>>The Cube presents UI Path Forward. Five. Brought to you by UI Path. >>Welcome back to The Cube's coverage of UI Path Forward. Five from Las Vegas. We're inside. The formerly was The Sands, now it's the Venetian Convention Center. Dave Nicholson. David, Deb. I've never seen it set up like this before. UI Path's. Very cool company. So of course the setup has to be cool, not like tons of concrete. James Furlong is here, the Vice President of Supply Chain Management and projects at Puma. And Derek Weimer is the CEO of Robo, who's an implementation partner, expert at Intelligent Automation. Folks, welcome to the Cube. Good to see you. Great to have you on. >>Thank you. It's a pleasure. >>So what's happening at Puma these days? I love your sneakers, but you guys probably do more than that, but let's tell us about, give us the update on Puma. >>Yeah, absolutely. Puma's one of the world's leading sports, sports brands. So we encompass all things sports. We do footwear, we do apparel, we do accessories. Cobra, Puma golf is underneath our umbrella as well. So we get the added benefit of having that category as well. And yeah, trade, trade all over the world and it's an exciting, exciting brand to be with. >>And di Robo Atlanta based really specialists in intelligent automation. That's pretty much all you do, is that right? >>Yeah, we are a pure play intelligence automation professional services firm. That's all we do. We're the world's largest firm that focuses only on automation headquarter in Germany, but with a large presence here in Americas. >>So we hear from a lot of customers. We've heard from like with the journey it started, you know, mid last decade, Puma James is just getting started. We April you mentioned. So take us through that. What was the catalyst as you're exiting the, the pandemic, the isolation economy we call it? Yeah. What was the catalyst tell, take us through the sort of business case for automation. >>Sure, absolutely. So Puma, our mission is forever faster. It's, it's our mantra and something we live and breathe. So naturally we have an intense focus on innovation and, and automation. So with that mindset, the way this all kicked off is that I had the opportunity to go into some of our distribution facility and I was unbelievably impressed with the automation that I saw there. So how automation augmented the employee workforce. And it was just very impressive to see that some of our state of the art technology and automation at the same time. Then I went back to the office with that excitement and that passion and I saw that we had the opportunity to take that to our employee base as well. We sort of lacked that same intense focus on how do we take automation and technology like I saw at the distribution facilities and bring it to our employees because picture a large workforce of talented, dedicated employees and they just couldn't keep up with the explosive growth who's seen explosive growth over the last couple of years and they just couldn't keep up with it. So I said that that's it. We need to, to take that same passion and innovation and enter in hyper hyper automation. So we went to the leadership team and no surprise they were all in. We went with them with the idea of bringing hyper automation, starting with RPA to, to our office employees. And they were in, they support innovation and they said, Great, what do you need? Really? Go for it. >>The first question wasn't how much, >>Actually the first question I will say that the funny part is, is they said, Well I like this, it sounds too good to be true. And because it, it really does. If you're new to it like we were and I'm pitching all the benefits that RPA could bring, it does sound too good to me. True. So they said, All right, you know, we trust you and, and go for it. What do you need? Resources, just let us know. So sure enough, I had a proof of concept, I had an idea, but now what? I didn't know where to go from there. So that's where we did some intensive research into software suppliers, but also implementation partners because now we knew what we wanted to do. We had excitement, we had leadership buy-in now, now what do I do? So this is when we entered our partnership to figure out, okay, help Puma on this journey. >>How'd you guys find each other? You know, >>Just intensive research and spoke with a lot of people here. Is there a lot of great organizations? But at the end of the day, they really supported everything that Houma stood for, what we're looking to do and had a lot of trust in the beginning and Dirk and his team and how he could help us on this journey. Yeah. >>Now James, your, your job title system for supply chain management. It is, but I understand that you have had a variety of roles within the organization. Now if we're talking about another domain, artificial intelligence, machine learning. Yeah. There's always this concept of domain expertise. Yeah. And how when you're trying to automate things in that realm, domain expertise is critical. Yeah. You have domain expertise outside of your job title. Yeah. So has that helped you with this journey looking at automation, being able to, being able to have insight into those other organizations? >>Yeah, absolutely. And I think when we were pitching it to the leadership team in the beginning, that enabled me to look at each one sitting at the table and saying, alright, and on the sales, on a commercial side, I was a head of sales for one of the trade channels. I could speak directly to him in the benefits it could have with not with tribal knowledge and with an expertise. So it wasn't something that, it was just, oh, that's supply chain. I could sit, you know, with the, our CFO and talk to him about the, the benefits for his group merchandising and legal so on. I was really able to kind of speak to each one of them and how it would support, because I had that knowledge from being blessed of 15 years experience at, at Puma. So yeah, I was able to take all of that and figure out how do I make sure not just supply chain benefits from rpa, but how does the whole organization benefit from not only RPA but the hyper automation strategy. >>So what's an engagement look like? You start, I presume you, you gotta do some type of assessment and, and you know, of some upfront planning work. Yeah. What does that look like? How, what's the starting point? Take us through that >>Journey. Yeah, so exactly. So the, the key when you're trying to get value from Intel automation is finding the right opportunities, right? And you can automate a lot of things, but which are the things that are gonna drive the most value and, and the value that actually matters to the company, right? So where are you trying to get to from a strategic level, your objectives and