>>from the bellagio Hotel >>in Las Vegas. >>It's the >>cube covering >>Ui Path forward. >>Four brought to >>you by Ui Path. >>Welcome back to Las Vegas. The cube is here. We've been here for two days covering Ui Path Forward for lisa martin here with David Monty. We've talked about automation and many industries. Now this segment is going to focus on automation and healthcare. We've got two guests joining us Jim Petrosea Cto of Blue Cross, Blue Shield and Gadget. Dhaliwal. The global C. I. O. Industry lead at you. I pass guys welcome to the program. Thank you. So let's start unpacking from the CTO level and the ceo level the agenda for automation. Jim let's start with you. What does that look like >>for us. It's actually pretty strategic and part of as we think about digital and what digital transformation means, it actually plays a pretty key role. Um There are a lot of processes that can be very manual within a big organization like Blue cross and Blue shield and to be able to streamline that and take away kind of what I would call the mundane work. Right? The the you know, going through a spreadsheet and then typing it into the screen, there are a lot of processes like that that are legacy. But what if you could take that away um and actually create a better work experience for the people that work there right? And and focus on higher value type uh type things and it's really key. And it really It goes down to our our business folks right? There are a lot of things we can drive with automation. We started a program um in 2019. Um that's been quite successful. We now have 250 box, we measure what we call annualized efficiency gains. So how much efficiency are we getting by these bots? So the bots are doing um this repetitive work that people would do. Um And what we're finding is, you know, we've got about $11 million in any wise efficiency gain through the process and we're just getting started. Um But we're all we're not stopping there too though, we're enabling citizen developers. So we're saying, hey business, if you want to automate, you know, parts of your job, we're gonna help you do that. So we've got about 60 people that were training. Um We run bad Ethan's where they come together and they actually create bots uh And it's really really creating some some impact and buzz in our business >>anywhere from your lens, where does automation fit within the C. I. O. S. Agenda? And how do you work together in unison with the C. T. O. To help roll this out across the enterprise? >>Yeah, no, definitely. And in fact as a part of introduction, I can actually share that. How I'm wearing a Ceo had within your path since I'm just joining join path and I'm actually now helping a client ceos in their automation strategy but I was a deputy ceo in my prior role at L. A. County where actually I ran the automation strategy. So if we look at from our organization perspective B complex as L. A County which is such a Federated organization. From a Ceo perspective, the way we look at the strategy is it's always driven by the business goals of the city or a county and we typically drive into three different areas. One is how we can transform our operational processes so that we can save the tax dollars. It's all about doing more with the less dollars. And then second is about how we can transform our residents experience because end of the day it is all about how we can improve the quality of life for our residents. So we've got 10 million people for L. A. County, the largest populous county in us. So it was an uphill task to serve that such a diverse population need and that the third area is about how to transform the new business models because as we are moving away from a government centric approach to the residents centric approach, you really need to come up with a new digital solutions. And Ceo is in the center of all these three elements when you look at it. So it's a very appear to us to keep keep improving your efficiency and then at a time keep adding the new digital solutions and that's where automation strategy is kind of a horizontal strategy which enables all these components. So what I hear from >>that is alignment with the business. Yeah. Right. Change management. Absolutely. That's like really fundamental and then see IOS this this agent of transformation uh you can see or she has a horizontal purview across the organization now now jim the cto role is the automation at blue cross blue shield lead by you or you there to make sure the technology plugs into your enterprise architecture. What's your shoulder? >>You know? Uh my my role is really to drive uh what I'll call technology enabled business change. Right. So I actually uh started our our automation journey uh at hc sc and I did that by partnering with our business. Um There was actually a lot of buzz around automation and there were actually some small pockets of it, none of it was enterprise scale. Um Right. And we really wanted to go big in this and and working with the business sponsors, they saw value in it. Um and we've you know, we've generated um a lot of uh efficiency, better quality of work because of it but but I very closely had a partner with our business, we have a committee that is lead of business folks that I facilitate. So I view my role as an enabler, um we have to communicate the change management pieces is huge. Uh the education just having a common vernacular on what is automation mean, Right, because everybody interpreted it differently um and then being able to do it at an enterprise scale is quite challenging. Um You know, I I really enjoyed um one of the key notes, I don't know if you had a chance to see shankar by Duncan from the hidden brain, right? But he talked a lot about the brain aspect and how do you get people to change? And and that's a large part of it. There's a lot about technology, but there's really a lot about being a change agent um and and really working very closely with your business, >>how does one measure? I'm hearing a lot time saved. Our saved. How does one measure that and quantify the dollar impact, which by the way, I'm on record as saying the soft dollars are way bigger. And but when you're talking to the, you know, the bottom line CFO and it's all about, you know, the cash flow, whatever is, how do you measure that? >>I can take it. So we, what we do is as we define these use cases right? We we go through an actual structure product process where we we gather them. Um we then rate them and we actually prioritize them based on those that are going to have the greatest impact. Um and we can tell based on, you know, what is the manual effort today. So we understand there are X number of people that do this X number of days and we think this body can take that some load off of them. Right? Um So we we go in with the business case. Um And then the Ui Path platform actually allows us to measure well, how much is that pot running? Right. So we can actually sit there and say, well we wanted that thing to run 10 hours a day and it did and it's generated this kind of efficiency because otherwise the human would have had to do that work. >>So the business case is kind of redeploying >>human. It really is is really maximizing human capital and make and and you know really using because the bots do repetitive stuff really well. They don't do higher level thinking and and we don't view it as replacing people, we view it as augmenting and actually making them more efficient and more effective at what, how do you get the dollars out of that? Well, a couple of ways. Right. And so one of the things we've we've done is we we create and measure the efficiency our business users and financed by the way is one of our bigger ones. And the CFO is one of the sponsors of the program, um can decide how to reinvest it in a lot of cases it is actually cost avoidance as we grow, literally being able to grow without adding staff. I mean that's very measurable. Um in some cases it is actually taking, you know cost out um in in certain cases, but a lot of times that's just through attrition, right? You don't back fill positions, you let it happen naturally. Um and and then there's just things that happen to your business that you have to respond to give you a great example, state of texas, um passes what's the equivalent of the no surprise attack. But they did it there before the federal government did it. Um but it requires a lot of processes to be put in place, because now you have providers and payers having to deal with disputes, right? It actually generates a boatload of work. And we thought there might be, you know, 5000 of these in the first year, where there were 21,000 in the first year. And so far this year we're doubling that amount, right. We were able to use automation to respond to that without having to add a bunch of stuff. If we had to add staff for that, it would have literally been, you know, maybe hundreds of people, right? And but now, you know, there's, you can clearly put a value on it and it's millions of dollars a year, that we would have otherwise had to expect. >>The reason I'm harping on this lease is because I've been through a lot of cycles, as you know, and after the dot com boom, the the cost avoidance meant not writing the check to the software company, right? And that's what nick Carr wrote this, i. T matter. And then, and then, you know, post the financial crisis, we've entered uh a decade plus of awareness on the impact of technology. And I wonder if it's, I think this, I think this the cycle is changing I think. And I wonder if you have an opinion here where people, I think organizations are going to look at Technology completely different than they did like in the early 2000s when it was just easy to cut. >>No, I think the other point I will add to it. I agree with the gym. So we typically look at our away but it doesn't always have to be the cost. Right? If you look from the outcomes of the value, there are other measures also right? If you look at the how automation was able to help in the Covid generate. It was never about costs at that time. It was about a human lives. So you always may not be able to quantify it what you look at. Okay. What how are we maximizing the value or what kind of situations where we are and where we may not even have a human power to do that work. And we are running against the time. It could be the compliance needs. I'll give example of our covid use case which was pretty big success uh within L. A. County we deployed bots for the covid contact tracing program. So we were actually interviewing all the people who were testing positive so that we actually can keep track of them and then bring back that data within our HR so that our criminologists actually can look at the trends and see how we are doing as a county as compared to other counties and nationally. And we were in the peak, we were interviewing about 5000 people a day And we had to process that data manually into our nature and we deployed 15 members to do that. And they were doing like about 600 interviews a day. So every day we had a backlog of 2500 interviews. So it is not about a cost saving or a dollar value here because nobody planned for these unplanned events and now we don't have a time and money to find more data entry operators and parts were able to actually clear up all the backlog. So the value which we were able to bring it is way beyond the cost element. >>I I believe that 100% and I've been fighting this battle for a long time and it's easier to fight now because we're in this economic cycle even despite the pandemic, but I think it can be quantified. I honestly believe it can be tied to the income statement or in the case of a public sector, it could be tied to the budget and the mission how that budget supports the mission of the company. But I really believe it. And and I've always said that those soft factors are dwarf the cost savings, but sometimes, you know, sometimes the CFO doesn't listen, you know, because he or she has to cut. I think automation could change that >>for public sector. We look at how we can do more about it. So it's because we don't look at bottom line, it's about the tax dollars, we have limited dollars, but how we can maximize the value which we are giving to residents, it is not about a profit for us. We look at the different lens when it comes to the commercial >>Side, it's similar for us. So as a as a health care pair, because we're a mutual right? Our members and we have 17 million of them are really the folks that own the company and we're very purpose driven. Our our purpose is to do everything in our power to stand by members in sickness and in health. So how do you get the highest quality, cost effective health care for them? So if automation allows you to be more effective and actually keep that cost down, that means you can cover more people and provide higher quality care to our members. So that's really the driver for mission driven, >>I was gonna ask you as a member as one of your 17 million members, what are some of the ways in which automation is benefiting me? >>Um you know, a number of different ways. First off, you know, um it lowers our administrative costs, right? So that means we can actually lower our rights as as we go out and and and work with folks? That's probably the the the the bottom line impact, but we're also automating processes uh to to make it easier for the member. Right? Uh the example I used earlier was the equivalent of no surprises. Right. How do we take the member out of the middle of this dispute between, you know, out of network providers and the payer and just make it go away. Right, and we take care of it. Um but that that creates potentially administrative burden on our side, but we want to keep their costs down and we do it efficiently using it. So there's a number of use cases that we've we've done across, you know, different parts of our business. We automate a lot of our customer service, right? When you call um there's bots in the background that are helping that that agent do their job. And what that means is you're on the show, you're on the phone a lot shorter of a period of time. And that agent can be more concise and more accurate in answering your question. >>So your employee experience is dramatically improved, as is the member experience? >>Yes, they go hand in hand. They do go hand, unhappy members means unhappy employees, 100% >>mentioned scale before, you said you can't scale in this particular, the departmental pockets. Talk about scale a little bit. I'm curious as to how important cloud is to scale. Is it not matter. Can you scale without cloud? What are the other dimensions of scale? >>Well, you know, especially with my CTO had, we're we're pushing very heavily to cloud. We view ourselves as a cloud first. We want to do things in a cloud versus our own data centers, partially because of the scale that it gives us. But because we're healthcare, we have to do it very securely. So. We are very meticulous about guarding our data, how we encrypt information um, not only in our data center but in the cloud and controlling the keys and having all the controls in place. You know, the C. So and I are probably the best friends right now in the company because we have to do it together and you have to take that that security mind set up front. Right cloud first. Put security first with it. Um, so we're moving what we can to the cloud because we think it's just going to give us better scale as we grow and better economics overall, >>Any thoughts on that? I think a similar thoughts but if we look from L. A. county because of the sheer volume itself because the data which we are talking about. We had 40 departments within the county. Each department is serving a different business purpose for the resident beit voting or B justice or being social services and all and the amount of data which we are generating for 10 million residents and the amount of duplicate asi which it comes out because it's a very government centering model. You have a different systems and they may not be talking to each other. The amount of diplomacy and identity delicacy which we are creating and as we are enabling the interoperability between these functions to give us seamless experience keeping security in mind so fully agree on that because the end of the day we have to ensure that customer guarantee but it's a sheer volume that as and when we are adding these data sets and the patient's data as well as the residents data and now we have started adding a machine data because we have deployed so many IOT solutions so the data which is coming from those machines, the logs and all its exponential so that's where the scale comes into picture and how we can ensure that we are future ready for the upscale which we need and that's where cloud ability definitely helps a lot. >>What do you mean by future ready? >>So if you look at from a future smart city or a smart community perspective, imagine when machines are everywhere machines and IOT solutions are deployed, beat even healthcare, your bad information, you're even patient information, everything is interconnected and amount of data which is getting generated in that your automobile they're going to start talking to entertainment or we have to potentially track a single resident might be going same person going to the justice or maybe same person might be having a mental health issues, A same person might be looking for a social services, how we're going to connect those dots and what all systems they are touching. So all that interconnections needs to happen. So that exponential increase of data is a future readiness, which I'm talking about. Are we future ready from a technology perspective? Are we future ready from the other ecosystem perspective and how and how we're gonna manage those situations? Uh, so those are the things which we >>look at it and it's a it's a multiplier to, right? We all have this influx of information and you need to figure out what to do with it. Right. This is where artificial intelligence, machine learning is so important. But you also have interoperability standards that are coming. So now we're we have this massive data that each of our organizations have. But now you have interoperability which is a good thing for the member saying now I need to be able to share that data. Yeah, I wanted to ask you about >>that because a lot of changes in health care, um, are meaningful use. You have to show that to get paid but the standards weren't mature. Right? And so now that's changing what role does automation play in facilitating those standards. >>So, you know, we're big, big supporters of the fire standard that's out there um to in order to be able to support the standards and and create a P. I. S. And and pull together the information. What what will happen sometimes in the background is there's actually um artificial intelligence, machine learning models that create algorithms right? The output of that though often has to be active. Now a person can do something with that information or a vodka. Right? So when you start taking the ideal of artificial intelligence and now you have a robotic process that can use that to pull together the information and assimilated in a way to make it higher quality. But now it's available. It's kind of in the background. You don't see it but it's there helping. >>What are some of the things that you see? I know we're out of time but I just have a couple more questions. Some of the things that you see here we are you I path forward for we're in person. This is a bold company that's growing very quickly. Some of the announcements that were made, what are what are some of your reaction to that? And how do you see it helping move blue crush blue shield forward even >>faster. Well you know a lot of the announcements in terms of some of the features that that they've added around their robotics processing are great right? The fact that they're in the cloud and and some of the capabilities and and and better ability to to support that the process mining is key. Right. In order for abouts to be effective, you have to understand your process and you just don't want to necessarily automate the bad practices. Right? So you want to take a look at those processes to figure out how you can automate things smartly. Um and some of their capabilities around that are very interesting. We're going to explore that quite a bit but but I think they're the ambition here is beyond robotics. Right. It's actually creating um you know, applications that actually are using bots in the background which is very intriguing and has a lot of potential potentially to drive even more digital transformation. This can really affect all of our workers and allow us to take digital solutions out to the market a lot faster >>and to see what was going to ask you, you are here for four weeks at UI Path, you got to meet a lot of your colleagues, which is great. But what about this company attracted you to leave your former role and come over here to the technology vendor side. >>Well, I think I was able to achieve the similar role within L. A. County, able to establish the automation practice and achieve the maturity, able to stand up things and I feel that this is the same practitioner activity which I can actually take it back to the other clients ceos because of one thing which I really like about your hypothesis. RP is just a small component of it. I really want to change that mindset that we have to start looking ui path as an end to end full automation enterprise solution and it is not only the business automation, it's the idea automation and it's a plus combination and whether we are developing a new industry solutions with our partners to help the different industry segments and we actually helping Ceo in the center of it because Ceo is the one who is driving the automation, enabling the business automation and actually managing the automation ceo and the governess. So CEO is in left and center of it and my role is to ensure that I actually help those Ceos to make successful and get that maturity and you will path as a platform is giving that ability of length and breath and that's what is really fascinating me and I'm really looking forward that how that spectrum is changing that we are getting matured in a process mining area and how we are expanding our horizons to look at the whole automation suit, not just the R. P. Product and that's something which I'm really looking forward and seeing that how we're going to continue expanding other magic quadrants and we're actually going to give the seamless experience so the client doesn't have to worry about okay for this, I have to pick this and further, I have to pick something else >>that's seamless experience is absolutely table stakes these days. Guys, we're out of time. But thank you so much for joining. David me, talking about automation and health care. Your recommendations for best practices, how to go about doing that and and the change management piece. That's a critical piece. We appreciate your time. >>Thanks for having. Thank >>you. Our pleasure for day Volonte. I'm lisa martin live in las Vegas. The cubes coverage of you a path forward for continues next. Mhm. Mhm mm.

>>Hello. And we're here at the cube startup showcase made possible by a Ws. Thanks so much for joining us today. You know when Jim McDaid Ghani was formulating her ideas around data mesh, She wasn't the only one thinking about decentralized data architecture. Hello, Fresh was going into hyper growth mode and realized that in order to support its scale, it needed to rethink how it thought about data. Like many companies that started in the early part of last decade, Hello Fresh relied on a monolithic data architecture and the internal team. It had concerns about its ability to support continued innovation at high velocity. The company's data team began to think about the future and work backwards from a target architecture which possessed many principles of so called data mesh even though they didn't use that term. Specifically, the company is a strong example of an early but practical pioneer of data mission. Now there are many practitioners and stakeholders involved in evolving the company's data architecture, many of whom are listed here on this on the slide to are highlighted in red are joining us today, we're really excited to welcome into the cube Clements cheese, the Global Senior Director for Data at Hello Fresh and christoph Nevada who's the Global Senior Director of data also, of course. Hello Fresh folks. Welcome. Thanks so much for making some time today and sharing your story. >>Thank you very much. Hey >>steve. All right, let's start with Hello Fresh. You guys are number one in the world in your field, you deliver hundreds of millions of meals each year to many, many millions of people around the globe. You're scaling christoph. Tell us a little bit more about your company and its vision. >>Yeah. Should I start or Clements maybe maybe take over the first piece because Clements has actually been a longer trajectory yet have a fresh. >>Yeah go ahead. Climate change. I mean yes about approximately six years ago I joined handle fresh and I didn't think about the startup I was joining would eventually I. P. O. And just two years later and the freshman public and approximately three years and 10 months after. Hello fresh was listed on the German stock exchange which was just last week. Hello Fresh was included in the Ducks Germany's leading stock market index and debt to mind a great great milestone and I'm really looking forward and I'm very excited for the future for the future for head of fashion. All our data. Um the vision that we have is to become the world's leading food solution group and there's a lot of attractive opportunities. So recently we did lounge and expand Norway. This was in july and earlier this year we launched the U. S. Brand green >>chef in the U. K. As >>well. We're committed to launch continuously different geographies in the next coming years and have a strong pipe ahead of us with the acquisition of ready to eat companies like factor in the U. S. And the planned acquisition of you foods in Australia. We're diversifying our offer now reaching even more and more untapped customer segments and increase our total addressable market. So by offering customers and growing range of different alternatives to shop food and consumer meals. We are charging towards this vision and the school to become the world's leading integrated food solutions group. >>Love it. You guys are on a rocket ship, you're really transforming the industry and as you expand your tam it brings us to sort of the data as a as a core part of that strategy. So maybe you guys could talk a little bit about your journey as a company specifically as it relates to your data journey. You began as a start up. You had a basic architecture like everyone. You made extensive use of spreadsheets. You built a Hadoop based system that started to grow and when the company I. P. O. You really started to explode. So maybe describe that journey from a data perspective. >>Yes they saw Hello fresh by 2015 approximately had evolved what amount of classical centralized management set up. So we grew very organically over the years and there were a lot of very smart people around the globe. Really building the company and building our infrastructure. Um This also means that there were a small number of internal and external sources. Data sources and a centralized the I team with a number of people producing different reports, different dashboards and products for our executives for example of our different operations teams, christian company's performance and knowledge was transferred um just via talking to each other face to face conversations and the people in the data where's team were considered as the data wizard or as the E. T. L. Wizard. Very classical challenges. And those et al. Reserves indicated the kind of like a silent knowledge of data management. Right? Um so a central data whereas team then was responsible for different type of verticals and different domains, different geographies and all this setup gave us to the beginning the flexibility to grow fast as a company in 2015 >>christoph anything that might add to that. >>Yes. Um Not expected to that one but as as clement says it right, this was kind of set up that actually work for us quite a while. And then in 2017 when L. A. Freshman public, the company also grew rapidly and just to give you an idea how that looked like. As was that the tech department self actually increased from about 40 people to almost 300 engineers And the same way as a business units as Clemens has described, also grew sustainable, sustainably. So we continue to launch hello fresh and new countries launching brands like every plate and also acquired other brands like much of a factor and with that grows also from a data perspective the number of data requests that centrally we're getting become more and more and more and also more and more complex. So that for the team meant that they had a fairly high mental load. So they had to achieve a very or basically get a very deep understanding about the business. And also suffered a lot from this context switching back and forth, essentially there to prioritize across our product request from our physical product, digital product from the physical from sorry, from the marketing perspective and also from the central reporting uh teams. And in a nutshell this was very hard for these people. And this that also to a situation that, let's say the solution that we have became not really optimal. So in a nutshell, the central function became a bottleneck and slowdown of all the innovation of the company. >>It's a classic case, isn't it? I mean Clements, you see you see the central team becomes a bottleneck and so the lines of business, the marketing team salesman's okay, we're going to take things into our own hands. And then of course I I. T. And the technical team is called in later to clean up the mess. Uh maybe, I mean was that maybe I'm overstating it, but that's a common situation, isn't it? >>Yeah. Uh This is what exactly happened. Right. So um we had a bottleneck, we have the central teams, there was always a little of tension um analytics teams then started in this business domains like marketing, trade chain, finance, HR and so on. Started really to build their own data solutions at some point you have to get the ball rolling right and then continue the trajectory um which means then that the data pipelines didn't meet the engineering standards. And um there was an increased need for maintenance and support from central teams. Hence over time the knowledge about those pipelines and how to maintain a particular uh infrastructure for example left the company such that most of those data assets and data sets are turned into a huge step with decreasing data quality um also decrease the lack of trust, decreasing transparency. And this was increasing challenge where majority of time was spent in meeting rooms to align on on data quality for example. >>Yeah. And and the point you were making christoph about context switching and this is this is a point that Jemaah makes quite often is we've we've we've contextualized are operational systems like our sales systems, our marketing system but not our our data system. So you're asking the data team, Okay. Be an expert in sales, be an expert in marketing, be an expert in logistics, be an expert in supply chain and it start stop, start, stop, it's a paper cut environment and it's just not as productive. But but on the flip side of that is when you think about a centralized organization you think, hey this is going to be a very efficient way, a cross functional team to support the organization but it's not necessarily the highest velocity, most effective organizational structure. >>Yeah, so so I agree with that. Is that up to a certain scale, a centralized function has a lot of advantages, right? That's clear for everyone which would go to some kind of expert team. However, if you see that you actually would like to accelerate that and specific and this hyper growth, right, you wanna actually have autonomy and certain teams and move the teams or let's say the data to the experts in these teams and this, as you have mentioned, right, that increases mental load and you can either internally start splitting your team into a different kind of sub teams focusing on different areas. However, that is then again, just adding another peace where actually collaboration needs to happen busy external sees, so why not bridging that gap immediately and actually move these teams and to end into into the function themselves. So maybe just to continue what, what was Clements was saying and this is actually where over. So Clements, my journey started to become one joint journey. So Clements was coming actually from one of these teams to build their own solutions. I was basically having the platform team called database housed in these days and in 2019 where basically the situation become more and more serious, I would say so more and more people have recognized that this model doesn't really scale In 2019, basically the leadership of the company came together and I identified data as a key strategic asset and what we mean by that, that if we leverage data in a proper way, it gives us a unique competitive advantage which could help us to, to support and actually fully automated our decision making process across the entire value chain. So what we're, what we're trying to do now or what we should be aiming for is that Hello, Fresh is able to build data products that have a purpose. We're moving away from the idea. Data is just a by problem products, we have a purpose why we would like to collect this data. There's a clear business need behind that. And because it's so important to for the company as a business, we also want to provide them as a trust versi asset to the rest of the organization. We say there's the best customer experience, but at least in a way that users can easily discover, understand and security access high quality data. >>Yeah, so and and and Clements, when you c J Maxx writing, you see, you know, she has the four pillars and and the principles as practitioners you look at that say, okay, hey, that's pretty good thinking and then now we have to apply it and that's and that's where the devil meets the details. So it's the four, you know, the decentralized data ownership data as a product, which we'll talk about a little bit self serve, which you guys have spent a lot of time on inclement your wheelhouse which is which is governance and a Federated governance model. And it's almost like if you if you achieve the first two then you have to solve for the second to it almost creates a new challenges but maybe you could talk about that a little bit as to how it relates to Hello fresh. >>Yes. So christophe mentioned that we identified economic challenge beforehand and for how can we actually decentralized and actually empower the different colleagues of ours. This was more a we realized that it was more an organizational or a cultural change and this is something that somebody also mentioned I think thought words mentioned one of the white papers, it's more of a organizational or cultural impact and we kicked off a um faced reorganization or different phases we're currently and um in the middle of still but we kicked off different phases of organizational reconstruct oring reorganization, try unlock this data at scale. And the idea was really moving away from um ever growing complex matrix organizations or matrix setups and split between two different things. One is the value creation. So basically when people ask the question, what can we actually do, what shall we do? This is value creation and how, which is capability building and both are equal in authority. This actually then creates a high urge and collaboration and this collaboration breaks up the different silos that were built and of course this also includes different needs of stuffing forward teams stuffing with more, let's say data scientists or data engineers, data professionals into those business domains and hence also more capability building. Um Okay, >>go ahead. Sorry. >>So back to Tzemach did johnny. So we the idea also Then crossed over when she published her papers in May 2019 and we thought well The four colors that she described um we're around decentralized data ownership, product data as a product mindset, we have a self service infrastructure and as you mentioned, Federated confidential governance. And this suited very much with our thinking at that point of time to reorganize the different teams and this then leads to a not only organisational restructure but also in completely new approach of how we need to manage data, show data. >>Got it. Okay, so your business is is exploding. Your data team will have to become domain experts in too many areas, constantly contact switching as we said, people started to take things into their own hands. So again we said classic story but but you didn't let it get out of control and that's important. So we actually have a picture of kind of where you're going today and it's evolved into this Pat, if you could bring up the picture with the the elephant here we go. So I would talk a little bit about the architecture, doesn't show it here, the spreadsheet era but christoph maybe you can talk about that. It does show the Hadoop monolith which exists today. I think that's in a managed managed hosting service, but but you you preserve that piece of it, but if I understand it correctly, everything is evolving to the cloud, I think you're running a lot of this or all of it in A W. S. Uh you've got everybody's got their own data sources, uh you've got a data hub which I think is enabled by a master catalog for discovery and all this underlying technical infrastructure. That is really not the focus of this conversation today. But the key here, if I understand it correctly is these domains are autonomous and not only that this required technical thinking, but really supportive organizational mindset, which we're gonna talk about today. But christoph maybe you could address, you know, at a high level some of the architectural evolution that you guys went through. >>Yeah, sure. Yeah, maybe it's also a good summary about the entire history. So as you have mentioned, right, we started in the very beginning with the model is on the operation of playing right? Actually, it wasn't just one model is both to one for the back end and one for the for the front and and or analytical plane was essentially a couple of spreadsheets and I think there's nothing wrong with spreadsheets, right, allows you to store information, it allows you to transform data allows you to share this information. It allows you to visualize this data, but all the kind of that's not actually separating concern right? Everything in one tool. And this means that obviously not scalable, right? You reach the point where this kind of management set up in or data management of isn't one tool reached elements. So what we have started is we've created our data lake as we have seen here on Youtube. And this at the very beginning actually reflected very much our operational populace on top of that. We used impala is a data warehouse, but there was not really a distinction between borders, our data warehouse and borders our data like the impala was used as a kind of those as the kind of engine to create a warehouse and data like construct itself and this organic growth actually led to a situation as I think it's it's clear now that we had to centralized model is for all the domains that will really lose kimball modeling standards. There was no uniformity used actually build in house uh ways of building materialized use abuse that we have used for the presentation layer, there was a lot of duplication of effort and in the end essentially they were missing feedbacks, food, which helped us to to improve of what we are filled. So in the end, in the natural, as we have said, the lack of trust and that's basically what the starting point for us to understand. Okay, how can we move away and there are a lot of different things that you can discuss of apart from this organizational structure that we have said, okay, we have these three or four pillars from from Denmark. However, there's also the next extra question around how do we implement our talking about actual right, what are the implications on that level? And I think that is there's something that we are that we are currently still in progress. >>Got it. Okay, so I wonder if we could talk about switch gears a little bit and talk about the organizational and cultural challenges that you faced. What were those conversations like? Uh let's dig into that a little bit. I want to get into governance as well. >>The conversations on the cultural change. I mean yes, we went through a hyper growth for the last year since obviously there were a lot of new joiners, a lot of different, very, very smart people joining the company which then results that collaboration uh >>got a bit more difficult. Of course >>there are times and changes, you have different different artifacts that you were created um and documentation that were flying around. Um so we were we had to build the company from scratch right? Um Of course this then resulted always this tension which I described before, but the most important part here is that data has always been a very important factor at l a fresh and we collected >>more of this >>data and continued to improve use data to improve the different key areas of our business. >>Um even >>when organizational struggles, the central organizational struggles data somehow always helped us to go through this this kind of change. Right? Um in the end those decentralized teams in our local geography ease started with solutions that serve the business which was very very important otherwise wouldn't be at the place where we are today but they did by all late best practices and standards and I always used sport analogy Dave So like any sport, there are different rules and regulations that need to be followed. These rules are defined by calling the sports association and this is what you can think about data governance and compliance team. Now we add the players to it who need to follow those rules and bite by them. This is what we then called data management. Now we have the different players and professionals, they need to be trained and understand the strategy and it rules before they can play. And this is what I then called data literacy. So we realized that we need to focus on helping our teams to develop those capabilities and teach the standards for how work is being done to truly drive functional excellence in a different domains. And one of our mission of our data literacy program for example is to really empower >>every employee at hello >>fresh everyone to make the right data informs decisions by providing data education that scaled by royal Entry team. Then this can be different things, different things like including data capabilities, um, with the learning paths for example. Right? So help them to create and deploy data products connecting data producers and data consumers and create a common sense and more understanding of each other's dependencies, which is important, for example, S. S. L. O. State of contracts and etcetera. Um, people getting more of a sense of ownership and responsibility. Of course, we have to define what it means, what does ownership means? But the responsibility means. But we're teaching this to our colleagues via individual learning patterns and help them up skill to use. Also, there's shared infrastructure and those self self service applications and overall to summarize, we're still in this progress of of, of learning, we are still learning as well. So learning never stops the tele fish, but we are really trying this um, to make it as much fun as possible. And in the end we all know user behavior has changed through positive experience. Uh, so instead of having massive training programs over endless courses of workshops, um, leaving our new journalists and colleagues confused and overwhelmed. >>We're applying um, >>game ification, right? So split different levels of certification where our colleagues can access, have had access points, they can earn badges along the way, which then simplifies the process of learning and engagement of the users and this is what we see in surveys, for example, where our employees that your justification approach a lot and are even competing to collect Those learning path batteries to become the # one on the leader board. >>I love the game ification, we've seen it work so well and so many different industries, not the least of which is crypto so you've identified some of the process gaps uh that you, you saw it is gloss over them. Sometimes I say paved the cow path. You didn't try to force, in other words, a new architecture into the legacy processes. You really have to rethink your approach to data management. So what what did that entail? >>Um, to rethink the way of data management. 