>>Welcome back to Vegas guys, Lisa Martin and Dave Lanta here wrapping up our coverage of day two of snowflake summit. We have given you a lot of content in the last couple of days. We've had a lot of great conversations with snowflake folks with their customers and with partners. And we have an alumni back with us. Please. Welcome back to the queue. Mike Palmer, CEO of Sigma computing. Mike. It's great to see you. >>Thanks for having me. And I guess again >>Exactly. >>It's fantastic me. >>So talk to the audience about Sigma before we get into the snowflake partnership and what you guys are doing from a technical perspective, give us that overview of the vision and some of the differentiators. >>Sure. You know, you've over the last 12 years, companies have benefited from enormous investments and improvements in technology in particular, starting with cloud technologies, obviously going through companies like snowflake, but in terms of the normal user, the one that makes the business decision in the marketing department and the finance team, you know, in the works in the back room of the supply chain, doing inventory very little has changed for those people. And the time had come where the data availability, the ability to organize it, the ability to secure it was all there, but the ability to access it for those people was not. And so what Sigma's all about is taking great technology, finding the skillset they have, which happens to be spreadsheets. There are billion license spreadsheet users in the world and connecting that skillset with all of the power of the cloud. >>And how do you work with snowflake? What are some of the, the what's the joint value proposition? >>How are they as an investor? That's what I wanna know. Ah, >>Quiet, which is the way we like them. No, I'm just kidding. Snowflake is, well, first of all, investment is great, but partnership is even better. Right. You know, and I think snowflake themselves are going through some evolution, but let's start with the basics of technology where this all starts because you know, all of the rest doesn't matter if the product is not great, we work directly on snowflake. And what that means is as an end user, when I, when I sit on that marketing team and I want to understand and, and connect, how did I get a, a customer where I had a pay to add? And they showed up on my website and from my website, they went to a trial. And from there, they touched a piece of syndicated contents. All of that data sits in snowflake and I, as a marketer, understand what it means to me. >>So for the first time, I want to be able to see that data in one place. And I want to understand conversion rates. I want to understand how I can impact those conversion rates. I can make predictions. What that user is doing is going to, to Sigma accessing live data in snowflake, they're able to ask ad hoc questions, questions that were never asked questions, that they don't exist in a filter that were never prepped by a data engineer. So they could truly do something creative and novel in a very independent sort of way. And the connection with Snowflake's live data, the performance, the security and governance that we inherit. These are all facilitators to really expand that access across the enterprise. So at, at a product level, we were built by a team of people, frankly, that also were the original investors in snowflake by two amazing engineers and founders, Rob will and Jason France, they understood how snowflake worked and that shows up in the product for our end customers. >>So, but if I may just to follow up on that, I mean, you could do that without snowflake, but what, it would be harder, more expensive. Describe what you'd have to go through to accomplish that outcome. >>And I think snowflake does a good job of enabling the ecosystem at large. Right. But you know, you always appreciate seeing early access to understand what the architecture's going to look like. You know, some of the things that I will, you know, leaning forward that we've heard here that we're very excited about is snowflake going to attack the TP market, right? The transactional market, one of the transactional database market. I, yeah. Right. You know, one of the things that we see coming, and, and one of the bigger things that we'll be talking about in Sigma is not just that you can do analytics out of snowflake. I think that's something that we do exceptionally well on an ad hoc basis, but we're gonna be the first that allow you to write into snowflake and to do that with good performance. And to do that reliably, we go away from OAP, which is the terminology for data warehousing. >>And we go toward transactional databases. And in that world, understanding snowflake and working collaboratively with them creates again, a much better experience for the end customer. So they, they allow us into those programs, even coming to these conferences, we talk to folks that run the industry teams, trying to up level that message and not just talk database and, and analytics, but talk about inventory management. How do we cut down the gap that exists between POS systems and inventory ordering, right? So that we get fewer stockouts, but also that we don't overorder. So that's another benefit, >>Strong business use cases. >>That's correct. >>And you're enabling those business users to have access to that data. I presume in near real time or near real time, so that they can make decisions that drive marketing forward or finance forward or legal >>Forward. Exactly. We had a customer panel yesterday. An example of that go puff is hopefully most of the viewers are familiar with, as a delivery company. This is a complicated business to run. It's run on the fringes. When we think about how to make money at it, which means that the decisions need to be accurate. They need to be real time. You can't have a batch upload for delivery when they're people are on the street, and then there's an issue. They need to understand the exact order at that time, not in 10 minutes, not from five minutes ago, right. Then they need to understand, do I have inventory in the warehouse when the order comes in? If they don't, what's a replacement product. We had a Mike came in from go puff and walked us through all of the complexity of that and how they're using Sigma to really just shorten those decision cycles and make them more accurate. You know, that's where the business actually benefits and, >>And actually create a viable business model. Cuz you think back to the early, think back to the.com days and you had pets.com, right? They couldn't make any money. Yeah. Without chewy. Okay. They appears to be a viable business model. Right? Part of that is just the efficiencies. And it's sort of a, I dunno if those are customers that they may or may not be, but they should be if they're not >>Chewy is, but okay. You know, and that's another example, but I'll even pivot to the various REI and other retailers. What do they care about cohorts? I'm trying to understand who's buying my product. What can I sell to them next? That, that idea of again, I'm sitting in a department, that's not data engineering, that's not BI now working collaboratively where they can get addend engineer, putting data sets together. They have a BI person that can help in the analytics process. But now it's in a spreadsheet where I understand it as a marketer. So I can think about new hierarchies. I wanna know it by customer, by region, by product type. I wanna see it by all of those things. I want to be able to do that on the fly because then it creates new questions that sort of flow. If you' ever worked in development, we use the word flow constantly, right? And as people that flow is when we have a question, we get an answer that generates a question. We have, we just keep doing that iteratively. That that is where Sigma really shines for them. >>What does a company have to do to really take advantage of, of this? I, if they're kind of starting from a company that's somewhat immature, what are the sort of expectations, maybe even outta scope expectations so they can move faster, accelerate analytics, a lot of the themes that we've heard today, >>What does an immature company is actually even a question in, in and of itself? You know, I think a lot of companies consider themselves to be immature simply because for various constraint reasons, they haven't leveraged the data in the way that they thought possible. Good, >>Good, good definition. Okay. So not, not, >>Not, I use this definition for digital transformation. It very simple. It is. Do you make better decisions, faster McKenzie calls this corporate metabolism, right? Can you speed up the metabolism of, of an enterprise and for me and for the Sigma customer base, there's really not much you have to do once. You've adopted snowflake because for the first time the barriers and the silos that existed in terms of accessing data are gone. So I think the biggest barrier that customers have is curiosity. Because once you have curiosity and you have access, you can start building artifacts and assets and asking questions. Our customers are up and running in the product in hours. And I mean that literally in hours, we are a user in snowflake, that's a direct live connection. They are able to explore tables, raw. They can do joins themselves if they want to. They can obviously work with their data engineering team to, to create data sets. If that's the preferred method. And once they're there and they've ever built a pivot table, they can be working in Sigma. So our customers are getting insights in the first one to two days, you referenced some, those of us are old enough to remember pest.com. Also old enough to remember shelfware that we would buy. We are very good at showing customers that within hours they're getting value from their investment in Sigma. And that, that just creates momentum, right? Oh, >>Tremendous momentum and >>Trust and trust and expansion opportunities for Sigma. Because when you're in one of those departments, someone else says, well, you know, why do you get access to that data? But I don't, how are you doing this? Yeah. So we're, you know, I think that there's a big movement here. People, I often compare data to communication. If you go back a hundred years, our communication was not limited. As it turns out by our desire to communicate, it was limited by the infrastructure. We had the typewriter, a letter and the us postal service and a telephone that was wired. And now we have walk around here. We, everything is, is enabled for us. And we send, you know, hundreds and thousands of messages a day and probably could do more. You will find that is true. And we're seeing it in our product is true of data. If you give people access, they have 10 times as many questions as they thought they had. And that's the change that we're gonna see in business over the next few years, >>Frank Salman's first book, what he was was CEO of snowflake was rise of the data cloud. And he talked about network effects. Basically what he described was Metcalf's law. Again, go back to the.com days, right? And he, Bob Metcalf used the phone system. You know, if there's two people in the phone system, it's not that valuable, right. >>You know, exactly, >>You know, grow it. And that's where the value is. And that's what we're seeing now applied to data. >>And even more than that, I think that's a great analogy. In fact, the direct comparison to what Sigma is doing actually goes one step beyond everything that I've been talking about, which is great at the individual level, but now the finance team and the marketing team can collaborate in the platform. They can see data lineage. In fact, one of our, our big emphasis points here is to eliminate the sweet products. You know, the ones where, you know, you think you're buying something, but you really have a spreadsheet product here and a document product there and a slide product over there. And they, you know, you can do all of that in Sigma. You can write a narrative. You can real time live, edit on numbers. You, you know, if you want to, you could put a picture in it. But you know, at Sigma we present everything out of our product. Every meeting is live data. Every question is answered on the spot. And that's when, you know, you know, to your point about met cap's law. Now everybody's involved in the decision making. They're doing it real time. Your meetings are more productive. You have fewer of them because they're no action items, right. We're answering our questions there and we're, and we're moving forward. >>You know, view were meeting sounds good. Productivity is, is weird now with the, the pandemic. But you know, if you go back to the nineties here am I'm, I'm dating myself again, but that's okay. You know, you, you didn't see much productivity going on when the PC boom started in the eighties, but the nineties, it kicked in and pre pandemic, you know, productivity in the us and Europe anyway has been going down. But I feel like Mike, listen to what you just described. I, how many meetings have we been in where people are arguing about them numbers, what are the assumptions on the numbers wasting so much time? And then nothing gets done and they, then they, they bolt cut that away and you drive in productivity. So I feel like we're on a Renaissance of productivity and a lot of that's gonna be driven by, by data. Yeah. And obviously communications the whole 5g thing. We'll see how that builds out. But data is really the main spring of, I think, a new, new Renaissance in productivity. >>Well, first of all, if you could find an enterprise where you ask the question, would you rather use your data better? And they say, no, like, you know, show me, tell me that I'll short their stock immediately. But I do agree. And I, unfortunately I have a career history in that meeting that you just described where someone doesn't like, what you're showing them. And their first reaction is to say, where'd you get that data? You know, I don't trust it. You know? So they just undermined your entire argument with an invalid way of doing so. Right. When you walk into a meeting with Sigma where'd, where'd you get that data? I was like, that's the live data right now? What question do you want answer >>Lineage, right. Yeah. And you know, it's a Sen's book about, you know, gotta move faster. I mean, this is an example of just cutting through making decisions faster because you're right. Mike and the P the P and L manager in a meeting can, can kill the entire conversation, you know, throw FUD at it. Yeah. You know, protect his or her agenda. >>True. But now to be fair to the person, who's tended to do that. Part of the reason they've done that is that they haven't had access to that data before the meeting and they're getting blindsided. Right. So going back to the collaboration point. Yes. Right. The fact we're coming to this discussion more informed in and of itself takes care of some of that problem. Yeah. >>For sure. And if, and if everybody then agrees, we can move on and now talk about the really important stuff. Yeah. That's good. It >>Seems to me that Sigma is an enabler of that curiosity that you mentioned that that's been lacking. People need to be able to hire for that, but you've got a platform that's going here. You go ask >>Away. That's right in the we're very good. You know, we love being a SaaS platform. There's a lot of telemetry. We can watch what we call our mouse to Dows, you know, which is our monthly average users to our daily average users. We can see what level of user they are, what type of artifacts they build. Are they, you know, someone that creates things from scratch, are they people that tend to increment them, which by the way, is helpful to our customers because we can then advise them, Hey, here's, what's really going on. You might wanna work with this team over here. They could probably be a little better of us using the data, but look at this team over here, you know, they've originated five workbooks in the last, you know, six days they're really on it. There's, there's, you know, that ability to even train for the curiosity that you're referring to is now there, >>Where are your customer conversations? Are they at the lines of business? Are they with the chief data officer? What does that look like these days? >>Great question. So stepping back a bit, what, what is Sigma here to do? And, and our first phase is really to replace spreadsheets, right? And so one of the interesting things about the company is that there isn't a department where a spreadsheet isn't used. So Sigma has an enormous Tam, but also isn't necessarily associated with any particular department or any particular vertical. So when we tend to have conversations, it really depends on, you know, either what kind of investment are you making? A lot of mid-market companies are making best technology investments. They're on a public cloud, they're buying snowflake and they wanna understand what's, what's built to really make this work best over the next number of years. And those are very short sales for us because we, we prove that, you know, in, in minutes to hours, if you're working at a large enterprise and you have three or four other tools, you're asking a different question. >>And often you're asking a question of what I call exploration. We have a product that has dashboards and they've been working for us and we don't wanna replace the dashboard. But when we have a question about the data in the dashboard, we're stuck, how do we get to the raw data? How do we get to the example that we can actually manage? You can't manage a dashboard. You can't manage a trend line, but if you get into the data behind the trend line, you can make decisions to change business process, to change quality, accuracy, to change speed of execution. That is what we're trying to enable. Those conversations happen between the it team who runs technology and the business teams who are responsible for the decisions. So we are, you know, we have a cross departmental sale, but across every department, >>One of the things we're not talking about at this event, which is kind of interesting, cause it's all we've been talking about is the macro supply chain challenges, Ukraine, blah, blah, blah, and the stock market. But, but how are you thinking about that? Macro? The impacts you're seeing, you know, a lot of private companies being, you know, recapped, et cetera, you guys obviously very well funded. Yeah. But how do you think about, I mean, I asked Frank a similar question. He's like, look, it's a marathon. We don't worry about it. We, you know, they made the public market, they get 5 billion in cash. Yeah. Yeah. How are you thinking about it? >>You know, first of all, what's the expression, right? You never, never waste a good, you know, in this case recession, no, we don't have one yet, but the impetus is there, right. People are worried. And when they're worried, they're thinking about their bottom lines, they're thinking about where they're going to get efficiency and their costs. They're already dealing with the supply chain issues of inventory. We all have it in our personal lives. If you've ordered anything in the last six months, you're used to getting it in, you know, days to weeks. And now you're getting in months, you know, we had customers like us foods as a good example, like they're constantly trying to align inventory. They have with transportation that gets that inventory to their end customers, right? And they do that with better data accuracy at the end point, working with us on what we are launching. >>And I mentioned earlier, having more people be able to update that data creates more data, accuracy creates better decisions. We align that then with them and better collaboration with the folks that then coordinate the trucks with Prologis and the panel yesterday, they're the only commercial public company that reports their, their valuations on a quarterly basis. They work with Sigma to trim the amount of time it takes their finance team to produce that data that creates investor confidence that holds up your stock price. So I mean the, the importance of data relative to all the stakeholders in enterprise cannot be overstated. Supply chain is a great example. And yes, it's a marathon because a lot of the technology that drives supply chain is old, but you don't have to rip out those systems to put your data into snowflake, to get better access through Sigma, to enable the people in your environment to make better decisions. And that's the good news. So for me, while I agree, there's a marathon. I think that most of the, I dunno if I could continue this metaphor, but I think we could run quite far down that marathon without an awful lot of energy by just making those couple of changes. >>Awesome. Mike, this has been fantastic. Last question. I, I can tell, I know a lot of growth for Sigma. I can feel it in your energy alone. What are some of the key priorities that you're gonna be focusing on for the rest of the year? >>Our number one priority, our number two priority and number three priority are always build the best product on the market, right? We, we want customers to increase usage. We want them to be delighted. You know, we want them to be RA. Like we have customers at our booth that walk up and it's like, you're building a great company. We love your product. I, if you want to show up happy at work, have customers come up proactively and tell you how your products changed their life. And that is, that is the absolute, most important thing because the real marathon here is that enablement over the long term, right? It is being a great provider to a bunch of great companies under that. We are growing, you know, we've been tripling the company for the fast few years, every year, that takes a lot of hiring. So I would've alongside product is building a great culture with bringing the best people to the company that I guess have my energy level. >>You know, if you could get paid in energy, we would've more than tripled it, you know, but that's always gonna be number two, where we're focused on the segment side, you know, is really the large enterprise customer. At this point, we are doing a great job in the mid-market. We have customer, we have hundreds of customers in our free trial on a constant basis. I think that without wanting to seem over confident or arrogant, I think our technology speaks for itself and the product experience for those users, making a great ROI case to a large enterprise takes effort. It's a different motion. We're, we're very committed to building that motion. We're very committed to building out the partner ecosystem that has been doing that for years. And that is now coming around to the, the snowflake and all of the ecosystem changes around snowflake because they've learned these customers for decades and now have a new opportunity to bring to them. How do we enable them? That is where you're gonna see Sigma going over the next couple of years. >>Wow, fantastic. Good stuff. And a lot of momentum, Mike, thank you so much for joining Dave and me talking about Sigma, the momentum, the flywheel of what you're doing with snowflake and what you're enabling customers to achieve the massive business outcomes. Really cool stuff. >>Thank you. And thank you for continuing to give us a platform to do this and glad to be back in conferences, doing it face to face. It's fantastic. >>It it's the best. Awesome. Mike, thank you for Mike Palmer and Dave ante. I'm Lisa Martin. You've been watching the cube hopefully all day. We've been here since eight o'clock this morning, Pacific time giving you wall the wall coverage of snowflake summit 22 signing off for today. Dave and I will see you right bright and early tomorrow morning. I will take care guys.

(soft electronic music) >> Hey everyone, welcome to this CUBE conversation as part of the AWS Startup Showcase, season three, episode one, featuring Astronomer. I'm your host, Lisa Martin. I'm in the CUBE's Palo Alto Studios, and today excited to be joined by a couple of guests, a couple of co-founders from Astronomer. Viraj Parekh is with us, as is Paola Peraza-Calderon. Thanks guys so much for joining us. Excited to dig into Astronomer. >> Thank you so much for having us. >> Yeah, thanks for having us. >> Yeah, and we're going to be talking about the role of data orchestration. Paola, let's go ahead and start with you. Give the audience that understanding, that context about Astronomer and what it is that you guys do. >> Mm-hmm. Yeah, absolutely. So, Astronomer is a, you know, we're a technology and software company for modern data orchestration, as you said, and we're the driving force behind Apache Airflow. The Open Source Workflow Management tool that's since been adopted by thousands and thousands of users, and we'll dig into this a little bit more. But, by data orchestration, we mean data pipeline, so generally speaking, getting data from one place to another, transforming it, running it on a schedule, and overall just building a central system that tangibly connects your entire ecosystem of data services, right. So what, that's Redshift, Snowflake, DVT, et cetera. And so tangibly, we build, we at Astronomer here build products powered by Apache Airflow for data teams and for data practitioners, so that they don't have to. So, we sell to data engineers, data scientists, data admins, and we really spend our time doing three things. So, the first is that we build Astro, our flagship cloud service that we'll talk more on. But here, we're really building experiences that make it easier for data practitioners to author, run, and scale their data pipeline footprint on the cloud. And then, we also contribute to Apache Airflow as an open source project and community. So, we cultivate the community of humans, and we also put out open source developer tools that actually make it easier for individual data practitioners to be productive in their day-to-day jobs, whether or not they actually use our product and and pay us money or not. And then of course, we also have professional services and education and all of these things around our commercial products that enable folks to use our products and use Airflow as effectively as possible. So yeah, super, super happy with everything we've done and hopefully that gives you an idea of where we're starting. >> Awesome, so when you're talking with those, Paola, those data engineers, those data scientists, how do you define data orchestration and what does it mean to them? >> Yeah, yeah, it's a good question. So, you know, if you Google data orchestration you're going to get something about an automated process for organizing silo data and making it accessible for processing and analysis. But, to your question, what does that actually mean, you know? So, if you look at it from a customer's perspective, we can share a little bit about how we at Astronomer actually do data orchestration ourselves and the problems that it solves for us. So, as many other companies out in the world do, we at Astronomer need to monitor how our own customers use our products, right? And so, we have a weekly meeting, for example, that goes through a dashboard and a dashboarding tool called Sigma where we see the number of monthly customers and how they're engaging with our product. But, to actually do that, you know, we have to use data from our application database, for example, that has behavioral data on what they're actually doing in our product. We also have data from third party API tools, like Salesforce and HubSpot, and other ways in which our customer, we actually engage with our customers and their behavior. And so, our data team internally at Astronomer uses a bunch of tools to transform and use that data, right? So, we use FiveTran, for example, to ingest. We use Snowflake as our data warehouse. We use other tools for data transformations. And even, if we at Astronomer don't do this, you can imagine a data team also using tools like, Monte Carlo for data quality, or Hightouch for Reverse ETL, or things like that. And, I think the point here is that data teams, you know, that are building data-driven organizations have a plethora of tooling to both ingest the right data and come up with the right interfaces to transform and actually, interact with that data. And so, that movement and sort of synchronization of data across your ecosystem is exactly what data orchestration is responsible for. Historically, I think, and Raj will talk more about this, historically, schedulers like KRON and Oozie or Control-M have taken a role here, but we think that Apache Airflow has sort of risen over the past few years as the defacto industry standard for writing data pipelines that do tasks, that do data jobs that interact with that ecosystem of tools in your organization. And so, beyond that sort of data pipeline unit, I think where we see it is that data acquisition is not only writing those data pipelines that move your data, but it's also all the things around it, right, so, CI/CD tool and Secrets Management, et cetera. So, a long-winded answer here, but I think that's how we talk about it here at Astronomer and how we're building our products. >> Excellent. Great context, Paola. Thank you. Viraj, let's bring you into the conversation. Every company these days has to be a data company, right? They've got to be a software company- >> Mm-hmm. >> whether it's my bank or my grocery store. So, how are companies actually doing data orchestration today, Viraj? >> Yeah, it's a great question. So, I think one thing to think about is like, on one hand, you know, data orchestration is kind of a new category that we're helping define, but on the other hand, it's something that companies have been doing forever, right? You need to get data moving to use it, you know. You've got it all in place, aggregate it, cleaning it, et cetera. So, when you look at what companies out there are doing, right. Sometimes, if you're a more kind of born in the cloud company, as we say, you'll adopt all these cloud native tooling things your cloud provider gives you. If you're a bank or another sort of institution like that, you know, you're probably juggling an even wider variety of tools. You're thinking about a cloud migration. You might have things like Kron running in one place, Uzi running somewhere else, Informatics running somewhere else, while you're also trying to move all your workloads to the cloud. So, there's quite a large spectrum of what the current state is for companies. And then, kind of like Paola was saying, Apache Airflow started in 2014, and it was actually started by Airbnb, and they put out this blog post that was like, "Hey here's how we use Apache Airflow to orchestrate our data across all their sources." And really since then, right, it's almost been a decade since then, Airflow emerged as the open source standard, and there's companies of all sorts using it. And, it's really used to tie all these tools together, especially as that number of tools increases, companies move to hybrid cloud, hybrid multi-cloud strategies, and so on and so forth. But you know, what we found is that if you go to any company, especially a larger one and you say like, "Hey, how are you doing data orchestration?" They'll probably say something like, "Well, I have five data teams, so I have eight different ways I do data orchestration." Right. This idea of data orchestration's been there but the right way to do it, kind of all the abstractions you need, the way your teams need to work together, and so on and so forth, hasn't really emerged just yet, right? It's such a quick moving space that companies have to combine what they were doing before with what their new business initiatives are today. So, you know, what we really believe here at Astronomer is Airflow is the core of how you solve data orchestration for any sort of use case, but it's not everything. You know, it needs a little more. And, that's really where our commercial product, Astro comes in, where we've built, not only the most tried and tested airflow experience out there. We do employ a majority of the Airflow Core Committers, right? So, we're kind of really deep in the project. We've also built the right things around developer tooling, observability, and reliability for customers to really rely on Astro as the heart of the way they do data orchestration, and kind of think of it as the foundational layer that helps tie together all the different tools, practices and teams large companies have to do today. >> That foundational layer is absolutely critical. You've both mentioned open source software. Paola, I want to go back to you, and just give the audience an understanding of how open source really plays into Astronomer's mission as a company, and into the technologies like Astro. >> Mm-hmm. Yeah, absolutely. I mean, we, so we at Astronomers started using Airflow and actually building our products because Airflow is open source and we were our own customers at the beginning of our company journey. And, I think the open source community is at the core of everything we do. You know, without that open source community and culture, I think, you know, we have less of a business, and so, we're super invested in continuing to cultivate and grow that. And, I think there's a couple sort of concrete ways in which we do this that personally make me really excited to do my own job. You know, for one, we do things like we organize meetups and we sponsor the Airflow Summit and there's these sort of baseline community efforts that I think are really important and that reminds you, hey, there just humans trying to do their jobs and learn and use both our technology and things that are out there and contribute to it. So, making it easier to contribute to Airflow, for example, is another one of our efforts. As Viraj mentioned, we also employ, you know, engineers internally who are on our team whose full-time job is to make the open source project better. Again, regardless of whether or not you're a customer of ours or not, we want to make sure that we continue to cultivate the Airflow project in and of itself. And, we're also building developer tooling that might not be a part of the Apache Open Source project, but is still open source. So, we have repositories in our own sort of GitHub organization, for example, with tools that individual data practitioners, again customers are not, can use to make them be more productive in their day-to-day jobs with Airflow writing Dags for the most common use cases out there. The last thing I'll say is how important I think we've found it to build sort of educational resources and documentation and best practices. Airflow can be complex. It's been around for a long time. There's a lot of really, really rich feature sets. And so, how do we enable folks to actually use those? And that comes in, you know, things like webinars, and best practices, and courses and curriculum that are free and accessible and open to the community are just some of the ways in which I think we're continuing to invest in that open source community over the next year and beyond. >> That's awesome. It sounds like open source is really core, not only to the mission, but really to the heart of the organization. Viraj, I want to go back to you and really try to understand how does Astronomer fit into the wider modern data stack and ecosystem? Like what does that look like for customers? >> Yeah, yeah. So, both in the open source and with our commercial customers, right? Folks everywhere are trying to tie together a huge variety of tools in order to start making sense of their data. And you know, I kind of think of it almost like as like a pyramid, right? At the base level, you need things like data reliability, data, sorry, data freshness, data availability, and so on and so forth, right? You just need your data to be there. (coughs) I'm sorry. You just need your data to be there, and you need to make it predictable when it's going to be there. You need to make sure it's kind of correct at the highest level, some quality checks, and so on and so forth. And oftentimes, that kind of takes the case of ELT or ETL use cases, right? Taking data from somewhere and moving it somewhere else, usually into some sort of analytics destination. And, that's really what businesses can do to just power the core parts of getting insights into how their business is going, right? How much revenue did I had? What's in my pipeline, salesforce, and so on and so forth. Once that kind of base foundation is there and people can get the data they need, how they need it, it really opens up a lot for what customers can do. You know, I think one of the trendier things out there right now is MLOps, and how do companies actually put machine learning into production? Well, when you think about it you kind of have to squint at it, right? Like, machine learning pipelines are really just any other data pipeline. They just have a certain set of needs that might not not be applicable to ELT pipelines. And, when you kind of have a common layer to tie together all the ways data can move through your organization, that's really what we're trying to make it so companies can do. And, that happens in financial services where, you know, we have some customers who take app data coming from their mobile apps, and actually run it through their fraud detection services to make sure that all the activity is not fraudulent. We have customers that will run sports betting models on our platform where they'll take data from a bunch of public APIs around different sporting events that are happening, transform all of that in a way their data scientist can build models with it, and then actually bet on sports based on that output. You know, one of my favorite use cases I like to talk about that we saw in the open source is we had there was one company whose their business was to deliver blood transfusions via drone into remote parts of the world. And, it was really cool because they took all this data from all sorts of places, right? Kind of orchestrated all the aggregation and cleaning and analysis that happened had to happen via airflow and the end product would be a drone being shot out into a real remote part of the world to actually give somebody blood who needed it there. Because it turns out for certain parts of the world, the easiest way to deliver blood to them is via drone and not via some other, some other thing. So, these kind of, all the things people do