(upbeat music) >> Hello, everyone. Welcome to the theCUBE's presentation of the AWS Startup Showcase. The theme, this showcase is MarTech, the emerging cloud scale customer experiences. Season two of episode three, the ongoing series covering the startups, the hot startups, talking about analytics, data, all things MarTech. I'm your host, John Furrier, here joined by Christian Wiklund, founder and CEO of unitQ here, talk about harnessing the power of user feedback to empower marketing. Thanks for joining us today. >> Thank you so much, John. Happy to be here. >> In these new shifts in the market, when you got cloud scale, open source software is completely changing the software business. We know that. There's no longer a software category. It's cloud, integration, data. That's the new normal. That's the new category, right? So as companies are building their products, and want to do a good job, it used to be, you send out surveys, you try to get the product market fit. And if you were smart, you got it right the third, fourth, 10th time. If you were lucky, like some companies, you get it right the first time. But the holy grail is to get it right the first time. And now, this new data acquisition opportunities that you guys in the middle of that can tap customers or prospects or end users to get data before things are shipped, or built, or to iterate on products. This is the customer feedback loop or data, voice of the customer journey. It's a gold mine. And it's you guys, it's your secret weapon. Take us through what this is about now. I mean, it's not just surveys. What's different? >> So yeah, if we go back to why are we building unitQ? Which is we want to build a quality company. Which is basically, how do we enable other companies to build higher quality experiences by tapping into all of the existing data assets? And the one we are in particularly excited about is user feedback. So me and my co-founder, Nik, and we're doing now the second company together. We spent 14 years. So we're like an old married couple. We accept each other, and we don't fight anymore, which is great. We did a consumer company called Skout, which was sold five years ago. And Skout was kind of early in the whole mobile first. I guess, we were actually mobile first company. And when we launched this one, we immediately had the entire world as our marketplace, right? Like any modern company. We launch a product, we have support for many languages. It's multiple platforms. We have Android, iOS, web, big screens, small screens, and that brings some complexities as it relates to staying on top of the quality of the experience because how do I test everything? >> John: Yeah. >> Pre-production. How do I make sure that our Polish Android users are having a good day? And we found at Skout, personally, like I could discover million dollar bugs by just drinking coffee and reading feedback. And we're like, "Well, there's got to be a better way to actually harness the end user feedback. That they are leaving in so many different places." So, you know what, what unitQ does is that we basically aggregate all different sources of user feedback, which can be app store reviews, Reddit posts, Tweets, comments on your Facebook ads. It can be better Business Bureau Reports. We don't like to get to many of those, of course. But really, anything on the public domain that mentions or refers to your product, we want to ingest that data in this machine, and then all the private sources. So you probably have a support system deployed, a Zendesk, or an Intercom. You might have a chatbot like an Ada, or and so forth. And your end user is going to leave a lot of feedback there as well. So we take all of these channels, plug it into the machine, and then we're able to take this qualitative data. Which and I actually think like, when an end user leaves a piece of feedback, it's an act of love. They took time out of the day, and they're going to tell you, "Hey, this is not working for me," or, "Hey, this is working for me," and they're giving you feedback. But how do we package these very messy, multi-channel, multiple languages, all over the place data? How can we distill it into something that's quantifiable? Because I want to be able to monitor these different signals. So I want to turn user feedback into time series. 'Cause with time series, I can now treat this the same way as Datadog treats machine logs. I want to be able to see anomalies, and I want to know when something breaks. So what we do here is that we break down your data in something called quality monitors, which is basically machine learning models that can aggregate the same type of feedback data in this very fine grained and discrete buckets. And we deploy up to a thousand of these quality monitors per product. And so we can get down to the root cause. Let's say, passive reset link is not working. And it's in that root cause, the granularity that we see that companies take action on the data. And I think historically, there has been like the workflow between marketing and support, and engineering and product has been a bit broken. They've been siloed from a data perspective. They've been siloed from a workflow perspective, where support will get a bunch of tickets around some issue in production. And they're trained to copy and paste some examples, and throw it over the wall, file a Jira ticket, and then they don't know what happens. So what we see with the platform we built is that these teams are able to rally around the single source of troop or like, yes, passive recent link seems to have broken. This is not a user error. It's not a fix later, or I can't reproduce. We're looking at the data, and yes, something broke. We need to fix it. >> I mean, the data silos a huge issue. Different channels, omnichannel. Now, there's more and more channels that people are talking in. So that's huge. I want to get to that. But also, you said that it's a labor of love to leave a comment or a feedback. But also, I remember from my early days, breaking into the business at IBM and Hewlett-Packard, where I worked. People who complain are the most loyal customers, if you service them. So it's complaints. >> Christian: Yeah. >> It's leaving feedback. And then, there's also reading between the lines with app errors or potentially what's going on under the covers that people may not be complaining about, but they're leaving maybe gesture data or some sort of digital trail. >> Yeah. >> So this is the confluence of the multitude of data sources. And then you got the siloed locations. >> Siloed locations. >> It's complicated problem. >> It's very complicated. And when you think about, so I started, I came to Bay Area in 2005. My dream was to be a quant analyst on Wall Street, and I ended up in QA at VMware. So I started at VMware in Palo Alto, and didn't have a driver's license. I had to bike around, which was super exciting. And we were shipping box software, right? This was literally a box with a DVD that's been burned, and if that DVD had bugs in it, guess what it'll be very costly to then have to ship out, and everything. So I love the VMware example because the test cycles were long and brutal. It was like a six month deal to get through all these different cases, and they couldn't be any bugs. But then as the industry moved into the cloud, CI/CD, ship at will. And if you look at the modern company, you'll have at least 20 plus integrations into your product. Analytics, add that's the case, authentication, that's the case, and so forth. And these integrations, they morph, and they break. And you have connectivity issues. Is your product working as well on Caltrain, when you're driving up and down, versus wifi? You have language specific bugs that happen. Android is also quite a fragmented market. The binary may not perform as well on that device, or is that device. So how do we make sure that we test everything before we ship? The answer is, we can't. There's no company today that can test everything before the ship. In particular, in consumer. And the epiphany we had at our last company, Skout, was that, "Hey, wait a minute. The end user, they're testing every configuration." They're sitting on the latest device, the oldest device. They're sitting on Japanese language, on Swedish language. >> John: Yeah. >> They are in different code paths because our product executed differently, depending on if you were a paid user, or a freemium user, or if you were certain demographical data. There's so many ways that you would have to test. And PagerDuty actually had a study they came out with recently, where they said 51% of all end user impacting issues are discovered first by the end user, when they serve with a bunch of customers. And again, like the cool part is, they will tell you what's not working. So now, how do we tap into that? >> Yeah. >> So what I'd like to say is, "Hey, your end user is like your ultimate test group, and unitQ is the layer that converts them into your extended test team." Now, the signals they're producing, it's making it through to the different teams in the organization. >> I think that's the script that you guys are flipping. If I could just interject. Because to me, when I hear you talking, I hear, "Okay, you're letting the customers be an input into the product development process." And there's many different pipelines of that development. And that could be whether you're iterating, or geography, releases, all kinds of different pipelines to get to the market. But in the old days, it was like just customer satisfaction. Complain in a call center. >> Christian: Yeah. >> Or I'm complaining, how do I get support? Nothing made itself into the product improvement, except for slow moving, waterfall-based processes. And then, maybe six months later, a small tweak could be improved. >> Yes. >> Here, you're taking direct input from collective intelligence. Okay. >> Is that have input and on timing is very important here, right? So how do you know if the product is working as it should in all these different flavors and configurations right now? How do you know if it's working well? And how do you know if you're improving or not improving over time? And I think the industry, what can we look at, as far as when it relates to quality? So I can look at star ratings, right? So what's the star rating in the app store? Well, star ratings, that's an average over time. So that's something that you may have a lot of issues in production today, and you're going to get dinged on star ratings over the next few months. And then, it brings down the score. NPS is another one, where we're not going to run NPS surveys every day. We're going to run it once a quarter, maybe once a month, if we're really, really aggressive. That's also a snapshot in time. And we need to have the finger on the pulse of product quality today. I need to know if this release is good or not good. I need to know if anything broke. And I think that real time aspect, what we see as stuff sort of bubbles up the stack, and not into production, we see up to a 50% reduction in time to fix these end user impacting issues. And I think, we also need to appreciate when someone takes time out of the day to write an app review, or email support, or write that Reddit post, it's pretty serious. It's not going to be like, "Oh, I don't like the shade of blue on this button." It's going to be something like, "I got double billed," or "Hey, someone took over my account," or, "I can't reset my password anymore. The CAPTCHA, I'm solving it, but I can't get through to the next phase." And we see a lot of these trajectory impacting bugs and quality issues in these work, these flows in the product that you're not testing every day. So if you work at Snapchat, your employees probably going to use Snapchat every day. Are they going to sign up every day? No. Are they going to do passive reset every day? No. And these things are very hard to instrument, lower in the stack. >> Yeah, I think this is, and again, back to these big problems. It's smoke before fire, and you're essentially seeing it early with your process. Can you give an example of how this new focus or new mindset of user feedback data can help customers increase their experience? Can you give some examples, 'cause folks watching and be like, "Okay, I love this value. Sell me on this idea, I'm sold. Okay, I want to tap into my prospects, and my customers, my end users to help me improve my product." 'Cause again, we can measure everything now with data. >> Yeah. We can measure everything. we can even measure quality these days. So when we started this company, I went out to talk to a bunch of friends, who are entrepreneurs, and VCs, and board members, and I asked them this very simple question. So in your board meetings, or on all hands, how do you talk about quality of the product? Do you have a metric? And everyone said, no. Okay. So are you data driven company? Yes, we're very data driven. >> John: Yeah. Go data driven. >> But you're not really sure if quality, how do you compare against competition? Are you doing as good as them, worse, better? Are you improving over time, and how do you measure it? And they're like, "Well, it's kind of like a blind spot of the company." And then you ask, "Well, do you think quality of experience is important?" And they say, "Yeah." "Well, why?" "Well, top of fund and growth. Higher quality products going to spread faster organically, we're going to make better store ratings. We're going to have the storefronts going to look better." And of course, more importantly, they said the different conversion cycles in the product box itself. That if you have bugs and friction, or an interface that's hard to use, then the inputs, the signups, it's not going to convert as well. So you're going to get dinged on retention, engagement, conversion to paid, and so forth. And that's what we've seen with the companies we work with. It is that poor quality acts as a filter function for the entire business, if you're a product led company. So if you think about product led company, where the product is really the centerpiece. And if it performs really, really well, then it allows you to hire more engineers, you can spend more on marketing. Everything is fed by this product at them in the middle, and then quality can make that thing perform worse or better. And we developed a metric actually called the unitQ Score. So if you go to our website, unitq.com, we have indexed the 5,000 largest apps in the world. And we're able to then, on a daily basis, update the score. Because the score is not something you do once a month or once a quarter. It's something that changes continuously. So now, you can get a score between zero and 100. If you get the score 100, that means that our AI doesn't find any quality issues reported in that data set. And if your score is 90, that means that 10% will be a quality issue. So now you can do a lot of fun stuff. You can start benchmarking against competition. So you can see, "Well, I'm Spotify. How do I rank against Deezer, or SoundCloud, or others in my space?" And what we've seen is that as the score goes up, we see this real big impact on KPI, such as conversion, organic growth, retention, ultimately, revenue, right? And so that was very satisfying for us, when we launched it. quality actually still really, really matters. >> Yeah. >> And I think we all agree at test, but how do we make a science out of it? And that's so what we've done. And when we were very lucky early on to get some incredible brands that we work with. So Pinterest is a big customer of ours. We have Spotify. We just signed new bank, Chime. So like we even signed BetterHelp recently, and the world's largest Bible app. So when you look at the types of businesses that we work with, it's truly a universal, very broad field, where if you have a digital exhaust or feedback, I can guarantee you, there are insights in there that are being neglected. >> John: So Chris, I got to. >> So these manual workflows. Yeah, please go ahead. >> I got to ask you, because this is a really great example of this new shift, right? The new shift of leveraging data, flipping the script. Everything's flipping the script here, right? >> Yeah. >> So you're talking about, what the value proposition is? "Hey, board example's a good one. How do you measure quality? There's no KPI for that." So it's almost category creating in its own way. In that, this net new things, it's okay to be new, it's just new. So the question is, if I'm a customer, I buy it. I can see my product teams engaging with this. I can see how it can changes my marketing, and customer experience teams. How do I operationalize this? Okay. So what do I do? So do I reorganize my marketing team? So take me through the impact to the customer that you're seeing. What are they resonating towards? Obviously, getting that data is key, and that's holy gray, we all know that. But what do I got to do to change my environment? What's my operationalization piece of it? >> Yeah, and that's one of the coolest parts I think, and that is, let's start with your user base. We're not going to ask your users to ask your users to do something differently. They're already producing this data every day. They are tweeting about it. They're putting in app produce. They're emailing support. They're engaging with your support chatbot. They're already doing it. And every day that you're not leveraging that data, the data that was produced today is less valuable tomorrow. And in 30 days, I would argue, it's probably useless. >> John: Unless it's same guy commenting. >> Yeah. (Christian and John laughing) The first, we need to make everyone understand. Well, yeah, the data is there, and we don't need to do anything differently with the end user. And then, what we do is we ask the customer to tell us, "Where should we listen in the public domain? So do you want the Reddit post, the Trustpilot? What channels should we listen to?" And then, our machine basically starts ingesting that data. So we have integration with all these different sites. And then, to get access to private data, it'll be, if you're on Zendesk, you have to issue a Zendesk token, right? So you don't need any engineering hours, except your IT person will have to grant us access to the data source. And then, when we go live. We basically build up this taxonomy with the customers. So we don't we don't want to try and impose our view of the world, of how do you describe the product with these buckets, these quality monitors? So we work with the company to then build out this taxonomy. So it's almost like a bespoke solution that we can bootstrap with previous work we've done, where you don't have these very, very fine buckets of where stuff could go wrong. And then what we do is there are different ways to hook this into the workflow. So one is just to use our products. It's a SaaS product as anything else. So you log in, and you can then get this overview of how is quality trending in different markets, on different platforms, different languages, and what is impacting them? What is driving this unitQ Score that's not good enough? And all of these different signals, we can then hook into Jira for instance. We have a Jira integration. We have a PagerDuty integration. We can wake up engineers if certain things break. We also tag tickets in your support system, which is actually quite cool. Where, let's say, you have 200 people, who wrote into support, saying, "I got double billed on Android." It turns out, there are some bugs that double billed them. Well, now we can tag all of these users in Zendesk, and then the support team can then reach out to that segment of users and say, "Hey, we heard that you had this bug with double billing. We're so sorry. We're working on it." And then when we push fix, we can then email the same group again, and maybe give them a little gift card or something, for the thank you. So you can have, even big companies can have that small company experience. So, so it's groups that use us, like at Pinterest, we have 800 accounts. So it's really through marketing has vested interest because they want to know what is impacting the end user. Because brand and product, the lines are basically gone, right? >> John: Yeah. >> So if the product is not working, then my spend into this machine is going to be less efficient. The reputation of our company is going to be worse. And the challenge for marketers before unitQ was, how do I engage with engineering and product? I'm dealing with anecdotal data, and my own experience of like, "Hey, I've never seen these type of complaints before. I think something is going on." >> John: Yeah. >> And then engineering will be like, "Ah, you know, well, I have 5,000 bugs in Jira. Why does this one matter? When did it start? Is this a growing issue?" >> John: You have to replicate the problem, right? >> Replicate it then. >> And then it goes on and on and on. >> And a lot of times, reproducing bugs, it's really hard because it works on my device. Because you don't sit on that device that it happened on. >> Yup. >> So now, when marketing can come with indisputable data, and say, "Hey, something broke here." And we see the same with support. Product engineering, of course, for them, we talk about, "Hey, listen, you you've invested a lot in observability of your stack, haven't you?" "Yeah, yeah, yeah." "So you have a Datadog in the bottom?" "Absolutely." "And you have an APP D on the client?" "Absolutely." "Well, what about the last mile? How the product manifests itself? Shouldn't you monitor that as well using machines?" They're like, "Yeah, that'd be really cool." (John laughs) And we see this. There's no way to instrument everything, lowering the stack to capture these bugs that leak out. So it resonates really well there. And even for the engineers who's going to fix it. >> Yeah. >> I call it like empathy data. >> Yup. >> Where I get assigned a bug to fix. Well, now, I can read all the feedback. I can actually see, and I can see the feedback coming in. >> Yeah. >> Oh, there's users out there, suffering from this bug. And then when I fix it and I deploy the fix, and I see the trend go down to zero, and then I can celebrate it. So that whole feedback loop is (indistinct). >> And that's real time. It's usually missed too. This is the power of user feedback. You guys got a great product, unitQ. Great to have you on. Founder and CEO, Christian Wiklund. Thanks for coming on and sharing, and showcase. >> Thank you, John. For the last 30 seconds, the minute we have left, put a plug in for the company. What are you guys looking for? Give a quick pitch for the company, real quick, for the folks out there. Looking for more people, funding status, number of employees. Give a quick plug. >> Yes. So we raised our A Round from Google, and then we raised our B from Excel that we closed late last year. So we're not raising money. We are hiring across go-to-markets, engineering. And we love to work with people, who are passionate about quality and data. We're always, of course, looking for customers, who are interested in upping their game. And hey, listen, competing with features is really hard because you can copy features very quickly. Competing with content. Content is commodity. You're going to get the same movies more or less on all these different providers. And competing on price, we're not willing to do. You're going to pay 10 bucks a month for music. So how do you compete today? And if your competitor has a better fine tuned piano than your competitor will have better efficiencies, and they're going to retain customers and users better. And you don't want to lose on quality because it is actually a deterministic and fixable problem. So yeah, come talk to us if you want to up the game there. >> Great stuff. The iteration lean startup model, some say took craft out of building the product. But this is now bringing the craftsmanship into the product cycle, when you can get that data from customers and users. >> Yeah. >> Who are going to be happy that you fixed it, that you're listening. >> Yeah. >> And that the product got better. So it's a flywheel of loyalty, quality, brand, all off you can figure it out. It's the holy grail. >> I think it is. It's a gold mine. And every day you're not leveraging this assets, your use of feedback that's there, is a missed opportunity. >> Christian, thanks so much for coming on. Congratulations to you and your startup. You guys back together. The band is back together, up into the right, doing well. >> Yeah. We we'll check in with you later. Thanks for coming on this showcase. Appreciate it. >> Thank you, John. Appreciate it very much. >> Okay. AWS Startup Showcase. This is season two, episode three, the ongoing series. This one's about MarTech, cloud experiences are scaling. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage here at re:MARS. I'm John Furrier, host of theCUBE. It's the event where it's part of the "re:" series: re:MARS, re:Inforce, re:Invent. MARS stands for machine learning, automation, robotics, and space. And a lot of conversation is all about AI machine learning. This one's about AI and business transformation. We've got Stepan Pushkarev CTO, CEO, Co-Founder of Provectus. Welcome to theCUBE. And Russ Lamb, eCommerce Retail Data Engineering Lead at PepsiCo, customer story. Gentlemen, thanks for coming on theCUBE. >> Great to be here, John. >> Yeah, thanks for having us. >> I love the practical customer stories because it brings everything to life. This show is about the future, but it's got all the things we want, we love: machine learning, robotics, automation. If you're in DevOps, or you're in data engineering, this is the world of automation. So what's the relationship? You guys, you're a customer. Talk about the relationship between you guys. >> Sure, sure. Provectus as a whole is a professional services firm, premier, a AWS partner, specializing in machine learning, data, DevOps. PepsiCo is our customer, our marquee customer, lovely customer. So happy to jointly present at this re:Invent, sorry, re:MARS. Anyway, Russ... >> I made that mistake earlier, by the way, 'cause re:Invent's always on the tip of my tongue and re:MARS is just, I'm not used to it yet, but I'm getting there. Talk about what are you guys working together on? >> Well, I mean, we work with Provectus in a lot of ways. They really helped us get started within our e-commerce division with AWS, provided a lot of expertise in that regard and, you know, just hands-on experience. >> We were talking before we came on camera, you guys just had another talk and how it's all future and kind of get back to reality, Earth. >> Russ: Get back to Earth. >> If we're on earth still. We're not on Mars yet, or the moon. You know, AI's kind of got a future, but it does give a tell sign to what's coming, industrial change, full transformation, 'cause cloud does the back office. You got data centers. Now you've got cloud going to the edge with industrial spaces, the ultimate poster child of edge and automation safety. But at the end of the day, we're still in the real world. Now people got to run businesses. And I think, you know, having you here is interesting. So I have to ask you, you know, as you look at the technology, you got to see AI everywhere. And the theme here, to me, that I see is the inflection point driving all this future robotics change, that everyone's been waiting for by the way, but it's like been in movies and in novels, is the machine learning and AI as the tipping point. This is key. And now you're here integrating AI into your company. Tell us your story. >> Well, I think that every enterprise is going to need more machine learning, more, you know, AI or data science. And that's the journey that we're on right now. And we've come a long way in the past six years, particularly with our e-commerce division, it's a really data rich environment. So, you know, going from brick and mortar, you know, delivering to restaurants, vending machines and stuff, it's a whole different world when you're, people are ordering on Amazon every couple minutes, or seconds even, our products. But they, being able to track all that... >> Can you scope the problem statement and the opportunity? Because if I just kind of just, again, I'm not, you're in, it's your company, you're in the weeds, you're at the data, you're everything, But it just seems me, the world's now more integration, more different data sources. You've got suppliers, they have their different IT back ends. Some are in the cloud, some aren't in the cloud. This is, like, a hard problem when you want to bring data together. I mean, API certainly help, but can you scope the problem, and, like, what we're talking about here? >> Well, we've got so many different sources of data now, right? So we used to be relying on a couple of aggregators who would pull all this data for us and hand us an aggregated view of things. But now we're able to partner with different retailers and get detail, granular information about transactions, orders. And it's just changed the game, changed the landscape from just, like, getting a rough view, to seeing the nuts and bolts and, like, all the moving parts. >> Yeah, and you see in data engineering much more tied into like cloud scale. Then you got the data scientists, more the democratization application and enablement. So I got to ask, how did you guys connect? What was the problem statement? How did you guys, did you have smoke and fire? You came in solved the problem? Was it a growth thing? How did this, how did you guys connect as a customer with Provectus? >> Yeah, I can elaborate on that. So we were in the very beginning of that journey when there was, like, just a few people in this new startup, let's call it startup within PepsiCo. >> John: Yeah. >> Calling like a, it's not only e-commerce, it was a huge belief from the top management that it's going to bring tremendous value to the enterprise. So there was no single use case, "Hey, do this and you're going to get that." So it's a huge belief that e-commerce is the future. Some industry trends like from brand-centric to consumer-centric. So brand, product-centric. Amazon has the mission to build the most customer-centric customer company. And I believe that success, it gets a lot of enterprises are being influenced by that success. So I remember that time, PepsiCo had a huge belief. We started building just from scratch, figuring out what does the business need? What are the business use cases? We have not started with the IT. We have not started with this very complicated migrations, modernizations. >> John: So clean sheet of paper. >> Yeah. >> From scratch. >> From scratch. >> And so you got the green light. >> Yeah. >> And the leadership threw the holy water on that and said, "Hey, we'll do this."