100%. So if I take the example of Revolution, Industrial Revolution or classical supply chain revolution, but just imagine that you have been riding a horse, for example, your whole life and suddenly you can operate a car or you suddenly receive just a complete new way of transporting assets from A to B. Um, so we needed to establish a new set of cross functional business processes to run faster, dry faster, um, more robustly and deliver data products which can be trusted and used by downstream processes and systems. Hence we had a subset of new standards and new procedures that would fall into the internal data governance and compliance sector with internal, I'm always referring to the data operations around new things like data catalog, how to identify >>ownership, >>how to change ownership, how to certify data assets, everything around classical software development, which we know apply to data. This this is similar to a new thinking, right? Um deployment, versioning, QA all the different things, ingestion policies, policing procedures, all the things that suffer. Development has been doing. We do it now with data as well. And in simple terms, it's a whole redesign of the supply chain of our data with new procedures and new processes and as a creation as management and as a consumption. >>So data has become kind of the new development kit. If you will um I want to shift gears and talk about the notion of data product and, and we have a slide uh that we pulled from your deck and I'd like to unpack it a little bit. Uh I'll just, if you can bring that up, I'll read it. A data product is a product whose primary objective is to leverage on data to solve customer problems where customers, both internal and external. So pretty straightforward. I know you've gone much deeper and you're thinking and into your organization, but how do you think about that And how do you determine for instance who owns what? How did you get everybody to agree? >>I can take that one. Um, maybe let me start with the data product. So I think um that's an ongoing debate. Right? And I think the debate itself is an important piece here, right? That visit the debate, you clarify what we actually mean by that product and what is actually the mindset. So I think just from a definition perspective, right? I think we find the common denominator that we say okay that our product is something which is important for the company has come to its value what you mean by that. Okay, it's it's a solution to a customer problem that delivers ideally maximum value to the business. And yes, it leverages the power of data and we have a couple of examples but it had a fresh year, the historical and classical ones around dashboards for example, to monitor or error rates but also more sophisticated ways for example to incorporate machine learning algorithms in our recipe recommendations. However, I think the important aspects of the data product is a there is an owner, right? There's someone accountable for making sure that the product that we are providing is actually served and is maintained and there are, there is someone who is making sure that this actually keeps the value of that problem thing combined with the idea of the proper documentation, like a product description, right that people understand how to use their bodies is about and related to that peace is the idea of it is a purpose. Right? You need to understand or ask ourselves, Okay, why does this thing exist does it provide the value that you think it does. That leads into a good understanding about the life cycle of the data product and life cycle what we mean? Okay from the beginning from the creation you need to have a good understanding, we need to collect feedback, we need to learn about that. We need to rework and actually finally also to think about okay benefits time to decommission piece. So overall, I think the core of the data product is product thinking 11 right that we start the point is the starting point needs to be the problem and not the solution and this is essentially what we have seen what was missing but brought us to this kind of data spaghetti that we have built there in in Russia, essentially we built at certain data assets, develop in isolation and continuously patch the solution just to fulfill these articles that we got and actually these aren't really understanding of the stakeholder needs and the interesting piece as a result in duplication of work and this is not just frustrating and probably not the most efficient way how the company should work. But also if I build the same that assets but slightly different assumption across the company and multiple teams that leads to data inconsistency and imagine the following too narrow you as a management for management perspective, you're asking basically a specific question and you get essentially from a couple of different teams, different kind of grass, different kind of data and numbers and in the end you do not know which ones to trust. So there's actually much more ambiguity and you do not know actually is a noise for times of observing or is it just actually is there actually a signal that I'm looking for? And the same is if I'm running in a B test right, I have a new future, I would like to understand what has it been the business impact of this feature. I run that specific source in an unfortunate scenario. Your production system is actually running on a different source. You see different numbers. What you've seen in a B test is actually not what you see then in production typical thing then is you're asking some analytics tend to actually do a deep dive to understand where the discrepancies are coming from. The worst case scenario. Again, there's a different kind of source. So in the end it's a pretty frustrating scenario and that's actually based of time of people that have to identify the root cause of this divergence. So in a nutshell, the highest degree of consistency is actually achieved that people are just reusing Dallas assets and also in the media talk that we have given right, we we start trying to establish this approach for a B testing. So we have a team but just providing or is kind of owning their target metric associated business teams and they're providing that as a product also to other services including the A B testing team, they'll be testing team can use this information defines an interface is okay I'm joining this information that the metadata of an experiment and in the end after the assignment after this data collection face, they can easily add a graph to the dashboard. Just group by the >>Beatles Hungarian. >>And we have seen that also in other companies. So it's not just a nice dream that we have right. I have actually worked in other companies where we worked on search and we established a complete KPI pipeline that was computing all this information. And this information was hosted by the team and it was used for everything A B test and deep dives and and regular reporting. So uh just one of the second the important piece now, why I'm coming back to that is that requires that we are treating this data as a product right? If you want to have multiple people using the things that I am owning and building, we have to provide this as a trust mercy asset and in a way that it's easy for people to discover and actually work with. >>Yeah. And coming back to that. So this is to me this is why I get so excited about data mesh because I really do think it's the right direction for organizations. When people hear data product they say well, what does that mean? Uh but then when you start to sort of define it as you did, it's it's using data to add value, that could be cutting costs, that could be generating revenue, it could be actually directly you're creating a product that you monetize, So it's sort of in the eyes of the beholder. But I think the other point that we've made is you made it earlier on to and again, context. So when you have a centralized data team and you have all these P NL managers a lot of times they'll question the data because they don't own it. They're like wait a minute. If they don't, if it doesn't agree with their agenda, they'll attack the data. But if they own the data then they're responsible for defending that and that is a mindset change, that's really important. Um And I'm curious uh is how you got to, you know, that ownership? Was it a was it a top down with somebody providing leadership? Was it more organic bottom up? Was it a sort of a combination? How do you decide who owned what in other words, you know, did you get, how did you get the business to take ownership of the data and what is owning? You know, the data actually mean? >>That's a very good question. Dave I think this is one of the pieces where I think we have a lot of learnings and basically if you ask me how we could start the feeling. I think that would be the first piece. Maybe we need to start to really think about how that should be approached if it stopped his ownership. Right? It means somehow that the team has a responsibility to host and self the data efforts to minimum acceptable standards. This minimum dependencies up and down string. The interesting piece has been looking backwards. What what's happening is that under that definition has actually process that we have to go through is not actually transferring ownership from the central team to the distributor teams. But actually most cases to establish ownership, I make this difference because saying we have to transfer ownership actually would erroneously suggests that the data set was owned before. But this platform team, yes, they had the capability to make the changes on data pipelines, but actually the analytics team, they're always the ones who had the business understands, you use cases and but no one actually, but it's actually expensive expected. So we had to go through this very lengthy process and establishing ownership. We have done that, as in the beginning, very naively. They have started, here's a document here, all the data assets, what is probably the nearest neighbor who can actually take care of that and then we we moved it over. But the problem here is that all these things is kind of technical debt, right? It's not really properly documented, pretty unstable. It was built in a very inconsistent over years and these people who have built this thing have already left the company. So there's actually not a nice thing that is that you want to see and people build up a certain resistance, e even if they have actually bought into this idea of domain ownership. So if you ask me these learnings, but what needs to happen as first, the company needs to really understand what our core business concept that they have, they need to have this mapping from. These are the core business concept that we have. These are the domain teams who are owning this concept and then actually link that to the to the assets and integrated better with both understanding how we can evolve actually, the data assets and new data build things new in the in this piece in the domain. But also how can we address reduction of technical death and stabilizing what we have already. >>Thank you for that christoph. So I want to turn a direction here and talk about governance and I know that's an area that's passionate, you're passionate about. Uh I pulled this slide from your deck, which I kind of messed up a little bit sorry for that, but but by the way, we're going to publish a link to the full video that you guys did. So we'll share that with folks. But it's one of the most challenging aspects of data mesh, if you're going to decentralize you, you quickly realize this could be the Wild West as we talked about all over again. So how are you approaching governance? There's a lot of items on this slide that are, you know, underscore the complexity, whether it's privacy, compliance etcetera. So, so how did you approach this? >>It's yeah, it's about connecting those dots. Right. So the aim of the data governance program is about the autonomy of every team was still ensuring that everybody has the right interoperability. So when we want to move from the Wild West riding horses to a civilised way of transport, um you can take the example of modern street traffic, like when all participants can manoeuvre independently and as long as they follow the same rules and standards, everybody can remain compatible with each other and understand and learn from each other so we can avoid car crashes. So when I go from country to country, I do understand what the street infrastructure means. How do I drive my car? I can also read the traffic lights in the different signals. Um, so likewise as a business and Hello Fresh, we do operate autonomously and consequently need to follow those external and internal rules and standards to set forth by the redistribution in which we operate so in order to prevent a car crash, we need to at least ensure compliance with regulations to account for society's and our customers increasing concern with data protection and privacy. So teaching and advocating this advantage, realizing this to everyone in the company um was a key community communication strategy and of course, I mean I mentioned data privacy external factors, the same goes for internal regulations and processes to help our colleagues to adapt to this very new environment. So when I mentioned before the new way of thinking the new way of um dealing and managing data, this of course implies that we need new processes and regulations for our colleagues as well. Um in a nutshell then this means the data governance provides a framework for managing our people the processes and technology and culture around our data traffic. And those components must come together in order to have this effective program providing at least a common denominator, especially critical for shared dataset, which we have across our different geographies managed and shared applications on shared infrastructure and applications and is then consumed by centralized processes um for example, master data, everything and all the metrics and KPI s which are also used for a central steering. Um it's a big change day. Right. And our ultimate goal is to have this noninvasive, Federated um ultimatum and computational governance and for that we can't just talk about it. We actually have to go deep and use case by use case and Qc buy PVC and generate learnings and learnings with the different teams. And this would be a classical approach of identifying the target structure, the target status, match it with the current status by identifying together with the business teams with the different domains have a risk assessment for example, to increase transparency because a lot of teams, they might not even know what kind of situation they might be. And this is where this training and this piece of illiteracy comes into place where we go in and trade based on the findings based on the most valuable use case um and based on that help our teams to do this change to increase um their capability just a little bit more and once they hand holding. But a lot of guidance >>can I kind of kind of trying to quickly David will allow me I mean there's there's a lot of governance piece but I think um that is important. And if you're talking about documentation for example, yes, we can go from team to team and tell these people how you have to document your data and data catalog or you have to establish data contracts and so on the force. But if you would like to build data products at scale following actual governance, we need to think about automation right. We need to think about a lot of things that we can learn from engineering before. And that starts with simple things like if we would like to build up trust in our data products, right, and actually want to apply the same rigor and the best practices that we know from engineering. There are things that we can do and we should probably think about what we can copy and one example might be. So the level of service level agreements, service level objectives. So that level indicators right, that represent on on an engineering level, right? If we're providing services there representing the promises we made to our customers or consumers, these are the internal objectives that help us to keep those promises. And actually these are the way of how we are tracking ourselves, how we are doing. And this is just one example of that thing. The Federated Governor governance comes into play right. In an ideal world, we should not just talk about data as a product but also data product. That's code that we say, okay, as most as much as possible. Right? Give the engineers the tool that they are familiar basis and actually not ask the product managers for example to document their data assets in the data catalog but make it part of the configuration. Have this as a, as a C D C I, a continuous delivery pipeline as we typically see another engineering task through and services we say, okay, there is configuration, we can think about pr I can think about data quality monitoring, we can think about um the ingestion data catalog and so on and forest, I think ideally in the data product will become of a certain templates that can be deployed and are actually rejected or verified at build time before we actually make them deploy them to production. >>Yeah, So it's like devoPS for data product um so I'm envisioning almost a three phase approach to governance and you kind of, it sounds like you're in early phases called phase zero where there's there's learning, there's literacy, there's training, education, there's kind of self governance and then there's some kind of oversight, some a lot of manual stuff going on and then you you're trying to process builders at this phase and then you codify it and then you can automate it. Is that fair? >>Yeah, I would rather think think about automation as early as possible in the way and yes, there needs to be certain rules but then actually start actually use case by use case. Is there anything that small piece that we can already automate? It's as possible. Roll that out and then actually extended step by step, >>is there a role though that adjudicates that? Is there a central Chief state officer who is responsible for making sure people are complying or is it how do you handle that? >>I mean from a from a from a platform perspective, yes, we have a centralized team to uh implement certain pieces they'll be saying are important and actually would like to implement. However, that is actually working very closely with the governance department. So it's Clements piece to understand and defy the policies that needs to be implemented. >>So Clements essentially it's it's your responsibility to make sure that the policy is being followed. And then as you were saying, christoph trying to compress the time to automation as fast as possible percent. >>So >>it's really it's uh >>what needs to be really clear that it's always a split effort, Right? So you can't just do one thing or the other thing, but everything really goes hand in hand because for the right automation for the right engineering tooling, we need to have the transparency first. Uh I mean code needs to be coded so we kind of need to operate on the same level with the right understanding. So there's actually two things that are important which is one its policies and guidelines, but not only that because more importantly or even well equally important to align with the end user and tech teams and engineering and really bridge between business value business teams and the engineering teams. >>Got it. So just a couple more questions because we gotta wrap I want to talk a little bit about the business outcome. I know it's hard to quantify and I'll talk about that in a moment but but major learnings, we've got some of the challenges that you cited. I'll just put them up here. We don't have to go detailed into this, but I just wanted to share with some folks. But my question, I mean this is the advice for your peers question if you had to do it differently if you had a do over or a Mulligan as we like to say for you golfers, what would you do differently? Yeah, >>I mean can we start with from a from the transformational challenge that understanding that it's also high load of cultural change. I think this is this is important that a particular communication strategy needs to be put into place and people really need to be um supported. Right? So it's not that we go in and say well we have to change towards data mesh but naturally it's in human nature, you know, we're kind of resistance to to change right? Her speech uncomfortable. So we need to take that away by training and by communicating um chris we're gonna add something to that >>and definitely I think the point that I have also made before right we need to acknowledge that data mesh is an architecture of scale, right? You're looking for something which is necessary by huge companies who are vulnerable, data productive scale. I mean Dave you mentioned it right, there are a lot of advantages to have a centralized team but at some point it may make sense to actually decentralized here and at this point right? If you think about data Mash, you have to recognize that you're not building something on a green field. And I think there's a big learning which is also reflected here on the slide is don't underestimate your baggage. It's typically you come to a point where the old model doesn't doesn't broke anymore and has had a fresh right? We lost our trust in our data and actually we have seen certain risks that we're slowing down our innovation so we triggered that this was triggering the need to actually change something. So this transition implies that you typically have a lot of technical debt accumulated over years and I think what we have learned is that potentially we have decentralized some assets to earlier, this is not actually taking into account the maturity of the team where we are actually distributed to and now we actually in the face of correcting pieces of that one. Right? But I think if you if you if you start from scratch you have to understand, okay, is are my team is actually ready for taking on this new uh, this news capabilities and you have to make sure that business decentralization, you build up these >>capabilities and the >>teams and as Clements has mentioned, right, make sure that you take the people on your journey. I think these are the pieces that also here, it comes with this knowledge gap, right? That we need to think about hiring and literacy the technical depth I just talked about and I think the last piece that I would add now which is not here on the flight deck is also from our perspective, we started on the analytical layer because that's kind of where things are exploding, right, this is the thing that people feel the pain but I think a lot of the efforts that we have started to actually modernize the current state uh, towards data product towards data Mash. We've understood that it always comes down basically to a proper shape of our operational plane and I think what needs to happen is is I think we got through a lot of pains but the learning here is this need to really be a commitment from the company that needs to happen and to act. >>I think that point that last point you made it so critical because I I hear a lot from the vendor community about how they're gonna make analytics better and that's that's not unimportant, but but through data product thinking and decentralized data organizations really have to operationalize in order to scale. So these decisions around data architecture an organization, their fundamental and lasting, it's not necessarily about an individual project are why they're gonna be project sub projects within this architecture. But the architectural decision itself is an organizational, its cultural and what's the best approach to support your business at scale. It really speaks to to to what you are, who you are as a company, how you operate and getting that right, as we've seen in the success of data driven driven companies is yields tremendous results. So I'll ask each of you to give give us your final thoughts and then we'll wrap maybe >>maybe it quickly, please. Yeah, maybe just just jumping on this piece that you have mentioned, right, the target architecture. If we talk about these pieces right, people often have this picture of mind like OK, there are different kind of stages, we have sources, we have actually ingestion layer, we have historical transformation presentation layer and then we're basically putting a lot of technology on top of that kind of our target architecture. However, I think what we really need to make sure is that we have these different kind of viewers, right? We need to understand what are actually the capabilities that we need in our new goals. How does it look and feel from the different kind of personas and experience view? And then finally, that should actually go to the to the target architecture from a technical perspective um maybe just to give an outlook but what we're what we're planning to do, how we want to move that forward. We have actually based on our strategy in the in the sense of we would like to increase that to maturity as a whole across the entire company and this is kind of a framework around the business strategy and it's breaking down into four pillars as well. People meaning the data, cultural, data literacy, data organizational structure and so on that. We're talking about governance as Clements has actually mentioned that, right, compliance, governance, data management and so on. You talk about technology and I think we could talk for hours for that one. It's around data platform, better science platform and then finally also about enablement through data, meaning we need to understand that a quality data accessibility and the science and data monetization. >>Great, thank you christophe clement. Once you bring us home give us your final thoughts. >>Can't can just agree with christoph that uh important is to understand what kind of maturity people have to understand what the maturity level, where the company where where people organization is and really understand what does kind of some kind of a change replies to that those four pillars for example, um what needs to be taken first and this is not very clear from the very first beginning of course them it's kind of like Greenfield you come up with must wins to come up with things that we really want to do out of theory and out of different white papers. Um only if you really start conducting the first initiatives you do understand. Okay, where we have to put the starts together and where do I missed out on one of those four different pillars? People, process technology and governance. Right? And then that kind of an integration. Doing step by step, small steps by small steps not boiling the ocean where you're capable ready to identify the gaps and see where either you can fill um the gaps are where you have to increase maturity first and train people or increase your text text, >>you know Hello Fresh is an excellent example of a company that is innovating. It was not born in Silicon Valley which I love. It's a global company. Uh and I gotta ask you guys, it seems like this is an amazing place to work you guys hiring? >>Yes, >>definitely. We do >>uh as many rights as was one of these aspects distributing. And actually we are hiring as an entire company specifically for data. I think there are a lot of open roles serious. Please visit or our page from better engineering, data, product management and Clemens has a lot of rules that you can speak about. But yes >>guys, thanks so much for sharing with the cube audience, your, your pioneers and we look forward to collaborations in the future to track progress and really want to thank you for your time. >>Thank you very much. Thank you very much. Dave >>thank you for watching the cubes startup showcase made possible by A W. S. This is Dave Volonte. We'll see you next time. >>Yeah.

>>from around the globe. It's the Cube presenting innovation for good, brought to you by on shape. >>Hello, everyone, and welcome to Innovation for Good Program, hosted by the Cuban. Brought to You by on Shape, which is a PTC company. My name is Dave Valentin. I'm coming to you from our studios outside of Boston. I'll be directing the conversations today. It's a very exciting, all live program. We're gonna look at how product innovation has evolved and where it's going and how engineers, entrepreneurs and educators are applying cutting edge, cutting edge product development techniques and technology to change our world. You know, the pandemic is, of course, profoundly impacted society and altered how individuals and organizations they're gonna be thinking about an approaching the coming decade. Leading technologists, engineers, product developers and educators have responded to the new challenges that we're facing from creating lifesaving products to helping students learn from home toe how to apply the latest product development techniques and solve the world's hardest problems. And in this program, you'll hear from some of the world's leading experts and practitioners on how product development and continuous innovation has evolved, how it's being applied toe positive positively affect society and importantly where it's going in the coming decades. So let's get started with our first session fueling Tech for good. And with me is John Hirschbeck, who is the president of the Suffers, a service division of PTC, which acquired on shape just over a year ago, where John was the CEO and co founder, and Dana Grayson is here. She is the co founder and general partner at Construct Capital, a new venture capital firm. Folks, welcome to the program. Thanks so much for coming on. >>Great to be here, Dave. >>All right, John. >>You're very welcome. Dana. Look, John, let's get into it for first Belated congratulations on the acquisition of Von Shape. That was an awesome seven year journey for your company. Tell our audience a little bit about the story of on shape, but take us back to Day zero. Why did you and your co founders start on shape? Well, >>actually, start before on shaping the You know, David, I've been in this business for almost 40 years. The business of building software tools for product developers and I had been part of some previous products in the industry and companies that had been in their era. Big changes in this market and about, you know, a little Before founding on shape, we started to see the problems product development teams were having with the traditional tools of that era years ago, and we saw the opportunity presented by Cloud Web and Mobile Technology. And we said, Hey, we could use Cloud Web and Mobile to solve the problems of product developers make their Their business is run better. But we have to build an entirely new system, an entirely new company, to do it. And that's what on shapes about. >>Well, so notwithstanding the challenges of co vid and difficulties this year, how is the first year been as, Ah, division of PTC for you guys? How's business? Anything you can share with us? >>Yeah, our first year of PTC has been awesome. It's been, you know, when you get acquired, Dave, you never You know, you have great optimism, but you never know what life will really be like. It's sort of like getting married or something, you know, until you're really doing it, you don't know. And so I'm happy to say that one year into our acquisition, um, PTC on shape is thriving. It's worked out better than I could have imagined a year ago. Along always, I mean sales are up. In Q four, our new sales rate grew 80% vs Excuse me, our fiscal Q four Q three. In the calendar year, it grew 80% compared to the year before. Our educational uses skyrocketing with around 400% growth, most recently year to year of students and teachers and co vid. And we've launched a major cloud platform using the core of on shape technology called Atlas. So, um, just tons of exciting things going on a TTC. >>That's awesome. But thank you for sharing some of those metrics. And of course, you're very humble individual. You know, people should know a little bit more about you mentioned, you know, we founded Solid Works, co founded Solid where I actually found it solid works. You had a great exit in the in the late nineties. But what I really appreciate is, you know, you're an entrepreneur. You've got a passion for the babies that you you helped birth. You stayed with the salt systems for a number of years. The company that quiet, solid works well over a decade. And and, of course, you and I have talked about how you participated in the the M I T. Blackjack team. You know, back in the day, a zai say you're very understated, for somebody was so accomplished. Well, >>that's kind of you, but I tend to I tend Thio always keep my eye more on what's ahead. You know what's next, then? And you know, I look back Sure to enjoy it and learn from it about what I can put to work making new memories, making new successes. >>Love it. Okay, let's bring Dana into the conversation. Hello, Dana. You look you're a fairly early investor in in on shape when you were with any A And and I think it was like it was a serious B, but it was very right close after the A raise. And and you were and still are a big believer in industrial transformation. So take us back. What did you see about on shape back then? That excited you. >>Thanks. Thanks for that. Yeah. I was lucky to be a early investment in shape. You know, the things that actually attracted me. Don shape were largely around John and, uh, the team. They're really setting out to do something, as John says humbly, something totally new, but really building off of their background was a large part of it. Um, but, you know, I was really intrigued by the design collaboration side of the product. Um, I would say that's frankly what originally attracted me to it. What kept me in the room, you know, in terms of the industrial world was seeing just if you start with collaboration around design what that does to the overall industrial product lifecycle accelerating manufacturing just, you know, modernizing all the manufacturing, just starting with design. So I'm really thankful to the on shape guys, because it was one of the first investments I've made that turned me on to the whole sector. And while just such a great pleasure to work with with John and the whole team there. Now see what they're doing inside PTC. >>And you just launched construct capital this year, right in the middle of a pandemic and which is awesome. I love it. And you're focused on early stage investing. Maybe tell us a little bit about construct capital. What your investment thesis is and you know, one of the big waves that you're hoping to ride. >>Sure, it construct it is literally lifting out of any what I was doing there. Um uh, for on shape, I went on to invest in companies such as desktop metal and Tulip, to name a couple of them form labs, another one in and around the manufacturing space. But our thesis that construct is broader than just, you know, manufacturing and industrial. It really incorporates all of what we'd call foundational industries that have let yet to be fully tech enabled or digitized. Manufacturing is a big piece of it. Supply chain, logistics, transportation of mobility or not, or other big pieces of it. And together they really drive, you know, half of the GDP in the US and have been very under invested. And frankly, they haven't attracted really great founders like they're on in droves. And I think that's going to change. We're seeing, um, entrepreneurs coming out of the tech world orthe Agnelli into these industries and then bringing them back into the tech world, which is which is something that needs to happen. So John and team were certainly early pioneers, and I think, you know, frankly, obviously, that voting with my feet that the next set, a really strong companies are going to come out of the space over the next decade. >>I think it's a huge opportunity to digitize the sort of traditionally non digital organizations. But Dana, you focused. I think it's it's accurate to say you're focused on even Mawr early stage investing now. And I want to understand why you feel it's important to be early. I mean, it's obviously riskier and reward e er, but what do you look for in companies and and founders like John >>Mhm, Um, you know, I think they're different styles of investing all the way up to public market investing. I've always been early stage investors, so I like to work with founders and teams when they're, you know, just starting out. Um, I happened to also think that we were just really early in the whole digital transformation of this world. You know, John and team have been, you know, back from solid works, etcetera around the space for a long time. But again, the downstream impact of what they're doing really changes the whole industry. And and so we're pretty early and in digitally transforming that market. Um, so that's another reason why I wanna invest early now, because I do really firmly believe that the next set of strong companies and strong returns for my own investors will be in the spaces. Um, you know, what I look for in Founders are people that really see the world in a different way. And, you know, sometimes some people think of founders or entrepreneurs is being very risk seeking. You know, if you asked John probably and another successful entrepreneurs, they would call themselves sort of risk averse, because by the time they start the company, they really have isolated all the risk out of it and think that they have given their expertise or what they're seeing their just so compelled to go change something, eh? So I look for that type of attitude experience a Z. You can also tell from John. He's fairly humble. So humility and just focus is also really important. Um, that there's a That's a lot of it. Frankly, >>Excellent. Thank you, John. You got such a rich history in the space. Uh, and one of you could sort of connect the dots over time. I mean, when you look back, what were the major forces that you saw in the market in in the early days? Particularly days of on shape on? And how is that evolved? And what are you seeing today? Well, >>I think I touched on it earlier. Actually, could I just reflect on what Dana said about risk taking for just a quick one and say, throughout my life, from blackjack to starting solid works on shape, it's about taking calculated risks. Yes, you try to eliminate the risk Sa's much as you can, but I always say, I don't mind taking a risk that I'm aware of, and I've calculated through as best I can. I don't like taking risks that I don't know I'm taking. That's right. You >>like to bet on >>sure things as much as you sure things, or at least where you feel you. You've done the research and you see them and you know they're there and you know, you, you you keep that in mind in the room, and I think that's great. And Dana did so much for us. Dana, I want to thank you again. For all that, you did it every step of the way, from where we started to to, you know, your journey with us ended formally but continues informally. Now back to you, Dave, I think, question about the opportunity and how it's shaped up. Well, I think I touched on it earlier when I said It's about helping product developers. You know, our customers of the people build the future off manufactured goods. Anything you think of that would be manufacturing factory. You know, the chair you're sitting in machine that made your coffee. You know, the computer you're using, the trucks that drive by on the street, all the covert product research, the equipment being used to make vaccines. All that stuff is designed by someone, and our job is given the tools to do it better. And I could see the problems that those product developers had that we're slowing them down with using the computing systems of the time. When we built solid works, that was almost 30 years ago. If people don't realize that it was in the early >>nineties and you know, we did the >>best we could for the early nineties, but what we did. We didn't anticipate the world of today. And so people were having problems with just installing the systems. Dave, you wouldn't believe how hard it is to install these systems. You need toe speck up a special windows computer, you know, and make sure you've got all the memory and graphics you need and getting to get that set up. You need to make sure the device drivers air, right, install a big piece of software. Ah, license key. I'm not making this up. They're still around. You may not even know what those are. You know, Dennis laughing because, you know, zero cool people do things like this anymore. Um, and it only runs some windows. You want a second user to use it? They need a copy. They need a code. Are they on the same version? It's a nightmare. The teams change, you know? You just say, Well, get everyone on the software. Well, who's everyone? You know, you got a new vendor today? A new customer tomorrow, a new employee. People come on and off the team. The other problem is the data stored in files, thousands of files. This isn't like a spreadsheet or word processor, where there's one file to pass around these air thousands of files to make one, even a simple product. People were tearing their hair out. John, what do we do? I've got copies everywhere. I don't know where the latest version is. We tried like, you know, locking people out so that only one person can change it At the time that works against speed, it works against innovation. We saw what was happening with Cloud Web and mobile. So what's happened in the years since is every one of the forces that product developers experience the need for speed, the need for innovation, the need to be more efficient with their people in their capital. Resource is every one of those trends have been amplified since we started on shape by a lot of forces in the world. And covert is amplified all those the need for agility and remote work cove it is amplified all that the same time, The acceptance of cloud. You know, a few years ago, people were like cloud, you know, how is that gonna work now They're saying to me, You know, increasingly, how would you ever even have done this without the cloud. How do you make solid works work without the cloud? How would that even happen? You know, once people understand what on shapes about >>and we're the >>Onley full SAS solution software >>as a service, >>full SAS solution in our industry. So what's happened in those years? Same problems we saw earlier, but turn up the gain, their bigger problems. And with cloud, we've seen skepticism of years ago turn into acceptance. And now even embracement in the cova driven new normal. >>Yeah. So a lot of friction in the previous environments cloud obviously a huge factor on, I guess. I guess Dana John could see it coming, you know, in the early days of solid works with, you know, had Salesforce, which is kind of the first major independent SAS player. Well, I guess that was late nineties. So his post solid works, but pre in shape and their work day was, you know, pre on shape in the mid two thousands. And and but But, you know, the bet was on the SAS model was right for Crick had and and product development, you know, which maybe the time wasn't a no brainer. Or maybe it was, I don't know, but Dana is there. Is there anything that you would invest in today? That's not Cloud based? >>Um, that's a great question. I mean, I think we still see things all the time in the manufacturing world that are not cloud based. I think you know, the closer you get to the shop floor in the production environment. Um e think John and the PTC folks would agree with this, too, but that it's, you know, there's reliability requirements, performance requirements. There's still this attitude of, you know, don't touch the printing press. So the cloud is still a little bit scary sometimes. And I think hybrid cloud is a real thing for those or on premise. Solutions, in some cases is still a real thing. What what we're more focused on. And, um, despite whether it's on premise or hybrid or or SAS and Cloud is a frictionless go to market model, um, in the companies we invest in so sass and cloud, or really make that easy to adopt for new users, you know, you sign up, started using a product, um, but whether it's hosted in the cloud, whether it's as you can still distribute buying power. And, um, I would I'm just encouraging customers in the customer world and the more industrial environment to entrust some of their lower level engineers with more budget discretionary spending so they can try more products and unlock innovation. >>Right? The unit economics are so compelling. So let's bring it, you know, toe today's you know, situation. John, you decided to exit about a year ago. You know? What did you see in PTC? Other than the obvious money? What was the strategic fit? >>Yeah, Well, David, I wanna be clear. I didn't exit anything. Really? You >>know, I love you and I don't like that term exit. I >>mean, Dana had exit is a shareholder on and so it's not It's not exit for me. It's just a step in the journey. What we saw in PTC was a partner. First of all, that shared our vision from the top down at PTC. Jim Hempleman, the CEO. He had a great vision for for the impact that SAS can make based on cloud technology and really is Dana of highlighted so much. It's not just the technology is how you go to market and the whole business being run and how you support and make the customers successful. So Jim shared a vision for the potential. And really, really, um said Hey, come join us and we can do this bigger, Better, faster. We expanded the vision really to include this Atlas platform for hosting other SAS applications. That P D. C. I mean, David Day arrived at PTC. I met the head of the academic program. He came over to me and I said, You know, and and how many people on your team? I thought he'd say 5 40 people on the PTC academic team. It was amazing to me because, you know, we were we were just near about 100 people were required are total company. We didn't even have a dedicated academic team and we had ah, lot of students signing up, you know, thousands and thousands. Well, now we have hundreds of thousands of students were approaching a million users and that shows you the power of this team that PTC had combined with our product and technology whom you get a big success for us and for the teachers and students to the world. We're giving them great tools. So so many good things were also putting some PTC technology from other parts of PTC back into on shape. One area, a little spoiler, little sneak peek. Working on taking generative design. Dana knows all about generative design. We couldn't acquire that technology were start up, you know, just to too much to do. But PTC owns one of the best in the business. This frustrated technology we're working on putting that into on shaping our customers. Um, will be happy to see it, hopefully in the coming year sometime. >>It's great to see that two way exchange. Now, you both know very well when you start a company, of course, a very exciting time. You