with the modern data stack is absolutely incredible, right? Like you were saying, every company's trying to be a data-driven company. What really energizes me is knowing that like, for all those best, super great tools out there that power a business, we get to be the connective tissue, or the, almost like the electricity that kind of ropes them all together and makes so people can actually do what they need to do. >> Right. Phenomenal use cases that you just described, Raj. I mean, just the variety alone of what you guys are able to do and impact is so cool. So Paola, when you're with those data engineers, those data scientists, and customer conversations, what's your pitch? Why use Astro? >> Mm-hmm. Yeah, yeah, it's a good question. And honestly, to piggyback off of Viraj, there's so many. I think what keeps me so energized is how mission critical both our product and data orchestration is, and those use cases really are incredible and we work with customers of all shapes and sizes. But, to answer your question, right, so why use Astra? Why use our commercial products? There's so many people using open source, why pay for something more than that? So, you know, the baseline for our business really is that Airflow has grown exponentially over the last five years, and like we said has become an industry standard that we're confident there's a huge opportunity for us as a company and as a team. But, we also strongly believe that being great at running Airflow, you know, doesn't make you a successful company at what you do. What makes you a successful company at what you do is building great products and solving problems and solving pin points of your own customers, right? And, that differentiating value isn't being amazing at running Airflow. That should be our job. And so, we want to abstract those customers from meaning to do things like manage Kubernetes infrastructure that you need to run Airflow, and then hiring someone full-time to go do that. Which can be hard, but again doesn't add differentiating value to your team, or to your product, or to your customers. So, folks to get away from managing that infrastructure sort of a base, a base layer. Folks who are looking for differentiating features that make their team more productive and allows them to spend less time tweaking Airflow configurations and more time working with the data that they're getting from their business. For help, getting, staying up with Airflow releases. There's a ton of, we've actually been pretty quick to come out with new Airflow features and releases, and actually just keeping up with that feature set and working strategically with a partner to help you make the most out of those feature sets is a key part of it. And, really it's, especially if you're an organization who currently is committed to using Airflow, you likely have a lot of Airflow environments across your organization. And, being able to see those Airflow environments in a single place and being able to enable your data practitioners to create Airflow environments with a click of a button, and then use, for example, our command line to develop your Airflow Dags locally and push them up to our product, and use all of the sort of testing and monitoring and observability that we have on top of our product is such a key. It sounds so simple, especially if you use Airflow, but really those things are, you know, baseline value props that we have for the customers that continue to be excited to work with us. And of course, I think we can go beyond that and there's, we have ambitions to add whole, a whole bunch of features and expand into different types of personas. >> Right? >> But really our main value prop is for companies who are committed to Airflow and want to abstract themselves and make use of some of the differentiating features that we now have at Astronomer. >> Got it. Awesome. >> Thank you. One thing, one thing I'll add to that, Paola, and I think you did a good job of saying is because every company's trying to be a data company, companies are at different parts of their journey along that, right? And we want to meet customers where they are, and take them through it to where they want to go. So, on one end you have folks who are like, "Hey, we're just building a data team here. We have a new initiative. We heard about Airflow. How do you help us out?" On the farther end, you know, we have some customers that have been using Airflow for five plus years and they're like, "Hey, this is awesome. We have 10 more teams we want to bring on. How can you help with this? How can we do more stuff in the open source with you? How can we tell our story together?" And, it's all about kind of taking this vast community of data users everywhere, seeing where they're at, and saying like, "Hey, Astro and Airflow can take you to the next place that you want to go." >> Which is incredibly- >> Mm-hmm. >> and you bring up a great point, Viraj, that every company is somewhere in a different place on that journey. And it's, and it's complex. But it sounds to me like a lot of what you're doing is really stripping away a lot of the complexity, really enabling folks to use their data as quickly as possible, so that it's relevant and they can serve up, you know, the right products and services to whoever wants what. Really incredibly important. We're almost out of time, but I'd love to get both of your perspectives on what's next for Astronomer. You give us a a great overview of what the company's doing, the value in it for customers. Paola, from your lens as one of the co-founders, what's next? >> Yeah, I mean, I think we'll continue to, I think cultivate in that open source community. I think we'll continue to build products that are open sourced as part of our ecosystem. I also think that we'll continue to build products that actually make Airflow, and getting started with Airflow, more accessible. So, sort of lowering that barrier to entry to our products, whether that's price wise or infrastructure requirement wise. I think making it easier for folks to get started and get their hands on our product is super important for us this year. And really it's about, I think, you know, for us, it's really about focused execution this year and all of the sort of core principles that we've been talking about. And continuing to invest in all of the things around our product that again, enable teams to use Airflow more effectively and efficiently. >> And that efficiency piece is, everybody needs that. Last question, Viraj, for you. What do you see in terms of the next year for Astronomer and for your role? >> Yeah, you know, I think Paola did a really good job of laying it out. So it's, it's really hard to disagree with her on anything, right? I think executing is definitely the most important thing. My own personal bias on that is I think more than ever it's important to really galvanize the community around airflow. So, we're going to be focusing on that a lot. We want to make it easier for our users to get get our product into their hands, be that open source users or commercial users. And last, but certainly not least, is we're also really excited about Data Lineage and this other open source project in our umbrella called Open Lineage to make it so that there's a standard way for users to get lineage out of different systems that they use. When we think about what's in store for data lineage and needing to audit the way automated decisions are being made. You know, I think that's just such an important thing that companies are really just starting with, and I don't think there's a solution that's emerged that kind of ties it all together. So, we think that as we kind of grow the role of Airflow, right, we can also make it so that we're helping solve, we're helping customers solve their lineage problems all in Astro, which is our kind of the best of both worlds for us. >> Awesome. I can definitely feel and hear the enthusiasm and the passion that you both bring to Astronomer, to your customers, to your team. I love it. We could keep talking more and more, so you're going to have to come back. (laughing) Viraj, Paola, thank you so much for joining me today on this showcase conversation. We really appreciate your insights and all the context that you provided about Astronomer. >> Thank you so much for having us. >> My pleasure. For my guests, I'm Lisa Martin. You're watching this Cube conversation. (soft electronic music)

(upbeat electronic music) >> Hello everyone, welcome to this CUBE conversation here from the studios in the CUBE in Palo Alto, California. I'm John Furrier, your host. We're featuring a startup, Astronomer. Astronomer.io is the URL, check it out. And we're going to have a great conversation around one of the most important topics hitting the industry, and that is the future of machine learning and AI, and the data that powers it underneath it. There's a lot of things that need to get done, and we're excited to have some of the co-founders of Astronomer here. Viraj Parekh, who is co-founder of Astronomer, and Paola Peraza Calderon, another co-founder, both with Astronomer. Thanks for coming on. First of all, how many co-founders do you guys have? >> You know, I think the answer's around six or seven. I forget the exact, but there's really been a lot of people around the table who've worked very hard to get this company to the point that it's at. We have long ways to go, right? But there's been a lot of people involved that have been absolutely necessary for the path we've been on so far. >> Thanks for that, Viraj, appreciate that. The first question I want to get out on the table, and then we'll get into some of the details, is take a minute to explain what you guys are doing. How did you guys get here? Obviously, multiple co-founders, sounds like a great project. The timing couldn't have been better. ChatGPT has essentially done so much public relations for the AI industry to kind of highlight this shift that's happening. It's real, we've been chronicalizing, take a minute to explain what you guys do. >> Yeah, sure, we can get started. So, yeah, when Viraj and I joined Astronomer in 2017, we really wanted to build a business around data, and we were using an open source project called Apache Airflow that we were just using sort of as customers ourselves. And over time, we realized that there was actually a market for companies who use Apache Airflow, which is a data pipeline management tool, which we'll get into, and that running Airflow is actually quite challenging, and that there's a big opportunity for us to create a set of commercial products and an opportunity to grow that open source community and actually build a company around that. So the crux of what we do is help companies run data pipelines with Apache Airflow. And certainly we've grown in our ambitions beyond that, but that's sort of the crux of what we do for folks. >> You know, data orchestration, data management has always been a big item in the old classic data infrastructure. But with AI, you're seeing a lot more emphasis on scale, tuning, training. Data orchestration is the center of the value proposition, when you're looking at coordinating resources, it's one of the most important things. Can you guys explain what data orchestration entails? What does it mean? Take us through the definition of what data orchestration entails. >> Yeah, for sure. I can take this one, and Viraj, feel free to jump in. So if you google data orchestration, here's what you're going to get. You're going to get something that says, "Data orchestration is the automated process" "for organizing silo data from numerous" "data storage points, standardizing it," "and making it accessible and prepared for data analysis." And you say, "Okay, but what does that actually mean," right, and so let's give sort of an an example. So let's say you're a business and you have sort of the following basic asks of your data team, right? Okay, give me a dashboard in Sigma, for example, for the number of customers or monthly active users, and then make sure that that gets updated on an hourly basis. And then number two, a consistent list of active customers that I have in HubSpot so that I can send them a monthly product newsletter, right? Two very basic asks for all sorts of companies and organizations. And when that data team, which has data engineers, data scientists, ML engineers, data analysts get that request, they're looking at an ecosystem of data sources that can help them get there, right? And that includes application databases, for example, that actually have in product user behavior and third party APIs from tools that the company uses that also has different attributes and qualities of those customers or users. And that data team needs to use tools like Fivetran to ingest data, a data warehouse, like Snowflake or Databricks to actually store that data and do analysis on top of it, a tool like DBT to do transformations and make sure that data is standardized in the way that it needs to be, a tool like Hightouch for reverse ETL. I mean, we could go on and on. There's so many partners of ours in this industry that are doing really, really exciting and critical things for those data movements. And the whole point here is that data teams have this plethora of tooling that they use to both ingest the right data and come up with the right interfaces to transform and interact with that data. And data orchestration, in our view, is really the heartbeat of all of those processes, right? And tangibly the unit of data orchestration is a data pipeline, a set of tasks or jobs that each do something with data over time and eventually run that on a schedule to make sure that those things are happening continuously as time moves on and the company advances. And so, for us, we're building a business around Apache Airflow, which is a workflow management tool that allows you to author, run, and monitor data pipelines. And so when we talk about data orchestration, we talk about sort of two things. One is that crux of data pipelines that, like I said, connect that large ecosystem of data tooling in your company. But number two, it's not just that data pipeline that needs to run every day, right? And Viraj will probably touch on this as we talk more about Astronomer and our value prop on top of Airflow. But then it's all the things that you need to actually run data and production and make sure that it's trustworthy, right? So it's actually not just that you're running things on a schedule, but it's also things like CICD tooling, secure secrets management, user permissions, monitoring, data lineage, documentation, things that enable other personas in your data team to actually use those tools. So long-winded way of saying that it's the heartbeat, we think, of of the data ecosystem, and certainly goes beyond scheduling, but again, data pipelines are really at the center of it. >> One of the things that jumped out, Viraj, if you can get into this, I'd like to hear more about how you guys look at all those little tools that are out. You mentioned a variety of things. You look at the data infrastructure, it's not just one stack. You've got an analytic stack, you've got a realtime stack, you've got a data lake stack, you got an AI stack potentially. I mean you have these stacks now emerging in the data world that are fundamental, that were once served by either a full package, old school software, and then a bunch of point solution. You mentioned Fivetran there, I would say in the analytics stack. Then you got S3, they're on the data lake stack. So all these things are kind of munged together. >> Yeah. >> How do you guys fit into that world? You make it easier, or like, what's the deal? >> Great question, right? And you know, I think that one of the biggest things we've found in working with customers over the last however many years is that if a data team is using a bunch of tools to get what they need done, and the number of tools they're using is growing exponentially and they're kind of roping things together here and there, that's actually a sign of a productive team, not a bad thing, right? It's because that team is moving fast. They have needs that are very specific to them, and they're trying to make something that's exactly tailored to their business. So a lot of times what we find is that customers have some sort of base layer, right? That's kind of like, it might be they're running most of the things in AWS, right? And then on top of that, they'll be using some of the things AWS offers, things like SageMaker, Redshift, whatever, but they also might need things that their cloud can't provide. Something like Fivetran, or Hightouch, those are other tools. And where data orchestration really shines, and something that we've had the pleasure of helping our customers build, is how do you take all those requirements, all those different tools and whip them together into something that fulfills a business need? So that somebody can read a dashboard and trust the number that it says, or somebody can make sure that the right emails go out to their customers. And Airflow serves as this amazing kind of glue between that data stack, right? It's to make it so that for any use case, be it ELT pipelines, or machine learning, or whatever, you need different things to do them, and Airflow helps tie them together in a way that's really specific for a individual business' needs. >> Take a step back and share the journey of what you guys went through as a company startup. So you mentioned Apache, open source. I was just having an interview with a VC, we were talking about foundational models. You got a lot of proprietary and open source development going on. It's almost the iPhone/Android moment in this whole generative space and foundational side. This is kind of important, the open source piece of it. Can you share how you guys started? And I can imagine your customers probably have their hair on fire and are probably building stuff on their own. Are you guys helping them? Take us through, 'cause you guys are on the front end of a big, big wave, and that is to make sense of the chaos, rain it in. Take us through your journey and why this is important. >> Yeah, Paola, I can take a crack at this, then I'll kind of hand it over to you to fill in whatever I miss in details. But you know, like Paola is saying, the heart of our company is open source, because we started using Airflow as an end user and started to say like, "Hey wait a second," "more and more people need this." Airflow, for background, started at Airbnb, and they were actually using that as a foundation for their whole data stack. Kind of how they made it so that they could give you recommendations, and predictions, and all of the processes that needed orchestrated. Airbnb created Airflow, gave it away to the public, and then fast forward a couple years and we're building a company around it, and we're really excited about that. >> That's a beautiful thing. That's exactly why open source is so great. >> Yeah, yeah. And for us, it's really been about watching the community and our customers take these problems, find a solution to those problems, standardize those solutions, and then building on top of that, right? So we're reaching to a point where a lot of our earlier customers who started to just using Airflow to get the base of their BI stack down and their reporting in their ELP infrastructure, they've solved that problem and now they're moving on to things like doing machine learning with their data, because now that they've built that foundation, all the connective tissue for their data arriving on time and being orchestrated correctly is happening, they can build a layer on top of that. And it's just been really, really exciting kind of watching what customers do once they're empowered to pick all the tools that they need, tie them together in the way they need to, and really deliver real value to their business. >> Can you share some of the use cases of these customers? Because I think that's where you're starting to see the innovation. What are some of the companies that you're working with, what are they doing? >> Viraj, I'll let you take that one too. (group laughs) >> So you know, a lot of it is... It goes across the gamut, right? Because it doesn't matter what you are, what you're doing with data, it needs to be orchestrated. So there's a lot of customers using us for their ETL and ELT reporting, right? Just getting data from other disparate sources into one place and then building on top of that. Be it building dashboards, answering questions for the business, building other data products and so on and so forth. From there, these use cases evolve a lot. You do see folks doing things like fraud detection, because Airflow's orchestrating how transactions go, transactions get analyzed. They do things like analyzing marketing spend to see where your highest ROI is. And then you kind of can't not talk about all of the machine learning that goes on, right? Where customers are taking data about their own customers, kind of analyze and aggregating that at scale, and trying to automate decision making processes. So it goes from your most basic, what we call data plumbing, right? Just to make sure data's moving as needed, all the ways to your more exciting expansive use cases around automated decision making and machine learning. >> And I'd say, I mean, I'd say that's one of the things that I think gets me most excited about our future, is how critical Airflow is to all of those processes, and I think when you know a tool is valuable is when something goes wrong and one of those critical processes doesn't work. And we know that our system is so mission critical to answering basic questions about your business and the growth of your company for so many organizations that we work with. So it's, I think, one of the things that gets Viraj and I and the rest of our company up every single morning is knowing how important the work that we do for all of those use cases across industries, across company sizes, and it's really quite energizing. >> It was such a big focus this year at AWS re:Invent, the role of data. And I think one of the things that's exciting about the open AI and all the movement towards large language models is that you can integrate data into these models from outside. So you're starting to see the integration easier to deal with. Still a lot of plumbing issues. So a lot of things happening. So I have to ask you guys, what is the state of the data orchestration area? Is it ready for disruption? Has it already been disrupted? Would you categorize it as a new first inning kind of opportunity, or what's the state of the data orchestration area right now? Both technically and from a business model standpoint. How would you guys describe that state of the market? >> Yeah, I mean, I think in a lot of ways, in some ways I think we're category creating. Schedulers have been around for a long time. I released a data presentation sort of on the evolution of going from something like Kron, which I think was built in like the 1970s out of Carnegie Mellon. And that's a long time ago, that's 50 years ago. So sort of like the basic need to schedule and do something with your data on a schedule is not a new concept. But to our point earlier, I think everything that you need around your ecosystem, first of all, the number of data tools and developer tooling that has come out industry has 5X'd over the last 10 years. And so obviously as that ecosystem grows, and grows, and grows, and grows, the need for orchestration only increases. And I think, as Astronomer, I think we... And we work with so many different types of companies, companies that have been around for 50 years, and companies that got started not even 12 months ago. And so I think for us it's trying to, in a ways, category create and adjust sort of what we sell and the value that we can provide for companies all across that journey. There are folks who are just getting started with orchestration, and then there's folks who have such advanced use case, 'cause they're hitting sort of a ceiling and only want to go up from there. And so I think we, as a company, care about both ends of that spectrum, and certainly want to build and continue building products for companies of all sorts, regardless of where they are on the maturity curve of data orchestration. >> That's a really good point, Paola. And I think the other thing to really take into account is it's the companies themselves, but also individuals who have to do their jobs. If you rewind the clock like 5 or 10 years ago, data engineers would be the ones responsible for orchestrating data through their org. But when we look at our customers today, it's not just data engineers anymore. There's data analysts who sit a lot closer to the business, and the data scientists who want to automate things around their models. So this idea that orchestration is this new category is right on the money. And what we're finding is the need for it is spreading to all parts of the data team, naturally where Airflow's emerged as an open source standard and we're hoping to take things to the next level. >> That's awesome. We've been up saying that the data market's kind of like the SRE with servers, right? You're going to need one person to deal with a lot of data, and that's data engineering, and then you're got to have the practitioners, the democratization. Clearly that's coming in what you're seeing. So I have to ask, how do you guys fit in from a value proposition standpoint? What's the pitch that you have to customers, or is it more inbound coming into you guys? Are you guys doing a lot of outreach, customer engagements? I'm sure they're getting a lot of great requirements from customers. What's the current value proposition? How do you guys engage? >> Yeah, I mean, there's so many... Sorry, Viraj, you can jump in. So there's so many companies using Airflow, right? So the baseline is that the open source project that is Airflow that came out of Airbnb, over five years ago at this point, has grown exponentially in users and continues to grow. And so the folks that we sell to primarily are folks who are already committed to using Apache Airflow, need data orchestration in their organization, and just want to do it better, want to do it more efficiently, want to do it without managing that infrastructure. And so our baseline proposition is for those organizations. Now to Viraj's point, obviously I think our ambitions go beyond that, both in terms of the personas that we addressed and going beyond that data engineer, but really it's to start at the baseline, as we continue to grow our our company, it's really making sure that we're adding value to folks using Airflow and help them do so in a better way, in a larger way, in a more efficient way, and that's really the crux of who we sell to. And so to answer your question on, we get a lot of inbound because they're... >> You have a built in audience. (laughs) >> The world that use it. Those are the folks who we talk to and come to our website and chat with us and get value from our content. I mean, the power of the opensource community is really just so, so big, and I think that's also one of the things that makes this job fun. >> And you guys are in a great position. Viraj, you can comment a little, get your reaction. There's been a big successful business model to starting a company around these big projects for a lot of reasons. One is open source is continuing to be great, but there's also supply chain challenges in there. There's also we want to continue more innovation and more code and keeping it free and and flowing. And then there's the commercialization of productizing it, operationalizing it. This is a huge new dynamic, I mean, in the past 5 or so years, 10 years, it's been happening all on CNCF from other areas like Apache, Linux Foundation, they're all implementing this. This is a huge opportunity for entrepreneurs to do this. >> Yeah, yeah. Open source is always going to be core to what we do, because we wouldn't exist without the open source community around us. They are huge in numbers. Oftentimes they're nameless people who are working on making something better in a way that everybody benefits from it. But open source is really hard, especially if you're a company whose core competency is running a business, right? Maybe you're running an e-commerce business, or maybe you're running, I don't know, some sort of like, any sort of business, especially if you're a company running a business, you don't really want to spend your time figuring out how to run open source software. You just want to use it, you want to use the best of it, you want to use the community around it, you want to be able to google something and get answers for it, you want the benefits of open source. You don't have the time or the resources to invest in becoming an expert in open source, right? And I think that dynamic is really what's given companies like us an ability to kind of form businesses around that in the sense that we'll make it so people get the best of both worlds. You'll get this vast open ecosystem that you can build on top of, that you can benefit from, that you can learn from. But you won't have to spend your time doing undifferentiated heavy lifting. You can do things that are just specific to your business. >> It's always been great to see that business model evolve. We used a debate 10 years ago, can there be another Red Hat? And we said, not really the same, but there'll be a lot of little ones that'll grow up to be big soon. Great stuff. Final question, can you guys share the history of the company? The milestones of Astromer's journey in data orchestration? >> Yeah, we could. So yeah, I mean, I think, so Viraj and I have obviously been at Astronomer along with our other founding team and leadership folks for over five years now. And it's been such an incredible journey of learning, of hiring really amazing people, solving, again, mission critical problems for so many types of organizations. We've had some funding that has allowed us to invest in the team that we have and in the software that we have, and that's been really phenomenal. And so that investment, I think, keeps us confident, even despite these sort of macroeconomic conditions that we're finding ourselves in. And so honestly, the milestones for us are focusing on our product, focusing on our customers over the next year, focusing on that market for us that we know can get valuable out of what we do, and making developers' lives better, and growing the open source community and making sure that everything that we're doing makes it easier for folks to get started, to contribute to the project and to feel a part of the community that we're cultivating here. >> You guys raised a little bit of money. How much have you guys raised? >> Don't know what the total is, but it's in the ballpark over $200 million. It feels good to... >> A little bit of capital. Got a little bit of cap to work with there. Great success. I know as a Series C Financing, you guys have been down. So you're up and running, what's next? What are you guys looking to do? What's the big horizon look like for you from a vision standpoint, more hiring, more product, what is some of the key things you're looking at doing? >> Yeah, it's really a little of all of the above, right? Kind of one of the best and worst things about working at earlier stage startups is there's always so much to do and you often have to just kind of figure out a way to get everything done. But really investing our product over the next, at least over the course of our company lifetime. And there's a lot of ways we want to make it more accessible to users, easier to get started with, easier to use, kind of on all areas there. And really, we really want to do more for the community, right, like I was saying, we wouldn't be anything without the large open source community around us. And we want to figure out ways to give back more in more creative ways, in more code driven ways, in more kind of events and everything else that we can keep those folks galvanized and just keep them happy using Airflow. >> Paola, any final words as we close out? >> No, I mean, I'm super excited. I think we'll keep growing the team this year. We've got a couple of offices in the the US, which we're excited about, and a fully global team that will only continue to grow. So Viraj and I are both here in New York, and we're excited to be engaging with our coworkers in person finally, after years of not doing so. We've got a bustling office in San Francisco as well. So growing those teams and continuing to hire all over the world, and really focusing on our product and the open source community is where our heads are at this year. So, excited. >> Congratulations. 200 million in funding, plus. Good runway, put that money in the bank, squirrel it away. It's a good time to kind of get some good interest on it, but still grow. Congratulations on all the work you guys do. We appreciate you and the open source community does, and good luck with the venture, continue to be successful, and we'll see you at the Startup Showcase. >> Thank you. >> Yeah, thanks so much, John. Appreciate it. >> Okay, that's the CUBE Conversation featuring astronomer.io, that's the website. Astronomer is doing well. Multiple rounds of funding, over 200 million in funding. Open source continues to lead the way in innovation. Great business model, good solution for the next gen cloud scale data operations, data stacks that are emerging. I'm John Furrier, your host, thanks for watching. (soft upbeat music)

