>> Narrator: Live from Midtown Manhattan it's theCUBE! Covering Big Data, New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsor. >> Okay, welcome back everyone. Live here in New York this is theCUBE's coverage of Big Data NYC, our event. We've been doing it for five years, it's our event in conjunction with Strata Data, which is the O'Reilly Media that we run, it's a separate event. But we've been covering the Big Data for eight years since 2010, Hadoop World. This is theCUBE. Of course theCUBE is never going to change, they might call it Strata AI next year, whatever trend that they might see. But we're going to keep it theCUBE. This is in New York City, our eighth year of coverage. Guys, welcome to theCUBE. Our next two guests is Andrew Burt, Chief Privacy Officer and Andrew Gillman, Chief Customer Officer and CMO. It's a start-up so you got all these fancy titles, but you're on the A-team from Immuta. Hot start-up. Welcome to theCUBE. Great to see you again. >> Thanks for having us, appreciate it. >> Okay, so you guys are the start-up feature here this week on theCUBE, our little segment here. I think you guys are the hottest start-up that is out there and that people aren't really talking a lot about. So you guys are brand new, you guys have got a really good reputation. Getting a lot of props inside the community. Especially in the people who know data, data science, and know some of the intelligence organizations. But respectful people like Dan Hutchin says you guys are rockstars and doing great. So why all the buzz inside the community? Now you guys are just starting to go to the market? What's the update on the company? >> So great story. Founded in 2014, (mumbles) Investment, it was announced earlier this year. And the team, group of serial entrepreneurs sold their last company CSC, ran the public sector business for them for a while. Really special group of engineers and technologists and data scientists. Headquartered out of D.C. Customer success organization out of Columbus, Ohio, and we're servicing Fortune 100 companies. >> John: So Immuta, I-M-M-U-T-A. >> Immuta.com we just launched the new website earlier this week in preparation for the show. And the easiest way-- >> Immuta, immutable, I mean-- >> Immutable, I'm sure there's a backstory. >> Immutable, yeah. We do not ever touch the raw data. So we're all about managing risk and managing the integrity of the data. And so risk and integrity and security are baked into everything we do. We want our customers to know that their data will be immutable, and that in using us they'll never pose an additional risk to that underlying data. >> I think of blockchain when I think of immutability, like I'm so into blockchaining these dayS as you guys know, I've been totally into it. >> There's no blockchain in their technology. >> I know, but let's get down to why the motivation to enter the market. There's a lot of noisy stuff out there. Why do we need another unified platform? >> The big opportunity that we saw was, organizations had spent basically the past decade refining and upgrading their application infrastructure. But in doing so under the guise of digital transformation. We've really built that organization's people processes to support monolithic applications. Now those applications are moving to the cloud, they're being rearchitected in a microsurfaces architecture. So we have all this data now, how do we manage it for the new application, which we see is really algorithm-centric? The Amazons of the world have proven, how do you compete against anyone? How do you disrupt any industry? That's operationalize your data in a new way. >> Oh, they were developer-centric right? They were very focused on the developer. You guys are saying you're algorithm-centric, meaning the software within the software kind of thing. >> It's really about, we see the future enterprise to compete. You have to build thousands of algorithms. And each one of those algorithms is going to do something very specific, very precise, but faster than any human can do. And so how do you enable an application, excuse me, an algorithm-centric infrastructure to support that? And today, as we go and meet with our customers and other groups, the people, the processes, the data is everywhere. The governance folks who have to control how the data is used, the laws are dynamic. The tooling is complex. So this whole world looks very much like pre-DevOps IT, or pre-cloud IT. It takes on average between four to six months to get a data scientist up and running on a project. >> Let's get into the company. I wanted to just get that gist out, put some context. I see the problem you solve: a lot of algorithms out there, more and more open sources coming up to the scene. With the Linux Foundation, having their new event Rebrand the Open Source summit, shows exponential growth in open source. So no doubt about it, software's going to be new guys coming on, new gals. Tons of software. What is the company positioning? What do you guys do? How many employees? Let's go down by the numbers and then talk about the problem that you solve. >> Okay, cool. So, company. We'll be about 40 people by Q1. Heavy engineering, go to market. We're operating and working with, as I mentioned, Fortune 100 clients. Highly regulated industries. Financial services, healthcare, government, insurance, et cetera. So where you have lots of data that you need to operationalize, that's very sensitive to use. What else? Company positioning. So we are positioned as data management for data science. So the opportunity that we saw, again, managing data for applications is very different than managing data for algorithm development, data sciences. >> John: So you're selling to the CDO, Chief Data Officer? Are you selling to the analytics? >> In a lot of our customers, like in financial services, we're going right into the line of business. We're working with managing directors who are building next generation analytics infrastructure that need to unify and connect the data in a new way that's dynamic. It's not just the data that they have within their organization, they're looking to bring data in from outside. They want to also work collaboratively with governance professionals and lawyers who in financial services, they are, you know, we always jest in the company that different organizations have these cool new tools, like data scientists have all their new tools. And the data owners have flash disks and they have all this. But the governance people still have Microsoft Word. And maybe the newer tools are like Wikis. So now we can get it off of Word and make it shareable. But what we allow them to do is, and what Andrew Burt has really driven, is the ability for you to take internal logic, internal policies, external regulations, and put them into code that becomes dynamically enforceable as you're querying the data, as you're using it, to train algorithms, and to drive, mathematical decision-making in the enterprise. >> Let's jump into some of the privacy. You're the Chief Privacy Officer, which is codeword for you're doing all the governance stuff. And there's a lot of stuff business-wise that's going on around GDPR which is actually relevant. There's a lot of dollars on table for that too, so it's probably good for business. But there's a lot of policy stuff going on. What's going on with you guys in this area? >> So I think policy is really catching up to the world of big data. We've known for a very long time that data is incredibly important. It's the lifeblood of an increasingly large number of organizations, and because data is becoming more important, laws are starting to catch up. I think GDPR is really, it's hot to talk about. I think it is just the beginning of a larger trend. >> People are scared. People are nervous. It's like they don't know, this could be a blank check that they're signing away. The enforcement side is pretty outrageous. >> So I mean-- >> Is that right? I mean people are scared, or do you think? >> I think people are terrified because they know that its important, and they're also terrified because data scientists, and folks in IT have never really had to think very seriously about implementing complex laws. I think GDPR is the first example of laws, forcing technology to basically blend software and law. The only way, I mean one of our theses is, the only way to actually solve for GDPR is to invent laws within the software you're using. And so, we're moving away from this meetings and memos type approach to governing data, which is very slow and can take months, and we need it to happen dynamically. >> This is why I wanted to bring you guys in. Not only, Andrew, we knew each other from another venture, but what got my attention for you guys was really this intersection between law and society and tech. And this is just the beginning. You look at the tell-signs there. Peter Burris who runs research for Wikibon coined the term programming the real world. Life basically. You've got wearables, you've got IOT, this is happening. Self-driving cars. Who decides what side of the street people walk on now? Law and code are coming together. That's algorithm. There'll be more of them. Is there an algorithm for the algorithms? Who teaches the data set, who shares the data set? Wait a minute, I don't want to share my data set because I have a law that says I can't. Who decides all this stuff? >> Exactly. We're starting to enter a world where governments really, really care about that stuff. Just in-- >> In Silicon Valley, that's not in their DNA. You're seeing it all over the front pages of the news, they can't even get it right in inclusion and diversity. How can they work with laws? >> Tension is brewing. In the U.S. our regulatory environment is a little more lax, we want to see innovation happen first and then regulate. But the EU is completely different. Their laws in China and Russia and elsewhere around the world. And it's basically becoming impossible to be a global organization and still take that approach where you can afford to be scared of the law. >> John: I don't know how I feel about this because I get all kinds of rushes of intoxication to fear. Look at what's going on with Bitcoin and Blockchain, underbelly is a whole new counterculture going on around in-immutable data. Anonymous cultures, where they're complete anonymous underbellies going on. >> I think the risk-factors going up, when you mentioned IOTs, so its where you are and your devices and your home. Now think about 23 and Me, Verily, Freenome, where you're digitizing your DNA. We've already started to do that with MRIs and other operations that we've had. You think about now, I'm handing over my DNA to an organization because I want find out my lineage. I want to learn about where I came from. How do I make sure that the derived data off of that digital DNA is used properly? Not just for me, as Andrew, but for my progeny. That introduces some really interesting ethical issues. It's an intersection of this new wave of investment, to your point, like in Silicon Valley, of bringing healthcare into data science, into technology and the intersection. And the underpinning of the whole thing is the data. How do we manage the data, and what do we do-- >> And AI really is the future here. Even though machine-learning is the key part of AI, we just put out an article this morning on SiliconANGLE from Gina Smith, our new writer. Google Brain Chief: AI tops humans in computer vision, and healthcare will never be the same. They talk about little things, like in 2011 you can barely do character recognition of pictures, now you can 100%. Now you take that forward, in Heidelberg, Germany, the event this week we were covering the Heidelberg Laureate Forum, or HLF 2017. All the top scientists were there talking about this specific issue of, this is society blending in with tech. >> Absolutely. >> This societal impact, legal impact, kind of blending. Algorithms are the only thing that are going to scale in this area. This is what you guys are trying to do, right? >> Exactly, that's the interesting thing. When you look at training models and algorithms in AI, right, AI is the new cloud. We're in New York, I'm walking down the street, and there's the algorithm you're writing, and everything is Ernestine Young. Billboards on algorithms, I mean who would have thought, right? An AI. >> John: theCUBE is going to be an AI pretty soon. "Hey, we're AI! "Brought to you by, hey, Siri, do theCUBE interview." >> But the interesting part of the whole AI and the algorithm is you have n number of models. We have lots of data scientists and AI experts. Siri goes off. >> Sorry Siri, didn't mean to do that. >> She's trying to join the conversation. >> Didn't mean to insult you, Siri. But you know, it's applied math by a different name. And you have n number of models, assuming 90% of all algorithms are single linear regression. What ultimately drives the outcome is going to be how you prepare and manage the data. And so when we go back to the governance story. Governance in applications is very different than governance in data science because how we actually dynamically change the data is going to drive the outcome of that algorithm directly. If I'm in Immuta, we connect the data, we connect the data science tools. We allow you to control the data in a unique way. I refer to that as data personalization. It's not just, can I subscribe to the data? It's what does the data look like based on who I am and what those internal and external policies are? Think about this for example, I'm training a model that doesn't mask against race, and doesn't generalize against age. What do you think is going to happen to that model when it goes to start to interact? Either it's delivered as-- >> Well context is critical. And the usability of data, because it's perishable at this point. Data that comes in quick is worth more, but historically the value goes down. But it's worth more when you train the machine. So it's two different issues. >> Exactly. So it's really about longevity of the model. How can we create and train a model that's going to be able to stay in? It's like the new availability, right? That it's going to stay, it's going to be relevant, and it's going to keep us out of jail, and keep us from getting sued as long as possible. >> Well Jeff Dean, I just want to quote one more thing to add context. I want to ask Andrew over here about his view on this. Jeff Dean, the Google Brain Chief behind all of the stuff is saying AI-enabled healthcare. The sector's set to grow at an annual rate of 40% through 2021, when it's expected to hit 6.6 billion spent on AI-enabled healthcare. 6.6 billion. Today it's around 600 million. That's the growth just in AI healthcare impact. Just healthcare. This is going to go from a policy privacy issue, One, healthcare data has been crippled with HIPPA slowing us down. But where is the innovation going to come from? Where's the data going to be in healthcare? And other verticals. This is one vertical. Financial services is crazy too. >> I mean, honestly healthcare is one of the most interesting examples of applied AI, and it's because there's no other realm, at least now, where people are thinking about AI, and the risk is so apparent. If you get a diagnosis and the doctor doesn't understand why it's very apparent. And if they're using a model that has a very low level of transparency, that ends up being really important. I think healthcare is a really fascinating sector to think about. But all of these issues, all of these different types of risks that have been around for a while are starting to become more and more important as AI takes-- >> John: Alright, so I'm going to wrap up here. Give you guys both a chance, and you can't copy each other's answer. So we'll start with you Andrew over here. Explain Immuta in a simple way. Someone who's not in the industry. What do you guys do? And then do a version for someone in the industry. So elevator pitch for someone who's a friend, who's not in the industry, and someone who is. >> So Immuta is a data management platform for data science. And what that actually gives you is, we take the friction out of trying to access data, and trying to control data, and trying to comply with all of the different rules that surround the use of that data. >> John: Great, now do the one for normal people. >> That was the normal pitch. >> Okay! (laughing) I can't wait to hear the one for the insiders. >> And then for the insiders-- >> Just say, "It's magic". >> It's magic. >> We're magic, you know. >> Coming from the infrastructure role, I like to refer to it as a VMWare for data science. We create an abstraction layer than sits between the data and the data science tools, and we'll dynamically enforce policies based on the values of the organization. But also, it drives better outcomes. Because today, the data owners aren't confident that you're going to do with the data what you say you're going to do. So they try to hold it. Like the old server-huggers, the data-huggers. So we allowed them to unlock that and make it universally available. We allow the governance people to get off those memos, that have to be interpreted by IT and enforced, and actually allow them to write code and have it be enforced as the policy mandates. >> And the number one problem you solve is what? >> Accelerate with confidence. We allow the data scientists to go and build models faster by connecting to the data in a way that they're confident that when they deploy their model, that it's going to go into production, and it's going to stay into production for as long as possible. >> And what's the GDPR angle? You've got the legal brain over here, in policy. What's going on with GDPR? How are you guys going to be a solution for that? >> We have the most, I'd say, robust option of policy enforcement on data, I think, available. We make it incredibly easy to comply with GDPR. We actually put together a sample memo that says, "Here's what it looks like to comply with GDPR." It's written from a governance department, sent to the internal data science department. It's about a page and a half long. We actually make that very onerous process-- >> (mumbles) GDPR, you guys know the size of that market? In terms of spend that's going to be coming around the corner? I think it's like the Y2K problem that's actually real. >> Exactly, it feels the same way. And actually Andrew and his team have taken apart the regulation article by article and have actually built-in product features that satisfy that. It's an interesting and unique--- >> John: I think it's really impressive that you guys bring a legal and a policy mind into the product discussion. I think that's something that I think you guys are doing a little bit different than I see anyone out there. You're bringing legal and policy into the software fabric, which is unique, and I think it's going to be the standard in my opinion. Hopefully this is a good trend, hopefully you guys keep in touch. Thanks for coming on theCUBE, thanks for-- >> Thanks for having us. >> For making time to come over. This is theCUBE, breaking out the start-up action sharing the hot start-ups here, that really are a good position in the marketplace, as the generation of the infrastructure changes. It's a whole new ballgame. Global development platform, called the Internet. The new Internet. It's decentralized, we even get into Blockchain, we want to try that a little later, maybe another segment. It's theCUBE in New York City. More after this short break.