know, a lot of baggage, you know, our customers pulling you in a lot of different directions and asking you for specials. You have this kind of clean slate, so to speak in it. I would think in many ways, John, despite you know, your install base, you have a bit of that dynamic occurring today especially, you know, driven by the forced march to digital transformation that cove it caused. So when you sit down with the team PTC and talk strategy. You now have more global resource is you got cohorts selling opportunities. What's the conversation like in terms of where you want to take the division? >>Well, Dave, you actually you sounds like we should have you coming in and talking about strategy because you've got the strategy down. I mean, we're doing everything said global expansion were able to reach across selling. We got some excellent PTC customers that we can reach reach now and they're finding uses for on shape. I think the plan is to, you know, just go, go, go and grow, grow, grow where we're looking for this year, priorities are expand the product. I mentioned the breath of the product with new things PTC did recently. Another technology that they acquired for on shape. We did an acquisition. It was it was small, wasn't widely announced. It, um, in an area related to interfacing with electrical cad systems. So So we're doing We're expanding the breath of on shape. We're going Maura, depth in the areas were already in. We have enormous opportunity to add more features and functions that's in the product. Go to market. You mentioned it global global presence. That's something we were a little light on a year ago. Now we have a team. Dana may not even know what we have. A non shape, dedicated team in Barcelona, based in Barcelona but throughout Europe were doing multiple languages. Um, the academic program just introduced a new product into that space that z even fueling more success and growth there. Um, and of course, continuing to to invest in customer success and this Atlas platform story I keep mentioning, we're going to soon have We're gonna soon have four other major PTC brands shipping products on our Atlas Saas platform. And so we're really excited about that. That's good for the other PTC products. It's also good for on shape because now there's there's. There's other interesting products that are on shape customers can use take advantage of very easily using, say, a common log in conventions about user experience there, used to invest of all they're SAS based, so they that makes it easier to begin with. So that's some of the exciting things going on. I think you'll see PTC, um, expanding our lead in SAS based applications for this sector for our our target, uh, sectors not just in, um, in cat and data management, but another area. PTC's Big and his augmented reality with of euphoria, product line leader and industrial uses of a R. That's a whole other story we should do. A whole nother show augmented reality. But these products are amazing. You can you can help factory workers people on, uh, people who are left out of the digital transformation. Sometimes we're standing from machine >>all day. >>They can't be sitting like we are doing Zoom. They can wear a R headset in our tools, let them create great content. This is an area Dana is invested in other companies. But what I wanted to note is the new releases of our authoring software. For this, our content getting released this month, used through the Atlas platform, the SAS components of on shape for things like revision management and collaboration on duh workflow activity. All that those are tools that we're able to share leverage. We get a lot of synergy. It's just really good. It's really fun to have a good time. That's >>awesome. And then we're gonna be talking to John MacLean later about that. Let's do a little deeper Dive on that. And, Dana, what is your involvement today with with on shape? But you're looking for you know, which of their customers air actually adopting. And they're gonna disrupt their industries. And you get good pipeline from that. How do you collaborate today? >>That sounds like a great idea. Um, Aziz, John will tell you I'm constantly just asking him for advice and impressions of other entrepreneurs and picking his brain on ideas. No formal relationship clearly, but continue to count John and and John and other people in on shaping in the circle of experts that I rely on for their opinions. >>All right, so we have some questions from the crowd here. Uh, one of the questions is for the dream team. You know, John and Dana. What's your next next collective venture? I don't think we're there yet, are we? No. >>I just say, as Dana said, we love talking to her about. You know, Dana, you just returned the compliment. We would try and give you advice and the deals you're looking at, and I'm sort of casually mentoring at least one of your portfolio entrepreneurs, and that's been a lot of fun for May on, hopefully a value to them. But also Dana. We uran important pipeline to us in the world of some new things that are happening that we wouldn't see if you know you've shown us some things that you've said. What do you think of this business? And for us, it's like, Wow, it's cool to see that's going on And that's what's supposed to work in an ecosystem like this. So we we deeply value the ongoing relationship. And no, we're not starting something new. I got a lot of work left to do with what I'm doing and really happy. But we can We can collaborate in this way on other ventures. >>I like this question to somebody asking With the cloud options like on shape, Wilmore students have stem opportunities s Oh, that's a great question. Are you because of sass and cloud? Are you able to reach? You know, more students? Much more cost effectively. >>Yeah, Dave, I'm so glad that that that I was asked about this because Yes, and it's extremely gratified us. Yes, we are because of cloud, because on shape is the only full cloud full SAS system or industry were able to reach. Stem education brings able to be part of bringing step education to students who couldn't get it otherwise. And one of most gratifying gratifying things to me is the emails were getting from teachers, um, that that really, um, on the phone calls that were they really pour their heart out and say We're able to get to students in areas that have very limited compute resource is that don't have an I T staff where they don't know what computer that the students can have at home, and they probably don't even have a computer. We're talking about being able to teach them on a phone to have an android phone a low end android phone. You can do three D modeling on there with on shape. Now you can't do it any other system, but with on shape, you could do it. And so the teacher can say to the students, They have to have Internet access, and I know there's a huge community that doesn't even have Internet access, and we're not able, unfortunately to help that. But if you have Internet and you have even an android phone, we can enable the educator to teach them. And so we have case after case of saving a stem program or expanding it into the students that need it most is the ones we're helping here. So really excited about that. And we're also able to let in addition to the run on run on whatever computing devices they have, we also offer them the tools they need for remote teaching with a much richer experience. Could you teach solid works remotely? Well, maybe if the student ran it had a windows workstation. You know, big, big, high end workstation. Maybe it could, but it would be like the difference between collaborating with on shape and collaborate with solid works. Like the difference between a zoom video call and talking on the landline phone. You know, it's a much richer experience, and that's what you need. And stem teaching stem is hard, So yeah, we're super super. Um, I'm excited about bringing stem to more students because of cloud yond >>we're talking about innovation for good, and then the discussion, John, you just had it. Really? There could be a whole another vector here. We could discuss on diversity, and I wanna end with just pointing out. So, Dana, your new firm, it's a woman led firm, too. Two women leaders, you know, going forward. So that's awesome to see, so really? Yeah, thumbs up on that. Congratulations on getting that off the ground. >>Thank you. Thank you. >>Okay, so thank you guys. Really appreciate It was a great discussion. I learned a lot and I'm sure the audience did a swell in a moment. We're gonna talk with on shaped customers to see how they're applying tech for good and some of the products that they're building. So keep it right there. I'm Dave Volonte. You're watching innovation for good on the Cube, the global leader in digital tech event coverage. Stay right there. >>Oh, yeah, it's >>yeah, yeah, around >>the globe. It's the Cube presenting innovation for good. Brought to you by on shape. >>Okay, we're back. This is Dave Volonte and you're watching innovation for good. A program on Cuba 3 65 made possible by on shape of PTC company. We're live today really live tv, which is the heritage of the Cube. And now we're gonna go to the sources and talkto on shape customers to find out how they're applying technology to create real world innovations that are changing the world. So let me introduce our panel members. Rafael Gomez Furberg is with the Chan Zuckerberg bio hub. A very big idea. And collaborative nonprofit was initiative that was funded by Mark Zuckerberg and his wife, Priscilla Chan, and really around diagnosing and curing and better managing infectious diseases. So really timely topic. Philip Tabor is also joining us. He's with silver side detectors, which develops neutron detective detection systems. Yet you want to know if early, if neutrons and radiation or in places where you don't want them, So this should be really interesting. And last but not least, Matthew Shields is with the Charlottesville schools and is gonna educate us on how he and his team are educating students in the use of modern engineering tools and techniques. Gentlemen, welcome to the Cuban to the program. This should be really interesting. Thanks for coming on. >>Hi. Or pleasure >>for having us. >>You're very welcome. Okay, let me ask each of you because you're all doing such interesting and compelling work. Let's start with Rafael. Tell us more about the bio hub and your role there, please. >>Okay. Yeah. So you said that I hope is a nonprofit research institution, um, funded by Mark Zuckerberg and his wife, Priscilla Chan. Um, and our main mission is to develop new technologies to help advance medicine and help, hopefully cure and manage diseases. Um, we also have very close collaborations with Universe California, San Francisco, Stanford University and the University California Berkeley on. We tried to bring those universities together, so they collaborate more of biomedical topics. And I manage a team of engineers. They by joining platform. Um, and we're tasked with creating instruments for the laboratory to help the scientist boats inside the organization and also in the partner universities Do their experiments in better ways in ways that they couldn't do before >>in this edition was launched Well, five years ago, >>it was announced at the end of 2016, and we actually started operation with at the beginning of 2017, which is when I joined, um, So this is our third year. >>And how's how's it going? How does it work? I mean, these things take time. >>It's been a fantastic experience. Uh, the organization works beautifully. Um, it was amazing to see it grow From the beginning, I was employee number 12, I think eso When I came in, it was just a nem P office building and empty labs. And very quickly we had something running about. It's amazing eso I'm very proud of the work that we have done to make that possible. Um And then, of course, that's you mentioned now with co vid, um, we've been able to do a lot of very cool work attire being of the pandemic in March, when there was a deficit of testing, uh, capacity in California, we spun up a testing laboratory in record time in about a week. It was crazy. It was a crazy project, Um, but but incredibly satisfying. And we ended up running all the way until the beginning of November, when the lab was finally shut down. We could process about 3000 samples a day. I think at the end of it all, we were able to test about 100 on the order of 100 and 50,000 samples from all over the state. We were providing free testing toe all of the Department of Public Health Department of Public Health in California, which at the media pandemic, had no way to do testing affordably and fast. So I think that was a great service to the state. Now the state has created that testing system that would serve those departments. So then we decided that it was unnecessary to keep going with testing in the other biopsy that would shut down. >>All right. Thank you for that. Now, Now, Philip, you What you do is mind melting. You basically helped keep the world safe. Maybe describe a little bit more about silver sod detectors and what your role is there and how it all works. >>Tour. So we make a nuclear bomb detectors and we also make water detectors. So we try and do our part thio keep the world from blowing up and make it a better place at the same time. Both of these applications use neutron radiation detectors. That's what we make. Put them out by import border crossing places like that. They can help make sure that people aren't smuggling. Shall we say very bad things. Um, there's also a burgeoning field of research and application where you can use neutrons with some pretty cool physics to find water so you could do things. Like what? A detector up in the mountains and measure snowpack. Put it out in the middle of the field and measure soil moisture content. And as you might imagine, there's some really cool applications in, uh, research and agronomy and public policy for this. >>All right, so it's OK, so it's a It's much more than, you know, whatever fighting terrorism, it's there's a riel edge or I kind of i o t application for what you guys >>do. We do both its's to plowshares. You might >>say a mat. I I look at your role is kind of scaling the brain power for for the future. Maybe tell us more about Charlottesville schools and in the mission that you're pursuing and what you do. >>Thank you. Um, I've been in Charlottesville City schools for about 11 or 12 years. I started their teaching, um, a handful of classes, math and science and things like that. But Thescore board and my administration had the crazy idea of starting an engineering program about seven years ago. My background is an engineering is an engineering. My masters is in mechanical and aerospace engineering and um, I basically spent a summer kind of coming up with what might be a fun engineering curriculum for our students. And it started with just me and 30 students about seven years ago, Um, kind of a home spun from scratch curriculum. One of my goals from the outset was to be a completely project based curriculum, and it's now grown. We probably have about six or 700 students, five or six full time teachers. We now have pre engineering going on at the 5th and 6th grade level. I now have students graduating. Uh, you know, graduating after senior year with, like, seven years of engineering under their belt and heading off to doing some pretty cool stuff. So it's It's been a lot of fun building a program and, um, and learning a lot in the process. >>That's awesome. I mean, you know, Cuba's. We've been passionate about things like women in tech, uh, diversity stem. You know, not only do we need more, more students and stem, we need mawr underrepresented women, minorities, etcetera. We were just talking to John Herstek and integrate gration about this is Do you do you feel is though you're I mean, first of all, the work that you do is awesome, but but I'll go one step further. Do you feel as though it's reaching, um, or diverse base? And how is that going? >>That's a great question. I think research shows that a lot of people get funneled into one kind of track or career path or set of interests really early on in their educational career, and sometimes that that funnel is kind of artificial. And so that's one of the reasons we keep pushing back. Um, so our school systems introducing kindergartners to programming on DSO We're trying to push back how we expose students to engineering and to stem fields as early as possible. And we've definitely seen the first of that in my program. In fact, my engineering program, uh, sprung out of an after school in Extracurricular Science Club that actually three girls started at our school. So I think that actually has helped that three girls started the club that eventually is what led to our engineering programs that sort of baked into the DNA and also our eyes a big public school. And we have about 50% of the students are under the poverty line and we e in Charlottesville, which is a big refugee town. And so I've been adamant from Day one that there are no barriers to entry into the program. There's no test you have to take. You don't have to have be taking a certain level of math or anything like that. That's been a lot of fun. To have a really diverse set of kids enter the program and be successful, >>that's final. That's great to hear. So, Philip, I wanna come back to you. You know, I think about maybe some day we'll be able to go back to a sporting events, and I know when I when I'm in there, there's somebody up on the roof looking out for me, you know, watching the crowd, and they have my back. And I think in many ways, the products that you build, you know, our similar. I may not know they're there, but they're keeping us safe or they're measuring things that that that I don't necessarily see. But I wonder if you could talk about a little bit more detail about the products you build and how they're impacting society. >>Sure, so There are certainly a lot of people who are who are watching, trying to make sure things were going well in keeping you safe that you may or may not be aware of. And we try and support ah lot of them. So we have detectors that are that are deployed in a variety of variety of uses, with a number of agencies and governments that dio like I was saying, ports and border crossing some other interesting applications that are looking for looking for signals that should not be there and working closely to fit into the operations these folks do. Onda. We also have a lot of outreach to researchers and scientists trying to help them support the work they're doing. Um, using neutron detection for soil moisture monitoring is a some really cool opportunities for doing it at large scale and with much less, um, expense or complication than would have been done. Previous technologies. Um, you know, they were talking about collaboration in the previous segment. We've been able to join a number of conferences for that, virtually including one that was supposed to be held in Boston, but another one that was held out of the University of Heidelberg in Germany. And, uh, this is sort of things that in some ways, the pandemic is pushing people towards greater collaboration than they would have been able to do. Had it all but in person. >>Yeah, we did. Uh, the cube did live works a couple years ago in Boston. It was awesome show. And I think, you know, with this whole trend toward digit, I call it the Force march to digital. Thanks to cove it I think that's just gonna continue. Thio grow. Rafael. What if you could describe the process that you use to better understand diseases? And what's your organization's involvement? Been in more detail, addressing the cove in pandemic. >>Um, so so we have the bio be structured in, Um um in a way that foster so the combination of technology and science. So we have to scientific tracks, one about infectious diseases and the other one about understanding just basic human biology, how the human body functions, and especially how the cells in the human body function on how they're organized to create tissues in the body. On Ben, it has this set of platforms. Um, mind is one of them by engineering that are all technology rated. So we have data science platform, all about data analysis, machine learning, things like that. Um, we have a mass spectrometry platform is all about mass spectrometry technologies to, um, exploit those ones in service for the scientist on. We have a genomics platform that it's all about sequencing DNA and are gonna, um and then an advanced microscopy. It's all about developing technologies, uh, to look at things with advanced microscopes and developed technologies to marry computation on microscopy. So, um, the scientists set the agenda and the platforms, we just serve their needs, support their needs, and hopefully develop technologies that help them do their experiments better, faster, or allow them to the experiment that they couldn't do in any other way before. Um And so with cove, it because we have that very strong group of scientists that work on have been working on infectious disease before, and especially in viruses, we've been able to very quickly pivot to working on that s O. For example, my team was able to build pretty quickly a machine to automatically purified proteins on is being used to purify all these different important proteins in the cove. It virus the SARS cov to virus Onda. We're sending some of those purified proteins all over the world. Two scientists that are researching the virus and trying to figure out how to develop vaccines, understand how the virus affects the body and all that. Um, so some of the machines we built are having a very direct impact on this. Um, Also for the copy testing lab, we were able to very quickly develop some very simple machines that allowed the lab to function sort of faster and more efficiently. Sort of had a little bit of automation in places where we couldn't find commercial machines that would do it. >>Um, eso Matt. I mean, you gotta be listening to this and thinking about Okay, So someday your students are gonna be working at organizations like like, like Bio Hub and Silver Side. And you know, a lot of young people they're just don't know about you guys, but like my kids, they're really passionate about changing the world. You know, there's way more important than you know, the financial angles and it z e. I gotta believe you're seeing that you're right in the front lines there. >>Really? Um, in fact, when I started the curriculum six or seven years ago, one of the first bits of feedback I got from my students is they said Okay, this is a lot of fun. So I had my students designing projects and programming microcontrollers raspberry, PiS and order we nose and things like that. The first bit of feedback I got from students was they said Okay, when do we get to impact the world? I've heard engineering >>is about >>making the world a better place, and robots are fun and all, but, you know, where is the real impact? And so um, dude, yeah, thanks to the guidance of my students, I'm baking that Maurin. Now I'm like day one of engineering one. We talk about how the things that the tools they're learning and the skills they're gaining, uh, eventually, you know, very soon could be could be used to make the world a better place. >>You know, we all probably heard that famous line by Jeff Hammer Barker. The greatest minds of my generation are trying to figure out how to get people to click on ads. I think we're really generally generationally, finally, at the point where young students and engineering a really, you know, a passionate about affecting society. I wanna get into the product, you know, side and understand how each of you are using on shape and and the value that that it brings. Maybe Raphael, you could start how long you've been using it. You know, what's your experience with it? Let's let's start there. >>I begin for about two years, and I switched to it with some trepidation. You know, I was used to always using the traditional product that you have to install on your computer, that everybody uses that. So I was kind of locked into that. But I started being very frustrated with the way it worked, um, and decided to give on ship chance. Which reputation? Because any change always, you know, causes anxiety. Um, but very quickly my engineers started loving it, Uh, just because it's it's first of all, the learning curve wasn't very difficult at all. You can transfer from one from the traditional product to entree very quickly and easily. You can learn all the concepts very, very fast. It has all the functionality that we needed and and what's best is that it allows to do things that we couldn't do before or we couldn't do easily. Now we can access the our cat documents from anywhere in the world. Um, so when we're in the lab fabricating something or testing a machine, any computer we have next to us or a tablet or on iPhone, we can pull it up and look at the cad and check things or make changes. That's something that couldn't do before because before you had to pay for every installation off the software for the computer, and I couldn't afford to have 20 installations to have some computers with the cat ready to use them like once every six months would have been very inefficient. So we love that part. And the collaboration features are fantastic, especially now with Kobe, that we have to have all the remote meetings eyes fantastic, that you can have another person drive the cad while the whole team is watching that person change the model and do things and point to things that is absolutely revolutionary. We love it. The fact that you have very, very sophisticated version control before it was always a challenge asking people, please, if you create anniversary and apart, how do we name it so that people find it? And then you end up with all these collection of files with names that nobody ever remembers, what they are, the person left. And now nobody knows which version is the right one. A mess with on shape on the version ING system it has, and the fact that you can go back in history off the document and go back to previous version so easily and then go back to the press and version and explore the history of the part that is truly, um, just world changing for us, that we can do that so easily on for me as a manager to manage this collection of information that is critical for our operations. It makes it so much easier because everything is in one place. I don't have to worry about file servers that go down that I have to administer that have to have I t taken care off that have to figure how to keep access to people to those servers when they're at home, and they need a virtual private network and all of that mess disappears. I just simply give give a person in accounting on shape and then magically, they have access to everything in the way I want. And we can manage the lower documents and everything in a way that is absolutely fantastic. >>Feel what was your what? What were some of the concerns you had mentioned? You had some trepidation. Was it a performance? Was it security? You know some of the traditional cloud stuff, and I'm curious as to how, How, whether any of those act manifested really that you had to manage. What were your concerns? >>Look, the main concern is how long is it going to take for everybody in the team to learn to use the system like it and buy into it? Because I don't want to have my engineers using tools against their will write. I want everybody to be happy because that's how they're productive. They're happy, and they enjoyed the tools they have. That was my main concern. I was a little bit worried about the whole concept of not having the files in a place where I couldn't quote unquote seat in some server and on site, but that That's kind of an outdated concept, right? So that took a little bit of a mind shift, but very quickly. Then I started thinking, Look, I have a lot of documents on Google Drive. Like, I don't worry about that. Why would I worry about my cat on on shape, right? Is the same thing. So I just needed to sort of put things in perspective that way. Um, the other, um, you know, the concern was the learning curve, right? Is like, how is he Will be for everybody to and for me to learn it on whether it had all of the features that we needed. And there were a few features that I actually discussed with, um uh, Cody at on shape on, they were actually awesome about using their scripting language in on shape to sort of mimic some of the features of the old cat, uh, in on, shaped in a way that actually works even better than the old system. So it was It was amazing. Yeah, >>Great. Thank you for that, Philip. What's your experience been? Maybe you could take us through your journey within shape. >>Sure. So we've been we've been using on shaped silver side for coming up on about four years now, and we love it. We're very happy with it. We have a very modular product line, so we make anything from detectors that would go into backpacks. Two vehicles, two very large things that a shipping container would go through and saw. Excuse me. Shape helps us to track and collaborate faster on the design. Have multiple people working a same time on a project. And it also helps us to figure out if somebody else comes to us and say, Hey, I want something new how we congrats modules from things that we already have put them together and then keep track of the design development and the different branches and ideas that we have, how they all fit together. A za design comes together, and it's just been fantastic from a mechanical engineering background. I will also say that having used a number of different systems and solid works was the greatest thing since sliced bread. Before I got using on shape, I went, Wow, this is amazing and I really don't want to design in any other platform. After after getting on Lee, a little bit familiar with it. >>You know, it's funny, right? I'll have the speed of technology progression. I was explaining to some young guns the other day how I used to have a daytime er and that was my life. And if I lost that daytime, er I was dead. And I don't know how we weigh existed without, you know, Google maps eso we get anywhere, I don't know, but, uh but so So, Matt, you know, it's interesting to think about, you know, some of the concerns that Raphael brought up, you hear? For instance, you know, all the time. Wow. You know, I get my Amazon bill at the end of the month that zip through the roof in, But the reality is that Yeah, well, maybe you are doing more, but you're doing things that you couldn't have done before. And I think about your experience in teaching and educating. I mean, you so much more limited in terms of the resource is that you would have had to be able to educate people. So what's your experience been with With on shape and what is it enabled? >>Um, yeah, it was actually talking before we went with on shape. We had a previous CAD program, and I was talking to my vendor about it, and he let me know that we were actually one of the biggest CAD shops in the state. Because if you think about it a really big program, you know, really big company might employ. 5, 10, 15, 20 cad guys, right? I mean, when I worked for a large defense contractor, I think there were probably 20 of us as the cad guys. I now have about 300 students doing cat. So there's probably more students with more hours of cat under their belt in my building than there were when I worked for the big defense contractor. Um, but like you mentioned, uh, probably our biggest hurdle is just re sources. And so we want We want one of things I've always prided myself and trying to do in this. Programs provide students with access two tools and skills that they're going to see either in college or in the real world. So it's one of the reason we went with a big professional cad program. There are, you know, sort of K 12 oriented software and programs and things. But, you know, I want my kids coding and python and using slack and using professional type of tools on DSO when it comes to cat. That's just that That was a really hurt. I mean, you know, you could spend $30,000 on one seat of, you know, professional level cad program, and then you need a $30,000 computer to run it on if you're doing a heavy assemblies, Um and so one of my dreams And it was always just a crazy dream. And I was the way I would always pitcher in my school system and say, someday I'm gonna have a kid on a school issued chromebook in subsidized housing, on public WiFi doing professional level bad and that that was a crazy statement until a couple of years ago. So we're really excited that I literally and you know, March and you said the forced march, the forced march into, you know, modernity, March 13th kids sitting in my engineering lab that we spent a lot of money on doing cad March 14th. Those kids were at home on their school issued chromebooks on public WiFi, uh, keeping their designs going and collaborating. And then, yeah, I could go on and on about some of the things you know, the features that we've learned since then they're even better. So it's not like this is some inferior, diminished version of Academy. There's so much about it. Well, I >>wanna I wanna ask you that I may be over my skis on this, but we're seeing we're starting to see the early days of the democratization of CAD and product design. It is the the citizen engineer, I mean, maybe insulting to the engineers in the room, But but is that we're beginning to see that >>I have to believe that everything moves into the cloud. Part of that is democratization that I don't need. I can whether you know, I think artists, you know, I could have a music studio in my basement with a nice enough software package. And Aiken, I could be a professional for now. My wife's a photographer. I'm not allowed to say that I could be a professional photographer with, you know, some cloud based software, and so, yeah, I do think that's part of what we're seeing is more and more technology is moving to the cloud. >>Philip. Rafael Anything you Dad, >>I think I mean, yeah, that that that combination of cloud based cat and then three d printing that is becoming more and more affordable on ubiquitous It's truly transformative, and I think for education is fantastic. I wish when I was a kid I had the opportunity to play with those kinds of things because I was always the late things. But, you know, the in a very primitive way. So, um, I think this is a dream for kids. Teoh be able to do this. And, um, yeah, there's so many other technologies coming on, like Arduino on all of these electronic things that live kids play at home very cheaply with things that back in my day would have been unthinkable. >>So we know there's a go ahead. Philip, please. >>We had a pandemic and silver site moved to a new manufacturing facility this year. I was just on the shop floor, talking with contractors, standing 6 ft apart, pointing at things. But through it all, our CAD system was completely unruffled. Nothing stopped in our development work. Nothing stopped in our support for existing systems in the field. We didn't have to think about it. We had other server issues, but none with our, you know, engineering cad, platform and product development in support world right ahead, which was cool, but also a in that's point. I think it's just really cool what you're doing with the kids. The most interesting secondary and college level engineering work that I did was project based, taken important problem to the world. Go solve it and that is what we do here. That is what my entire career has been. And I'm super excited to see. See what your students are going to be doing, uh, in there home classrooms on their chromebooks now and what they do building on that. >>Yeah, I'm super excited to see your kids coming out of college with engineering degrees because, yeah, I think that Project based experience is so much better than just sitting in a classroom, taking notes and doing math problems on day. I think it will give the kids a much better flavor. What engineering is really about Think a lot of kids get turned off by engineering because they think it's kind of dry because it's just about the math for some very abstract abstract concept on they are there. But I think the most important thing is just that hands on a building and the creativity off, making things that you can touch that you can see that you can see functioning. >>Great. So, you know, we all know the relentless pace of technology progression. So when you think about when you're sitting down with the folks that on shape and there the customer advisor for one of the things that that you want on shape to do that it doesn't do today >>I could start by saying, I just love some of the things that does do because it's such a modern platform. And I think some of these, uh, some some platforms that have a lot of legacy and a lot of history behind them. I think we're dragging some of that behind them. So it's cool to see a platform that seemed to be developed in the modern era, and so that Z it is the Google docks. And so the fact that collaboration and version ing and link sharing is and like platform agnostic abilities, the fact that that seems to be just built into the nature of the thing so far, That's super exciting. As far as things that, uh, to go from there, Um, I don't know, >>Other than price. >>You can't say >>I >>can't say lower price. >>Yeah, so far on P. D. C. S that work with us. Really? Well, so I'm not complaining. There you there, >>right? Yeah. Yeah. No gaps, guys. Whitespace, Come on. >>We've been really enjoying the three week update. Cadence. You know, there's a new version every three weeks and we don't have to install it. We just get all the latest and greatest goodies. One of the trends that we've been following and enjoying is the the help with a revision management and release work flows. Um, and I know that there's more than on shape is working on that we're very excited for, because that's a big important part about making real hardware and supporting it in the field. Something that was cool. They just integrated Cem markup capability. In the last release that took, we were doing that anyway, but we were doing it outside of on shapes. And now we get to streamline our workflow and put it in the CAD system where We're making those changes anyway when we're reviewing drawings and doing this kind of collaboration. And so I think from our perspective, we continue to look forward. Toa further progress on that. There's a lot of capability in the cloud that I think they're just kind of scratching the surface on you, >>right? I would. I mean, you're you're asking to knit. Pick. I would say one of the things that I would like to see is is faster regeneration speed. There are a few times with convicts, necessities that regenerating the document takes a little longer than I would like. It's not a serious issue, but anyway, I I'm being spoiled, >>you know? That's good. I've been doing this a long time, and I like toe ask that question of practitioners and to me, it It's a signal like when you're nit picking and that's what you're struggling to knit. Pick that to me is a sign of a successful product, and and I wonder, I don't know, uh, have the deep dive into the architecture. But are things like alternative processors. You're seeing them hit the market in a big way. Uh, you know, maybe helping address the challenge, But I'm gonna ask you the big, chewy question now. Then we maybe go to some audience questions when you think about the world's biggest problems. I mean, we're global pandemics, obviously top of mind. You think about nutrition, you know, feeding the global community. We've actually done a pretty good job of that. But it's not necessarily with the greatest nutrition, climate change, alternative energy, the economic divides. You've got geopolitical threats and social unrest. Health care is a continuing problem. What's your vision for changing the world and how product innovation for good and be applied to some of the the problems that that you all are passionate about? Big question. Who wants toe start? >>Not biased. But for years I've been saying that if you want to solve the economy, the environment, uh, global unrest, pandemics, education is the case. If you wanna. If you want to, um, make progress in those in those realms, I think funding funding education is probably gonna pay off pretty well. >>Absolutely. And I think Stam is key to that. I mean, all of the ah lot of the well being that we have today and then industrialized countries. Thanks to science and technology, right improvements in health care, improvements in communication, transportation, air conditioning. Um, every aspect of life is touched by science and technology. So I think having more kids studying and understanding that is absolutely key. Yeah, I agree, >>Philip, you got anything to add? >>I think there's some big technical problems in the world today, Raphael and ourselves there certainly working on a couple of them. Think they're also collaboration problems and getting everybody to be able to pull together instead of pulling separately and to be able to spur the ideas on words. So that's where I think the education side is really exciting. What Matt is doing and it just kind of collaboration in general when we could do provide tools to help people do good work. Uh, that is, I think, valuable. >>Yeah, I think that's a very good point. And along those lines, we have some projects that are about creating very low cost instruments for low research settings, places in Africa, Southeast Asia, South America, so that they can do, um, um, biomedical research that it's difficult to do in those place because they don't have the money to buy the fancy lab machines that cost $30,000 an hour. Um, so we're trying to sort of democratize some of those instruments. And I think thanks to tools like Kahn shape then is easier, for example, to have a conversation with somebody in Africa and show them the design that we have and discuss the details of it with them on. But it's amazing, right to have somebody, you know, 10 time zones away, Um, looking really life in real time with you about your design and discussing the details or teaching them how to build a machine, right? Because, um, you know, they have a three D printer. You can you can just give them the design and say like, you build it yourself, uh, even cheaper than and, you know, also billing and shipping it there. Um, so all that that that aspect of it is also super important. I think for any of these efforts to improve some of the hardest part was in the world for climate change. Do you say, as you say, poverty, nutrition issues? Um, you know, availability of water. You have that project at about finding water. Um, if we can also help deploy technologies that teach people remotely how to create their own technologies or how to build their own systems that will help them solve those forms locally. I think that's very powerful. >>Yeah, the point about education is right on. I think some people in the audience may be familiar with the work of Erik Brynjolfsson and Andrew McAfee, the second machine age where they sort of put forth the premise that, uh, is it laid it out. Look, for the first time in history, machines air replacing humans from a cognitive perspective. Machines have always replaced humans, but that's gonna have an impact on jobs. But the answer is not toe protect the past from the future. The answer is education and public policy that really supports that. So I couldn't agree more. I think it's a really great point. Um, we have We do have some questions from the audience. If if we could If I can ask you guys, um, you know, this one kind of stands out. How do you see artificial intelligence? I was just talking about machine intelligence. Um, how do you see that? Impacting the design space guys trying to infuse a I into your product development. Can you tell me? >>Um, absolutely, like, we're using AI for some things, including some of these very low cost instruments that will hopefully help us diagnose certain diseases, especially this is that are very prevalent in the Third World. Um, and some of those diagnostics are these days done by thes armies of technicians that are trained to look under the microscope. But, um, that's a very slow process. Is very error prone and having machine learning systems that can to the same diagnosis faster, cheaper and also little machines that can be taken to very remote places to these villages that have no access to a fancy microscope. To look at a sample from a patient that's very powerful. And I we don't do this, but I have read quite a bit about how certain places air using a Tribune attorneys to actually help them optimize designs for parts. So you get these very interesting looking parts that you would have never thought off a person would have never thought off, but that are incredibly light ink. Earlier, strong and I have all sort of properties that are interesting thanks to artificial intelligence machine learning in particular >>yet another. The advantage you get when when your work is in the cloud I've seen. I mean, there's just so many applications that so if the radiology scan is in the cloud and the radiologist is goes to bed at night, Radiologist could come in in the morning and and say, Oh, the machine while you were sleeping was using artificial intelligence to scan these 40,000 images. And here's the five that we picked out that we think you should take a closer look at. Or like Raphael said, I can design my part. My, my, my, my, my you know, mount or bracket or whatever and go to sleep. And then I wake up in the morning. The machine has improved. It for me has made it strider strider stronger and lighter. Um And so just when your when your work is in the cloud, that's just that's a really cool advantage that you get that you can have machines doing some of your design work for you. >>Yeah, we've been watching, uh, you know, this week is this month, I guess is AWS re invent and it's just amazing to see how much effort is coming around machine learning machine intelligence. You