(soft music) >> Hello everyone, welcome to this Cube conversation here from the studios of theCube in Palo Alto, California. John Furrier, your host. We're featuring a startup, Astronomer, astronomer.io is the url. Check it out. And we're going to have a great conversation around one of the most important topics hitting the industry, and that is the future of machine learning and AI and the data that powers it underneath it. There's a lot of things that need to get done, and we're excited to have some of the co-founders of Astronomer here. Viraj Parekh, who is co-founder and Paola Peraza Calderon, another co-founder, both with Astronomer. Thanks for coming on. First of all, how many co-founders do you guys have? >> You know, I think the answer's around six or seven. I forget the exact, but there's really been a lot of people around the table, who've worked very hard to get this company to the point that it's at. And we have long ways to go, right? But there's been a lot of people involved that are, have been absolutely necessary for the path we've been on so far. >> Thanks for that, Viraj, appreciate that. The first question I want to get out on the table, and then we'll get into some of the details, is take a minute to explain what you guys are doing. How did you guys get here? Obviously, multiple co-founders sounds like a great project. The timing couldn't have been better. ChatGPT has essentially done so much public relations for the AI industry. Kind of highlight this shift that's happening. It's real. We've been chronologicalizing, take a minute to explain what you guys do. >> Yeah, sure. We can get started. So yeah, when Astronomer, when Viraj and I joined Astronomer in 2017, we really wanted to build a business around data and we were using an open source project called Apache Airflow, that we were just using sort of as customers ourselves. And over time, we realized that there was actually a market for companies who use Apache Airflow, which is a data pipeline management tool, which we'll get into. And that running Airflow is actually quite challenging and that there's a lot of, a big opportunity for us to create a set of commercial products and opportunity to grow that open source community and actually build a company around that. So the crux of what we do is help companies run data pipelines with Apache Airflow. And certainly we've grown in our ambitions beyond that, but that's sort of the crux of what we do for folks. >> You know, data orchestration, data management has always been a big item, you know, in the old classic data infrastructure. But with AI you're seeing a lot more emphasis on scale, tuning, training. You know, data orchestration is the center of the value proposition when you're looking at coordinating resources, it's one of the most important things. Could you guys explain what data orchestration entails? What does it mean? Take us through the definition of what data orchestration entails. >> Yeah, for sure. I can take this one and Viraj feel free to jump in. So if you google data orchestration, you know, here's what you're going to get. You're going to get something that says, data orchestration is the automated process for organizing silo data from numerous data storage points to organizing it and making it accessible and prepared for data analysis. And you say, okay, but what does that actually mean, right? And so let's give sort of an example. So let's say you're a business and you have sort of the following basic asks of your data team, right? Hey, give me a dashboard in Sigma, for example, for the number of customers or monthly active users and then make sure that that gets updated on an hourly basis. And then number two, a consistent list of active customers that I have in HubSpot so that I can send them a monthly product newsletter, right? Two very basic asks for all sorts of companies and organizations. And when that data team, which has data engineers, data scientists, ML engineers, data analysts get that request, they're looking at an ecosystem of data sources that can help them get there, right? And that includes application databases, for example, that actually have end product user behavior and third party APIs from tools that the company uses that also has different attributes and qualities of those customers or users. And that data team needs to use tools like Fivetran, to ingest data, a data warehouse like Snowflake or Databricks to actually store that data and do analysis on top of it, a tool like DBT to do transformations and make sure that that data is standardized in the way that it needs to be, a tool like Hightouch for reverse ETL. I mean, we could go on and on. There's so many partners of ours in this industry that are doing really, really exciting and critical things for those data movements. And the whole point here is that, you know, data teams have this plethora of tooling that they use to both ingest the right data and come up with the right interfaces to transform and interact with that data. And data orchestration in our view is really the heartbeat of all of those processes, right? And tangibly the unit of data orchestration, you know, is a data pipeline, a set of tasks or jobs that each do something with data over time and eventually run that on a schedule to make sure that those things are happening continuously as time moves on. And, you know, the company advances. And so, you know, for us, we're building a business around Apache Airflow, which is a workflow management tool that allows you to author, run and monitor data pipelines. And so when we talk about data orchestration, we talk about sort of two things. One is that crux of data pipelines that, like I said, connect that large ecosystem of data tooling in your company. But number two, it's not just that data pipeline that needs to run every day, right? And Viraj will probably touch on this as we talk more about Astronomer and our value prop on top of Airflow. But then it's all the things that you need to actually run data and production and make sure that it's trustworthy, right? So it's actually not just that you're running things on a schedule, but it's also things like CI/CD tooling, right? Secure secrets management, user permissions, monitoring, data lineage, documentation, things that enable other personas in your data team to actually use those tools. So long-winded way of saying that, it's the heartbeat that we think of the data ecosystem and certainly goes beyond scheduling, but again, data pipelines are really at the center of it. >> You know, one of the things that jumped out Viraj, if you can get into this, I'd like to hear more about how you guys look at all those little tools that are out there. You mentioned a variety of things. You know, if you look at the data infrastructure, it's not just one stack. You've got an analytic stack, you've got a realtime stack, you've got a data lake stack, you got an AI stack potentially. I mean you have these stacks now emerging in the data world that are >> Yeah. - >> fundamental, but we're once served by either a full package, old school software, and then a bunch of point solution. You mentioned Fivetran there, I would say in the analytics stack. Then you got, you know, S3, they're on the data lake stack. So all these things are kind of munged together. >> Yeah. >> How do you guys fit into that world? You make it easier or like, what's the deal? >> Great question, right? And you know, I think that one of the biggest things we've found in working with customers over, you know, the last however many years, is that like if a data team is using a bunch of tools to get what they need done and the number of tools they're using is growing exponentially and they're kind of roping things together here and there, that's actually a sign of a productive team, not a bad thing, right? It's because that team is moving fast. They have needs that are very specific to them and they're trying to make something that's exactly tailored to their business. So a lot of times what we find is that customers have like some sort of base layer, right? That's kind of like, you know, it might be they're running most of the things in AWS, right? And then on top of that, they'll be using some of the things AWS offers, you know, things like SageMaker, Redshift, whatever. But they also might need things that their Cloud can't provide, you know, something like Fivetran or Hightouch or anything of those other tools and where data orchestration really shines, right? And something that we've had the pleasure of helping our customers build, is how do you take all those requirements, all those different tools and whip them together into something that fulfills a business need, right? Something that makes it so that somebody can read a dashboard and trust the number that it says or somebody can make sure that the right emails go out to their customers. And Airflow serves as this amazing kind of glue between that data stack, right? It's to make it so that for any use case, be it ELT pipelines or machine learning or whatever, you need different things to do them and Airflow helps tie them together in a way that's really specific for a individual business's needs. >> Take a step back and share the journey of what your guys went through as a company startup. So you mentioned Apache open source, you know, we were just, I was just having an interview with the VC, we were talking about foundational models. You got a lot of proprietary and open source development going on. It's almost the iPhone, Android moment in this whole generative space and foundational side. This is kind of important, the open source piece of it. Can you share how you guys started? And I can imagine your customers probably have their hair on fire and are probably building stuff on their own. How do you guys, are you guys helping them? Take us through, 'cuz you guys are on the front end of a big, big wave and that is to make sense of the chaos, reigning it in. Take us through your journey and why this is important. >> Yeah Paola, I can take a crack at this and then I'll kind of hand it over to you to fill in whatever I miss in details. But you know, like Paola is saying, the heart of our company is open source because we started using Airflow as an end user and started to say like, "Hey wait a second". Like more and more people need this. Airflow, for background, started at Airbnb and they were actually using that as the foundation for their whole data stack. Kind of how they made it so that they could give you recommendations and predictions and all of the processes that need to be or needed to be orchestrated. Airbnb created Airflow, gave it away to the public and then, you know, fast forward a couple years and you know, we're building a company around it and we're really excited about that. >> That's a beautiful thing. That's exactly why open source is so great. >> Yeah, yeah. And for us it's really been about like watching the community and our customers take these problems, find solution to those problems, build standardized solutions, and then building on top of that, right? So we're reaching to a point where a lot of our earlier customers who started to just using Airflow to get the base of their BI stack down and their reporting and their ELP infrastructure, you know, they've solved that problem and now they're moving onto things like doing machine learning with their data, right? Because now that they've built that foundation, all the connective tissue for their data arriving on time and being orchestrated correctly is happening, they can build the layer on top of that. And it's just been really, really exciting kind of watching what customers do once they're empowered to pick all the tools that they need, tie them together in the way they need to, and really deliver real value to their business. >> Can you share some of the use cases of these customers? Because I think that's where you're starting to see the innovation. What are some of the companies that you're working with, what are they doing? >> Raj, I'll let you take that one too. (all laughing) >> Yeah. (all laughing) So you know, a lot of it is, it goes across the gamut, right? Because all doesn't matter what you are, what you're doing with data, it needs to be orchestrated. So there's a lot of customers using us for their ETL and ELT reporting, right? Just getting data from all the disparate sources into one place and then building on top of that, be it building dashboards, answering questions for the business, building other data products and so on and so forth. From there, these use cases evolve a lot. You do see folks doing things like fraud detection because Airflow's orchestrating how transactions go. Transactions get analyzed, they do things like analyzing marketing spend to see where your highest ROI is. And then, you know, you kind of can't not talk about all of the machine learning that goes on, right? Where customers are taking data about their own customers kind of analyze and aggregating that at scale and trying to automate decision making processes. So it goes from your most basic, what we call like data plumbing, right? Just to make sure data's moving as needed. All the ways to your more exciting and sexy use cases around like automated decision making and machine learning. >> And I'd say, I mean, I'd say that's one of the things that I think gets me most excited about our future is how critical Airflow is to all of those processes, you know? And I think when, you know, you know a tool is valuable is when something goes wrong and one of those critical processes doesn't work. And we know that our system is so mission critical to answering basic, you know, questions about your business and the growth of your company for so many organizations that we work with. So it's, I think one of the things that gets Viraj and I, and the rest of our company up every single morning, is knowing how important the work that we do for all of those use cases across industries, across company sizes. And it's really quite energizing. >> It was such a big focus this year at AWS re:Invent, the role of data. And I think one of the things that's exciting about the open AI and all the movement towards large language models, is that you can integrate data into these models, right? From outside, right? So you're starting to see the integration easier to deal with, still a lot of plumbing issues. So a lot of things happening. So I have to ask you guys, what is the state of the data orchestration area? Is it ready for disruption? Is it already been disrupted? Would you categorize it as a new first inning kind of opportunity or what's the state of the data orchestration area right now? Both, you know, technically and from a business model standpoint, how would you guys describe that state of the market? >> Yeah, I mean I think, I think in a lot of ways we're, in some ways I think we're categoric rating, you know, schedulers have been around for a long time. I recently did a presentation sort of on the evolution of going from, you know, something like KRON, which I think was built in like the 1970s out of Carnegie Mellon. And you know, that's a long time ago. That's 50 years ago. So it's sort of like the basic need to schedule and do something with your data on a schedule is not a new concept. But to our point earlier, I think everything that you need around your ecosystem, first of all, the number of data tools and developer tooling that has come out the industry has, you know, has some 5X over the last 10 years. And so obviously as that ecosystem grows and grows and grows and grows, the need for orchestration only increases. And I think, you know, as Astronomer, I think we, and there's, we work with so many different types of companies, companies that have been around for 50 years and companies that got started, you know, not even 12 months ago. And so I think for us, it's trying to always category create and adjust sort of what we sell and the value that we can provide for companies all across that journey. There are folks who are just getting started with orchestration and then there's folks who have such advanced use case 'cuz they're hitting sort of a ceiling and only want to go up from there. And so I think we as a company, care about both ends of that spectrum and certainly have want to build and continue building products for companies of all sorts, regardless of where they are on the maturity curve of data orchestration. >> That's a really good point Paola. And I think the other thing to really take into account is it's the companies themselves, but also individuals who have to do their jobs. You know, if you rewind the clock like five or 10 years ago, data engineers would be the ones responsible for orchestrating data through their org. But when we look at our customers today, it's not just data engineers anymore. There's data analysts who sit a lot closer to the business and the data scientists who want to automate things around their models. So this idea that orchestration is this new category is spot on, is right on the money. And what we're finding is it's spreading, the need for it, is spreading to all parts of the data team naturally where Airflows have emerged as an open source standard and we're hoping to take things to the next level. >> That's awesome. You know, we've been up saying that the data market's kind of like the SRE with servers, right? You're going to need one person to deal with a lot of data and that's data engineering and then you're going to have the practitioners, the democratization. Clearly that's coming in what you're seeing. So I got to ask, how do you guys fit in from a value proposition standpoint? What's the pitch that you have to customers or is it more inbound coming into you guys? Are you guys doing a lot of outreach, customer engagements? I'm sure they're getting a lot of great requirements from customers. What's the current value proposition? How do you guys engage? >> Yeah, I mean we've, there's so many, there's so many. Sorry Raj, you can jump in. - >> It's okay. So there's so many companies using Airflow, right? So our, the baseline is that the open source project that is Airflow that was, that came out of Airbnb, you know, over five years ago at this point, has grown exponentially in users and continues to grow. And so the folks that we sell to primarily are folks who are already committed to using Apache Airflow, need data orchestration in the organization and just want to do it better, want to do it more efficiently, want to do it without managing that infrastructure. And so our baseline proposition is for those organizations. Now to Raj's point, obviously I think our ambitions go beyond that, both in terms of the personas that we addressed and going beyond that data engineer, but really it's for, to start at the baseline. You know, as we continue to grow our company, it's really making sure that we're adding value to folks using Airflow and help them do so in a better way, in a larger way and a more efficient way. And that's really the crux of who we sell to. And so to answer your question on, we actually, we get a lot of inbound because they're are so many - >> A built-in audience. >> In the world that use it, that those are the folks who we talk to and come to our website and chat with us and get value from our content. I mean the power of the open source community is really just so, so big. And I think that's also one of the things that makes this job fun, so. >> And you guys are in a great position, Viraj, you can comment, to get your reaction. There's been a big successful business model to starting a company around these big projects for a lot of reasons. One is open source is continuing to be great, but there's also supply chain challenges in there. There's also, you know, we want to continue more innovation and more code and keeping it free and and flowing. And then there's the commercialization of product-izing it, operationalizing it. This is a huge new dynamic. I mean, in the past, you know, five or so years, 10 years, it's been happening all on CNCF from other areas like Apache, Linux Foundation, they're all implementing this. This is a huge opportunity for entrepreneurs to do this. >> Yeah, yeah. Open source is always going to be core to what we do because, you know, we wouldn't exist without the open source community around us. They are huge in numbers. Oftentimes they're nameless people who are working on making something better in a way that everybody benefits from it. But open source is really hard, especially if you're a company whose core competency is running a business, right? Maybe you're running e-commerce business or maybe you're running, I don't know, some sort of like any sort of business, especially if you're a company running a business, you don't really want to spend your time figuring out how to run open source software. You just want to use it, you want to use the best of it, you want to use the community around it. You want to take, you want to be able to google something and get answers for it. You want the benefits of open source. You don't want to have, you don't have the time or the resources to invest in becoming an expert in open source, right? And I think that dynamic is really what's given companies like us an ability to kind of form businesses around that, in the sense that we'll make it so people get the best of both worlds. You'll get this vast open ecosystem that you can build on top of, you can benefit from, that you can learn from, but you won't have to spend your time doing undifferentiated heavy lifting. You can do things that are just specific to your business. >> It's always been great to see that business model evolved. We used to debate 10 years ago, can there be another red hat? And we said, not really the same, but there'll be a lot of little ones that'll grow up to be big soon. Great stuff. Final question, can you guys share the history of the company, the milestones of the Astronomer's journey in data orchestration? >> Yeah, we could. So yeah, I mean, I think, so Raj and I have obviously been at astronomer along with our other founding team and leadership folks, for over five years now. And it's been such an incredible journey of learning, of hiring really amazing people. Solving again, mission critical problems for so many types of organizations. You know, we've had some funding that has allowed us to invest in the team that we have and in the software that we have. And that's been really phenomenal. And so that investment, I think, keeps us confident even despite these sort of macroeconomic conditions that we're finding ourselves in. And so honestly, the milestones for us are focusing on our product, focusing on our customers over the next year, focusing on that market for us, that we know can get value out of what we do. And making developers' lives better and growing the open source community, you know, and making sure that everything that we're doing makes it easier for folks to get started to contribute to the project and to feel a part of the community that we're cultivating here. >> You guys raised a little bit of money. How much have you guys raised? >> I forget what the total is, but it's in the ballpark of 200, over $200 million. So it feels good - >> A little bit of capital. Got a little bit of cash to work with there. Great success. I know it's a Series C financing, you guys been down, so you're up and running. What's next? What are you guys looking to do? What's the big horizon look like for you? And from a vision standpoint, more hiring, more product, what is some of the key things you're looking at doing? >> Yeah, it's really a little of all of the above, right? Like, kind of one of the best and worst things about working at earlier stage startups is there's always so much to do and you often have to just kind of figure out a way to get everything done, but really invest in our product over the next, at least the next, over the course of our company lifetime. And there's a lot of ways we wanting to just make it more accessible to users, easier to get started with, easier to use all kind of on all areas there. And really, we really want to do more for the community, right? Like I was saying, we wouldn't be anything without the large open source community around us. And we want to figure out ways to give back more in more creative ways, in more code driven ways and more kind of events and everything else that we can do to keep those folks galvanized and just keeping them happy using Airflow. >> Paola, any final words as we close out? >> No, I mean, I'm super excited. You know, I think we'll keep growing the team this year. We've got a couple of offices in the US which we're excited about, and a fully global team that will only continue to grow. So Viraj and I are both here in New York and we're excited to be engaging with our coworkers in person. Finally, after years of not doing so, we've got a bustling office in San Francisco as well. So growing those teams and continuing to hire all over the world and really focusing on our product and the open source community is where our heads are at this year, so. >> Congratulations. - >> Excited. 200 million in funding plus good runway. Put that money in the bank, squirrel it away. You know, it's good to kind of get some good interest on it, but still grow. Congratulations on all the work you guys do. We appreciate you and the open sourced community does and good luck with the venture. Continue to be successful and we'll see you at the Startup Showcase. >> Thank you. - >> Yeah, thanks so much, John. Appreciate it. - >> It's theCube conversation, featuring astronomer.io, that's the website. Astronomer is doing well. Multiple rounds of funding, over 200 million in funding. Open source continues to lead the way in innovation. Great business model. Good solution for the next gen, Cloud, scale, data operations, data stacks that are emerging. I'm John Furrier, your host. Thanks for watching. (soft music)

>>The Cube Presents HPD Discovered 2020 >>two. >>Brought to You by H. P E >>Good >>Morning Live from the Venetian Expo Centre Lisa Martin Day Volonte Day two of the Cubes Coverage of HP Discover 22 We've had some great conversations yesterday. Today, full day, a content coming your way. We've got one of our alumni back with us. Neil MacDonald joins us, the executive vice president and general manager of Compute at HPD Neale, Great to have you back on the Cube. >>It's great to be back. And how cool is it to be able to do this face to face again instead of on zoom. Right. So >>great. Great. The keynote yesterday absolutely packed, so refreshing to see that many people eager to hear what HP has been doing. It's been three years since we've all gotten together in person. >>It is, and we've been busy. We've been busy. We've got to share some great news yesterday about some of the work that we're doing with HB Green Lake Cloud Platform and really bringing together all the capabilities across the company in a very unified, cohesive way to enable our customers to embrace that as a service experience we committed to Antonio three years ago, said we were gonna deliver everything we do as a company as a service through Green Lake and we've done it. And it's fantastic to see the momentum that that's really building and how it's breaking down the silos from different types of infrastructure and offer to really create integrated solutions for our customers. So that's been a lot of fun. >>Give us the scope of your role, your areas of responsibility. And then I'd love to hear some feedback. You've been a couple of days here around customers. What some of the feedback help us understand that. >>So at HP, I lead the Compute business, which is our largest business. That includes our hardware and software and services in the compute space. Both, um, what flows through the green late model, but also what throws flows through a traditional purchase model. So, um, that's, uh, that's about $13 billion business for the company and the core of so much of what we do, and it's a real honour to be leading a business that's such a a legacy in a franchise with with 30 years of innovation for our customers in an ocean of followers. Um and it's great to be able to start to share some of the next chapters in that with our customers this week. >>Well, it's almost half the business H p e and as we've talked about, it's an awesome time to be in the computer business. What are you seeing in terms of the trends? Obviously you're all in on as a service. But some customers say, Tell me I got a lot of capital. Yeah, absolutely. I'm fine with Capex. What are you hearing from customers in that regard? And presumably you're happy to sell them in a kind of Capex model? >>Absolutely. And in the current environment, in particular with with some of the economic headwinds that we're starting to stare down here, it's really important for organisations to continue to transform digitally but to be able to match their investments with the revenues as they're building new services and new capabilities. And for some organisations, the challenge of investing all the Capex up front is a big lift and there's quite a delay before they can really monetise all of that. So the power of HP Green Lake is enabling them to match their investment in the infrastructure on a pay as you go basis with the actual revenue they're going to generate from their new capability. So for lots of people that works. But for many other customers, it's it's much more palatable to continue in a Capex purchase, but and we're delighted to do that. A lot of my business still is in that mode. What's changing the or what are the needs, whether you're in the green light environment or in the Capex environment? Um, increasingly, the edge has become a bigger and bigger part of all of our worlds, right, the edges where we all live and work. We've all seen over the last couple of years enormous change in how that work experience and how the shape of businesses has changed, and that creates some challenges for infrastructure. So one of the things that we've announced and we shared some more details of this week is HP Green Light for Computer Ops Management, which is a location agnostic, cloud based management set up that enables you to automate and lifecycle, manage your physical compute infrastructure wherever it lies, so that might be in a distributed environment in hotel locations or out at the edge for so much more data is now being gathered and has to be computed on. So we're really excited about that. And the great thing is because it's fully integrated with HP. Green Light Cloud Platform is in there alongside the storage, alongside the connectivity alongside all the other capabilities. And we can bring those together in a very cohesive infrastructure view for our customers and then build workloads and services and tops. And that's that's really exciting. How have >>your customer conversations evolved, especially over the last couple of years as the edge has exploded? But we've been living in such uncertain times. Are you seeing a change there in the stakeholders rising up the C suite stack in terms of how do we really fine tune this? Because we've got to be competitive. We've got to be a data company. >>Well, that's so true because everybody has seen seen data as a currency and is desperately innovating and Modernising their business model, and with it, the underlying infrastructure and how they think about development. And nowhere is that truer than in enterprises that really becoming digital. First, organisations more and more companies are doing their own in house full stack, cloud native development and pivoting hard from a more traditional view of in house enterprise i t. And in that regard, >>let's >>start to look a lot like a Saas company or a service provider in terms of the needs of the infrastructure you want linear performance scaling. You want to be very sensitive not just to the cost, as you call it, but also to the environmental cost and the power efficiency. And so yesterday we were really thrilled to announce the HBP Reliant are all 300 General Live in, which is the first of our general living platforms. And that's in partnership with Ampere is the first of several things that we're gonna go do together. We're looking forward to building out the rest of our Gen 11 portfolio broadly with all of our industry partners in the in the coming quarters. But we're thrilled about the feedback that we're starting to get from some of our customers about the gains in power efficiency that they're getting from using this new server line that we've developed with amber. >>So, you know, this is an area that I'm very interested in what I write about this a lot. So tell us the critical aspects of Gen 11, where ampere fits, is it is it being used for primarily offloads and there's a core share with us. So >>if you look at the opportunity here is really as a core compute tool for organisations that are doing that in house full snack cloud native development and in that environment, being able to do it with great power efficiency at a great cost point is the great combination. The maturity of the ecosystem, um, is really, really improving to the point where is much, much more accessible for those loads? And if you consider how the infrastructure evolves underneath it, the gains that you get from power efficiency multiply. It's a TCO benefit. It's obviously an environmental benefit, and we all have much, much more to do as an industry on that journey. But every little helps, and we're really excited about being able to bring that to market. The other thing that we've done is recognising the value that we bring in the prelim experience, everything with our integrated lights out management, all of the security, the, uh, hardware root of trust, the secure boot chains, all of that Reliant family values we brought to that platform, just as we do with our others. But we've also recognised that for some of our service provider customers, there's a lot of interest in leveraging open BMC and being able to integrate the management plane and control that in house and tie it to whatever orchestrations being done in the service product. So we have full support for open BMC out of the box out of the gate with Janna Levin. And that's one of the ways that we're evolving. Are offering to meet our customers where they are, including not just the assassin service providers but the enterprises who are starting to adopt more and more of those practises as they build out digital. First, >>tell us more about the architecture. If you would kneel. I mean, so where does ampere and that partnership add value? That's incremental to what you what you might think is a traditional server architecture. How's that evolving? >>Well, it's another alternative for certain workloads in that full stack in house proud Native Development model. Um, it's another choice. It's another option and something that's very excited about >>That's the right course for the horse, for the course that was back in internal development because it's just more efficient. It's lower power, more sustainable. All those things exactly. >>And the wonderful thing for us in the uh in this juncture in the market is there is so much architectural innovation. There are so many innovators out there in the industry creating different optimizations in technology with the lesson silicon or other aspects of the system. And that gives us a much broader palette to paint from as we meet our customers' needs as their businesses involving the requirements are evolving, we can be much more creative as we bring this all together. It's a real thrill to be able to bring some of these technologies into the HP reliant space because we've always felt that compute matters. We've always known that hardware matters, and we've been leading and innovating and meeting these needs as they've evolved over the decades, and it's really fun to be able to continue to do that. Hardware still >>matters. It doesn't matter. We know that here on the Cube, talk about the influence of the customer with so much architectural innovation. There's a lot of choice for customers in every industry. When you're in customer conversations, how are you helping them make decisions? One of the key differentiators that you articulate that's going to really help them achieve outcomes that they have to achieve? >>Well, I think that's exactly as you say. It's about the outcome. Too often, I think the conversation can get down into the lower level details of component, tree and technology and our philosophy. HP has always been focused on what it is that the customer is trying to achieve. How are they trying to serve their customers? What are their needs? And then we can bring an opinionated point of view on the best way to solve that problem, whether that's recommendations on the particular Capex, infrastructure and architecture to build or increasingly, the opportunity to serve that through HP Green Lake, either as hard or as a service. Or is HP Green Lake services further up the stack? Because when you start talking about what is the outcome you're trying to achieve, you have you have a much, much better opportunity to focus the technology to serve the business and not get wrapped up in managing the infrastructure and that's what we love to do. >>So where? Give us the telescope vision. Maybe not to tell a binocular vision as to where compute is going. We're clearly seeing more diversity in silicon. Uh, it's not just a you know x 86 CPU world anymore. There's all these other supporting components new workloads coming in. Where do you you mentioned Edge, whole new ballgame ai inference sing. And that was kind of new workloads, offloads and things of that. Where do you see it all going in the next 3 to 5 years? >>I think it's gonna be really, really exciting time because more and more of our data is getting captured to the edge. And because of the experiences that companies are trying to deliver and organisations are trying to deliver that requires more and more stories are more and more compute at the edge. The edge is not just about connectivity, and again, that's why with the F B green light cloud platform, the power of bringing together the connectivity with the compute with the storage with the other capabilities in that integrated way gives us the ability to serve that combined need at the edge in a very, very compelling way. The room moves a lot of friction and a lot of work for our customers. But as you see that happen, you're going to see more and more combining of functionalities. The silos are going to start to break down between different classes of building block in the data centre, and you've already seen shifts with more and more software to find more and more hybrid offerings running across a computing substrate. But perhaps delivering storage services are analytic services or other workloads, and you're gonna see that to conduct that continue to evolve. So it's gonna be very fun over the next few years to see that, uh, that diversification and a much more opinionated set of offers for particular use cases and workloads and at our job and value is going to be simplifying that complexity because choices great right up to the point where you're paralysed by too many choices. So the wonderful thing about the world that's been done here is that we're able to bring that opinionated point of view and help guide, and again it's all about starting with what are you trying to achieve. What are the outcomes you're trying to deliver? And if you start there were having a great time helping our customers find the right path forward. >>Wow, it sounds like a fun job. Talk to me about, you know, maybe one of your favourite examples that you really think articulates the value of of the choice and the opportunities that HP can deliver to customers, maybe favourite customer example where you think we really nailed it here and they're achieving some incredible outcomes. >>Well, we're really excited about this week as I was chatting with the CEO of Cloud Sigma, which is a global ideas and pass provider who's actually been using our new HP per client moral 300 general live in Are you on purpose? Server line? And, uh, their CEO was reporting to me yesterday that based on his benchmarking, they're seeing a significant improvement in power efficiency, and that's that's that's cool to an engineer. But what's even better is the next thing, he said. That's enabling them to deliver better cost to their customers and advanced their sustainability goals, which is such a core part of what we as an industry and we as society are going to have to continue to make stepwise progress against over the next decade in order to confront those challenges in the environment so that that's that's really fulfilling, not just to see the tech, which is always interesting to an engineer but actually see the impact that it's having an enabling that outcome foreclosed signal >>so many customers, including Cloud Sigma and customers in every industry. E S G is an incredibly important initiative. And so it's vital for companies that have a core focus on E. S G to partner with companies like HP who will help them facilitate that actually demonstrate outcomes to their own users. >>It's such an important journey and it's gonna be a journey of many steps together. But I think it's one of the most critical partnerships that as an industry and as an ecosystem, we still have a lot of work to do and we have to stay focused on it every day, continuing, moving the bar. >>You >>know, to your point about E. S G. You see these E s G reports. Now that they're unbelievable, the data that is in them and the responsibility that organisations mid and large organisations have to actually publish that and be held accountable. It's actually kind of daunting, but there's a lot of investments going on there. You're absolutely right. The >>accountability is key, and it's it's it's necessary to have an accountability partner and ecosystem that can facilitate that. Exactly. >>We just published last week our Own Living Progress report this year, talking about some of the steps that we're making the commitments that we pulled in in time. Um, and we're looking forward to continue to work on that with our customers and with the industry, because it's so critical that we make faster progress together on that >>last question. What's your favourite comment that you've heard the last couple of days being back in person with about 8000 customers, partners and execs? It's >>not. It's not the common. It's the sparkles in the eyes. It's the energy. It is so great to be back together, face to face. I think we, uh, we've soldiered through a couple of tough years. We've done a lot of things remotely together, but there's no substitute for being back together, and the energy is just palpable and it's it's fantastic to be able to share some of what we've been up to in the interim and see the excitement about getting adopted by customers and partners. >>I agree the energy has been fantastic. We were talking about that yesterday. You brought it today, Neil, Thank you so much for joining us. We're excited about Antonio coming up next, going to unpack all the announcements. Really good customers. Perspective from the top of H P E for Neil and Dave Volonte. I'm Lisa Martin joins us in just a few minutes as the CEO of HP, Antonio Neary joins us next.