how do you actually use automation to help you get to there? So the first thing is, what are the opportunities gonna help you do that? And then once you identify, what we recommend is start with something that's gonna be, you know, accessible, small, You're gonna get a quick win. Cuz then the important thing is once you get that out there, you build the momentum and excitement in the organization that then leads to more and more. And then you build a proper pipeline and you and you get that the, the engagement. >>So what was that discovery like? Was it you fly up there and do a, a chalk talk? Or did you already know James, like where you wanted to focus? >>Yeah, I knew I had a solid proof of concept with the disruptions in supply chain we couldn't keep up with, with all the changes and supply. So right away I knew that I have a very substantial impact on the organization and it would be a solid proof of concept. It was something that not only would supply chain steal, but our customers would feel that we would be servicing them better. Our sales team, the commercial team, marketing impacted everybody. But at the same time it was tangible. I saw two people that just physically couldn't get their, their work done despite how talented and hardworking they were. So I, I was in on that proof of concept and then I just took that idea with some strong advice from Dirk and and his team on, okay, well how do I take that? But then also use that to evangelize through the organization. What are some pitfalls to avoid? Because as a proof of concept, they just told me it's too good to be true. I believe in it. So it was so important to me that it >>Was successful. >>It get your neck out. Oh, I sure was. Which is a little scary, but I had confidence that we would >>Do it. But your poc you had to have a systems view. Yes. Right? Cuz you were trying to, I think you, I'm inferring that you had two people working really hard, but they couldn't get their job done. Yeah, for sure. They were just sitting on their hands. Right. Waiting. Okay. So you kind of knew where the bottlenecks were. Yes. And that's what you attacked and or you helped James and her the team think through that or, >>Yeah, exactly. So, so a couple points you were asking about her domain model of knowledge earlier, and I think that's really key to the puma's success with it, is that they've come at it from a business point of view, what matters to the business. And at the point, you know, supply chain challenges, how do we use automation to address that? And then, you know, and then it's gonna, it's actually gonna, you know, pick opportunities that are gonna matter to the business. Yeah, >>Yeah. At the same time, we, we knew this could be a scary thing, right? If it's not done right, you know, automation definitely can, can take a, a wrong path. So what we relied on them for is tell us how to make this successful. We wanted structure, we wanted oversight, we wanted to balance that with speed and really, you know, developing our pipeline, but at the same time, tell us how to do this right? How do we set up a center, our first ever center of excellence? They help us set that up. Our steerco, our process definition documents are like, they really helped us add that structure to how to make this successful, sustainable and make sure that we were standing things up the right way versus launching into a strong proof, proof of concept. But then it's not gonna be scalable if we didn't really take their strong advice on how to make this something, you know, that had the right oversight, the right investment. So that was, that was key as >>Well for us. So when you looked at the POC and James was saying there were potential pitfalls, what were those pitfalls? Like what did you tell Puma, Hey, watch out for this, watch out for that. What was sort of the best advice there? >>Yeah, so I think one is understanding complexity, right? So a lot of opportunities sound good, but you want to make sure that it's, it's feasible with the right tool set. And also that you're not bit off too much in the beginning is really important. And so some of that is that bringing that expertise to say, Okay, yeah, look, that does something, a good process. You're gonna get value out. It's not gonna be overly complicated. It's a good place to start. And then also, I guess the thing too to mention is it's more than just a technology project. And that's the thing that we also really focus on is it's actually as much about the change management, it's much about, you know, what is the right story, the business case around it, the technology actually in a way is the easy part and it's all the stuff around it that really makes the POC effective, >>Obviously the process. Yeah. Been the people I presume getting to adopt, >>Right? And I think, again, with our, our brand mantra forever faster, we, we get that support that the buy-in from the top is is there from, from the beginning. So that's a benefit that some companies don't, they don't have, right? They have a little resistance maybe from the top. We're trying to get everyone's buy in it. And we had that. So we had, you know, the buy-in the engagement, we were ready to go. So now we just needed someone to kind of help us. >>One more if I may. Yeah, yeah. Gabe, six months in. Yes. That's the business impact that, can >>You tell you? That was tremendous. Yeah. >>Really already six months. Wow. >>Yeah, >>Absolutely. Cfo, CFO's dream. Yeah. >>And again, and, and we had a CFO change mid, mid project. So the new CFO comes in, not new to Puma, the same thing. Super, super smart guy. And I had to sit and again pitch, you know, pitch what it is and the support that I needed by way of investment. And he saw the results and he was all in, you know, what do you need, what's next? And instantly was challenging his departments, Why don't he got competitive, right? We're a competitive bunch, so why don't you know, you should have more in the pipeline. And he was, he was bought in. So there was that fear of a new CFO coming in and how do you show value? Because some of it is, it's very easy to show right away, You know, we were able to refocus those two full-time employees on, on higher value chain activity and you know, they're doing a tremendous job and they're, you know, they have the, the bot and the automation supporting them. So he saw that right away. And we can show him that. But he also understands, as does the whole leadership team, the concept of downstream impacts that you can't necessarily, you know, touch and, and put on paper. So he sees some, but then he also recognizes all the other upstream and downstream impacts that it's had and he's all in and supports whatever, whatever we need. >>Yeah. New CFOs like George Seaford taking