know Amazon has sage maker Google's got, you know, embedded you no ML and big query. Uh, certainly Microsoft with Azure is doing tons of stuff and machine learning. I think the point there is that that these things will be infused in tow R and D and in tow software product by the vendor community. And you all will apply that to your business and and build value through the unique data that your collecting, you know, in your ecosystems. And and that's how you add value. You don't have to be necessarily, you know, developers of artificial intelligence, but you have to be practitioners to apply that. Does that make sense to you, Philip? >>Yeah, absolutely. And I think your point about value is really well chosen. We see AI involved from the physics simulations all the way up to interpreting radiation data, and that's where the value question, I think, is really important because it's is the output of the AI giving helpful information that the people that need to be looking at it. So if it's curating a serious of radiation alert, saying, Hey, like these air the anomalies. You need to look at eyes it, doing that in a way that's going to help a good response on. In some cases, the II is only as good as the people. That sort of gave it a direction and turn it loose. And you want to make sure that you don't have biases or things like that underlying your AI that they're going to result in less than helpful outcomes coming from it. So we spend quite a lot of time thinking about how do we provide the right outcomes to people who are who are relying on our systems? >>That's a great point, right? Humans air biased and humans build models, so models are inherently biased. But then the software is hitting the market. That's gonna help us identify those biases and help us, you know? Of course. Correct. So we're entering Cem some very exciting times, guys. Great conversation. I can't thank you enough for spending the time with us and sharing with our audience the innovations that you're bringing to help the world. So thanks again. >>Thank you so much. >>Thank you. >>Okay. Welcome. Okay. When we come back, John McElheny is gonna join me. He's on shape. Co founder. And he's currently the VP of strategy at PTC. He's gonna join the program. We're gonna take a look at what's next and product innovation. I'm Dave Volonte and you're watching innovation for good on the Cube, the global leader. Digital technology event coverage. We'll be right back. >>Okay? Okay. Yeah. Okay. >>From around >>the globe, it's the Cube. Presenting innovation for good. Brought to you by on shape. >>Okay, welcome back to innovation. For good. With me is John McElheny, who is one of the co founders of On Shape and is now the VP of strategy at PTC. John, it's good to see you. Thanks for making the time to come on the program. Thanks, Dave. So we heard earlier some of the accomplishments that you've made since the acquisition. How has the acquisition affected your strategy? Maybe you could talk about what resource is PTC brought to the table that allowed you toe sort of rethink or evolve your strategy? What can you share with us? >>Sure. You know, a year ago, when when John and myself met with Jim Pepperman early on is we're we're pondering. Started joining PTC one of things became very clear is that we had a very clear shared vision about how we could take the on shape platform and really extended for, for all of the PTC products, particular sort of their augmented reality as well as their their thing works or the i o. T business and their product. And so from the very beginning there was a clear strategy about taking on shape, extending the platform and really investing, um, pretty significantly in the product development as well as go to market side of things, uh, toe to bring on shape out to not only the PTC based but sort of the broader community at large. So So So PTC has been a terrific, terrific, um, sort of partner as we've we've gonna go on after this market together. Eso We've added a lot of resource and product development side of things. Ah, lot of resource and they go to market and customer success and support. So, really, on many fronts, that's been both. Resource is as well a sort of support at the corporate level from from a strategic standpoint and then in the field, we've had wonderful interactions with many large enterprise customers as well as the PTC channels. So it's been really a great a great year. >>Well, and you think about the challenges of in your business going to SAS, which you guys, you know, took on that journey. You know, 78 years ago. Uh, it's not trivial for a lot of companies to make that transition, especially a company that's been around as long as PTC. So So I'm wondering how much you know, I was just asking you How about what PCP TC brought to the table? E gotta believe you're bringing a lot to the table to in terms of the mindset, uh, even things is, is mundane is not the right word, but things like how you compensate salespeople, how you interact with customers, the notion of a service versus a product. I wonder if you could address >>that. Yeah, it's a it's a really great point. In fact, after we had met Jim last year, John and I one of the things we walked out in the seaport area in Boston, one of things we sort of said is, you know, Jim really gets what we're trying to do here and and part of let me bring you into the thinking early on. Part of what Jim talked about is there's lots of, you know, installed base sort of software that's inside of PTC base. That's helped literally thousands of customers around the world. But the idea of moving to sass and all that it entails both from a technology standpoint but also a cultural standpoint. Like How do you not not just compensate the sales people as an example? But how do you think about customer success? In the past, it might have been that you had professional services that you bring out to a customer, help them deploy your solutions. Well, when you're thinking about a SAS based offering, it's really critical that you get customers successful with it. Otherwise, you may have turned, and you know it will be very expensive in terms of your business long term. So you've got to get customers success with software in the very beginning. So you know, Jim really looked at on shape and he said that John and I, from a cultural standpoint, you know, a lot of times companies get acquired and they've acquired technology in the past that they integrate directly into into PTC and then sort of roll it out through their products, are there just reached channel, he said. In some respects, John John, think about it as we're gonna take PTC and we want to integrate it into on shape because we want you to share with us both on the sales side and customer success on marketing on operations. You know all the things because long term, we believe the world is a SAS world, that the whole industry is gonna move too. So really, it was sort of an inverse in terms of the thought process related to normal transactions >>on That makes a lot of sense to me. You mentioned Sharon turns the silent killer of a SAS company, and you know, there's a lot of discussion, you know, in the entrepreneurial community because you live this, you know what's the best path? I mean today, You see, you know, if you watch Silicon Valley double, double, triple triple, but but there's a lot of people who believe, and I wonder, if you come in there is the best path to, you know, in the X Y axis. If if it's if it's uh, growth on one and retention on the other axis. What's the best way to get to the upper right on? Really? The the best path is probably make sure you've nailed obviously the product market fit, But make sure that you can retain customers and then throw gas on the fire. You see a lot of companies they burn out trying to grow too fast, but they haven't figured out, you know that. But there's too much churn. They haven't figured out those metrics. I mean, obviously on shape. You know, you were sort of a pioneer in here. I gotta believe you've figured out that customer retention before you really, You know, put the pedal to the >>metal. Yeah, and you know, growth growth can mask a lot of things, but getting getting customers, especially the engineering space. Nobody goes and sits there and says, Tomorrow we're gonna go and and, you know, put 100 users on this and and immediately swap out all of our existing tools. These tools are very rich and deep in terms of capability, and they become part of the operational process of how a company designs and builds products. So any time anybody is actually going through the purchasing process. Typically, they will run a try along or they'll run a project where they look at. Kind of What? What is this new solution gonna help them dio. How are we gonna orient ourselves for success? Longer term. So for us, you know, getting new customers and customer acquisition is really critical. But getting those customers to actually deploy the solution to be successful with it. You know, we like to sort of, say, the marketing or the lead generation and even some of the initial sales. That's sort of like the Kindle ing. But the fire really starts when customers deploy it and get successful. The solution because they bring other customers into the fold. And then, of course, if they're successful with it, you know, then in fact, you have negative turn which, ironically, means growth in terms of your inside of your install. Bates. >>Right? And you've seen that with some of the emerging, you know, SAS companies, where you're you're actually you know, when you calculate whatever its net retention or renew ALS, it's actually from a dollar standpoint. It's up in the high nineties or even over 100%. >>So >>and that's a trend we're gonna continue. See, I >>wonder >>if we could sort of go back. Uh, and when you guys were starting on shape, some of the things that you saw that you were trying to strategically leverage and what's changed, you know, today we were talking. I was talking to John earlier about in a way, you kinda you kinda got a blank slate is like doing another startup. >>You're >>not. Obviously you've got installed base and customers to service, but But it's a new beginning for you guys. So one of the things that you saw then you know, cloud and and sas and okay, but that's we've been there, done that. What are you seeing? You know today? >>Well, you know, So So this is a journey, of course, that that on shape on its own has gone through it had I'll sort of say, you know, several iterations, both in terms of of of, you know, how do you How do you get customers? How do you How do you get them successful? How do you grow those customers? And now that we've been part of PTC, the question becomes okay. One, There is certainly a higher level of credibility that helps us in terms of our our megaphone is much bigger than it was when we're standalone company. But on top of that now, figuring out how to work with their channel with their direct sales force, you know, they have, um, for example, you know, very large enterprises. Well, many of those customers are not gonna go in forklift out their existing solution to replace it with with on shape. However, many of them do have challenges in their supply chain and communications with contractors and vendors across the globe. And so, you know, finding our fit inside of those large enterprises as they extend out with their their customers is a very interesting area that we've really been sort of incremental to to PTC. And then, you know, they they have access to lots of other technology, like the i o. T business. And now, of course, the augmented reality business that that we can bring things to bear. For example, in the augmented reality world, they've they've got something called expert capture. And this is essentially imagine, you know, in a are ah, headset that allows you to be ableto to speak to it, but also capture images still images in video. And you could take somebody who's doing their task and capture literally the steps that they're taking its geo location and from their builds steps for new employees to be, we'll learn and understand how todo use that technology to help them do their job better. Well, when they do that, if there is replacement products or variation of of some of the tools that that they built the original design instruction set for they now have another version. Well, they have to manage multiple versions. Well, that's what on shape is really great at doing and so taking our technology and helping their solutions as well. So it's not only expanding our customer footprint, it's expanding the application footprint in terms of how we can help them and help customers. >>So that leads me to the tam discussion and again, as part of your strategist role. How do you think about that? Was just talking to some of your customers earlier about the democratization of cat and engineering? You know, I kind of joked, sort of like citizen engineering, but but so that you know, the demographics are changing the number of users potentially that can access the products because the it's so much more of a facile experience. How are you thinking about the total available market? >>It really is a great question, You know, it used to be when you when you sold boxes of software, it was how many engineers were out there. And that's the size of the market. The fact that matter is now when, When you think about access to that information, that data is simply a pane of glass. Whether it's a computer, whether it's a laptop, UH, a a cell phone or whether it's a tablet, the ability to to use different vehicles, access information and data expands the capabilities and power of a system to allow feedback and iteration. I mean, one of the one of the very interesting things is in technology is when you can take something and really unleash it to a larger audience and builds, you know, purpose built applications. You can start to iterate, get better feedback. You know there's a classic case in the clothing industry where Zara, you know, is a fast sort of turnaround. Agile manufacturer. And there was a great New York Times article written a couple years ago. My wife's a fan of Zara, and I think she justifies any purchases by saying, You know, Zara, you gotta purchase it now. Otherwise it may not be there the next time. Yet you go back to the store. They had some people in a store in New York that had this woman's throw kind of covering Shaw. And they said, Well, it would be great if we could have this little clip here so we can hook it through or something. And they sent a note back toe to the factory in Spain, and literally two weeks later they had, you know, 4000 of these things in store, and they sold out because they had a closed loop and iterative process. And so if we could take information and allow people access in multiple ways through different devices and different screens, that could be very specific information that, you know, we remove a lot of the engineering data book, bring the end user products conceptually to somebody that would have had to wait months to get the actual physical prototype, and we could get feedback well, Weaken have a better chance of making sure whatever product we're building is the right product when it ultimately gets delivered to a customer. So it's really it's a much larger market that has to be thought of rather than just the kind of selling A boxes software to an engineer. >>That's a great story. And again, it's gonna be exciting for you guys to see that with. The added resource is that you have a PTC, Um, so let's talk. I promise people we wanna talk about Atlas. Let's talk about the platform. A little bit of Atlas was announced last year. Atlas. For those who don't know it's a SAS space platform, it purports to go beyond product lifecycle management and you You're talking cloud like agility and scale to CAD and product design. But John, you could do a better job than I. What do >>we need to know about Atlas? Well, I think Atlas is a great description because it really is metaphorically sort of holding up all of the PTC applications themselves. But from the very beginning, when John and I met with Jim, part of what we were intrigued about was that he shared a vision that on shape was more than just going to be a cad authoring tool that, in fact, you know, in the past these engineering tools were very powerful, but they were very narrow in their purpose and focus. And we had specialty applications to manage the versions, etcetera. What we did in on shape is we kind of inverted that thinking. We built this collaboration and sharing engine at the core and then kind of wrap the CAD system around it. But that collaboration sharing and version ING engine is really powerful. And it was that vision that Jim had that he shared that we had from the beginning, which was, how do we take this thing to make a platform that could be used for many other applications inside of inside of any company? And so not only do we have a partner application area that is is much like the APP store or Google play store. Uh, that was sort of our first Stan Shih ation of this. This this platform. But now we're extending out to broader applications and much meatier applications. And internally, that's the thing works in the in the augmented reality. But there'll be other applications that ultimately find its way on top of this platform. And so they'll get all the benefits of of the collaboration, sharing the version ing the multi platform, multi device. And that's an extremely extremely, um, strategic leverage point for the company. >>You know, it's interesting, John, you mentioned the seaport before. So PTC, for those who don't know, built a beautiful facility down at the Seaport in Boston. And, of course, when PTC started, you know, back in the mid 19 eighties, there was nothing at the seaport s. >>So it's >>kind of kind of ironic, you know, we were way seeing the transformation of the seaport. We're seeing the transformation of industry and of course, PTC. And I'm sure someday you'll get back into that beautiful office, you know? Wait. Yeah, I'll bet. And, uh and but I wanna bring this up because I want I want you to talk about the future. How you how you see that our industry and you've observed this has moved from very product centric, uh, plat platform centric with sass and cloud. And now we're seeing ecosystems form around those products and platforms and data flowing through the ecosystem powering, you know, new innovation. I wonder if you could paint a picture for us of what the future looks like to you from your vantage point. >>Yeah, I think one of the key words you said there is data because up until now, data for companies really was sort of trapped in different applications. And it wasn't because people were nefarious and they want to keep it limited. It was just the way in which things were built. And, you know, when people use an application like on shape, what ends up happening is there their day to day interaction and everything that they do is actually captured by the platform. And, you know, we don't have access to that data. Of course it's it's the customer's data. But as as an artifact of them using the system than doing their day to day job, what's happening is they're creating huge amounts of information that can then be accessed and analyzed to help them both improve their design process, improve their efficiencies, improve their actual schedules in terms of making sure they can hit delivery times and be able to understand where there might be roadblocks in the future. So the way I see it is companies now are deploying SAS based tools like on shape and an artifact of them. Using that platform is that they have now analytics and tools to better understand and an instrument and manage their business. And then from there, I think you're going to see, because these systems are all you know extremely well. Architected allow through, you know, very structured AP. I calls to connect other SAS based applications. You're gonna start seeing closed loop sort of system. So, for example, people design using on shape, they end up going and deploying their system or installing it, or people use the end using products. People then may call back into the customers support line and report issues, problems, challenges. They'll be able to do traceability back to the underlying design. They'll be able to do trend analysis and defect analysis from the support lines and tie it back and closed loop the product design, manufacture, deployment in the field sort of cycles. In addition, you can imagine there's many things that air sort of as designed. But then when people go on site and they have to install it. There's some alterations modifications. Think about think about like a large air conditioning units for buildings. You go and you go to train and you get a large air conditioning unit that put up on top of building with a crane. They have to build all kinds of adaptors to make sure that that will fit inside of the particulars of that building. You know, with on shape and tools like this, you'll be able to not only take the design of what the air conditioning system might be, but also the all the adapter plates, but also how they installed it. So it sort of as designed as manufactured as stalled. And all these things can be traced, just like if you think about the transformation of customer service or customer contacts. In the early days, you used to have tools that were PC based tools called contact management solution, you know, kind of act or gold mine. And these were basically glorified Elektronik role in Texas. It had a customer names and they had phone numbers and whatever else. And Salesforce and Siebel, you know, these types of systems really broadened out the perspective of what a customer relationship? Waas. So it wasn't just the contact information it was, you know, How did they come to find out about you as a company? So all of the pre sort of marketing and then kind of what happens after they become a customer and it really was a 3 60 view. I think that 3 60 view gets extended to not just to the customers, but also tools and the products they use. And then, of course, the performance information that could come back to the manufacturer. So, you know, as an engineer, one of the things you learn about with systems is the following. And if you remember, when the CD first came out CDs that used to talk about four times over sampling or eight times over sampling and it was really kind of, you know, the fidelity the system. And we know from systems theory that the best way to improve the performance of a system is to actually have more feedback. The more feedback you have, the better system could be. And so that's why you get 16 60 for example, etcetera. Same thing here. The more feedback we have of different parts of a company that a better performance, The company will be better customer relationships. Better, uh, overall financial performance as well. So that's that's the view I have of how these systems all tied together. >>It's a great vision in your point about the data is I think right on. It used to be so fragmented in silos, and in order to take a system view, you've gotta have a system view of the data. Now, for years, we've optimized maybe on one little component of the system and that sometimes we lose sight of the overall outcome. And so what you just described, I think is, I think sets up. You know very well as we exit. Hopefully soon we exit this this covert era on John. I hope that you and I can sit down face to face at a PTC on shape event in the near term >>in the seaport in the >>seaport would tell you that great facility toe have have an event for sure. It >>z wonderful >>there. So So John McElhinney. Thanks so much for for participating in the program. It was really great to have you on, >>right? Thanks, Dave. >>Okay. And I want to thank everyone for participating. Today we have some great guest speakers. And remember, this is a live program. So give us a little bit of time. We're gonna flip this site over toe on demand mode so you can share it with your colleagues and you, or you can come back and and watch the sessions that you heard today. Uh, this is Dave Volonte for the Cube and on shape PTC. Thank you so much for watching innovation for good. Be well, Have a great holiday. And we'll see you next time. Yeah.

>>from around the globe. It's the Cube presenting innovation for good. Brought to >>you by on shape. Okay, welcome back to innovation. For good. With me is John McElheny, who is one of the co founders of on Shape and is now the VP of strategy at PTC. John, good to see you. Thanks for making the time to come on the program. Thanks, Dave. So we heard earlier some of the accomplishments that you've made since the acquisition. How has the acquisition affected your strategy? Maybe you could talk about what resource is PTC brought to the table that allowed you toe sort of rethink or evolve your strategy? What can you share with us? >>Sure. You know, a year ago when John and myself met with Jim Hempleman early on is we're we're pondering started joining PTC. One of things became very clear is that we had a very clear shared vision about how we could take the on shape platform and really extended for for all of the PTC products, particular sort of their augmented reality as well as their their thing works or the i o. T business and their product. And so from the very beginning, there was a clear strategy about taking on shape, extending the platform and really investing, um, pretty significantly in the product development as well as go to market side of things, uh, toe to bring on shape out to not only the PTC based but sort of the broader community at large. So So So PTC has been terrific. Terrific, um, sort of partner as we've we've gonna go on after this market together. Eso we've added a lot of resource and product development side of things. Ah, lot of resource and to go to market and customer success and support. So really, on many fronts, that's with both resource is, as well a sort of support at the corporate level from from a strategic standpoint and then in the field, we've had wonderful interactions with many large enterprise customers as well as the PTC channels. So it's been really a great a great year. >>Well, and you think about the challenges of your business going to sas what you guys, you know, took on that journey, you know, 78 years ago. Uh, it's not trivial for a lot of companies to make that transition, especially company. That's been around as long as PTC. So So I'm wondering how much you know, I was just asking you what PC PTC brought the table. E gotta believe you're bringing a lot to the table to in terms of the mindset, uh, even things is, is mundane is not the right word. But things like how you compensate sales people, how you interact with customers, the notion of a service versus a product. I wonder if you could address >>that. Yeah, it's a It's a really great point. In fact, after we had met Jim last year, John and I one of the things we walked out in the seaport area in Boston one of things we sort of said is you know, Jim really gets what we're trying to do here and and part of let me bring you into the thinking early on. Part of what Jim talked about is there's lots of, you know, installed base sort of software that's inside of PTC base. That helped literally thousands of customers around the world. But the idea of moving to sass and all that it entails both from a technology standpoint, but also a cultural standpoint, like how do you not not just compensate the sales people as an example? But how do you think about customers? Success? In the past, it might have been that you had professional services that you bring out to a customer, help them deploy your solutions. Well, when you're thinking about a SAS based offering, it's really critical that you get customers successful with it. Otherwise, you may have turned, and you know it will be very expensive in terms of your business long term. So you've got to get customers success with software in the very beginning. So you know, Jim really looked at on shape and he said that John and I from a cultural standpoint, you know, a lot of times companies get acquired and they've acquired technology in the past that they integrate directly into into PTC and then sort of roll it out through their products or their distribution channels, he said. In some respects, John John, think about it as we're gonna take PTC and we want to integrate it into on shape because we want you to share with us both on the sales side and customer success on marketing on operations, you know, all the things because long term, we believe the world is a SAS world, that the whole industry is gonna move too. So, really, it was sort of an inverse in terms of the thought process related to normal transactions >>on that makes a lot of sense to me. You mentioned Sharon turns the silent killer of a SAS company. And you know, there's a lot of discussion, you know, in the entrepreneurial community because you live this, you know, what's the best path? I mean, today, you see, you know, you you watch Silicon Valley double, double, triple triple. But but there's a lot of people who believe, and I wonder, if you come in there is the best path to, you know, in the X Y axis. If if it's if it's, uh, growth on one and retention on the other axis, what's the best way to get to the upper right on? Really, the the best path is probably make sure you've nailed obviously the product market fit, but make sure that you can retain customers and then throw gas on the fire. You see a lot of companies they burn out trying to grow too fast, but they haven't figured out, you know that. But there's too much churn. They haven't figured out those metrics. I mean, obviously on shape. You know, you were sort of a pioneer in here. I gotta believe you've figured out that customer retention before you really? You know, put the pedal to the >>metal. Yeah. And you know, growth growth can mask a lot of things, but getting getting customers, especially the engineering space. Nobody goes and sits there and says, Tomorrow we're gonna go and and, you know, put 100 users on this and and immediately swap out all of our existing tools. These tools are very rich and deep in terms of capability, and they become part of the operational process of how a company designs and builds products. So any time anybody is actually going through the purchasing process, typically they will run a try along or they'll run a project where they look at Kind of What? What is this new solution gonna help them dio. How are we gonna orient ourselves for success? Longer term. So for us, you know, getting new customers and customer acquisition is really critical. But getting those customers to actually deploy the solution to be successful with it. You know, we like to sort of, say, the marketing or the lead generation and even some of the initial sales. That's sort of like the Kindle ing. But the fire really starts when customers deploy it and get successful with the solution because they bring other customers into the fold. And then, of course, if they're successful with it, you know, then in fact, you have negative turn which, ironically, means growth in terms of your inside of your install Bates. >>Right? And you've seen that with some of the emerging, you know, SAS companies, where you're you're actually you know, when you calculate whatever its net retention or renew ALS, it's actually from a dollar standpoint that's up in the high nineties or even over 100% >>so and >>that's a trend we're gonna continue. See, I wonder if we could sort of go back. Uh, and when you guys were starting on shape, some of the things that you saw that you were trying to strategically leverage and what's changed, you know, today we were talking. I was talking to John earlier about in a way, you kinda you kinda got a blank slate is like doing another startup. You're not. Obviously you've got installed base and customers to service, but but it's a new beginning for you guys. So one of the things that you saw then you know, cloud and and sas and okay, but that's we've been there, done that. What are you seeing? You know, today? >>Well, you know, So So this is a journey, of course, that that on shape on its own has gone through. And had, I'll sort of say, you know, several iterations, both in terms of of of, you know, how do you How do you get customers? How do you How do you get them successful? How do you grow those customers? And now that we've been part of PTC, the question becomes okay, One, there is certainly a higher level of credibility that helps us in terms of our our megaphone is much bigger than it was when we're standalone company. But on top of that now, figuring out how to work with their channel with their direct sales force, you know, they have, um, for example, you know, very large enterprises. Well, many of those customers are not gonna go in forklift out their existing solution to replace it with with on shape. However, many of them do have challenges in their supply chain and communications with contractors and vendors across the globe. And so, you know, finding our fit inside of those large enterprises as they extend out with their their customers is a very interesting area that we've really been sort of incremental to to PTC. And then, you know, they they have access to lots of other technology, like the i O. T business. And now, of course, the augmented reality business that that we can bring things to bear. For example, in the augmented reality world they've they've got something called expert capture. And this is essentially imagined, you know, in a are, ah, headset that allows you to be ableto to speak to it but also capture images, still images in video, and you could take somebody who's doing their task and capture literally the steps that they're taking its geo location and from their builds steps for new employees. We'll learn and understand how todo use that technology to help them do their job better. Well, when they do that if there is replacement products or variation of of some of the tools that that they built the original design instruction set for they now have another version. Well, they have to manage multiple versions. Well, that's what on shape is really great at doing and so taking our technology and helping their solutions as well. So it's not only expanding our customer footprint, it's expanding the application footprint in terms of how we can help them and help customers. >>So that leads me to the tam discussion. And again, it was part of your strategist role. How do you think about that? Was just talking to some of your customers earlier about the democratization of cat and engineering. You know, I kind of joked, sort of like citizen engineering, but but so that, you know, the demographics are changing the number of users potentially that can access the products because the it's so much more of a facile experience. How are you thinking about the total available market? >>It really is a great question, you know, It used to be when you when you sold boxes of software, it was how many engineers were out there, and that's the size of the market. The fact that matter is now when, When you think about access to that information, that data is simply a pane of glass. Whether it's a computer, whether it's a laptop, uh, a cell phone or whether it's a tablet, the ability to to use different vehicles, access information and data expands the capabilities and power of a system to allow feedback and iteration. I mean, one of the one of the very interesting things is in technology is when you can take something and really unleash it to a larger audience and builds, you know, purpose built applications. You can start to iterate, get better feedback. You know, there's a classic case in the clothing industry where Zara, you know, is a fast, sort of turnaround agile manufacturer. And there was a great New York Times article written a couple years ago. My wife's a fan of Zara, and I think she justifies any purchases by saying, you know, was Are you gotta purchase it now. Otherwise it may not be there the next time. Yet you go back to the store. They had some people in the store in New York that had this woman's throw kind of covering Shaw, and they said, Well, it would be great if we could have this little clip here so we could hook it through or something. And they sent a note back toe to the factory in Spain and literally two weeks later they had, you know, 4000 of these things in store, and they sold out because they had a closed loop and iterative process. And so if we could take information and allow people access in multiple ways through different devices and different screens, that could be very specific information that, you know, we remove a lot of the engineering data book, bring the end user products conceptually to somebody that would have had to wait months to get the actual physical prototype, and we could get feedback. Well, Weaken have a better chance of making sure whatever product we're building is the right product when it ultimately gets delivered to a customer. So it's really it's a much larger market that has to be thought of rather than just the kind of selling a boxes off where to an engineer, >>that's a great story, and and again, it's gotta be exciting for you guys to see that on day with the added resource is that you have a PTC eso. Let's talk. I promise people we want to talk about Atlas. Let's talk about the platform. A little bit of Atlas was announced last year. Atlas. For those who don't know it's a SAS space platform, it purports to go beyond product lifecycle management and you you're talking cloudlike agility and scale to CAD and product design. But, John, you could do a better job than I. What do >>we need to know about Atlas? Well, I think Atlas is a great description because it really is metaphorically, sort of holding up all of the PTC applications themselves. But from the very beginning, when John and I met with Jim, part of what we were intrigued about was that he shared a vision that on shape was more than just going to be a cad authoring tool that, in fact, you know, in the past, these engineering tools were very powerful, but they were very narrow in their purpose and focus, and we had specialty applications to manage diversions, etcetera. What we did in on shape is we kind of inverted that thinking we built this collaboration and sharing engine at the core and then kind of wrap the CAD system around it. But that collaboration sharing and version ING engine is really powerful. And it was that vision that Jim had that he shared that we had from the beginning, which was, how do we take this thing to make a platform that could be used for many other applications inside of inside of any company? And so not only do we have a partner application area that is is much like the APP store or Google play store. Uh, that was sort of our first misty initiation of this this this platform. But now we're extending out to broader applications and much meatier applications. And internally, that's the thing works in the in the augmented reality. But there'll be other applications that ultimately find its way on top of this platform, and so they'll get all the benefits of of the collaboration, sharing the version ing the multi platform multi device. And that's an extremely extremely, um, strategic leverage point for the company. >>You know, it's interesting, John, you mentioned the seaport before, So PTC For those who don't know built a beautiful facility down at the seaport in Boston. And of course, when PTC started back in the mid 19 eighties, this there was nothing at the seaport s. >>So it's >>kind of kind of ironic, you know, we were way seeing the transformation of the seaport. We're seeing the transformation of industry and of course, PTC. And I'm sure someday you'll get back into that beautiful office, you know? Wait. Yeah, I'll Bet. And, uh and but I wanna bring this up because I want I want you to talk about the future. How you how you see that our industry and you've observed this has moved from very product centric, uh, plat platform centric with sass and cloud. And now we're seeing ecosystems form around those products and platforms and in data flowing through the ecosystem, powering you new innovation. I wonder if you could paint a picture for us of what the future looks like to you from your vantage point. >>Yeah, I think one of the key words you said there is data because up until now, data for companies really was sort of trapped in different applications. And it wasn't because people with nefarious and they want to keep it limited. It was just the way in which things were built, and you know, when people use an application like on shape, what ends up happening is there their day to day interactions and everything that they dio is actually captured by the platform. And you know, we don't have access to that data. Of course it's it's the customer's data. But as as an artifact of them using the system than doing their day to day job, what's happening is they're creating huge amounts of information that can then be accessed and analyzed to help them both improve their design process, improve their efficiencies, improve their actual schedules in terms of making sure they can hit delivery times and be able to understand where there might be roadblocks in the future. So the way I see it is, companies now are deploying SAS based tools like an shape and an artifact of them. Using that platform is that they have now analytics and tools to better understand and an instrument and manage their business. And then from there, I think you're going to see, because these systems are all you know extremely well. architected allow through, you know, very structured AP. I calls to connect other SAS based applications. You're gonna start seeing closed loop sort of system. So, for example, people design using on shape. They end up going and deploying their system or installing it, or people use the end using products. People then may call back into the customers support line and report issues problems, challenges. They'll be able to do traceability back to the underlying design. They'll be able to do trend analysis and defect analysis from the support lines and tie it back and closed loop the product design, manufacture, deployment in the field sort of cycles. In addition, you can imagine there's many things that air sort of as designed. But then when people go on site and they have to install it, there's some alterations modifications. Think about think about like a large air conditioning units for buildings. You go and you go to train and you get a large air conditioning unit that put up on the top of building with a crane. They have to build all kinds of adaptors to make sure that that will fit inside of of of the particulars of that building. You know, with on shape and tools like this, you'll be able to not only take the design of what the air conditioning system might be, but also the all the adapter plates, but also how they installed it. So it sort of as designed as manufactured as stalled. And all these things can be traced just like if you think about the transformation of customer service or customer contacts. In the early days, you used to have tools that were PC based tools called contact management solution, you know, kind of act or gold mine. And these were basically glorified Elektronik role in Texas. It had a customer names, and they had phone numbers and whatever else. And Salesforce and Siebel, these types of systems really broadened out the perspective of what a customer relationship waas. So it wasn't just the contact information it was, you know, How did they come to find out about you as a company? So all the pre sort of marketing and then kind of what happens after they become a customer and it really was a 3 60 view. I think that 3 60 view gets extended to not just to the customers, but also tools and the products they use. And then, of course, the performance information that could come back to the manufacturer. So, you know, as an engineer, one of the things you learn about with systems is the following. And if you remember, when the 501st came out CDs that used to talk about four times over sampling or eight times over sampling and it was really kind of, you know, the fidelity the system. And we know from systems theory that the best way to improve the performance of a system is to actually have more feedback. The more feedback you have, the better system could be. And so that's why you got 16 60 for example, etcetera. Same thing here. The more feedback we have of different parts of a company that a better performance. The company will be better customer relationships, better overall financial performance as well. So that's that's the view I have of how these systems all tied together. >>The great vision in your point about the data is, I think, right on. It used to be so fragmented in silos, and in order to take a system view, you've gotta have a system view of the data. Uh, for years we've optimized maybe on one little component of the system and that sometimes we lose sight of the overall outcome. And so what you just described, I think is, I think sets up. You know very well as we exit. Hopefully soon we exit this this covert era on John. I hope that you and I can sit down face to face at a PTC on shape event in the near term. Who's >>in the seaport in the >>seaport Would tell you that great facility toe have have an event for sure. It >>z wonderful >>there. So So, John McElhinney. Thanks so much for for participating in the program. It was really great to have you on. >>Right. Thanks, Dave. >>Okay. And I want to thank everyone for participating. Today. We have some great guest speakers. And remember, this is a live program, so give us a little bit of time. We're gonna flip this site over to on demand mode so you can share it with your colleagues and you, or you can come back and and watch the sessions that you heard today. Uh, this is Dave Volonte for the Cube and on shape PTC. Thank you so much for watching innovation for good. Be well, have a great holiday and we'll see you next time.