? >> That's exactly what happened. It was from the top down. The CEO kind of set aside the e-commerce vision as kind of being able to, in a rapidly evolving business place like e-commerce, it's a growing field. Not everybody's figured it out yet, but to be able to change quickly, right? The business needs to change quickly. The technology needs to change quickly. And that's what we're doing here. >> So this is interesting. A lot of companies don't have that, actually, luxury. I mean, it's still more fun because the tools are available now that all the hyper scales built on their own. I mean, back in the day, 10 years ago, they had to build it all, Facebook. You didn't know, I had people on here from Pinterest and other companies. They had to build all of that from scratch. Now cloud's here. So how did you guys do this? What was the playbook? Take us through the AI because it sounds like the AI is core, you know, belief principle of the whole entire system. What did you guys do? Take me through the journey there. >> Yeah. Beyond management decisions, strategic decisions that has been made as a separate startup, whatever- >> John: That's great. >> So some practical, tactical. So it may sound like a cliche, but it's a huge thing because I work with many enterprises and this, like, "center of excellence" that does a nice technology stuff and then looks for the budget on the different business units. It just doesn't go anywhere. It could take you forever to modernize. >> We call that the Game of Thrones environment. >> Yes. >> Yeah. Nothing ever gets done 'till it blows up at the end. >> Here, these guys, and I have to admit, I don't want to steal their thunder. I just want to emphasize it as an external person. These guys just made it so differently. >> John: Yeah. >> They even physically sat in a different office in a WeWork co-working and built that business from scratch. >> That's what Andy Jackson talked about two years ago. And if you look at some of the big successes on AWS, Capital One, all the big, Goldman Sachs. The leadership, real commitment, not like BS, like total commitment says, "Go." But enough rope to give you some room, right? >> Yeah. I think that's the thing is, there was always an IT presence, right, overseeing what we were doing within e-commerce, but we had a lot of freedoms to make design choices, technology choices, and really accelerate the business, focus on those use cases where we could make a big impact with a technology choice. >> Take me through the stages of the AI transformation. What are some of the use cases and specific tactics you guys executed on? >> Well, I think that the supply chain, which I think is a hot topic right now, but that was one use case where we're using, like, data real time, real time data to inform our sales projections and delivery logistics. But also our marketing return on investment, I feel like that was a really interesting, complex problem to solve using machine learning, Because there's so much data that we needed to process in terms of countries, territories, products, like where do you spend your limited marketing budget when you have so many choices, and, using machine learning, boil that all down to, you know, this is the optimal choice, right now. >> What were some of the challenges and how did you overcome them in the early days to get things set up, 'cause it takes a lot of energy to get it going, to get the models. What were some of the challenges and how did you overcome them? >> Well, I think some of it was expertise, right? Like having a partner like Provectus and Stepan really helped because they could guide us, Stepan could guide us, give his expertise and what he knows in terms of what he's seen to our budding and growing business. >> And what were the things that you guys saw that you contributed on? And was there anything new that you had to do together? >> Yeah, so yeah. First of all, just a very practical tip. Yes, start with the use cases. Clearly talk to the business and say, "Hey, these are the list of the use cases" and prioritize them. So not with IT, not with technology, not with the migration thing. Don't touch anything on legacy systems. Second, get data in. So you may have your legacy systems or some other third party systems that you work with. There's no AI without data. Get all the pipelines, get data. Quickly boat strap the data lake house. Put all the pipelines, all the governance in place. And yeah, literally took us three months to get up and running. And we started delivering first analytical reports. It's just to have something back to business and keep going. >> By the way, that's huge, speed. I mean, this is speed. You go back and had that baggage of IT and the old antiquated systems, you'd be dragging probably months. Right? >> It's years, years. Imagine you should migrate SAP to the cloud first. No, you don't do don't need to do that. >> Pipeline. >> Just get data. I need data. >> Stream that data. All right, where are we now? When did you guys start? I want to get just going to timeline my head 'cause I heard three months. Where are we now? You guys threw it. Now you have impact. You have, you have results. >> Yeah. I mean that for our marketing ROI engine, we've built it and it's developed within e-commerce, but we've started to spread it throughout the organization now. So it's not just about the digital and the e-commerce space. We're deploying it to, you know, regionally to other, to Europe, to Latin America, other divisions within PepsiCo. And it's just grown exponentially. >> So you have scale to it right now? >> Yeah. Well- >> How far are you in now? What, how many years, months, days? >> E-commerce, the division was created six years ago, which is, so we've had some time to develop this, our machine learning capabilities and this use case particular, but it's increasingly relevant and expansion is happening as we speak. >> What are you most proud of? You look back at the impact. What are you most proud of? >> I think the relationship we built with the people, you know, who use our technology, right. Just seeing the impact is what makes me proud. >> Can you give an example without revealing any confidential information? >> Yeah. Yeah. I mean, there was an example from my talk about, I was approached recently by our sales team. They were having difficulty with supply chain, monitoring our fill rate of our top brands with these retailers. And they come up to me, they have this problem. They're like, "How do we solve it?" So we work together to find a data source, just start getting that data in the hands of people who can use it within days. You know, not talking like a long time. Bring that data into our data warehouse, and then surface the data in a tool they can use, you know, within a matter of a week or two. >> I mean, the transformation is just incredible. In fact, we were talking on theCUBE earlier today around, you know, data warehouses in the cloud, data meshes of different pros and cons. And the theme that came out of that conversation was data's a product now. >> Yes. >> Yes. >> And what you're kind of describing is, just gimme the product or find it. >> Russ: Right. >> And bring it in with everything else. And there's some, you know, cleaning and stuff people do if they have issues with that. But, if not, it's just bring it in, right? It's a product. >> Well, especially with the data exchanges now. AWS has a data exchange and this, I think, is the future of data and what's possible with data because you don't have to start from, okay, I've got this Excel file somebody's been working with on their desktop. This is a, someone's taken that file, put it into a warehouse or a data model, and then they can share it with you. >> John: So are you happy with these guys? >> Absolutely, yeah. >> You're actually telling the story. What was the biggest impact that they did? Was it partnering? Was it writing code, bringing development in, counseling, all the above, managed services? What? >> I think the biggest impact was the idea, you know, like being able to bring ideas to the table and not just, you know, ask us what we want, right? Like I think Provectus is a true partner and was able to share that sort of expertise with us. >> You know, Andy Jackson, whenever I interview on theCUBE, he's now in charge of all Amazon. But when he was at (inaudible). He always had to use their learnings, get the learnings out. What was the learnings you look back now and say, Hey, those were tough times. We overcome them. We stopped, we started, we iterated, we kept moving forward. What was the big learning as you look back, some of the key success points, maybe some failures that you overcome. What was the big learnings that you could share with folks out there now that are in the same situation where they're saying, "Hey, I'd rather start from scratch and do a reset." >> Yeah. So with that in particular, yes, we started this like sort of startup within the enterprise, but now we've got to integrate, right? It's been six years and e-commerce is now sharing our data with the rest of the organization. How do we do that, right? There's an enterprise solution, and we've got this scrappy or, I mean, not scrappy anymore, but we've got our own, you know, way of doing. >> Kind of boot strap. I mean, you were kind of given charter. It's a start up within a big company, I mean- >> But our data platform now is robust, and it's one of the best I've seen. But how do we now get those systems to talk? And I think Provectus has came to us with, "Here, there's this idea called data mesh, where you can, you know, have these two independent platforms, but share the data in a centralized way. >> So you guys are obviously have a data mesh in place, big part of the architecture? >> So it is in progress, but we know the next step. So we know the next step. We know the next two steps, what we're going to do, what we need to do to make it really, to have that common method, data layer. between different data products within organization, different locations, different business units. So they can start talking to each other through the data and have specific escalates on the data. And yeah. >> It's smart because I think one of the things that people, I think, I'd love to get your reaction to this is that we've been telling the story for many, many years, you have horizontally scalable cloud and vertically specialized domain solutions, you need machine learning that's smart, but you need a lot of data to help it. And that's not, a new architecture, that's a data plane, it's control plane, but now everyone goes, "Okay, let's do silos." And they forget the scale side. And then they go, "Wait a minute." You know, "I'm not going to share it." And so you have this new debate of, and I want to own my own data. So the data layer becomes an interesting conversation. >> Yeah, yes. Meta data. >> Yeah. So what, how do you guys see that? Because this becomes a super important kind of decision point architecturally. >> I mean, my take is that there has to be some, there will always be domains, right? Everyone, like there's only so much that you can find commonality across, like in industry, for example. But there will always be a data owner. And, you know, kind of like what happened with rush to APIs, how that enabled microservices within applications and being sharing in a standardized way, I think something like that has to happen in the data space. So it's not a monolithic data warehouse, it's- >> You know, the other thing I want to ask you guys both, if you don't mind commenting while I got you here, 'cause you're both experts. >> We just did a showcase on data programmability. Kind of a radical idea, but like data as code, we called it. >> Oh yeah. >> And so if data's a product and you're acting on, you've got an architecture and system set up, you got to might code it's programmable. You need you're coding with data. Data becomes like a part of the development process. What do you guys think of when you hear data as code and data being programmable? >> Yeah, it's a interesting, so yeah, first of all, I think Russ can elaborate on that, Data engineering is also software engineering. Machine learning engineering is a software. At the end of the day, it's all product. So we can use different terms and buzz words for that but this is what we have at the end of the day. So having the data, well I will use another buzz word, but in terms of the headless architecture- >> Yes. >> When you have a nice SDK, nice API, but you can manipulate with the data as your programming object to build reach applications for your users, and give it, and share not as just a table in Redshift or a bunch of CSV files in S3 bucket, but share it as a programmable thing that you can work with. >> Data as code. >> Yeah. This is- >> Infrastructure code was a revolution for DevOps, but it's not AI Ops so it's something different. It's really it's data engineering. It's programming. >> Yeah. This is the way to deliver data to your consumers. So there are different ways you can show it on a dashboard. You can show it, you can expose it as an API, or you can give it as an object, programmable interface. >> So now you're set up with a data architecture that's extensible 'cause that's the goal. You don't want to foreclose. You must think about that must keep you up at night. What's going to foreclose that benefit? 'Cause there's more coming. Right? >> Absolutely. There's always more coming. And I think that's why it's important to have that robust data platform to work from. And yeah, as Stepan mentioned, I'm a big believer in data engineering as software engineering. It's not some like it's not completely separate. You have to follow the best practices software engineers practice. And, you know, really think about maintainability and scalability. >> You know, we were riffing about how cloud had the SRE managing all those servers. One person, data engineering has a many, a one to many relationships too. You got a lot going on. It's not managing a database. It's millions of data points and data opportunity. So gentlemen, thanks for coming on theCUBE. I really appreciate it. And thanks for telling the story of Pepsi. >> Of course, >> And great conversation. Congratulations on this great customer. And thanks for >> coming on theCUBE. >> Thanks, thank you. Thanks, Russ, would you like to wrap it up with the pantry shops story? >> Oh, yeah! I think it will just be a super relevant evidence of the agility and speed and some real world applicable >> Let's go. Close us out. >> So when, when the pandemic happened and there were lockdowns everywhere, people started buying things online. And we noticed this and got a challenge from our direct to consumer team saying, "Look, we need a storefront to be able to sell to our consumers, and we've got 30 days to do it." We need to be able to work fast. And so we built not just a website, but like everything that behind it, the logistics of supply chain aspects, the data platform. And we didn't just build one. We built two. We got pantry shop.com and snacks.com, within 30 days. >> Good domains! >> The domain broker was happy on that one. Well continue the story. >> Yeah, yeah. So I feel like that the agility that's required for that kind of thing and the like the planning to be able to scale from just, you know, an idea to something that people can use every day. And, and that's, I think.- >> And you know, that's a great point too, that shows if you're in the cloud, you're doing the work you're prepared for anything. The pandemic was the true test for who was ready because it was unforeseen force majeure. It was just like here it comes and the people who were in the cloud had that set up, could move quickly. The ones that couldn't. >> Exactly. >> We know what happened. >> And I would like to echo this. So they have built not just a website, they have built the whole business line within, and launched that successfully to production. That includes sales, marketing, supply chain, e-commerce, aside within 30 days. And that's just a role model that could be used by other enterprises. >> Yeah. And it was not possible without, first of all, right culture. And second, without cloud Amazon elasticity and all the tools that we have in place. >> Well, the right architecture allows for scale. That's the whole, I mean, you did everything right at the architecture that's scale. I mean, you're scaling. >> And we empower our engineers to make those choices, right. We're not, like, super bureaucratic where every decision has to be approved by the manager or the managers manager. The engineers have the power to just make good decisions, and that's how we move fast. >> That's exactly the future right there. And this is what it's all about. Reliability, scale agility, the ability to react and have applications roll out on top of it without long timeframes. Congratulations. Thanks for being on theCUBE. Appreciate it. All right. >> Thank you. >> Okay, you're watching theCUBE here at re:MARS 2020, I'm John Furrier. Stay tuned. We've got more coverage coming after this short break. (upbeat music)

>>Okay, welcome back everyone to the cubes coverage here in Las Vegas for AWS re Mars, Remar stands for machine learning, automation, robotics, and space. Part of thehow is reinforces security. And the big show reinvent at the end of the year is the marquee event. Of course, the queues at all three and more coverage here. We've got a great guest here. Ryan re practice lead data analytics, machine learning at mission cloud. Ryan. Thanks for joining me. Absolutely >>Glad. >>So we were talking before he came on camera about mission cloud. It's not a mission as in a space mission. That's just the name of the company to help people with their mission to move to the cloud. And we're a space show to make that it's almost like plausible. I can see a mission cloud coming someday. >>Yeah, absolutely. >>You got >>The name. We got it. We're ready. >>You guys help customers get to the cloud. So you're working with all the technologies on AWS stack and people who are either lifting and shifting or cloud native born in the cloud, right? Absolutely. >>Yeah. I mean, we often see some companies talk about lift and shift, but you know, we try to get them past that because often a lift and shift means like, say you're on Oracle, you're bringing your Oracle licensing, but a lot of companies want to, you know, innovate and migrate more than they want to lift and shift. So that's really what we're seeing in market. >>You see more migration. Yeah. Less lift and shift. >>Yeah, exactly. Because they, they're trying to get out of an Oracle license. Right. They're seeing if that's super expensive and you know, you can get a much cheaper product on AWS. >>Yeah. What's the cutting up areas right now that you're seeing with cloud Amazon. Cause you know, Amazon, you know, is at their, their birthday, you know, dynamo you to sell with their 10th birthday. Where are they in your mind relative to the enterprise in terms of the services and where this goes next in terms of the on-prem you got the hybrid model. Everyone sees that, but like you got outpost. Mm. Not doing so as good as say EKS or other cool serverless stuff. >>Yeah. I mean, that's a great question. One of the things that's you see from AWS is really innovation, right? They're out there, they have over 400 microservices. So they're looking at all the different areas you have on the cloud and that people are trying to use. And they're creating these microservices that you string together, you architect them all up so that you can create what you're looking for. One of the big things we're seeing, right, is with SageMaker. A lot of people are coming in, looking for ML projects, trying to use all the hype that you see around that doing prediction, NLP and computer vision are super hot right now we've helped a lot of companies, you know, start to build out these NLP models where they're doing, you know, all kinds of stuff you use. 'em in gene research, you know, they're trying to do improvements in drugs and therapeutics. It's really awesome. And then we do some eCommerce stuff where people are just looking at, you know, how do I figure out what are similar things on similar websites, right. For, for search companies. So >>Awesome. Take me through the profile of your customer. You have the mix of business. Can you break down the, the target of the small, medium size enterprise, large all the above. >>Yeah. So mission started working with a lot of startups and SMBs and then as we've grown and become, you know, a much larger company that has all the different focus areas, we started to get into enterprise as well and help a lot of pretty well known enterprises out there that are, you know, not able to find the staff that they need and really want to get into >>The cloud. I wanted to dig into the staffing issues and also to the digital transformation journey. Okay. It okay. We all kind of know what's turning into the more dashboards, more automation, DevOps, cloud, native applications. All good. Yeah. And I can see that journey path. Now the reality is how do you get people who are gonna be capable of doing the ML, doing the DevOps dev sec ops. But what about cyber security? I mean is a ton of range of issues that you gotta be competent on to kind of survive in this multi-disciplined world, just to the old days of I'm the top of rack switch guy is over. >>Absolutely. Yeah. You know, it's a really good question. It's really hard. And that's why, you know, AWS has built out that partner ecosystem because they know companies can't hire enough people to do that. You know, if you look at just a migration into a data lake, you know, on-prem often you had one guy doing it, but if you want to go to the cloud, it's like you said, right, you need a security guy. You need to have a data architect. You need to have a cloud architect. You need to have a data engineer. So, you know, in the old days maybe you needed one guy. Now you have to have five. And so that's really why partners are valuable to customers is we're able to come in, bring those resources, get everything done quickly, and then, you know, turn >>It over. Yeah. We were talking again before we came on camera here live, you, you guys have a service led business, but the rise of MSPs managed service providers is huge. We're seeing it everywhere mainly because the cloud actually enables that you're seeing it for things like Kubernetes, serverless, certain microservices have certain domain expertise and people are making a living, providing great managed services. You guys have managed services. What's that phenomenon. Do you agree with it? And how do you, why did that come about and what, how does it keep going? Is it a trend or is it a one trick pony? >>I think it's a trend. I mean, what you have, it's the same skills gap, right? Is companies no longer want that single point of failure? You know, we have a pool model with our managed services where your team's working with a group of people. And so, you know, we have that knowledge and it's spread out. And so if you're coming in and you need help with Kubernetes, we got a Kubernetes guy in that pool to help you, right. If you need, you know, data, we got a data guy. And so it just makes it a lot easier where, Hey, I can pay the same as one guy and get a whole team of like 12 people that can be interchangeable onto my project. So, you know, I think you're gonna see managed services continue to rise and companies, you know, just working in that space. >>Do you see a new skill set coming? That's kind of got visibility right now, but not full visibility. That's going to be needed. I asked this because the environment's changing for the better obviously, but you're seeing companies that are highly valued, like data bricks, snowflake, they're getting killed on valuation. So they gotta have a hard time retaining talent. In my opinion, my opinion probably be true, but you know, you can't, you know, if you're data breach, you can't raise that 45 billion valuation try to hire senior people. They're gonna be underwater from day one. So there's gonna be a real slow down in these unicorns, these mega unicorns, deck, unicorns, whatever they're called because they gotta refactor the company, stock equity package. They attract people. So they gotta put them on a flat foot. And the next question is, do they actually have the juice, the goods to go to the new market? That's another question. So what I mean, what's your take on you're in the trenches. You're in the front lines. >>Yeah, that's a great question. I mean, and it's hard for me to think about whether they have the juice. I think snowflake and data bricks have been great for the market. They've come in. They've innovated, you know, snowflake was cloud native first. So they were built for the cloud. And what that's done is push all the hyperscalers to improve their products, right. AWS has gone through and you know, drastically over the last three years, improved Redshift. Like, I mean it's night and day from three years ago. Did, >>And you think snowflake put that pressure on them? >>Snowflake. Absolutely. Put that pressure on them. You know, I don't know whether they would've gotten to that same level if snowflake wasn't out there stealing market share. But now when you look at it, Redshift is much cheaper than snowflake. So how long are people gonna pay that tax to have snowflake versus switching over snowflakes? >>Got a nice data. Clean room, had some nice lock in features. Only on snowflake. The question is, will that last clean room? I see you smiling. Go ahead. >>Clean. Room's a concept that was actually made by Google. I know Snowflake's trying to capture it as their own, but, but Google's the one that actually launched the clean room concept because of marketing and, and all of that. >>Google also launches semantic layer, which Snowflake's trying to copy that. Does that, what does that mean to you when you hear the word semantic layer? What does that mean? >>And semantic layer just is really all about meta tags, right? How am I going through to figure out what data do I actually have in my data lake so that I can pull it for whatever I'm trying to do, whether it's dashboarding or whether it's machine learning. You're just trying to organize your data better. >>Ryan, you should be a cue post. You're like a masterclass here in, in it and cloud native. I gotta ask you since you're here, since we're having the masterclass being put in a clinic here, lot of clients are confused between how to handle the control plane and the data plane cause machine learning right now is at an all time high. You're seeing deep racer. You're seeing robotic space, all driving by machine learning. SW. He said it today, the, the companion coder, right? The, the code whisperer, that's only gonna get stronger. So machine learning needs data. It feeds on data. So everyone right now is trying to put data in silos. Okay? Cause they think, oh, compliance, you gotta create a data plane and a control plane that makes it highly available. So that can be shared >>Right >>Now. A lot of people are trying to own the data plane and some are trying to own the control plane or both. Right? What's your view on that? Because I see customers say, look, I want to own my own data cause I can control it. Control plane. I can maybe do other things. And some are saying, I don't know what to do. And they're getting forced to take both to control plane and a data plane from a vendor, right? What's your, what's your reaction to that? >>So it's pretty interesting. I actually was presenting at a tech target conference this week on exactly this concept, right, where we're seeing more and more words out there, right? It was data warehouse and it was data lake and it's lake house. And it's a data mesh and it's a data fabric. And some of the concepts you're talking about really come into that data, match data fabric space. And you know, what you're seeing is data's gonna become a product right, where you're gonna be buying a product and the silos yes. Silos exist. But what, what companies have to start doing is, and this is the whole data mesh concept is, Hey yes, you finance department. You can own your silo, but now you have to have an output product. That's a data product that every other part of your company can subscribe to that data product and use it in their algorithms or their dashboard so that they can get that 360 degree view of the customer. So it's really, you know, key that, you know, you work within your business. Some business are gonna have that silo where the data mesh works. Great. Others are gonna go. >>And what do you think about that? Because I mean, my thesis would be, Hey, more data, better machine learning. Right. Is that the concept? >>So, or that's a misconception or, >>Okay. So what's the, what's the rationale to share the data like that and data mission. >>So having more of the right data here, it is improves. Just having more data in general, doesn't improve, right? And often the problem is in the silos you're getting to is you don't have all the data you want. Right. I was doing a big project about shipping and there's PII data. When you talk about shipping, right? Person's addresses, that's owned by one department and you can't get there. Right. But how am I supposed to estimate the cost of shipping if I can't get, you know, data from where a person lives. Right. It's just >>Not. So none of the wrinkle in the equation is latency. Okay. The right data at the right time is another factor is that factored into data mesh versus these other approaches. Because I mean, you can, people are streaming data. I get that. We're seeing a lot of that. But talking about getting data fast enough before the decisions are made, is that an issue or is this just BS? >>I'm going with BS. Okay. So people talk about real time real. Time's great if you need it, but it's really expensive to do. Most people don't need real time. Right. They're really looking for, I need an hourly dashboard or I need a daily dashboard. And so pushing into real time, just gonna be an added expense that you don't >>Really need. Like cyber maybe is that not maybe need real time. >>Well, cyber security add. I mean, there's definitely certain applications that you need real time, >>But don't over invest in fantasy if you don't need an an hour's fine. Right, >>Right. Yeah. If you're, if you're a business and you're looking at your financials, do you need your financials every second? Is that gonna do anything for you? Got >>It. Yeah. Yeah. And so this comes back down to data architecture. So the next question I asked, cause I had a great country with the Fiddler AI CEO, CEO earlier, and he was at Facebook and then Pinterest, he was a data, you know, an architect and built everything. He said themselves. We were talking about all the stuff that's available now are all the platforms and tools available to essentially build the next Facebook if someone wanted to from scratch. I mean, hypothetically thought exercise. So the ability to actually ramp up and code a complete throwaway and rebuild from the ground up is possible. >>Absolutely. >>And so the question is, okay, how do you do it? How long would it take? I mean, in an ideal scenario, not, you know, make some assumptions here, you got the budget, you got the people, how long to completely roll out a brand new platform. >>Now it's funny, you asked that because about a year ago I was asked that exact same question by a customer that was in the religious space that basically wanted to build a combination of Facebook, Netflix, and Amazon altogether for the religious space, for religious goods and you know, church sermons, we estimated for him about a year and about $9 million to do it. >>I mean, that's a, that's a, a round these days. Yeah. Series a. So it's possible. Absolutely. So enterprises, what's holding them back, just dogma process, old school legacy, or are people taking the bold move to take more aggressive, swiping out old stuff and just completely rebuilding? Or is it a talent issue? What's the, what's the enterprise current mode of reset, >>You know, I think it really depends on the enterprise and their aversion to risk. Right. You know, some enterprises and companies are really out there wanting to innovate, you know, I mean there's companies, you know, an air conditioning company that we worked with, that's totally, you know, nest was eaten all their business. So they came in and created a whole T division, you know, to, to chase that business, that nest stole from them. So I think it, I think often a company's not necessarily gonna innovate until somebody comes in and starts stealing their >>Lunch. You know, Ryan, Andy, Jess, we talked about this two reinvents ago. And then Adam Eski said the same thing this year on a different vector, but kind of building on what Andy Jessey said. And it's like, you could actually take new territory down faster. You don't have to kill the old, no I'm paraphrasing. You don't have to kill the old to bring in the new, you can actually move on new ideas with a clean sheet of paper if you have that builder mindset. And I think that to me is where I'm seeing. And I'd love to get your reaction because if you see an opportunity to take advantage and take territory and you have the right budget time and people, you can get it. Oh absolutely. It's gettable. So a lot of people have this fear of, oh, we're, that's not our core competency. And, and they they're the frog and boiling water. >>You know, my answer to that is I think part of it's VCs, right? Yeah. VCs have come in and they see the value of a company often by how many people you hire, right. Hire more people. And the value is gonna go up. But often as a startup, you can't hire good people. So I'm like, well, why are you gonna go hire a bunch of random people? You should go to a firm like ours that knows AWS and can build it quickly for you, cuz then you're gonna get to the market faster versus just trying to hire a bunch of people in >>Someone. Right. I really appreciate you coming on. I'd love to have you back on the cube again, sometime your expertise and your insights are awesome. Give a commercial for the company, what you guys are doing, who you're looking for, what you want to do, hiring or whatever your goals are. Take a minute to explain what you guys are doing and give a quick plug. >>Awesome. Yeah. So mission cloud, you know, we're a premier AWS consulting firm. You know, if you're looking to go to AWS or you're in AWS and you need help and support, we have a full team, we do everything. Resell, MSP professional services. We can get you into the cloud optimize. You make everything run as fast as possible. I also have a full machine learning team. Since we're here at re Mars, we can build you models. We can get 'em into production, can make sure everything's smooth. The company's hiring. We're looking to double in size this year. So, you know, look me up on LinkedIn, wherever happy to, to take, >>You mentioned the cube, you get a 20% discount. He's like, no, I don't approve that. Thanks for coming on the key. Really appreciate it. Again. Machine learning swaping said on stage this, you can be a full time job just tracking just the open source projects. Never mind all the different tools and like platform. So I think you're gonna have a good, good tailwind for your business. Thanks for coming on the queue. Appreciate it. Ryan Reese here on the queue. I'm John furry more live coverage here at re Mars 2022. After this short break, stay with us.

