(bright music) >> You know, there's a lot of negative press around the technology industry these days. The tech lash is somewhat understandable, people are struggling and yet the tech industry is booming, creating incredible wealth for a relatively select group of people. I get it. But the reality is, that the technology industry has guided us through the pandemic, allowing us to work remotely, securing our employees, keeping goods and services flowing, and using data and analytics to track COVID and accelerate the development of vaccines. And many in the tech industry are passionate about giving back and applying their talents to solve real world problems. I'll give you an example. After accidents, cancer is the number one cause of death among young people. In the middle of the 20th century, the survival rate for kids with cancer was 0.0%. Today, it's above 85%. Cancer in kids is much different than in adults. The types of cancer, the diagnoses, the treatments, they vary. Different types of research are required to attack the problem. And that takes money. And one of the people here in Boston and beyond that's using his talents, his creativity, his network, and yeah, his wealth, to attack this problem, is my friend, Chris Lynch, entrepreneur, investor, and philanthropist. Chris, awesome to see ya. Welcome back to theCUBE my friend. >> Thanks, Dave. It's great to be here. >> So, listen, this personal story of yours, how'd you get into, where's the passion come from for kids with cancer? >> Dave, it's actually related to one of my startup endeavors. When you're starting, bootstrapping your company, you're typically staying at people's homes to save money. >> Sleeping on couches. Yeah. >> Yeah, yeah. Pretty much. And for the years of these startups, I've developed relationships with families all over the world, 'cause I've literally lived with them for periods of time until the companies got to points where we didn't have to do that. And there was a family in Seattle that I used to stay with, and they had a son that was a similar age to one of mine and he ultimately passed of cancer. And I stayed with the family, and I stayed with them a few times while they were going through this, and I was touched, I was inspired by their courage, how positive they were. I was thinking in my own circumstance, how could I, I would just hate the world. And in these families, I stay there, they call me Uncle Chris. And I was having dinner at the family home and I was looking at the boy, and I excused myself, went to the bathroom and I started sobbing, and he knocks on the door, comes in and says, "Uncle Chris, it's okay. My dad tells me you can do anything. Just do whatever you can so that other kids don't have what I have." You know and... >> Wow. Wow. And I can see the emotion that you're feeling right now, bringing us back to that moment. >> Well. Yeah. >> It's unbelievable. All right, so you got Tech Tackles Cancer. Is this your latest venture? I think the last one was 2018. It's coming back, took a break 'cause of COVID, and this is going to go down on the 21st at The Sinclair in Harvard Square. Bring a bunch of people in. We got a number of people who have signed up to, actually you're one of them, of course, but to sing karaoke, raise a bunch of dough, and then there's like a little contest, right? So... (he chuckles) Alex, bring up that slide. I got to show the audience who we got here. And this is, Chris, this is your competition. So, here you go. We got, Steve Duplessie, right? That's a great picture, Steve. Thanks for doing this, right. Nathan Hall, who's at Pure Storage. Steiny, Ken Steinhardt, from INFINIDAT. And you got George Hope at HPE. And Joe Lemay, who's an inventor, he's the CEO of Rocketbook. Any of these guys worry you? >> I'm going to sleep easy tonight. (Dave laughs) >> So, how did you get into rock and roll? You wrote a blog one time. You quoted Nietzsche saying that life without music would be a mistake. Rock and roll. Rock on. How'd you get into rock and what's your passion there? >> Well, I always loved rock and roll but I had someone that was staying with us who was a student at BU, and he went to his semester abroad, he went to the UK. And he came back with all this punk rock music, the Sex Pistols and all this stuff. And I heard it and it just triggered something in me. And then I didn't want to do anything but play music and try to be a musician, and my grades and everything else suffered as a result. But music's always inspired me, the creativity, the boldness. A lot of things that I think I apply to my startup life. >> How could people help? Let's say they want to get involved. I mean, obviously, they can attend the event, they donate. What should people do? They could sing? >> Yeah. So they can certainly sponsor the event. There are a number of sponsorship opportunities. They can participate. They can volunteer for the event. It is an all-volunteer organization. Every dollar that we raise goes to the charities that we've listed. And we handle everything else through a lot of arm twisting and whatnot. >> Great. So it's June 24th, sorry, June 21st, at The Sinclair, which is right in Harvard Square. So it's live band karaoke, right? >> Correct. >> I've seen some of the, we're going to share a little clip there. And so, it's a call to action to all you rock and roll technology gods out there. You know, we showed you the five folks plus Chris who were doing it, and so we're dying to see you up there again, you must be really excited about it. >> I am, I am. I'm going to be much better than last time. >> Okay. Well, so just on that note we'll close with a little taste of what's in store for June 21st. We'll see you there. ♪ Now my loneliness ♪ ♪ Is killing me now ♪ ♪ You know I still believe ♪ ♪ Midnight, midnight to six ♪ ♪ Midnight, midnight to six ♪ ♪ Midnight, midnight to six ♪ ♪ Believe in things that you don't understand ♪ ♪ then you're su... ♪ (bright music)

[Music] you know there's a lot of negative press around the technology industry these days the tech lash that's somewhat understandable people are struggling and yet the tech industry is booming creating incredible wealth for a relatively select group of people i get it but the reality is that the technology industry has guided us through the pandemic allowing us to work remotely securing our employees keeping goods and services flowing and using data and analytics to track covet and accelerate the development of vaccines and many in the tech industry are passionate about giving back and applying their talents to solve real world problems i'll give an example after accidents cancer is the number one cause of death among young people in the middle of the 20th century the survival rate for kids with cancer was 0.0 percent today it's above 85 percent cancer in kids is a much different than in adults the types of cancer the diagnoses the treatments they vary different types of research are required to attack the problem and that takes money and one of the people here in boston and beyond that's using his talents his creativity his network and yeah his wealth to attack this problem is my friend chris lynch entrepreneur investor and philanthropist chris awesome to see you welcome back to thecube my friend thanks dave it's great to be here so listen this personal story of yours how did you get into where's the passion come from for kids with cancer dave it's actually related to one of my startup endeavors when you're starting bootstrapping company you're typically staying at people's homes and you know to save money sleeping on couches yeah yeah pretty much and um you know through the years of these startups i've developed relationships with families all over the world you know because i've literally lived with them you know for periods of time until the companies get to points where we didn't have to do that and um there was a family in seattle that i used to stay with and they had a son that was a similar age to one of mine and he ultimately passed of cancer and i stayed with the family and i stayed with them a few times while they were going through this and i was touched i was inspired by their courage how positive they were i was thinking in my own circumstance how could i i would just hate the world and you know in the you know in these families i stay there you know they call me uncle chris and um i was having dinner at the you know at the family home and i was looking at the boy and uh i excused myself went to the to the bathroom and i started sobbing and um he knocks on the door comes comes in and says uncle chris it's okay my dad tells me you can do anything just do whatever you can so that other kids don't have what i have you know in it wow wow and i can see the emotion that you're feeling right now bringing bringing us back to that moment it's it's unbelievable and and the thing is when you started st baldrick's it wasn't it was obviously about the kids but it was also about the family as well right because they're going through right i mean you know we all know as parents how hard it is to be a parent can you imagine having a parent that's you know got a disease like that so it's not just about you know the the cancer and the research it's about the supporting the families as well right that's right and that's why one mission is you know one one of our um you know big beneficiaries you know of of the work we do um because it's obviously we want to find cures um but people you know families are affected every day and we need to provide them the kind of support um you know that that they any child should have and any family should have in this circumstance all right so you got tech tackles cancer this is your latest venture i think the last one was uh 2018. it's coming back took a break because of covid obviously uh but so it's live band karaoke it's the tech industry your network and beyond really kind of giving back how does that all work well basically you know we we once i learned that pediatric cancer was different and that there was it was underfunded we wanted to raise awareness for that we wanted to raise funds to take a different approach applying sort of venture principles how i invest in companies and find the best research in the world which is not in any four walls of any sort of research center so we get the best research from around the world and that we decided to put the money invest the money as well as the support services around those that you know are affected today yeah okay so we've got actually so what's going to happen and this is going to go down on the the 21st at the sinclair and harvard square bring a bunch of people in we've got a number of people who have signed up to actually you're one of them of course but to to sing karaoke raise a bunch of dough and then there's a little contest right no alex bring up that slide i gotta i gotta show the audience who we got here this is chris this is your competition uh so here you go you got we got steve duplessi right he has great picture steve thanks for doing this right nathan hall who's at pure storage steiny ken steinhardt from infinidat and you got george hope at hpe and joe lemay who's uh he's inventor he's a ceo a rocket book any of these guys where are you i'm going to sleep easy tonight [Laughter] how did you get into rock and roll you wrote a blog one time you you quoted nietzsche is saying that life without music would be a mistake you know rock and roll rock on how did you get into rock and roll well i always loved rock and roll but i had that was staying with us he was a student at bu and he he went to his semester abroad he went to the uk and he came back with all this punk rock music the sex pistols and all the stuff and um i heard it and it just triggered something in me and that i didn't want to do anything but play music and you know try to be a musician and um you know my grades and everything else suffered as a result but music's always inspired me the creativity the boldness a lot of things that i think i apply to my startup life how can people help let's say they want to get involved i mean obviously they can attend the event they donate what what should people do they could sing yeah so they could they could certainly sponsor the event there are a number of sponsorship opportunities um they can participate they can volunteer for the event it is an all volunteer organization every dollar that we raise goes to the charities that we've listed um and we handle everything else through a lot of arm twisting and you know and whatnot great so it's june 24th uh sorry june 21st at the sinclair which is right in harvard square so it's live band karaoke right i've seen some of the we're gonna share a little a little a little clip there and so it's a call to action to all you you rock and roll technology gods out there you know we showed you the the five folks plus chris who were doing it um and so we're dying to to see you up there again you must be really excited about it i am i am i'm going to be much better than last time okay well so just on that note we'll close with a little taste of what's in store for june 21st we'll see you there [Music] midnight midnight midnight midnight six midnight midnight six things that you don't understand in yourself [Music] you

>>Hello, and welcome to this cube conversation. I'm Sean for, with the cube here in Palo Alto, California, actually coming out of the pandemic this year. Hopefully we'll be back to real life soon. Uh it's uh, in March, shouldn't it be? April spring, 2021. Got a great guest Chris Lynch, who is executive chairman, CEO of scale, who took over at the helm of this company about two and a half years ago, or so, um, lots of going on Chris. Great to see you, uh, remotely, uh, in Boston, we're here in Palo Alto. Great to see you. >>Great to see you as well, but hope to see you in person, this sprint. >>Yeah. I got to say people really missing real life. And I started to see events coming back to vaccines out there, but a lot going on. I mean, Dave and I Volante, I was just talking about how, um, you know, when we first met you and big data world was kicking ass and taking names a lot's changed at Duke went the way it went. Um, you know, Vertica coming, you led, did extremely well sold. HP continue to be a crown jewel for HPE. Now the world has changed in the data and with COVID more than ever, you starting to see more and more people really doubling down. You can see who the winners and losers are. You starting to see kind of the mega trend, and now you've got the edge and other things. So I want to get your take at scale, took advantage of that pivot. You've been in charge. Give us the update. What's the current strategy of that scale? >>Sure. Well, when I took the company over about two and a half years ago, it was very focused on accelerating the dupe instances. And, uh, as you mentioned earlier, the dupe is sort of plateaued, but the ability to take that semantic layer and deliver it in the cloud is actually even more relevant with the advent of snowflake and Databricks and the emergence of, uh, Google big query, um, and Azure as the analytic platforms, in addition to Amazon, which obviously was, was the first mover in the space. So I would say that while people present big day in as sort of a passing concept, I think it's been refined and matured and companies are now digitizing their environment to take advantage of being able to deliver all of this big data in a way that, um, they could get actionable insights, which I don't think has been the case through the early stages of the development of big data concepts. >>Yeah, Chris, we've always followed your career. You've been a strong operator, but also see things a little bit early, get on the wave, uh, and help helps companies turn around also on public, a great career. You've had, I got to ask you in your opinion and you, and you can make sense for customers and make sure customers see the value proposition. So I got to ask you in this new world of the semantic layer, you mentioned snowflake, Amazon and cloud scales. Huge. Why is the semantic layer important? What is it and why is it important for customers? What are they really buying with this? >>Well, they're buying a few things, the buying freedom and choice because we're multicloud, um, they're, they're buying the ability to evolve their environments versus your evolution versus revolution. When they think about how they move forward in the next generation of their enterprise architecture. And the reason that you need the semantic layer, particularly in the cloud is that we separate the source from the actual presentation of the data. So we allow data to stay where it is, but we create one logical view that was important for legacy data workloads, but it's even more important in a world of hybrid compute models in multi-vendor cloud models. So having one source of truth, consistency, consistent access, secure access, and actual insights to wall, and we deliver this with no code and we allow you to turbocharge the stacks of Azure and Amazon Redshift and Google big query while being able to use the data that you've created your enterprise. So, so there's a demand for big data and big data means being able to access all your data into one logical form, not pockets of data that are in the cloud that are behind the firewall that are constrained by, um, vendor lock-in, but open access to all of the data to make the best decisions. >>So if I'm an enterprise and I'm used to on-premise data warehouses and data management, you know, from whether it's playing with a dupe clusters or whatever, I see snowflake, I see the cloud scale. How do I get my teams kind of modernized if you had to kind of go in and say, cause most companies actually have a hard time doing that. They're like they got to turn their existing it into cloud powerhouses. That's what they want to do. So how do you get them there? What's the secret in your opinion, to take a team and a company that's used to doing it on prem, on premises to the cloud? >>Sure. It's a great question. So as I mentioned before, the difference between evolution and revolution today, without outscale to do what you're suggesting is a revolution. And you know, it's very difficult to perform heart surgery on the patient while he's running the Boston marathon. And that's the analog I would give you for trying to digitize your environment without this semantic layer that allows you to first create a logical layer, right? This information in a logical mapping so that you can gradually move data to the appropriate place. Without us. You're asked to go from, you know, one spot to another and do that while you're running your business. And that's what discourages companies or creates tremendous risk with digitizing your environment or moving to cloud. They have to be able to do it in a way that's non-disruptive to their business and seamless with respect to their current workflows. >>No, Chris, I got to ask you without, I know you probably not expecting this question, but um, most people don't know that you are also an investor before you as CEO, um, angel investor as well. You did an angel investment deal with a chemical data robot. We've had a good outcome. And so you've seen the wave, you've seen a kind of how the progress, you mentioned snowflake earlier. Um, as you look at those kinds of deals, as they've evolved, you know, you're seeing this acceleration with data science, what's your take on this because you know, those companies that have become successful or been acquired that you've invested in now, you're operating at scale as a company, you got to direct the company into the right direction. Where is that? Where are you taking this thing? >>Sure. It's a great, great question. So with respect to AI and ML and the investment that I made almost 10 years ago and data robot, um, I believe then, and I believe now more than ever that AI is going to be the next step function in industrial productivity. And I think it's going to change, you know, the composition of our lives. And, um, I think I have enough to have been around when the web was commercialized in the internet, the impact that's having had on the world. I think that impact pales in comparison to what AI, the application of AI to all walks of life has gone going to do. Um, I think that, um, within the next 24 months companies that don't have an AI strategy will be shorted on wall street. I think every phone, every, every vertical function in the marketplace is going to be impacted by AI. >>And, um, we're just seeing the infancy of mass adoption application when it comes to at scale. I think we're going to be right in the middle of that. We're about the democratization of those AI and machine learning models. One of the interesting things we developed it, this ML ops product, where we're able to allow you with your current BI tool, we're able to take machine learning models and just all the legacy BI data into those models, providing better models, more accurate, and precise models, and then re publish that data back out to the BI tool of your choice, whether it be Tableau, Microsoft power, BI Excel, we don't care. >>So I got to ask you, okay, the enterprises are easy targets, large enterprises, you know, virtualization of the, of this world that we're living with. COVID virtualization being more, you know, virtual events, virtual meetings, virtual remote, not, not true virtualization, as we know it, it virtualization, but like life of virtualization of life companies, small companies like the, even our size, the cube, we're getting more data. So you start to see people becoming more data full, not used to dealing with data city mission. They see opportunities to pivot, leverage the data and take advantage of the cloud scale. McKinsey, just put out a report that we covered. There's a trillion dollars of new Tam in innovation, new use cases around data. So a small company, the size of the cube Silicon angle could be out there and innovate and build a use case. This is a new dynamic. This is something that was seen, this mid-market opportunity where people are starting to realize they can have a competitive advantage and disrupt the big guys and the incumbents. How do you see this mid market opportunity and how does at-scale fit into that? >>So you're as usual you're spot on John. And I think the living breathing example of snowflake, they brought analytics to the masses and to small and medium enterprises that didn't necessarily have the technical resources to implement. And we're taking a page out of their book. We're beginning to deliver the end of this quarter, integrated solutions, that map SME data with public markets, data and models, all integrated in their favorite SAS applications to make it simple and easy for them to get EnLink insight and drive it into their business decisions. And we think we're very excited about it. And, you know, if, if we can be a fraction, um, if we can, if we get a fraction of the adoption that snowflake has will be very soon, we'll be very successful and very happy with the results this year. >>Great to see you, Chris, I want to ask you one final question. Um, as you look at companies coming out of the pandemic, um, growth strategies is going to be in play some projects going to be canceled. There's pretty obvious, uh, you know, evidence that, that has been exposed by working at remote and everyone working at home, you can start to see what worked, what wasn't working. So that's going to be clear. You're gonna start to see pattern of people doubling down on certain projects. Um, at scales, a company has a new trajectory for folks that kind of new the old company, or might not have the update. What is at scale all about what are what's the bumper sticker? What's the value proposition what's working that you're doubling down on. >>We want to deliver advanced multi-dimensional analytics to customers in the cloud. And we want to do that by delivering, not compromising on the complexity of analytics, um, and to do that, you have to deliver it, um, in a seamless and easy to use way. And we figure out a way to do that by delivering it through the applications that they know and love today, whether it be their Salesforce or QuickBooks or you name, the SAS picked that application, we're going to turbocharge them with big data and machine learning in a way that's going to enhance their operations without, uh, increase the complexity. So it's about delivering analytics in a way that customers can absorb big customers and small customers alike. >>While I got you here, one final final question, because you're such an expert at turnarounds, as well as growing companies that have a growth opportunity. There's three classes of companies that we see emerging from this new cloud scale model where data's involved or whatever new things out there, but mainly data and cloud scale. One is use companies that are either rejuvenating their business model or pivoting. Okay. So they're looking at cost optimization, things of that nature, uh, class number two innovation strategy, where they're using technology and data to build new use cases or changed existing use cases for kind of new capabilities and finally pioneers, pioneering new net, new paradigms or categories. So each one has its own kind of profile. All, all are winning with data as a former investor and now angel investor and someone who's seen turnarounds and growing companies that are on the innovation wave. What's your takeaway from this because it's pretty miraculous. If you think about what could happen in each one of those cases, there's an opportunity for all three categories with cloud and data. What's your personal take on that? >>So I think if you look at, um, ways we've seen in the past, you know, particularly the, you know, the internet, it created a level of disruption that croup that delivered basically a renewed, um, playing field so that the winners and losers really could be reset and be based on their ability to absorb and leverage the new technology. I think the same as an AI and ML. So I think it creates an opportunity for businesses that were laggerts to catch, operate, or even supersede the competitors. Um, I think it has that kind of an impact. So from my, my view, you're going to see as big data and analytics and artificial intelligence, you know, mature and coalesce, um, vertical integration. So you're going to see companies that are full stack businesses that are delivered through AI and cloud, um, that are completely new and created or read juvenile based on leveraging these new fundamentals. >>So I think you're going to see a set of new businesses and business models that are created by this ubiquitous access to analytics and data. And you're going to see some laggerts catch up that you're going to see some of the people that say, Hey, if it isn't broke, don't fix it. And they're going to go by the wayside and it's going to happen very, very quickly. When we started this business, John, the cycle of innovation was five it's now, you know, under a year, maybe, maybe even five months. So it's like the difference between college for some professional sports, same football game, the speed of the game is completely different. And the speed of the game is accelerating. >>That's why the startup actions hot, and that's why startups are going from zero to 60, if you will, uh, very quickly, um, highly accelerated great stuff. Chris Lynch veteran the industry executive chairman CEO of scale here on the cube conversation with John furrier, the host. Thank you for watching Chris. Great to see you. Thanks for coming on. >>Great to see you, John, take care. Hope to see you soon. >>Okay. Let's keep conversation. Thanks for watching.