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Welcome back to the Cube's coverage here from Palo Alto, California in our studio with remote interviews during this time of covert 19 with our quarantine crew. I'm John Furrier, your host of the Cube, and we have here the award winners for the best CDU solution from North based loses. Jim Keller, the president and from Harvard Business Publishing and University of Pittsburgh, Chad Burden PhD in data privacy officer of University of Pittsburgh. Thanks for coming on, gentlemen. Appreciate it. >>Thank you. >>So, Jim, we'll start with you. What is the solution that you guys have got the award for and talk about how it all came about? >>Yeah. Thank you for asking. And, uh, it's been a pleasure Worldwide chat and the entire you pitch team. So? So as we as we enter this this this whole covitz situation, our team really got together and started to think about how we could help AWS customers continue their journey with AWS, but also appreciate the fact that everyone was virtual. The budgets were very tight, but Nonetheless, the priorities remained the same. Um, So So we devised a solution which which we call jam sessions, AWS jam sessions and the whole principle behind the notion is that many customers go through AWS training and AWS has a number of other offerings, immersion days and boot camps and other things. But we felt it was really important that we brought forth a solution that enables customers to focus on a use case but do it rapidly in a very concentrated way with our expert team. So we formulated what we call jam sessions, which are essentially very focused, too. Weak engagements, rapid prototyping engagements. So in the context of Chad on the pitch team, it was around a data lake and they had been channels certainly speak to this in much more detail. But the whole notion here was how do you How does the customer get started out? Is how does a customer prove the efficacy of AWS proved that they can get data out of their on premises systems, get it into AWS, make it accessible in the form in this case, a data lake solution, and have the data be consumable. So we have an entire construct that we use, which includes structured education, virtual simultaneous rooms where development occurs with our joint sap prototyping teams. We come back again and do learnings, and we do all of this in the construct of the agile framework. And ideally, by the time we're done with the two weeks, um, the customer achieves some success around achieving the goal of the jam session. But more importantly, their team members have learned a lot about AWS with hands on work, real work. Learn by doing if you will, um, and really marry those two concepts of education and doing and come out of that with an opportunity then to think about the next step in that journey, which in this case be Thea implementation of a data lake in a full scale project kind of initiative. >>Talk about the relationship with the North based solutions. So your customer, you guys were partnering on this, so it's kind of your partnering, but also your they're helping you talk about the relationship and how the interactions went. >>Yeah, so I was faced with a challenge that I think a lot of people in my role is faced with where the demand for data is increasing and demand for more variety of data. And I'm faced with a lot of aging on premise hardware that, um I really don't want to invest any further. And so I know the clouds in the future, but we are so new with the cloud that we don't even know what we don't know. So it has zeroed in on AWS and I was talking with them and I made it very clear. I said, you know, because of our inexperience, you know, we have talented data engineers, but they don't have this type of experience, but I'm confident they can learn. What I'm looking for is a partner who can help us not only prove this out, that it can work, which I had high confidence that it could, but help us identify where we need to be putting our still skilling up. You know what gaps do we have? And you know, aws has so many different components. But we also needed help zeroing in on or our need. You know, what are the pieces we should really be paying attention to and developing those skills. So we got introduced to North Bay and they introduced us to the idea of the jam session, which was perfect. It was really exactly what I was looking for. Um, you know, we made it very clear in the early conversations that this would be side by side development, that my priority was, of course, to meet our deliverables. But it also for my team to learn how to use some of this and learn what they need to dive deeper in at the end of the engagement. I think that's how we got started on then. It was very successful engagement after that >>talk about the jam sessions because I love this. First of all, this is in line with what we're seeing in the marketplace, with rapid innovation now more than ever, with virtual workforces at home given situation, rapid, agile, rapid innovation, rapid development is a key kind of thing. What is a jam session was the approach. Give me a little bit about of it out, but what's your take on the jam sessions? Had it all has it all work? >>It was great because of the large team that north a broad and the variety of skills they brought and then they just had a playbook that worked, right? They broke us up into different groups from the people who be making the data pipeline to the people who then would be consuming it to develop analytics projects. Um, so that part works really well. And, yes, this rapid iterative development, You know, right now, with our current kind of process in our current tools, I have a hard time telling anybody how long it will take to get that new data source online and available to our data analysts who are data scientists because it takes months sometimes and nobody wants that answer. And I don't want to be giving that answer. So what we're really focused on is how do we tighten up our process? How do we still like the right tools so that we can pay, you know, will be two weeks from start to finish and you know you'll be able to make the data available. So the engagement with North of the jam session scheduled like that really helped us prove that. You know, once you have the skills and have the right people, you can do this rapid development and bring more value to our business more quickly, which is really what it's all about. We're out, >>Jim. I want get your thoughts because, you know, we see time and time again with the use cases with the cloud When you got smart people, certainly people who play with data and work with data, they're not. They're pretty savvy. They know the limitations. But when you get the cloud, it's like a car versus a horse or, you know, get a go from point A to point B. But again, the faster is the key. How did you put this all together And what were the key learnings? >>Yeah. So, uh, John, you know, a couple of things that are really important. One is, as Chad mentioned, really smart people, um, on the it side that wanted to wanted to really learn and had had a thirst for learning. Um, and then couple that with the thing that they're trying to learn in the actual use case that we're trying to jointly jointly implement a couple of things that we've learned that they're they're really important. One is, although we have structure, we have a Silla by and we have sort of a pattern of execution. We never lose sight of the fact that every customer's different. Every team members different and in fact chat in this case that team members some had more skills on AWS than others, so we had to be sensitive to that. So what we did was we sort of use our general formula for for the two weeks one week one is very structured, focused on getting folks up to speed and normalize in terms of where they are in their education of aws solution we're building, um, and then we two is really meant to sort of multiple together and really take this the solution that we're trying to execute around, um, and tailor it to the customer. So they were addressing the specific needs both from their team member of perspective and, uh, and the institutions perspective, Um, in total. We've learned that starting the day together and ending today with the recap of that day is really important in terms of ensuring that everyone's on the same page, that they have commonality of knowledge. And then we were addressing any concerns. You know, this stuff we move fast, right? Two weeks is is not a long time to get a lot of rapid prototyping done. So if there is anxiety or folks feel like they're falling behind, you want to make sure we knew that we want to address that quickly that evening or the next morning, recalibrate and and then continue. The other thing that we've learned is that and Chad, the entire Cube team did a phenomenal job of this was really preparation. So we want to We we We have a set of preliminary set of activities that we that we work with our customers sort of lay the foundation for, so that on day one of the jam session, we're ready to go. And with this we're doing this virtually. We don't have the luxury of being in a physical room and having time to sort of get acclimated to the physical constructive of organizing rooms and shares and tables. All of that, we're doing all that virtually so. Joe and the team were tremendous and getting all the preparatory work done. The thing about was involved in a data lake. It's the data and security and access of things Our team needed to work with their team and the prescription that in the formula that we use is really 33 critical things. One is our team members have to be adept that educating on a white board in this case. Secondly, we want to do side by side element. That's that's the whole goal. And then we want team members to to build trust and relationship side by side and then, thirdly and importantly, we want to be able to do over the shoulder mentoring. So as Chad's team members were executing, UI could guide them as we go. And those really those three ingredients really >>talk about the Data Lake on the outcome. As you guys went through this, what was the results of the Data Lake? How did it all? How'd it all turn out? >>Yeah, the result was great. It was exactly what we're looking for. The way I had structured the engagement and working with Jim to do this is I wanted to accomplish two things. I wanted to one prove that we can do what we do today with a star schema Martin model that creates a lot of reports that are important to the business but doesn't really help us grow in our use of data. There was a second component of it that I said, I want I want to show how we do something new and different that we can't do with our existing tools so that I can go back to our executive leadership and say, Hey, you know, by investing in this year's all the possibilities we can do and we've got proof that we can do it. So some natural language processing was one of those and leveraging aws comprehend with key and And the idea here was there are unfortunately relevant today with Cove it. But there are events happening all around campus. And how do students find the right events for them? You know, they're all in the calendar will live pricing national language processing using AWS comprehend and link them to a student's major so that we can then bubble these up to a student. Hey, you know of all these thousands of events here and you might be most interested in you can't do that right now, but using these tools using the skills that north they helped us develop working side by side will help us get there, >>you know, beautiful thing is with these jam sessions. You want to get some success, You go for the next one. You get this Sounds like another jam session opportunity to go in there and do the virtual version as well. As the fall comes up, you have the new reality. And this >>is >>really kind of What I like about this story is you guys did the jam session. First of all, great project, but right in the middle of this new shift of virtual, so it's very interesting. So I want to get your thoughts, Chad, You know, as you guys look at this, I mean on any given Sunday, this is a great project. You get people together, you have the cloud get more agile, get the proof points, show it double down on it. Playbook check. But now you've got the virtual workforce. How did that all play out? Anything surprise you any expectations that were met or things that were new that came out of this? Because this is something that everyone is going through right now. How do I come out of this or deal with current Cove it as it evolves and when I come out of it. I don't have a growth strategy in a team that's deploying and building. What's your take on? >>Yeah, so, yeah, you know, it's a good question. And I was a little concerned about it at first, cause when we had first begun conversations with North Bay, we were planning on a little bit on site and a little bit virtual. And of course, Cove. It happened. Our campuses closed. Nobody's permitted to be there. And so we had to just pivot to 100% virtual. I have to say I didn't notice any problems with it. It didn't impede our progress that didn't impede our communication. I think the playbook that North they had really just worked for that. Now they may have had to adjust it, and Jim can certainly part of that. But you know those morning stand ups for each group that's working the end of day worn out right? That's what those were the things I was joining in on, you know, it wasn't involved in it throughout the day, but I wanted to check in at the end of the day to make sure things are kind of moving along and the communication the transparency that was provided with key, and because of that transparency and that kind of schedule, they already have set up North Bay. We didn't see we didn't have any problems having a fully virtual engagement. In fact, I would probably prefer to do for two engagements moving forward because we can cut down on travel costs for everybody. >>You know, Jim O. Negative thoughts that I think is a huge point that's not just representing with here and illustrate with the example of the success of the EU solution. You guys got the award for, but in a way, covert exposes all the people that are been relying on waterfall based processes. You got to be in a room and argue things out. Our have meetings set up. It takes a lot of time when you when you have a virtual space and an agile process, you make some adjustments. But if you're already agile, it doesn't really impact too much. Can you share your thoughts because you deployed this very successfully? Virtually. >>Yeah, I know it is. Certainly, um, the key is always preparation and on our team did a phenomenal job of making sure that we could deliver equal to or better than virtual experience than we could on site and on site experience. But, John, you're right. You're absolutely right. But it forces you to really do is think about all the things that come natural when you're when you're in a physical room together, you can't take for granted virtually, um, even even interpersonal relationships and how those were built and the trust that's built in. And this whole, as much as this is a technical solution and as much as the teams did you really phenomenal aws work, foundational Lee. It all comes down to trust it, as Chad said, transparency, and it's hard, often hard to to build that into a virtual experience. So part of that preparatory work that I mentioned, we actually spend time doing that. And we spent time with Chad and other team members understanding each of their team members and understanding their strengths, understanding where they were in the education journey and experiential journey a little bit about them personally, right? So so I think. Look, I think the reality in the short and near term is that everything is gonna be virtual North Bay delivers much of their large scale projects. Virtually now, we have a whole methodology around that, and, um, and it's proven. Actually, it's made us better at what we do. >>Yeah, definitely puts the pressure on getting the job done and focusing on the creativity the building out. I want to ask you guys both the same question on this next round, because I think it's super important as people see the reality of cloud and there certainly has been around the benefits of there. But still you have, you know, mentality of, you know, we have to do it ourselves, not invented here. It's a managed services security. You know, there's plenty of objections. If you really want to avoid cloud, you can come up with something if you really look for it. Um, but the reality is, is that there are benefits for the folks out there that are now being accelerated into the cloud for the reasons we cove it and other reasons. What's your advice to them? Why cloud, what's the what's the bet? What comes? What comes out of making a good choice with the cloud? Chad? Is people sitting there going? Okay, I got to get my cloud mojo going What's your What's your What's your advice to those folks sitting out there watching this? >>Yeah, so I would say it. And Jim does this, you know, we have a big vision for data, you know, the whole universe of data. Where does everything is made available? And, um, I can't estimate the demand for all of that yet, right, That's going to evolve over time. So if I'm trying to scale some physical hardware solution, I'm either going to under scale it and not be able to deliver. Or I'm gonna invest too much money for the value in getting what? By moving to the cloud. What that enables me to do is just grow organically and make sure that our spend and the value we're getting from the use are always aligned. Um And then, of course, all the questions that you have availability and acceptability, right? We can just keep growing. And if we're not seeing value in one area, we can just we're no longer spending on that particular area, and we contract that money to a different components of the cloud, so just not being locked into a huge expense up front is really key, I think, >>Jim, your thoughts on Why Cloud? Why now? It's pretty obvious reasons, but benefits for the naysayers sitting on the fence who are? >>Yeah, it's It's a really important question, John and I think that had a lot of important points. I think there's two others that become important. One is, um, agility. Whether that's agility with respect to your in a competitive marketplace, place agility in terms of just retaining team members and staff in a highly competitive environment will go nowhere in particularly in the I t world, um, agility from a cost perspective. So So agility is a theme that comes through and through, over and over and over again in this change, right? So, he said, most companies and most organizations don't they don't know the entirety of what it is they're facing or what the demands are gonna be on their services. The agility is really is really key, and the 2nd 1 is, you know, the notion has often been that you have to have it all figured out. You could start and really our mantra and the jam session was sort of born this way. It's really start by doing, um, pick a use case, Pick a pain point, pick an area of frustration, whatever it might be. And just start the process you learn as you go. Um, and you know, not everything is the right fit for cloud. There are some things for the right reasons where alternatives might be might be appropriate. But by and large, if you if you start by doing And in fact, you know the jam session, learn by doing, and you start to better understand, enterprise will start to better understand what's most applicable to that where they can leverage the best of this bang for the buck if you will, um, and ultimately deliver on the value that that I t is is meant to deliver to the line of business, whatever that whatever that might be. And those two themes come through and through. And thirdly, I'll just add speed now. Speed of transformation, Speed of cost reduction, speed of feature rollout. Um, you know, Chad has users begging for information and access to data. Right? And the team we're sitting there trying to figure how to give it to him quickly. Um, so speed of execution with quality is really paramount as well these days >>and channels. You mentioned scale too, because he's trying to scale up as key and again getting the cloud muscles going for the teams. And culture is critical because, you know, matching that incentives. I think the alignment is critical. Point point. So congratulations, gentlemen. On great award best edu solution, Chad, While I have you here, I want to just get your personal thoughts. Put your industry expert PhD hat on because, you know, one of the things we've been reporting on is a lot of in the edu space higher ed in other areas with people having different education policies. The new reality is with virtual virtualized students and faculty alumni nine in community, the expectations and the data flows are different. Right? So you you had stuff that people use systems, legacy systems, >>kind of. >>It's a good opportunity to look at cloud to build a new abstraction layer and again create that alignment of what can we do? Development wise? I'm sure you're seeing new data flows coming in. I'm sure there's kind of thinking going on around. Okay. As we go forward, how >>do >>we find out who's what. Classes to attend if they're not on site this another jam session. So I see more, more things happening pretty innovative in your world. What's your take on all this? >>Um, I take, you know, So when we did the pivot, we did a pivot right after spring. Great toe. Be virtual for our students, Like a lot of universities dead. And, um, you learn a lot when you go through a crisis kind of like that. And you find all the weaknesses And we had finished the engagement. I think north by that point, or it were in it. And, um, seeing how if we were at our future state, you know, the way I envision the future state, I can now point to the specific things and get specific examples of how we would have been able to more effectively on when these new demands on data came up when new data flows were being created very quickly and, you know, able to point out to the weaknesses of our current ecosystem and how that would be better. Um, so that was really key. And then, you know, it's a This whole thing is an opportunity. It's really accelerated a lot of things that were kind of already in the works, and that's why it's exciting. It's obviously very challenging, you know, and that if it were really right now trying to focus on how do we have a safe campus environment and going with a maximum flexibility and older technology that's involved in that? And, you know, I've already got you know, I've had more unique data requests. >>My desk >>is coded and in the previous five years, you know, >>new patterns, new opportunities to write software. And it's great to see you guys focused on the hierarchy of needs. Really appreciate. I want to just share a funny story. Not funny, but interesting story, because this highlights the creativity that's coming. I was riffing on Zoom with someone in Higher Ed University out here in California, and it was wasn't official. Business was just more riffing on the future, and I said, Hey, wouldn't it be cool if you have, like an abstraction layer that had leverage, canvas, zoom and discord and all the kids are on discourse, their game received. Okay, why discord? It's the hang space people are his connective tissue Well, how do you build notifications through the different silos? So canvas doesn't support certain things? And campuses? The software. Most companies never say years, but that's a use case that we were just riffing on. But that's the kind of ideation that's going to come out of these kinds of jam sessions. You guys having that kind of feeling to? How do you see this new ideation? Rapid prototyping. I only think it's gonna get faster. Accelerated >>It was. Chad said, you know, his requests are multiplying. I'm sure on people are you know, folks are not willing to wait, you know, we're in a hurry up. Hurry up. I wanted now mentality these days with with both, um college attendees as well as those of us. We're trying to deliver on that promise. And I think, John, I think you're absolutely right. And I think that, um, whether it be the fail fast mantra or whether it be can we may even make this work right? Doesn't have lakes, is it is even viable. Um, and is it even cost effective? I can tell you that the we do a lot of work in tech. We do a lot of work in other industries as well. And what what the courseware delivery companies and the infrastructure companies are all trying to deal with and as a result of coaches, they've all had to try to innovate. Um, so we're being asked to challenge ourselves in ways we never been asked to challenge ourselves in terms of speed, of execution, speed of deployment, because these folks need answers, you know, tomorrow, Today, yesterday, not not six months from now. So the the I'll use the word legacy way of thinking is really not one that could be sustained or tolerated any longer. And and I want Chad and others to be able to call us and say, Hey, we need help. We need help quickly. How we go work together, side by side and go prove something. It may not be the most elegant. It may not be the most robust, but we need. We need it kind of tomorrow, and that's really the spirit of the whole. The whole notion of transition >>and new expectations means new solutions that will give you the final word going forward. You're on this wave right now. You got new things coming at you. You get in that foundation set. What's your mindset as you ride this wave? >>I'm optimistic it really It's an exciting time to be in this role. The progress we've made in the county or 2020 despite the challenges we've been faced with with, um cove it and budget issues. Um, I'm optimistic. I love what I saw in the in the jam session. It just kind of confirmed my I believe that this is really the future for the University of Pittsburgh in order to fully realize our vision of maximizing the value of data. >>Awesome. Best Edu solution award for AWS Public sector Congratulations and North based solutions. Jim Keller, President and University of Pittsburgh Chadbourne. Thank you for coming on and sharing your story. Great insights. And again, the wave is here. New expectation, new solutions. Clouds There. You guys got a good approach. Congratulations on the jam session. Thanks. >>Thank you, John. Pleasure. Thank you. Through >>the cube coverage of AWS Public Sector Partner Awards. I'm John Furrow, your host of the Cube. Thanks for watching. Yeah, yeah, yeah, yeah

>>from the Keep studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is a cube conversation. Hi, I'm John Furry with the Cube. We're here for a special Q conversation, housing with remote, where in studio most of the time. But on the weekends, I get an opportunity to talk to friends and experts, and he I wanted to really dig in with an awesome case study around AWS Cloud in a use case that I think is game changing for local community, especially this time of Cove. It you have local community work, local journalism suffering, but also connected this and connected experiences was gonna make. The difference is we come out of this pandemic a societal impact. But there's a real tech story here I want to dig into. We're here with Dan. True is the vice president of engineering for Chemical. Did you? They make a nap coat local be TV, which basically takes over the air television and streams it to an app in your local area, enabling access to many your TV and on demand as well. For local communities, it's a phenomenal project and its unique, somewhat misunderstood right now, but I think it's gonna be something that's going to really put Dan, thank you for coming along and chatting. Thanks >>for having me appreciate it. >>Okay, so I'm a big fan. I've been using the APP in San Francisco. I know New York's on the docket. I might be deployed. You guys have a unique infrastructure capability that's powering this new application, and this is the focus of the conversations. Q. Talk Amazon is a big part of this. Talk about your local be TV that you are protected. This platform for broadcast television has a unique hybrid cloud. Architecture. Can you tell us about that? >>Certainly. I mean, one of our challenges, as you know, is that we are local television eso unlike a lot of products on the markets, you know, like your Hulu's or other VM PV products, which primarily service sort of national feeds and things like that. Ah, we have to be able to receive, um, over the air signals in each market. Um, many channels that serve local content are still over the air, and that is why you don't see a lot of them on those types of services. They tend to get ignored and unavailable to many users. So that's part of our value. Proposition is to not only allow more people to get access to these stations, but, uh, allow the station's themselves to reach more people. So that means that we have to have a local presence in each market in order to receive those signals. Eso that's sort of forces us to have this hybrid model where we have local data centers. But then we also want to be able to effectively manage those in a central way. On. We do that in our cloud platform, which is hosted on Amazon and using Amazon service. >>Let me take take a breath. Here. You have a hybrid architecture on Amazon. So such a using a lot of the plumbing take us through what the architectures ram is on using a variety of their services. Can you unpack that? >>Yeah. So, um, obviously starts with some of the core services, like easy to s three already us, which everybody on planet uses. Um, we're also very focused on using PCs were completely containerized, which allows us to more effectively deploy our services and scale them. Um, and one of the benefits on that front that Amazon provides is that because they're container services wired into all the other services, like cloud, What metrics? Auto scaling policies. I am policies. Things like that. It means it allows us to manage those things in a much more effective way. Um, and use those services too much more effectively make those things reliable and scalable. Um, we also use a lot of their technologies, for example, for collecting metrics. So we use kinesis and red shift to collect real time metrics from all of our markets across the U. S. Uh, that allows us to do that reliably and at scale without having to manage complex each l systems like Kafka and other things. Um, as well a stored in a, uh, large data lake like red shift in Korea for analytics. And you know, things like that. Um, we also use, um, technologies like media Taylor s O, for example, one of the big features that, uh, most stations do not have access to Israel. Time targeted advertising in the broadcast space. Many ads are sold and placed weeks in advance. Um, and not personalized, obviously. You know, for that reason. Where is one of the big features we can bring to the table? Using our system and technologies like Media Taylor is we can provide real time targeted advertising, which is a huge win for these stations. >>What are some of the unique capabilities that you guys are? Offer broadcast station partners because you're basically going in and partnering with broadcast ages as well, but also your enabling new broadcasters to jump. And it's well, what are some of the unique capability that you're delivering? What is that? It's on the table there. What are you doing? This You >>well again. It allows us because we can do things centrally. You know as well as the local reception allows us to do some interesting things. Like if we have channels that, um, are allowed to broadcast even outside their market, Um, then we can easily put them in other markets and get them even more of years. That way we have the ability to even do, like hyper local or community channels, you know that are not necessarily broadcasting over the standard antennas, um, but could get us a feed from, you know, whatever. Zip code in whatever market and we can give them away toe reach viewers in the entire market and other markets, or even just in their local area. So, you know, consider the case where maybe a high school or a college you know, wants to show games or local content. Um, we provide a platform where they can now do that and reach more people, Um, using our app in our platform very, very easily. So that's another area that we want toe help Expand is not just your typical view of local of what's available in Phoenix, Um, but what's available in a particular city in that area or a local community where they want toe, um, reach their community more effectively, or even have content that might be interesting to other communities in Phoenix or one of the other markets? >>No, I think just is not going to side tension here. I talked with your partner. Jim longs to see you guys have an amazing business opportunity again. I think it's kind of misunderstood, but it's very clear to me that follows in. It has huge passion of local journalism. You see awesome efforts out there by Charlie Senate from the ground Truth project report for America. They take a journalism kind of friend few. But if you add like that, did you business model ought to This local journalism you can enable more video locally. I mean, that's really the killer app of video. And now it Koven. More than ever. I really want to know things like this. A mural with downtown Palo Alto Black lives matters. I want to know what's going on. Local summer restaurants, putting people out of sidewalks. Right now I'm limited to, like, next door or very Laghi media, whether it's the website. So again, I think this is an opportunity to that plus education. I mean Amazon educated Prince, that you can get a degree cloud computing by sitting on the couch. So, you know, this is again. This is a paradigm shift from an application standpoint, but you're providing essentially linear TV toe because in the local economy, So I just want to give you a shout out for that because I think it's super important. I think you know, people should get behind this. Eso congratulates. Okay, I'm often my little rant there. Let's get back down to some of that cloud steps. I think what super interesting to me is you guys can stand up infrastructure very quickly and what you've done here, you delivery of the benefits of Amazon of the goodness of cloud you, especially in stand up a metro region pretty quickly try it. And it pretty impressive. So I gotta ask you what? Amazon services are most important for your business. >>Um, well, like I said, I think for us it's matching the central services. So we sort of talked about, uh, managing the software, the AP eyes, um, and those kind of the glue. So, you know, for us standing up a new metro is obviously, you know, getting the data center contracts and all the other you know, >>and >>ask yourself, you have to deal with just have a footprint. But essentially, once we have that in place, we can spin up the software in the data center and have it hooked into our central service within hours. Right? And we could be starting channels >>literate >>literally within half a day. Um, so that's the rial win for us is, um, having all that central blue and the central management system and the scalability where You know, we can just add another 10 20 5100 markets. And the system is set up to scale centrally, um, where we can start collecting metrics their cloudwatch from those data centers. We're collecting logs and diagnostic information. Eso weaken the type health and everything else centrally and monitor and operate all of these things centrally in a way that is saying and not crazy. We don't need a 24 7 knock of 1000 people to do this. Um, you know, and do that in a way that, you know, we, as a relatively small company can still scale and do that in a sensible way, a cost effective way, which is obviously very important for us at our size. But at any size, um, you want to make sure if you're gonna go into 200 plus markets, that you have a really good cost model. Um and that's one of the things that where Amazon has really really helped us is allow us to do some really complex things and an efficient, scalable, reliable and cost effective way. You know, the cost for us to go into the New Metro now is so small, you know, relatively speaking. Um, but that's really allows. What allows us to do is a business of now. We just opened up New York, you know, and we're going to keep expanding on that model. So that's been a huge win for us. Is evaluating what Amazon could bring to the table versus other third parties and or building our own? You know, obviously which >>So Amazon gives you the knock, basically leverage and scale the data center you're referring to. That's pretty much just to get an origination point in the derrick. Exactly. That's right. It's not like it's a super complex data center. You can just go in making sure they got all the normal commute back of recovery in the North stuff. It's not like a heavy duty buildup. Can you explain that? >>Yeah. So one thing we do do in our data centres is because we are local. Um, we have sort of primary data centers. Ah, where we do do trance coating and origination of the video eso we receive the video locally, and then we want to transport and deliver it locally. And that way we're not sending video across the country and back trying to think so that that is sort of the hybrid part of our model. Right? So we stand that up, but then that is all managed by the central service. Right? So we essentially have another container cluster using kubernetes in this case. But that kubernetes cluster is essentially told what to do by everything that's running in Amazon. So we essentially stand up the kubernetes cluster, we wire it up to the Central Service, and then from then on, it just we just go into the Central Service and say, Stand up these channels. Um and it all pops up >>with my final question on the Amazon pieces is really about future capabilities Besides having a cube channel, which I would love to head on there. And I told my guys, We'll get there. But what is this too busy working around the clock is You guys are with Kobe tonight? Yeah, sand. I can almost see a slew of new services coming out just on the Amazon site if I'm on the Amazon. So I'm thinking, OK, outposts. The opportunity from a I got stage maker machine learning coming in any value for user experience and also, you know, enabling in their own stuff. They got a ton of stuff with prime the moving people around and delivering the head room for Amazon. This thing is off the charts. But that being said, that's Amazon could see them winning with this. I'm certainly I know using elemental as well. But for you guys on the consumer side, what features and what new things do you see on the road map or what? You might envision the future looking like, >>Well, I think part of it. I think there's two parts. One is what are we gonna deliver ourselves, you know? So we sort of talked about adding community content and continuing to evolve the local beauty product. Um, but we also see ourselves primarily as a local TV platform. Um, and you know, for example, you mentioned prime. And a lot of people are now realizing, especially with Cove, it and what's going on the importance of local television. Ah, and so we're in discussions on a lot of fronts with people to see how how we can be the provider of that local TV content, you know, um and that's really a lot of stationed are super psyched about that to just, you know, again looking to expand their own footprint and their own reach. You know, we're basically the way that we conjoined those two things together between the station's the other video platforms and distribution mechanisms and the viewers. Obviously, at the end of the day, um, you know, we want to make sure local viewers can get more local content and stuff this interesting to them. You know, like you said with the news, it is not uncommon that you may have your Bay area stations, but the news is still may be very focused on L. A or San Francisco or whatever. Um and so being able to enable, uh, you know, the smaller regional outlets to reach people in that area in a more local fashion, uh, is definitely a big way that we can facilitate that from the platform. And, you know, if you were perspective, so we're hoping to do that in any way we can. You know, our main focus is make local great, you know, uh, get the broadcast world out there, and that's not going anywhere, especially with things like HSC tree. Uh, you know on the front. Um, and you know, we just want to make sure that those people are successful, um, and can reach people and make revenue. And, you know, >>you got a lot of it and search number two. But I think one of the things that's just think about your project that I find is a classic case of people who focus in on that Just, you know, current market value investing versus kind of game changing shifts is that you guys air horizontally, enabling in the sense that there's so many different use cases. I was pointing out from my perspective journalism, you know, I'm like, I look at that and I'm like, OK, that's a huge opportunity. Just they're changing the game on, you know, societal impact on journalism, huge education, opportunity for cord cutters. You're talking about a whole nother thing around TV. I gotta ask you, you know, pretend I'm an idiot for a minute by our pretending that this person from this making I amenity after I don't understand is it Isn't this just TV? What are you doing? Different? Because it's only local. I can't watch San Francisco. I'm in Chicago and I can't watch Chicago in San Francisco. I get that. You know why? Why is this important? Isn't this just TV? Can I just get on YouTube? Mean Tic tac? Well, talk about the yes >>or no. I mean, there's TV, and then there's TV, You know, as you know, um and, you know, if you look at the TV landscape just pretty fracture. But typically, when you're talking about YouTube or who you're talking about, sort of cable TV channels, you know, you're gonna get your Annie, you're going to get some of your local to ABC and what not? Um, but you're not really getting local contact. And So, for example, in our Los Angeles market, um, we there are There are about 100 something over the air channels. If you look at the cross section of which of those channels you can get on your other big name products like you lose your YouTube TV, you're talking about maybe 1/2 a dozen or a dozen, right? So there's like 90 plus channels that are local to L. A. That you can only get through an antenna, right? And those air hitting the type of demographics. You know, quite frankly, some of these other players or just, you know, don't see is important >>under other minorities. Back with immigrants, you know, hit the launch printers of our country. Yes, >>exactly. You know, So, you know, we might see a lot of Korean channels or Spanish channels or other. You know, um, minority channels that you just won't get over your cable channels or your typical online video providers. So that's again Why, you know, we feel like we've got something that is really unique. Um, and that is really underserved, you know, as far as on a television sampling, Um, the other side that we bring to the table is that a lot of these broadcast channels are underserved themselves in terms of technology. Right? If you look at, you know, at insertion, um and you know, a lot of the technical discussions about how to do live TV and how to get live tv out there. It's very focused on the o t T market. So again, going back to who lose and >>the utility well, over the top of >>over the top. Yeah. Um and so this broadcast market basically had no real evolution on that front in a while, you know? And I sort of mentioned, like the way ad buying works. You know, it's still sort of the traditional and buying that happens a couple weeks in front. Not a lot of targeted or anything ability. Um, And even when we get to the HSC three, you're now relying on having an H s street TV and you're still tied to an antenna, etcetera, etcetera, which is again, a good move forward, but still not covering the spectrum of what these guys really want to reach and do. So that's where we kind of fill in the gaps, you know, using technology and filling in the gap of receiving a signal and bringing these technologies. So not only the ad insertion and stuff we can do for the life stream, Um, but providing analytics and other tools to the stations, uh, that they really don't have right now, unless you're willing to shell out a lot of money for Nielsen, which a lot of local small stations don't do s so we can provide a lot of analytics on viewership and targeting and things like that that they're really looking forward to and really excited >>about. I gotta ask you, put you on the spot. He'll because I don't see Andy Jassy. It reinvented might. Hopefully I'll see him this year. They do a person event. He's really dynamic. And you just said it made me think he tends to read his emails a lot. And if your customer and you are. But if you bumped into Andy Jassy on the elevators like, Hey, why should I pay attention to? Did you? What's why is it important for Amazon? And why is it important for the world? How does it raise the bar on society? >>Well, I think part of what Amazon's goal And you know, especially if you get into, you know, their work in the public sector on education. Um, you know, that's really where you know, we see we're focusing with the community on local television and enabling new types of local television eso. I think there's a lot of, uh, advantage, and, um, I hate the word synergy, but I'm going to use the word synergies, you know, um, this for us, You know, our goals in those areas around, you know, really helping, you know, Uh, you know, one of the terms flying around now is the dot double bottom line, where it's not just about revenue. It's about how do we help people and communities be better as well? Um, so there's a bottom line in terms of, uh, people benefit and revenue in that way, not just financial revenue, Right? And you know, that's very important to us as a business as well is, you know, that's why we're focused on local TV. And we're not just doing another food. Go where it's really easy to get a night. The national feed. You know, it's really important to us to enable the local, um, community and the local broadcasters and local channels and the local viewers to get that content, Um, that they're missing out on right now. Um, so I think there's a energy on that front A so >>far, synergy and the new normal to have energy in the near normal. You know, I think I think Kobe did. >>And you know, um, and some of the other, uh, things that have been happening in the news of the black lives matter and, um, you know, a lot of things going around where you know, local and community has been in the spotlight right and getting the word out and having really local things versus 100. Seeing this thing from, you know, three counties away, which I don't really care about, it's not telling me what's happening down the street, like you said, Um, and that's really what we want to help improve and support. >>Yeah, I know it's a great mission is one we care a lot of cute. We've seen the data content drives, community engagement and communities where the truth is so in an era where we need more transparency and more truth, you get more cameras on the street, you're gonna start to see things. That's what we're seeing, a lot of things. And as more data is exposed as you turn the lights on, so this week that kind of data will only help communities grow, heal and thrive. So, to me, big believer in what you guys are doing local be TV is a great mission. Wish you guys well and thanks for explaining the infrastructure on Amazon. I think you guys have a really killer use case. Technically, I mean to me. I think the technical superiority of what you've done. Abilities stand up. These kinds of networks with massive number potential reach out of the gate. It's just pretty impressive. Congratulations, >>Right. Thank you very much. And thanks for taking the time. >>Okay. Dan Drew, vice president of James. Did you start up? That's a lot of potential. Will. See. Let's go check out the comments on YouTube while we're here. Since we got you, let's see what's going on the YouTube front year. Yeah. The one question was from someone asked me, Was stiff from TV Cres that William Dan, Great to see you. Thanks for taking the time on Sunday and testing out this new zoom home recording my home studio, which I got to get cleaned up a little. Thank you for your time problem. Okay, take care.