>>live from Miami, Florida. It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, Everybody. You're watching the Cube, the leader in live tech coverage. We're here covering the IBM data and a I form. Rob Thomas is here. He's the general manager for data in A I and I'd be great to see again. >>Right. Great to see you here in Miami. Beautiful week here on the beach area. It's >>nice. Yeah. This is quite an event. I mean, I had thought it was gonna be, like, roughly 1000 people. It's over. Sold or 17. More than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands on presentations. Tell us a little bit more about how this event has evolved. >>It started as a really small training event, like you said, which goes back five years. And what we saw those people, they weren't looking for the normal kind of conference. They wanted to be hands on. They want to build something. They want to come here and leave with something they didn't have when they arrived. So started as a little small builder conference and now somehow continues to grow every year, which were very thankful for. And we continue to kind of expand at sessions. We've had to add hotels this year, so it's really taken off >>you and your title has two of the three superpowers data. And of course, Cloud is the third superpower, which is part of IBMs portfolio. But people want to apply those superpowers, and you use that metaphor in your your keynote today to really transform their business. But you pointed out that only about a eyes only 4 to 10% penetrated within organizations, and you talked about some of the barriers that, but this is a real appetite toe. Learn isn't there. >>There is. Let's go talk about the superpower for a bit. A. I does give employees superpowers because they can do things now. They couldn't do before, but you think about superheroes. They all have an origin story. They always have somewhere where they started and applying a I an organization. It's actually not about doing something completely different. It's about extenuating. What you already d'oh doing something massively better. That's kind of in your DNA already. So we're encouraging all of our clients this week like use the time to understand what you're great at, what your value proposition is. And then how do you use a I to accentuate that? Because your superpower is only gonna last if it's starts with who you are as a company or as a >>person who was your favorite superhero is a kid. Let's see. I was >>kind of into the whole Hall of Justice. Super Superman, that kind of thing. That was probably my cartoon. >>I was a Batman guy. And the reason I love that movie because all the combination of tech, it's kind of reminds me, is what's happening here today. In the marketplace, people are taking data. They're taking a I. They're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's a There's an issue with the quality of data and and there's a there's a skills gap as well. So let's let's start with the data quality problem described that problem and how are you guys attacking it? >>You're a I is only as good as your data. I'd say that's the fundamental problem and organization we worked with. 80% of the projects get slowed down or they get stopped because the company has a date. A problem. That's why we introduce this idea of the A i ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports a I. So how they collect their data, organize their data, analyze their data and ultimately begin to infuse a I into business processes soap. Every organization needs to climb that ladder, and they're all different spots. So for someone might be, we gotta focus on organization a data catalogue. For others, it might be we got do a better job of data collection data management. That's for every organization to figure out. But you need a methodical approach to how you attack the data problem. >>So I wanna ask you about the Aye aye ladder so you could have these verbs, the verbs overlay on building blocks. I went back to some of my notes in the original Ai ai ladder conversation that you introduced a while back. It was data and information architecture at the at the base and then building on that analytics machine learning. Aye, aye, aye. And then now you've added the verbs, collect, organized, analyze and infused. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model, >>I would think of it as building blocks and the methodology, which is you got to decide. Do wish we focus on our data collection and doing that right? Is that our weakness or is a data organization or is it the sexy stuff? The Aye. Aye. The data science stuff. We just This is just a tool to help organizations organize themselves on what's important. I asked every company I visit. Do you have a date? A strategy? You wouldn't believe the looks you get when you ask that question, you get either. Well, she's got one. He's got one. So we got seven or you get No, we've never had one. Or Hey, we just hired a CDO. So we hope to have one. But we use the eye ladder just as a tool to encourage companies to think about your data strategy >>should do you think in the context I want follow up on that data strategy because you see a lot of tactical data strategies? Well, we use Data Thio for this initiative of that initiative. Maybe in sales or marketing, or maybe in R and D. Increasingly, our organization's developing. And should they develop a holistic data strategy, or should they trying to just get kind of quick wins? What are you seeing in the marketplace? >>It depends on where you are in your maturity cycle. I do think it behooves every company to say We understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities gonna be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable tohave an overarching data strategy, and I recommended companies think about a hub and spokes model for this. Have a centralized chief date officer, but your business units also need a cheap date officer. So strategy and one place execution in another. There's a best practice to going about this >>the next you ask the question. What is a I? You get that question a lot, and you said it's about predicting, automating and optimizing. Can we unpack that a little bit? What's behind those three items? >>People? People overreact a hype on topics like II. And they think, Well, I'm not ready for robots or I'm not ready for self driving Vehicles like those Mayor may not happen. Don't know. But a eyes. Let's think more basic it's about can we make better predictions of the business? Every company wants to see a future. They want the proverbial crystal ball. A. I helped you make better predictions. If you have the data to do that, it helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do you software to automate that there's about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far off thing. We're talking about basics better predictions, better automation, better optimization. >>Now interestingly, use the term black magic because because a lot of a I is black box and IBM is always made a point of we're trying to make a I transparent. You talk a lot about taking the bias out, or at least understanding when bias makes sense. When it doesn't make sense, Talk about the black box problem and how you're addressing. >>That starts with one simple idea. A eyes, not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box. With Watson, we have a few things. We've got tools for clients that want to build their own. Aye, aye, to think of it as a tool box you can choose. Do you want a hammer and you want a screwdriver? You wanna nail you go build your own, aye, aye. Using Watson. We also have applications, so it's basically an end user application that puts a I into practice things like Watson assistant to virtually no create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So, aye, aye, for Watson is about tools. You want to build your own applications if you want to consume an application, but we've also got in bed today. I capability so you can pick up Watson and put it inside of any software product in the >>world. He also mentioned that Watson was built with a lot of of of, of open source components, which a lot of people might not know. What's behind Watson. >>85% of the work that happens and Watson today is open source. Most people don't know that it's Python. It's our it's deploying into tensorflow. What we've done, where we focused our efforts, is how do you make a I easier to use? So we've introduced Auto Way. I had to watch the studio, So if you're building models and python, you can use auto. I tow automate things like feature engineering algorithm, selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist could do their job better >>so again come back to a adoption. We talked about three things. Quality, trust and skills. We talked about the data quality piece we talked about the black box, you know, challenge. It's not about skills you mention. There's a 250,000 person Gap data science skills. How is IBM approaching how our customers and IBM approaching closing that gap? >>So think of that. But this in basic economic terms. So we have a supply demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me, who helped a client get to their first big win with a I. It's that simple. We go in for 4 to 6 weeks. It's an elite team. It's not a long project we're gonna get you do for your success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto. Aye, aye. But we also do things like using a eye for building data catalogs, metadata creation data matching so making that data prep process automated through A. I can also help that supply demand. Miss Max. The way that you solve this is we put skills on the field, help clients, and we do a lot of automation in software. That's how we can help clients navigate this. So the >>data science elite team. I love that concept because way first picked up on a couple of years ago. At least it's one of the best freebies in the business. But of course you're doing it with the customers that you want to have deeper relationships with, and I'm sure it leads toe follow on business. What are some of the things that you're most proud of from the data science elite team that you might be able to share with us? >>The clients stories are amazing. I talked in the keynote about origin stories, Roll Bank of Scotland, automating 40% of their customer service. Now customer SATs going up 20% because they put their customer service reps on those hardest problems. That's data science, a lead helping them get to a first success. Now they scale it out at Wonderman Thompson on stage, part of big W P p big advertising agency. They're using a I to comb through customer records they're using auto Way I. That's the data science elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own. Once we left, we got countless examples where this team has gone in for very short periods of time. And clients don't talk about this because they have to talk about it cause they're like, we can't believe what this team did. So we're really excited by the >>interesting thing about the RVs example to me, Rob was that you basically applied a I to remove a lot of these mundane tasks that weren't really driving value for the organization. And an R B s was able to shift the skill sets. It's a more strategic areas. We always talk about that, but But I love the example C. Can you talk a little bit more about really, where, where that ship was, What what did they will go from and what did they apply to and how it impacted their businesses? A improvement? I think it was 20% improvement in NPS but >>realizes the inquiry's they had coming in were two categories. There were ones that were really easy. There were when they were really hard and they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said, we can automate all the easy stuff, we can put all of our people in the hardest things customer sat shot through the roof. Now what is a virtual agent do? Let's decompose that a bit. We have a thing called intent classifications as part of Watson assistant, which is, it's a model that understands customer a tent, and it's trained based on the data from Royal Bank of Scotland. So this model, after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent, because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software, and you can only do that with things like a I. >>And what is the role of the human element in that? How does it interact with that virtual agent. Is it a Is it sort of unattended agent or is it unattended? What is that like? >>So it's two pieces. So for the easiest stuff no humans needed, we just go do that in software for the harder stuff. We've now given the RVs, customer service agents, superpowers because they've got Watson assistant at their fingertips. The hardest thing for a customer service agent is only finding the right data to solve a problem. Watson Discovery is embedded and Watson assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff the humans don't want to do in the first place. >>I'm gonna shift gears a little bit. Talk about, uh, red hat in open shift. Obviously huge acquisition last year. $34 billion Next chapter, kind of in IBM strategy. A couple of things you're doing with open shift. Watson is now available on open shifts. So that means you're bringing Watson to the data. I want to talk about that and then cloudpack for data also on open shifts. So what has that Red had acquisition done for? You obviously know a lot about M and A but now you're in the position of you've got to take advantage of that. And you are taking advantage of this. So give us an update on what you're doing there. >>So look at the cloud market for a moment. You've got around $600 million of opportunity of traditional I t. On premise, you got another 600 billion. That's public clouds, dedicated clouds. And you got about 400 billion. That's private cloud. So the cloud market is fragmented between public, private and traditional. I t. The opportunity we saw was, if we can help clients integrate across all of those clouds, that's a great opportunity for us. What red at open shift is It's a liberator. It says right. Your application once deployed them anywhere because you build them on red hot, open shift. Now we've brought cloudpack for data. Our data platform on the red hot open shift certified on that Watson now runs on red had open shift. What that means is you could have the best data platform. The best Aye, Aye. And you can run it on Google. Eight of us, Azure, Your own private cloud. You get the best, Aye. Aye. With Watson from IBM and run it in any of those places. So the >>reason why that's so powerful because you're able to bring those capabilities to the data without having to move the date around It was Jennifer showed an example or no, maybe was tail >>whenever he was showing Burt analyzing the data. >>And so the beauty of that is I don't have to move any any data, talk about the importance of not having Thio move that data. And I want I want to understand what the client prerequisite is. They really take advantage of that. This one >>of the greatest inventions out of IBM research in the last 10 years, that hasn't gotten a lot attention, which is data virtualization. Data federation. Traditional federation's been around forever. The issue is it doesn't perform our data virtualization performance 500% faster than anything else in the market. So what Jennifer showed that demo was I'm training a model, and I'm gonna virtualized a data set from Red shift on AWS and on premise repositories a my sequel database. We don't have to move the data. We just virtualized those data sets into cloudpack for data and then we can train the model in one place like this is actually breaking down data silos that exist in every organization. And it's really unique. >>It was a very cool demo because what she did is she was pulling data from different data stores doing joins. It was a health care application, really trying to understand where the bias was peeling the onion, right? You know, it is it is bias, sometimes biases. Okay, you just got to know whether or not it's actionable. And so that was that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? >>Start using cloudpack for data. We've got something on the Web called cloudpack experiences. Anybody can go try this in less than two minutes. I just say go try it. Because cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call scheme a folding. So a skiing version of the schema from your source writing compact for data. This is like instant access to your data. >>It sounds like magic. OK, last question. One of the big takeaways You want people to leave this event with? >>We are trying to inspire clients to give a I shot. Adoption is 4 to 10% for what is the largest economic opportunity we will ever see in our lives. That's not an acceptable rate of adoption. So we're encouraging everybody Go try things. Don't do one, eh? I experiment. Do Ah, 100. Aye, aye. Experiments in the next year. If you do, 150 of them probably won't work. This is where you have to change the cultural idea. Ask that comes into it, be prepared that half of them are gonna work. But then for the 52 that do work, then you double down. Then you triple down. Everybody will be successful. They I if they had this iterative mindset >>and with cloud it's very inexpensive to actually do those experiments. Rob Thomas. Thanks so much for coming on. The Cuban great to see you. Great to see you. All right, Keep right, everybody. We'll be back with our next guest. Right after this short break, we'll hear from Miami at the IBM A I A data form right back.