over for bill walls. >>Yeah, exactly. Exactly. We >>Have, we have to keep showing results and it has to be sustainable. So that's, again, we'll rely on our partnership to say, okay, this is the beginning, you know, what's next? Keep us, you know, honest on oversight and, and any pitfalls that we should avoid because he's excited. But at the same time, we need to make sure that we sustain those results and, and show what's next. Now they all gotta taste to the apple and they're very eager to see what's next in, in, in this hyper automation journey. >>Well, Dirk, you've partnered on this journey, this specific journey with, with, with Puma. But from your perspective in the broader marketplace, what would be the perfect low hanging fruit opportunity that you would like to have somebody call you and say, Hey, we've got, we've got this perspective engagement with a client. What would be the, what would be the like, Oh yeah, that's easy, that's huge roi really quickly, What does that look like? >>Yeah, I think there's, there's a few areas, right? You know, one task automation RPA is a, is a really good entry point, right? Because it's, it's, it's not overly complex. It doesn't involve a lot of complicated technologies. And I'd say the, the usual starting areas, you know, you, you finance back office, you know, shared service, invoice processing, you know, payables is a very good opportunity area. HR is also an area I would look at, you know, in new, new employee onboarding process or you know, payroll, et cetera. And then supply chain is actually becoming more and more, more common, right? So those would be I guess, top three areas I would mention. And >>Then, and then kind of follow onto that, what's the tip of this sphere? What's the sort of emerging market Yeah. >>For >>This kind of technology? >>I think there's two things. One, it's taking a holistic into end view and leveraging multiple, you know, technology, you know, beyond just rpa, right? You know, intelligent document processing, iml, you know, bringing all this to bear to actually do a true digital transformation. That's, that's number one. And then I'd say the second is going from focusing on cost and efficiency to actually getting into the front office and how do you, how do you actually increase revenue? How do you increase margin? How do you actually, you know, help with that, that top line growth. I think that's really, and that's where you're leveraging technologies, you know, like the, the AI as an example to really help you understand how do you optimize. >>So James, that's, that becomes then an enterprise wide initiative. Yeah. That's, that's, is that your vision? Maybe maybe lay that out for >>Us a bit. Yeah, ab absolutely. The, the vision is now that we've seen what, what it can do, how do we take it from being managed by just, you know, supply chain and this proof of concept cuz I manage projects, but now it's bigger than just a supply chain project. And how do we sort of evangelize that through the whole organization And you know, they mentioned on main stage this, the creation of new jobs and, and roles and how a, a company might set out their strategic directive now is, is changing and evolving. So you know that that's our idea now and that what we'll need support next is how should we structure now for success. And so that it's across the whole enterprise. But that's, that's the vision for >>Sure. What worries you do, you worried about it like taking off and getting outta control and not being governed and so you have to be a little bit careful there. >>Yeah, for sure. That was really important to us. And we actually got to leverage a lot of heavy lifting that Puma Global had done at the same time that we were coming up and, and thinking of the idea of rpa. They were having the same thoughts and they did a lot of heavy lifting again, about not only the software providers but also what does the structure look like, the oversight, a center of excellence globally. So we were able to really leverage a lot of best practices and SOPs that they had set out and we were able to kind of leverage those, bring those to Puma North America so that we didn't face that fear cuz that would be a limiting factor for us. So because we were so disciplined and we could leverage the work that they had done, that fear wasn't, wasn't there. Now we have to stay, you know, on top of it. And as people get excited, how do you kind of mirror the excitement and with it at the same time that the oversight and not getting, you know, too, too big, too fast. So that's the balance that we'll, we'll work through now. It's a good problem to have. >>Well, exactly. It is super exciting. Great story. Congratulations on, on the success and good luck. Thank you. Yeah, you very much for coming to the, Yeah. Thank you. Thank you. All right. And thank you for watching. Keep it right there. Dave Nicholson Andante right back, the cube live from Las Vegas UI path forward. Five.

>> 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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>> (upbeat music) >> production: From around the globe, it's the Cube covering Google cloud Next on-Air 20. (Upbeat music) >> Welcome back. I'm Stu Miniman and this is the CUBE coverage of Google cloud Next on Air 20. Of course, the nine week distributed all online program that Google cloud is doing and going to be talking about, of course, multi-cloud, Google of course had a big piece in multi-cloud. When they took what was originally Borg, They built Kubernetes. They made that open source and gave that to the CNCF and one of Google's partners and a leader in that space is of course, Red Hat. Happy to welcome to the program Sathish Balakrishnan, he is the Vice President of hosted platforms at Red Hat. Sathish, thanks so much for joining us. >> Thank you. It's great to be here with you on Google Cloud Native insights. >> Alright. So I, I tied it up, of course, you know, we talk about, you know, the hybrid multicloud and open, you know, two companies. I probably think of the most and that I've probably said the most about the open cloud are Google and Red Hat. So maybe if we could start just, uh, you hosted platforms, help us understand what that is. And, uh, what was the relationship between Red Hat and the Open Shift team and Google cloud? >> Absolutely. Great question. And I think Google has been an amazing partner for us. I think we have a lot of things going on with them upstream in the community. I think, you know, we've been with Google and the Kubernetes project since the beginning and you know, like the second biggest contributor to Kubernetes. So we have great relationships upstream. We also made Red Hat Enterprise Linux as well as Open Shift available on Google. So