>>from around the globe. It's the Cube covering HP Discover. Virtual experience Brought to you by HP >>Everybody welcome back to the Cube's continuous coverage of Discover 2020. That virtual experience. The Cube has been been virtualized really excited to have Marissa Freeman here. She's the chief brand officer, Hewlett Packard Enterprise. And, of course, he joined by Jim Jackson. Who's the CMO of HP? Guys, Great to see you Wish we were face to face. But thanks so much for coming on the Cube. >>Great to be here. Hope that you and your family and your friends are safe and well, >>and we're back at you both. Jim, let me start with you. So, uh, this kind of got dumped on you with this pandemic. Different mindset. You have to do a bit flip to goto virtual you talk about some of the things that you focused in on some of the things you want to keep. And some of the things you knew you couldn't. And you had to do things differently. >>Yeah, You know, we pretty much had to rethink everything about this event platforms, how we thought about messaging, how we thought about content. Um audience acquisition demos, really everything. And for us, it really all boiled down to having a vision. And our vision was to bring the Discover experience, all that energy, the excitement that you get the in person event. We wanted to bring that to all of our customers and our partners and our team members around the world. So for us, it wasn't about virtualized discover. It was about bringing the Discover experience to a 12 inch screen. In many cases for our customers and our partners and our team members, I think another thing that was really eye opening for us. Waas thinking of opening up the aperture and thinking, Hey, we can now take this and drive. This is the true global events and we can reach people all over the world, reach customers and partners that can't come to discover because they can't physically come to the event. That was a couple of things that really we had to put a lot of thought into, and it was really exciting for us. I think one other thing is now customers, and how we think about their experience at the event became very, very important for us because you know, at an in person event, it's three days, and we can you know, there's a lot of things people can do, but you have three days of content, and then people move on for us. Now. Our customers might go through three weeks or three months, and we really needed to think about that experience in a very simple, seamless, easy way for them so that they could to consume the content digitally in a way that made the most sense for them. So a lot of new thinking for us. But we're really excited about the opportunities that virtual brings in that digital brings >>now immerse. So I gotta ask you so No, no meter boards at least know for a physical meter boards, you know, How did you think about continuing that branding in a virtual event? >>Well, it's, uh, it's really a beautiful experience when you look at the the intro of the platform that we're on. It's beautifully branded all the way throughout. The branding is really coming through, though, in the content, um, and in the people, So we always say, Jim and I always say every year, Gosh, if we could just have every estimate on every prospect come to discover they would see our brand come to life they would feel are our purpose. They would understand, just with a new and different energized and fully charged a company, we are they would get to meet Antonio and Security. And Liz and Jennifer Income are honored and Jim and feel for themselves, uh, the power of the company. And now everyone can So the brand really is coming to life through the people. I appreciate that you love the the beautiful graphics, and we work really hard. Um, I'm all of that stuff, Sure, but the real branding is in the content itself. So >>now, Jim asses. Well, you were kind of lucky in the sense that, you know, this show wasn't in March or April. You had some time. So to see what others were doing. And you saw early on when this thing first hit, there were some the missteps there, There's there, still are even. But So what do you What do you tell people that is really unique about the Discover virtual experience? >>Yeah, I think a couple things and you're right. We did have a little more runway, and that was to our advantage. But we feel like we've taken full advantage of it. I think the first is coming back to that global experience that I talked about. So we're delivering this on 10 different with translating into 10 different languages, and that makes it easy for people to consume our key content around the world. We're truly delivering our content on time zones that are very appropriate, or our customers and our partners again, all around the world, in different Geos, we're bringing in our geo MVS where they are now having geo lounges, um, specific addresses and other things locally that really enables us to have that local experience. But derive it is making it part of a global event. I think another thing, Dave and you've been Teoh Discover. But you've seen that amazing Discover Expo Hall that we have out there with, you know, literally thousands of people and lots of demos. We had to figure out How do we bring that to a a ah, digital or a virtual experience? And I think the teams have done just an amazing job here. So what we did is we have 61 demos, and this is part of really 150 sessions. But if you just think of demos, we're going to deliver these live over 1717 100 times the first week. That's really, really powerful. This is >>live, meaning >>somebody from HP, a subject matter expert, talking to our customers, answering questions in real time. So that's unique. I think another thing that we're doing is we're not stopping after the first week. The first week is going to be extremely powerful and we can't wait for it. And but, you know, we're gonna extend, if you will, the value we're gonna double click and follow on Wave focused on SMB. Focus on software and containers for more of a developer, audience, Cloud services and other things like that, as well as data and storage. And then finally, I'll say, You know, we're really excited about the great speakers that we have Marissa >>talks >>about. You know, Antonio Qwerty, Irv etcetera. But we've got some great outside speakers as well. Lewis Hamilton from Mercedes Formula 16 time Formula One champion Simone Biles, uh, who's Olympian and world champion, 25 medals. We've got Steve Kerr and they're going to be part of a panel talking about performing under pressure, and we're all doing that. But it's gonna be again a great story we've got, um, John Chambers is going to be joining Antonio and talking about what great companies do during a crisis and how they prepare to come out of this kind of a situation to deliver better solutions to their customers. Soledad O Brien, who is moderating, are women leaders in I t session, and this is one of our most powerful sessions. In fact, Marissa is part of that as well. So we're really excited about this, the amount of things that we were able to bring together. And of course, we also have our CEO Summit and our Global Partner Summit happening at the same time. So we've got a lot of things that we've been able to coordinate all of this and really think about the experience from a digital in a virtual expect perspective to make it great for our customers and our partners and our attendees. A >>lot of rich content layers. Yeah. So what if you could talk about that here here to help Sort of the cultural aspects of that. What it means to your customers, your clients, your employees and your just broader community. >>Well, you know, Dave one when covert first hit the United States, we We had a lot of social media out there, a lot of digital media out there. And even before it came to the United States, when Italy and China were really suffering, we gathered as a team and audited every piece of content that we had pulled all back in. I met daily Jim and I and Jennifer temples. Teams met daily to talk about what is our tone of voice? What are we saying? How are we helping our customers get through? This time we knew how difficult it was for us with business continuity, remote workforce, we needed to help our customers and let them know that we were at the ready right now to help. So we chose to speak through the voices of our leaders. Antonio did several blocks and videos, and we rallied and redid the website completely to be all about over response and how we had many solutions for our cost. Most implement immediately from $2 billion financing Teoh setting up remote workforces, too, doing WiFi in parking lots and turning ships into hospitals. It ran the gamut, Um, and so it was really important to us that we conveyed a message of here to help. Ultimately, we ended up doing a television commercial. Antonio's voice. It was a personal letter from Antonio to his fellows, business leaders and engineers and said, Look, we know what you're going through. We're going through it ourselves. We're here to help. Here's how and it's been really motivating and successful and joy and driving people to find out more about what HP could do to help. So >>I would just add >>to what >>Murtha said. She outlined it really well. But we have some great customer examples and great customer stories as well. They're very emotional talking about how customers really needed our help and our combination of technology. People really came together to enable them to get their businesses up and running, or to address a pain point or problem for their audiences. The first point you know, there's the concept of here to help with the recovery and then here to help with the transformation as well as they look to the future. >>So how are you guys thinking about just sort of growth marketing strategies, branding strategies not only for HP but in the spirit of helping customers in this post isolation economy. Merson. Maybe you could start start us off. >>Well, we we've been talking about how this crisis has brought the future forward, nor our doorsteps. So where our customers may have been on a digital transformation path and they were accelerating it. Now there's there's an impetus to do it right now. So whether you're in recovery, um, or whether you're one of the customers for whom this crisis created a surge of demand and you needed to scale way up, these are the moments of transformation that our company is. Is there to help you with Jim? Do you want to build on that? >>Now? I think you hit the highlights there, Marissa, you know, again for us, I think we wanted to just be authentic and true to who we are as a company. And, you know, our purpose is to advance the way people live and work. And I think we live that during this time and will continue to live that as we go forward. It it's really core to who we are. And what we saw is that many of our customers really valued the fact that when they needed us the most, we were there for them and we were there for them all around the world. And, um, you know, and our goal is to continue to do that and continue to delight them and to be the best transformation partner for the future. >>I mean, culturally, we obviously re observe all this stuff, but culturally, you kind of be kind of had a heads down approach to all of this. I mean, there was there was not a hint of ambulance chasing in what you got. How you guys approach this. So I mean, I think I think culturally that here to help message it seemed like a very strong roots in citizenship. Um, you know, And then, of course, with social uprising, respect for individuals that seemed to shine through. I don't know. I know versus deliberate or that's just again cultural. Maybe >>it's it's all of the above. You can't change who you are and we need at Hewlett Packard Enterprise are people who care about other people our purpose. As Jim said, Our purpose is to advance the way people live in or every one of us every day gets up and goes to work or goes to work at home at HP to do just that. That is who we are. And so it would be an authentic for I think, true to this crisis in any other way. >>I think I wanna make an observation and see if you guys to respond. So we always talk about technology disruptions. Mercy you mentioned about, you know, the future was put forward. I'm sure you've seen the wrecking ball. You know, the folks in the building, the executives very complacent. A digital transformation not in my day. And in the 19 wrecking bald covert 19 survey, you probably saw that Who's who's leading your digital transformation CEO CTO or Covert 19. But it's really now. I mean, if you're not digital, you're not doing business. So but my observation is that it seems like despite all this technology that global disruptions are going to probably have a bigger impact in this coming decade, whether it's pandemics of social upheaval, of natural disasters, etcetera. But technology can play a huge role in supporting us through those things. Jim, I wonder if you have any thoughts on that comment. >>I mean, I think it's it's a great question, you know, if you think about it, What what happened with the macro economy Cove? It It's been a catalyst for, I think, everybody to understand that they needed to really accelerate their digital transformation. And, more importantly, they need a partner who can help them on that journey as well. I mean, if you just look at what we're talking about here >>with >>this event, right, most of h p e. And, um, you know, our >>competitors to >>cancel their virtual events >>are canceled their physical >>events rather, and they're moving now to a digital event in any way. This is going to be the new normal for us, right? So I think as we go >>forward, we're gonna >>see this only continue to accelerate. And for us, you know, our edge to cloud platform as a service strategy plays really well to helping customers accelerate that digital transformation. And, you know, it just kind of comes back to what Marissa said. You know, here to help is very very HP in terms of it's authentic and it's here. We want to be here to help our customers in their biggest hour of need. And we're doing everything we can and will continue to do that for the future as well. >>Versus, you know, having done many, many discovers we've noticed over the last several years you guys made a much bigger emphasis on the sort of post discover which a lot of organizations don't have a big physical event, and it's sort of on to the next thing. And how do you see the post from a branding standpoint? Messaging, etcetera. How do you see taking advantage of that from a virtual standpoint? And what have you learned? >>Well, we've been on our own digital transformation journey, and, you know, through Jim's leadership, we have built a pretty serious digital engine, which allows us to have a personal relationship with the customer, meet them where they are on their terms. For example, with this platform, it's even using your now because we we actually will know what content would see what sessions, what demos someone interested in. Maybe they put it, you know, on their schedule, and then didn't get to do it. So we'll go back to them later and say, Hey, we saw that you wanted to do this. It's still here. Why don't you come and have a look and then watch to that We do sort of the Netflix engine, the been newsworthy playlist of If you like that, you like this. And if you like this, you like that and we bring them through the breadcrumbs all the way through. And it's a self directed journey, but we're there to help. And that is really the true power of digital is to have that interaction, that conversation with the customer and where they want to be and with what they want to learn and read about. We'll see. >>Yeah, And everything, of course, is instrumented gym. We'll give you the last word and you were involved, as was Marissa in sort of the new HP. The new branding and the whole purpose of that was really to get Hewlett Packard enterprise focus and really back to sort of the roots of innovation. And I wonder if you could comment on from a strategy standpoint, innovation and from a competitive standpoint, you know where you're at over the last several years, we've obviously transformed as a company and where you see your competitive posture going forward. >>Yeah, you know, for us, um, we're so excited about this event because this is a great opportunity for us to showcase progress against our edge to cloud platform as a service strategy, and we roll this out last year. It's differentiated. It's unique in the marketplace. It demonstrates the transformation happening across as a service and software at Hewlett Packard Enterprise. So we are a company in transition, aligned to what we feel, our companies, our customers, biggest pain points. And when you look at some of the acquisitions that we've made some of the organic investments that we've done, we're just very well positioned to deliver against, you know, some very unique pain points that our customers have. Plus, I think another thing is, at the end of the day, really, what our customers are saying is, help me take all this data and translate that data into insight and that insight into action. You're going to hear us talk about the age of insight and how we're really again unifying across edge the cloud to deliver that for our customers. Stone. We're excited for this event because you're going to hear a significant industry revealed, focused around cloud services around software and really a lot of the things that we've been talking about. And we're going to show a lot of progress as we continue on that journey. And then, you know, Murtha mentioned digital. I'm really excited about digital because that enables us to understand and learn and help our customers and deliver a better experience for them. And then finally, you know, huge opportunity for us. Two. Take this message out globally, you know? Ah, great opportunity for people all around the world who maybe haven't heard from HP for a while to see our message, to feel the new energy to see who we are to see. Uh, you know that we're doing some very interesting things that we can help them. So we're excited. There's a lot of energy right now inside the company, and, uh, we're ready to kick it off and get rolling here. >>Well, it's quite amazing. I mean, we started off 2020 with the gut punch, but the reality is, is that 20 twenties? A lot different than 20 pens. If it weren't for technology and companies like HP here to help center, you know, we would not be in such such good shape and good in quotes. But think about it. The technology is really helping his power through this. So Jim Morrison, Thanks so much for coming on the Cube. Thank you, HB. Everything you're doing for customers in the community. Really? Thank >>you for having us. Thank you for having me. Good to see you. >>Great to see you guys to and keep it right there. Everybody, this is Dave Volante for the Cube. Our continuous coverage of hpe discover virtual experience in 2020. We're right back right after this short break. >>Yeah, yeah, yeah, yeah.

>>from around the globe. It's the Cube with digital coverage of Red Hat. Summit 2020 Brought to you by Red Hat. >>Hi, I'm Stew Minimum And and this is the Cube's coverage of Red Hat Summit 2020 course Digital event This year. We're not together at Mosconi, but we are bringing together many of the speakers thought leaders, customers in this very important ecosystem. Really excited to welcome back to our program. Stephanie Cheers. Who's the vice president and general manager of the Red Hat Enterprise Linux business unit inside of Red Hat. Stephanie. So great to see you have to give you a virtual hug high five year, but you know, always great to see and have you on the program. >>Oh, thank you. So it's great to be here, and this is what together means today. But it's great to be together with you >>again. Here it's limit. >>Yeah, the discussion is you talk on it together apart for for a time we talk in tact. That change is one of the only constants that we have, and there are more changes than ever happening right now. So before we get into kind of your B you talk a little bit about, You know, some of the big changes. There's organizational changes, you know, I know we spoke to you about in 2019 at IBM Think and Red Hat Summit because you've worked for both sides of the equation here, Uh, give us kind of the latest from your standpoint. >>Yes, certainly the leadership changes which have been public now for a couple weeks. Those were a big change >>for for us. I think one of the things that has come through is IBM has really been respecting what red hat is. What? Um, what we do. But also how we do it is very important and valued. And we at red Hat >>believe in it so strongly. We're sticking to what Red Hat does best. Everything is open source. Everything is collaborative. And honestly, I have to say it. It >>feels great as a red Hatter to see Jim in the position he's in at IBM Um, Paul's passion, >>which clearly comes across in his keynotes and >>his passion for how we do have an open source development model. It's great to have them now take over the CEO role for Red hat. So it's it's really exciting times. I think. Last year when we spoke, it was, um it was a bit of a wait and see and see what happens. And I think now the recent announcements really solidify this sort of synergy and partnership that IBM and Red Hat have and what our intentions are in the market. But at Red Hat, we still stay red hat, and we're still driving things the way we always have. And that's great. Feels that >>that that's great. And thank you so much for the update. So when we talk about your business unit that the Red Hat Enterprise, Linux, of course, Rehl, um, you know, I've got a little too much history, you know. I go back when it was, whereas, you know, before well and kind of wash the growth of Linux sto become really, you know, the underlying fabric of so much of what we see out there today for all of businesses, so many companies could not exist if it wasn't for Linux. And in the seven years we've been having the Cube, of course, we've really watched that that moved from Lennox to not only be some of the foundations of what's happening in customer's environments, but also a major piece of cloud and cloud. Native S O. You know, give us that up date as to, you know, here in 2020. You know why? You know Linux has been around for quite a long time, but, you know, it's still is relevant. >>Yeah, so that's it. That's a >>great leader and ties exactly to how we look at well in the red hat sort of entire portfolio. Um, when you look at Lenox of how it evolved, it started out as being a bit of a cheaper alternative to units. But it quickly became, because of the open source way and collaborative way it's developed. It quickly became sort of this springboard for innovation because you have all these incredible innovators collaborating upstream. All of that has fed to a whole different view of what Linux is. Is cloud exists because of Linux is containers are just a different deployment mechanism or Lennox workloads, artificial intelligence. All those APS are built on Linux, so it's become this standardized foundation upon which innovation is done today, And for me, that's the most exciting thing, because it red hat and rail. Our goal is to one. Have it just work right? It has >>to be the standard. And, um well, sometimes that can be misinterpreted. It >>is boring or a commodity. It is anything but a commodity. It's probably one of the most strategic decisions that someone makes. Is which Raoul Distribution? Which Red Hat, which Lennox distribution did they use and that really take real pride that it's built for the enterprise? It's build for security. It's built for resiliency, and all of that build it once deploy anywhere, translates into also using all the innovation, all the container ization capabilities, using it across multiple public clouds. So it's really that combination of having it just work, be the foundation of where you build once and then being able to leverage all the innovation that's coming out of the open source world today. >>Yeah, really interesting points. Stephanie, I think back to when we talked for years about the consumer nation consumers, consumer ization. Excuse me of I t and people thought that therefore, there wouldn't be differentiation, you know, just by white box things and everything will be off the shelf. But if you look at how most companies build things, they really hyper optimize that. I need to build what I need. I need to use the tools that are available, and I need to be able to be agile. You know, I want one of my highlights last year talking to a lot of companies going through their digital transformation and a number of them at Red Hat Summit last year where they talked about both the organization and technology changes that they're making to move faster. And, of course, your portfolio is a big piece of helping them move forward. >>And that's one thing we're seeing that that ability to consume, innovation and get the >>most and extract the most out of what they're running today in their data center. As customers transform and take on this digital transformation, it's not just a technology statement. In most cases, it's an organizational statement as well. And how do you bridge both those and move it forward? It's one thing we focus a lot on right with the open innovation labs, with a lot of customers as well, because it's not just about the technology, it's about the way we work in the way we do things as well. >>Yeah. So, Stephanie, you know, every every year or so I hear it's like, Oh, well, we've got a new way to To the operating system. There was the Jeff just enough operating system for for a while when container ization came out, there was little company named core Os. That was like, Oh, we're going to make a thin version or core OS is now Ah, piece of red hat. Um, so still, with the cloud, there's always, you know, we're going to change the way the operating system it's done. Um, we just love your viewpoint as to, you know, Red Hat has, you know, a few options and kind of a spectrum of offerings. But how do your customers think about the OS these days? And you know, how should we be thinking about rail specifically in that overall spectrum? >>No, it's so that's a great question, too. And we look at >>it as Lennox and Rehl is be one thing that stays the same and helps you get the value out of all the work you've already put in all the development work you've already put in. And make sure that that translates to the future, where everything is changing, how you deploy where you deploy what you deploy. All of that may change, but if you want to get the value out of the work in the development that has been done yesterday, you need something to stay the same. In our view, that's real. We build it with um in mind for the enterprise along lifecycle security support. We build all of that into it so that when you build on a rail monorail kernel, you can take that. If you want to deploy it in a container, you can deploy on Rehl itself. Or if you need orchestration, you can deploy it on open shift. And that's part of the reason why you mentioned Core OS. So we now have a rail core. OS is within open shift 4.0, on beyond, of course. But what we did was we tailor down what is. In reality, it's the same packages. It's the same certification, security, all of that work that we put in. We take the core OS piece of it, what's essential and really optimized for open shift. We build that into an immutable image, and it goes out as part of open shift. It's not available separately because it's really tailored. What we pick the life cycle is all matching open shift, >>and what that does >>is provide you on open shift experience. That's easy to update fully across the board, all the way down to the kernel. But you know, it's the same Lennox that you have in rail, >>and it's that consistency >>of technology that we really strive for. Um, same thing in public Cloud. So when you build an image on Prem on REL, you can take that image up into the public cloud. And no, it's the same level of security and it just will work, you know, part of part of my team. And we take a lot of pride in the fact that it will just work on. And while that >>may not sound super exciting, particularly in days >>like like right now, being dependable and being reliable and knowing that it's secure, all of that is really important when you run your business that those those features or anything but commoditized >>Well, yeah, I think one of the real volumes that customers see with real specifically is there's so much change going on there, and you look at the Linux community, you look at what open shifts doing in the Kubernetes community. There's so much coaches going on red hat packages that make sure that you don't need to think about the almost chaos that's going out there in all of those communities. But you packaged those together. So Stephanie rarely was, of course, one of the highlights of last year's Red Hat Summit. So we'd love to hear you know, if you've got any good customer stories, really, the momentum of relate as you've seen it, you roll out around the world as and then we'll talk about the new updates. You have this. >>Yeah, great. So Rehl eight was a big deal for us last year, as you remember, and partly because not >>only all the features and functions, of course, which we put into it, but also because we really wanted to reposition what the value of an operating system is within a data center and within their innovation future. So we really focus all the features and functions into two buckets. One is about how do we help you with the operating system? Run your business better, more efficiently If the most out of the systems you have in the critical workloads that you run today and how do we use the operating system to help you bridge into the next level of innovation? What's coming down the pipeline? Things like containers. >>And we really wanted to >>make sure that, as we see you know, most customers are looking to how they digitally transform. But of course, no one has the freedom to throw away everything they've done in the past. They want to build upon that and get value out of it. So we really focused on balancing those two things now, as we look at. In fact, one of the commitments we made because we heard it from customers was they wanted a more predictable deployment of our minor releases and our major releases. And we committed, um, at the REHL eight launch that we would be delivery minor releases every six months, major releases every three years, and we have held to that. We delivered 816 months after we delivered eight. And now you saw last week we delivered eight dot too. Um, this is what it means for us to stand by our world and be dependable as an operating system. And the beauty of the subscription with well is that if you're a customer and you're running REL seven, particularly in times right now, it's It's not that easy to get into your data center, perhaps. And so if you don't choose to update to eight now, you can stay on seven until that time works. That's to me. That's part of the beauty and the flexibility of the subscription model. We have course want to continue to bring your new capabilities and new features. But the subscription Our goal is to have a value subscription that you can you can get the most value from No matter when you decide to upgrade or no forward with, uh, with a different releases, we have >>Well, you can go. And congratulations on keeping the releases going on schedule. One of the nice things about open source is we can see the roadmap out there. You've made this Ah, this promise and you're keeping to it. So ah is you said the announcements we made has been talked about in the keynote. So give us a couple of highlights. Says what people to be looking at and looking to learn more when they dig into a thought to >>Yeah, great. So we really wanted to stick with a few key >>messages with it, and they do really tie to How do we help you run your business? And how do we help you grow your business? It's one thing that we announced and what we pivoted to, um, with the eight dot io is we >>really moved to? How do we How do we >>deliver what we called an intelligent OS, which means an OS that helps you bridge the gap and brings more value to you in your data center than you got before? One of the key aspects to this was adding in the capability of red hat insights, and we added insights capability into every single rail subscription that is under current support. So whether or not you moved to relate whether you have real seven, if you have a supported version of real six, all of those had insights added to it, and what insights is is a as a service on cloud at red hat dot com and link up your servers, and >>it will give you insight >>into operational capability. Is it configured correctly is it could be optimized for better performance. Where are you on your C V E updates and what it does is take all that knowledge that Red Hat has from all the support cases and things that we're seeing what's happening in the industry, what we're seeing other customers have, and we can even proactively help customers. The feedback on this capability has been huge. In fact, you'll see in the announcement last week we've added a lot of new capabilities into this specifically For that reason we've had customers, you know, it's like having it's like having more ops people on my team because I'm getting this input in directly from Red Hat for things to look at. And so that, to me, was probably one of the key aspects that, as we look from going to eight into eight dot too, how do we build up that capability? And of course, last week you saw we added a lot to that, and I think now more than ever, we want to make sure that everyone who has a real subscription is getting the most value out of that and I think insights is one of the places where if you have a subscription and, um, you can value or you can get more value from operational help, insights is a place where we want to help you. Um, we everything we had prior we have now bucket sized into a capability and insights called advisor is really about performance, stability and security and doing an analysis for you. We've added a new capabilities around vulnerability, Right. How do you re mediate common vulnerabilities and exposures, compliance aspect, patch aspect policies and drift? Um, kind of all of those we've now bucket it in into that insights capability. So this friends a lot more value to something that we have already seen. Customers say, You know, we didn't expect to get this amount of input and continuous growth because we constantly add new new rules into that engine. And so you know what? What we what we knew yesterday will be what we know tomorrow, and we look forward to sharing with that with everyone >>who has a subscription. So this is >>a place where I think it's ah, it's an important place for folks to look, particularly now because operational efficiency is really key. And security is really we have a lot of capabilities in both. What? Yes, Please, >>please, please, go ahead. Now, >>one other aspect on that that I wanted to mention >>was we also added a capability called subscription watch and subscription Watch helps you get a very simple, clean view of all the subscriptions you have and where they're running. And that was one thing that we saw. Customers say there was friction. And how do I know where my entitlements are? How I'm using them across my entire enterprise Corruption watch can help with that. So, um, this sort of cloud dot red hat dot com capability that we can assist with and is already part of your subscription. These are the kinds of things that we really want to help augment this to make Really intelligent os for the enterprise. >>Yeah. Stuff Stephanie. The comment I was gonna make is there's certain shows that I go to that every year. You go to it, You say Okay, it's a little bit bigger. They announce something. They made some progress on it. What has impressed me most about going to the red Hat show year after year is really the the growth of the of the portfolio, if you will. So when I first started going to it, it was, you know, a lot of the people there were, you know, the hard core Linux people. Um And then, you know, there's some storage people, some networking people is cloud containers really grew. It really blossomed into this really robust ecosystem. Oh, and growth there. So would love just to get your viewpoint on, you know, the skill set because, you know, I'm sure there's plenty of companies out there that are like, Well, you know, I've got some people that are, you know, my limits people, and they do things that aren't there. But, you know, how do you see kind of the skill set and what what Red Hat's doing really permeating more and more of, of companies, day to day activity. >>I think one of the things that I'm >>most proud of is even since last year's all the deeper collaborations we have between the various product lines. Certainly we'll talk with Joe Fitzgerald, and he and I work together very closely. Capabilities like insights. How do we add answerable capabilities directly into real. And what that does is really help. I think in any customer today, skills is probably one of the biggest concerns that they have. How do they grow those skills? How do they help folks grow and learn more and progress into the innovation areas? But clearly they still need their their mission critical applications to run and how do they span that? And I think what we're really trying to do is be able to bring the strength of the portfolio together to help a customer have more flexibility in how they leverage their skills and how they grow their skills. >>Because I think coming back to >>that statement that that you made earlier it's not just about technology. It's about how, if >>you really want to be, have agile, it's about >>how a company has organized. And I think we're hoping that we bring together the strength of the portfolio so that a customer is able todo leverage their organization and leverage their skills and the best way possible. I think another place where we worked hard on eight dot too. Some similar lines of bridging the portfolio was, you know, we announced back in eight dot io. We were putting container ization tools directly in Terrell with build a pod made in scope e 08 dot too. We brought in the newest versions of Scope EO and Build Up. In fact, in tech preview, you get containerized versions of those, and so we're continuing to add. What we are seeing is the container ization is a journey for customers. Many customers just want to deploy a single container on a server. Or they were. They want to deploy a single container in a VM Um, they're not ready for orchestration. We wanted to put the tools in so that a customer could do that on REHL. Get started, get those containers deployed on REHL. Put those tools directly, and we added it to old protocol, which is a tool built for security. It brings that security of SC Lennox and brings that up and adds value at the container level. It's those kinds of things as you see the bridge from well into open shift. How do we help a customer rich? That skill journey as well along that path and I think right now in kubernetes and Containers skills is a is a big, big area of focus, so the more we can help ease that across the portfolio and bring those things together is really important. And I know we're working very closely with the chefs in the, um and the team there in order to help bridge that. >>Excellent. Stephanie, I just want to give you the last word. We talked a lot about the ongoing journey that customers are going through. So give us your final take away as to how customers should be thinking about red hat in general and role specifically as their journey goes forward. >>I think I think one of the things >>we're very proud of here at Red Hat is that we always, particularly in the open source communities with our customers, with our partners, we want to roll up our sleeves and help, and that's we want. So, developer, we wanna work upstream with you. It's one of the things we're very proud of, and now, particularly in this time it's We want to make sure that folks understand we're here to help, and we want to make sure that you're getting the most out of the subscriptions you have, Um, and we help. We help you on that journey both to get the most out of you can out of your data center today. But also be ready for the innovation that you want to consume going forward. And we're collectively working across red Hat in order to make that happen. But it's, um >>even though this is different and it's there the virtual Experience edition of Red Hat Summit. It's >>great to be together and be able to share the whole message. >>Well, Stephanie, the open source community is definitely used to collaborating remotely. So thank you so much for joining us. It's a pleasure to see you. And we would hope to talk again soon. >>Great to see you too. Thank you for the time. >>Alright. You're watching the Cube's coverage of Red Hat Summit 2020 digitally with remote guests from around the globe. Instrument a man and thank you for watching the Cube. >>Yeah, yeah, yeah.

