>>Good morning and welcome back to Detroit, Michigan. My name is Savannah Peterson and I'm here on set of the cube, my co-host John Farer. How you doing this morning, John? >>Doing great. Feeling fresh. Day two of three days of coverage, feeling >>Fresh. That is that for being in the heat of the conference. I love that attitude. It's gonna >>Be a great day today. We'll see you at the end of the day. Yeah, >>Well, we'll hold him to it. All right, everyone hold 'em accountable. Very excited to start the day off with an internet, a legend as well as a cube og. We are joined this morning by Matt Klein. Matt, welcome to the show. >>Thanks for having me. Good to see you. Yep. >>It's so, what's the vibe? Day two, Everyone's buzzing. What's got you excited at the show? You've been here before, but it's been three years you >>Mentioned. I, I was saying it's been three years since I've been to a conference, so it's been interesting for me to see what is, what is the same and what is different pre and post covid. But just really great to see everyone here again and nice to not be sitting in my home by myself. >>You know, Savannah said you're an OG and we were referring before we came on camera that you were your first came on the Cub in 2017, second Cuban event. But you were, I think, on the first wave of what I call the contributor momentum, where CNCF really got the traction. Yeah. You were at Lift, Envoy was contributed and that was really hyped up and I remember that vividly. It was day zero they called it back then. Yeah. And you got so much traction. People are totally into it. Yeah. Now we've got a lot of that going on now. Right. A lot of, lot of day Zero events. They call 'em co, co-located events. You got web assembly, a lot of other hype out there. What do you see out there that you like? How would you look at some of these other Sure. Communities that are developing, What's the landscape look like as you look out? Because Envoy set the table, what is now a standard >>Practice. Yeah. What's been so interesting for me just to come here to the conference is, you know, we open source Envoy in 2016. We donated in 2017. And as you mentioned at that time, Envoy was, you know, everyone wanted to talk about Envoy. And you know, much to my amazement, Envoy is now pervasive. I mean, it's used everywhere around the world. It's like, never in my wildest dreams would I have imagined that it would be so widely used. And it's almost gotten to the point where it's become boring. You know, It's just assumed that Envoy is, is everywhere. And now we're hearing a lot about Eeb p f and Web assembly and GI ops and you know, AI and a bunch of other things. So it's, it's actually great. It's made me very happy that it's become so pervasive, but it's also fun. Yeah. We mention to, to look around all other stuff >>Like congratulate. It's just a huge accomplishment really. I think it's gonna be historic, historical moment for the industry too. But I like how it progressed. I mean, I don't mind hype cycles as long as it's some vetting. Sure. Of course. You know, use cases that are clearly defined, but you gotta get that momentum in the community, but then you start gotta get down to, to business. Yep. So, so to speak and get it deployed, get traction. Yep. What should projects look like? And, and give us the update on Envoy. Cause you guys have a, a great use case of how you got traction. Right. Take us through some of the early days of what made Envoy successful in your opinion. Great question. >>Yeah. You know, I, I think Envoy is fairly unique around this conference in the sense that Envoy was developed by Lyft, which is an end user company. And many of the projects in this ecosystem, you know, no judgment, for better or worse, they are vendor backed. And I think that's a different delivery mechanism when it's coming from an end user where you're solving a, a particular business case. So Envoy was really developed for Lyft in a, you know, very early scaling days and just, you know, trying to help Lyft solve its business problems. So I think when Envoy was developed, we were, you know, scaling, we were falling over and actually many other companies were having similar problems. So I think Envoy became very widely deployed because many companies were having similar issues. So Envoy just became pervasive among lift peer companies. And then we saw a lot of vendor uptake in the service mesh space in the API gateway space among large internet providers. So, I I I, I think it's just, it's an interesting case because I think when you're solving real problems on the ground, in some ways it's easier to actually get adoption than if you're trying to develop it from a commercial backing. >>And that's the class, I mean, almost, It's almost like open source product market fit. It is in its own way. Cause you have a problem. Absolutely. Other people have the same problem finding >>Too. I mean, it's, it's designed thinking from >>A different, When, when I talk to people about open source, I like to tell people that I do not think it's any different than starting a company. I actually think it's all the same problems finding pro product, market fit, hiring, like finding contributors and maintainers, like doing PR and marketing. Yeah. Getting team together, traction, getting, getting funding. I mean, you have to have money to do all these things. Yeah. So I think a lot of people think of open source as I, I don't know, you know, this fantastic collaborative effort and, and it is that, but there's a lot more to it. Yeah. And it is much more akin to starting a >>Company. Let's, let's just look at that for a second. Cause I think that's a good point. And I was having a conversation in the hallway two nights ago on this exact point. If the power dynamics of a startup in the open source, as you point out, is just different, it's community based. So there are things you just gotta be mindful of. It's not top down. >>Exactly. It's not like, >>Right. You know, go take that hill. It's really consensus based, but it is a startup. All those elements are in place. Absolutely. You need leadership, you gotta have debates, alignment, commit, You gotta commit to a vision. Yep. You gotta make adjustments. Build the trajectory. So based on that, I mean, do you see more end user traction? Cause I was, we were talking also about Intuit, they donated some of their tow code R goes out there. Yep. R go see the CDR goes a service. Where's the end user contributions to these days? Do you feel like it's good, still healthy? >>I, I mean, I, I'm, I'm biased. I would like to see more. I think backstage outta Spotify is absolutely fantastic. That's an area just in terms of developer portals and developer efficiency that I think has been very underserved. So seeing Backstage come outta Spotify where they've used it for years, and I think we've already seen they had a huge date, you know, day one event. And I, I think we're gonna see a lot more out of that >>Coming from, I'm an end user, pretend I'm an end user, so pretend I have some code. I want to, Oh man, I'm scared. I don't am I'm gonna lose my competitive edge. What's the, how do you talk to the enterprise out there that might be thinking about putting their project out there for whether it's the benefit of the community, developing talent, developing the product? >>Sure. Yeah. I would say that I, I would ask everyone to think through all of the pros and cons of doing that because it's not for free. I mean, doing open source is costly. It takes developer time, you know, it takes management time, it takes budgeting dollars. But the benefits if successful can be huge, right? I mean, it can be just in terms of, you know, getting people into your company, getting users, getting more features, all of that. So I would always encourage everyone to take a very pragmatic and realistic view of, of what is required to make that happen. >>What was that decision like at Lyft >>When you I I'm gonna be honest, it was very naive. I I think we've, of that we think we need to know. No, just didn't know. Yeah. I think a lot of us, myself included, had very minimal open source experience. And had we known, or had I known what would've happened, I, I still would've done it. But I, I'm gonna be honest, the last seven years have aged me what I feel like is like 70 or a hundred. It's been a >>But you say you look out in the landscape, you gotta take pride, look at what's happened. Oh, it's, I mean, it's like you said, it >>Matured fantastic. I would not trade it for anything, but it has, it has been a journey. What >>Was the biggest surprise? What was the most eye opening thing about the journey for you? >>I, I think actually just the recognition of all of the non-technical things that go into making these things a success. I think at a conference like this, people think a lot about technology. It is a technology conference, but open source is business. It really is. I mean, it, it takes money to keep it going. It takes people to keep >>It going. You gotta sell people on the concepts. >>It takes leadership to keep it going. It takes internal, it takes marketing. Yeah. So for me, what was most eyeopening is over the last five to seven years, I feel like I actually have not developed very many, if any technical skills. But my general leadership skills, you know, that would be applicable again, to running a business have applied so well to, to >>Growing off, Hey, you put it out there, you hear driving the ship. It's good to do that. They need that. It really needs it. And the results speak for itself and congratulations. Yeah. Thank you. What's the update on the project? Give us an update because you're seeing, seeing a lot of infrastructure people having the same problem. Sure. But it's also, the environments are a little bit different. Some people have different architectures. Absolutely different, more cloud, less cloud edges exploding. Yeah. Where does Envoy fit into the landscape they've seen and what's the updates? You've got some new things going on. Give the updates on what's going on with the project Sure. And then how it sits in the ecosystem vis-a-vis what people may use it for. >>Yeah. So I'm, from a core project perspective, honestly, things have matured. Things have stabilized a bit. So a lot of what we focus on now are less Big bang features, but more table stakes. We spend a lot of time on security. We spend a lot of time on software supply chain. A topic that you're probably hearing a lot about at this conference. We have a lot of software supply chain issues. We have shipped Quicken HTB three over the last year. That's generally available. That's a new internet protocol still work happening on web assembly where ha doing a lot of work on our build and release pipeline. Again, you would think that's boring. Yeah. But a lot of people want, you know, packages for their fedora or their ADU or their Docker images. And that takes a lot of effort. So a lot of what we're doing now is more table stakes, just realizing that the project is used around the world very widely. >>Yeah. The thing that I'm most interested in is, we announced in the last six months a project called Envoy Gateway, which is layered on top of Envoy. And the goal of Envoy Gateway is to make it easier for people to run Envoy within Kubernetes. So essentially as an, as an ingress controller. And Envoy is a project historically, it is a very sophisticated piece of software, very complicated piece of software. It's not for everyone. And we want to provide Envoy Gateway as a way of onboarding more users into the Envoy ecosystem and making Envoy the, the default API gateway or edge proxy within Kubernetes. But in terms of use cases, we see Envoy pervasively with service mesh, API gateway, other types of low balancing cases. I mean, honestly, it's, it's all over the place at >>This point. I'm curious because you mentioned it's expanded beyond your wildest dreams. Yeah. And how could you have even imagined what Envoy was gonna do? Is there a use case or an application that really surprised you? >>You know, I've been asked that before and I, it's hard for me to answer that. It's, it's more that, I mean, for example, Envoy is used by basically every major internet company in China. I mean, like, wow. Everyone in China uses Envoy, like TikTok, like Alibaba. I mean like everyone, all >>The large sale, >>Everyone. You know, and it's used, it's used in the, I'm just, it's not just even the us. So I, I think the thing that has surprised me more than individual use cases is just the, the worldwide adoption. You know, that something could be be everywhere. And that I think, you know, when I open my phone and I'm opening all of these apps on my phone, 80 or 90% of them are going through Envoy in some form. Yeah. You know, it's, it's just that pervasive, I blow your mind a little bit sometimes >>That does, that's why you say plumber on your Twitter handle as your title. Cause you're working on all these things that are like really important substrate issues, Right. For scale, stability, growth. >>And, you know, to, I, I guess the only thing that I would add is, my goal for Envoy has always been that it is that boring, transparent piece of technology. Kind of similar to Linux. Linux is everywhere. Right? But no one really knows that they're using Linux. It's, it's justs like Intel inside, we're not paying attention. It's just there, there's >>A core group working on, if they have pride, they understand the mission, the importance of it, and they make their job is to make it invisible. >>Right. Exactly. >>And that's really ease of use. What's some of the ease of use sways and, and simplicity that you're working on, if you can talk about that. Because to be boring, you gotta be simpler and easier. All boring complex is unique is not boring. Complex is stressful. No, >>I I think we approach it in a couple different ways. One of them is that because we view Envoy as a, as a base technology in the ecosystem, we're starting to see, you know, not only vendors, but other open source projects that are being built on top of Envoy. So things like API Gateway, sorry, Envoy Gateway or you know, projects like Istio or all the other projects that are out there. They use Envoy as a component, but in some sense Envoy is a, as a transparent piece of that system. Yeah. So I'm a big believer in the ecosystem that we need to continue to make cloud native easier for, for end users. I still think it's too complicated. And so I think we're there, we're, we're pushing up the stack a bit. >>Yeah. And that brings up a good point. When you start seeing people building on top of things, right? That's enabling. So as you look at the enablement of Envoy, what are some of the things you see out on the horizon if you got the 20 mile stare out as you check these boring boxes, make it more plumbing, Right? Stable. You'll have a disruptive enabling platform. Yeah. What do you see out there? >>I am, you know, I, again, I'm not a big buzzword person, but, so some people call it serverless functions as a service, whatever. I'm a big believer in platforms in the sense that I really believe in the next 10 to 15 years, developers, they want to provide code. You know, they want to call APIs, they want to use pub subsystems, they want to use cas and databases. And honestly, they don't care about container scheduling or networking or load balancing or any of >>These things. It's handled in the os >>They just want it to be part of the operating system. Yeah, exactly. So I, I really believe that whether it's an open source or in cloud provider, you know, package solutions, that we're going to be just moving increasingly towards systems likes Lambda and Fargate and Google Cloud Run and Azure functions and all those kinds of things. And I think that when you do that much of the functionality that has historically powered this conference like Kubernetes and Onvoy, these become critical but transparent components that people don't, they're not really aware of >>At that point. Yeah. And I think that's a great call out because one of the things we're seeing is the market forces of, of this evolution, what you just said is what has to happen Yep. For digital transformation to, to get to its conclusion. Yep. Which means that everything doesn't have to serve the business, it is the business. Right. You know it in the old days. Yep. Engineers, they serve the business. Like what does that even mean? Yep. Now, right. Developers are the business, so they need that coding environment. So for your statement to happen, that simplicity in visibility calling is invisible os has to happen. So it brings up the question in open source, the trend is things always work itself out on the wash, as we say. So when you start having these debates and the alignment has to come at some point, you can't get to those that stay without some sort of defacto or consensus. Yep. And even standards, I'm not a big be around hardcore standards, but we can all agree and have consensus Sure. That will align behind, say Kubernetes, It's Kubernetes a standard. It's not like an i e you know, but this next, what, what's your reaction to this? Because this alignment has to come after debate. So all the process contending for I am the this of that. >>Yeah. I'm a look, I mean, I totally see the value in like i e e standards and, and there's a place for that. At the same time, for me personally as a technologist, as an engineer, I prefer to let the, the market as it were sort out what are the defacto standards. So for example, at least with Envoy, Envoy has an API that we call Xds. Xds is now used beyond Envoy. It's used by gc, it's used by proprietary systems. And I'm a big believer that actually Envoy in its form is probably gonna go away before Xds goes away. So in some ways Xds has become a defacto standard. It's not an i e e standard. Yeah. We, we, we have been asked about whether we should do that. Yeah. But I just, I I think the >>It becomes a component. >>It becomes a component. Yeah. And then I think people gravitate towards these things that become de facto standards. And I guess I would rather let the people on the show floor decide what are the standards than have, you know, 10 people sitting in a room figure out >>The community define standards versus organizational institutional defined standards. >>And they both have places a >>Hundred percent. Yeah, sure. And, and there's social proof in both of them. Yep. >>Frankly, >>And we were saying on the cube that we believe that the developers will decide the standard. Sure. Because that's what you're basically saying. They're deciding what they do with their code. Right. And over time, as people realize the trade of, hey, if everyone's coding this right. And makes my life easier to get to that state of nirvana and enlightenment, as we would say. Yeah. Yeah. >>Starting strong this morning. John, I I love this. I'm curious, you mentioned Backstage by Spotify wonderful example. Do you think that this is a trend we're gonna see with more end users >>Creating open source projects? Like I, you know, I hope so. The flip side of that, and as we all know, we're entering an uncertain economic time and it can be hard to justify the effort that it takes to do it well. And what I typically counsel people when they are about to open source something is don't do it unless you're ready to commit the resources. Because opensourcing something and not supporting it. Yeah. I actually can be think, I think it'd be worse. >>It's an, it's insult that people, you're asking to commit to something. Exactly. Needs of time, need the money investment, you gotta go all in and push. >>So I, so I very much want to see it and, and I want to encourage that here, but it's hard for me to look into the crystal ball and know, you know, whether it's gonna happen more >>Or less at what point there were, are there too many projects? You know, I mean, but I'm not, I mean this in, in a, in a negative way. I mean it more in the way of, you know, you mentioned supply chain. We were riffing on the cube about at some point there's gonna be so much code open source continuing thundering away with, with the value that you're just gluing things. Right. I don't need the code, this code there. Okay. What's in the code? Okay. Maybe automation can help out on supply chain. Yeah. But ultimately composability is the new >>Right? It is. Yeah. And, and I think that's always going to be the case. Case. Good thing. It is good thing. And I, I think that's just, that's just the way of things for sure. >>So no code will be, >>I think, I think we're seeing a lot of no code situations that are working great for people. And, and, but this is actually really no different than my, than my serverless arguing from before. Just as a, as a, a slight digression. I'm building something new right now and you know, we're using cloud native technologies and all this stuff and it's still, >>What are you building? >>Even as a I'm, I'm gonna keep that, I'm gonna keep that secret. I know I'm, but >>We'll find out on Twitter. We're gonna find out now that we know it. Okay. Keep on mystery. You open that door. We're going down see in a couple weeks. >>Front >>Page is still an angle. >>But I, I was just gonna say that, you know, and I consider myself, you know, you're building something, I'm, I see myself an expert in the cloud native space. It's still difficult, It's difficult to, to pull together these technologies and I think that we will continue to make it easier for people. >>What's the biggest difficulties? Can you give us some examples? >>Well, just, I mean, we still live in a big mess of yammel, right? Is a, there's a, there's a lot of yaml out there. And I think just wrangling all of that in these systems, there's still a lot of cobbling together where I think that there can be unified platforms that make it easier for us to focus on our application logic. >>Yeah. I gotta ask you a question cuz I've talked to college kids all the time. My son's a junior in CS and he's, you know, he's coding away. What would you, how does a student or someone who's learning figure out where, who they are? Because there's now, you know, you're either into the infrastructure under the hood Yeah. Or you're, cuz that's coding there option now coding the way your infrastructure people are working on say the boring stuff so everyone else can have ease of use. And then what is just, I wanna just code, there's two types of personas. How does someone know who they are? >>My, when I give people career advice, my biggest piece of advice to them is in the first five to seven to 10 years of their career, I encourage people to do different things like every say one to two to three years. And that doesn't mean like quitting companies and changing companies, it could mean, you know, within a company that they join doing different teams, you know, working on front end versus back end. Because honestly I think people don't know. I think it's actually very, Yeah. Our industry is so broad. Yeah. That I think it's almost impossible to >>Know. You gotta get your hands dirty to jump >>In order to know what you like. And for me, in my career, you know, I've dabbled in different areas, but I've always come back to infrastructure, you know, that that's what I enjoy >>The most. Okay. You gotta, you gotta taste everything. See what you, what >>You like. Exactly. >>Right. Last question for you, Matt. It's been three years since you were here. Yep. What do you hope that we're able to say next year? That we can't say this year? Hmm. Beyond the secrets of your project, which hopefully we will definitely be discussing then. >>You know, I I, I don't have anything in particular. I would just say that I would like to see more movement towards projects that are synthesizing and making it easier to use a lot of the existing projects that we have today. So for example, I'm, I'm very bullish on backstage. Like I, I've, I've always said that we need better developer UIs that are not CLIs. Like I know it's a general perception among many people. Totally agree with you. Frankly, you're not a real systems engineer unless you type on the command line. I, I think better user interfaces are better for humans. Yep. So just for a project like Backstage to be more integrated with the rest of the projects, whether that be Envo or Kubernete or Argo or Flagger. I, I just, I think there's tremendous potential for further integration of some >>Of these projects. It just composability That makes total sense. Yep. Yep. You're, you're op you're operating and composing. >>Yep. And there's no reason that user experience can't be better. And then more people can create and build. So I think it's awesome. Matt, thank you so much. Thank you. Yeah, this has been fantastic. Be sure and check out Matt on Twitter to find out what that next secret project is. John, thank you for joining me this morning. My name is Savannah Peterson and we'll be here all day live from the cube. We hope you'll be joining us throughout the evening until a happy hour today. Thanks for coming. Thanks for coming. Thanks for watching.

