>> Hey everyone, welcome to theCUBE's coverage of the AWS startup showcase. This is season two, episode three of our ongoing series featuring AWS and its big ecosystem of partners. This particular season is focused on MarTech, emerging cloud scale customer experiences. I'm your host, Lisa Martin, and I'm pleased to be joined by James Fang, the VP of product marketing at mparticle. James, welcome to the program. Great to have you on. >> Thanks for having me. >> Tell us a little bit about mparticle, what is it that you guys do? >> Sure, so we're mparticle, we were founded in 2013, and essentially we are a customer data platform. What we do is we help brands collect and organize their data. And their data could be coming from web apps, mobile apps, existing data sources like data warehouses, data lakes, et cetera. And we help them help them organize it in a way where they're able to activate that data, whether it's to analyze it further, to gather insights or to target them with relevant messaging, relevant offers. >> What were some of the gaps in the market back then as you mentioned 2013, or even now, that mparticle is really resolving so that customers can really maximize the value of their customer's data. >> Yeah. So the idea of data has actually been around for a while, and you may have heard the buzzword 360 degree view of the customer. The problem is no one has really been actually been able to, to achieve it. And it's actually, some of the leading analysts have called it a myth. Like it's a forever ending kind of cycle. But where we've kind of gone is, first of all customer expectations have really just inflated over the years, right? And part of that was accelerated due to COVID, and the transformation we saw in the last two years, right. Everyone used to, you know, have maybe a digital footprint, as complimentary perhaps to their physical footprint. Nowadays brands are thinking digital first, for obvious reasons. And the data landscape has gotten a lot more complex, right? Brands have multiple experiences, on different screens, right? And, but from the consumer perspective, they want a complete end to end experience, no matter how you're engaging with the brand. And in order to, for a brand to deliver that experience they have to know, how the customers interacted before in each of those channels, and be able to respond in as real time as possible, to those experiences. >> So I can start an interaction on my iPad, maybe carry it through or continue it on my laptop, go to my phone. And you're right, as a, as a consumer, I want the experience across all of those different media to be seamless, to be the same, to be relevant. You talk about the customer 360, as a marketer I know that term well. It's something that so many companies use, interesting that you point out that it's really been, largely until companies like mparticle, a myth. It's one of those things though, that everybody wants to achieve. Whether we're talking about healthcare organization, a retailer, to be able to know everything about a customer so that they can deliver what's increasingly demanded that personalized, relevant experience. How does mparticle fill some of the gaps that have been there in customer 360? And do you say, Hey, we actually deliver a customer 360. >> Yeah, absolutely. So, so the reason it's been a myth is for the most part, data has been- exists either in silos, or it's kind of locked behind this black box that the central data engineering team or sometimes traditionally referred to as IT, has control over, right? So brands are collecting all sorts of data. They have really smart people working on and analyzing it. You know, being able to run data science models, predictive models on it, but the, the marketers and the people who want to draw insights on it are asking how do I get it in, in my hands? So I can use that data for relevant targeting messaging. And that's exactly what mparticle does. We democratize access to that data, by making it accessible in the very tools that the actual business users are are working in. And we do that in real time, you don't have to wait for days to get access to data. And the marketers can even self-service, they're able to for example, build audiences or build computed insights, such as, you know, average order value of a customer within the tool themselves. The other main, the other main thing that mparticle does, is we ensure the quality of that data. We know that activation is only as as good, when you can trust that data, right? When there's no mismatching, you know, first name last names, identities that are duplicated. And so we put a lot of effort, not only in the identity resolution component of our product but also being able to ensure that the consistency of that data when it's being collected meets the standard that you need. >> So give us a, a picture, kind of a topology of a, of a customer data platform. And what are some of the key components that it contains, then I kind of want to get into some of the use cases. >> Yeah. So at, at a core, a lot of customer data platforms look similar. They're responsible first of all for the collection of data, right? And again, that could be from web mobile sources, as well as existing data sources, as well as third party apps, right? For example, you may have e-commerce data in a Shopify, right. Or you may have, you know, a computer model from a, from a warehouse. And then the next thing is to kind of organize it somehow, right? And the most common way to do that is to unify it, using identity resolution into this idea of customer profiles, right. So I can look up everything that Lisa or James has done, their whole historical record. And then the third thing is to be able to kind of be able to draw some insights from that, whether to be able to build an audience membership on top of that, build a predictive model, such as the churn risk model or lifetime value of that customer. And finally is being able to activate that data, so you'll be able to push that data again, to those relevant downstream systems where the business users are actually using that data to, to do their targeting, or to do more interesting things with it. >> So for example, if I go to the next Warrior's game, which I predict they're going to win, and I have like a mobile app of the stadium or the team, how, and I and I'm a season ticket holder, how can a customer data platform give me that personalized experience and help to, yeah, I'd love to kind of get it in that perspective. >> Yeah. So first of all, again, in this modern day and age consumers are engaging with brands from multiple devices, and their attention span, frankly, isn't that long. So I may start off my day, you know, downloading the official warriors app, right. And I may be, you know browsing from my mobile phone, but I could get distracted. I've got to go join a meeting at work, drop off my kids or whatever, right? But later in the day I had in my mind, I may be interested in purchasing tickets or buying that warriors Jersey. So I may return to the website, or even the physical store, right. If, if I happen to be in the area and what the customer data platform is doing in the background, is associating and connecting all those online and offline touchpoints, to that user profile. And then now, I have a mar- so let's say I'm a marker for the golden state warriors. And I see that, you know, this particular user has looked at my website even added to their cart, you know, warriors Jersey. I'm now able to say, Hey, here's a $5 promotional coupon. Also, here's a special, limited edition. We just won, you know, the, the Western conference finals. And you can pre-book, you know, the, you know the warriors championships Jersey, cross your fingers, and target that particular user with that promotion. And it's much more likely because we have that contextual data that that user's going to convert, than just blasting them on a Facebook or something like that. >> Right. Which all of us these days are getting less and less patient with, Is those, those broad blasts through social media and things like that. That was, I love that example. That was a great example. You talked about timing. One of the things I think that we've learned that's in very short supply, in the last couple of years is people's patience and tolerance. We now want things in nanoseconds. So, the ability to glean insights from data and act on it in real time is no longer really a nice to have that's really table stakes for any type of organization. Talk to us about how mparticle facilitates that real time data, from an insights perspective and from an activation standpoint. >> Yeah. You bring up a good point. And this is actually one of the core differentiators of mparticle compared to the other CDPs is that, our architecture from the ground up is built for real time. And the way we do that is, we use essentially a real time streaming architecture backend. Essentially all the data points that we collect and send to those downstream destinations, that happens in milliseconds, right? So the moment that that user, again, like clicks a button or adds something to their shopping cart, or even abandons that shopping cart, that downstream tool, whether it's a marketer, whether it's a business analyst looking at that data for intelligence, they get that data within milliseconds. And our audience computations also happens within seconds. So again, if you're, if you have a targeted list for a targeted campaign, those updates happen in real time. >> You gave an- you ran with the Warrior's example that I threw at you, which I love, absolutely. Talk to me. You must have though, a favorite cu- real world customer example of mparticle's that you think really articulates the value to organizations, whether it's to marketers operators and has some nice, tangible business outcomes. Share with me if you will, a favorite customer story. >> Yeah, definitely one of mine and probably one of the- our most well known's is we were actually behind the scenes of the Whopper jr campaign. So a couple of years ago, Burger King ran this really creative ad where the, effectively their goal was to get their mobile app out, as well as to train, you know, all of us back before COVID days, how to order on our mobile devices and to do things like curbside checkout. None of us really knew how to do that, right. And there was a challenge of course that, no one wants to download another app, right? And most apps get downloaded and get deleted right out away. So they ran this really creative promotion where, if you drove towards a McDonald's, they would actually fire off a text message saying, Hey, how about a Whopper for 99 cents instead? And you would, you would, you would receive a text message personalized just for you. And you'd be able to redeem that at any burger king location. So we were kind of the core infrastructure plumbing the geofencing location data, to partner of ours called radar, which handles you geofencing, and then send it back to a marketing orchestration vendor to be able to fire that targeted message. >> Very cool. I, I, now I'm hungry. You, but there's a fine line there between knowing that, okay, Lisa's driving towards McDonald's let's, you know, target her with an ad for a whopper, in privacy. How do you guys help organizations in any industry balance that? Cause we're seeing more and more privacy regulations popping up all over the world, trying to give consumers the ability to protect either the right to forget about me or don't use my data. >> Yeah. Great question. So the first way I want to respond to that is, mparticle's really at the core of helping brands build their own first party data foundation. And what we mean by that is traditionally, the way that brands have approached marketing is reliant very heavily on second and third party data, right? And most that second-third party data is from the large walled gardens, such as like a Facebook or a TikTok or a Snapchat, right? They're they're literally just saying, Hey find someone that is going to, you know fit our target profile. And that data is from people, all their activity on those apps. But with the first party data strategy, because the brand owns that data, we- we can guarantee that or the brands can guarantee to their customers it's ethically sourced, meaning it's from their consent. And we also help brands have governance policies. So for example, if the user has said, Hey you're allowed to collect my data, because obviously you want to run your business better, but I don't want any my information sold, right? That's something that California recently passed, with CPRA. Then brands can use mparticle data privacy controls to say, Hey, you can pass this data on to their warehouses and analytics platforms, but don't pass it to a platform like Facebook, which potentially could resell that data. >> Got it, Okay. So you really help put sort of the, the reigns on and allow those customers to make those decisions, which I know the mass community appreciates. I do want to talk about data quality. You talked about that a little bit, you know, and and data is the lifeblood of an organization, if it can really extract value from it and act on it. But how do you help organizations maintain the quality of data so that what they can do, is actually deliver what the end user customer, whether it's a somebody buying something on a, on a eCommerce site or or, a patient at a hospital, get what they need. >> Yeah. So on the data quality front, first of all I want to highlight kind of our strengths and differentiation in identity resolution. So we, we run a completely deterministic algorithm, but it's actually fully customizable by the customer depending on their needs. So for a lot of other customer data providers, platform providers out there, they do offer identity resolution, but it's almost like a black box. You don't know what happens. And they could be doing a lot of fuzzy matching, right. Which is, you know, probabilistic or predictive. And the problem with that is, let's say, you know, Lisa your email changed over the years and CDP platform may match you with someone that's completely not you. And now all of a sudden you're getting ads that completely don't fit you, or worse yet that brand is violating privacy laws, because your personal data is is being used to target another user, which which obviously should not, should not happen, right? So because we're giving our customers complete control, it's not a black box, it's transparent. And they have the ability to customize it, such as they can specify what identifiers matter more to them, whether they want to match on email address first. They might've drawn on a more high confidence identifier like a, a hash credit card number or even a customer ID. They have that choice. The second part about ensuring data quality is we act actually built in schema management. So as those events are being collected you could say that, for example, when when it's a add to cart event, I require the item color. I require the size. Let's say it's a fashion apparel. I require the size of it and the type of apparel, right? And if, if data comes in with missing fields, or perhaps with fields that don't match the expectation, let's say you're expecting small, medium, large and you get a Q, you know Q is meaningless data, right? We can then enforce that and flag that as a data quality violation and brands can complete correct that mistake to make sure again, all the data that's flowing through is, is of value to them. >> That's the most important part is, is to make sure that the data has value to the organization, and of course value to whoever it is on the other side, the, the end user side. Where should customers start, in terms of working with you guys, do you recommend customers buy an all in one marketing suite? The best, you know, build a tech stack of best of breed? What are some of those things that you recommend for folks who are going, all right, We, maybe we have a CDP it's been under delivering. We can't really deliver that customer 360, mparticle, help us out. >> Yeah, absolutely. Well, the best part about mparticle is you can kind of deploy it in phases, right. So if you're coming from a world where you've deployed a, all in one marketing suite, like a sales force in Adobe, but you're looking to maybe modernize pieces of a platform mparticle can absolutely help with that initial step. So let again, let's say all you want to do is modernize your event collection. Well, we can absolutely, as a first step, for example, you can instrument us. You can collect all your data from your web and mobile apps in real time, and we can pipe to your existing, you know Adobe campaign manager, Salesforce, marketing cloud. And later down the line, let's say, you say I want to, you know, modernize my analytics platform. I'm tired of using Adobe analytics. You can swap that out, right again with an mparticle place, a marketer can or essentially any business user can flip the switch. And within the mparticle interface, simply disconnect their existing tool and connect a new tool with a couple of button clicks and bam, the data's now flowing into the new tool. So it mparticle really, because we kind of sit in the middle of all these tools and we have over 300 productized prebuilt integrations allows you to move away from kind of a locked in, you know a strategy where you're committed to a vendor a hundred percent to more of a best of breed, agile strategy. >> And where can customers that are interested, go what's your good and market strategy? How does that involve AWS? Where can folks go and actually get and test out this technology? >> Yeah. So first of all, we are we are AWS, a preferred partner. and we have a couple of productized integrations with AWS. The most obvious one is for example, being able to just export data to AWS, whether it's Redshift or an S3 or a kinesis stream, but we also have productized integrations with AWS, personalized. For example, you can take events, feed em to personalize and personalize will come up with the next best kind of content recommendation or the next best offer available for the customer. And mparticle can ingest that data back and you can use that for personalized targeting. In fact, Amazon personalize is what amazon.com themselves use to populate the recommended for use section on their page. So brands could essentially do the same. They could have a recommended for you carousel using Amazon technology but using mparticle to move the data back and forth to, to populate that. And then on top of that very, very soon we'll be also launching a marketplace kind of entry. So if you are a AWS customer and you have credits left over or you just want to transact through AWS, then you'll have that option available as well. >> Coming soon to the AWS marketplace. James, thank you so much for joining me talking about mparticle, how you guys are really revolutionizing the customer data platform and allowing organizations and many industries to really extract value from customer data and use it wisely. We appreciate your insights and your time. >> Thank you very much, Lisa >> For James Fang, I'm Lisa Martin. You're watching theCube's coverage of the AWS startup showcase season three, season two episode three, leave it right here for more great coverage on theCube, the leader in live tech coverage.