>> From the Hard Rock Hotel in Las Vegas, it's theCUBE! Covering HoshoCon 2018! Brought to you by Hosho. >> Okay, welcome back everyone, we're here live in Las Vegas for the first security blockchain conference's inaugural event, HoshoCon, and it's all about the top brains in the industry coming together, with experience and tech chops to figure out the future in security. I'm John Furrier, the host of theCUBE. Our next guest, Andre McGregor, who's the partner and head of global security for TLDR. Welcome to theCUBE, thanks for joining me. >> Thank you for having me. >> So you have a background, we were just talking off-camera, FBI, you've been doing the cyber for a long time, cyber-security, mostly enterprise-grade, large-scale. Now we're in crypto, where you have small set of teams, running massive scale, with money involved. >> Correct. So guess what, money attracts. >> Right. People who want it, want that money. Lot of hacks, $400 million in Japan, plus 60 million over here, you add it all up, there's a billion so far this year, who knows what really the number is, it's pretty big. >> It is, and what's concerning and the reason why I came over in this space was the number of hacks that were happening. My company, we get probably a call a week, whether it's high net worth individuals, CEO, exchanges, we've helped a couple, some that you'd know of if I told you who they were, trying to get out of a very bad situation. And interim response has been big, but what we've learned is that it's the same old fraud, the same old security tactics that are being used against some of these crypto-companies. >> And we've seen it all the time, everyone's had fraud alerts on their credit card, this is like classic blocking and tackling, at a whole 'nother level. >> It is, because if you think about it from, like a traditional start-up, you have a company that's small, they have time to develop their MVP, they go out and do maybe a seed round, friends and family, they're sort of ramping up over time, whereas we basically flipped the model upside-down, the same six founders now have $10 million worth of crypto, and they're not protecting it in the ways they think they should, because they're in hyper-growth mode. So the bad guys have determined that as a great place to target, and now as we see in the news, it's actually happening. >> Yeah, and Hartej, the co-founder of Hosho, was just one talking about physical security, in the sense of you got to watch out where you go too now, it's not just online security, it's physical security. So start-ups have that kind of fast and loose kind of culture. >> Well, if you think about it, traditional security in corporations, I can put everyone in a building, I have this similar or same network egress points, I can protect those, I can do the gates, guards, guns, perimeters around, but I got people working from home now in the crypto space, everyone's got their own setup. If someone's in an audience, they say oh, I've been in the blockchain space since 2010 or 11, I can make assumptions about them, about their financial worth, and other people are doing the same, but having nefarious reasons. >> Yeah, you connected the dots okay, it was $0.22 in 2011, so therefore, if they had kept a little bit of Bitcoin-- >> They would be doing very well. >> They're a target. >> Therefore, they're a target now. So when you think about it, you put all those scams together, it becomes sort of a hot topic for-- >> I just got into crypto. (laughs) >> Good answer, good answer. >> Alright, so let's talk about this security hack. Because obviously, in the enterprise tech, we cover a lot of those events across the year. IoT Edge is a huge topic, cloud computing booming, so now you have a lot of compute, which is good, and for bad actors too. So you have now a service area that's now, no perimeter, there's no egress points to manage. Is there a digital way to kind of map this out, and does blockchain give us any advantages or is there anything on the horizon that you see, where we can, in digital form? >> Well, I mean the true reason I came to the blockchain space, having worked hundreds of victim notifications and several dozen actual intrusions, from large intrusions at banks that are top five in the world, all the way down to small core defense contractors, you realize it's always a server you didn't know about, credentials that had more access than they should, obviously gaining access to a centralized server, that then gets exposed and allows that data to be leaked out. So the idea of blockchain and being able to decentralize, distribute that data, own it, and keep it cryptographically pure, and also being able to essentially remove the single source of failure that we saw in a lot of these hacks is exciting. Obviously, blockchain is also not the answer to everything. So in some ways, the spread sheet is still a spread sheet, and the MongoDB will still be the MongoDB, but-- >> The post-it next to your computer, your private key on it. >> But at the same point in time, it all comes down to cyber-hygiene, right? I mean, the stuff that we're looking at, the hacks that we're seeing, the hacks that I'm dealing with and my company dealing with, day in and day out, are not sophisticated. They may be sophisticated actors, but they're using insophisticated means, and of course, I hate to harp on it, but e-mail is still the number one intrusion vector, we all have it, we all use it. You could take stats from the FBI that says 92%, you could take stats from Verizon that says 93%, but that will be the number one way in. >> And phishing is the classic attack point. >> It will always be, because-- >> It's easy. >> I can manipulate people, I find the right opportunity, I always say even I've been phished. It happens, the way your mind is, it's just how you react, is what we need to teach people. >> It's really clicking on that one thing, that just takes one time. >> Yep. >> A PDF that you think is a document from work, or potentially a job opportunity, a new thing, sports scores, your favorite team, girlfriend, boyfriend, whatever, I mean, you don't know! >> But, I'm going to challenge you on this, you get, you click on that bad link, or you feel like your computer has been hacked, who do you call? Do you actually have someone that you can call? There's no cyber 911. Unless you are a high net worth individual, or being targeted by a nation-state, you're not calling the FBI. So who do you call? And that's a problem that we have in our industry right now. I mean, I guess I've been the person that people have been calling, which is fine, I want to help them. 12 years as a firefighter on top of my FBI career, I'm used to helping people in time of need. But really, in the grand scheme of things, there's not enough Mandiants or Verizons are too big. So for these smaller, six-person companies, that don't have $500,000 to spend on instant response, they actually have no one to call when they actually do click something bad. >> And the people they punch in a call, the ones that aren't actually there to help them. Sometimes they get honey-potted into another vector. >> Sure. >> Which is hey, how can I help you? >> Or I even challenge it a bit further. You call any of these companies when your phone has been hacked, you SIM-swap, whatever it is, and you need to sign a master services agreement, you need to go through all the legalese, while you're actively being hacked. Like, it's happening hour after hour, and you're seeing it, your accounts are being compromised and being taken over, and you're trying to find outside counsel to do redline. So in emergency services, we say, don't exchange business cards at the disaster site. It's not the time that you should be saying hi, I'm introducing myself, we should figure out all the retainers, inter-response, legal questions beforehand, so that at 2:00 in the morning, someone calls, and you have someone pick up the phone. >> Yeah, and you know what the costs are going to be, 'cause it's solve the problem at hand, put out that fire, if you will. Okay, so I got to ask you a question on how do people protect themselves? 'Cause we know Michael Terpin's doing a fireside chat, it's well known that he sued AT&T, he had his phone SIM swapped out, this is a known vector in the crypto community. Most people maybe in the mainstream might not know it. But you know, your phone can be hacked. >> Yes. >> Simple two-factor authentication's not enough. >> Correct. >> What is the state-of-the-art solution for people who want to hold crypto, any meaningful amount, could be casual money, to high net worth individual wants to have a lot of crypto. >> I mean, I spent a good amount of my time talking about custody. We've sort of pivoted off to a new part of our business line, that deals specifically around institutional custody solutions, and helping people get through this particular process. But we all know, especially from that particular case, that SMS compromises, after account takeover of a phone, is high. Hardware tokens are always going to be something that I'm going to, Harp or YubiKey, or something like that, where I'm still having the ability to keep a remote adversary away from being able to attack my system that has my private keys, or whatever high-value data I have on it. But if I think about it at the end of the day, I'm going to need to transfer that risk. I would like to say that we can transfer all risk, but instead for the people that have a lot of crypto, you're going to need to look for a good custody solution, you're going to need to look and trust the team, you're going to need to look and trust the technology they have, and you're going to have to get insurance. Because there are so many vectors, in a certain point in time, we can't go back to the wild west, where we're actually >> The insider job is, is really popular now too. >> It is, but there are ways around the collusion, counterparty, third party risk of ensuring that not one person can take the billion dollars worth of crypto and run away off to Venezuela and never appear again. But again, it comes down to basic hygiene. I ask people, I've surveyed hundreds of people in the crypto space, and I ask simple questions like VPNs, and I'm still getting a third to a half of people are using VPNS. Very simple things that people are not doing. When you looks at password for example, if anyone still has a password under 12 characters, then game over. I mean, there are a variety of ways of hacking them. I can use GPU servers to do them very quickly. I won't go into all the different options that are there. People still-- >> So 12 characters, alphanumeric obviously, with-- >> With special characters as well. >> Special characters. >> But the assumption, let's just make the assumption, that either those passwords have been cracked already, because they've already been dumped, people share passwords, they get used again, and then the entropy is exponentially higher with every single character after 12. So my password's 22 characters, sure it's a pain to type it in, but when you think about it, at the end of the day, when I combine that with a password manager that also has a YubiKey that's a hardware token, and I require that access all the time, then I don't run into the problem that someone's going to compromise a single system to get into multiple systems. >> And then also, I know there's a lot of Google people as well, they're looking at security at the hardware level, down to the firmware. >> Sure, sure. >> There's all kinds of-- >> I mean, obviously, you could use the TPM chip as well, and that's something that we should be better at, as a society. >> So while I got you here, I might as well ask you about the China super micro modchip baseboard management controller, BMC, that was reported in Bloomberg, debunked, Apple and Amazon both came out and said no, that's been confirmed. They shift their story a little bit too, the reality probably there is some mods going on, it's manufactured in China. I mean, it's a zero-margin business going to zero, why not just let the Chinese continue to develop, and have a higher-value security solution somewhere else, that's what some people are discussing, like okay, like the DRAM market was. >> Yep. >> Let the Japanese own that, they did, and then Intel makes the Pentium. Wall Street Journal reported that, Andy Kessler. So the shifts in the industry, certainly China's manufacturing the devices. There's no surprise when you go to China, and if you turn on your iPhone, it says Apple would like to push an update, but that's not Apple, it's a forged certificate, pretty much public knowledge. The DNS is controlled by China, and a certificate, these are things that they can control, that's, this is the new normal. >> It, it-- >> If you know the hardware, you can exploit it. >> We've been dealing with supply-chain issues since Maxtor hard drives in Indonesia. So was I shocked when I hear stories about that? No, I'm sort of scared myself into a corner, working in skiffs over the years and reading the various reports that come out about supply chain poisoning. >> Certainly possible. >> It's happening. I mean, it's just to what extent is still something that may or may not be known to its full extent, but it's something that will happen, always happens, and will continue to happen. And so at a certain point in time, capitalism does step in and says alright, well, guess what, China, the way I