(upbeat music) >> Welcome back. Day two of theCUBE's coverage of re:MARS in Las Vegas. Amazon re:MARS, it's part of the Re Series they call it at Amazon. re:Invent is their big show, re:Inforce is a security show, re:MARS is the new emerging machine learning automation, robotics, and space. The confluence of machine learning powering a new industrial age and inflection point. I'm John Furrier, host of theCUBE. We're here to break it down for another wall to wall coverage. We've got a great guest here, CUBE alumni from our AWS startup showcase, Krishna Gade, founder and CEO of fiddler.ai. Welcome back to theCUBE. Good to see you. >> Great to see you, John. >> In person. We did the remote one before. >> Absolutely, great to be here, and I always love to be part of these interviews and love to talk more about what we're doing. >> Well, you guys have a lot of good street cred, a lot of good word of mouth around the quality of your product, the work you're doing. I know a lot of folks that I admire and trust in the AI machine learning area say great things about you. A lot going on, you guys are growing companies. So you're kind of like a startup on a rocket ship, getting ready to go, pun intended here at the space event. What's going on with you guys? You're here. Machine learning is the centerpiece of it. Swami gave the keynote here at day two and it really is an inflection point. Machine learning is now ready, it's scaling, and some of the examples that they were showing with the workloads and the data sets that they're tapping into, you know, you've got CodeWhisperer, which they announced, you've got trust and bias now being addressed, we're hitting a level, a new level in ML, ML operations, ML modeling, ML workloads for developers. >> Yep, yep, absolutely. You know, I think machine learning now has become an operational software, right? Like you know a lot of companies are investing millions and billions of dollars and creating teams to operationalize machine learning based products. And that's the exciting part. I think the thing that that is very exciting for us is like we are helping those teams to observe how those machine learning applications are working so that they can build trust into it. Because I believe as Swami was alluding to this today, without actually building trust into AI, it's really hard to actually have your business users use it in their business workflows. And that's where we are excited about bringing their trust and visibility factor into machine learning. >> You know, a lot of us all know what you guys are doing here in the ecosystem of AWS. And now extending here, take a minute to explain what Fiddler is doing for the folks that are in the space, that are in discovery mode, trying to understand who's got what, because like Swami said on stage, it's a full-time job to keep up on all the machine learning activities and tool sets and platforms. Take a minute to explain what Fiddler's doing, then we can get into some, some good questions. >> Absolutely. As the enterprise is taking on operationalization of machine learning models, one of the key problems that they run into is lack of visibility into how those models perform. You know, for example, let's say if I'm a bank, I'm trying to introduce credit risk scoring models using machine learning. You know, how do I know when my model is rejecting someone's loan? You know, when my model is accepting someone's loan? And why is it doing it? And I think this is basically what makes machine learning a complex thing to implement and operationalize. Without this visibility, you cannot build trust and actually use it in your business. With Fiddler, what we provide is we actually open up this black box and we help our customers to really understand how those models work. You know, for example, how is my model doing? Is it accurately working or not? You know, why is it actually rejecting someone's loan application? We provide these both fine grain as well as coarse grain insights. So our customers can actually deploy machine learning in a safe and trustworthy manner. >> Who is your customer? Who you're targeting? What persona is it, the data engineer, is it data science, is it the CSO, is it all the above? >> Yeah, our customer is the data scientist and the machine learning engineer, right? And we usually talk to teams that have a few models running in production, that's basically our sweet spot, where they're trying to look for a single pane of glass to see like what models are running in their production, how they're performing, how they're affecting their business metrics. So we typically engage with like head of data science or head of machine learning that has a few machine learning engineers and data scientists. >> Okay, so those people that are watching, you're into this, you can go check it out. It's good to learn. I want to get your thoughts on some trends that I see emerging, and I want to get your reaction to those. Number one, we're seeing the cloud scale now and integration a big part of things. So the time to value was brought up on stage today, Swami kind of mentioned time to value, showed some benchmark where they got four hours, some other teams were doing eight weeks. Where are we on the progression of value, time to value, and on the scale side. Can you scope that for me? >> I mean, it depends, right? You know, depending upon the company. So for example, when we work with banks, for them to time to operationalize a model can take months actually, because of all the regulatory procedures that they have to go through. You know, they have to get the models reviewed by model validators, model risk management teams, and then they audit those models, they have to then ship those models and constantly monitor them. So it's a very long process for them. And even for non-regulated sectors, if you do not have the right tools and processes in place, operationalizing machine learning models can take a long time. You know, with tools like Fiddler, what we are enabling is we are basically compressing that life cycle. We are helping them automate like model monitoring and explainability so that they can actually ship models more faster. Like you get like velocity in terms of shipping models. For example, one of the growing fintech companies that started with us last year started with six models in production, now they're running about 36 models in production. So it's within a year, they were able to like grow like 10x. So that is basically what we are trying to do. >> At other things, we at re:MARS, so first of all, you got a great product and a lot of markets that grow onto, but here you got space. I mean, anyone who's coming out of college or university PhD program, and if they're into aero, they're going to be here, right? This is where they are. Now you have a new core companies with machine learning, not just the engineering that you see in the space or aerospace area, you have a new engineering. Now I go back to the old days where my parents, there was Fortran, you used Fortran was Lingua Franca to manage the equipment. Little throwback to the old school. But now machine learning is companion, first class citizen, to the hardware. And in fact, and some will say more important. >> Yep, I mean, machine learning model is the new software artifact. It is going into production in a big way. And I think it has two different things that compare to traditional software. Number one, unlike traditional software, it's a black box. You cannot read up a machine learning model score and see why it's making those predictions. Number two, it's a stochastic entity. What that means is it's predictive power can wane over time. So it needs to be constantly monitored and then constantly refreshed so that it's actually working in tech. So those are the two main things you need to take care. And if you can do that, then machine learning can give you a huge amount of ROI. >> There is some practitioner kind of like craft to it. >> Correct. >> As you said, you got to know when to refresh, what data sets to bring in, which to stay away from, certainly when you get to the bias, but I'll get to that in a second. My next question is really along the lines of software. So if you believe that open source will dominate the software business, which I do, I mean, most people won't argue. I think you would agree with that, right? Open source is driving everything. If everything's open source, where's the differentiation coming from? So if I'm a startup entrepreneur or I'm a project manager working on the next Artemis mission, I got to open source. Okay, there's definitely security issues here. I don't want to talk about shift left right now, but like, okay, open source is everything. Where's the differentiation, where do I have the proprietary edge? >> It's a great question, right? So I used to work in tech companies before Fiddler. You know, when I used to work at Facebook, we would build everything in house. We would not even use a lot of open source software. So there are companies like that that build everything in house. And then I also worked at companies like Twitter and Pinterest, which are actually used a lot of open source, right? So now, like the thing is, it depends on the maturity of the organization. So if you're a Facebook or a Google, you can build a lot of things in house. Then if you're like a modern tech company, you would probably leverage open source, but there are lots of other companies in the world that still don't have the talent pool to actually build, take things from open source and productionize it. And that's where the opportunity for startups comes in so that we can commercialize these things, create a great enterprise experience, so actually operationalize things for them so that they don't have to do it in house for them. And that's the advantage working with startups. >> I don't want to get all operating systems with you on theory here on the stage here, but I will have to ask you the next question, which I totally agree with you, by the way, that's the way to go. There's not a lot of people out there that are peaked. And that's just statistical and it'll get better. Data engineering is really narrow. That is like the SRE of data. That's a new role emerging. Okay, all the things are happening. So if open source is there, integration is a huge deal. And you start to see the rise of a lot of MSPs, managed service providers. I run Kubernetes clusters, I do this, that, and the other thing. So what's your reaction to the growth of the integration side of the business and this role of new services coming from third parties? >> Yeah, absolutely. I think one of the big challenges for a chief data officer or someone like a CTO is how do they devise this infrastructure architecture and with components, either homegrown components or open source components or some vendor components, and how do they integrate? You know, when I used to run data engineering at Pinterest, we had to devise a data architecture combining all of these things and create something that actually flows very nicely, right? >> If you didn't do it right, it would break. >> Absolutely. And this is why it's important for us, like at Fiddler, to really make sure that Fiddler can integrate to all varies of ML platforms. Today, a lot of our customers use machine learning, build machine learning models on SageMaker. So Fiddler nicely integrate with SageMaker so that data, they get a seamless experience to monitor their models. >> Yeah, I mean, this might not be the right words for it, but I think data engineering as a service is really what I see you guys doing, as well other things, you're providing all that. >> And ML engineering as a service. >> ML engineering as a- Well it's hard. I mean, it's like the hard stuff. >> Yeah, yeah. >> Hear, hear. But that has to enable. So you as a business entrepreneur, you have to create a multiple of value proposition to your customers. What's your vision on that? What is that value? It has to be a multiple, at least 5 to 10. >> I mean, the value is simple, right? You know, if you have to operationize machine learning, you need visibility into how these things work. You know, if you're CTO or like chief data officer is asking how is my model working and how is it affecting my business? You need to be able to show them a dashboard, how it's working, right? And so like a data scientist today struggles to do this. They have to manually generate a report, manually do this analysis. What Fiddler is doing them is basically reducing their work so that they can automate these things and they can still focus on the core aspect of model building and data preparation and this boring aspect of monitoring the model and creating reports around the models is automated for them. >> Yeah, you guys got a great business. I think it's a lot of great future there and it's only going to get bigger. Again, the TAM's going to expand as the growth rising tide comes in. I want to ask you on while we're on that topic of rising tides, Dave Malik and I, since re:Invent last year have been kind of kicked down around this term that we made up called supercloud. And supercloud was a word that came out of these clouds that were not Amazon hyperscalers. So Snowflake, Buildman Sachs, Capital One, you name it, they're building massive proprietary value on top of the CapEx of Amazon. Jerry Chen at Greylock calls it castles in the cloud. You can create these moats. >> Yeah, right. >> So this is a phenomenon, right? And you land on one, and then you go to the others. So the strategies, everyone goes to Amazon first, and then hits Azure and GCP. That then creates this kind of multicloud so, okay, so super cloud's kind of happening, it's a thing. Charles Fitzgerald will disagree, he's a platformer, he says he's against the term. I get why, but he's off base a little. We can't wait to debate him on that. So superclouds are happening, but now what do I do about multicloud, because now I understand multicloud, I have this on that cloud, integrating across clouds is a very difficult thing. >> Krishna: Right, right, right. >> If I'm Snowflake or whatever, hey, I'll go to Azure, more TAM expansion, more market. But are people actually working together? Are we there yet? Where it's like, okay, I'm going to re-operationalize this code base over here. >> I mean, the reality of it, enterprise wants optionality, right? I think they don't want to be locked in into one particular cloud vendor on one particular software. And therefore you actually have in a situation where you have a multicloud scenario where they want to have some workloads in Amazon, some workloads in Azure. And this is an opportunity for startups like us because we are cloud agnostic. We can monitor models wherever you have. So this is where a lot of our customers, they have some of their models are running in their data centers and some of their models running in Amazon. And so we can provide a universal single pan of glass, right? So we can basically connect all of those data and actually showcase. I think this is an opportunity for startups to combine the data streams come from various different clouds and give them a single pain of experience. That way, the sort of the where is your data, where are my models running, which cloud are there, is all abstracted out from the customer. Because at the end of the day, enterprises will want optionality. And we are in this multicloud. >> Yeah, I mean, this reminds me of the interoperability days back when I was growing into the business. Everything was interoperability and OSI and the standards came out, but what's your opinion on openness, okay? There's a kneejerk reaction right now in the market to go silo on your data for governance or whatever reasons, but yet machine learning gurus and experts will say, "Hey, you want to horizon horizontal scalability and have the best machine learning models, you've got to have access to data and fast in real time or near real time." And the antithesis is siloing. >> Krishna: Right, right, right. >> So what's the solution? Customers control the data plane and have a control plane that's... What do customers do? It's a big challenge. >> Yeah, absolutely. I think there are multiple different architectures of ML, right, you know? We've seen like where vendors like us used to deploy completely on-prem, right? And they still do it, we still do it in some customers. And then you had this managed cloud experience where you just abstract out the entire operations from the customer. And then now you have this hybrid experience where you split the control plane and data plane. So you preserve the privacy of the customer from the data perspective, but you still control the infrastructure, right? I don't think there's a right answer. It depends on the product that you're trying to solve. You know, Databricks is able to solve this control plane, data plane split really well. I've seen some other tools that have not done this really well. So I think it all depends upon- >> What about Snowflake? I think they a- >> Sorry, correct. They have a managed cloud service, right? So predominantly that's their business. So I think it all depends on what is your go to market? You know, which customers you're talking to? You know, what's your product architecture look like? You know, from Fiddler's perspective today, we actually have chosen, we either go completely on-prem or we basically provide a managed cloud service and that's actually simpler for us instead of splitting- >> John: So it's customer choice. >> Exactly. >> That's your position. >> Exactly. >> Whoever you want to use Fiddler, go on-prem, no problem, or cloud. >> Correct, or cloud, yeah. >> You'll deploy and you'll work across whatever observability space you want to. >> That's right, that's right. >> Okay, yeah. So that's the big challenge, all right. What's the big observation from your standpoint? You've been on the hyperscaler side, your journey, Facebook, Pinterest, so back then you built everything, because no one else had software for you, but now everybody wants to be a hyperscaler, but there's a huge CapEx advantage. What should someone do? If you're a big enterprise, obviously I could be a big insurance, I could be financial services, oil and gas, whatever vertical, I want a supercloud, what do I do? >> I think like the biggest advantage enterprise today have is they have a plethora of tools. You know, when I used to work on machine learning way back in Microsoft on Bing Search, we had to build everything. You know, from like training platforms, deployment platforms, experimentation platforms. You know, how do we monitor those models? You know, everything has to be homegrown, right? A lot of open source also did not exist at the time. Today, the enterprise has this advantage, they're sitting on this gold mine of tools. You know, obviously there's probably a little bit of tool fatigue as well. You know, which tools to select? >> There's plenty of tools available. >> Exactly, right? And then there's like services available for you. So now you need to make like smarter choices to cobble together this, to create like a workflow for your engineers. And you can really get started quite fast, and actually get on par with some of these modern tech companies. And that is the advantage that a lot of enterprises see. >> If you were going to be the CTO or CEO of a big transformation, knowing what you know, 'cause you just brought up the killer point about why it's such a great time right now, you got platform as a service and the tooling essentially reset everything. So if you're going to throw everything out and start fresh, you're basically brewing the system architecture. It's a complete reset. That's doable. How fast do you think you could do that for say a large enterprise? >> See, I think if you set aside the organization processes and whatever kind of comes in the friction, from a technology perspective, it's pretty fast, right? You can devise a data architecture today with like tools like Kafka, Snowflake and Redshift, and you can actually devise a data architecture very clearly right from day one and actually implement it at scale. And then once you have accumulated enough data and you can extract more value from it, you can go and implement your MLOps workflow as well on top of it. And I think this is where tools like Fiddler can help as well. So I would start with looking at data, do we have centralization of data? Do we have like governance around data? Do we have analytics around data? And then kind of get into machine learning operations. >> Krishna, always great to have you on theCUBE. You're great masterclass guest. Obviously great success in your company. Been there, done that, and doing it again. I got to ask you, since you just brought that up about the whole reset, what is the superhero persona right now? Because it used to be the full stack developer, you know? And then it's like, then I call them, it didn't go over very well in theCUBE, the half stack developer, because nobody wants to be a half stack anything, a half sounds bad, worse than full. But cloud is essentially half a stack. I mean, you got infrastructure, you got tools. Now you're talking about a persona that's going to reset, look at tools, make selections, build an architecture, build an operating environment, distributed computing operating. Who is that person? What's that persona look like? >> I mean, I think the superhero persona today is ML engineering. I'm usually surprised how much is put on an ML engineer to do actually these days. You know, when I entered the industry as a software engineer, I had three or four things in my job to do, I write code, I test it, I deploy it, I'm done. Like today as an ML engineer, I need to worry about my data. How do I collect it? I need to clean the data, I need to train my models, I need to experiment with what it is, and to deploy them, I need to make sure that they're working once they're deployed. >> Now you got to do all the DevOps behind it. >> And all the DevOps behind it. And so I'm like working halftime as a data scientist, halftime as a software engineer, halftime as like a DevOps cloud. >> Cloud architect. >> It's like a heroic job. And I think this is why this is why obviously these jobs are like now really hard jobs and people want to be more and more machine learning >> And they get paid. >> engineering. >> Commensurate with the- >> And they're paid commensurately as well. And this is where I think an opportunity for tools like Fiddler exists as well because we can help those ML engineers do their jobs better. >> Thanks for coming on theCUBE. Great to see you. We're here at re:MARS. And great to see you again. And congratulations on being on the AWS startup showcase that we're in year two, episode four, coming up. We'll have to have you back on. Krishna, great to see you. Thanks for coming on. Okay, This is theCUBE's coverage here at re:MARS. I'm John Furrier, bringing all the signal from all the noise here. Not a lot of noise at this event, it's very small, very intimate, a little bit different, but all on point with space, machine learning, robotics, the future of industrial. We'll back with more coverage after the short break. >> Man: Thank you John. (upbeat music)

>>Welcome everyone to this cube conversation featuring unit Q. I'm your host, Lisa Martin. And we are excited to be joined by Christian Vickle, the founder and CEO of unit Q Christian. Thank you so much for joining me today. >>Thank you so much, Lisa pleasure to be here. >>Let's talk a little bit about unit Q. You guys were founded in 2018, so pretty recent. What is it that unit Q does. And what were some of the gaps in the market that led you to founding the company? >>Yep. So me and my co-founder Nick, we're actually doing our second company now is the unit Q is number two, and our first company was called scout years ago. We were back ES wicks and it was very different from unit Q. It's a social network for meeting people. And it was really during that experience where we saw the impact that quality of the experience quality of the product can have on your growth trajectory and the challenges we faced. How do we test everything before we ship it? And in reality, a modern company will have, let's say, 20 languages supported you support Android, Iowas, web big screen, small screen, you have 20 plus integrations and you have lots of different devices out there that might run your binary a little differently. So who is the ultimate test group of all of these different permutation and that's the end user. >>And we, we saw the, the big gap in the market, sort of the dream platform for us was unit queue. So if, if this would've existed back in the day, we would've been a, a happy purchaser and customer, and it really comes down to how do we, how do we harness the power of user feedback? You know, the end user, that's testing your product every single day in all different configurations. And then they're telling you that, Hey, something didn't work for me. I got double build or the passive recent link didn't work, or I couldn't, you know, when music, when the ad is finished playing on, on my app, the music doesn't resume. So how do we capture those signals into something that the company and different teams can align on? So that's where, you know, unit Q the, the vision here is to build a quality company, to help other companies build higher quality products. >>So really empowering companies to take a data driven approach to product quality. I was looking on your website and noticed that Pandora is one of your customers, but talk to me a little bit about a customer example that you think really articulates the value of what Q unit he was delivering. >>Right? So maybe we should just go back one little step and talk about what is quality. And I think quality is something that is, is a bit subjective. It's something that we live and breathe every day. It's something that can be formed in an instant first impressions. Last it's something that can be built over time that, Hey, I'm using this product and it's just not working for me. Maybe it's missing features. Maybe there are performance related bots. Maybe there is there's even fulfillment related issues. Like we work with Uber and hello, fresh and, and other types of more hybrid type companies in addition to the Pandoras and, and Pinterest and, and Spotify, and these more digital, only products, but the, the end users I'm producing this data, the reporting, what is working and not working out there in many different channels. So they will leave app produce. >>They will write into support. They might engage with a chat support bot. They will post stuff on Reddit on Twitter. They will comment on Facebook ads. So like this data is dispersed everywhere. The end user is not gonna fill out a perfect bug report in a form somewhere that gets filed into gr like they're, they're producing this content everywhere in different languages. So the first value of what we do is to just ingest all of that data. So all the entire surface area of use of feedback, we ingest into a machine and then we clean the data. We normalize it, and then we translate everything into English. And it was actually a surprise to us when we started this company, that there are quite a few companies out there that they're only looking at feedback in English. So what about my Spanish speaking users? What about my French speaking users? >>And when, when, when that is done, like when all of that data is, is need to organized, we extract signals from that around what is impacting the user experience right now. So we break these, all of this data down into something called quality monitors. So quality monitor is basically a topic which can be again, passive reset, link noting, or really anything that that's impacting the end user. And the important part here is that we need to have specific actionable data. For instance, if I tell you, Hey, Lisa music stops playing is a growing trend that our users are reporting. You will tell me, well, what can I do with that? Like what specifically is breaking? So we deploy up to 1500 unique quality monitors per customer. So we can then alert different teams inside of the organization of like, Hey, something broke and you should take a look at it. >>So it's really breaking down data silos within the company. It aligns cross-functional teams to agree on what should be fixed next. Cause there's typically a lot of confusion, you know, marketing, they might say, Hey, we want this fixed engineering. They're like, well, I can't reproduce, or that's not a high priority for us. The support teams might also have stuff that they want to get fixed. And what we've seen is that these teams, they struggle to communicate. So how do we align them around the single source of truth? And I think that's for unit two is early identification of stuff. That's not working in production and it's also aligning the teams so they can quickly triage and say, yes, we gotta fix this right before it snowballs into something. We say, you know, we wanna, we wanna cap catch issues before you go into crisis PR mode, right? So we want to get this, we wanna address it early in the cycle. >>Talk to me about when you're in customer conversations, Christian, the MarTech landscape is competitive. There's nearly 10,000 different solutions out there, and it's growing really quickly quality monitors that you just described is that one of the key things that, that you talk to customers about, that's a differentiator for unit Q. >>Yeah. So I mean, it, it, it comes down to, as you're building your product, right, you, you have, you have a few different options. One is to build new features and we need to build new features and innovate and, and, and that's all great. We also need to make sure that the foundation of the product is working and that we keep improving quality and what, what we see with, with basically every customer that we work with, that, that when quality goes up, it's supercharges the growth machine. So quality goes up, you're gonna see less support tickets. You're gonna see less one star reviews, less one star reviews is of course good for making the store front convert better. You know, I, I want install a 4.5 star app, not a 3.9 star app. We also see that sentiment. So for those who are interested in getting that NPS score up for the next time we measure it, we see that quality is of course a very important piece of that. >>And maybe even more importantly, so sort of inside of the product machine, the different conversion steps, let's say sign up to activate it to coming back in second day, 30 day, 90 day, and so forth. We see a dramatic impact on how quality sort of moves that up and down the retention function, if you will. So it, it really, if you think about a modern company, like the product is sort of the center of the existence of the company, and if the product performs really well, then you can spend more money in marketing because it converts really good. You can hire more engineers, you can hire, you can hire more support people and so forth. So it's, it's really cool to see that when quality improves its supercharges, everything else I think for marketing it's how do you know if you're spending into a broken product or not? >>And I, and I, I feel like marketing has, they have their insights, but it's, it's not deep enough where they can go to engineering and say, Hey, these 10 issues are impacting my MPS score and they're impacting my conversion and I would love for you to fix it. And when you can bring tangible impact, when you can bring real data to, to engineering and product, they move on it cause they also wanna help build the company. And, and so I think that's, that's how we stand out from the more traditional MarTech, because we need to fix the core of, of sort of this growth engine, which is the quality of the product >>Quality of the product. And obviously that's directly related to the customer experience. And we know these days, one of the things I think that's been in short supply the last couple of years is patience. We know when customers are unhappy with the product or service, and you talked about it a minute ago, they're gonna go right to, to Reddit or other sources to complain about that. So being able to, for uniq, to help companies to improve the customer experience, isn't I think table stakes for businesses it's mission critical these days. Yeah, >>It is mission critical. So if you look at the, let's say that we were gonna start a, a music app. Okay. So how do we, how do we compete as a music app? Well, if you, if you were to analyze all different music apps out there, they have more or less the same features app. Like they, the feature differentiation is minimal. And, and if you launch a new cool feature than your competitor will probably copy that pretty quickly as well. So competing with features is really hard. What about content? Well, I'm gonna get the same content on Spotify as apple SD. So competing with content is also really hard. What about price? So it turns out you'll pay 9 99 a month for music, but there's no, there's no 1 99. It's gonna be 9 99. So quality of the experience is one of the like last vectors or areas where you can actually compete. >>And we see consistently that if you' beating your competition on quality, you will do better. Like the best companies out there also have the highest quality experience. So it's, it's been, you know, for us at our last company, measuring quality was something that was very hard. How do we talk about it? And when we started this company, I went out and talked to a bunch of CEOs and product leaders and board members. And I said, how do you talk about quality in a board meeting? And they were, they said, well, we don't, we don't have any metrics. So actually the first thing we did was to define a metrics. We have, we have this thing called this unit Q score, which is on our website as well, where we can base it's like the credit score. So you can see your score between zero and a hundred. >>And if your score is 100, it means that we're finding no quality issues in the public domain. If your score is 90, it means that 10% of the data we look at refers to a quality issue. And the definition of a quality issue is quite simple. It is when the user experience doesn't match the user expectation. There is a gap in between, and we've actually indexed the 5,000 largest apps out there. So we're then looking at all the public review. So on our website, you can go in and, and look up the unit Q score for the 5,000 largest products. And we republish these every night. So it's an operational metric that changes all the time. >>Hugely impactful. Christian, thank you so much for joining me today, talking to the audience about unit Q, how you're turning qualitative feedback into pretty significant product improvements for your customers. We appreciate your insights. >>Thank you, Lisa, have a great day. >>You as well, per Christian Lin, I'm Lisa Martin. You're watching a cube conversation.