we have customers using both our offerings as well as our other offerings on Google cloud as well. And more recently with the hosted our offerings. You know, we actually manage Open Shift on multiple clouds. We relaunched our Open Shift dedicated offering on Google cloud back at Red Hat Summit. There's a lot of interest for the offering. We had back offered the offering in 2017 with Open Shift Three and we just relaunched this with Open Shift Four and we received considerable interest for the Google cloud Open Shift dedicated offering. >> Yeah, Sathish maybe it makes sense if we talk about kind of the maturation of open source solutions, managed services has seen really tremendous growth, something we've seen, especially if we were talking about in the cloud space. Maybe if you could just walk us through a little bit out that, you know, what are you hearing from customers? How does Red Hat think about managed solutions? >> Absolutely. Stu, I think it was a good question, right? I think, uh, as we say, the customers are looking at, you know, multiple infrastructure footprints, Be iteither the public cloud or on-prem. They'll start looking at, you know, if I go to the cloud, you know, there's this concept of, I want something to be managed. So what Open Shift is doing is in Open Shift, as you know it's Red Hat's hybrid cloud platform and with Open Shift, all the things that we strive to do is to enable the vision of the Open Hybrid Cloud. Uh, so, but Open Hybrid Cloud, it's all about choice, So we want to make sure the customers have both the managed as well as the self managed option. Uh, so if you really look at it, you know, Red Hat has multiple offerings from a managed standpoint. One as you know, we have Open Shift dedicated, which runs from AWS and Google. And, you know, we just have, as I mentioned earlier. We relaunched our Google service at Red Hat Summit back in May. So that's actually getting a lot of traction. We also have joint offerings with Azure that we announced a couple of years back and, there's a lot of interest for that offering as well as the new offering that we announced post-summit, the Amazon-Red Hat Open Shift, which basically is another native offering that we have on Amazon. If you really look at, having, having spoken about these offerings, if you really look at Red Hat's evolution as a managed service provider in the public cloud, we've been doing this since 2011. You know, that's kind of surprising for a lot of people, but you know, we've been doing Open Shift online, which is kind of a multi-tenant parcel multi-talent CaaS solution 2011. And we are one of the earliest providers of managed kubernetes, you know, along with Google Kubernetes engine GKE, we are our Open Shift dedicated offering back in 2015. So we've been doing Kubernetes managed since, Open Shift 3.1. So that's actually, you know, we have a lot of experience with management of Kubernetes and, you know, the devolution of Open Shift we've now made it available and pretty much all the clouds. So that customers have that exact same experience that they can get any one cloud across all clouds, as well as on-prem. Managed service customers now have a choice of a self managed Open Shift or completely managed Open Shift. >> Yeah. You mentioned the choice and one of the challenges we have right now is there's really the paradox of choice. If you look in the Kubernetes space, you know, there are dozens of offerings. Of course, every cloud provider has their offerings. You know, Google's got GKE, they have Anthos, uh, they, they have management tools around there. You, you talked a bit about the, you know, the experience and all the customers you have, the, you know, there's one of the fighters talks about, there's no compression algorithm for experience. So, you know, what is Red Hat Open Shift? What really differentiates in the market place from, you know, so many of the other offerings, either from the public high providers, some of the new startups, that we should know. >> Yeah. I think that's an interesting question, right? I think all Google traders start with it's complete open source and, you know, we are a complete open source company. So there is no proprietary software that we put into Open Shift. Open Shift, basically, even though it has, you know, OC command, it basically has CPR. So you can actually use native Google networks as you choose on any Google network offering that you have be it GKE, EKS or any of the other things that are out there. So that's why I think there are such things with google networks and providers and Red Hat does not believe in open provider. It completely believes in open source. We have everything that we is open source. From an it standpoint, the value prop for Red Hat has always been the value of the subscription, but we actually make sure that, you know, Google network is taken from an upstream product. It's basically completed productized and available for the enterprise to consume. But that right, when we have the managed offering, we provide a lot more benefits to it, right? The benefits are right. We actually have customer zero for Open Shift. So what does that mean? Right. We will not release Open Shift if we can't run open Shift dedicated or any of their (indistinct) out Open Shift for them is under that Open Shift. Really really well. So you won't get a software version out there. The second thing is we actually run a lot of workloads, but then Red Hat that are dependent on our managed or open shift off. So for example, our billing systems, all of those internal things that are important for Red Hat run on managed Open Shift, for example, managed Open Shift. So those are the important services for Red Hat and we have to make sure that those things are running really, really well. So we provide that second layer of enterprise today. Then having put Open Shift online, out that in public. We have 4 million applications and a million developers that use them. So that means, I've been putting it out there in the internet and, you know, there's security hosts that are constantly being booked that are being plugged in. So that's another benefit that you get from having a product that's a managed service, but it also is something that enterprises can now use it. From an Open Shift standpoint, the real difference is we add a lot of other things on top of google network without compromising the google network safety. That basically helps customers not have to worry about how they're going to get the CIC pipeline or how they have to do a bunch of in Cobra Net as an outside as the inside. Then you have technologies like Store Street Metrics kind of really help customers not to obstruct the way the containerization led from that. So those are some of the benefits