>> From Cambridge, Massachusetts it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by, SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts, everybody. You're watching theCUBE, the leader in live tech coverage. I'm Dave Vellante with my co-host, Paul Gillan. Chris Lynch, good friend is here CEO, newly minted CEO and AtScale and legend. Good to see you. >> In my own mind. >> In mine too. >> It's great to be here. >> It's awesome, thank you for taking time. I know how busy you are, you're running around like crazy your next big thing. I was excited to hear that you got back into it. I predicted it a while ago you were a very successful venture capitalists but at heart, you're startup guy, aren't ya? >> Yeah 100%, 100%. I couldn't be more thrilled, I feel invigorated. I think I've told you many times, when you've interviewed me and asked me about the transition from being an entrepreneur to being a VC and since it's a PG show, I've got a different analog than the one I usually give you. I used to be a movie star and now I'm an executive producer of movies. Now am back to being a movie star, hopefully. >> yeah well, so you told me when you first became a VC you said, I look for startups that have a 10X impact either 10X value, 10X cost reduction. What was it that attracted you to AtScale? What's the 10X? >> AtScale, addresses $150 billion market problem which is basically bringing traditional BI to the cloud. >> That's the other thing you told me, big markets. >> Yeah, so that's the first thing massive market opportunity. The second is, the innovation component and where the 10X comes we're uniquely qualified to virtualize data into the pipeline and out. So I like to say that we're the bridge between BI and AI and back. We make every BI user, a citizen data scientist and that's a game changer. And that's sort of the new futuristic component of what we do. So one part is steeped in, that $150 billion BI marketplace in a traditional analytics platforms and then the second piece is into you delivering the data, into these BI excuse me, these AI machine learning platforms. >> Do you see that ultimately getting integrated into some kind of larger, data pipeline framework. I mean, maybe it lives in the cloud or maybe on prem, how do you see that evolving over time? >> So I believe that, with AtScale as one single pane of glass, we basically are providing an API, to the data and to the user, one single API. The reason that today we haven't seen the delivery of the promise of big data is because we don't have big data. Fortunate 2000 companies don't have big data. They have lots of data but to me big data means you can have one logical view of that data and get the best data pumped into these models in these tools, and today that's not the case. They're constricted by location they're constricted by vendor they're constricted by whether it's in the cloud or on prem. We eliminate those restrictions. >> The single API, I think is important actually. Because when you look at some of these guys what they're doing with their data pipeline they might have 10 or 15 unique API's that they're trying to manage. So there's a simplification aspect to, I suppose. >> One of the knocks on traditional BI has always been the need for extract databases and all the ETL that goes that's involved in that. Do you guys avoid that stage? You go to the production data directly or what's the-- >> It's a great question. The way I put it is, we bring Moses to the mountain the mountain being the data, Moses being the user. Traditionally, what people have been trying to do is bring the mountain to Moses, doesn't scale. At AtScale, we provide an abstraction a logical distraction between the data and the BI user. >> You don't touch, you don't move the data. >> We don't move the data. Which is what's unique and that's what's delivering I think, way more than a 10X delivery in value. >> Because you leave the data in place you bring that value to wherever the data is. Which is the original concept of Hadoop, by the way. That was what was profound about Hadoop everybody craps on it now, but that was the game changer and if you could take advantage of that that's how you tap your 10X. >> To the difference is, we're not, to your point we're not moving the data. Hadoop, in my humble opinion why it plateaued is because to get the value, you had to ask the user to bring and put data in yet another platform. And the reason that we're not delivering on big data as an industry, I believe is because we've too many data sources, too many platforms too many consumers of data and too many producers. As we build all these islands of data, with no connectivity. The idea is, we'll create this big data lake and we're going to physically put everything in there. Guess what? Someday turned out to be never. Because people aren't going to deal with the business disruption. We move thousands of users from a platform like Teradata to a platform like Snowflake or Google BigQuery, we don't care. We're a multi-cloud and we're a hybrid cloud. But we do it without any disruption. You're using Excel, you just continue and use it. You just see the results are faster. You use Tableau, same difference. >> So we had all the vertical rock stars in here. So we had Colin in yesterday, we had Stonebraker around earlier. Andy Palmer just came on and Chris here with the CEO who ultimately sold the company to HP. That really didn't do anything with it and then spun it off and now it's back. Aaron was, he had a spring in his step yesterday. So when you think about, Vertica. The technology behind Vertica go back 10 years and where we come now give us a little journey of, your data journey. >> So I think it plays into the, the original assertion is that, vertical is a best-in-class platform for analytics but it was yet another platform. The analog I give now, is now we have Snowflake and six months, 12 months from now we're going to have another one. And that creates a set of problems if you have to live in the physical world. Because you've all these islands of data and I believe, it's about the data not about the models, it's about the data. You can't get optimal results if you don't have an optimal access to the pertinent data. I believe that having that Universal API is going to make the next platform that more valuable. You're not going to be making the trade-off is, okay we have this platform that has some neat capability but the trade-off is from an enterprise architecture perspective we're never going to be able to connect all this stuff. That's how all of these things proliferated. My view is, in a world where you have that single pane of glass, that abstraction layer between the user and the data. Then innovation can be spawned quicker and you can use these tools effectively 'cause you're not compromising being able to get a logical view of the data and get access to it as a user. >> What's your issue with Snowflake you mentioned them, Mugli's company-- >> No issue, they're a great partner of ours. We eliminate the friction between the user going from an on-prem solution to the cloud. >> Slootman just took over there. So you know where that's going. >> Yep (laughing) >> Frank's got the magic touch. Okay good, you say they're a partner yours how are you guys partnering? >> They refer us into customers that, if you want to buy Snowflake now the next issue is, how do i migrate? You don't. You put our virtualization layer in and then we allow you access to Snowflake in a non-disruptive way, versus having to move data into their system or into a particular cloud which creates sales friction. >> Moving data is just, you want to avoid it at all cost. >> I do want to ask you because I met with your predecessors, Dave Mariani last year and I know he was kind of a reluctant CEO he didn't really want to be CEO but wanted to be CTO, which is what he is now. How did that come about, that they found you that you connected with them and decided this was the right opportunity. >> That's a great question. I actually looked at the company at the seed stage when I was in venture, but I had this thing as you know that, I wanted to move companies to Boston and they're about my vintage age-wise and he's married with four kids so that wasn't in the cards. I said look, it doesn't make sense for me to seed this company 'cause I can't give you the time you're out in California everything I'm instrumenting is around Boston. We parted friends. And I was skeptical whether he could build this 'cause people have been talking about building a heterogeneous universal semantic layer, for years and it's never come to fruition. And then he read in Fortune or Forbes that I was leaving Accomplice and that I was looking for one more company to operate. He reached out and he told me what they were doing that hey, we really built it but we need help and I don't want to run this. It's not right for the company and the opportunity So he said, "I'll come and I'll consult to you." I put together a plan and I had my Vertica and data robot. NekTony guys do the technical diligence to make sure that the architecture wasn't wedded to the dupe, like all the other ones were and when I saw it wasn't then I knew the market opportunity was to take that, rifle and point it at that legacy $150 billion BI market not at the billion dollar market of Hadoop. And when we did that, we've been growing at 162% quarter-over-quarter. We've built development centers in Bulgaria. We've moved all operations, non-technical to Boston here down in our South Station. We've been on fire and we are the partner of choice of every cloud manner, because we eliminate the sales friction, for customers being able to take advantage of movement to the cloud and we're able through our intelligent pipeline and capability. We're able to reduce the cost significantly of queries because we understand and we were able to intelligently cash those queries. >> Sales ops is here, all-- >> Sales marketing, customer support, customer success and we're building a machine learning team here at Dev team here. >> Where are you in that sort of Boston build-out? >> We have an office on 711 Atlantic that we opened in the fall. We're actually moving from 4,000 square feet to 10,000 this month. In less than six months and we'll house by the first year, 100 employees in Boston 100 in Bulgaria and about that same hundred in San Mateo. >> Are you going after net new business mainly? Or there's a lot of legacy BI out there are you more displacing those products? >> A couple of things. What we find is that, customers want to evolve into the cloud, they don't want a revolution they want a evolution. So we allow them, because we support hybrid cloud to keep some data behind the firewall and then experiment with moving other data to the cloud platform of choice but we're still providing that one logical view. I would say most of our customers are looking to reap platform, off of Teradata or something onto a, another platform like Snowflake. And then we have a set of customers that see that as part of the solution but not the whole solution. They're more true hybrids but I would say that 80% of our customers are traditional BI customers that are trying to contemporize their environments and be able to take advantage of tabular support and multidimensional, the things that we do in addition to the cube world. >> They can keep whatever they're using. >> Correct, that's the key. >> Did you do the series D, you did, right? >> Yes, Morgan Stanely led. >> So you're not actively but you're good for now, It was like $50 million >> Yeah we raised $50 million. >> You're good for a bit. Who's in the Chris Lynch target? (laughs) Who's the enemy? Vertica, I could say it was the traditional database guys. Who's the? >> We're in a unique position, we're almost Switzerland so we could be friend to foe, of anybody in that ecosystem because we can, non-disruptively re-platform customers between legacy platforms or from legacy platforms to the cloud. We're an interesting position. >> So similar to the file sharing. File virtualization company >> The Copier. >> Copier yeah. >> It puts us in an interesting position. They need to be friends with us and at the same time I'm sure that they're concerned about the capabilities we have but we have a number of retail customers for instance that have asked us to move down from Amazon to Google BigQuery, which we accommodate and because we can do that non-disruptively. The cost and the ability to move is eliminated. It gives customers true freedom of choice. >> How worried are you, that AWS tries to replicate what you guys do. You're in their sights. >> I think there are technical, legal and structural barriers to them doing that. The technical is, this team has been at it for six and a half years. So to do what we do, they'll have to do what we've done. Structurally from a business perspective if they could, I'm not sure they want to. The way to think about Amazon is, they're no different than Teradata, except for they want the same vendor lock-in except they want it to be the Amazon Cloud when Teradata wanted it to be, their data warehouse. >> They don't promote multi-cloud versus-- >> Yeah, they don't want multi-cloud they don't want >> On Prem >> Customers to have a freedom of choice. Would they really enable a heterogeneous abstraction layer, I don't think they would nor do I think any of the big guys would. They all claim to have this capability for their system. It's like the old IBM adage I'm in prison but the food's going to get three squares a day, I get cable TV but I'm in prison. (laughing) >> Awesome, all right, parting thoughts. >> Parting thoughts, oh geez you got to give me a question I'm not that creative. >> What's next, for you guys? What should we be paying attention to? >> I think you're going to see some significant announcements in September regarding the company and relationships that I think will validate the impact we're having in the market. >> Give you some leverage >> Yeah, will give us, better channel leverage. We have a major technical announcement that I think will be significant to the marketplace and what will be highly disruptive to some of the people you just mentioned. In terms of really raising the bar for customers to be able to have the freedom of choice without any sort of vendor lock-in. And I think that that will create some counter strike which we'll be ready for. (laughing) >> If you've never heard of AtScale before trust me you're going to in the next 18 months. Chris Lynch, thanks so much for coming on theCUBE. >> It's my pleasure. >> Great to see you. All right, keep it right there everybody we're back with our next guest, right after this short break you're watching theCUBE from MIT, right back. (upbeat music)

[Music] [Applause] hello everyone welcome to the cube conversation here in palo alto california i'm john furrier host of thecube we got a special breaking news at scale has hired a new ceo chris lynch is the big news and taking over dave mariano with the evp of technology important because one cube alumni and also we've seen about all the big data events doing amazing work in the cloud scale data market for many many years we're very familiar at scale both david and chris lynch both cube alumni chris great to see you congratulations new ceo of atscale thanks john i really appreciate being here so um we know each other you've been on the cube before you're formerly the ceo of vertica sold to hp other ceos before that also a venture capitalist at atlas ventures for that distinguished career why this move right now what's attracted you to at scale take over the helm from the co-founder who will be partnering with you on this it's a great question um i'm still asking myself that but i think what it comes down to is i met dave years ago when i was at atlas and um it didn't work out the time for us to make an investment but i tracked the company and what they were doing and i left accomplished a little over a year ago and working with some companies out in the west coast app and be out here and i reached out to dave and said hey you want to grab dinner which we did and um by the end of the evening he was like you know you should come help us really commercialize this and take it to the next level they've been on our radar they've been on our radar for a while obviously i mean david at hadoop summit i think three four years ago and he was formerly a cloud we kind of hit it off clearly a big data um visionary and also entrepreneur but they had a unique model at that time hadoop was certainly viewed as you know the king of the castle for in big data at that time but cloud scale wasn't on everyone's radar on the mainstream they had a unique perspective has anything changed with the tech is that what's attracted to you the scaled piece of it what's the what's the secret sauce that that got you enticing so so i'm aware of the company's history that's not what got me interested um what got me interested is i think that they're the only player today in in the market that has a production product that can actually take customers from the data center to the cloud and do so transparently and i liken it to what we did at copia we virtualized file systems and frankly when we virtualized http traffic at arrow point so the idea of an abstraction layer a federation layer made sense to me and um you know as a venture capitalist i've seen the lack of adoption of big data workloads in the cloud you know there's a 200 billion dollar opportunity i think these guys are uniquely qualified to take advantage of it so that's really what drove me from a from a business perspective i see this opportunities unique versus anything i've seen on either coast we get a great reputation as an operator also someone who can manage and operate businesses and grow them and ultimately you have some great exits in your day vertica is well known in in the history of tech for two reasons one is it was probably the best deal hp ever has ever done on acquisition value-wise also it was before and during the whole vertica and autonomy autonomy acquisition was billions of dollars so and they ended up throwing that away keeping vertica and that became the flagship so you've seen how companies can take a wave and get in the right position you've done that with stone breaker in the past founder of vertica do you get the same movement here with this company it's the same playbook what's different is it the same what's the opportunity for this company so i think the opportunity for this company is different at vertica it was about executing against the excellent product that they had built in a known market they were targeted for my vision for at scale is to move beyond the data lake hadoop market and really take all the legacy warehousing vendors to the cloud cloud proof those solutions behind the firewall and begin to deliver those workloads in earnest to the cloud transparent to the user irrespective of the bi tool whether the the technology is behind the firewall in front of the firewall and i think that's a game changer certainly we've seen on the cube and the big conversation in the industry has been hybrid cloud multi-cloud but if you squint through that those trend lines it's really about integration right so you mentioned getting people to the cloud how big is that right now from an action standpoint is it is it accelerating is it early stages where is the progress bar on companies accelerating to the cloud it's it's stalled frankly because there are thousands of tens of thousands of applications in the fortune 500 that the the ability to take those applications and that data and move it to the cloud is it's on par with trying to operate do heart surgery on a patient while they're running the boston marathon so it's too difficult it's too disruptive to a business too risky what we do is we create a federation layer that basically abstracts all that complexity from the user and makes that transition transparent so to the user they don't have to care whether it's behind the firewall in front of the firewall what cloud it sits on what analytics store you're drawing from what bi tool it doesn't matter to the user so they've basically been able to separate those two things and that's going to allow people to scale and evolve into the cloud right today cloud is a revolution not an evolution it needs to be an evolution for fortune 500 companies to take advantage of it i got to ask you the hard question because ultimately let amazon they're kicking ass 10 ways from saturday they're obviously the numbers are off the chart even in public sectors just down there last week you got azure retooling and essentially they're going to try to replicate the the uh the congress of scale i think they're going to have a hard time but still no they're not going anywhere either and you get google changing the game focusing on their core competencies and where they can differentiate all that is potentially competition so this company at scale they definitely have tech chops so that's you know we know the team there so they had a lot of credit for that but 25 million dollars raised in their last round of funding total capital day 45 million how are you going to compete how are you going to take this and commercialize this opportunity and not be driftwood instead ride that wave it's a terrific question i actually think that one of the things that excites me about this opportunity it's the first opportunity as an operator that i've had that i haven't been in the david goliath thing i actually don't think that any of those people are competitors i think when atscale wins bi vendors win traditional data stores win and the cloud provider wins and ultimately the customer wins so my view is all those companies you mentioned if google wants to be relevant in the enterprise they need to get those big data workloads to their cloud we can do that we can continue to help amazon do that we can help oracle secure cloud do that we can help microsoft do that and all the time we're future proofing the legacy data stores of the teradatas and the oracles and the ibms so this is the first opportunity that i've seen where the game isn't to go disrupt and call out the competition it's to work with all these people to drive workloads to the cloud in a in a scale that hasn't been done before so you'd have to unseat anyone you've got to ride the cloud wave pretty much yeah we have to we have to demonstrate to these guys that we do what we say we we do um but my view is when we win all those participants can potentially win awesome how about uh staff funding you feel good that enough try powder in there is there another round of funding on the horizon or yeah i mean you know i haven't even started yet but you know my expectation is that in this marketplace with my track record raising capital or attracting capital will not be an issue it'll be about figuring out the business model and making sure it's right and then investing behind that business model it's enough cash now certainly do that talk about the boston california you're going to stay in boston that's news companies based in california you have a pedigree in boston certainly and being a vc down there but also you run businesses down there there's talent down there is there plans for a boston expansion a boston bi-coastal situation what's their opportunity so the company will remain headquartered in san mateo and i'll take up residence here and i'll go back and forth so my family's not moving so i'll have a residence in boston and one here um but you can absolutely expect that i'm going to leverage the ecosystem that i've grown up in and we will have a significant presence on the east coast awesome chris final question machine learning you guys were close to that for a long time at vertica you guys were doing some of the most cutting-edge machine learning before it became super popular as it is now as they call it ai now but essentially that was the beginning of the commodore store database which you guys pioneered speed and using data for competitive advantage how is that now scaled up in the market now how how robust is it how mature is it how ready is it and how does atscale take advantage of of that of that growth so i think that the world of data science in general has matured if you look at one of my proudest investments company called data robot they are the leaders in automated machine learning and their business is growing triple digits every year the level of adoption is really only gated by practitioners and people to apply the technology to these business problems but it's gaining incredible momentum for us i plan to integrate automated ai into components of our architecture which i think makes it really a game changer so of course we expect competition um but by the time that you know they get you know 100 miles behind us you know we're going to hit that what's that button that you have in those teslas you guys drive out here insane mode and it'll be automated machine learning will be powering that what's your impression of the marketplace right now is that um you know obviously you're seeing global landscape you know we see the china situation going on asia a lot of activity a lot of growth outside the united states um and obviously cloud you're seeing region specialties any thoughts on how that's going to play into it is it not relevant to you guys right now what's the what's your thoughts on the global landscape i mean i think it's it's relevant to everyone because i think it's what's driving valuations and this influx of money coming from these different places i mean if if you look at the middle east you know they're writing checks to any sort of tech company they can because they're pr trying to divest of what they know is a dead business right so that's going to drive valuations it's going to drive in my opinion um a lack of discipline and and bad behavior as we've seen you know in 2000 and other times in 2008 um i think for us as a company we're going to be disciplined and you know the fact that we can raise money and raise money attractive valuations isn't a reason to do it if you have a business model of fund that's a reason to do it so you know i don't think it'll be a distraction for us but i think it will increase you know the amount of noise in all the key markets and i think cyber we've seen it you know ai for sure um iot bitcoin all these what's the most exciting thing in the data business that's as it evolves now to the center value proposition that you see and as the ceo now of at scala you're going to capture this i think i would say two things in the in the ai machine learning space i think the fact that with democratization of data you're now actually seeing people applying machine learning way broader in organizations and way deeper than ever before and that's going to transform businesses low-tech business as well as high-tech businesses for us i think that the real opportunity exists it's a question of just taking these lit these legacy workloads and moving them to the cloud and that's not a trivial task not just technically but um you always have to be sensitive to companies ability to absorb technology i think one of the challenges is you know you're trying to transform a business that you know basically was informed as it developed in technology that was 1980. well chris congratulations on the ceo opportunity at at scale um what can people expect from you what what if you can write the narrative of the first couple moves off the line of scrimmage here what are you going to do what's your order of business what can they expect from you well the first thing i'd like to do after i meet the customers and the employees and the par existing partners is go out and get two significant partnerships i like to see a couple partnerships in cloud a couple partnerships with the classic data store vendors so that's probably going to be my first mission to get that moving and you know we'll see how quickly it goes but i think that's super important to do yeah and certainly scale right now has been a big competitive advantage here's a company at scale five year on their five year anniversary interesting gestation period for this big data world because hadoop you can look back into 2010 days hadoop was supposed to be the biggest thing since sliced bread but what happened was the world became bigger and from a date not just outside hadoop gave these guys an opportunity and their architecture fits well you see it's scaling quicker what's your what's your where's your point of scale how do you see this so i think i think that the company probably rightly so at the time hitch their wagon to hadoop but i think as you said it's it's really a subset of the data landscape and it's actually a pretty small one the real opportunity is in driving all the legacy data and analytics stores those islands of analytics and bringing them to the cloud and that like i said i think is a you know 100 billion dollar business well certainly great to see you congratulations getting back at the chief position and did a great job at vertica great journey we followed you on that one that was fantastic and then certainly watch it unfold certainly at hpe create a lot of value congratulations and at scale's got a good hire there congratulations thank you i appreciate it alright this is thecube conversation here inside the palo alto studios i'm john furrier thanks for watching [Music]