>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back everybody Jeff Frick here with the cube. This is a special week it's Grace Hopper week, and Grace Hopper is the best name in tech conferences. The celebration of women in computing, and we've been going there for years we're not there this year, but one of the themes that comes up over and over at Grace Hopper is women and girls need to see women in positions that they can envision themselves being in someday. That is a really important piece of the whole diversity conversation is can I see people that I can role model after and I just want to bring up something from a couple years back from 2016 when we were there, we were there with Mimi Valdez, Christina Deoja and Dr. Jeanette Epps, Dr. Jeanette Epps is the astronaut on the right. They were there talking about "The Hidden Figures" movie. If you remember it came out 2016, it was about Katherine Johnson and all the black women working at NASA. They got no credit for doing all the math that basically keep all the astronauts safe and they made a terrific movie about it. And Janet is going up on the very first Blue Origin Space Mission Next year. This was announced a couple of months ago, so again, phenomenal leadership, black lady astronaut, going to go into space and really provide a face for a lot of young girls that want to get into that and its clearly a great STEM opportunity. So we're excited to have four terrific women today that well also are the leaders that the younger women can look up to and follow their career. So we're excited to have them so we're just going to go around. We got four terrific guests, our first one is Annabel Chang, She is the Head of State Policy and Government Regulations at Waymo. Annabel great to see you, where are you coming in from today? >> from San Francisco >> Jeff: Awesome. Next up is Inamarie Johnson. She is the Chief People and Diversity Officer for Zendesk Inamarie, great to see you. Where are you calling in from today? >> Great to be here. I am calling in from Palos Verdes the state >> Jeff: awesome >> in Southern California. >> Jeff: Some of the benefits of a virtual sometimes we can, we couldn't do that without the power of the internet. And next up is Jennifer Cabalquinto she is the Chief Financial Officer of the Golden State Warriors. Jennifer, great to see you Where are you coming in from today? >> Well, I wish I was coming in from the Chase Center in San Francisco but I'm actually calling in from Santa Cruz California today. >> Jeff: Right, It's good to see you and you can surf a lot better down there. So that's probably not all bad. And finally to round out our panelists, Kate Hogan, she is the COO of North America for Accenture. Kate, great to see you as well. Where are you coming in from today? >> Well, it's good to see you too. I am coming in from the office actually in San Jose. >> Jeff: From the office in San Jose. All right, So let's get into it . You guys are all very senior, you've been doing this for a long time. We're in a kind of a crazy period of time in terms of diversity with all the kind of social unrest that's happening. So let's talk about some of your first your journeys and I want to start with you Annabel. You're a lawyer you got into lawyering. You did lawyering with Diane Feinstein, kind of some politics, and also the city of San Francisco. And then you made this move over to tech. Talk about that decision and what went into that decision and how did you get into tech? 'cause we know part of the problem with diversity is a pipeline problem. You came over from the law side of the house. >> Yes, and to be honest politics and the law are pretty homogenous. So when I made the move to tech, it was still a lot of the same, but what I knew is that I could be an attorney anywhere from Omaha Nebraska to Miami Florida. But what I couldn't do was work for a disruptive company, potentially a unicorn. And I seized that opportunity and (indistinct) Lyft early on before Ride Hailing and Ride Sharing was even a thing. So it was an exciting opportunity. And I joined right at the exact moment that made myself really meaningful in the organization. And I'm hoping that I'm doing the same thing right now at Waymo. >> Great, Inamarie you've come from one of my favorite stories I like to talk about from the old school Clorox great product management. I always like to joke that Silicon Valley needs a pipeline back to Cincinnati and Proctor and Gamble to get good product managers out here. You were in the classic, right? You were there, you were at Honeywell Plantronics, and then you jumped over to tech. Tell us a little bit about that move. Cause I'm sure selling Clorox is a lot different than selling the terrific service that you guys provide at Zendesk. I'm always happy when I see Zendesk in my customer service return email, I know I'm going to get taken care of. >> Oh wow, that's great. We love customers like you., so thank you for that. My journey is you're right from a fortune 50 sort of more portfolio type company into tech. And I think one of the reasons is because when tech is starting out and that's what Zendesk was a few five years back or so very much an early stage growth company, two things are top of mind, one, how do we become more global? And how do we make sure that we can go up market and attract enterprise grade customers? And so my experience having only been in those types of companies was very interesting for a startup. And what was interesting for me is I got to live in a world where there were great growth targets and numbers, things I had never seen. And the agility, the speed, the head plus heart really resonated with my background. So super glad to be in tech, but you're right. It's a little different than a consumer products. >> Right, and then Jennifer, you're in a completely different world, right? So you worked for the Golden State Warriors, which everybody knows is an NBA team, but I don't know that everyone knows really how progressive the Warriors are beyond just basketball in terms of the new Chase Center, all the different events that you guys put on it. And really the leadership there has decided we really want to be an entertainment company of which the Golden State Warrior basketball team has a very, very important piece, you've come from the entertainment industry. So that's probably how they found you, but you're in the financial role. You've always been in the financial role, not traditionally thought about as a lot of women in terms of a proportion of total people in that. So tell us a little bit about your experience being in finance, in entertainment, and then making this kind of hop over to, I guess Uber entertainment. I don't know even how you would classify the warriors. >> Sports entertainment, live entertainment. Yeah, it's interesting when the Warriors opportunity came up, I naturally said well no, I don't have any sports background. And it's something that we women tend to do, right? We self edit and we want to check every box before we think that we're qualified. And the reality is my background is in entertainment and the Warriors were looking to build their own venue, which has been a very large construction project. I was the CFO at Universal Studios Hollywood. And what do we do there? We build large attractions, which are just large construction projects and we're in the entertainment business. And so that sort of B to C was a natural sort of transition for me going from where I was with Universal Studios over to the Warriors. I think a finance career is such a great career for women. And I think we're finding more and more women entering it. It is one that you sort of understand your hills and valleys, you know when you're going to be busy and so you can kind of schedule around that. I think it's really... it provides that you have a seat at the table. And so I think it's a career choice that I think is becoming more and more available to women certainly more now than it was when I first started. >> Yeah, It's interesting cause I think a lot of people think of women naturally in human resources roles. My wife was a head of human resources back in the day, or a lot of marketing, but not necessarily on the finance side. And then Kate go over to you. You're one of the rare birds you've been at Accenture  for over 20 years. So you must like airplanes and travel to stay there that long. But doing a little homework for this, I saw a really interesting piece of you talking about your boss challenging you to ask for more work, to ask for a new opportunity. And I thought that was really insightful that you, you picked up on that like Oh, I guess it's incumbent on me to ask for more, not necessarily wait for that to be given to me, it sounds like a really seminal moment in your career. >> It was important but before I tell you that story, because it was an important moment of my career and probably something that a lot of the women here on the panel here can relate to as well. You mentioned airplanes and it made me think of my dad. My father was in the air force and I remember him telling stories when I was little about his career change from the air force into a career in telecommunications. So technology for me growing up Jeff was, it was kind of part of the dinner table. I mean it was just a conversation that was constantly ongoing in our house. And I also, as a young girl, I loved playing video games. We had a Tandy computer down in the basement and I remember spending too many hours playing video games down there. And so for me my history and my really at a young age, my experience and curiosity around tech was there. And so maybe that's, what's fueling my inspiration to stay at Accenture for as long as I have. And you're right It's been two decades, which feels tremendous, but I've had the chance to work across a bunch of different industries, but you're right. I mean, during that time and I relate with what Jennifer said in terms of self editing, right? Women do this and I'm no exception, I did this. And I do remember I'm a mentor and a sponsor of mine who called me up when I'm kind of I was at a pivotal moment in my career and he said you know Kate, I've been waiting for you to call me and tell me you want this job. And I never even thought about it. I mean I just never thought that I'd be a candidate for the job and let alone somebody waiting for me to kind of make the phone call. I haven't made that mistake again, (laughing) but I like to believe I learned from it, but it was an important lesson. >> It's such a great lesson and women are often accused of being a little bit too passive and not necessarily looking out for in salary negotiations or looking for that promotion or kind of stepping up to take the crappy job because that's another thing we hear over and over from successful people is that some point in their career, they took that job that nobody else wanted. They took that challenge that really enabled them to take a different path and really a different Ascension. And I'm just curious if there's any stories on that or in terms of a leader or a mentor, whether it was in the career, somebody that you either knew or didn't know that was someone that you got kind of strength from kind of climbing through your own, kind of career progression. Will go to you first Annabel. >> I actually would love to talk about the salary negotiations piece because I have a group of friends about that we've been to meeting together once a month for the last six years now. And one of the things that we committed to being very transparent with each other about was salary negotiations and signing bonuses and all of the hard topics that you kind of don't want to talk about as a manager and the women that I'm in this group with span all types of different industries. And I've learned so much from them, from my different job transitions about understanding the signing bonus, understanding equity, which is totally foreign to me coming from law and politics. And that was one of the most impactful tools that I've ever had was a group of people that I could be open with talking about salary negotiations and talking about how to really manage equity. Those are totally foreign to me up until this group of women really connected me to these topics and gave me some of that expertise. So that is something I strongly encourage is that if you haven't openly talked about salary negotiations before you should begin to do so. >> It begs the question, how was the sensitivity between the person that was making a lot of money and the person that wasn't? And how did you kind of work through that as a group for the greater good of everyone? >> Yeah, I think what's really eye opening is that for example, We had friends who were friends who were on tech, we had friends who were actually the entrepreneurs starting their own businesses or law firm, associates, law firm partners, people in PR, so we understood that there was going to be differences within industry and frankly in scale, but it was understanding even the tools, whether I think the most interesting one would be signing bonus, right? Because up until a few years ago, recruiters could ask you what you made and how do you avoid that question? How do you anchor yourself to a lower salary range or avoid that happening? I didn't know this, I didn't know how to do that. And a couple of women that had been in more senior negotiations shared ways to make sure that I was pinning myself to a higher salary range that I wanted to be in. >> That's great. That's a great story and really important to like say pin. it's a lot of logistical details, right? You just need to learn the techniques like any other skill. Inamarie, I wonder if you've got a story to share here. >> Sure. I just want to say, I love the example that you just gave because it's something I'm super passionate about, which is transparency and trust. Then I think that we're building that every day into all of our people processes. So sure, talk about sign on bonuses, talk about pay parody because that is the landscape. But a quick story for me, I would say is all about stepping into uncertainty. And when I coach younger professionals of course women, I often talk about, don't be afraid to step into the role where all of the answers are not vetted down because at the end of the day, you can influence what those answers are. I still remember when Honeywell asked me to leave the comfort of California and to come to the East coast to New Jersey and bring my family. And I was doing well in my career. I didn't feel like I needed to do that, but I was willing after some coaching to step into that uncertainty. And it was one of the best pivotal moment in my career. I didn't always know who I was going to work with. I didn't know the challenges and scope I would take on, but those were some of the biggest learning experiences and opportunities and it made me a better executive. So that's always my coaching, like go where the answers aren't quite vetted down because you can influence that as a leader. >> That's great, I mean, Beth Comstock former vice chair at GE, one of her keynotes I saw had a great line, get comfortable with being uncomfortable. And I think that its a really good kind of message, especially in the time we're living in with accelerated change. But I'm curious, Inamarie was the person that got you to take that commitment. Would you consider that a sponsor, a mentor, was it a boss? Was it maybe somebody not at work, your spouse or a friend that said go for it. What kind of pushed you over the edge to take that? >> It's a great question. It was actually the boss I was going to work for. He was the CHRO, and he said something that was so important to me that I've often said it to others. And he said trust me, he's like I know you don't have all the answers, I know we don't have this role all figured out, I know you're going to move your family, but if you trust me, there is a ton of learning on the other side of this. And sometimes that's the best thing a boss can do is say we will go on this journey together. I will help you figure it out. So it was a boss, but I think it was that trust and that willingness for him to stand and go alongside of me that made me pick up my family and be willing to move across the country. And we stayed five years and really, I am not the same executive because of that experience. >> Right, that's a great story, Jennifer, I want to go to you, you work for two owners that are so progressive and I remember when Joe Lacob came on the floor a few years back and was booed aggressively coming into a franchise that hadn't seen success in a very long time, making really aggressive moves in terms of personnel, both at the coaches and the players level, the GM level. But he had a vision and he stuck to it. And the net net was tremendous success. I wonder if you can share any of the stories, for you coming into that organization and being able to feel kind of that level of potential success and really kind of the vision and also really a focus on execution to make the vision real cause vision without execution doesn't really mean much. If you could share some stories of working for somebody like Joe Lacob, who's so visionary but also executes so very, very effectively. >> Yeah, Joe is, well I have the honor of working for Joe, for Rick Welts to who's our president. Who's living legend with the NBA with Peter Guber. Our leadership at the Warriors are truly visionary and they set audacious targets. And I would say from a story the most recent is, right now what we're living through today. And I will say Joe will not accept that we are not having games with fans. I agree he is so committed to trying to solve for this and he has really put the organization sort of on his back cause we're all like well, what do we do? And he has just refused to settle and is looking down every path as to how do we ensure the safety of our fans, the safety of our players, but how do we get back to live entertainment? And this is like a daily mantra and now the entire organization is so focused on this and it is because of his vision. And I think you need leaders like that who can set audacious goals, who can think beyond what's happening today and really energize the entire organization. And that's really what he's done. And when I talked to my peers and other teams in there they're talking about trying to close out their season or do these things. And they're like well, we're talking about, how do we open the building? And we're going to have fans, we're going to do this. And they look at me and they're like, what are you talking about? And I said, well we are so fortunate. We have leadership that just is not going to settle. Like they are just always looking to get out of whatever it is that's happening and fix it. So Joe is so committed His background, he's an epidemiologist major I think. Can you imagine how unique a background that is and how timely. And so his knowledge of just around the pandemic and how the virus is spread. And I mean it's phenomenal to watch him work and leverage sort of his business acumen, his science acumen and really think through how do we solve this. Its amazing. >> The other thing thing that you had said before is that you basically intentionally told people that they need to rethink their jobs, right? You didn't necessarily want to give them permission to get you told them we need to rethink their jobs. And it's a really interesting approach when the main business is just not happening, right? There's just no people coming through the door and paying for tickets