>> Live from Mountain View, California, it's the Cube! Covering Devnet Create 2019. Brought to you by Cisco. >> Welcome back to the Cube's coverage, day one, Cisco Devnet Create 2019 at the Computer History Museum in Mountain View, California. Lisa Martin with John Furrier, pleased to welcome to the Cube for the first time, Mathieu Gerard, the co-founder and CTO of Mapwize. Mathieu, it's lovely to have you on the program. >> Thank you. >> So Mapwize and Cisco are partners, but first, give our audience an idea of Mapwize. What you are, what you deliver, where you're located. >> Yeah, Mapwize is a startup company, we are based in France. And so we want to bring digital services inside buildings. We feel that a lot of our life has been digitalized, but that there are still a lot of services that can be brought inside those buildings. And one of the key elements, when you speak about digital services in buildings, is to have a map. A map where you can show all the different details about the buildings, the live data that is generating from all the sensors that's in the building. That's where the partnership with Cisco actually comes in to bring all those infrastructure sensors that you get, bring that to be displayed on the map as well and bring services to the user. >> So one of the hot announcements is the Wi-Fi 6. I'm jazzed about. It was 802.11 something A or B, I forget what it was. But you're now calling it Wi-Fi 6, thank God. Although even numbers, I'm skeptical of that. You know, odds tend to be better, bug-free, going back to our old days as you know. But Wi-Fi 6 changes the game at many levels. What are some of the things that will help you guys? Because we've all been in the buildings where, concrete, bounces RF, you can't get through certain things, we've all been in stadiums where it's kind of like a nightmare with bandwidth. Wi-Fi's like, you know, part of Maslow's hierarchy of needs now. We want our Wi-Fi. Businesses want Wi-Fi, so new things are happening. What's your take on Wi-Fi 6? >> So our take is that we really want to bring all those services. Of course bandwidth is something, but for us it's not necessarilly the critical part. For us it's really the kind of data that you can get from the Wi-Fi. Making sure that all the IOT devices can be deployed in more and more of those buildings. Everybody now wants to know if a meeting room is available or not. So what's the best way of doing that, and just having a small sensor that detects presence, and can be broadcasted back to the cloud and then displayed on the map. So there are so many sensors, that's one of them. But in terms of pollution of temperature, if you have those in the building, can bring new services around all those mapping. >> So bandwidth is not an issue, obviously this is like gig ethernet now, just helps with the signaling. What about range and coverage area, antenna chains? These are the kind of things we're hearing about, some of the benefits. Does that help you guys at all? Does that help the maps and get more range? >> Yeah, and at the same time the challenge we are facing when we look at the Wi-Fi is to be able to use it to locate people and to know where I am so that I can be provided services around me. And so that usually came with a need for more density of access points because the more density you have the better you can access the location of a user. And so what we see is a lot of evolution in the Wi-Fi in the kind of capabilities that they have in positioning people. So we hope to see that as well in Wi-Fi 6. >> What's your vision on location services inside an enterprise? Because we saw that movie play out on the consumer side with mobile, iPhones and Androids now everywhere. We've all seen it, we know when art was showing up, all the things that were happening on the maps, map mashups back in the old web 2.0 days. What's the new sets of things that will come out that you see? What's your vision? >> What we see is that, as you were mentioning, mapping and wave finding is something we are using everyday. And nobody would even imagine how it was back in the time when we had paper maps. And so we believe that that is also coming into all the office and industry environments. For example the possibility of seeing live, what's available, what's going on in my building, what's available as services where are the people that I need to interact with, where are the assets I need to actually go grab? That's something that today, seems like complicated to do, and I'm pretty convinced that in a few years from now, it's going to be natural, like waze is natural everyday for everybody. >> And this is the opportunity for Mapwize and with Cisco as well, to convert existing structures into these smart buildings. Are you seeing that as well as with the development of new buildings that are kind of built natively smart? >> Yeah, of course the new buildings are built more smart. And with new infrastructure, that allows a lot more. But the new buildings are still a very small percentage of the buildings that are out there. And so the great thing is that all the infrastructure that already exists is already capable of a lot. And so even with the updates that are being done there, there is a lot of data that today are totally not used, that we believe still can bring a lot of new services and a lot of potential. >> Is there any industries in particular where you and Cisco are working together where this is a really, they're right for this type of transformation. I can think of hospitals as one thing that comes to mind with being able to identify where everything is, censor services. Especially in life and death situations. >> Yeah, so what we see is that everybody that works in a hospital has the same reaction. It's like, where is everything? It's the kind of campuses where it's really easy to get lost. And so, whenever you get there, you need to get to your appointment, and if you don't find it, what're you going to do is to ask the medical staff. So you ask people that are actually saving life, how to get to your next appointment, which we feel is kind of a waste. >> Huge efficiencies. Not just asset tracking, which is low-hanging fruit. IOT devices in terms of instrumentation, but just supply chain services. It's a tsunami of new things. Limited by a lot of old school, either technical limitations on connectivity at the edge or just software. >> You know that in health care, there is a lot of time where a surgery room is ready with all the surgeons and the staff and the patient is not there because the person who is supposed to go get him in his room and bring to the surgery is actually late. And so we think that that's such a waste of time and money. >> Absolutely. >> Could be much better utilized. >> You could bring surge pricing in to the surgery room. (laughing) We're backed up, or hey I got low pricing, I got a price line ... But all joking aside, this is really important. This is like real value. High priced resources, idle in a hospital. There's probably a zillion examples of those. Okay, what's the low-hanging fruit that you guys see? When you start rolling out Mapwize. Is it just getting a physical footprint of it? Is it just a graphic rendering? Is the mashup piece? Is it visualization? What are some of the key things that you guys are doing, or have done to remove the blockers for adoption and create more movement towards that value? >> So what we see is really the first step is bring some wave finding, helping people navigate around the buildings. And so basically taking the old stock of technical floor plans that everybody has, that usually just a few architects use in a company. And being able to drag and drop that into a web platform. And from one day to another, making it available to a hundred percent of the people that actually live in that building on a daily basis. So that's really the first step we see. And then together with Cisco, being able to bring the location of the users. So that I have the same experience outside of the GPS as I have inside the building with the Wi-Fi infrastructure. >> It'd be great to know too, there's a lot of people streaming video around one access point. Might want to add another one. These kinds of things just are natural ideas that people would do. >> Yeah and where the bandwidth is the best, where the noise is the lowest, where potentially is the temperature higher, lower. Today in the flex office, people can choose to sit wherever they want. So what are the key reasons to choose one spot or the other? And I think there are a lot more value that we can bring to those as occupants. >> So you have here at Devnet Create 2019, you have a breakout, or had today a breakout and a workshop. Tell us about the workshop first in terms of the title, the conversations and some of the interesting conversations that you had with some of the participants. >> The workshop was about how to bring the link between the map and the more key infrastructure that you have. So potentially, even before anyone connects to a Wi-Fi, we're actually already showing him usually a portal, a captive portal where he can look in. And how we can add in that captive portal, already services. Like showing him, where is, on a map, how to get to any destination, potentially services that are around him. So that was the goal of the workshop. And it was great because everybody was saying directly in his industry. I had somebody from a university say this is exactly what we need as well for our campus. So I think it's something we can bring to much more industries. >> There's much more of a horizontal opportunity like you said, across industries. And you also had a breakout session. What did that dive into? >> The breakout session was specifically around location analytics. So it's completely different world, but it's about them using the location of the crow of every single device in the building and see how people move. Where do they go. And to understand the behavior of the people that are there. Just to give you an example, if you look at an event like this one, maybe the organizers would like to understand how much time people spend looking at the talk, looking at the workshop, getting around. So basically using all the data that's collected by the Wi-Fi, we can get a lot of analytics and numbers to better assess if the spaces are well organized. >> Making sure people are at their desks doing their job. (laughing) >> Oh no, no. No big brother. >> That's potentially the downside around it. It's something we need to be careful about. >> Innovation versus creepiness. It's always a trade off, privacy. >> It is a trade off and I think we need to be aware of when we allow it. When there is somebody working alone in a building, you actually do want to know where he is, because it's good for his safety. >> It's all over. We all have privacy problems. The GPS knows everything I'm doing here. Get over it, people. >> I think it's good to know which cases and to have opt-in. Like sometimes I want people to know where I am exactly, because that can actually help me. And I've other cases where I do not want it. So I think it's important that any developer who is building application with that data, is aware of that privacy issue and can know when to anonymize data, when not. >> Great stuff. >> Mathieu, thank you so much for joining John and me talking about Mapwize, what you're doing with Cisco. Really, really interesting technology. Maybe next year at Devnet Create you can tell us all of the analytic from this year. >> Yeah, absolutely. >> All right, we appreciate your time. >> Thank you so much. >> Thank you. >> For John Furrier, I'm Lisa Martin, live on the Cube from Cisco Devnet Create 2019. Thanks for watching.