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all, as we know, data is changing the world, and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to the Cube's presentation of "Democratizing Analytics Across the Enterprise," made possible by Alteryx. An Alteryx-commissioned IDC InfoBrief entitled, Four Ways to Unlock Transformative Business Outcomes From Analytics Investments, found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special Cube presentation, Jason Klein, Product Marketing Director of Alteryx, will join me to share key findings from the new Alteryx-commissioned IDC Brief, and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, Chief Data and Analytics Officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then, in our final segment, Paula Hansen, who is the President and Chief Revenue Officer of Alteryx, and Jacqui Van der Leij-Greyling, who is the Global Head of Tax Technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, Product Marketing Director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research which spoke with about 1500 leaders? What nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees. And this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity, and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics. And we're able to focus on the behaviors driving higher ROI. >> So the InfoBrief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the InfoBrief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack what's driving this demand, this need for analytics across organizations? >> Sure, well, first, there's more data than ever before. The data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins, and to improve customer experiences. And analytics, along with automation and AI, is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> Yet not all analytics spending is resulting in the same ROI. So, what are some of the discrepancies that the InfoBrief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow, and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead, they're relying on outdated spreadsheet technology. Nine out of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone, regardless of skill level, should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically then, what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value >> from their data and analytics and achieved more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics, across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture, and this begins with people. But we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources compared to only 67% among the ROI laggards. >> So interesting that you mentioned people. I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand. We know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right. So analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also, among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well, compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively, and letting them do so cross-functionally >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side, and is expected to spend more on analytics than other IT. What risks does this present to the overall organization? If IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this is because the lines of business have recognized the value of analytics and plan to invest accordingly. But a lack of alignment between IT and business, this will negatively impact governance, which ultimately impedes democratization and hence, ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more, you know, on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up an Alteryx environment. But also to take a look at your analytics stack, and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process and technologies. Jason, thank you so much for joining me today, unpacking the IDC InfoBrief and the great nuggets in there. Lots that organizations can learn, and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you. It's been a pleasure. >> In a moment, Alan Jacobson, who's the Chief Data and Analytics Officer at Alteryx, is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching the Cube, the leader in tech enterprise coverage. (gentle music)

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all, as we know, data is changing the world, and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to the Cube's presentation of "Democratizing Analytics Across the Enterprise," made possible by Alteryx. An Alteryx-commissioned IDC InfoBrief entitled, Four Ways to Unlock Transformative Business Outcomes From Analytics Investments, found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special Cube presentation, Jason Klein, Product Marketing Director of Alteryx, will join me to share key findings from the new Alteryx-commissioned IDC Brief, and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, Chief Data and Analytics Officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then, in our final segment, Paula Hansen, who is the President and Chief Revenue Officer of Alteryx, and Jacqui Van der Leij-Greyling, who is the Global Head of Tax Technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, Product Marketing Director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research which spoke with about 1500 leaders? What nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees. And this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity, and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics. And we're able to focus on the behaviors driving higher ROI. >> So the InfoBrief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the InfoBrief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack what's driving this demand, this need for analytics across organizations? >> Sure, well, first, there's more data than ever before. The data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins, and to improve customer experiences. And analytics, along with automation and AI, is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> One of the things that the study also showed was that not all analytics spending is resulting in the same ROI. What are some of the discrepancies that the InfoBrief uncovered with respect to the the changes in ROI that organizations are achieving? >> Our research with IDC revealed significant roadblocks across people, processes, and technologies. They're preventing companies from reaping greater benefits from their investments. So for example, on the people side, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy, as compared to the technology itself. And next, while data is everywhere, most organizations, 63%, from our survey, are still not using the full breadth of data types available. Yet, data's never been this prolific. It's going to continue to grow, and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytics tools to help everyone unlock the power of data. They instead rely on outdated spreadsheet technology. In our survey, 9 out of 10 respondents said less than half of their knowledge workers are active users of analytics software beyond spreadsheets. But true analytic transformation can't happen for an organization in a few select pockets or silos. We believe everyone, regardless of skill level, should be able to participate in the data and analytics process and be driving value. >> Should we retake that, since I started talking over Jason accidentally? >> Yep, absolutely, you can do so. Yep, we'll go back to Lisa's question. Let's retake the question and the answer. >> That'll be not all analog spending results in the same ROI. What are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we can get that clean question and answer. >> Okay. >> Thank you for that. on your ISO, we're still speeding, Lisa. So give it a beat in your head, and then on you. >> Yet not all analytics spending is resulting in the same ROI. So, what are some of the discrepancies that the InfoBrief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow, and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead, they're relying on outdated spreadsheet technology. Nine out of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone, regardless of skill level, should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically then, what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value from their data and analytics and achieved more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics, across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So very strategic investments. Did the survey uncover any specific areas where most companies are falling short, like any black holes that organizations need to be aware of at the outset? >> It did. It did. So organizations, they need to build a data-centric culture. And this begins with people. But what the survey told us is that the people aspect of analytics is the most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone in the organization has access to the data and analytics technology they need. And then the organizations also have to align their investments with upskilling in data literacy to enjoy that higher ROI. Companies who did so experience higher ROI than companies who underinvested in analytics literacy. So among the high ROI achievers, 78% have a good or great alignment between analytics investment and workforce upskilling compared to only 64% among those without positive ROI. And as more orgs adopt cloud data warehouses or cloud data lakes, in order to manage the massively increasing workloads. Can I start that one over? Can I redo this one? >> Sure. >> Yeah >> Of course. Stand by. >> Tongue tied. >> Yep. No worries. >> One second. >> If we could get, if we could do the same, Lisa, just have a clean break. We'll go to your question. Yep. >> Yeah. >> On you Lisa. Just give that a count and whenever you're ready, here, I'm going to give us a little break. On you Lisa. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture, and this begins with people. But we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources compared to only 67% among the ROI laggards. >> So interesting that you mentioned people. I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand. We know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right. So analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also, among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well, compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively, and letting them do so cross-functionally >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side, and is expected to spend more on analytics than other IT. What risks does this present to the overall organization? If IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this is because the lines of business have recognized the value of analytics and plan to invest accordingly. But a lack of alignment between IT and business, this will negatively impact governance, which ultimately impedes democratization and hence, ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more, you know, on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up an Alteryx environment. But also to take a look at your analytics stack, and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process and technologies. Jason, thank you so much for joining me today, unpacking the IDC InfoBrief and the great nuggets in there. Lots that organizations can learn, and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you. It's been a pleasure. >> In a moment, Alan Jacobson, who's the Chief Data and Analytics Officer at Alteryx, is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching the Cube, the leader in tech enterprise coverage. (gentle music)

>>Live from Seattle, Washington. It's the cube covering Smartsheet engage 2019 brought to you by Smartsheet. >>Welcome back everyone. You are watching the cube and we are here in Seattle, Washington at Smartsheet engage 2019 I'm your host, Rebecca Knight along with my cohost Jeff Frick. We're joined by Mark Klein. He is the principal at populace. Thank you so much for coming on the show. My pleasure. Thank you. So you have a very cool job. Tell our viewers a little bit about populace and about what you do. Sure. So populace is actually an architect firm. Our main focuses architecture. We one of the largest sports architecture firms in the world. So we build stadiums and arenas and convention centers and airports and places that people gather is our bread and butter is over 500 worldwide employees that work on that. But we have an event office out of Denver, Colorado where we take our architectural principles and apply them to major gatherings of people in the sporting world and other areas. >>And these are sporting include the includes include the final four >> in the Olympics and all of your NFL major events that are not a regular season game. All of your inner NHL events that happen in stadiums outdoors, all star games, things like that. Any major event, it's a nonstandard event. They really call on us to help make sure that that goes off without a hitch. Yup. >> All right, so talk a little bit about what it was like before you believe what it was like before you use Smartsheet and entered the, the headaches and the problems and that and now what life is like now. Sure, >> so a little more than 10 years ago when I joined the firm, we had a good stable of events and events. We're still kind of just operating off spreadsheets and back in napkins and drawings and things like that as security and to nine 11 was actually a major factor in kind of the growth of our industry where events now had to be planned a little more with more scrutiny. >>We needed a way to better pull our information together and get everybody to to, to, to collaborate on one set of drawings, one schedule who's doing what and when. And Smartsheet has become that prime resource for all of our event planning >> and for in for an event, there's so many outside contractors that you guys have to orchestrate with, whether it be the teams and the associations and the security and the venue and the concessions. The list goes on transportation, on and on and on. So to be able to bring outsiders into your project, >> and that's a new set every year with every event. So you think of the final four, we're going to a new city every year. So EV, I have literally eight months to work with a team to plan a major event that's going to be seen by hundreds of millions of people. And then I've got to pick up and do it again in another city and then another. >>And we're doing that across dozens of events across our team every year. So we may have a vendor that touches the system once. We may have someone who sees us once every third year. So within our environment we have extremely high turnover of people. We have very short period to get them up to speed and working with us. So Smartsheet has been really, really a big part of Hey I need you to better get in here, get your information and work with the tool, get us the information and guess what, you're going to get some feedback on this one too. So it benefits them. >>Right. It's just interesting to me that the level of granularity and detail, you know, we get, we go to a lot of events, obviously there's so much minutia that you have to keep track of from printing on the napkins, you know, to signage, etc. But at the same time, especially in the sporting world, you know, there can be huge changes, you know, especially at the same plow who wins a game, changes the venue. Right? So how do you, how do you use a tool to manage the boat? The tremendous detail when you have the opportunity to plan versus the change of plan a we got to got to shift, >>he hears well so, so we use a lot of the tools that Smartsheet is has built into it for automation. So for example, at the final four, we don't know our teams until Sunday night and that that that Monday we have decor going up, team specific decor. So locker room assignments. As soon as the game is final we send out notifications in Smartsheet to the decor printers that you're printing this graphic, this size, these a locker room assignments, these are the bus assignments. So all of that is, is queued up and ready to go. Um, so a lot of those last minute things that you may think of, we've thought through them and are ready to trigger as many as we can. You're never 100%, but if we can get that 80% 90% triggered and out the door as soon as the decision is made or the team has decided that lets us deal with those others that are a little less planned. >>So, but those are ones where, you know, those are sort of the known unknowns. What about when you have the unknown unknowns, when things like bad weather can affect an event or, I mean, how do you, how do you use Smartsheet into change on a dime when that happened? >>So, um, we, we plan and we plan and we plan. So for example, bad weather is something we have multiple plans for. But where Smartsheet comes into play as I have real time scheduling information sitting on my screen in a control room at an event. So if we have a weather event, we have two or three options that we can pick from. But I'm now looking at the realtime Smartsheet schedule going, all right, if we select option one, be aware we're going to affect these items. If we go with option two, these are the items. So it's the information that has been gathered through that planning phase and everybody's put their information in. So I know what our action is going to cause and the ripple effects of those. >>And Lindsay, the smart, the choose your own adventures when you were a kid reading those, choose your own adventure, want to open >>a door and guess what's there. I want to open a door of a decision and know that this is the follow on effect and I can look at the schedule and the vendors involved of who I'm about to impact with my decision. Right. And do you have the car, you have the comms and all that stuff dialed in there as well? Correct. Yeah. So we're on radio and we're, you know, these, these events, we run control centers. So there's eight or nine of us sitting in a control room. I, I send Mark meter a picture every year of my Smartsheet screen with some field of play behind it, beautiful ball or basketball field and go smart. We're ready to go, keep it up, keep it running for the next few hours. So, um, yeah, it's a, it's a, it's a fairly intense time. Um, when, when we opened doors or we turn on the cameras if those events, because let's face it, there's 70,000 people sitting there and there's usually three triple digit, a hundred millions of people watching on television. >>So it has to go right. That's a lot of pressure. Yes. How do you deal with it? How does your team deal with it? I mean you're used to it of course, but is there, uh, it's the confidence in the plan. I think that has really shaped how we get to that point and don't and don't overreact or get too caught up in the moment. So, um, what we do within the planning of, of our events and with our staff and is we put everybody's tasks in in Smartsheet of course. So my tunnel captain only has to focus on the 40 things that he or she is responsible for. So he may be standing at a team tunnel and we've extracted from the schedule are Austin, here are your 40 items. Don't worry about every, all the chaos going around you. Cause I've got 40 other people out working those items. >>So we filter schedules by either location or staff member so that they can put their blinders on and stay focused on their tasks. And that's really how people can focus and stay. Stay in the moment. What's coming next? What do I need to worry about? Cause there's 4,000 line items in that schedule. I can't have him trying to figure out what are his right at that moment. Mark, I would shift gears a little bit cause you guys came from an architectural bet, the company's architectural background and buildings, venues and stadiums. We just had the new chase. Then you just got finished in San Francisco. Beautiful new facility as the way you guys think about, it's kind of people centric. It's Vinnie's for people in its events for people. What are some of the kind of the guiding principles that make for a good event? A good venue from the people experience point of views. >>There's really multiple sets of customers that I look at at every venue. Obviously we always started the field of play. You gotta get that right? It's gotta be a hundred yards long. It's gotta be. And I thought they broke that rule the other day. We won't go there. Um, so feel to play out. So you've got your competitors, your spectators, and then your operators. All three of those. We focus on all of them equally because if one piece of that triad doesn't work, then the overall experience doesn't work. So obviously the field of play honestly is the easiest part to deal with. But it's an important part. So you look at how a team is going to arrive at a venue bus, whatever the case may be, so that they get to their locker room, get to their services that out to their field and back and forth to media obligations. >>So you don't want to put a media work room halfway across the stadium because then they're making a long Trek. It's a little things like that in the, in the team component, spectators, obviously theirs could be 50 to, if it's a baseball park, 50,000 up to 70,000 in a stadium. We want to ensure that they're going to fully enjoy their two to four hours in that building. Um, so we work on scheduling with our vendor. The one of the biggest things we found in the, in that area is we have really engaged with our contractors, the concessions folks, because they were kind of operating on their own. So engaging concessions to say, don't be moving product when there are people in the building, no one, the timeouts are, we'll call you from control based on the schedule so that we're synchronizing building operations so that they're, the customers are running out of water. >>Well we didn't run out of water, we couldn't get it to you. So things like that are really important to our planning. And then the group that really gets overlooked at, I spend a lot of time on is the people that helped build and get the building ready. Because if my vendors are having a rough time getting their things in the building or building the platform I've asked for or setting up the stage, they're just not going to be in a good frame of mind when the lights turn on. And I want everybody to be, yeah, let's go. We've had a great experience in the five days leading up to this event, whatever it may be. I'm ready now to put on a show. So we use Smartsheet IX so much with our vendors to help guide them through the build process, scheduling, deliveries, getting their credentials where they're going to park and where do I take my breaks? >>Everything is there at their fingertips. So even the mom and pop vendors that I deal with, and there are quite a few of them from city to city, feel like they're as important as my Avi company. So they're excited. They do their load in there like, Hey, this is a great experience and now they're here to help support the event. And then when I call and go, guess what? We have a problem. I need your help. They're going to share, Mark, what can we do? Right? Cause they're there, they're enthusiastic and they didn't feel like I beat 'em up right during that load in great, great insight. People centric. But you're talking about it's treating people like people, not just that they are some cog in the wheel that they are to to execute this task. Right, right. Yeah. No happy staff deliver happy events. >>So what's next in terms of, in terms of a broader adoption in terms of more improvements that you're seeing on the pipeline? Um, so I'm really excited about the collaboration component that was announced today at the keynote. Um, we are an architect firm, so the base of all of our plant, all of our events is a set of drawings, drawings that show what we need, where it is, when it's gonna happen. So all of our non drawing material has lived in Smartsheet for 10 years. I'm now gonna be able to bring those drawings in and get the collaborative information to feedback. So we take a drawing, we'll send it to CBS and say, please Mark up how you think we've drawn your broadcast compound. That has all been email. Now with this collaboration tool, it's going to live in Smartsheets. So I cannot tell you how excited I am about the collaboration component. >>It's gonna. It's gonna really streamline how we do our business. I, I'm kinda lost for words to get in there and try it. My staff is gonna probably go Mark. You can't go to any more conferences, but, uh, I think it's really going to be a great addition to our work process. Um, the other one that has been a personal part of mine, a personal goal that I've seen is the adoption by our staff are the to day work process. Um, I listened in the office, we have a big open work plan space and I listened for my staff going, I've got to put this plan together, attract this and I go, I literally will stand up and walk over. Have you thought about using Smartsheet? And half of the time they haven't. And um, I will say, let me help you through it. Let me get you started and see if it works for you. >>Um, so that organic growth with Smartsheet, um, is, is the big step that we're doing on a day to day basis, um, to get staff introduced to a new way to work and be more collaborative of how they, they manage your information. So, um, just that that kind of growth is, is, is ongoing. Um, but after I've been to the conference, I can say I've got a little more knowledge about it. Let me, uh, let me, uh, help you out a little bit and get you to use it. Right, right, right, right. And you're even finding ways to use it in your personal life, you said? Sure. I use it for home tasks. We plan, we plan our kid's birthday celebrations in it. So my wife and I will share a sheet about who's visiting for graduation. My daughter's high school graduation is coming up. We actually post a forum on Smartsheet coming where they staying at the tag that I put up on the wall over there as people think I work for Smartsheet with how much we use it. So yes, it bleeds into the personal life, but why not exactly a word. I don't fix it. Thank you so much for coming on. The show is a lot of fun talking pleasure. Thank you. Thank you both. Thank you. I'm Rebecca Knight for Jeff Frick. Stay tuned. Have more of engaged 2019 here in Seattle. You're watching the cube.

(funky music) >> From our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBE conversation. >> Welcome back everybody, Jeff Frick here with the CUBE. We're in our Palo Alto studios havin' a CUBE conversation, but for a little bit of something different. Instead of having our guest here locally in Palo Alto we've got him all the way across the country, across the pond, all the way over to Holland, and he's in Utrecht, and we're happy to welcome Erik Klein. He is the infrastructure architect for FrieslandCampina. Erik thanks for joining us today. >> Thank you for having me. >> Absolutely, so before we get started, a little background on FrieslandCampina for people that aren't familiar with the company. >> FrieslandCampina is a co-operative company owned by farmers, predominantly in the Netherlands, Belgium and Germany. It's a international company. We have about 34 countries with, we have, at our sales offices, our plans in there, we are one of the biggest dairy companies in the world, and love to be there. It's a very good company to work for. >> It's amazing, I was doing a little research, I mean the scale is amazing. You guys, you operate in 100 countries, exporting. You've got offices in 34 countries. I think it said of 23,000 plus employees. It's quite a big operation. >> Yup. >> So, >> A big operation doing about 10 billion liters, or kilograms, of milk a year. >> Great, so, it's a dairy, we're here talking about digital transformation; it's always fascinating to me, kind of, the reach of digital transformation in everybody's company. Everyone says everyone's really a software company, you know, kind of built around a different product or service. So what were some of the challenges that you were looking towards in 2018-2019 in terms of digital transformation in this mature industry of dairy? >> The challenges that we're having is that you have to make sure that everything is safe. The products are safe, but also the data is safe. But also that we have a lot of things move through the Cloud, and also that the performance of those applications moves through the Cloud, is to the end user's satisfaction as well. So you're not looking only at transferring data safely from the Cloud into our offices, into our production environment, also protecting our production environments from everything that's going bad on the Internet, but also having to make sure that the applications are performing to the liking of the end user, so to speak, to our customer and our consumers. >> And was the objective to build new applications in the Cloud, or was it more kind of lift-and-shift some of your older applications in the Cloud? Because those are two very different challenges. >> Yeah, it's a lift-and-shift of our older applications. For example we're now in the middle of moving our SAP environment to the Cloud, at least the development test and user environments are moved to the Cloud. The other ones remain still within a traditional data center environment, and we have moved all of our Office 365, so that's Skype for Business, SharePoint, but all the other applications to the Cloud as well. >> Ha ha. >> And there we have all this additional transformation, the challenges that really comes back to the end user. >> Those are huge applications; SAP and Office 365. Those are not insignificant >> Yup. >> applications at all. So what were some of the challenges, I'm sure we have a lot of your peers watching this. What is some of the tips and tricks that you can share with them? Big challenges that you had to overcome? Things you thought about, maybe some things that you didn't think about in that transformation? >> If you look at the SAP landscape, it's the sheer amount of interfaces between the different components of SAP. That's was something that made us decide not to move SAP to the Cloud, not the production environment and the systems Environment. That was too big of an impact. That would take too long to do and we don't have that time. If you look at Office 365, the fact that Microsoft is very averse in having anything in the middle, that brought us some real challenges. And and we did that already in 2014-2015 and we had our fair share of all fun and games. >> Ha ha ha, so what was different about it then than today? I mean obviously the Cloud has moved quite a bit. I don't know if you can mention which Cloud you put it in? >>Yeah correct, the fact that Zscaler now, does the updating, and all the changes within the Microsoft environment. So you don't have to do it yourself. You don't have to constantly monitor the ARS feeds from Microsoft, do all the changes yourself. Now it's all done by Zscaler, all the SSL bypass, the authentication bypass has been set correctly. So when that came on board that made our life a lot easier. >> Wow. >> The first part of the migration that we did in in Europe, especially in the bigger locations like Amersfoort, which has our headquarters, we really had our challenges to keep the end user satisfied. >> So just, again, kind of the scale of the end users. You mentioned that a couple of times. Is this in support of all the 23,000 people that are employed at FrieslandCampina? Is it a subset, or is it remote workers? How are you, kind of, allocating this effort? >> It is indeed all users, except for the factory workers. We don't allow people that work in production direct access to the internet. So those people are not as much excluded, but they have special PCs where they work on. So you're looking currently at about 15,000 people that are working with Office 365 directly on a day-to-day basis within FrieslandCampina. >> Wow, so the other thing you've talked about repeatedly is not only satisfaction with the users who are interfacing with the systems, but security. So what were some of the >> Yup. >> security considerations that you considered? How did you, kind of, bake security into your process? And, as we hear all the time as we go to different shows, including security shows, you know, it's not a bolt-on anymore; you have to be thinking security throughout the whole pipeline of the process. So how did you think about it? How did you attack it? How did you solve some of those problems? >> We started thinking about it already in 2012. We had, at that time within FrieslandCampina, a program specifically driven out of the LT environment, so the operational technology, so the production IT, so to speak, and they come up with an architecture based on the ISO 9599 norm, and we took that on board as IT and continued to work on that. So from 2014 we already had in our plans, the architecture to separate the various layers of the ISO 9599 framework into security zones, and we're constantly building on that one. We're refining it, we're improving it. >> Another question on security, really, and kind of the network architecture. Did you have to re-do anything within your network architecture to make this move to the Cloud possible? How did you address the network? >> It was a completely redesigned. It was a complete redesign. In the, previous to that, we just had IT, and we had one or two firewalls on-site that connects to a certain part of OT, and that was it. And now we have an architecture where we can integrate all different flavors of OT. There's no need for OT to have their own internet connections for maintenance, for support, et cetera. It's all integrated and secure. We made, and the reason for that is that you can't, in this day and age, have an island structure. Everything needs to be integrated. Everything needs to talk to each other, et cetera. >>  So Erik, this interview is sponsored by Zscaler. You're a customer of theirs. I'm just curious if you can talk a little bit about how, you know, their offering enabled you to do stuff that maybe you couldn't do before. How did you get involved with them? How are they working with them throughout this project? And how has that really been an enabler for your, you know, your move to the Cloud? >> In 2013-2014 there was a request from the business, a very strong drive from the business, that looked into breakouts, specifically to get localized contact, driven out of the, how do say that, marketing department. And then we looked at, okay, how can we enable that without creating firewalls on every location we're having, making it very expensive, etcetera. And at that time our provider, Verizon, came up, let's do a Cloud security with Verizon, with Zscaler, and do a proof of concept, and build on that one. So that worked. That gave us more regularity, if the people in the countries that needed localized content got the localized content, speeding up the application for the specific countries, so no happening from Tokyo, Japan, back to Singapore, back to websites in Japan. So that helps a lot, but like I said it was early days so we had our challenges in getting that working, getting it secure, getting the traffic to the correct Zscaler node, and so on. So we did make, from the initial set-up of this network, a number of iterations to come to where we are today. >> Great. >> So it's not one decision and then it works. No, it's a decision, see what has worked, which challenge you're getting, and then take it to the next level. >> Right. >> If we do the same thing with Zscaler as they're offering today it will be a lot quicker. We will have a number of those challenges that we had at that time, we will not have today. >> So as you look forward, what's kind of next. As you mentioned this isn't a one-stop shop. This is an ongoing process. What are, kind of, your next priorities, you know, over the next six months or so as you guys continue on this journey? >> To another data center, so not to the Cloud but to a different data center, so that's a big, really a big program. The other thing we're looking at is how can we improve remote access, provide extra benefits as part. We also look at the ZPA product of Zscaler. We're doing a proof of concept, probably in the second half of this year. So, but on the other side, this year, 2019, FrieslandCampina is a, how do you say that in proper English, stop and look back and see what's really important, what we need to go forward. So it's not going crazy on all different kind of projects. It is, okay, what will actually contribute to the profitability of FrieslandCampina going forward. >> I think that's a really great close. I know it's late in Utrecht. I appreciate you taking some time out of your evening, and I was going to ask you the last question, you know, what advice would you have for your peers, for other practitioners that are looking at this, and, you know, either in the process or planning out their journey, but I think you hit on a big one right there which is really focus on the things that matter, focus on the things that really make a difference, and just don't start doing science experiments all over the place because you can, or it's fun, or it's interesting. >> Well, what my worries are for the future, and what, not keeps me awake at night, but that that's too much, is the bad that's going around in this world is getting stronger. They have more resources than we, as a company, has to defend for us against, and the acute challenge would be, is identifying what is your traffic that is good flowing in your network. Because if you're knowing what is good everything that's not defined as being good can be immediately defined as being bad. In that case you'll have a better position in preventing yourself against everything that's going wrong, like WannaCry. If you know that WannaCry is using a well known port used all over the place in FrieslandCampina. But if you then see that same port being used to communicate between servers that never communicated before, or to workstations to servers that never communicated before, then you say, okay, stop that one immediately, because that's not good. >> Right. >> And at that moment our biggest challenge is identifying what is the traffic that's good within our network. >> Well that's a great tip, you know, that's great. You know what the positives are, and if it doesn't make the the green list then shut 'er down and (chuckling) find out what's going on. >> Correct. >> All right. >> Correct. And the reason why we identified WannaCry is that somebody, for some reason, identified Hey this server never talked with that device: Why? >> Yeah, we're hearing that, >> And because, all. >> because with IOT you have to do that, right? >> You have to do that. >> 'Cause everything's IP connected, right? Whether it's the shades and the HVAC system all the way down to all your manufacturing processes, distribution processes, >> Correct. >> IT systems. >> Correct, correct. Our big advantage was that the call back to the command and control servers was already blocked by Zscaler so it didn't hurt us that much. >> Yeah, well good, we got to keep the cows safe, keep the milk safe, and the, >> Yeah, absolutely. >> what did you say, the 10 billion gallons of milk that you guys kick out a year, or something like that? >> Yep. >> It's amazing, ha ha. >> It's amazing. >> All right Erik, well thanks for sharing your story. Good luck on your future transformations, and good luck next week; thanks for stopping by. >> Thank you very much. >> All right. >> All right. >> All right, he's Erik, I'm Jeff, you're watching the CUBE. We're in our Palo Alto studios and Utrecht, Holland. Thanks for watching, we'll see you next time. (funky music)