that we provide with Open Shift. >> Yeah. So, so, so Sathish, as it's said, there's lots of options when it comes to Kubernetes, even from a Red Hat offering, you've got different competing models there. If I look inside your portfolio, if it's something that I want to put on my infrastructure, if I haven't read the Open Shift container platform, is that significantly different from the managed platform. Maybe give us a little compare contrast, you know. What do I have to do as a customer? Is the code base the same? Can I do, you know, hybrid environments between them and you know, what does that mean? >> It's a smart questions. It's a really, really good question that you asked. So we actually, you know, as I've said, we add a lot of things on top of google network to make it really fast, but do you want to use the cast, you can use the desktop. So one of the things we've found, but you know, what we've done with our managed offering is we actually take Open Shift container platform and we manage that. So we make sure that you get like a completely managed source, you know. They'll be managed, the patching of the worker nodes and other things, which is, again, another difference that we have with the native Cobra Net of services. We actually give plush that admin functionality to customers that basically allows them to choose all the options that they need from an Open Shift container platform. So from a core base, it's exactly the same thing. The only thing is, it's a little bit opinionated. It to start off when we deploy the cluster for the customer and then the customer, if they want, they can choose how to customize it. So what this really does is it takes away any of the challenges the customer may have with like how to install and provision a cluster, which we've already simplified a lot of the open shift, but with the managed the Open Shift, it's actually just a click of it. >> Great. Sathish Well, I've got the trillion dollar question for you. One of the things we've been looking at for years of course, is, you know, what do I keep in my data center? What do I move to the cloud? How do I modernize it? We understand it's a complex and nuanced solution, but you talk to a lot of customers. So I, you know, here in 2020, what's the trends? What are some of the pieces that you're seeing some change and movement that, you know, might not have been the case a year ago? >> I think, you know, this is an interesting question and it's an evolving question, right? And it's something that if you ask like 10 people you'll get real answers, but I'm trying to generalize what I've seen just from all the customer conversations I've been involved. I think one thing is very clear, right? I think that the world is right as much as anybody may want to say that I'm going to go to a single cloud or I'm going to just be on prem. It is inevitable that you're going to basically end up with multiple infrastructure footprint. It's either multicloud or it's on Prem versus a single cloud or on prem versus multiple cloud. So the main thing is that, we've been noticing as, what customers are saying in a whole. How do I make sure that my developers are not confused by all these difference than one? How do I give them a consistent way to develop and build their applications? Not really worry about, what is the infrastructure. What is the footprint that they're actually servicing? So that's kind of really, really important. And in terms of, you know, things that, you know, we've seen customers, you know, I think you always start with compliance requirements and data regulations. Back there you got to figure it out. What compliance do I need? And as the infrastructure or the platform that I'm going to go to meet the compliance requirements that I have, and what are the data regulations? You know, what is the data I'm going to be setting? Is it going to meet the data submitted rules that my country or my geo has? I got to make sure I worry about that. And then I got to figure out if I'm going to basically more to the cloud from the data center or from one cloud to another cloud. I might just be doing a lift or shift. Am I doing a transformation? What is it that I really worry about? In addition to the transformation, they got to figure it out, or I need to do that. Do I not need to do that? And then, you know, we've got to figure out what your data going to set? What your database going to look in? And do you need to connect to some legacy system that you have on prem? Or how do you go? How do you have to figure that out and give them all of these complexities? This is really, really common for any large enterprise that has like an enterprise ID for that multi-cloud. That's basically in multiple geographies, servicing millions of customers. So that has a lot of experience doing all these things. We have open innovation labs, which are really, really awesome experience for customers. Whether they take a small project, they figured out how to change things. Not only learn how to change things from a technology standpoint, but also learn how to culturally change things, because a lot of these things. So it's not just moving from one infrastructure to another, but also learning how to do things differently. Then we have things like the container adoption programmer, which is like, how do you take a big legacy monolith application? How do you containerize it? How do you make it micro services? How do you make sure that you're leveraging the real benefits that you're going to get out of moving to the cloud or moving to a container platform? And then we have a bunch of other things like, how do you get started with Open Shift and all of that? So we've had a lot of experience with like our 2,400 plus customers doing this kind of really heavy workload migration and lifting. So the customers really get the benefits that they see out of Open Shift. >> Yeah. So Sathish, if I think about Google, specifically talking about Google cloud, one of the main reasons we hear customers using Google is to have access to the data services. They have the AI services they have. So how does that tie into what we were just talking about? If I, if I use Open Shift and you know. I'm living in Google cloud, can, can I access all of those cloud native services? Are there any nuances things I need to think about to be able to really unleash that innovation of the platform that I'm tying into? >> Yeah, absolutely not. Right. I think it's a great question. And I think customers are always wondering about. Hey, if I use Open Shift, am I going to be locked out of using the cloud services? And if anything run out as antilock. We want to make sure that you can use the best services that you need for your enterprise, like the strategy as well as for