>> Announcer: From the Cube Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is The Cube Conversation. >> Everyone welcome to this special live stream here in The Cube Studios. I'm John Furrier, your host. We've got a great panel discussion here for one hour, sponsored by Lumina PR, not sponsored but organized by Lumina PR. An authentic conversation around professionals in the news media, and communication professionals, how they can work together. As we know, pitching stories to national media takes place in the backdrop in today's market, which is on full display. The Coronavirus, racial unrest in our country and a lot of new tech challenges from companies, their role in society with their technology and of course, an election all make for important stories to be developed and reported. And we got a great panel here and the purpose is to bridge the two worlds. People trying to get news out for their companies in a way that's relevant and important for audiences. I've got a great panelists here, Gerard Baker Editor at Large with the Wall Street Journal, Eric Savitz, Associate Editor with Barron's and Brenna Goth who's a Southwest Staff Correspondent with Bloomberg Publications. Thanks for joining me today, guys, appreciate it. >> Thank you. >> So we're going to break this down, we got about an hour, we're going to probably do about 40 minutes. I'd love to get your thoughts in this power panel. And you guys are on the front lines decades of experience, seeing these waves of media evolve. And now more than ever, you can't believe what's happening. You're seeing the funding of journalism really challenging at an all time high. You have stories that are super important to audiences and society really changing and we need this more than ever to have more important stories to be told. So this is really a challenge. And so I want to get your thoughts on this first segment. The challenge is around collecting the data, doing the analysis, getting the stories out, prioritizing stories in this time. So I'd love to get your thoughts. We'll start with you, Brenna, what's your thoughts on this as you're out there in Arizona. Coronavirus on the worst is one of the states there. What are your challenges? >> I would say for me, one of the challenges of the past couple months is just the the sheer influx of different types of stories we've had and the amount of news coming out. So I think one of the challenging things is a lot of times we'll get into a bit of a routine covering one story. So early on maybe the Coronavirus, and then something else will come up. So I personally have been covering some of the Coronavirus news here in Arizona and in the Southwest, as well as some of the protests we've seen with the Black Lives Matter movement. And prioritizing that is pretty difficult. And so one thing that I I've been doing is I've noticed that a lot of my routine projects or things I've been working on earlier in the year are off the table, and I'll get back to them when I have time. But for now, I feel like I'm a little bit more on breaking news almost every day in a way that I wasn't before. >> Gerard, I want to get your thoughts on this. Wall Street Journal has been since I could remember when the web hit the scene early on very digital savvy. Reporting, it's obviously, awesome as well. As you have people in sheltering in place, both journalists and the people themselves and the companies, there's an important part of the digital component. How do you see that as an opportunity and a challenge at the same time because you want to get data out there, you want to be collecting and reporting those stories? How do you see that opportunity, given the challenge that people can't meet face to face? >> First of all, thank you very much for having me. I think as we've all discovered in all fields of endeavor in the last three months, it's been quite a revelation, how much we can do without using without access to the traditional office environment. I think one of the things that Coronavirus, this crisis will have done we all agree I think is that it will have fundamentally changed the way people work. There'll be a lot more people quite a bit more working from home. They'll be a lot more remote working. Generally, there'll be a lot less travel. So on the one hand, it's been eye opening. actually how relatively easy, I use that word carefully. But how we've managed, and I think it's true of all news organizations, how we've managed surprisingly well, I think, without actually being at work. At the Wall Street Journal, we have a big office, obviously in midtown Manhattan, as well as dozens of bureaus around the world. Nobody has really been in that office since the middle of March. And yet we've put out a complete Wall Street Journal product, everything from the print edition, obviously, through every aspect of digital media, the website, all of the apps, video, everything, audio, podcasts. We've been able to do pretty well everything that we could do when we were all working in the office. So I think that will be an important lesson and that will clearly induce some change, some long term changes, I think about the way we work. That said, I'd point to two particular challenges that I think we have not properly overcome. Or if you like that we have, the two impediments, that the crisis has produced for us. One is, as you said, the absence of face to face activity, the hive process, which I think is really important. I think that a lot of the best ideas, a lot of the best, the best stories are developed through conversations between people in an office which don't necessarily we can't necessarily replicate through the online experience through this kind of event or through the Zoom meetings that we've all been doing. I think that has inhibited to some extent, some of the more creative activity that we could have done. I think the second larger problem which we all must face with this is that being essentially locked up in our homes for more than three months, which most of us has been I think accentuates a problem that is already that has been a problem in journalism for a long time, which is that journalists tend to cluster in the major metropolitan areas. I think, a couple of years ago, I read a study which said, I think that more than three quarters of journalists work for major news organizations, print, digital TV, radio, whatever, live and work in one of four major metropolises in the US. That's the New York area, the Washington DC area, the San Francisco area and the LA area. And that tends to create a very narrow worldview, unfortunately, because not enough people either come from those areas, but from outside those areas or spend enough time talking to people from outside those areas. And I think the Coronavirus has accentuated that. And I think in terms of coverage, I'm here in New York. I've been in New York continuously for three and a half months now which is quite unusual, I usually travel a lot. And so my reporting, I write columns now, mainly, but obviously I talk to people too. But the reporting, the editing that we're doing here is inevitably influenced by the experience that we've had in New York, which has obviously been, frankly, devastating. New York has been devastated by Coronavirus in a way that no where else in the country has. And I think to some extent, that does, perhaps have undue influence on the coverage. We're all locked up. We're all mindful of our own health. We're all mindful of people that we know who've gone to hospital or have been very, very sick or where we are, we are heavily influenced by our own immediate environment. And I think that has been a problem if we had been, imagine if the journalists in the country, instead of being clustered in New York and LA and San Francisco had been sort of spread over Texas and Missouri and Florida, things like that. I think you'd have a very different overall accounting of this story over the last three months. So I think it's just, it's accentuated that phenomenon in journalism, which I think we're mindful of, and which we all need to do a better job of addressing. >> It's really interesting. And I want to come back to that point around, who you're collaborating with to get this, now we have virtual ground truth, I guess, how you collaborate. But decision making around stories is, you need an open mind. And if you have this, I guess, I'll call it groupthink or clustering is interesting, now we have digital and we have virtual, it opens up the aperture but we still have the groupthink. But I want to get Eric's take first on his work environment, 'cause I know you've lived on both sides of New York and San Francisco area, as well as you've worked out in the field for agencies, as well on the other side, on the storytelling side. How has this current news environment, journalism environment impacted your view and challenges and your opportunities that you're going after