>>Welcome everybody to the cubes live coverage of AWS reinvent 2021. We're here in the main hall. Yes, this is a physical event. It's a hybrid event, probably the industry's most important hybrid event in the year. We're super excited to be here. Of course, last year during the lockdown, reinvent was purely virtual. This year. They go in hybrid 20 plus thousand people. I hear the whisper numbers like 25, 20 7,000 hundreds of thousands of people online. The cubes here, two sets, we've got two remote studios, super excited. I'd like to introduce my co-host David Nicholson. He'll be here all week with us. Uh, John furrier is also here, Lisa Martin for the cubes wall-to-wall coverage. And we're so psyched to start off this session with Kenneth Chestnut. Who's the head of technology partnerships at Stripe. Stripe's an amazing company, Ken. Great to see you. Thanks for coming on. >>Thanks for having me, Dave and David. I greatly appreciate it. How about this? >>Right. Finally live event. We've done a few. We probably done four or five this year, but >>It's good to be back in person. It is. Yeah, absolutely. It's >>A Stripe. I mean, wow. Can a powering the new economy. Tell us a little bit more for those people who may not be familiar with Stripe. They probably use it without even knowing it when they sign it away. Yeah. So tell us about the >>Well, uh, Stripe was founded in 2010 by two brothers, Patrick and John Colson. And really it was from their first business and realizing how hard it was to actually charge for things on online. Um, you had to acquire a relationship with, uh, with a gateway provider to accept payments. You had to acquire a relationship with a, with a acquiring bank. Um, and you had to do that for each and every country that you wanted to service. Uh, so the same way that AWS reduced the barrier in terms of not having to procure, spend millions of dollars on storage, computers, networking, uh, effectively, what we we've done at Stripe is reduce the barriers around economic infrastructure, accepting payments online, >>Use that undifferentiated heavy lifting for payments. So describe Ken, what it was like kind of pre Stripe. You would literally have to install servers, get storage and put, put software on there, get a database. And then what if you had any money left over, you can actually do some business, but, but describe the sort of what the experience is like with Stripe. >>Sure. So, uh, the R R with, with Stripe, we literally talk about seven lines of code. So we, we allow any developer to, um, uh, provide a set of APIs for any developer to accept payments on online. And we do the undifferentiated heavy lifting in terms of accepting payments, accepting those payments, processing them revenue, reporting, and reconciliation, um, all ensuring compliance and security. Um, so it's like you said, uh, taking care of the undifferentiated heavy lifting are around accepting payments online in the enabling >>The enabler. There is the cloud. I mean, it was 2009, 2010. You guys were founded, the cloud was only like three years old. Right. And so you had to really sort of take a chance on leveraging the cloud or maybe early on you just installed it yourself and said, this isn't going to scale. So maybe tell us how you sort of leverage the cloud. >>Sure. Um, so we're a long time, uh, AWS, uh, customer and user, um, uh, back in the early days of, of Stripe in the early days of, of AWS. And we've just grown, uh, with, with AWS and the ecosystem. And it's interesting because a lot of, uh, a lot of the companies that have been built on, on AWS and grown to be successful, they're also Stripe customers as well. So they use Stripe for their economic infrastructure. >>We use Stripe, we run our company on AWS and we use Stripe. It it's true. The integration took like minutes. It was so simple. Hey it, test it, make sure it scales. But so what, what's the stack look like? What is there, is there such thing as a payment stack? What's the technology stuff? >>Sure. So we initially started with payments and being able to accept payments, uh, on online. Uh we've we brought in out our, our, our Stripe product portfolio now to effectively provide economic, uh, infrastructure for the internet. So that could be accepting payments. Uh, it could be setting up marketplaces. So companies like Lyft and Deliveroo, uh, use Stripe to power their marketplaces with their, with their drivers and, and, um, uh, delivers, um, uh, we provide, uh, a product called radar that, uh, that, um, prevents fraud, uh, around, around the globe. Um, based upon the data that we're seeing from our, from our customers, um, we have, uh, issuing and treasury so that companies can provide their users or their merchants with banking services. So loans, uh, issuing credit cards. So we we've really broadened out the product portfolio of Stripe to provide sort of economic infrastructure for the internet. So >>We talked about strike being in the cloud from an infrastructure perspective and how that enables certain things, but that in and of itself, doesn't change the dynamics around sovereignty and governance from country to country. Sure. Uh, I imagine that the global nature of AWS sort of dovetails with your strategy, but how, how do you address that? It's one thing to tell me in Northern California, you can process payments for me, but now globally go across 150 countries. How do you make that work? Yeah, >>Uh, absolutely. So we, we establish relationships, uh, within, within each company country that we operate in we're in about 47, uh, countries, uh, today, um, and that's rapidly expanding so that companies can, can process or accept payments and do, uh, financial transactions within, within, within those countries. So we're in 47 countries today. We, we accept a multitude of different payment, uh, different currencies, different payment types. So the U S is very, uh, credit card focused. But if you go to other, other parts of the globe, it could be a debit cards. It could be, um, uh, wallets, uh, uh, Google pay, Ali pay, uh, others. So really it's, uh, providing sort of the payment methods that users prefer in, in the different countries, uh, and meeting and meeting those users where, where they are. >>Are you out of the box compliant? What integration is required to do that? Uh, what about things like data sovereignty, is that taken care of by the cloud provider or you guys, and where, w w where does, where does AWS end and you guys pick up? Yes, >>We're, we're PCI compliant. Um, we, we leverage AWS as our, as our infrastructure, um, to grow, grow and scale. So, um, one of the things that we're, we're proud of is, uh, through, throughout 2020 and 2021, we've, we've had 11 nines of, uh, of, of, uh, or five nines of uptime, um, even through, um, uh, black Friday and cyber Monday. So providing AWS provides that, that infrastructure, which we built on top of to provide, uh, you know, five nines of uptime for our, for our users. >>You describe in more detail, Kenya, your ecosystem. I mean, you're responsible for tech partnerships. What does that ecosystem, how I paint a picture of it? >>Sure. So, um, uh, a number of users want to be able to use Stripe with, with their other, uh, it infrastructure and, and their business processes. So a customer may start, uh, with a salesperson may start with a quote or order, uh, in, in Salesforce, want to automate the invoicing and billing and payment of that with, with Stripe and then, uh, reconcile re revenue and an ERP solution like SAP or Oracle or NetSuite or into it, um, in the case of, of small, medium businesses. So really, um, what we're focused on is building out that, that ecosystem to allow, uh, um, our, our customers to streamline their business processes, um, and, and integrate Stripe into their existing it infrastructure and, and business processes. >>You mentioned a lot of different services, but broadly speaking, if I think about payments, correct me if I'm wrong, but you were one of the early, uh, sort of software companies, if I can call you that, um, platforms, whatever, but to really focus on a usage based pricing, but how do I, how do I engage with you? What's, what's the pricing model. Maybe you could describe that a little. >>Sure. So the pricing model is very, very transparent. Uh, it's on, it's on the website. So, uh, we, we take a, um, a percentage of each transaction. So literally you can, you can set up a, a Stripe account it's self-service, um, uh, we, we take a 2.9% plus 30 cents on every, uh, Tran transaction. Um, we don't, you don't start getting, um, uh, charged until, uh, you start accepting payments from your, from your customers or from your users. >>Um, can you give us a sense of the business scope, maybe any metrics you can share, customers, whatever. >>Sure. So there's a couple of things we can share publicly, just in terms of the size of the business. I think since, uh, since 2020, uh, more than 2 million businesses have launched on, on Stripe. Uh, so, uh, 2 million in, in, in, in 2020, um, we've, uh, uh, in the past 12 months, we've, uh, uh, uh, processed over 173 billion, uh, API calls. Uh, we do we process about, um, uh, hundreds of billions of, of, of, uh, payment volume, uh, every, every year. Um, if you look at sort of the macros of the business, the business is growing faster than the broader e-commerce space. So the amount of payment volume that we did in this past year is more than the entire industry did when Patrick and John founded the company. And in 2010, just to give you a, uh, an idea of the, the, the size of the business and sort of the pace of the business >>You're growing as e-commerce grows, but you're also stealing share from other sort of traditional payment systems. Okay. So that's a nice flywheel effect. And of course, Stripe's a private company they've raised well over a billion dollars of Peter teal, and it wasn't original founders, so are funders. So, you know, that's, he's talking scale. I want to go back to something you said about radar. Sure. So there's tech in your stack fraud detection, right. So some of >>That in machine learning, right. >>So, and so you guys, I mean, are you a technology company, are you a F a FinTech company? What are you? >>We're a software company. We provide software and we provide technology for developers, uh, to make online businesses and make, uh, uh, commerce, uh, more seamless and more frictionless >>Cloud-first API first. I mean, maybe describe how that is different maybe than, you know, the technical debt that's been built up over, you know, decades with traditional payment systems. >>Yes, it's very similar to the early, earlier days of AWS where a lot of tech forward companies leveraged Stripe, um, to, um, whether it be large enterprises to transform their businesses and move online, or, or, uh, uh, startups and developers that want to, uh, start a new business online and, and do that, uh, as quickly and seamlessly as possible. So it's, it's quite the gamut from large enterprises that are digitally transforming themselves companies like Marske and, and NASDAQ and others, as well as, uh, um, startups and developers that have started their businesses and born on born on Stripe. So >>When you talk about a startup, how small of an entity makes sense, uh, when you think of, if you look at, from an economic perspective, lowering the friction associated with transactions can lift up a large part of the world with sort of, you know, w with very, very small businesses. Is that something that this is all about? >>Yeah, absolutely. So, like I said, you know, two, 2 million business have sub launched on, on, on Stripe, uh, in, in the past year. And, and those businesses vary, but it could be literally a, a developer or a, uh, uh, a small, uh, SMB that wants to be able to accept payments on online. And it can just set up a Stripe account and start accepting payments. >>Yeah. So this is not a one hit wonder, um, lay out the vision for Stripe, right? I mean, you're, you're a platform, uh, you're, you're becoming a fundamental ingredient of the digital economy sounds pre pandemic. That was all a bunch of buzzwords, but today we all know how important that is, but what lay out the vision for us can, >>Yeah, it really are. The mission of Stripe is to grow the GDP of the internet. Um, and, and so what that means is, uh, more and more our, our, our basic belief is more and more and more businesses, uh, will, will, uh, go, go online, uh, with, uh, with the pandemic that that was, uh, accelerated. But I think that the general trend of businesses moving online, uh, will continue to accelerate, and we want to provide, uh, economic infrastructure to support those businesses. Um, you know, um, uh, uh, Andreessen talked about sort of software, software eating the world well fit. Our belief has FinTech is eating software. So in, in the fullness of time, I think the opportunity is for, uh, any, any company to be a financial services company. And we want to empower any company that wants to, or any user that wants to be a financial services company to, to provide the economic infrastructure for them to do so. >>And, and, you know, I mean your data company in that sense, you're moving bits around, you know, and those datas, I like to say data's eating software, you know, cause really you gotta have your data act together. Absolutely. And that's an evolving, I mean, you guys started to, to 2010, I would imagine your data strategy has evolved quite dramatically. Yeah. >>It's a great, it's a great call out Dave. Uh, one of our other products is a product called Sigma. So Sigma allows, uh, merchants or our customers to query payment and transaction data. So they want to be able to understand who, who, who are their customers, what are the payment methods that those customers prefer in different countries, in different regions? Um, so we're, we're starting to have some interesting use cases, um, working with, with AWS and other partners when you can start combining payment and transaction data in Stripe with other data to understand customer segmentation, customer 360 lifetime value of a customer customer acquisition costs, being able to close the books faster in your ERP, because you can apply that payment and transaction data to your general ledger to, to close the books faster at the end of the month or at the end of the, at the end of the year. So, uh, yeah, we we're, um, uh, as, as more and more companies are using Stripe, um, they want to be able to take advantage of that data and combine it with other, other sources of data to drive business. >>Yeah. You mentioned some of those key metrics that are, that are so important to companies today. I'll give you the last word re-invent this hall is packed, um, a little bit surprising, frankly, you know, but, uh, but exciting. Uh, what are you looking forward to this? >>Yeah, I'm just looking forward to meeting people in person again, it's, uh, it's great to be here and, and, you know, uh, uh, we have a strong relationship with AWS. We have lots of partners in, in, in common here, uh, as well, both consulting partners and technology partners. So really looking forward to meeting with partners and customers, and especially as we, as we plan for next year and, uh, launching our, our, our partner program beginning of next year. Uh, there's a lot of, uh, uh, groundwork and things to learn from, from here. As we, as we, we, we, we launch our, our, our partner business formula next >>I'll bet. Looking forward to that, Ken, thanks so much for coming to the cure. You so much. It was great to have a chat at the time. All right. And we want to thank our sponsors, uh, AWS, of course, and also AMD who's making the editorial segments that we bring you this week possible for Dave Nicholson. I'm Dave Volante. You're watching the cube at AWS reinvent 2021. Keep it right there, right back.

>> (Announcer) From the Bellagio hotel in Las Vegas. It's theCube covering UiPath FORWARD IV, brought to you by UiPath. >> Good afternoon. Welcome back to theCube's live coverage of UiPath FORWARD IV. Lisa Martin here with Dave Vellante. We're on day two of our coverage. We've been talking a lot about automation, all of the opportunities that it's uncovering across industries. We're now going to be talking about a big company undergoing its own automation-led digital transformation. Joining us next, Mariesa Coughanour, head of Automation Advisory Services at Cognizant. Mariesa, welcome to the program. >> Thank you for having me. I'm excited to be here today. >> So let's talk. So Cognizant is a part, both a partner and a customer of UiPath. >> Yes. >> We're going to talk about you in the customer realm today. Cognizant is undergoing its own automation-led digital transformation. Let's talk about that. Talk to me about some of the business outcomes that are, that you're expecting, how it's going to transform the employee experience, the customer experience. >> (Mariesa) Sure, absolutely. We actually started working with automation ourselves back in 2018, where we just put in a CoE, we said we want to drive it into our business operations. But about a year ago, we said, let's go further. I really wanted to play with all of our employees. We wanted to empower them. We talk about citizen development, of robot for every person. And we know that that's really the future. That's where we're going. We're digitally transforming our organizations. And so what we did is we sat down and we worked really closely with UiPath on how do we do this? What kind of training do we need? We're going to need some process, some governance in there. And so we put that in place and, you know, we said, let's get this going this year. So we went out, we did, our first Hackathon, went really well. It was Bring Your Own Bots. So BYOB, so, fun themes. And we got some good savings. We actually drove over 10,000, close to 20,000 hours back into the organization. And we said after that, let's go a bit bigger though. And we did what's called Game of Bots. So obviously we know where that came from, right? And we said, we're going to go a little bit longer and we want to go bigger. So we went and had 2,500 people participate over eight weeks. We built over a thousand bots. And guess what? We drove over 200,000 hours back into the organization in just eight weeks. So super big success story. People loved it. Our teams were excited. We recognized over 200 people out of that with team awards, who submitted the most ideas. And even our top leadership said, let's do a presentation. So the guys and gals who had the top, biggest impact automations got to meet with our top senior leaders and present out to them. It's been awesome. And now we're starting to move that force. We're scaling bigger. We're actually going pretty big in Cognizant. We have some big goals right now. >> That's a gob of hours. Game of Bots, get it? GOB. >> (laughs) >> Come on, with me. >> It is a gob of hours. >> How do you measure the hours? Is it a back of napkin kind of thing? We ask people, Hey, how much, how what, how do you actually measure it? >> No, we actually track it. We could see how many hours people were doing a certain tasks and things that they do every day, whether they're running reports, submitting claims for a customer. And so we're able to see that that time is actually going down. We're faster. We get better quality. People were also able to get hours back in their day so they could do more value added work in the organization. So we actually do track it. And we're able to really measure those tangible outcomes for the teams. >> Sounds like you guys have been moving pretty quickly on this. >> We are. >> So the appetite at Cognizant was there, the culture was there to embrace it. Those are probably, I imagine, two big facilitators of being able to move at the speed and the scale, >> Yeah >> that, with what you're doing. >> Culture was there. We're really digitally savvy. I would say we're digital at heart in Cognizant. We are, we're really a tech company and we really focus on how to be at the forefront of all things when it comes to technology. But we said, we also want to transform how we work. So starting to shift the conversation from, you know, do you want to automate, to why not? How do we actually start talking about, you know, I have this to do list, but you know what, actually, we can improve if we did some of this other stuff instead. So let's free up that time, but use automation there and we can actually grow things. We can add more value. We do all that stuff on your to-do list that I think everybody has and they want to get to, but you get caught up in your day-to-day job all the time. So we're actually getting people to be more excited about and have a real voice. And I actually think that's important. Is that, it's not just about giving people the tool, it's about shifting our culture to really embrace digital, embrace this technology, because we're trying to transform how we all work. And we want to lead by example. >> So we talk about BPO. Business Process Optimization, right? It was kind of the buzz word of the '90s and early 2000s. A lot of times it meant putting in SAP. (chuckles) >> (Mariesa) (laughs) Yeah. >> So that's evolved. And there's some companies that would say, "Hey, we specialize in that," technology companies, obviously, >> (Mariesa) Yep. >> you know, SI's as well. How do you think about the difference between end-to-end enterprise automation and, and sort of traditional BPO? >> I think it has to come together a bit, is one thing. So when you do the BPO, or you do shared services, or you outsource some of the work. We actually put into those contracts, because we do a lot of that for our customers. And we put in automation. The step we took further was we actually started to empower people to actually build the automations themselves, which meant we actually had to work with customers too. So they knew we were doing this. We wanted to make sure they understood, they were comfortable. We put any controls in place that they also needed, to make sure that, you know, we didn't impact any of their services. We want to make it better. We want them to feel nothing but bigger, better results in outcomes. And then as you think about the enterprise side, we have to compliment, because a lot of those processes do feed back into how you run a business. And so we focus on how do you bring both of those stories together so that you're driving synergies across the board. And actually some good lessons learned along the way because some of this stuff becomes reusable. You have best practices you could share across the board. And we want to make sure that we are connecting the dots from the shared services BPO work, back into the enterprise because really a process is end to end, an organization. And we want to help people think that way and also get the results that way too. >> Is that end to end automation, at enterprise automation, more tech heavy, or, or maybe it's tech light in a way, whereas BPO is maybe a lot more, sort of, lean thinking, a lot more chalkboard. Are we deep into the, so I, sometimes, you're saying they have to come together. >> They do yeah. >> But from, from where I guess is, is what I'm trying to better understand. >> So I would think about it this way. When you think about a process, right, from when you even placed an order, the whole way through when you fulfill it for a customer. There's work that we, we do outsource all the time, right? So maybe it's the, the PO process, some of the order transactions from the payment, but you also have the pieces is actually touching the customer, too. You have the pieces that are fulfilling the order. So we say end to end, that is really thinking about that beginning, from a conversation with the customer, the whole way to when we're delivering. And I do think there's a lot of technology. That is something I think everyone gravitates to because there is a lot. Especially if you're going to go end to end, you have to be able to take in documents. You have automation. You're not going to know all the rules, no matter how many times you ask, you're going to need machine learning to be able to help figure it out and get smarter as what, as you go along the way. But as you're putting this into place, what's important is: as you're thinking about, kind of, transforming that business so that they're feeling the results the whole way through, because if you just focus on one, you might create a bottleneck, right? You might've got super fast, but the guy who's going to get the work from you, they're going to feel like, oh my goodness, there's all this work on my plate. So we really want to make sure that we create that seamless experience for everyone across the board, as we put it in. >> And how does UiPath, help facilitate that? >> Across the board, I mean, we were sitting down, we were laying out our program. 'Cause we're actually trying to get to 60,000 strong. So we have 7,000 trained today. We're going to get to 60,000. That's our plan. So we're working very closely with UiPath on what does that training that you need to have in place? What's that model? How do we get people comfortable? Because one thing you'll find is not everyone's in the same spot. Some are going to jump in, dive right in, give me the tool. I want to build. I love this. Others might need a little bit more confidence boost. They might need more handholding. And I think that's really important. And it's probably the one thing I would add too, as you do talk a lot about the technologies, we put it in, but it's the people at the end of the day, it's how you help them adopt, feel comfortable with this technology and really embrace it. That's really going to be the difference on whether, how fast you get down that line for transformation. >> Is it a classic bell curve? You got your 10% early adopters, you got a big fat middle, and then you've got some laggards who come along. >> It kind of is. And I think what's important is that middle is all up in how you do it because 10% are always going to love it. You're always going to have a few people, they're a little extra nervous maybe. But in the middle, if you really think about it, and you're able to put in that culture, you're able to put in your leadership is engaged. You're putting us in gamification, make it fun. That's what we found is, if we got people really having fun up front with it, it gives people a reason to be a part of it. And also, why don't we let people partner up? We can give them the technology, but if someone's not as comfortable, let us do teams. Let's meet people where they're at and then move them along this journey. And let's try to accelerate the best we can. >> How did you gamify it? Crypto. No. (chuckles) >> (laughs) >> (laughs) No, no crypto. But I will say we have some really cool prizes and people were super excited to get to do the presentations because they got to show their, their bots live, their creations, to the team. And I think that was important. Not everyone always is able to capture all the results, but we wanted to actually talk about like, what were the ideas, share it across the board. Cause it also generated ideas. because what you'll find is, when you hear something like, you know what, that's kind of what I do. Wonder if I could do some automation too. At least submit an idea, and then, maybe they're moving down the line, they're getting their hands on the technology. And I think that's how we all push the needle forward and move this along faster. >> One of the most important things about automation is letting people be able to move away from the mundane, the repetitive tasks, that they probably don't enjoy. And being able to focus more on their core competencies or more strategic initiatives that really make them more relevant to themselves and to their company. And it sounds like you guys have achieved that pretty quickly and, and you have an aggressive plan >> (chuckles) We do. >> to go from 7,000 to 60,000. >> Yes. And that's really the power of automation, if you think about it. We all have things in our job we don't like to do. I don't know about you guys, but there's things that I'm like, oh man, like, can we please automate this? Expense reports, for example. All about automated expense reports. (laughs) But it's really about freeing people up. Think about it. These people went to school, they often have degrees and things, and they do get caught in a lot of the manual things, downloading reports, consolidating data, you know, submitting spreadsheets and forms. Imagine if we're able to make that easier for people, we give them what they need to do their job. So that all that stuff you would like to do, that you know would improve things. You know would make the company better. The culture better. Heck, maybe it's a new product that people know would be really awesome to go build, but everyone feels like they're so busy. They don't have the time to do it. I mean, that's one of the big values of automation. Is this value creation conversation that you get to have with people. And you get to start asking 'why not' a little bit more. >> You've mentioned a couple of times the IDC presentation this morning. And we were talking about earlier, and the pie chart of, of, of value benefits was cost savings, which was very large, new revenue, which was very large. And then I think 15% was quality improvements. And that, I think that's an underappreciated slice of the pie. Somebody, I think years ago, of the UiPath FORWARD said to me, I can very inexpensively apply Six Sigma to business process. >> (Mariesa) Yeah. >> And I could never afford to do that before RPA. And, and so I wonder if you could talk to the quality impacts that you're seeing. >> Absolutely. I actually spent a lot of time in Lean Six Sigma in my early career days. And one of the things about it too, is when you're doing automation, we actually asked that question upfront, can we just simplify, can we just stop doing this? Because you don't want to automate a bad process either. So you want to ask some of those questions. >> (Dave) Yeah. >> But you're spot on. There's a ton of quality benefits that you get from automation. And one of the things I've actually seen is if you focus on some of the quality upfront, process gets better. Get better impact, as when you get faster. If you have better quality, and get faster, you also get your cost out targets. And I, that really matters because quality also, beyond being able to drive the cost out, it also helps a lot with the experience that people face. Customers are frustrated if they have poor quality, something doesn't work the way it's supposed to, a site's not working the way it should. And also even employees, they go, how many times, if you try to do something and you try to follow a process and something's hung up or who knows what happened, right. It's frustrating. So if you're able to improve the quality in the process, not only do you get the cost savings, but you get these, it's softer tan, there's still tangible experiences that get better and actually motivates people to want to do more. >> And those motivated people are probably dealing with customers much, much better. >> Yes, yeah. >> I mean, it's, I always think the employee experience is so, is, is a critical component. >> It is. >> But the customer experience. So how has the customer experience improved at Cognizant as a result of building in automation and enabling all these people? >> Yeah, they're loving the results because we're giving them back efficiencies in their process immediately by putting this automation in. These are quick impacts they're feeling and we're able to do more for them as well. So we're actually having conversations now on how do we drive more efficiencies for you and also, you know, how can we do more? Is there more volume of work? Is there more we could be doing to add value back to your organization? And that's what you want to talk about with customers is we're able to give you this value. And by the way, we actually did X for you now as well, because we knew you needed it. And we have the capacity to do this for you. So it's a really positive conversation, but we did have to upfront talk to them about it, to make sure that we, everyone was on board. They're comfortable. And we're continuing to have those conversations because you know, sometimes you're in a regulated business and we did put a little extra control in. Absolutely okay. But we want to be able to drive these efficiencies back for them. So they feel it in their own operations internally too. And it hits their bottom line and oftentimes helps their employees too, because we interact with them. So those downstream benefits and sometimes even upstream get some nice returns there as well. >> We've heard from, well, we're going to have Daniel on soon. He's the CEO. We've of course heard from CFOs. We've, that's kind of one of the main springs of RPA in the early days. We've heard a lot more CIOs at this event and we have a CTO coming on later. Are these C suite executives totally aligned in their objectives? Do they have they have different agendas? What are you seeing in terms of serving the C suite? >> Yeah. They're all going to have a little bit different agendas, right? Cause that's, their roles have different objectives, but they all align back to the strategy, obviously, for their company. But they're going to have portions of it that they're trying to drive and deliver. What we do see is that there's still some merging that needs to happen between the operations, the more business focused side and the more technical side. But we do, we're starting to see that convergence happen. Because what happens is, is that, you have these technologists, who really are going to have to help move you forward. We're, you know, we're applying AI, ML. Very technical technologies, and we want to make sure we do it right, that we put the right governance in. And we think about the security that we have to have in place for this too. And but we also have the business outcomes and coming together is where you really see the results. If you look at all of those that have reached true maturity, it's where you see these agendas aligning a bit more because you also have to shift the culture too. And it's a collaboration point. You need to be able to have the tech savvy folks. It helps bring them along this journey, but you also have to have the business depth as because you're looking at a process and you're going really deep into it to apply the technology. So it's when people partner is when we really see the results become more exponential. >> So digital transformation, you know, we hear that term a lot. And automation-led digital transformation. >> Yep. >> I hear a lot of data led digital transformations are those parallel tracks, or they can talk a lot about convergence. >> Yeah. >> Are they? I mean, they're not competing. They're obviously very much related. How do you see the data agenda and the automation agenda coming together. >> They have to. Because you really need good quality data to be able to enable your automation at the end of the day. And, but they actually play nicely together. You can actually use automation to help go back and improve your master data management too, which is the core of your information because that's actually where a lot of the struggle sometimes comes, is in the quality of the data that everyone has to work with. So you see the data agenda working on, "How do I clean this? How do I get more insightful, predictful information?" And then from an automation standpoint, how do I then use that to go take action? So all we see is you bring it together, to be able to identify where do you need to get in the process? How do I get the right information? So the automation also is proven data behind it, that we drove the outcomes, because that's where you take it to the bank at the end of the day. Is that you see it in the data itself. But I think one of the things I've seen with automation that helps drive the digital transformation conversation is, the business and IT teams are coming together and having a joint conversation now. People are excited. They're understanding it. I think that's why people jumped on with RPA so quickly, was because they're like, I get this, this is rule-based, this is my business process. I just tell it what to do. I'll take that. I want to do that. And so people got excited about that. And then they said, let's do more. How do I make it more intelligent? How do I help it do things in my process that it's harder for me to explain because there's just so much information here. There's so many nuances. Well, we have the technology can help make it more intelligent, smarter, and learn, so that we're able to drive that back into the business itself to transform. >> You mentioned Master Data Management is, is the data agenda as it relates to automation, primarily reporting, is it moving? Is it transcending reporting into the building of data products, for example, data services that can be monetized either within Cognizant or in your customer base? >> So it's really evolving. I would say some start with reports. That's easy. That's where we'll start, but I'll just kind of give you maybe a little example. So we have a customer and we work with them. So they have customers where they need to, when they call in, the sales folks and the contact centers, they have to upsell. So they work with a lot of different restaurants and different, maybe, bars and, you know, different companies that have different type of like beverages and things like that. So we worked with them to show, how are they performing today with all their sales reps? And then we started to use some automation to be able to get them more helpful information the moment the customer's calling in. And we also did some semantic analysis on a voice, how people were, how were they sounding? How was their tone? Were they happy? Were they upset? Were they sad during the call? And we fed that information back to those teams, back to those managers, and went back even to their training programs. What they actually saw was a ton of top line growth. They saw all of their metrics starting to get better, and they also start to get more predictive on ways that they can use more data to drive the support for those teams and their customers. Like for example, if you know holidays are coming up or a certain time of year with weather, we're able to actually put that type of information in and helps those sales reps better serve their customers. >> Last question, some of the announcements that came out yesterday and some of the news today about UiPath, what excites you about the technology and how it's going to continue to enable you to, to foster this new culture that you've shifted? >> I think, so one thing about UiPath that we've always loved to be able to partner with them as we're still customer centric. And you see that in every announcement that they're doing, and also they're focused on this true process transformation, intelligent end to end thinking, because I think a lot of times when we've had conversations most get stuck in kind of point solutions. And that's just because people are trying to solve today's problems. But with where UiPath is moving and where we want to move to, is how do we help you to really transform how people work? We know automation is a part of our future. We know it's going to be how we work in the future. And we love about UiPath is to really think about how do we integrate it? How do we make those connections? So we can drive the bigger results, we can make it easier for people to adopt and really embrace it because we need to bring the people along this journey and we need to be able to actually impact our processes too, so we can transform them. So I think that's one thing that's been really exciting is just watching them in general. Involved with the announcements the last couple of days, we really see them continue to push that needle. >> Excellent. Well, Mariesa, thanks for joining us. Talking to us about the automation-led digital transformation at Cognizant. Good luck raising your trained individuals from 7K to 60K. It sounds like the momentum is there. The culture's there. We can't wait to hear what happens next! >> Awesome. Thanks again for having me today. >> Our pleasure. >> Good to see you. >> Thank you. >> For Dave Vellante, I'm Lisa Martin live in Las Vegas at the Bellagio. UiPath FORWARD IV is the event we're covering. We'll be right back with our next guest. >> (bubbly outro music)