see it is, China wants to be a super-power. At a certain point, they know that people are looking at them, and saying we can't trust you. So they're going to clean up their house, just like anyone else. >> It's inevitable for them. >> It is inevitable. Because they need to show that they can be a trusting force, in the world economy. And at the same time, we're going to have competition out there that's essentially going to say, alright, we can actually prove to have a much better, stronger, validated supply chain that you'll use. >> I mean, IoT and blockchain, great solutions for supply chain. >> 100%. >> I mean, so this is where-- >> I mean, we're talking, I mean, I was actually on a plane flying from Phoenix, to Santa Fe, New Mexico, and I was sitting next to a guy, who was just like, I just want to use a blockchain to be able to deal with a supply chain around compromised food. So in the sense that if you think about it, fish for example, there's a lot of fake fish, fake type of tuna and other stuff that's out there, that people don't know the difference. But the restaurants are paying double, triple the amount of money for it. You start taking things like elephant tusks, you take things like just being able to track things that no one's really thinking about, and you're just like huh, I never thought of it that way. So at the end of the day, I still get surprised with what people are thinking about, that they can do with the blockchain. >> So Andre, question for you here, this event, what's the impact of this event and for the industry, in your opinion? Obviously, a lot of smart people here talking, candidly, sometimes maybe a little bit contentious about philosophies, regulation, no regulation, self-governance, lot of different things being discussed as exploration, to a new proficiency level that we need to get to. What are some of the hallway conversations you're hearing, and involved in? >> A lot of mine are obviously around custody. That is the topic of the moment. And for me, I'm in learning mode. I recognize that I've spent a lot of time in cyber-security. However, whereas it relates to blockchain and digital asset custody, whether it's utility tokens or security tokens, I'm on the CFTC Technology Advisory Committee, specifically, with cyber-security and custody, and so I want to take in as much information as I can, bring it back to the committee, bring it back to the commissioners, and help them create the proper regulations and standards, whether it's through an SRO, or it's through the government itself. >> For the folks that may watch this video later, that are new to the area, what does custody actually mean? Obviously, holding crypto, but define custody in context of these conversations, what is it, what's the threshold issues that are being discussed? >> Sure. I mean, to break it down, custody is very similar to a bank. So you are, you're saying I have a lot of X. It could be baseball cards, it could be gold bars, it could be fiat cash. And I want to have someone hold it, and I'm going to trust them with that. Of course, I'm transferring that risk, and with that, I have an expectation to have a qualified custodian, that has rules and regulations of how they're going to actually manage it, how they're going to control it, ensure that the risk, that people aren't going to take it. It could be, again, the Monet, it could be the Johnny Bench Ricky card, it could be 100 million blocks of gold. But I also want to have a level of insurance. That insurance could come from the insurance industry themselves, and allowing me to protect it in case something does happen to that, or the government. The FDIC, $250,000 for your bank account is a type of insurance that people are using. By the end of the day, from an institutional perspective, you want a pure custodian that takes all the risk. The government wants to say a certain point, that that custodian can allow for margin call, so that the client can't come in and say, well I'm not going to pay out $100 million worth of crypto, and I'm going to seize, or seizure of funds as well. And that's what's being set up right now. Traditional banks are not ready to handle that. Traditional auditing firms, like PWC or Ernst & Young, are still trying to figure out how they'd even be given a qualified opinion, as it relates to how-- >> So it's not so much that they are not have the appetite to do it, they don't have systems, they don't have expertise, >> They don't have systems, they don't have expertise, >> They don't have workflows. >> And right now, things are so new and so volatile, that they're sort of almost putting their toe in the water, but really not sure what the temperature is yet of the water to hop in. >> If someone wants to go to court, you say hey, prove it. Well, it's encrypted, I don't know who did it. >> Well, and the thing is is that when you have 53 states and territories with different money-transmitting laws, on top of the countless federal agencies and departments that are managing that, it is hard to come to consensus. It is much easier in a place like Bermuda, where the government is small enough where everyone can get together pretty quickly, have consensus on an opinion of how they want to deal with the crypto market, deal with custody, pass a regulation, and what's nice about Bermuda is it has crown ascendancy, so the UK government still approves it. >> And they move fast on the regulation side. They literally just passed-- >> They are the only jurisdiction that has a fully complete law surrounding cryptocurrency. >> You're bullish on Bermuda. >> I am, because I saw the efficiency there. And I expressed my same opinion with the CFTC, when I was doing my hearing last week, that it's nice to see the speed, but it's also a small island that allows for that speed. >> And they have legitimate practices that have been going on for years in other industries. >> Right, so there's no dirty money, there's no anything that people are sort of concerned with, they have the same AML, KYC, anti-money laundering and know your customer regulations that you would expect if you had your money in the United States. >> Yeah, we had a chance to interview the honorable charge there. >> Premier Burt, oh very nice. >> Yeah, he's great, and Toronto, so it's awesome. >> Nice. >> Alright, so final takeaway, for this show here, what's your takeaway about this event, the impact to the industry? >> This is a very important event, because I think people are still trying to get their footing around blockchain, they're still trying to get their footing around digital asset protections. And if we can get the smart people in one room, and they can share knowledge, and then we can come together as a community, and create some standards that make sense, then we're protecting the world. >> Well Andre, I'm glad you're in the industry, 'cause your expertise and background on the commercial side and government side certainly lend well to the needs. (laughs) So to speak. We need you, we need more of you. Thanks for coming on theCUBE, really appreciate your commentary and your insight. It's theCUBE, bringing the insights here, we are live in Las Vegas for HoshoCon, I'm John Furrier with theCUBE, we'll be back with more coverage after this short break. (upbeat music)

>> Announcer: Live from the Bill Graham Auditorium in San Francisco, it's theCUBE, covering Pure Storage Accelerate 2018. Brought to be you by Pure Storage. >> Welcome back to theCUBE, we are live at Pure Storage Accelerate 2018 in San Francisco. I'm Lisa Prince Martin with Dave The Who Vellante, and we're with David Hatfield, or Hat, the president of Purse Storage. Hat, welcome back to theCUBE. >> Thank you Lisa, great to be here. Thanks for being here. How fun is this? >> The orange is awesome. >> David: This is great. >> Super fun. >> Got to represent, we love the orange here. >> Always a good venue. >> Yeah. >> There's not enough orange. I'm not as blind yet. >> Well it's the Bill Graham, I mean it's a great venue. But not generally one for technology conferences. >> Not it's not. You guys are not conventional. >> So far so good. >> But then-- >> Thanks for keeping us out of Las Vegas for a change. >> Over my dead body I thin I've said once or twice before. >> Speaking of-- Love our customers in Vegas. Unconventional, you've said recently this is not your father's storage company. What do you mean by that? >> Well we just always want to do things a little bit less conventional. We want to be modern. We want to do things differently. We want to create an environment where it's community so our customers and our partners, prospective customers can get a feel for what we mean by doing things a little bit more modern. And so the whole orange thing is something that we all opt in for. But it's more about really helping transform customer's organizations think differently, think out of the box, and so we wanted to create a venue that forced people to think differently, and so the last three years, one was on Pier 48, we transformed that. Last year was in a big steelworkers, you know, 100 year old steel manufacturing, ship building yard which is now long since gone. But we thought the juxtaposition of that, big iron rust relative to what we're doing from a modern solid state perspective, was a good metaphor. And here it's about making music, and how can we together as an industry, develop new things and develop new songs and really help transform organizations. >> For those of you who don't know, spinning disk is known as spinning rust, right? Eventually, so very clever sort of marketing. >> The more data you put on it the slower it gets and it gets really old and we wanted to get rid of that. We wanted to have everything be online in the data center, so that was the point. >> So Hat, as you go around and talk to customers, they're going through a digital transformation, you hear all this stuff about machine intelligence, artificial intelligence, whatever you want to call it, what are the questions that you're getting? CEO's, they want to get digital right. IT professionals are wondering what's next for them. What kind of questions and conversations are you having? >> Yeah, I think it's interesting, I was just in one of the largest financial services companies in New York, and we met with the Chief Data Officer. The Chief Data Officer reports into the CEO. And he had right next to him the CIO. And so they have this development of a recognition that moving into a digital world and starting to harness the power of data requires a business context. It requires people that are trying to figure out how to extract value from the data, where does our data live? But that's created the different organization. It drives devops. I mean, if you're going to go through a digital transformation, you're going to try and get access to your data, you have to be a software development house. And that means you're going to use devops. And so what's happened from our point of view over the last 10 years is that those folks have gone to the public cloud because IT wasn't really meeting the needs of what devops needed and what the data scientists were looking for, and so what we wanted to create not only was a platform and a tool set that allowed them to bridge the gap, make things better today dramatically, but have a platform that gets you into the future, but also create a community and an ecosystem where people are aware of what's happening on the devop's side, and connect the dots between IT and the data scientists. And so we see this exploding as companies digitize, and somebody needs to be there to help kind of bridge the gap. >> So what's your point of view and advice to that IT ops person who maybe really good at provisioning LUNS, should they become more dev like? Maybe ops dev? >> Totally, I mean I think there's a huge opportunity to kind of advance your career. And a lot of what Charlie talked about and a lot of what we've been doing for nine years now, coming up on nine years, is trying to make our customers heroes. And if data is a strategic asset, so much so they're actually going to think about putting it on your balance sheet, and you're hiring Chief Data Officers, who knows more about the data than the storage and infrastructure team. They understand the limitations that we had to go through over the past. They've recognized they had to make trade offs between performance and cost. And in a shared accelerated storage platform where you have tons of IO and you can put all of your applications (mumbles) at the same time, you don't have to make those trade offs. But the people that really know that are the storage leads. And so what we want to do is give them a path for their career to become strategic in their organization. Storage should be self driving, infrastructure should be self driving. These are not things that in a boardroom people care about, gigabytes and petabytes and petaflops, and whatever metric. What they care about is how they can change their business and have a competitive advantage. How they can deliver better customer experiences, how they can put more money on the bottom line through better insights, etc. And we want to teach and work with and celebrate data heroes. You know, they're coming from the infrastructure side and connecting the dots. So the value of that data is obviously something that's new in terms of it being front and center. So who determines the value of that data? You would think it's the business line. And so there's got to be a relationship between that IT ops person and the business line. Which maybe here to for was somewhat adversarial. Business guys are calling, the clients are calling again. And the business guys are saying, oh IT, they're slow, they say no. So how are you seeing that relationship changing? >> It has to come together because, you know, it does come down to what are the insights that we can extract from our data? How much more data can we get online to be able to get those insights? And that's a combination of improving the infrastructure and making it easy and removing those trade offs that I talked about. But also being able to ask the right questions. And so a lot has to happen. You know, we have one of the leaders in devops speaking tomorrow to go through, here's what's happening on the software development and devops side. Here's what the data scientists are trying to get at. So our IT professionals understand the language, understand the problem set. But they have to come together. We have Dr. Kate Harding as well from MIT, who's brilliant and thinking about AI. Well, there's only .5% of all the data has actually been analyzed. You know, it's all in these piggy banks as Burt talked about onstage. And so we want to get rid of the piggy banks and actually create it and make it more accessible, and get more than .5% of the data to be usable. You know, bring as much of that online as possible, because it's going to provide richer insights. But up until this point storage has been a bottleneck to making that happen. It was either too costly or too complex, or it wasn't performing enough. And with what we've been able to bring through solid state natively into sort of this platform is an ability to have all of that without the trade offs. >> That number of half a percent, or less than half a percent of all data in the world is actually able to be analyzed, is really really small. I mean we talk about, often you'll here people say data's the lifeblood of an organization. Well, it's really a business