the news? >> Well, so there's there's a few elements here. So one, Barron's Of course, covers the world, looks at the world through a financial lens. We cover the stock market every day. The stock market is not the center of story, but it is an important element of what's been unfolding over the last few months and the markets have been incredibly volatile, we change the way that we approach the markets. Because everything, the big stories are macro stories, huge swings in stock prices, huge swings in the price of oil, dramatic moves in almost every financial security that you can imagine. And so there's a little bit of a struggle for us as we try and shift our daily coverage to be a little more focused on the macro stories as we're still trying to tell what's happening with individual stocks and companies, but these bigger stories have changed our approach. So even if you look at say the covers of our magazine over the last few months, typically, we would do a cover on a company or an investor, that sort of thing. And now they're all big, thematic stories, because the world has changed. And world is changing how it looks at the financial markets. I think one thing that that Gerard touched on is the inability to really leave your house. I'm sitting in my little home office here, where I've been working since March, and my inability to get out and talk to people in person to have some, some interface with the companies and people that I cover, makes it tougher. You get story ideas from those interactions. I think Gerard said some of it comes from your interactions with your colleagues. But some of that also just comes from your ability to interact with sources and that is really tougher to do. It's more formalistic if you do it online. It's just not the same to be on a Zoom call as to be sitting in a Starbucks with somebody and talking about what's going on. I think the other elements of this is that there's, we have a lot of attempts, trying new things trying to reach our readers. We'll do video sessions, we'll do all sorts of other things. And it's one more layer on top of everything else is that there's a lot of demands on the time for the people who are working in journalism right now. I would say one other thing I'll touch on, John, which is, you mentioned, I did use, I worked for public communications for a while, and I do feel their pain because the ability to do any normal PR pitching for new products, new services, the kinds of things that PR people do every day is really tough. It's just really hard to get anybody's attention for those things right now. And the world is focused on these very large problems. >> Well, we'll unpack the PR comms opportunities in the next section. But I want to to just come back to this topic teased out from Gerard and Brenna when you guys were getting out as well. This virtual ground truth, ultimately, at the end of the day, you got to get the stories, you got to report them, they got to be distributed. Obviously, the Wall Street Journal is operating well, by the way, I love the Q&A video chats and what they got going on over there. So the format's are evolving and doing a good job, people are running their business. But as journalists and reporters out there, you got to get the truth and the ground truth comes from interaction. So as you have an aperture with digital, there's also groupthink on, say, Twitter and these channels. So getting in touch with the audience to have those stories. How are you collecting the data? How are you reporting? Has anything changed or shifted that you can point to because ultimately, it's virtual. You still got to get the ground truth, you still got to get the stories. Any thoughts on this point? >> I think in a way what we're seeing is in writ large actually is a problem again, another problem that I think digital journalism or the digital product digital content, if you like, actually presents for us today, which is that it's often said, I think rightly, that one of the, as successful as a lot of digital journalism has been and thank you for what you said about the Wall Street Journal. And we have done a tremendous job and by the way, one of the things that's been a striking feature of this crisis has been the rapid growth in subscriptions that we've had at the Journal. I know other news organizations have too. But we've benefited particularly from a hunger for the quality news. And we've put on an enormous number subscriptions in the last three months. So we've been very fortunate in that respect. But one of the challenges that people always say, one of the one of the drawbacks that people always draw attention to about digital content is that there's a lack of, for want of a better words, serendipity about the experience. When you used to read a newspaper, print newspapers, when may be some of us are old enough to remember, we'd get a newspaper, we'd open it up, we'd look at the front page, we look inside, we'd look at what other sections they were. And we would find things, very large number of things that we weren't particularly, we weren't looking for, we weren't expecting to, we're looking for a story about such. With the digital experience, as we know, that's a much it's a much less serendipitous experience. So you tend to a lot of search, you're looking, you find things that you tend to be looking for, and you find fewer things that, you follow particular people on social media that you have a particular interest in, you follow particular topics and have RSS feeds or whatever else you're doing. And you follow things that, you tend to find things that you were looking for. You don't find many things you weren't. What I think that the virus, the being locked up at home, again, has had a similar effect. That we, again, some of the best stories that I think anybody comes across in life, but news organizations are able to do are those stories that you know that you come across when you might have been looking for something else. You might have been working on a story about a particular company with a particular view to doing one thing and you came across somebody else. And he or she may have told you something actually really quite different and quite interesting and it took you in a different direction. That is easier to do when you're talking to people face to face, when you're actually there, when you're calling, when you're tasked with looking at a topic in the realm. When you are again, sitting at home with your phone on your computer, you tend to be more narrowly so you tend to sort of operate in lanes. And I think that we haven't had the breadth probably of journalism that I think you would get. So that's a very important you talk about data. The data that we have is obviously, we've got access broadly to the same data that we would have, the same electronically delivered data that we would have if we'd been sitting in our office. The data that I think in some ways is more interesting is the non electronically delivered data that is again, the casual conversation, the observation that you might get from being in a particular place or being with someone. The stimuli that arise from being physically in a place that you just aren't getting. And I think that is an important driver of a lot of stories. And we're missing that. >> Well, Gerard, I just want to ask real quick before I go to Brenna on her her take on this. You mentioned the serendipity and taking the stories in certain directions from the interactions. But also there's trust involved. As you build that relationship, there's trust between the parties, and that takes you down that road. How do you develop trust as you are online now? Is there a methodology or technique? Because you want to get the stories out fast, it's a speed game. But there's also the development side of it where a trust equation needs to build. What's your thoughts on that piece? Because that's where the real deeper stories come from. >> So I wasn't sure if you're asking me or Gerard. >> Gerard if he wants can answer that is the trust piece. >> I'll let the others speak to that too. Yeah, it is probably harder to... Again, most probably most people, most stories, most investigative stories, most scoops, most exclusives tend to come from people you already trust, right? So you've developed a trust with them, and they've developed a trust with you. Perhaps more importantly, they know you're going to treat the story fairly and properly. And that tends to develop over time. And I don't think that's been particularly impaired by this process. You don't need to have a physical proximity with someone in order to be able to develop that trust. My sources, I generally speak to them on the phone 99% of the time anyway, and you can still do that from home. So I don't think that's quite... Obviously, again, there are many more benefits from being able to actually physically interact with someone. But I think the level of, trust takes a long time to develop, let's be honest, too, as well. And I think you develop that trust both by developing good sources. and again, as I said, with the sources understanding that you're going to do the story well. >> Brenna, speed game is out there, you got to get stories fast. How do you balance speed and getting the stories and doing some digging into it? What's your thoughts on all this? >> I would say, every week is looking different for me these days. A lot of times there are government announcements coming out, or there are numbers coming out or something that really does require a really quick story. And so what I've been trying to do is get those stories out as quick as possible with maybe sources I already have, or really just the facts on the ground I can get quickly. And then I think in these days, too, there is a ton of room for following up on things. And some news event will come out but it sparks another idea. And that's the time to that when I'm hearing from PR people or I'm hearing from people who care about the issue, right after that first event is really useful for me to hear who else is thinking about these things and maybe ways I can go beyond the first story for something that more in depth and adds more context and provides more value to our readers. >> Awesome. Well, guys, great commentary and insight there on the current situation. The next section is with