>>Hello, everyone. Welcome to the cubes presentation of the 80 minutes startup showcase. Season two, episode two, the theme is data as code, the future of analytics. I'm John furry, your host. We had a great day lineup for you. Fast growing startups, great lineup of companies, founders, and stories around data as code. And we're going to kick it off here with our opening keynote with Rahul Pathak VP of analytics at AWS cube alumni. Right? We'll thank you for coming on and being the opening keynote for this awesome event. >>Yeah. And it's great to see you, and it's great to be part of this event, uh, excited to, um, to help showcase some of the great innovation that startups are doing on top of AWS. >>Yeah. We last spoke at AWS reinvent and, uh, a lot's happened there, service loss of serverless as the center of the, of the action, but all these start-ups rock set Dremio Cribble monks next Liccardo, a HANA imply all doing great stuff. Data as code has a lot of traction. So a lot of still momentum going on in the marketplace. Uh, pretty exciting. >>No, it's, uh, it's awesome. I mean, I think there's so much innovation happening and you know, the, the wonderful part of working with data is that the demand for services and products that help customers drive insight from data is just skyrocketing and has no sign of no sign of slowing down. And so it's a great time to be in the data business. >>It's interesting to see the theme of the show getting traction, because you start to see data being treated almost like how developers write software, taking things out of branches, working on them, putting them back in, uh, machine learnings, uh, getting iterated on you, seeing more models, being trained differently with better insights, action ones that all kind of like working like code. And this is a whole nother way. People are reinventing their businesses. This has been a big, huge wave. What's your reaction to that? >>Uh, I think it's spot on, I mean, I think the idea of data's code and bringing some of the repeatability of processes from software development into how people built it, applications is absolutely fundamental and especially so in machine learning where you need to think about the explainability of a model, what version of the world was it trained on? When you build a better model, you need to be able to explain and reproduce it. So I think your insights are spot on and these ideas are showing up in all stages of the data work flow from ingestion to analytics to I'm out >>This next way is about modernization and going to the next level with cloud-scale. Uh, thank you so much for coming on and being the keynote presenter here for this great event. Um, I'll let you take it away. Reinventing businesses, uh, with ads analytics, right? We'll take it away. >>Okay, perfect. Well, folks, we're going to talk about, uh, um, reinventing your business with, uh, data. And if you think about it, the first wave of reinvention was really driven by the cloud. As customers were able to really transform how they thought about technology and that's well on her way. Although if you stop and think about it, I think we're only about five to 10% of the way done in terms of it span being on the cloud. So lots of work to do there, but we're seeing another wave of reinvention, which is companies reinventing their businesses with data and really using data to transform what they're doing to look for new opportunities and look for ways to operate more efficiently. And I think the past couple of years of the pandemic, it really only accelerated that trend. And so what we're seeing is, uh, you know, it's really about the survival of the most informed folks for the best data are able to react more quickly to what's happening. >>Uh, we've seen customers being able to scale up if they're in, say the delivery business or scale down, if they were in the travel business at the beginning of all of this, and then using data to be able to find new opportunities and new ways to serve customers. And so it's really foundational and we're seeing this across the board. And so, um, you know, it's great to see the innovation that's happening to help customers make sense of all of this. And our customers are really looking at ways to put data to work. It's about making better decisions, finding new efficiencies and really finding new opportunities to succeed and scale. And, um, you know, when it comes to, uh, good examples of this FINRA is a great one. You may not have heard of them, but that the U S equities regulators, all trading that happens in equities, they keep track of they're look at about 250 billion records per day. >>Uh, the examiner, I was only EMR, which is our spark and Hadoop service, and they're processing 20 terabytes of data running across tens of thousands of nodes. And they're looking for fraud and bad actors in the market. So, um, you know, huge, uh, transformation journey for FINRA over the years of customer I've gotten to work with personally since really 2013 onward. So it's been amazing to see their journey, uh, Pinterest, not a great customer. I'm sure everyone's familiar with, but, um, you know, they're about visual search and discovery and commerce, and, um, they're able to scale their daily lot searches, um, really a factor of three X or more, uh, drive down their costs. And they're using the Amazon Opus search service. And really what we're trying to do at AWS is give our customers the most comprehensive set of services for the end-to-end journey around, uh, data from ingestion to analytics and machine learning. And we will want to provide a comprehensive set of capabilities for ingestion, cataloging analytics, and then machine learning. And all of these are things that our partners and the startups that are run on us have available to them to build on as they build and deliver value for their customers. >>And, you know, the way we think about this is we want customers to be able to modernize what they're doing and their infrastructure. And we provide services for that. It's about unifying data, wherever it lives, connecting it. So the customers can build a complete picture of their customers and business. And then it's about innovation and really using machine learning to bring all of this unified data, to bear on driving new innovation and new opportunities for customers. And what we're trying to do AWS is really provide a scalable and secure cloud platform that customers and partners can build on a unifying is about connecting data. And it's also about providing well-governed access to data. So one of the big trends that we see is customers looking for the ability to make self-service data available to that customer there and use. And the key to that is good foundational governance. >>Once you can define good access controls, you then are more comfortable setting data free. And, um, uh, the other part of it is, uh, data lakes play a huge role because you need to be able to think about structured and unstructured data. In fact, about 80% of the data being generated today, uh, is unstructured. And you want to be able to connect data that's in data lakes with data that's in purpose-built data stores, whether that's databases on AWS databases, outside SAS products, uh, as well as things like data warehouses and machine learning systems, but really connecting data as key. Uh, and then, uh, innovation, uh, how can we bring to bear? And we imagine all processes with new technologies like AI and machine learning, and AI is also key to unlocking a lot of the value that's in unstructured data. If you can figure out what's in an imagine the sentiment of audio and do that in real-time that lets you then personalize and dynamically tailor experiences, all of which are super important to getting an edge, um, in, uh, in the modern marketplace. And so at AWS, we, when we think about connecting the dots across sources of data, allowing customers to use data, lakes, databases, analytics, and machine learning, we want to provide a common catalog and governance and then use these to help drive new experiences for customers and their apps and their devices. And then this, you know, in an ideal world, we'll create a closed loop. So you create a new experience. You observe our customers interact with it, that generates more data, which is a data source that feeds into the system. >>And, uh, you know, on AWS, uh, thinking about a modern data strategy, uh, really at the core is a data lakes built on us three. And I'll talk more about that in a second. Then you've got services like Athena included, lake formation for managing that data, cataloging it and querying it in place. And then you have the ability to use the right tool for the right job. And so we're big believers in purpose-built services for data because that's where you can avoid compromising on performance functionality or scale. Uh, and then as I mentioned, unification and inter interconnecting, all of that data. So if you need to move data between these systems, uh, there's well-trodden pathways that allow you to do that, and then features built into services that enable that. >>And, um, you know, some of the core ideas that guide the work that we do, um, scalable data lakes at key, um, and you know, this is really about providing arbitrarily scalable high throughput systems. It's about open format data for future-proofing. Uh, then we talk about purpose-built systems at the best possible functionality, performance, and cost. Uh, and then from a serverless perspective, this has been another big trend for us. We announced a bunch of serverless services and reinvented the goal here is to really take away the need to manage infrastructure from customers. They can really focus about driving differentiated business value, integrated governance, and then machine learning pervasively, um, not just as an end product for data scientists, but also machine learning built into data, warehouses, visualization and a database. >>And so it's scalable data lakes. Uh, data three is really the foundation for this. One of our, um, original services that AWS really the backbone of so much of what we do, uh, really unmatched your ability, availability, and scale, a huge portfolio of analytics services, uh, both that we offer, but also that our partners and customers offer and really arbitrary skin. We've got individual customers and estimator in the expert range, many in the hundreds of petabytes. And that's just growing. You know, as I mentioned, we see roughly a 10 X increase in data volume every five years. So that's a exponential increase in data volumes, Uh, from a purpose-built perspective, it's the right tool for the right job, the red shift and data warehousing Athena for querying all your data. Uh, EMR is our managed sparking to do, uh, open search for log analytics and search, and then Kinesis and Amex care for CAFCA and streaming. And that's been another big trend is, uh, real time. Data has been exploding and customers wanting to make sense of that data in real time, uh, is another big deal. >>Uh, some examples of how we're able to achieve differentiated performance and purpose-built systems. So with Redshift, um, using managed storage and it's led us and since types, uh, the three X better price performance, and what's out there available to all our customers and partners in EMR, uh, with things like spark, we're able to deliver two X performance of open source with a hundred percent compatibility, uh, almost three X and Presto, uh, with on two, which is our, um, uh, new Silicon chips on AWS, better price performance, about 10 to 12% better price performance, and 20% lower costs. And then, uh, all compatible source. So drop your jobs, then have them run faster and cheaper. And that translates to customer benefits for better margins for partners, uh, from a serverless perspective, this is about simplifying operations, reducing total cost of ownership and freeing customers from the need to think about capacity management. If we invent, we, uh, announced serverless redshifts EMR, uh, serverless, uh, Kinesis and Kafka, um, and these are all game changes for customers in terms of freeing our customers and partners from having to think about infrastructure and allowing them to focus on data. >>And, um, you know, when it comes to several assumptions in analytics, we've really got a very full and complete set. So, uh, whether that's around data warehousing, big data processing streaming, or cataloging or governance or visualization, we want all of our customers to have an option to run something struggles as well as if they have specialized needs, uh, uh, instances are available as well. And so, uh, really providing a comprehensive deployment model, uh, based on the customer's use cases, uh, from a governance perspective, uh, you know, like information is about easy build and management of data lakes. Uh, and this is what enables data sharing and self service. And, um, you know, with you get very granular access controls. So rule level security, uh, simple data sharing, and you can tag data. So you can tag a group of analysts in the year when you can say those only have access to the new data that's been tagged with the new tags, and it allows you to very, scaleably provide different secure views onto the same data without having to make multiple copies, another big win for customers and partners, uh, support transactions on data lakes. >>So updates and deletes. And time-travel, uh, you know, John talked about data as code and with time travel, you can look at, um, querying on different versions of data. So that's, uh, a big enabler for those types of strategies. And with blue, you're able to connect data in multiple places. So, uh, whether that's accessing data on premises in other SAS providers or, uh, clouds, uh, as well as data that's on AWS and all of this is, uh, serverless and interconnected. And, um, and really it's about plugging all of your data into the AWS ecosystem and into our partner ecosystem. So this API is all available for integration as well, but then from an AML perspective, what we're really trying to do is bring machine learning closer to data. And so with our databases and warehouses and lakes and BI tools, um, you know, we've infused machine learning throughout our, by, um, the state of the art machine running that we offer through SageMaker. >>And so you've got a ML in Aurora and Neptune for broths. Uh, you can train machine learning models from SQL, directly from Redshift and a female. You can use free inference, and then QuickSight has built in forecasting built in natural language, querying all powered by machine learning, same with anomaly detection. And here are the ideas, you know, how can we up our systems get smarter at the surface, the right insights for our customers so that they don't have to always rely on smart people asking the right questions, um, and you know, uh, really it's about bringing data back together and making it available for innovation. And, uh, thank you very much. I appreciate your attention. >>Okay. Well done reinventing the business with AWS analytics rural. That was great. Thanks for walking through that. That was awesome. I have to ask you some questions on the end-to-end view of the data. That seems to be a theme serverless, uh, in there, uh, Mel integration. Um, but then you also mentioned picking the right tool for the job. So then you've got like all these things moving on, simplify it for me right now. So from a business standpoint, how do they modernize? What's the steps that the clients are taking with analytics, what's the best practice? How do they, what's the what's the high order bit here? >>Uh, so the basic hierarchy is, you know, historically legacy systems are rigid and inflexible, and they weren't really designed for the scale of modern data or the variety of it. And so what customers are finding is they're moving to the cloud. They're moving from legacy systems with punitive licensing into more flexible, more systems. And that allows them to really think about building a decoupled, scalable future proof architecture. And so you've got the ability to combine data lakes and databases and data warehouses and connect them using common KPIs and common data protection. And that sets you up to deal with arbitrary scale and arbitrary types. And it allows you to evolve as the future changes since it makes it easy to add in a new type of engine, as we invent a better one a few years from now. Uh, and then, uh, once you've kind of got your data in a cloud and interconnected in this way, you can now build complete pictures of what's going on. You can understand all your touch points with customers. You can understand your complete supply chain, and once you can build that complete picture of your business, you can start to use analytics and machine learning to find new opportunities. So, uh, think about modernizing, moving to the cloud, setting up for the future, connecting data end to end, and then figuring out how to use that to your advantage. >>I know as you mentioned, modern data strategy gives you the best of both worlds. And you've mentioned, um, briefly, I want to get a little bit more, uh, insight from you on this. You mentioned open, open formats. One of the themes that's come out of some of the interviews, these companies we're going to be hearing from today is open source. The role opens playing. Um, how do you see that integrating in? Because again, this is just like software, right? Open, uh, open source software, open source data. It seems to be a trend. What does open look like to you? How do you see that progressing? >>Uh, it's a great question. Uh, open operates on multiple dimensions, John, as you point out, there's open data formats. These are things like JSI and our care for analytics. This allows multiple engines tend to operate on data and it'll, it, it creates option value for customers. If you're going to data in an open format, you can use it with multiple technologies and that'll be future-proofed. You don't have to migrate your data. Now, if you're thinking about using a different technology. So that's one piece now that sort of software, um, also, um, really a big enabler for innovation and for customers. And you've got things like squat arc and Presto, which are popular. And I know some of the startups, um, you know, that we're talking about as part of the showcase and use these technologies, and this allows for really the world to contribute, to innovating and these engines and moving them forward together. And we're big believers in that we've got open source services. We contribute to open-source, we support open source projects, and that's another big part of what we do. And then there's open API is things like SQL or Python. Uh, again, uh, common ways of interacting with data that are broadly adopted. And this one, again, create standardization. It makes it easier for customers to inter-operate and be flexible. And so open is really present all the way through. And it's a big part, I think, of, uh, the present and the future. >>Yeah. It's going to be fun to watch and see how that grows. It seems to be a lot of traction there. I want to ask you about, um, the other comment I thought was cool. You had the architectural slides out there. One was data lakes built on S3, and you had a theme, the glue in lake formation kind of around S3. And then you had the constellation of, you know, Kinesis SageMaker and other things around it. And you said, you know, pick the tool for the right job. And then you had the other slide on the analytics at the center and you had Redshift and all the other, other, other services around it around serverless. So one was more about the data lake with Athena glue and lake formation. The other one's about serverless. Explain that a little bit more for me, because I'm trying to understand where that fits. I get the data lake piece. Okay. Athena glue and lake formation enables it, and then you can pick and choose what you need on the serverless side. What does analytics in the center mean? >>So the idea there is that really, we wanted to talk about the fact that if you zoom into the analytics use case within analytics, everything that we offer, uh, has a serverless option for our customers. So, um, you could look at the bucket of analytics across things like Redshift or EMR or Athena, or, um, glue and league permission. You have the option to use instances or containers, but also to just not worry about infrastructure and just think declaratively about the data that you want to. >>Oh, so basically you're saying the analytics is going serverless everywhere. Talking about volumes, you mentioned 10 X volumes. Um, what are other stats? Can you share in terms of volumes? What are people seeing velocity I've seen data warehouses can't move as fast as what we're seeing in the cloud with some of your customers and how they're using data. How does the volume and velocity community have any kind of other kind of insights into those numbers? >>Yeah, I mean, I think from a stats perspective, um, you know, take Redshift, for example, customers are processing. So reading and writing, um, multiple exabytes of data there across from each shift. And, uh, you know, one of the things that we've seen in, uh, as time has progressed as, as data volumes have gone up and did a tapes have exploded, uh, you've seen data warehouses get more flexible. So we've added things like the ability to put semi-structured data and arbitrary, nested data into Redshift. Uh, we've also seen the seamless integration of data warehouses and data lakes. So, um, actually Redshift was one of the first to enable a straightforward acquiring of data. That's sitting in locally and drives as well as feed and that's managed on a stream and, uh, you know, those trends will continue. I think you'll kind of continue to see this, um, need to query data wherever it lives and, um, and, uh, allow, uh, leaks and warehouses and purpose-built stores to interconnect. >>You know, one of the things I liked about your presentation was, you know, kind of had the theme of, you know, modernize, unify, innovate, um, and we've been covering a lot of companies that have been, I won't say stumbling, but like getting to the future, some go faster than others, but they all kind of get stuck in an area that seems to be the same spot. It's the silos, breaking down the silos and get in the data lakes and kind of blending that purpose built data store. And they get stuck there because they're so used to silos and their teams, and that's kind of holding back the machine learning side of it because the machine learning can't do its job if they don't have access to all the data. And that's where we're seeing machine learning kind of being this new iterative model where the models are coming in faster. And so the silo brake busting is an issue. So what's your take on this part of the equation? >>Uh, so there's a few things I plan it. So you're absolutely right. I think that transition from some old data to interconnected data is always straightforward and it operates on a number of levels. You want to have the right technology. So, um, you know, we enable things like queries that can span multiple stores. You want to have good governance, you can connect across multiple ones. Uh, then you need to be able to get data in and out of these things and blue plays that role. So there's that interconnection on the technical side, but the other piece is also, um, you know, you want to think through, um, organizationally, how do you organize, how do you define it once data when they share it? And one of the asylees for enabling that sharing and, um, think about, um, some of the processes that need to get put in place and create the right incentives in your company to enable that data sharing. And then the foundational piece is good guardrails. You know, it's, uh, it can be scary to open data up. And, uh, the key to that is to put good governance in place where you can ensure that data can be shared and distributed while remaining protected and adhering to the privacy and compliance and security regulations that you have for that. And once you can assert that level of protection, then you can set that data free. And that's when, uh, customers really start to see the benefits of connecting all of it together, >>Right? And then we have a batch of startups here on this episode that are doing a lot of different things. Uh, some have, you know, new lake new lakes are forming observability lakes. You have CQL innovation on the front end data, tiering innovation at the data tier side, just a ton of innovation around this new data as code. How do you see as executive at AWS? You're enabling all this, um, where's the action going? Where are the white spaces? Where are the opportunities as this architecture continues to grow, um, and get traction because of the relevance of machine learning and AI and the apps are embedding data in there now as code where's the opportunities for these startups and how can they continue to grow? >>Yeah, the, I mean, the opportunity is it's amazing, John, you know, we talked a little bit about this at the beginning, but the, there is no slow down insight for the volume of data that we're generating pretty much everything that we have, whether it's a watch or a phone or the systems that we interact with are generating data and, uh, you know, customers, uh, you know, we talk a lot about the things that'll stay the same over time. And so, you know, the data volumes will continue to go up. Customers are gonna want to keep analyzing that data to make sense of it. They're going to want to be able to do it faster and more cheaply than they were yesterday. And then we're going to want to be able to make decisions and innovate, uh, in a shorter cycle and run more experiments than they were able to do. >>And so I think as long as, and they're always going to want this data to be secure and well-protected, and so I think as long as we, and the startups that we work with can continue to push on making these things better. Can I deal with more data? Can I deal with it more cheaply? Can I make it easier to get insight? And can I maintain a super high bar in security investments in these areas will just be off. Um, because, uh, the demand side of this equation is just in a great place, given what we're seeing in terms of theater and the architect for forum. >>I also love your comment about, uh, ML integration being the last leg of the equation here or less likely the journey, but you've got that enablement of the AIP solves a lot of problems. People can see benefits from good machine learning and AI is creating opportunities. Um, and also you also have mentioned the end to end with security piece. So data and security are kind of going hand in hand these days, not just the governments and the compliance stuff we're talking about security. So machine learning integration kind of connects all of this. Um, what's it all mean for the customers, >>For customers. That means that with machine learning and really enabling themselves to use machine learning, to make sense of data, they're able to find patterns that can represent new opportunities, um, quicker than ever before. And they're able to do it, uh, dynamically. So, you know, in a prior version of the world, we'd have little bit of systems and they would be relatively rigid and then we'd have to improve them. Um, with machine learning, this can be dynamic and near real time and you can customize them. So, uh, that just represents an opportunity to deepen relationships with customers and create more value and to find more efficiency in how businesses are run. So that piece is there. Um, and you know, your ideas around, uh, data's code really come into play because machine learning needs to be repeatable and explainable. And that means versioning, uh, keeping track of everything that you've done from a code and data and learning and training perspective >>And data sets are updating the machine learning. You got data sets growing, they become code modules that can be reused and, uh, interrogated, um, security okay. Is a big as a big theme data, really important security is seen as one of our top use cases. Certainly now in this day and age, we're getting a lot of, a lot of breaches and hacks coming in, being defended. It brings up the open, brings up the data as code security is a good proxy for kind of where this is going. What's your what's take on that and your reaction to that. >>So I'm, I'm security. You can, we can never invest enough. And I think one of the things that we, um, you know, guide us in AWS is security, availability, durability sort of jobs, you know, 1, 2, 3, and, um, and it operates at multiple levels. You need to protect data and rest with encryption, good key management and good practices though. You need to protect data on the wire. You need to have a good sense of what data is allowed to be seen by whom. And then you need to keep track of who did what and be able to verify and come back and prove that, uh, you know, uh, only the things that were allowed to happen actually happened. And you can actually then use machine learning on top of all of this apparatus to say, uh, you know, can I detect things that are happening that shouldn't be happening in near real time so they could put a stop to them. So I don't think any of us can ever invest enough in securing and protecting my data and our systems, and it is really fundamental or adding customer trust and it's just good business. So I think it is absolutely crucial. And we think about it all the time and are always looking for ways to raise >>Well, I really appreciate you taking the time to give the keynote final word here for the folks watching a lot of these startups that are presenting, they're doing well. Business wise, they're being used by large enterprises and people buying their products and using their services for customers are implementing more and more of the hot startups products they're relevant. What's your advice to the customer out there as they go on this journey, this new data as code this new future of analytics, what's your recommendation. >>So for customers who are out there, uh, recommend you take a look at, um, what, uh, the startups on AWS are building. I think there's tremendous innovation and energy, uh, and, um, there's really great technology being built on top of a rock solid platform. And so I encourage customers thinking about it to lean forward, to think about new technology and to embrace, uh, move to the cloud suite, modernized, you know, build a single picture of our data and, and figure out how to innovate and when >>Well, thanks for coming on. Appreciate your keynote. Thanks for the insight. And thanks for the conversation. Let's hand it off to the show. Let the show begin. >>Thank you, John pleasure, as always.