and buying beers and hotdogs. It's a really interesting talk. And I'm curious, kind of what was the reception from the people like hey, you're the boss, you just figure it out or were they like hey, this is terrific that he pressed me to come up with some good ideas. >> Yeah, I think when all of this happened, we were resolved to make sure that our workforce is safe and that they had the tools that they needed to get through their day. But then we really challenged them with re imagining what the next normal is. Because when we come out of this, we want to be ahead of everybody else. And that comes again from the vision that Joe set, that we're going to use this time to make ourselves better internally because we have the time. I mean, we had been racing towards opening Chase Center and not having time to pause. Now let's use this time to really rethink how we're doing business. What can we do better? And I think it's really reinvigorated teams to really think and innovate in their own areas because you can innovate anything, right?. We're innovating how you pay payables, we're all innovating, we're rethinking the fan experience and queuing and lines and all of these things because now we have the time that it's really something that top down we want to come out of this stronger. >> Right, that's great. Kate I'll go to you, Julie Sweet, I'm a big fan of Julie Sweet. we went to the same school so go go Claremont. But she's been super aggressive lately on a lot of these things, there was a get to... I think it's called Getting to 50 50 by 25 initiative, a formal initiative with very specific goals and objectives. And then there was a recent thing in terms of doing some stuff in New York with retraining. And then as you said, military being close to your heart, a real specific military recruiting process, that's formal and in place. And when you see that type of leadership and formal programs put in place not just words, really encouraging, really inspirational, and that's how you actually get stuff done as you get even the consulting businesses, if you can't measure it, you can't improve it. >> Yeah Jeff, you're exactly right. And as Jennifer was talking, Julie is exactly who I was thinking about in my mind as well, because I think it takes strong leadership and courage to set bold bold goals, right? And you talked about a few of those bold goals and Julie has certainly been at the forefront of that. One of the goals we set in 2018 actually was as you said to achieve essentially a gender balance workforce. So 50% men, 50% women by 2025, I mean, that's ambitious for any company, but for us at the time we were 400,000 people. They were 500, 6,000 globally. So when you set a goal like that, it's a bold goal and it's a bold vision. And we have over 40% today, We're well on our path to get to 50%, I think by 2025. And I was really proud to share that goal in front of a group of 200 clients the day that it came out, it's a proud moment. And I think it takes leaders like Julie and many others by the way that are also setting bold goals, not just in my company to turn the dial here on gender equality in the workforce, but it's not just about gender equality. You mentioned something I think it's probably at as, or more important right now. And that's the fact that at least our leadership has taken a Stand, a pretty bold stand against social injustice and racism, >> Right which is... >> And so through that we've made some very transparent goals in North America in terms of the recruitment and retention of our black African American, Hispanic American, Latinex communities. We've set a goal to increase those populations in our workforce by 60% by 2025. And we're requiring mandatory training for all of our people to be able to identify and speak up against racism. Again, it takes courage and it takes a voice. And I think it takes setting bold goals to make a change and these are changes we're committed to. >> Right, that's terrific. I mean, we started the conversation with Grace Hopper, they put out an index for companies that don't have their own kind of internal measure to do surveys again so you can get kind of longitudinal studies over time and see how you're improving Inamarie, I want to go to you on the social justice thing. I mean, you've talked a lot about values and culture. It's a huge part of what you say. And I think that the quote that you use, if I can steal it is " no culture eats strategy for breakfast" and with the social injustice. I mean, you came out with special values just about what Zendesk is doing on social injustice. And I thought I was actually looking up just your regular core mission and value statement. And this is what came up on my Google search. So I wanted to A, you published this in a blog in June, taking a really proactive stand. And I think you mentioned something before that, but then you're kind of stuck in this role as a mind reader. I wonder if you can share a little bit of your thoughts of taking a proactive stand and what Zendesk is doing both you personally, as well as a company in supporting this. And then what did you say as a binder Cause I think these are difficult kind of uncharted waters on one hand, on the other hand, a lot of people say, hello, this has been going on forever. You guys are just now seeing cellphone footage of madness. >> Yeah Wow, there's a lot in there. Let me go to the mind reader comments, cause people are probably like, what is that about? My point was last December, November timing. I've been the Chief People Officer for about two years And I decided that it really was time with support from my CEO that Zendesk have a Chief Diversity Officer sitting in at the top of the company, really putting a face to a lot of the efforts we were doing. And so the mind reader part comes in little did I know how important that stance would become, in the may June Timing? So I joked that, it almost felt like I could have been a mind reader, but as to what have we done, a couple of things I would call out that I think are really aligned with who we are as a company because our culture is highly threaded with the concept of empathy it's been there from our beginning. We have always tried to be a company that walks in the shoes of our customers. So in may with the death of George Floyd and the world kind of snapping and all of the racial injustice, what we said is we wanted to not stay silent. And so most of my postings and points of view were that as a company, we would take a stand both internally and externally and we would also partner with other companies and organizations that are doing the big work. And I think that is the humble part of it, we can't do it all at Zendesk, we can't write all the wrongs, but we can be in partnership and service with other organizations. So we used funding and we supported those organizations and partnerships. The other thing that I would say we did that was super important along that empathy is that we posted space for our employees to come together and talk about the hurt and the pain and the experiences that were going on during those times and we called those empathy circles. And what I loved is initially, it was through our mosaic community, which is what we call our Brown and black and persons of color employee resource group. But it grew into something bigger. We ended up doing five of these empathy circles around the globe and as leadership, what we were there to do is to listen and stand as an ally and support. And the stories were life changing. And the stories really talked about a number of injustice and racism aspects that are happening around the world. And so we are committed to that journey, we will continue to support our employees, we will continue to partner and we're doing a number of the things that have been mentioned. But those empathy circles, I think were definitely a turning point for us as an organization. >> That's great, and people need it right? They need a place to talk and they also need a place to listen if it's not their experience and to be empathetic, if you just have no data or no knowledge of something, you need to be educated So that is phenomenal. I want to go to you Jennifer. Cause obviously the NBA has been very, very progressive on this topic both as a league, and then of course the Warriors. We were joking before. I mean, I don't think Steph Curry has ever had a verbal misstep in the history of his time in the NBA, the guy so eloquent and so well-spoken, but I wonder if you can share kind of inside the inner circle in terms of the conversations, that the NBA enabled right. For everything from the jerseys and going out on marches and then also from the team level, how did that kind of come down and what's of the perception inside the building? >> Sure, obviously I'm so proud to be part of a league that is as progressive and has given voice and loud, all the teams, all the athletes to express how they feel, The Warriors have always been committed to creating a diverse and equitable workplace and being part of a diverse and equitable community. I mean that's something that we've always said, but I think the situation really allowed us, over the summer to come up with a real formal response, aligning ourselves with the Black Lives Matter movement in a really meaningful way, but also in a way that allows us to iterate because as you say, it's evolving and we're learning. So we created or discussed four pillars that we wanted to work around. And that was really around wallet, heart, beat, and then tongue or voice. And Wallet is really around putting our money where our mouth is, right? And supporting organizations and groups that aligned with the values that we were trying to move forward. Heart is around engaging our employees and our fan base really, right? And so during this time we actually launched our employee resource groups for the first time and really excited and energized about what that's doing for our workforce. This is about promoting real action, civic engagement, advocacy work in the community and what we've always been really focused in a community, but this really hones it around areas that we can all rally around, right? So registration and we're really focused on supporting the election day results in terms of like having our facilities open to all the electorate. So we're going to have our San Francisco arena be a ballot drop off, our Oakland facilities is a polling site, Santa Cruz site is also a polling location, So really promoting sort of that civic engagement and causing people to really take action. heart is all around being inclusive and developing that culture that we think is really reflective of the community. And voice is really amplifying and celebrating one, the ideas, the (indistinct) want to put forth in the community, but really understanding everybody's culture and really just providing and using the platform really to provide a basis in which as our players, like Steph Curry and the rest want to share their own experiences. we have a platform that can't be matched by any pedigree, right? I mean, it's the Warriors. So I think really getting focused and rallying around these pillars, and then we can iterate and continue to grow as we define the things that we want to get involved in. >> That's terrific. So I have like pages and pages and pages of notes and could probably do this for hours and hours, but unfortunately we don't have that much time we have to wrap. So what I want to do is give you each of you the last word again as we know from this problem, right? It's not necessarily a pipeline problem, it's really a retention problem. We hear that all the time from Girls in Code and Girls in Tech. So what I'd like you to do just to wrap is just a couple of two or three sentences to a 25 year old, a young woman sitting across from you having coffee socially distanced about what you would tell her early in the career, not in college but kind of early on, what would the be the two or three sentences that you would share with that person across the table and Annabel, we'll start with you. >> Yeah, I will have to make a pitch for transportation. So in transportation only 15% of the workforce is made up of women. And so my advice would be that there are these fields, there are these opportunities where you can make a massive impact on the future of how people move or how they consume things or how they interact with the world around them. And my hope is that being at Waymo, with our self driving car technology, that we are going to change the world. And I am one of the initial people in this group to help make that happen. And one thing that I would add is women spend almost an hour a day, shuttling their kids around, and we will give you back that time one day with our self driving cars so that I'm a mom. And I know that that is going to be incredibly powerful on our daily lives. >> Jeff: That's great. Kate, I think I might know what you're already going to say, but well maybe you have something else you wanted to say too. >> I don't know, It'll be interesting. Like if I was sitting across the table from a 25 year old right now I would say a couple of things first I'd say look intentionally for a company that has an inclusive culture. Intentionally seek out the company that has an inclusive culture, because we know that companies that have inclusive cultures retain women in tech longer. And the companies that can build inclusive cultures will retain women in tech, double, double the amount that they are today in the next 10 years. That means we could put another 1.4 million women in tech and keep them in tech by 2030. So I'd really encourage them to look for that. I'd encouraged them to look for companies that have support network and reinforcements for their success, and to obviously find a Waymo car so that they can not have to worry where kids are on for an hour when you're parenting in a few years. >> Jeff: I love the intentional, it's such a great word. Inamarie, >> I'd like to imagine that I'm sitting across from a 25 year old woman of color. And what I would say is be authentically you and know that you belong in the organization that you are seeking and you were there because you have a unique perspective and a voice that needs to be heard. And don't try to be anything that you're not, be who you are and bring that voice and that perspective, because the company will be a better company, the management team will be a better management team, the workforce will be a better workforce when you belong, thrive and share that voice. >> I love that, I love that. That's why you're the Chief People Officer and not Human Resources Officer, cause people are not resources like steel and cars and this and that. All right, Jennifer, will go to you for the wrap. >> Oh my gosh, I can't follow that. But yes, I would say advocate for yourself and know your value. I think really understanding what you're worth and being willing to fight for that is critical. And I think it's something that women need to do more. >> Awesome, well again, I wish we could go all day, but I will let you get back to your very, very busy day jobs. Thank you for participating and sharing your insight. I think it's super helpful. And there and as we said at the beginning, there's no better example for young girls and young women than to see people like you in leadership roles and to hear your voices. So thank you for sharing. >> Thank you. >> All right. >> Thank you. >> Okay thank you. >> Thank you >> All right, so that was our diversity panel. I hope you enjoyed it, I sure did. I'm looking forward to chapter two. We'll get it scheduled as soon as we can. Thanks for watching. We'll see you next time. (upbeat music)

>> Narrator: Live from Seattle, Washington it's theCUBE covering Smartsheet ENGAGE 2019. Brought to you by Smartsheet. >> Welcome back, everyone, to theCUBE's live coverage of Smartsheet ENGAGE here in Seattle. I'm your host, Rebecca Knight, along with my co-host, Jeff Frick. We're joined by Kristin Komassa. She is the VP Process Improvement at Colliers International Wisconsin. Thanks so much for coming on the show. >> Thanks for having me, I'm excited to be here. >> So you're here direct from Milwaukee. Tell our viewers a little bit about Colliers International Wisconsin. >> Yeah, so Colliers International Wisconsin, we are recognized as the largest full-service commercial real estate company in the state of Wisconsin. And when I say full-service it means we have everything from brokerage to property management to facilities, architecture, development. We cover the gamut on both the commercial and we've actually started a residential program, as well. So, we've got you covered. >> Excellent, so tell us now about your Smartsheet story. There was a movie that played during the keynote address where we heard a lot about your Smartsheet experience. But you tell our viewers now. >> Yeah, so I started using Smartsheet in 2012 when I came to Colliers and really it was a one specific project that we needed to really wrap our arms around and other methods weren't doing it at all. So I discovered Smartsheet. And ironically if you took Smartsheet from 2012 and put it next to Smartsheet 2019, you wouldn't think they're the same product at all. But it solved our issues at that time. We were able to really elevate what we were doing with that client. We were recognized, and the company ownership saw that if you can do this with one client, what could you do with the whole company? And over the past years we have really rolled it out both internally through the operational side, from how we just manage our day-to-day business to also how do we get in with those clients and how do we manage their real estate with this software program? So that's kind of been my journey and it's been fun and it's been amazing and I'm looking forward to the next phase. >> So what was the killer app in 2012 that you couldn't do with any other tool that was so breakthrough? >> We were starting with Excel and it was just an extremely large portfolio. We tried to do Google Sheets, that didn't work. And Smartsheet was the app of choice, that we could collaboratively work on this entire portfolio but manage it with a security level, because it was a banking institution, that they were concerned. And Smartsheet, even at that time, they knew that security was a big issue with their clients. >> So was it the ability to cross-company collaborate with the banking client as well as your own team? That was-- >> It was. It was a large team, we had 15 people, so you can imagine version control was huge. >> Nightmare. >> Yeah, a nightmare. Nobody wants to see an Excel document sent to 15 people asking for revisions. And, again, we had to be able to report to this banking customer in their own format and we had again really aggregate that data in a consistent and repeatable way, but yet still maintain that control. And Smartsheet allowed us to do that in a very flexible and customizable way. So we didn't buy something off the shelf that we'll maybe use 50% of it, we used 100% of what we purchased. >> So 2012, that's a while ago. >> A little bit. >> Can you talk about the cultural change from your company now that you use Smartsheet on a regular basis and how that has helped you collaborate and helped you be more creative with each other, helped you understand the big picture? >> Yeah, so really in 2012 we were a slightly smaller company. It was coming right out of the recession and when there was a lot of REO properties and just there was some issues in real estate in general. And we were able to really ride that