>> From The Cube Studios in Palo Alto and Boston, bringing you data driven insights from The Cube at ETR. This is "Breaking Analysis" with Dave Vellante. >> The reverse momentum in tech stocks caused by rising interest rates, less attractive discounted cash flow models, and more tepid forward guidance, can be easily measured by public market valuations. And while there's lots of discussion about the impact on private companies and cash runway and 409A valuations, measuring the performance of non-public companies isn't as easy. IPOs have dried up and public statements by private companies, of course, they accentuate the good and they kind of hide the bad. Real data, unless you're an insider, is hard to find. Hello and welcome to this week's "Wikibon Cube Insights" powered by ETR. In this "Breaking Analysis", we unlock some of the secrets that non-public, emerging tech companies may or may not be sharing. And we do this by introducing you to a capability from ETR that we've not exposed you to over the past couple of years, it's called the Emerging Technologies Survey, and it is packed with sentiment data and performance data based on surveys of more than a thousand CIOs and IT buyers covering more than 400 companies. And we've invited back our colleague, Erik Bradley of ETR to help explain the survey and the data that we're going to cover today. Erik, this survey is something that I've not personally spent much time on, but I'm blown away at the data. It's really unique and detailed. First of all, welcome. Good to see you again. >> Great to see you too, Dave, and I'm really happy to be talking about the ETS or the Emerging Technology Survey. Even our own clients of constituents probably don't spend as much time in here as they should. >> Yeah, because there's so much in the mainstream, but let's pull up a slide to bring out the survey composition. Tell us about the study. How often do you run it? What's the background and the methodology? >> Yeah, you were just spot on the way you were talking about the private tech companies out there. So what we did is we decided to take all the vendors that we track that are not yet public and move 'em over to the ETS. And there isn't a lot of information out there. If you're not in Silicon (indistinct), you're not going to get this stuff. So PitchBook and Tech Crunch are two out there that gives some data on these guys. But what we really wanted to do was go out to our community. We have 6,000, ITDMs in our community. We wanted to ask them, "Are you aware of these companies? And if so, are you allocating any resources to them? Are you planning to evaluate them," and really just kind of figure out what we can do. So this particular survey, as you can see, 1000 plus responses, over 450 vendors that we track. And essentially what we're trying to do here is talk about your evaluation and awareness of these companies and also your utilization. And also if you're not utilizing 'em, then we can also figure out your sales conversion or churn. So this is interesting, not only for the ITDMs themselves to figure out what their peers are evaluating and what they should put in POCs against the big guys when contracts come up. But it's also really interesting for the tech vendors themselves to see how they're performing. >> And you can see 2/3 of the respondents are director level of above. You got 28% is C-suite. There is of course a North America bias, 70, 75% is North America. But these smaller companies, you know, that's when they start doing business. So, okay. We're going to do a couple of things here today. First, we're going to give you the big picture across the sectors that ETR covers within the ETS survey. And then we're going to look at the high and low sentiment for the larger private companies. And then we're going to do the same for the smaller private companies, the ones that don't have as much mindshare. And then I'm going to put those two groups together and we're going to look at two dimensions, actually three dimensions, which companies are being evaluated the most. Second, companies are getting the most usage and adoption of their offerings. And then third, which companies are seeing the highest churn rates, which of course is a silent killer of companies. And then finally, we're going to look at the sentiment and mindshare for two key areas that we like to cover often here on "Breaking Analysis", security and data. And data comprises database, including data warehousing, and then big data analytics is the second part of data. And then machine learning and AI is the third section within data that we're going to look at. Now, one other thing before we get into it, ETR very often will include open source offerings in the mix, even though they're not companies like TensorFlow or Kubernetes, for example. And we'll call that out during this discussion. The reason this is done is for context, because everyone is using open source. It is the heart of innovation and many business models are super glued to an open source offering, like take MariaDB, for example. There's the foundation and then there's with the open source code and then there, of course, the company that sells services around the offering. Okay, so let's first look at the highest and lowest sentiment among these private firms, the ones that have the highest mindshare. So they're naturally going to be somewhat larger. And we do this on two dimensions, sentiment on the vertical axis and mindshare on the horizontal axis and note the open source tool, see Kubernetes, Postgres, Kafka, TensorFlow, Jenkins, Grafana, et cetera. So Erik, please explain what we're looking at here, how it's derived and what the data tells us. >> Certainly, so there is a lot here, so we're going to break it down first of all by explaining just what mindshare and net sentiment is. You explain the axis. We have so many evaluation metrics, but we need to aggregate them into one so that way we can rank against each other. Net sentiment is really the aggregation of all the positive and subtracting out the negative. So the net sentiment is a very quick way of looking at where these companies stand versus their peers in their sectors and sub sectors. Mindshare is basically the awareness of them, which is good for very early stage companies. And you'll see some names on here that are obviously been around for a very long time. And they're clearly be the bigger on the axis on the outside. Kubernetes, for instance, as you mentioned, is open source. This de facto standard for all container orchestration, and it should be that far up into the right, because that's what everyone's using. In fact, the open source leaders are so prevalent in the emerging technology survey that we break them out later in our analysis, 'cause it's really not fair to include them and compare them to the actual companies that are providing the support and the security around that open source technology. But no survey, no analysis, no research would be complete without including these open source tech. So what we're looking at here, if I can just get away from the open source names, we see other things like Databricks and OneTrust . They're repeating as top net sentiment performers here. And then also the design vendors. People don't spend a lot of time on 'em, but Miro and Figma. This is their third survey in a row where they're just dominating that sentiment overall. And Adobe should probably take note of that because they're really coming after them. But Databricks, we all know probably would've been a public company by now if the market hadn't turned, but you can see just how dominant they are in a survey of nothing but private companies. And we'll see that again when we talk about the database later. >> And I'll just add, so you see automation anywhere on there, the big UiPath competitor company that was not able to get to the public markets. They've been trying. Snyk, Peter McKay's company, they've raised a bunch of money, big security player. They're doing some really interesting things in developer security, helping developers secure the data flow, H2O.ai, Dataiku AI company. We saw them at the Snowflake Summit. Redis Labs, Netskope and security. So a lot of names that we know that ultimately we think are probably going to be hitting the public market. Okay, here's the same view for private companies with less mindshare, Erik. Take us through this one. >> On the previous slide too real quickly, I wanted to pull that security scorecard and we'll get back into it. But this is a newcomer, that I couldn't believe how strong their data was, but we'll bring that up in a second. Now, when we go to the ones of lower mindshare, it's interesting to talk about open source, right? Kubernetes was all the way on the top right. Everyone uses containers. Here we see Istio up there. Not everyone is using service mesh as much. And that's why Istio is in the smaller breakout. But still when you talk about net sentiment, it's about the leader, it's the highest one there is. So really interesting to point out. Then we see other names like Collibra in the data side really performing well. And again, as always security, very well represented here. We have Aqua, Wiz, Armis, which is a standout in this survey this time around. They do IoT security. I hadn't even heard of them until I started digging into the data here. And I couldn't believe how well they were doing. And then of course you have AnyScale, which is doing a second best in this and the best name in the survey Hugging Face, which is a machine learning AI tool. Also doing really well on a net sentiment, but they're not as far along on that access of mindshare just yet. So these are again, emerging companies that might not be as well represented in the enterprise as they will be in a couple of years. >> Hugging Face sounds like something you do with your two year old. Like you said, you see high performers, AnyScale do machine learning and you mentioned them. They came out of Berkeley. Collibra Governance, InfluxData is on there. InfluxDB's a time series database. And yeah, of course, Alex, if you bring that back up, you get a big group of red dots, right? That's the bad zone, I guess, which Sisense does vis, Yellowbrick Data is a NPP database. How should we interpret the red dots, Erik? I mean, is it necessarily a bad thing? Could it be misinterpreted? What's your take on that? >> Sure, well, let me just explain the definition of it first from a data science perspective, right? We're a data company first. So the gray dots that you're seeing that aren't named, that's the mean that's the average. So in order for you to be on this chart, you have to be at least one standard deviation above or below that average. So that gray is where we're saying, "Hey, this is where the lump of average comes in. This is where everyone normally stands." So you either have to be an outperformer or an underperformer to even show up in this analysis. So by definition, yes, the red dots are bad. You're at least one standard deviation below the average of your peers. It's not where you want to be. And if you're on the lower left, not only are you not performing well from a utilization or an actual usage rate, but people don't even know who you are. So that's a problem, obviously. And the VCs and the PEs out there that are backing these companies, they're the ones who mostly are interested in this data. >> Yeah. Oh, that's great explanation. Thank you for that. No, nice benchmarking there and yeah, you don't want to be in the red. All right, let's get into the next segment here. Here going to look at evaluation rates, adoption and the all important churn. First new evaluations. Let's bring up that slide. And Erik, take us through this. >> So essentially I just want to explain what evaluation means is that people will cite that they either plan to evaluate the company or they're currently evaluating. So that means we're aware of 'em and we are choosing to do a POC of them. And then we'll see later how that turns into utilization, which is what a company wants to see, awareness, evaluation, and then actually utilizing them. That's sort of the life cycle for these emerging companies. So what we're seeing here, again, with very high evaluation rates. H2O, we mentioned. SecurityScorecard jumped up again. Chargebee, Snyk, Salt Security, Armis. A lot of security names are up here, Aqua, Netskope, which God has been around forever. I still can't believe it's in an Emerging Technology Survey But so many of these names fall in data and security again, which is why we decided to pick those out Dave. And on the lower side, Vena, Acton, those unfortunately took the dubious award of the lowest evaluations in our survey, but I prefer to focus on the positive. So SecurityScorecard, again, real standout in this one, they're in a security assessment space, basically. They'll come in and assess for you how your security hygiene is. And it's an area of a real interest right now amongst our ITDM community. >> Yeah, I mean, I think those, and then Arctic Wolf is up there too. They're doing managed services. You had mentioned Netskope. Yeah, okay. All right, let's look at now adoption. These are the companies whose offerings are being used the most and are above that standard deviation in the green. Take us through this, Erik. >> Sure, yet again, what we're looking at is, okay, we went from awareness, we went to evaluation. Now it's about utilization, which means a survey respondent's going to state "Yes, we evaluated and we plan to utilize it" or "It's already in our enterprise and we're actually allocating further resources to it." Not surprising, again, a lot of open source, the reason why, it's free. So it's really easy to grow your utilization on something that's free. But as you and I both know, as Red Hat proved, there's a lot of money to be made once the open source is adopted, right? You need the governance, you need the security, you need the support wrapped around it. So here we're seeing Kubernetes, Postgres, Apache Kafka, Jenkins, Grafana. These are all open source based names. But if we're looking at names that are non open source, we're going to see Databricks, Automation Anywhere, Rubrik all have the highest mindshare. So these are the names, not surprisingly, all names that probably should have been public by now. Everyone's expecting an IPO imminently. These are the names that have the highest mindshare. If we talk about the highest utilization rates, again, Miro and Figma pop up, and I know they're not household names, but they are just dominant in this survey. These are applications that are meant for design software and, again, they're going after an Autodesk or a CAD or Adobe type of thing. It is just dominant how high the utilization rates are here, which again is something Adobe should be paying attention to. And then you'll see a little bit lower, but also interesting, we see Collibra again, we see Hugging Face again. And these are names that are obviously in the data governance, ML, AI side. So we're seeing a ton of data, a ton of security and Rubrik was interesting in this one, too, high utilization and high mindshare. We know how pervasive they are in the enterprise already. >> Erik, Alex, keep that up for a second, if you would. So yeah, you mentioned Rubrik. Cohesity's not on there. They're sort of the big one. We're going to talk about them in a moment. Puppet is interesting to me because you remember the early days of that sort of space, you had Puppet and Chef and then you had Ansible. Red Hat bought Ansible and then Ansible really took off. So it's interesting to see Puppet on there as well. Okay. So now let's look at the churn because this one is where you don't want to be. It's, of course, all red 'cause churn is bad. Take us through this, Erik. >> Yeah, definitely don't want to be here and I don't love to dwell on the negative. So we won't spend as much time. But to your point, there's one thing I want to point out that think it's important. So you see Rubrik in the same spot, but Rubrik has so many citations in our survey that it actually would make sense that they're both being high utilization and churn just because they're so well represented. They have such a high overall representation in our survey. And the reason I call that out is Cohesity. Cohesity has an extremely high churn rate here about 17% and unlike Rubrik, they were not on the utilization side. So Rubrik is seeing both, Cohesity is not. It's not being utilized, but it's seeing a high churn. So that's the way you can look at this data and say, "Hm." Same thing with Puppet. You noticed that it was on the other slide. It's also on this one. So basically what it means is a lot of people are giving Puppet a shot, but it's starting to churn, which means it's not as sticky as we would like. One that was surprising on here for me was Tanium. It's kind of jumbled in there. It's hard to see in the middle, but Tanium, I was very surprised to see as high of a churn because what I do hear from our end user community is that people that use it, like it. It really kind of spreads into not only vulnerability management, but also that endpoint detection and response side. So I was surprised by that one, mostly to see Tanium in here. Mural, again, was another one of those application design softwares that's seeing a very high churn as well. >> So you're saying if you're in both... Alex, bring that back up if you would. So if you're in both like MariaDB is for example, I think, yeah, they're in both. They're both green in the previous one and red here, that's not as bad. You mentioned Rubrik is going to be in both. Cohesity is a bit of a concern. Cohesity just brought on Sanjay Poonen. So this could be a go to market issue, right? I mean, 'cause Cohesity has got a great product and they got really happy customers. So they're just maybe having to figure out, okay, what's the right ideal customer profile and Sanjay Poonen, I guarantee, is going to have that company cranking. I mean they had been doing very well on the surveys and had fallen off of a bit. The other interesting things wondering the previous survey I saw Cvent, which is an event platform. My only reason I pay attention to that is 'cause we actually have an event platform. We don't sell it separately. We bundle it as part of our offerings. And you see Hopin on here. Hopin raised a billion dollars during the pandemic. And we were like, "Wow, that's going to blow up." And so you see Hopin on the churn and you didn't see 'em in the previous chart, but that's sort of interesting. Like you said, let's not kind of dwell on the negative, but you really don't. You know, churn is a real big concern. Okay, now we're going to drill down into two sectors, security and data. Where data comprises three areas, database and data warehousing, machine learning and AI and big data analytics. So first let's take a look at the security sector. Now this is interesting because not only is it a sector drill down, but also gives an indicator of how much money the firm has raised, which is the size of that bubble. And to tell us if a company is punching above its weight and efficiently using its venture capital. Erik, take us through this slide. Explain the dots, the size of the dots. Set this up please. >> Yeah. So again, the axis is still the same, net sentiment and mindshare, but what we've done this time is we've taken publicly available information on how much capital company is raised and that'll be the size of the circle you see around the name. And then whether it's green or red is basically saying relative to the amount of money they've raised, how are they doing in our data? So when you see a Netskope, which has been around forever, raised a lot of money, that's why you're going to see them more leading towards red, 'cause it's just been around forever and kind of would expect it. Versus a name like SecurityScorecard, which is only raised a little bit of money and it's actually performing just as well, if not better than a name, like a Netskope. OneTrust doing absolutely incredible right now. BeyondTrust. We've seen the issues with Okta, right. So those are two names that play in that space that obviously are probably getting some looks about what's going on right now. Wiz, we've all heard about right? So raised a ton of money. It's doing well on net sentiment, but the mindshare isn't as well as you'd want, which is why you're going to see a little bit of that red versus a name like Aqua, which is doing container and application security. And hasn't raised as much money, but is really neck and neck with a name like Wiz. So that is why on a relative basis, you'll see that more green. As we all know, information security is never going away. But as we'll get to later in the program, Dave, I'm not sure in this current market environment, if people are as willing to do POCs and switch away from their security provider, right. There's a little bit of tepidness out there, a little trepidation. So right now we're seeing overall a slight pause, a slight cooling in overall evaluations on the security side versus historical levels a year ago. >> Now let's stay on here for a second. So a couple things I want to point out. So it's interesting. Now Snyk has raised over, I think $800 million but you can see them, they're high on the vertical and the horizontal, but now compare that to Lacework. It's hard to see, but they're kind of buried in the middle there. That's the biggest dot in this whole thing. I think I'm interpreting this correctly. They've raised over a billion dollars. It's a Mike Speiser company. He was the founding investor in Snowflake. So people watch that very closely, but that's an example of where they're not punching above their weight. They recently had a layoff and they got to fine tune things, but I'm still confident they they're going to do well. 'Cause they're approaching security as a data problem, which is probably people having trouble getting their arms around that. And then again, I see Arctic Wolf. They're not red, they're not green, but they've raised fair amount of money, but it's showing up to the right and decent level there. And a couple of the other ones that you mentioned, Netskope. Yeah, they've raised a lot of money, but they're actually performing where you want. What you don't want is where Lacework is, right. They've got some work to do to really take advantage of the money that they raised last November and prior to that. >> Yeah, if you're seeing that more neutral color, like you're calling out with an Arctic Wolf, like that means relative to their peers, this is where they should be. It's when you're seeing that red on a Lacework where we all know, wow, you raised a ton of money and your mindshare isn't where it should be. Your net sentiment is not where it should be comparatively. And then you see these great standouts, like Salt Security and SecurityScorecard and Abnormal. You know they haven't raised that much money yet, but their net sentiment's higher and their mindshare's doing well. So those basically in a nutshell, if you're a PE or a VC and you see a small green circle, then you're doing well, then it means you made a good investment. >> Some of these guys, I don't know, but you see these small green circles. Those are the ones you want to start digging into and maybe help them catch a wave. Okay, let's get into the data discussion. And again, three areas, database slash data warehousing, big data analytics and ML AI. First, we're going to look at the database sector. So Alex, thank you for bringing that up. Alright, take us through this, Erik. Actually, let me just say Postgres SQL. I got to ask you about this. It shows some funding, but that actually could be a mix of EDB, the company that commercializes Postgres and Postgres the open source database, which is a transaction system and kind of an open source Oracle. You see MariaDB is a database, but open source database. But the companies they've raised over $200 million and they filed an S-4. So Erik looks like this might be a little bit of mashup of companies and open source products. Help us understand this. >> Yeah, it's tough when you start dealing with the open source side and I'll be honest with you, there is a little bit of a mashup here. There are certain names here that are a hundred percent for profit companies. And then there are others that are obviously open source based like Redis is open source, but Redis Labs is the one trying to monetize the support around it. So you're a hundred percent accurate on this slide. I think one of the things here that's important to note though, is just how important open source is to data. If you're going to be going to any of these areas, it's going to be open source based to begin with. And Neo4j is one I want to call out here. It's not one everyone's familiar with, but it's basically geographical charting database, which is a name that we're seeing on a net sentiment side actually really, really high. When you think about it's the third overall net sentiment for a niche database play. It's not as big on the mindshare 'cause it's use cases aren't as often, but third biggest play on net sentiment. I found really interesting on this slide. >> And again, so MariaDB, as I said, they filed an S-4 I think $50 million in revenue, that might even be ARR. So they're not huge, but they're getting there. And by the way, MariaDB, if you don't know, was the company that was formed the day that Oracle bought Sun in which they got MySQL and MariaDB has done a really good job of replacing a lot of MySQL instances. Oracle has responded with MySQL HeatWave, which was kind of the Oracle version of MySQL. So there's some interesting battles going on there. If you think about the LAMP stack, the M in the LAMP stack was MySQL. And so now it's all MariaDB replacing that MySQL for a large part. And then you see again, the red, you know, you got to have some concerns about there. Aerospike's been around for a long time. SingleStore changed their name a couple years ago, last year. Yellowbrick Data, Fire Bolt was kind of going after Snowflake for a while, but yeah, you want to get out of that red zone. So they got some work to do. >> And Dave, real quick for the people that aren't aware, I just want to let them know that we can cut this data with the public company data as well. So we can cross over this with that because some of these names are competing with the larger public company names as well. So we can go ahead and cross reference like a MariaDB with a Mongo, for instance, or of something of that nature. So it's not in this slide, but at another point we can certainly explain on a relative basis how these private names are doing compared to the other ones as well. >> All right, let's take a quick look at analytics. Alex, bring that up if you would. Go ahead, Erik. >> Yeah, I mean, essentially here, I can't see it on my screen, my apologies. I just kind of went to blank on that. So gimme one second to catch up. >> So I could set it up while you're doing that. You got Grafana up and to the right. I mean, this is huge right. >> Got it thank you. I lost my screen there for a second. Yep. Again, open source name Grafana, absolutely up and to the right. But as we know, Grafana Labs is actually picking up a lot of speed based on Grafana, of course. And I think we might actually hear some noise from them coming this year. The names that are actually a little bit more disappointing than I want to call out are names like ThoughtSpot. It's been around forever. Their mindshare of course is second best here but based on the amount of time they've been around and the amount of money they've raised, it's not actually outperforming the way it should be. We're seeing Moogsoft obviously make some waves. That's very high net sentiment for that company. It's, you know, what, third, fourth position overall in this entire area, Another name like Fivetran, Matillion is doing well. Fivetran, even though it's got a high net sentiment, again, it's raised so much money that we would've expected a little bit more at this point. I know you know this space extremely well, but basically what we're looking at here and to the bottom left, you're going to see some names with a lot of red, large circles that really just aren't performing that well. InfluxData, however, second highest net sentiment. And it's really pretty early on in this stage and the feedback we're getting on this name is the use cases are great, the efficacy's great. And I think it's one to watch out for. >> InfluxData, time