(upbeat music) >> Hey everyone. Welcome back to theCUBE's live coverage of Women in Data Science, WiDS 2022, coming to live from Stanford University. I'm Lisa Martin. My next guest is here. Nandi Leslie, Doctor Nandi Leslie, Senior Engineering Fellow at Raytheon Technologies. Nandi, it's great to have you on the program. >> Oh it's my pleasure, thank you. >> This is your first WiDS you were saying before we went live. >> That's right. >> What's your take so far? >> I'm absolutely loving it. I love the comradery and the community of women in data science. You know, what more can you say? It's amazing. >> It is. It's amazing what they built since 2015, that this is now reaching 100,000 people 200 online event. It's a hybrid event. Of course, here we are in person, and the online event going on, but it's always an inspiring, energy-filled experience in my experience of WiDS. >> I'm thoroughly impressed at what the organizers have been able to accomplish. And it's amazing, that you know, you've been involved from the beginning. >> Yeah, yeah. Talk to me, so you're Senior Engineering Fellow at Raytheon. Talk to me a little bit about your role there and what you're doing. >> Well, my role is really to think about our customer's most challenging problems, primarily at the intersection of data science, and you know, the intersectional fields of applied mathematics, machine learning, cybersecurity. And then we have a plethora of government clients and commercial clients. And so what their needs are beyond those sub-fields as well, I address. >> And your background is mathematics. >> Yes. >> Have you always been a math fan? >> I have, I actually have loved math for many, many years. My dad is a mathematician, and he introduced me to, you know mathematical research and the sciences at a very early age. And so, yeah, I went on, I studied in a math degree at Howard undergrad, and then I went on to do my PhD at Princeton in applied math. And later did a postdoc in the math department at University of Maryland. >> And how long have you been with Raytheon? >> I've been with Raytheon about six years. Yeah, and before Raytheon, I worked at a small to midsize defense company, defense contracting company in the DC area, systems planning and analysis. And then prior to that, I taught in a math department where I also did my postdoc, at University of Maryland College Park. >> You have a really interesting background. I was doing some reading on you, and you have worked with the Navy. You've worked with very interesting organizations. Talk to the audience a little bit about your diverse background. >> Awesome yeah, I've worked with the Navy on submarine force security, and submarine tracking, and localization, sensor performance. Also with the Army and the Army Research Laboratory during research at the intersection of machine learning and cyber security. Also looking at game theoretic and graph theoretic approaches to understand network resilience and robustness. I've also supported Department of Homeland Security, and other government agencies, other governments, NATO. Yeah, so I've really been excited by the diverse problems that our various customers have you know, brought to us. >> Well, you get such great experience when you are able to work in different industries and different fields. And that really just really probably helps you have such a much diverse kind of diversity of thought with what you're doing even now with Raytheon. >> Yeah, it definitely does help me build like a portfolio of topics that I can address. And then when new problems emerge, then I can pull from a toolbox of capabilities. And, you know, the solutions that have previously been developed to address those wide array of problems, but then also innovate new solutions based on those experiences. So I've been really blessed to have those experiences. >> Talk to me about one of the things I heard this morning in the session I was able to attend before we came to set was about mentors and sponsors. And, you know, I actually didn't know the difference between that until a few years ago. But it's so important. Talk to me about some of the mentors you've had along the way that really helped you find your voice in research and development. >> Definitely, I mean, beyond just the mentorship of my my family and my parents, I've had amazing opportunities to meet with wonderful people, who've helped me navigate my career. One in particular, I can think of as and I'll name a number of folks, but Dr. Carlos Castillo-Chavez was one of my earlier mentors. I was an undergrad at Howard University. He encouraged me to apply to his summer research program in mathematical and theoretical biology, which was then at Cornell. And, you know, he just really developed an enthusiasm with me for applied mathematics. And for how it can be, mathematics that is, can be applied to epidemiological and theoretical immunological problems. And then I had an amazing mentor in my PhD advisor, Dr. Simon Levin at Princeton, who just continued to inspire me, in how to leverage mathematical approaches and computational thinking for ecological conservation problems. And then since then, I've had amazing mentors, you know through just a variety of people that I've met, through customers, who've inspired me to write these papers that you mentioned in the beginning. >> Yeah, you've written 55 different publications so far. 55 and counting I'm sure, right? >> Well, I hope so. I hope to continue to contribute to the conversation and the community, you know, within research, and specifically research that is computationally driven. That really is applicable to problems that we face, whether it's cyber security, or machine learning problems, or others in data science. >> What are some of the things, you're giving a a tech vision talk this afternoon. Talk to me a little bit about that, and maybe the top three takeaways you want the audience to leave with. >> Yeah, so my talk is entitled "Unsupervised Learning for Network Security, or Network Intrusion Detection" I believe. And essentially three key areas I want to convey are the following. That unsupervised learning, that is the mathematical and statistical approach, which tries to derive patterns from unlabeled data is a powerful one. And one can still innovate new algorithms in this area. Secondly, that network security, and specifically, anomaly detection, and anomaly-based methods can be really useful to discerning and ensuring, that there is information confidentiality, availability, and integrity in our data >> A CIA triad. >> There you go, you know. And so in addition to that, you know there is this wealth of data that's out there. It's coming at us quickly. You know, there are millions of packets to represent communications. And that data has, it's mixed, in terms of there's categorical or qualitative data, text data, along with numerical data. And it is streaming, right. And so we need methods that are efficient, and that are capable of being deployed real time, in order to detect these anomalies, which we hope are representative of malicious activities, and so that we can therefore alert on them and thwart them. >> It's so interesting that, you know, the amount of data that's being generated and collected is growing exponentially. There's also, you know, some concerning challenges, not just with respect to data that's reinforcing social biases, but also with cyber warfare. I mean, that's a huge challenge right now. We've seen from a cybersecurity perspective in the last couple of years during the pandemic, a massive explosion in anomalies, and in social engineering. And companies in every industry have to be super vigilant, and help the people understand how to interact with it, right. There's a human component. >> Oh, for sure. There's a huge human component. You know, there are these phishing attacks that are really a huge source of the vulnerability that corporations, governments, and universities face. And so to be able to close that gap and the understanding that each individual plays in the vulnerability of a network is key. And then also seeing the link between the network activities or the cyber realm, and physical systems, right. And so, you know, especially in cyber warfare as a remote cyber attack, unauthorized network activities can have real implications for physical systems. They can, you know, stop a vehicle from running properly in an autonomous vehicle. They can impact a SCADA system that's, you know there to provide HVAC for example. And much more grievous implications. And so, you know, definitely there's the human component. >> Yes, and humans being so vulnerable to those social engineering that goes on in those phishing attacks. And we've seen them get more and more personal, which is challenging. You talking about, you know, sensitive data, personally identifiable data, using that against someone in cyber warfare is a huge challenge. >> Oh yeah, certainly. And it's one that computational thinking and mathematics can be leveraged to better understand and to predict those patterns. And that's a very rich area for innovation. >> What would you say is the power of computational thinking in the industry? >> In industry at-large? >> At large. >> Yes, I think that it is such a benefit to, you know, a burgeoning scientist, if they want to get into industry. There's so many opportunities, because computational thinking is needed. We need to be more objective, and it provides that objectivity, and it's so needed right now. Especially with the emergence of data, and you know, across industries. So there are so many opportunities for data scientists, whether it's in aerospace and defense, like Raytheon or in the health industry. And we saw with the pandemic, the utility of mathematical modeling. There are just so many opportunities. >> Yeah, there's a lot of opportunities, and that's one of the themes I think, of WiDS, is just the opportunities, not just in data science, and for women. And there's obviously even high school girls that are here, which is so nice to see those young, fresh faces, but opportunities to build your own network and your own personal board of directors, your mentors, your sponsors. There's tremendous opportunity in data science, and it's really all encompassing, at least from my seat. >> Oh yeah, no I completely agree with that. >> What are some of the things that you've heard at this WiDS event that inspire you going, we're going in the right direction. If we think about International Women's Day tomorrow, "Breaking the Bias" is the theme, do you think we're on our way to breaking that bias? >> Definitely, you know, there was a panel today talking about the bias in data, and in a variety of fields, and how we are, you know discovering that bias, and creating solutions to address it. So there was that panel. There was another talk by a speaker from Pinterest, who had presented some solutions that her, and her team had derived to address bias there, in you know, image recognition and search. And so I think that we've realized this bias, and, you know, in AI ethics, not only in these topics that I've mentioned, but also in the implications for like getting a loan, so economic implications, as well. And so we're realizing those issues and bias now in AI, and we're addressing them. So I definitely am optimistic. I feel encouraged by the talks today at WiDS that you know, not only are we recognizing the issues, but we're creating solutions >> Right taking steps to remediate those, so that ultimately going forward. You know, we know it's not possible to have unbiased data. That's not humanly possible, or probably mathematically possible. But the steps that they're taking, they're going in the right direction. And a lot of it starts with awareness. >> Exactly. >> Of understanding there is bias in this data, regardless. All the people that are interacting with it, and touching it, and transforming it, and cleaning it, for example, that's all influencing the veracity of it. >> Oh, for sure. Exactly, you know, and I think that there are for sure solutions are being discussed here, papers written by some of the speakers here, that are driving the solutions to the mitigation of this bias and data problem. So I agree a hundred percent with you, that awareness is you know, half the battle, if not more. And then, you know, that drives creation of solutions >> And that's what we need the creation of solutions. Nandi, thank you so much for joining me today. It was a pleasure talking with you about what you're doing with Raytheon, what you've done and your path with mathematics, and what excites you about data science going forward. We appreciate your insights. >> Thank you so much. It was my pleasure. >> Good, for Nandi Leslie, I'm Lisa Martin. You're watching theCUBE's coverage of Women in Data Science 2022. Stick around, I'll be right back with my next guest. (upbeat flowing music)

>>from around the globe. It's the Cube with digital coverage of AWS >>reinvent 2020 sponsored >>by Intel, AWS and our community partners. Welcome back to the cubes. Virtual coverage of AWS reinvent 2020. It's virtual this year. We're not in person, so we're doing remote interviews. Part of the three weeks we'll be covering wall to wall a lot of great conversations. News to cover and joining me today Off Fresh off the news off Andy Jackson's keynote, We have two great guests here. Jason Kapoor, senior product manager for Accelerated Computing at A. W S and eight time Medina Chief business officer, Havana Labs, which was recently acquired by Intel Folks. Thanks for coming on, gentlemen. Thank you for spending the time for coming on the key. Appreciate it. >>Thanks for having us. >>J Town. So talk about the news, actually. Uh, computers changing. It's being reinvented. That's the theme from Andy's keynote. What did Andy announced? Could you take a minute to explain the announcement? What services? What ap What's gonna be supported? What's this about? Take a minute to explain. >>Yeah, absolutely. Yeah. So today >>we >>announced our plans to launch and easy to instance based on hardware accelerators from Havana labs. We expect these businesses to be available in the first time from next year. And these air custom designed for accelerating training off deep learning models, a zoo we all know like training of deep learning models is a really competition. Aly extensive task. Oftentimes it takes too long and cost too much. And we're really excited about getting these instances out of the market as we expect for them to provide up to 40% better price performance. Thani on top of the line GPU instances, >>a lot of improvements. Why did anybody do this? Why heaven or what's the what the working backwards document tell you? What is it customers looking for here is or specific use case? >>Yeah, absolutely. So, you know, over the years, uh, the use of machine learning and deep learning has, like, really skyrocketed, right? So we're seeing companies from all the way like 14, 500 to like start ups just reinventing their business models and use using deep learning more pervasively. Right. So we have companies like Pinterest, you know, you'd use deep learning for content recommendations and object detection to Toyota Research Institute that are advancing the science behind autonomous vehicles. And there's a consistent cream from a lot of these customers that are, you know, innovating in the deep learning space that you know the cost it takes to experiment, train and optimize the deep learning models. It's too high. And, you know, they're looking at us as one of their partners to help them optimize their costs, you know, bring them as well as possible while giving them really performing products and enable them to actually bring their markets, their innovations to market as soon as possible. Right? S o. Do you answer your questions straight on your wants? The working backwards. It's a feedback from customers that they want choice on. They want our help Thio lower. Uh, the amount of compute resources and the cost it takes to train the new planning models. >>Hey, Tom, why don't you weigh in here on Havana and now part of intel? What trends are driving this? What's the motivation? Were you guys fit in? What's your view on this? >>Yeah, So Havana was founded in 2016 to deliver a I processors for the data center and cloud for training and inference deep learning models. So while building chips is hard, building, the software and ecosystem is even harder. So joining forces with intel simply helps us connect the dots. Ever since the acquisition last year, we were able to significantly boost our armed. The resource is, and now we're leveraging inter scale in number of customers and ecosystem and partner support. >>So what's the name of the product? Is there a chip name got? Was it Gowdy is the name? >>Yes, the product is man angle. >>Okay. And so it's gonna be hardware. So it's the hardware software. What's involved? Take us through the product. >>Yes. So Gandhi was designed from the ground up to do one task which is training deep learning models. To do that well, we focus the architectural to aspect efficiency and scalability. The computer architectures is a combination of fully programmable TPC tensile process, of course, and a central g M n G. These DPC course are programmable Villa W seen the machines that we designed with custom instruction, set architecture, er and special functions that will developed specifically for a I. The Gandhi cheap integrates also 32 gigabyte off H B M to memory which makes it easy to port to. For GPU developers, Gandhi is unique in integrating 10 parts of 100 gigabit Internet rocky on cheap. And this is opposed to other architectural, which use proprietary interfaces. So overall, improving the cost performance is achieved through efficiency, namely higher utilization off the computer and memory resource is on cheap and the native integration off the rocky interfaces >>J Town. This is actually interesting, as this is the theme for reinvent. We're seeing it right on stage today. Play out again another command performance by Andy Jassy. Slew of announcements. How does Gowdy fit into the AI portfolio or Amazon strategy? Because what a town saying is it sounds like he's doing the heavy lifting on all this training stuff when people want to just get to the outcome. I mean, the theme has been, just let the product do what they do kind of put stuff under the covers and just let it scale. Is that the theme here is this. >>What does this >>all fit in? Take us through how this fits into the A, I strategy for Amazon and also what what what is Havana Intel bring to the table? >>Absolutely. Yeah. So with respect to our overall strategy and portfolio units, it's relatively straightforward, right? So we're laser focused on making sure we have the broadest and deepest portfolio off services for machine learning, right? So these range from infrastructure services specifically compute networking and storage all the way up to, like, managed and all services, which come with pre trained models and customers can simply invoke them using an A P. I call right eso. So from a strategy perspective, you want to make sure that we provide a customer to a choice, uh, enable them to pick the right platform for the right use case, help them get to the Khan structure they actually want, right eso with Havana. And you know, their acquisition with Intel, we finally have access to hardware software and the ability to kind of build out a ecosystem beyond what you know judicially is being used. Which is was a GP used right eso. So the engagement with with Havana, you know, allows us to take their products and capabilities, wrap it around, and easy to instance, which is what customers will be able to launch right on doing so. We're enabling them to tap into the innovation that Teton the rest of the Havana team are working on while having a solution that is integrated with the full AWS stack. Right? So you don't you don't have to rack in stock hard. Bring your data center thes. They're gonna be available standard. Easy to instances. You can just click and launch them. Get access to software that's already pre integrated and big den and ready to go right. Eso so it actually comes down to taking their innovations, coupling it with an AWS solution and making it too easy for customers together. I've been running with the respective training the deepened models. >>Well, here is the question that I want to get to. I think everyone's on everyone's mind is how is it Gowdy different or similar than other GPU? Specifically, you mentioned the software stack on the AWS What you get the software stack inside the chip. How is this different or similar? Two other GP use. And what's the difference between the software stack versus a traditional libraries? >>So from day one, we were focused on the software experience and we were mindful in the need to make it easy for developers to use the innovations we have in the hardware. Most developers, if not all of them, are using deep learning frameworks such as tensile flowing pytorch for building their deep learning models. So God is synapse AI software suite comes integrated and optimized for tensorflow and pilotage, so we expect most developers to be able to take their existing models and with minor changes to the training strips to be able to run them on Gowdy based instances. In addition, expert developers that are familiar with writing their own kernels will be provided with food too sweet for writing their own TPC kernels that can augment the Havana provided library. >>So that's the user experience for the developers, right? That's what you're saying >>exactly, exactly, and we will provide detailed guides for developers. In doing that, Havana will provide open access to documentation library software models and left toe Havana's kita and bi directional communication with the Havana developer community. All these resources will be available concurrently with the AWS Instances launch. >>Okay, so I'm a developer. How did I get involved? It's software on git hub I use the hardware is on Amazon, obviously, in their instances. It's a new instance. Take me through the workflow develop. I'm into this. I wanna I wanna get involved. What I what am I doing? Take me >>through? Yes, I think it s so If the developer is accustomed to using GPS for training the deep learning models three experience is gonna be practically the same, right? So they'll have multiple options to get started. One of them would be, for example, to take our deep learning, Um, it's or Amazon machine images that will come integrated with software from Havana labs. Right. So customers will take the deep Learning Army and launch it on an easy to instance, featuring the gaudy accelerators. Right? So when with that, they'll have, you know, the baseline construct off software and hardware available to get up and running with right, we'll support, you know, all different types of work flows. So if customers want to use containerized solutions, thes instances will be supported R E C s and E s services. Eso using containerized kubernetes you know, thes the solution will just work on. Lastly, we also intend to support these instances through sage maker eso. Just a quick recap on stage maker. That's a manage service that does end to end that provides end to end capabilities for training, debugging, building and deploying machine learning applications. Eso these instances will also be supporting sage maker. So if you're fiddling with sage maker, you can get up and running with this. This is fairly quickly. >>It sounds like it's gonna enable a lot of action and sage maker level. Then can that layer on the use cases? I gotta ask you guys quickly, What's the low hanging fruit use case applications for this product thing? This partnership, Because you know that's gonna be the first Traction said, What are some of these applications gonna be used for? What can we expect to see? >>So typical applications would be image classifications, object detection, natural language processing, the recommendation systems. You'll find reference models in our get up for that and will be growing at least a Z you can imagine. >>Okay, where can people find more info? Give us the data. Take him in to explain. Put a plug in for how What's all the coordinates? U r l sites support how people create, Um, how people get involved. The community. >>Yeah, so customers will be able to access information on AWS websites and also on Havana Labs website. So you will be kicking off a preview early next year. Eso I would highly recommend for customers to find our product pages and signed up for already access and previous information. Utah. >>Yes, and you'll find more information on Havana. A swell a Savannah's get up over time. >>Great announcement. Congratulations. Thanks for sharing the news and some commentary on it. This is really the big theme. You know what Cove in 19 and this pandemic has shown is massive acceleration of digital transformation and having the software and hardware out there that accelerates the heavy lifting and creates value around the data. Super valuable. Thanks for for doing that. Appreciate taking the time. Thank >>you so much. >>Yeah. Thanks for having >>us. Okay, this is the cubes coverage at 80. Best reinvent next three weeks. We're here on the ground. Will remote. We're live inside the studio. We wish we could be there in person, but it's remote this year. But stay tuned. Check out silicon angle dot com. Exclusive interviews with Andy Jassy and Amazon executives and the big news covering. They're all there in one spot. Check it out. We'll be back with more coverage after this break. Thanks for watching. Yeah.

la from Las Vegas it's the cube covering AWS reinvent 2019 brought to you by Amazon Web Services and in care along with its ecosystem partners hey welcome back everyone cubes live coverage of eight of us reinvent 2019 this is the cube seventh year covering Amazon reinvent it's their eighth year of the conference and want to just shout out to Intel for their sponsorship for these two amazing sets without their support we would be able to bring our mission of great content to you I'm John Force to many men we're here with the chief of AWS the chief executive officer Andy chassis tech athlete and himself three our keynotes welcome to the cube again great to see you great to be here thanks for having me guys congratulations on a great show a lot of great buzz thank you a lot of good stuff your keynote was phenomenal you get right into you giddy up right into as you say three hours 30 announcements you guys do a lot but what I liked the new addition in the last year and this year is the band house man yeah they're pretty good they hit the Queen note so that keeps it balanced so we're going to work on getting a band for the cube awesome so if I have to ask you what's your walk-up song what would it be there's so many choices depends what kind of mood I'm in but maybe times like these by the Foo Fighters these are unusual times right now Foo Fighters playing at the Amazon intersect show they are Gandy well congratulations on the intersect you got a lot going on intersect is the music festival I'll get that in a second but I think the big news for me is two things obviously we had a one-on-one exclusive interview and you laid out essentially what looks like was gonna be your keynote it was transformation key for the practice I'm glad to practice use me anytime yeah and I like to appreciate the comments on Jedi on the record that was great but I think the transformation story is a very real one but the NFL news you guys just announced to me was so much fun and relevant you had the Commissioner of NFL on stage with you talking about a strategic partnership that is as top-down aggressive goals you could get yeah I have Roger Goodell fly to a tech conference to sit with you and then bring his team talk about the deal well you know we've been partners with the NFL for a while with the next-gen stats are they using all their telecasts and one of the things I really like about Roger is that he's very curious and very interested in technology in the first couple times I spoke with him he asked me so many questions about ways the NFL might be able to use the cloud and digital transformation to transform their various experiences and he's always said if you have a creative idea or something you think that could change the world for us just call me is it or text me or email me and I'll call you back within 24 hours and so we've spent the better part of the last year talking about a lot of really interesting strategic ways that they can evolve their experience both for fans as well as their players and the player health and safe safety initiative it's so important in sports and particularly important with the NFL given the nature of the sport and they've always had a focus on it but what you can do with computer vision and machine learning algorithms and then building a digital athlete which is really like a digital twin of each athlete so you understand what does it look like when they're healthy what and compare that when it looks like they may not be healthy and be able to simulate all kinds of different combinations of player hits and angles and different plays so that you can try to predict injuries and predict the right equipment you need before there's a problem can be really transformational so it was super excited about it did you guys come up with the idea it was the collaboration between there's really a collaboration I mean they look they are very focused on player's safety and health and it's it's a big deal for their you know they have two main constituents that the players and fans and they care deeply about the players and it's a it's a hard problem in a sport like football but you watch it yeah I gotta say it does point out the use cases of what you guys are promoting heavily at the show here of the stage maker studio which is a big part of your keynote where they have all this data right and they're dated hoarders they've the hoard data but they're the manual process of going through the data it was a killer problem this is consistent with a lot of the enterprises that are out there they have more data than they even know so this seems to be a big part of the strategy how do you get the customers to actually a wake up to the fact that they got data and how do you tie that together I think in almost every company they know they have a lot of data and there are always pockets of people who want to do something with it but when you're gonna make these really big leaps forward these transformations so things like Volkswagen is doing with they're reinventing their factories in their manufacturing process or the NFL where they're gonna radically transform how they do players health and safety it starts top-down and if they if the senior leader isn't convicted about wanting to take that leap forward and trying something different and organizing the data differently and organizing the team differently and using machine learning and getting help from us and building algorithms and building some muscle inside the company it just doesn't happen because it's not in the normal machinery of what most companies do and so it all wait almost always starts top-down sometimes it can be the commissioner or the CEO sometimes it can be the CIO but it has to be senior level conviction or it does get off the ground and the business model impact has to be real for NFL they know concussions hurting their youth pipelining this is a huge issue for them is their business model they they lose even more players to lower extremity injuries and so just the notion of trying to be able to predict injuries and you know the impact it can have on rules the impact it can have on the equipment they use it's a huge game changer when they look at the next 10 to 20 years all right love geeking out on the NFL but no more do you know off camera a 10 man is here defeated season so everybody's a Patriots fan now it's fascinating to watch you and your three-hour keynote Vernor in his you know architectural discussion really showed how AWS is really extending its reach you know it's not just a place for a few years people have been talking about you know cloud as an operation operational model it's not a destination or a location but I felt that really was laid out is you talked about breadth and depth and Verna really talked about you know architectural differentiation people talk about cloud but there are very there are a lot of differences between the vision for where things are going help us understand and why I mean Amazon's vision is still a bit different from what other people talk about where this whole cloud expansion journey but put over what tagger label you want on it but you know the control plane and the technology that you're building and where you see that going well I think that we've talked about this a couple times we we have two macro types of customers we have those that really want to get at the load level building blocks and stitch them together creatively and however they see fit to create whatever is in there in their heads and then we have this second segment of customers who say look I'm willing to give up some of that flexibility in exchange for getting 80% of the way they're much faster in an abstraction that's different from those low level building blocks in both segments of builders we want to serve and serve well and so we built very significant offerings in both areas I think when you look at micro services you know some of it has to do with the fact that we have this very strongly held belief born out of several years at Amazon where you know the first seven or eight years of Amazon's consumer business we basically jumbled together all of the parts of our technology and moving really quickly and when we wanted to move quickly where you had to impact multiple internal development teams it was so long because it was this big ball this big monolithic piece and we got religion about that and trying to move faster in the consumer business and having to tease those pieces apart and it really was a lot of the impetus behind conceiving AWS where it was these low-level very flexible building blocks that don't try and make all the decisions for customers they get to make them themselves and some of the micro services that you saw Verner talking about just you know for instance what we what we did with nitro or even what we do with firecracker those are very much about us relentlessly working to continue to to tease apart the different components and even things that look like low-level building blocks over time you build more and more features and all of a sudden you realize they have a lot of things that are they were combined together that you wished weren't that slowed you down and so nitro was a completely reimagining of our hypervisor and virtualization layer to allow us both to let customers have better performance but also to let us move faster and have a better security story for our customers I got to ask you the question around transformation because I think it all points to that all the data points you got all the references goldman-sachs on stage at the keynote Cerner and the healthcare just an amazing example because I mean this demonstrating real value there there's no excuse I talked to someone who wouldn't be named last night and then around the area said the CIA has a cost bar like this cost up on a budget like this but the demand for mission based apps is going up exponentially so there's need for the cloud and so seeing more and more of that what is your top-down aggressive goals to fill that solution base because you're also very transformational thinker what is your what is your aggressive top-down goals for your organization because you're serving a market with trillions of dollars of span that's shifting that's on the table a lot of competition now sees it too they're gonna go after it but at the end of the day you have customers that have that demand for things apps yeah and not a lot of budget increase at the same time this is a huge dynamic what's your goals you know I think that at a high level are top-down aggressive goals so that we want every single customer who uses our platform to have an outstanding customer experience and we want that outstanding customer experience in part is that their operational performance and their security are outstanding but also that it allows them to build and it build projects and initiatives that change their customer experience and allow them to be a sustainable successful business over a long period of time and then we also really want to be the technology infrastructure platform under all the applications that people build and they were realistic we know that that you know the market segments we address with infrastructure software hardware and data center services globally are trillions of dollars in the long term it won't only be us but we have that goal of wanting to serve every application and that requires not just the security operational performance but also a lot of functionality a lot of capability we have by far the most amount of capability out there and yet I would tell you we have three to five years of items on our roadmap that customers want us to add and that's just what we know today well and any underneath the covers you've been going through some transformation when we talked a couple years ago about how serverless is impacting things I've heard that that's actually in many ways glue behind the two pizza teams to work between organizations talk about how the internal transformations are happening how that impacts your discussions with customers that are going through that transformation well I mean there's a lot of a lot of the technology we build comes from things that we're doing ourselves you know and that we're learning ourselves it's kind of how we started thinking about microservices serverless - we saw the need we know we would have we would build all these functions that when some kind of object came into an object store we would spin up compute all those tasks would take like three or four hundred milliseconds then we spin it back down and yet we'd have to keep a cluster up in multiple availability zones because we needed that fault tolerance and it was we just said this is wasteful and that's part of how we came up with lambda and that you know when we were thinking about lambda people understandably said well if we build lambda and we build the serverless event-driven computing a lot of people who are keeping clusters of instances aren't going to use them anymore it's going to lead to less absolute revenue for us but we we have learned this lesson over the last 20 years at Amazon which is if