applications. So with that, right. And we've developed the operator framework, which I think Google has been a very early supporter of. They've built a lot of operators around their services. So you can develop those operators to monitor the life cycle of these services, right from Open Shift. So you can actually connect to an AI service if you want. That's absolutely fine. You can connect the database services as well. And you can leverage all of those things while your application runs on Open Shift from Google cloud. Also I think that done us right. We recognize that, when you're talking about the open hybrid cloud, you got to make sure that customers can actually leverage services that are the same across different clouds. So when you can actually leverage the Google services from On Prem as well, if you choose to have localized services. We have a large catalog of operators that we have in our operator hub, as well as in the Red Hat marketplace that you can actually go and leverage from third party, third party ISV, so that you're basically having the same consistent experience if you choose to. But based on the consistent experience, that's not tied to a cloud. You can do that as well. But we would like for customers to use any service that they want, right from Open Shift without any restrictions. >> Yeah. One of the other things we've heard a lot from Google over the last year or so has been, you know, just helping customers, especially for those mission, critical business, critical applications, things like SAP. You talked a bit about databases. What advice would you give customers these days? They're, they're looking at, you know, increasing or moving forward in their cloud journeys. >> I think it sounds as an interesting question because I think customers really have to look at, you know, what is the ID and technology strategy? What are the different initiatives to have? Is it digital transformation? Is it cloud native development? Is it just containerization or they have an overarching theme over? They've got to really figure that out and I'm sure they're looking at it. They know which one is the higher priority when all of them are interrelated and in some ways. They also got to figure out how they going to expand to new business. Because I think as we said, right, ID is basically what is driving personal software is eating the load. Software services are editing them. So you got to figure out, what are your business needs? Do you need to be more agile? Do you need to enter new businesses? You know, those are kind of important things. For example, BMW is a great example, they use Open Shift container platform as well as they use Open Shift dedicated, you know. They are like a hundred hundred plus year old car, guess, you know what they're trying to do. They're actually now becoming connected car infrastructure. That's the main thing that they're trying to build so that they can actually service the cars in any job. So in one shoe, they came from a car manufacturing company to now focus on being a SAS, an Edge and IOT company. If you really look at the cars as like the internet of things on an edge computer and what does that use case require? That use case cannot anymore have just one data center in Munich, they have to basically build a global platform of data centers or they can really easily go to the cloud. And then they need to make sure that that application double close when they're starting to run on multiple clouds, multiple geographies, they have the same abstraction layer so that they can actually apply things fast. Develop fast. They don't have to worry about the infrastructure frequently. And that's basically why they started using Open Shift. And don't know why they're big supporters of Open Shift. And then I think it's the right mission for their use. So I think it really depends on, you know, what the customer is looking for, but irrespective of what they're looking for, I think Open Shift nicely fits in because what it does, is it provides you that commonality across all infrastructure footprints. It gives you all the productivity gains and it allows you to connect to any service that you want anywhere because we are agnostic to that and as well as we bring a whole lot of services from Red Hat marketplace so you can actually leverage your status. >> Well, Sathish Balakrishnan, thank you so much for the updates. Great to hear about the progress you've got with your customers. And thank you for joining us on the Google cloud Next On Air Event. >> Thank you Stu. It's been great talking to you and look forward to seeing you in person one day. >> Alright. I'm Stu Miniman. And thank you as always for watching the Cube. (upbeat music) (upbeat music)

>>from around the globe. >>It's the Cube with coverage >>of Coop con and cloud, Native con Europe 2020 Virtual brought to you by Red Hat, The Cloud Native Computing Foundation and its ecosystem partners. Welcome back. I'm stew minimum. And this is the Cube's coverage of cube con cloud, native con Europe 2020 of course, happening virtual this year. We always love when we get to talk to the practitioners in this community. So much happening in the developer space and really excited to have on the program first time guest in a very timely topic, we welcome our bod. Hassani, Who is the back and lead for house? My flattening, which is a joint research project. It related to code 19 associated with the University of Toronto. About thanks so much for joining us. >>Thank you. >>All right, so maybe explain how is my flattening? You know, the term flattening the curve is something that I think everyone around the globe is familiar with. Now, um, you know, Canada, you've got some great initiatives going. So help us understand how you got involved in this in what? What is the project? Sure, So I'll >>take a stock to March, which now feels like years ago. Um, back in March, way could look across in Europe, and we saw that. You know, I feel we're being overwhelmed. This new Cobra thing was happening, and there seems to be nothing happening here despite the fact that we know what was going on in Europe. So this whole collaboration started. It's really the brainchild of Dr Ben. Fine. Who's the radiologist that actually and partners on the idea was, Why don't we put all the data that is related to co bid, uh, for the province of Ontario, where I'm from in one place, right. So for the data mining people, like a lot of people on the on the program here and for the data minded people of Ontario to be able to have the information they need to make targeted both of the general public on that policy makers to really empower them with the right tools. We know the data was siloed in health care, and we know, you know, when this whole thing