series database. The other interesting things I just noticed here, you got Tamer on here, which is that little small green. Those are the ones we were saying before, look for those guys. They might be some of the interesting companies out there and then observe Jeremy Burton's company. They do observability on top of Snowflake, not green, but kind of in that gray. So that's kind of cool. Monte Carlo is another one, they're sort of slightly green. They are doing some really interesting things in data and data mesh. So yeah, okay. So I can spend all day on this stuff, Erik, phenomenal data. I got to get back and really dig in. Let's end with machine learning and AI. Now this chart it's similar in its dimensions, of course, except for the money raised. We're not showing that size of the bubble, but AI is so hot. We wanted to cover that here, Erik, explain this please. Why TensorFlow is highlighted and walk us through this chart. >> Yeah, it's funny yet again, right? Another open source name, TensorFlow being up there. And I just want to explain, we do break out machine learning, AI is its own sector. A lot of this of course really is intertwined with the data side, but it is on its own area. And one of the things I think that's most important here to break out is Databricks. We started to cover Databricks in machine learning, AI. That company has grown into much, much more than that. So I do want to state to you Dave, and also the audience out there that moving forward, we're going to be moving Databricks out of only the MA/AI into other sectors. So we can kind of value them against their peers a little bit better. But in this instance, you could just see how dominant they are in this area. And one thing that's not here, but I do want to point out is that we have the ability to break this down by industry vertical, organization size. And when I break this down into Fortune 500 and Fortune 1000, both Databricks and Tensorflow are even better than you see here. So it's quite interesting to see that the names that are succeeding are also succeeding with the largest organizations in the world. And as we know, large organizations means large budgets. So this is one area that I just thought was really interesting to point out that as we break it down, the data by vertical, these two names still are the outstanding players. >> I just also want to call it H2O.ai. They're getting a lot of buzz in the marketplace and I'm seeing them a lot more. Anaconda, another one. Dataiku consistently popping up. DataRobot is also interesting because all the kerfuffle that's going on there. The Cube guy, Cube alum, Chris Lynch stepped down as executive chairman. All this stuff came out about how the executives were taking money off the table and didn't allow the employees to participate in that money raising deal. So that's pissed a lot of people off. And so they're now going through some kind of uncomfortable things, which is unfortunate because DataRobot, I noticed, we haven't covered them that much in "Breaking Analysis", but I've noticed them oftentimes, Erik, in the surveys doing really well. So you would think that company has a lot of potential. But yeah, it's an important space that we're going to continue to watch. Let me ask you Erik, can you contextualize this from a time series standpoint? I mean, how is this changed over time? >> Yeah, again, not show here, but in the data. I'm sorry, go ahead. >> No, I'm sorry. What I meant, I should have interjected. In other words, you would think in a downturn that these emerging companies would be less interesting to buyers 'cause they're more risky. What have you seen? >> Yeah, and it was interesting before we went live, you and I were having this conversation about "Is the downturn stopping people from evaluating these private companies or not," right. In a larger sense, that's really what we're doing here. How are these private companies doing when it comes down to the actual practitioners? The people with the budget, the people with the decision making. And so what I did is, we have historical data as you know, I went back to the Emerging Technology Survey we did in November of 21, right at the crest right before the market started to really fall and everything kind of started to fall apart there. And what I noticed is on the security side, very much so, we're seeing less evaluations than we were in November 21. So I broke it down. On cloud security, net sentiment went from 21% to 16% from November '21. That's a pretty big drop. And again, that sentiment is our one aggregate metric for overall positivity, meaning utilization and actual evaluation of the name. Again in database, we saw it drop a little bit from 19% to 13%. However, in analytics we actually saw it stay steady. So it's pretty interesting that yes, cloud security and security in general is always going to be important. But right now we're seeing less overall net sentiment in that space. But within analytics, we're seeing steady with growing mindshare. And also to your point earlier in machine learning, AI, we're seeing steady net sentiment and mindshare has grown a whopping 25% to 30%. So despite the downturn, we're seeing more awareness of these companies in analytics and machine learning and a steady, actual utilization of them. I can't say the same in security and database. They're actually shrinking a little bit since the end of last year. >> You know it's interesting, we were on a round table, Erik does these round tables with CISOs and CIOs, and I remember one time you had asked the question, "How do you think about some of these emerging tech companies?" And one of the executives said, "I always include somebody in the bottom left of the Gartner Magic Quadrant in my RFPs. I think he said, "That's how I found," I don't know, it was Zscaler or something like that years before anybody ever knew of them "Because they're going to help me get to the next level." So it's interesting to see Erik in these sectors, how they're holding up in many cases. >> Yeah. It's a very important part for the actual IT practitioners themselves. There's always contracts coming up and you always have to worry about your next round of negotiations. And that's one of the roles these guys play. You have to do a POC when contracts come up, but it's also their job to stay on top of the new technology. You can't fall behind. Like everyone's a software company. Now everyone's a tech company, no matter what you're doing. So these guys have to stay in on top of it. And that's what this ETS can do. You can go in here and look and say, "All right, I'm going to evaluate their technology," and it could be twofold. It might be that you're ready to upgrade your technology and they're actually pushing the envelope or it simply might be I'm using them as a negotiation ploy. So when I go back to the big guy who I have full intentions of writing that contract to, at least I have some negotiation leverage. >> Erik, we got to leave it there. I could spend all day. I'm going to definitely dig into this on my own time. Thank you for introducing this, really appreciate your time today. >> I always enjoy it, Dave and I hope everyone out there has a great holiday weekend. Enjoy the rest of the summer. And, you know, I love to talk data. So anytime you want, just point the camera on me and I'll start talking data. >> You got it. I also want to thank the team at ETR, not only Erik, but Darren Bramen who's a data scientist, really helped prepare this data, the entire team over at ETR. I cannot tell you how much additional data there is. We are just scratching the surface in this "Breaking Analysis". So great job guys. I want to thank Alex Myerson. Who's on production and he manages the podcast. Ken Shifman as well, who's just coming back from VMware Explore. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some great editing for us. Thank you. All of you guys. Remember these episodes, they're all available as podcast, wherever you listen. All you got to do is just search "Breaking Analysis" podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch david.vellante@siliconangle.com. You can DM me at dvellante or comment on my LinkedIn posts and please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for Erik Bradley and The Cube Insights powered by ETR. Thanks for watching. Be well. And we'll see you next time on "Breaking Analysis". (upbeat music)

(upbeat music) >> On June 21st in Cambridge mass at the Sinclair in Harvard Square, Tech Tackles Cancer is back after a COVID hiatus with live band karaoke and some local tech celebrities raising money for a great cause. The Cube is a media sponsor of the event and Josh Epstein, local marketing exec and one of the events organizers is here to tell us more. Josh, good to see you, welcome. >> Good to be here, Dave. >> So tell us about this event. What's going on? What are the logistics? How's that all work? >> Yeah, we're super excited. So as you said, June 21st at the Sinclair in Harvard Square, Sinclair, if you haven't been there is just the great old school rock club. So we'll be there from 6:00 to 10:00. We will have live band karaoke. So the main event and kind of the primary fundraising approach here is that we have some celebrity technology rock gods these featured performers like Chris Lynch who was the founder of Tech Tackles Cancer, who are are raising money from basically now, up until June 21st. Then at the event, their fundraising will culminate with them singing a live song backed by a live band. And the awards will be given out to the most money raised, the best performance and the best stage presence. So it will be a lot of fun. >> So the fundraising format is I sign up to sing do the karaoke with a live band which is a little bit different. And then I raise as much dough as possible. So obviously that's competitive. >> It's competitive, I think that we ask for a minimum of $10,000 targeted for each of the fundraisers but knowing these guys, knowing guys like Chris Lynch, they don't like to lose. So the bet here is that people are going to go out, they're going to hit their network and they are going to look to kind of raise the most money. So we anticipate this to be a great event with a lot of money raised and a lot of fun. >> So we have a graphic from Alex. If you could bring that up of the people who have signed up for this already. We got Steve Duplessie, founder of of ESG, senior analyst. They sold their company to Tech Target, which is awesome. Congratulations to those guys and thank you for stepping up. George Hope, who heads partner sales for HPE, Joe Lemay of Rocketbook Nathan Hall from Pure Storage, system engineering guy and of course, Steiny, Ken Steinhardt from Infinidat. He was at EMC, he's the field CTO now. He's going to be up there singing. So of course, Chris. >> Absolutely, these are just the early entrance here. So we just started really working our networks. And obviously, I'm a Boston tech guy kind of working the storage networks, the networking networks and kind of the other folks that are around. So as we come out of stealth here in April and start really recruiting, we anticipate having probably 10 to 15 of these featured performers, really fundraising performers that we'll sing. And then we're also obviously soliciting broader donations from anyone who wants to come to the event or just give to the cause and the corporate sponsorships as well. >> All right, so you got corporate sponsorships. You can sing, you can donate you can be there just to support it. That's fantastic and the awards, how's that work? >> Yeah, so we're excited. So first off, most money raised wins an award. So we'll have a leaderboard on the website, we'll be able to kind of track who's raised what, at the event, we're going to have some celebrity judges that will be actually voting for their favorites and then have a crowdsource component as well. So we'll introduce what that mechanism is. But as people, either at the events or a watching in streamed live on LinkedIn live, we'll actually vote for their favorite performance as well as their their pick for best stage presence which we know in rock and roll is half the battle. >> Now this cause has raised a bunch of, I think last time, you guys did this, it was probably a quarter million or close to it and you support multiple causes. What causes are you supporting? >> Sure, yeah, actually I think since they founded the event several years ago they raised over $2 million. This year for this format where we're looking, we can really up our game here but this year we're supporting two really great causes that are both focused on pediatric cancer. The first is St. Batrick's that is really committed to raising funds for research to really help stamp out pediatric cancer really. The approach to researching cures and treatments to pediatric cancer is very different from regular adult cancer. So St. Batrick's does a great job of picking those research projects that really target in on those pediatric cancer causes. And then the second is one mission. And one mission really outlooks to help make pediatric cancer patients that are spending time in the hospital, making their time less stressful, less painful, less sad, less boring. And so they do a lot of fundraising and contributions targeting children's hospitals, really around the country for those pediatric cancer floors. >> Josh, amazing cause. Thanks so much for coming onto the Cube and explaining all that. >> Great, thanks David. >> All right, June 21st, go to ttcfund.org, Tech Tackles Cancer fund, ttcffund.org for more information and you can donate. We'll see you there. (soft music)

>>Welcome back everybody to AWS reinvent. You're watching the cube, the leader in high tech coverage. My name is Dave Volante with my co-host David Nicholson. We're here all week. We got two sets, 20 plus thousand people here live at AWS reinvent. 21 of course last year was virtual. We got a hybrid event running. We had two studios running before the show running. A lot of pre-records really excited to have ninja Bardelli Walla, who is the chief product officer at data robot. Really interesting AI company. We're going to talk about insights with machine intelligence and then shout. It's great to see you again. It's been awhile. >>Great to see you as well. And I'm so happy to be on the cube. I think eight years since I first came on. >>When you launched the company that you founded back then Peck Sada on the cube, that was part >>Of the inner robot >>Family part of data, robot family. And of course, friend of the cube. Chris Lynch is the executive chairman of data robot. So a lot of connections, I always joke a hundred people in our industry, 99 seats, but tell us about data robot. What's the, what's the scoop these days. >>Thanks. Thanks very much for the opportunity to speak with both of you. Uh, I think we're seeing some very interesting trends. Uh, we've all been in the industry long enough to recognize, uh, that hype cycles they're cycles. They go in waves and, uh, the level of interest in AI has never been higher. Uh, every company in the world is looking for the opportunity to take advantage of AI, to improve their business processes, whether it's to improve their revenue it's to lower their cost profile or it's to lower their risk. What we're seeing that's most interesting is that, uh, we spend a lot of time working with companies on what we consider applied AI. That is how do we solve real business problems, uh, with the technology and not just run a bunch of experiments. You know, it's very tempting for a lot of us, Dave and David, uh, to, to do, uh, you know, spin up a spark cluster with 10,000 nodes and slosh a bunch of data through it. >>But the question we always ask at data robot is what is the business value of doing this? Why are we using these AI techniques and in order to solve what problem? So the biggest trend we see a data robot and one that we feel we're very well positioned to solve is that companies are coming out of that experimental phase. There's still a lot of experimentation going on and they're saying, okay, we, we stood up a cluster. Uh, we got a bunch of Python notebooks running around here, but we haven't really seen a return on our investment yet data robot, can you help us actually make AI real and concrete in terms of achieving a specific business outcome for us? >>Well, and I want to test something on your niche. That's something we've talked about a lot on the cube is a change in the way in which companies are architecting their data. When we first, it was like, okay, create a Hadoop cluster. And that spark came along to make that easier, but it was still this highly technical, highly centralized, hyper specialized roles where the business, people who have a really good understanding of the outcome had to kind of beg to get what they wanted because it was so technical and the success was defined as, Hey, it worked or we ran the experiment and it looks like it has promise. So now it seems like with companies like data robot, you're democratizing AI, allowing organizations to inject AI into their business processes, their applications. And it seems to be more business led. One of you could comment on that. >>I think that is a various dude observation. Uh, we launched this concept a little bit earlier this year of AI cloud. And the idea behind AI cloud is if you want to democratize AI, which is in fact has been DataRobot's vision since 2012, we were the first company on the cloud. The first AI cloud that ever existed was data robots in 2014. And the entire idea was that we knew that data scientists would always play a very important role in an organization, but yet the demand for AI would vastly outstrip the supply. And so in order to solve that challenge, we built AI cloud. We've actually spent over a million engineering hours in building this technology over the, over the last decade and put this together in a way where all of the different personas and the organizations, you have people who create AI applications. >>Those are the folks we usually think about, but those are the data scientists. Those are the analysts, those are the data engineers, but then you actually have to put it into production. You've got to run the system. So you also have to democratize this capability for the folks who are going to operate the system for the folks in risk and compliance. We're actually going to, uh, ensure that the system is operating in accordance with your policies and compliance regimes. And then the third wave of democratization, which we've just embarked on is then how do you bring AI into the hands of the actual business people? How do you put on a mobile device or a web browser, or in context, in an application with the decision, the ability for AI to drive a decision in your organization, which leads to an action, which helps drive you towards the outcome you're trying to optimize for. >>So AI cloud is about this pervasive tapestry, bringing together the creators, the consumers, the individuals who operate these systems into a single system that can lower the barrier to entry for people who don't have the skills, but allow you to plug in and go deep underneath the covers and modify whatever you need to, if you have that level of technical skill and that ability for us to kind of slide, slide the slider in one direction or the other, I could slide it to the right and say, I want all automation, something data robot has pioneered and is absolutely the leader in, but we can also, especially in these last couple of years, say, I want to be able to use as much code as I want to bring in. And the beauty of the model is that customers can choose how much they want to let the machine drive or how much they want to let the human being drive. David. I love that, >>That idea of a slider, because now you're talking about generalists getting access to really powerful tools. >>Yeah, no, exactly. And I, I'm curious, what's your view on where we are culturally with AI at this point? And what I mean by culturally is the idea that, okay, that's great. You put powerful tools in the hands of business users. Um, do most of us still need to have a lot of visibility under the covers to understand the inner workings so that we trust what we're being told? You know, I'm fine pulling a lever and having a little biscuit come out of SWOT as long as I've gotten a tour of the kitchen at some point in time. Yes. I mean, where are we with that? Where where's the level of >>Absolutely fantastic question and it's one that's, it's actually pervasive to the way data robot operates. So trust gets, uh, engendered by multiple different capabilities that you build throughout the platform. The first one is around, uh, explainability. So when you get a prediction from a system, just like you mentioned, you know, if, if the stakes are not very high, you know, you, uh, we're here in Las Vegas, of course I'm thinking of slot machines. If you get a biscuit at the end of it and it tastes pretty good. Hey, great. Right? When you're making a mission critical business decision, you don't want to be in the position where you don't understand why the system is making the decision. It does. So we have historically invested an enormous amount of effort in explainability tools, having the system actually at a prediction level, explain to you, why is it making the recommendation it's making? >>For example, the system says this customer has a high likelihood of churn. Why? Because their account balance has been declining over the last five months. Uh, number two, because their credit score has been going down. And what gives you the trust is actually the machine and the human able to communicate in the same language and same vernacular about the business value. So that's one part of it. The second part is about transparency, right? So one of the things that the automated machine learning movement, that data robot pioneered, uh, has been, I'd say rightfully criticized for frankly, is that it's too much of a black box. It's too much magic. I load my dataset. I press the start button and data robot does everything else for me. Well, that's not very satisfying when you have a 10 or a hundred million dollar decision coming on the other side, even if the technology is actually doing the job correctly, which data robot usually does. >>So where we've morphed and evolved our position in the market and where I have driven our technology portfolio at data robot is to say, you know what? There is a very important aspect of trust that needs to be brought to bear here, which is that if somebody wants to see code, let them see code. And in fact, the beauty of AI cloud is that on the same platform, the people who don't like code, but are, are very good at understanding the business domain con uh, the business domain knowledge and the context. They now have the ability to do that. But when they're at the stage before they're going to deploy anything to production. Now you can raise your hand at data robot and actually use our workflow and say, I need a coder to review this. I want the professional data scientist who has all this knowledge who understands and has read up on the latest advances in hyper parameter tuning to look at the model and tell me that this is going to be okay. And so we allow both the less technical folks and the very deep technical data scientists, the ability to collaborate on the same environment, which allows you to build trust in terms of the human side of, Hey, I don't want to just let anybody throw a model into production. I like, >>I mean, I see those, the transparency and the explainability is almost two sides of the same coin, right? Because you know, if you're gonna be accused of gender bias, you can say, no, here's how the system may, it's not like, you know, you think about the internet. It tells you it's a cat, but you don't really know how the machine determined that you're breaking apart, blowing away that black box. And the other thing I like what you said was you have data producers and data consumers, and you also talked about context because a lot of times the data producers, they don't necessarily care about the context or the PI data pipeline. People necessarily care about the context. So, okay. So now we're at the point where you're democratizing data, you're doing some great work. What are some of the blockers that you see today that you're obliterating with data robot? Maybe you could talk about that a little bit. Sure. >>So, so I think, uh, you know, one very important concept is that, uh, in a democracy, we talked about democratization. You still have rules, you still have governance. It's not a free for all the free for all version of that is called NRG. That's not what any company wants, right? So we have to blend the freedom and flexibility that we want businesses to have with the compliance and regulatory observability that we need in order to be successful. So what we're seeing in, in our, in our customer base and what companies are coming to data robot to discuss is, okay, we've tried these experiments. Now we want to actually get to real business value. And one of the things that's really unique about data robot is that we have put, uh, we have, we've worked in our system on over 1 million projects, training models, inside data robot. >>We have seen every type of use case across different industries, whether it's healthcare or manufacturing, uh, or, or retail, uh, we have the ability to understand those different data sets and actually to come up with models. So we have that breadth of information there if you aggregate that over time, right? So again, we did not come to AI. This is not a fad for us. We didn't start as one kind of company than slap the AI label on and say, Hey, we're an AI company now, right? We have been AI native since day one. And in that process, what we have found is working on these, this million plus projects on these data sets across these industries, we have a very good sense of which projects will actually deliver value and which don't. And that gets to a previous point that you were making, which is that you have to know and partner with an organization who it's not just about the technology. So we have fantastic people who we call our customer facing data scientists who will tell the customer, look, I know you think this is a really high value use case, but we've tried it at other customers. And unfortunately it didn't work very well. Let's steer you, cause you need with a, with a technology that is largely at the early stage and the maturity that organizations have with it, you need to help them in order to deliver success. And no vendor has delivered more successful production deployment of AI than data road. >>No, don't go down that path. It's a dead end as a cul-de-sac. So just avoid it. So we talked about transparency, explainability governance. Can you get that to the point where it's self-serve as you, as you put data in the hands of business, people where the context lives, the domain experts, can you get to self-serve and federate that governance? Yes. >>So you can, uh, that's one of the key principles of what we, what we do at data robot. And it comes back to a concept that I learned, uh, you, you both will remember. We were in the Sarbanes-Oxley crazy world of, I dunno, was that 15 years of saved data warehousing. >>Everybody wanted to talk about socks. You know, my wife would hear me on the phone. She'd be like, what is your sudden obsession with socks? I'm like, no, no, it's not what you fit. And so, um, but what came from Sarbanes Oxley are, are these, uh, longstanding principles around the segregation of duties and segregation of responsibilities. You can have democracy democratization with governance, if you have the right segregation of duties. So for example, I have somebody who can generate lots of different models, right? But I don't allow them to, to, uh, in a self-service way, just deploy into production. I actually have a workflow system which will go through multiple rigorous approvals and say, these three people have signed off, they've done an audit, uh, an, an audit assessment of this model. It's good to go, let's go and drop it into production. So the way that you get to self-service with governance is to have the right controls and policies and frameworks that surround the self-service model with the right checks and balances that implement the segregation of duties I'm talking >>And you get that right. And then you can automate it and then you can really scale, right? You gotta have your back because it's such a great topic. We, we barely scratched the surface. It was great to see you again, congratulations on all the success. And, uh, as I say any time, let's do this again. Fantastic. Thank >>You so much. All right, you're welcome. And thank you for watching you watching the cubes coverage of AWS reinvent 2021, Dave Volante for David Nicholson. Keep it right there. You're watching the cube, the leader in high-tech coverage.