it's something it's good for customers you're much better off cannibalizing yourself and doing the right thing for customers and being part of shaping something and I think if you look at the history of Technology you always build things and people say well that's gonna cannibalize this and people are gonna spend less money what really ends up happening is they spend spend less money per unit of compute but it allows them to do so much more that the ultimately long-term end up being you know more significant customers I mean you are like beating the drum all the time customers what they say we implement the roadmap I got that you guys have that playbook down that's been really successful for you yeah two years ago you told me machine learning was really important to you because your customers told what's the next tranche of importance for customers what's on top of mine now as you look at this reinvent kind of coming to a close replays tonight you had conversations your your tech a fleet you're running around doing speeches talking to customers what's that next hill from from my fist machine learning today there's so much I mean that's not it's not a soup question you know I think we're still in this in the very early days of machine learning it's not like most companies have mastered yet even though they're using it much more than they did in the past but you know I think machine learning for sure I think the edge for sure I think that we're optimistic about quantum computing even though I think it'll be a few years before it's really broadly useful we're very enthusiastic about robotics I think the amount of functions are going to be done by these robotic applications are much more expansive than people realize it doesn't mean humans won't have jobs they're just going to work on things that are more value-added I thought we're believers in augmented and virtual reality we're big believers and what's going to happen with voice and I'm also I think sometimes people get bored you know I think you're even bored with machine learning maybe already but yet people get bored with the things you've heard about but I think just what we've done with the chips you know in terms of giving people 40% better price performance in the latest generation of x86 processors it's pretty unbelievable and the difference in what people are going to be able to do or just look at big data I mean big date we haven't gotten through big data where people have totally solved it the amount of data that companies want to store process and analyze is exponentially larger than it was a few years ago and it will I think exponentially increase again in the next few years you need different tools the service I think we're not we're not for with machine learning we're excited to get started because we have all this data from the video and you guys got sage maker yeah we call it a stairway to machine learning heaven we start with the data move up what now guys are very sophisticated with what you do with technology and machine learning and there's so much I mean we're just kind of again in this early innings and I think that it was soaked before sage maker was so hard for everyday developers and data scientists to build models but the combination of sage maker and what's happened with thousands of companies standardizing on it the last two years Plus now sage maker studio giant leap forward we hope to use the data to transform our experience with our audience and we're on Amazon Cloud I really appreciate that and appreciate your support if we're with Amazon and Instant get that machine learning going a little faster for us a big that'll be better if you have requests so any I'm you talked about that you've got the customers that are builders and the customers that need simplification traditionally when you get into the you know the heart of the majority of adoption of something you really need to simplify that environment but when I think about the successful enterprise of the future they need to be builders yeah so has the model flipped if you know I normally would said enterprise want to pay for solutions because they don't have the skill set but if they're gonna succeed in this new economy they need to go through that transformation that yeah so I mean are we in just a total new era when we look back will this be different than some of these previous waves it's a it's a really good question Stu and I I don't think there's a simple answer to it I think that a lot of enterprises in some ways I think wish that they could just skip the low level building blocks and and only operate at that higher level abstraction it's why people were so excited by things like sage maker or code guru or Kendra or contact lens these are all services that allow them to just send us data and then run it on our models and get back the answers but I think one of the big trends that we see with enterprises is that they are taking more and more of their development in-house and they are wanting to operate more and more like startups I think that they admire what companies like Airbnb and Pinterest and slack and and you know Robin Hood and a whole bunch of those companies stripe have done and so when you know I think you go through these phases and errors where there are waves of success at different companies and then others want to follow that success and and replicate and so we see more and more enterprises saying we need to take back a lot of that development in-house and as they do that and as they add more developers those developers in most cases like to deal with the building blocks and they have a lot of ideas on how they can create us to creatively stitch them together on that point I want to just quickly ask you on Amazon versus other clouds because you made a comment to me in our interview about how hard it is to provide a service that to other people and it's hard to have a service that you're using yourself and turn that around and the most quoted line in my story was the compression algorithm there's no compression outliving for experience which to me is the diseconomies of scale for taking shortcuts yeah and so I think this is a really interesting point just add some color comments or I think this is a fundamental difference between AWS and others because you guys have a trajectory over the years of serving at scale customers wherever they are whatever they want to do now you got micro services it's even more complex that's hard yeah how about that I think there are a few elements to that notion of there's no compression algorithm I think the first thing to know about AWS which is different is we just come from a different heritage in a different background we sweep ran a business for a long time that was our sole business that was a consumer retail business that was very low margin and so we had to operate a very large scale given how many people were using us but also we had to run infrastructure services deep in the stack compute storage and database in reliable scalable data centers at very low costs and margins and so when you look at our our business it actually today I mean it's it's a higher margin business in our retail business the lower margin business and software companies but at real scale it's a it's a high-volume relatively low margin business and the way that you have to operate to be successful with those businesses and the things you have to think about and that DNA come from the type of operators that we have to be in our consumer retail business and there's nobody else in our space that does that you know the way that we think about cost the way we think about innovation and the data center and and I also think the way that we operate services and how long we've been operating services of the company it's a very different mindset than operating package software then you look at when you think about some of the issues and very large scale cloud you can't learn some of those lessons until you get two different elbows of the curve and scale and so what I was telling you is it's really different to run your own platform for your own users where you get to tell them exactly how it's going to be done but that's nothing really the way the real world works I mean we have millions of external customers who use us from every imaginable country and location whenever they want without any warning for lots of different use cases and they have lots of design patterns and we don't get to tell them what to do and so operating a cloud like that at a scale that's several times larger the next few providers combined is a very different endeavor and a very different operating rigor well you got to keep raising the bar you guys do a great job really impress again another tsunami of announcements in fact you had to spill the beans early with quantum the day before the event tight schedule I gotta ask you about the music festival because I think there's a really cool innovation it's the inaugural intersex conference yeah it's not part of replay which is the concert tonight right it's a whole new thing big music act you're a big music buff your daughter's an artist why did you do this what's the purpose what's your goal yeah it's an experiment I think that what's happened is that reinvent has gotten so big with 65,000 people here that to do the party which we do every year it's like a thirty five forty thousand person concert now which means you have to have a location that has multiple stages and you know we thought about it last year when we were watching it and we said we're kind of throwing like a four hour music festival right now there's multiple stages and it's quite expensive to set up that set for our partying we said well maybe we don't have to spend all that money for four hours in the rip it apart because actually the rent to keep those locations for another two days is much smaller than the cost of actually building multiple stages and so we we would try it this year we're very passionate about music as a business and I think we are I think our customers feel like we throw in a pretty good music party the last few years and we thought we were trying at a larger scale as an experiment and if you look at the economics the headliners real quick the Foo Fighters are headlining on Saturday night Anderson Park and the free Nashville free Nationals Brandi Carlile Shawn Mullins Willie Porter it's a good set Friday night it's back in Kacey Musgraves so it's it's a really great set of about 30 artists and we're hopeful that if we can build a great experience that people want to attend that we can do it it's scale and it might be something that you know both pays for itself and maybe helps pay for reinvent to overtime and you know I think that we're also thinking about it as not just a music concert and festival the reason we named it intersect is that we want an intersection of music genres and people and ethnicities and age groups and art and Technology all there together and this will be the first year we try it it's an experiment and we're really excited about I'm gone congratulations all your success and I want to thank you we've been seven years here at reinvent we've been documenting the history two sets now once-dead upstairs so appreciate a cube is part of reinvent you know you guys really are a part of the event and we really appreciate your coming here and I know people appreciate the content you create as well and we just launched cube 365 on Amazon Marketplace built on AWS so thanks for letting us cool build on the platform appreciate it thanks for having me guys Jesse the CEO of AWS here inside the cube it's our seventh year covering and documenting they're just the thunderous innovation that Amazon is doing they're really doing amazing work building out the new technologies here in the cloud computing world I'm John Force too many men be right back with more after this short break [Music]

>> Live from Las Vegas, it's theCUBE Covering Informatica World 2019 Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019 here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Ariel Kelman. He is the VP, Worldwide Marketing at AWS. Thank you so much for coming on theCUBE. >> Thanks so much for having me on today. >> So let's start out just at ten thousand feet and talk a little bit about what you're seeing as the major cloud and AI trends and what your customers are telling you. >> Yeah, so I mean, clearly, machine learning and AI is really the forefront of a lot of discussions in enterprise IT and there's massive interest but it's still really early. And one of the things that we're seeing companies really focused on now is just getting all their data ready to do the machine learning training. And as opposed to also, in addition I mean, training up all their people to be able to use these new skills. But we're seeing tons of interest, it's still very early, but you know one of the reasons here at Informatica World is that getting all the data imported and ready is, you know, it's almost doubled or tripled in importance as it was when people were just trying to do analytics. Now they're doing machine learning as well. You know, we're seeing huge interest in that. >> I want to get into some of the cloud trends with your business, but first, what's the relationship with Informatica, and you know we see them certainly at re:Invent. Why are you here? Was there an announcement? What's the big story? >> I mean, we've been working together for a long time and it's very complementary products and number varies. I think the relationship really started deepening when we released Redshift in 2013, and having so many customers that wanted to get data into the cloud to do data we're housing, we're already using Informatica in, to help get the data loaded and cleansed and so really they're one of the great partners that's fueling moving data into the cloud and helping our customers be more successful with Redshift. >> Yeah, one of the things I really admire about you guys is that you're very customer centric. We've been following Amazon as you know since their, actually second reinvent, Cube's been there every time, and just watching the growth, you know, Cloud certainly has been a power source for innovation, SAS companies that are born in the cloud have exponentially scaled faster than most enterprises because they use data. And so data's been a heart of all the successful SAS businesses, that's why start ups gravitated to the Cloud right away. But now that you guys got enterprise adoption, you guys have been customer centric and as you listen to customers, what are you guys hearing from that? Because the data on premises, you've got more compliance, you've got more regulation, you've got-- news today-- more privacy and now you've got regions, countries with different laws. So the complexity around even just regulatory, nevermind tech complexity, how are you guys helping customers when they say, you know what, I want to get to the cloud, love Amazon, love the cloud, but I've got my, I've got to clean up my on param house. >> Yeah, I would say like a lot, if you look at a lot of the professional services work that we do, a lot of it is around getting the company prepared and organized with all their data before they move to the cloud: segmenting it, understanding the different security regulatory requirements, coming up with a plan of what they need, what data they're going to maybe abstract up, before they load it, and there's a lot of work there. And, you know, we've been focused on trying to help customers.. >> And is there a part in you're helping migrate to the cloud, is that.. >> Yeah, there's technology pieces, companies like Informatica helping to extract and transform and load the data and on data governance policies. But then also, for a lot of our systems integrator partners, Cognizant, Accenture, Deloitte-- they're very involved in these projects. There's a lot of work that goes on; a lot of people don't talk about just before you can even start doing the machine learning, and a lot of that's getting your data ready. >> So how, what are some of the best practices that have emerged in working with companies that, as you said, there's a lot of pre-work that needs to be done and they need to be very thoughtful about about sort of getting their data sorted. >> Well I think the number one thing that I see and I recommend is to actually first take a step back from the data and to focus on what are the business requirements of, what questions are you trying to answer, let's say with machine learning, or with data science advanced analytics, and then back out the data from that. What we see a lot of, you know companies sometimes will have it be a data science driven project. Okay, here's all the data that we have, let's put it in one place, when you may not be spending time proportionate to the value of the data. And so that's one of the key things that we see, and to come up-- just come up with a strong plan around what answers you're, what business questions you're trying to answer. >> On the growth of Amazon, you guys certainly have had great record numbers, growth, even in the double digit kind of growth you're seeing on top of your baseline has been phenomenal. Clearly number one on the cloud. Enterprise has been a big focus. I noticed that on the NHL, your logo's on the ice during the playoffs; you've got the Statcast. You guys are creating a lot of aware-- I see a lot of billboards everywhere, a lot of TV ads. Is that part of the strategy is to get you guys more brand awareness? What's the.. >> We're trying, you know, it's part of our overall brand awareness strategy. What we're trying to do is to help, we're trying to communicate to the world how our customers are being successful using our technology, specifically machine learning and AI. It's one of these things where so many companies want to do it but they say, well, what am I supposed to use it for? And so, you know, one of, if you dumb down what marketing is at AWS, it's inspiring people about what they can run in the cloud with AWS, what use cases they should consider us for, and then we spend a lot of energy giving them the technical education and enablement so they can be successful using our products. At the end of the day, we make money when our customers are successful using our products. >> One of the hot products was SageMaker, we see in that group, AI's gone mainstream. That's a great tail wind for you guys because it kind of encapsulates or kind of doesn't have to get all nerdy about cloud, you know, infrastructure and SAS. AI kind of speaks to many people. It's one of the hottest curriculums and topics in the world. >> Yeah, and with SageMaker, we're trying to address a problem that we see in most of our customers where the everyday developer is not, does not have expertise in machine learning. They want to learn it, so we think that anything we can do to make it easier for every developer to ramp up on machine learning the better. So that's why we came up with SageMaker as a platform to really make all three stages of machine learning easier: getting your data prepared for training, training in optimized models, and then running inference to make the predictions and incorporate that into people's applications. >> One of the themes that's really emerging in this conversation is the need to make sure developers are ready and that your people are skilled up and know what they need to know. How are, how is AWS thinking about the skills gap, and what are you doing to remedy it? >> Yeah, a couple things. I mean, we're really, like a lot of things we do, we'll say what are all the ways we can attack the problem and let's try and help. So, we have free training that we've been creating online. We've been partnering with large online training firms like Udacity and Coursera. We have an ML solutions lab that help companies prototype, we have a pretty significant professional services team, and then we're working with all of out systems integrators partners to build up their machine learning practices. It's a new area for a lot of them and we've been pushing them to add more people so they can help their customers. >> Talk about the conferences, you have re:Invent, the CORE conference, we've been theCUBE there. We've just also covered London, Amazon's Web Services summit, and 22,000 registered, 14,000 showed up. Got huge global reach now. How do you keep up with this? I mean it's a... >> Well we're trying to help our customers keep up with all the technology. I mean, really, we have about, maybe 25 or so of these summits around the world-- usually around two days, several thousand people, free conferences. And what we're trying to do is >> They're free? >> The summits are free and it's like, we introduce so much new technology, new services, deeper functionality within our exiting services, and our customers are very hungry to learn the latest best practices and how they can use these, and so we're trying to be in all the major areas to come in and provide deep educational content to help our customers be more successful. >> And re:Invent's coming around the corner. Any themes there early on, numbers wise? Last year you had, again, record numbers. I mean at some point, is Vegas too small >> Yeah, we had over 50,000 people. We're going to have even more, and we've been expanding to more and more locations around Las Vegas and you know we're going to keep growing. There's a lot of demand. I mean, we want to be able to provide the re:Invent experience for as many people as want to attend. >> What's the biggest skill set, you know the folks graduating this month, my daughter's graduating from Cal Berkeley, and a lot of others are graduating >> Congratulations >> high school. Everyone wants to either jump into some sort of data related field, doesn't have to be computer science, those numbers are up. What's your view of skill sets that are needed right now that weren't in curriculum, or what pieces of curriculum should people be learning to be successful if machine learning continues to grow from helping videos surface to collecting customer data. Machine learning's going to be feeding the AI applications and SAS businesses. >> Yeah, I mean look, you just forget about machine learning, you go to a higher level. There's not enough good developers. I mean, we're in a world now where any enterprise that is going to be successful is going to have their own software developers. They're going to be writing their own software. That's not how the world was 15 years ago. But if you're a large corporation and you're outsourcing your technology, you're going to get disrupted by someone else who does believe in custom software and developers. So the demand for really good software engineers, I mean we deal with all the time, we're hiring. It is always going to outstrip supply. And so, for young people, I would encourage them to start coding and to not be over reliant on the university curriculums, which don't always keep pace with, you know, with the latest trends. >> And you guys got a ton of material online too, you can always go to your site. Okay, on the next question around, as someone figures out, okay, enterprise versus pure SAS, you guys have proven with the Cloud that start ups can grow very fast and then the list goes on: AirBnB, Pinterest, Zoom Communications, disrupting existing big, mature markets by having access to the data. So how do you talk about customers when you say, hey, you know, I want to be like a SAS company, like a consumer company, leverage data, but I've got a lot of stuff on premise. So how do I not make that data constrained? How do you guys feel about that conversation because that seems to be the top conversation here, is you know, it's not to say be consumer, it's consumer-like. Leveraging data, cause if data's not into AI, there's no, AI doesn't work, right? So >> Right >> It can't be constrained by anything. >> Well, you know, you talk to all these companies and at first they don't even know what they don't know in terms of what is that data? And where is it? And what are the pieces that are important? And so, you know, we encourage people to do a good amount of strategy work before they even start to move bits up to the cloud. And of course, then we have a lot of ways we can help them, from our Snowball machines that they can plug in, all the way to our Snowmobile, which is the semi truck that you can drive up to your data center and offload very large amounts of data and drive it over to our data centers. >> One of the things that is trending-- we had Ali from Data Bricks talk about, he absolutely believes a lot of the same philosophies you guys do-- data in the cloud. And one of his arguments was is that there's a lot of data sets in these marketplaces now where you can really leverage other people's data, and we see that on cybersecurity where people are starting to share data, and Cloud is a better model for that than trying to ship drives around, and there's a time for Snowball, I get that, and Snowmobile, the big trucks for large ingestion into the cloud, but the enterprise, this is a new phenomenon. No one really shared a lot in the old days. This is a new dynamic. Talk about that, is it-- >> I mean, sharing, selling, monetizing data. If there's something that is important, there will be a market for it. And I think we're seeing that just the hunger, everything from enterprises to startups, that want more data, whether it's for machine learning to train their models, or it's just to run analytics and compare against their data sets. So I think the commercial opportunity is pretty large. >> I think you're right on that. I think that's a great insight. I mean, no one ever thought about data as a service from our data set standpoint, 'cause data sets feed machine learning. All right, so let's do, give the plug on what's going on with AWS. What's new, what's on your plate, what's notable. I mean I love the NHL, I couldn't resist that plug for you being a hockey fan. But what's new in your world? >> Um, you know, we're, we're in early planning stages on our re:Invent conference, our engineers are hard at work on a lot of new technology that we're going to have ready between now and our re:Invent show. You know, also we're, my team's been doing a lot of work with the sports organizations. We've had some interesting machine learning work with major league baseball. They rolled out this year a new machine learning model to do stolen base predictions. So, you can see on some of the broadcasts, as a runner goes past first base, we'll have a ticker that will show what the probability is that they'll be successful stealing second base if they choose to run. Trying to make a little more entertaining all those scenes we've seen in the past of the pitcher throwing the ball back to first, trying to use AI machine leaning to give a little bit more insight into what's going on. >> And that's the Statcast. Part of that's the Statcast >> That's Statcast, yeah >> And you got anything new coming around that besides that new.. >> Yeah, I think that yeah, major league baseball is hard at work on some new models that I think will be announced fairly soon. >> All right, to wrap up Informatica real quick, an announcement here, news coming I hear. How are you guys working with Informatica in the field? Is there any, can you share more about relationship >> Yeah I mean I think we're going to have an announcement a little bit later today, I mean it's around the subject we've been talking about: making it easier for customers to, you know, be successful moving their data to the Cloud so that they can start to benefit from the agility, the speed and the cost savings of data analytics and machine learning in the Cloud. >> And so when you're working with customers, I mean, because this is the thing about Amazon. It is a famously innovative, cutting edge company, and when you talk about the hunger that you describe, that these customers, isn't it just that they want to be around Amazon and kind of rub shoulders with this really creative, thinking four steps ahead kind of company. I mean how do you let your innovation rub off on these customers? >> I mean there's a couple ways We do, one of the things we've done recently is these innovation workshops. We have this thing we talk about a lot this working backwards process where we force the engineers to write a press release before we'll green light the product because we feel like if you can't clearly articulate the customer benefit, then we probably shouldn't start investing, right? And so we, that's one of the processes that we use to help us innovate better, more effectively and so we've been walk-- we walk customers through this. We have them come, you know there's an international company that I was, part of one of the efforts we did in Palo Alto last year where we had a bunch of their leadership team out for two days of workshops where we worked a bunch of ideas through, through our process. And so we do some of that but the other area is we try and capture area where we think that we've innovated in some interesting way into a service that then customers can use. Like Amazon Connect I think is a good example of it. This is our contact center call routing technology and you know, one of the things Amazon's consumer business is known for is having great customer support, customer service, and they spent a lot of time and energy making sure that calls get routed intelligently to the right people, that you don't sit on hold forever, and so we figure we're probably not the only company that could benefit from that. Kind of like with AWS, when we figure out how to run infrastructure securely and high performance and availability, and so we turn that into a service and it's become a very successful service for us. A lot of companies have similar contact center problems. >> As a customer, I can attest to being on hold a lot. Ariel, thank you so much for coming on theCUBE. It's been great talking to you. >> I appreciate it. Thank you. >> Thanks for coming out, appreciate it. >> I'm Rebecca Knight, for John Furrier. You are watching theCUBE. Stay tuned. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE covering Magento Imagine 2019, brought to you by Adobe. >> Hey, welcome back to Las Vegas. Lisa Martin with Jeff Frick. We're coming to you live from Magento Imagine 2019. Welcoming to theCUBE for the first time Gary Specter, the VP of Commerce, Sales and Customer Success at Adobe. Gary, welcome to theCUBE! >> Thank you, I'm thrilled to be here. >> So there's about 3,500 people here, you guys have, from 60-plus countries. >> Gary: That's right. >> I think 100 sessions, 150 speakers. People coming down from ceilings, up from the floor. >> Gary: And we're streaming live. >> First ever live stream, yes. >> On the general set, first ever. That's right. Someone tweeted out that there are 35,000 people watching. >> Marketing probably loved that and then had a heart attack at the same time. >> Yeah, I'm sure they did. Not exactly accurate but I'll take what I can get. >> Tell us about the event, the spirit of the event. This is kind of, yesterday evening things kicked off. What of some of the things you've hearing from customers, partners, developers? >> So, I think the thing that's really unique about Imagine is that it does involve partners, the community, developers, along with Magento and our customers and our prospects. And it makes it really different because the developer community and our partners are so passionate about Magento. And I think everybody feels really good about the marriage of Adobe and Magento. You had technologies that were very well aligned, not overlapping. It enables us to extend the capabilities of what we can do from both the Adobe side or the Magento side. I like to say that the color palette got a lot bigger, and I think there's a lot of excitement around that and what that means to all of these people, developers, partners, the ecosystem, customers, prospects. So the energy is really high. I think obviously people are, what's next? And what does this mean for Magento? And I think it means investment, I think it means a higher rate of agility and an expansion of what we do. Acceleration of our roadmap. So I think people are very, very positive. And this is my fourth Imagine, and it's really, I've never felt the energy higher than at this Imagine. So it's exciting for me. >> Gary, one of the interesting ways that you talked about community and everybody wants developer communities, right? And you guys also have open source as a passion. But you phrased it in a way I've never heard before, is that you like going to sleep at night knowing that there's a whole bunch of other CEOs betting their business-- >> That's right. >> On this platform. >> Yeah. >> And it's not just you guys, so it's a really different way to think about open source. We often think of the developers and there's smart people outside your four walls contributing code. But it's not often couched in terms of the business terms. >> No. >> If there's are other people betting their business, thinking about how are they gonna help grow your business by building their business on top of Magento. >> That's what drives the passion of the community. These people realize that there's a symbiotic relationship here. If Magento successful, the ability for them to be successful is very broad. And if Magento's not successful, then you have to ask yourselves did I make the right bet? So a lot of our tech partners have build these great solutions on top of Magento, and it's a partnership. And you don't have that anywhere else, and again, I sleep better at night, to your point. I don't know where you got that quote, but it's actually mine, it's phenomenal. >> No, no, I think I got it from your Argentina 2017 talk perhaps. >> Actually, it's true. I know that all of these tech partners, these CEOs, they have my back. I'd like them to know I have theirs. And I don't think Adobe has any, there's no reason or rhyme why that would ever change. I think Adobe will enhance it. And I think that's why there so much excitement here. >> Well, and it's really a validation and what we talked about before, the prior segment, was now to bring the marketing tools, and the AI and all the power that's in that big building in San Jose, free the commerce transaction, really, to your point, adds so much more horse power to the total solution. >> Like I said, color palette just got a lot bigger. There's so many more things that we can do and so many more colors we can use to create these great experience for our brands and our customers, that we could've done before but it was a lot of work, but now we've got all of the makings of a platform that will enable that and we're already pretty far along in taking the Adobe experience cloud and making that work. And I'm just really excited about the future and what this offers for our customers and our brands. >> We've heard a number of guests that talk about just what you were referring to a minute ago, and that was really this symbiosis of Adobe, the power that Adobe brings, the data that Adobe brings, along with Magento, So a new Adobe commerce buy was just launched a couple of months ago, at Adobe Summit powered by Magento Commerce, but you look at it as analytics, advertising, marketing, commerce, fundamentals for managing what is a changing and highly demand customer experience, 'cause we want more and more things accessible from right here. So some of the feedback from customers, partners, developers since that announcement and now going "Ahh, okay now I can actually touch and see and play with this two symbiosis machines coming together." >> Yeah, I think it's not a hard thing to get. I think when the acquisition first happened, there's a little let's wait and see and make sure they get it right. And I think what I feel today, or what people have given to me today is the feedback that they're believers. They know that we're gonna execute on this strategy, and this strategy is gonna allow us to extend our lead on our competitors, which in return, allows these brands and these commerce players to extend their lead on their competitors. >> Let's talk about the small/medium business folks for a minute. When the announcement was made last year, the intention, right after Imagine 2018 I believe, for Adobe to acquire Magento, and then right after they acquired Marketo, there was some concern for is Adobe gonna kind of shift what Magento has been doing, so successfully for so long, away from focusing on those smaller merchants to the enterprise folks. Yesterday and today, we heard some great, exciting announcements with what you guys are doing with Amazon Sales Channel, with Google Shopping, and it sounded like the small and medium business size folks were going "Yes, this is what we need." Talk to us a little bit about that. >> I mean, you mentioned two, along with PWA and some of the other things that we're doing. While these can be leveraged in the enterprise, they were built for the mid-market in the SMB space. And there is no doubt that Adobe and Magento both understand how important SMB and the mid-market is. And in fact, we've seen acceleration in the SMB space since the acquisition, from the Magento side of the house. And Adobe is fully committed and knows that there's market share there to be had. And the application or the business problems that we solve at the enterprise, are still applicable for the mid-market and the SMB space. They're handled in a little bit different of a manner, but they have same aspirations. And the solution's gonna be able, when you look across everything that you're gonna be able to do, it plays for both markets. And Adobe has an incredible opportunity to really drive market share in this mid-market. They don't have a big footprint there today. Even if you capture just a small portion of it, and its our plans to capture a large portion of it, but even a small portion of it is gonna make a big impact on Adobe. So I think that we will see acceleration in the mid-market and in the SMB space with what we're doing, what we're developing together, and the different types of products that we can offer to those markets that Adobe has in its broader portfolio. >> And of course on the enterprise side, what we don't see here that we saw at Adobe Summit a couple weeks back are some of the really big integrators who have huge practices built around and on top of the Adobe tool set that now you get to leverage. I'm sure you're pretty excited about as running field. There's, again, a whole nother group of people, not necessarily CEOs, but managing partners, who have bet their jobs, bet their livelihood, bet their practices on this, and now you getta take advantage of those resources as well. >> Absolutely, and