the role of PR, because it's changing. I've heard the term earned media is a term that's been kicked around. Now we're all virtual, and we're all connected. The media is all virtual. It's all earned at this point. And that's not just a journalistic thing, there's storytelling. There's new voices emerging. You got these newsletter services, audiences are moving very quickly around trying to figure out what's real. So comms folks are trying to get out there and do their job and tell a story. And sometimes that story doesn't meet the cadence of say, news and/or reporting. So let's talk about that. Eric, you brought this up. You have been on both sides. You said you feel for the folks out there who are trying to do their job. How is the job changing? And what can they do now? >> The news cycle is so ferocious at the moment that it's very difficult to insert your weigh in on something that doesn't touch on the virus or the economy or social unrest or the volatility of the financial markets. So I think there's certain kinds of things that are probably best saved for another moment in time, If you're trying to launch new products or trying to announce new services, or those things are just tougher to do right now. I think that the most interesting questions right now are, If I'm a comms person, how can I make myself and my clients a resource to media who are trying to tell stories about these things, do it in a timely way, not overreach, not try insert myself into a story that really isn't a good fit? Now, every time one of these things happen, we got inboxes full of pitches for things that are only tangentially relevant and are probably not really that helpful, either to the reporter generally or to the client of the firm that is trying to pitch an idea. But I will say on the on this at the same time that I rely on my connections to people in corporate comms every single day to make connections with companies that I cover and need to talk to. And it's a moment when almost more than ever, I need immediacy of response, accurate information access to the right people at the companies who I'm trying to cover. But it does mean you need to be I think sharper or a little more pointed a little more your thinking about why am I pitching this person this story? Because the there's no time to waste. We are working 24 hours a day is what it feels like. You don't want to be wasting people's time. >> Well, you guys you guys represent big brands in media which is phenomenal. And anyone would love to have their company mentioned obviously, in a good way, that's their goal. But the word media relations means you relate to the media. If there's no media to relate to, the roles change, and there's not enough seats at the table, so to speak. So getting a clip on in the clip book that gets sent to management, look, "We're on Bloomberg." "Great, check." But is at it? So people, this is a department that needs to do more. Is there things that they can do, that isn't just chasing, getting on your franchises stories? Because it obviously would be great if we were all on Barron's Wall Street Journal, and Bloomberg, but they can't always get that. They still got to do more. They got to develop the relationships. >> John, one thing I would be conscious of here is that many of our publications, it's certainly true for journalists, true for us at Barron's and it's certainly true for Bloomberg. We're all multimedia publishers. We're doing lots of things. Barron's has television show on Fox. We have a video series. We have podcasts and newsletters, and daily live audio chats and all sorts of other stuff in addition to the magazine and the website. And so part of that is trying to figure out not just the right publication, but maybe there's an opportunity to do a very particular, maybe you'd be great fit for this thing, but not that thing. And having a real understanding of what are the moving parts. And then the other part, which is always the hardest part, in a way, is truly understanding not just I want to pitch to Bloomberg, but who do I want to pitch at Bloomberg. So I might have a great story for the Wall Street Journal and maybe Gerard would care but maybe it's really somebody you heard on the street who cares or somebody who's covering a particular company. So you have to navigate that, I think effectively. And even, more so now, because we're not sitting in a newsroom. I can't go yell over to somebody who's a few desks away and suggest they take a look at something. >> Do you think that the comm-- (talk over each other) Do you think the comms teams are savvy and literate in multimedia? Are they still stuck in the print ways or the group swing is they're used to what they're doing and haven't evolved? Is that something that you're seeing here? >> I think it varies. Some people will really get it. I think one of the things that that this comes back to in a sense is it's relationship driven. To Gerard's point, it's not so much about trusting people that I don't know, it's about I've been at this a long time, I know what people I know, who I trust, and they know the things I'm interested in and so that relationship is really important. It's a lot harder to try that with somebody new. And the other thing is, I think relevant here is something that we touched on earlier, which is the idiosyncratic element. The ability for me to go out and see new things is tougher. In the technology business, you could spend half your time just going to events, You could go to the conferences and trade shows and dinners and lunches and coffees all day long. And you would get a lot of good story ideas that way. And now you can't do any of that. >> There's no digital hallway. There are out there. It's called Twitter, I guess or-- >> Well, you're doing it from sitting in this very I'm still doing it from sitting in the same chair, having conversations, in some ways like that. But it's not nearly the same. >> Gerard, Brenna, what do you guys think about the comms opportunity, challenges, either whether it's directly or indirectly, things that they could do differently? Share your thoughts. Gerard, we'll start with you? >> Well, I would echo Eric's point as far as knowing who you're pitching to. And I would say that in, at least for the people I'm working with, some of our beats have changed because there are new issues to cover. Someone's taking more of a role covering virus coverage, someone's taking more of a role covering protests. And so I think knowing instead of casting a really wide net, I'm normally happy to try to direct pitches in the right direction. But I do have less time to do that now. So I think if someone can come to me and say, "I know you've been covering this, "this is how my content fits in with that." It'd grab my attention more and makes it easier for me. So I would say that that is one thing that as beats are shifting and people are taking on a little bit of new roles in our coverage, that that's something PR and marketing teams could definitely keep an eye on. >> I agree with all of that. And all everything everybody said. I'd say two very quick things. One, exactly as everybody said, really know who you are pitching to. It's partly just, it's going to be much more effective if you're pitching to the right person, the right story. But when I say that also make the extra effort to familiarize yourself with the work that that reporter or that editor has done. You cannot, I'm sorry to say, overestimate the vanity of reporters or editors or anybody. And so if you're pitching a story to a particular reporter, in a field, make sure you're familiar with what that person may have done and say to her, "I really thought you did a great job "on the reporting that you did on this." Or, "I read your really interesting piece about that," or "I listened to your podcast." It's a relatively easy thing to do that yields extraordinarily well. A, because it appeals to anybody's fantasy and we all have a little bit of that. But, B, it also suggests to the reporter or the editor or the person involved the PR person communications person pitching them, really knows this, has really done their work and has really actually takes this seriously. And instead of just calling, the number of emails I get, and I'm sure it's the same for the others too, or occasional calls out of the blue or LinkedIn messages. >> I love your work. I love your work. >> (voice cuts out) was technology. Well, I have a technology story for you. It's absolutely valueless. So that's the first thing, I would really emphasize that. The second thing I'd say is, especially on the specific relation to this crisis, this Coronavirus issue is it's a tricky balance to get right. On the one hand, make sure that what you're doing what you're pitching is not completely irrelevant right now. The last three months has not been a very good time to pitch a story about going out with a bunch of people to a crowded restaurant or whatever or something like that to do something. Clearly, we know that. At the same time, don't go to the other extreme and try and make every little thing you have seen every story you may have every product or service or idea that you're pitching don't make it the thing that suddenly is really important because of Coronavirus. I've seen too many of those too. People trying too hard to say, "In this time of crisis, "in this challenging time, what people really want to hear "about is "I don't know, "some new diaper "baby's diaper product that I'm developing or whatever." That's trying too hard. So there is something in the middle, which is, don't pitch the obviously irrelevant story that is just not going to get any attention through this process. >> So you're saying don't-- >> And at the same time, don't go too far in the other direction. And essentially, underestimate the reporter's intelligence 'cause that reporter can tell you, "I can see that you're trying too hard." >> So no shotgun approach, obviously, "Hey, I love your work." Okay, yeah. And then be sensitive to what you're working on not try to force an angle on you, if you're doing a story. Eric, I want to get your thoughts on the evolution of some of the prominent journalists that I've known and/or communication professionals that are taking roles in the big companies to be storytellers, or editors of large companies. I