>>From the Bellagio hotel in Las Vegas. It's the cube covering UI path forward for brought to you by UI path. >>Welcome back to Las Vegas. Live the cube. Yes, it's live in Las Vegas at the Bellagio. Lisa Martin, with Dave Alante, we are covering UI path forward for very excited to be here, talking with customers, UI path, employees, partners, lots of great conversations going on about automation and the acceleration that we're seeing, especially in the last 18 months. We've got two guests here with me today to talk about emerging technologies, specifically continuous process discovery. Please welcome Paula Katikia VP of product management at UI path and Layla Deleage CIO and digital officer at Coca Cola. Ladies, welcome to >>The program. Thank you. It's great to be here. So let's >>Talk about public. Let's start with you. Continuous process discovery. Define that for us. What does that mean? >>So process discovery has been, um, a concept that's been around for awhile, right? It's enterprises have a bunch of processes that are deployed and people are following them. Um, the concept of discovery has existed. What we're trying to do with continuous process discovery is enable you to identify the processes, figure out how to optimize them and then automate them once they're automated, we want to monitor them and then keep doing that cycle over and over again, using technology rather than having fill in, having people fill in paperwork and then having those processes go out of, um, out of, um, status, like right away, because they're just becoming stale with continuous process discovery. They don't become stale. You're getting that real time feedback loop and you're getting the processes to work and to end continuously. >>So I wonder if I could follow up on that because I remember when you guys made the acquisition of process gold. And so as somebody who's heavily involved in product management, how did you go about, I mean, it's been, sounds like it's seamless, but it never is. Right. But how did you go about integrating and making it appear as though it's just kind of part of the platform? >>I mean, there's a lot goes into that right. Process gold was a great technology to begin with. So it wasn't a huge stretch for us to take it and integrate it and make it part of the platform. Um, typically when we acquire companies, we look for product market fit. We look for a technology fit. We look for people fit and we had that with process gold. The other thing to add there is a process discovery, um, specifically with Parsis gold and automation go hand in hand, you can't having one without the other is kind of leaving half of your solution on the table and just focusing on understanding and not focusing on implementation. And so it was very easy to take that technology and make it part of the hyper automation platform. >>Well, the reason why I asked that question is because it sort of coincides with a customer's journey where you go from sort of a individual department. And then now you're saying, I always say pave the cow path. And I kind of take a process that I know I'll just implement that even might not be the best I'm going to repeat and takes you to a new realm. And so this is, to me, this is all about how incumbent companies, a hundred plus year old companies can actually be digital disruptors as opposed to being disrupted themselves. Right? A lot of smart people running these big companies. So last time we talked, you were relatively new inside of a year. So how's the journey going. And, and how does it tie in to some of the advancements that UI path has made? Yeah, >>Absolutely. So the journey is going great. I like to work to use accelerate. So I'm here to accelerate and transform and why we have to do it is so that we don't become obsolete and we continue to be relevant for our customers, for our employees. They're important and for our community. So the are doing a lot of finished running a lot of initiatives. When you look at being relevant for the customer, that means we have to transform the way we operate and our business models. We have to generate new revenue streams now that are enabled and based on data and technology, while you do that, you have to create efficiency internally. You cannot create great experiences with customers and you work with very monolithic and very old school, traditional processes or based off working and systems. So you have to make sure that you adapt and change and transform the way you work internally to meet the customer's needs and demand and generate these new business models. >>So our starting position was automation. We have to automate at an extreme speed, but we also wanted to go really far without automation, not just fast and hit with task automation and just automate these traditional 50, 60 year old processes, but have Doobie identify what else is there? There's a wealth of opportunity when you look at an end to end process. So that's where process mining as Polak described, comes into play. And actually we started affiliating with process mining during process gold. So your question around how the integration went, we actually went through that. I think the UI pads, one key value that they have, and they should never use is listening to the customer. So the got to get her with iPads. And we said, there's more to what we can do with automation. And we implemented process mining for one end to end process, amazing results, just one country, one end to end process, amazing results. But it's because of the partnership. We know what we need to achieve, but we have to do, and they know how to help us to get the technology up and running or adapt to technology and improve the technology. So that's where we are achieving outcomes. We are generating new business model, new revenue stream, automating internally re-skilling and up-skilling our people, which is extremely important that comes along with automation that redesign exciters sorry, but that redesign a work is >>Very important in the CEO's role is very important in that as well. I wanted to talk though about something that you just said with respect to the listening piece that you have is so good at this morning in the keynote. Mary said too, you know, all that, which was standing room only, which was amazing to see, um, in this day and age, but that they wanted to hear from customers. What are we doing? Right? What are we not doing that you want to see more of? What do you want to see less of? Talk to me about the direction and advice that you, as the CIO of Coca-Cola is able to provide to flock and the team about where you I've had this going, right. It's really on a very fast cadence. >>Absolutely. So as Coca-Cola TJ, we started the journey with two iPad, three years of work. Exactly. I was on the job and the second big technology decision I made was the iPad. And since then it was fear consistently think. But during our cab meeting, Daniel said something, he said, I'm not welcoming the request. He said, we welcome. He said, no, no, sorry. I am not welcoming. I'm requesting you to give us insight. And I think that's very critical. That's what we want to hear. At the end of the day, we are technologists. We are total leaders, but the are better taught leaders with our technology partners. So we want technology partners to show us the way sometimes. And with low code, no code type of approaches. And the evolution of the technology that UI path is, has been running since the past three years is helping us remove so many barriers. >>When it comes to people, they are listening to us in terms of the roadmap and what should be implemented and what should be prioritized VR, providing with them, our roadmap, our vision on where we want to go in automation and hugged battle. We want to integrate with other ecosystem and environments that we have. They are listening to us in terms of, for the existing products, what can be improved, what can work better? And we don't need a cab actually for you iPad to listen to us. We work hand in hand with two iPad team continuously be coil, you know, eight sometimes. So, and that's what we want them to continue to do. They are great technologists, as long as they continue to listen to us, they're going to be greater technology. >>Yeah. And I'll share my perspective on this, this, this, you know, these partnerships actually make us build better products, right? We get to, this is how we stay ahead of the curve by listening to our customers, because they're the ones who are doing the implementations. They understand how our product works. We can design it, we can test it. But that's the extent to which we can go once they implement it is when we know what's working, what's not working. And how do we take that feedback and make better products. So it's a two-way street. We love hearing from them constantly. >>You have to decode what the customer is saying sometimes, right? Like Steve jobs said, yeah, if you just ask the customer what they want, you'll never build, you know, something that's game changing the world changing. And so, so you have to talk to Layla, you get the input from COVID, Coca-Cola maybe many and then other customers to figure out, okay, how can I apply this? So that actually can scale and meet the needs of many customers. Not just so, because otherwise you end up being, you know, a custom development shop, which ironically is what you guys were 20 years ago. Right? So it's kind of some art involved in the science of listening. Isn't it? >>There is definitely, I mean, most of our job as product managers is to design the product, right? It's very much art and the feedback that we get from Layla and others, it really just helps us focus on a vision. But, you know, keeping up with new technology trends, figuring out how to figuring out how to, um, bring AI into our product vision and looking beyond what we're being told and asked for and looking forward at what the next trends are going to be in technology is what helps us continue to innovate. So it's both, it's the balance of what we're hearing, but also technologies. And what's possible with what's available >>Question for you. You said three years ago, you guys brought in UI path, right after you joined the company as it's CIO, why U I path, clearly you looked at some of the other folks, you mentioned that company that they acquired, but what in your mind differentiates what they're able to deliver on the partnership side and the technology side? >>Yeah. Very important question. We have a definition for a technology partner for us, the technology partner needs to meet criteria of innovating. So how much do you invest in innovation? And Daniel says, I don't even know the number, right? So because we want them to be on the forefront. Sometimes they have to pull us and sometimes we have to pull them. The second one is very important for a company to be successful in automation or in any advanced technology, you have to build intellectual property within your enterprise. And we did not want to art source technology. We wanted to insource technology and we asked you, I pad, if they would be reeling to co-innovate, co-develop collaborate with us. They were the only ones who allowed us to build the intellectual property within my enterprise, because that's the way I'm going to innovate. And that's the way I'm going to help product leaders like Pollock to create better products. Right? So, and the third one is just building expertise. Low-code no-code the technology company needs to, you know, wait where they remove some of the barriers for me to find the skills or develop talent, how easy it is to find the talent and skills to develop this technology. Right. And what, what does the technology company do to develop skills? So these are a few criteria that we have, and then when the company takes all of those, they are in, >>I'm interested in, um, to kind of shift the conversation. If I may, in your, your role, it's not uncommon to see a CIO and a chief digital officer together, but it's quite uncommon at a, at a large firm like Coca-Cola. And, and I'm wondering, is that how the company, cause your group sees information in digital? Is that how the company's organized? You know, that you plug into somebody who has that to a role. Can you talk about, >>Yeah, absolutely. So cocoli too. Jake is within the Coca-Cola system. We are one of the leading butlers within the Coca-Cola system. The reason I merged the two roles is to be successful in the digital era. When you have the digital and it separated. If it goes a little slower, you can not be successful in digital and you cannot be successful in generating new revenue streams or new business models. So you have to orchestrate that evolution and transformation of it and the rest of the business together. And that's why I merged the two roles. We are unique as Coca-Cola >>Merged them. You say you merged those roles, like, did you come at it from the, where you digital first and then CIO first >>Digital first. Okay. Great point. I built from scratch and started with the digital strategy. And then we went into defining what roles, what skills do we need? And then we redefined, what are the improvements we need on the it side? But it was all digital product based >>Because I think, uh, I think it would be much harder for a CIO, let alone a woman CIO, no offense, but I don't think there's any offense there, but oh, she's trying to do a land grab. I could see that happening, but the digital officer title, because that's the hot title and it's the visionary. Right. And it's a lot of times it's undefined. Yeah. So that's that and that, and that that's the structure of the organization. So you roll up into it. >>Uh, so yeah, because I came into the ex-con role. I had the privilege to kind of shape it from scratch. >>Exactly. And >>Like Shankar was talking about hidden brain and all the change this morning, it was a change in terms of how are we going to approach digital? It was a change in terms of all the people who are part of the company and people who have been in technology or it before right now, the expectations are very different. You have to be product organization, you have to be outcome centric. You have to generate the revenue streams. So it's very different from the world of it. I think any it or any technology leader can do this, if they are willing to transform themselves first and then their organization, and then they can transform the rest of the company, >>Chief digital officer data is a big part of your role. You're not the chief data officer, >>The organization, that's >>Part of your, okay, so the CDL reports into, okay, and that individual sure is responsible for governance and compliance. >>Well look, the data management, data governance, the foundation, and all the database solutions, I think >>You got it right. I think this idea of creating stovepipes, it just it's, it's not as productive and it's harder to make decisions that are aligned with the organization's goals, >>Boulder. So we're going to disrupt further. Our goal now is to create platforms and then democratize the platforms. So our operating partners can learn the new skills and they can develop their own use cases on the platforms. And that way they'll go much, further and much faster in terms of the generational new revenue, streams, changing, operating models, data and technology. I call it the new operating system of any business and everybody must learn >>Well. And that's what I want to ask you about, because if you think about, uh, uh, a company and incumbent, like Coca-Cola your processes over the years have in your data, maybe they were organized around the bottlers or the distribution channel, et cetera. And that might not be the best process. So you have to take a look at that and then use process mining to say, actually, what is the best process, reinvent yourself? Okay. >>Absolutely VRD and re-engineering and reinventing in a lot of places. Process mining helped us in short order to cash cycle. Everybody, every company has ordered to cash process. We took an order to cash process, which we recently standardized, by the way we thought we did. And every process mining told us that very few times you go through the happy path. Most of the times you go out of the happy path. So gave us a lot of tangible outcomes where we improve the cycle time. And it's an interesting process because you touch the customer it's impacts your delivery and your commitments to the customer. And it makes life easier for the employees. When you improve the process, this is only one piece VR also transforming the way we are interacting with our customers using digital means and digital channel. But one thing is very valuable with us while we do all of this staying hybrid is very important. Like with everything else, they do that human touch and personal relationship with our customers and consumers is invaluable. So we going to keep that doesn't matter how digital we go or how much technology we implement. They're going to keep the customer and consumer connect the most valuable asset that we have. >>Absolutely. It is. I'll go ahead. >>I was going to say, this is the one thing that, that we think about when we're designing our products, right? It's how can process my mining help you optimize your workflows, such that you can spend more time with the customer such that you can spend more time and get back to them faster. >>Yeah, that's critical. They, I always say the employee experience is inextricably linked to the customer experience. And so what you just talked about, you talked about so much stuff that I'd love to unpack. We probably don't have time, but coming in as with a transformation mindset, one being, you mentioned, you know, leaders need to be willing to embrace that. Obviously you were, but as a CIO, >>Working with UI path, you're really helping to redefine work. And also that customer experience, to an extent, how's your iPod helped facilitate that. So because they are listening and they are willing to partner with, and I think the most importantly, they're going to be part of our outcomes. They care about our outcomes. And going back to your question, how do we select a technology partner? That was one of the critical items. Outcomes are very critical. If there's no outcome, there's no point in it are not doing technology for the sake of doing it. We are, yes. We are all excited with what technology can bring and removing barriers very important, which is a huge, another huge topic. But if you don't generate an outcome it's meaningless and you AIPAC is willing to understand the outcome we are generating. So it's less of a commercial discussion, more of a technology and outcome conversation. >>So whether it's an customer outcome or an employee outcome or a cash outcome, financial outcome, I think that's why we have been successful. And they have been on the journey with you, iPad process mining. I think they are one of the very few clients, right? Customers of UI path who are using it. And because we are very progressive organization, you AIPAC is listening to our feedback and implementing back to your earlier question, you have so many customers who do you listen, right? So when you are progressive and when you really know what you are doing, you're also pulling your iPad, a big technology company into a direction that is more meaningful. So they listen to us in terms of what to improve with process mining. And that's why we were able to achieve the outcomes. And now they are listening to us further on further improvements on process mining so that we can capitalize on further outcomes and benefits of process mining >>In order to cash is common use cases. So what, what, uh, were there any diamonds in the rough, or do you suspect there are with, >>We already realized, yes. We realized multiple tangible outcomes. We discussed this with Polak earlier today. One of them is some very interesting, I'm not able to share, but the most critical one is be focused on improving cash cycle. It's scent. You can imagine extremely full flow business, even within FMCG, right? We as Coca-Cola system, we are an extremely flow business. It's an instant consumption business. Hence your delivery and cash cycles are very different compared to other industries. So we said, we want to improving cash. We discovered that the improved, the invoice due date change, which impacts the payment terms by 20%, we improved credit limits approvals by 5% by removing unnecessary approval steps. We realized there were unnecessary approvals. These two are directly impacting our customers as well because it's waiting in somebody's queue to handle those approvals. And the customer is not getting to delay delivery because it's payment, payment and delivery go hand in hand. >>And the third one is, and I'm not able to articulate it exact outcome, but it's a very critical day, every day gain on getting cash. So it's a cash game. The next big outcome is the cycle time improvements. So we significantly improve the cycle time of the process. And this means efficiency for our employees. We are making life easier for them. The last one is again, a tangible one 30,000 hours back in terms of productivity, one process, one country, 30,000 hours. And that translates into exactly that translates into benefit for the customer. You increase customer satisfaction, you increase employee satisfaction. 'cause you remove all the non-available for it. So going back to Pollock's point around continuous discovery, that's why we love it. It's like good old lean six Sigma lean six Sigma is exactly that you continuously, you want to continuously improve the process. You don't do it once with process mining. We don't want to do it once. We want to do it continuously, but this time with automation, >>But before we go, I'm the lone male on the panel. So I have to ask. So, so you CIO seat, chief digital role, very uncommon, let alone uncommon for a woman. Big time product management person. Okay. That's cool check. Right? You've been in the industry for a while now, a celebrity on the, on the cube and elsewhere. So has the pandemic, how has the pandemic affected the whole women in tech trend? Has it slowed it down? Has it accelerated? We were talking earlier about the working moms feeling like way stressed out more than the working dads, double 30% versus 15%. Has the pandemic in your minds altered in any way, was women in tech meme? How so positive. Negative. >>So we are trying to turn the negative into a positive. It is negative. Absolutely. I think it's impacted everybody, all, all women in all industries and in all areas of operation and workforce women in technology is already a very slim, right? It's a very tiny layer within any company and out there in the society. And unfortunately the challenges that came with COVID impacted and some of them had to leave and they couldn't stick around. Right. So we are trying to turn that into positive. As a digital function, we have a big give back initiative. It's a priority of the digital team. I'll be talking about that very in, in, and our technology removes barriers. So we have to turn this into a positive, yes, COVID has impacted everybody personally and directly or indirectly. But now with technology, we can remove barriers. We have now flexible working and hybrid working models, being ramped up across all geographies and all industries and all companies, technology removes barriers. >>We can teach technology to a lot of people and our communities and they can join because we have huge skill gaps in technology that would sat is we have huge scarcity of skills in technology. And we have very few people, but we are talking about women dropping out or any type of minor to dropping out, right? So we can leverage and improve and turn it around. I hope we'll accomplish to do that. We started doing that in our company and in Turkey. And we are trying to expand that across multiple other countries with NGO partnerships, helping women to gain certain skills so that they can join the economy again from wherever they are. >>And from my point of view, I think there are two aspects to it. As Layla said, it has affected women a little bit more, but I've also seen, in some cases it has leveled the playing field a little bit because there's, you know, everybody's on zoom. The kids show up on zoom cameras for men, just as much as they do for women. So it helps shine a light on things that we would normally go through that nobody would know about. And I thought that was a really cool outcome to some degree of this. You know, my manager prom has little kids and they'd be in his background all the time, just as my little kids would be by background. And I'm like, oh wow. So you know how it feels to be the caregiver at home. And I thought, I thought that was a positive outcome of the whole being a female in technology. I liked that >>That's something that I hadn't thought about in terms of leveling the playing field like that there's in this situation, there are both positives and negatives. I like how you're seeing the playing field level a bit more and how you're at. Coca-Cola looking to, how can we turn this negative into a positive lots of opportunities there we uncovered a lot in the last, I'm going to guess 20 minutes talking about continuous process discovery, all the way to women in technology, how you're each doing that and what your perspectives are. I wish we had more time. We could keep going, but ladies, thank you for joining David. >>It's been a pleasure >>For Dave Volante. I'm Lisa Martin live in Las Vegas at the Bellagio UI path forward for it. We'll be right back.

(upbeat music) >> From the Bellagio Hotel in Las Vegas, It's theCUBE. Covering UiPath FORWARD IV. Brought to you by UiPath. >> Welcome back to theCUBE's coverage of UiPath FORWARD IV. Live from the Bellagio, in Las Vegas. I'm Lisa Martin with Dave Vellante. We're here all day today and tomorrow. We're going to talk about process mining next. We've got two guests here. Mike Engel is here, intelligent automation and process intelligence leader at PWC. And Ryan McMahon, the SVP of growth at UiPath. Gentlemen, welcome to the program. >> Thank you, Lisa. >> Thank you. >> So Ryan, I'm going to start with you. Talk to us about process mining. How does UiPath do it differently and what are some of the things being unveiled at this event? >> So look, I would tell you it's actually more than process mining and hopefully, not only you but others saw this this morning with Param. It's really about the full capabilities of that discovery suite. In which, obviously, process mining is part of. But it starts with task capture. So, going out and actually working with subject matter experts on a process. Accounts payable, accounts receivable, order to cash, digitally capturing that process or how they believe it should work or execute across one's environment. Right Mike? And then from there, actually validating or verifying with things or capabilities like process mining. Giving you a full digital x-ray of actually how that process is being executed in the enterprise. Showing you process bottlenecks. For things like accounts payable, showing you days outstanding, maverick buying, so you can actually pin point and do a few things. Fix your process, right? Where process should be fixed. Fix your application because it's probably not doing what you think it is, and then third, and where the value comes, is in our platform of which process mining is a capability, our PA platform. Really moving directly to automations, right? And then, having the ability with even task mining to drill into a specific bottleneck. Capturing keystrokes, clicks, and then moving to, with both of those, process mining and task mining, into Automation Hub, as part of our discovery platform as well. Being able to crowdsource, prioritize, all of those potential, if you will, just capabilities of automations, and saying, "Okay, let's go and prioritize these. These deliver to the greatest value," and executing across them. So, as much as it is about process mining, it's actually the whole entire discovery suite of capabilities that differentiates UiPath from other RPA vendors, as the only RPA vendor that delivers process mining, task mining and this discovery suite as part of our enterprise automation platform. >> Such a critical point, Ryan. I mean, it's multi-dimensional. It's not just one component. It's not just process mining or task mining, it's the combination that's really impactful. Agree with you a hundred percent. >> So, one of the things that people who watch our shows know, I'm like a broken record on this, the early days of RPA, I called it paving the cow path. And that was good because somebody knew the process, they just repeat it. But the problem was, the process wasn't necessarily the best process. As you just described. So, when you guys made the acquisition of ProcessGold, I said, "Okay, now I'm starting to connect the dots," and now a couple years on, we're starting to see that come together. This is what I think is most misunderstood about UiPath, and I wonder, from a practitioner's perspective, if you can sort of fill in some of those gaps. It's that, it's different from a point tool, it's different from a productivity tool. Like Power Automate, I'll just say it, that's running in Azure Cloud, that's cool or a vertically integrated part of some ERP Stack. This is a horizontal play that is end to end. Which is a bigger automation agenda, it's bold but it's potentially huge. $60 billion dollar TAM, I think that's understated. Maybe you could, from a practitioner's perspective, share with us the old way, >> Yeah. >> And kind of, the new way. >> Well obviously, we all made a lot of investments in this space, early on, to determine what should we be automating in the first place? We even went so far as, we have platforms that will transcribe these kind of surveys and discussions that we're having with our clients, right. But at the end of the day all we're learning is what they know about the process. What they as individuals know about the process. And that's problematic. Once we get into the next phase of actually developing something, we miss something, right? Because we're trying to do this rapidly. So, I think what we have now is really this opportunity to have data driven insights and our clients are really grabbing onto that idea, that it's good to have a sense of what they think they do but it's more important to have a sense of what they actually do. >> Are you seeing, in the last year in a half we've seen the acceleration of a lot of things, there's some silver linings but we've also seen the acceleration in automation as a mandate. Where is it? In terms of a priority, that you're seeing with customers, and are there any industries that you're seeing that are really leading the edge here? >> Well I do see it as a priority and of course, in the role that I have, obviously everybody I talk to, it's a priority for them. But I think it's kind of changing. People are understanding that it's not just a sense of, as Ryan was pointing out, it's not just a sense of getting an understanding of what we do today, it's really driving it to that next step of actually getting something impactful out the other end. Clients are starting to understand that. I like to categorize them, there's three types of clients, there's starters, there's stall-ers and those that want to scale. >> Right? So we're seeing a lot more on the other ends of this now, where clients are really getting started and they're getting a good sense that this is important for them because they know that identifying the opportunities in the first place is the most difficult part of automation. That's what's stalling the programs. Then on the other end of the spectrum, we've got these clients that are saying, "Hey, I want to do this really at scale, can you help us do that?" >> (Ryan) Right. >> And it's quite a challenge. >> How do I build a pipeline of automations? So I've had success in finance and accounting, fantastic. How do I take this to operations? How do I take this this to supply chain? How do I take this to HR? And when I do that, it all starts with, as Wendy Batchelder, Chief Data Officer at VMware, would say and as a customer, "It starts with data but more importantly, process." So focusing on process and where we can actually deliver automation. So it's not just about those insights, it's about moving from insights to actionable next steps. >> Right. >> And that is where we're seeing this convergence, if you will, take place. As we've seen it many times before. I mentioned I worked at Cisco in the past, we saw this with Voice Over IP converging on the network. We saw this at VMware, who I know you guys have spoken to multiple times. When a move from a hypervisor to including NSX with the network, to including cloud management and also VSAN for storage, and converging in software. We're seeing it too with process, really. Instead of kids and clipboards, as they used to call it, and many Six Sigma and Lean workshops, with whiteboards and sticky papers, to actually showing people within, really, days how a process is being executed within their organization. And then, suggesting here's where there's automation capabilities, go execute against them. >> So Ryan, this is why sometimes I scoff at the TAM analysis. I get you've got to do the TAM analysis, you've got to communicate to Wall Street. But basically what you do is you pull out IDC or Gartner data, which is very stovepipe, and you kind of say, "Okay we're in this market." It's the convergence of these markets. It's cloud, it's containers, it's IS, it's PaaS, it's Saas, it's blockchain, it's automation. They're all coming together to form this, it sound like a buzzword but this digital matrix, if you will. And it's how well you leverage that digital matrix, which defines your digital business. So, talk about the role that automation, generally, RPA specifically, process mining specifically, play in a digital business. >> Do you want to take that Mike or do you want me to take it? >> We can both do it? How about that? >> Yeah, perfect. >> So I'll start with it. I mean all this is about convergence at this point, right? There are a number of platform providers out there, including UiPath, that are kind of teaching us that. Often times led by the software vendors in terms of how we think of it but what we know is that there's no one solution. We went down the RPA path, lots of clients and got a lot of excitement and a lot of impact but if you really want to drive it broader, what clients are looking at now, is what is the ecosystem of tools that we need to have in place to make that happen? And from our perspective, it's got to start with really, process intelligence. >> What I would say too, if you look at digital transformation, it was usually driven from an application. Right? Really. And what I think customers found was that, "Hey," I'm going to name some folks here, "Put everything in SAP and we'll solve all your problems." Larry Ellison will tell you, "Put everything into Oracle and we'll solve all your problems." Salesforce, now, I'm a salesperson, I've never used an out of the box Salesforce dashboard in my life, to run my business because I want to run it the way I want to run it. Having said that though, they would say the same thing, "Put everything into our platform and we'll make sure that we can access it and you can use it everywhere and we'll solve all of your problems." I think what customers found is that that's not the case. So they said, "Okay, where are there other ways. Yes, I've got my application doing what it's doing, I've improved my process but hang on. There's things that are repeatable here that I can remove to actually focus on higher level orders." And that's where UiPath comes in. We've kind of had a bottom up swell but I would tell you that as we deliver ROI within days or weeks, versus potentially years and with a heavy, heavy investment up front. We're able to do it. We're able to then work with our partners like PWC, to then demonstrate with business process modeling, the ability to do it across all those, as I call, Silo's of excellence in an organization, to deliver true value, in a timeline, with integrated services from our partner, to execute and deliver on ROI. >> You mentioned some of the great software companies that have been created over the years. One you didn't mention but I want you to comment on it is Service Now. Because essentially McDermott's trying to create the platform of platforms. All about workflow and service management. They bought an RPA company, "Hey we got this too." But it's still a walled garden. It's still the same concept is put everything in here. My question is, how are you different? Yeah look, we're going to integrate with customers who want to integrate because we're an open platform and that's the right approach. We believe there will be some overlap and there'll be some choices to be made. Instead of that top down different approach, which may be a little bit heavy and a large investment up front, with varied results, as far as what that looks like, ours is really a bottoms up. I would tell you too, if you look at our community, which is a million and a half, I believe, strong now and growing, it's really about that practitioner and those people that have embraced it from the bottom up that really change how it gets implemented. And you don't have what I used to call the white blood cells, pushing back when you're trying to say, "Hey, let's take it from this finance and accounting to HR, to the supply chain, to the other sides of the organization," saying, "Hey look, be part of this," instead of, "No, you will do." >> Yeah, there's no, at least that I know of, there's no SAP or Salesforce freemium. You can't try it before you buy. And the entry price is way higher. I mean generally. I guess Salesforce not necessarily but I could taste automation for well under $100,000. I could get in for, I bet you most of your customers started at 25 of $50,000 departmental deployments. >> It's a bottoms up ground swell, that's exactly right. And it's really that approach. Which is much more like an Atlassian, I will tell you and it's really getting to the point where we obviously, and I'm saying this, I work at UiPath, we make really good software. And so, out of the box, it's getting easier and easier to use. It all integrates. Which makes it seamless. The reason people move to RPA first was because they got tired of bouncing between applications to do a task. Now we deliver this enterprise automation platform where you can go from process discovery to crowd sourcing and prioritizing your automations with your pipeline of automations, into Studio, into creating those automations, into testing them and back again, right? We give you the opportunity not to leave the platform and extract the most value out of our, what we call enterprise automation platform. Inclusive of process mining. Inclusive of testing and all those capabilities, document understanding, which is also mine, and it's fantastic. It's very differentiated from others that are out there. >> Well it's about having the right framework in place. >> That's it. From an automation perspective. I think that's a little bit different from what you would expect from the SAP's of the world. Mike, where are you seeing, in the large organizations that you work with, we think of what you describe as the automation pipeline, where are some of the key priorities that you're finding in large organizations? What's in that pipeline and in what order? >> It's interesting because every time we have a conversation whether it's internal or with our clients, we come up with another use case for this type of technology. Obviously, when we're having the initial conversations, what we're talking about is really automation. How do we stuff that pipe with automation. But you know, we have clients that are saying, "Hey listen, I'm trying to carve out of a parent company and what I need to do is document all of my processes in a meaningful way, that I can, at some point, take action on, so there's meaningful outcomes." Whether it be a shared services organization that's looking to outsource, all different types of use cases. So, prioritizing is, I think, it's about impact and the quickest way to impact seems to be automation. >> Is it fair to say, can I look at you UiPath as automation infrastructure? Is that okay or do you guys want to say, "Oh, we're an application." The reason I ask, so then you can answer, is if you look at the great infrastructure plays, they all had a role. The DBA, the CCIE from Cisco, the Cloud Architect, the VMware admin, you've been at all of them, Ryan. So, is there a role emerging here and if it's not plumbing or infrastructure, I know, okay that's cool but course correct me on the infrastructure comment and then, is there a role emerging? >> You know, I think the difference between UiPath and some of the infrastructure companies is, it used to take, Dave, years to give an ROI, really. You'd invest in infrastructure and it's like, if we build it they will come. In fact, we've seen this with Cloud, where we kind of started doing some of that on prem, right? We can do this but then you had Amazon, Azure and others kind of take it and say, "Look, we can do it better, faster and cheaper." It's that simple. So, I would say that we are an application and that we reference it as an enterprise automation platform. It's more than infrastructure. Now, are we going to, as I mentioned, integrate to an open platform, to other capabilities? Absolutely. I think, as you see with our investments and as we continue to build this out, starting in core RPA, buying ProcessGold and getting into our discovery suite of capabilities I covered, getting into, what I see next is, as you start launching many bots into your organization, you're touching multiple applications, so you got to test it. Any time you would launch an application you're going to test it before you go live, right? We see another convergence with testing and I know you had Garrett on and Matt, earlier, with testing, application testing, which has been a legacy, kind of dinosaur market, converging with RPA, where you can deliver automations to do it better, faster and cheaper. >> Thank you for that clarification but now Mike, is that role, I know roles are emerging in RPA and automation but is there, I mean, we're seeing centers of excellence pop up, is there an analogy there or sort of a similar- >> Yeah, I think the new role, if you will, it's not super new but it's really that sense of an automation solution architect. It's a whole different thing. We're talking about now more about recombinant innovation. >> Mike: Yeah. >> Than we are about build it from scratch. Because of the convergence of these low-code, no-code types of solutions. It's a different skill set. >> And we see it at PWC. You have somebody who is potentially a process expert but then also somebody who understands automations. It's the convergences of those two, as well, that's a different skill set. It really is. And it's actually bringing those together to get the most value. And we see this across multiple organizations. It starts with a COE. We've done great with our community, so we have that upswell going and then people are saying, "Hang on, I understand process but I also understand automations. let me put the two together," and that's where we get our true value. >> Bringing in the education and training. >> No question. >> That's a huge thing. >> The traditional components of it still need to exist but I think there are new roles that are emerging, for sure. >> It's a big cultural shift. >> Oh absolutely, yeah. >> How do you guys, how does PWC and UiPath, and maybe you each can answer this in the last minute or so, how do you help facilitate that cultural shift in a business that's growing at warp speed, in a market that is very tumultuous? How do you do that? >> Want to go first or I can go? >> I'll go ahead and go first. It's working with great partners like Mike because they see it and they're converging two different practices within their organization to actually bring this value to customers and also that executive relevance. But even on our side, when we're meeting with customers, just in general, we're actually talking about, how do we deal with, there's what? 13 and a half million job openings, I guess, right now and there's 8500 people that are unemployed, is the last number that I heard. We couldn't even fill all of those jobs if we wanted to. So it's like, okay, what is it that we could potentially automate so maybe we don't need all those jobs. And that's not a negative, it's just saying, we couldn't fill them anyway. So let's focus on where we can and where, there again, can extract the most value in working with our partners but create this new domain that's not networking or virtualization but it's actually, potentially, process and automation. It's testing and automation. It might even be security and automation. Which, I will tell you, is probably coming next, having come out of the security space. You know, I sit there and listen to all these threats and I see these people chasing, really, automated threats. It's like, guys a threat hunter that's really good goes through the same 15 steps that they would when they're chasing a false positive, as if a bot would do that for them. >> I mean, I've written about the productivity declines over the past several decades in western countries, it's not universal around the world and maybe we have a productivity boost because of Covid but it's like this perpetual workday now. That's not sustainable. So we're not going to be able to solve the worlds great problems. Whether it's climate change, diversity, massive deaths, on and on and on, unless we deal with that labor gap. >> That's right. >> And the only way to do that is automation. It's so clear to me that that's the answer. Part of the answer. >> It is part of the answer and I think, to your point Lisa, it's a cultural shift that's going to happen whether we want it to or not. When you think about people that are coming into the work force, it's an expectation now. So if you want to retain or you know, attract and retain the right people, you'd better be prepared for it as an organization. >> Yeah, remember the old, proficient in Word and Excel. Makes it almost trivial. It's trivial compared to that. I think if you don't have automation chops, going forward, it's going to be an issue. Hey, we have whatever, 5000 bots running at our company, how could you help? Huh? What's a bot? >> That's right. You're right. We see this too. I'll give you an example at Cisco. One of their financial analysts, junior starter, he says, "Part of our training program, is creating automations. Why? Because it's not just about finance anymore. It's about what can I automate in my role to actually focus on higher level orders and this for me, is just amazing." And you know, it's Rajiv Ramaswamy's son who's over there at Cisco now as a financial analyst. I was sitting on my couch on a Saturday, no kidding, right Dave? And I get a text from Rajiv, who's now CEO at Nutanix, and he says, "I can't believe I just created a bot." And I said, "I'm at the right place." Really. >> That's cool, I mean hey, you're right too. You want to work for Amazon, you got to know how to provision a EC2 instance or you don't get the job. >> Yeah. >> You got to train for that. And these are the types of skills that are expected- >> That's right. >> For the future. >> Awesome. Guys- >> I'm glad I'm older. >> Are you no longer proficient in Word is the question. >> Guys, thanks for joining us, talking about what you guys are doing together, how you're really facilitating this massive growth trajectory. It's great to be back in person and we look forward to hearing from some of your customers later today. >> Terrific. >> Great. >> Thank you for the opportunity. >> Thank you for having us. >> Thank you guys. >> Our pleasure. For Dave Vellante, I'm Lisa Martin, you're watching theCUBE live from the Bellagio in Las Vegas, at UiPath FORWARD IV. Stick around. We'll be back after a short break. (upbeat music)