catalyst. >> David: Oil. >> Right, but catalysts need to be applied to multiple reactions simultaneously. And that's what a company needs to be able to do to maximize the value. Because if you can't do that there's no value in that. >> Right. >> How are you guys helping to kind of maybe abstract storage? We hear a lot, we heard the word simplicity a lot today from Mercedes Formula One, for example. How are you partnering with customers to help them identify, where do we start narrowing down to find those needles in the haystack that are going to open up new business opportunities, new services for our business? >> Well I think, first of all, we recognize at Pure that we want to be the innovators. We want to be the folks that are, again, making things dramatically better today, but really future-proofing people for what applications and insights they want to get in the future. Charlie talked about the three-legged stool, right? There's innovations that's been happening in compute, there's innovations that have been happening over the years in networking, but storage hasn't really kept up. It literally was sort of the bottleneck that was holding people back from being able to feed the GPUs in the compute that's out there to be able to extract the insights. So we wanted to partner with the ecosystem, but we recognize an opportunity to remove the primary bottleneck, right? And if we can remove the bottleneck and we can partner with firms like NVIDIA and firms like Cisco, where you integrate the solution and make it self driving so customers don't have to worry about it. They don't have to make the trade offs in performance and cost on the backend, but it just is easy to stamp out, and so it was really great to hear Service Now and Keith walk through is story where he was able to get a 3x level improvement and something that was simple to scale as their business grew without having an impact on the customer. So we need to be part of an ecosystem. We need to partner well. We need to recognize that we're a key component of it because we think data's at the core, but we're only a component of it. The one analogy somebody shared with me when I first started at Pure was you can date your compute and networking partner but you actually get married to your storage partner. And we think that's true because data's at the core of every organization, but it's making it available and accessible and affordable so you can leverage the compute and networking stacks to make it happen. >> You've used the word platform, and I want to unpack that a little bit. Platform versus product, right? We hear platform a lot today. I think it's pretty clear that platforms beat products and that allows you to grow and penetrate the market further. It also has an implication in terms of the ecosystem and how you partner. So I wonder if you could talk about platform, what it means to you, the API economy, however you want to take that. >> Yeah, so, I mean a platform, first of all I think if you're starting a disruptive technology company, being hyper-focused on delivering something that's better and faster in every dimension, it had to be 10x in every dimension. So when we started, we said let's start with tier one block, mission critical data workloads with a product, you know our Flash Array product. It was the fastest growing product in storage I think of all time, and it still continues to be a great contributor, and it should be a multi-billion dollar business by itself. But what customers are looking for is that same consumer like or cloud like experience, all of the benefits of that simplicity and performance across their entire data set. And so as we think about providing value to customers, we want to make sure we capture as much of that 99.5% of the data and make it online and make it affordable, regardless of whether it's block, file, or object, or regardless if it's tier one, tier two, and tier three. We talk about this notion of a shared accelerated storage platform because we want to have all the applications hit it without any compromise. And in an architecture that we've provided today you can do that. So as we think about partnering, we want to go, in our strategy, we want to go get as much of the data as we possibly can and make it usable and affordable to bring online and then partner with an API first open approach. There's a ton of orchestration tools that are out there. There's great automation. We have a deep integration with ACI at Cisco. Whatever management and orchestration tools that our customer wants to use, we want to make those available. And so, as you look at our Flash Array, Flash Deck, AIRI, and Flash Blade technologies, all of them have an API open first approach. And so a lot of what we're talking about with our cloud integrations is how do we actually leverage orchestration, and how do we now allow and make it easy for customers to move data in and out of whatever clouds they may want to run from. You know, one of the key premises to the business was with this exploding data growth and whether it's 30, 40, 50 zettabytes of data over the next you know, five years, there's only two and a half or three zettabytes of internet connectivity in those same period of time. Which means that companies, and there's not enough data platform or data resources to actually handle all of it, so the temporal nature of the data, where it's created, what a data center looks like, is going to be highly distributed, and it's going to be multi cloud. And so we wanted to provide an architecture and a platform that removed the trade offs and the bottlenecks while also being open and allowing customers to take advantage of Red Shift and Red Hat and all the container technologies and platform as a service technologies that exist that are completely changing the way we can access the data. And so we're part of an ecosystem and it needs to be API and open first. >> So you had Service Now on stage today, and obviously a platform company. I mean any time they do M and A they bring that company into their platform, their applications that they build are all part of that platform. So should we think about Pure? If we think about Pure as a platform company, does that mean, I mean one of your major competitors is consolidating its portfolio. Should we think of you going forward as a platform company? In other words, you're not going to have a stovepipe set of products, or is that asking too much as you get to your next level of milestone. >> Well we think we're largely there in many respects. You know, if you look at any of the competitive technologies that are out there, you know, they have a different operating system and a different customer experience for their block products, their file products, and their object products, etc. So we wanted to have a shared system that had these similar attributes from a storage perspective and then provide a very consistent customer experience with our cloud-based Pure One platform. And so the combination of our systems, you hear Bill Cerreta talk about, you have to do different things for different protocols to be able to get the efficiencies in the data servers as people want. But ultimately you need to abstract that into a customer experience that's seamless. And so our Pure One cloud-based software allows for a consistent experience. The fact that you'll have a, one application that's leveraging block and one application that's leveraging unstructured tool sets, you want to be able to have that be in a shared accelerated storage platform. That's why Gartner's talking about that, right? Now you can do it with a solid state world. So it's super key to say, hey look, we want consistent customer experience, regardless of what data tier it used to be on or what protocol it is and we do that through our Pure One cloud-based platform. >> You guys have been pretty bullish for a long time now where competition is concerned. When we talk about AWS, you know Andy Jassy always talks about, they look forward, they're not looking at Oracle and things like that. What's that like at Pure? Are you guys really kind of, you've been also very bullish recently about NVME. Are you looking forward together with your partners and listening to the voice of the customer versus looking at what's blue over the corner? >> Yes, so first of all we have a lot of respect for companies that get big. One of my mentors told me one time that they got big because they did something well. And so we have a lot of respect for the ecosystem and companies that build a scale. And we actually want to be one of those and are already doing that. But I think it's also important to listen and be part of the community. And so we've always wanted to the pioneers. We always wanted to be the innovators. We always wanted to challenge conventions. And one of the reasons why we founded the company, why Cos and Hayes founded the company originally was because they saw that there was a bottleneck and it was a media level bottleneck. In order to remove that you need to provide a file system that was purpose built for the new media, whatever it was going to be. We chose solid state because it was a $40 billion industry thanks to our consumer products and devices. So it was a cost curve where I and D was going to happen by Samsung and Toshiba and Micron and all those guys that we could ride that curve down, allowing us to be able to get more and more of the data that's out there. And so we founded the company with the premise that you need to remove that bottleneck and you can drive innovation that was 10x better in every dimension. But we also recognize in doing so that putting an evergreen ownership model in place, you can fundamentally change the business model that customers were really frustrated by over the last 25 years. It was fair because disk has lots of moving parts, it gets slower with the more data you put on, etc., and so you pass those maintenance expenses and software onto customers. But in a solid state world you didn't need that. So what we wanted to do was actually, in addition to provide innovation that was 10x better, we wanted to provide a business model that was evergreen and cloud like in every dimension. Well, those two forces were very disruptive to the competitors. And so it's very, very hard to take a file system that's 25 years old and retrofit it to be able to really get the full value of what the stack can provide. So we focus on innovation. We focus on what the market's are doing, and we focus on our customer requirements and where we anticipate the use cases to be. And then we like to compete, too. We're a company of folks that love to win, but ultimately the real focus here is on enabling our customers to be successful, innovating forward. And so less about looking sidewise, who's blue and who's green, etc. >> But you said it before, when you were a startup, you had to be 10x better because those incumbents, even though it was an older operating system, people's processes were wired to that, so you had to give them an incentive to do that. But you have been first in a number of things. Flash itself, the sort of All-Flash, at a spinning disk price. Evergreen, you guys set the mark on that. NVME you're doing it again with no premium. I mean, everybody's going to follow. You can look back and say, look we were first, we led, we're the innovator. You're doing some things in cloud which are similar. Obviously you're doing this on purpose. But it's not just getting close to your customers. There's got to be a technology and architectural enabler for you guys. Is that? >> Well yeah, it's software, and at the end of the day if you write a file system that's purpose built for a new media, you think about the inefficiencies of that media and the benefits of that media, and so we knew it was going to be memory, we knew it was going to be silicon. It behaves differently. Reads are effectively free. Rights are expensive, right? And so that means you need to write something that's different, and so you know, it's NVME that we've been plumbing and working on for three years that provides 44,000 parallel access points. Massive parallelism, which enables these next generation of applications. So yeah we have been talking about that and inventing ways to be able to take full advantage of that. There's 3D XPoint and SCM and all kinds of really interesting technologies that are coming down the line that we want to be able to take advantage of and future proof for our customers, but in order to do that you have to have a software platform that allows for it. And that's where our competitive advantage really resides, is in the software. >> Well there are lots more software companies in Silicon Valley and outside Silicon Valley. And you guys, like I say, have achieved that escape velocity. And so that's pretty impressive, congratulations. >> Well thank you, we're just getting started, and we really appreciate all the work you guys do. So thanks for being here. >> Yeah, and we just a couple days ago with the Q1FY19, 40%, you have a year growth, you added 300 more customers. Now what, 4800 customers globally. So momentum. >> Thank you, thank you. Well we only do it if we're helping our customers one day at a time. You know, I'll tell you that this whole customer first philosophy, a lot of customers, a lot of companies talk about it, but it truly has to be integrated into the DNA of the business from the founders, and you know, Cos's whole pitch at the very beginning of this was we're going to change the media which is going to be able to transform the business model. But ultimately we want to make this as intuitive as an iPhone. You know, infrastructure should just work, and so we have this focus on delivering simplicity and delivering ownership that's future proofed from the very beginning. And you know that sort of permeates, and so you think about our growth, our growth has happened because our customers are buying more stuff from us, right? If you look at our underneath the covers on our growth, 70 plus percent of our growth every single quarter comes from customers buying more stuff, and so, as we think about how we partner and we think about how we innovate, you know, we're going to continue to build and innovate in new areas. We're going to keep partnering. You know, the data protection staff, we've got great partners like Veeam and Cohesity and Rubrik that are out there. And we're going to acquire. We do have a billion dollars of cash in the bank to be able to go do that. So we're going to listen to our customers on where they want us to do that, and that's going to guide us to the future. >> And expansion overseas. I mean, North America's 70% of your business? Is that right? >> Rough and tough. Yeah, we had 28%-- >> So it's some upside. >> Yeah, yeah, no any mature B2B systems company should line up to be 55, 45, 55 North America, 45, in line with GDP and in line with IT spend, so we made investments from the beginning knowing we wanted to be an independent company, knowing we wanted to support global 200 companies you have to have operations across multiple countries. And so globalization is always going to be key for us. We're going to continue our march on doing that. >> Delivering evergreen from an orange center. Thanks so much for joining Dave and I on the show this morning. >> Thanks Lisa, thanks Dave, nice to see you guys. >> We are theCUBE Live from Pure Accelerate 2018 from San Francisco. I'm Lisa Martin for Dave Vellante, stick around, we'll be right back with our next guests.