wave and come back a lot stronger than we were because we were able to cross-collaborate between all of our different company divisions, and really show our clients, one of our taglines is Better Together, and that's what we were. And it's easy to be better together when you have a platform that helps you build that up. And our company has since kind of shed some of those maybe less desirable properties or product type and really moved into the class-A downtown markets because we're able to now work with some of those more sophisticated owners of real estate and those sophisticated clients that are, they're really looking for not just a real estate expert, but an advisor for them. How do you help me take my real estate and make it work for my business? And Smartsheet was a big part of that. >> It really has evolved your role. As you said, it's much more of a, you're much more of an advisor now. >> Yes, we are definitely much more of an advisor, of a consultant, of a trusted partner, is what we are. And it's not always just about real estate anymore, it's about building those relationships. But showing them as well as to, how can we put all those pieces together and then still have full transparency with you? And with our other vendors and our clients and bringing everybody together. >> So I love that you, looking at the big picture and big changes in the big picture, but you've also talked about it's a combination of lots of little things that add up to the big thing. I think one of your videos you talked about a push notice for an accept/decline was a game changer. And then today we heard in the keynote, a copy/paste from one to the other got a standing ovation. So what was your favorite feature for today? And I'm just curious, is that approach something that you've adopted also in the way that you use the tool to engage with your clients? >> Every ENGAGE that I've been to I leave and I'm just so excited to get back and start implementing everything because, again, Smartsheet really listens to their clients. But really from what the things that were announced today, it seems like a simple thing but I'm really excited about Move Row. Because when you're done with a project, it doesn't take a lot of time to actually grab it and move it down, but if somebody forgot to do it and it's rolling up to your aggregate data and all that, it's just such a little thing but it makes such a big difference. Show me only my active in-flight projects. I don't want to see my completed ones or my closed, or my on-hold, if I change the status. Give me what I care about, front and center. So Move Row wass my big thing. >> Love it. >> But that is what we've been talking about, frankly, all day, is how these little things can add up to be the big aggravations of work. And so when you are slowly chipping away at all of the annoyances, that leads to a much more pleasant work day. >> Kristin: It definitely does. >> And a much more satisfying work life. >> Yep, I'll take any second I can gain back in a day. >> Right, so we talked about how Colliers International Wisconsin has really evolved from sort of, not a small-time real estate, but now you are this trusted partner of so many wealthier clients. Talk about the internal culture, though, in terms of how you all work together. >> Yeah, so some of our key features are like we like Warrior-Spirit, and this Better Together, and being innovators. And that's really what Smartsheet has encouraged us to be, is more of these innovators and working together and really being a champion internally. You'd be amazed, a lot of real estate companies, they have a lot of brokers and then employees and maybe not everybody, there are different personality types and all that, but our company has been able to figure out a way to pull everybody together and aggregate that data for a real big picture from both sides. Instead of looking at employees versus consultants, but just everybody. What is Colliers? And it's been amazing because Smartsheet has been that platform that we've utilized to do that and to bring everybody up. The collaboration that it has encouraged between different departments. Everybody knowing what is going on with a project or knowing that if you're talking to the same client that I'm talking to and how do we now work together, versus you make a phone call and you just called my client. I don't want that happening and it makes you sound kind of silly. How do we work together for a common purpose, basically, is what's happened. >> So is it the primary work tool that's open on people's desks? >> Yes, it is. It's open on my desk 100% of the time and we have actually created individualized dashboards for every single one of our brokers and it is their ground zero where they go to for all of their information. For if they have a new listing, if they have to submit commission information, if they want to submit a referral to another one of our lines. That is where they go. Our property managers, we're working right now to create their individual dashboards where, again, they're going to be living in there, and how they're communicating with their landlords and their owners and, how do you aggregate that tenant data in there so that everybody on your team is all on the same page? But again, it's living in Smartsheet is what the entire company is doing these days. >> So you talked about how this was 2012 when you first adopted it. The real estate business particularly, and commercial real estate not in a great position, in a much better, more solid position today. What are you thinking about for the future in terms of how your industry evolves and how you're going to need tools to help you evolve? >> Yeah, our clients, it's a tech world, everything. Your fridge can order milk for you these days. If you have a real estate and they're not an advisor, they're just a real estate broker and they're not accessing the technology that is out there to help you get market intel at the touch of your fingertips. They almost want you to anticipate what their question is going to be before they ask it. And they want that data available at night, on the weekends, in the morning, at their own schedule. If you're not able to provide that but you have to send them an email and they have to wait on it, I think that you're going to fall behind. You have to be able to keep up with the world of technology and becoming less of a one, I'm just going to help you on this single transaction to I'm helping you on this one, but what's the next one? And how does it affect your business? And how do I become your partner and your advisor and just that trusted partner? And that's where it's going, I think. >> And have you been able to, are you able to do those things because it has freed up your time? Because that's another thing we hear about this technology, is that because it is automating so many of the manual, repetitive tasks, you do have more time to be creative, to think more holistically and more about the future. >> Yeah and that's really what we're pushing is, if it's an administrative task, if it's something that you can automate it, do it. Don't take another day sending a repetitive email or you checking your calendar, did somebody finish something? Have the system do it for you. Did somebody, if you assigned a task, did they do it? You shouldn't have to babysit them for it. And yes, it should free you up to, how do I look strategically? How do I look forward into something? Instead of constantly trying to look backwards as to what did we do? Has it been completed? It should be done and we should be on to the next step at this point. >> So you said that you always come away from ENGAGEs so excited, so happy to come back to your office and talk about what you've learned. What do you think it's going to be from this one? Besides Move Row? Which I know is going to change your life, Kristin. >> Move Row will change my life, but there's a lot of things. You know what, so many things. Again, Smartsheet, I can't reiterate enough, they listen to their customers. And going back and figuring out how do I optimize something that I already thought was the apex thing that I was going to create, how do I now make it better? How do I make it so that it frees up somebody else's time? So that maybe them moving a row down, they no longer have to do that. How do I now make the next one even better? So I'm just, I'm excited, again, about that continuous process improvement. >> Excellent. Well, thank you so much for coming on the show. It was a pleasure having you. >> Thank you, I'm excited to be here. >> And now you're a CUBE veteran. >> Now I'm a CUBE veteran, thank you. >> I'm Rebecca Knight for Jeff Frick, stay tuned for more of theCUBE's live coverage of ENGAGE 2019. (upbeat electronic music)

>> Announcer: From Austin, Texas it's theCube, covering Pure Storage Accelerate 2019. Brought to you by Pure Storage. >> Howdy from Austin, Lisa Martin with Dave Vellante we are on day one of our coverage of Pure Accelerate 2019. Welcoming a couple of guests to theCube. One is an alumni, Nathan Hall, VP of America's Systems Engineering from Pure, Nathan welcome back to theCube. >> Thanks, thanks very much. >> Lisa: And you brought a buddy from Cisco. We have Tom Sutliff, director of systems engineering and the America's data center, welcome to the Cube Tom. >> Thanks for having me. >> Dave: It's howdy you all. >> Howdy you all, okay. Thank you, it took the wicked smart guy from Boston to figure that out. >> A local. >> All right, so you all, let's talk about Cisco and Pure, you guys have been partners now since, Nathan we were chatting, since about the IPO, about four years ago. Let's start with you Nathan, our Pure guy. The Cisco, Pure partnership evolution, better together? What have you done over those last five years that sets you up for another first that you're going to share with us today? >> Sure, so it's a deep relationship that's only getting deeper and it's really at all levels. It starts with the executive alignment and think about Charlie Giancarlo from Cisco we've got a lot of just common, cross pollination there. But now it extends, certainly the field level, Tom and I are doing a lot of planning together in terms of having our teams go after common use cases. But now it extends to engineering as well, we had a UCS director plugin that we've had for some time now but Pure is now first in terms of having integration into Cisco intersight, so we are first and only to have storage integration of the Cisco intersight so that Cisco and Pure customers can really manage their environment from one console, so a lot of simplicity, just single SaaS interface for managing everything. >> Tom why Pure, why first with them? >> Well you know Nathan he articulated it well, we can look at the executive level, we talked about Charlie, but even, you know all of our Cisco executives but also to the engineering. We started really strong with the field sales teams but even if you look at the little things that our customers notice but a lot of people may not like the internal development of validated design guides, use cases. We churn them out with Pure as our top ecosystem partner, more than anybody and there's a lot of work being done, our customers see that and it's really helped drive our goal to market together it's really a very strong strategy. >> So there's a CVD around this is that right? >> Yeah there's many there's 22 right now and we're churning them out about one or two a quarter. With some vendors we might put out some initially we might do one or two things well, we do a lot of things well I guess you could say we do 22 things well with the CVD's but more than that. >> So this really started in the field if I understand correctly is that right? [Nathan] - Yes. >> So I always look for these deals and say is it a Barney deal, you know Barney deal I love you, you love me. And if there's real engineering going on then you say okay it's beyond a Barney deal. So it starts in the field with what, hey we should you know a customer wants us to work together and then how does the partnership evolve into where you're putting engineering resources and what does that look like? >> I think a lot of it evolves from just showing progress and showing success. If you look at, we just have a lot of common goals and from a portfolio perspective we fill in a lot of each others gaps so that's really where it started was having the success in the field and that drove, we should actually make greater investments in terms of engineering development, those 22 CVD's, the intersight integration, et cetera. >> So we were talking earlier about CI, HCI for audience members who it's kind of nuanced, how do you guys look at the intersection of those two? >> I say it's another better together story, for example we have a recent joint customer win where essentially across their entire SAP landscape we have Cisco hyper flex the HX managing the database portion, we have FlashStack with Pure Storage managing the Hanna portion, and really it all comes down to single console which is intersight. So we're really able to provide the best type of infrastructure for the right workload at the right time but all make it look like one single experience to the customer. >> So from a customer conversation perspective let's go back to you know we've talked about now this exciting new first engineering alignment. Going back to the field where customers have a multitude of workloads, SAP, Oracle, Microsoft, FEEdi, and there's FlashStack like 31 flavors of FlashStack right. What's that conversation like in terms of CI versus HCI when you guys come into play? Obviously FlashStack being I mentioned a number of flavors of that have been around for awhile, how do you help the customers determine what infrastructure is optimal for their workloads and their business objectives? >> You know there's a clear delineation between a hyper convergence, our HX platform, a hyper flex platform, and the converged infrastructure that we have with FlashStacks. If you look at a FlashStack it's an all in one solution, compute, fabric, storage. It's more for tier one apps, something that's you know scalable, something that's a highly dense tier one application. Latency obviously plays into this you know, I'd say it's a little less with the hyper flex platform and hyper convergence, much easier to stand up, much quicker to stand up within a half an hour. It's a storage play it does many of the similar same things but you know we're kind of closing the gap on both of them because even what you would call that smaller platform that started off at more tier one, excuse me tier two and tier three is now moving into the tier one space so. But it's really about scalability, ease of use, some of them are stronger in some markets like maybe a higher enterprise. But we can sell them across anywhere whether it be public sector, commercial, mid market, smaller customers. But they each have use cases that they fit in very well. >> This morning in the key notes we heard a lot about API's, I want to get into Multi Cloud in a second but before I do we talk a lot about infrastructures code, DevOps, we heard a lot about Kubernetes, a little bit about Kubernetes this morning. And the Cisco DevNet I've often said on theCUBE that they're the only large established company that's figured out how to do something for developers. Now does your partnership extend into sort of infrastructures code, how does that all sort of go through? Is DevNet a play here or even on the roadmap? >> Nathan: So from DevNet can you take that one? >> Well I can say yes it is a play, if you take a look at all of our solutions, primarily the compute and the fabric solutions, programmability is really a key function that we have and the customers can go in and they can actually working with our API's, API's that we work with separate with other vendors too that are dedicated to other vendors. It is a key thing and DevNet became to the forefront probably about five years ago and it was really built off of that development effort so that's critical for us going forward here there's a lot that we're doing I know we're going to talk about intersight and some other things where that was a key element of it. >> Yeah so this is important. You were at Cisco Live. >> And Cisco DevNet. >> And we were in the DevNet zone and you remember, you had many many booths, very specialized, then you have CCIE's learning python, learning how to program infrastructure for new use cases, edge comes in. Anything you'd add Nathan to sort of programmability? >> So I think just from day one from Pure Storage just having our restful API interface, having code.purestorage.com we've tried to make it as much automatable as possible, as easy for to really create a community of developers that can create these integrations very quickly, and honestly evidence of that is in intersight itself. How quickly we got that integration happening is because of that restful API interface. We were able to take the kind of AI Ops of Pure One and bring it into intersight, be able to get intersight to talk to Pure Storage very easily because of that strength of API first. >> What do we need to know about intersight? Add some color there, what is it, how's it work, what's the kind of history and how do you guys turn what you're doing in integration into customer value? >> So if I look at, going back to your comments around why converge versus hyper converge, it's often really a story of simplicity right? Customers want something simple for the data center, they know they can get it out in the Cloud but they can't always run their workloads out in the external Cloud. So simplicity is for intersight, no matter what it is, if it's converged or hyper converged, if it's Pure Storage, being able to have single interface to monitor your infrastructure, lifecycle it, to get really specific imagine a VMware administrator is able to in that single console, provision storage from Pure to a UCS server, format it for VMware ESX and VMFS, and in that single console so doesn't have to go to a bunch of different consoles, gets that Cloud like experience and that's what intersight delivers. So you get that simplicity whether its converged or hyper converged with intersight. >> Whether it's in the Cloud, it's the Edge, it's the Branch, Hybrid Cloud, instead of having to manage it I think that Nathan just hit on these single clusters of storage, compute, what have you. These can all be managed from one single console world wide no matter where they sit. >> So I want to talk about Multi Cloud if we can. So if I look at the players in Multi Cloud, the big whales, VMware, Red Hat, Google, Microsoft, and Cisco, you partner with all of those pretty much I think. AWS is not on the list but you figure they're kind of the facto part of the Multi Cloud scene but they're not going after Multi Cloud, Cisco was a relatively new entrant there. You got companies that have a Cloud like Microsoft and Google that want to participate, you've got companies that don't have a Cloud like Cisco that want to participate, where does Pure fit in to that Multi Cloud opportunity and how does it relate to the partnership? >> Well I think where we found a solid partnership with Cisco and Multi Cloud is the same approach to Multi Cloud and that is I'd call it open Multi Cloud. As opposed to having, forcing a single type of hyper visor on one side or a single Cloud, external Cloud on the other side, how do we make certain that our customers can run any app, anywhere? How do we appear and provide the data fabric having the most