started, everything was on a website, you would get a daily update, but it wasn't something that you could analyze. Something you couldn't use. Really? It was unusable. How everything kind of started it. What if we did something about that? What if we brought all the data in one place? What if we visualize it and put all the resources in place that was released? How is my fattening got a Which is this initiative that I got involved with back in March and what we've been doing is building a number of dashboards based on Kobe data that are close to real time as possible. Doing a number of analyses. Um, the answer, your specific questions and doing deep dives into specific question. We have a team of scientific experts where our leadership, um you know Dr Ben Fine. I mentioned earlier. There's Dr Laura Rosello, the epidemiologists out of Ah, Perceptron. Oh, and then we have a Dr Alley that he's Austin Oy. Who the data science lead over it. Quick. Also, we got this kind of three perfect or the organization of the right talent required, and we've been trying Yeah, and whatever way we can by making the data transparent, >>Yeah, there's been a lot of initiatives, obviously that have had to accelerate really fast during this time it bring us inside a little bit. How long did it take to spend the site up? How do you make sure you're getting good data in Who decides? You know which visualizations love to hear a little bit about? You know how that has matured over the months that you've had project out there >>for sure. So when we started what people were doing out on Twitter, really, where there's a lot of this activity was happening was people were grabbing expect sheets and typing out every day what was happening. And I mean, coming from I'm not by any means a technical developer. That's not what I specialize in, but having some development dot com, and it makes sense that things could be done so much better. So we started to build data pipelines. Starting in March. We had a couple of government sources that were public. It was basically scrapping the government website and recording that in a database. Um, and then we start to visualize that we're using, you know, whatever we could that we started with Pablo just because we had a few. We're trying to build a community, right? So a community people want help and do this. But we have some tableau experts on our team and our community and, you know, the way we went. So we had the database. We started to connect with tableau and visualize it. Do you know, besides into and also that and then the project has matured from that web stopper ever since, with more complex data, pipeline building and data from different sources and visualizing them in different ways and expanding our dash boarding and expanding our now >>well in the cube con show that we're here at is so much about community. Obviously, open source is a major driver of what's going on there. So it sounded like that was that was a big piece of what you're working on. Help us bring inside out of that community build. I'd love to hear if there's any projects and tools you mentioned tableau for visualization, but anything from open source also that you're using. >>So actually, I I've never been involved in open source project before That this was kind of my first attempt, if you will, on we started, uh, on get hub quite early on. Actually, one of the partners I got involved in re shots was was Red hat off course. They're known for doing open source and for selling at it, and we have some amazing help from them into how we can organize community. Um, and we started to move the community over from getting up to get lab. You know, we started to the way we collaborate in slack. Ah, lot of times. And there's a lot of silos that we started to break those down and move them into get lab. And all conversations were happening in public that would beam or more closer to an open source approach. And honestly, a lot of people that are involved are our students, grass students who want to help our people in the community that want to help people from all kind of different backgrounds. I think we're really bringing in open source is not not a known concept in a lot of these clinical scientific communities, right? It's a lot more developer oriented, and I think it's been it's been learning opportunity for everyone involved. Uh, you know, something that may seem kind of default or basic have been a big learning opportunity for everyone of, you know, issues shocking and labeling and using comments and I'll going back into our own old ways of like, emailing people are people. Um, they had been digital art to it, and we'll get a lot of the big one. Um, we went from having this kind of monolithic container rising it and using Kubernetes, of course, were developed with the help of Red Hat. We're able to move everything over to their open shift dedicated platform, and that was that allowed us to do is really do a lot of do things a lot better and do things in a more mature way. Um, that's that's quite a bit of information, but that's kind of high level. What it? >>Well, no, it's great. We One of the things we've been poking out for the last few years is you know, in the early days you talk about kubernetes. It was Oh, I need things at a scale on And, you know, while I'm sure that the amount of data and scale is important, speed was a major major piece of what you need to be involved in and you'll be able to rally and James So can you talk a little bit more. Just open shift. What did that bring to the environment? Any aspects related to the data that red hat help you with. >>So a few things there. The one thing that open shift I think really helped us with was really mean and how to help us with generally was establishing a proper see I CD pipeline. Right. So now we we use git lab itself. We have get lab runners that everyone, basically all developers involved have their own branches when they push code to get auto. We like to their branch. It just made everything a lot easier and a lot faster to be able to push things quickly without worrying about everything breaking That was definitely a big plus. Um, the other thing that we're doing with, uh that is using containers. Actually, we've been working on this open data hub, which is, you know, working on another great open source project which is again built on kubernetes and trying to break down some of the barriers when it comes to sharing data in the healthcare system. Um, we're using that and we, with the help of red, how we're able to deploy that to be able to collaborate between hospitals, share data securely. You do security analytics and try to break down some of these silos that I've gone up due to fears over security and find the so That's another great