>>Um, okay, thanks to the studio there for the handoff. Appreciate it. We're here for breaking news and it's exciting that we have who's the managing director. Google is breaking some hard news here, Dave. We want to bring him in and get commentary while we end up Dave too. Honestly, the story here is cloud city. We are in the cloud city and all, thanks for coming on remotely into our physical hybrid set here. Thanks for coming >>On. Thank you, John. And very excited to be here. What Juliet. >>Well, we got Bon Jovi ready to play. Everyone's waiting for that concert in the year. The only thing standing between bunch of LV and all the great stuff. So a lot of people watching. Thanks for coming on, sir. So you guys got some big news, um, first Erickson partners with you guys on 5g platform, deal with Anthem, as well as, uh, open ran Alliance. You guys are joining huge, a Testament to the industry. I see Google with all your innovation you guys have in the big three cloud hyperscalers. Obviously you guys invented SRE, so you know, you no stranger to large scale. What's the news. Let's tell us why this Erickson news is so important. Let's start with the Erickson announcement. >>Sure. So John, I mean, we are very excited today to finally bring to the market, the strategic partnership that we've been building with Erickson for the last few months, uh, the partnership to recent retreat, which is very important to the industry is you're actually doing this in conjunction with very large CSPs. So it's not been in isolation. You are in fact in the press release that we have already launched something to get the big telecom Italia in Italy, because you will see that also in the past. And really the partnership is on three pillars. Number one, how can CSBs monetize 5g and edge, which is the real team at the moment using Google clouds solutions like the edge computing platform and, and POS, and Erikson's cutting edge 5g components, 5g solutions. And if we can onboard these together at the CSP, such as telecom Italia, that creates massive pain to market efficiency. So that's 0.1 because speed and agility is key John, but then point to it also unlocks a lot of edge use cases for a bunch of verticals, retail, manufacturing, healthcare, so on, which are already starting to launch together with that. Excellent. And so that's the second pillar. And then the final pillar of course, is this continuously cloud native innovation that you just highlighted. John, we are going to try and double down on it between ourselves and Ericsson to really time created this cloud native application suite or 5g or whatever. >>Talk about the innovations around cloud, because the message we're hearing him this year at mobile world Congress, is that the public cloud is driving the innovation. And, you know, I can be a little bit over the top. So the telcos are slow. They're like glaciers, they move slow, but they're just moving packets. They are there. They're moving the network around. The innovation is happening on top. So there's some hardened operations operating the networks. Now you have a build concept cloud native enables that. So you've got containers. You can put that encapsulate that older technology and integrated in. So this is not a rip and replace. Someone has to die to win. This is a partnership with the tellers. Can you share your thoughts on that piece? >>Smart Antone's photo? We believe that it's a massive partnership opportunity. There's zero conflict or tensions in this sort of ecosystem. And the reason for that is when you talk about that containerization and right once and deploy everywhere type architecture that we are trying to do, that's where the cloud native really helps. Like when you create Ericsson 5g solutions with the operators, adjust telecom Italia, once you build a solution, you don't have to worry about, do I need to kick it back again and again, but every deployment, as long as your mantra, genetics and working, you shouldn't be able to have the same experience. >>Yeah, I'm John. I talk all the time in the cube about how developers are really going to drive the edge. You're clearly doing that with your distributed cloud, building out a telco cloud. I wonder if you could talk a little bit more about how you see that evolving. A lot of the AI that's done today is done in the cloud. A lot of modeling being done. When you think about edge, you think about AI inferencing, you think about all these monetization opportunities. How are you thinking about that? >>So I think David, first of all, it's a fan best six Sigma in how we are looked in at analytics at the edge, right? So we, uh, we have realized that is a very, very, uh, uh, uh, data computing, heavy operation. So certainly the training of the models is still going to stay in cloud for the foreseeable future. But the influencing part that you mentioned is there something that we can offer to the edge? Why is that so important in the pandemic era, think of running a shop or a factory floor, completely autonomously meeting zero minimal human intervention. And if you want to look at an assembly line and look at AI influencing as a way to find out assembly line defects on products in manufacturing, that's very difficult problem to solve unless you actually create those influencing models at the edge. So creating that ecosystem of an Erickson and a Google cloud carrier gives you that edge placement of the workloads that would fit right next to our factory floor in our manufacturing example. And then on top of that, you could run that AI influence thing to really put in the hands of the manufacturer, a visual inspection capability to just bring this to life. >>Great. Thank you for that. And now the other piece of the announcement of course, is the open, open ran. We've been talking about that all weekend and you know, you well, remember when cloud first came out, people were concerned about security. Of course. Now everybody's asking the question, can we still get the reliability and the security that we're used to with the telcos? And of course over time we learned that you guys actually pretty good at security. So how do you see the security component, maybe first talk about the open ran piece, why that's important and how security fits? >>Sure. So first of all, open trend is something that we have taken great interest in the last year or so as it started evolving. And the reason for that is fairly simple. Dave, this aggregation of networks has been happening for some time in the radio layer. We believe that's the final frontier of sort of unlocking and dis-aggregating that radio layer. And why is it so important? 80% of the operators spend globally is on radio. 80% is on radio. If you disaggregate that. And if the internet synergies for your CSP partners and clients, that meant you have standard purpose hardware standard for software with open interfaces, number one, massive difference in VCO. Number two, the supply chain gets streamlined and become still really, really simple way to manage a fairly large distribution. That's about to get larger in 5g and the capital clarity that 5g needs. >>You're thinking of tens of thousands of micro cells and radio cells going everywhere. And having that kind of standardized hardware software with openings of Essex is an extremely important cost dimension to every new site finished that the reason we got to exact open brand was you can now run for a lot of API APIs on the radio net, cetera, that then certainly brings a whole developer community on the radio later. That then helps you do a bunch of things like closed loop automation for network optimization, as well as potentially looking at monetization opportunities by hyper personalizing, yours and mine experiences at the waist level from the self-doubt. And so that really is what is driving us towards this open grind paper. Come on, we go and >>Got a minute and a half. I want to get your thoughts real quick on, on open source and the innovation. Um, Danielle Royston, who's the CEO of telco, Dr. She's at a keynote today. And she mentioned that the iPhone 14 years ago was launched. Okay. And you think about open and you mentioned proprietary with the 5g and having Iran be more commodity and industry standard. That's going to lower the costs increase the surface here of infrastructure. Everyone wins because everyone wants more connectivity options. Software is going to be the key to success for the telco industry. And open source is driving. That is Android. The playbook that you guys pioneered, obviously at Google with the smart phones was very successful. How is that a playbook or an indicator to what could happen at telecom? >>Absolutely. John and the parallel and analogy that you raised is photon. Be believed in the telco world and tossed multi cloud as a unifying software development layer. The app development platform is the way that people will start to drive this innovation, whether it's radio or whether it's in the core or whether it's on the side of pups, same software planning, everywhere that really allows you that whole development models that we are familiar with, but on the telecom side. And that's where we are seeing some massive innovation opportunities for systems to come on board. >>That's great stuff. And I was just heard someone in the hallway just yesterday and say, you want to be the smartphone. You don't want to be the Blackberry going forward. That's pretty much the consensus here at mobile world Congress. I'm all. Thank you for coming on and sharing the hard news and Google regulations on the Erickson Anthem platform, a deal as well as the open Ranton Alliance. Uh, congratulations. Good to see you. And by the way, you'll be keynoting tomorrow on the cube featured segment. So >>Watch that in there. Thank you, John. Thank you. Glad >>To be here. Benching director telecom, industry, solicitor, Google, obviously player. He's managing that business. Big opportunities for Google because they have the technology. They got the chops, Dave, and we're going to now bring this Daniel. Russia says here when to bring up on the stage, Bon Jovi is about to go on behind us Bon Jovi's here. And this is like a nightclub, small intimate setting here in cloud city. Dave Bon. Jovi's right there. He's going to come on stage after we close down here, but first let's bring up the CEO of telco. Dr. And yeah, it was great to see she's hot off the keynote. We're going to see you to Mike. Great to see you. Oh, it's great to be there. We're going to see you tomorrow for an official unpacking of the keynote, but thanks for coming by and closing, >>Swinging by. I never closed down the show. It's been a big, it's been a big day-to-day at MWC and in cloud city, really starting to get packed. I mean, everyone's coming in the band's warming up. You can kind of hear it. Um, I think Elon Musk is about to go on as well. So I mean, it's really happening all the buzz about cloud city out there in the hallway. Yeah. Yeah, no, I mean, I think everyone's talking about it. I'm really, really excited with how it's going. >>Well, this is awesome. While we got you here, we want to put you to work being the cube analyst for the segment. You just heard Google. Uh, we broke them in for a breaking news segment. So hard news Erickson partnership. We're in the factory, former Erickson booth. They're not even here, it's now the Calco VR booth, but that's a relation. And then open ran again, open source, you got five G you got open source all happening. What's your take on this? >>You see, you know, there's two big. And I, I talked about it, my keynote this morning, and there's two big technological changes that are happening in our industry simultaneously. And I don't think we could have had it MWC 21. I certainly wanted to make it about the public cloud. I think I'm sort of successful in doing that. And I think the other piece is open ramp, right? And I think these two big shifts are happening and, um, I'm really thrilled about it. And so, yeah, >>Well I loved your keynote. We were here, live. Chloe was here filling in for Dave while David was going to do some research and some breaking stories to you are on stage. And we were talking well, he's like, there's trillions of dollars, John on the table. And I was making the point, the money is at the middle of the table and it's changing hands if people don't watch it. And then you onstage that this trillions of dollars, this is a real competitive shift with dollars on the table. And you've got cultural collision. You got operators and builders trying to figure out it feels like dev ops is coming in here. Yeah. I mean, what's the, what's the holistic vibe. What's >>The, yeah, I think my message is about, we can use the software and specifically the software, the public cloud to double your ARPU without massive cap X expenditure. And I think the CSPs is always viewed to get the increase in ARPU. I got to build out the network. I got to spend a lot of money. And with these two technologies that require might be dropped. And then in exchange for doubling our poo, why not? We should do that. Absolutely. >>You know, your message has been pretty clear that you got to get on, on the wave that arrived the way you're going to become driftwood. As John said yesterday. And I think it's pretty, it's becoming pretty clear that that's the case for the telcos. I feel like Danielle, that they were entering this decade, perhaps with a little bit more humility than they have in the past. And then, you know, maybe, especially as it relates to developers, we're just talking about building out the edge. We always talk about how developers are really going to be a key factor in the edge. And that's not a wheelhouse necessarily. It's obviously they're going to have to partner for that to have going to have to embrace cloud native. I mean, it's pretty clear that your premise is right on it. We'll see how long it takes, but if it, if they don't move fast, you know, what's going to happen. Well, I >>Think you look at it from the enterprise's perspective. And I think we just heard Google talking about it. We need to provide a tech stack that the enterprises can write to now, historically they haven't had this opportunity historically that CSPs have provided it. Now you're going to be able to write against Google's tech stack. And that's something that is documented. It's available. There's developers out there that know it. Um, and so I think that's the big opportunity and this might be the big use case that they've been looking for with 5g and looking forward to 16th. And so it's a huge opportunity for CSS. >>I think that's an important point because you got to place bets. And if I'm betting on Google or Amazon, Microsoft, okay. Those are pretty safe bets, right. Those guys are going to be around. >>I mean, they're like, no, don't trust the hyperscalers. I'm like, um, are you guys nuts? If they're safe, right. Safe >>Bets in terms of your investment in technology, now you got to move fast. Yeah. That's the other piece of it. You've got to change your business model. >>Well, you gotta be in the right side of history too. I mean, I mean, what is trust actually really mean? The snowflake trust Amazon, it sure did to get them where they are. Um, but now that's a >>Great example, John. It really is because there's a company that can move fast, but at the same time they compete with the same time they add incremental value. And so yeah, >>Here, the, you can see the narrative like, oh no, we're partnering telcos. Aren't bad. No one needs to die to bring in the new containers. Do we'll help them manage that operational legacy. But if they don't move, they're going to have an asset. That'll get rolled up into a SPAC or some sort of private equity deal. And because the old model of building cap backs and extract rents is kind of shifting because the value shifting. So to me, I think this is what we're watching still kind of unknown. Danielle Love to get your thoughts on this because if the value shifts to services, which is a consumption model like cloud, yeah. Then you can, don't have to try and extract the rents out of the cap ex >>Yeah. I don't think you need to own the entire stack to provide value. And I think that's where we are today in telco, right there. I mean, nuts and bolts of the stack, the servers, you know, the cabling, everything. And I'm like, stand on the shoulders of these amazing tech giants that have solved, you know, mega data centers, right. Huge data centers at scale, and just leverage their, their investment and uh, for your own benefit, it starts to focus. And we heard, um, all talking about it starts to focus on your subscriber and driving a great experience for us. Right? Yeah. Well, you're >>Talking about that many times they can do, but you're right. If the conversation hasn't has to go beyond, okay, we're just conductivity. It's gotta be ongoing and be like, oh, it's $10 a month for roaming charges. Ah, great. Yeah. Tick that box, right? It's those value added services that you're talking about and it's an infinite number of those that can be developed. And that's where the partnerships come in a creativity in the industry. It's just >>A blank piece of paper for, well, we, you know, everyone thinks Google knows everything about you, right. We've had the experience on our phone where they're serving of ads and you're like, how did you write Facebook? But you know, who knows more about us than, than Google or your mother, even your telco, you take your phone with you everywhere. Right? And so it's time to start unlocking all of that knowledge and using it to provide >>A really great, by the way, congratulations on the CEO to Toby and the investment a hundred million dollars. That's a game changer statement again, back to the billing. And there's a good, there's a whole new chain, even all up and down the stack of solutions, great stuff. And I want to unpack that tomorrow. I don't hold that. We're going and we're going to meet tomorrow. I want, I wanna want to leave that to stay >>In the data for a second, because you made the point before in your keynote as well. That it's, that it's the data that drives the value of these companies. Why is it that apple, Amazon, Google Facebook now trillion dollar evaluations. It's all about the data and the telcos have the data, but they can't figure out how to turn that into valuation. >>There's two parts of the data problem, which is number one, the data is trapped in on-premise siloed systems that are not open. You can't connect them and they certainly can't do without. And we talked about it, I think yesterday, you know, millions of dollars of expenditure. And I think the other piece that's really interesting is that it's not connected to a mechanism to get it out in a timely manner, right? This is data that's aging by the minute. And when it takes you weeks to get the insight it's useless. Right? And so to Togi we announced the launch to Togi, I'll get a little to Tokyo plug in there, right. To Toby is connecting that insight to the charger, to the engagement engine and getting it out to subscribers. I think that's the beginning of this connection. I think it's a hard problem to solve and would have been solved already. >>But I think the key is leveraging the public cloud to get your data out of on-premise and, and mashing it up against these great services that Google and Azure and Amazon provides to drive it into the hands of the subscriber, make it very actionable, very monetizeable right at the end, that's what they want. More ARPU, more revenue. Right. And you know, we heard some keynotes from GSA yesterday, some big, big guys, you know, talking about how, you know, it's not fair that these other communication platforms are not regulated. You know, telco is heavily regulated and they're like, it's not fair. And I'm like, yep. It's not fair. That's like right. South complaining about it and start treating your customers better. So they are, they're happy to give you more. >>Yeah. And I think that's the message about the assets do, um, well, one thing I will say is this mobile world Congress is that we've been having a lot of fun here in cloud city. I have to ask you a personal question. Have you been having fun? You look great on the keynote of spring to your staff, cloud cities. Beautiful. Spectacular here. Give us some highlights, personal highlights from your trip. So far, >>Number one, I'm, I'm psyched that the keynote is delivered and, and done. I mean, I think it takes my blood pressure down a blind, um, you know, the spring in my step, I wore these fun little tennis shoes and, and that was really fun, but yeah, I'm having, I'm having, I think a lot of things, great conversations. Yes. The attendance has reduced, um, you know, usually you see hundreds of people from the big group carriers, especially the European groups and yeah, the attendance is reduced, but the senior guys are here, right. The senior leadership teams are in the booth or having meetings, running amazing conversations. I think the last year we really did live a decade in one year. I think they woke up to the power of the public cloud. I mean, there was no way that they got business done without cloud based tools. And I think the light bulb went off, I think I'm right in the right moment. Awesome. Do you think that, >>Do you think that they'd think in there, like left money on the table because you look at the pandemic, there were three categories of companies, losers, people who held the line struggled and then winners. Yeah. Big time tailwind booming. Obviously the zooms of the world telcos did well. They were up and running. Uh, this, this was good. You think we might've left some money on the table? They could have done more. >>Yeah. I think the ones that were, you know, people talk about digital transformation where digital telco we're digitally enabled, but I think the pandemic really tested this. Right. Can you deliver a contactless SIM or do you need to go to a store in person to get to go pick it up? And I had a broken SIM during the pandemic. My provider made me go to the store and I'm like, is it even open? And so I heard other stories of telcos that were very digitally enabled, right. They were using Uber to deliver Sims, all sorts of fun, crazy stuff and new ideas. And they were able to pivot right. Agile. And so I think, I think that was a really big telemedicine booming. So >>If you were in a digital business during the pandemic in general, you're out of business maybe unless you were telco, but I think you're right. I think the light bulb went off. It was an aha moment. And they said, oh, if >>We don't, I mean, I am not kidding. Right. As an ex CEO where I was trying to collect signatures on renewals, right. Here's a DocuSign, which for the world is like, duh. I mean, our school uses DocuSign. I had telcos that required an in-person signature, right. In some country once a month on Tuesday between 10 and two. And I'm like, how are you doing business? Like that? That's like the dark ages. >>Yeah. This is where the crypto guys got it right. With know your customer. Right. >>Because they have the data. Well, there's a lot of things that come in wrong. We don't want to get the whole show on that, but then you have great to have you drop biopsy Bon Jovi's here. How did you get Bon Jovi? Huge fan, New Jersey boy Patriots fan. We'd love it. Well, >>Yeah. I mean, who doesn't love Bon Jovi. Right. Um, we knew we wanted a rocker, right. Rock and roll is all about challenging the status quo. Um, that, I mean, since the beginning and that's what we're doing here, right. We're really challenging. Like the way things have been done in telco kind of just shattering the glass ceiling and lots of different ways. Right. Calling the old guys dinosaurs. I'm sure those guys love me. Right. I mean, how much do they hate me right now? Or they're like that girl? Oh, we're punk >>Rock. They're rock and roll. Right, right. I mean, maybe we should have gotten the clash >>Right. Black flag. Right. I'm a little bit old. >>Accessible. Still >>Edgy. Yeah. So really excited to get them here. Um, I've met him before. Um, and so hopefully he'll remember me. It's been a couple of years since I've seen him. So can't wait to connect with him again. I think we have Elon Musk coming up and that's going to be, it's always exciting to hear that guy talk. So >>Yeah, it could be inspiration off after you've talked to space, space X and kind to star lake. >>Right. I mean, those guys are launching rockets and deploying satellites. And >>I think that's really interesting for >>Rural right. In telco. Right. Being able to deploy very quickly in rural where the, maybe the cost, um, you know, per gig doesn't make sense. You know, the cost for deployment of tower. I think, I mean, that's an interesting idea right there. It's exciting. It's exciting. >>He's inspirational. I think a lot of people look at the younger generation coming into this issue. Why are we doing things? A lot of people are questioning and they see the cloud. They're saying, oh, Hey, you're a B, why are we doing this? This is such an easier, better way. Yeah. I think eventually the generation shifts >>It's coming. I'm so excited to be a part of it. Yeah. Great, >>Great leadership. And I want to say that you are real innovative, glad to have us here and presenting with you here. >>Awesome team. I'm psyched to have you guys. We talked last night about how great this partnership has said. Yeah. >>Cuba's keep us rocking inside the cloud city. The streets of the city are packed in here. All stuff. Great stuff. Thanks for coming on. Thanks. Bon Jovi is here. We've got a shot. A bunch of we do we have a screenshot of Bon Jovi? Yup. There it is. Okay. He's about to come on stage and uh, we're gonna take a break here. We're gonna take and send it back to Adam and the team in the studio. Thanks guys.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by Silicon Angle. Hi, this is David Linton. You're watching the Cubes coverage of the Cube on Cloud, our own virtual event where we're trying to understand the future of cloud, where we've come from and where we're going. And we're bringing in visionaries to really have that detailed conversation. Daniel Jones is here. He's the CEO of automation specialist. You. I path Daniel. Thanks for coming on and sharing your insights here. >>Thank you so much for inviting me. They've appreciated. >>That's always a pleasure to get together with folks that have started companies with a seed of a vision and have exploded in tow. You know, great success. And when I wanna go back to the the the founding days of you, I path 2005. It was a pre cloud. There's certainly pre cloud as we know it today. A w s came out in 2006. Aw, and then we saw the clouds Ascendancy. But but your original founding premise there was no cloud, you know, it wasn't like a startup could just spend up stuff in the cloud. But what you've seen that evolution. So when you first started to see cloud evolved, What did you think? Did you think Oh, well, we'll see what happens. Or did you? Did you know at the time that this was gonna be a bigas? It actually has become. What were your thoughts back then? >>Well, I honestly, I thought that we are kind of agent. And maybe it's stupid to not to pie foot in tow, The new trends in technology like Cloud Mobile social and I we kept, you know, working on this computer vision technology that 15 years ago, war was not really hot. But with the evolution of self driving cars and the latest development in AI, we we've been able to capture our investments in the domain that was not hot. But suddenly, you know, became the word the of the greatest minds in I t. And we definitely we specialize Our computer vision toe a narrow use case, but still, it's the It's the key of what we've done in, uh, in the end, the robots are powered by computer vision technology. This kind of a robot emulate how human user work. So obviously we use vision a lot in our day by day work and having the best technology that allows our robots to interact with the computer screen more like human user is quintessential and, uh, making our business reliable and easy to use. So we were lucky. But I always felt that maybe I should change it. And we were feeling I remember you know, many discussions with my, you know, initial developers because we like what you're doing. What we felt a bit left outside my door. What way? Got lucky in the end. >>So So I have a premise here and that when you go back to the early days of cloud, what they got right was they were attacking the human labor problem and they automate it was storage. It was it was networking. It was compute. But really the automation that they brought toe i t. And the quality that that drove and the flexibility was, you know, a game changer. Of course, we know that now. And you know, many of us at the time were very excited about Cloud. I'm not sure we predicted the impact that it had, but my premise is that there's a parallel in your business with the automation that you're driving into the business. We've talked toe people, for instance, that some of your customers have said, You know I can't do Six Sigma. I can't afford to do six Sigma before things like R P. A. For business process. I do that for Mission critical things, but now I can apply six Sigma thinking across my entire business that drives quality. It takes costs out of my business. So what do you think about that premise? That there's a parallel between the early days of cloud taking human labor out of the equation and driving quality and flexibility, cost saving speed and revenue, etcetera and what you're doing on the business side, >>it is clearly a parallel. I can tell that the cloud was built by looking at ICTY Automation use cases first of all, because this is all software engineers understand the most software engineers. Let's be you little on this. They don't understand the business work. They don't understand all how the rial work is performing a big enterprise and they don't care. Sometimes when in my own discussions with our CFO, he is surprised that I don't know all the use cases in the world. Yes, of course. I don't know exactly how an insurance company work All the processes in a health care, all the banking processes. I have intellectual curiosity how they were. But what interests me the most is our computer vision technology that works uniformly well across different. That was the same from the cloud. So initially they built and they build a cow cloud one toe, help them when what they know the best. And now, for we were put in the face of having great technology, this computer region technology, but without having a great use case in the I t world that we understood. And when we when I'm speaking about our early days like 12, 13, 14, I believe this technology has a lot less applicable bility in the real world. Because again, we were thinking of some sorts of small I T automation gigs that were not possible just doing the AP ice. But when I discovered the messy world of business processes and how important is to emulate people when you think automation, that was a big ah ha moment. So I believe that we can do for business processes what the cloud has done for I t processes on. We are really patient now about this business processes on helping people toe eliminate all the repetitive work that is their delegate. This work two robots and have the people that are required to do this work do do better. A smaller number of tasks every day. Everyone has own, as on her or him played today like, let's say, 10, 20 different activities. Some of them can be completely delegated to rob to robots, and they are the low value type of activities, while they can focus on the high value activities like interaction with people, creativity, decision making and this type of human like things that we as humans really love. >>I love that you shared that story, but you thought it was a very narrow, sort of set of use cases when you first started and then, you know, that's that's just an awesome founders, you know, really ization. I love it when we've often said in the Cube that, you know, for decades we've marched to the tune of Moore's Law. That was the innovation engine. No longer is that case. It's a combination of of data, applied machine intelligence and cloud for scale. And I guess the computer vision pieces How you in just the data you've you've made some investments in a I and there's many more to come the industry in general and the cloud is sort of the piece of that equation that we see for scale. So I wonder how you see those pieces fitting to your business. Uh, and how important is the cloud for your scale? At last? Uh, at last year, I path forward. There was a lot of talk amongst your customers about scaling. Is the cloud critical for that scale? >>Yeah, I believe so. And we are thinking of clouds in tow. Distinct ways number one. We're offering Onda manage automation service in our own close, using where we host everything by ourselves, including our orchestrator, and then be next to have the plans to include our the robots that execute the automation And people simply can't connect to our cloud building automation and just scheduled to run without any maintainers. And they will have access to oh, great analytics, Everything integrated. So this is a major force to us, and the way we launching G a. This cloud offering in April this year, and I can tell you that until now, 20% of our customers already are in a shape or another in this type of offering, not 20% dollar amount, but 20% of our customers. And it's clear that at this point this has mawr applicability into the long tail, a smaller customers than in the on our biggest customers. But the second, this thing type of cloud offering that we focus on is toe have best in class support and best in class multi cloud support for the cloud of choice of our customers. For instance, if you go in if you go in a w, g, c, p usher and you buy a subscription there, you wear buildings. Specialized editions were with one click. You will be able to install our technology in those clouds and you'll be ableto scale up and down your robots. You can connect your robots to our many service were within your tenant, but basically the angle is toe lesson. Ah lot the administration, the maintainers footprint of your installation, either on our own cloud, even on your cloud of choice. I'm a strong believer that we will see an accelerated transition from the completely on Prem Workloads into these two source of cloud workloads. >>I wanna ask you, is a a technologist if you see. So you mentioned that you're gonna take your products and your support. Multiple clouds will run on any cloud in A lot of companies are talking about that, you know, for their respective whether it's a database or, you know, whatever storage device, etcetera. Do you see the day where you'll actually start? You're collaborating across clouds. Where the user, uh, maybe maybe the user today doesn't know, but maybe a developer does know which cloud it's running on. But do you see any value in actual, you know, connecting across clouds where the data and one cloud is relevant for the data? Another cloud is I know there are latent see issues. Is that you know, technically feasible. And is it it? Will it drive business value? What do you think about that cross cloud connection? >>I believe it is already happening. There is a mesh between between various services and who knows in which cloud they are awful. Already. I feel the Leighton see is less and less of a problem as much as the biggest cloud provider have have a very distributed geographically president. So as long as I can playing AWS in East Coast, on on Asia in East Coast, it's not such a big Leighton see issue. Uh huh. Frankly, in the past, our customers at least start telling us they seen how it is to be completely looking toe one technology on people would like Toa have optionality. It's not necessarily that I will use three clothes, but I would like to use the vendor that gives me optionally even. And this is what we're trying to offer. >>Do you, when you think about the future of work? I mean, e said before the cloud one dato was infrastructure storage, networking, computing Uh, it seems like to Dato we're bringing in more ai new workloads. We're seeing, you know, analytics and machine intelligence applied to the data and then, you know, distributed at scale self serve to the business. How do you see the future of work specifically as it relates toe automation affecting that, uh on you know what role does cloud play there? What's your vision? >>So as the workloads will move to cloud. It's absolutely critical that the processes will move to cloud, so there is no way back. I think, that moving in tow, moving from home for and software into cloud will make even easier toe automate this type of workloads into the cloud. It's gonna be less maintain us. You will deal less with legacy applications that require some special care. It's kind of a bit more easier to automate modern Onley, Web based type of application so that Z we'll see an acceleration on the moving to cloud. But again, there will be different sorts of cloud from a completely manage automation service from us toe managing yourself the automation in your cloud tenant, but not on prayer. I'm not a big believer that we will accept unless very few critical sectors I don't think that we will see home Primor roads in the past five years. >>I mean, I agree in this case, the business case for on Prem just gets, you know, less and less. I mean, it'll be a certain applications for sure. My last question is, when thinking about from a software developer standpoint, you obviously you're gonna wanna run in a W S and G, C P and Azure. Uh, perhaps Alibaba, Uh, do you look at other clouds? Whether their regional clouds, of course. You got your own cloud. Maybe Oracle. IBM. How do you think about those? Do you just sort of evaluated on a case by case basis? You let customers, you know, tell you where you need to be. >>Yeah, way focus on the on the three big clouds today, but we're building on the top off Q Burnett is most of our way. We have a big shift in tow building que Burnett is micro services. And my guess is that all mother clouds would offer fantastic support for kubernetes. So what What it takes when you create a new edition for another cloud is toe is toe have the underlying services. Like if we plan to use snowflake, for instance in our analytics offering, you better have snowflake in another cloud. Otherwise, probably the the analytics will will have toe be delayed or use a less of one part technology. So it's not only about what we are building, but it's also, you know, the vast availability of other set of technologies that we try toe use when you choose a technology. Now, first of all, we are looking. We need to choose something that is multi cloud. There's who's dedicated from one cloud vendor. That's that's our first priority. This is why I've mentioned snowflake and then when when we moved into a cloud. We are limited by the offerings that are there, but I my belief is in the main clouds, probably in the US I don't know one of the region's what's gonna happen, but in the main crowds in the U. S. In I believe that they will. In the end, they will catch up in terms off offering and convincing of other defenders toe have kind of kind of similar offering on their own. I don't know if, besides, the Big Three, or you'll see someone and that is able to compete could be too much fragmented. Maybe they will be dedicated clouds for certain services. But for General Cloud, I think three is more than enough. >>Yeah, and so, you know, in the early days of cloud, people talked about dial tone, and essentially, that's what's becoming. It's the it's the value that's running on top of the cloud from software companies like ey Path and others that is really driving. So the cloud to Dato the next generation Daniel Dennett is thanks so much for sharing your vision on participating in the Cuban cloud. Really appreciate it. >>My pleasure, Dave. Thank you so much for inviting. >>You're welcome. You always great to talk to you. And thank you for watching everybody keep it right there. We'll be back with our next guest right into this short break. This is Dave Volonte for the Cube. Yeah.