(techno music) >> Live from Barcelona, Spain, it's theCUBE. Covering Cisco Live! Europe. Brought to you by Cisco and it's ecosystem partners. >> Welcome back to theCUBE's exclusive coverage here at Barcelona, Spain of Cisco LIVE! Europe 2019. I'm John Furrier. Stu Miniman, and Dave Vellante here this week covering all the action in cloud, data center, multi-cloud. Our next is Jim Frey who's the Vice President's alliances at Kentik Technologies. Groundbreaking report that came of the Amazon Reinvent Conference. A lot of customers. Part of the multi-cloud discussion. Jim, great to see you, welcome to theCUBE. >> Thanks. It's Frye by the way. >> Frye. I'm sorry. >> Okay. No worries. No worries. >> Multi-cloud, your report has some interesting data. Talk about the survey, the results. What is is telling us? >> Yeah, we've been working hard at Kentik on extending our solution to start covering the cloud, multi-cloud server hybrid environments. And so we were at the AWS re:Invent show and we decided to take the opportunity to talk to some of the attendees and just sort of get their view of what some of the challenges are. So we talked to a little over 300 of em and we asked them a few questions not a, you know, rigorous thing, you're doing it on the show floor, right? But we found some really interesting things out of that. So the first thing is is that it really is a multi-cloud world already. More so even in hybrid. And so we had nearly 60 percent. 58 percent of the people who we talked to had more than just one of the cloud in play. They almost all had AWS of course cause it was an AWS event, but not all, of which is really interesting. But, you know, they either had AWS plus Google or plus Azure or plus some other cloud. More so than even hybrid. And so we also asked, are you using AWS in conjunction with you know, your own private data center or a third party host to go low center. Only 33 percent were doing that. So, we were surprised. And the reason that that is really significant is monitoring in management of these environments is much more complex in a, well. It's complex in a hybrid environment. It's even more complex in a multi-cloud environment. So it sounds like there's some real need for some help there. >> What are the challenges and what are the some of the complexities? What are the challenges in the monitoring? >> Well, so that was the next question. What's the key challenge, ya know? And usually whenever you ask someone about the challenges, the number one answer is always, oh, security is my biggest concern. That did not turn out to be the case here. The biggest overriding concern across all the different sort of levels of people we talked to was actually cost management. And cost management is, it was a bit surprising. You know, but, usually, you hear security, security, security and then something else. This was cost management either number one or number two. And number one for most of the constituencies. And in some of the subgroups, like VP level, SVP level, architect level, it was overwhelmingly the first choice. 40 and 50 percent of them are saying yeah, cost control is their biggest issue. Even ahead of other things like performance, like visibility, like actual, you know, control of the environment. You know, its cost was really the biggest concern. That's the big issue. >> Jim, something we've been tracking especially at shows like this, at the Cisco show is the challenges I used to understand kind of the stuff I had in my data center. I could get my arms around it. I might not love the management tools that I have. I might complain about some of the cost. But, it's all very well understand. It's bought most of the cap and freight. When you get to the public cloud, like totally understand what you are saying. multi-cloud. Now I've got all these different pieces and how will I have them defined. There's different skill sets between them. >> Right >> And when it comes to cost, right, the big unknown is oh wait, am I getting surprised by what happens, in that environment and across all them, I mean, I've talked to plenty of companies that will dedicate an engineering resource just to manage cloud or >> Right. >> I have many friends in the industry that are helping you know, cost optimization is something that is, you know, software consulting, there's huge business in that because we're still early in this getting to the steady stage. Help us connect the dots. Where does Kentik play into this then. So you talk to all these customers. >> Thank you. Our viewpoint is network and we're trying to give a viewpoint of what's happening in this environment by watching the network. And that's always super valueable because it helps you localize where things are, you know, what activity's happening and it helps you see, you know, which workloads are talking to which workloads. And that reveals sometimes things you don't expect. And this is where the cost control come in because you know, the cloud environment, you have to pay for certain network traffic. Especially between availability zones or when you're shipping it out of the cloud back to your other, you know, your home environment. And we have talked to a lot of customers who have said, hey, end of the month comes around, I get my bill and there's this big number there for data, you know, transfer. I don't know what drove that. And why am I being surprised time and time again by this. Well then there were viewpoints really awesome for seeing that. And if you can do it with a monitoring system that's watching for that all the time, the good news is that you can catch it, figure it out if it's real or not, needed or not, and fix it before 20 days later you get a big, fat bill. >> What does fixing it mean, does it mean like keeping it contained in the cloud, or on frame, or managing what's moving around? >> Could be combination of things, one of the things that we've seen in some of our earlier deployments are someone moved a workload into a different availability zone. Well, there was an application dependency they didn't recognize. And, you know, that workload was talking to, you know, home datacenter, or the another availability zone, and creating traffic across there and just running up the meter on the network costs. So if you can see that and it becomes very obvious to watch the traffic patterns. You can at least have someone go say, Hmm, okay, that's a surprise. They had a big rise to my zone to zone traffic or my, you know, cloud to home traffic. Let's just take a look to who's driving that and whether something that should be or shouldn't be. >> One of the interesting trends we've been watching on scene with cloud and hybrid cloud is kind of the consumption and deployments of cloud and hybrid's interesting because hybrid's with a cloud operation on premise. Which is been slowest to deploy. WikiBound's done a lot of research on private cloud and why that's happening. But it seems that clouds sprawl on the public side has been there. So yeah, I've got some Amazon, easy to stand up. I've got some Azure and now Google. So it's probably easier to get stuff in the clouds and then now they've got repurpose on premise to kind of have this seamless cloud native environment and Cisco's announcements, et cetera, et cetera. >> Yeah >> So as that's happened, what have you guys learned and scene in terms of the customer behavior. They wake up obviously, the bills are higher, so makes sense that the cloud is higher than hybrid and the cost containment is a concern. How did they get there? What are you seeing? And what's the psychology the customer just share some insight into the customer behavior. >> Well. >> Oh shoot, I got to unwind this, do I double down? What's going on? >> I think it really depends a lot on what the projects are, what the objectives are and what the skillset is. But one of the things that we found in this survey is that, network viewpoint that helps you understand what's really happening in the production environment is often being underutilized or underappreciated in the cloud environments, in the cloud, you know, deployments in cloud infrastructure. So one of the things we asked about was, how many of you folks at this event are actually taking advantage of, for instance, VPC Flow Logs, which can tell you exactly what's happening with an AWS, and between the availability zones. And it was surprising, they've been around, VPC Flow Logs have been around for years as a technology and as an additional service available. But, only about a third of the response were actually using them. So they weren't taking advantage of this important insight and viewpoint ceilometery set. About a third kind of knew about em, but wasn't using em yet. And then another third didn't even know what they were. >> Yeah. >> So I think there's still some maturity happening some maturation happening in terms of understanding what can I do about this? How can I get ahead of this? What's at my disposal? So part of the challenge of course then is that I have that piece covered, but as you said now, how do I cover my home, you know, home front? And where do I find, you know, some sort of tools that can be put these things together so I can see it all as one. >> That's where you guys fit in. >> That's where we fit in, yeah. >> So let me get some anecdotes from you. One it's clear that's a, there's a pain point. Take the aspirin. Understand what's going on, contain the bills. Is, give a scenario of what they're doing to contain the, you mentioned a few of them, but also to give an example of where they're using the data to be proactive, so there's the vitamin side of it. The vitamin, aspirin, whatever metaphor. So, you know, I've got contain my cost, I get that. How are people using the data to be more proactive in either architecting or deploying? >> So I think the, I don't know that anyone's being proactive yet. That is certainly the promise and the opportunity. Most organizations are simply want to be more aware of what happened. Or more affectively reactive and you start there. And once you start to realize, hey, I can do this then you can start turning toward being more proactive. So, for instance our solution was built to allow you to trigger corrective actions back to the environment. We don't take the actions, but we can trigger the systems that would change configurations or change policy and then form those systems of, you know, what's happening and what sort of parameters can we recognize that indicated and issue? So we believe that in especially watching the change in patterns of activity, noticing the anomalies. Anomaly detection often times used around security use cases. We do that. But also, it should be applied to operational use cases. When does a new workload pop up or a new, you know, volume of traffic show up that they didn't expect? And if it's something that I recognize happens at a regular basis and I know the answer, let's automate the corrective response. So that's kind of our theory of provide you the understanding of what's happening then with the tools to trigger and automatic corrective action. >> Alright, so Jim, we're talking a lot about multi-cloud this week with Cisco. Of course, you know, Cisco dominant in the networking space. Really feeling out where they live in multi-cloud, how networking plays across all of them. What's the relationship between Kentik and Cisco? How does that work? >> Thanks, so we're a member of CSPP Program. We are a partner. We joined because we manage a lot of Cisco gear. (laugh) So, a lot of our customers have Cisco. A lot of our use cases, historically, have been at the edge of the network, in particular the service providers. So, those that are delivering internet services or using the internet to reach their customers in some way. So, what's really different about us is we do a really deep and detailed approach of integrating a path, BGP path data, PGP rev data and correlating that with the traffic. As well with other enhancements, and augmentations of the data that give business and service context to the network traffic. Makes it more actionable. >> Yes, and what are you doing in the container space? You mentioned edge computing got some interesting use cases maybe explain a little bit where you play there? >> So when I say edge, I'm saying internet edge, not EDGE computing, although we're fascinated by what EDGE computing represents and the new challenges that's going to bring. Now when it comes to containers, actually we're very fascinated working in that area too, because, Jon, as you mentioned, moving and implementation of new cloud workloads is cloud native, using Kubernetes, using things like Gist T O, you know, that changes the environment once again. So, we've actually built a connector into Kubernetes so that we can use that to pull service information, you know, in terms of what workloads, what containers are out there. What are they doing? What's there purpose? So when we show you activity map of, you know, site to site communications we can say, here are the actual, you know, services that are being, participating in this activity. Its G was another place where we're really interested in to look at the service mesh, you know, that's being set up to run and operate communication between containers. Cause that's a new sort of virtual cloud network. It's a way that these containers are communicating. and again, the more you understand about the communication patterns, the better you can recognize problems, the better you can balance and plan, the better you really get a handle on what's really happening. >> Jim, I want to get your thoughts on since you brought up edge of the internet, multi-cloud and hybrid cloud, data moves around, certainly brings up the question of which routes the packets are moving around? There's always been debates but SL lays around, you know, direct connection versus go through the internet? Is China looking at it? So, there's a security kind of concern. >> Yep. >> What's the trend that you're seeing with respect to say either direct connects, cause I'm a company that I have multiple clouds. I have the connections in there. I'm concerned about latency, certainly cost, you know, whether it's cat videos or whatever, or application too. It still costs money. >> Yep. >> So latency's important so each cloud is its own kind of latency issues. What have you seen? >> Well, getting to the cloud and then within the cloud. >> Yeah, exactly. So it's complicated. So, this is a new dynamic, but it's similar concept. Is there standard latencies? Is it getting better? What's the trend look like? >> That's a great question, and I honestly don't have a good answer for you. But I recognize and agree that those are common concerns that we hear. And the best thing at least for what Kentik is doing is to provide the means to measure and understand that. So you can compare what's working. You can, you know, document a baseline, your different options and your different paths, and recognize when there's a real problem occurring. When you start to see latencies spike to any particular cloud service or location or zone so that you can try and get on top of it and figure it. >> That's a classic case of evolution. Get it instrumented. >> Yeah. >> Get the providers, get better what there services. That's the out of, really out of your hands. >> Yeah. >> That's not really. Okay, so, getting back to the survey kind of wrap things up. Interesting it said Amazon the biggest cloud show Azure pops up on the list as pretty high. >> It sure does. >> Makes sense Microsoft's got great performance. I mean Azure's kind of like, they move a lot of stuff into Azure preexisting Microsoft stuff plus they're investing. What's the bottom line summary as you kind of, you know, the aroma of the rapport. What's coming out of the rapport? What's the key insights that you can glean out of this? >> So I think it indicates normal pattern of adoption, and sort of we're growing into this marketplace. It's evolving as we go, you know. We saw big early-end option hopping in like lift and ship approaches to just move stuff into the cloud. Throw it in the cloud. It's going to be cheaper. It doesn't turn out to be cheaper. It can be. Then you've got another, you know, set of organizations that are born in the cloud, right? And they've started out from the beginning. So those two early approaches are merging into how do we really use this as a true, strategic approach to I.T.? What are the real world complexities we're going to deal with? And how are we going to deal with those? It's really no different from the way that technology has evolved within traditional data centers. And why, the way virtualization came in and changed the way we build and architect datacenters. It's awesome. It's great. It save you money in one area, but then it created huge blindspots, cause you couldn't tell what was going on in those virtualization layers, so we had to adapt our operational monitoring, and operational practices to accommodate the new technology. I think we're going through the same thing now with cloud. People recognize that they don't necessarily want to be holded to a single cloud provider. They want alternatives. They want, you know, cost competitiveness. They want redundancy. And so multi-cloud, I think, is becoming more and more real in part because people don't want to put all of their eggs in that one basket. >> And cost certainly looks good on paper at the beginning. >> Yeah. >> But then as you said, there's side effects. It's a system so there's consequences to the system. >> Yes, absolutely. >> When you start growing or whatever. And that's just where people have to work it better. Right? >> Yep. >> That's pretty much the operational. >> I mean, let's apply the same rigor that we used to apply to traditional data center environments. And let's start embracing the cloud, right? >> So, Jim, you've talked about the multi-cloud bid. Why don't you put a fine point on it. There's a reason why you jump from being an analyst into the vendor world. Some people on the outside will be like, well, you know, cloud's been going on for ten years, seems we understand where this is going. But, tell us why, you know, now is so important for this multi-cloud environment and the opportunity that you see again. >> Sure. >> In this ecosystem. >> Kentik in particular what we're starting to hear, very loud and clear amongst the what. Our traditional an initial base of customers was facilities based, service providers and digital enterprise that managed big routed networks and needed to understand better control their relationship with the internet and delivery across the internet. There coming to us and saying, hey look. We're splitting. We're adding cloud workloads. So, we're moving our content that we're serving up into the cloud, you know, more and more of our systems are moving into the cloud and we rely on you for this visibility in our production environment. We need you to add this. So, we saw a demand from our customers to, you know, accommodate this and in parallel we're just really inspired by this next generation of cloud native application development. It seems to be starting to reach that point where's it's becoming reality and it's becoming mature, and it's becoming a reliable approach to I.T. That now's the time to really get serious about bringing these other best practices for the traditional world, and applying them there. >> And the survey data has created proved multi-cloud and hybrid all here, costs can run out of control. You've got to work. You've got to operationalize cloud. And same rigor. I love that. Great insights, Jim. Thanks for coming on theCUBE. Appreciate it. >> Sure. >> Live CUBE coverage here in Barcelona for Cisco Live! Europe 2019. It's theCUBE. Day three, or three days of coverage. We'll be back with more, after this short break. (techno music)

(upbeat music) >> Hi, welcome to this year's Annual Wikibon Predictions. This is our 2018 version. Last year, we had a very successful webinar describing what we thought was going to happen in 2017 and beyond and we've assembled a team to do the same thing again this year. I'm very excited to be joined by the folks listed here on the screen. My name is Peter Burris. But with me is David Floyer, Jim Kobielus is remote. George Gilbert's here in our Pal Alto studio with me. Neil Raden is remote. David Vellante is here in the studio with me. And Stuart Miniman is back in our Marlboro office. So thank you analysts for attending and we look forward to a great teleconference today. Now what we're going to do over the course of the next 45 minutes or so is we're going to hit about 13 of the 22 predictions that we have for the coming year. So if you have additional questions, I want to reinforce this, if you have additional questions or things that don't get answered, if you're a client, give us a call. Reach out to us. We'll leave you with the contact information at the end of the session. But to start things off we just want to make sure that everybody understands where we're coming from. And let you know who is Wikibon. So Wikibon is a company that starts with the idea of what's important as to research communities. Communities are where the action is. Community is where the change is happening. And community is where the trends are being established. And so we use digital technologies like theCUbE, CrowdChat and others to really ensure that we are surfacing the best ideas that are in a community and making them available to our clients so that they can succeed successfully, they can be more successful in their endeavors. When we do that, our focus has always been on a very simple premise. And that is that we're moving to an era of digital business. For many people, digital business can mean virtually anything. For us it means something very specific. To us, the difference between business and digital business is data. A digital business uses data to differentially create and keep a customer. So borrowing from what Peter Drucker said if the goal of business is to create customers and keep and sustain customers, the goal of digital business is to use data to do that. And that's going to inform an enormous number of conversations and an enormous number of decisions and strategies over the next few years. We specifically believe that all businesses are going to have establish what we regard as the five core digital business capabilities. First, they're going to have to put in place concrete approaches to turning more data into work. It's not enough to just accrete data, to capture data or to move data around. You have to be very purposeful and planful in how you establish the means by which you turn that data into work so that you can create and keep more customers. Secondly, it's absolutely essential that we build kind of the three core technology issues here, technology capabilities of effectively doing a better job of capturing data and IoT and people, or internet of things and people, mobile computing for example, is going to be a crucial feature of that. You have to then once you capture that data, turn it into value. And we think this is the essence of what big data and in many respects AI is going to be all about. And then once you have the possibility, kind of the potential energy of that data in place, then you have to turn it into kinetic energy and generate work in your business through what we call systems of agency. Now, all of this is made possible by this significant transformation that happens to be conterminous with this transition to digital business. And that is the emergence of the cloud. The technology industry has always been defined by the problems it was able to solve, catalyzed by the characteristics of the technology that made it possible to solve them. And cloud is crucial to almost all of the new types of problems that we're going to solve. So these are the five digital business capabilities that we're going to talk about, where we're going to have our predictions. Let's start first and foremost with this notion of turn more data into work. So our first prediction relates to how data governance is likely to change in a global basis. If we believe that we need to turn more data into work well, businesses haven't generally adopted many of the principles associated with those practices. They haven't optimized to do that better. They haven't elevated those concepts within the business as broadly and successfully as they have or as they should. We think that's going to change in part by the emergence of GDPR or the General Data Protection Regulation. It's going to go in full effect in May 2018. A lot has been written about it. A lot has been talked about. But our core issues ultimately are is that the dictates associated with GDPR are going to elevate the conversation on a global basis. And it mandates something that's now called the data protection officer. We're going to talk about that in a second David Vellante. But if is going to have real teeth. So we were talking with one chief privacy officer not too long ago who suggested that had the Equifax breach occurred under the rules of GDPR that the actual finds that would have been levied would have been in excess of 160 billion dollars which is a little bit more than the zero dollars that has been fined thus far. Now we've seen new bills introduced in Congress but ultimately our observation and our conversations with a lot of data chief privacy officers or data protection officers is that in the B2B world, GDPR is going to strongly influence not just our businesses behavior regarding data in Europe but on a global basis. Now that has an enormous implication David Vellante because it certainly suggest this notion of a data protection officer is something now we've got another potential chief here. How do we think that's going to organize itself over the course of the next few years? >> Well thank you Peter. There are a lot of chiefs (laughs) in the house and sometimes it gets confusing as the CIO, there's the CDO and that's either chief digital officer or chief data officer. There's the CSO, could be strategy, sometimes that could be security. There's the CPO, is that privacy or product. As he says, it gets confusing sometimes. On theCUbE we talked to all of these roles so we wanted to try to add some clarity to that. First thing we want to say is that the CIO, the chief information officer, that role is not going away. A lot of people predict that, we think that's nonsense. They will continue to have a critical role. Digital transformations are the priority in organizations. And so the chief digital officer is evolving from more than just a strategy role to much more of an operation role. Generally speaking, these chiefs tend to report in our observation to the chief operating officer, president COO. And we see the chief digital officer as increasing operational responsibility aligning with the COO and getting incremental responsibility that's more operational in nature. So the prediction really is that the chief digital officer is going to emerge as a charismatic leader amongst these chiefs. And by 2022, nearly 50% of organizations will position the chief digital officer in a more prominent role than the CIO, the CISO, the CDO and the CPO. Those will still be critical roles. The CIO will be an enabler. The chief information security officer has a huge role obviously to play especially in terms of making security a teams sport and not just falling on IT's shoulders or the security team's shoulders. The chief data officer who really emerged from a records and data management role in many cases, particularly within regulated industries will still be responsible for that data architecture and data access working very closely with the emerging chief privacy officer and maybe even the chief data protection officer. Those roles will be pretty closely aligned. So again, these roles remain critical but the chief digital officer we see as increasing in prominence. >> Great, thank you very much David. So when we think about these two activities, what we're really describing is over the course of the next few years, we strongly believe that data will be regarded more as an asset within business and we'll see resources devoted to it and we'll see certainly management devoted to it. Now, that leads to the next set of questions as data becomes an asset, the pressure to acquire data becomes that much more acute. We believe strongly that IoT has an enormous implication longer term as a basis for thinking about how data gets acquired. Now, operational technology has been in place for a long time. We're not limiting ourselves just operational technology when we talk about this. We're really talking about the full range of devices that are going to provide and extend information and digital services out to consumers, out to the Edge, out to a number of other places. So let's start here. Over the course of the next few years, the Edge analytics are going to be an increasingly important feature overall of how technology decisions get made, how technology or digital business gets conceived and even ultimately how business gets defined. Now David Floyer's done a significant amount of work in this domain and we've provided that key finding on the right hand side. And what it shows is that if you take a look at an Edge based application, a stylized Edge based application and you presume that all the data moves back to an centralized cloud, you're going to increase your costs dramatically over a three year period. Now that moderates the idea or moderates the need ultimately for providing an approach to bringing greater autonomy, greater intelligence down to the Edge itself and we think that ultimately IoT and Edge analytics become increasingly synonymous. The challenge though is that as we evolve, while this has a pressure to keep more of the data at the Edge, that ultimately a lot of the data exhaust can someday become regarded as valuable data. And so as a consequence of that, there's still a countervailing impression to try to still move all data not at the moment of automation but for modeling and integration purposes, back to some other location. The thing that's going to determine that is going to be rate at which the cost of moving the data around go down. And our expectation is over the next few years when we think about the implications of some of the big cloud suppliers, Amazon, Google, others, that are building out significant networks to facilitate their business services may in fact have a greater impact on the common carriers or as great an impact on the common carriers as they have on any server or other infrastructure company. So our prediction over the next few years is watch what Amazon, watch what Google do as they try to drive costs down inside their networks because that will have an impact how much data moves from the Edge back to the cloud. It won't have an impact necessarily on the need for automation at the Edge because latency doesn't change but it will have a cost impact. Now that leads to a second consideration and the second consideration is ultimately that when we talk about greater autonomy at the Edge we need to think about how that's going to play out. Jim Kobielus. >> Jim: Hey thanks a lot Peter. Yeah, so what we're seeing at Wikibon is that more and more of the AI applications, more of the AI application development involves AI and more and more of the AI involves deployment of those models, deep learning machine learning and so forth to the Edges of the internet of things and people. And much of that AI will be operating autonomously with little or no round-tripping back to the cloud. What that's causing, in fact, we're seeing really about a quarter of the AI development projects (static interference with web-conference) as Edge deployment. What that involves is that more and more of that AI will be, those applications will be bespoke. They'll be one of a kind, or unique or an unprecedented application and what that means is that, you know, there's a lot of different deployment scenarios within which organizations will need to use new forms of learning to be able to ready that data, those AI applications to do their jobs effectively albeit to predictions of real time, guiding of an autonomous vehicle and so forth. Reinforcement learning is the core of what many of these kinds of projects, especially those that involve robotics. So really software is hitting the world and you know the biggest parts are being taken out of the Edge, much of that is AI, much of that autonomous, where there is no need or less need for real time latency in need of adaptive components, AI infused components where as they can learn by doing. From environmental variables, they can adapt their own algorithms to take the right actions. So, they'll have far reaching impacts on application development in 2018. For the developer, the new developer really is a data scientist at heart. They're going to have to tap into a new range of sources of data especially Edge sourced data from the senors on those devices. They're going to need to do commitment training and testing especially reinforcement learning which doesn't involve trained data so much as it involves being able to build an algorithm that can learn to maximum what's called accumulative reward function and if you do the training there adaptly in real time at the Edge and so forth and so on. So really, much of this will be bespoke in the sense that every Edge device increasingly will have its own set of parameters and its own set of objective functions which will need to be optimized. So that's one of the leading edge forces, trends, in development that we see in the coming year. Back to you Peter. >> Excellent Jim, thank you very much. The next question here how are you going to create value from data? So once you've, we've gone through a couple trends and we have multiple others about what's going to happen at the Edge. But as we think about how we're going to create value from data, Neil Raden. >> Neil: You know, the problem is that data science emerged rapidly out of sort of a perfect storm of big data and cloud computing and so forth. And people who had been involved in quantitative methods you know rapidly glommed onto the title because it was, lets face it, it was very glamorous and paid very well. But there weren't really good best practices. So what we have in data science is a pretty wide field of things that are called data science. My opinion is that the true data scientists are people who are scientists and are involved in developing new or improving algorithms as opposed to prepping data and applying models. So the whole field really kind of generated very quickly, in really, just in a few years. To me I called it generation zero which is more like data prep and model management all done manually. And it wasn't really sustainable in most organizations because for obvious reasons. So generation one, then some vendors stepped up with tool kits or benchmarks or whatever for data scientists and made it a little better. And generation two is what we're going to see in 2018, is the need for data scientists to no longer prep data or at least not spend very much time with it. And not to do model management because the software will not only manage the progression of the models but even recommend them and generate them and select the data and so forth. So it's in for a very big change and I think what you're going to see is that the ranks of data scientists are going to sort of bifurcate to old style, let me sit down and write some spaghetti code in R or Java or something and those that use these advanced tool kits to really get the work done. >> That's great Neil and of course, when we start talking about getting the work done, we are becoming increasingly dependent upon tools, aren't we George? But the tool marketplace for data science, for big data, has been somewhat fragmented and fractured. And hasn't necessarily focused on solving the problems of the data scientists. But in many respects focusing the problems that the tools themselves have. What's going to happen in the coming year when we start thinking about Neil's prescription that as the tools improve what's going to happen to the tools. >> Okay so, the big thing that we see supporting what Neil's talking about, what Neil was talking about is partly a symptom of a product issue and a go to market issue where the produce issue was we had a lot of best of breed products that were all designed to fit together. That in the broader big data space, that's the same issue that we faced with more narrowly with ArpiM Hadoop where you know, where we were trying to fit together a bunch of open source packages that had an admin and developer burden. More broadly, what Neil is talking about is sort of a richer end to end tools that handle both everything from the ingest all to the way to the operationalization and feedback of the models. But part of what has to go on here is that with open source, these open source tools the price point and the functional footprints that many of the vendors are supporting right now can't feed an enterprise sales force. Everyone talks with their open source business models about land and expand and inside sales. But the problem is once you want to go to wide deployment in an enterprise, you still need someone negotiating commercial terms at a senior level. You still need the technical people fitting the tools into a broader architecture. And most of the vendors that we have who are open source vendors today, don't have either the product breadth or the deal size to support traditional enterprise software. An account team would typically a million and a half to two million quota every year so we see consolidation and the consolidation again driven by the need for simplicity for the admins and the developers and for business model reasons to support enterprise sales force. >> All right, so what we're going to see happen in the course of the coming year is a lot of specialization and recognition of what is data science, what are the practices, how is it going to work, supported by an increasing quality of tools and a lot of tool vendors are going to be left behind. Now the third kind of notion here for those core technology capabilities is we still have to enact based on data. The good new is that big data is starting to show some returns in part because of some of the things that AI and other technologies are capable of doing. But we have to move beyond just creating the potential for, we have to turn that into work and that's what we mean ultimately by this notion of systems of agency. The idea that data driven applications will increasingly be act on behalf of a brand, on behalf of a company and building those systems out is going to be crucial. It's going to have a whole new set of disciplines and expertise required. So when we think about what's going to be required, it always starts with this notion of AI. A lot of folks are presuming however, that AI is going to be relatively easy to build or relatively easy to put together. We have a different opinion George. What do we think is going to happen as these next few years unfold related to AI adoption in large enterprises? >> Okay so, let's go back to the lessons we learned from sort of the big data, the raw, you know, let's put a data link in place which was sort of the top of everyone's agenda for several years. The expectation was it was going to cure cancer, taste like chocolate and cost a dollar. And uh. (laughing) It didn't quite work out that way. Partly because we had a burden on the administrator again of so many tools that weren't all designed to fit together, even though they were distributed together. And then the data scientists, the guys who had to take all this data that wasn't carefully curated yet. And turn that into advanced analytics and machine learning models. We have many of the same problems now with tool sets that are becoming more integrated but at lower levels. This is partly what Neil Raden was just talking about. What we have to recognize is something that we see all along, I mean since the beginning of (laughs) corporate computing. We have different levels of extraction and you know at the very bottom, when you're dealing with things like Tensorflow or MXNet, that's not for mainstream enterprises. That's for you know, the big sophisticated tech companies who are building new algorithms on those frameworks. There's a level above that where you're using like a spark cluster in the machine learning built into that. That's slightly more accessible but when we talk about mainstream enterprises taking advantage of AI, the low hanging fruit is for them to use the pre-trained models that the public cloud vendors have created with all the consumer data on speech, image recognition, natural language processing. And then some of those capabilities can be further combined into applications like managing a contact center and we'll see more from like Amazon, like recommendation engines, fulfillment optimization, pricing optimization. >> So our expectation ultimately George is that we're going to see a lot of this, a lot of AI adoption happen through existing applications because the vendors that are capable of acquiring a talent, taking or experimenting, creating value, software vendors are going to be where a lot of the talent ends up. So Neil, we have an example of that. Give us an example of what we think is going to happen in 2018 when we start thinking about exploiting AI and applications. >> Neil: I think that it's fairly clear to be the application of what's called advanced analytics and data science and even machine learning. But really, it's rapidly becoming a commonplace in organizations not just at the bottom of the triangle here. But I like the example of SalesForce.com. What they've done with Einstein, is they've made machine learning and I guess you can say, AI applications available to their customer base and why is that a good thing? Because their customer base already has a giant database of clean data that they can use. So you're going to see a huge number of applications being built with Einstein against Salesforce.com data. But there's another thing to consider and that is a long time ago Salesforce.com built connectors to a zillion times of external data. So, if you're a SalesForce.com customer using Einstein, you're going to be able to use those advanced tools without knowing anything about how to train a machine learning model and start to build those things. And I think that they're going to lead the industry in that sense. That's going to push their revenue next year to, I don't know, 11 billion dollars or 12 billion dollars. >> Great, thanks Neil. All right so when we think about further evidence of this and further impacts, we ultimately have to consider some of the challenges associated with how we're going to create application value continually from these tools. And that leads to the idea that one of the cobblers children, it's going to gain or benefit from AI will in fact be the developer organization. Jim, what's our prediction for how auto-programming impacts development? >> Jim: Thank you very much Peter. Yeah, automation, wow. Auto-programming like I said is the epitome of enterprise application development for us going forward. People know it as co-generation but that really understates the control of auto-programming as it's evolving. Within 2018, what we're going to see is that machine learning driven co-generation approach of becoming the forefront of innovation. We're seeing a lot of activity in the industry in which applications use ML to drive the productivity of developers for all kinds of applications. We're also seeing a fair amount of what's called RPA, robotic process automation. And really, how they differ is that ML will deliver or will drive co-generation, from what I call the inside out meaning, creating reams of code that are geared to optimize a particular application scenario. This is RPA which really takes over the outside in approach which is essentially, it's the evolution of screen scraping that it's able to infer the underlined code needed for applications of various sorts from the external artifacts, the screens and from sort of the flow of interactions and clips and so forth for a given application. We're going to see that ML and RPA will compliment each other in the next generation of auto-programming capabilities. And so, you know, really application development tedium is really the enemy of, one of the enemies of productivity (static interference with web-conference). This is a lot of work, very detailed painstaking work. And what they need is to be better, more nuanced and more adaptive auto-programming tools to be able to build the code at the pace that's absolutely necessary for this new environment of cloud computing. So really AI-related technologies can be applied and are being applied to application development productivity challenges of all sorts. AI is fundamental to RPA as well. We're seeing a fair number of the vendors in that stage incorporate ML driven OCR and natural language processing and screen scraping and so forth into their core tools to be able to quickly build up the logic albeit to drive sort of the verbiage outside in automation of fairly complex orchestration scenario. In 2018, we'll see more of these technologies come together. But you know, they're not a silver bullet. 