example open source helping us kind of pushing forward. >>Well, that that's I'm glad you brought that up The open data hub, that collaboration with other places when you have data being able to share that, you know, has to be important talk. This was a collaboration to start with, you know, what's the value of being able to work with other groups and to share your data beyond beyond just the community that's working on it. >>So if you think about what's happening right now in a lot of hospitals in Canada, and I mean it's the same in the US is everyone is in this re opening stage. We shut down the economy. We should down a lot of elective surgeries and a lot of procedures. I know hospitals are trying to reopen right so and trying to figure out how to go back to their old capacity, and in that they're all trying to solve the same problem in different ways. So everyone is in their silo trying to tackle the same problems in a way. So what we're trying to do is basically get everyone together and collaborate on this open, open source environments, right? And what this open data allows us to do in to some degree alleviate some of the fears over sharing data so that we're not all doing the same thing in parallel are not talking to each other. We're able to share code, share data, get each other's opinions and, you know, use your resources in the healthcare system or official the drill, you know, all trying to address the same goal here. >>So imagine if you've had a lot of learnings from this project that you've done. Have you given any thought to? You know, once you get past that kind of the immediate hurdle of covert 19 you know what? Will this technology be able to help you going forward? You know, what do you see? Kind of post dynamic, if you will. >>I think the last piece I touched on, there is a big thing that I'm really hoping we'll be able to push forward past the pandemic. I think what? What the pandemic has shown us is the need for more transparency and more collaboration and being able to be more agile in response to things faster. And that's know how they're operating. And I think we know that now we can see that. I'm hoping that can be used as an opportunity to be able to bring people together to collaborate on projects like, How's my funding outside of this, right? We're not Not only the next pandemic. Hopefully I never come. Um but but for other, bigger problem that we face every day, collaboration can only help things, not tender thing. I'm hoping that's one big side effect that comes out of this. And I think the data transparency thing is is another big one that I'm hoping can improve outside of the situation. >>Yeah, I I wonder if I can ask you just a personal question. We've heard certain organizations say that, you know, years of planning have been executed in months. When I think about all the technologies that you had thrown at you, all the new things you learned often that something that would have taken years. But you didn't month. So how do you work through that? You know, there's only 24 hours in any day, and we do need some sleep. So what was important from your standpoint? What partners into tools helped, you know, and And the team, you know, take advantage of all of these new technologies. >>Yeah, honestly, I think that the team is really, really important. We've had an amazing set of people that are quite diverse and then usually would, quite honestly, never be seen in the same room together just because of all the different backgrounds that are there. Um, so that was a big driver. I think everyone was motivated to get things done. What happens when we first launched the site? We, you know, put it together. Basic feedback mechanism. Where we where we could hear from the public on. We've got an outpouring of support, people saying that they found that information really useful. And I think that pushed everyone to work harder and ah, and kind of reinforces our belief that this is what we're doing is helpful on, is making a difference in someone's life. And I think everyone that helped everyone work harder in terms of some of the tools that we use. Yeah, I totally agree. I think there was a 1,000,000 things that we all learned. Um, and it definitely wasn't amazing. Growing opportunity, I think, for the whole group. Um, I I don't know if there's a There's any wisdom I can impart. They're more than I think we were just being pushed by the need and being driven by the support that we're getting. Okay, >>well, you know, when there's a necessity to get things done, it's great to see the team execute the last question I have for you. You've got all this data. You've got visualizations. You've been going through a lot of things any any interesting learnings that you had or something that you were. You able to visualize things in a certain way in the community, reacted anything that you've learned along the way. That may be surprised you. >>That's a really interesting question there. I think the biggest, the biggest learning opportunity or surprise for me was what? How much people are willing to help if you just write, um, a lot of people involved. I mean, this is a huge group of volunteers who are dedicating their time to this because they believe in it on because they think they're doing the right thing and they're doing it for a bigger cause. It sounds very cheesy. Um, but I think that was wonderful to me to see that we can bring together such diverse people to dedicate their time for freedom to do something for the public. >>Yeah, well, and along that note, I I see on the website there is a get involved. But so is there anything you know, skill set or people that you're looking for, uh, further to help the team >>100%. So I think when I every time we do a presentation of any thought really got for anyone who's watching to just go on our site and get involved, there's a 1,000,000 different things that you can get involved with. If you're a developer, we can always use help. If you're a data, this person, we can always use help If you're a designer, honestly, there were a community driven organization. Uhm and we can always use more people in that community. That's that's the unique thing about the organization. 100%. Please do to house my finding, Dr and you get involved in get Lab. >>Well, so far, but thank you so much for sharing. We definitely encourage the unity get involved. It's projects like this that are so critically important. Especially right now during the pandemic. Thanks so much for joining. And thank you for all the work the team did. >>Thank you for having me. >>Alright. And stay tuned for more coverage from Cube Con Cloud native on 2020 in Europe Virtual Edition. I'm Stew Minimum. And thank you for watching the Cube. Yeah, yeah, yeah, yeah, yeah, yeah