(funky music) >> From our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBE conversation. >> Welcome back everybody, Jeff Frick here with the CUBE. We're in our Palo Alto studios havin' a CUBE conversation, but for a little bit of something different. Instead of having our guest here locally in Palo Alto we've got him all the way across the country, across the pond, all the way over to Holland, and he's in Utrecht, and we're happy to welcome Erik Klein. He is the infrastructure architect for FrieslandCampina. Eric thanks for joining us today. >> Thank you for having me. >> Absolutely, so before we get started, a little background on FrieslandCampina for people that aren't familiar with the company. >> FrieslandCampina is a co-operative company owned by farmers, predominantly in the Netherlands, Belgium and Germany. It's a international company. We have about 34 countries with, we have, at our sales offices, our plans in there, we are one of the biggest dairy companies in the world, and love to be there. It's a very good company to work for. >> It's amazing, I was doing a little research, I mean the scale is amazing. You guys, you operate in 100 countries, exporting. You've got offices in 34 countries. I think it said of 23,000 plus employees. It's quite a big operation. >> Yup. >> So, >> A big operation doing about 10 billion liters, or kilograms, of milk a year. >> Great, so, it's a dairy, we're here talking about digital transformation; it's always fascinating to me, kind of, the reach of digital transformation in everybody's company. Everyone says everyone's really a software company, you know, kind of built around a different product or service. So what were some of the challenges that you were looking towards in 2018-2019 in terms of digital transformation in this mature industry of dairy? >> The challenges that we're having is that you have to make sure that everything is safe. The products are safe, but also the data is safe. But also that we have a lot of things move through the Cloud, and also that the performance of those applications moves through the Cloud, is to the end user's satisfaction as well. So you're not looking only at transferring data safely from the Cloud into our offices, into our production environment, also protecting our production environments from everything that's going bad on the Internet, but also having to make sure that the applications are performing to the liking of the end user, so to speak, to our customer and our consumers. >> And was the objective to build new applications in the Cloud, or was it more kind of lift-and-shift some of your older applications in the Cloud? Because those are two very different challenges. >> Yeah, it's a lift-and-shift of our older applications. For example we're now in the middle of moving our SAP environment to the Cloud, at least the development test and user environments are moved to the Cloud. The other ones remain still within a traditional data center environment, and we have moved all of our Office 365, so that's Skype for Business, SharePoint, but all the other applications to the Cloud as well. >> Ha ha. >> And there we have all this additional transformation, the challenges that really comes back to the end user. >> Those are huge applications; SAP and Office 365. Those are not insignificant >> Yup. >> applications at all. So what were some of the challenges, I'm sure we have a lot of your peers watching this. What is some of the tips and tricks that you can share with them? Big challenges that you had to overcome? Things you thought about, maybe some things that you didn't think about in that transformation? >> If you look at the SAP landscape, it's the sheer amount of interfaces between the different components of SAP. That's was something that made us decide not to move SAP to the Cloud, not the production environment and the systems Environment. That was too big of an impact. That would take too long to do and we don't have that time. If you look at Office 365, the fact that Microsoft is very adverse in having anything in the middle, that brought us some real challenges. And and we did that already in 2014-2015 and we had our fair share of all fun and games. >> Ha ha ha, so what was different about it then than today? I mean obviously the Cloud has moved quite a bit. I don't know if you can mention which Cloud you put it in? >> Yeah correct, the fact that Zscaling now, does the updating, and all the changes within the Microsoft environment. So you don't have to do it yourself. You don't have to constantly monitor the ARS feeds from Microsoft, do all the changes yourself. Now it's all done by Zscaler, all the SSL bypass, the authentication bypass has been set correctly. So when that came on board that made our life a lot easier. >> Wow. >> The first part of the migration that we did in in Europe, especially in the bigger locations like Amersfoort, which has our headquarters, we really had our challenges to keep the end user satisfied. >> So just, again, kind of the scale of the end users. You mentioned that a couple of times. Is this in support of all the 23,000 people that are employed at FrieslandCampina? Is it a subset, or is it remote workers? How are you, kind of, allocating this effort? >> It is indeed all users, except for the factory workers. We don't allow people that work in production direct access to the internet. So those people are not as much excluded, but they have special PCs where they work on. So you're looking currently at about 15,000 people that are working with Office 365 directly on a day-to-day basis within FrieslandCampina. >> Wow, so the other thing you've talked about repeatedly is not only satisfaction with the users who are interfacing with the systems, but security. So what were some of the >> Yup. >> security considerations that you considered? How did you, kind of, bake security into your process? And, as we hear all the time as we go to different shows, including security shows, you know, it's not a bolt-on anymore; you have to be thinking security throughout the whole pipeline of the process. So how did you think about it? How did you attack it? How did you solve some of those problems? >> We started thinking about it already in 2012. We had, at that time within FrieslandCampina, a program specifically driven out of the LT environment, so the operational technology, so the production IT, so to speak, and they come up with an architecture based on the ISO 9599 norm, and we took that on board as IT and continued to work on that. So from 2014 we already had in our plans, the architecture to separate the various layers of the ISO 9599 framework into security zones, and we're constantly building on that one. We're refining it, we're improving it. >> Another question on security, really, and kind of the network architecture. Did you have to re-do anything within your network architecture to make this move to the Cloud possible? How did you address the network? >> It was a completely redesigned. It was a complete redesign. In the, previous to that, we just had IT, and we had one or two firewalls on-site that connects to a certain part of OT, and that was it. And now we have an architecture where we can integrate all different flavors of OT. There's no need for OT to have their own internet connections for maintenance, for support, et cetera. It's all integrated and secure. We made, and the reason for that is that you can't, in this day and age, have an island structure. Everything needs to be integrated. Everything needs to talk to each other, et cetera. >> So Erik, this interview is sponsored Zscaler. You're a customer of theirs. I'm just curious if you can talk a little bit about how, you know, their offering enabled you to do stuff that maybe you couldn't do before. How did you get involved with them? How are they working with them throughout this project? And how has that really been an enabler for your, you know, your move to the Cloud? >> In 2013-2014 there was a request from the business, a very strong drive from the business, that looked into breakouts, specifically to get localized contact, driven out of the, how do say that, marketing department. And then we looked at, okay, how can we enable that without creating firewalls on every location we're having, making it very expensive, et cetera. And at that time our provider, Verizon, came up, let's do a Cloud security with Verizon, with Zscaler, and do a proof of concept, and build on that one. So that worked. That gave us more regularity, if the people in the countries that needed localized content got the localized content, speeding up the application for the specific countries, so no happening from Tokyo, Japan, back to Singapore, back to websites in Japan. So that helps a lot, but like I said it was early days so we had our challenges in getting that working, getting it secure, getting the traffic to the correct Zscaler node, and so on. So we did make, from the initial set-up of this network, a number of iterations to come to where we are today. >> Great. >> So it's not one decision and then it works. No, it's a decision, see what has worked, which challenge you're getting, and then take it to the next level. >> Right. >> If we do the same thing with Zscaler as they're offering today it will be a lot quicker. We will have a number of those challenges that we had at that time, we will not have today. >> So as you look forward, what's kind of next. As you mentioned this isn't a one-stop shop. This is an ongoing process. What are, kind of, your next priorities, you know, over the next six months or so as you guys continue on this journey? >> To another data center, so not to the Cloud but to a different data center, so that's a big, really a big program. The other thing we're looking at is how can we improve remote access, provide extra benefits as part. We also look at the CPA product of Zscaler. We're doing a proof of concept, probably in the second half of this year. So, but on the other side, this year, 2019, FrieslandCampina is a, how do you say that in proper English, stop and look back and see what's really important, what we need to go forward. So it's not going crazy on all different kind of projects. It is, okay, what will actually contribute to the profitability of FrieslandCampina going forward. >> I think that's a really great close. I know it's late in Utrecht. I appreciate you taking some time out of your evening, and I was going to ask you the last question, you know, what advice would you have for your peers, for other practitioners that are looking at this, and, you know, either in the process or planning out their journey, but I think you hit on a big one right there which is really focus on the things that matter, focus on the things that really make a difference, and just don't start doing science experiments all over the place because you can, or it's fun, or it's interesting. >> Well, what my worries are for the future, and what, not keeps me awake at night, but that that's too much, is the bad that's going around in this world is getting stronger. They have more resources than we, as a company, has to defend for us against, and the acute challenge would be, is identifying what is your traffic that is good flowing in your network. Because if you're knowing what is good everything that's not defined as being good can be immediately defined as being bad. In that case you'll have a better position in preventing yourself against everything that's going wrong, like WannaCry. If you know that WannaCry is using a well known port used all over the place in FrieslandCampina. But if you then see that same port being used to communicate between servers that never communicated before, or to workstations to servers that never communicated before, then you say, okay, stop that one immediately, because that's not good. >> Right. >> And at that moment our biggest challenge is identifying what is the traffic that's good within our network. >> Well that's a great tip, you know, that's great. You know what the positives are, and if it doesn't make the the green list then shut 'er down and (chuckling) find out what's going on. >> Correct. >> All right. >> Correct. And the reason why we identified WannaCry is that somebody, for some reason, identified Hey this server never talked with that device: Why? >> Yeah, we're hearing that, >> And because, all. >> because with IOT you have to do that, right? >> You have to do that. >> 'Cause everything's IP connected, right? Whether it's the shades and the HVAC system all the way down to all your manufacturing processes, distribution processes, >> Correct. >> IT systems. >> Correct, correct. Our big advantage was that the call back to the command and control servers was already blocked by Zscaler so it didn't hurt us that much. >> Yeah, well good, we got to keep the cows safe, keep the milk safe, and the, >> Yeah, absolutely. >> what did you say, the 10 billion gallons of milk that you guys kick out a year, or something like that? >> Yep. >> It's amazing, ha ha. >> It's amazing. >> All right Erik, well thanks for sharing your story. Good luck on your future transformations, and good luck next week; thanks for stopping by. >> Thank you very much. >> All right. >> All right. >> All right, he's Erik, I'm Jeff, you're watching the CUBE. We're in our Palo Alto studios and Utrecht, Holland. Thanks for watching, we'll see you next time. (funky music)

>> Live from Seattle, Washinton it's theCUBE, covering KubeCon and CloudNativeCon North America 2018. Brought to you by Red Hat, the Cloud Native Computing Foundation, and it's ecosystem partners. >> Hey, welcome back everyone. We're live here at KubeCon, Cloud Native. This is theCUBE's live coverage of three days of three days of wall to wall coverage. Day two, I'm John Furrier with Stu Miniman. Our next guess is an end user, also a program chair of EnvoyCon, which is sold out. Matt Klein, software engineer with Lyft. Great to have you on again, good to see you. Thanks for spending the time. >> Thank you great to be here. >> I know you've been busy, your voice is getting hoarse. You guys had a successful EnvoyCon, sold out. Was on the front-end of KubeCon and CloudNativeCon. Interesting, right? This is the rising tide. What's going on? How'd that go? Why all the interest? >> It's been I continue to be blown away by the overall reaction. So we had EnvoyCon on Monday. We had, I think almost 350 people come, sold out. I think we could have had a larger room if it was available, but we didn't. Just amazing to walk around this conference and see all the cloud vendors getting behind Envoy, lots of companies building on top of Envoy, all of the end users. It just seems to be everywhere here and to have only been open source for a little over two years, I mean it's just unbelievable. >> Matt you know I think a year ago service mesh was something we were still getting the basic understanding of what it was and it definitely, there's certain interviews we've done this week, you know service mesh, you know Envoy, thing likes Istio are going to be even bigger than Kubernetes. >> Yeah, well you know I've been to the last few KubeCons and every KubeCon, I think that it can't get much bigger or more nuts, and no, no. Everyone seems to be a little bit crazier. But no, just from the community perspective, EnvoyCon was fantastic because we had mostly end user talks so it was really fun to get people together and to see all the different things they're building on top of Envoy. >> One of the things that's impressive and I think is a real notable story, and of course we talked about it a bit last time you were on, is that Lyft as an end user kind of encapsulates and epitomizes kind of the innovation building going on. A lot of people have been building a lot of cool stuff using cloud look and getting down and dirty and rolling their own. And actually creating business value, not in a classic IT by IT, just build IT, build systems >> Yep >> To build business value and then donating it in to scale up with the community is pretty notable so congratulations on that. >> Thanks. >> Now you have startups kind of acting the same way so the line between a vendor and end user is certainly changing. I mean, we were end users. Well they're all kind of end users. This is a dynamic that is, I think notable for this generation and it's real. Talk about that dynamic because I think this is a real success story and also a trend in the industry. >> You know so I think for us what's fun for me about not only building Envoy but seeing how it's evolved is really what you said is that I like solving actual problems for people, right? We can have different opinions on what the different vendors are doing, of course. There's lots of people doing different things, but for me at least working at a company like Lyft it's super fun to be able to build technology that solves specific problems that the business is actually happening. Now if something becomes successful sure we're going to see a lot of vendors come in hopefully build products that can help other folks. The way that I look at it and this has been an interesting evolution for me over the last year is I would say a year ago, people would come to me and say "Hey Matt, I've heard about Envoy I'd like to use to help solve some problems and I went to the website and I don't understand it, like it's too complicated to use. The documentation is not good enough." And I think over the last year my thinking has evolved a little bit in the sense that we've seen so many people or end users or companies build fantastic products on top of Envoy and I think one of the reasons Envoy's become so successful is that it's a building block that other people can come and add vertical value. So whether that's a more sophisticated internet company like Lyft or a vendor or a cloud vendor. I think that's what's made the community so successful is that we can build this base thing and it's amazing but then we can allow people to add vertical value. >> And you know that's an interesting dynamic of both cloud and open source. You look at Amazon, the most successful public cloud Their core building blocks was EC2 and S3 originally. Open source is about building on top of other things. Again the dynamic between open source and cloud scale is really kind of the magic. >> Well and just in terms of how we actually go through and I think fund some of these projects ends up being very interesting. Just in the sense that we have a lot of full time people working on Envoy and they're working on it actually for different reasons. We have people working on it as end users, we have people working on it because they're building vertical products but in the end everyone wins because the base technology stays technology focused. I think that has been what has been successful, is that we allow people to succeed in different ways. >> Alright, so Matt, you're at the forefront of one of the most difficult problems that we're looking at these days. It's scale, distributed systems, and edge and how that ties in. I want to get your kind of macro level viewpoint as to how we're doing in this industry? What are some of those tough challenges we've talked about? We talk about things like IoT and Edge and vehicles of course have a lot of them. >> Yeah so I mean, I think when you say scale there's two things that comes to mind. There's physical scale, and I do agree actually that we are continuing to push more compute out to the edge and in fact, I talked about this a little at EnvoyCon, but I have some very exciting projects or plans to bring Envoy actually to mobile phones and to Edge devices starting next year. I'll have more to say about that in the spring. I'm very excited about that. I do think there's a lot of opportunity to better evolve how we ingress data from the edge, how we do compute out at the edge, a bunch of other things. And I think Envoy will be at the forefront of that but when you talk about scale I still think that there's a lot of human scale involved of how we scale the number of developers that are working on all of these architectures. And I do think that Service Mesh and Kubernetes and a bunch of other stuff ultimately if we're successful it helps us grow the number of product developers that can successfully work on these systems. I still think we have a long way to go but I think that's one of those areas where I think some of these technologies help people both at physical internet scale but also at human scale. >> Well I really appreciate your work you're doing. Your contributions to the community, both on solving the problems with Envoy and also being the program chair of EnvoyCon I think is going to be great for the community. I got to ask you as you get pulled into a lot of these, I won't say political, or media kind of conversations you got to kind of be a helicopter and get above and get high level and talk to people who are discovering and learning for the first time which is part of what communities do. How do you talk about those other end users that say "Hey Matt, I'm going to reshape our company, I'm going to reshape their IT investments all based on open source and I really want to learn more about Envoy and just the benefits of Cloud Native in general. I got to go, and I'm a believer, I got to go talk to some wanna-believers or non-believers in my company and I got to make my point home?" How do they be successful? What's your advice to that? Because that's a challenge a lot of people are having. >> I totally agree My advice, first and foremost, is to start by understanding what problems are trying to be solved. And I actually think that sounds very obvious but I think that people don't do it enough because I think sometimes we come to conferences like this and we see all the amazing technology that people are building and it seems fantastic but if one tries to adopt everything that they see here without understanding the incremental steps and the things that are the problems that are being solved that can be very problematic. >> It's a new kind of technical depth. It's kind of a new way >> My advice is to start with what are the actual problems, right? And whether that be observability issues, or authentication issues, or security issues, or whatever, is to start with the problems and then work backwards and my advice is always incremental, no big bang. And try to figure out the right incremental path of adopting the smallest piece of technology that solves a particular problem and go from there. >> And build economies of scale to the mission. >> Right, and whether that means working with a vendor or working with the raw open source technology that's a personal decision of each company to figure out what their comfort level is. But that really is my advice, is start with the problem statement and then figure out the easiest and the quickest incremental path forward. >> The trends that we're seeing Stu was talking earlier, a lot of hyper-scalers here, a lot of diversity coming into the community just what's the hallway conversation amongst the people in the community around as the community grows larger? I mean open source community core persona or constituency, then you got the down-stream impact of that is IT is changing, developers are coming in. So it's not so much changing personas and target audiences of the environment. Open source is still core. That's kind of the down-stream impacts. So you're seeing a lot of people come in, IT people, new developers. How does the community look at that? What's your view on how to engage but also not alienate new people? >> Well I think ultimately we are attempting to build systems help people be successful and be more productive, right? I think the natural evolution of that is bringing some of this technology into the enterprise. We have to recognize that as the community scales the base line level of knowledge is different. I mean we all come at it with different understanding of whether it be networking or orchestration or security. And I think what I would say is that we're never going to build one technology that makes everyone happy. It is impossible. It's impossible to build a technology that satisfies both the expert user and the entry level user. So I believe that we need to build layered technologies, layered abstraction that allow people to plug in at different levels and some of them are more opinionated than others. And I think it is recognizing and supporting a community that has base level technology, has vendors adding value at different layers to help people, and really just respecting the fact that people come at it with different levels. >> I mean application assembly is really where it's going. >> Exactly, I agree >> Matt, I'm wondering if you could reflect back for us. You're the creator of Envoy, I saw you up on stage yesterday, the supportive team and the community that helped this grow. And you've reached graduation. What does that mean to you, for the team? It's different than a school graduation, this is not the end of something, you don't get a diploma out of it. >> Is there a party? >> I don't know if there was. I don't think they invited me. >> Get pictures? >> Cloud Foundation picking up the bar tab? >> I don't know, maybe. So like from a project perspective, in terms of how we go about our day to day I don't think that much changes. I think we have been operating as a mature graduated level project probably for quite some time, in terms of adoption and methodology and stuff like that. I think what graduation means for the project is it's a vote of respect from the larger industry and the community that Envoy isn't going to disappear, it's not going to become an abandoned project on GitHub if for example if Lyft stops investing in it. I think we've reached a critical mass of project success and I think what that means is that it allows folks that may be at more conservative organizations who may be a little later to adopt newer technologies to give them the confidence that says Envoy is not going disappear, that we can potentially bet some of our future on Envoy. So I think it's a vote of confidence, I don't think it changes a lot about how we operate on a day to day basis. >> Matt, thanks for coming on theCUBE. Again, congratulations. Seminal work, you guys are doing great. Lyft is really, I think, a great example of the new dynamic in open source where they're building and they're working with the community to continue to extend that. And this is what we want, that's what open source is all about. >> It is. >> Congratulations. And we got to have a graduation party for Envoy. We'll figure it out, get photos and pictures and everything else. Thanks for coming on theCUBE. >> Cool, thank you very much. >> theCUBE coverage here live, I'm John Furrier with Stu Miniman. More coverage after this short break, stay with us. (upbeat music)