okay here we go hi everybody this is dave vellante and you're watching thecube's coverage of the cube on cloud our own virtual event where we're trying to understand the future of cloud where we've come from and where we're going and we're bringing in visionaries to really have that that detailed conversation daniel dines is here he's the ceo of automation specialist uipath daniel thanks for coming on and sharing your insights here thank you so much for inviting me dave appreciate it that's always a pleasure to get together with with folks that have started companies with a seed of a vision and have exploded into you know great success and and but i want to go back to the the founding days of uipath 2005. it was pre-cloud there's certainly pre-cloud as we know it today aws came out in 2006 and then we we saw the clouds ascendancy but but your original founding premise there was no cloud you know it wasn't like a startup that could just spin up stuff in the cloud but you've seen that evolution so when you first started to see cloud evolve what did you think did you think oh well we'll see what happens or did you did you know at the time that this was going to be as big as it actually has become what were your thoughts back then well i honestly i i thought that we are kind of ancient and maybe it's stupid to not to private into the new trends in technology like cloud mobile social but i we kept you know working on this computer vision technology that uh 15 years ago was not really hot but with the evolution of self-driving cars and the latest development in ai we we've been able to capture our investments in a domain that was not hot but suddenly you know became the word of the greatest minds in i.t and we definitely we we specialize our computer vision to a narrow use case but still it's the it's the key of what we've done in uh in the end the robots are powered by computer vision technology this kind of a robot emulate how human user work so obviously we use vision a lot in our day by day work and having the best technology that allows our robots to interact with the computer screen more like a human user is quintessential in uh in making our business reliable and easy to use so we were lucky but i always felt that maybe i should change it and we we were feeling i i remember you know many discussions with my you know initial developers because we like what we were doing but we felt a bit left outside by the world but we got lucky in the end so so i have a premise here and that when you go back to the early days of cloud what they got right was they were attacking the the human labor problem and they automated it was storage it was it was networking it was compute but really the automation that they brought to it and the quality that that drove and the flexibility uh was you know a game changer of course we we know that now and you know many of us at the time were very excited about cloud i'm not sure we predicted the impact that it had but my premise is that there's a parallel in your business with the automation that you're driving into the business we've talked to people for instance some of your customers have said you know i can't do six sigma i can't afford to do six sigma before things like rpa for business process i do that for mission critical things but now i can apply six sigma thinking across my entire business that drives quality it takes cost out of my business so what do you think about that premise that there's a parallel between the early days of cloud taking human labor out of the equation and driving quality and flexibility cost saving speed and revenue etc and what you're doing on the business side it is clearly a parallel i can tell that the cloud was built by looking at it automation use cases first of all because this is what software engineers understand the most software engineers let's be you know honest they don't understand the business work they they don't understand all how the real work is performed in a big enterprise and they don't care sometimes when in my own discussions with our cfo he is surprised that i don't know all the use cases in the world yes of course i don't know exactly how an insurance company work all the processes in healthcare all the banking processes i have intellectual curiosity how they work but what interests me the most is our computer vision technology that works uniformly well across different that was the same from the cloud so initially they built in the they built the cloud to help them when what they know the best and now for we were put in the face of having great technology this computer vision technology but without having a great use case in the iit world that we understood and when we when i'm speaking about our early days like 12 13 14 i believe this technology has a lot less applicability in the real world because again we were thinking of some sorts of small it automation gigs that were not possible just doing the apis but when i discovered the messy world of business processes and how important is to emulate people when you think automation that was the big aha moment so i believe that we can do for business processes what the cloud has done for i.t processes and we are really patient now about these business processes and helping people to eliminate all the repetitive work that is there delegate this work to robots and have the people that are required to do this work do do better a smaller number of tasks every day everyone has one as on her or him played today like let's say 10 20 different activities some of them can be completely delegated to rock to robots and they are the low value type of activities while they can focus on the high value activities like interaction with people creativity decision making and this type of human-like things that we as humans really love i love that uh you shared that story but you you thought it was a very narrow sort of set of use cases when you first started and then you know that's that's just an awesome founders you know realization i i love it when we've often said in the cube that you know for decades we've marched to the the tune of moore's law that was the innovation engine no longer is the case it's a combination of of data applied machine intelligence and cloud for scale and i guess the computer vision piece is how you ingest the data uh you you've you've made some investments in in ai and there's many more to come the industry in general and the cloud is is sort of the piece of that equation that we see for scale so i wonder how you see those pieces fitting to your business and how important is the cloud for your scale at last uh at last ui path forward there was a lot of talk amongst our customers about scaling is the cloud critical for that scale yeah i believe so and we are thinking of cloud in two distinct ways number one we are offering and manage automation service in our own cloud using uh where we host everything by ourselves including our orchestrator and then next we have the plans to include our the robots that execute the automation and people simply can connect to our cloud build an automation and just schedule it to run without any maintenance and they will have have access to great analytics everything integrated so this is a major focus to us and the way we launching ga this cloud offering in april this year and i can tell you that until now 20 percent of our customers already are in a shape or another in this type of offering not 20 dollar amount but 20 of our customers and it's clear that at this point this has more applicability into the long tail a smaller customers than in the on our biggest customers but the second distinct type of cloud offering that we focus on is to have best-in-class support in best-in-class multi-cloud support for the cloud of choice of our customers for instance if you go in if you go in aws gcp azure and you buy a subscription there you we we are building specialized editions where with one click you will be able to install our technology in those clouds and you will be able to to scale up and down your robots you can connect your robots to our many services were within your tenant but basically the end goal is to lessen a lot the administration the maintenance footprint of your installation either on our own cloud even on your cloud of choice i'm a strong believer that we will see an accelerated transition from the completely on prem workloads into these two source cloud workloads i want to ask you as a as a technologist if you see so you mentioned that you're going to take your products and you support multiple clouds it'll run on any cloud and a lot of companies are talking about that you know for their respective whether it's a database or you know whatever storage device etc do you see the the day where you'll actually start you're collaborating across clouds where the user uh maybe maybe the user today doesn't know but but maybe a developer does know which cloud it's running on but do you see any value in actual you know connecting across clouds where the data in one cloud is relevant for the data another cloud is i know there are latency issues is is that you know technically feasible and is it will it drive business value what do you think about that cross-cloud connection i believe it is already happening there is a mesh between between various services and who knows in which cloud they are offered already i feel the latency is less and less of a problem as much as the biggest cloud provider have have a very distributed geographically present so as long as i can play in aws in east coast on azure in east coast it's not such a big latency issue and frankly in the past our customers at least are telling us they seen how it is to be completely locked into one technology and people would like to have optionality it's not necessarily that i will use three clouds but i would like to use a vendor that gives me optionality even the and this is what we are trying to offer do you when you think about the future of work i mean as i said before the cloud 1.0 is infrastructure storage networking compute and it seems like 2.0 we're bringing in more ai new workloads uh we're seeing you know analytics and and machine intelligence applied to the data and then you know distributed at scale self-serve to the business how do you see the future of work specifically as it relates to automation affecting that uh and you know what role does cloud play there what's your vision so as the workloads will move to cloud it's absolutely critical that the processes will move to cloud so there is no way back and i i think that uh moving into moving from opera and software into cloud will make even easier to automate this type of workloads into the cloud it's gonna be less maintenance you will deal less with legacy applications that require some special care it's kind of a bit more easier to automate modern only web-based type of application so that's uh we will see an acceleration on the on the moving to cloud but again there will be different sorts of cloud from a completely managed automation service from us to managing yourself the automation in your cloud tenant but not on-prem i'm not a big believer that we will ex unless very few critical sectors i don't think that we will see on-prem workloads in the past five years i mean i agree in the business the business case for on-prem just gets you know less and less i mean it'll there's certain applications for sure my last question is when thinking about from a software developer standpoint you obviously you're going to want to run in aws and gcp and azure uh perhaps alibaba do you look at other clouds whether they're regional clouds of course you've got your own cloud maybe oracle ibm how do you think about those do you just sort of evaluate them on a case-by-case basis you let customers you know tell you where where you need to be yeah we we we focus on the on the three big clouds today but we are building on the top of kubernetes most of our we we have a big shift into building kubernetes microservices and my guess is that all modern clouds will offer fantastic support for kubernetes so what what it takes when you create an edition for another cloud is to is to have the underlying services like if we plan to use snowflake for instance in our analytics offering you better have snowflake in another cloud otherwise probably the the analytics will will have to be delayed or use less of one part technology so it's not only about what we are building but it's also you know the vast availability of other set of technologies that we try to use when you choose a technology now first of all we are looking we need to choose something that is multi-cloud versus dedicated from one cloud vendor that's that's our first priority this is why i've mentioned snowflake and then when you when we move into a cloud we are limited by the offerings that are there but i my belief is in the in the main clouds probably in the us i don't know on other regions what's going to happen but in the main clouds in the us and i believe that they will in the end they will catch up in terms of offering and convincing other other vendors to to have kind of similar offering on their own i don't know if besides the big three we will see someone and that is able to compete could be too much fragmented maybe they will be dedicated clouds for certain services but for general cloud i think three is more than enough yeah and so you know in the early days of cloud people talked about dial tone and essentially that's what's becoming it's the it's the value that's running on top of the cloud from software companies like uipath and others that is really driving sort of the cloud 2.0 the next generation daniel dines thanks so much for sharing your your vision uh and participating in the cube on cloud really appreciate it my pleasure dave thank you so much for inviting you're welcome always great to talk to you and thank you for watching everybody keep it right there we'll be back with our next guest ready for this short break this is dave vellante for the cube

>> Narrator: From around the globe, it's theCUBE. With digital coverage of smart data marketplaces. Brought to you by Io-Tahoe. >> Digital transformation has really gone from a buzzword to a mandate, but digital business is a data business. And for the last several months we've been working with Io-Tahoe on an ongoing content series, focused on smart data and automation to drive better insights and outcomes, essentially putting data to work. And today we're going to do a deeper dive on automating data discovery. And one of the thought leaders in this space is Ajay Vohora, who's the CEO of Io-Tahoe. Once again, joining me, Ajay good to see you. Thanks for coming on. >> Great to be here, David, thank you. >> So let's, let's start by talking about some of the business realities and what are the economics that are driving automated data discovery? Why is that so important? >> Yeah, on this one, David it's a number of competing factors. We've got the reality of data which may be sensitive. So there's control. Three other elements wanting to drive value from that data to innovation. You can't really drive a lot of value without exchanging data. So the ability to exchange data and to manage those cost overheads and data discovery is at the root of managing that in an automated way to classify that data and set some policies to put that automation in place. >> Yeah, look, we have a picture of this. If we could bring it up guys, cause I want to, Ajay, help the audience understand kind of where data discovery fits in here. This is, as we talked about, this is a complicated situation for a lot of customers. They've got variety of different tools and you've really laid it out nicely here in this diagram. So, take us through sort of where that piece fits. >> Yeah, I mean, we're at the right hand side of this exchange, you know. We're really now in a data driven economy that is everything's connected through APIs that we consume online through mobile apps. And what's not apparent is the chain of activities and tasks that have to go into serving that data to an API at the outset. They may be many legacy systems, technologies, platforms On-premise, in cloud, hybrid, you name it and across those silos, getting to a unified view is the heavy lifting. I think we've seen some, some great impacts that BI tools, such as Power BI, Tableau, Looker, and so on, and Qlik have had, and they're in our ecosystem on visualizing Data and, you know, CEOs, managers, people that are working in companies day-to-day get a lot of value from saying, "What's the real time activity? "What was the trend over this month versus last month?" The tools to enable that, you know, we hear a lot of good things that we're doing with Snowflake, MongoDB on the public Cloud platforms, GCP Azure about enabling building those pipelines to feed into those analytics. But what often gets hidden is how do you source that data that could be locked into a mainframe, a data warehouse, IOT data, and pull over all of that together. And that is the reality of that is it's a lot of heavy lifting. It's hands on work that can be time consuming. And the issue there is that data may have value. It might have potential to have an impact on the top line for a business, on outcomes for consumers, but you're never really sure unless you've done the investigation, discovered it, unified that, and be able to serve that through to other technologies. >> Guys, if you would bring that picture back up again, because Ajay you made a point and I want to land on that for a second. There's a lot of manual curating. An example would be the data catalog. You know, data scientists complain all the time that they're manually wrangling data. And so you're trying to inject automation into the cycle. And then the other piece that I want you to address is the importance of APIs. You really can't do this without an architecture that allows you to connect things together that sort of enables some of the automation. >> Yep, I mean, I'll take that in two parts, David, the APIs, so virtual machines connected by APIs, business rules, and business logic driven by APIs, applications, so everything across the stack from infrastructure down to the network, hardware is all connected through APIs and the work of serving data through to an API, building those pipelines, is often miscalculated, just how much manual effort that takes and that manual effort, we've got a nice list here of what we automate down at the bottom, those tasks of indexing, labeling, mapping across different legacy systems, all of that takes away from the job of a data scientist or data engineer, looking to produce value, monetize data, and to help that business convey to consumers. >> Yeah, it's that top layer that the business sees, of course, there's a lot of work that has to go into achieving that. I want to talk about some of the key tech trends that you're seeing. And one of the things that we talk about a lot is metadata. The importance of metadata, you know, can't be understated. What are some of the big trends that you're seeing metadata and others? >> Yeah, I'll summarize it as five. There's a trend now look at metadata more holistically across the enterprise. And that really makes sense from trying to look across different data silos and apply a policy to manage that data. So that's the control piece. That's that lever. The other side, sometimes competing with that control around sensitive data around managing the cost of data is innovation. Innovation being able to speculate and experiment and try things out where you don't really know what the outcome is if you're a data scientist and engineer, you've got a hypothesis and therefore you've got that tension between control over data and innovation and driving value from it. So enterprise wide metadata management is really helping to unlock where might that latent value be across that sets of data. The other piece is adaptive data governance. Those controls that stick from the data policemen, data stewards, where they're trying to protect the organization, protect the brand, protect consumers data necessary, but in different use cases, you might want to nuance and apply a different policy to govern that data relevant to the context where you might have data that is less sensitive, that can be used for innovation and adapting the style of governance to fit the context is another trend that we're seeing coming up here. A few others is where we're sitting quite extensively in working with automating data discovery. We're now breaking that down into what can we direct? What do we know is a business outcome is a known upfront objective and direct that data discovery to towards that. And that means applying our algorithms around technology and our tools towards solving a known problem. The other one is autonomous data discovery. And that means, you know, trying to allow background processes to understand what changes are happening with data over time, flagging those anomalies. And the reason that's important is when you look over a length of time to see different spikes, different trends and activity, that's really giving a data ops team the ability to manage and calibrate how they're applying policies and controls the data. And the last two, David, that we're seeing is this huge drive towards self-service. So re-imagining how to apply policy data governance into the hands of a data consumer inside a business, or indeed the consumer themselves, to self-service if they're a banking customer or healthcare customer and the policies and the controls and rules, making sure that those are all in place to adaptively serve those data marketplaces that when are involved in creating. >> I want to ask you about the autonomous data discovering, the adaptive data governance, is the problem we're addressing there one of quality, in other words, machines are better than humans are at doing this? Is it one of scale? That humans just don't don't scale that well? Is it both? Can you add some color to that? >> Yeah, honestly, it's the same equation that existed 10 years ago, 20 years ago, it's being exacerbated, but it's that equation of how do I control all the things that I need to protect? How do I enable innovation where it is going to deliver business value? How do I exchange data between a customer, somebody in my supply chain safely, and do all of that whilst managing the fourth leg, which is cost overheads. There's not an open checkbook here. I've got to figure out if I'm the CIO and CDO, how I do all of this within a fixed budget. So those aspects have always been there, now with more choices, infrastructure in the Cloud, API driven applications, On-premises, and that is expanding the choices that a business has and how they put their data to work. It's also then creating a layer of management and data governance that really has to now manage those four aspects, control, innovation, exchange of data, and the cost overhead. >> That top layer of the first slide that we showed was all about the business value. So, I wonder if we could drill into the business impact a little bit. What are your customers seeing specifically in terms of the impact of all this automation on their business? >> Yeah, so we've had some great results. I think a few of the biggest have been helping customers move away from manually curating their data and their metadata. It used to be a time where if data initiatives or data governance initiatives, there'd be teams of people manually feeding a data catalog. And it's great to have that inventory of classified data to be able to understand single version of the truth, but having 10, 15 people manually process that, keep it up to date, when it's moving feet, the reality of it is what's true about data today, add another few sources and a few months time to your business, start collaborating with new partners, suddenly the landscape has changed. The amount of work has gone up, but what we're finding is through automating, creating that data discovery, feeding our data catalog, that's releasing a lot more time for our customers to spend on innovating and managing their data. A couple of others is around self service data analytics, moving the choices of what data might have business value into the hands of business users and data consumers to have faster cycle times around generating insights. And we're really helping them by automating the creation of those data sets that are needed for that. And the last piece, I'd have to say where we're seeing impacts more recently is in the exchange of data. There are a number of marketplaces out there who are now being compelled to become more digital, to rewire their business processes and everything from an RPA initiative to automation involving digital transformation is having CIOs, chief data officers and enterprise architects rethink how do they, how do they rewire the pipelines for their data to feed that digital transformation? >> Yeah, to me, it comes down to monetization. Now, of course, that's for a for-profit industry. For non-profits, for sure, the cost cutting or in the case of healthcare, which we'll talk about in a moment, I mean, it's patient outcomes, but the job of a Chief Data Officer has gone from data quality and governance and compliance to really figuring out how data can be monetized, not necessarily selling the data, but how it contributes to the monetization of the company. And then really understanding specifically for that organization, how to apply that. And that is a big challenge. We sort of chatted about 10 years ago, the early days of a dupe. And then 1% of the companies had enough engineers to figure it out, but now the tooling is available. The technology is there and the practices are there. And that really, to me is the bottom line, Ajay, is it's show me the money. >> Absolutely. It's definitely is focusing in on the single view of that customer and where we're helping there is to pull together those disparate, siloed sources of data to understand what are the needs of the patient, of the broker of the, if it's insurance? What are the needs of the supply chain manager, if it's manufacturing? And providing that 360 view of data is helping to see, helping that individual unlock the value for the business. So data's providing the lens provided, you know which data it is that can assist in doing that. >> And, you know, you mentioned RPA before, I had an RPA customer tell me she was a Six Sigma expert and she told me, "We would never try to apply Six Sigma "to a business process, "but with RPA we can do so very cheaply." Well, what that means is lower costs. It means better employee satisfaction and really importantly, better customer satisfaction and better customer outcomes. Let's talk about healthcare for a minute because it's a really important industry. It's one that is ripe for disruption and has really been, up until recently, pretty slow to adopt a lot of the major technologies that have been made available. But what are you seeing in terms of this theme we're using a putting data to work in healthcare specifically? >> Yeah, I mean, health care's has had a lot thrown at it. There's been a lot of change in terms of legislation recently, particularly in the U.S. market, in other economies, healthcare is on a path to becoming more digital. And part of that is around transparency of price. So, to be operating effectively as a healthcare marketplace, being able to have that price transparency around what an elective procedure is going to cost before taking that step forward. It's super important to have an informed decision around that. So if we look at the U.S., for example, we've seen that healthcare costs annually have risen to $4 trillion, but even with all of that cost, we have healthcare consumers who are reluctant sometimes to take up healthcare even if they have symptoms. And a lot of that is driven through not knowing what they're opening themselves up to. And, you know, I think David, if you or I were to book travel a holiday, maybe, or trip, we'd want to know what we're in for, what we're paying for upfront. But sometimes in healthcare that choice, the option might be the plan, but the cost that comes with it isn't. So recent legislation in the U.S. is certainly helpful to bring forward that price transparency. The underlying issue there though is the disparate different format types of data that are being used from payers, patients, employers, different healthcare departments to try and make that work. And where we're helping on that aspect in particular related to price transparency is to help make that data machine readable. So, sometimes with data, the beneficiary might be a person, but in a lot of cases, now we're seeing the ability to have different systems interact and exchange data in order to process the workflow to generate online lists of pricing from a provider that's been negotiated with a payer is really an enabling factor. >> So guys, I wonder if you could bring up the next slide, which is kind of the nirvana. So, if you saw the previous slide that the middle there was all different shapes and presumably to disparate data, this is the outcome that you want to get, where everything fits together nicely. And you've got this open exchange. It's not opaque as it is today. It's not bubble gum, band-aids and duct tape, but describe this sort of outcome that you're trying to achieve and maybe a little bit about what it's going to take to get there. >> Ajay: Yeah, that that's the culmination of a number of things. It's making sure that the data is machine readable, making it available to APIs, that could be RPA tools. We're working with technology companies that employ RPA for healthcare, and specifically to manage that patient and payer data to bring that together. In our data discovery, what we're able to do is to classify that data and have it made available to a downstream tool technology or person to apply that, that workflow to the data. So this looks like nirvana, it looks like utopia, but it's, you know, the end objective of a journey that we can see in different economies, that are at different stages of maturity in turning healthcare into a digital service even so that you can consume it from where you live, from home with telemedicine and tele care. >> Yeah, so, and this is not just for healthcare, but you know, you want to achieve that self-service data marketplace in virtually any industry. You're working with TCS, Tata Consulting Services to achieve this. You know, a company like Io-Tahoe has to have partnerships with organizations that have deep industry expertise. Talk about your relationship with TCS and what you guys are doing specifically in this regard. >> Yeah, we've been working with TCS now for a long while and we'll be announcing some of those initiatives here where we're now working together to reach their customers where they've got a brilliant framework of business, 4.0, where they're re-imagining with the clients, how their business can operate with AI, with automation and become more agile and digital. Our technology, now, the reams of patients that we have in our portfolio, being able to apply that at scale, on a global scale across industries, such as banking, insurance and healthcare is really allowing us to see a bigger impact on consumer outcomes, patient outcomes. And the feedback from TCS is that we're really helping in those initiatives remove that friction. They talk a lot about data friction. I think that's a polite term for the image that we just saw with the disparate technologies that the legacy that has built up. So if we want to create a transformation, having that partnership with TCS across industries is giving us that reach and that impact on many different people's day-to-day jobs and lives. >> Let's talk a little bit about the Cloud. It's a topic that we've hit on quite a bit here in this content series. But, but you know, the Cloud companies, the big hyper-scalers, they've put everything into the Cloud, right? But customers are more circumspect than that. But at the same time, machine intelligence, ML, AI, the Cloud is a place to do a lot of that. That's where a lot of the innovation occurs. And so what are your thoughts on getting to the Cloud, putting data to work, if you will, with machine learning, stuff that you're doing with AWS, what's your fit there? >> Yeah, we, David, we work with all of the Cloud platforms, Microsoft Azure, GCP, IBM, but we're expanding our partnership now with AWS. And we're really opening up the ability to work with their Greenfield accounts, where a lot of that data, that technology is in their own data centers at the customer. And that's across banking, healthcare, manufacturing, and insurance. And for good reason, a lot of companies that have taken the time to see what works well for them with the technologies that the Cloud providers are offering, and a lot of cases, testing services or analytics using the Cloud to move workloads to the Cloud to drive data analytics is a real game changer. So there's good reason to maintain a lot of systems On-premise. If that makes sense from a cost, from a liability point of view and the number of clients that we work with that do have, and will keep their mainframe systems when in Cobra is no surprise to us, but equally they want to tap into technologies that AWS has such as SageMaker. The issue is as a Chief Data Officer, I didn't have the budget to move everything to the Cloud they want, I might want to show some results first upfront to my business users and work closely with my Chief Marketing Officer to look at what's happening in terms of customer trends and customer behavior> What are the customer outcomes, patient outcomes and partner outcomes that you can achieve through analytics, data science? So, working with AWS and with clients to manage that hybrid topology of some of that data being in the Cloud, being put to work with AWS SageMaker and Io-Tahoe being used to identify where is the data that needs to be amalgamated and curated to provide the dataset for machine learning, advanced analytics to have an impact for the business. >> So what are the critical attributes of what you're looking at to help customers decide what to move and what the keep if you will? >> Well, one of the quickest outcomes that we help customers achieve is to buy that business glossary, you know, that the items of data, that means something to them across those different silos and pull all of that together into a unified view. Once they've got that data engineer working with a business manager to think through, how do we want to create this application? Now, what is the churn model, the loyalty or the propensity model that we want to put in place here? How do we use predictive analytics to understand what needs for a patient that sort of innovation is what we're unlocking, applying a tools such as SageMaker on AWS to then do the computation and to build those models to deliver that outcome is across that value chain. And it goes back to the first picture that we put up, David, you know, the outcome is that API on the back of it, you've got a machine learning model that's been developed in a tool such as Databricks or Jupiter notebook. That data has to be sourced from somewhere. Somebody has to say that, "Yep, "You've got permission to do what you're trying to do without falling foul "of any compliance around data." And it all goes back to discovering that data, classifying it, indexing it in an automated way to cut those timelines down to hours and days. >> Yeah, it's the innovation part of your data portfolio, if you will, that you're going to put into the Cloud, apply tools like SageMaker and others, your tool Azure. I mean, whatever your favorite tool is, you don't care. The customer's going to choose that. And you know, the Cloud vendors, maybe they want you to use their tool, but they're making their marketplaces available to everybody, but it's that innovation piece, the ones that you, where you want to apply that self-service data marketplace to, and really drive, as I said before, monetization, All right, give us your final thoughts. Ajay, bring us home. >> So final thoughts on this, David, is at the moment, we're seeing a lot of value in helping customers discover their data using automation, automatically curating a data catalog. And that unified view is then being put to work through our API is having an open architecture to plug in whatever tool technology our clients have decided to use. And that open architecture is really feeding into the reality of what CIOs and Chief Data Officers are managing, which is a hybrid On-premise Cloud approach to use best of breed. But business users wanting to use a particular technology to get their business outcome, having the flexibility to do that no matter where your data is sitting On-premise, on Cloud is where self-service comes in so that sales service view of what data I can plug together, jive exchange, monetizing that data is where we're starting to see some real traction with customers. Now accelerating, becoming more digital to serve their own customers. >> Yeah, we really have seen a cultural mind shift going from sort of complacency, and obviously COVID has accelerated this, but the combination of that cultural shift, the Cloud machine intelligence tools give me a lot of hope that the promises of big data will ultimately be lived up to in this next 10 years. So Ajay Vohora, thanks so much for coming back on theCUBE. You're a great guest and appreciate your insights. >> Appreciate it, David. See you next time. >> All right, keep it right there, everybody, right back after this short break. 