I think that a lot of the large integrators and partners, I think everybody's starting to understand that commerce is very different now than it was five or 10 years ago, right? I call it bite small, chew fast. And HP is a great example, where they started in some of the smaller APAC countries and then went to Brazil, and they're looking at the US last, but they're taking it a step at a time. One country, one country, one country. And a lot of our big retailers or brands that wanna expand globally are doing the same things, or companies that have portfolios of brands, one at a time. Bite small, chew fast. Launch, be successful, launch, be successful. And I think the SIs, including the large partners, understand that and they're changing the way that they look at businesses holistically. So I think right time, right place. >> Yeah, we had Gillian Campbell from HP on right after her keynote this morning, and it was an interesting kinda POC program. And I said what was some of the market dynamics that identified APAC as the right market to start in. And part of that, I think, was that from a historical legacy perspective of using Magento on the HP Inc. side. But some of the things I found interesting to them was that leveraging the data to understand the cultural e-commerce differences snd how different cultures interact with different social media platforms or purchasing platforms differently, and how important it is to really understand those commerce patterns and start to drive conversions from there there and then go success, roll it out, rinse and repeat. >> And she nailed it right? I mean, buy online, pick up in store versus having it delivered to your home, if you live in the middle of India, what's the reality of you getting that delivered in an hour? And if you look at country like Russia, which is very spread out, right, so there's not a high density outside of a lot of their major cities and you have a lot of the same issues. If you're gonna have it ship to your home, how long is it gonna take? It might be easier just to go pick it up in the store. And I think it's different in every region. And it's good to be able to have access to that data to get a good read on what are the things our customers want specifically to drive the experience they need within that region. >> Right, key for a company whether it's something the size of an HP Inc. or not, to be able to scale globally, but also have that sort of local market adaptation where you're able to react, understand the preferences in your markets, and deliver exactly what those consumers want. So having a tool like Magento as the power to enable that global scale regional adaptation, it's a driver. >> And I think you start to add complexity when you look at do they use their phone, do they use their computer? Do they use social networks and buy buttons? I have an interesting dynamic in my own house where I've got a 13-year-old, and the way that she would shop online is different than the way that my wife would shop online, which is very different from how I would shop online. I browse and go to the store. My wife uses her computer. My daughter shops on Pinterest, or Instagram, or Facebook. Very different journeys for the three of us, and we could be buying the same thing, and we're all gonna do it differently. So it crosses generations as well. >> So, Gary, it feels like kinda the dust has settled post-Adobe acquisition where everybody feels kinda comfortable, and it's been a year and everything didn't go bananas. So as you look forward now, after things have kinda settled, what are some of your priorities over the next year, If we sit down a year from now, what are you working on? >> I can tell you that for me, the biggest priority for me is to make sure that the mid-market and the SMB flywheel is effective, the way that we go to market, the way that we target that segment. And it's not that I'm not interested in the enterprise. I'm extremely interested in the enterprise. But we have a lot of people that are working on the enterprise. And Adobe doesn't have deep domain expertise around the mid-market. But with Marketo and Magento, you now do. So for me personally, I wanna make sure that that flywheel is well-run, it's well-oiled, it's set up for success, that operationally, the things that we do to drive market share in that segment run as effectively as the rest of Adobe on the enterprise side. It's a new sales motion for Adobe. But the good news is I think Adobe understands that. We understand that as a company, and I think over the next year, for me, that's where my focus is gonna be. >> So if we keep looking out to the next year, this is your fourth Magento Imagine. >> It is. >> Is there gonna be a Magento Imagine 2020? >> So I will tell you that there will be an Imagine 2020, and I will share details around that Wednesday. I've been asked to help close Imagine out, and when I do, I will be thrilled to announce our plans for Imagine 2020. >> So can folks watch that on the livestream tomorrow, Wednesday, that 15th? >> They can. >> Are you gonna be coming up from the floor, the ceiling? >> I think I'm probably just gonna dance on out. I have been invigorated, I love being here. Imagine is the one opportunity every year where I come out of this thing just feeling really good about the opportunities that we had ahead of us. And by Wednesday, although tired, I'm usually really happy to be going back and getting in the field with my teams and just driving opportunity. And I think we had an amazing one. >> Well, we'll be all watching. Is it imagine.magento.com to watch the livestream ? Or magento.imagine.com. go to to the Magento.com site, Wednesday tomorrow in the afternoon, you're gonna be able to hear more about what's to come next year. Gary, thank you so much for giving us time today. >> Thanks for having me, enjoy it. >> Our pleasure. >> It's great to meet you all. >> Excellent >> Thank you. >> For Jeff Frick, I'm Lisa Martin. Tou're watching theCUBE live from Magento Imagine 2019 from Vegas. Thanks for watching. (upbeat music)

(electronic music) >> Welcome back to London everybody, this is Dave Vellante with theCUBE, the leader in tech coverage. We're here with a special session in London, we've been following the career of Teresa Carlson around, we asked, "hey, can we come to London to your headquarters there and interview some of the leaders and some of the startups and innovators both in public sector and commercial?" Andy Isherwood is here, he's the managing director of AWS EMEA. Andy, thanks for coming on theCUBE. >> Dave, great to be here, thank you very much for your time. >> So you're about a year in, so that's plenty of time to get acclimated, what are your impressions of AWS and then we'll get into the market? >> Yeah, so it's nearly a year and a half actually, so time definitely goes pretty quickly. So I'd say it's pretty different, I'd say probably a couple of things kind of jump out at me. One is, I think we just have a startup mentality in everything we do. So, y'know, if you think about everything we do kind of works back from the customer and we really feel like a kind of startup at heart. And we always say, y'know, within the organization, we should also make it feel like day one. If we get to day two, y'know, the game's over. So we always try and make day one something that's kind of relevant in what we're doing. I think the second thing is customer obsession. I think we are truly customer obsessed. And you could say that most organizations actually say, y'know, they're customer obsessed. I'd say we're truly customer obsessed in everything we do so if you think about our re:Invent program, if you think about, y'know, London, the summit coming up, what you will notice is that there will be customers everywhere, speaking about their experiences and that's really important. So we start with the customer and we always work back. So super important that we never forget that and if you think about how we develop our services, they start with the customer. We don't go out like a product company would and make great products and sell them. We start with the customer, work back, develop the solutions and then let the customer use them, and we iterate on those developments. So I'd say it's pretty different in those two aspects. I'd say the other thing is, it's just hugely relevant. Every customer I go into, and I've seen hundreds of customers in the last year and a half, were hugely relevant. Y'know, we are at the heart of what people want to do and need to do, which makes it important. >> Yeah, so we've been following the career of Andy Jassy for years and we've learnt about the Working Backwards documents, certainly you guys are raising the bar all the time, is sort of the mantra, and yeah, customer centricity, you said it's different, y'know, we do over a hundred events every year and every company out there talks about, "we're focused on the customer", but what makes AWS different? >> I think it's the fact that we truly listen and work back from the customer. So, y'know, we're not a product company, we don't make products with great R&D people and then take them and sell them. We don't obsess about the competition, y'know, we start with the customer, we go and speak to the customer, I think we listen intently to what they need, and we help them look round corners. We help them think about what they need to do for them to be successful, then we work back and probably 90% of what we do is fundamentally developed from those insights that the customer gives us. That's quite different. That really is a working back methodology. >> We run most of our business on AWS and it's true, so I remember we were in a meeting with Andy Jassy one time and he started asking us how we use the platform and what we like about it and don't like about it, and my business partner, John Furrier, he's kind of our CTO, he starts rattling off a number of things that he wanted to see, and Andy pulls out his pad and he starts writing it down, and he was asking questions back and forth, so I think I've seen that in action. One of the things that we've observed is that the adoption of cloud in EMEA and worldwide is pretty consistent and ubiquitous, there's not like a big gap, y'know, you used to see years later, y'know, Europe would maybe adopt a technology and you're seeing actually in many cases, you certainly see it with mobile, you're seeing greater advancements. GDPR, obviously, is a template for privacy, what are you seeing in Europe in terms of some of the major trends of cloud adoption? >> Yeah, I don't think we're seeing major differences, y'know, people talk a lot about, "well, Europe must be two years behind North America" in terms of adoption. We don't see that, I think it is slightly slower in some countries, but I don't think that's kind of common across the piste. So I'd say that the adoption, and if you think back to some customers that were very early adopters, just from an overall global cloud perspective, companies like Shell, for example, y'know they were really early adopters, and those were European-based companies, you could say they're global companies, absolutely, but a lot of what they did was developed in Europe. So I would say that there are countries that are slower to adopt, sometimes driven by the fact that, y'know, security is an issue, or was an issue, that data sovereignty was a bigger issue for some of these countries. But I think all of those are pretty much passed now, so I think we are very quickly kind of catching up with regards to the North American market. So, yeah. >> You mentioned your sort of startup mentality, you mentioned BP. Is it divisions within a large company like that that are startup-like? Is that what you're seeing in terms of the trends? >> No, I'm seeing three patterns. So I'm seeing a pattern which is, y'know, large organizations that go all-in very quickly, typically, y'know, strong leadership, clear vision, need to move quickly. >> Dave Vellante: We're going cloud? >> Yeah, we're going cloud, and we're going all in and that may be, like an NL would be a great example. So NL's a really good example of a top-down approach, very progressive CIO, very clear-thinking CEO that's driven adoption. So I'd say that's pattern one. For me, pattern two is where large organizations create an entity alongside, so almost a separate business. So probably Openbank is probably a good example, part of Santander. And now that organization has about one and a half million customers, obviously started in Spain, but they built a digital bank, clearly tapping into all of the data and customer sets within Santander, but building an experience which is fundamentally different. >> So a skunkworks that really grew and grew? >> Correct, absolutely, a skunkworks that grew, but grew quickly and now it's becoming y'know, a key part of their business. And then the third area, or the third pattern for me is very much a kind of a bottoms-up-led approach. So this is where the developers basically love the services that we have, they use the services, they typically put them on their credit card or AMEX, and then they'll go and use the services and create real value. That value is then seen and it snowballs. So those are kind of the three patterns. I'd say the only outlier to those three patterns is a startup organization, and as you know we've been hugely successful with startups, from, y'know, Pinterest, to Uber, to Careem, to all of these organizations and those organizations it's really important to influence them early on, to make sure that they are aware, and the developer community and the founders are aware of what we can do and we have a number of programs to really help them do that. And they start to use our services, and as those organizations are successful then our business grows alongside them and they, y'know, typically start to use a lot more of the services. >> One of the defining patterns of three, the bottoms-up and four, the start-ups, is they code infrastructure. And, y'know, sometimes the one, the top-down may not have the skillsets and the disciplines and the structure to do that. What are you seeing in terms of that whole programmable infrastructure, the skillsets, programmers essentially coding the infrastructure? Are you seeing CIOs come in and say, "Okay, we need to re-skill", are they bringing in new staff, kind of like number two, the Openbank example might be, y'know, some rockstars that they wanna sort of assign to the skunkwork. How is the number one category dealing with that in terms of their digital transformation? >> Yeah, so y'know, skills is something that is critically important, having the right skills in the right place at the right time. And if you think about Europe it's a big outsourced market, so a lot of those skills were outsourced typically to a lot of the outsourcing companies, as you'd expect. What you're seeing now is organizations, BP's a good example of this, where they're building the innovation capability back into their organizations to make sure that they can create the offerings and create the user experience and create the business models for the new world. And what we're doing is really trying to make sure that we're enabling those organizations to build the skills. So probably at a number of different levels, kind of, y'know, very basic level, or at a very junior level we're kind of influencing people in schools. So, y'know, we're going to be announcing, or announcing at the summit, Guess IT, which is basically a program to train up year eight students. So you start there, and basically you go all the way through to offering training and certification, we have a very big function associated with that to make sure that we're building the right skills for organizations to be successful, and also then working with partners, so all of those training and certification skills, we are working with the partners like the Cloudreaches of this world, but also the DXCs of this world, the Accentures of this world, the Atoses of this world, really to make sure that they have the right skills and capability, not only around our services but around the movement to cloud which is what these organizations need to do to help them innovate. >> And it sounds like your customers wanna learn how to fish, they see that as IP, in a sense, still work with partners, but help them transfer that knowledge and then, y'know, continue to innovate, raise the bars, as we like to say. >> Yes, yes. >> One of the biggest challenges that we see, we talk to customers all the time, is the data challenge. Particularly companies that have been around for a while, they have a lot of technical debt, the data's locked into these hardened silos, obviously I'm sure you see that as a challenge, maybe can you address that, how you're helping customers deal with that challenge and some of the other things that you see cloud addressing? >> Yeah, so y'know, we're really trying to help customers be successful in doing what they do in the timescale that they're setting themselves, and we're helping them be successful. I think from a data point of view, we have a lot of capability, so just to give you a perspective, so since I've been here that year and a half, we started with 125 services. That number of services has gone to 170-odd services now and the innovation that we have within those services has now reached, I think last year, just over the 1900 level so this is iterations on the product. In addition to that, we are continually building new offerings, so if you think about our database strategy, y'know, it's very much to create databases that customers can use in the right way at the right time to do the right job and that's just not one database, it's a number of different databases tuned for specific needs. So we have 14 databases, for example, which are really geared to make customers use the right database at the right time to achieve the right outcome, and we think that's really important, so that's helping people basically use their data in a different way. Obviously our S3, our core storage offering is critically important and hugely successful. We think that as-is, the bedrock for how people think about their data and then they expand and use data lakes, and then underpinning that is making sure that they've got the right databases to support and use that data effectively. >> At the start of this millennium there was like a few databases, databases was a boring marketplace and now it's exploded, as Inova says, dozens a minute it's actually amazing >> Yep >> how much innovation there is occurring in that space. What's your vision for AWS in EMEA? >> Yeah, so you know the overall Amazon vision is to be the world's most customer-obsessed organization, so y'know, here in EMEA, that holds true, so y'know, we start with the customer, we work back, and we wanna make sure that every single customer's happy with what we're doing. I think the second thing is making sure that we are bringing and enabling customers to be innovative. This is really important to us, and it's really important to the customers that we sell to, y'know, there's many insurgents kind of attacking historic business models, it's really important that we give all of the organizations the ability to use technology, whether they're a small company or a big company. And we call that the democratization of IT, we're making things available that were only available to big companies a while back. Now, we have made those services available to pretty much every single company, whether you're a startup in garage, y'know, to a large global organization. So that's really important that we bring and we continue to democratize IT to make it available for the masses, so that they can go out there and innovate and do what ultimately, customers wanna do, y'know, customers want people to innovate. Customers want a different experience. And it's important that we give organizations the tools and the wherewithal to go and do that. >> Well you've been in the industry long enough, and you've worked at product companies prior to this part of your career, and you know the innovation engine used to be Moore's Law. It used to be how fast can I take advantage of that curve, and that's totally changed now. You see a number of things happening, it's get rid of the heavy lifting, so you can focus on your business, that's what cloud does for you, but it's kind of this combination, the cocktail of data, plus machine intelligence, and then the cloud brings scale, it attracts innovative companies. How do you see, first of all do you buy that sort of new cocktail, and how do you see customers applying that innovation engine? >> Yeah, y'know, to answer the first bit first, we definitely see that cocktail. So y'know, the kind of undifferentiated work that was historically done to kind of build servers and make sure that they ran and all of those things, people don't need to do that now. We do that really really effectively. So they can really focus their time, attention, their money, their efforts, their innovation, on creating new experiences, new products, new offerings, for their customers. And they should also work back from customers themselves and work out what's really required. Every single business model, every single offering, needs to be questioned, by every single organization and I think that's what we do. We give the ability to organizations to really think differently about how they use what we have to do the really important things, the things that differentiate them and the things that ultimately give customers a different experience. And that's why I think we've seen so many very successful companies, y'know, from Airbnb, to Pinterest, to Uber. It's giving people a fundamentally different experience and that's what people want, so y'know, we're here to I think give people the ability to create those different experiences. >> Kind of amazing when you go back and you remember the book Does IT Matter? the Havard Business Review famous... It couldn't have been more wrong, at the same time it couldn't have been more right because it really underscored that IT was broken and that preceded 2006 introduction of EC2 and now technology matters more than ever before, every company's a technology company, y'know, you hear Marc Bennioff talk about software's eating the world, it's so true, and so as companies become technology companies, what's your advice to them? I mean obviously you gotta say, "Let us handle the heavy lifting," but what do they have to do to succeed in their digital transformation in your view? >> Yeah, I think it's about changing the mindset and changing the culture of organizations. So I think you can try and instill new processes and new tools on an organization but fundamentally you've gotta change the culture and I think we have to create and enable cultures to be created that are innovative and that requires, I think, a very different mindset. That requires a mindset which is about, "we don't mind if you fail". Y'know, and we'll applaud failure. We in Amazon have had many failures but it's applauded, and if it's applauded, people try again so they'll dust themselves off and they'll move on. You can see this in Israel which is, y'know, very much a startup nation. You can see people start a business, they might fail. Next day, they start a new one. So I think it's having this culture of innovation that allows people to experiment. Experimentation's good, but it's also prone to failure. But, y'know, out of 10 experiments you're gonna get one that's successful. That one could be the make or break for your organization to move forward, and give customers what they actually need, so, y'know, super important. >> Break things, move fast, right? >> Exactly. >> I love it. All right, what should we expect tomorrow at the London summit? We gotta big crowd coming, it's at the ExCeL Center >> Yeah, I think you'll see us continue to innovate, I think you'll see a lot of people, and I think you'll see a lot of customers talk about their experience and share their experience, y'know, these are learning summits, y'know, they're not kind of show and tell, they're very much about explaining what other customers are doing, how people can use the innovation and you'll see lots of experiences from different customers that people will be able to take away and learn from and go back to their offices and do similar things, but probably in a different way. So, y'know there'll be lots of exciting announcements, as you saw from re:Invent, we continue to innovate at a fair clip, as I said, 1950-odd innovations, y'know, significant releases last year, so not surprisingly you'll see a few of those. >> These summits are like mini re:Invents, aren't they? And as you said, Andy, very customer-focused, customer-centric; a lot of customer content. So, Andy Isherwood, thanks so much for coming on theCUBE, it was really great to have you. >> Great >> All right. >> Thank you >> You're welcome Keep it right there everybody, we'll be back with our next guest right after this short break. This is Dave Vellente, you're watching theCUBE.

(funky music) >> From our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBEConversation. >> Everyone, welcome to this CUBEConversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. Here in theCUBE Studios here with Abby Kearns, Executive Director, Cloud Foundry Foundation, CUBE alumni. Great to see you again. I think this is your eighth time on theCUBE chatting. Always great to get the update. Thanks for spending the time. >> My pleasure, and it's a joy to drive down to your actual studios. >> (laughs) This is where all happens Wednesdays and Thursdays when we're not on the road doing CUBE events. I think we'll have over 120 events this year. We'll certainly see you at a bulk of them. Cloud Foundry, give us the update. Yeah, we took 'em joking before we came on camera. Boy this cloud thing is kind of working out. I mean, I think IBM CEO calls it chapter two. I'm like, we're still in chapter one, two, three? Give us the update Cloud Foundry, obviously open-source. Things are rocking. Give us the update. >> I do feel like we're moving into chapter two. Chapter one was a really long chapter. (laughs) It spanned about 10 years. But I do think we're starting to see actual growth and actual usage. And I think a lot of people are like, no, there's actually been usage for a while. Me, no no no not on a real scale. And we haven't seen any of the workloads for organizations running at massive scale. At the scale that we know that they can run at. But we're starting to see interesting scale. Like 40, 50 thousand applications, you know. Billions of transactions now passing through. A lot of cloud native technology. So we're starting to see real interesting volume. And so that's going to actually dictate how the next five years unfold because scale is going to dictate how the technologies unfold, how they're used. And they're going to feed into this virtuous cycle of how the technologies unfold, and how they're going to be used, which feedback into how enterprises are using them, and you know, and the cycle continues. >> Give us the update on the foundation. What's going on with the foundation, status, momentum, clouds out there. Obviously open-source continues to drive however we saw a lot of acquisitions and fundings around people who are using open-source to build a business around that. >> I love that. >> Your favorite conversation. But, I mean you know the technical challenges with open-source allow for technical challenges but also the people side is they're learning. What's the update with the foundation? >> Well open-source is really tricky, and I think there is a lot of people that are really enthusiastic as it is a because model. I mean last year 2018 was a pretty substantial year for open-source. The year ended with Red Hat's acquisition by IBM. One of their biggest acquisitions, $34 billion. But we saw in December alone, we also saw Heptio get picked up by VMware which is a services company which is really based on Kubernetes on an open-source technology. But we also saw HashiCorp get another round of funding. And then earlier in the year, Pivotal IPO'd. And so if you look at 2018 at a bigger level, you saw a lot of momentum around open-source and how it's actually being commercialized. Now you and I were talking a little bit prior and I'm a big believer that open-source has the potential and is going to change fundamentally how technology is used and consumed. But at the end of the day for the commercial aspects of it you still have to have a business around that. And I think there's always going to be that fine line. And that line is actually always be going to be moving because how you provide value in, around, and on top of open-source, has to evolve with both the market and your customer needs. >> Yeah and where you are on that wave, whatever wave that is, is it an early wave or is it more mature so the metrization certainly matters? >> Sure. >> You could be early on setting the table or if it's growing when there's some complexity. So it kind of depends, it's always that depends is it the cloud air or is it the Red Hat? There's different approaches and people kind of get confused on that and your answer to that is just pick one that works for, that's a good business model. Don't get hung up on kind of the playbook if you will, is that kind of what you're saying? >> Well I think we're seeing this play out this week with AWS's Elastic announcement, right? And there's been a lot of conversation around how do we think about open-source. Who has access to it? Who has the right to commercialize it? What does commercialization look like? And I think, I've always cautioned people that are proceeding down the path to open-source is really be thoughtful about why you're doing open-source. Like what is your, what are you hoping to achieve? There's a lot of potential that comes with open sourcing your technology. You gain ecosystem, community, momentum. There's a lot of positives that come with that but there's also a lot of work that comes with that too. Managing your community. Managing a much more varied share of stakeholders and people that are going to have thoughts and opinions around how that technology unfolds. And then of course it's because it's open-sources there's more opportunity for people to use that and build their own ideas and their own solutions on top of that. And potentially their own commercial products. And so really figuring out that fine line and what works best for your business. What works best for the technology. And then what your hopes are at the end of the day with that. >> And what are some of the momentums or points for the Foundation, with Cloud Foundry, obviously seeing Pivotal went public, you mentioned that VMWare, I talk to Michael Dell all the time, the numbers are great coming from that operation. Pat Kelson near the Amazon deal think that clear and where VMWare was. But still you have a lot more cloud, multi-cloud conversations happening than ever before. >> Well, for sure I mean at Cloud Foundry, we've actually been talking about multicloud since 2016. We saw that trend coming based on user behavior. And now you've seen everyone is multicloud, even the public clouds are multicloud. >> I think you had the first study out on that, too on multicloud. We did. We were we were firm believers in multicloud. Last year we've actually moved more broadly to multi-platform. Because at the end of the day there isn't one technology that solves all of these problems. Multicloud is you know is pervasive and at the end of the day multicloud means a lot of different things to a lot of people. But for many enterprises what it gives is optionality. You don't want to be locked into a single provider. You don't want to be locked into a single cloud or single solution because you know if I'm an enterprise, I don't know where I'm going to be in five years. Do I want to make a five year or a 10 year or a 20 year commitment to a single infrastructure provider when I don't know what my needs are going to be. So having that optionality and also being able to use the best of what clouds can provide, the best services, the best outcomes. And so for me, I want to have that optionality. So I'm going to look at technologies that give me that portability and then I'm going to use that to allow me to choose the best cloud that I need for right now for my business and maybe again a different one in the future. >> I want to get your thoughts on this. I just doubled down on this conversation because I think there's two things going on that I'm saying we'll get your reaction to. One is I've heard things like pick the right cloud for the right workload and I heard analogies. Hey, if you got an airplane you need to have two engines. You have one engine if it works for that plane, but your whole fleet of planes could be other clouds. So, pick the right cloud for the right workload. Meaning workload is defined spec. >> Yeah. >> I've also heard that the people side of the equation, where people are behaving like they are comfortable with API's tooling is potentially a lock-in, kind of by default. Not a technical lock-in, but people are comfortable with the API's and the tooling. >> Yeah. >> And the workloads need a certain cloud. Then maybe that cloud would be it. That's not saying pick that cloud for the entire company. Right, so certainly that the trend seems to be coming from a lot of people in the news saying hey, this whole sole-cloud, multi-cloud thing argument really isn't about one cloud vs. multiple clouds. It's workload cloud for the use case in the tooling, if it fits and the people are there to do it. Then you can still have other clouds and that's in the multi-cloud architecture. So is that real? What's your thoughts on that? >> Let's dissect that 'cause I think that's actually solving for two different outcomes. Like one multi-cloud for optionality's purpose and workload specific. I think it's a great one. There's a lot of services that are native to certain clouds that maybe you really would like to get greater access to. And so I think you're going to choose the best. You know that's going to drive your workload. Now also factoring in that you know you're going to have a much more mediated access to cloud based on what people are comfortable with. I do think it's at some point as an organization you want to have a better control over that. You know historically over the last decade what we've seen. Shadow IT really dictates your Cloud spend right. You know everyone's got a credit card. I got I've got access to AWS. >> And they got most of that business. Amazon did. >> Yes and that served them quite well. If I am an organization that's trying to digitally transform, I'm also trying to get a better handle on what we're spending, how we're spending it and frankly, now if I have compliance requirements, where's my data? These are going to be important questions for you when you're starting to run production workloads at scale on multiple clouds and so, I predict we're going to see a lot more tension there in internal organizations. Like, hey I'd love for you to use cloud, you know? Where this no longer needs to be a shadow thing, but let's figure out a way to do it that's strategically and intentional versus just random pockets. Choosing to do cloud because of the workflow that they like. >> Well you bring up a good point. The cost thing was never a problem, but then you have sprawl and you realize there's a cost to Optimizer component which means you might be overpaying because as you think about the system aspects, you got networking and you got Cloud management factors. So you start as you get into that Shadow IT expansion. You got to realize, wait a minute, I'm still spending a lot of cash here. >> This adds up really really quickly. I mean, I think the information piece a couple weeks ago where they talked about the Pinterest bill, this stuff, it starts adding up. And for organizations, this is like not just thousands of dollars. It's now hundreds of thousands of dollars. If not you know, tens of millions of dollars. And so, if I'm trying to figure out ways to optimize my business and my scale, I'm going to look at that because that is not an insignificant amount of money. And so if I'm in it, that's money that could be better invested in more developers, better outcomes, a better alignment with my business, then that's where I want to spend my time and money, and so, I'm going to spend more time being really thoughtful about what clouds we're using, what infrastructure we're using, and the tools we're using to allow us to have that optionality. >> So you would agree with the statement if I said, generally, multi-cloud is here, it already exists. >> Yes. >> And that multi-cloud architecture thinking is really the conversation that needs to be had. Not so much cloud selection, per say. It's not a mutually exclusive situation. Meaning, I'm not all in on Amazon. I'm going to have clouds plural? >> Well, yeah you are. Like we have already seen as of early last year over half of our users. Which right now over half the Fortune 500 are multi-cloud already, and that number has gone up since last year I'm for sure. Some workloads were on-prem and some are in a public cloud. Be it GCP, AWS, Azure, or AliCloud. And so that is a statement of fact. And I have every executive that I've talked to with every enterprise has been like, yes, we're doing multi-cloud. >> Yeah, they're going to have some kind of on-prem anyway, So we know that's there. That's not going to go away. >> No, PRIM is not going to go away. >> Then an IOT edge, and an Enterprise Edge, SDWAN comes back into vogue as people start using SAS across network connections. >> Yeah. >> I mean, SDWAN is essentially the internet basically. >> I feel like the older I get the more I'm like, wow, didn't I have this conversation like, 20 years ago? (laughs) >> I was talking about something earlier when I came in. The old becomes the new again. It's what's happening, right? Distributor computing now goes to cloud, you got the Enterprise. What are the big players doing? Google Next is coming up next month, big event. >> It is the week after Cloud Foundry Summit. >> They got Amit Zavery, big news over there they poached from Oracle. So Thomas Kurian brought in his Oracle, who is Cube alumni as well. Really smart guy. Diane is not there. What do you expect from Google Next for the week? What are we going to see there? What's the sentiment? What's the vibe? What do you see happening? >> Well, I think it's going to be all about the Enterprise right. That's why Thomas was brought in. And then I think they really give Google that Enterprise focus and say, how do we end up? As it's