>> Narrator: Live from Austin Texas. It's theCUBE, covering KubeKon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux Foundation, and theCUBE's ecosystem partners. >> Welcome back everyone, live here in Austin Texas, theCUBE's exclusive coverage of CloudNativeConference and KubeKon, for Kubernetes' Conference. I'm John Furrier, co-founder of SiliconANGLE and my co-host Stu Miniman, our analyst. And next is Matt Klein, a software engineer at Lyft, ride-hailing service, car sharing, social network, great company, everyone knows that everyone loves Lyft. Thanks for coming on. >> Thanks very much for having me. >> All right so you're a customer of all this technology. You guys built, and I think this is like the shiny use cases of our generation, entrepreneurs and techies build their own stuff because they can't get product from the general market. You guys had a large-scale demand for the service, you had to go out and build your own with open source and all those tools, you had a problem you had to solve, you build it, used some open source and then give it back to open source and be part of the community, and everybody wins, you donated it back. This is, this is the future, this is what it's going to be like, great community work. What problem were you solving? Obviously Lyft, everyone knows it's hard, they see their car, lot of real time going on, lot of stuff happening >> Matt: Yeah, sure. >> magic's happening behind the scenes, you had to build that. Talk about the problem you solved. >> Well, I think, you know, when people look at Lyft, like you were saying, they look at the app and the car, and I think many people think that it's a relative simple thing. Like how hard could it be to bring up your app and say, I want a ride, and you know, get that car from here to there, but it turns out that it's really complicated. There's a lot of real-time systems involved in actually finding what are all the cars that are near you, and what's the fastest route, all of that stuff. So, I think what people don't realize is that Lyft is a very large, real-time system that, at current scale, operates at millions of requests per second, and has a lot of different use cases around databases, and caching, you know, all those technologies. So, Lyft was built on open source, as you say, and, you know Lyft grew from what I think most companies do, which is a very simple, monolithic stack, you know, it starts with a PHP application, we're a big user of MongoDB, and some load balancer, and then, you know-- >> John: That breaks (laughs) >> Well, well no but but people do that because that's what's very quick to do. And I think what happened, like most companies, is, or that most companies that become very successful, is Lyft grew a lot, and like the few companies that can become very successful, they start to outgrow some of that basic software, or the basic pieces that they're actually using. So, as Lyft started to grow a lot, things just didn't actually start working, so then we had to start fixing and building different things. >> Yeah, Matt, scale is one of those things that gets talked about a lot. But, I mean Lyft, you know, really does operate at a significant scale. >> Matt: Yeah, sure. >> Maybe you can talk a little bit about, you know, what kind of things were breaking, >> Matt: Absolutely, yeah, and then what led to Envoy and why that happened. >> Yeah, sure. I mean, I think there's two different types of scale, and I think this is something that people don't talk about enough. There's scale in terms of things that people talk about, in terms of data throughput or requests per second, or stuff like that. But there's also people scale, right. So, as organizations grow, we go from 10 developers to 50 developers to 100, where Lyft is now many hundreds of developers and we're continuing to grow, and what I think people don't talk about enough is the human scale, so you know, so we have a lot of people that are trying to edit code, and at a certain size, that number of people, you can't all be editing on that same code base. So that's I think the biggest move where people start moving towards this microservice or service-oriented architecture, so you start splitting that apart to get people-scale. People-scale probably usually comes with requests per second scale and data scale and that kind of stuff. But these problems come hand in hand, where as you grow the number of people, you start going into microservices, and then suddenly you have actual scale problems. The database is not working, or the network is not actually reliable. So from Envoy perspective, so Envoy is an open source proxy we built at Lyft, it's now part of CNCF, it's having tremendous uptake across the industry, which is fantastic, and the reason that we built Envoy is what we're seeing now in the industry is people are moving towards polyglot architectures, so they're moving towards architectures with many different applications, or many different languages. And it used to be that you could use Java and you could have one particular library that would do all of your networking and service discovery and load balancing, and now you might have six different languages. So how as an organization do you actually deal with that? And what we decided to do was build an out-of-process proxy, which allows people to build a lot of functionality into one place, around load balancing, and service discovery, and rate limiting, and buffering, and all those kinds of things, and also most importantly, observability. So things like tracing and stats and logging. And that allowed us to actually understand what was going on in the network, so that when problems were happening, we could actually debug what was going on. And what we saw at Lyft, about three years ago, is we had started our microservices journey, but it was actually almost, it was almost stopped, because what people found is they had started to build services because supposedly it was faster than the monolith, but then we would start having problems with tail latency and other things, and they didn't know hot to debug it. So they didn't trust those services, and then at that point they say, not surprisingly, we're just going to go back and we're going to build it back into the monolith. So, we're almost in that situation where things are kind of in that split. >> So Matt I have to think that's the natural, where you led to service mesh, and Istio specifically and Lyft, Google, IBM all working on that. Talk a little bit about, more about what Istio, it was really the buzz coming in with service mesh, there's also there's some competing offerings out there, Conduit, new one announced this week, maybe give us the landscape, kind of where we are, and what you're seeing. >> So I think service mesh is, it's incredible to look around this conference, I think there's 15 or more talks on service mesh between all of the Buoyant talks on Linker D and Conduit and Istio and Envoy, it's super fantastic. I think the reason that service mesh is so compelling to people is that we have these problems where people want to build in five or six languages, they have some common problems around load balancing and other types of things, and this is a great solution for offloading some of those problems into a common place. So, the confusion that I see right now around the industry is service mesh is really split into two pieces. It's split into the data plane, so the proxy, and the control plane. So the proxy's the thing that actually moves the bytes, moves the requests, and the control plane is the thing that actually tells all the proxies what to do, tells it the topology, tells it all the configurations, all the settings. So the landscape right now is essentially that Envoy is a proxy, it's a data plane. Envoy has been built into a bunch of control planes, so Istio is a control plane, it's reference proxy is Envoy, though other companies have shown that they can integrate with Istio. Linker D has shown that, NGINX has shown that. Buoyant just came out with a new combined control plane data plane service mesh called Conduit, that was brand new a couple days ago, and I think we're going to see other companies get in there, because this is a very popular paradigm, so having the competition is good. I think it's going to push everyone to be better. >> How do companies make sense of this, I mean, if I'm just a boring enterprise with complexity, legacy, you know I have a lot of stuff, maybe not the kind of scale in terms of transactions per second, because they're not Lyft, but they still have a lot of stuff. They got servers, they got data center, they got stuff in the cloud, they're trying to put this cloud native package in because the developer movement is clearly pushing the legacy guy, old guard, into cloud. So how does your stuff translate into the mainstream, how would you categorize it? >> Well, what I counsel people is, and I think that's actually a problem that we have within the industry, is that I think sometimes we push people towards complexity that they don't necessarily need yet. And I'm not saying that all of these cloud native technologies aren't great, right, I mean people here are doing fantastic things. >> You know how to drive a car, so to speak, you don't know how to use the tech. >> Right, and I advise companies and organizations to use the technology and the complexity that they need. So I think that service mesh and microservices and tracing and a lot of the stuff that's being talked about at this conference are very important if you have the scale to have a service-oriented microservice architecture. And, you know, some enterprises they're segmented enough where they may not actually need a full microservice real-time architecture. So I think that the thing to actually decide is, number one, do you need a microservice architecture, and it's okay if you don't, that's just fine, take the complexity that you need. If you do need a microservice architecture, then I think you're going to have a set of common problems around things like networking, and databases, and those types of things, and then yes, you are probably going to need to build in more complicated technologies to actually deal with that. But the key takeaway is that as you bring on, as you bring on more complexity, the complexity is a snowballing effect. More complexity yields more complexity. >> So Matt, might be a little bit out of bounds for what we're talking about, but when I think about autonomous vehicles, that's just going to put even more strain on the kind of the distributed natured systems, you know, things that have to have the edge, you know. Are we laying the groundwork at a conference like this? How's Lyft looking at this? >> For sure, and I mean, we're obviously starting to look into autonomous a lot, obviously Uber's doing that a fair amount, and if you actually start looking at the sheer amount of data that is generated by these cars when they're actually moving around, it's terabytes and terabytes of data, you start thinking through the complexity of ingesting that data from the cars into a cloud and actually analyzing it and doing things with it either offline or in real-time, it's pretty incredible. So, yes, I think that these are just more massive scale real-time systems that require more data, more hard drives, more networks, and as you manage more things with more people, it becomes more complicated for sure. >> What are you doing inside Lyft, your job. I mean obviously, you're involved in open source. Like, what are you coding specifically these days, what's the current assignment? >> Yeah, so I'm a software engineer at Lyft, I lead our networking team. Our networking team owns obviously all the stuff that we do with Envoy, we own our edge system, so basically how internet traffic comes into Lyft, all of our service discovery systems, rate limiting, auth between services. We're increasingly owning our GRPC communications, so how people define their APIs, moving from a more polling-based API to a more push-based API. So our team essentially owns the end-to-end pipe from all of our back-end services to the client, so that's APIs, analytics, stats, logging, >> So to the app >> Yeah, right, right, to the app, so, on the phone. So that's my job. I also help a lot with general kind of infrastructure architecture, so we're increasingly moving towards Kubernetes, so that's a big thing that we're doing at Lyft. Like many companies of Lyft's kind of age range, we started on VMs and AWS and we used SaltStack and you know, it's the standard story from companies that were probably six or eight years old. >> Classic dev ops. >> Right, and >> Gen One devops. >> And now we're trying to move into the, as you say, Gen Two world, which is pretty fantastic. So this is becoming, probably, the most applicable conference for us, because we're obviously doing a lot with service mesh, and we're leading the way with Envoy. But as we integrate with technologies like Istio and increasingly use Kubernetes, and all of the different related technologies, we are trying to kind of get rid of all of our bespoke stuff that many companies like Lyft had, and we're trying to get on that general train. >> I mean you guys, I mean this is going to be written in the history books, you look at this time in a generation, I mean this is going to define open source for a long, long time, because, I say Gen one kind of sounds pejorative but it's not. It's really, you need to build your own, you couldn't just buy Oracle database, because, you probably have some maybe Oracle in there, but like, you build your own. Facebook did it, you guys are doing it. Why, because you're badass, you had to. Otherwise you don't build customers. >> Right and I absolutely agree about that. I think we are in a very unique time right now, and I actually think that if you look out 10 years, and you look at some of the services that are coming online, and like Amazon just did Fargate, that whole container scheduling system, and Azure has one, and I think Google has one, but the idea there is that in 10 years' time, people are really going to be writing business logic, they're going to insert that business logic >> They may do a powerpoint slides. >> That would be nice. >> I mean it's easy to me, like powerpoint, it's so easy, that's, I'm not going to say that's coding, but that's the way it should be. >> I absolutely agree, and we'll keep moving towards that, but the way that's going to happen is, more and more plumbing if you will, will get built into these clouds, so that people don't have to worry about all this stuff. But we're in this intermediate time, where people are building these massive scale systems, and the pieces that they need is not necessarily there. >> I've been saying in theCUBE now for multiple events, all through this last year, kind of crystallized and we were talking about with Kelsey about this, Hightower, yesterday, craft is coming back to programming. So you've got software engineering, and you've got craftsmanship. And so, there's real software engineering being done, it's engineering. Application development is going to go back to the old school of real craft. I mean, Agile, all it did was create a treadmill of de-risking rapid build scale, by listening to data and constantly iterating, but it kind of took the craft out of it. >> I agree. >> But that turned into engineering. Now you have developers working on say business logic or just solving, building a healthcare app. That's just awesome software. Do you agree with this craft? >> I absolutely agree, and actually what we say about Envoy, so kind of the catchword buzz phrase of Envoy is to make the network transparent to applications. And I think most of what's happening in infrastructure right now is to get back to a time where application developers can focus on business logic, and not have to worry about how some of this plumbing actually works. And what you see around the industry right now, is it is just too painful for people to operate some of these large systems. And I think we're heading in the right direction, all of the trends are there, but it's going to take a lot more time to actually make that happen. >> I remember when I was graduating college in the 80s, sound old but, not to date myself, but the jobs were for software engineering. I mean that is what they called it, and now we're back to this devops brought it, cloud, the systems kind of engineering, really at a large scale, because you got to think about these things. >> Yeah, and I think what's also kind of interesting is that companies have moved toward this devops culture, or expecting developers to operate their systems, to be on call for them and I think that's fantastic, but what we're not doing as an industry is we're not actually teaching and helping people how to do this. So like we have this expectation that people know how to be on-call and know how to make dashboards, and know how to do all this work, but they don't learn it in school, and actually we come into organizations where we may not help them learn these skills. >> Every company has different cultures, that complicates things. >> So I think we're also, as an industry, we are figuring out how to train people and how to help them actually do this in a way that makes sense. >> Well, fascinating conversation Matt. Congratulations on all your success. Obviously a big fan of Lyft, one of the board members gave a keynote, she's from Palo Alto, from Floodgate. Great investors, great fans of the company. Congratulations, great success story, and again open source, this is the new playbook, community scale contribution, innovation. TheCUBE's doing it's share here live in Austin, Texas, for KubeKon, for Kubernetes conference and CloudNativeCon. I'm John Furrrier, for Stu Miniman, we'll be back with more after this short break. (futuristic music)

>> Narrator: Live from Las Vegas, it's the Cube, covering VMWorld 2017. Brought to you by VMWare and its ecosystem partners. (bright music) >> Hi, I'm Stu Miniman with the Cube, here with my guest host, Justin Warren. Happy to have a returning Cube alum, but in a different role then we had. It's been a few years. Tal Klein, who is the author of The Punch Escrow. >> Au-tor, please. No, I'm just kidding. (laughing) Tal, thanks so much for joining us. It's great for you to be able to find time to hang out with the tech geeks rather than all the Hollywood people that you've been with recently. (laughing) >> You guys are more interesting. (laughing) >> Well thank you for saying that. So last time we interviewed you, you were working for a sizable tech company. You were talking about things like, you know, virtualization, everything like that. Your Twitter handle's VirtualTal. So how does a guy like that become not only an author but an author that's been optioned for a movie, which those of us that, you know, are geeks and everything are looking at, as a matter of fact, Pac Elsiger this morning said, "we are seeing science fiction become science fact." >> That's right. >> Stu: So tell us a little of the journey. >> Yeah, cool, I hope you read the book. (laughing) I don't know, the journey is really about marketing, right? Cause a lot of times when we talk about virtual, like, in fact last time I was on the Cube, we were talking about the idea that desktops could be virtual. Cause back then it was still this, you know, almost hypothetical notion, like could desktops be virtual, and so today, you know, so much of our life is virtual. So much of the things that we do are not actually direct. I was watching this great video by Apple's new augmented reality product, where you sit in the restaurant and you look at it with your iPad, and it's your plate, and you can just shift the menu items, and you see the menu items on your plate in the context of the restaurant and your seat and the person you're sitting across from. So I think the future is now. >> Yeah, it reminds of, you know, the movie Wall-E, the animated one. We're all going to be sitting in chairs with our devices or Ready Player One, you know, very popular sci-fi book that's being done by Speilberg, I believe. >> Yes, yeah, very exciting. >> Tell us a little bit about your book, you know, we talked, when I was younger and used to read a lot of sci-fi, it was like, what stuff had they done 50 years ago that now's reality, and what stuff had they predicted, like, you know, we're going to go away from currency and go digital currency, and it's like we're almost there. But we still don't have flying cars. >> Yeah, we're, I mean, the main problem with flying cars is that we need pilots. And I think actually we're very close to flying cars, cause once we have self-driving vehicles and we no longer need to worry about it being a person behind the joystick, then we're in really good shape. That's really the issue, you know, the problem with flying cars is that we are so incompetent at driving and or flying. That's not our core competency, so let's just put things that do understand how to make those things happen and eliminate us from the equation. >> Everything is a people problem. >> Yeah, so when I wrote the book, Punch Escrow, Punch Escrow, (laughing) when I wrote the book, I really thought about all the things that I read growing up in science fiction, you know, things like teleportation, things like nanotechnology, things like digital currency, you know, how do we make those, how do we present those in a viable way that doesn't seem too science fictiony. Like one of the things I really get when people read the book is it feels really near-future, even though it's set like 100 plus years in the future, all the concepts in it feel very pragmatic or within reach, you know? >> Yeah, absolutely. It's interesting, we look at, you know, what things happen in a couple of years and what things take a long time. So artificial intelligence, machine learning, it's not like these are new concepts, you know? I read a great book by, you know, it was Isaacson, The Innovators. You go back to like Aida Lovelace, and the idea of what a machine or computer would be able to do. So 100 years from now, what's real, what's not real? We still all have jobs or something? >> We have jobs but different. Remember, I don't know if you're a historian, but back in the industrial age, there was a whole bunch of people screaming doom and gloom. In fact, if we go way back to the age of the Luddites, who just hated machines of any kind. I think that in general, we don't like, you know, we're scared of change. So I do think a lot of the jobs that exist today are going to be done by machines or code. That doesn't mean the jobs are going away. It means jobs are changing. A lot of the jobs that people have today didn't exist in the industrial age. So I think that we have to accept that we are going to be pragmatic enough to accept the fact that humans will continue to evolve as the infrastructure powering our world evolves, you know? We talk about living in the age of the quantified self, right? There's a whole bunch that we don't understand how to do yet. For example, I can think of a whole industry that tethers my FitBit to my nutrition. You know, like there's so much opportunity that for us to say, oh that's going to be the end of jobs, or the end of innovation or the end of capitalism, is insane. I think this just ushers in a whole new age of opportunity. And that's me, I'm just an optimist that way, you know. >> So the Luddites did famously try to destroy the machines. But the thing is, the Luddites weren't wrong. They did lose their jobs. So what about the people whose jobs are replaced, as you say net new, there's a net new number of jobs. But specific individuals, like people who manufacture cars for example, lose their jobs because a robot can do that job safer and better and faster than a human can do it. So what do we do with those humans? Because how do we get people to have new jobs and retrain themselves? >> I address some of these notions in the book. For example, one of the weird things that we're suffering from is the lack of welders in society today, cause welding has become this weird thing that we don't think we need people for, so people don't really get trained up in it because, you know, machines do a lot of welding but there's actually specialty welding that machines can't do. So I think the people who are really good at the things that they do will continue to have careers. I think their careers will become more niche. Therefore they'll be able to create, to demand a higher wage for it because almost like a carpenter, you know, a specialist carpenter will be able to earn a much higher wage today by having fewer customers who want really custom carpentry versus things that can be carved up by a machine. So I think what we end up seeing is that it's not that those jobs go away. It's they become more specialized. People still want Rolls Royces. People still want McLarens. Those are not done by machines. Those are hand-made, you know? >> That's an interesting point, so the value of something being hand-made becomes, instead of it being a worse product, it's actually- >> Tal: That's a big concept in the book. >> Oh okay, right. >> A big concept in the book is that we place a lot of value on the uniqueness of an object. And that parlays in multiple ways. So one of the examples that I use in the book is the value of a Big Mac actually coming from McDonald's. Like, you can make a Big Mac. We know the recipe for a Big Mac. But there is a weird sort of nacent value to getting a Big Mac from McDonald's. It's something in our brain that clicks that tethers it to an originality. Diamonds, another really good example. Or you know, we know there's synthetic diamonds. We still want the ones that get mined in the cave. Why? We don't know. Right, they're just special. >> Because De Beers still has really good marketing. (laughing) >> So I think there's- >> That's interesting, so the concept of uniqueness, which again comes to scarcity and so on. As an author, someone who is no doubt, signed a lot of his book, that means that that book is unique because it's signed by the author, unlike something which is mass produced and there is hopefully thousands and thousands of copies that you sell. >> Going into this, I actually thought about that a lot. And that's why I've created like multiple editions of the book. So like the first 500 people who pre-ordered it, they get like a special edition of the book that's like stamped and all this kind of stuff. I even used different pens. (laughs) I appreciate that because I'm also a collector. I collect music, I collect books. And you know, so I see those aspects in myself. So I know what I value about them, you know? >> And the crossover between music and books is interesting. So as someone who has a musical background, I know that there's a lot of musicians who'll come out with special editions, and you know, because this is an age where we can download it. You can download the book. Do you think there is something, is there something that is intrinsic to having a physical object in a virtual world? >> I think to our generation, yes. I'm not so sure about millennials, when they grow up. But there are, for example, I'm going to see U2 next week, I'm very lucky to see that. But part of the U2 buying experience, to get access to the presale, you need to be part of their fan club. To be a part of their fan club, you need to get, you get like a whole bunch of limited edition posters, limited edition vinyl, and all this kind of stuff. So there's an experience. It's no longer just about going to see U2 at a concert. There's like the entire package of you being a special U2 fan. And they surround it with uniqueness. It's not necessarily limited, but there's an enhanced experience that can't just be, it's not just about you having a ticket to a single concert. >> Justin: Yeah, okay. >> I'm curious, the genre, if you'd call it, is hard science fiction. >> Yes. >> The challenge with that is, you know, what is an extension of what we're doing, and what is fiction? And people probably poke at that. Have you had any interesting experience, things like that? I mean, I've listened to a lot of stuff like Andy Weir, like let the community give feedback before he created the final The Martian. (laughing) But so yeah, what's it like, cause we can, the geeks can be really harsh. >> Yes, I've learned from my Reddit experience that, so what's really funny about it is the first draft of this novel was hard as nails. It was crazy. And my publisher read it, and it would have made all the hard science fiction guys super happy. My publisher read it, he was like, you've written a really great hard science fiction book, and all five people who read it are going to love it. (laughing) You know, but like, I came here with my buddy Danny. He couldn't even get through the first three pages of it. He's like, he wanted to read it. So part of working through the editorial process is saying, look, I care a lot about the science because one of my deep goals is to write a STEM-oriented book that gets people excited about technology and present the future as not a dystopian place. And so I wanted the science to be there and have a sort of gravity to the narrative. But yeah, it's tough. I worked with a physicist, a biologist, a geneticist, an anthropologist, and a lawyer. (laughs) Just to try to figure out, how do we carve out, you know, what does the future look like, what does the evolution of each individual sciences, we talked about the mosquitoes, right? You know, we're already doing a lot of crazy stuff with mosquitoes. We're modifying them so that the males mate with females that carry the Zika virus, you know, give birth to offspring that never reach maturity. I mean, this is just crazy, it's science fiction. And now that they're working on modifying female mosquitoes into vaccine carriers instead of disease carriers. I mean, this is science fiction, right? Like who believes this stuff? It's crazy. >> Christopher is amazing. >> Yeah, I've loved, there's been a bunch of movies recently that have kind of helped to educate on STEM some, you know, Martian got a lot of people excited, you know, Hidden Figures, the one that I could being my kids that are teenagers now into it and they get excited, oh, science is great. So the movie, how much will you be involved? You know, what can you share about that experience, too, so far? >> It's been, it's very surreal. That's the word is use to describe it, the honest, god's honest truth, I mean. I've been very lucky in that my representation in Hollywood is this rock-solid guy called Howie Sanders. And he's this bigger-than-life Hollywood agent guy. He's hooked me up, we've made a lot of business decisions that we're focused less on the money and more on the team, which is nice to be, like when you're in your 40s and you're more financially settled, you're not in the kind of situation where you might be in your 20s and just going to sign the first deal that people give you. So we really focused on hooking up with like the director, James Bovin is, you know, he's the guy who co-created Flight of the Concords. He did the Muppets movie, you know, Alice Through the Looking Glass. Really professional guy but also really understands the tone of the book, which is like humorous, you know, kind of sarcastic. It's not just about the technology. It's also about the characters. Same thing with the production team. The two producers, Mandeville Productions, I was just talking to Todd Lieberman, and we're talking about just what is augmented reality, like how does it look like on the screen? So I'm not- >> It's not going to look like Blade Runner is what I'm hearing. >> (laughs) I don't know. It's going to look real. I imagine, I don't know, they're going to make whatever movie they're going to make, but their perspective, one of the things we talked about is keeping the movie very grounded. Like you know, one of the big questions they ask first going into it is before we even had any sort of movie discussions is like is this more of like a Looper, Gattica, or District Nine, or is it more like The Fifth Element, you know, I mean, is it like, do you want it to be this sort of grounded movie that feels authentic and real and near future or do you want this to be like completely alien and weird and out of it. And the story is more grounded. So I think a lot, hopefully what we display on the screen will not feel that far away from reality. >> Okay, yeah. >> You do marketing in your day job. >> I do. >> I'm curious as you look at this, kind of the balance of educating, reaching a broad audience, you have passion for STEM, what's your thoughts around that? Is it, I worry there's so much general, like television or things like that, when I see the science stuff, it like makes me groan. Because you know, it's like I don't understand that. >> I am the worst, because I got a security background too, so that's the one I get scrambled on. The war, I mean, like. >> Wait, thank goodness I updated my firewall settings because I saved the world from terrorists. >> Hang on, we're breaking through the first firewall. Now we're through the second firewall. (laughing) Now we're going through the third firewall, like 15 firewalls. And let me upload the virus, like all that stuff. It's difficult for me. I think that, you know, hopefully, there's also a group in Hollywood called the Hollywood Science and Entertainment Exchange. And they're a group of scientists who work with film makers on, you know, reigning things in. And film makers don't usually take all their advice, i.e. Interstellar, (laughing) but you know, I think (laughing) in many cases there's some really good ideas that come to play into it that hopefully bring up, like I think Jarvis for example, in Iron Man or the Avengers is a really cool implementation of what the future of AI systems might be like. And I know they used the Hollywood Science Exchange to figure out how is that going to work? And I think the marketing aspect is, you know, the reason I came up with the idea for this book is because my CEO of a company I used to work for, he had this whole conversation about teleportation, like teleportation was impossible. And he's like, it's not because the science, yes, the science is a problem right now, but we'll get over it. The main issue is that nobody would ever step foot into a device that vaporizes them and then printed them out somewhere else. And I said, well that's great, cause that's a marketing problem. (laughing) >> Yeah, you're dead every time you do it. But it's the same you, I can't tell the difference. >> Well, you say you're dead, I'm saying you're just moving. (laughing) >> Artificial intelligence, you know, kind of a big gap between the hype to where we need to go. What's your thoughts on that space in general? >> I think that we have, it's a great question because I feel like that's a term that gets thrown around a lot, and I think as a result it's becoming watered down. So you've this sort of artificial intelligence that comes with like, you know, Google building an app that can beat the world's best Go player, which is a really, really difficult puzzle. The problem is, that app can do one thing, and that's play Go. You put in it a chess game, and it's like I don't know what's going on. >> It's a very specialized kind of intelligence, yeah. >> Now with Open AI, you know, they just had some pretty interesting implementations where they actually played video games with a real live competition and won. Again, you know, but without the smack talk, which really I think would add a lot. Now you got to get an AI to smack talk. So I think the problem is we haven't figured out a really good way of creating a general purpose AI. And there's a lot of parallels to the evolution of computing in general because if you look at how computers were before we had general purpose operating systems like Unix, every computer was built to do a very, very specific function, and that's kind of what AI is right now. So we're still waiting to have a sort of general purpose AI that can do a lot of specialized activities. >> Even most robots are still very single-purpose today. >> That's the fundamental problem. But you're seeing the Cambridge guys are working on sort of the bipedal robot that can do lots of things. And Siri's getting better, Cortana's getting better, Watson's getting better, but we're not there. We still need to find a really good way of integrating deep knowledge with general purpose conversational AI. Cause that's really what you need to like, Stu, what do you need? Here, let me give it to you, you know? >> Do you draw a distinction between AI that's able to simply sort of react as a fairly complex machine or something that can create new things and add something? >> That's in the book as well. So the fundamental thing that I don't think we get around even in the future is giving computers the ability to actually come up with new ideas. There's actually a career, the main job of the protagonist in the book, his job is a salter. And his job is to salt AI algorithms to introduce entropy so they can come up with new ideas. >> Okay, interesting. >> So based off the sort of chaos theory. >> Like chaos monkey, right? >> Yeah. And that's really what you're trying to do is like, okay, react to things that are happening because you can't just come up with them on their own. There's a whole, I don't want to bore you, but there's a whole bunch of stuff in the book about how that works. >> It's like hand-carving ideas that are then mass produced by machines. >> Yeah, I don't know if you guys are going to have Simon Crosby on here, he's kind of like an expert on that. He was the Dean of Kings College, which is where Turing came from. So he really knows a lot about that. He's got a lot of strong ideas about it. But I learned a lot from him in that regard. There's a lot of like, the snarky spirit of Simon Crosby lives on in my book somewhere. But he's just funny cause he's, coming from that field, he immediately sees a lot of BS right off the bat, whenever anybody's presenting. He's got like the ability to just cut through it. Because he understands what it would actually take to make that happen, you know? So I tried to preserve some of that in the book. >> That is refreshing in the tech industry. >> So Tal, I need to let you, you know, wrap this up. Give us a plug for the book, tell us, when are we going to be able to see this on the big screen? >> I don't know about the big screen, but the Punch Escrow is now available. You can get it on Amazon, Barnes and Noble, anywhere books are sold. It's been optioned by Lionsgate. The director attached to it is James Bovin, production team is Mandeville Productions. I'm very excited about it. Go check it out. It's a pretty quick read, reads like a technothriller. It's not too hard. And it's fun for the whole family. I think one of the coolest things about it is that the feedback I've been getting has been that it really is appealing to everybody. I've got mother-in-laws reading it, you know, it's pretty cool. Initially I sold it, my initial audience is like us, but it's kind of cool, like, Stu will finish the book, he'll give it to, you know, wife, daughter, anything, and they're really digging it. So it's kind of fun. >> Justin: Thanks a lot. >> Tal Klein, really appreciate you coming. Congratulations on the book, we look forward to the movie. Maybe, you know, we'll get the Cube involved down the road. (laughing) >> And we're giving away 75 copies of it here at Lakeside booth, if you guys want to come. >> Tal Klein, author of The Punch Escrow, also CMO of Lakeside, who is here in the thing. But yeah, (laughing) a lot of stuff. Justin and I will be back with more coverage here from VMWorld 2017. You're watching the Cube. (bright music)