(bright music) >> Welcome back to the CUBE presents the AWS Startup Showcase The Next Big Thing in cloud startups with AI security and life science tracks, 15 hottest growing startups are presented. And we had a great opening keynote with luminaries in the industry. And now our closing keynote is to get a deeper dive on cracking the code in the enterprise, how startups are changing the game and helping companies change. And they're also changing the game of open source. We have a great guest, Katie Drucker, Head of Business Development, Madrona Venture Group. Katie, thank you for coming on the CUBE for this special closing keynote. >> Thank you for having me, I appreciate it. >> So one of the topics we talked about with Soma from Madrona on the opening keynote, as well as Ali from Databricks is how startups are seeing success faster. So that's the theme of the Cloud speed, agility, but the game has changed in the enterprise. And I want to really discuss with you how growth changes and growth strategy specifically. They talk, go to market. We hear things like good sales to enterprise sales, organic, freemium, there's all kinds of different approaches, but at the end of the day, the most successful companies, the ones that might not be known that just come out of nowhere. So the economics are changing and the buyers are thinking differently. So let's explore that topic. So take us through your view 'cause you have a lot of experience. But first talk about your role at Madrona, what you do. >> Absolutely all great points. So my role at Madrona, I think I have personally one of the more enviable jobs and that my job is to... I get the privilege of working with all of these fantastic entrepreneurs in our portfolio and doing whatever we can as a firm to harness resources, knowledge, expertise, connections, to accelerate their growth. So my role in setting up business development is taking a look at all of those tools in the tool chest and partnering with the portfolio to make it so. And in our portfolio, we have a wide range of companies, some rely on enterprise sales, some have other go to markets. Some are direct to consumer, a wide range. >> Talk about the growth strategies that you see evolving because what's clear with the pandemic. And as we come out of it is that there are growth plays happening that don't look a little bit differently, more obvious now because of the Cloud scale, we're seeing companies like Databricks, like Snowflake, like other companies that have been built on the cloud or standalone. What are some of the new growth techniques, or I don't want to say growth hacking, that is a pejorative term, but like just a way for companies to quickly describe their value to an enterprise buyer who's moving away from the old RFP days of vendor selection. The game has changed. So take us through how you see secret key and unlocking that new equation of how to present value to an enterprise and how you see enterprises evaluating startups. >> Yes, absolutely. Well, and that's got a question, that's got a few components nestled in what I think are some bigger trends going on. AWS of course brought us the Cloud first. I think now the Cloud is more and more a utility. And so it's incumbent upon thinking about how an enterprise 'cause using the Cloud is going to go up the value stack and partner with its cloud provider and other service providers. I think also with that agility of operations, you have thinning, if you will, the systems of record and a lot of new entrance into this space that are saying things like, how can we harness AIML and other emerging trends to provide more value directly around work streams that were historically locked into those systems of record? And then I think you also have some price plans that are far more flexible around usage based as opposed to just flat subscription or even these big clunky annual or multi-year RFP type stuff. So all of those trends are really designed in ways that favor the emerging startup. And I think if done well, and in partnership with those underlying cloud providers, there can be some amazing benefits that the enterprise realizes an opportunity for those startups to grow. And I think that's what you're seeing. I think there's also this emergence of a buyer that's different than the CIO or the site the CISO. You have things with low code, no code. You've got other buyers in the organization, other line of business executives that are coming to the table, making software purchase decisions. And then you also have empowered developers that are these citizen builders and developer buyers and personas that really matter. So lots of inroads in places for a startup to reach in the enterprise to make a connection and to bring value. That's a great insight. I want to ask that just if you don't mind follow up on that, you mentioned personas. And what we're seeing is the shift happens. There's new roles that are emerging and new things that are being reconfigured or refactored if you will, whether it's human resources or AI, and you mentioned ML playing a role in automation. These are big parts of the new value proposition. How should companies posture to the customer? Because I don't want to say pivot 'cause that means it's not working but mostly extending our iterating around their positioning because as new things have not yet been realized, it might not be operationalized in a company or maybe new things need to be operationalized, it's a new solution for that. Positioning the value is super important and a lot of companies often struggle with that, but also if they get it right, that's the key. What's your feeling on startups in their positioning? So people will dismiss it like, "Oh, that's marketing." But maybe that's important. What's your thoughts on the great positioning question? >> I've been in this industry a long time. And I think there are some things that are just tried and true, and it is not unique to tech, which is, look, you have to tell a story and you have to reach the customer and you have to speak to the customer's need. And what that means is, AWS is a great example. They're famous for the whole concept of working back from the customer and thinking about what that customer's need is. I think any startup that is looking to partner or work alongside of AWS really has to embody that very, very customer centric way of thinking about things, even though, as we just talked about those personas are changing who that customer really is in the enterprise. And then speaking to that value proposition and meeting that customer and creating a dialogue with them that really helps to understand not only what their pain points are, but how you were offering solves those pain points. And sometimes the customer doesn't realize that that is their pain point and that's part of the education and part of the way in which you engage that dialogue. That doesn't change a lot, just generation to generation. I think the modality of how we have that dialogue, the methods in which we choose to convey that change, but that basic discussion is what makes us human. >> What's your... Great, great, great insight. I want to ask you on the value proposition question again, the question I often get, and it's hard to answer is am I competing on value or am I competing on commodity? And depending on where you're in the stack, there could be different things like, for example, land is getting faster, smaller, cheaper, as an example on Amazon. That's driving down to low cost high value, but it shifts up the stack. You start to see in companies this changing the criteria for how to evaluate. So an enterprise might be struggling. And I often hear enterprises say, "I don't know how to pick who I need. I buy tools, I don't buy many platforms." So they're constantly trying to look for that answer key, if you will, what's your thoughts on the changing requirements of an enterprise? And how to do vendor selection. >> Yeah, so obviously I don't think there's a single magic bullet. I always liked just philosophically to think about, I think it's always easier and frankly more exciting as a buyer to want to buy stuff that's going to help me make more revenue and build and grow as opposed to do things that save me money. And just in a binary way, I like to think which side of the fence are you sitting on as a product offering? And the best ways that you can articulate that, what opportunities are you unlocking for your customer? The problems that you're solving, what kind of growth and what impact is that going to lead to, even if you're one or two removed from that? And again, that's not a new concept. And I think that the companies that have that squarely in mind when they think about their go-to market strategy, when they think about the dialogue they're having, when they think about the problems that they're solving, find a much faster path. And I think that also speaks to why we're seeing so many explosion in the line of business, SAS apps that are out there. Again, that thinning of the systems of record, really thinking about what are the scenarios and work streams that we can have happened that are going to help with that revenue growth and unlocking those opportunities. >> What's the common startup challenge that you see when they're trying to do business development? Usually they build the product first, product led value, you hear that a lot. And then they go, "Okay, we're ready to sell, hire a sales guy." That seems to be shifting away because of the go to markets are changing. What are some of the challenges that startups have? What are some that you're seeing? >> Well, and I think the point that you're making about the changes are really almost a result of the trends that we're talking about. The sales organization itself is becoming... These work streams are becoming instrumented. Data is being collected, insights are being derived off of those things. So you see companies like Clary or Highspot or two examples or tutorial that are in our portfolio that are looking at that action and making the art of sales and marketing far more sophisticated overall, which then leads to the different growth hacking and the different insights that are driven. I think the common mistakes that I see across the board, especially with earlier stage startups, look you got to find product market fit. I think that's always... You start with a thesis or a belief and a passion that you're building something that you think the market needs. And it's a lot of dialogue you have to have to make sure that you do find that. I think once you find that another common problem that I see is leading with an explanation of technology. And again, not focusing on the buyer or the... Sorry, the buyer about solving a problem and focusing on that problem as opposed to focusing on how cool your technology is. Those are basic and really, really simple. And then I think setting a set of expectations, especially as it comes to business development and partnering with companies like AWS. The researching that you need to adequately meet the demand that can be turned on. And then I'm sure you heard about from Databricks, from an organization like AWS, you have to be pragmatic. >> Yeah, Databricks gone from zero a software sales a few years ago to over a billion. Now it looks like a Snowflake which came out of nowhere and they had a great product, but built on Amazon, they became the data cloud on top of Amazon. And now they're growing just whole new business models and new business development techniques. Katie, thank you for sharing your insight here. The CUBE's closing keynote. Thanks for coming on. >> Appreciate it, thank you. >> Okay, Katie Drucker, Head of Business Development at Madrona Venture Group. Premier VC in the Seattle area and beyond they're doing a lot of cloud action. And of course they know AWS very well and investing in the ecosystem. So great, great stuff there. Next up is Peter Wagner partner at Wing.VX. Love this URL first of all 'cause of the VC domain extension. But Peter is a long time venture capitalist. I've been following his career. He goes back to the old networking days, back when the internet was being connected during the OSI days, when the TCP IP open systems interconnect was really happening and created so much. Well, Peter, great to see you on the CUBE here and congratulations with success at Wing VC. >> Yeah, thanks, John. It's great to be here. I really appreciate you having me. >> Reason why I wanted to have you come on. First of all, you had a great track record in investing over many decades. You've seen many waves of innovation, startups. You've seen all the stories. You've seen the movie a few times, as I say. But now more than ever, enterprise wise it's probably the hottest I've ever seen. And you've got a confluence of many things on the stack. You were also an early seed investor in Snowflake, well-regarded as a huge success. So you've got your eye on some of these awesome deals. Got a great partner over there has got a network experience as well. What is the big aha moment here for the industry? Because it's not your classic enterprise startups anymore. They have multiple things going on and some of the winners are not even known. They come out of nowhere and they connect to enterprise and get the lucrative positions and can create a moat and value. Like out of nowhere, it's not the old way of like going to the airport and doing an RFP and going through the stringent requirements, and then you're in, you get to win the lucrative contract and you're in. Not anymore, that seems to have changed. What's your take on this 'cause people are trying to crack the code here and sometimes you don't have to be well-known. >> Yeah, well, thank goodness the game has changed 'cause that old thing was (indistinct) So I for one don't miss it. There was some modernization movement in the enterprise and the modern enterprise is built on data powered by AI infrastructure. That's an agile workplace. All three of those things are really transformational. There's big investments being made by enterprises, a lot of receptivity and openness to technology to enable all those agendas, and that translates to good prospects for startups. So I think as far as my career goes, I've never seen a more positive or fertile ground for startups in terms of penetrating enterprise, it doesn't mean it's easy to do, but you have a receptive audience on the other side and that hasn't necessarily always been the case. >> Yeah, I got to ask you, I know that you're a big sailor and your family and Franks Lubens also has a boat and sailing metaphor is always good to have 'cause you got to have a race that's being run and they have tactics. And this game that we're in now, you see the successes, there's investment thesises, and then there's also actually bets. And I want to get your thoughts on this because a lot of enterprises are trying to figure out how to evaluate startups and starts also can make the wrong bet. They could sail to the wrong continent and be in the wrong spot. So how do you pick the winners and how should enterprises understand how to pick winners too? >> Yeah, well, one of the real important things right now that enterprise is facing startups are learning how to do and so learning how to leverage product led growth dynamics in selling to the enterprise. And so product led growth has certainly always been important consumer facing companies. And then there's a few enterprise facing companies, early ones that cracked the code, as you said. And some of these examples are so old, if you think about, like the ones that people will want to talk about them and talk about Classy and want to talk about Twilio and these were of course are iconic companies that showed the way for others. But even before that, folks like Solar Winds, they'd go to market model, clearly product red, bottom stuff. Back then we didn't even have those words to talk about it. And then some of the examples are so enormous if think about them like the one right in front of your face, like AWS. (laughing) Pretty good PLG, (indistinct) but it targeted builders, it targeted developers and flipped over the way you think about enterprise infrastructure, as a result some how every company, even if they're harnessing relatively conventional sales and marketing motion, and you think about product led growth as a way to kick that motion off. And so it's not really an either word even more We might think OPLJ, that means there's no sales keep one company not true, but here's a way to set the table so that you can very efficiently use your sales and marketing resources, only have the most attractive targets and ones that are really (indistinct) >> I love the product led growth. I got to ask you because in the networking days, I remember the term inevitability was used being nested in a solution that they're just going to Cisco off router and a firewall is one you can unplug and replace with another vendor. Cisco you'd have to go through no switching costs were huge. So when you get it to the Cloud, how do you see the competitiveness? Because we were riffing on this with Ali, from Databricks where the lock-in might be value. The more value provider is the lock-in. Is their nestedness? Is their intimate ability as a competitive advantage for some of these starts? How do you look at that? Because startups, they're using open source. They want to have a land position in an enterprise, but how do they create that sustainable competitive advantage going forward? Because again, this is what you do. You bet on ones that you can see that could establish a model whatever we want to call it, but a competitive advantage and ongoing nested position. >> Sometimes it has to do with data, John, and so you mentioned Snowflake a couple of times here, a big part of Snowflake's strategy is what they now call the data cloud. And one of the reasons you go there is not to just be able to process data, to actually get access to it, exchange with the partners. And then that of course is a great reason for the customers to come to the Snowflake platform. And so the more data it gets more customers, it gets more data, the whole thing start spinning in the right direction. That's a really big example, but all of these startups that are using ML in a fundamental way, applying it in a novel way, the data modes are really important. So getting to the right data sources and training on it, and then putting it to work so that you can see that in this process better and doing this earlier on that scale. That's a big part of success. Another company that I work with is a good example that I call (indistinct) which works in sales technology space, really crushing it in terms of building better sales organizations both at performance level, in terms of the intelligence level, and just overall revenue attainment using ML, and using novel data sources, like the previously lost data or phone calls or Zoom calls as you already know. So I think the data advantages are really big. And smart startups are thinking through it early. >> It's interest-- >> And they're planning by the way, not to ramble on too much, but they're betting that PLG strategy. So their land option is designed not just to be an interesting way to gain usage, but it's also a way to gain access to data that then enables the expand in a component. >> That is a huge call-out point there, I was going to ask another question, but I think that is the key I see. It's a new go to market in a way. product led with that kind of approach gets you a beachhead and you get a little position, you get some data that is a cloud model, it means variable, whatever you want to call it variable value proposition, value proof, or whatever, getting that data and reiterating it. So it brings up the whole philosophical question of okay, product led growth, I love that with product led growth of data, I get that. Remember the old platform versus a tool? That's the way buyers used to think. How has that changed? 'Cause now almost, this conversation throws out the whole platform thing, but isn't like a platform. >> It looks like it's all. (laughs) you can if it is a platform, though to do that you can reveal that later, but you're looking for adoption, so if it's down stock product, you're looking for adoption by like developers or DevOps people or SOEs, and they're trying to solve a problem, and they want rapid gratification. So they don't want to have an architectural boomimg, placed in front of them. And if it's up stock product and application, then it's a user or the business or whatever that is, is adopting the application. And again, they're trying to solve a very specific problem. You need instant and immediate obvious time and value. And now you have a ticket to the dance and build on that and maybe a platform strategy can gradually take shape. But you know who's not in this conversation is the CIO, it's like, "I'm always the last to know." >> That's the CISO though. And they got him there on the firing lines. CISOs are buying tools like it's nobody's business. They need everything. They'll buy anything or you go meet with sand, they'll buy it. >> And you make it sound so easy. (laughing) We do a lot of security investment if only (indistinct) (laughing) >> I'm a little bit over the top, but CISOs are under a lot of pressure. I would talk to the CISO at Capital One and he was saying that he's on Amazon, now he's going to another cloud, not as a hedge, but he doesn't want to focus development teams. So he's making human resource decisions as well. Again, back to what IT used to be back in the old days where you made a vendor decision, you built around it. So again, clouds play that way. I see that happening. But the question is that I think you nailed this whole idea of cross hairs on the target persona, because you got to know who you are and then go to the market. So if you know you're a problem solving and the lower in the stack, do it and get a beachhead. That's a strategy, you can do that. You can't try to be the platform and then solve a problem at the same time. So you got to be careful. Is that what you were getting at? >> Well, I think you just understand what you're trying to achieve in that line of notion. And how those dynamics work and you just can't drag it out. And they could make it too difficult. Another company I work with is a very strategic cloud data platform. It's a (indistinct) on systems. We're not trying to foist that vision though (laughs) or not adopters today. We're solving some thorny problems with them in the short term, rapid time to value operational needs in scale. And then yeah, once they found success with (indistinct) there's would be an opportunity to be increasing the platform, and an obstacle for those customers. But we're not talking about that. >> Well, Peter, I appreciate you taking the time and coming out of a board meeting, I know that you're super busy and I really appreciate you making time for us. I know you've got an impressive partner in (indistinct) who's a former Sequoia, but Redback Networks part of that company over the years, you guys are doing extremely well, even a unique investment thesis. I'd like you to put the plug in for the firm. I think you guys have a good approach. I like what you guys are doing. You're humble, you don't brag a lot, but you make a lot of great investments. So could you take them in to explain what your investment thesis is and then how that relates to how an enterprise is making their investment thesis? >> Yeah, yeah, for sure. Well, the concept that I described earlier that the modern enterprise movement as a workplace built on data powered by AI. That's what we're trying to work with founders to enable. And also we're investing in companies that build the products and services that enable that modern enterprise to exist. And we do it from very early stages, but with a longterm outlook. So we'll be leading series and series, rounds of investment but staying deeply involved, both operationally financially throughout the whole life cycle of the company. And then we've done that a bunch of times, our goal is always the big independent public company and they don't always make it but enough for them to have it all be worthwhile. An interesting special case of this, and by the way, I think it intersects with some of startup showcase here is in the life sciences. And I know you were highlighting a lot of healthcare websites and deals, and that's a vertical where to disrupt tremendous impact of data both new data availability and new ways to put it to use. I know several of my partners are very focused on that. They call it bio-X data. It's a transformation all on its own. >> That's awesome. And I think that the reason why we're focusing on these verticals is if you have a cloud horizontal scale view and vertically specialized with machine learning, every vertical is impacted by data. It's so interesting that I think, first start, I was probably best time to be a cloud startup right now. I really am bullish on it. So I appreciate you taking the time Peter to come in again from your board meeting, popping out. Thanks for-- (indistinct) Go back in and approve those stock options for all the employees. Yeah, thanks for coming on. Appreciate it. >> All right, thank you John, it's a pleasure. >> Okay, Peter Wagner, Premier VC, very humble Wing.VC is a great firm. Really respect them. They do a lot of great investing investments, Snowflake, and we have Dave Vellante back who knows a lot about Snowflake's been covering like a blanket and Sarbjeet Johal. Cloud Influencer friend of the CUBE. Cloud commentator and cloud experience built clouds, runs clouds now invests. So V. Dave, thanks for coming back on. You heard Peter Wagner at Wing VC. These guys have their roots in networking, which networking back in the day was, V. Dave. You remember the internet Cisco days, remember Cisco, Wellfleet routers. I think Peter invested in Arrow Point, remember Arrow Point, that was about in the 495 belt where you were. >> Lynch's company. >> That was Chris Lynch's company. I think, was he a sales guy there? (indistinct) >> That was his first big hit I think. >> All right, well guys, let's wrap this up. We've got a great program here. Sarbjeet, thank you for coming on. >> No worries. Glad to be here todays. >> Hey, Sarbjeet. >> First of all, really appreciate the Twitter activity lately on the commentary, the observability piece on Jeremy Burton's launch, Dave was phenomenal, but Peter was talking about this dynamic and I think ties this cracking the code thing together, which is there's a product led strategy that feels like a platform, but it's also a tool. In other words, it's not mutually exclusive, the old methods thrown out the window. Land in an account, know what problem you're solving. If you're below the stack, nail it, get data and go from there. If you're a process improvement up the stack, you have to much more of a platform longer-term sale, more business oriented, different motions, different mechanics. What do you think about that? What's your reaction? >> Yeah, I was thinking about this when I was listening to some of the startups pitching, if you will, or talking about what they bring to the table in this cloud scale or cloud era, if you will. And there are tools, there are applications and then they're big monolithic platforms, if you will. And then they're part of the ecosystem. So I think the companies need to know where they play. A startup cannot be platform from the get-go I believe. Now many aspire to be, but they have to start with tooling. I believe in, especially in B2B side of things, and then go into the applications, one way is to go into the application area, if you will, like a very precise use cases for certain verticals and stuff like that. And other parties that are going into the platform, which is like horizontal play, if you will, in technology. So I think they have to understand their age, like how old they are, how new they are, how small they are, because when their size matter when you are procuring as a big business, procuring your technology vendors size matters and the economic viability matters and their proximity to other windows matter as well. So I think we'll jump into that in other discussions later, but I think that's key, as you said. >> I would agree with that. I would phrase it in my mind, somewhat differently from Sarbjeet which is you have product led growth, and that's your early phase and you get product market fit, you get product led growth, and then you expand and there are many, many examples of this, and that's when you... As part of your team expansion strategy, you're going to get into the platform discussion. There's so many examples of that. You take a look at Ali Ghodsi today with what's happening at Databricks, Snowflake is another good example. They've started with product led growth. And then now they're like, "Okay, we've got to expand the team." Okta is another example that just acquired zero. That's about building out the platform, versus more of a point product. And there's just many, many examples of that, but you cannot to your point, very hard to start with a platform. Arm did it, but that was like a one in a million chance. >> It's just harder, especially if it's new and it's not operationalized yet. So one of the things Dave that we've observed the Cloud is some of the best known successes where nobody's not known at all, database we've been covering from the beginning 'cause we were close to that movement when they came out of Berkeley. But they still were misunderstood and they just started generating revenue in only last year. So again, only a few years ago, zero software revenue, now they're approaching a billion dollars. So it's not easy to make these vendor selections anymore. And if you're new and you don't have someone to operate it or your there's no department and the departments changing, that's another problem. These are all like enterprisey problems. What's your thoughts on that, Dave? >> Well, I think there's a big discussion right now when you've been talking all day about how should enterprise think about startups and think about most of these startups they're software companies and software is very capital efficient business. At the same time, these companies are raising hundreds of millions, sometimes over a billion dollars before they go to IPO. Why is that? A lot of it's going to promotion. I look at it as... And there's a big discussion going on but well, maybe sales can be more efficient and more direct and so forth. I really think it comes down to the golden rule. Two things really mattered in the early days in the startup it's sales and engineering. And writers should probably say engineering and sales and start with engineering. And then you got to figure out your go to market. Everything else is peripheral to those two and you don't get those two things right, you struggle. And I think that's what some of these successful startups are proving. >> Sarbjeet, what's your take on that point? >> Could you repeat the point again? Sorry, I lost-- >> As cloud scale comes in this whole idea of competing, the roles are changing. So look at IOT, look at the Edge, for instance, you got all kinds of new use cases that no one actually knows is a problem to solve. It's just pure opportunity. So there's no one's operational I could have a product, but it don't know we can buy it yet. It's a problem. >> Yeah, I think the solutions have to be point solutions and the startups need to focus on the practitioners, number one, not the big buyers, not the IT, if you will, but the line