>> Narrator: Live from San Francisco. It's theCUBE, covering DevNet Create 2017, brought to you by Cisco. >> Welcome back, everyone. We're live in San Francisco for CUBE's special coverage, exclusive coverage, of Cisco Systems DevNet Create. It's an inaugural event for DevNet, a new extension to their developer program. DevNet, which is their classic developer program for the Cisco ecosystem, network guys, so on and so forth, moving packets around, hardware guys. DevNet Create is about developers and dev ops and cloud-native, all the goodness of application developers. Where apps meets infrastructure, certainly with the Cisco acquisition of AppDynamics, a new world order is coming down the pipe. Cisco's moving up the stack. I'm John Furrier, Peter Burris is my co-host, our next guest is Val Bercovici, CUBE alumni and also guest analyst in our studio in Palo Alto, also the cofounder of Peritus.ai, and now you can talk about it. Welcome to theCUBE. >> Thanks, John. We get to talk about it, finally. >> So before we get into your company, and I want to drill into it because the first public CUBE interview, drilling down on what you're working on. What's your take on Cisco's event here, because, I've known Cisco since I moved to Silicon Valley, 18 years, and even before then, and they scaled all the internet connecting networks. There's always been a discussion internally inside Cisco about moving up the stack. And it's always been kind of like a Civil War. Half the company wants to move up the stack, half doesn't, and now, you've been in NetApp, you know this world and its infrastructure, its hardware, its gear, its boxes, network packets. This is a seminal moment for Cisco. They've tried some open source before, but this seems like an all-in bet. Your thoughts? >> It is, and I was just telling Yodi Rahm, before we went live on stage that I think this is like Goldilocks event, right? It's my first. Apparently, it's the first one of its kind here at Cisco, and for me it's not too big. It's not too small. I find it really just the right size, and I find it very well-targeted, in terms of the fellow speakers, panelists that I was on with today in terms of, I see the right amount of laptops, the right amount of code, basically, amongst the attendees on the floor here. So my first impression, 'cause that's all I have so far, is it's a very well-targeted show, and it's not unique anymore. You'll notice Intel kind of pulled back from having one large event, one large annual event, and smaller more targeted events for developers, for operators, for other ISVs, and so forth. >> You're talking about the IDF Intel Developer Forum. >> The IDF, yeah, it's no longer a big, monolithic event. They split it up into more- >> And IBM has also collapsed their shows into one monster show. So little micro-events seem to be the norm. >> Yeah, I wouldn't even call it quite even a micro-event. It's a bit bigger than that, but it's not a VMworld . >> It's not a Dreamforce. >> It's not a COMDEX to VR sales. >> Interesting, I like they did their homework on the panels. So in terms of subject matter, the agenda looks great, but I do agree with you. I like how principals are here. It's not just staff here. It's both people in the trenches at Cisco, and the execs are here. Susie Wee and some other folks, they run Cisco a lot. They're all here. >> And their CTO, this morning, I caught the opening keynote livestream on the way over here. She did a fantastic job describing the role of the infrastructure developer, which is something that is a bit nebulous to nail down, at least it has been in the past, and I'm really glad that Cisco is echoing that, because I think it helps their entire ecosystem, their partner ecosystem, particularly former employers like NetApp of mine. >> I'm usually critical of big companies trying to put their toe in the water with some event that looks like a little cloud washing or you know, here or there, but I think Cisco's got a legit opportunity with programmable infrastructure. And I think, just in general, straight up, they do, because their infrastructure, and Peter and I talked about that. But I think IoT is really the big driver. They could really, that's a network connection. It's at the edge. It requires intelligence. That's a good angle for them. >> It's a great angle. The only beef I have, the gripe I have, is they still call it IoE, I think. If it's going to be Internet of Everything, and it's Internet of nothing, right? I really wish they'd kind of stick to the agreed term, and what they are doing of course is giving- >> But they were first with IoE. They were, you got to give Cisco, when they ran those commercials, what 10 years ago? >> Yeah. >> You know. >> It's a personal in for me. The commercials are fantastic. It's just the term bothers me. >> They got dogma with IoE, come on, get rid of it. Okay, tell me about your company? >> So Peritus.ai, we've realized now there's a chance to go beyond traditional digital disruption of existing industries to cognitive disruption. Let me explain what I mean by that. We're seeing a lot of increasing pace of change in data centers. The conference here, and all the technologies spoken about here, are very foreign to more traditional data center operators, and so the new environments, microservice architectures, or cloud-native apps or so forth. It's a pace of change that we haven't seen before. Agile business and agile software developer models can push code out realistically on a daily basis, whereas the waterfall model and the iCode models in most IT service practitioners practice, that's a manual or quarterly update cycle, with formal change managed practices. >> John: A more settled, structured. >> Yeah, yeah. >> Slow. >> Familiar. >> John: Reliable. >> You know, but it's the past, and the pace of change now is creating stress within IT organizations and stress within the product support organizations of the vendors that they choose to deploy. You couple that with increasing complexity of the environments we have here. We have a lot going on, the ethos of CNCF, which is container packaged, dynamically orchestrated, and micro services architected apps, cloud-native apps. The abstraction layers are masking a lot of complexity there, but the complexity is still there. And you have very good availability if you're able to write to cloud-native principles as a developer, but nevertheless, you still got that .001% of your outages so forth. And the last line of defense towards business continuity is still a human. You still got escalation engineers and support organizations that go through pretty contrived and complicated workflows to triage and diagnose problems, perhaps a case manager to assign a case or subject manager expert, get that back and forth information with the customer and finally resolve the case, and this is what we term cognitive disruption. The maturity of the AI platforms now have reached a point where you can take these complicated workflows that require nuance and inference, and actually apply true machine learning and deep learning to them. And if not entirely automate the resolution of these complex cases, better prepare a scarce resource, an escalation engineer with lots of experience, with more context up front when they encounter the case, so they can close it more quickly, and this has- >> So you're targeting, so if I understand this correctly, you're targeting the personnel in the data center. >> Val: The supportability space. >> Escalation engineers, the human labor, the last mile, if you will, or whatever, first mile, how you look at it. >> Correct. We see APM vendors in this space, we see ITSM vendors in the space. They're partners and even platforms for us. No one really is focused on supportability and automating those workflows using cognitive techniques. >> John: Give an example, give an example. >> The best example I can give actually is firsthand. I'll try and be as generic as I can to protect the innocent, but if you take a look- >> John: NetApp. >> (laughs) It's not even specifically a NetApp case. >> John: Okay, all right. >> If you look at the supply chain upstream, let's talk about electronic supply chain. If a particular manufacturer defect occurs upstream, that defect gets shipped in bundles, purchased by an equipment supplier vendor in bundles, and deployed by customers in bundles. So it's not like one of these one node outage situations is the best case scenario, traditional triple replication, you know. >> John: It's a bad batch basically. >> A bad batch. >> A lot of bad product. >> That can take out not just a node, it can take out a rack. It can take out multiple racks of storage gear, switching gear, server hosts, and so forth. In that case, again, your last line of defense is a human. You basically got to triage and diagnose the problem, could be hardware problem, could be a driver-software problem, could be an upstream OS or database problem. And it's a very stressful environment, a very stressful situation. You can take a look at prior case notes. You can take a look at machine logs and data. You can take a look at product documentation and bill of materials from suppliers, and you can pre-analyze a lot of that, and factor that into your diagnosis, effectively having it almost ready before the case is even opened, so that when the escalation engineer is assigned the case, they don't start from ground zero. They start from third base and almost they're rounding their way to home, and they're able to apply all the prior knowledge, algorithms never sleep. All the prior knowledge in terms of all the cases that have actually been dealt with that match that to a degree. They're never perfect matches, because that's just business process automation. There's a degree of inference required, and using AI techniques, we're able to guess that you know what? I've seen this before. It's very obscure, but it's actually going to be this resolution. >> So AI's technology that you're using in machine learning and data, what problem are you solving specifically? Saving them time, getting them faster resolutions? >> So we're improving the efficiency of support operations. There's always margin pressures within customer support operations. We're fundamentally solving complex system problems. We've reached a point now where business process automation can solve trivial support cases. >> John: Wait a minute, wait a minute, hold on. Expert system's supposed to do this. >> And they did in the past, and now we're evolving beyond the expert. >> Not really. Remember those expert system stays? >> Yeah, I remember LISP and all those early days, so yeah. >> So this kind of sounds like a modern version of an expert system to aid the support engineers to either have a predetermined understanding of options and time to solution. >> So we're able to do so much more than that, right? We're able to create what we call otologies. We're able to categorize all the cases that you've seen in the past, find out whether this new one fits an existing category, if so, if it matches other criteria, if not, defining a category. We're able to orchestrate. Resolution is not just a one-shot deal. Resolution is diagnose the problem, find out if you have some subject matter experts available to resolve the issue, assign it to them, track their progress, close the case, follow up on customer satisfaction. All those things are pretty elaborate workflows that can be highly automated today with cognitive approach-- >> Congratulations on launching. Thanks for spending the time to lay that out. What's next? You've got some seed funding? >> Val: We've got some seed funding. >> You got some in an incubator at the Hive in Palo Alto, which we know quite well. Rob is great, Rob is a great friend. He's done great, he's done great. How many people do have, what are you guys looking to do? What are some of the priorities? >> We are hiring. We're definitely looking to get more data scientists on staff, more full-stack engineers particularly with log experience. We're still looking for a CTO and leadership team. So there's a lot of hiring coming in place. >> John: How many in there now? >> We have about, less than 10 people working right now. >> It's a great opportunity for a classic early-stage opportunity. >> Yeah, early stage opportunity. We're addressing a hot space, and what I love is I personally shifted from being a provider of cloud-native solutions in this market to being a consumer. So I'm seeing exactly how a perfect storm is coming together of machine and deep learning algorithms, running on, orchestrated-- >> John: Both sides of the table. You should talk to Mark Sister. >> Yeah. He's been on both sides. What's it like to be on the other side now? >> It's everything I actually thought it would be, because at the end of the day, I always say, developers are the ultimate pragmatists. So it's not so much about brand loyalty at any particular vendors. What solution, whether it's an open source library, whether it's a commercial library, whether it's a propietary cloud service or something in between. What solution can solve my task, this next task? And composite applications are a very real thing right now. >> So we had a question I posted into the crowd chat, from this social net. I'm going to ask you the same questions. So Burt's watching and maybe you'll find that thread, and I'll add to it later. Here's the question. What challenges still remain as part of implementing DevOps, in your opinion? How did you see the landscape, and how are people addressing them? In your expert opinion, what's the answer to that question? What's your opinion? >> It's a two-faceted answer, at least. The first one, it's not a cliche. It's still a cultural challenge. If you want to actually want to map, it's not even a cultural challenge specifically, it's Conway's Law. Any product output, software output, is a function of your organizational structure that created it. So I find that whether you want to call it culture, whether you want to call it org structure, the org structure's rarely in place to incentivize entire teams to collaborate together throughout a full CICD pipeline process. You've still got incentive structures and org structures in place for people to develop code, unit test it, perhaps even integration test it, but I see more often than I'd like to, isolated or fenced off operations teams that take that and try to make it something real. They might call themselves SREs, and outside recovery engineers, but they're not integrated enough into the development process, in my mind. >> So you're saying the organization structures are also foreclosing their ability be agile, even though they're trying