>>Good afternoon everyone, and welcome back to the Cube. I am Savannah Peterson here, coming to you from Detroit, Michigan. We're at Cuban Day three. Such a series of exciting interviews. We've done over 30, but this conversation is gonna be extra special, don't you think, John? >>Yeah, this is gonna be a good one. Griffon Labs is here with us. We're getting the conversation of what's going on in the industry management, watching the Kubernetes clusters. This is large scale conversations this week. It's gonna be a good one. >>Yeah. Yeah. I'm very excited. He's also got a fantastic Twitter handle, twitchy. H Please welcome Richie Hartman, who is the director of community here at Griffon. Richie, thank you so much for joining us. Thanks >>For having me. >>How's the show been for you? >>Busy. I, I mean, I, I, >>In >>A word, I have a ton of talks at at like maintain a thing and like the covering board searches at the TLC panel. I run forme day. So it's, it's been busy. It, yeah. Monday, I didn't have to run anything. That was quite nice. But there >>You, you have your hands in a lot. I'm not even gonna cover it. Looking at your bio, there's, there's so many different things that you're working on. I know that Grafana specifically had some announcements this week. Yeah, >>Yeah, yeah. We had quite a few, like the, the two largest ones is a, we now have a field Kubernetes integration on Grafana Cloud. So our, our approach is generally extremely open source first. So we try to push stuff into the exporters, like into the open source exporters, into mixes into things which are out there as open source for anyone to use. But that's little bit like a tool set, not a ready made solution. So when we talk integrations, we actually talk about things where you get this like one click experience, You log into your Grafana cloud, you click, I have a Kubernetes, which probably most of us have, and things just work like you in just the data. You have to write dashboards, you have to write alerts, you have to write everything to just get started with extremely opinionated dashboards, SLOs, alerts, again, all those things made by experts, so anyone can use them. And you don't have to reinvent the view for every single user. So that's the one. The other is, >>It's a big deal. >>Oh yeah, it is. Yeah. It is. It, we, we has, its heavily in integrations course. While, I mean, I don't have to convince anyone that perme is a DD factor standard in everything. Cloudnative. But again, it's, it's, it's sometimes a little bit hard to handle or a little bit not easy to get into. So, so smoothing this, this, this path onto onboarding yourself onto this stack and onto those types of solutions. Yes. Is what a lot of people need. Course, if you, if you look at the statistics from coupon, and we just heard this in the governing board session yesterday. Yeah. Like 60% of the people here are first time attendees. So there's a lot of people who just come into this thing and who need, like, this is your path. This is where you should be going. Or at least if you want to go, go there. This is how to get there. >>Here's your runway for takeoff. Yes. Yeah. I think that's a really good point. And I love that you, you had those numbers. I was curious. I, I had seen on Twitter, speaking of Twitter, I had seen, I had seen that, that there were a lot of people here coming for the first time. You're a community guy. Are we at an inflection point where this community is about to continue to scale? >>That's a very good question. Which I can't really answer. So I mean, >>Obviously I bet you're gonna try. >>I covid changed a few things. Yeah. Probably most people, >>A couple things. I mean, you know, casually, it's like such a gentle way of putting that, that was >>Beautiful. I'm gonna say yes, just to explode. All these new ERs are gonna learn Prometheus. They're gonna roll in with a open, open metrics, open telemetry. I love it, >>You know, But, but at the same time, like Cuban is, is ramping back up. But if you look at the, if you look at the registration numbers between Valencia Andro, it was more or less the same. Interesting. Which, so it didn't go onto this, onto this flu trajectory, which it was on like, up to, up to 2019. I expect this to take up again. But also with the economic situation, everything, I, I don't think >>It's, I think the jury's still out on hybrid. I think there's a lot, lot more hybrid. Let's see how the projects are gonna go. That's what I think it's gonna be the tell sign. How many people are in participating? How are the project's advancing? Some of the momentum, >>I mean, from the project level, Most of this is online anyway. Of course. That's how open source, right. I've been working for >>Ages. That's >>Cause you don't have any trouble budget or, or any office or, It's >>Always been that way. >>Yeah, precisely. So the projects are arguably spearheading this, this development and the, the online numbers. I I, I have some numbers in my head, but I'm, I'm not a hundred percent certain to, but they're higher for this time in Detroit than in volunteer as far somewhere. Cool. So that is growing and it's grown in parallel, which also is great. Cause it's much more accessible, much more inclusive. You don't have to have a budget of at least, let's say, I don't know, two to five k to, to fly over the pond and, and attend this thing. You can just do it from your home. So that is, that's a lot more inclusive. And I expect this to, to basically be a second more or less orthogonal growth, growth path. But the best thing about coupon is the hallway track. I'm just meeting people, talking to people and that kind of thing is not really possible with, >>It's, it's great to see people >>In person. No, and it makes such a difference. I mean, yeah. Even and interviewing people in person too. I mean, it does a, it's, it's, and, and this, this whole, I mean cncf, this whole community, every company here is community first. It's how these projects come to be. I think it's awesome. I feel like you got something you're saying to say, Johnny. >>Yeah. And I love some of the advancements. Rich Richie, we talked last time about, you know, open telemetry, open metrics. You're involved in dashboards. Yeah. One of the themes here is ease of use, simplicity, developer productivity. Where do you see the ease of use going from a project standpoint? For me, as you mentions everywhere, it's pretty much, it is, it's almost all corners of the world. Yep. And new people coming in. How, how are you making it easier? What's going on? Give us the update on that. >>So we also, funnily enough at precisely this topic in the TC panel just a few hours ago, about ease of use and about how to, how to make things easier to, to handle how developers currently, like if they just want to get into the cloud native seen, they have like, like we, we did some neck and math, like maybe 10 tools at least, which you have to be somewhat proficient in to just get started, which is honestly horrendous. Yeah. Course. Like with a server, I just had my survey install my thing and it runs, maybe I need a database, but that's roughly it. And this needs to change again. Like it's, it's nice that everything is, is un unraveled. And you have, you, you, you, you don't have those service boundaries which you had before. You can do all the horizontal scaling, you can do all the automatic scaling, all those things that they're super nice. But at the same time, this complexity, which used to be nicely compartmentalized, was deliberately broken up. And so it's becoming a lot harder to, to, like, we, we need to find new ways to compartmentalize this complexity back to, to human understandable levels again, in particular, as we keep onboarding new and new and new, new people, of course it's just not good use of anyone's time to, to just like learn the basics again and again and again. This is something which should be just compartmentalized and automated away. We're >>The three, We were talking to Matt Klein earlier and he was talking about as projects become mature and all over the place and have reach and and usage, you gotta work on the boring stuff. Yes. And when it's boring, that means you have success. Yes. But then you gotta work on the plumbing. What are some of the things that you guys are working on? Because people are relying on the product. >>Oh yeah. So for with my premises head on, the highlight feature is exponential or native or spars. Histograms. There's like three different names for one single concept. If you know Prometheus, you ha you currently have hard bucket boundaries where I say my latency is lower equal two seconds, one second, a hundred milliseconds, what have you. And I can put stuff into those histogram buckets accordingly to those predefined levels, which is extremely efficient, but like on the, on the code level. But it's not very nice for the humans course you need to understand your system before you're able to, to, to choose good cutoff points. And if you, if you, if you add new ones, that's completely fine. But if you want to actually change them, course you, you figured out that you made a fundamental mistake, you're going to have a break in the continue continuity of your observability data. And you cannot undo this in, into the past. So this is just gone native histograms. On the other hand, allow me to, to, okay, I'm not going to get get into the math, but basically you define a single formula, which there comes a good default. If you have good reasons, then you can change it. But if you don't, just don't talk, >>The people are in the math, Hit him up on Twitter. Twitter, h you'll get you that math. >>So the, >>The thing is people want the math, believe me. >>Oh >>Yeah. I mean we don't have time, but hit him up. Yeah. >>There's ProCon in two weeks in Munich and there will be whole talk about like the, the dirty details of all of the stuff. But the, the high level answer is it just does what people would expect it to do. And with very little overhead, you become, you get highly, highly or high resolution histograms, which is really important for a lot of use cases. But this is not just Prometheus with my open metrics head on the 2.0 feature, like the breaking highlight feature of Open Metrics 2.0 will be you guested precisely the same with my open telemetry head on. Low and behold the same underlying technology is being put or has been put into open telemetry. And we've worked for month and month and month and even longer between all different projects to, to assert that we have one single standard which is actually compatible with each other course. One of the worst things which you can have in the cloud ecosystem is if you have soly different things and they break in subtly wrong ways, like it's much better to just not work than to break in a way, which is just a little bit wrong. Of course you won't figure this out until it's too late. So we spent, like with all three hats, we spent insane amounts of time on making this happen and, and making this nice. >>Savannah, one of the things we have so much going on at Cube Con. I mean just you're unpacking like probably another day of cube. We can't go four days, but open time. >>I know, I know. I'm the same >>Open telemetry >>Challenge acceptance open. >>Sorry, we're gonna stay here. All the, They >>Shut the lights off on us last night. >>They literally gonna pull the plug on us. Yeah, yeah, yeah, yeah. They've done that before. It's not the first time we go until they kick us out. We love, love doing this. But Open telemetry is got a lot of news too. So that's, We haven't really talked much about that. >>We haven't at >>All. So there's a lot of stuff going on that, I won't call it boring. That's like code word's. That's cube talk for, for it's working. Yeah. So it's not bad, but there's a lot of stuff going on. Like open telemetry, open metrics, This is the stuff that matters cuz when you go in large scale, that's key. It's just what, missing all the, all the stuff. >>No, >>What are we missing? What are people missing? What's going on in the show that you think that's not actually being reported on? I mean it's a lot of high web assembly for instance got a lot >>Of high. Oh yeah, I was gonna say, I'm glad you're asking this because you, you've already mentioned about seven different hats that you wear. I can only imagine how many hats are actually in your hat cabinet. But you, you are someone with your, with your fingers in a lot of different things. So you can kind of give us a state of the union. Yeah. So go ahead. Let's talk about >>It. So I think you already hit a few good points. Ease of use is definitely one of them. And, and improving the developer experience and not having this like a value of pain. Yeah. That is one of the really big ones. It's going to be interesting cause it is boring. It is janitorial and it needs a different type of persona. A lot of, or maybe not most, but a large fraction of developers like the shiny stuff. And we could see this in Prometheus where like initially the people who contributed this the most where like those restless people who need to fix that one thing, this is impossible, are going to do it. Which changed over the years where the people who now contribute the most are off the janitorial. Like keep things boring, keep things running, still have substantial changes. But but not like more on the maintenance level. >>Yeah. The maintainers. I was just gonna bring that >>Up. Yeah. On the, on the keep things boring while still pushing 'em forward. Yeah. And the thing about ease of use is a lot of this is boring. A lot of this is strategy. A lot of this is toil. A lot of this takes lots of research also in areas where developers are not really good at, like UX for example, and ui like most software developers are really bad at those cause they just think differently from normal humans, I guess. >>So that's an interesting observation that you just made. I we could unpack that on a whole nother show as well. >>So the, the thing is this is going to be interesting for the open source scene course. This needs deliberate investment by companies who assign people to those projects and say, okay, fix that one thing or make it easier to use what have you. That is a lot easier with, with first party products and projects from companies cuz they can invest directly into the thing and they see much more of a value prop. It's, it's kind of normal by now to, to allow developers or even assigned developers onto open source projects. That's not so much the case for the tpms, for the architects, for the UX and your I people like for the documentation people that there's not as much awareness of that this is also driving value for everyone. Yes. And also there's not much as much. >>Yeah, that's a great point. This whole workflow production system of open source, which has grown and keeps growing and we'll keep growing. These be funded. And one of the things we were talking earlier in another session about is about the recession potentially we're hitting and the global issues, macroeconomics that might force some of these projects or companies not to get VC >>Funding. It's such a theme at the show. So, >>So to me, I said it's just not about VC funding. There's other funding mechanisms that's community oriented. There's companies participating, there's other meccas. Richie, if you could have your wishlist of how things could progress an open source, what would you want to see happen in terms of how it's, how things are funded, how things are executed. Cuz developers are going to run businesses. Cuz ultimately if you follow digital transformation to completion, it and developers aren't a department serving the business. They are the business. And that's coming fast. You know, what has to happen in your opinion, if you had the wish magic wand, what would you, what would you snap your fingers to make happen? >>If I had a magic wand that's very different from, from what is achievable. But let, let's >>Go with, Okay, go with the magic wand first. Cause we'll, we'll, we'll we'll riff on that. So >>I'm here for dreams. Yeah, yeah, >>Yeah. I mean I, I've been in open source for more than two, two decades, but now, and most of the open source is being driven forward by people who are not being paid for those. So for example, Gana is the first time I'm actually paid by a company to do my com community work. It's always been on the side. Of course I believe in it and I like doing it. I'm also not bad at it. And so I just kept doing it. But it was like at night on the weekends and everything. And to be honest, it's still at night and in the weekends, but the majority of it is during paid company time, which is awesome. Yeah. Most of the people who have driven this space forward are not in this position. They're doing it at night, they're doing it on the weekends. They're doing it out of dedication to a cause. Yeah. >>The commitment is insane. >>Yeah. At the same time you have companies mostly hyperscalers and either they have really big cloud offerings or they have really big advertisement business or both. And they're extracting a huge amount of value, which has been created in large part elsewhere. Like yes, they employ a ton of developers, but a lot of the technologies they built on and the shoulders of the giants they stand upon it are really poorly paid. And there are some efforts to like, I think the core foundation like which redistribute a little bit of money and such. But if I had my magic wand, everyone who is an open source and actually drives things forwards, get, I don't know, 20% of the value which they create just magically somehow. Yeah. >>Or, or other companies don't extract as much value and, and redistribute more like put more full-time engineers onto projects or whichever, like that would be the ideal state where the people who actually make the thing out of dedication are not more or less left on the sideline. Of course they're too dedicated to just say, Okay, I'm, I'm not doing this anymore. You figure this stuff out and let things tremble and falter. So I mean, it's like with nurses and such who, who just like, they, they know they have something which is important and they keep doing it. Of course they believe in it. >>I think this, I think this is an opportunity to start messaging this narrative because yeah, absolutely. Now we're at an inflection point where there's a big community, there is a shared responsibility in my opinion, to not spread the wealth, but make sure that it's equally balanced and, and the, and I think there's a way to do that. I don't know how yet, but I see that more than ever, it's not just come in, raid the kingdom, steal all the jewels, monetize it, and throw some token token money around. >>Well, in the burnout. Yeah, I mean I, the other thing that I'm thinking about too is it's, you know, it's, it's the, it's the financial aspect of this. It's the cognitive load. And I'm curious actually, when I ask you this question, how do you avoid burnout? You do a million different things and we're, you know, I'm sure the open source community that passion the >>Coach. Yeah. So it's just write code, >>It's, oh, my, my, my software engineering days are firmly over. I'm, I'm, I'm like, I'm the cat herer and the janitor and like this type of thing. I, I don't really write code anymore. >>It's how do you avoid burnout? >>So a i I didn't curse ahead burnout a few years ago. I was not nice, but that was still when I had like a full day job and that day job was super intense and on top I did all the things. Part of being honest, a lot of the people who do this are really dedicated and are really bad at setting boundaries between work >>And process. That's why I bring it up. Yeah. Literally why I bring it up. Yeah. >>I I I'm firmly in that area and I'm, I'm, I don't claim I have this fully figured out yet. It's also even more risky to some extent per like, it's, it's good if you're paid for this and you can do it during your work time. But on the other hand, if it's so nice and like if your hobby and your job are almost completely intersectional, it >>Becomes really, the lines are blurry. >>Yeah. And then yeah, like have work from home. You, you don't even commute anything or anymore. You just sit down at your computer and you just have fun doing your stuff and all of a sudden it's deep at night and you're still like, I want to keep going. >>Sounds like God, something cute. I >>Know. I was gonna say, I was like, passion is something we all have in common here on this. >>That's the key. That is the key point There is a, the, the passion project becomes the job. But now the contribution is interesting because now yeah, this ecosystem is, is has a commercial aspect. Again, this is the, this is the balance between commercialization and keeping that organic production system that's called open source. I mean, it's so fascinating and this is amazing. I want to continue that conversation. It's >>Awesome. Yeah. Yeah. This is, this is great. Richard, this entire conversation has been excellent. Thank you so much for joining us. How can people find you? I mean, I give em your Twitter handle, but if they wanna find out more about Grafana Prometheus and the 1700 things you do >>For grafana grafana.com, for Prometheus, promeus.io for my own stuff, GitHub slash richie age slash talks. Of course I track all my talks in there and like, I don't, I currently don't have a personal website cause I stop bothering, but my, like that repository is, is very, you find what I do over, like for example, the recording link will be uploaded to this GitHub. >>Yeah. Great. Follow. You also run a lot of events and a lot of community activity. Congratulations for you. Also, I talked about this last time, the largest IRC network on earth. You ran, built a data center from scratch. What happened? You done >>That? >>Haven't done a, he even built a cloud hyperscale compete with Amazon. That's the next one. Why don't you put that on the >>Plate? We'll be sure to feature whatever Richie does next year on the cube. >>I'm game. Yeah. >>Fantastic. On that note, Richie, again, thank you so much for being here, John, always a pleasure. Thank you. And thank you for tuning in to us here live from Detroit, Michigan on the cube. My name is Savannah Peterson and here's to hoping that you find balance in your life this weekend.