of business, even within that sphere, like just focus on the practitioners who are going to use that technology. I talked to, I think it wasn't Fiddler, no, it was CoreLogics. I think that story was great today earlier in how they kind of struggle in the beginning, they were trying to do a big bang approach as a startup, but then they almost stumbled. And then they found their mojo, if you will. They went to Don the market, actually, that's a very classic theory of disruption, like what we study from Harvard School of Business that you go down the market, go to the non-consumers, because if you're trying to compete head to head with big guys. Because most of the big guys have lot of feature and functionality, especially at the platform level. And if you're trying to innovate in that space, you have to go to the practitioners and solve their core problems and then learn and expand kind of thing. So I think you have to focus on practitioners a lot more than the traditional oracle buyers. >> Sarbjeet, we had a great thread last night in Twitter, on observability that you started. And there's a couple of examples there. Chaos searches and relatively small company right now, they just raised them though. And they're part of this star showcase. And they could've said, "Hey, we're going to go after Splunk." But they chose not to. They said, "Okay, let's kind of disrupt the elk stack and simplify that." Another example is a company observed, you've mentioned Jeremy Burton's company, John. They're focused really on SAS companies. They're not going after initially these complicated enterprise deals because they got to get it right or else they'll get churn, and churn is that silent killer of software companies. >> The interesting other company that was on the showcase was Tetra Science. I don't know if you noticed that one in the life science track, and again, Peter Wagner pointed out the life science. That's an under recognized in the press vertical that's exploding. Certainly during the pandemic you saw it, Tetra science is an R&D cloud, Dave, R&D data cloud. So pharmaceuticals, they need to do their research. So the pandemic has brought to life, this now notion of tapping into data resources, not just data lakes, but like real deal. >> Yeah, you and Natalie and I were talking about that this morning and that's one of the opportunities for R&D and you have all these different data sources and yeah, it's not just about the data lake. It's about the ecosystem that you're building around them. And I see, it's really interesting to juxtapose what Databricks is doing and what Snowflake is doing. They've got different strategies, but they play a part there. You can see how ecosystems can build that system. It's not one company is going to solve all these problems. It's going to really have to be connections across these various companies. And that's what the Cloud enables and ecosystems have all this data flowing that can really drive new insights. >> And I want to call your attention to a tweet Sarbjeet you wrote about Splunk's earnings and they're data companies as well. They got Teresa Carlson there now AWS as the president, working with Doug, that should change the game a little bit more. But there was a thread of the neath there. Andy Thry says to replies to Dave you or Sarbjeet, you, if you're on AWS, they're a fine solution. The world doesn't just revolve around AWS, smiley face. Well, a lot of it does actually. So (laughing) nice point, Andy. But he brings up this thing and Ali brought it up too, Hybrid now is a new operating system for what now Edge does. So we got Mobile World Congress happening this month in person. This whole Telco 5G brings up a whole nother piece of the Cloud puzzle. Jeff Barr pointed out in his keynote, Dave. Guys, I want to get your reaction. The Edge now is... I'm calling it the super Edge because it's not just Edge as we know it before. You're going to have these pops, these points of presence that are going to have wavelength as your spectrum or whatever they have. I think that's the solution for Azure. So you're going to have all this new cloud power for low latency applications. Self-driving delivery VR, AR, gaming, Telemetry data from Teslas, you name it, it's happening. This is huge, what's your thoughts? Sarbjeet, we'll start with you. >> Yeah, I think Edge is like bound to happen. And for many reasons, the volume of data is increasing. Our use cases are also expanding if you will, with the democratization of computer analysis. Specialization of computer, actually Dave wrote extensively about how Intel and other chip players are gearing up for that future if you will. Most of the inference in the AI world will happen in the field close to the workloads if you will, that can be mobility, the self-driving car that can be AR, VR. It can be healthcare. It can be gaming, you name it. Those are the few use cases, which are in the forefront and what alarm or use cases will come into the play I believe. I've said this many times, Edge, I think it will be dominated by the hyperscalers, mainly because they're building their Metro data centers now. And with a very low latency in the Metro areas where the population is, we're serving the people still, not the machines yet, or the empty areas where there is no population. So wherever the population is, all these big players are putting their data centers there. And I think they will dominate the Edge. And I know some Edge lovers. (indistinct) >> Edge huggers. >> Edge huggers, yeah. They don't like the hyperscalers story, but I think that's the way were' going. Why would we go backwards? >> I think you're right, first of all, I agree with the hyperscale dying you look at the top three clouds right now. They're all in the Edge, Hardcore it's a huge competitive battleground, Dave. And I think the missing piece, that's going to be uncovered at Mobile Congress. Maybe they'll miss it this year, but it's the developer traction, whoever wins the developer market or wins the loyalty, winning over the market or having adoption. The applications will drive the Edge. >> And I would add the fourth cloud is Alibaba. Alibaba is actually bigger than Google and they're crushing it as well. But I would say this, first of all, it's popular to say, "Oh not everything's going to move into the Cloud, John, Dave, Sarbjeet." But the fact is that AWS they're trend setter. They are crushing it in terms of features. And you'd look at what they're doing in the plumbing with Annapurna. Everybody's following suit. So you can't just ignore that, number one. Second thing is what is the Edge? Well, the edge is... Where's the logical place to process the data? That's what the Edge is. And I think to your point, both Sarbjeet and John, the Edge is going to be won by developers. It's going to be one by programmability and it's going to be low cost and really super efficient. And most of the data is going to stay at the Edge. And so who is in the best position to actually create that? Is it going to be somebody who was taking an x86 box and throw it over the fence and give it a fancy name with the Edge in it and saying, "Here's our Edge box." No, that's not what's going to win the Edge. And so I think first of all it's huge, it's wide open. And I think where's the innovation coming from? I agree with you it's the hyperscalers. >> I think the developers as John said, developers are the kingmakers. They build the solutions. And in that context, I always talk about the skills gravity, a lot of people are educated in certain technologies and they will keep using those technologies. Their proximity to that technology is huge and they don't want to learn something new. So as humans we just tend to go what we know how to use it. So from that front, I usually talk with consumption economics of cloud and Edge. It has to focus on the practitioners. And in this case, practitioners are developers because you're just cooking up those solutions right now. We're not serving that in huge quantity right now, but-- >> Well, let's unpack that Sarbjeet, let's unpack that 'cause I think you're right on the money on that. The consumption of the tech and also the consumption of the application, the end use and end user. And I think the reason why hyperscalers will continue to dominate besides the fact that they have all the resource and they're going to bring that to the Edge, is that the developers are going to be driving the applications at the Edge. So if you're low latency Edge, that's going to open up new applications, not just the obvious ones I did mention, gaming, VR, AR, metaverse and other things that are obvious. There's going to be non-obvious things that are going to be huge that are going to come out from the developers. But the Cloud native aspect of the hyperscalers, to me is where the scales are tipping, let me explain. IT was built to build a supply resource to the businesses who were writing business applications. Mostly driven by IBM in the mainframe in the old days, Dave, and then IT became IT. Telcos have been OT closed, "This is our thing, that's it." Now they have to open up. And the Cloud native technologies is the fastest way to value. And I think that paths, Sarbjeet is going to be defined by this new developer and this new super Edge concept. So I think it's going to be wide open. I don't know what to say. I can't guess, but it's going to be creative. >> Let me ask you a question. You said years ago, data's new development kit, does low code and no code to Sarbjeet's point, change the equation? In other words, putting data in the hands of those OT professionals, those practitioners who have the context. Does low-code and no-code enable, more of those protocols? I know it's a bromide, but the citizen developer, and what impact does that have? And who's in the best position? >> Well, I think that anything that reduces friction to getting stuff out there that can be automated, will increase the value. And then the question is, that's not even a debate. That's just fact that's going to be like rent, massive rise. Then the issue comes down to who has the best asset? The software asset that's eating the world or the tower and the physical infrastructure. So if the physical infrastructure aka the Telcos, can't generate value fast enough, in my opinion, the private equity will come in and take it over, and then refactor that business model to take advantage of the over the top software model. That to me is the big stare down competition between the Telco world and this new cloud native, whichever one yields in valley is going to blink first, if you say. And I think the Cloud native wins this one hands down because the assets are valuable, but only if they enable the new model. If the old model tries to hang on to the old hog, the old model as the Edge hugger, as Sarbjeet says, they'll just going to slowly milk that cow dry. So it's like, it's over. So to me, they have to move. And I think this Mobile World Congress day, we will see, we will be looking for that. >> Yeah, I think that in the Mobile World Congress context, I think Telcos should partner with the hyperscalers very closely like everybody else has. And they have to cave in. (laughs) I usually say that to them, like the people came in IBM tried to fight and they cave in. Other second tier vendors tried to fight the big cloud vendors like top three or four. And then they cave in. okay, we will serve our stuff through your cloud. And that's where all the buyers are congregating. They're going to buy stuff along with the skills gravity, the feature proximity. I've got another term I'll turn a coin. It matters a lot when you're doing one thing and you want to do another thing when you're doing all this transactional stuff and regular stuff, and now you want to do data science, where do you go? You go next to it, wherever you have been. Your skills are in that same bucket. And then also you don't have to write a new contract with a new vendor, you just go there. So in order to serve, this is a lesson for startups as well. You need to prepare yourself for being in the Cloud marketplaces. You cannot go alone independently to fight. >> Cloud marketplace is going to replace procurement, for sure, we know that. And this brings up the point, Dave, we talked about years ago, remember on the CUBE. We said, there's going to be Tier two clouds. I used that word in quotes cause nothing... What does it even mean Tier two. And we were talking about like Amazon, versus Microsoft and Google. We set at the time and Alibaba but they're in China, put that aside for a second, but the big three. They're going to win it all. And they're all going to be successful to a relative terms, but whoever can enable that second tier. And it ended up happening, Snowflake is that example. As is Databricks as is others. So Google and Microsoft as fast as they can replicate the success of AWS by enabling someone to build their business on their cloud in a way that allows the customer to refactor their business will win. They will win most of the lion's share my opinion. So I think that applies to the Edge as well. So whoever can come in and say... Whichever cloud says, "I'm going to enable the next Snowflake, the next enterprise solution." I think takes it. >> Well, I think that it comes back... Every conversation coming back to the data. And if you think about the prevailing way in which we treated data with the exceptions of the two data driven companies in their quotes is as we've shoved all the data into some single repository and tried to come up with a single version of the truth and it's adjudicated by a centralized team, with hyper specialized roles. And then guess what? The line of business, there's no context for the business in that data architecture or data Corpus, if you will. And then the time it takes to go from idea for a data product or data service commoditization is way too long. And that's changing. And the winners are going to be the ones who are able to exploit this notion of leaving data where it is, the point about data gravity or courting a new term. I liked that, I think you said skills gravity. And then enabling the business lines to have access to their own data teams. That's exactly what Ali Ghodsi, he was saying this morning. And really having the ability to create their own data products without having to go bow down to an ivory tower. That is an emerging model. All right, well guys, I really appreciate the wrap up here, Dave and Sarbjeet. I'd love to get your final thoughts. I'll just start by saying that one of the highlights for me was the luminary guests size of 15 great companies, the luminary guests we had from our community on our keynotes today, but Ali Ghodsi said, "Don't listen to what everyone's saying in the press." That was his position. He says, "You got to figure out where the puck's going." He didn't say that, but I'm saying, I'm paraphrasing what he said. And I love how he brought up Sky Cloud. I call it Sky net. That's an interesting philosophy. And then he also brought up that machine learning auto ML has got to be table stakes. So I think to me, that's the highlight walk away. And the second one is this idea that the enterprises have to have a new way to procure and not just the consumption, but some vendor selection. I think it's going to be very interesting as value can be proved with data. So maybe the procurement process becomes, here's a beachhead, here's a little bit of data. Let me see what it can do. >> I would say... Again, I said it was this morning, that the big four have given... Last year they spent a hundred billion dollars more on CapEx. To me, that's a gift. In so many companies, especially focusing on trying to hang onto the legacy business. They're saying, "Well not everything's going to move to the Cloud." Whatever, the narrative should change to, "Hey, thank you for that gift. We're now going to build value on top of the Cloud." Ali Ghodsi laid that out, how Databricks is doing it. And it's clearly what Snowflake's new with the data cloud. It basically a layer that abstracts all that underlying complexity and add value on top. Eventually going out to the Edge. That's a value added model that's enabled by the hyperscalers. And that to me, if I have to evaluate where I'm going to place my bets as a CIO or IT practitioner, I'm going to look at who are the ones that are actually embracing that investment that's been made and adding value on top in a way that can drive my data-driven, my digital business or whatever buzzword you want to throw on. >> Yeah, I think we were talking about the startups in today's sessions. I think for startups, my advice is to be as close as you can be to hyperscalers and anybody who awards them, they will cave in at the end of the day, because that's where the whole span of gravity is. That's what the innovation gravity is, everybody's gravitating towards that. And I would say quite a few times in the last couple of years that the rate of innovation happening in a non-cloud companies, when I talk about non-cloud means are not public companies. I think it's like diminishing, if you will, as compared to in cloud, there's a lot of innovation. The Cloud companies are not paying by power people anymore. They have all sophisticated platforms and leverage those, and also leverage the marketplaces and leverage their buyers. And the key will be how you highlight yourself in that cloud market place if you will. It's like in a grocery store where your product is placed and you have to market around it, and you have to have a good story telling team in place as well after you do the product market fit. I think that's a key. I think just being close to the Cloud providers, that's the way to go for startups. >> Real, real quick. Each of you talk about what it takes to crack the code for the enterprise in the modern era now. Dave, we'll start with you. What's it take? (indistinct) >> You got to have it be solving a problem that is 10X better at one 10th a cost of anybody else, if you're a small company, that rule number one. Number two is you obviously got to get product market fit. You got to then figure out. And I think, and again, you're in your early phases, you have to be almost processed builders, figure out... Your KPIs should all be built around retention. How do I define customer success? How do I keep customers and how do I make them loyal so that I know that my cost of acquisition is going to be at least one-third or lower than my lifetime value of that customer? So you've got to nail that. And then once you nail that, you've got to codify that process in the next phase, which really probably gets into your platform discussion. And that's really where you can start to standardize and scale and figure out your go to market and the relationship between marketing spend and sales productivity. And then when you get that, then you got to move on to figure out your Mot. Your Mot might just be a brand. It might be some secret sauce, but more often than not though, it's going to be the relationship that you build. And I think you've got to think about those phases and in today's world, you got to move really fast. Sarbjeet, real quick. What's the secret to crack the code? >> I think the secret to crack the code is partnership and alliances. As a small company selling to the bigger enterprises, the vendors size will be one of the big objections. Even if they don't say it, it's on the back of their mind, "What if these guys disappear tomorrow what would we do if we pick this technology?" And another thing is like, if you're building on the left side, which is the developer side, not on the right side, which is the operations or production side, if you will, you have to understand the sales cycles are longer on the right side and left side is easier to get to, but that's why we see a lot more startups. And on the left side of your DevOps space, if you will, because it's easier to sell to practitioners and market to them and then show the value correctly. And also understand that on the left side, the developers are very know how hungry, on the right side people are very cost-conscious. So understanding the traits of these different personas, if you will buyers, it will, I think set you apart. And as Dave said, you have to solve a problem, focus on practitioners first, because you're small. You have to solve political problems very well. And then you can expand. >> Well, guys, I really appreciate the time. Dave, we're going to do more of these, Sarbjeet we're going to do more of these. We're going to add more community to it. We're going to add our community rooms next time. We're going to do these quarterly and try to do them as more frequently, we learned a lot and we still got a lot more to learn. There's a lot more contribution out in the community that we're going to tap into. Certainly the CUBE Club as we call it, Dave. We're going to build this actively around Cloud. This is another 20 years. The Edge brings us more life with Cloud, it's really exciting. And again, enterprise is no longer an enterprise, it's just the world now. So great companies here, the next Databricks, the next IPO. The next big thing is in this list, Dave. >> Hey, John, we'll see you in Barcelona. Looking forward to that. Sarbjeet, I know in a second half, we're going to run into each other. So (indistinct) thank you John. >> Trouble has started. Great talking to you guys today and have fun in Barcelona and keep us informed. >> Thanks for coming. I want to thank Natalie Erlich who's in Rome right now. She's probably well past her bedtime, but she kicked it off and emceeing and hosting with Dave and I for this AW startup showcase. This is batch two episode two day. What do we call this? It's like a release so that the next 15 startups are coming. So we'll figure it out. (laughs) Thanks for watching everyone. Thanks. (bright music)

>> from Cambridge, Massachusetts. It's three Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back. We're here to wrap up the M I T. Chief data officer officer, information quality. It's hashtag m i t CDO conference. You're watching the Cube. I'm David Dante, and Paul Gill is my co host. This is two days of coverage. We're wrapping up eyes. Our analysis of what's going on here, Paul, Let me let me kick it off. When we first started here, we talked about that are open. It was way saw the chief data officer role emerged from the back office, the information quality role. When in 2013 the CEO's that we talked to when we asked them what was their scope. We heard things like, Oh, it's very wide. Involves analytics, data science. Some CEOs even said Oh, yes, security is actually part of our purview because all the cyber data so very, very wide scope. Even in some cases, some of the digital initiatives were sort of being claimed. The studios were staking their claim. The reality was the CDO also emerged out of highly regulated industries financialservices healthcare government. And it really was this kind of wonky back office role. And so that's what my compliance, that's what it's become again. We're seeing that CEOs largely you're not involved in a lot of the emerging. Aye, aye initiatives. That's what we heard, sort of anecdotally talking to various folks At the same time. I feel as though the CDO role has been more fossilized than it was before. We used to ask, Is this role going to be around anymore? We had C I. Ose tell us that the CEO Rose was going to disappear, so you had both ends of the spectrum. But I feel as though that whatever it's called CDO Data's our chief analytics off officer, head of data, you know, analytics and governance. That role is here to stay, at least for for a fair amount of time and increasingly, issues of privacy and governance. And at least the periphery of security are gonna be supported by that CD a role. So that's kind of takeaway Number one. Let me get your thoughts. >> I think there's a maturity process going on here. What we saw really in 2016 through 2018 was, ah, sort of a celebration of the arrival of the CDO. And we're here, you know, we've got we've got power now we've got an agenda. And that was I mean, that was a natural outcome of all this growth and 90% of organizations putting sea Dios in place. I think what you're seeing now is a realization that Oh, my God, this is a mess. You know what I heard? This year was a lot less of this sort of crowing about the ascendance of sea Dios and Maura about We've got a big integration problem of big data cleansing problem, and we've got to get our hands down to the nitty gritty. And when you talk about, as you said, we had in here so much this year about strategic initiatives, about about artificial intelligence, about getting involved in digital business or customer experience transformation. What we heard this year was about cleaning up data, finding the data that you've got organizing it, applying meditator, too. It is getting in shape to do something with it. There's nothing wrong with that. I just think it's part of the natural maturation process. Organizations now have to go through Tiu to the dirty process of cleaning up this data before they can get to the next stage, which was a couple of three years out for most of >> the second. Big theme, of course. We heard this from the former head of analytics. That G s K on the opening keynote is the traditional methods have failed the the Enterprise Data Warehouse, and we've actually studied this a lot. You know, my analogy is often you snake swallowing a basketball, having to build cubes. E D W practitioners would always used to call it chasing the chips until we come up with a new chip. Oh, we need that because we gotta run faster because it's taking us hours and hours, weeks days to run these analytics. So that really was not an agile. It was a rear view mirror looking thing. And Sarbanes Oxley saved the E. D. W. Business because reporting became part of compliance thing perspective. The master data management piece we've heard. Do you consistently? We heard Mike Stone Breaker, who's obviously a technology visionary, was right on. It doesn't scale through this notion of duping. Everything just doesn't work and manually creating rules. It's just it's just not the right approach. This we also heard the top down data data enterprise data model doesn't works too complicated, can operationalize it. So what they do, they kick the can to governance. The Duke was kind of a sidecar, their big data that failed to live up to its promises. And so it's It's a big question as to whether or not a I will bring that level of automation we heard from KPMG. Certainly, Mike Stone breaker again said way heard this, uh, a cz well, from Andy Palmer. They're using technology toe automate and scale that big number one data science problem, which is? They spend all their time wrangling data. We'll see if that if that actually lives up >> to his probable is something we did here today from several of our guests. Was about the promise of machine learning to automate this day to clean up process and as ah Mark Ramsay kick off the conference saying that all of these efforts to standardize data have failed in the past. This does look, He then showed how how G s K had used some of the tools that were represented here using machine learning to actually clean up the data at G S. K. So there is. And I heard today a lot of optimism from the people we talked to about the capability of Chris, for example, talking about the capability of machine learning to bring some order to solve this scale scale problem Because really organizing data creating enterprise data models is a scale problem, and the only way you can solve that it's with with automation, Mike Stone breaker is right on top of that. So there was optimism at this event. There was kind of an ooh, kind of, ah, a dismay at seeing all the data problems they have to clean up, but also promised that tools are on the way that could do that. >> Yeah, The reason I'm an optimist about this role is because data such a hard problem. And while there is a feeling of wow, this is really a challenge. There's a lot of smart people here who are up for the challenge and have the d n a for it. So the role, that whole 360 thing. We talked about the traditional methods, you know, kind of failing, and in the third piece that touched on, which is really bringing machine intelligence to the table. We haven't heard that as much at this event. It's now front and center. It's just another example of a I injecting itself into virtually every aspect every corner of the industry. And again, I often jokes. Same wine, new bottle. Our industry has a habit of doing that, but it's cyclical, but it is. But we seem to be making consistent progress. >> And the machine learning, I thought was interesting. Several very guest spoke to machine learning being applied to the plumbing projects right now to cleaning up data. Those are really self contained projects. You can manage those you can. You can determine out test outcomes. You can vet the quality of the of the algorithms. It's not like you're putting machine learning out there in front of the customer where it could potentially do some real damage. There. They're vetting their burning in machine, learning in a environment that they control. >> Right, So So, Amy, Two solid days here. I think that this this conference has really grown when we first started here is about 130 people, I think. And now it was 500 registrants. This'd year. I think 600 is the sort of the goal for next year. Moving venues. The Cube has been covering this all but one year since 2013. Hope to continue to do that. Paul was great working with you. Um, always great work. I hope we can, uh we could do more together. We heard the verdict is bringing back its conference. You put that together. So we had column. Mahoney, um, had the vertical rock stars on which was fun. Com Mahoney, Mike Stone breaker uh, Andy Palmer and Chris Lynch all kind of weighed in, which was great to get their perspectives kind of the days of MPP and how that's evolved improving on traditional relational database. And and now you're Stone breaker. Applying all these m i. Same thing with that scale with Chris Lynch. So it's fun to tow. Watch those guys all Boston based East Coast folks some news. We just saw the news hit President Trump holding up jet icon contractors is we've talked about. We've been following that story very closely and I've got some concerns over that. It's I think it's largely because he doesn't like Bezos in The Washington Post Post. Exactly. You know, here's this you know, America first. The Pentagon says they need this to be competitive with China >> and a I. >> There's maybe some you know, where there's smoke. There's fire there, so >> it's more important to stick in >> the eye. That's what it seems like. So we're watching that story very closely. I think it's I think it's a bad move for the executive branch to be involved in those type of decisions. But you know what I know? Well, anyway, Paul awesome working with you guys. Thanks. And to appreciate you flying out, Sal. Good job, Alex Mike. Great. Already wrapping up. So thank you for watching. Go to silicon angle dot com for all the news. Youtube dot com slash silicon angles where we house our playlist. But the cube dot net is the main site where we have all the events. It will show you what's coming up next. We've got a bunch of stuff going on straight through the summer. And then, of course, VM World is the big kickoff for the fall season. Goto wicked bond dot com for all the research. We're out. Thanks for watching Dave. A lot day for Paul Gillon will see you next time.