interviewed Andy Cunningham last year, who used to be With Cunningham Communications, and formerly of Apple, better in the tech space and NPR. She said, "Companies have to own their own story "and tell it and put it out there." I've seen journalists say on Facebook, "I'm working on a story of x." And then crowdsource a little inbound. Thoughts on this new role of corporations telling their own story, going direct to the consumers. >> I think to a certain extent, that's valuable. And in some ways, it's a little overrated. There are a lot of companies creating content on their websites, or they're creating their own podcasts or they're creating their own newsletter and those kinds of things. I'm not quite sure how much of that, what the consumption level is for some of those things. I think, to me, the more valuable element of telling your story is less about the form and function and it's more about being able to really tell people, explain to them why what they do matters and to whom it matters, understanding the audience that's going to want to hear your story. There are, to your point, there are quite a few journalists who have migrated to either corporate communications or being in house storytellers of one kind or another for large businesses. And there's certainly a need to figure out the right way to tell your story. I think in a funny way, this is a tougher moment for those things. Because the world is being driven by external events, by these huge global forces are what we're all focused on right now. And it makes it a lot tougher to try and steer your own story at this particular moment in time. And I think you do see it Gerard was talking about don't try and... You want to know what other people are doing. You do want to be aware of what others are writing about. But there's this tendency to want to say, "I saw you wrote a story about Peloton "and we too have a exercise story that you can, "something that's similar." >> (chuckles) A story similar to it. We have a dance video or something. People are trying to glam on to things and taking a few steps too far. But in terms of your original question, it's just tougher at the moment to control your story in that particular fashion, I think. >> Well, this brings up a good point. I want to get to Gerard's take on this because the Wall Street Journal obviously has been around for many, many decades. and it's institution in journalism. In the old days, if you weren't relevant enough to make the news, if you weren't the most important story that people cared about, the editors make that choice and you're on the front page or in a story editorially. And companies would say, "No, but I should be in there." And you'd say, "That's what advertising is for." And that's the way it seemed to work in the past. If you weren't relevant in the spirit of the decision making of important story or it needs to be communicated to the audience, there's ads for that. You can get a full page ad in the old days. Now with the new world, what's an ad, what's a story? You now have multiple omni-channels out there. So traditionally, you want to get the best, most important story that's about relevance. So companies might not have a relevant story and they're telling a boring story. There's no there, there, or they miss the story. How do you see this? 'Cause this is the blend, this is the gray area that I see. It's certainly a good story, depending on who you're talking to, the 10 people who like it. >> I think there's no question. We're in the news business, topicality matters. You're going to have a much better chance of getting your story, getting your product or service, whatever covered by the Wall Street Journal, Barron's or anywhere else for that matter, if it seems somehow news related, whether it's the virus or the unrest that we've been seeing, or it's to do with the economy. Clearly, you can have an effect. Newspapers, news organizations of all the three news organizations we represent don't just, are not just obviously completely obsessed with what happened this morning and what's going on right now. We are all digging into deeper stories, especially in the business field. Part of what we all do is actually try to get beyond the daily headlines. And so what's happening with the fortunes of a particular company. Obviously, they may be impacted by they're going to be impacted by the lockdown and Coronavirus. But they actually were doing some interesting things that they were developing over the long term, and we would like to look into that too. So again, there is a balance there. And I'm not going to pretend that if you have a really topical story about some new medical device or some new technology for dealing with this new world that we're all operating in, you're probably going to get more attention than you would if you don't have that. But I wouldn't also underestimate, the other thing is, as well as topicality, everybody's looking at the same time to be different, and every journalist wants to do something original and exclusive. And so they are looking for a good story that may be completely unrelated. In fact, I would also underestimate, I wouldn't underestimate either the desire of readers and viewers and listeners to actually have some deeper reported stories on subjects that are not directly in the news right now. So again, it's about striking the balance right. But I wouldn't say that, that there is not at all, I wouldn't say there is not a strong role for interesting stories that may not have anything to do what's going on with the news right now. >> Brenna, you want to add on your thoughts, you're in the front lines as well, Bloomberg, everyone wants to be on Bloomberg. There's Bloomberg radio. You guys got tons of media too, there's tons of stuff to do. How do they navigate? And how do you view the interactions with comms folks? >> It looks we're having a little bit of challenge with... Eric, your thoughts on comm professionals. The questions in the chats are everything's so fast paced, do you think it's less likely for reporters to respond to PR comms people who don't have interacted with you before? Or with people you haven't met before? >> It's an internal problem. I've seen data that talks about the ratio of comms people to reporters, and it's, I don't know, six or seven to one or something like that, and there are days when it feels like it's 70 to one. And so it is challenging to break through. And I think it's particularly challenging now because some of the tools you might have had, you might have said, "Can we grab coffee one day or something like that," trying to find ways to get in front of that person when you don't need them. It's a relationship business. I know this is a frustrating answer, but I think it's the right answer which is those relationships between media and comms people are most successful when they've been established over time. And so you're not getting... The spray and pray strategy doesn't really work. It's about, "Eric, I have a story that's perfect for you. "And here's why I think you you should talk to this guy." And if they really know me, there's a reasonable chance that I'll not only listen to them, but I'll at least take the call. You need to have that high degree of targeting. It is really hard to break through and people try everything. They try, the insincere version of the, "I read your story, it was great. "but here's another great story." Which maybe they read your story, maybe they didn't at least it was an attempt. Or, "if you like this company, you'll love that one." People try all these tricks to try and get get to you. I think the highest level of highest probability of success comes from the more information you have about not just what I covered yesterday, but what do I cover over time? What kinds of stories am I writing? What kinds of stories does the publication write? And also to keep the pitching tight, I was big believer when I was doing comms, you should be able to pitch stories in two sentences. And you'll know from that whether there's going to be connection or not, don't send me five or more pitches. Time is of the essence, keep it short and as targeted as possible. >> That's a good answer to Paul Bernardo's question in the chat, which is how do you do the pitch. Brenna, you're back. Can you hear us? No. Okay. We'll get back to her when she gets logged back in. Gerard, your thoughts on how to reach you. I've never met you before, if I'm a CEO or I'm a comms person, a company never heard of, how do I get your attention? If I can't have a coffee with you with COVID, how do I connect with you virtually? (talk over each other) >> Exactly as Eric said, it is about targeting, it's really about making sure you are. And again, it's, I hate to say this, but it's not that hard. If you are the comms person for a large or medium sized company or even a small company, and you've got a particular pitch you want to make, you're probably dealing in a particular field, a particular sector, business sector or whatever. Let's say it says not technology for change, let's say it's fast moving consumer goods or something like that. Bloomberg, Brenna is in an enormous organization with a huge number of journalist you deal and a great deal of specialism and quality with all kinds of sectors. The Wall Street Journal is a very large organization, we have 13, 1400 reporters, 13 to 1400 hundred journalist and staff, I should say. Barron's is a very large organization with especially a particularly strong field coverage, especially in certain sectors of business and finance. It's not that hard to find out A, who is the right person, actually the right person in those organizations who's been dealing with the story that you're trying to sell. Secondly, it's absolutely not hard to find out what they have written or broadcast or produced on in that general field in the course of the last, and again, as Eric says, going back not just over the last week or two, but over the last year or two, you can get a sense of their specialism and understand them. It's really not that hard. It's the work of an hour to go back and see who the right person is and to find out what they've done. And then to tailor the pitch that you're making to that person. And again, I say that partly, it's not purely about the vanity of the reporter, it's that the