(soft music) >> Good morning from Detroit, Michigan. theCUBE is live on our second day of coverage of KubeCon + CloudNativeCon North America 2022. Lisa Martin here with John Furrier. John, great to be back with you. The buzz is here, no doubt. We've been talking a lot about the developers. And one of the biggest bottlenecks that they face in software delivery, is when they're stuck waiting for access to environments. >> Yeah, this next segment's going to be very interesting. It's a company that's making DevOps more productive, but recognizing the reality of how people are working remotely, but also company to company developers. People are collaborating in all kinds of forms, so this is really going to be a great segment. >> Exactly. Two new guests to theCUBE who know theCUBE, but are first time on theCUBE from Release Hub, Tommy McClung, it's CEO and Matt Carter, it's CMO. Guys, great to have you on the program. >> Thank you. >> Thanks for having us here. >> So we want to dig into Release Hub, so the audience really gets an understanding. But Tommy, I want to get an understanding of your background. >> Sure. >> You've been at Release Hub for what, three years? >> Yep, I'm the co-founder. >> Before that you were at TrueCar? >> I was, yeah, I was the CTO at TrueCar. And prior to that, I've been a software engineer my entire career. I've started a couple of companies before this. Software engineer at heart. I've been working on systems management and making developers productive since 2000, long time. So it's fun to be working on developer productivity stuff. And this is our home and this is where I feel the most comfortable. >> Lisa: Yeah. And Matt, you're brand new to the company as it's chief marketing officer. >> Matt: Yeah, so I just joined earlier this month, so really excited to be here. I came over from Docker, so it's great to be able to keep working with developers and helping them, not only get their jobs done better and faster, but just get more delight out of what they do every day, that's a super important privilege to me and it's exciting to go and work on this at Release here. >> Well, they're lucky to have you. And we work together, Matt, at Docker, in the past. Developer productivity's always been a key, but communities are now more important. We've been seeing on theCUBE that developers are going to decide the standards, they're going to vote with their axes and their code. And what they decide to work on, it has to be the best. And that's going to be the new defacto standard. You guys have a great solution that I like. And I love the roots from the software engineering background because that's the hardest thing right now, is how do you scale the software, making things simpler and easier. And when things happen, you don't want to disrupt the tool chains, you want to make sure the code is right, you guys have a unique solution. Can you take a minute to explain what it is and why it's so important? >> Tommy: Yeah, I'll use a little bit of my experience to explain it. I was the CTO of a company that had 300 engineers, and sharing a handful of environments, really slowed everybody down, you bottleneck there. So in order to unlock the productivity of that team, developers need environments for development, they need it for testing, they need it for staging, you run your environments in production. So the environment is the key building block in every software development process. And like my last company, there were very few of them, one or two, everybody sharing them. And so the idea at Release is to make environments available on demand, so if a developer needs one for anything, they can spin one up. So if they want to write their code in a environment based in the cloud, they can do that, if they want to test on a poll request, an environment will automatically spin up. And the environments are full stack, include all the services, data, settings, configuration that runs the app. So developers literally get an isolated copy of the application, so they can develop knowing they're not stepping on other developers' toes. >> John: Can you give an example of what that looks like? Do they have to pre-configure the environment, or how does that work? Can you give an example? >> Yeah, sure. You have to, just like infrastructure is code, we call this environments is code. So you need to define your environment, which we have a lot of tools that help you do that. Analyze your repositories, help you define that environment. Now that you have the template for that, you can easily use that template to derive multiple environments out of it. A key part of this is everybody wants to make sure their development and data is secure. It runs within the AWS account of our customer. So we're the control plane that orchestrates it and the data and applications run within the context of their AWS account, so it's- >> John: What's the benefit? >> Tommy: Well, bottlenecking, increased developer productivity, developer happiness is a big one. Matt talks about this all the time, keeping developers in flow, so that they're focused on the job and not being distracted with, "Hey DevOps team, I need you to go spin up an environment." And a lot of times in larger organizations, not just the environments, but the process to get access to resources is a big issue. And so DevOps was designed to let developers take control of their own development process, but were still bottlenecking, waiting for environments, waiting for resources from the DevOps team, so this allows that self-service capability to really be there for the developer. >> Lisa: Matt, talk about... Target audience is the developer, talk about though... Distill that down into the business value. What am I, if I'm a financial services organization, or a hospital, or a retailer in e-commerce, what is my business value going to be with using technology like this and delighting those developers? >> Matt: I think there's three things that really matter to the developers and to the financial leader in the organization, A, developers are super expensive and they have a lot of opportunities. So if a developer's not happy and finding joy and productivity in what they're doing, they're going to look elsewhere. So that's the first thing, the second thing is that when you're running a business, productivity is one measure, but also, are you shipping something confidently the first time, or do you have to go back and fix things? And by having the environment spun up with all of your name space established, your tendencies are managed, all of your data being brought in, you're testing against a very high fidelity version of your application when you check in code. And so by doing that, you're testing things more quickly, and they talk a lot about shifting left, but it's making that environment as fully functional and featured as possible. So you're looking at something as it will appear in production, not a subset of that. And then the last thing, and this is one where the value of Figma is very important, a lot of times, you'll spin up an environment on AWS and you may forget about it and might just keep running and chewing up resources. Knowing that when you're done it goes away, means that you're not spending money on things just sitting there on your AWS instance, which is very important for competitors. >> Lisa: So I hear retention of developers, you're learning that developers, obviously business impact their speed to value as well. >> Tommy: Yep. >> And trust, you're enabling your customers to instill trust in their developers with them. >> Tommy: That's right, yeah. >> Matt: And trust and delight, they can be across purposes, a developer wants to move fast and they're rewarded for being creative, whereas your IT team, they're rewarded for predictability and consistency, and those can be opposing forces. And by giving developers a way to move quickly and the artifact that they're creating is something that the IT team understands and works within their processes, allows you to let both teams do what they care about and not create a friction there. >> John: What about the environment as a service? I love that 'cause it makes it sound like it's scaling in the cloud, which you have mentioned you do that. Is it for companies that are working together? So I don't want to spin up an environment, say we're a businesses, "Hey, let's do a deal. "I'm going to integrate my solution into yours. "I got to get my developers to maybe test it out, "so I'm spinning up an environment with you guys," then what do I do? >> Tommy: Well as far as if you're a customer of ours, is that the way you're asking? Well, a lot of times, it's being used a lot in internal development. So that's the first use case, is I'm a developer, you have cross collaboration amongst teams, so a developer tools. And what you're talking about is more, I'm using an environment for a demo environment, or I'm creating a new feature that I want to share with a customer, That's also possible. So if I'm a developer and I'm building a feature and it's for a specific customer of mine, I can build that feature and preview it with the customer before it actually goes into production. So it's a sandbox product development area for the developers to be actually integrating with their customers very, very quickly before it actually makes its way to all of the end users. >> A demo? >> It could be a demo. >> It's like a collaboration feature? >> Sandbox environment. We have customers- >> Kind of like we're seeing more of this collaboration with developers. This becomes a well- >> Tommy: And it's not even just collaboration with internal teams, it's now you're collaborating with your customer while you're building your software, which is actually really difficult to do if you only have one environment, you can't have- >> John: Yeah, I think that's a killer right there, that's the killer app right there. >> Matt: Instead of sending a Figma to a customer, this is what's going to look like, it's two dimensions, this is the app. That is a massive, powerful difference. >> Absolutely. In terms of customer delay, customer retention, employee engagement, those are all inextricably linked. Can you share, Matt, the voice of the customer? I just saw the release with TripActions, I've been a TripActions user myself, but give us this sense, I know that you're brand new, but the voice of the customer, what is it? What is it reflecting? How is it reinforcing your value prop? >> Matt: I think the voice that comes through consistently is instead of spending time building the system that is hard to do and complicated and takes our engineering cycles, our engineers can focus on whether it's platform engineering, new features and whatnot, it's more valuable to the company to build features, it's more exciting for a developer to build features and to not have to keep going back and doing things manually, which you're doing a... This is what we do all day long. To do it as a sideline is hard. And the customers are excited 'cause they get to move onto higher value activities with their time. >> Lisa: And everybody wants that, everybody wants to be able to contribute high value projects, programs for their organization rather than doing the boring stuff. >> Tommy: Yeah. I think with TripActions specifically, a lot of platform engineering teams are trying to build something like this in house, and it's a lot of toil, it's work that isn't value added, it enables developers to get their job done, but it's not really helping the business deliver a feature to the user. And so this whole movement of platform engineering, this is what those groups are doing and we're a big enabler to those teams, to get that to market faster. >> John: You're targeting businesses, enterprises, developers. >> That's right. >> Mainly, right, developers? >> Yeah. >> What's the business model? How are you guys making money? What's the strategy there? >> Yeah, I mean we really like to align with the value that we deliver. So if a user creates an environment, we get paid when that happens. So it's an on-demand, if you use the environment, you pay us, if you don't, you don't. >> John: Typical cloud-based pricing. >> Yeah. >> Pay as you go. >> Tommy: Usage based pricing. >> Is there a trigger on certain of how it gets cost? Is it more of the environment size, or what's the- >> Yeah, I mean there's a different tier for if you have really large, complicated environments. And that's the trend, that distributed applications aren't simple anymore, so if you have a small little rails app, it's going to be cheaper than if you have a massive distributed system. But manageable, the idea here is that this should help you save money over investing deeply into a deep platform engineering team. So it's got to be cost effective and we're really cognizant of that. >> So you got a simple approach, which is great. Talk about the alternative. What does it look like for a customer that you want to target? What's their environment? What does it look like, so that if I'm a customer, I would know I need to call you guys at Relief Hub. Is it sprawl? Is it multiple tool chains? Chaos, mayhem? What does it look like? >> Tommy: Yeah, let's have Matty, Matt could do this one. >> When you look at the systems right now, I think complexity is the word that keeps coming up, which is that, whether you're talking about multi-cloud or actually doing it, that's a huge thing. Microservices proliferation are happening over and over again, different languages. What I'm excited about with Release, is not dissimilar from what we saw in the Docker movement, which is that there's all this great stuff out there, but there's that common interface there, so you can actually run it locally on your machine, do your dev and test, and know that it's going to operate with, am I using Couchbase or Postgres or whatever, I don't care, it's going to work this way. Similar with Release, people are having to build a lot of these bespoke solutions that are purpose built for one thing and they're not designed to the platform. And the platform for platform engineering gives us a way to take that complexity out the equation, so you're not limited to what you can do, or, "Oh crud, I want to move to something else, "I have to start over again," that process is going to be consistent no matter what you're doing. So you're not worried about evolution and success and growth, you know that you've got a foundation that's going to grow. Doing it on your own, you have to build things in that very bespoke, specific manner, and that just creates a lot more toil than you'd want to get if you were using a platform and focusing on the value after your company. >> Matt Klein was just on here. He was with Lyft, he was the one who open source Envoy, which became very popular. We asked him what he thought about the future and he's like, it's too hard to work with all this stuff. He was mentioning Yamo code, but he got triggered a little bit, but his point was there's a lot to pull together. And it sounds like you guys have this solution, back in the old days, spin up some EC2, compute, similar way, right? "Hey, I don't want to person a server, I person a server, rack and stack, top of rack switch, I'm going to go to the cloud, use EC2. >> Tommy: Yeah, I mean just think about if- >> You're an environment version of that. Why wait for it to be built? >> Yeah. >> Is that what I'm getting- >> Yeah, I mean, and an application today isn't just the EC2 instances, it's all of your data, it's your configuration. Building it one time is actually complicated to get your app to work it, doing it lots of times to make your developers productive with copies of that, is incredibly difficult. >> John: So you saw the problem of developers waiting around for someone to provision an environment. >> Tommy: That's right. >> So they can do whatever they want to do. >> Tommy: That's right. >> Test, ship, do, play around, test the customer. Whatever that project scope is, they're waiting around versus spinning up an environment. >> Yeah, absolutely, 100%. >> And that's the service. >> That's what it is. >> Take time, reduce the steps it takes, make it more productive. >> And build an amazing developer experience that you know your developers are going to love. If you're at Facebook or Google, they have thousands of DevOps people building platforms. If you're a company that doesn't have that resource, you have a choice of go build this yourself, which is a distraction, or invest in something like us and focus on your core. >> John: You got Matt on board, got a new CMO, you got enterprise class features and I saw the press release. Talk about the origination story, why you developed it, and then take a minute to give a plug for the company, on what you're looking for, I'm sure you're hiring, what's going on? >> Tommy: Yeah, I've been an entrepreneur for 20 years. My last experience at TrueCar, I saw this problem firsthand. And as the CTO of that company, I looked into the market for a solution to this, 'cause we had this problem of 300 developers, environments needed for everything. So we ended up building it ourselves and it costs multiple millions of dollars to build it. And so as the buyer at the time, I was like, man, I would've spent to solve this, and I just couldn't. So as a software engineer at heart, having seen this problem my entire career, it was just a natural thing to go work on. So yeah, I mean, for anybody that wants to create unlimited environments for their team, just go to releasehub.com. It's pretty self-explanatory, how to give it a shot and try it out. >> Environments is a service, from someone who had the problem, fixed it, built it- >> That's right. >> For other people. What are you guys hiring, looking for some people? >> Yeah, we have engineering hires, sales hires, Matt's got a few marketing hires coming, >> Matt: I was going to say, got some marketing coming. >> Selfishly he has that. (John laughs) The team's growing and it's a really great place to work. We're 100% remote. Part of this helps that, we build this product and we use it every day, so you get to work on what you build and dog food, it's pretty cool. >> Great solution. >> We love remote development environments. Being here and watching that process where building a product and a feature for the team to work better, wow, we should share this with customers. And the agility to deliver that was really impressive, and definitely reinforced how excited I am to be here 'cause we're building stuff for ourselves, which is- >> Matt: Well we're psyched that you're here in theCUBE. Matt, what's your vision for marketing? You got a hiring plan, you got a vision, I'm sure you got some things to do. What's your goals? What's your objective? >> My goal is... The statement people say, you can't market to developers. And I don't want to market to developers, I want to make sure developers are made aware of how they can learn new things in a really efficient way, so their capabilities grow. If we get people more and more successful with what they're doing, give them joy, reduce their toil and create that flow, we help them do things that make you excited, more creative. And that's to me, the reward of this. You teach people how to do that. And wow, these customers, they're building the greatest innovations in the world, I get to be part of that, which is awesome. >> Lisa: Yeah. Delighted developers has so many positive business outcomes that I'm sure organizations in any industry are going to be able to achieve. So exciting stuff, guys. Thank you so much for joining John and me on the program. Good luck with the growth and congrats on what you've enabled so far in just a few short years. >> Thank you, appreciate it. >> Thanks you so much. >> Thank you for having us on. >> Appreciate it. >> Pleasure. >> Thank you. >> For our guests and for John Furrier, I'm Lisa Martin. You're watching theCUBE, live in Detroit, at KubeCon + CloudNativeCon '22. We're back after a short break. (soft music)

>> Good afternoon everyone, and welcome back to theCUBE where we're coming to you live from Detroit, Michigan at KubeCon and Cloud Native Con. We're going to keep theCUBE puns coming this afternoon because we have the pleasure of being joined by not one but two guests from Kubiya. John Furrier, my wonderful co-host. You're familiar with these guys. You just chatted with them last week. >> We broke the story of their launch and featured them on theCUBE in our studio conversation. This is a great segment. Real innovative company with lofty goals, and they're really good ones. Looking forward to it. >> If that's not a tease to keep watching I don't know what is. (John laughs) Without further ado, on that note, allow me to introduce Amit and Shaked who are here to tell us all about Kubiya. And I'm going to blow the pitch for you a little bit just because this gets me excited. (all laugh) They're essentially the Siri of DevOps, but that means you can, you can create using voice or chat or any medium. Am I right? Is this? Yeah? >> You're hired. >> Excellent. (all laugh) >> Okay. >> We'll take it. >> Who knows what I'll tell the chat to do or what I'll, what I will control with my voice, but I love where you're. >> Absolutely. I'll just give the high level. Conversational AI for the world of DevOps. Kind of redefining how self-service DevOps is supposed to be essentially accessed, right? As opposed to just having siloed information. You know, having different platforms that require an operator or somebody who's using it to know exactly how they're accessing what they're doing and so forth. Essentially, the ability to express your intent in natural language, English, or any language I use. >> It's quite literally the language barrier sometimes. >> Precisely. >> Both from the spoken as well as code language. And it sounds like you're eliminating that as an obstacle. >> We're essentially saying, turn simple, complex cast into simple conversations. That's, that's really what we're saying here. >> So let's get into the launch. You just launched a fresh startup. >> Yeah, yeah, yeah. >> Yeah. >> So you guys are going to take on the world. Lofty goals if that. I had the briefing. Where's the origination story come from? What, how did you guys get here? Was it a problem that you saw, you were experiencing, an itch you were scratching? What was the motivation and what's the origination story? >> Shaked: So. >> Amit: Okay, go first please. >> Essentially everything started with my experience as being an operator. I used to be a DevOps engineer for a few years for a large (indistinct) company. On later stages I even managed an SRE team. So all of these access requires Q and A staff is something that I experience nonstop on Slack or Teams, all of these communication channels. And usually I find out that everything happens from the chat. So essentially back then I created a chat bot. I connect this chat bot to the different organizational tools, and instead of the developers approaching to me or the team using the on call channel or directly they will just approach the bot. But essentially the bot was very naive, and they still needed to know what they, they want to do inside the bot. But it's still managed to solve 70% of the complexity and the toil on us as a team so we could focus on innovation. So Kubiya's a more advanced version of it. Basically with Kubiya you can define what we call workflows, and we convert all of these complexity of access request into simple conversations that the end users, which could be developers, but not only, are having with a DevOps team. So that's essentially how it works, and we're very excited about it. >> So you were up all night answering the same question over and over again. (all laugh) And you said, Hey, screw it. I'm going to just create a bot, bot myself up. (Shaked laughs) But it gets at something important. I mean, I'm not just joking. It probably happened, right? That was probably the case. You were up all night telling. >> Yeah, I mean it was usually stuff that we didn't need to maintain. It was training requests and questions that just keep on repeating themselves. And actually we were in Israel, but we sell three different time zones of developers. So all of these developers, as soon as the day finishes in Israel, the day in the US started. So they will approach us from the US. So we didn't really sleep. (all laugh) As with these requests non-stop. >> It's that 24 hour. >> Yeah, yeah. 24 hours for a single team. >> So the world clock global (indistinct) catches you a little sometimes. Yeah. >> Yeah, exactly. >> So you basically take all the things that you know that are common and then make a chat bot answering as if you're you. But this brings up the whole question of chat bot utilization. There's been a lot of debate in the AI circles that chat bots really haven't made it. They're not, they haven't been good enough. So 'cause NLP and other trivial, >> Amit: Sure. or things that haven't really clicked. What's different now? How do you guys see your approach cracking the code to go that kind of reasoning level? Bots can reason? Now we're in business. >> Yeah. Most of the chat bots are general purpose, right? We're coming with the domain expertise. We know the pain from the inside. We know how the operators want to define such conversations that users might have with the virtual assistant. So we combined all of the technical tools that are needed in order to get it going. So we have a a DSL, domain specific language, where the operators can define these easy conversations and combine all of the different organizational tools which can be done using DSDK. Besides this fact, we have a no code, for less technical people to create such workflows even with no code interface. And we have a CLI, which you could use to leverage the power of the virtual assist even right from your terminal. So that's how I see the domain expertise coming in that we have different communication channels for everyone that needs to be inside the loop. >> That's awesome. >> And I, and I can add to that. So that's one element, which is the domain expertise. The other one is really our huge differentiator, the ability to let the end users influence the system itself. So essentially. >> John: Like how? Give me an example. >> Sure. We call it teach me feature, but essentially if you have any type of a request and the system hasn't created an automation or hasn't, doesn't recognize it, you can go ahead and bind that into your intent and next time, and you can define the scope for yourself only, for the team, or even for the entire organization that actually has to have permission to access the request and control and so on. >> Savannah: That's something. Yeah, I love that as a knowledge base. I mean a custom tool kit. >> Absolutely. >> And I like that you just said for the individual. So let's say I have some crazy workflows that I don't need anybody else to know about. >> 100 percent. >> I can customize my experience. >> Mm hmm. >> Do you see your, this is really interesting, and I'm, it's surprising to me we haven't seen a lot of players in this space before because what you're doing makes a lot of sense to me, especially as someone who is less technical. >> Yeah. >> Do you view yourselves as a gateway tool for more folks to be involved in more complex technology? >> So, so I'll take that. It's not that we haven't seen advanced virtual assistants. They've existed in different worlds. >> Savannah: Right. >> Up until now they've existed more in CRM tools. >> Savannah: Right. >> Call centers, right? >> Shaked: Yeah. >> You go on to Ralph Lauren, Calvin Klein, you go and chat with. Now imagine you can bring that into a world of dev tools that has high domain expertise, high technical amplitude, and now you can go and combine the domain expertise with the accessibility of conversational AI. That's, that's a unique feature here. >> What's the biggest thing that's surprised you with the launch so far? The reaction to the name, Kubiya, which is Cube in Hebrew. >> Amit: Yes. >> Apparently. >> Savannah: Which I love. >> Which by the way, you know, we have a TM and R on our Cube. (all laugh) So we can talk, you know, license rights. >> Let's drop the trademark rules today, John, here. We're here to share information. Confuse the audience. Sorry about that, by the way. (all laugh) >> We're an open source, inclusive culture. We'll let it slide this time. >> The KubeCon, theCUBE, and Kubiya. (John laughs) In the Hebrew we have this saying, third time we all have ice cream. So. (all laugh) >> I think there's some ice cream over there actually. >> There is. >> Yeah, yeah. There you go. >> All kidding aside, all fun. What's, what's been the reaction? Got some press coverage. We had the launch. You guys launched with theCUBE in here, big reception. What's been the common feedback? >> And really, I think I expected this, but I didn't expect this much. Really, the fact that people really believe in our thesis, really expect great things from us, right? We've starting to working with. >> Savannah: Now the pressure's on. >> Rolling out dozens of POCs, but even that requires obviously a lot of attention to the detail, which we're rolling out. This is effectively what we're seeing. People love the fact that you have a unique and fresh way to approaching the self-service which really has been stalled for a while. And we've recognized that. I think our thesis is where we. >> Okay, so as a startup you have lofty goals, you have investors now. >> Amit: Yeah. >> Congratulations. >> Amit: Thank you. >> They're going to want to keep the traction going, but as a north star, what's your, what are you going to, what are you going to take? What territory are you going to take? Is it new territory? Are you eating someone's lunch? Who are you going to be competing with? What's the target? What's the, what's the? >> Sure, sure. >> I'm sure you guys have it. Who are you takin' over? >> I think the gateway, the entry point to every organization is a bottleneck. You solve the hard problem first. That's where you can go into other directions, and you can imagine where other operational workflows and pains that we can help solve once we have essentially the DevOps. >> John: So you see this as greenfield, new opportunity? >> I believe so. >> Is there any incumbent you see out there? An old stodgy? >> Today we're on internal developer platform service catalog type of, you know, use cases. >> John: Yeah. >> But that's kind of where we can grow from there and have the ecosystem essentially embrace us. >> John: How about the technology platform? >> Amit: Yeah. >> What's the vision for the innovation? >> Essentially want to be able to integrate with all of the different cloud providers, cloud solutions, SaaS platforms, and take the atlas approach that they were using right to the chats from everywhere to anywhere. So essentially we want in the end that users will be able to do anything that they need inside all of these complicated platforms, which some of them are totally complicated, with plain English. >> So what's the biggest challenge for you then on that front leading the technology side of the team? >> So I would say that the conversational AI part is truly complicated because it requires to extract many types of intentions from different types of users and also integrate with so many tools and solutions. >> Savannah: Yeah. So it requires a lot of thinking, a lot of architecture, but we are doing it just fine. >> Awesome. What do you guys think about KubeCon this week? What's, what's the top story that you see emerging out of this? Just generally as an industry observer, what's the most important? >> Savannah: Maybe it's them. Announcement halo. >> What's the cover story that you see? (all laugh) I mean you guys are in the innovation intent-based infrastructure. I get that. >> So obviously everyone's looking to diversify their engineering, diversify their platforms to make sure they're as decoupled from the main CSPs as possible. So being able to build their own, and we're really helping enable a lot of that in there. We're really helping improve upon that open source together with managed platforms can really play a very nice game together. So. >> Awesome. So are you guys hiring, recruiting? Tell us about the team DNA. Now you're in Tel Aviv. You're in the bay. >> Shaked: Check our openings on LinkedIn. (all laugh) >> We have a dozen job postings on our website. Obviously engineering and sales then go to market. >> So when theCUBE comes to Tel Aviv, and we have a location there. >> Yeah. >> Will you be, share some space? >> Savannah: Is this our Tel Aviv office happening right now? I love this. >> Amit: We will be hosting you. >> John: theCube with a C and Kube with a K over there. >> Yeah. >> All one happy family. >> Would love that. >> Get some ice cream. >> Would love that. >> All right, so last question for you all. You just had a very big exciting announcement. It's a bit of a coming out party for you. What do you hope to be able to say in a year that you can't currently say right now? When you join us on theCUBE next time? >> No, no, it's absolutely. I think our thesis that you can turn conversations into operations. It's, it sounds obvious to you when you think about it, but it's not trivial when you look into the workflows, into the operations. The fact that we can actually go a year from today and say we got hundreds of customers, happy customers who've proven the thesis or sharing knowledge between themselves, that would be euphoric for us. >> All right. >> You really are about helping people. >> Absolutely. >> It doesn't seem like it's a lip service from both of you. >> No. (all laugh) >> Is there going to be levels of bot, like level one bot level two, level three, and then finally the SRE gets on the phone? Is that like some point? >> Is there going to be bot singularity? Is that, is that what we're exploring right now? (overlapping chatter) >> Some kind of escalation bot. >> Enlightenment with bots. >> We actually planning a feature we want to call a handoff where a human in the loop is required, which often is needed. Machine cannot do it alone. We'll just. >> Yeah, I think it makes total sense for geos, ops at the same. >> Shaked: Yeah. >> But not exactly the same. Really good, good solution. I love the direction. Congratulations on the launch. >> Shaked: Thank you so much. >> Amit: Thank you very much. >> Yeah, that's very exciting. You can obviously look, check out that news on Silicon Angle since we had the pleasure of breaking it. >> Absolutely. >> If people would like to say hi, stalk you on the internet, where's the best place for them to do that? >> Be on our Twitter and LinkedIn handles of course. So we have kubiya.ai. We also have a free trial until the end of the year, and we also have free forever tier, that people can sign up, play, and come say hi. I mean, we'd love to chat. >> I love it. Well, Amit, Shaked, thank you so much for being with us. >> Shaked: Thank you so much. >> John, thanks for sitting to my left for the entire day. I sincerely appreciate it. >> Just glad I can help out. >> And thank you all for tuning in to this wonderful edition of theCUBE Live from Detroit at KubeCon. Who knows what my voice will be controlling next, but either way, I hope you are there to find out. >> Amit: Love it.