not just about I'm going to sell to enterprises. That's not, you know, when you're selling to an enterprise there is a whole different approach and you have to write how to the teams, the sales teams. You have to write how to the ecosystem, the services, the enablement capabilities, the support, the training, the product strategy? All of that takes a very different slant when you're thinking about an enterprise. And so I'm sure, that's going to be front-and-center for everything that they talk about. >> And certainly he's very public about, you know, the position Oracle Cloud, he knows the Enterprise Oracle was the master of enterprise gamesmanship for sure. >> Yes, for sure. You don't get a whole lot more enterprising than Oracle. >> What's going on in the CNCF any news there? What's happening on the landscape? What's the Abby take on the landscape of cloud? >> Well, speaking as someone that does not run CNCF. >> Feel free to elaborate. >> Cloud Native Computing Foundation, for those of you that aren't aren't, you know, aren't familiar is a sister open-source organization that is a clearing house or collective of cloud made of technologies. The anchor project is the very well-known Kubernetes, but it also spans a variety of technologies from everything from LINKerD to SEDA to Envoy, so it's just a variety of cloud-native technologies. And you know they're continuing to grow because obviously cloud-native is becoming you know it's coming into its own time right now. Because we're starting to really think about how to do better with workloads. Particularly workloads that I can run across a cloud. I mean and that seems pretty pedantic but we've been talking about Cloud since 2007. And we were talking about what cloud brings. What did cloud bring, it brings resiliency. You can auto-scale. You can burst into the cloud, remember bursting? Now all the things we talked about in 2007 to 2008 but weren't really reality because the applications that were written weren't necessarily written to do that. >> And that's exactly the point. >> So now we're actually seeing a lot more of these applications written we call them microservices, 12 Factor apps, serverless apps. What have you but it's applications written to run and scale across the cloud. And that is a really defining point because now these technologies are actually relevant because we're starting to see more of these created and run and now run at scale. >> Yeah, I think that's the point. I think you nailed it. The applications are driving everything And I think that's the chapter two narrative. In my opinion, chapter one was, let's get infrastructures code going. And chapter two is apps dictating policy and then you're going to see microservices start to emerge. Kind of new different vibe in terms of like what it means for scale as less of about, hey, I'm doing cloud, I got some stuff in the public cloud. Here the conversation is around apps, the workloads and that's where the business value is. It's not like people who is trying to do transformation. They're not saying hey I stood up a Kubernetes Cluster. They're saying I got to deploy my banking app or I got to do, I got to drive this workload. >> And I have to iterate now. I can't do a banking app and then update it in a year. That's not acceptable anymore. You are constantly having to update. You're constantly having to iterate, and that is not something you can do with a large application. I mean the whole reason we talk a lot about monolithic vs 12 factor or cloud in a box is because it isn't that my monolithics are inherently bad, it's just they're big and they're complex. Which means in order to make any updates it takes time. That's where the year comes in, the 18-months come in. And I think that is no longer acceptable you know. I remember the time and I'm going to date myself here, but I remember the time when you know banks would or any e-commerce site would be down. They'd have what they call the orange page. But the orange page would come up, site down tonight 'cause we're doing maintenance for the weekend, right? >> Under construction. >> Under construction. Okay, well I'll just come back on Monday. That's fine. And now, you're like, if it's down for 5 minutes you're like what is actually happening right now. Why is this not here. >> Yeah like when Facebook went down the other day. I was like, what the hell? Facebook sucks. >> You know, the internet blows up if Instagram is down. Oh my God, my life is over and I think our our expectation now is not only constant availability. So you know always available. But also our expectation is real-time access to data transparency and a visibility into what's actually happening at all times. That I've said something that a lot of organizations are really having to figure out. How to develop the applications to expose that. And that takes time and that takes change. And there's a ton of culture change. it has to happen and that is the more important thing if I'm a business I care more about how do I make that a reality and I should care a lot less about the technologies that you use. >> It's interesting you mention about the monolith versus the decomposed application of being agile. Because if you don't have the culture and the people to do it it's still a monolithic effort in the sense of the holistic thinking and the architectural, it's a systems architecture. You have to look at it like a system and that's not easy either. Once get that done the benefits are multifold in terms of like what you can do. But its it's that systems thinking setup is becoming more of an architectural concept that's super important. >> For sure if I have a microservice app, but it takes a 150 people to get that through change management and get it into production well that will still take me a year. Does it matter if there's maybe 12 lines of code in that application? It doesn't matter and so, you know I spend a lot of time. Even though I run Cloud Foundry, I spend a lot of time talking about culture change. All the writing I do is really around cultural change and what does that look like. Because at the end of the day if you're not willing to make those changes, you're not willing to structure your teams and allow for that collaboration and if you're doing iterative work, feedback loops from your customers. If you're not willing to put those pieces into place there is no technology that's going to make you better. >> I totally agree, so let me ask you a question on that point, great point, by the way. Most followed your you're writing your blog posts in the links, but I think that's the question. When do you know when it's not working? So I've seen companies that are rearranging the deckchairs, if you will, to use an analogy with all the culture rah, rah! And then nothing ever happens right? So they've gone into that paralysis mode. When do you look at a culture? When does the executive, what should they be thinking about because people kind of aspire to do this execution that you said is critical? When do you know it's not working or what should they be doing? What's the best practice? How does someone say hey you know what I really want is to be more holistic in my architecture. I don't want to spend two years on that the architecture and then find out it's now just starting. I want to get an architecture in place. I want to hit the ground running. >> I mean it's twofold, one, start small. I mean you're not going to change you know if you're an 85 year old company with 200,000 people you're not going to change that overnight and you should expect that's going to be an 8 to 10 year process now what that's also going to mean is you're going to have to have a really clear vision and you're going to have to be really committed like this is going to be a hard road but conversely when someone says what does success look like, when you're looking at a variety of companies how do you know which ones which ones you think are going to be the most successful at the end of the day because no one's ever actually done any of this before there's no one that's ever gone through this digital transformation and it should have come out on the other side no one. There isn't and so I think what does success look and I said well for me, what I look for are companies that are investing and re-skilling their workforce. That's what I'm looking for. I get real excited when companies talk about their internal boot camps or their programs to rescale or upscale their teams because it's not like you're going to lay off 20,000 people and hire 20,000 cloud native developers, they don't exist and they're certainly not going to exists for thousands of companies to go and do that so you know how are you investing in re-skilling because-- >> It's easy to grow your own internally from pre-existing positions. >> Well sure, they know your business. >> Rather than go to a job board that has no one available. >> And you know at the end of the day that needs to be your new business model what is digital transformation actually it's just a different way of working and there isn't, there is no destination to the digital trend. This isn't a journey that has an end and so you need to really think about how are you going to invest differently in your people so that they can continuously learn continuously learning needs to be part of your model and your mantra and that needs to be in everything you do from hiring to HR to MBO's to you know how do you how do you structure your teams like how do you make sure that people can constantly learn and evolve because if that's not happening it doesn't you know everything else is going to fall by the wayside >> Is the technology gap easy to fill? Lot of tech out there. Talent gap hard to fill. >> For sure. >> That's the real challenge. >> If you have all the best tech in the world but you don't have the right people or the right structure are you going to be successful, probably not. >> Yeah, that's a challenge. Alright, so final question for you where are you going to be, what's your schedule look like, where can people find you, what events going to be at? You guys have an event coming up? >> April 2nd through 4th in Philly. We're going to have a summit you want to see some people that are actually running cloud at scale that's the place to go >> April 5th? >> 2nd through 4th. First week of April Philly, fingers crossed good weather lots of cloud talk and it's a great way. >> City of Brotherly Love >> Yes, we're bringing it. >> Philadelphia. The Patriots couldn't make it to the playoffs last year but love the Philly fans down there Paul Martino and friends down there. Abby thanks for coming on. Appreciate it-good to see you. Thanks for the update. We'll see you around the events, I won't be able to make your event I'll be taking the week off skiing. >> Well one of us has to. >> First vacation of the year, two years. Thanks for coming in. >> You should do that. >> Abby Kearns here inside theCUBE for CUBEConversation I'm John Furrier, thanks for watching (funky music)

>> From Sand Hill Road in the heart of Silicon Valley, it's theCUBE presenting the People First Network, insights from entrepreneurs and tech leaders. >> Everyone, I'm John Furrier with theCUBE. We are here for a special conversation on Sand Hill Road at Mayfield's 50th anniversary, part of their People First Network. I'm here with Jonah Goodhart, co-founder and CEO of Moat, now with Oracle, sold their company in 2017, entrepreneur, serial entrepreneur. Thanks for joining me today. >> Thanks for having me, John, excited to be here. >> So we're talking before you came on camera. You've been an entrepreneur since you were a small kid doing all kinds of hustles and side things. What's happening with you now? Obviously, you sold your company in 2017, part of Oracle. Oracle not known for the entrepreneurial activity, but you brought that company in, still goin' on. Give us an update. >> So I started Moat back in 2010. Like you said in 2017, Oracle decided to make us an offer, and we decided to sell our company. And it's been frankly exciting for me to be part of a company that has a 40-year history in Oracle. To have a company that has played a pretty pivotal role in Silicon Valley. We're sitting here right in the heart of Silicon Valley, and to be a part of a company that I think is... So important to the future development of software and databases and hardware. I think is interesting and exciting. And certainly not the path that I thought I would be on, but I'm excited to be here today. >> It's always nice to have an entrepreneurial success the level you guys had. Great exit, the numbers that was reported almost close to a billion dollars in value to Oracle, sorry, the company you started. But you got a unique journey. You started with your brother. Was in New York. Take us through that journey. What were some of the things that you did? And how did it get started? What was the main drive? >> Sure, so I got to take us back a little bit. So I've been in business with my brother, Noah, for 20 years. So we started a company in the late 1990's when I was an undergrad at Cornell. And the Internet was going crazy. E-commerce companies were going public. And the first of everything was starting, the first Internet credit card, the first of x, y, and z, fill in the blank. And so we decided, sort of haphazardly at the time, that we would start a business. And we started by helping companies acquire customers using the Internet. And so we really built, I think in sort of looking back on it now, it was somewhat of a marketing agency but at the time we were building-- >> What year was that? >> This is '98, '98, '99. >> So sort right in Internet boom. Things are going crazy. >> Things are going crazy. We're in college. We were building email lists. We were essentially trying to figure out how do you tell stories and advertise online, but we didn't know we were doing that. We were just trying to simply make some money. I was working for $5 an hour at the Computer Center in Ithaca, New York at Cornell, and I didn't own a computer. So I'm sitting there. Part of the reason I worked for the Computer Center was 'cause I got 24-hour access to the Internet and to a computer. And so we started our first business there. And things went really well almost out of the gate. So '98, '99, and then 2000 happened. And 2001 happened, and the world changed. Business certainly changed. The so-called sort of bust of a lot of, I think, the ideas that people had. I think people realized that there was going to have to be real business that were built. And eventually those businesses were built in many cases. But I think it didn't happen the way that people expected. And we were certainly surprised by it. We were 21-year-old, I was 21 at the time. My brother was two years older than me. And so we had this business that was going really well, and then we sort of ran off of a cliff. And so were profitable, growing, on top of the world, and then hit a challenge. And it was one of the first business lessons that I really learned back in 2000, 2001, which is that you have to have something that is sticky. That's going to be able to stick around through the tough times. It can't only work when things are going up. It can't only work when people are spending money. And so we learned a lot of lessons about how do you build a long-term sustainable business. In 2002, someone that we had done business with for a couple years called me. And he said, "I'm going to start a new business. "And I think there's an opportunity to build a business "to trade digital advertising and to do it more effectively "and efficiently than has been done to date." This guy said, "I think there's something to be done. "I think now is the time to do it." My brother and I decided to partner with him. We decided to write a check to become his first client and to help him start a company that he started in 2002 called Right Media. Right Media ended up becoming a big success. It was the first big ad exchange. The first platform to trade digital advertising inventory. Yahoo! ended up acquiring the company in 2007. And so we were sort of on our way as entrepreneurs slash now investors, but enter the world of 2008. Once again, the economy changes. The world changes. And we start to think, "Alright, maybe when the market "goes down, when everything crashes, maybe that's the time "to start thinking about starting a new business. "Maybe when competition dries out a little bit "it's the right time to get back into building companies." And so Noah and I, my brother and I, decided, "Alright, let's go start a new business." And we got started with Moat in 2010. And it's been a pretty fun ride. >> And how long did you work on Moat for? How many years? >> So we started in 2010. We spent a year or two trying to figure out what we would do. Really got started in earnest in 2010. Raised, invested the initial amount of money ourselves through myself, and Noah, and our third partner, Mike Walrath, the guy from Right Media. And in 2011, raised the friends and family round. 2012, we're fortunate to get Mayfield to invest. And at that point was when our business really took off. So we ran the company from 2010 to 2012 with zero dollars in revenue. Mayfield invested in us when we had zero dollars in revenue. And things started to go off from there. So from 2012 to 2017 when we sold the company, we built a pretty sizable SaaS business. >> So interesting experiences as to Mayfield, no revenue, that's the way they like it. Like to build businesses. Take a piece of the action. You also did that early on. But I think what's interesting about your story, and I want to get your thoughts on this is that entrepreneurs sometimes they hit a wall and sometimes they can't get back up. You hit multiple kind of market timings. I'll say the bubble crash, 2001-2002 time frame. You mentioned 2008. Seeing transitions is a big part of having that entrepreneurial antenna, if you will, having a feeling for the market, knowing what the wave is, when to start, when to invest, invest in down markets. As you grew from that first venture and you're on top of the world, college, that first crash, how did you figure out the market transition kind of dynamic? What was, did it jump out at you? Was it just scar tissue? What was some of the feelings there? >> Yeah, I mean my view is that so the market changed, and we had all these expectations about our revenue was going to continue to grow forever, and our profits were going to continue to grow forever. And when the market changed and outside dynamics changed our business. This is Colonize. I'm talking about our first company. All of a sudden we went, "Uh oh, what do you now?" And I think it was more having lived through that experience that we said, "Alright, we need to figure out "when we build businesses, how do we build them "to be sort of fool-proof? "Or as much fool-poof as we can be. "How do we have something that's sticky, sustainable, "that can't simply be turned off with the ebb and flow "of the market?" And I think it, for me, taught me something which was you need to build something that's long-lasting. Something that is not driven by market conditions. If your business is driven by external market conditions, that should be a big signal that there's potentially a problem, 'cause if those conditions change you're going to be in a tough spot. And so we decided then and there, "Alright, we need "to really build businesses that are here for the long run." We sat on the board of Right Media, helped start the company, but we didn't operate it. Mike ran this company, and we watched. We watched very closely and carefully, and he did something else that was interesting. It's that he learned how to story tell. He learned how to think about where we were going as a business in Right Media not where we were. And so I combined, with my brother, these two themes. Sustainable, sticky business with storytelling. Think about where you're going not just where you are. And I think as we created Moat, we thought, "Alright, how do you actually turn that "into a long-term business?" And part of the way you do it is by trying to project forward, trying to think, "Alright, not what are we doing today? "But where are we going into the future?" And that really became a critical part of product development, a part of our vision, of where we wanted to be as a business. And I think it was a critical part of our success. >> What can other entrepreneurs learn from that? Because I think I see a lot of entrepreneurs here in Silicon Valley and around the world, now that entrepreneurship's kind of gone global, is they get stuck in with dogma and like, "We got to make this work." And sometimes they might not be self-aware that they might have to just take their head up and look around and get a feel for what's goin' on around them. What's your advice for those guys? >> I think you have to be honest with yourself. You know, as an entrepreneur, in your heart of hearts is what's happening to you real? You know, you should know I think, whether or not what's happening to you is because of some conditions, because of one customer that's doing something that's good or bad, or because of a broader trend or a broader movement. I try to ask questions about not just what does it look like a year from now or two years from now or three years from now? I think about the world ten years from now. What do I know to be the case ten years from now? I think this is something that Jeff Bezos talks about. Which is what do you for sure know is going to be the case with your business ten years from now? If you can plan towards that, you can build something that's sustainable. And so we knew ten years from now marketers are still going to want to reach people. They're still going to want to story tell. They're still going to want to measure how effective it was to actually reach those people. And so we knew that wouldn't change. What might change are the mechanisms. How they reach people, how they story tell, what platforms they do it on, whether it's Facebook or Snapchat or Pinterest or whatever the next new platform is, that may change. But the fact that marketers will need to reach people won't. And so we felt really confident that ten years from now that's going to still be the case. And I felt if you know that then you can build towards this vision and so-- >> Medium and the channels are all going to change all the time, but the stories need to be told. >> That's right, and interestingly, I think that when you start a business you come up with a theme. You come up with a vision. And so for us it was how do marketers tell their stories increasingly in a world that's digital? That's not something that's going to change overnight. And I felt like over the long haul that's not going to change very quickly. Increasingly we're going to be digital consumers, and marketers are going to have to tell their stories. Now the business that we started at Moat in 2010 ended up changing dramatically. We started a crowd-sourced creative marketplace. We ended as a measurement and analytics company. Pretty different place from creative. The vision was still the same. The vision was still about helping companies, marketers, tell their stories in a world that's increasingly digital. And if you look at successful businesses, they tend to have the same vision from when they started. Now the underlying business may change. Hopefully, the underlying business iterates and finds the right path, but the overall, the high level of where you're going ideally doesn't change. And I think that's part of the key to success. >> That's a great point. I think, I always get in a debate here among entrepreneurs and investors. The word pivot versus adjusting. When you have a North Star or a mission, you just got to kind of tack with the wind and make it a tailwind not a headwind versus a full pivot which might be, "Hey, there's no business here. "We have to do something different." Can you talk about the nuances between what a pivot is? And how you find that tailwind, the wind in the sails if you will, for the entrepreneur to hit that vision? >> Yeah, so first of all, any successful business that I've ever seen never starts off how it ends. In other words, there are always iterations that go through. Pick any company that you can think of right now. They've iterated. They've started off with one theme, and they've gone this slight different path. So I would argue that every good business is going to iterate. Now whether you want to call it a pivot or not, I think is more nomenclature or semantics. My view is you're going to iterate. They key is having that North Star. So in ten years, what do we believe to be the case? Forget about what do we believe, what do we know to be the case? What do we know this is going to be the case ten years from now? And if you're right about that then it can qualify as your North Star. By the way, if you don't know ten years from now this is going to be the case then maybe that shouldn't be your North Star. Maybe that shouldn't be the guiding light for your business. Once you get that part right then it almost frees you to be flexible. It frees you to say, "Okay, so if the world's moving "this way or that way, I'm going to adjust." One of the things that I learned from Moat was actually somebody gave me advice early on. They said, "Go have a thousand meetings. "Go have a thousand meetings in your industry, "in your category. "Go meet with every single person in the business." And I did that. It took me probably 18 months, but I went out and met with everyone who would take my meeting. What I learned from that is that in the B2B world we have an advantage. You can talk to your customers. Your customers will literally tell you, "Here are the issues we're having. "Here are the things we're trying to solve for. "If you can help us solve for this, we will pay you money "to provide a service to us to actually solve this problem." And so I learned, "Wow, that's pretty amazing!" If you actually meet with enough people, you get a sense of the market. You get a sense of what people are buying. You get a sense of the trends. As my oldest brother says, "The world kind of slows down "a little bit." Markets move in slow motion when you really get into it. And so if you go out and have a thousand meetings in your industry, you actually learn what's happening in that business. And you can tweak your business accordingly. I walked away with Moat feeling like if you're not in a meeting talking your story, telling your pitch, telling your vision, and they're not nodding their head going, "Yep, yep, yep, 100% on the same page." Then you're not in the right place. >> I love that comment about slowing the game down. Reminds me of baseball batters up there slowing that game down, watch that ball come in, really slow. And I think that's good advice because you want to slow it down. You want to make sure you're kind of capturing the right things that's happening at the right time, not try to go too fast. >> That's right. Things don't happen overnight. I think oftentimes when you're not in the industry, and you just read the headlines, you think, "Oh my God, that's crazy that this thing happened "and that thing happened!" When it's your space, it doesn't move quite that fast. There's work that has to be done. Contracts that have to be put in place. You see it evolving. And so I always tell people when you want to get to know an industry, read every single piece of content there is about the industry, read every article that comes out about it, and take as many meetings as you can possibly take in the space. And it'll slow down. It'll move at a pace that you can kind of go, "Got it! "It feels like if we do this and this then we can actually "start to build a business here." And again, I think there's a bright line test in B2B if you walk into a meeting and you start telling your story, and you're not getting the nods, and you're not getting the, "Yep, yep, yeah, "that's an issue for us." If that's not happening, then you're not in the right space. Doesn't mean your North Star is wrong, but it means you got to iterate a bit. >> You got to find your groove. I want to change gears a little bit and talk about this People First Network concept that I love because you hear, "Mobile first, cloud first." And the notion of people first, we live in a very social world now, you're seeing a lot of stuff happening where we're connected now almost with digital 100%. Everyone's kind of got mobile even in emerging countries you got connections. Yet there's a lot of new dynamics emerging on the social scene and checking around you're well-known for networking. You're known for connecting with people certainly in your area and beyond. And so there's two things I want to get your thoughts on. One is networks. Who to work with. How do I make decisions on? How do you want to spend your time with other entrepreneurs or other peers? And social entrepreneurship, there's a lot of emphasis around mission-driven things. These are people dynamics where you're starting to see the role of the relationships between people start to take a really important role in entrepreneurship not just, "Let's hire and fire fast." Certainly some basic business knowledge that's common sense. But as you're starting to see this next generation of entrepreneurs emerge, there's an eye on social, mission-driven, but spending time with the right people. What's your thoughts on that? >> So first of all, businesses are about people. In the end of the day, you want to do business with people that you like, with people that you trust, with people that you want to hang out with. That was one of the lessons I learned somewhat early on, and I think it's critical. Businesses are not automated. Businesses are about, "Alright, a group of people "come together with a shared idea of what they can do. "And they can hopefully go support a group of other people "who are trying to get their vision done." And so once you realize that, you realize it's about people. You want to build relationships. You want to build connections. You want to figure out, "Alright, how can I help people? "And hopefully with good karma something will happen "in my favor at some point." And so I always operate under the idea that you just try to do good, you try to help people, and hopefully as a result, good things will happen. In terms of social entrepreneurship what I would tell you is that having a mission that you feel deep down inside of you that is not just, "We're going to make money. "And we're going to deliver on behalf of shareholders." Yes, of course that's important. But when you wake up, and you go to work or you get online, you want to feel something for it. You want to feel like, "Alright, this is something "that I feel good about doing." When you do that, when you know that you've done it right, it doesn't feel like work. It doesn't feel like a job. It feels like you want to wake up, and you can't get enough of it. And I think that's when you know you've done something right. So I think the more that we can lead mission-driven businesses, mission-driven lives, the better that will be. In the end of the day, I think that life and business converge. I think in the end of the day when you do it right, it doesn't feel like work, and it doesn't feel like you're working or not working. It just feels like you're trying to do good, you're trying to help other people, and hopefully good things happen. >> Great stuff. The thing I love about digital is you start to see that blending of analog and digital where lives are now part of each other. If you could go back and be 18 and 20 again with all the tools that we have out there now, open-source at a whole new level, you have everyone's connected, what were some of the things that you would do? If you had to go back and talk to your 18-year-old self going into Cornell with your brother, a lot more on the table to play with. Certainly, it's easier to do ventures, easier to come up with ideas, maybe more lean. What are some of the things that you would do if you were in your 20's? >> Yeah, I guess if I went back I would tell myself to make big bets and make them on where you know the future is going to be ten years from now. I think oftentimes, particularly when I was a young entrepreneur, you were living day to day or week to week where you were going, "Alright, we need to get this thing done by this day "so that we can do this tomorrow." And so we need to fly and stay up all night and end up eating and sort of doing things that are not the best sort of health-wise in order just to try to get things done or what you thought would just get things done. I think I would play a longer game, and I would encourage myself to think about, "Alright, what do I know to be the case "ten years from now and how can I focus on that?" If we go back 20 some years, two or three of the biggest companies in the world were really created in Amazon, in Apple, in Google. And I think the opportunity existed back then. So if I could go back to my-- >> You'd buy some Apple stock for sure. (laughs) >> I don't know if I would bought Apple stock, but certainly I would've made longer term bets. What those companies do that I think is phenomenal is they think about where the world's going not where the world is today. >> I think that's great advice. And it's interesting, too. You go back, and you always, everyone has those experiences in life where they would say, "I could've been there "or there." Looking forward is the key. And I think one of the interesting things about your journey is you had the time in college, make some money, put some dough in your pocket. Then you go out and you have some cash. You make an investment. You ride the wave with Right Media, and then you go the venture-backed startup. Talk about the dynamics. Specifically the venture-backed startup, because now the dynamics are changed. I mean, hell, I might go do an ICL and suddenly get subpoenaed if I did that. But you got all kinds of new opportunities to get funded, either to venture capital, either with Mayfield. Different venture architecture there you mentioned, no revenue, but funding to go build it out. What was different about doing a venture-backed startup versus the other ones? >> Yeah, I guess what I would say is first of all we have to step back and realize that when we're in these industries, we have a hard time understanding what they're doing. What venture capitalists do is just what any money manager does. They're doing allocation of capital so that they can get returns for their investors. And so in the end of the day, they're trying to make bets. Now the bets that a venture capital makes are different from someone who's buying public equities for sure, but the same sort of ideals are there which is they want to make bets on the right companies, on the right people so that they can drive profits and returns and hopefully make a difference. In the case of Moat, we were really impressed by Mayfield. We were impressed by the way that they approached the conversation with us, the way that they leaned forward. I tell entrepreneurs when you have venture capitalist meetings if three out of ten of them go well, you're in the Hall of Fame. It's like baseball. Most of the time you're not going to get that perfect chemistry. You're not going to get that feeling where, "Ah, there's something interesting here." The other thing I tell entrepreneurs is if they're not leaning forward, if they're not going, "You know what we could do? "We could do this, this, and this. "I could connect you with so and so. "We could build a business doing this. "You should think about this." If they're not doing that, they're probably not the right fit. I think about it. I'm happily married for many years with four kids. When you meet your spouse you tend to know that that's the right person. If you have to go home and say, "Alright, why don't you "send me some reasons to try to justify "why you might be the right fit for me," maybe that's not the right spouse. I think it's the same thing with venture capitalism. You ultimately want to have chemistry. Again, it comes back to people. And so Mayfield I think does a really good job of thinking about people and putting people first in that conversation. >> And it's also a team environment almost because you want to have a spouse and a venture partner who's going to be there for the good, bad, and the ugly. >> That's right. >> And be there. And that's, I think a lot of people don't get that. They want the valuation, "Oh, I got a better deal." There's no better deal when you look at the long run impact of potentially making the wrong decision. >> One of the first things that Navin Chaddha from Mayfield said to me when I first met with him is he said, "This is going to take you seven to ten years "to build this business." And I thought, "Wow, that sounds like a long time!" >> I'm going to do it in three. >> Yeah, that seems crazy. (John laughing) But he was right, and one of the things that he said to me after they invested and we had gone through a couple quarters of working. I came in and I actually had pretty high expectations of what we could do as a business. I said, "Well, if we really push the accelerator "I think we could do this number instead of this number." And he said, "Relax. "We have plenty of time. "Don't try to knock it out of the park, "and you'll make mistakes if you do that. "Just try to deliver on the numbers that you think "you can deliver realistically. "And focus on building the business." And he was right. Having that approach is smart. It's not about, "Can I make this work next quarter?" It's about, "Can I make this work over the long run?" And I learned a lot in that process. >> Well, Jonah, I really appreciate the conversation. You're an inspiration to a lot of entrepreneurs out there. And congratulations on all your great success. I guess the question is what's next for you? You got that ten year vision. What's going to happen in the next ten years? Which wave will you be riding? >> Well, I think, increasingly, we're going to live in a connected society where data is information, and data is knowledge. And I think for me I'm excited about a future world where will we use more or less data to make decisions. I think more. Will we make smarter decisions over time? Hopefully smarter decisions over time. Will we be able to catch diseases earlier? I think so. Will we be able to leave longer lives? I think so. And so some of those things end up being themes-- (no audio) >> Great, Jonah Goodhart, at Oracle now, first a founder, entrepreneur, serial entrepreneur, here as part of theCUBE's People First Network series. I'm John Furrier. Thanks for watching. (upbeat electronic music)