>> from Cambridge, Massachusetts. It's the Cube covering M. I. T. Chief Data officer and Information Quality Symposium 2019 Brought to you by Silicon Angle Media >> Welcome back to M I. T. Everybody watching the Cube. The leader in live tech coverage we hear a Day two of the M I t chief data officer information Quality Conference Day Volonte with Paul Dillon. Andy Palmer's here. He's the co founder and CEO of Tamer. Good to see again. It's great to see it actually coming out. So I didn't ask this to Mike. I could kind of infirm from someone's dances. But why did you guys start >> Tamer? >> Well, it really started with an academic project that Mike was doing over at M. I. T. And I was over in of artists at the time. Is the chief get officer over there? And what we really found was that there were a lot of companies really suffering from data mastering as the primary bottleneck in their company did used great new tech like the vertical system that we've built and, you know, automated a lot of their warehousing and such. But the real bottleneck was getting lots of data integrated and mastered really, really >> quickly. Yeah, He took us through the sort of problems with obviously the d. W. In terms of scaling master data management and the scanning problems was Was that really the problem that you were trying to solve? >> Yeah, it really was. And when we started, I mean, it was like, seven years ago, eight years ago, now that we started the company and maybe almost 10 when we started working on the academic project, and at that time, people weren't really thinking are worried about that. They were still kind of digesting big data. A zit was called, but I think what Mike and I kind of felt was going on was that people were gonna get over the big data, Um, and the volume of data. And we're going to start worrying about the variety of the data and how to make the data cleaner and more organized. And, uh, I think I think way called that one pretty much right. Maybe >> we're a little >> bit early, but but I think now variety is the big problem >> with the other thing about your big day. Big data's oftentimes associated with Duke, which was a batch and then you sort of saw the shifter real time and spark was gonna fix all that. And so what are you seeing in terms of the trends in terms of how data is being used to drive almost near real time business decisions. >> You know, Mike and I came out really specifically back in 2007 and declared that we thought, uh, Hadoop and H D f s was going to be far less impactful than other people. >> 07 >> Yeah, Yeah. And Mike Mike actually was really aggressive and saying it was gonna be a disaster. And I think we've finally seen that actually play out of it now that the bloom is off the rose, so to speak. And so they're They're these fundamental things that big companies struggle with in terms of their data and, you know, cleaning it up and organizing it and making it, Iike want. Anybody that's worked at one of these big companies can tell you that the data that they get from most of their internal system sucks plain and simple, and so cleaning up that data, turning it into something it's an asset rather than liability is really what what tamers all about? And it's kind of our mission. We're out there to do this and it sort of pails and compare. Do you think about the amount of money that some of these companies have spent on systems like ASAP on you're like, Yeah, but all the data inside of the systems so bad and so, uh, ugly and unuseful like we're gonna fix that problem. >> So you're you're you're special sauce and machine learning. Where are you applying machine learning most most effectively when >> we apply machine learning to probably the least sexy problem on the planet. There are a lot of companies out there that use machine learning and a I t o do predictive algorithms and all kinds of cool stuff. All we do with machine learning is actually use it to clean up data and organize data. Get it ready for people to use a I I I started in the eye industry back in the late 19 eighties on, you know, really, I learned from the sky. Marvin Minsky and Mark Marvin taught me two things. First was garbage in garbage out. There's no algorithm that's worth anything unless you've got great data, and the 2nd 1 is it's always about the human in the machine working together. And I've really been working on those two same principles most of my career, and Tamer really brings both of those together. Our goal is to prepare data so that it can be used analytically inside of these companies, that it's actually high quality and useful. And the way we do that involves bringing together the machine, mostly these advanced machine learning algorithms with humans, subject matter experts inside of these companies that actually know all the ins and outs and all the intricacies of the data inside of their company. >> So say garbage in garbage out. If you don't have good training data course you're not going good ML model. How much how much upfront work is required. G. I know it was one of your customers and how much time is required to put together on ML model that can deal with 20,000,000 records like that? >> Well, you know, the amazing thing that this happened for us in the last five years, especially is that now we've got we've built enough models from scratch inside of these large global 2000 companies that very rarely do we go into a place where there we don't already have a model that's pre built. That they can use is a starting point. And I think that's the same thing that's happening in modeling in general. If you look a great companies like data robot Andi and even in in the Python community ml live that the accessibility of these modeling tools and the models themselves are actually so they're commoditized. And so most of our models and most of the projects we work on, we've already got a model. That's a starting point. We don't really have to start from scratch. >> You mentioned gonna ta I in the eighties Is that is the notion of a I Is it same as it was in the eighties and now we've just got the tooling, the horsepower, the data to take advantage of it is the concept changed? The >> math is all the same, like, you know, absolutely full stop, like there's really no new math. The two things I think that have changed our first. There's a lot more data that's available now, and, you know, uh, neural nets are a great example, right? in Marvin's things that, you know when you look at Google translate and how aggressively they used neural nets, it was the quantity of data that was available that actually made neural nets work. The second thing that that's that's changed is the cheap availability of Compute that Now the largest supercomputer in the world is available to rent by the minute. And so we've got all this data. You've got all this really cheap compute. And then third thing is what you alluded to earlier. The accessibility of all the math that now it's becoming so simple and easy to apply these math techniques, and they're becoming you know, it's It's almost to the point where the average data scientists not the advance With the average data, scientists can do a practice. Aye, aye. Techniques that 20 years ago required five PhDs. >> It's not surprising that Google, with its new neural net technology, all the search data that it has has been so successful. It's a surprise you that that Amazon with Alexa was able to compete so effectively. >> Oh, I think that I would never underestimate Amazon and their ability to, you know, build great tact. They've done some amazing work. One of my favorite Mike and I actually, one of our favorite examples in the last, uh, three years, they took their red shift system, you know, that competed with with Veronica and they they re implemented it and, you know, as a compiled system and it really runs incredibly fast. I mean, that that feat of engineering, what was truly exceptional >> to hear you say that Because it wasn't Red Shift originally Park. So yeah, that's right, Larry Ellison craps all over Red Shift because it's just open source offer that they just took and repackage. But you're saying they did some major engineering to Oh >> my gosh, yeah, It's like Mike and I both way Never. You know, we always compared par, excelled over tika, and, you know, we always knew we were better in a whole bunch of ways. But this this latest rewrite that they've done this compiled version like it's really good. >> So as a guy has been doing a eye for 30 years now, and it's really seeing it come into its own, a lot of a I project seems right now are sort of low hanging fruit is it's small scale stuff where you see a I in five years what kind of projects are going our bar company's gonna be undertaking and what kind of new applications are gonna come out of this? But >> I think we're at the very beginning of this cycle, and actually there's a lot more potential than has been realized. So I think we are in the pick the low hanging fruit kind of a thing. But some of the potential applications of A I are so much more impactful, especially as we modernize core infrastructure in the enterprise. So the enterprise is sort of living with this huge legacy burden. And we always air encouraging a tamer our customers to think of all their existing legacy systems is just dated generating machines and the faster they can get that data into a state where they can start doing state of the art A. I work on top of it, the better. And so really, you know, you gotta put the legacy burden aside and kind of draw this line in the sand so that as you really get, build their muscles on the A. I side that you can take advantage of that with all the data that they're generating every single day. >> Everything about these data repose. He's Enterprise Data Warehouse. You guys built better with MPP technology. Better data warehouses, the master data management stuff, the top down, you know, Enterprise data models, Dupin in big data, none of them really lived up to their promise, you know? Yeah, it's kind of somewhat unfair toe toe like the MPP guys because you said, Hey, we're just gonna run faster. And you did. But you didn't say you're gonna change the world and all that stuff, right? Where's e d? W? Did Do you feel like this next wave is actually gonna live up to the promise? >> I think the next phase is it's very logical. Like, you know, I know you're talking to Chris Lynch here in a minute, and you know what? They're doing it at scale and at scale and tamer. These companies are all in the same general area. That's kind of related to how do you take all this data and actually prepare it and turn it into something that's consumable really quickly and easily for all of these new data consumers in the enterprise and like so that that's the next logical phase in this process. Now, will this phase be the one that finally sort of meets the high expectations that were set 2030 years ago with enterprise data warehousing? I don't know, but we're certainly getting closer >> to I kind of hoped knockers, and we'll have less to do any other cool stuff that you see out there. That was a technology just >> I'm huge. I'm fanatical right now about health care. I think that the opportunity for health care to be transformed with technology is, you know, almost makes everything else look like chump change. What aspect of health care? Well, I think that the most obvious thing is that now, with the consumer sort of in the driver seat in healthcare, that technology companies that come in and provide consumer driven solutions that meet the needs of patients, regardless of how dysfunctional the health care system is, that's killer stuff. We had a great company here in Boston called Pill Pack was a great example of that where they just build something better for consumers, and it was so popular and so, you know, broadly adopted again again. Eventually, Amazon bought it for $1,000,000,000. But those kinds of things and health care Pill pack is just the beginning. There's lots and lots of those kinds of opportunities. >> Well, it's right. Healthcare's ripe for disruption on, and it hasn't been hit with the digital destruction. And neither is financialservices. Really? Certainly, defenses has not yet another. They're high risk industry, so Absolutely takes longer. Well, Andy, thanks so much for making the time. You know, You gotta run. Yeah. Yeah. Thank you. All right, keep it right. Everybody move back with our next guest right after this short break. You're watching the Cube from M I T c B O Q. Right back.

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, you're watching The Cube, the leader in tech coverage. My name is Dave Vellante here with my cohost Paul Gillin. This is day one of our two day coverage of the MIT CDOIQ conferences. CDO, Chief Data Officer, IQ, information quality. Colin Mahoney is here, he's a good friend and long time CUBE alum. I haven't seen you in awhile, >> I know >> But thank you so much for taking some time, you're like a special guest here >> Thank you, yeah it's great to be here, thank you. >> Yeah, so, this is not, you know, something that you would normally attend. I caught up with you, invited you in. This conference has started as, like back office governance, information quality, kind of wonky stuff, hidden. And then when the big data meme took off, kind of around the time we met. The Chief Data Officer role emerged, the whole Hadoop thing exploded, and then this conference kind of got bigger and bigger and bigger. Still intimate, but very high level, very senior. It's kind of come full circle as we've been saying, you know, information quality still matters. You have been in this data business forever, so I wanted to invite you in just to get your perspectives, we'll talk about what's new with what's going on in your company, but let's go back a little bit. When we first met and even before, you saw it coming, you kind of invested your whole career into data. So, take us back 10 years, I mean it was so different, remember it was Batch, it was Hadoop, but it was cool. There was a lot of cool >> It's still cool. (laughs) projects going on, and it's still cool. But, take a look back. >> Yeah, so it's changed a lot, look, I got into it a while ago, I've always loved data, I had no idea, the explosion and the three V's of data that we've seen over the last decade. But, data's really important, and it's just going to get more and more important. But as I look back I think what's really changed, and even if you just go back a decade I mean, there's an insatiable appetite for data. And that is not slowing down, it hasn't slowed down at all, and I think everybody wants that perfect solution that they can ask any question and get an immediate answers to. We went through the Hadoop boom, I'd argue that we're going through the Hadoop bust, but what people actually want is still the same. You know, they want real answers, accurate answers, they want them quickly, and they want it against all their information and all their data. And I think that Hadoop evolved a lot as well, you know, it started as one thing 10 years ago, with MapReduce and I think in the end what it's really been about is disrupting the storage market. But if you really look at what's disrupting storage right now, public clouds, S3, right? That's the new data league. So there's always a lot of hype cycles, everybody talks about you know, now it's Cloud, everything, for maybe the last 10 years it was a lot of Hadoop, but at the end of the day I think what people want to do with data is still very much the same. And a lot of companies are still struggling with it, hence the role for Chief Data Officers to really figure out how do I monetize data on the one hand and how to I protect that asset on the other hand. >> Well so, and the cool this is, so this conference is not a tech conference, really. And we love tech, we love talking about this, this is why I love having you on. We kind of have a little Vertica thread that I've created here, so Colin essentially, is the current CEO of Vertica, I know that's not your title, you're GM and Senior Vice President, but you're running Vertica. So, Michael Stonebreaker's coming on tomorrow, >> Yeah, excellent. >> Chris Lynch is coming on tomorrow, >> Oh, great, yeah. >> we've got Andy Palmer >> Awesome, yeah. >> coming up as well. >> Pretty cool. (laughs) >> So we have this connection, why is that important? It's because, you know, Vertica is a very cool company and is all about data, and it was all about disrupting, sort of the traditional relational database. It's kind of doing more with data, and if you go back to the roots of Vertica, it was like how do you do things faster? How do you really take advantage of data to really drive new business? And that's kind of what it's all about. And the tech behind it is really cool, we did your conference for many, many years. >> It's coming back by the way. >> Is it? >> Yeah, this March, so March 30th. >> Oh, wow, mark that down. >> At Boston, at the new Encore Hotel. >> Well we better have theCUBE there, bro. (laughs) >> Yeah, that's great. And yeah, you've done that conference >> Yep. >> haven't you before? So very cool customers, kind of leading edge, so I want to get to some of that, but let's talk the disruption for a minute. So you guys started with the whole architecture, MPP and so forth. And you talked about Cloud, Cloud really disrupted Hadoop. What are some of the other technology disruptions that you're seeing in the market space? >> I think, I mean, you know, it's hard not to talk about AI machine learning, and what one means versus the other, who knows right? But I think one thing that is definitely happening is people are leveraging the volumes of data and they're trying to use all the processing power and storage power that we have to do things that humans either are too expensive to do or simply can't do at the same speed and scale. And so, I think we're going through a renaissance where a lot more is being automated, certainly on the Vertica roadmap, and our path has always been initially to get the data in and then we want the platform to do a lot more for our customers, lots more analytics, lots more machine-learning in the platform. So that's definitely been a lot of the buzz around, but what's really funny is when you talk to a lot of customers they're still struggling with just some basic stuff. Forget about the predictive thing, first you've got to get to what happened in the past. Let's give accurate reporting on what's actually happening. The other big thing I think as a disruption is, I think IOT, for all the hype that it's getting it's very real. And every device is kicking off lots of information, the feedback loop of AB testing or quality testing for predictive maintenance, it's happening almost instantly. And so you're getting massive amounts of new data coming in, it's all this machine sensor type data, you got to figure out what it means really quick, and then you actually have to do something and act on it within seconds. And that's a whole new area for so many people. It's not their traditional enterprise data network warehouse and you know, back to you comment on Stonebreaker, he got a lot of this right from the beginning, you know, and I think he looked at the architectures, he took a lot of the best in class designs, we didn't necessarily invent everything, but we put a lot of that together. And then I think the other you've got to do is constantly re-invent your platform. We came out with our Eon Mode to run cloud native, we just got rated the best cloud data warehouse from a net promoter score rating perspective, so, but we got to keep going you know, we got to keep re-inventing ourselves, but leverage everything that we've done in the past as well. >> So one of the things that you said, which is kind of relevant for here, Paul, is you're still seeing a real data quality issue that customers are wrestling with, and that's a big theme here, isn't it? >> Absolutely, and the, what goes around comes around, as Dave said earlier, we're still talking about information quality 13 years after this conference began. Have the tools to improve quality improved all that much? >> I think the tools have improved, I think that's another area where machine learning, if you look at Tamr, and I know you're going to have Andy here tomorrow, they're leveraging a lot of the augmented things you can do with the processing to make it better. But I think one thing that makes the problem worse now, is it's gotten really easy to pour data in. It's gotten really easy to store data without having to have the right structure, the right quality, you know, 10 years ago, 20 years ago, everything was perfect before it got into the platform. Right, everything was, there was quality, everything was there. What's been happening over the last decade is you're pumping data into these systems, nobody knows if it's redundant data, nobody knows if the quality's any good, and the amount of data is massive. >> And it's cheap to store >> Very cheap to store. >> So people keep pumping it in. >> But I think that creates a lot of issues when it comes to data quality. So, I do think the technology's gotten better, I think there's a lot of companies that are doing a great job with it, but I think the challenge has definitely upped. >> So, go ahead. >> I'm sorry. You mentioned earlier that we're seeing the death of Hadoop, but I'd like you to elaborate on that becuase (Dave laughs) Hadoop actually came up this morning in the keynote, it's part of what GlaxoSmithKline did. Came up in a conversation I had with the CEO of Experian last week, I mean, it's still out there, why do you think it's in decline? >> I think, I mean first of all if you look at the Hadoop vendors that are out there, they've all been struggling. I mean some of them are shutting down, two of them have merged and they've got killed lately. I think there are some very successful implementations of Hadoop. I think Hadoop as a storage environment is wonderful, I think you can process a lot of data on Hadoop, but the problem with Hadoop is it became the panacea that was going to solve all things data. It was going to be the database, it was going to be the data warehouse, it was going to do everything. >> That's usually the kiss of death, isn't it? >> It's the kiss of death. And it, you know, the killer app on Hadoop, ironically, became SQL. I mean, SQL's the killer app on Hadoop. If you want to SQL engine, you don't need Hadoop. But what we did was, in the beginning Mike sort of made fun of it, Stonebreaker, and joked a lot about he's heard of MapReduce, it's called Group By, (Dave laughs) and that created a lot of tension between the early Vertica and Hadoop. I think, in the end, we embraced it. We sit next to Hadoop, we sit on top of Hadoop, we sit behind it, we sit in front of it, it's there. But I think what the reality check of the industry has been, certainly by the business folks in these companies is it has not fulfilled all the promises, it has not fulfilled a fraction on the promises that they bet on, and so they need to figure those things out. So I don't think it's going to go away completely, but I think its best success has been disrupting the storage market, and I think there's some much larger disruptions of technologies that frankly are better than HTFS to do that. >> And the Cloud was a gamechanger >> And a lot of them are in the cloud. >> Which is ironic, 'cause you know, cloud era, (Colin laughs) they didn't really have a cloud strategy, neither did Hortonworks, neither did MapR and, it just so happened Amazon had one, Google had one, and Microsoft has one, so, it's just convenient to-- >> Well, how is that affecting your business? We've seen this massive migration to the cloud (mumbles) >> It's actually been great for us, so one of the things about Vertica is we run everywhere, and we made a decision a while ago, we had our own data warehouse as a service offering. It might have been ahead of its time, never really took off, what we did instead is we pivoted and we say "you know what? "We're going to invest in that experience "so it's a SaaS-like experience, "but we're going to let our customers "have full control over the cloud. "And if they want to go to Amazon they can, "if they want to go to Google they can, "if they want to go to Azure they can." And we really invested in that and that experience. We're up on the Amazon marketplace, we have lots of customers running up on Amazon Cloud as well as Google and Azure now, and then about two years ago we went down and did this endeavor to completely re-architect our product so that we could separate compute and storage so that our customers could actually take advantage of the cloud economics as well. That's been huge for us, >> So you scale independent-- >> Scale independently, cloud native, add compute, take away compute, and for our existing customers, they're loving the hybrid aspect, they love that they can still run on Premise, they love that they can run up on a public cloud, they love that they can run in both places. So we will continue to invest a lot in that. And it is really, really important, and frankly, I think cloud has helped Vertica a lot, because being able to provision hardware quickly, being able to tie in to these public clouds, into our customers' accounts, give them control, has been great and we're going to continue on that path. >> Because Vertica's an ISV, I mean you're a software company. >> We're a software company. >> I know you were a part of HP for a while, and HP wanted to mash that in and run it on it's hardware, but software runs great in the cloud. And then to you it's another hardware platform. >> It's another hardware platform, exactly. >> So give us the update on Micro Focus, Micro Focus acquired Vertica as part of the HPE software business, how many years ago now? Two years ago? >> Less than two years ago. >> Okay, so how's that going, >> It's going great. >> Give us the update there. >> Yeah, so first of all it is great, HPE and HP were wonderful to Vertica, but it's great being part of a software company. Micro Focus is a software company. And more than just a software company it's a company that has a lot of experience bridging the old and the new. Leveraging all of the investments that you've made but also thinking about cloud and all these other things that are coming down the pike. I think for Vertica it's been really great because, as you've seen Vertica has gotten its identity back again. And that's something that Micro Focus is very good at. You can look at what Micro Focus did with SUSE, the Linux company, which actually you know, now just recently spun out of Micro Focus but, letting organizations like Vertica that have this culture, have this product, have this passion, really focus on our market and our customers and doing the right thing by them has been just really great for us and operating as a software company. The other nice thing is that we do integrate with a lot of other products, some of which came from the HPE side, some of which came from Micro Focus, security products is an example. The other really nice thing is we've been doing this insource thing at Micro Focus where we open up our source code to some of the other teams in Micro Focus and they've been contributing now in amazing ways to the product. In ways that we would just never be able to scale, but with 4,000 engineers strong in Micro Focus, we've got a much larger development organization that can actually contribute to the things that Vertica needs to do. And as we go into the cloud and as we do a lot more operational aspects, the experience that these teams have has been incredible, and security's another great example there. So overall it's been great, we've had four different owners of Vertica, our job is to continue what we do on the innovation side in the culture, but so far Micro Focus has been terrific. >> Well, I'd like to say, you're kind of getting that mojo back, because you guys as an independent company were doing your own thing, and then you did for a while inside of HP, >> We did. >> And that obviously changed, 'cause they wanted more integration, but, and Micro Focus, they know what they're doing, they know how to do acquisitions, they've been very successful. >> It's a very well run company, operationally. >> The SUSE piece was really interesting, spinning that out, because now RHEL is part of IBM, so now you've got SUSE as the lone independent. >> Yeah. >> Yeah. >> But I want to ask you, go back to a technology question, is NoSQL the next Hadoop? Are these databases, it seems to be that the hot fad now is NoSQL, it can do anything. Is the promise overblown? >> I think, I mean NoSQL has been out almost as long as Hadoop, and I, we always say not only SQL, right? Mike's said this from day one, best tool for the job. Nothing is going to do every job well, so I think that there are, whether it's key value stores or other types of NoSQL engines, document DB's, now you have some of these DB's that are running on different chips, >> Graph, yeah. >> there's always, yeah, graph DBs, there's always going to be specialty things. I think one of the things about our analytic platform is we can do, time series is a great example. Vertica's a great time series database. We can compete with specialized time series databases. But we also offer a lot of, the other things that you can do with Vertica that you wouldn't be able to do on a database like that. So, I always think there's going to be specialty products, I also think some of these can do a lot more workloads than you might think, but I don't see as much around the NoSQL movement as say I did a few years ago. >> But so, and you mentioned the cloud before as kind of, your position on it I think is a tailwind, not to put words in your mouth, >> Yeah, yeah, it's a great tailwind. >> You're in the Amazon marketplace, I mean they have products that are competitive, right? >> They do, they do. >> But, so how are you differentiating there? >> I think the way we differentiate, whether it's Redshift from Amazon, or BigQuery from Google, or even what Azure DB does is, first of all, Vertica, I think from, feature functionality and performance standpoint is ahead. Number one, I think the second thing, and we hear this from a lot of customers, especially at the C-level is they don't want to be locked into these full stacks of the clouds. Having the ability to take a product and run it across multiple clouds is a big thing, because the stack lock-in now, the full stack lock-in of these clouds is scary. It's really easy to develop in their ecosystems but you get very locked into them, and I think a lot of people are concerned about that. So that works really well for Vertica, but I think at the end of the day it's just, it's the robustness of the product, we continue to innovate, when you look at separating compute and storage, believe it or not, a lot of these cloud-native databases don't do that. And so we can actually leverage a lot of the cloud hardware better than the native cloud databases do themselves. So, like I said, we have to keep going, those guys aren't going to stop, and we actually have great relationships with those companies, we work really well with the clouds, they seem to care just as much about their cloud ecosystem as their own database products, and so I think that's going to continue as well. >> Well, Colin, congratulations on all the success >> Yeah, thank you, yeah. >> It's awesome to see you again and really appreciate you coming to >> Oh thank you, it's great, I appreciate the invite, >> MIT. >> it's great to be here. >> All right, keep it right there everybody, Paul and I will be back with our next guest from MIT, you're watching theCUBE. (electronic jingle)