(light upbeat music) (mouse clicks) >> Hey, everyone. Welcome back to the program. Lisa Martin here. I've got two guests joining me. Please welcome back to The Cube, Paula Hansen, the chief revenue officer and president at Alteryx. And Jacqui Van der Leij - Greyling joins us as well, the global head of tax technology at eBay. They're going to share with you how Alteryx is helping eBay innovate with analytics. Ladies, welcome. It's great to have you both on the program. >> Thank you, Lisa. >> Thank you, Lisa. >> It's great to be here. >> Yeah, Paula. We're going to start with you. In this program, we've heard from Jason Klein, we've heard from Alan Jacobson, they talked about the need to democratize analytics across any organization to really drive innovation. With analytics as they talked about at the forefront of software investments, how's Alteryx helping its customers to develop roadmaps for success with analytics? >> Well, thank you, Lisa. It absolutely is about our customer's success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts, of course, with our innovative technology and platform but ultimately, we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics through things like enablement programs, skills assessments, hackathons, setting up centers of excellence to help their organizations scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practices so they can make better business decisions and compete in their respective industries. >> Excellent. Sounds like a very strategic program. We're going to unpack that. Jacqui let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How, Jacqui, did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >> So I think the main thing for us is just when we started out was, is that, you know, our, especially in finance they became spreadsheet professionals, instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and be more effective. So ultimately, we really started very, very actively embedding analytics in our people and our data and our processes. >> Starting with people is really critical. Jacqui, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >> So I think, you know, eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them to move forward. The other thing is, is that you know, people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals, right? And there was no, we're not independent. You couldn't move forward. You would've been dependent on somebody else's roadmap to get to data and to get the information you wanted. So really finding something that everybody could access analytics or access data. And finally, we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy and that is not so daunting on somebody who's brand new to the field? And I would call those out as your major roadblocks because you always have, not always, but most of the times you have support from the top in our case, we have, but in the end of the day, it's our people that need to actually really embrace it and making that accessible for them, I would say is definitely not per se, a roadblock but basically some, a block you want to be able to move. >> It's really all about putting people first. Question for both of you, and Paula will start with you, and then Jacqui will go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone, should be for everyone. Let's talk now about how both of your organizations are empowering people those in the organization that may not have technical expertise to be able to leverage data so that they can actually be data driven? Paula? >> Yes. Well, we leverage our platform across all of our business functions here at Alteryx. And just like Jacqui explained at eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jacqui mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO, Kevin Rubin has been a key sponsor for using our own technology. We use Alteryx for forecasting, all of our key performance metrics for business planning across our audit function to help with compliance and regulatory requirements, tax and even to close our books at the end of each quarter so it's really remained across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases. And so one of the other things that we've seen many companies do is to gamify that process to build a game that brings users into the experience for training and to work with each other, to problem solve, and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jacqui mentioned, it's really about ensuring that people feel comfortable, that they feel supported that they have access to the training that they need. And ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >> That confidence is key. Jacqui, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >> Yeah, I think it means to what Paula has said in terms of you know, getting people excited about it but it's also understanding that this is a journey. And everybody is the different place in their journey. You have folks that's already really advanced who has done this every day, and then you have really some folks that this is brand new and, or maybe somewhere in between. And it's about how you could get everybody in their different phases to get to the initial destination. I say initially, because I believe the journey is never really complete. What we have done is that we decided to invest in a... We build a proof of concepts and we got our CFO to sponsor a hackathon. We opened it up to everybody in finance in the middle of the pandemic. So everybody was on Zoom. And we told people, "Listen, we're going to teach you this tool, super easy. And let's just see what you can do." We ended up having 70 entries. We had only three weeks. So, and these are people that has... They do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon. From the 70 entries with people that have never, ever done anything like this before and there you had the result. And then it just went from there. It was people had a proof of concept, they knew that it worked, and they overcame that initial barrier of change. And that's where we are seeing things really, really picking up now. >> That's fantastic. And the business outcome that you mentioned there, the business impact is massive helping folks get that confidence to be able to overcome sometimes the cultural barriers is key here. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you're empowering the next generation of data workers? Paula will start with you. >> Absolutely. And Jacqui says it so well, which is that it really is a journey that organizations are on. And we, as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Alteryx to help address this skillset gap on a global level is through a program that we call SparkED, which is essentially a no-cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay, and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with SparkED, we started last May, but we currently have over 850 educational institutions globally engaged across 47 countries. And we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close gap and empower more people with the necessary analytics skills to solve all the problems that data can help solve. >> So SparkED just made a really big impact in such a short time period. It's going to be fun to watch the progress of that. Jacqui let's go over to you now. Talk about some of the things that eBay is doing to empower the next generation of data workers. >> So we basically wanted to make sure that we kicked that momentum from the hackathon. Like we don't lose that excitement, right? So we just launched a program called eBay Masterminds. And what it basically is, it's an inclusive innovation initiative, where we firmly believe that innovation is for upscaling for all analytics role. So it doesn't matter your background, doesn't matter which function you are in, come and participate in this, where we really focus on innovation, introducing new technologies and upscaling our people. We are... Apart from that, we also said... Well, we should just keep it to inside eBay. We have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use alter Alteryx. And we're working with actually, we're working with SparkED and they're helping us develop that program. And we really hope that, let us say, by the end of the year have a pilot and then also next, was hoping to roll it out in multiple locations, in multiple countries, and really, really focus on that whole concept of analytics role. >> Analytics role, sounds like Alteryx and eBay have a great synergistic relationship there, that is jointly aimed at, especially, kind of, going down the stuff and getting people when they're younger interested and understanding how they can be empowered with data across any industry. Paula let's go back to you. You were recently on The Cube's Supercloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating what is by default a multi-cloud world? How does the Alteryx Analytics Cloud platform enable CIOs to democratize analytics across their organization? >> Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last, I check there was 2 million data scientists in the world. So that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. (Paula clears throat) So what we're seeing now with CIOs, with business leaders is that they're integrating data analysis and the skillset of data analysis into virtually every job function. And that is what we think of when we think of analytics for all. And so our mission with Alteryx Analytics Cloud, is to empower all of those people in every job function regardless of their skillset. As Jacqui pointed out from people that would, you know are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Alteryx Analytics Cloud and it operates in a multi-cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze and report out so that we can break down data silos across the enterprise and help drive real business outcomes as a result of unlocking the potential of data. >> As well as really lessening that skills gap as you were saying, there's only 2 million data scientists. You don't need to be a data scientist. That's the beauty of what Alteryx is enabling and eBay is a great example of that. Jacqui let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where Alteryx fits in as that analytics maturity journey continues. And what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >> When we started about getting excited about things when it comes to analytics, I can go on all day but I'll keep it short and sweet for you. I do think we are on the topic full of data scientists. And I really feel that that is your next step, for us anyways, it's just that, how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's something completely different. And it's something that is in everybody in a certain extent. So again, partnering with Alteryx would just release the AI/ML solution, allowing, you know, folks to not have a data scientist program but actually build models and be able to solve problems that way. So we have engaged with Alteryx and we purchased the licenses quite a few. And right now, through our mastermind program we're actually running a four-months program for all skill levels. Teaching them AI/ML and machine learning and how they can build their own models. We are really excited about that. We have over 50 participants without the background from all over the organization. We have members from our customer services, we have even some of our engineers, are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I want to give you a quick example of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all was able to develop a solution where, you know, there is a checkout feedback, checkout functionality on the eBay site, where sellers or buyers can verbatim add information. And she build a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we, as a human even step in. And now instead of us or somebody going to the bay to try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value. And it's a beautiful tool, and I'm very impressed when you saw the demo and they've been developing that further. >> That sounds fantastic. And I think just the one word that keeps coming to mind and we've said this a number of times in the program today is, empowerment. What you're actually really doing to truly empower people across the organization with varying degrees of skill level going down to the high school level, really exciting. We'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I want to thank you so much for joining me on the program today and talking about how Alteryx and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you >> Thank you, Lisa. >> Thank you so much. (light upbeat music) >> As you heard over the course of our program, organizations where more people are using analytics who have deeper capabilities in each of the four E's that's, everyone, everything, everywhere and easy analytics. Those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling an empowering line of business users to use analytics. Not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We want to thank you so much for watching the program today. Remember you can find all of the content on thecube.net. You can find all of the news from today on siliconangle.com, and of course alteryx.com. We also want to thank Alteryx for making this program possible and for sponsoring The Cube. For all of my guests, I'm Lisa Martin. We want to thank you for watching and bye for now. (light upbeat music)

>>Hey everyone. Welcome back to the program. Lisa Martin here, I've got two guests joining me, please. Welcome back to the cube. Paula Hansen, the chief revenue officer and president at Al alters and Jackie Vander lake grayling joins us as well. The global head of tax technology at eBay. They're gonna share with you how an alter Ricks is helping eBay innovate with analytics. Ladies. Welcome. It's great to have you both on the program. >>Thank you, Lisa. It's great to be here. >>Yeah, Paula, we're gonna start with you in this program. We've heard from Jason Klein, we've heard from Alan Jacobson, they talked about the need to democratize analytics across any organization to really drive innovation with analytics. As they talked about at the forefront of software investments, how's alters helping its customers to develop roadmaps for success with analytics. >>Well, thank you, Lisa. It absolutely is about our customer's success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course, with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics through things like enablement programs, skills, assessments, hackathons, setting up centers of excellence to help their organizations scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics, maturity curve with proven technologies and best practices so they can make better business decisions and compete in their respective industries. >>Excellent. Sounds like a very strategic program. We're gonna unpack that Jackie, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jackie did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >>So I think the main thing for us is just when we started out was is that, you know, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and be more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes, >>Starting with people is really critical. Jackie, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >>So I think, you know, eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and, and just finding those data sources and finding ways to connect to them to move forward. The other thing is, is that, you know, people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals, right? And we, there was no, we're not independent. You couldn't move forward. You would've opinion on somebody else's roadmap to get to data and to get the information you wanted. So really finding something that everybody could access analytics or access data. >>And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy? And that is not so daunting on somebody who's brand new to the field. And I would, I would call those out as your, as your major roadblocks, because you always have not always, but most of the times you have support from the top in our case, we have, but in the end of the day, it's, it's our people that need to actually really embrace it and, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically some, a block you wanna be able to move. >>It's really all about putting people. First question for both of you and Paula will start with you. And then Jackie will go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data so that they can actually be data driven Paula. >>Yes. Well, we leverage our platform across all of our business functions here at Altrix and just like Jackie explained it, eBay finances is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jackie mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO, Kevin Rubin has been a, a key sponsor for using our own technology. We use Altrix for forecasting, all of our key performance metrics for business planning across our audit function, to help with compliance and regulatory requirements tax, and even to close our books at the end of each quarter. So it's really remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? >>And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jackie mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need. And ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >>That confidence is key. Jackie, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >>Yeah, I think it means to what Paula has said in terms of, you know, you know, getting people excited about it, but it's also understanding that this is a journey and everybody's the different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new and, or maybe somewhere in between. And it's about how you put, get everybody in their different phases to get to the, the initial destination. I say initially, because I believe the journey is never really complete. What we have done is, is that we decided to invest in an Ebola group of concept. And we got our CFO to sponsor a hackathon. We opened it up to everybody in finance, in the middle of the pandemic. So everybody was on zoom and we had, and we told people, listen, we're gonna teach you this tool super easy. >>And let's just see what you can do. We ended up having 70 entries. We had only three weeks. So, and these are people that has N that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 inches with people that have never, ever done anything like this before and there you had the result. And then it just went from there. It was, people had a proof of concept. They, they knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up. Now >>That's fantastic. And the, the business outcome that you mentioned there, the business impact is massive helping folks get that confidence to be able to overcome. Sometimes the, the cultural barriers is key. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you are empowering the next generation of data workers, Paula will start with you? >>Absolutely. And, and Jackie says it so well, which is that it really is a journey that organizations are on. And, and we, as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Altrix to help address this skillset gap on a global level is through a program that we call sparked, which is essentially a, no-cost a no cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to, to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with sparked. We started last may, but we currently have over 850 educational institutions globally engaged across 47 countries. And we're gonna continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close gap and empower more people within necessary analytics skills to solve all the problems that data can help solve. >>So spark has made a really big impact in such a short time period. And it's gonna be fun to watch the progress of that. Jackie, let's go over to you now talk about some of the things that eBay is doing to empower the next generation of data workers. >>So we basically wanted to make sure that we keep that momentum from the hackathon that we don't lose that excitement, right? So we just launched a program called Ebo masterminds. And what it basically is, it's an inclusive innovation initiative where we firmly believe that innovation is all up scaling for all analytics for. So it doesn't matter. Your background doesn't matter which function you are in, come and participate in, in this where we really focus on innovation, introducing new technologies and upskilling our people. We are apart from that, we also say, well, we should just keep it to inside eBay. We, we have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use alter alter. And we're working with actually, we're working with spark and they're helping us develop that program. And we really hope that as a say, by the end of the year, have a pilot and then also make you, so we roll it out in multiple locations in multiple countries and really, really focus on, on that whole concept of analytics, role >>Analytics for all sounds like ultra and eBay have a great synergistic relationship there that is jointly aimed at, especially kind of going down the staff and getting people when they're younger, interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you. You were recently on the Cube's super cloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating. What is by default a multi-cloud world? How does the alters analytics cloud platform enable CIOs to democratize analytics across their organization? >>Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I check there was 2 million data scientists in the world. So that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs with business leaders is that they're integrating data analysis and the skill of data analysis into virtually every job function. And that is what we think of when we think of analytics for all. And so our mission with Altrics analytics cloud is to empower all of those people in every job function, regardless of their skillset. As Jackie pointed out from people that would, you know, are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Altrics analytics cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and drive real business outcomes. As a result of unlocking the potential of data, >>As well as really re lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist. That's the, the beauty of what Altrics is enabling. And, and eBay is a great example of that. Jackie, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where alters fits in on as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >>When we start about getting excited about things, when it comes to analytics, I can go on all day, but I I'll keep it short and sweet for you. I do think we are on the topic full of, of, of data scientists. And I really feel that that is your next step for us anyways, is that, how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's, it's something completely different. And it's something that, that is in everybody to a certain extent. So again, partner with three X would just released the AI ML solution, allowing, you know, folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with alters and we, we purchased a license, this quite a few. And right now through our mastermind program, we're actually running a four months program for all skill levels, teaching, teaching them AI ML and machine learning and how they can build their own models. >>We are really excited about that. We have over 50 participants without the background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I wanna give you a quick example of, of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where, you know, there is a checkout feedback checkout functionality on the eBay site where sellers or buyers can verbatim add information. And she build a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we, as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value. >>And it's a beautiful tool and very impressed. You saw the demo and they developing that further. >>That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with, with varying degrees of skill level, going down to the high school level, really exciting, we'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I wanna thank you so much for joining me on the program today and talking about how alters and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >>Thank you. >>As you heard over the course of our program organizations, where more people are using analytics who have the deeper capabilities in each of the four E's, that's, everyone, everything everywhere and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling an empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We wanna thank you so much for watching the program today. Remember you can find all of the content on the cue.net. You can find all of the news from today on Silicon angle.com and of course, alter.com. We also wanna thank alt alters for making this program possible and for sponsored in the queue for all of my guests. I'm Lisa Martin. We wanna thank you for watching and bye for now.