reporter will just be much more favorably inclined to deal with someone who clearly knows, frankly, not just what they're pitching, but what the journalist is doing and what he or she, in his or her daily activity is actually doing. Target it as narrowly as you can. And again, I would just echo what Eric and I think what Brenna was also saying earlier too that I'm really genuinely surprised at how many very broad pitches, again, I'm not directly in a relative role now. But I was the editor in chief of the Journal for almost six years. And even in that position, the number of extraordinarily broad pitches I get from people who clearly didn't really know who I was, who didn't know what I did, and in some cases, didn't even really know what Wall Street Journal was. If you can find that, if you actually believe that. It's not hard. It's not that hard to do that. And you will have so much more success, if you are identifying the organization, the people, the types of stories that they're interested in, it really is not that difficult to do. >> Okay, I really appreciate, first of all, great insight there. I want to get some questions from the crowd so if you're going to chat, there was a little bit of a chat hiccup in there. So it should be fixed. We're going to go to the chat for some questions for this distinguished panel. Talk about the new coffee. There's a good question here. Have you noticed news fatigue, or reader seeking out news other than COVID? If so, what news stories have you been seeing trending? In other words, are people sick and tired of COVID? Or is it still on the front pages? Is that relevant? And if not COVID, what stories are important, do you think? >> Well, I could take a brief stab at that. I think it's not just COVID per se, for us, the volatility of the stock market, the uncertainties in the current economic environment, the impact on on joblessness, these massive shifts of perceptions on urban lifestyles. There's a million elements of this that go beyond the core, what's happening with the virus story. I do think as a whole, all those things, and then you combine that with the social unrest and Black Lives Matter. And then on top of that, the pending election in the fall. There's just not a lot of room left for other stuff. And I think I would look at it a little bit differently. It's not finding stories that don't talk on those things, it's finding ways for coverage of other things whether it's entertainment. Obviously, there's a huge impact on the entertainment business. There's a huge impact on sports. There's obviously a huge impact on travel and retail and restaurants and even things like religious life and schooling. I have the done parents of a college, was about to be a college sophomore, prays every day that she can go back to school in the fall. There are lots of elements to this. And it's pretty hard to imagine I would say to Gerard's point earlier, people are looking for good stories, they're always looking for good stories on any, but trying to find topics that don't touch on any of these big trends, there's not a lot of reasons to look for those. >> I agree. Let me just give you an example. I think Eric's exactly right. It's hard to break through. I'll just give you an example, when you asked that question, I just went straight to my Wall Street Journal app on my phone. And of course, like every organization, you can look at stories by sections and by interest and by topic and by popularity. And what are the three most popular stories right now on the Wall Street Journal app? I can tell you the first one is how exactly do you catch COVID-19? I think that's been around since for about a month. The second story is cases accelerate across the United States. And the third story is New York, New Jersey and Connecticut, tell travelers from areas with virus rates to self isolate. So look, I think anecdotally, there is a sense of COVID fatigue. Well, we're all slightly tired of it. And certainly, we were probably all getting tired, or rather distressed by those terrible cases and when we've seen them really accelerate back in March and April and these awful stories of people getting sick and dying. I was COVID fatigued. But I just have to say all of the evidence we have from our data, in terms of as I said earlier, the interest in the story, the demand for what we're doing, the growth in subscriptions that we've had, and just as I said, little things like that, that I can point you at any one time, I can guarantee you that our among our top 10 most read stories, at least half of them will be COVID-19. >> I think it's safe to say general interest in that outcome of progression of that is super critical. And I think this brings up the tech angle, which we can get into a minute. But just stick with some of these questions I just want to just keep these questions flowing while we have a couple more minutes left here. In these very challenging times for journalism, do byline articles have more power to grab the editors attention in the pitching process? >> Well, I think I assume what the questioner is asking when he said byline articles is contributed. >> Yes. >> Contributed content. Barron's doesn't run a lot of contributing content that way in a very limited way. When I worked at Forbes, we used to run tons of it. I'm not a big believer that that's necessarily a great way to generate a lot of attention. You might get published in some publication, if you can get yourself onto the op ed page of The Wall Street Journal or The New York Times, more power to you. But I think in most cases-- >> It's the exception not the rule Exception not the rule so to speak, on the big one. >> Yeah. >> Well, this brings up the whole point about certainly on SiliconANGLE, our property, where I'm co founder and chief, we basically debate over and get so many pitches, "hey, I want to write for you, here's a contributed article." And it's essentially an advertisement. Come on, really, it's not really relevant. In some case we (talk over each other) analysts come in and and done that. But this brings up the question, we're seeing these newsletters like sub stack and these services really are funding direct journalism. So it's an interesting. if you're good enough to write Gerard, what's your take on this, you've seen this, you have a bit of experience in this. >> I think, fundamental problem here is that is people like the idea of doing by lines or contributed content, but often don't have enough to say. You can't just do, turn your marketing brochure into a piece of an 800 word with the content that that's going to be compelling or really attract any attention. I think there's a place for it, if you truly have something important to say, and if you really have something new to say, and it's not thinly disguised marketing material. Yeah, you can find a way to do that. I'm not sure I would over-rotate on that as an approach. >> No, I just briefly, again, I completely agree. At the Journal we just don't ever publish those pieces. As Eric says, you're always, everyone is always welcome to try and pitch to the op ed pages of the Journal. They're not generally going to I don't answer for them, I don't make those decisions. But I've never seen a marketing pitch run as an op ed effectively. I just think you have to know again, who you're aiming at. I'm sure it's true for Bloomberg, Barron's and the Journal, most other major news organizations are not really going to consider that. There might be organizations, there might be magazines, digital and print magazines. There might be certain trade publications that would consider that. Again, at the Journal and I'm sure most of the large news organizations, we have very strict rules about what we can publish. And how and who can get published. And it's essentially journal editorials, that journal news staff who can publish stories we don't really take byline, outside contribution. >> Given that your time is so valuable, guys, what's the biggest, best practice to get your attention? Eric, you mentioned keeping things tight and crisp. Are there certain techniques to get your attention? >> Well I'll mention just a couple of quick things. Email is better than most other channels, despite the volume. Patience is required as a result because of the volume. People do try and crawl over the transom, hit you up on LinkedIn, DM you on Twitter, there's a lot of things that people try and do. I think a very tightly crafted, highly personalized email with the right subject line is probably still the most effective way, unless it's somebody you actually, there are people who know me who know they have the right to pick up the phone and call me if they really think they have... That's a relationship that's built over time. The one thing on this I would add which I think came up a little bit before thinking about it is, you have to engage in retail PR, not not wholesale PR. The idea that you're going to spam a list of 100 people and think that that's really going to be a successful approach, it's not unless you're just making an announcement, and if you're issuing your earnings release, or you've announced a large acquisition or those things, fine, then I need to get the information. But simply sending around a very wide list is not a good strategy, in most cases, I would say probably for anyone. >> We got Brenna back, can you hear me? She's back, okay. >> I can hear you, I'm back. >> Well, let's go back to you, we missed you. Thanks for coming back in. We had a glitch on our end but appreciate it, bandwidth internet is for... Virtual is always a challenge to do live, but thank you. The trend we're just going through is how do I pitch to you? What's the best practice? How do I get your attention? Do bylines lines work? Actually, Bloomberg doesn't do that very often either as well as like the Journal. but your thoughts on folks out there who are really trying to figure out how to do a good job, how to get your attention, how to augment your role and responsibilities. What's your thoughts? >> I would say, going back to what we said a little bit before about really knowing who you're pitching to. If you know something that I've written recently that you can reference, that gets my attention. But I would also encourage people to try to think about different ways that they can be part of a