>> wait. >> Okay, We're back. We are in Cambridge, Massachusetts. This is Dave Volante. I'm here with Jeff Kelly. Where with Wicked Bond. This is the Cube Silicon Angles production. We're here at the Mighty Information Quality Symposium in the heart of database design and development. We've had some great guests on Scott Hauser is here. He's the head of marketing at Adapt Company that we've introduced to our community. You know, quite some time ago, Um, really bringing multiple channels into the Duke Duke ecosystem and helping make sense out of all this data bringing insights to this data. Scott, welcome back to the Cube. >> Thanks for having me. It's good to be here. >> So this this notion of data quality, the reason why we asked you to be on here today is because first of all, you're a practitioner. Umm, you've been in the data warehousing world for a long, long time. So you've struggled with this issue? Um, people here today, uh, really from the world of Hey, we've been doing big data for a long time. This whole big data theme is nothing new to us. Sure, but there's a lot knew. Um, and so take us back to your days as a zoo. A data practitioner. Uh, data warehousing, business intelligence. What were some of the data quality issues that you faced and how did you deal with him? So >> I think a couple of points to raise in that area are no. One of things that we like to do is try and triangulate on user to engage them. And every channel we wanted to go and bring into the fold, creating unique dimension of how do we validate that this is the same person, right? Because each channel that you engage with has potentially different requirements of, um, user accreditation or, ah, guarantee of, you know, single user fuel. That's why I think the Holy Grail used to be in a lot of ways, like single sign on our way to triangulate across the spirit systems, one common identity or person to make that world simple. I don't think that's a reality in the in the sense that when you look at, um, a product provider or solution provider and a customer that's external, write those those two worlds Avery spirit and there was a lot of channels and pitch it potentially even third party means that I might want to engage this individual by. And every time I want to bring another one of those channels online, it further complicates. Validating who? That person eighty. >> Okay, so So when you were doing your data warehouse thing again as an I t practitioner, Um, you have you You try to expand the channels, but every time he did that and complex if I hide the data source So how did you deal with that problem? So just create another database and stole five Everything well, >> unfortunately, absolutely creates us this notion of islands of information throughout the enterprise. Because, as you mentioned, you know, we define a schema effectively a new place, Um, data elements into that schema of how you identified how you engage in and how you rate that person's behaviors or engagement, etcetera. And I think what you'd see is, as you'd bring on new sources that timeto actually emerge those things together wasn't in the order of days or weeks. It's on months and years. And so, with every new channel that became interesting, you further complicate the problem and effectively, What you do is, you know, creating these pools of information on you. Take extracts and you try and do something to munch the data and put in a place where you give access to an analyst to say, Okay, here's it. Another, um, Sample said a day to try and figure out of these things. Align and you try and create effectively a new schema that includes all the additional day that we just added. >> So it's interesting because again, one of the themes that we've been hearing a lot of this conference and hear it a lot in many conferences, not the technology. It's the people in process around the technology. That's certainly any person person would agree with that. But at the same time, the technology historically has been problematic, particularly data. Warehouse technology has been challenging you. So you've had toe keep databases relatively small and despair, and you had to build business processes around those that's right a basis. So you've not only got, you know, deficient technology, if you will, no offense, toe data, warehousing friends, but you've got ah, process creep that's actually fair. That's occurred, and >> I think you know what is happening is it's one of the things that's led to sort of the the revolution it's occurring in the market right now about you know, whether it's the new ecosystem or all the tangential technologies around that. Because what what's bound not some technology issues in the past has been the schema right. As important as that is because it gives people a very easy way to interact with the data. It also creates significant challenges when you want to bring on these unique sources of information. Because, you know, as you look at things that have happened over the last decade, the engagement process for either a consumer, a prospect or customer have changed pretty dramatically, and they don't all have the same stringent requirements about providing information to become engaged that way. So I think where the schema has, you know, has value you obviously, in the enterprise, it also has a lot of, um, historical challenges that brings along with >> us. So this jump movement is very disruptive to the traditional market spaces. Many folks say it isn't traditional guy, say, say it isn't but clearly is, particularly as you go Omni Channel. I threw that word out earlier on the channels of discussion that we had a dupe summit myself. John Ferrier, Hobby lobby meta and as your and this is something that you guys are doing that bringing in data to allow your customers to go Omni Channel. As you do that, you start again. Increase the complexity of the corpus of data at the same time. A lot of a lot of times into do you hear about scheme alight ski, but less so how do you reconcile the Omni Channel? The scheme of less It's their scheme alight. And the data quality >> problems, Yes, I think for, you know, particular speaking about adapt one of things that we do is we give customers the ability to take and effectively dump all that data into one common repository that is HD if s and do and leverage some of those open source tools and even their own, you know, inventions, if you will, you know, with m R code pig, whatever, and allow them to effectively normalized data through it orations and to do and then push that into tables effectively that now we can give access to the sequel interface. Right? So I think for us the abilities you're absolutely right. The more channels. You, Khun, give access to write. So this concept of anomie channel where Irrespective of what way we engaged with a customer what way? They touch us in some way. Being able to provide those dimensions of data in one common repository gives the marketeer, if you will, an incredible flexibility and insights that were previous, Who'd be discoverable >> assuming that data qualities this scene >> right of all these So so that that was gonna be my question. So what did the data quality implications of using something like HD FSB. You're essentially scheme unless you're just dumping data and essentially have a raw format and and it's raw format. So now you've gotto reconcile all these different types of data from different sources on build out that kind of single view of a customer of a product, Whatever, whatever is yours. You're right. >> So how do you go >> about doing that in that kind of scenario? So I think the repository in Hindu breach defense himself gives you that one common ground toa workin because you've got, you know, no implications of schema or any other preconceived notions about how you're going toe to toe massage weight if you will, And it's about applying logic and looking for those universal ides. There are a bunch of tools around that are focused on this, but applying those tools and it means that doesn't, um, handy captain from the start by predisposing them to some structure. And you want them to decipher or call out that through whether it's began homegrown type scripts, tools that might be upstairs here and then effectively normalizing the data and moving it into some structure where you can interact with it on in a meaningful way. So that really the kind the old way of trying to bring, you know, snippets of the data from different sources into ah, yet another database where you've got a play structure that takes time, months and years in some cases. And so Duke really allows you to speed up that process significantly by basically eliminating that that part of the equation. Yeah, I think there's and there's a bunch of dimensions we could talk about things like even like pricing exercises, right quality of triangulating on what that pricing should be per product for geography, for engagement, etcetera. I think you see that a lot of those types of work. Let's have transitioned from, you know, mainframe type environments, environments of legacy to the Duke ecosystem. And we've seen cases where people talk about they're going from eight month, you know, exercises to a week. And I think that's where the value of this ecosystem in you know, the commodity scalability really provides you with flexibility. That was just previously you unachievable. >> So could you provide some examples either >> you know, your own from your own career or from some customers you're seeing in terms of the data quality implications of the type of work they're doing. So one of our kind of *** is that you know the data quality measures required for any given, uh, use case various, in some cases, depending on the type of case. You know, in depending on the speed that you need, the analysis done, uh, the type of data quality or the level data qualities going is going to marry. Are you seeing that? And if >> so, can you give some examples of the different >> types of way data quality Gonna manifest itself in a big data were close. Sure. So I think that's absolutely fair. And you know. Obviously there's there's gonna be some trade off between accuracy and performance, right? And so you have to create some sort of confidence coefficient part, if you will, that you know, within some degree of probability this is good enough, right? And there's got to be some sort of balance between that actor Jerseyan time Um, some of the things that you know I've seen a lot of customers being interested in is it is a sort of market emerging around providing tools for authenticity of engagement. So it's an example. You know, I may be a large brand, and I have very, um, open channels that I engage somebody with my B e mail might be some Web portal, etcetera, and there's a lot of fishing that goes on out there, right? And so people fishing for whether it's brands and misrepresenting themselves etcetera. And there's a lot of, you know, desire to try and triangulate on data quality of who is effectively positioned themselves as me, who's really not me and being able to sort of, you know, take a cybersecurity spin and started to block those things down and alleviate those sort of nefarious activities. So We've seen a lot of people using our tool to effectively understand and be able to pinpoint those activities based upon behavior's based upon, um, out liars and looking at examples of where the engagement's coming from that aren't authentic if that >> makes you feel any somewhat nebulous but right. So using >> analytics essentially to determine the authenticity of a person of intensity, of an engagement rather than taking more rather than kind of looking at the data itself using pattern detection to determine. But it also taking, you know, there's a bunch of, um, there's a bunch of raw data that exists out there that needs you when you put it together again. Back to this notion of this sort of, you know, landing zone, if you will, or Data Lake or whatever you wanna call it. You know, putting all of this this data into one repository where now I can start to do you know, analytics against it without any sort of pre determined schema. And start to understand, you know, are these people who are purporting to be, you know, firm X y Z are there really from X y Z? And if they're not, where these things originating and how, when we start to put filters or things in place to alleviate those sort of and that could apply, it sounds like to certainly private industry. But, I mean, >> it sounds like >> something you know, government would be very interested in terms ofthe, you know, in the news about different foreign countries potentially being the source of attacks on U. S. Corporations are part of the, uh, part of our infrastructure and trying to determine where that's coming from and who these people are. And >> of course, people were trying to get >> complicated because they're trying to cover up their tracks, right? Certainly. But I think that the most important thing in this context is it's not necessarily about being able to look at it after the fact, but it's being able to look at a set of conditions that occur before these things happen and identify those conditions and put controls in place to alleviate the action from taking place. I think that's where when you look at what is happening from now an acceleration of these models and from an acceleration of the quality of the data gathering being able to put those things into place and put effective controls in place beforehand is changing. You know the loss prevention side of the business and in this one example. But you're absolutely right. From from what I see and from what our customers were doing, it is, you know, it's multi dimensional in that you know this cyber security. That's one example. There's pricing that could be another example. There's engagements from, ah, final analysis or conversion ratio that could be yet another example. So I think you're right in it and that it is ubiquitous. >> So when you think about the historical role of the well historical we had Stewart on earlier, he was saying, the first known chief data officer we could find was two thousand three. So I guess that gives us a decade of history. But if you look back at the hole, I mean data quality. We've been talking about that for many, many decades. So if you think about the traditional or role of an organization, trying tio achieved data quality, single version of the truth, information, quality, information value and you inject it with this destruction of a dupe that to me anyway, that whole notion of data quality is changing because in certain use, cases inference just fine. Um, in false positives are great. Who cares? That's right. Now analyzing Twitter data from some cases and others like healthcare and financial services. It's it's critical. But so how do you see the notion of data quality evolving and adapting to this >> new world? Well, I think one of these you mentioned about this, you know, this single version of the truth was something that was, you know, when I was on the other side of the table, >> they were beating you over the head waken Do this, We >> can do this, and it's It's something that it sounds great on paper. But when you look at the practical implications of trying to do it in a very finite or stringent controlled way, it's not practical for the business >> because you're saying that the portions of your data that you can give a single version of the truth on our so small because of the elapsed time That's right. I think there's that >> dimension. But there's also this element of time, right and the time that it takes to define something that could be that rigid and the structure months. It's months, and by that time a lot of the innovations that business is trying to >> accomplish. The eyes have changed. The initiatives has changed. Yeah, you lost the sale. Hey, but we got the data. It would look here. Yeah, I think that's your >> right. And I think that's what's evolving. I think there's this idea that you know what Let's fail fast and let's do a lot of it. Orations and the flexibility it's being provided out in that ecosystem today gives people an opportunity. Teo iterated failed fast, and you write that you set some sort of, you know confidence in that for this particular application. We're happy with you in a percent confidence. Go fish. You are something a little >> bit, but it's good enough. So having said that now, what can we learn from the traditional date? A quality, you know, chief data officer, practitioners, those who've been very dogmatic, particularly in certain it is what can we learn from them and take into this >> new war? I think from my point of view on what my experience has always been is that those individuals have an unparalleled command of the business and have an appreciation for the end goal that the business is trying to accomplish. And it's taking that instinct that knowledge and applying that to the emergence of what's happening in the technology world and bringing those two things together. I think it's It's not so much as you know, there's a practical application in that sense of Okay, here's the technology options that we have to do these, you know, these desired you engaged father again. It's the pricing engagement, the cyber security or whatever. It's more. How could we accelerate what the business is trying to accomplish and applying this? You know, this technology that's out there to the business problem. I think in a lot of ways, you know, in the past it's always been here. But this really need technology. How can I make it that somewhere? And now I think those folks bring a lot of relevance to the technology to say Hey, here's a problem. Trying to solve legacy methodologies haven't been effective. Haven't been timely. Haven't been, uh, scaleable. Whatever hock me. Apply what's happening. The market today to these problems. >> Um, you guys adapt in particular to me any way a good signal of the maturity model and with the maturity of a dupe, it's It's starting to grow up pretty rapidly, you know, See, due to two auto. And so where are we had? What do you see is the progression, Um, and where we're going. >> So, you know, I mentioned it it on the cue for the last time it So it and I said, I believe that you know who do busy operating system of big data. And I believe that, you know, there's a huge transition taking place that was there were some interesting response to that on Twitter and all the other channels, but I stand behind that. I think that's really what's happening. Lookit. You know what people are engaging us to do is really start to transition away from the legacy methodologies and they're looking at. He's not just lower cost alternatives, but also more flexibility. And we talked about, you know, its summit. The notion of that revenue curve right and cost takeouts great on one side of the coin, and I are one side of the defense here. But I think equally and even more importantly, is the change in the revenue curve and the insights that people they're finding because of these unique channels of the Omni Challenge you describe being able to. So look at all these dimensions have dated one. Unified place is really changing the way that they could go to market. They could engage consumers on DH that they could provide access to the analyst. Yeah. I mean, ultimately, that's the most >> we had. Stewart Madness con who's maybe got written textbooks on operating systems. We probably use them. I know I did. Maybe they were gone by the time you got there, but young, but the point being, you know, a dupe azan operating system. The notion of a platform is really it's changing dramatically. So, um, I think you're right on that. Okay. So what's what's next for you guys? Uh, we talked about, you know, customer attraction and proof points. You're working. All right on that. I know. Um, you guys got a great tech, amazing team. Um, what's next for >> you? So I think it's it's continuing toe. Look at the market in being flexible with the market around as the Hughes case is developed. So, you know, obviously is a startup We're focused in a couple of key areas where we see a lot of early adoption and a lot of pain around the problem that we can solve. But I think it's really about continuing to develop those use cases, um, and expanded the market to become more of a, you know, a holistic provider of Angelique Solutions on top of a >> house. Uh, how's Cambridge working out for you, right? I mean, the company moved up from the founders, moved up from New Haven and chose shows the East Coast shows cameras were obviously really happy about. That is East Coast people. You don't live there full time, but I might as well. So how's that working out talent pool? You know, the vibrancy of the community, the the you know, the young people that you're able to tap. So >> I see there's a bunch of dimensions around that one. It's hot. It's really, really hot >> in human, Yes, but it's been actually >> fantastic. And if you look it not just a town inside the team, but I think around the team. So if you look at our board right Jet Saxena. Chris Lynch, I've been very successful. The database community over decades of experience, you know, and getting folks like that onto the board fell. The Hardiman has been, you know, in this space as well for a long time. Having folks like that is, you know, advisors and providing guidance to the team. Absolutely incredible. Hack Reduce is a great facility where we do things like hackathons meet ups get the community together. So I think there's been a lot of positive inertia around the company just being here in Cambridge. But, you know, from AA development of resource or recruiting one of you. It's also been great because you've got some really exceptional database companies in this area, and history will show you like there's been a lot of success here, not only an incubating technology, but building real database companies. And, you know, we're on start up on the block that people are very interested in, and I think we show a lot of, you know, dynamics that are changing in the market and the way the markets moving. So the ability for us to recruit talent is exceptional, right? We've got a lot of great people to pick from. We've had a lot of people joined from no other previously very successful database companies. The team's growing, you know, significantly in the engineering space right now. Um, but I just you know, I can't say enough good things about the community. Hack, reduce and all the resource is that we get access to because we're here in Cambridge. >> Is the hacker deuces cool? So you guys are obviously leveraging that you do how to bring people into the Sohag produces essentially this. It's not an incubator. It's really more of a an idea cloud. It's a resource cloud really started by Fred Lan and Chris Lynch on DH. Essentially, people come in, they share ideas. You guys I know have hosted a number of how twos and and it's basically open. You know, we've done some stuff there. It's it's very cool. >> Yeah, you know, I think you know, it's even for us. It's also a great place to recruit, right. We made a lot of talented people there, and you know what? The university participation as well We get a lot of talent coming in, participate in these activities, and we do things that aren't just adapt related, that we've had people teach had obsessions and just sort of evangelize what's happening in the ecosystem around us. And like I said, it's just it's been a great resource pool to engage with. And, uh, I think it's been is beneficial to the community, as it has been to us. So very grateful for that. >> All right. Scott has always awesome. See, I knew you were going to have some good practitioner perspectives on data. Qualities really appreciate you stopping by. My pleasure. Thanks for having to see you. Take care. I keep right to everybody right back with our next guest. This is Dave a lot. They would. Jeff Kelly, this is the Cube. We're live here at the MIT Information Quality Symposium. We'LL be right back.