efficient amenity of fabric out there to kind of get around the data gravity problems of moving workloads, and we do that now with Pure Flash right on premises, Cloud block store out in the Cloud, our ability to Cloud snap to Azure, to AWS, and that's part of the story. The other part of the story is the fabric and the compute. So with ACI anywhere really that compeletes the any workload anywhere story, and keeping it open so it's not just one hyper visor or one Cloud provider on the other side. >> So you be the data plane in that equation, with the management of that data plane, and Cisco is the overall management framework the control plane I guess we could call that. Is that the right way to think about it? >> I'd say part of the control plane and the network fabric as well, and we're part of essentially the consistent data services no matter where you go. So really upleveling for example EBS to an enterprise grade of storage that it wasn't before, now we have something that whether you're on hardware on premises or in the cloud, you can run that monolithic application in places you couldn't do it before. >> So let's look at this in the real world in a customer environment, talk to me about whatever kind of whether it's a bank or an airline or what have you, what are the business benefits that, we'll use delta Airlines as an example, what would they get out of this if they think of all of the things that they need to achieve internally and be able to deliver to their customers? What's that you know TCO, ROI, what are all those sexy things that you guys are delivering? >> So I'd say they get essentially a lot of the barriers to getting the TCO you want for a given workload are based on compatibility. Maybe you want to run it out in Amazon but you can't get it there because it's this massive monolithic gap, the sync would take days, the SLA out there isn't quite what you want. Now being able to provide a consistent experience no matter where that data plane is, you get that choice. You can go and evaluate AWS or Azure and say that's ultimately the right TCO for my application and I know it could run out there because I've essentially standardized my data fabric anywhere, and it's the same story essentially now with ACI anywhere as well. So the ability to keep essentially the fundamental elements of the application, the infrastructure around it consistent no matter where it is, freeze that IT decision maker to put it in the right place. You don't have to be constrained by compatibility anymore. >> So internal operations can be dialed way up which means those folks are free to resources to work on other higher value projects, and the customer on the other end who doesn't know any of this stuff is under the hood is getting what they need when they want it. >> Exactly, yeah you can manage if you look at ACI you can manage the automation of the applications across the network fabric again wherever it may be, and there's robustness there, there's telemetry, there's measurements. So instead of just looking at the application you look at the robustness of that on the network and the network here us absolutely critical, none of this is going to run I think as Nathan hit on that it could be in the Cloud, it could be in the Branch, you still want the same level of performance the SLA, the five nines and that's where the network comes in that's what's critical. >> Well and the security piece as well. >> Absolutely. >> You guys are largely coming at the Multi Cloud from of course the network strength that you have but you've also got a security angle there because you can go deep packet inspection and that's a sweet spot for you guys. >> Tom: Absolutely. >> Talk about security and it's importance and so on. >> Well I think the security I mean one of the big plays that we have with ACI and with Tetration is being able to look in literally billions of packets a second and being able to track and make realtime decisions on any type of threat, threat defense that's built right in. So normally obviously you have firewall and you try to keep everything out but a lot of what will happen a lot of the penetration security hack happens inside. So this is able to look at all of the flows, at every single packet the flow of the application and the information to see if there's a threat in real time. It takes a lot of processing power a lot of storage and a lot of capacity but you know that's a Tetration product and it's a huge play, our security team is actually out selling that in addition to the data center teams. >> So is Wallingford Yankee's country or Red Sox country? >> Oh it's right on the border so I've got my in laws Yankee's, my parents Redsox, so it's very difficult at home. >> You're a Pat's fan of course, did you feel dirty watching the game on Sunday or? >> Tom: No not at all. >> Oh you felt good? >> Maybe 19 and O this year we'll see. >> And you're Switzerland in this whole debate? >> I try to be it's hard. >> Well you know this company is Warrior's so we can talk NBA too. >> You bet! >> There's a really interesting NBA season coming up now. Not so much for our team but. (laughter) >> Lisa: You never know! >> You never know. >> I had to try to be Switzerland too cause I was the West Coaster with the East Coaster boss, you know how it goes. So Tom last question for you, whole bunch of announcements that came out of Pure today as we look at all of the partnerships that Pure has we talked about that, that Cisco has as well, what are some of the things that as a partner as a valued strategic partner, that Cisco hears when they hear Pure talking about delivering everything as a service and what they're doing with AI and dialing up things there, what is Ciscos reaction to that news? >> Well the thing with Pure and it preceded this conference but you know I really heard it with the new announcements and Nate and I we have a lot of things we're going to work with our systems engineers on in the Americas, it's just the innovation which is pretty incredible. You know you kind of have the big four products here but primarily with the Flash arrays the CI platforms, the Flash blades, what's going on with Pure one, that's going to be critical going forward and we have very similar messages with Multi Cloud. We talked about the validated designs, this is really going to lead us to almost like it's kind of funny when you have an innovative partner you can do reboots every year and people don't think you're just throwing work at them or what have you. It's like now we really innovated again, 12, 15 months later we're going to hit this again and come at it. And so Pure is probably one of the only partners we have that type of relationship with. >> Alright well guys thank you so much for joining Dave and me on theCUBE today we appreciate it. We look forward to following the evolution of this Cisco Pure partnership, thanks for your time. >> Thank you. >> Thank you guys. >> For Dave Vellante, I'm Lisa Martin, you're watching theCUBE ya'll from Pure Accelerate in Austin, Texas. (upbeat music)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBEConversation. >> Hi everyone, welcome to the special CUBEConversation. We're in Palo Alto, California, at theCUBE studio. I'm John Furrier, co-host of the CUBE. We're here with Chris Yeh. He's the co-founder and general partner of Blitzscaling Ventures, author of the book Blitzscaling with Reid Hoffman, founder of LinkedIn and a variety of other ventures, also a partner at Greylock Partners. Chris, great to see you. I've known you for years. Love the book, love Reid. You guys did a great job. So congratulations. But the big news is you're now a TV star as one of the original inaugural contestants on the Mental Samurai, just premiered on Fox, was it >> On Fox. >> On Fox, nine o'clock, on which days? >> So Mental Samurai is on Fox, Tuesdays at 9 p.m. right after Master Chef Junior. >> Alright. So big thing. So successful shows. Take us through the journey. >> Yeah. >> It's a new show, so it's got this kind of like Jeopardy vibe where they got to answer tough questions in what looks like a roller coaster kind of arm that moves you around from station to station, kind of jar you up. But it's a lot of pressure, time clock and hard questions. Tell us about the format. How you got that. Gives all the story. >> So the story behind Mental Samurai is it's from the producers of American Ninja Warrior, if you've ever seen that show. So American Ninja Warrior is a physical obstacle course and these incredible athletes go through and the key is to get through the obstacle course. If you miss any of the obstacles, you're out. So they took that and they translated it to the mental world and they said, okay, we're going to have a mental obstacle course where you going to have different kinds of questions. So they have memory questions, sequence questions, knowledge questions, all these things that are tapping different elements of intelligence. And in order to win at the game, you have to get 12 questions right in five minutes or less. And you can't get a single question wrong. You have to be perfect. >> And they do try to jar you up, to kind of scrabble your brain with those devices, it makes it suspenseful. In watching last night at your watch party in Palo Alto, it's fun to watch because yeah, I'm like, okay, it's going to be cool. I'll support Chris. I'll go there, be great and on TV, and oh my, that's pretty interesting. It was actually riveting. Intense. >> Yeah. You have that element of moving around from station to station and it's dramatic. It's kind of a theater presence. But what's it like in there? Give us some insight. You're coming on in April 30th so you're yet to come on. >> Yes. >> But the early contestants, none of them made it to the 100,000. Only one person passed the first threshold. >> Right >> Take us through the format. How many thresholds are there? What's the format? >> Perfect, so basically when a competitor gets strapped into the chair, they call it Ava, it's like a robot, and basically they got it from some company in Germany and it has the ability to move 360 degrees. It's like an industrial robot or something. It makes you feel like you're an astronaut or in one those centrifugal force things. And the idea is they're adding to the pressure. They're making it more of a challenge. Instead of just Jeopardy where you're sitting there, and answering questions and bantering with Alex Trebek, you're working against the clock and you're being thrown around by this robot. So what happens is first you try to answer 12 questions correctly in less than five minutes. If you do that, then you make it through to the next round, what they call the circle of samurai and you win $10,000. The circle of samurai, what happens is there are four questions and you get 90 seconds plus whatever you have left over from your first run, to answer those four questions. Answer all four questions correctly, you win $100,000 and the official title of Mental Samurai. >> So there's only two levels, circle of samurai but it gets harder. Now also I noticed that it's, their questions have certain puzzles and there's certain kinds of questions. What's the categories, if you will, what's the categories they offer? >> Yes, so the different categories are knowledge, which is just classic trivia, it's a kind of Jeopardy stuff. There's memory, where they have something on screen that you have to memorize, or maybe they play an audio track that you have to remember what happened. And then there's also sequence where you have to put things in order. So all these different things are represented by these different towers which are these gigantic television screens where they present the questions. And the idea is in order to be truly intelligent, you have to be able to handle all of these different things. You can't just have knowledge. You can't just have pop culture. You got to have everything. >> So on the candidates I saw some from Stanford. >> Yeah. >> I saw an athlete. It's a lot of diversity in candidates. How do they pick the candidates? How did you get involved? Did your phone ring up one day? Were you identified, they've read your blog. Obviously they've, you're smart. I've read your stuff on Facebook. How did you get in there? (laughs) >> Excellent question. So the whole process, there's a giant casting department that does all these things. And there's people who just cast people for game shows. And what happened with me is many years ago back in 2014, my sister worked in Hollywood when I was growing up. She worked for ER and Baywatch and other companies and she still keeps track of the entertainment industry. And she sent me an email saying, hey, here's a casting call for a new show for smart people and you should sign up. And so I replied to the email and said hey I'm Chris Yeh. I'm this author. I graduate from Stanford when I was 19, blah blah blah blah. I should be on your show. And they did a bunch of auditions with me over the phone. And they said we love you, the network loves you. We'll get in touch and then I never heard. Turns out that show never got the green light. And they never even shot that show. But that put me on a list with these various casting directors. And for this show it turns out that there was an executive producer of the show, the creator of the show, his niece was the casting director who interviewed me back in 2014. And she told her uncle, hey, there's this guy, Chris Yeh, in Palo Alto. I think would be great for this new show you're doing. Why don't you reach out to him. So they reached out to me. I did a bunch of Skype auditions. And eventually while I was on my book tour for Blitzscaling, I got the email saying, congratulations, you're part of the season one cast. >> And on the Skype interviews, was it they grilling you with questions, or was it doing a mock dry run? What was some of interview vetting questions? >> So they start off by just asking you about yourself and having you talk about who you are because the secret to these shows is none of the competitors are famous in advance, or at least very few of them are. There was a guy who was a major league baseball pitcher, there's a guy who's an astronaut, I mean, those guys are kind of famous already, but the whole point is, they want to build a story around the person like they do with the Olympics so that people care whether they succeed or not. And so they start off with biographical questions and then they proceed to basically use flash cards to simulate the game and see how well you do. >> Got it, so they want to basically get the whole story arc 'cause Chris, obviously Chris is smart, he passed the test. Graduate when he's 19. Okay, you're book smart. Can you handle the pressure? If you do get it, there's your story line. So they kind of look from the classic, kind of marketing segmentation, demographics is your storylines. What are some of the things that they said to you on the feedback? Was there any feedback, like you're perfect, we like this about you. Or is it more just cut and dry. >> Well I think they said, we love your energy. It's coming through very strongly to the screen. That's fantastic. We like your story. Probably the part I struggle the most with, was they said hey, you know, talk to us about adversity. Talk to us about the challenges that you've overcome. And I tell people, listen, I'm a very lucky guy. A lot of great things have happened to me in life. I don't know if there's that much adversity that I can really complain about. Other people who deal with these life threatening illnesses and all this stuff, I don't have that. And so that was probably the part I struggled the most with. >> Well you're certainly impressive. I've known you for years. You're a great investor, a great person. And a great part of Silicon Valley. So congratulations, good luck on the show. So it's Tuesdays. >> 9 p.m. >> 9 p.m. >> On fox. >> On Fox. Mental Samurai. Congratulations, great. Great to be at the launch party last night. The watch party, there'll be another one. Now your episode comes out on April 30th. >> Yes. So on April 30th we will have a big Bay area-wide watch party. I'm assuming that admission will be free, assuming I find the right sponsors. And so I'll come back to you. I'll let you know where it's going to be. Maybe we should even film the party. >> That's, well, I got one more question on the show. >> Yeah. >> You have not been yet on air so but you know the result. What was it like sitting in the chair, I mean, what was it personally like for you? I mean you've taken tests, you've been involved with the situation. You've made some investments. There's probably been some tough term sheets here and there, board meetings. And all that experience in your life, what was it compared to, what was it like? >> Well, it's a really huge adrenaline rush because if you think about there's so many different elements that already make it an adrenaline rush and they all combine together. First of all, you're in this giant studio which looks like something out of a space-age set with this giant robotic arm. There's hundreds of people around cheering. Then you're strapped into a robotic arm which basically makes you feel like an astronaut, like every run starts with you facing straight up, right? Lying back as if you're about to be launched on a rocket. And then you're answering these difficult questions with time pressure and then there's Rob Lowe there as well that you're having a conversation with. So all these things together, and your heart, at least for me, my heart was pounding. I was like trying very hard to stay calm because I knew it was important to stay clam, to be able to get through it. >> Get that recall, alright. Chris, great stuff. Okay, Blitzscaling. Blitzscaling Ventures. Very successful concept. I remember when you guys first started doing this at Stanford, you and Reid, were doing the lectures at Stanford Business School. And I'm like, I love this. It's on YouTube, kind of an open project initially, wasn't really, wasn't really meant to be a book. It was more of gift, paying it forward. Now it's a book. A lot of great praise. Some criticism from some folks but in general it's about scaling ventures, kind of the Silicon Valley way which is the rocket ship I call. The rocket ship ventures. There's still the other venture capitals. But great book. Feedback from the book and the original days at Stanford. Talk about the Blitzscaling journey. >> And one of the things that happened when we did the class at Stanford is we had all these amazing guests come in and speak. So people like Eric Schmidt. People like Diane Greene. People like Brian Chesky, who talked about their experiences. And all of those conversations really formed a key part of the raw material that went into the book. We began to see patterns emerge. Some pretty fascinating patterns. Things like, for example, a lot of companies, the ones that'd done the best job of maintaining their culture, have their founders involved in hiring for the first 500 employees. That was like a magic number that came up over and over again in the interviews. So all this content basically came forward and we said, okay, well how do we now take this and put it into a systematic framework. So the idea of the book was to compress down 40 hours of video content, incredible conversations, and put it in a framework that somebody could read in a couple of hours. >> It is also one of those things where you get lightning in a ball, the classic and so then I'd say go big or go home. But Blitzscaling is all about something new and something different. And I'm