story if they are looking to be mentioned in one of our articles. And what I mean by that is, maybe you are launching new products or you have a new initiative, but think about other ways that your companies relate to what's going on right now. So for instance, one thing that I'm really interested in is just the the changing nature of work in the office place itself. So maybe you know of something that's going on at a company, unlimited vacation for the first time or sabbaticals are being offered to working parents who have nowhere to send their children, or something that's unique about the current moment that we're living in. And I think that those make really good interviews. So it might not be us featuring your product or featuring exactly what your company does, but it still makes you part of the conversation. And I think it's still, it's probably valuable to the company as well to get that mention, and people may be looking into what you guys do. So I would say that something else we are really interested in right now is really looking at who we're quoting and the diversity of our sources. So that's something else I would put a plug in for PR people to be keeping an eye on, is if you're always putting up your same CEO who is maybe of a certain demographic, but you have other people in your company who you can give the opportunity to talk with the media. I'm really interested in making sure I'm using a diverse list of sources and I'm not just always calling the same person. So if you can identify people who maybe even aren't experienced with it, but they're willing to give it a try, I think that now's a really good moment to be able to get new voices in there. >> Rather than the speed dial person you go to for that vertical or that story, building out those sources. >> Exactly. >> Great, that's great insight, Everyone, great insights. And thank you for your time on this awesome panel. Love to do it again. This has been super informative. I love some of the engagement out there. And again, I think we can do more of these and get the word out. I'd like to end the panel on an uplifting note for young aspiring journalists coming out of school. Honestly, journalism programs are evolving. The landscape is changing. We're seeing a sea change. As younger generation comes out of college and master's programs in journalism, we need to tell the most important stories. Could you each take a minute to give your advice to folks either going in and coming out of school, what to be prepared for, how they can make an impact? Brenna, we'll start with you, Gerard and Eric. >> That's a big question. I would say one thing that has been been encouraging about everything going on right now as I have seen an increased hunger for information and an increased hunger for accurate information. So I do think it can obviously be disheartening to look at the furloughs and the layoffs and everything that is going on around the country. But at the same time, I think we have been able to see really big impacts from the people that are doing reporting on protests and police brutality and on responses to the virus. And so I think for young journalists, definitely take a look at the people who are doing work that you think is making a difference. And be inspired by that to keep pushing even though the market might be a little bit difficult for a while. >> I'd say two things. One, again, echoing what Brenna said, identify people that you follow or you admire or you think are making a real contribution in the field and maybe directly interact with them. I think all of us, whoever we are, always like to hear from young journalists and budding journalists. And again, similar advice to giving to the advice that we were giving about PR pitches. If you know what that person has been doing, and then contact them and follow them. And I know I've been contacted by a number of young journalists like that. The other thing I'd say is and this is more of a plea than a piece of advice. But I do think it will work in the long run, be prepared to go against the grain. I fear that too much journalism today is of the same piece. There is not a lot of intellectual diversity in what we're seeing There's a tendency to follow the herd. Goes back a little bit to what I was saying right at the opening about the fact that too many journalists, quite frankly, are clustered in the major metropolitan areas in this country and around the world. Have something distinctive and a bit different to say. I'm not suggesting you offer some crazy theory or a set of observations about the world but be prepared to... To me, the reason I went into journalism was because I was always a bit skeptical about whenever I saw something in any media, which especially one which seemed to have a huge amount of support and was repeated in all places, I always asked myself, "Is that really true? "Is that actually right? "Maybe there's an alternative to that." And that's going to make you stand out as a journalist, that's going to give you a distinctiveness. It's quite hard to do in some respects right now, because standing out from the crowd can get you into trouble. And I'm not suggesting that people should do that. Have a record of original storytelling, of reporting, of doing things perhaps that not, because look, candidly, there are probably right now in this country, 100,00 budding putative journalists who would like to go out and write about, report on Black Lives Matter and the reports on the problems of racial inequality in this country and the protests and all of that kind of stuff. The problem there is there are already 100,000 of those people who want to do that in addition to probably the 100,000 journalists who are already doing it. Find something else, find something different. have something distinctive to offer so that when attention moves on from these big stories, whether it's COVID or race or politics or the election or Donald Trump or whatever. Have something else to offer that is quite distinctive and where you have actually managed to carve out for yourself a real record as having an independent voice. >> Brenna and Gerard, great insight. Eric, take us home close us out. >> Sure. I'd say a couple things. So one is as a new, as a young journalist, I think first of all, having a variety of tools in your toolkit is super valuable. So be able to write long and write short, be able to do audio, blogs, podcast, video. If you can shoot photos and the more skills that you have, a following on social media. You want to have all of the tools in your toolkit because it is challenging to get a job and so you want to be able to be flexible enough to fill all those roles. And the truth is that a modern journalist is finding the need to do all of that. When I first started at Barron's many, many years ago, we did one thing, we did a weekly magazine. You'd have two weeks to write a story. It was very comfortable. And that's just not the way the world works anymore. So that's one element. And the other thing, I think Gerard is right. You really want to have a certain expertise if possible that makes you stand out. And the contradiction is, but you also want to have the flexibility to do lots of different stories. You want to get (voice cuts out) hold. But if you have some expertise, that is hard to find, that's really valuable. When Barron's hires we're always looking for people who have, can write well but also really understand the financial markets. And it can be challenging for us sometimes to find those people. And so I think there's, you need to go short and long. It's a barbell strategy. Have expertise, but also be flexible in both your approach and the things you're willing to cover. >> Great insight. Folks, thanks for the great commentary, great chats for the folks watching, really appreciate your valuable time. Be original, go against the grain, be skeptical, and just do a good job. I think there's a lot of opportunity. And I think the world's changing. Thanks for your time. And I hope the comms folks enjoyed the conversation. Thank you for joining us, everyone. Appreciate it. >> Thanks for having us. >> Thank you. >> I'm John Furrier here in the Cube for this Cube Talk was one hour power panel. Awesome conversation. Stay in chat if you want to ask more questions. We'll come back and look at those chats later. But thank you for watching. Have a nice day. (instrumental music)

>> Narrator: Hi, everyone, thanks for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled toward Zero Unplanned Downtime of Medical Imaging Systems using Big Data. My name is Sue LeClaire, Director of Marketing at Vertica, and I'll be your host for this webinar. Joining me is Mauro Barbieri, lead architect of analytics at Philips. Before we begin, I want to encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click Submit. There will be a Q&A session at the end of the presentation. And we'll answer as many questions as we're able to during that time. Any questions that we don't get to we'll do our best to answer them offline. Alternatively, you can also visit the vertical forums to post your question there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And yes, this virtual session is being recorded, and we'll be available to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Mauro, over to you. >> Thank you, good day everyone. So medical imaging systems such as MRI scanners, interventional guided therapy machines, CT scanners, the XR system, they need to provide hospitals, optimal clinical performance but also predictable cost of ownership. So clinicians understand the need for maintenance of these devices, but they just want to be non intrusive and scheduled. And whenever there is a problem with the system, the hospital suspects Philips services to resolve it fast and and the first interaction with them. In this presentation you will see how we are using big data to increase the uptime of our medical imaging systems. I'm sure you have heard of the company Phillips. Phillips is a company that was founded in 129 years ago in actually 1891 in Eindhoven in Netherlands, and they started by manufacturing, light bulbs, and other electrical products. The two brothers Gerard and Anton, they took an investment from their father Frederik, and they set up to manufacture and sale light bulbs. And as you may