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>>from around the globe. It's the Cube with digital coverage of smart data. Marketplace is brought to You by Io Tahoe Digital transformation is really gone from buzzword to a mandate. Additional businesses, a data business. And for the last several months, we've been working with Iot Tahoe on an ongoing content. Serious, serious, focused on smart data and automation to drive better insights and outcomes, essentially putting data to work. And today we're gonna do a deeper dive on automating data Discovery. And one of the thought leaders in this space is a J ahora who is the CEO of Iot. Tahoe's once again joining Me A J Good to see you. Thanks for coming on. >>A great to be here, David. Thank you. >>So let's start by talking about some of the business realities. And what are the economics that air? That air driving, automated data Discovery? Why is that so important? >>Yeah, and on this one, David, it's It's a number of competing factors we've got. The reality is data which may be sensitive, so this control on three other elements are wanting to drive value from that data. So innovation, you can't really drive a lot of value without exchanging data. So the ability to exchange data and to manage those costs, overheads and data discovery is at the roots of managing that in an automated way to classify that data in sets and policies to put that automation in place. >>Yeah. Okay, look, we have a picture of this. We could bring it up, guys, because I want oh, A j help the audience. Understand? Unaware data Discovery fits in here. This is as we talked about this, a complicated situation for a lot of customers. They got a variety of different tools, and you really laid it out nicely here in this diagram. So take us through. Sort of where that he spits. >>Yeah. I mean, where at the right hand side, This exchange. You know, we're really now in a data driven economy that is, everything's connected through AP, eyes that we consume on mine free mobile relapse. And what's not a parent is the chain of activities and tasks that have to go into serving that data two and eight p. I. At the outset, there may be many legacy systems, technologies, platforms on premise and cloud hybrids. You name it. Andi across those silos. Getting to a unified view is the heavy lifting. I think we've seen Cem some great impacts that be I titles such as Power Bi I tableau looker on DSO on in Clear. Who had Andi there in our ecosystem on visualising Data and CEO's managers, people that are working in companies day to day get a lot of value from saying What's the was the real time activity? What was the trend over this month? First his last month. The tools to enable that you know, we here, Um, a lot of good things are work that we're doing with snowflake mongo db on the public cloud platforms gcpd as your, um, about enabling building those pay planes to feed into those analytics. But what often gets hidden is have you sauce that data that could be locked into a mainframe, a data warehouse? I ot data on DPA, though, that all of that together that is the reality of that is it's it's, um, it's a lot of heavy lifting It z hands on what that, um, can be time consuming on the issue There is that data may have value. It might have potential to have an impact on the on the top line for a business on outcomes for consumers. But you never any sure unless you you've done the investigation discovered it unified that Onda and be able to serve that through to other technologies. >>Guys have. You would bring that picture back up again because A. J, you made a point, and I wanna land on that for a second. There's a lot of manual curating. Ah, an example would be the data catalogue if they decide to complain all the time that they're manually wrangling data. So you're trying to inject automation in the cycle, and then the other piece that I want you to addresses the importance of AP eyes. You really can't do this without an architecture that allows you to connect things together. That sort of enables some of the automation. >>Yeah, I mean, I don't take that in two parts. They would be the AP eyes so virtual machines connected by AP eyes, um, business rules and business logic driven by AP eyes applications. So everything across the stack from infrastructure down to the network um, hardware is all connected through AP eyes and the work of serving data three to an MP I Building these pipelines is is often, um, miscalculated. Just how much manual effort that takes and that manual ever. We've got a nice list here of what we automate down at the bottom. Those tasks of indexing, labeling, mapping across different legacy systems. Um, all of that takes away from the job of a data scientist today to engineer it, looking to produce value monetize data on day two to help their business day to conceive us. >>Yes. So it's that top layer that the business sees, of course, is a lot of work that has to go went into achieving that. I want to talk about some of the key tech trends that you're seeing and one of the things that we talked about a lot of metadata at the importance of metadata. It can't be understated. What are some of the big trends that you're seeing metadata and others? >>Yeah, I'll summarize. It is five. There's trains now, look, a metadata more holistically across the enterprise, and that really makes sense from trying. Teoh look across different data silos on apply, um, a policy to manage that data. So that's the control piece. That's that lever the other side's on. Sometimes competing with that control around sense of data around managing the costs of data is innovation innovation, being able to speculate on experiment and trying things out where you don't really know what the outcome is. If you're a data scientist and engineer, you've got a hypothesis. And now, before you got that tension between control over data on innovation and driving value from it. So enterprise wide manage data management is really helping to enough. Where might that latent value be across that sets of data? The other piece is adaptive data governance. Those controls that that that stick from the data policemen on day to steer its where they're trying to protect the organization, protect the brand, protect consumers data is necessary. But in different use cases, you might want to nuance and apply a different policy to govern that data run of into the context where you may have data that is less sensitive. Um, that can me used for innovation. Andi. Adapting the style of governance to fit the context is another trend that we're seeing coming up here. A few others is where we're sitting quite extensively and working with automating data discovery. We're now breaking that down into what can we direct? What do we know is a business outcome is a known up front objective on direct that data discovery to towards that. And that means applying around with Dems run technology and our tools towards solving a known problem. The other one is autonomous data discovery. And that means, you know, trying to allow background processes do winds down what changes are happening with data over time flagging those anomalies. And the reason that's important is when you look over a length of time to see different spikes, different trends and activity that's really giving a day drops team the ability to to manage and calibrate how they're applying policies and controls today. There, in the last two David that we're seeing is this huge drive towards self service so reimagining how to play policy data governance into the hands off, um, a day to consumer inside a business or indeed, the consumer themselves. The South service, um, if their banking customer or healthcare customer and the policies and the controls and rules, making sure that those are all in place to adaptive Lee, um, serve those data marketplaces that, um when they're involved in creating, >>I want to ask you about the autonomous data discovering the adaptive data. Governance is the is the problem where addressing their one of quality. In other words, machines air better than humans are doing this. Is that one of scale that humans just don't don't scale that well, is it? Is it both? Can you add some color to that >>yet? Honestly, it's the same equation that existed 10 years ago, 20 years ago. It's It's being exacerbated, but it's that equation is how do I control both things that I need to protect? How do we enable innovation where it is going to deliver business value? Had to exchange data between a customer, somebody in my supply chains safely. And all of that was managing the fourth that leg, which is cost overheads. You know, there's no no can checkbook here. I've got a figure out. If only see io and CDO how I do all of this within a fixed budget so that those aspects have always been there. Now, with more choices. Infrastructure in the cloud, um, NPR driven applications own promise. And that is expanding the choices that a a business has and how they put mandated what it's also then creating a layer off management and data governance that really has to now, uh, manage those full wrath space control, innovation, exchange of data on the cost overhead. >>That that top layer of the first slide that we showed was all about business value. So I wonder if we could drill into the business impact a little bit. What do your customers seeing you know, specifically in terms of the impact of all this automation on their business? >>Yeah, so we've had some great results. I think view the biggest Have Bean helping customers move away from manually curating their data in their metadata. It used to be a time where for data quality initiatives or data governance initiative that be teams of people manually feeding a data Cavallo. And it's great to have the inventory of classified data to be out to understand single version of the trees. But in a having 10 15 people manually process that keep it up to date when it's moving feet. The reality of it is what's what's true about data today? and another few sources in a few months. Time to your business on start collaborating with new partners. Suddenly the landscape has changed. The amount of work is gonna But the, um, what we're finding is through automating creating that data discovery feeding a dent convoke that's releasing a lot more time for our CAS. Mr Spend on innovating and managing their data. A couple of others is around cell service data and medics moving the the choices of what data might have business value into the hands of business users and and data consumers to They're faster cycle times around generating insights. Um, we really helping that by automating the creation of those those data sets that are needed for that. And in the last piece, I'd have to say where we're seeing impacts. A more recently is in the exchange of data. There are a number of marketplaces out there who are now being compelled to become more digital to rewire their business processes. Andi. Everything from an r p a initiative. Teoh automation involving digital transformation is having, um, see iose Chief data officers Andi Enterprise architects rethink how do they how they re worthy pipelines? But they dated to feed that additional transformation. >>Yeah, to me, it comes down to monetization. Of course, that's for for profit in industry, from if nonprofits, for sure, the cost cutting or, in the case of healthcare, which we'll talk about in a moment. I mean, it's patient outcomes. But you know, the the job of ah, chief data officer has gone from your data quality and governance and compliance to really figuring out how data and be monetized, not necessarily selling the data, but how it contributes for the monetization of the company and then really understanding specifically for that organization how to apply that. And that is a big challenge. We chatted about it 10 years ago in the early days of a Duke. And then, you know, 1% of the companies had enough engineers to figure it out. But now the tooling is available, the technology is there and the the practices air there, and that really to me, is the bottom line. A. J is it says to show me the money. >>Absolutely. It's is definitely then six sing links is focusing in on the saying over here, that customer Onda, where we're helping there is dio go together. Those disparities siloed source of data to understand what are the needs of the patient of the broker of the if it's insurance? Ah, one of the needs of the supply chain manager If its manufacturing onda providing that 3 60 view of data, um is helping to see helping that individual unlock the value for the business. Eso data is providing the lens, provided you know which data it is that can God assist in doing that? >>And you know, you mentioned r p A. Before an r p A customer tell me she was a six Sigma expert and she told me we would never try to apply six segment to a business process. But with our P A. We can do so very cheaply. Well, what that means is lower costs means better employee satisfaction and, really importantly, better customer satisfaction and better customer outcomes. Let's talk about health care for a minute because it's a really important industry. It's one that is ripe for disruption on has really been up until recently, pretty slow. Teoh adopt ah, lot of the major technologies that have been made available, but come, what are you seeing in terms of this theme, we're using a putting data to work in health care. Specific. >>Yeah, I mean, healthcare's Havlat thrown at it. There's been a lot of change in terms of legislation recently. Um, particularly in the U. S. Market on in other economies, um, healthcare ease on a path to becoming more digital on. Part of that is around transparency of price, saying to be operating effectively as a health care marketplace, being out to have that price transparency, um, around what an elective procedure is going to cost before taking that that's that forward. It's super important to have an informed decision around there. So we look at the US, for example. We've seen that health care costs annually have risen to $4 trillion. But even with all of that on cost, we have health care consumers who are reluctant sometimes to take up health care if they even if they have symptoms on a lot of that is driven through, not knowing what they're opening themselves up to. Andi and I think David, if you are, I want to book, travel, holiday, maybe, or trip. We want to know what what we're in for what we're paying for outfront, but sometimes in how okay, that choice, the option might be their plan, but the cost that comes with it isn't so recent legislation in the US Is it certainly helpful to bring for that tryst price, transparency, the underlying issue there? There is the disparity. Different formats, types of data that being used from payers, patients, employers, different healthcare departments try and make that make that work. And when we're helping on that aspect in particular related to track price transparency is to help make that date of machine readable. So sometimes with with data, the beneficiary might be on a person. I've been a lot of cases now we're seeing the ability to have different systems, interact and exchange data in order to process the workflow. To generate online at lists of pricing from a provider that's been negotiated with a payer is, um, is really a neighboring factor. >>So, guys, I wonder if you bring up the next slide, which is kind of the Nirvana. So if you if you saw the previous slide that the middle there was all different shapes and presumably to disparage data, this is that this is the outcome that you want to get. Everything fits together nicely and you've got this open exchange. It's not opaque as it is today. It's not bubble gum band aids and duct tape, but but but described this sort of outcome the trying to achieve and maybe a little bit about what gonna take to get there. >>Yeah, that's a combination of a number of things. It's making sure that the data is machine readable. Um, making it available to AP eyes that could be our ph toes. We're working with technology companies that employ R P. A full health care. I'm specifically to manage that patient and pay a data. Teoh, bring that together in our data Discovery. What we're able to do is to classify that data on having made available to eight downstream tour technology or person to imply that that workflow to to the data. So this looks like nirvana. It looks like utopia. But it's, you know, the end objective of a journey that we can see in different economies there at different stages of maturity, in turning healthcare into a digital service, even so that you could consume it from when you live from home when telling medicine. Intellicast >>Yes, so And this is not just health care but you wanna achieve that self service doing data marketplace in virtually any industry you working with TCS, Tata Consultancy Services Toe Achieve this You know, if you are a company like Iota has toe have partnerships with organizations that have deep industry expertise Talk about your relationship with TCS and what you guys are doing specifically in this regard. >>Yeah, we've been working with TCS now for room for a long while. Andi will be announcing some of those initiatives here where we're now working together to reach their customers where they've got a a brilliant framework of business for that zero when there re imagining with their clients. Um, how their business cause can operate with ai with automation on, become more agile in digital. Um, our technology, the dreams of patients that we have in our portfolio being out to apply that at scale on the global scale across industries such as banking, insurance and health care is is really allowing us to see a bigger impact on consumer outcomes. Patient outcomes And the feedback from TCS is that we're really helping in those initiatives remove that friction. They talk a lot about data. Friction. Um, I think that's a polite term for the the image that we just saw with the disparity technologies that the legacy that has built up. So if we want to create a transformation, Um, having a partnership with TCS across Industries is giving us that that reach and that impacts on many different people's day to day jobs and knives. >>Let's talk a little bit about the cloud. It's It's a topic that we've hit on quite a bit here in this in this content Siri's. But But you know, the cloud companies, the big hyper scale should put everything into the cloud, right? But but customers are more circumspect than that. But at the same time, machine intelligence M. L. A. The cloud is a place to do a lot of that. That's where a lot of the innovation occurs. And so what are your thoughts on getting to the cloud? Ah, putting dated to work, if you will, with machine learning stuff you're doing with aws. What? You're fit there? >>Yeah, we we and David. We work with all of the cloud platforms. Mike stuffed as your G, c p IBM. Um, but we're expanding our partnership now with AWS Onda we really opening up the ability to work with their Greenfield accounts, where a lot of that data that technology is in their own data centers at the customer, and that's across banking, health care, manufacturing and insurance. And for good reason. A lot of companies have taken the time to see what works well for them, with the technologies that the cloud providers ah, are offered a offering in a lot of cases testing services or analytics using the cloud to move workloads to the cloud to drive Data Analytics is is a real game changer. So there's good reason to maintain a lot of systems on premise. If that makes sense from a cost from a liability point of view on the number of clients that we work with, that do have and we will keep their mainframe systems within kobo is is no surprise to us, but equally they want to tap into technologies that AWS have such a sage maker. The issue is as a chief data officer, I don't have the budget to me, everything to the cloud day one, I might want to show some results. First upfront to my business users Um, Onda worked closely with my chief marketing officer to look at what's happening in terms of customer trains and customer behavior. What are the customer outcomes? Patient outcomes and partner at comes I can achieve through analytics data signs. So I, working with AWS and with clients to manage that hybrid topology of some of that data being, uh, in the cloud being put to work with AWS age maker on night, I hope being used to identify where is the data that needs to bay amalgamated and curated to provide the data set for machine learning advanced and medics to have an impact for the business. >>So what are the critical attributes of what you're looking at to help customers decide what what to move and what to keep, if you will. >>Well, what one of the quickest outcomes that we help custom achieve is to buy that business blustery. You know that the items of data that means something to them across those different silos and pour all of that together into a unified view once they've got that for a data engineer working with a a business manager to think through how we want to create this application. There was the turn model, the loyalty or the propensity model that we want to put in place here. Um, how do we use predictive and medics to understand what needs are for a patient, that sort of innovation is what we're looking applying the tools such a sagemaker, uh, night to be west. So they do the the computation and to build those models to deliver the outcome is is across that value chain, and it goes back to the first picture that we put up. David, you know the outcome Is that a P I On the back of it, you've got the machine learning model that's been developed in That's always such as data breaks. But with Jupiter notebook, that data has to be sourced from somewhere. Somebody has to say that yet you've got permission to do what you're trying to do without falling foul of any compliance around data. Um, it'll goes back to discovering that data, classifying it, indexing it in an automated way to cut those timelines down two hours and days. >>Yeah, it's the it's the innovation part of your data portfolio, if you will, that you're gonna put into the cloud. Apply tools like sage maker and others. You told the jury. Whatever your favorite tool is, you don't care. The customer's gonna choose that and hear the cloud vendors. Maybe they want you to use their tool, but they're making their marketplaces available to everybody. But it's it's that innovation piece, the ones that you where you want to apply that self service data marketplace to and really drive. As I said before monetization. All right, give us your final thoughts. A. J bring us home. >>So final thoughts on this David is that at the moment we're seeing, um, a lot of value in helping customers discover that day the using automation automatically curating a data catalogue, and that unified view is then being put to work through our A B. I's having an open architecture to plug in whatever tool technology our clients have decided to use, and that open architecture is really feeding into the reality of what see Iose in Chief Data Officers of Managing, which is a hybrid on premise cloud approach. Do you suppose to breed Andi but business users wanting to use a particular technology to get their business outcome having the flexibility to do that no matter where you're dating. Sitting on Premise on Cloud is where self service comes in that self service. You of what data I can plug together, Dr Exchange. Monetizing that data is where we're starting to see some real traction. Um, with customers now accelerating becoming more digital, uh, to serve their own customers, >>we really have seen a cultural mind shift going from sort of complacency. And obviously, cove, it has accelerated this. But the combination of that cultural shift the cloud machine intelligence tools give give me a lot of hope that the promises of big data will ultimately be lived up to ah, in this next next 10 years. So a J ahora thanks so much for coming back on the Cube. You're you're a great guest. And ah, appreciate your insights. >>Appreciate, David. See you next time. >>All right? And keep it right there. Very right back. Right after this short break

>> Narrator: From around the globe, it's theCUBE, covering HPE Discover Virtual Experience, brought to you by HPE. >> Hello and welcome to theCUBE's coverage of HPE Discover 2020, The Virtual Experience. I'm Lisa Martin and I've got a couple of guests joining me, Stephen Laaper principal at Deloitte consulting and Brenna Sniderman the Executive Director for the Center of Integrated Research at Deloitte Services, Stephen and Brenna, nice to have you on the program today. >> Thank you, >> (mumbles) >> So we're going to be talking about The Smart Factory. I'd love for you to start Brenna, we'll start with you. Give our audience an overview of Deloitte's definition of The Smart Factory then we can dig into some of the very interesting research that Deloitte has been doing the last few years. >> Sure, absolutely. So the way we think about The Smart Factory is it is a system that's quite flexible that uses data and information from throughout, physical assets to optimized performance, to enable the facility to be more agile, to be proactive, to optimize its assets and to react and change as quickly as possible to shifts going on. It overall enables organizations to just be more intelligent about the way they use their assets to use data, to make more informed decisions and to drive a more optimized process. >> And Stephen for you, one of the things that I found interesting looking at some of Deloitte's research is that the last few years or so, there's been net zero growth in manufacturing labor productivity and labor productivity being an indicator of economic impact. Why in Deloitte's perspective, has that manufacturing labor productivity growth been flat? >> Yeah, it's a really interesting observation. And what we've seen is really decades and decades of management principles, companies using things like Lean, like Six Sigma, taking advantage of labor arbitrage in many cases. And the reality is that a lot of that low hanging fruit is gone. Those projects have been executed well and we're now seeing what we would consider to be diminishing returns as it relates to the investments in those same types of tools. And that is really what's leading many organizations now towards things like the capabilities that you'd find in a Smart Factory. Adding additional technologies to the capability set to really bring companies to that new productivity frontier. >> One of the things that I saw too, is that Smart Factory adoption in one of your studies, can result in a threefold productivity increase. So talk to me about in the last few years, some of the early adopters, Brenna we'll start with you, what are some of the trends that you've seen with those early adopters? any industries in particular that are leading in that respect? >> Well, that's a good question. I think when we recently published a study on lessons from early adopters in the Smart Factory and what we found was that a lot of the organizations that have adopted the Smart Factory have learned lessons that are not necessarily new but some that are new as well. Really I think the biggest challenge has been to figure out how to gather data from a lot of assets that maybe haven't had to produce data before to find out where all the information is from throughout the facility to bring together different groups and different cultures within the organization, whether it's IT and OT and have them figure out how to share information and data and really just to figure out what to do with that information once we've gotten it. Some of the organizations that we spoke with for our research really ran the gamut from aerospace to automotive, to consumer products, to industrial manufacturing. It really has been an interesting spread that we've looked at. >> Stephen walk us through the last three years or so of research that Deloitte has been doing into the Smart Factory from the 2017 study to the 2019 study, to the one that was just released, what's some of the progress that you've seen over the last three years? Is it what you anticipated it would be? >> Yeah, it's interesting. I mean, three years ago, I think a lot of people were talking about Industry 4.0, they were talking about the industrial internet of things, they were talking about The Smart Factory, but we saw relatively few very concentrated efforts to advance those. Now as we fast forward three years, we're seeing that the specific capabilities that each one of those topic areas can enable for organizations, has become much clearer. So correspondingly companies have been planning for these types of investments and they're taking action on much of the capability build and quite frankly, starting to see the value. One of the underlying kind of architectural elements that I think are critical as part of the modern Smart Factory is exactly what Brenna touched on. And that was as it relates to the data. Many assets out there even if they're several decades old likely have a wealth of data associated with them. The challenge is that data is either not readily accessible or it's not well understood. And much of the effort that organizations have now undertaken is not only how do they connect, extract and use that information many times on a real time or near real time basis, but now also combining that information with other assets, other parts of the manufacturing facility, or even their manufacturing network to generate that value. >> So Stephen follow on question, how does an organization, a company start that process, if as you said, there's myriad assets of varying age, some really advanced, some really old as well as even from, I guess, a generational perspective in the workforce, you've got multiple generations, for organizations that know we've got data that's hidden, where do they start? >> Yeah, absolutely. And I think a really important element of your question is how do you determine where to start? And the reality is that not all of these solutions are created equal. Not all of the assets have data that's interesting enough to be equal. And so really going through a very concerted effort to understand what are the capabilities we're trying to build And what value does it create for our organization? Aligning that to the objectives and the goals of the organization is critical right from the outset. And we see companies that are being most successful in their implementation of the Smart Factory, following that value orientation. And that might not mean that that value comes tomorrow, It might not come next month, but there's a very clear guidance in terms of how the particular capabilities that are being built will lead to value. Organizations that are not doing that, we tend to see random X visual. We see a lot of different efforts underway with very little tied value and correspondingly many of those efforts don't continue because the executive team, the shareholders aren't going to continue those investments in that space without showing them (mumbles). >> So Brenna walk us through, along what Stephen was just saying. I was reading in your 2020 study that positioning a Smart Factory initiative for value starts with human-centered design and I thought this was really interesting that Deloitte research demonstrated successful teams generally focus on the user first, not the technology. >> Well, yeah. And I think to follow on a little bit to what Stephen said about understanding the value and the goal of what you're trying to do before thinking about the technology you need to rush out and implement goes along with this as well. You want to think about what the user is actually going to be using that data for, what is their job, what information are they going to need and think about from their perspective, what is going to be most helpful and effective for them. And I think the value of this is twofold. One is if talent within your organization and folks on the shop floor, see the value of this data and information, they're going to be more inclined to adopt it because it makes their job easier. But also if you have a tremendous amount of data and information from all the different assets and parts of your facility, if an individual has to sift through all of that, to find what's going to be valuable to them, it's not really going to make their job easier. So human-centered design is really thinking about what that individual needs to do their role, and in a lot of the work that we've done, we've almost thought about it as personas where this particular persona or job needs this information, needs to go through these steps and here's the data information we need to show them to enable them to do that. It's just a way for people to leverage information, to make smarter decisions more quickly. >> How does a manufacturing company do that, Brenna, excuse me, without being siloed, like in business units, so I'm thinking, getting cross-functional support all the way up to the top level. >> Mhhh, that's something that we saw quite a bit in our research that many of the groups or organizations that have successfully enacted a Smart Factory have done so because it's not just coming down from the top, it's also coming up from the bottom. You know, although that may sound like a pejorative term, but coming from all angles of the organization. So we see from the strategic level, this is what we need to do to change the way our organization operates in a more effective way. But from the line of business individuals that are using this information and data every day, we need to think about sort of having a groundswell of support work there as well, so that our team members are using this information. So I think it has to be something that comes from throughout the organization. What we've also found your point about silos is bringing in diverse teams and individuals from throughout the organization who have different types of expertise, different perspectives, different things that they're looking at in different ways that they need to use this technology to do their job, will enable us to make sure that, what we're producing is something that's going to be of value to them. >> And along those lines, Stephen question for you, this must need to be looked at, not as what can we do today or the next six months, but over the long-term. So that ongoing enablement and education is going to be critical. >> Yeah, absolutely. Right. And you know, the reality is that some of these investments that organizations are making into Smart Factory, do take quite a bit of thought, research and assessment and those aren't investments that they're making for the short-term, many of them are long-term. The important part about those investments that organizations are making is that they're creating platforms by which teams can continue to evolve the persona-based type solutions that Brenna referred to, so critical. And so, the flexibility, the adaptability, the agility of those platforms and the investments that are being made, really is one of their critical factors. I did want to just revisit the user adoption of these types of solutions. And, I'm a engineer by education. And I could look up back to early in my career and say, "Hey, look, I built solutions, "using data for manufacturing shop floor equipment. "And I created those solutions for others." But the reality was that I created it in a way that an engineer would you consume that data. And the reality is the persona-based approach really lets us focus on how is a particular individual in their job going to consume that data in a way that enables them to make the best next decision which ultimately has a positive outcome for the company. And in some cases that might mean not exposing them to all the complexities that happen underneath the surface. The modern smartphone, for example, enormously complex device, yet intuitive to use, easy to pick up, easy to interact with. The modern Smart Factory is also very similar in that frame. >> Along those lines of agility, but also designing with certain mindset, culturally IT and OT are different. Brenna, one of the things that I found interesting in the research was the marriage of IT and OT, how do you advise or let's go to clients that were part of that 2020 study, what lessons can the next wave of adopters learn where it comes to bridging those two IT OT mentalities and different cultures? >> Yeah, that's a good question. And I think the different cultures is sort of, key insight that is helpful. With respect to IT, they work on different timeframes, they think about investments in a different way, they think about technology in a different way than individuals who are in OT, who are on the shop floor, who are using these tools every day. and what we found was that bridging that divide and bringing them together, is a challenge that many overlook and something that really the importance of it, can't be overstated. I think to get back to Stephen's point about adoption, if those within the OT space have an understanding of what IT is doing and why, they're just likelier to adopt and to use. And conversely, if those in IT have a deeper understanding of what those in OT are doing and what types of tools they need, they're likelier to come up with solutions that are going to be effective. I think the cultural divide is something that's practically important to understand, to address and not to overlook because I think the last thing that anyone implementing any sort of Smart Factory solution wants is to roll out a solution that was sort of baked in one area, but not taking into account the other as well. >> Great point. Stephen, I want to go back to you for a second. Just understanding along the lines of the cultural differences and the design principles that need to be factored in. When the COVID-19 pandemic hit in March of 2020, for clients that you were talking to that were in whatever stage process of rolling out Smart Factory initiatives, where are they now? And what are some of the advantages that you see that organizations that aren't yet adopting Smart Factory initiatives should be doing to prepare to thrive in this new normal? >> Yeah, absolutely. Let me start with some of those advantages right at the outset. So many organizations now have been looking at advanced solutions, perhaps, to enforce social distancing within the manufacturing environment or perhaps contact tracing within the manufacturing environment and the advantages organizations are seeing that are already on that Smart Factory journey is they're finding they have largely a lot of the infrastructure required to be able to do that already in place. So that has been an enormous accelerant for companies that are already on the journey. The reality is that many organizations, are unable to have their experts, their engineers, their vendors, many of the people that are supporting the equipment and the people in their manufacturing plants around the world, they're not able to get them there. And companies that have been on the Smart Factory journey, specifically as it relates to creating what we would call the digital twin of many of their assets, where they can now see not only visual representations of those assets, but can also see that the data flowing off those assets and in the most advanced solutions, being able to see those together, they'd be able to unlock remote support, in a way that organizations that have not been on this journey simply can't. And we're starting to see some very distinct results, as it relates to those who are able to continue running at scale, and those who are struggling in the COVID environment. >> And Stephen, last question for you. I know you've got a session or a demo on Smart Factory an AI that you're doing at Discover 2020. Tell us a little bit about that and what the participants can anticipate. >> Yeah. So we're really excited to be able to bring Factory AI as we call it, in a live virtualized session. That session is going to cover what we have built around we'll call it a mini manufacturing line. And usually we'd have that with you at the conference, or we take that around the country, to many of our manufacturing clients to really show them, the power of adopting many of these different types of capabilities in the manufacturing environment. So what we're going to be showing and what viewers can expect to see is a demonstration of edge capabilities, of computer vision, of advanced internet of things, all wrapped into several high-impact use cases. So we're looking forward to you're doing that. >> Excellent. Well, Stephen, Brenna, thank you so much for your time discussing The Smart Factor. This is such an interesting provocative topic. I wish we had more time, but appreciate you speaking with me today. >> Thanks for having us. >> Thank you. >> You're watching the cube, Lisa Martin for HPE Discover 2020, The Virtual Experience. Thanks for watching. (upbeat music)