reading a book right now called Loonshots, which is a goof on moonshots. It's about the loonies who start the real companies and a lot of companies that are successful like Airbnb was passed over on and they call those loonies. Those aren't moonshots. Moonshots are well known, build-outs. This is where the blitzscaling kind of magic happens. Can you just share your thoughts on that because that's something that's not always talked about in the mainstream press, is that a lot of there blitzscaling companies, are the ones that don't look good on paper initially. >> Yes. >> Or ones that no one's talking about is not in a category or herd mentality of investors. It's really that outlier. >> Yes. >> Talk about that dynamic. >> Yeah, and one of the things that Reid likes to say is that the best possible companies usually sound like they're dumb ideas. And in fact the best investment he's been a part of as a venture capitalist, those are the ones where there's the greatest controversy around the table. It's not the companies that come in and everyone's like this is a no-brainer, let's do it. It's the companies where there's a big fight. Should we do this, should we not? And we think the reason is this. Blitzscaling is all about being able to be the first to scale and the winner take most or the winner take all market. Now if you're in a market where everyone's like, this is a great market, this is a great idea. You're going to have huge competition. You're going to have a lot of people going after it. It's very difficult to be the first to scale. If you are contrarian and right you believe something that other people don't believe, you have the space to build that early lead, that you can then use to leverage yourself into that enduring market leadership. >> And one of the things that I observed from the videos as well is that the other fact that kind of plays into, I want to get your reaction, this is that there has to be a market shift that goes on too because you have to have a tailwind or a wave to ride because if you can be contrarian if there's no wave, >> Right. >> right? so a lot of these companies that you guys highlight, have the wave behind them. It was mobile computing, SaaSification, cloud computing, all kind of coming together. Talk about that dynamic and your reaction 'cause that's something where people can get confused on blitzscaling. They read the book. Oh I'm going to disrupt the dry cleaning business. Well I mean, not really. I mean, unless there's something different >> Exactly. >> in market conditions. Talk about that. >> Yeah, so with blitzscaling you're really talking about a new market or a market that's transforming. So what is it that causes these things to transform? Almost always it's some new form of technological innovation, or perhaps a packaging of different technological innovations. Take mobile computing for example. Many of the components have been around for a while. But it took off when Apple was able to combine together capacitative touchscreens and the form factor and the processor strength being high enough finally. And all these things together created the technological innovation. The technological innovation then enables the business model innovation of building an app store and creating a whole new way of thinking about handheld computing. And then based on that business model innovation, you have the strategy innovation of blitzscaling to allow you to grow rapidly and keep from blowing up when you grow. >> And the spirit of kind of having, kind of a clean entrepreneurial segmentation here. Blitzscaling isn't for everybody. And I want you to talk about that because obviously the book's popular when this controversy, there's some controversy around the fact that you just can't apply blitzscaling to everything. We just talk about some of those factors. There are other entrepreneurialship models that makes sense but that might not be a fit for blitzscaling. Can you just unpack that and just explain, a minute to explain the difference between a company that's good for blitzscaling and one that isn't. >> Well, a key thing that you need for blitzscaling is one of these winner take most or winner take all markets that's just enormous and hugely valuable, alright? The whole thing about blitzscaling is it's very risky. It takes a lot of effort. It's very uncomfortable. So it's only worth doing when you have those market dynamics and when that market is really large. And so in the book we talk about there being many businesses that this doesn't apply to. And we use the example of two companies that were started at the same time. One company is Amazon, which is obviously a blitzscaling company and a dominant player and a great, great company. And the other is the French Laundry. In fact, Jeff Bezos started Amazon the same year that Thomas Keller started the French Laundry. And the French Laundry still serves just 60 people a day. But it's a great business. It's just a very different kind of business. >> It's a lifestyle or cash flow business and people call it a lifestyle business but mainly it's a cash flow or not a huge growing market. >> Yeah. >> Satisfies that need. What's the big learnings that you learned that was something different that you didn't know coming out of blitzscaling experience? Something that surprised you, something that might have shocked you, something that might have moved you. I mean you're well-read. You're smart. What was some learnings that you learned from the journey? >> Well, one of the things that was really interesting to me and I didn't really think about it. Reid and I come from the startup world, not the big company world. One of the things that surprised me is the receptivity of big companies to these ideas. And they explained it to me and they said, listen, you got to understand with a big company, you think it's just a big company growing at 10, 15% a year. But actually there's units that are growing at 100% a year. There's units that are declining at 50% a year. And figuring out how you can actually continue to grow new businesses quicker than your old businesses die is a huge thing for the big, established companies. So that was one of the things that really surprised me but I'm grateful that it appears that it's applicable. >> It's interesting. I had a lot of conversations with Michael Dell before, and before they went private and after they went private. He essentially was blitzscaling. >> Yeah. >> He said, I'm going to winner take most in the mature, somewhat declining massive IT enterprise spend against the HPs of the world, and he's doing it and VMware stock went to an all time high. So big companies can blitz scale. That's the learning. >> Exactly. And the key thing to remember there is one of the reasons why somebody like Michael Dell went private to do this is that blitzscaling is all about prioritizing speed over efficiency. Guess who doesn't like that? Wall street doesn't like because you're taking a hit to earnings as you invest in a new business. GM for example is investing heavily in autonomous vehicles and that investment is not yet delivering cash but it's something that's going to create a huge value for General Motors. And so it's really tough to do blitzscaling as a publicly traded company though there are examples. >> I know your partner in the book, Reid Hoffman as well as in the blitzscaling at Stanford was as visible in both LinkedIn and as the venture capitalist of Greylock. But also he was involved with some failed startups on the front end of LinkedIn. >> Yeah. >> So he had some scar tissue on social networking before it became big, I'll say on the knowledge graph that he's building, he built at LinkedIn. I'm sure he had some blitzscaling lessons. What did he bring to the table? Did he share anything in the classes or privately with you that you can share that might be helpful for people to know? >> Well, there's a huge number of lessons. Obviously we drew heavily on Reid's life for the book. But I think you touched on something that a lot of people don't know, which is that LinkedIn is not the first social network that Reid created. Actually during the dot-com boom Reid created a company called SocialNet that was one of the world's first social networks. And I actually was one of the few people in the world who signed up and was a member of SocialNet. I think I had the handle, net revolutionary on that if you can believe that. And one of the things that Reid learned from his SocialNet experience turned into one of his famous sayings, which is, if you're not embarrassed by your first product launch, you've launched too late. With SocialNet they spent so much time refining the product and trying to get it perfectly right. And then when they launched it, they discovered what everyone always discovers when they launch, which is the market wants something totally different. We had no idea what people really wanted. And they'd wasted all this time trying to perfect something that they've theoretically thought was what the market wanted but wasn't actually what the market wanted. >> This is what I love about Silicon Valley. You have these kind of stories 'cause that's essentially agile before agile came out. They're kind of rearranging the deck chairs trying to get the perfect crafted product in a world that was moving to more agility, less craftsmanship and although now it's coming back. Also I talked to Paul Martino, been on theCUBE before. He's a tribe with Pincus. And it's been those founding fathers around these industries. It's interesting how these waves, they start off, they don't get off the ground, but that doesn't mean the category's dead. It's just a timing issue. That's important in a lot of ventures, the timing piece. Talk about that dynamic. >> Absolutely. When it comes to timing, you think about blitzscaling. If you start blitzscaling, you prioritize speed over efficiency. The main question is, is it the right time. So Webvan could be taken as an example of blitzscaling. They were spending money wildly inefficiently to build up grocery delivery. Guess what? 2000 was not the right time for it. Now we come around, we see Instacart succeeding. We see other delivery services delivering some value. It just turns out that you have to get the timing right. >> And market conditions are critical and that's why blitzscaling can work when the conditions are right. Our days back in the podcast, it was, we were right but timing was off. And this brings up the question of the team. >> Yeah. >> You got to have the right team that can handle the blitzscaling culture. And you need the right investors. You've been on both sides of the table. Talk about that dynamic because I think this is probably one of the most important features because saying you going to do blitzscaling and then getting buy off but not true commitment from the investors because the whole idea is to plow money into the system. You mentioned Amazon, one of Jeff Bezos' tricks was, he always poured money back into his business. So this is a capital strategy, as well financial strategy capital-wise as well as a business trait. Talk about the importance of having that stomach and the culture of blitzscaling. >> Absolutely. And I think you hit on something very important when you sort of talk about the importance of the investors. So Reid likes to refer to investors as financing partners. Or financing co-founders, because really they're coming on with you and committing to the same journey that you're going on. And one of the things I often tell entrepreneurs is you really have to dig deep and make sure you do more due diligence on your investors than you would on your employees. Because if you think about it, if you hire an employee, you can actually fire them. If you take money from an investor, there's no way you can ever get rid of them. So my advice to entrepreneurs is always, well, figure out if they're going to be a good partner for you. And the best way to do that is to go find some of the entrepreneurs they backed who failed and talked to those people. >> 'Cause that's where the truth will come out. >> Well, that's right. >> We stood by them in tough times. >> Exactly. >> I think that's classic, that's perfect but this notion of having the strategies of the elements of the business model in concert, the financial strategy, the capital strategy with the business strategy and the people strategy, all got to be pumping that can't be really any conflict on that. That's the key point. >> That's right, there has to be alignment because again, you're trying to go as quickly as possible and if you're running a race car and you have things that are loose and rattling around, you're not going to make it across the finish line. >> You're pulling for a pit stop and the guys aren't ready to change the tires, (snapping fingers) you know you're out of sync. >> Bingo. >> Chris, great stuff. Blitzscaling is a great book. Check it out. I recommend it, remember blitz scale is not for anyone, it's for the game changers. And again, picking your investors is critical on this. So if you picked the wrong investors, blitzscaling will blow up in a bad way. So don't, don't, pick properly on the visa and pick your team. Chris, so let's talk about you real quick to end the segment and the last talk track. Talk about your background 'cause I think you have a fascinating background. I didn't know that you graduated when you're 19, from Stanford was it? >> Yes. >> Stanford at 19, that's a great accomplishment. You've been an entrepreneur. Take us through your journey. Give us a quick highlight of your career. >> So the quick highlight is I grew up in Southern California and Santa Monica where I graduated from Santa Monica High School along with other luminaries such as Rob Lowe, Robert Downey, Jr., and Sean Penn. I didn't go at the same time that they did. >> They didn't graduate when they were 17. >> They did not, (John laughing) and Charlie Sheen also attended Santa Monica High School but dropped out or was expelled. (laughing) Go figured. >> Okay. >> I came up to Stanford and I actually studied creative writing and product design. So I was really hitting both sides of the brain. You could see that really coming through in the rest of my career. And then at the time I graduated which was the mid-1990s that was when the internet was first opening up. I was convinced the internet was going to be huge and so I just went straight into the internet in 1995. And have been in the startup world ever since. >> Must love that show, Halt and Catch Fire a series which I love reminiscing. >> AMC great show. >> Just watching that my life right before my eyes. Us old folks. Talk about your investment. You are at Wasabi Ventures now. Blitzscaling Ventures. You guys looks like you're going to do a little combination bring capital around blitzscaling, advising. What's Blitzscaling Ventures? Give a quick commercial. >> So the best way to think about it is for the entrepreneurs who are actually are blitzscaling, the question is how are you going to get the help you need to figure out how to steer around the corners to avoid the pitfalls that can occur as you're growing rapidly. And Blitzscaling Ventures is all about that. So obviously I bring a wealth of experience, both my own experience as well as everything I learned from putting this book together. And the whole goal of Blitzscaling Ventures is to find those entrepreneurs who have those blitzscalable opportunities and help them navigate through the process. >> And of course being a Mental Samurai that you are, the clock is really important on blitzscaling. >> There are actually are a lot of similarities between the startup world and Mental Samurai. Being able to perform under pressure, being able to move as quickly as possible yet still be accurate. The one difference of course is in our startup world you often do make mistakes. And you have a chance to recover from them. But in Mental Samurai you have to be perfect. >> Speed, alignment, resource management, capital deployment, management team, investors, all critical factors in blitzscaling. Kind of like entrepreneurial going to next level. A whole nother lesson, whole nother battlefields. Really the capital markets are flush with cash. Post round B so if you can certainly get altitude there's a ton of capital. >> Yeah. And the key is that capital is necessary for blitzscaling but it's not sufficient. You have to take that financial capital and you have to figure out how to combine it with the human capital to actually transform the business in the industry. >> Of course I know you've got to catch a plane. Thanks for coming by in the studio. Congratulations on the Mental Samurai. Great show. I'm looking forward to April 30th. Tuesdays at 9 o'clock, the Mental Samurai. Chris will be an inaugural contestant. We'll see how he does. He's tight-lipped, he's not breaking his disclosure. >> I've got legal requirements. I can't say anything. >> Just say he's sticking to his words. He's a man of his words. Chris, great to see you. Venture capitalist, entrepreneur, kind of venture you want to talk to Chris Yeh, co-founder, general partner of blitzscaling. I'm John Furrier for theCUBE. Thanks for watching. (upbeat music)