'Cause fundamentally and for organizations that are considering going deeply down into auto-programming they're going to have to factor AI into their overall plans. They need to get knowledgeable about AI. They're going to need to bring more AI specialists into their core development teams to be able to select from the growing range of tools that are out there, RPA and ML driven auto-programming. Overall, really what we're seeing is that the AI, the data scientists, who's been the fundamental developer of AI, they're coming into the core of development tools and skills in organizations. And they're going to be fundamental to this whole trend in 2018 and beyond. If AI gets proven out in auto-programming, these developers will then be able to evangelize the core utility of the this technology, AI. In a variety of other backend but critically important investments that organizations will be making in 2018 and beyond. Especially in IT operations and in management, AI is big in that area as well. Back to you there, Peter. >> Yeah, we'll come to that a little bit later in the presentation Jim, that's a crucial point but the other thing we want to note here regarding ultimately how folks will create value out of these technologies is to consider the simple question of okay, how much will developers need to know about infrastructure? And one of the big things we see happening is this notion of serverless. And here we've called it serverless, developer more. Jim, why don't you take us through why we think serverless is going to have a significant impact on the industry, at least certainly from a developer perspective and developer productivity perspective. >> Jim: Yeah, thanks. Serverless is really having an impact already and has for the last several years now. Now, everybody, many are familiar in the developer world, AWS Lambda which is really the ground breaking public cloud service that incorporates the serverless capabilities which essentially is an extraction layer that enables developers to build stateless code that executes in a cloud environment without having to worry about and to build microservices, we don't have to worry about underlined management of containers and virtual machines and so forth. So in many ways, you know, serverless is a simplification strategy for developers. They don't have to worry about the underlying plumbing. They can worry, they need to worry about the code, of course. What are called Lambda functions or functional methods and so forth. Now functional programming has been around for quite a while but now it's coming to the form in this new era of serverless environment. What we'll see in 2018 is that we're predicting is that more than 50% of lean microservices employees, in the public cloud will be deployed in serverless environments. There's AWS and Microsoft has the Azure function. IMB has their own. Google has their own. There's a variety of private, there's a variety of multiple service cloud code bases for private deployment of serverless environments that we're seeing evolving and beginning to deploy in 2018. They all involve functional programming which really, along, you know, when coupled with serverless clouds, enables greater scale and speed in terms of development. And it's very agile friendly in the sense that you can quickly Github a functionally programmed serverless microservice in a hurry without having to manage state and so forth. It's very DevOps friendly. In the very real sense it's a lot faster than having to build and manage and tune. You know, containers and DM's and so forth. So it can enable a more real time and rapid and iterative development pipeline going forward in cloud computing. And really fundamentally what serverless is doing is it's pushing more of these Lamba functions to the Edge, to the Edges. If you're at an AWS Green event last week or the week before, but you notice AWS is putting a big push on putting Lambda functions at the Edge and devices for the IoT as we're going to see in 2018. Pretty much the entire cloud arena. Everybody will push more of the serverless, functional programming to the Edge devices. It's just a simplification strategy. And that actually is a powerful tool for speeding up some of the development metabolism. >> All right, so Jim let me jump in here and say that we've now introduced the, some of these benefits and really highlighted the role that the cloud is going to play. So, let's turn our attention to this question of cloud optimization. And Stu, I'm going to ask you to start us off by talking about what we mean by true private cloud and ultimately our prediction for private cloud. Do we have, why don't you take us through what we think is going to happen in this world of true private cloud? >> Stuart: Sure Peter, thanks a lot. So when Wikibon, when we launched the true private cloud terminology which was about two weeks ago next week, two years ago next week, it was in some ways coming together of a lot of trends similar to things that you know, George, Neil and James have been talking about. So, it is nothing new to say that we needed to simplify the IT stack. We all know, you know the tried and true discussion of you know, way too much of the budget is spent kind of keeping lights on. What we'd like to say is kind of running the business. If you squint through this beautiful chart that we have on here, a big piece of this is operational staffing is where we need to be able to make a significant change. And what we've been really excited and what led us to this initial market segment and what we're continuing to see good growth on is the move from traditional, really siloed infrastructure to you want to have, you know, infrastructure where it is software based. You want IT to really be able to focus on the application services that they're running. And what our focus for the this for the 2018 is of course it's the central point, it's the data that matters here. The whole reason we've infrastructured this to be able to run applications and one of the things that is a key determiner as to where and what I use is the data and how can I not only store that data but actually gain value from that data. Something we've talked about time and again and that is a major determining factor as to am I building this in a public cloud or am I doing it in you know my core. Is it something that is going to live on the Edge. So that's what we were saying here with the true private cloud is not only are we going to simplify our environment and therefore it's really the operational model that we talked about. So we often say the line, cloud is not a destination. But it's an operational model. So a true private cloud giving me some of the you know, feel and management type of capability that I had had in the public cloud. It's, as I said, not just virtualization. It's much more than that. But how can I start getting services and one of the extensions is true private cloud does not live in isolation. When we have kind of a core public cloud and Edge deployments, I need to think about the operational models. Where data lives, what processing happens need to be as environments, and what data we'll need to move between them and of course there's fundamental laws of physics that we need to consider in that. So, the prediction of course is that we know how much gear and focus has been on the traditional data center. And true private cloud helps that transformation to modernization and the big focus is many of these applications we've been talking about and uses of data sets are starting to come into these true private cloud environments. So, you know, we've had discussions. There's Spark, there's modern databases. Many of these, there's going to be many reasons why they might live in the private cloud environment. And therefore that's something that we're going to see tremendous growth and a lot of focus. And we're seeing a new wave of companies that are focusing on this to deliver solutions that will do more than just a step function for infrastructure or get us outside of our silos. But really helps us deliver on those cloud native applications where we pull in things like what Jim was talking about with serverless and the like. >> All right, so Stu, what that suggests ultimately is that data is going to dictate that everything's not going to end up in the private or in the public cloud or centralized public clouds because of latency costs, data governance and IP protection reasons. And there will be some others. At bare minimum, that means that we're going to have it in most large enterprises as least a couple of clouds. Talk to us about what this impact of multi cloud is going to look like over the course of the next few years. >> Stuart: Yeah, critical point there Peter. Because, right, unfortunately, we don't have one solution. There's nobody that we run into that say, oh, you know, I just do a single you know, one environment. You know it would be great if we only had one application to worry about. But as you've done this lovely diagram here, we all use lots of SaaS and increasingly, you know, Oracle, Microsoft, SalesForce, you know, all pushing everybody to multiple SaaS environments that has major impacts on my security and where my data lives. Public clouds, no doubt is growing at leaps and bounds. And many customers are choosing applications to live in different places. So just as in data centers, I would kind of look at it from an application standpoint and build up what I need. Often, there's you know, Amazon doing phenomenal. But you know, maybe there's things that I'm doing with Azure. Maybe there's things that's I'm doing with Google or others as well as my service providers for locality, for you know, specialized services, that there's reasons why people are doing it. And what customers would love is an operational model that can actually span between those. So we are very early in trying to attack this multi cloud environment. There's everything from licensing to security to you know, just operationally how do I manage those. And a piece of them that we were touching on in this prediction year, is Kubernetes actually can be a key enabler for that cloud native environment. As Jim talked about the serverless, what we'd really like is our developer to be able to focus on building their application and not think as much about the underlined infrastructure whether that be you know, racket servers that I built myself or public cloud infrastructures. So we really want to think more it's at the data and application level. It's SaaS and pass is the model and Kubernetes holds the promise to solve a piece of this puzzle. Now Kubernetes is not by no means a silver bullet for everything that we need. But it absolutely, it is doing very well. Our team was at the Linux, the CNCF show at KubeCon last week and there is you know, broad adoption from over 40 of the leading providers including Amazon is now a piece. Even SalesForce signed up to the CNCF. So Kubernetes is allowing me to be able to manage multi cloud workflows and therefore the prediction we have here Peter is that 50% of developing teams will be building, sustaining multi cloud with Kubernetes as a foundational component of that. >> That's excellent Stu. But when we think about it, the hardware of technology especially because of the opportunities associated with true private cloud, the hardware technologies are also going to evolve. There will be enough money here to sustain that investment. David Floyer, we do see another architecture on the horizon where for certain classes of workloads, we will be able to collapse and replicate many of these things in an economical, practical way on premise. We call that UniGrid, NVME is, over fabric is a crucial feature of UniGrid. >> Absolutely. So, NVMe takes, sorry NVMe over fabric or NVMe-oF takes NVMe which is out there as storage and turns it into a system framework. It's a major change in system architecture. We call this UniGrid. And it's going to be a focus of our research in 2018. Vendors are already out there. This is the fastest movement from early standards into products themselves. You can see on the chart that IMB have come out with NVMe over fabrics with the 900 storage connected to the power. Nine systems. NetApp have the EF750. A lot of other companies are there. Meta-Lox is out there looking for networks, for high speed networks. Acceler has a major part of the storage software. So and it's going to be used in particular with things like AI. So what are the drivers and benefits of this architecture? The key is that data is the bottleneck for application. We've talked about data. The amount of data is key to making applications more effective and higher value. So NVMe and NVMe over fabrics allows data to be accessed in microseconds as opposed to milliseconds. And it allows gigabytes of data per second as opposed to megabytes of data per second. And it also allows thousands of processes to access all of the data in very very low latencies. And that gives us amazing parallelism. So what's is about is disaggregation of storage and network and processes. There are some huge benefits from that. Not least of which is you save about 50% of the processor you get back because you don't have to do storage and networking on it. And you save from stranded storage. You save from stranded processor and networking capabilities. So it's overall, it's going to be cheaper. But more importantly, it makes it a basis for delivering systems of intelligence. And systems of intelligence are bringing together systems of record, the traditional systems, not rewriting them but attaching them to real time analytics, real time AI and being able to blend those two systems together because you've got all of that additional data you can bring to bare on a particular problem. So systems themselves have reached pretty well the limit of human management. So, one of the great benefits of UniGrid is to have a single metadata lab from all of that data, all of those processes. >> Peter: All those infrastructure elements. >> All those infrastructure elements. >> Peter: And application. >> And applications themselves. So what that leads to is a huge potential to improve automation of the data center and the application of AI to operations, operational AI. >> So George, it sounds like it's going to be one of the key potential areas where we'll see AI be practically adopted within business. What do we think is going to happen here as we think about the role that AI is going to play in IT operations management? >> Well if we go back to the analogy with big data that we thought was going to you know, cure cancer, taste like chocolate, cost a dollar, and it turned out that the application, the most wide spread application of big data was to offload ETL from expensive data warehouses. And what we expect is the first widespread application of AI embedded in applications for horizontal use where Neil mentioned SalesForce and the ability to use Einstein as SalesForce data and connected data. Now because the applications we're building are so complex that as Stu mentioned you know, we have this operational model with a true private cloud. It's actually not just the legacy stuff that's sucking up all the admin overhead. It's the complexity of the new applications and the stringency of the SLA's, means that we would have to turn millions of people into admins, the old you know, when the telephone networks started, everyone's going to have to be an operator. The only way we can get past this is if we sort of apply machine learning to IT Ops and application performance management. The key here is that the models can learn how the infrastructure is laid out and how it operates. And it can also learn about how all the application services and middleware works, behaving independently and with each other and how they tie with the infrastructure. The reason that's important is because all of a sudden you can get very high fidelity root cause analysis. In the old management technology, if you had an underlined problem, you'd have a whole sort of storm of alerts, because there was no reliable way to really triangulate on the or triage the root cause. Now, what's critical is if you have high fidelity root cause analysis, you can have really precise recommendations for remediation or automated remediation which is something that people will get comfortable with over time, that's not going to happen right away. But this is critical. And this is also the first large scale application of not just machine learning but machine data and so this topology of collecting widely desperate machine data and then applying models and then reconfiguring the software, it's training wheels for IoT apps where you're going to have it far more distributed and actuating devices instead of software. >> That's great, George. So let me sum up and then we'll take some questions. So very quickly, the action items that we have out of this overall session and again, we have another 15 or so predictions that we didn't get to today. But one is, as we said, digital business is the use of data assets to compete. And so ultimately, this notion is starting to diffuse rapidly. We're seeing it on theCUbE. We're seeing it on the the CrowdChats. We're seeing it in the increase of our customers. Ultimately, we believe that the users need to start preparing for even more business scrutiny over their technology management. For example, something very simple and David Floyer, you and I have talked about this extensively in our weekly action item research meeting, the idea of backing up and restoring a system is no longer in a digital business world. It's not just backing up and restoring a system or an application, we're talking about restoring the entire business. That's going to require greater business scrutiny over technology management. It's going to lead to new organizational structures. New challenges of adopting systems, et cetera. But, ultimately, our observations is that data is going to indicate technology directions across the board whether we talk about how businesses evolve or the roles that technology takes in business or we talk about the key business capability, digital business capabilities, of capturing data, turning it into value and then turning into work. Or whether we talk about how we think about cloud architecture and which organizations of cloud resources we're going to utilize. It all comes back to the role that data's going to play in helping us drive decisions. The last action item we want to put here before we get to the questions is clients, if we don't get to your question right now, contact us. Send us an inquiry. Support@silicongangle.freshdesk.com. And we'll respond to you as fast as we can over the course of the next day, two days, to try to answer your question. All right, David Vellante, you've been collecting some questions here. Why don't we see if we can take a couple of them before we close out. >> Yeah, we got about five or six minutes in the chat room, Jim Kobielus has been awesome helping out and so there's a lot of detailed answer there. The first, there's some questions and comments. The first one was, are there too many chiefs? And I guess, yeah. There's some title inflation. I guess my comment there would be titles are cheap, results aren't. So if you're creating chief X officers just for the, to check a box, you're probably wasting money. So you've got to give them clear roles. But I think each of these chiefs has clear roles to the extent that they are you know empowered. Another comment came up which is we don't want you know, Hadoop spaghetti soup all over again. Well true that. Are we at risk of having Hadoop spaghetti soup as the centricity of big data moves from Hadoop to AI and ML and deep learning? >> Well, my answer is we are at risk of that but that there's customer pressure and vendor economic pressure to start consolidating. And we'll also see, what we didn't see in the ArpiM big data era, with cloud vendors, they're just going to start making it easier to use some of the key services together. That's just natural. >> And I'll speak for Neil on this one too, very quickly, that the idea ultimately is as the discipline starts to mature, we won't have people that probably aren't really capable of doing some of this data science stuff, running around and buying a tool to try to supplement their knowledge and their experience. So, that's going to be another factor that I think ultimately leads to clarity in how we utilize these tools as we move into an AI oriented world. >> Okay, Jim is on mute so if you wouldn't mind unmuting him. There was a question, is ML a more informative way of describing AI? Jim, when you and I were in our Boston studio, I sort of asked a similar question. AI is sort of the uber category. Machine learning is math. Deep learning is a more sophisticated math. You have a detailed answer in the chat. But maybe you can give a brief summary. >> Jim: Sure, sure. I don't want too pedantic here but deep learning is essentially, it's a lot more hierarchical deeper stacks of neural network of layers to be able to infer high level extractions from data, you know face recognitions, sentiment analysis and so forth. Machine learning is the broader phenomenon. That's simply along a different and part various approaches for distilling patterns, correlations and algorithms from the data itself. What we've seen in the last week, five, six tenure, let's say, is that all of the neural network approaches for AI have come to the forefront. And in fact, the core often market place and the state of the art. AI is an ancient paradigm that's older than probably you or me that began and for the longest time was rules based system, expert systems. Those haven't gone away. The new era of AI we see as a combination of both statical approaches as well as rules based approaches, and possibly even orchestration based approaches like graph models or building broader context or AI for a variety of applications especially distributed Edge application. >> Okay, thank you and then another question slash comment, AI like graphics in 1985, we move from a separate category to a core part of all apps. AI infused apps, again, Jim, you have a very detailed answer in the chat room but maybe you can give the summary version. >> Jim: Well quickly now, the most disruptive applications we see across the world, enterprise, consumer and so forth, the advantage involves AI. You know at the heart of its machine learning, that's neural networking. I wouldn't say that every single application is doing AI. But the ones that are really blazing the trail in terms of changing the fabric of our lives very much, most of them have AI at their heart. That will continue as the state of the art of AI continues to advance. So really, one of the things we've been saying in our research at Wikibon `is that the data scientists or those skills and tools are the nucleus of the next generation application developer, really in every sphere of our lives. >> Great, quick comment is we will be sending out these slides to all participants. We'll be posting these slides. So thank you Kip for that question. >> And very importantly Dave, over the course of the next few days, most of our predictions docs will be posted up on Wikibon and we'll do a summary of everything that we've talked about here. >> So now the questions are coming through fast and furious. But let me just try to rapid fire here 'cause we only got about a minute left. True private cloud definition. Just say this, we have a detailed definition that we can share but essentially it's substantially mimicking the public cloud experience on PRIM. The way we like to say it is, bringing the cloud operating model to your data versus trying to force fit your business into the cloud. So we've got detailed definitions there that frankly are evolving. about PaaS, there's a question about PaaS. I think we have a prediction in one of our, you know, appendices predictions but maybe a quick word on PaaS. >> Yeah, very quick word on PaaS is that there's been an enormous amount of effort put on the idea of the PaaS marketplace. Cloud Foundry, others suggested that there would be a PaaS market that would evolve because you want to be able to effectively have mobility and migration and portability for this large cloud application. We're not seeing that happen necessarily but what we are seeing is that developers are increasingly becoming a force in dictating and driving cloud decision making and developers will start biasing their choices to the platforms that demonstrate that they have the best developer experience. So whether we call it PaaS, whether we call it something else. Providing the best developer experience is going to be really important to the future of the cloud market place. >> Okay great and then George, George O, George Gilbert, you'll follow up with George O with that other question we need some clarification on. There's a question, really David, I think it's for you. Will persistent dims emerge first on public clouds? >> Almost certainly. But public clouds are where everything is going first. And when we talked about UniGrid, that's where it's going first. And then, the NVMe over fabrics, that architecture is going to be in public clouds. And it has the same sort of benefits there. And NV dims will again develop pretty rapidly as a part of the NVMe over fabrics. >> Okay, we're out of time. We'll look through the chat and follow up with any other questions. Peter, back to you. >> Great, thanks very much Dave. So once again, we want to thank you everybody here that has participated in the webinar today. I apologize for, I feel like Hans Solo and saying it wasn't my fault. But having said that, none the less, I apologize Neil Raden and everybody who had to deal with us finding and unmuting people but we hope you got a lot out of today's conversation. Look for those additional pieces of research on Wikibon, that pertain to the specific predictions on each of these different things that we're talking about. And by all means, Support@silicongangle.freshdesk.com, if you have an additional question but we will follow up with as many as we can from those significant list that's starting to queue up. So thank you very much. This closes out our webinar. We appreciate your time. We look forward to working with you more in 2018. (upbeat music)

>> Narrator: Live from Orlando, Florida it's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to sunny Orlando everybody. This is theCUBE, the leader in live tech coverage, and this is our second day covering PentahoWorld 2017. theCUBE was here in 2015 when Pentaho had just been recently acquired by Hitachi. We then, let's see, around September timeframe we saw Hitachi rebrand, Hitachi Data Systems rebrand as Hitachi Vantara, bringing together three components of its business, the Hitachi Data Systems business, the Hitachi Insights business, and of course, the Pentaho Analytics platform. We heard yesterday from Brian Householder, the president and COO of Hitachi Vantara, what the strategy was. I thought he was a very crisp, clear presenter. The strategy made a lot of sense, it resonated. Obviously a lot of execution to be done. And then subsequently at the last two days we've heard largely from Pentaho practitioners who are applying this end to end analytics platform to really transform their businesses, to really become data driven supporting those digital transformations. So pretty positive story overall. A lot of work to be done. We got to see how this whole edge to outcome plays out. Sounds good. There's got to be some execution there. We got to see the ecosystem grow for sure. These guys got a great story. This conference should explode. >> It's really a validation for Pentaho. They've been on the market for more than a decade now as the spearhead for the open source analytics revolution in business analytics, and in predictive modeling, and in data integration, all of it open source. And they've come very far and they're really a blue chip solution program. I think this show has been a great validation of Pentaho's portfolio presence in the market. Now Hitachi Vantara has a gem of a core asset. Clearly, the storage market, the data center converged infrastructure, the core Hitachi Data Systems product lines, are starting to experience the low growth that such a mature space experiences. And clearly they're placing a strong bet on Hitachi Vantara that the IoT, that the edge analytics market, will just boom wide open. Hitachi Insight Group, which was only created last year by their corporate parent, was chartered to explore opportunities in IoT. They've got the Lumata platform. They had, Hitachi Next, their conference last month, focused on IoT. I think that's really the capstone, the Lumata portfolio, in this overall story. Now, I think what we're hearing this week is that great, they've got the components, the building blocks, of potential growth, but I don't think they're going to be able to achieve takeoff growth until such time, Hitachi Vantara, they have a stronger, more credible reach out to the developer community, specifically the developers who are building the AI and machine learning for deployment to the edge. That will require to have credibility in that space. Clearly it's going to have to be the new set of frameworks, such as TensorFlow, and MXNet, and Fee-an-o, and so forth. They're going to need some sort of a modeling framework or abstraction from it that sits on top of the Pentaho platform or really across all of their offerings, including Lumata, and enables a developer to using, the mainstream application developer to use code, whether it be Python or R or Java, whatever, to build the deep learning and AI models at the highest level of abstraction, the business level of abstraction, then to automatically compile those models, which are computational graphs, down to formats that are optimized and efficient to run on devices of all sorts, chip sets of all sorts, that are increasingly resource constrained. They're not there yet. I'm not hearing that overall developer story at this show. I think they've got a lot of smart people, including Brian, pushing them in that direction. Hopefully next year's PentahoWorld or however they may rebrand this show, I think they'll probably have more of that put together, but we'll keep on waiting to see. >> And that's something that I pushed on a little bit this week. In particular, that requires a whole new go to market where the starting point is developers and then you're nurturing those developers. And certainly Pentaho has experience with community editions, but that was more to get enterprise buyers to kind of try before they buy. As you know well, Jim, the developer community is, they're very fickle, they're persnickety, they're demanding, and they're super smart, and they can be your best advocates or they'll just ignore you. That's just kind of the way it is with developers. And if you can appeal to them you can get a foothold in markets. We've seen it. Look at what Microsoft has done, look at what Amazon has done, certainly Docker, you know, on and on and on. >> Community marketing that's full bore (mumbles) user groups, developer days, hackathons, the whole nine yards, I'm not seeing a huge emphasis on community marketing in that really evangelistic sense. They need to go there seriously. They need to win the hearts and minds of the next generation developer, the next generation developer who actually won't care about whether it's TensorFlow backends or the other ones. What they will care is the high level framework, and really a collaborative framework, that's a solution provider gives them for their teams to collaborate on building and training and deploying all this stuff. I'm not hearing from this solution provider, devops really, here this year. Hopefully in the coming years there will be. Other vendors are a bit further along than they are. We see a bit further along IBM is. We see a bit further along like Cloudera and others are in putting together really a developer friendly ecosystem of components within a broader data lake framework. >> Yeah, and that's not been the historical Pentaho DNA. However, as you know, to reach out, have a community effort to reach out to developers requires resources and commitment, and it's not a one shot deal. But, it also requires a platform, and what we're seeing today is the formation of that. The reformation of Hitachi into Hitachi Vantara with a lot of resources that has a vision of a platform, of which Pentaho is a critical component, but it's going to take a lot of effort, a lot of cultivating. I presume they're having those conversations internally. They're not ready to have them externally, which is I presume why they're not having them. But that's something that we're going to certainly watch for in the coming years. What else? You gave a talk this afternoon. >> Yeah, AI is Eating the Edge, and it was well received. In fact, when I prepared my thoughts and my research about a month ago for this event I was thinking, "Am I way too far ahead?" This is Pentaho. I've been of course familiar with them since their inception. I thought, "Are there other users? "Are there developers? "Is their community going deep into AI "and all the IoT stuff?" And the last day or so here at this event it's like, "Whoa, everybody here is into that. "They know this stuff." So, not only was I relieved that I wouldn't have to explain the ABCs of all that, they were ahead of me in terms of the questions I got. The questions are, once again, what framework should we adopt for AI, the whole TensorFlow, all those framework wars, which I think are sort of overblown and they will be fairly soon, it'll be irrelevant, but those kinds of questions. Those are actually developer level questions that people are just here and they're coming to me with. >> Well, you know, I tell you, I'm no expert in frameworks, but my advice would be whatever framework you adopt you're probably not going to be using that same framework down the road. So you have to be flexible as an organization. A lot of technical leaders tell me this is look, technology is going to come and it's going to go. We got to have great people. We've got to be able to respond to the market requirements. We have to have processes that allow us to be proactive and responsive, and that your choice of framework should ensure that it doesn't constrict you in those areas. >> And you know, the framework that actually appeals to this crowd, including the people in my room, it's a wiki bot framework, it's also what Brian Hopkins of Forrester presented, the three tier architecture. There's the edge devices. There are the gateways or hubs. There's the cloud. We call them primary, secondary, tertiaries. Whatever you call them, you put different data, you put different analytics on each of those tiers. And then really in many ways in a modular fashion then you begin to orchestrate with Kubernetes and so forth these AI infused apps and these distributed architectures, like self driving vehicles or whatever. And the buzz I've been getting here, including in my session, everybody is saying, "Yeah, that's exactly the way to go." In other words, thinking in those terms prevents you as a developer from thinking that AI has to be some monolithic frigging stack on one single node. No, it actually has to be massively parallel and distributed, because these are potentially very compute intensive applications. I think there's a growing realization in the developer community that when you're talking about developing AI you're really talking about developing two core workloads. There's the inferencing, which is where the magic happens in terms of predictions and classifications, but even more resource consumptive is the training that has to happen in the cloud, and that's data, that's exabytes, petabytes intensive potentially. That's compute intensive. Very different workload. That definitely needs to happen in the cloud primarily. There's a little bit of federated training that goes out to the edge, but that's really the exception right now. So there's a growing realization in the developer community that boy, we better get a really good platform for training. And actually they could leverage, we've seen it in our research of wiki bot is that, many AI developers, many deep learning developers, actually leverage their Spark clusters for training of TensorFlow and so forth, because of in memory massive parallelism, so forth and so on. I think there will be a growing realization in the developer community that the investments they've been making in Hadoop and Spark will just be leveraged for this growing stack, for training if nothing else. >> Well, in 8.0 that was sort of the big buzz here. And you and I talked at the open with Rebecca, our other co-host, about 8.0 A lot of incremental improvements. But you know what, in talking to customers that's kind of what they want. They want Pentaho to do a good job of incorporating, curating, open source content, open source platforms and products, bringing them into their system, and making sure that their customers can take advantage of them. That's what they consistently kept asking for. They weren't freaked out about lack of AI and lack of deep learning and ML and Weka is fine. Now maybe it's a blind spot, I don't know. >> No, no, actually I've had 24 hours since they announced to chew on it. In fact, I have a SiliconANGLE article going up fairly soon with essentially my trip report and my basic takeaway. And actually what I like about 8.0 is that it focuses on streaming, bringing open source analytic streaming more completely into the Pentaho data integration platform, in other words, their stronger interoperability with Spark streaming, with Kafka, and so forth, but also they have the ability within 8.0 to better match realtime streaming workloads to execution engines in a distributed fabric. In other words, what I think that represents not only in terms of Hitachi Vantara's portfolio, but in terms of where the industry is going with all things to do with big data applications whether or not they involve AI is streaming is coming into the mainstream, pun intended, and data at rest platforms are starting to become marginalized in a lot of applications. In other words, Hadoop is data at rest par excellence, so are a fair number of other no SQL platforms. Those are not going away. Those are the core of your data lakes. But most development is being developed now, most AI and machine learning is being developed for streaming environments that increasingly are edge oriented. So Pentaho, Hitachi Vantara, for 8.0 have put in the right incremental features for the market that lies ahead. So in many ways I think that was actually a well thought out release for this particular event. >> Great. Okay, some of the highlights here. We had a lot of different industries, gaming, we had experts on autonomous vehicles, we had the NASDAQ guys on, that was a very interesting segment, the German police interview you did, the chief data officer of community colleges in Indiana. So, a lot of diversity, which underscores the platformness of Pentaho. It's not some industry specific system. It is a horizontal capabilities platform. Final thoughts on the show, some interesting things that you saw, things you learned? >> Yeah, on the show itself, they did a really good job. Hitachi Vantara, of course it's a new brand, but it's an old company, and it's even an old established set of product teams that have come together in a hurry essentially, though it's really been two years since the acquisition. They did a really good job of presenting a unified go to market message. That's a good start They've done a good job of the fact that they had these two shows in a rapid sequence, Hitachi Next, which was IoT and Lumata, but it was Hitachi Vantara, and now this one where it's all data analytics. The fact that here in the peak of fall event season they had these two shows really highlighting their innovations and their romance for those two core of their portfolio, and have done a good job of positioning themselves in each case, that shows that the teams are orchestrating well in terms of at least go to market presenting their value prop. I think in terms of the actual, we've had a lot of great customer and partner interviews on this show. And I think, you mentioned gaming first, I wasn't actually on the gaming related CUBE interview, but gaming is a hot, of course it's a hot, hot market for AI increasingly. A lot of AI that gets developed now for lots of applications involves simulations of whatever scenario you're building, including like autonomous vehicles. So gaming is in many ways a set of practices that are well established and mature that are becoming fundamental to development of all AI, because you're developing synthetic data based on simulation environments. The fact that Hitachi Vantara has strong presence as a data provider in the gaming market I think in many ways indicates that they've got ... It's a crowded marketplace. They have much larger competitors and deeper pocketed, but I think the fact is they've got all the piece parts needed to be a roaring success in this new era, and they've got strong and very loyal customers I'm discovering, not discovering, I've known this all along. But, since I've rejoined the analysts' space it's been revalidated that Pentaho how strong in blue chip they are. Now that they're a new brand in a new era, they're turning themselves around fairly well. I don't think that they'll be isolated by ... Clearly, I mean, with AI ... AI right now belongs to AWS and Microsoft and Google and IBM to some degree. We have to recognize that the Hitachi Vantaras of the world right now are still a second tier in that arena. They probably have to hitch their wagon to at least one of those core cloud providers as a core partner going forward to really prevail. >> Dave: Which they can do. >> Yeah, they can do. >> Alright. Jim, thanks very much for closing with me. Thanks to you all for watching. theCUBE puts out a lot of content. You can go to SiliconAngle.com to see all the news. theCUBE.net is where we host all these videos. Wikibon.com is our research site, so check that out, as well. We've got CrowdChats going on, CrowdChat.net. It's just unbelievable. >> Unbelievable. >> Rush of content. We're all about the data, we're all about sharing, so check those sites out. Thanks very much to the crew here. Great job. And next week a lot going on. We're in New York City. We've got some stuff going on there. Want to thank our sponsor, without whom this show, this CUBE show, would not be possible, Hitachi Vantara slash Pentaho. >> Thank you to sunny Orlando. It's great and wonderful. >> This has been theCUBE at PentahoWorld 2017. We'll see you next time. Thanks for watching. (techno music)