hard, that the incentives are too grounded in there. >> So I still see a lot of skunkworks projects as DevOps projects, and it shouldn't be that way anymore, right? There should be, where there's a legitimate business reason for more agile businesses, there should be a much more formal DevOps structure, as opposed to skunkworks DevOps structure. So that's one challenge, and it's not new, but it's also not resolved. And the other one really is this blind spot for the autonomous data center vision, this blind spot for operations being 100% automated and really just never having to deal with the problem. The blind spot is everything breaks. New technology just happens to break in new ways, but it does fundamentally break, and if your last line of defense is a human or a group of humans, you can expect a very, very different sort of responsiveness and agility as opposed to having something automated. >> Peter and I have been talking all morning the Ford firing of Mark Fields, which was announced yesterday. He quote retired by the Twitter handle of Ford, which is just code words for he got pushed aside. One, we're big fans of Mark Fields, before we covered Ford there in Palo Alto, doing some innovative centers over there, and also a Cloud Foundry customer. So I was actually, took notice of that. We were commenting on not so much the tech, but the guy got fired in less than three years into his journey as chief executive. >> Val: Yeah. >> Now the stock's down 39% so the hammer's coming down from either the family, Ford family, or Wall Street, Peter thinks Wall Street. But this brings up the question, how are you going to be a transformational leader, if you don't have the runway? Back to your org structure. This is, this is-- >> I'm like a broken record. I was thinking that yesterday as I was watching CNBC, and just thinking in my mind, processing what they were announcing. I'm realizing in my head, I bet why, because I don't know, but I bet why, I speculate why he got fired, because he wasn't able to put the org structure and incentives in place to run faster, and that's what the board asked his successor is run faster, and if his successor doesn't put the org structure and the incentives in place to be an agile business. That's the definition of insanity. It's banging your head against the wall. >> If I had to add one more thing to that comment, which by the way I agree with you. If you could configure an asset in a company besides the organizational structure, so you did that, what would your next asset be? More cloud, more data-centric, what would be? >> It might be cliche, but it's totally true, I would have a cloud-first approach to everything. So we don't remember this guy called Obama anymore, but really he did a pretty revolutionary thing, when he brought in a CIO eight, nine years ago, and he made every federal government department defend a capital purchase. And they basically have to go through a multi-hundred page document to defend a capital IT acquisition, but to actually go cloud first or cloud native, didn't require almost any pre-approval at all to get funding. >> So we made it easier incentives to go cloud. >> Created incentives, and I'm a big believer that cloud is not a panacea. >> That helped Amazon, not IBM, as the CIA case now. >> I'm a big believer in life cycles, so it's not like cloud is the rubber stamp solution for every problem, but the beginning innovation phase of every new product line or revenue stream really should be in the cloud right now. The amazing services, forget about IS and all that. Look at the machine language and APIs, IoT APIs, the entire CICD pipelines that are automated and simplistic, the innovation phase for everyone should be in the crowd. Then you got to take a step back, look at that bill, get over your sticker shock, and figure out whether you can afford to stay in a cloud using maybe some of those higher-level proprietary high-margin services and whether you want to re-factor. And that's where professional services kick in, and I think that might be the next great disruption for AI, is re-factoring apps. >> I think one of the things, final question I want to get your thoughts on. Pretend that we're at Cisco and we go back to the ranch, and someone says, "Hey, what's that DevNet Create?" What's our advice to our peers, if we had an opinion that people valued inside Cisco, doubled down on DevNet Create, continue, merge it DevNet? What would your advice be? >> I'm a long time James Governor fan. Developers are the new kingmakers. Actually I think we're in this situation that's not very well understood by business leaders right now, where developers are influencing all the technology infrastructure decisions we're making, but they don't necessarily write the checks. But if you want to run an agile business, a digital business today, you can't do it without happy developers and a good developer experience, so you have to cater to their needs and their biases and so forth, and at shows like this I think, bring Cisco's large ecosystem to bear, where we can figure out how Cisco can maximize the developer experience, how partners, and I'm soon to be a Cisco partner myself at Peritus.ai can maximize their developer experience and just drive more modern business. >> Bring the developer community in with the networking, get those margins connected. Val Bercovici, cofounder of Peritus.ai, this is theCUBE with exclusive coverage of the inaugural event of Cisco's DevNet Create. I'm John Furrier, Peter Burris, returning after this short break. (electric music) >> Hi, I'm April Mitchell, and I'm the senior director.

the cube and hadoop summit 2014 is brought to you by anchor sponsor Hortonworks we do have do and headline sponsor when disco we make hadoop invincible okay welcome back and when we're here at the dupe summit live is looking valance the cube our flagship program we go out to the events expect a signal from noise i'm john per year but Jeff Rick drilling down on the topics we're here with wind disco welcome welcome Brett room Stein about senior director tell us what's going on for you guys I'll see you at big presence here so all the guys last night you guys have a great great booth so causing and the crew what's happening yeah I mean the show is going is going very well what's really interesting is we have a lot of very very technical individuals approaching us they're asking us you know some of the tougher more technical in-depth questions about how our consensus algorithm is able to do all this distributor replication which is really great because there's a little bit of disbelief and then of course we get to do the demonstration for them and then suspend disbelief if you will and and I think the the attendance has been great for our brief and okay I always get that you always we always have the geek conversations you guys are a very technical company Jeff and I always comment certainly de volada and Jeff Kelly that you know when disco doesn't has has their share pair of geeks and that dudes who know they're talking about so I'm sure you get that but now them in the business side you talk to customers I want to get into more the outcome that seems to be the show focused this year is a dupe of serious what are some of the outcomes then your customers are talking about when they get you guys in there what are their business issues what are they tore what are they working on to solve yeah I mean I think the first thing is to look at you know why they're looking at us and then and then with the particular business issues that we solve and the first thing and sort of the trend that we're starting to see is the prospects and the customers that we have are looking at us because of the data that they have and its data that matters so it's important data and that's when people start to come to is that's when they look to us as they have data that's very important to them in some cases if you saw some of the UCI stuff you see that the data is you know doing live monitoring of various you know patient activity where it's not just about about about a life and monitoring a life but potentially about saving the life and systems that go down not only can't save lives but they can potentially lose them so you have a demos you want to jump into this demo here what is this all about you know the demo that the demonstration that I'm going to do for you today is I want to show you our non-stop a new product i'm going to show you how we can basically stand up a single HDFS or a single Hadoop cluster across multiple data centers and I think that's one of the tough things that people are really having trouble getting their heads wrapped around because most people when they do multi data center Hadoop they tend to do two different clusters and then synchronize the data between the two of them the way they do that is they'll use you know flume or they'll use some form of parallel ingest they'll use technologies like dis CP to copy data between the data centers and each one of those has sort of an administrative burden on them and then some various flaws in their and their underlying architecture that don't allow them to do a really really detailed job as ensuring that all blocks are replicated properly that no mistakes are ever made and again there's the administrative burden you know somebody who always has to have eyes in the system we alleviate all those things so I think the first thing I want to start off with we had somebody come to our booth and we were talking about this consensus algorithm that we that we perform and the way we synchronize multiple name nodes across multiple geographies and and again and that sort of spirit of disbelief I said you know one of the key tenants of our application is it doesn't underlie it doesn't change the behavior of the application when you go from land scope to win scope and so I said for example if you create a file in one data center and 3,000 miles apart or 7,000 miles apart from that you were to hit the same create file operation you would expect that the right thing happens what somebody gets the file created and somebody gets file already exists even if at 7,000 miles distance they both hit this button at the exact same time I'm going to do a very quick demonstration of that for you here I'm going to put a file into HDFS the my top right-hand window is in Northern Virginia and then 3,000 miles distance from that my bottom right-hand window is in Oregon I'm going to put the etsy hosts file into a temp directory in Hadoop at the exact same time 3,000 miles distance apart and you'll see that exact behavior so I've just launched them both and again if you look at the top window the file is created if you look at the bottom window it says file already exists it's exactly what you'd expect a land scope up a landscape application and the way you'd expect it to behave so that is how we are ensure consistency and that was the question that the prospect has at that distance even the speed of light takes a little time right so what are some of the tips and tricks you can share this that enable you guys to do this well one of the things that we're doing is where our consensus algorithm is a majority quorum based algorithm it's based off of a well-known consensus algorithm called paxos we have a number of significant enhancements innovations beyond that dynamic memberships you know automatic scale and things of that nature but in this particular case every transaction that goes into our system gets a global sequence number and what we're able to do is ensure that those sequence numbers are executed in the correct order so you can't create you know you can't put a delete before a create you know everything has to happen in the order that it actually happened occurred in regardless of the UN distance between data centers so what is the biggest aha moment you get from customer you show them the demo is it is that the replication is availability what is the big big feature focus that they jump on yeah I think I think the biggest ones are basically when we start crashing nodes well we're running jobs we separate the the link between the win and maybe maybe I'll just do that for you now so let's maybe kick into the demonstration here what I have here is a single HDFS cluster it is spanning two geographic territory so it's one cluster in Northern Virginia part of it and the other part is in Oregon I'm going to drill down into the graphing application here and inside you see all of the name notes so you see I have three name nodes running in Virginia three name nodes running in Oregon and the demonstration is as follows I'm going to I'm going to run Terrigen and Terra sort so in other words i'm going to create some data in the cluster I'm then going to go to sort it into a total order and then I'm going to run Tara validate in the alternate data center and prove that all the blocks replicated from one side to the other however along the way I'm going to create some failures I am going to kill some of that active name nodes during this replication process i am going to shut down the when link between the two data centers during the replication paris's and then show you how we heal from from those kinds of conditions because our algorithm treats failure is a first class citizen so there's really no way to deal in the system if you will so let's start unplug John I'm active the local fails so let's go ahead and run the Terrigen in the terrorists or I'm going to put it in the directory called cube one so we're creating about 400 megabytes of data so a fairly small set that we're going to replicate between the two data centers now the first thing that you see over here on the right-hand side is that all of these name nodes kind of sprung to life that is because in an active active configuration with multiple name nodes clients actually load balance their requests across all of them also it's a synchronous namespace so any change that I make to one immediately Curzon immediately occurs on all of them the next thing you might notice in the graphing application is these blue lines over and only in the Oregon data center the blue lines essentially represent what we call a foreign block a block that is not yet made its way across the wide area network from the site of ingest now we move these blocks asynchronously from the site of in jeff's oh that I have land speed performance in fact you can see I just finished the Terrigen part of the application all at the same time pushing data across the wide area network as fast as possible now as we start to get into the next phase of the application here which is going to run terrace sort i'm going to start creating some failures in the environment