>> Live from Stanford University, it's theCUBE. Covering Global Women in Data Science Conference. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE I'm Lisa Martin. We've been live all day at the fourth annual Women in Data Science Conference. I'm with John Furrier, John, this is not just WiDS fourth annual, it's theCUBE's fourth time covering this event. There were, as Margot Gerritsen, Co-Founder stopped by this afternoon and was chatting with me saying, there's over 20,000 people they expect today just to watch the WiDS livestream from Stanford. Another 100,000 engaging in over 150 regional WiDS events, and 50 countries, CUBE's been there since the beginning tell us a little bit about that. >> Well what's exciting about this event is that we've been there from the beginning, present at creation with these folks. Great community, Judy Logan, Karen Matthys, Margot. They're all been great, but the vision from day one has been put together smart people, okay, on a stage, in a room, and bring it, syndicate it out to anyone who's available, meet ups and groups around the world. And if you bet on good content and quality people the community with self-form. And with the Stanford brand behind it, it really was a formula for success from day one. And this is the new model, this is the new reality, where, if you have high quality people in context, the global opportunity around the content and community work well together, and I think they cracked the code. Something that we feel similar at theCUBE is high quality conversations, builds community so content drives community and keep that fly wheel going this is what Women in Data Science have figured out. And I'm sure they have the data behind it, they have the women who can analyze the data. But more importantly is a great community and it's just it's steamrolling forward ahead, it's just great to see. 50 countries, 125 cities, 150 events. And it's just getting started so, we're proud to be part of it, and be part of the creation but continue to broadcast and you know you're doing a great job, and I wish I was interviewing, some of the ladies myself but, >> I know you do >> I get jealous. >> you're always in the background, yes I know you do. You know you talk about fly wheel and Margot Gerritsen we had her on the WiDS broadcast last year, and she said, you know, it's such a short period of time its been three and a half years. That they have generated this incredible momentum and groundswell that every time, when you walk in the door, of the Stanford Arrillaga Alumni Center it's one of my favorite events as you know, you feel this support and this positivity and this movement as soon as you step foot in the door. But Margot said this actually really was an idea that she and her Co-Founders had a few years ago. As almost sort of an anti, a revenge conference. Because they go to so many events, as do we John, where there are so many male, non-female, keynote speakers. And you and theCUBE have long been supporters of women in technology, and the time is now, the momentum is self-generating, this fly wheel is going as you mentioned. >> Well I think one of the things that they did really well was they, not only the revenge on the concept of having women at the event, not being some sort of, you know part of an event, look we have brought women in tech on stage, you know this is all power women right? It's not built for the trend of having women conference there's actual horsepower here, and the payload of the content agenda is second to none. If you look at what they're talking about, it's hardcore computer science, its data analytics, it's all the top concepts that the pros are talking about and it just happens to be all women. Now, you combine that with what they did around openness they created a real open environment around opening up the content and not making it restrictive. So in a way that's, you know, counter intuitive to most events and finally, they created a video model where they livestream it, theCUBE is here, they open up the video format to everybody and they have great people. And I think the counter intuitive ones become the standard because not everyone is doing it. So that's how success is, it's usually the ones you don't see coming that are doing it and they think they did it. >> I agree, you know this is a technical conference and you talked about there's a lot of hardcore data science and technology being discussed today. Some of the interesting things, John, that I really heard thematically across all the guests that I was able to interview today is, is the importance, maybe equal weight, maybe more so some of the other skills, that, besides the hardcore data analysis, statistical analysis, computational engineering and mathematics. But it's skills such as communication, collaboration collaboration was key throughout the day, every person in academia and the industry that we talked to. Empathy, the need to have empathy as you're analyzing data with these diverse perspectives. And one of the things that kind of struck me as interesting, is that some of the training in those other skills, negotiation et cetera, is not really infused yet in a lot of the PhD Programs. When communication is one of the key things that makes WiDS so effective is the communication medium, but also the consistency. >> I think one of the things I'm seeing out of this trend is the humanization of data and if you look at I don't know maybe its because its a women's conference and they have more empathy than men as my wife always says to me. But in seriousness, the big trend right now in machine learning is, is it math or is it cognition? And so if you look at the debate that machine learning concepts, you have two schools of thought. You have the Berkeley School of thought where it's all math all math, and then you have, you know kind of another school of thought where learning machines and unsupervised machine learning kicks in. So, machines have to learn, so, in order to have a humanization side is important and people who use data the best will apply human skills to it. So it's not just machines that are driving it, it's the role of the humans and the machines. This is something we have been talking a lot in theCUBE about and, it's a whole new cutting edge area of science and social science and look at it, fake news and all these things in the mainstream press as you see it playing out everyday, without that contextual analysis and humanization the behavioral data gets lost sometimes. So, again this is all data, data science concepts but without a human application, it kind of falls down. >> And we talked about that today and one of the interesting elements of conversation was, you know with respect to data ethics, there's 2.5 trillion data sets generated everyday, everything that we do as people is traceable there's a lot of potential there. But one of the things that we talked about today was this idea of, almost like a Hippocratic Oath that MDs take, for data scientists to have that accountability, because the human component there is almost one that can't really be controlled yet. And it's gaining traction this idea of this oath for data science. >> Yeah and what's interesting about this conference is that they're doing two things at the same time. If you look at the data oath, if you will, sharing is a big part, if you look at cyber security, we are going to be at the RSA conference this week. You know, people who share data get the best insights because data, contextual data, is relevant. So, if you have data and I'm looking at data but your data could help me figure out my data, data blending together works well. So that's an important concept of data sharing and there's an oath involved, trust, obviously, privacy and monitoring and being a steward of the data. The second thing that's going on at this event is because it's a global event broadcast out of Stanford, they're activating over 50 countries, over 125 cities, they're creating a localization dynamic inside other cities so, they're sharing their data from this event which is the experts on stage, localizing it in these markets, which feeds into the community. So, the concept of sharing is really important to this conference and I think that's one of the highlights I see coming out of this is just that, well, the people are amazing but this concept of data sharing it's one of those big things. >> And something to that they're continuing to do is not just leverage the power of the WiDS brand that they're creating in this one time of year in the March of the year where they are generating so much interest. But Margot talked about this last year, and the idea of developing content to have this sustained inspiration and education and support. They just launched a podcast a few months ago, which is available on iTunes and GooglePlay. And also they had their second annual datathon this year which was looking at palm oil production, plantations rather, because of the huge biodiversity and social impact that these predictive analytics can have, it's such an interesting, diverse, set of complex challenges that they tackle and that they bring more awareness to everyday. >> And Padmasree Warrior talked about her keynote around, former Cisco CTO, and she just ran, car, she's working on a new start up. She was talking about the future of how the trends are, the old internet days, as the population of internet users grew it changed the architecture. Now mobile phones, that's changing the architecture. Now you have a global AI market, that's going to change the architecture of the solutions, and she mentioned at the end, an interesting tidbit, she mentioned Blockchain. And so I think that's something that's going to be kind of interesting in this world is, because there's, you know about data and data science, you have Blockchain it's the data store potentially out there. So, interesting to see as you start getting to these supply chains, managing these supply chains of decentralization, how that's going to impact the WiDS community, I'm curious to see how the team figures that out. >> Well I look forward to being here at the fifth annual next year, and watching and following the momentum that WiDS continues to generate throughout the rest of 2019. For John Furrier, I'm Lisa Martin, thanks so much for watching theCUBE's coverage, of the fourth annual Women in Data Science Conference Bye for now. (upbeat electronic music)

>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time CUBE alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.

>> Narrator: From San Mateo, California, it's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. >> Welcome back, Jeff Frick here with theCUBE. We're at the Crossroads, that's 92 and 101 in the Bay Area if you've been through it, you've had time to take a minute and look at all the buildings, 'cause traffic's usually not so great around here. But there's a lot of great software companies that come through here. It's interesting, I always think back to the Siebel Building that went up and now that's Rakuten, who we all know from the Warrior jerseys, the very popular Japanese retailer. But that's not why we're here. We're here to talk to SnapLogic. They're doing a lot of really interesting things, and they have been in data, and now they're doing a lot of interesting things in integration. And we're excited to have a many time Cube alum. He's Greg Benson, let me get that title right, chief scientist at SnapLogic and of course a professor at University of San Francisco. Greg great to see you. >> Great to see you, Jeff. >> So I think the last time we see you was at Fleet Forward. Interesting open-source project, data, ad moves. The open-source technologies and the technologies available for you guys to use just continue to evolve at a crazy breakneck speed. >> Yeah, it is. Open source in general, as you know, has really revolutionized all of computing, starting with Linux and what that's done for the world. And, you know, in one sense it's a boon, but it introduces a challenge, because how do you choose? And then even when you do choose, do you have the expertise to harness it? You know, the early social companies really leveraged off of Hadoop and Hadoop technology to drive their business and their objectives. And now we've seen a lot of that technology be commercialized and have a lot of service around it. And SnapLogic is doing that as well. We help reduce the complexity and make a lot of this open-source technology available to our customers. >> So, I want to talk about a lot of different things. One of the things is Iris. So Iris is your guys' leverage of machine learning and artificial intelligence to help make integration easier. Did I get that right? >> That's correct, yeah. Iris is the umbrella terms for everything that we do with machine learning and how we use it to enhance the user experience. And one way to think about it is when you're interacting with our product, we've made the SnapLogic designer a web-based UI, drag-and-drop interface to construct these integration pipelines. We connect these things called Snaps. It's like building with Legos to build out these transformations on your data. And when you're doing that, when you're interacting with the designer, we would like to believe that we've made it one of the simplest interfaces to do this type of work, but even with that, there are many times we have to make decisions, like what type of transformation do you do next? How do you configure that transformation if you're talking to an Oracle database? How do you configure it? What's your credentials if you talk to SalesForce? If I'm doing a transformation on data, which fields do I need? What kind of operations do I need to apply to those fields? So as you can imagine, there's lots of situations as you're building out these data integration pipelines to make decisions. And one way to think about Iris is Iris is there to help reduce the complexity, help reduce what kind of decision you have to make at any point in time. So it's contextually aware of what you're doing at that moment in time, based on mining our thousands of existing pipelines and scenarios in which SnapLogic has been used. We leverage that to train models to help make recommendations so that you can speed through whatever task you're trying to do as quickly as possible. >> It's such an important piece of information, because if I'm doing an integration project using the tool, I don't have the experience of the vast thousands and thousands, and actually you're doing now, what, a trillion document moves last month? I just don't have that expertise. You guys have the expertise, and truth be told, as unique as I think I am, and as unique as I think my business processes are, probably, a lot of them are pretty much the same as a lot of other people that are hooking up to SalesForce to Oracle or hooking up Marketta to their CRM. So you guys have really taken advantage of that using the AI and ML to help guide me along, which is probably a pretty high-probability prediction of what my next move's going to be. >> Yeah, absolutely, and you know, back in the day, we used to consider, like, wizards or these sorts of things that would walk you through it. And really that was, it seemed intelligent, but it wasn't really intelligence or machine learning. It was really just hard-coded facts or heuristics that hopefully would be right for certain situations. The difference today is we're using real data, gigabytes of metadata that we can use to train our models. The nice thing about that it's not hard-coded it's adaptive. It's adaptive both for new customers but also for existing customers. We have customers that have hundreds of people that just use SnapLogic to get their business objectives done. And as they're building new pipelines, as they are putting in new expressions, we are learning that for them within their organization. So like their coworkers, the next day, they can come in and then they get the advantages of all the intellectual work that was done to figure something out will be learned and then will be made available through Iris. >> Right. I love this idea of operationalizing machine learning and the augmented intelligence. So how do you apply it? Don't just talk about it, don't give it a name of some dead smart person, but actually apply it to an application where you can start to see the benefit. And that's really what Iris is all about. So what's changed the most in the last year since you launched it? >> You know, one thing I'll say: The most interesting thing that we discovered when we first launched Iris, and I should say one of the first Iris technologies that we introduced was something called the integration assistant. And this was an assistant that would make, make recommendations of the next Snap as you're building out your pipeline, so the next transformation or the next connector, and before we launched it, we did lots of experimentation with different machine learning models. We did different training to get the best accuracy possible. And what we really thought was that this was going to be most useful for the new user, somebody who hasn't really used the product and it turns out, when we looked at our data, and we looked at how it got used, it turns out that yes, new users did use it, but existing or very skilled users were using it just as much if not more, 'cause it turned out that it was so good at making recommendations that it was like a shortcut. Like, even if they knew the product really well, it's still actually a little more work to go through our catalog of 400 plus Snaps and pick something out when if it's just sitting right there and saying, "Hey, the next thing you need to do," you don't even have to think. You just have to click, and it's right there. Then it just speeds up the expert user as well. That was an interesting sort of revelation about machine learning and our application of it. In terms of what's changed over the last year, we've done a number of things. Probably the operationalizing it so that instead of training off of SnapShot, we're now training on a continuous basis so that we get that adaptive learning that I was talking about earlier. The other thing that we have done, and this is kind of getting into the weeds, we were using a decision tree model, which is a type of machine learning algorithm, and we switched to neural nets now, so now we use neural nets to achieve higher accuracy, and also a more adaptive learning experience. The neural net allowed us to bring in sort of like this organizational information so that your recommendations would be more tailored to your specific organization. The other thing we're just on the cusp of releasing is, in the integration assistant, we're working on sort of a, sort of, from beginning-to-end type recommendation, where you were kind of working forward. But what we found is, in talking to people in the field, and our customers who use the product, is there's all kinds of different ways that people interact with a product. They might know know where they want the data to go, and then they might want to work backwards. Or they might know that the most important thing I need this to do is to join some data. So like when you're solving a puzzle with the family, you either work on the edges or you put some clumps in the middle and work to get to. And that puzzle solving metaphor is where we're moving integration assistance so that you can fill in the pieces that you know, and then we help you work in any direction to make the puzzle complete. That's something that we've been adding to. We recently started recommending, based on your context, the most common sources and destinations you might need, but we're also about to introduce this idea of working backwards and then also working from the inside out. >> We just had Gaurav on, and he's talking about the next iteration of the vision is to get to autonomous, to get to where the thing not only can guess what you want to do, has a pretty good idea, but it actually starts to basically do it for you, and I guess it would flag you if there's some strange thing or it needs an assistant, and really almost full autonomy in this integration effort. It's a good vision. >> I'm the one who has to make that vision a reality. The way I like to explain is that customers or users have a concept of what they want to achieve. And that concept is as a thought in their head, and the goal is how to get that concept or thought into something that is machine executable. What's the pathway to achieve that? Or if somebody's using SnapLogic for a lot of their organizational operations or for their data integration, we can start looking at what you're doing and make recommendations about other things you should or might be doing. So it's kind of like this two-way thing where we can give you some suggestions but people also know what they want to do conceptually but how do we make that realizable as something that's executable. So I'm working on a number of research projects that is getting us closer to that vision. And one that I've been very excited about is we're working a lot with NLP, Natural Language Processing, like many companies and other products are investigating. For our use in particular is in a couple of different ways. To be sort of concrete, we've been working on a research project in which, rather than, you know, having to know the name of a Snap. 