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all as we know, data is changing the world and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to "theCUBE"'s presentation of democratizing analytics across the enterprise, made possible by Alteryx. An Alteryx commissioned IDC info brief entitled, "Four Ways to Unlock Transformative Business Outcomes from Analytics Investments" found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special "CUBE" presentation, Jason Klein, product marketing director of Alteryx, will join me to share key findings from the new Alteryx commissioned IDC brief and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, chief data and analytics officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then in our final segment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who is the global head of tax technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, product marketing director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research, which spoke with about 1500 leaders, what nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees, and this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics, and we're able to focus on the behaviors driving higher ROI. >> So the info brief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the info brief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack, what's driving this demand, this need for analytics across organizations? >> Sure, well first there's more data than ever before, the data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins and to improve customer experiences. And analytics along with automation and AI is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> One of the things that the study also showed was that not all analytics spending is resulting in the same ROI. What are some of the discrepancies that the info brief uncovered with respect to the changes in ROI that organizations are achieving? >> Our research with IDC revealed significant roadblocks across people, processes, and technologies. They're preventing companies from reaping greater benefits from their investments. So for example, on the people side, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% from our survey, are still not using the full breadth of data types available. Yet data's never been this prolific, it's going to continue to grow, and orgs should be using it to their advantage. And lastly organizations, they need to provide the right analytics tools to help everyone unlock the power of data. >> So they- >> They instead rely on outdated spreadsheet technology. In our survey, nine out of 10 respondents said less than half of their knowledge workers are active users of analytics software beyond spreadsheets. But true analytic transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and be driving value. >> Should we retake that, since I started talking over Jason accidentally? >> Yep, absolutely we can do so. We'll just go, yep, we'll go back to Lisa's question. Let's just, let's do the, retake the question and the answer, that'll be able to. >> It'll be not all analytics spending results in the same ROI, what are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we get that clean question and answer. >> Okay. >> Thank you for that. On your ISO, we're still speeding, Lisa, so give it a beat in your head and then on you. >> Yet not all analytics spending is resulting in the same ROI. So what are some of the discrepancies that the info brief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes, and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead they're relying on outdated spreadsheet technology. Nine of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically, then what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value from their data and analytics and achieve more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So very strategic investments. Did the survey uncover any specific areas where most companies are falling short, like any black holes that organizations need to be aware of at the outset? >> It did, it did. So organizations, they need to build a data-centric culture. And this begins with people. But what the survey told us is that the people aspect of analytics is the most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone in the organization has access to the data and analytics technology they need. And then the organizations also have to align their investments with upskilling in data literacy to enjoy that higher ROI. Companies who did so experience higher ROI than companies who underinvested in analytics literacy. So among the high ROI achievers, 78% have a good or great alignment between analytics investment and workforce upskilling compared to only 64% among those without positive ROI. And as more orgs adopt cloud data warehouses or cloud data lakes, in order to manage the massively increasing workloads- Can I start that one over. >> Sure. >> Can I redo this one? >> Yeah. >> Of course, stand by. >> Tongue tied. >> Yep, no worries. >> One second. >> If we could do the same, Lisa, just have a clean break, we'll go your question. >> Yep, yeah. >> On you Lisa. Just give that a count and whenever you're ready. Here, I'm going to give us a little break. On you Lisa. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture and this begins with people, but we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources, compared to only 67% among the ROI laggards. >> So interesting that you mentioned people, I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand, we know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right, so analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively and letting them do so cross-functionally. >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side. And it's expected to spend more on analytics than other IT. What risks does this present to the overall organization, if IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this isn't because the lines of business have recognized the value of analytics and plan to invest accordingly, but a lack of alignment between IT and business. This will negatively impact governance, which ultimately impedes democratization and hence ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up in Alteryx environment, but also to take a look at your analytics stack and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process, and technologies. Jason, thank you so much for joining me today, unpacking the IDC info brief and the great nuggets in there. Lots that organizations can learn and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you, it's been a pleasure. >> In a moment, Alan Jacobson, who's the chief data and analytics officer at Alteryx is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching "theCUBE", the leader in tech enterprise coverage. >> Somehow many have come to believe that data analytics is for the few, for the scientists, the PhDs, the MBAs. Well, it is for them, but that's not all. You don't have to have an advanced degree to do amazing things with data. You don't even have to be a numbers person. You can be just about anything. A titan of industry or a future titan of industry. You could be working to change the world, your neighborhood, or the course of your business. You can be saving lives or just looking to save a little time. The power of data analytics shouldn't be limited to certain job titles or industries or organizations because when more people are doing more things with data, more incredible things happen. Analytics makes us smarter and faster and better at what we do. It's practically a superpower. That's why we believe analytics is for everyone, and everything, and should be everywhere. That's why we believe in analytics for all. (upbeat music) >> Hey, everyone. Welcome back to "Accelerating Analytics Maturity". I'm your host, Lisa Martin. Alan Jacobson joins me next. The chief of data and analytics officer at Alteryx. Alan, it's great to have you on the program. >> Thanks, Lisa. >> So Alan, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics? >> You're spot on, many organizations really aren't leveraging the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole. We just launched an assessment tool on our website that we built with the International Institute of Analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >> So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >> So domain experts are really in the best position. They know where the gold is buried in their companies. They know where the inefficiencies are. And it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a logistics expert of your company. Much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional if they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics to stay current and be capable for their companies. And companies need people who can do that. >> Absolutely, it seems like it's table stakes these days. Let's look at different industries now. Are there differences in how you see analytics in automation being employed in different industries? I know Alteryx is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams. Any differences in industries? >> Yeah, there's an incredible actually commonality between the domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are much larger than you might think. And even on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use Alteryx across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Alteryx, and if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 Sports has, and I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see Fortune 500 finance departments doing to optimize their budget, and so really the commonality is very high, even across industries. >> I bet every Fortune 500 or even every company would love to be compared to the same department within McLaren F1. Just to know that wow, what they're doing is so incredibly important as is what we're doing. >> So talk- >> Absolutely. >> About lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature? >> Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if your company isn't going on this journey and your competition is, it can be a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear, organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment, and so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey, can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies that didn't. And so picking technologies that'll help everyone do this and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key. >> So faster, able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >> Absolutely the IDC, or not the IDC, the International Institute of Analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company, they showed correlation to revenue and they showed correlation to shareholder values. So across really all of the key measures of business, the more analytically mature companies simply outperformed their competition. >> And that's key these days, is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I got to ask you, is it really that easy for the line of business workers who aren't trained in data science to be able to jump in, look at data, uncover and extract business insights to make decisions? >> So in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Alteryx, they're Alteryx certified and it was quite easy. It took 'em about 20 hours and they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant that's been doing the best accounting work in your company for the last 20 years, and all you happen to know is a spreadsheet for those 20 years, are you ready to learn some new skills? And I would suggest you probably need to, if you want to keep up with your profession. The big four accounting firms have trained over a hundred thousand people in Alteryx. Just one firm has trained over a hundred thousand. You can't be an accountant or an auditor at some of these places without knowing Alteryx. And so the hard part, really in the end, isn't the technology and learning analytics and data science, the harder part is this change management, change is hard. I should probably eat better and exercise more, but it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to help them become the digitally enabled accountant of the future, the logistics professional that is E enabled, that's the challenge. >> That's a huge challenge. Cultural shift is a challenge, as you said, change management. How do you advise customers if you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >> Yeah, that's a great question. So people entering into the workforce today, many of them are starting to have these skills. Alteryx is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce, have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can be great fun. We have a great time with many of the customers that we work with, helping them do this, helping them go on the journey, and the ROI, as I said, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that have really made great impact to society as a whole. >> Isn't that so fantastic, to see the difference that that can make. It sounds like you guys are doing a great job of democratizing access to Alteryx to everybody. We talked about the line of business folks and the incredible importance of enabling them and the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alteryx customers that really show data breakthroughs by the lines of business using the technology? >> Yeah, absolutely, so many to choose from. I'll give you two examples quickly. One is Armor Express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We see how important the supply chain is. And so adjusting supply to match demand is really vital. And so they've used Alteryx to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a dollar standpoint. They cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer demand. And so when people have orders and are looking to pick up a vest, they don't want to wait. And it becomes really important to get that right. Another great example is British Telecom. They're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and this is crazy to think about, over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and report, and obviously running 140 legacy models that had to be done in a certain order and length, incredibly challenging. It took them over four weeks each time that they had to go through that process. And so to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Alteryx and learn Alteryx. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours it took to run in a 60% run time performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and pasting data into a spreadsheet. And that was just one project that this group of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in other areas. So you can imagine the impact by the end of the year that they will have on their business, potentially millions upon millions of dollars. And this is what we see again and again, company after company, government agency after government agency, is how analytics are really transforming the way work is being done. >> That was the word that came to mind when you were describing the all three customer examples, transformation, this is transformative. The ability to leverage Alteryx, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And also the business outcome you mentioned, those are substantial metrics based business outcomes. So the ROI in leveraging a technology like Alteryx seems to be right there, sitting in front of you. >> That's right, and to be honest, it's not only important for these businesses. It's important for the knowledge workers themselves. I mean, we hear it from people that they discover Alteryx, they automate a process, they finally get to get home for dinner with their families, which is fantastic, but it leads to new career paths. And so knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytic and automate processes actually matches the needs of the employees, and they too want to learn these skills and become more advanced in their capabilities. >> Huge value there for the business, for the employees themselves to expand their skillset, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there, Alan. Is there anywhere that you want to point the audience to go to learn more about how they can get started? >> Yeah, so one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who want to experience Alteryx, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning, and see where you are on the journey and just reach out. We'd love to work with you and your organization to see how we can help you accelerate your journey on analytics and automation. >> Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >> Thank you so much. >> In a moment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who's the global head of tax technology at eBay, will join me. You're watching "theCUBE", the leader in high tech enterprise coverage. >> 1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops. >> Make that 2.3. >> Sector times out the wazoo. >> Way too much of this. >> Velocities, pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into winning insights, they turn to Alteryx. Alteryx, analytics automation. (upbeat music) >> Hey, everyone, welcome back to the program. Lisa Martin here, I've got two guests joining me. Please welcome back to "theCUBE" Paula Hansen, the chief revenue officer and president at Alteryx, and Jacqui Van der Leij Greyling joins us as well, the global head of tax technology at eBay. They're going to share with you how Alteryx is helping eBay innovate with analytics. Ladies, welcome, it's great to have you both on the program. >> Thank you, Lisa, it's great to be here. >> Yeah, Paula, we're going to start with you. In this program, we've heard from Jason Klein, we've heard from Alan Jacobson. They talked about the need to democratize analytics across any organization to really drive innovation. With analytics, as they talked about, at the forefront of software investments, how's Alteryx helping its customers to develop roadmaps for success with analytics? >> Well, thank you, Lisa. It absolutely is about our customers' success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics, through things like enablement programs, skills assessments, hackathons, setting up centers of excellence to help their organization scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practices, so they can make better business decisions and compete in their respective industries. >> Excellent, sounds like a very strategic program, we're going to unpack that. Jacqui, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jacqui did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >> So I think the main thing for us is when we started out was is that, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and being more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes. >> Starting with people is really critical. Jacqui, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >> So I think eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them to move forward. The other thing is that people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals. And there was no, we were not independent. You couldn't move forward, you would've put it on somebody else's roadmap to get the data and to get the information if you want it. So really finding something that everybody could access analytics or access data. And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy, and that is not so daunting on somebody who's brand new to the field? And I would call those out as your major roadblocks, because you always have, not always, but most of the times you have support from the top, and in our case we have, but at the end of the day, it's our people that need to actually really embrace it, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically a block you want to be able to move. >> It's really all about putting people first. Question for both of you, and Paula we'll start with you, and then Jacqui we'll go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone, should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data, so that they can actually be data driven. Paula. >> Yes, well, we leverage our platform across all of our business functions here at Alteryx. And just like Jacqui explained, at eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jacqui mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO Kevin Rubin has been a key sponsor for using our own technology. We use Alteryx for forecasting all of our key performance metrics, for business planning, across our audit function, to help with compliance and regulatory requirements, tax, and even to close our books at the end of each quarter. So it's really going to remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jacqui mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need, and ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >> That confidence is key. Jacqui, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >> Yeah, I think it means to what Paula has said in terms of getting people excited about it, but it's also understanding that this is a journey and everybody is at a different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new or maybe somewhere in between. And it's about how you get everybody in their different phases to get to the initial destination. I say initial, because I believe a journey is never really complete. What we have done is that we decided to invest, and built a proof of concept, and we got our CFO to sponsor a hackathon. We opened it up to everybody in finance in the middle of the pandemic. So everybody was on Zoom and we told people, listen, we're going to teach you this tool, it's super easy, and let's just see what you can do. We ended up having 70 entries. We had only three weeks. So and these are people that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 entries with people that have never, ever done anything like this before. And there you have the result. And then it just went from there. People had a proof of concept. They knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up now. >> That's fantastic. And the business outcome that you mentioned there, the business impact is massive, helping folks get that confidence to be able to overcome sometimes the cultural barriers is key here. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you're empowering the next generation of data workers? Paula, we'll start with you. >> Absolutely, and Jacqui says it so well, which is that it really is a journey that organizations are on and we as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Alteryx to help address this skillset gap on a global level is through a program that we call SparkED, which is essentially a no-cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with SparkED. We started last May, but we currently have over 850 educational institutions globally engaged across 47 countries, and we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close the gap and empower more people with the necessary analytics skills to solve all the problems that data can help solve. >> So SparkED has made a really big impact in such a short time period. It's going to be fun to watch the progress of that. Jacqui, let's go over to you now. Talk about some of the things that eBay is doing to empower the next generation of data workers. >> So we basically wanted to make sure that we kept that momentum from the hackathon, that we don't lose that excitement. So we just launched the program called eBay Masterminds. And what it basically is, is it's an inclusive innovation in each other, where we firmly believe that innovation is for upskilling for all analytics roles. So it doesn't matter your background, doesn't matter which function you are in, come and participate in in this where we really focus on innovation, introducing new technologies and upskilling our people. We are, apart from that, we also said, well, we shouldn't just keep it to inside eBay. We have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use Alteryx. And we're working with, actually, we're working with SparkED and they're helping us develop that program. And we really hope that at, say, by the end of the year, we have a pilot and then also next year, we want to roll it out in multiple locations in multiple countries and really, really focus on that whole concept of analytics for all. >> Analytics for all, sounds like Alteryx and eBay have a great synergistic relationship there that is jointly aimed at especially going down the stuff and getting people when they're younger interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you, you were recently on "theCUBE"'s Supercloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating what is by default a multi-cloud world. How does the Alteryx Analytics Cloud platform enable CIOs to democratize analytics across their organization? >> Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I checked, there was 2 million data scientists in the world, so that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs, with business leaders is that they're integrating data analysis and the skillset of data analysis into virtually every job function, and that is what we think of when we think of analytics for all. And so our mission with Alteryx Analytics Cloud is to empower all of those people in every job function, regardless of their skillset, as Jacqui pointed out from people that are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Alteryx Analytics Cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and help drive real business outcomes as a result of unlocking the potential of data. >> As well as really lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist, that's the beauty of what Alteryx is enabling and eBay is a great example of that. Jacqui, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where Alteryx fits in as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >> When we're starting up and getting excited about things when it comes to analytics, I can go on all day, but I'll keep it short and sweet for you. I do think we are on the top of the pool of data scientists. And I really feel that that is your next step, for us anyways, is that how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's something completely different. And it's something that is in everybody in a certain extent. So again, partnering with Alteryx who just released the AI ML solution, allowing folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with Alteryx and we purchased the licenses, quite a few. And right now through our Masterminds program, we're actually running a four month program for all skill levels, teaching them AI ML and machine learning and how they can build their own models. We are really excited about that. We have over 50 participants without a background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I want to give you a quick example of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where there is a checkout feedback functionality on the eBay side where sellers or buyers can verbatim add information. And she built a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value, and it's a beautiful tool and I was very impressed when I saw the demo and definitely developing that sort of thing. >> That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with varying degrees of skill level, going down to the high school level, really exciting. We'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I want to thank you so much for joining me on the program today and talking about how Alteryx and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >> Thank you, Lisa. >> Thank you so much. (cheerful electronic music) >> As you heard over the course of our program, organizations where more people are using analytics who have deeper capabilities in each of the four Es, that's everyone, everything, everywhere, and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling and empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We want to thank you so much for watching the program today. Remember you can find all of the content on thecube.net. You can find all of the news from today on siliconangle.com and of course alteryx.com. We also want to thank Alteryx for making this program possible and for sponsoring "theCUBE". For all of my guests, I'm Lisa Martin. We want to thank you for watching and bye for now. (upbeat music)

>>Hey, everyone, welcome back to the programme. Lisa Martin here. I've got two guests joining me. Please welcome back to the Q. Paula Hanson, the chief Revenue officer and president at all tricks. And Jackie Vanderlei Grayling joins us as well. The global head of tax technology at eBay. They're gonna share with you how an all tricks is helping eBay innovate with analytics. Ladies, welcome. It's great to have you both on the programme. >>Thank you, Lisa. Not great to be >>here. >>Yeah, Paula, we're gonna start with you in this programme. We've heard from Jason Klein. We've heard from Allan Jacobsen. They talked about the need to democratise analytics across any organisation to really drive innovation with analytics as they talked about at the forefront of software investments. House all tricks, helping its customers to develop roadmaps for success with analytics. >>Well, thank you, Lisa. Absolutely is about our customers success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts, of course, with our innovative technology and platform. But ultimately we help our customers to create a culture of data literacy and analytics from the top of the organisation starting with the C suite and we partner with our customers to build their road maps for scaling that culture of analytics through things like enablement programmes, skills assessments, hackathons, uh, setting up centres of excellence to help their organisation scale and drive governance of this, uh, analytics capability across the Enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practises so they can make better business decisions and compete in their respective industries. >>Excellent. Sounds like a very strategic programme. We're gonna unpack that, Jackie, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the I. D. C report that showed that 93% of organisations are not utilising the analytic skills of their employees. But then there's eBay. How Jackie did eBay become one of the 7% of organisations who's really maturing and how are you using analytics across the organisation at bay? >>So I think the main thing for us is when we started out was is that you know our especially in finance. They became spreadsheet professionals instead of the things that we really want our influence to add value to. And we realised we have to address that. And we also knew we couldn't wait for all our data to be centralised until we actually start using the data or start automating and be more effective. Um, so ultimately, we really started very, very actively embedding analytics in our people and our data and our processes. >>Starting with people is really critical jacket continuing with you. What was in the roadblocks to analytics adoption that you faced and how did you overcome them? >>So I think you know, Eva is a very data driven company. We have a lot of data. I think we are 27 years around this year. So we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them, um, to move forward. The other thing is that you know, people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals, right? And there was no we're not independent. You couldn't move forward. You're dependent on somebody else's roadmap to get to data to get the information you want it. So really finding something that everybody could access analytics or access data. And finally we have to realise, is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy and that is not so daunting on somebody who's brand new to the field? And I would I would call those out as your as your major roadblocks, because you always have always. But most of the times you have support from the top. In our case we have. But in the end of the day, it's it's our people that need to actually really embrace it and making that accessible for them. I would say it's not to say a road block a block you want to be able to do. >>It's really all about putting people first question for both of you and Paula will start with you and then Jackie will go to you. I think the message in this programme that the audience is watching with us is very clear. Analytics is for everyone should be for everyone. Let's talk now about how both of your organisations are empowering people, those in the organisation that may not have technical expertise to be able to leverage data so that they can actually be data driven colour. >>Yes, well, we leverage our platform across all of our business functions here at all tricks. And just like Jackie explained that eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jackie mentioned, we have this huge amount of data, uh, flowing through our enterprise, and the opportunity to leverage that into insights and analytics is really endless. So our CFO, Kevin Ruben has been a key sponsor for using our own technology. We use all tricks for forecasting all of our key performance metrics for business planning across our audit function, uh, to help with compliance and regulatory requirements, tax and even to close our books at the end of each quarter. So it's really remain across our business. And at the end of the day, it comes to How do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other to problem solve and, along the way, maybe earn badges, depending on the capabilities and trainings that they take and just have a little healthy competition, Uh, as an employee based around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jackie mentioned, it's really about ensuring that people feel comfortable that they feel supportive, that they have access to the training that they need, and ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >>That confidence is key. Jackie talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >>I think it means to what Paula has said in terms of, you know, getting people excited about it. But it's also understanding that this is a journey and everybody is the different place in their journey. You have folks that's already really advanced. Who's done this every day. And then you have really some folks that this is brand new and, um, or maybe somewhere in between. And it's about how you could get everybody in their different phases to get to the the initial destination. And I say initial because I believe the journey is never really complete. Um, what we have done is that we decided to invest in a group of concept when we got our CFO to sponsor a hackathon. Um, we open it up to everybody in finance, um, in the middle of the pandemic. So everybody was on Zoom, um, and we had and we told people, Listen, we're gonna teach you this tool. It's super easy, and let's just see what you can do. We ended up having 70 injuries. We had only three weeks. So these are people that that do not have a background. They are not engineers and not data scientists and we ended up with 25,000 our savings at the end of the hackathon. Um, from the 70 countries with people that I've never, ever done anything like this before. And there you have the results. And they just went from there because people had a proof of concept. They knew that it worked and they overcame the initial barrier of change. Um, and that's what we are seeing things really, really picking up now >>that's fantastic. And the business outcome that you mentioned that the business impact is massive, helping folks get that confidence to be able to overcome. Sometimes the cultural barriers is key there. I think another thing that this programme has really highlighted is there is a clear demand for data literacy in the job market, regardless of organisation. Can each of you share more about how your empowering the next generation of data workers Paula will start with you? >>Absolutely. And Jackie says it so well, which is that it really is a journey that organisations are on and we, as people in society, are on in terms of up skilling our capabilities. Uh, so one of the things that we're doing here at all tricks to help address the skill set gap on a global level is through a programme that we call Sparked, which is essentially a no cost analyst education programme that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this programme is really developed just to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with sparked we started last May, but we currently have over 850 educational institutions globally engaged across 47 countries, and we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises when we close gap and empower more people with the necessary analytic skills to solve all the problems that data can help solve. >>So >>I just made a really big impact in such a short time period is gonna be fun to watch the progress of that. Jackie, let's go over to you now Talk about some of the things that eBay is doing to empower the next generation of data workers. >>So we definitely wanted to make sure that we kept implemented from the hackathon that we don't lose that excitement life. So we just launched a programme for evil masterminds and what it basically is. It's an inclusive innovation initiative where we firmly believe that innovation is all upscaling for all analytics role. So it doesn't matter. Your background doesn't matter which function you are in. Come and participate in this where we really focus on innovation, introducing these technologies and upscaling of people. Um, we are apart from that. We also said, Well, we should just keep it to inside the way we have to share this innovation with the community. So we are actually working on developing an analytics high school programme which we hope to pilot by the end of this year. We will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, But also, um, how to use all tricks and we're working with Actually, we're working with spark and they're helping us develop that programme. And we really hope that it is said by the end of the year, have a pilot and then also makes you must have been rolled out in multiple locations in multiple countries and really, really, uh, focused on that whole concept of analytic school >>analytics. Girl sounds like ultra and everybody have a great synergistic relationship there that is jointly aimed at especially kind of going down the stock and getting people when they're younger, interested and understanding how they can be empowered with data across any industry. Paula, let's go back to you. You were recently on the cubes Super Cloud event just a couple of weeks ago and you talked about the challenges the companies are facing as they are navigating what is by default, a multi cloud world. How does the all tricks analytics cloud platform enable CEO s to democratise analytics across their organisation? >>Yes, business leaders and CEO s across all industries are realising that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organisations. Last I checked, there was two million data scientists in the world. So that's, uh, woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CEO s with business leaders is that they are integrating data analysis and the skill set of data analysis into virtually every job function. Uh, and that is what we think of when we think of analytics for all. And so our mission with all tricks analytics cloud is to empower all of those people in every job function, regardless of their skill set, as Jackie pointed out, from people that would are just getting started all the way to the most sophisticated of technical users. Um, every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organisations. So that's our goal with all tricks, analytics cloud and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyse and report out so that we can break down data silos across the Enterprise and Dr Real Business Outcomes. As a result, of unlocking the potential of data >>as well as really listening that skills gap. As you were saying, There's only two million data scientists. You don't need to be a data scientist. That's the beauty of what all tricks is enabling. And eBay is a great example of that. Jackie, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where all tricks fits in as that analytics maturity journey continues. And what are some of the things that you're most excited about as analytics truly gets democratised across eBay >>when we start about getting excited about things when it comes to analytics, I can go on all day, but I'll keep it short and sweet for you. Um, I do think we're on the topic full of data scientists, and I really feel that that is your next step for us, anyway. Is that how do we get folks to not see data scientist as this big thing like a rocket scientist it's something completely different and it's something that is in everybody in a certain extent. So, um, game partnering with all tricks to just release uh, ai ml um, solution allowing. You know, folks do not have a data scientist programme but actually build models and be able to solve problems that way. So we have engaged with all turrets and we purchase the licence is quite a few. And right now, through our masterminds programme, we're actually running a four months programme. Um, for all skill levels, um, teaching them ai ml and machine learning and how they can build their own models. Um, we are really excited about that. We have over 50 participants without the background from all over the organisation. We have members from our customer services. We have even some of our engineers are actually participating in the programme will just kick it off. And I really believe that that is our next step. Um, I want to give you a quick example of the beauty of this is where we actually, um, just allow people to go out and think about ideas and come up with things and one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution. Where, um, you know there is a checkout feedback checkout functionality on the eBay side, There's sellers or buyers can pervade them at information. And she built a model to be able to determine what relates to tax specific what is the type of problem and even predict how that problem can be solved before we as human, even stepped in. And now, instead of us or somebody going to debate and try to figure out what's going on there, we can focus on fixing their versus, um, actually just reading through things and not adding any value and its a beautiful tool. And I'm very impressed when we saw the demo and they've been developing that further. >>That sounds fantastic. And I think just the one word that keeps coming to mind. And we've said this a number of times in the programme. Today's empowerment, what you're actually really doing to truly empower people across the organisation with with varying degrees of skill level, going down to the high school level really exciting. We'll have so stay tuned to see what some of the great things are that come from this continued partnership? Ladies, I wanna thank you so much for joining me on the programme today and talking about how all tricks and eBay are really partnering together to democratise analytics and to facilitate its maturity. It's been great talking to you. >>Thank you. >>Thank you so much.