so the first thing I'm going to do is want to pick two named nodes I'm going to fail a local named node and then we're also going to fail a remote name node so let's pick one of these i'm going to pick HD p 2 is the name of the machine so want to do ssh hd2 and i'm just going to reboot that machine so as I hit the reboot button the next time the graphing application updates what you'll notice here in the monitor is that a flat line so it's no longer taking any data in but if you're watching the application on the right hand side there's no interruption of the service the application is going to continue to run and you'd expect that to happen maybe in land scope cluster but remember this is a single cluster a twin scope with 3,000 miles between the two of them so I've killed one of the six active named nodes the next thing I'm going to do is kill one of the name nodes over in the Oregon data center so I'm going to go ahead and ssh into i don't know let's pick the let's pick the bottom one HTTP nine in this case and then again another reboot operation so I've just rebooted two of the six name nose while running the job but if again if you look in the upper right-hand corner the job running in Oregon kajabi running in North Virginia continues without any interruption and see we just went from 84 to eighty eight percent MapReduce and so forth so again uninterruptedly like to call continuous availability at when distances you are playing that what does continuous availability and wins because that's really important drill down on yeah I mean I think if you look at the difference between what people traditionally call high availability that means that generally speaking the system is there there is a very short time that the system will be unavailable and then it will then we come available again a continuously available system ensures that regardless of the failures that happen around it the system is always up and running something is able to take the request and in a leaderless system like ours where no one single node actually it actually creates a leadership role we're able to continue replication we're and we're also able to continue the coordinator that's two distinct is high availability which everyone kind of know was in loves expensive and then continues availability which is a little bit kind of a the Sun or cousin I guess you know saying can you put in context and cost implementation you know from a from a from a from a perspective of a when disco deployment it's kind of a continuously available system even though people look at us as somewhat traditional disaster recovery because we are replicating data to another data center but remember it's active active that means both data centers are able to write at the same time you have you get to maximize your cluster resources and again if we go back to one of the first questions you asked what are what a customer's doing this with this what a prospects want to do they want to maximize their resource investment if they have half a million dollars sitting in another data center that only is able to perform an emergency recovery situation that means they either have to a scale the primary data center or be what they want to do is utilize existing resource in an active active configuration which is why i say continuous availability they're able to do that in both data centers maximizing all their resource so you versus the consequences of not having that would be the consequences of not being able to do that is you have a one-way synchronization a disaster occurs you then have to bring that data center online you have to make sure that all the appropriate resources are there you have to you have an administrative burden that means a lot of people have to go into action very quickly with the win disco systems right what that would look like I mean with time effort cost and you have any kind of order of magnitude spec like a gay week called some guy upside dude get in the office login you have to look at individual customer service level agreements a number that i hear thrown out very very often is about 16 hours we can be back online within 16 hours really RTO 44 when disco deployment is essentially zero because both sites are active you're able to essentially continue without without any doubt some would say some would say that's contingent availability is high available because essentially zero 16 that's 16 hours I mean any any time down bad but 16 hours is huge yeah that's the service of level agreement then everyone says but we know we can do it in five hours the other of course the other part of that is of course ensuring that once a year somebody runs through the emergency configure / it you know procedure to know that they truly can be back up in line in the service level agreement timeframe so again there's a tremendous amount of effort that goes into the ongoing administrating some great comments here on our crowd chatter out chat dot net / hadoop summit joined the conversation i'll see ya we have one says nice he's talking about how the system has latency a demo is pretty cool the map was excellent excellent visual dave vellante just weighed in and said he did a survey with Jeff Kelly said large portion twenty-seven percent of respondents said lack of enterprises great availability was the biggest barriers to adoption is this what you're referring to yeah this is this is exactly what we're seeing you know people are not able to meet the uptime requirements and therefore applications stay in proof-of-concept mode or those that make it out of proof of concept are heavily burdened by administrators and a large team to ensure that same level of uptime that can be handled without error through software configuration like Linda scope so another comment from Burt thanks Burt for watching there's availability how about security yeah so security is a good one of course we are you know we run on standard dupe distributions and as such you know if you want to run your cluster with on wire encryption that's okay if you want to run your cluster with kerberos authentication that's fine we we fully support those environments got a new use case for crowd chapel in the questions got more more coming in so send them in we're watching the crowd chat slep net / hadoop summit great questions and a lot of people aren't i think people have a hard time partial eh eh versus continues availability because you can get confused between the two is it semantics or is it infrastructure concerns what is what is the how do you differentiate between those two definitions me not I think you know part of it is semantics but but but also from a win disco perspective we like to differentiate because there really isn't that that moment of downtime there is there really isn't that switch over moment where something has to fail over and then go somewhere else that's why I use that word continuous availability the system is able to simply continue operating by clients load balancing their requests to available nodes in a similar fashion when you have multiple data centers as I do here I'm able to continue operations simply by running the jobs in the alternate data center remember that it's active active so any data ingest on one side immediately transfers to the other so maybe let me do the the next part I showed you one failure scenario you've seen all the nodes have actually come back online and self healed the next part of this I want to do an separation I want to run it again so let me kick up kick that off when I would create another directory structure here only this time I'm going to actually chop the the network link between the two data centers and then after I do that I'm going to show you some some of our new products in the works give you a demonstration of that as well well that's far enough Britain what are some of the applications that that this enables people to use the do for that they were afraid to before well I think it allows you know when we look at our you know our customer base and our prospects who are evaluating our technologies it opens up all the all the regulated industries you know things like pharmaceutical companies financial services companies healthcare companies all these people who have strict regulations auditing requirements and now have a very clear concise way to not only prove that they're replicating data that data has actually made its way it can prove that it's in both locations that it's not just in both locations that it's the correct data sometimes we see in the cases of like dis CP copying files between data centers where the file isn't actually copied because it thinks it's the same but there is a slight difference between the two when the cluster diverges like that it's days of administration hour depending on the size of the cluster to actually to put the cluster you know to figure out what went wrong what went different and then of course you have to involve multiple users to figure out which one of the two files that you have is the correct one to keep so let me go ahead and stop the van link here of course with LuAnn disco technology there's nothing to keep track of you simply allow the system to do HDFS replication because it is essentially native HDFS so I've stopped the tunnel between the two datacenters while running this job one of the things that you're going to see on the left-hand size it looks like all the notes no longer respond of course that's just I have no visibility to those nodes there's no longer replicating any data because the the tunnel between the two has been shut down but if you look on the right hand side of the application the upper right-hand window of course you see that the MapReduce job is still running it's unaffected and what's interesting is once I start replicating the data again or once i should say once i start the tunnel up again between the two data centers i'll immediately start replicating data this is at the block level so again when we look at other copy technologies they are doing things of the file level so if you had a large file and it was 10 gigabytes in size and for some reason you know your your file crash but in that in that time you and you were seventy percent through your starting that whole transfer again because we're doing block replication if you had seventy percent of your box that had already gone through like perhaps what I've done here when i start the tunnel backup which i'm going to do now what's going to happen of course is we just continue from those blocks that simply haven't made their way across the net so i've started the tunnel back up the monitor you'll see springs back to life all the name nodes will have to resync that they've been out of sync for some period of time they'll learn any transactions that they missed they'll be they'll heal themselves into the cluster and we immediately start replicating blocks and then to kind of show you the bi-directional nature of this I'm going to run Tara validate in the opposite data center over in Oregon and I'll just do it on that first directory that we created and in what you'll see is that we now wind up with foreign blocks in both sides I'm running applications at the same time across datacenters fully active active configuration in a single Hadoop cluster okay so the question is on that one what is the net net summarized that demo reel quick bottom line in two sentences is that important bottom line is if name notes fail if the wind fails you are still continuously operational okay so we have questions from the commentary here from the crowd chat does this eliminate the need for backup and what is actually transferring certainly not petabytes of data ? I mean you somewhat have to transfer what what's important so if it's important for you to I suppose if it was important for you to transfer a petabyte of data then you would need the bandwidth that support I transfer of a petabyte of data but we are to a lot of Hollywood studios we were at OpenStack summit that was a big concern a lot of people are moving to the cloud for you know for workflow and for optimization Star Wars guys were telling us off the record that no the new film is in remote locations they set up data centers basically in the desert and they got actually provisioned infrastructure so huge issues yeah absolutely so what we're replicating of course is HDFS in this particular case I'm replicating all the data in this fairly small cluster between the two sites or in this case this demo is only between two sites I could add a third site and then a failure between any two would actually still allow complete you know complete availability of all the other sites that still participate in the algorithm Brent great to have you on I want to get the perspective from you in the trenches out in customers what's going on and win disco tell us what the culture there what's going on the company what's it like to work there what's the guys like I mean we we know some of the dudes there cause we always drink some vodka with him because you know likes to tip back a little bit once in a while but like great guy great geeks but like what's what's it like it when disco I think the first you know you touched on a little piece of it at first is there are a lot of smart people at windows go in fact I know when I first came on board I was like wow I'm probably the most unsmoked person at this company but culturally this is a great group of guys they like to work very hard but equally they like to play very hard and as you said you know I've been out with cause several times myself these are all great guys to be out with the culture is great it's a it's a great place to work and you know so you know people who are who are interested should certainly yeah great culture and it fits in we were talking last night very social crowd here you know something with a Hortonworks guide so javi medicate fortress ada just saw him walk up ibm's here people are really sociable this event is really has a camaraderie feel to it but yet it's serious business and you didn't the days they're all a bunch of geeks building in industry and now it's got everyone's attention Cisco's here in Intel's here IBM's here I mean what's your take on the big guys coming in I mean I think the big guys realize that that Hadoop is is is the elephant is as large as it appears elephant is in the room and exciting and it's and everybody wants a little piece of it as well they should want a piece of it Brett thanks for coming on the cube really appreciate when discs are you guys a great great company we love to have them your support thanks for supporting the cube we appreciate it we right back after this short break with our next guest thank you