes it much easier to control a multi-cloud deployment, to control stuff in the cloud, but it also means you don't have to update software, we do it for you. It's just way simpler, so as time goes on, you know, on-prem infrastructure, in our belief, will become more and more the, you know, the thing operated by the cloud and the sort of puppet master will be outside. >> And performance, can you address performance? >> Yeah, so we ran, so basically our system scales quite well because of the way we built it, and we ran, you know, benchmark to take some of the vendors because we wanted to prove it that we're really good at this stuff, and we are the fastest probably on the planet. Our performance is really, really, very good, and it's not because everybody wants it, it's because you don't have to think about it anymore. You don't have, you don't, like it's one of those things again, don't think about it, just works for you, the performance is super high. >> Hm... >> We have customer validations, via the way, we have gotten our reviews from customers who are really, really five-star. We have, like, raving fans for our product. >> Excellent, well guys, thanks very much for helping us parse through that and appreciate you coming back on. >> Okay, thank you very much. >> Thanks for having us. >> All right, thanks for watching, everybody, this special CUBE conversation from out Palo Alto studios. This is Dave Vellante, we'll see you next time. (techy music)