'Cause right now, you get this thing called a Snap catalog, and like I said, 400 plus Snaps. To go through the whole list, it's pretty long. You can start to type a name, and yeah, it'll limit it, but you still have to know exactly what that Snap is called. What we're doing is we're applying machine learning in order to allow you to either speak or type what the intention is of what you're looking for. I want to parse a CSV file. Now, we have a file reader, and we have a CSV parser, but if you just typed, parse a CSV file, it may not find what you're looking for. But we're trying to take the human description and then connect that with the actual Snaps that you might need to complete your task. That's one thing we're working on. I have two more. The second one is a little bit more ambitious, but we have some preliminary work that demonstrates this idea of actually saying or typing what you want an entire pipeline to do. I might say I want to read data from SalesForce, I want to filter out only records from the last week, and then I want to put those records into Redshift. And if you were to just say or type what I just said, we would give you a pipeline that maybe isn't entirely complete, but working and allows you to evolve it from there. So you didn't have to go through all the steps of finding each individual Snap and connecting them together. So this is still very early on, but we have some exciting results. And then the last thing we're working on with NLP is, in SnapLogic, we have a nice view eye, and it's really good. A lot of the heavy lifting in building these pipelines, though, is in the actual manipulation of the data. And to actually manipulate the data, you need to construct expressions. And expressions in SnapLogic, we have a JavaScript expression language, so you have to write these expressions to do operations, right. One of our next goals is to use natural language to help you describe what you want those expressions to do and then generate those expressions for you. To get at that vision, we have to chisel. We have to break down the barriers on each one of these and then collectively, this will get us closer to that vision of truly autonomous integration. >> What's so cool about it, and again, you say autonomous and I can't help but think autonomous vehicles. We had a great interview, he said, if you have an accident in your car, you learn, the person you had an accident learns a little bit, and maybe the insurance adjuster learns a little bit. But when you have an accident in an autonomous vehicle, everybody learns, the whole system learns. That learning is shared orders of magnitude greater, to greater benefit of the whole. And that's really where you guys are sitting in this cloud situation. You've got all this integration going on with customers, you have all this translation and movement of data. Everybody benefits from the learning that's gained by everybody's participation. That's what is so exciting, and why it's such a great accelerator to how things used to be done before by yourself, in your little company, coding away trying to solve your problems. Very very different kind of paradigm, to leverage all that information of actual use cases, what's actually happening with the platform. So it puts you guys in a pretty good situation. >> I completely agree. Another analogy is, look, we're not going to get rid of programmers anytime soon. However, programming's a complex, human endeavor. However, the Snap pipelines are kind of like programs, and what we're doing in our domain, our space, is trying to achieve automated programming so that, you're right, as you said, learning from the experience of others, learning from the crowd, learning from mistakes and capturing that knowledge in a way that when somebody is presented with a new task, we can either make it very quick for them to achieve that or actually provide them with exactly what they need. So yeah, it's very exciting. >> So we're running out of time. Before I let you go, I wanted to tie it back to your professor job. How do you leverage that? How does that benefit what's going on here at SnapLogic? 'Cause you've obviously been doing that for a long time, it's important to you. Bill Schmarzo, great fan of theCUBE, I deemed him the dean of big data a couple of years ago, he's now starting to teach. So there's a lot of benefits to being involved in academe, so what are you doing there in academe, and how does it tie back to what you're doing here in SnapLogic? >> So yeah, I've been a professor for 20 years at the University of San Francisco. I've long done research in operating systems and distributed systems, parallel computing programming languages, and I had the opportunity to start working with SnapLogic in 2010. And it was this great experience of, okay, I've done all this academic research, I've built systems, I've written research papers, and SnapLogic provided me with an opportunity to actually put a lot of this stuff in practice and work with real-world data. I think a lot of people on both sides of the industry academia fence will tell you that a lot of the real interesting stuff in computer science happens in industry because a lot of what we do with computer science is practical. And so I started off bringing in my expertise in working on innovation and doing research projects, which I continue to do today. And at USF, we happened to have a vehicle already set up. All of our students, both undergraduates and graduates, have to do a capstone senior project or master's project in which we pair up the students with industry sponsors to work on a project. And this is a time in their careers where they don't have a lot of professional experience, but they have a lot of knowledge. And so we bring the students in, and we carve out a project idea. And the students under my mentorship and working with the engineering team work toward whatever project we set up. Those projects have resulted in numerous innovations now that are in the product. The most recent big one is Iris came out of one of these research projects. >> Oh, it did? >> It was a machine learning project about, started around three years ago. We continuously have lots of other projects in the works. On the flip side, my experience with SnapLogic has allowed me to bring sort of this industry experience back to the classroom, both in terms of explaining to students and understanding what their expectations will be when they get out into industry, but also being able to make the examples more real and relevant in the classroom. For me, it's been a great relationship that's benefited both those roles. >> Well, it's such a big and important driver to what goes on in the Bay Area. USF doesn't get enough credit. Clearly Stanford and Cal get a lot, they bring in a lot of smart people every year. They don't leave, they love the weather. It is really a significant driver. Not to mention all the innovation that happens and cool startups that come out. Well, Greg thanks for taking a few minutes out of your busy day to sit down with us. >> Thank you, Jeff. >> All right, he's Greg, I'm Jeff. You're watching theCUBE from SnapLogic in San Mateo, California. Thanks for watching.

>> Narrator: Live from Las Vegas, it's theCUBE, covering VMworld 2017. Brought to you by VMware and its ecosystem partners. >> Welcome back to VMworld. You are watching theCUBE here, live on day two, continuing coverage from the show this year. I'm Lisa Martin, my cohost is Stu Miniman, and we're very excited to welcome our next guest. First time on theCUBE is Scott Delandy. >> First time. >> Lisa: First time technology director at Dell EMC. Welcome to theCUBE. >> Thrilled to be here. >> We're thrilled to have you, and you have a couple of really interesting things that I want to kick off with. First off all, you played vodgeball. If you're not familiar, vodgeball is a really cool, starts on the Sunday right before VMworld, benefits Wounded Warriors, which is fantastic, but it's a serious game. I've played before, I was terrified for my life. What was your experience like this year? >> It's a great event and they've been doing it for the last several years, I mean, so it was my first time I was able to participate, but it basically is a lot of the partners and exhibitors here, they put a team together, and it's to support the Wounded Warrior Foundation, so it's a great charity and a great cause. But yeah, it was very intense, because when they asked me to play, I was like, "Dodgeball, vodgeball, how hard could it be, right? "You just pick up the ball "and you just throw it at somebody, right?" I had no idea that this is like a legit thing. There's referees, there's rules, there's strategy. I mean, it was intense. And, you know, we had fun. I think everybody had fun, but I will say there were, there were some teams that were very serious and very determined to do well. And they did. >> Nobody injured, I hope. >> Not that I recall. Oh, no, there was one injury, there was one injury. Somebody was going backwards and fell into somebody who was taking a picture and there was blood. Yeah, there was a little bit of blood. But hey, again, for a good cause, right? >> The people at VMworld, they're serious about whatever they're doing. >> Very serious. >> There you go. >> That's for sure. >> Something also that interests me about your background is you have a really interesting connection with an industry that people wouldn't think, oh, there's a similarity between wrestling, WWE, and Dell EMC. On the customer experience side, you've talked with John Cena, who I admire for what he does on TV. Tell us about the similarities that you and he discussed about the customer experience. >> Yeah, so it was last year. There's an event, it's actually a legit thing, called Customer Experience Day. And so, at Dell EMC, we had, you know, different events planned at the different locations, and there were speakers that came in. Matter of fact, if you were in the Santa Clara area, they had Matthew McConaughey, was the individual that they had come there. But we had John Cena, which I think we probably got a better deal out of that. But your point, it's like, what's the similarities, and I even asked him as we were getting ready to do the interview, I was chatting with him a bit, and I was like, "You probably have no idea what we do," and "Why are you here? "This is like completely different." And he was like, "Absolutely not, "I am so looking forward to this because "I'm going to talk to new people that "I've never talked to before. "What we do and what you do is very similar "because it really is about that customer experience "and making sure that people enjoy it, "you connect with those customers, "you connect with those users out there. "It's all about, you know, how the technology "on our side is getting consumed "and what our users are able to do, "but it's also the products that they're putting out there, "just from an entertainment perspective." And he got up there and he spoke for 20 minutes, and it was amazing. I mean, he just did such a great job. >> So, Scott, I actually worked with you at EMC, and you've been at EMC for just a few years. I still have to say, it's now Dell EMC, 'cause for some reason, LinkedIn says I worked for Dell EMC for 10 years. I worked for EMC Corporation. Those of us in Massachusetts, EMC had a profound impact on technology, but how long's it been now, you've been there? And tell us how you got to your current roles. >> With EMC and now Dell EMC, I just hit my 27th year, so going on 28 years now. Badge number 399, for anybody that's still keeping score. >> Lisa: You started as a child, right? >> I was 11 when I started. It was before they changed the child labor laws. But no, it's great. I mean, you think about how the company's changed and evolved in that period of time, and I think the thing that I've always loved and continued to love about the company and the organization is just how we continued to evolved, we continued to change, we continued to adapt to what's happening in the technology space because, you know, as you know, things are constantly moving, and I think that the difference over the last several years is that the rate of change has completely accelerated, with new ways to be able to deliver IT, new ways to basically consume the things that we've been developing for years. I come on the storage side of things, and just from a company perspective, the portfolio has expanded to include pretty much anything from a technology perspective. So it's really, really cool to be able to be a part of that. >> Okay, so, Scott, you know, there are many in the storage industry that have perspective, but I mean, you've been there since, like, I guess day one of Symmetrix. And Symmetrix, through DMX, through VMAX, it's still a product line, it's still going strong. You know, why is VMAX important in enterprise tech today? >> You know, you think about it, and it really is cool, and it's something that I work closely with throughout my career, but you think about examples of technology that have been available on the market for 30 or so years. I mean, I can only come up with two. If you can come up with one, let me know, but I think of mainframes, and I think of Symmetrix VMAX, right? And they're still a key part of technology because there's a tremendous amount of trust. The world's most mission-critical workloads run on those environments. It's a proven platform that still continues to be really, really, a core part of an IT infrastructure for many, many organizations. >> Yeah, it always resonated with me. You talk to anyone in that storage organization, and they've all ready Only the Paranoid Survive. So, you know, until microprocessor's going strong, you know, lots of discussion about where Moore's Law is going. But right, you know, I think back to the early days of things like SRDF, really changed what's going on. But now, I mean, you know, Flash is the discussion. We've just been talking to some of your peers about software-defined storage. What are some of those key customer conversations you're seeing these days out there in the market? >> I think, you know, from a modernization perspective, clearly Flash is becoming the predominant way people want to store their information, right? That's, you know, you think about Flash when it was initially introduced years and years ago, it provided a solution for high performance requirements. It was really, really fast, much faster than mechanical media at the time, but it was also really, really expensive, and I think what's changed is kind of two things. Number one, the media costs have come down pretty dramatically, right? But still more expensive than spinning drives. But the arrays themselves have also become much more efficient in terms of how they're able to take advantage of Flash. You think of things like data reduction technologies, compression, dedupe, fim provisioning, snapshots, all of these types of things, where we typically see about a four to one space efficiency. So if I've got 100 terabytes, I'm paying for that 100 terabytes of capacity, but through all of these technologies, I can make that look like 400 terabytes to the outside world. So that dramatically changes the cost curb and makes it way more efficient, way more affordable than what people have previously done with things like hybrid arrays or even spinning drives. So it's cool, and, you know, you think of what's happening in the future, there are different memory-based technologies, storage class memory technologies that are going to start to become available in the marketplace, and it'll be interesting to see architecturally how that's going to impact some of the things that are available in the marketplace today, so it's going to be very interesting, I think, in the next couple of years, as the technology continues to evolve, and you're able to do things from a performance density capacity perspective that, you know, today you're just kind of getting to sort of the tip of the iceberg in terms of some of the niche technologies that are out there. These are things that are going to become much, much more mainstream going forward. So, again, people often think that storage, snoreage, right? It's the boring stuff, right? The only time people care about storage is if something breaks, right? They just assume that it's going to work. But again, there's a lot of really cool things happening from an innovation, from a technology perspective, and again, being on the technology side and getting to work very closely with the engineering guys, and the product managers, and then being able to talk to customers and users and understand kind of what challenges they're facing today and where they see things going in the future. Again, it's a great opportunity because you get to see all of this stuff coming together. So, it continues to be fun. I don't know if I can do another 27 years, but I'm hoping to get at least a couple more good ones. >> You've got like another 30 before retirement age. >> Right, right. >> Yeah, I think you're right. I'll do the math on that. Maybe not quite 30, but I appreciate it anyway, Stu. >> So, speaking of innovation, Michael Dove was talking about that this morning, and I thought it was cool that he and Pat shared some laughs on, you know, now that the accommodation is done with Dell EMC and they own VMware, there's competitors that are now partners, et cetera. Can you talk to us, you talked about kind of talking with product groups. How are you facilitating innovation and integration, say, with the VMAX with VMware? How is that kind of going? >> So, VMware is definitely a big, obviously, partner for us. But they also, their customers, in the use cases that they have, fit in very well with our technology and our systems, specifically, I'll talk specifically around VMAX. You know, you look at some of the really large environments that are out there. I know customers that have 50,000 plus VMs running on a single storage system, right? And, you know, you think of just how massive that is, and you put 50,000 anything on one storage system, you know, you need to make sure that you've got the performance, you've got the scale, you've got the reliability, you've got the data services. Those are the things that people need to be able to do consolidation at that scale, and that's where certainly VMAX is kind of the technology that continues to be core for those types of workloads. But again, there's always new things that are coming up, and there's also, you know, a set of new challenges that users are always looking at. And again, Flash is a good example where, you know, you're starting to hit the limits in terms of what you can do with traditional mechanical media, but the Flash was still too expensive at the time. But again, taking advantage of that data reduction technology and building it into the system, and being able to do it in a way that doesn't compromise any of the data services, it doesn't impact performance, it doesn't change the reliability, or the availability of the applications and the workloads. I mean, that's what kind of users sort of expect from us, and that's what we deliver. >> I think you've still got 30 years in you just, you know, with this passion and excitement that you're talking about now. >> We'll see, we'll see. Well, maybe you guy will have me back next year and we can see where we are then. >> Well, we are so thankful to you for stopping by theCUBE for your first time. You're now part of theCUBE alumni. >> Awesome, I am so thrilled. >> I don't think we have John Cena on. We do have a few professional athletes. I've interviewed a couple of former Patriots, and the like. >> As I told John when I interviewed him, he may be bigger than me, but I have better hair, I think at least. >> By far, by far. Well, Scott Delandy, thank you so much for stopping by theCUBE and sharing some of the innovations that you're doing, and we'll look forward to seeing you on theCUBE next time. >> Scott: Awesome, thank you. >> All right, and for Scott, my co-host Stu Miniman, I'm Lisa Martin, you're watching day two, live from VMworld 2017 from Las Vegas. Stick around, we will be right back.