know, a key technology for making light bulbs is, was glass and vacuum. So when you're good at making glass products and vacuum and light bulbs, then there is an easy step to start making radicals like they did but also X ray tubes. So Philips actually entered very early in the market of medical imaging and healthcare technology. And this is what our is our core as a company, and it's also our future. So, healthcare, I mean, we are in a situation now in which everybody recognize the importance of it. And and we see incredible trends in a transition from what we call Volume Based Healthcare to Value Base, where, where the clinical outcomes are driving improvements in the healthcare domain. Where it's not enough to respond to healthcare challenges, but we need to be involved in preventing and maintaining the population wellness and from a situation in which we episodically are in touch with healthcare we need to continuously monitor and continuously take care of populations. And from healthcare facilities and technology available to a few elected and reach countries we want to make health care accessible to everybody throughout the world. And this of course, has poses incredible challenges. And this is why we are transforming the Philips to become a healthcare technology leader. So from Philips has been a concern realizing and active in many sectors in many sectors and realizing what kind of technologies we've been focusing on healthcare. And we have been transitioning from creating and selling products to making solutions to addresses ethical challenges. And from selling boxes, to creating long term relationships with our customers. And so, if you have known the Philips brand from from Shavers from, from televisions to light bulbs, you probably now also recognize the involvement of Philips in the healthcare domain, in diagnostic imaging, in ultrasound, in image guided therapy and systems, in digital pathology, non invasive ventilation, as well as patient monitoring intensive care, telemedicine, but also radiology, cardiology and oncology informatics. Philips has become a powerhouse of healthcare technology. To give you an idea of this, these are the numbers for, from 2019 about almost 20 billion sales, 4% comparable sales growth with respect to the previous year and about 10% of the sales are reinvested in R&D. This is also shown in the number of patents rights, last year we filed more than 1000 patents in, in the healthcare domain. And the company is about 80,000 employees active globally in over 100 countries. So, let me focus now on the type of products that are in the scope of this presentation. This is a Philips Magnetic Resonance Imaging Scanner, also called Ingenia 3.0 Tesla is an incredible machine. Apart from being very beautiful as you can see, it's a it's a very powerful technology. It can make high resolution images of the human body without harmful radiation. And it's a, it's a, it's a complex machine. First of all, it's massive, it weights 4.6 thousand kilograms. And it has superconducting magnets cooled with liquid helium at -269 degrees Celsius. And it's actually full of software millions and millions of lines of code. And it's occupied three rooms. What you see in this picture, the examination room, but there is also a technical room which is full of of of equipment of custom hardware, and machinery that is needed to operate this complex device. This is another system, it's an interventional, guided therapy system where the X ray is used during interventions with the patient on the table. You see on the left, what we call C-arm, a robotic arm that moves and can take images of the patient while it's been operated, it's used for cardiology intervention, neurological intervention, cardiovascular intervention. There's a table that moves in very complex ways and it again it occupies two rooms, this room that we see here and but also a room full of cabinets and hardwood and computers. This is another another characteristic of this machine is that it has to operate it as it is used during medical interventions, and so it has to interact with all kind of other equipment. This is another system it's a, it's a, it's a Computer Tomography Scanner Icon which is a unique, it is unique due to its special detection technology. It has an image resolution up to 0.5 millimeters and making thousand by thousand pixel images. And it is also a complex machine. This is a picture of the inside of a compatible device not really an icon, but it has, again three rotating, which waits two and a half turn. So, it's a combination of X ray tube on top, high voltage generators to power the extra tube and in a ray of detectors to create the images. And this rotates at 220 right per minutes, making 50 frames per second to make 3D reconstruction of the of the body. So a lot of technology, complex technology and this technology is made for this situation. We make it for clinicians, who are busy saving people lives. And of course, they want optimal clinical performance. They want the best technology to treat the patients. But they also want predictable cost of ownership. They want predictable system operations. They want their clinical schedules not interrupted. So, they understand these machines are complex full of technology. And these machines may have, may require maintenance, may require software update, sometimes may even say they require some parts, horrible parts to be replaced, but they don't want to have it unplanned. They don't want to have unplanned downtime. They would hate send, having to send patients home and to have to reschedule visits. So they understand maintenance. They just want to have a schedule predictable and non intrusive. So already a number of years ago, we started a transition from what we call Reactive Maintenance services of these devices to proactive. So, let me show you what we mean with this. Normally, if a system has an issue system on the field, and traditional reactive workflow would be that, this the customer calls a call center, reports the problem. The company servicing the device would dispatch a field service engineer, the field service engineer would go on site, do troubleshooting, literally smell, listen to noise, watch for lights, for, for blinking LEDs or other unusual issues and would troubleshoot the issue, find the root cause and perhaps decide that the spare part needs to be replaced. He would order a spare part. The part would have to be delivered at the site. Either immediately or the engineer would would need to come back another day when the part is available, perform the repair. That means replacing the parts, do all the needed tests and validations. And finally release the system for clinical use. So as you can see, there is a lot of, there are a lot of steps, and also handover of information from one to between different people, between different organizations even. Would it be better to actually keep monitoring the installed base, keep observing the machine and actually based on the information collected, detect or predict even when an issue is is going to happen? And then instead of reacting to a customer calling, proactively approach the customer scheduling, preventive service, and therefore avoid the problem. So this is actually what we call Corrective Service. And this is what we're being transitioning to using Big Data and Big Data is just one ingredient. In fact, there are more things that are needed. The devices themselves need to be designed for reliability and predictability. If the device is a black box does not communicate to the outside world the status, if it does not transmit data, then of course, it is not possible to observe and therefore, predict issues. This of course requires a remote service infrastructure or an IoT infrastructure as it is called nowadays. The passivity to connect the medical device with a data center in enterprise infrastructure, collect the data and perform the remote troubleshooting and the predictions. Also the right processes and the right organization is to be in place, because an organization that is, you know, waiting for the customer to call and then has a number of few service engineers available and a certain amount of spare parts and stock is a different organization from an organization that actually is continuously observing the installed base and is scheduling actions to prevent issues. And in other pillar is knowledge management. So in order to realize predictive models and to have predictive service action, it's important to manage knowledge about failure modes, about maintenance procedures very well to have it standardized and digitalized and available. And last but not least, of course, the predictive models themselves. So we talked about transmitting data from the installed base on the medical device, to an enterprise infrastructure that would analyze the data and generate predictions that's predictive models are exactly the last ingredient that is needed. So this is not something that I'm, you know, I'm telling you for the first time is actually a strategic intent of Philips, where we aim for zero unplanned downtime. And we market it that way. We also is not a secret that we do it by using big data. And, of course, there could be other methods to to achieving the same goal. But we started using big data already now well, quite quite many years ago. And one of the reasons is that our medical devices already are wired to collect lots of data about the functioning. So they collect events, error logs that are sensor connecting sensor data. And to give you an idea, for example, just as an order of magnitudes of size of the data, the one MRI scanner can log more than 1 million events per day, hundreds of thousands of sensor readings and tens of thousands of many other data elements. And so this is truly big data. On the other hand, this data was was actually not designed for predictive maintenance, you have to think a medical device of this type of is, stays in the field for about 10 years. Some a little bit longer, some of it's shorter. So these devices have been designed 10 years ago, and not necessarily during the design, and not all components were designed, were designed with predictive maintenance in mind with IoT, and with the latest technology at that time, you know, progress, will not so forward looking at the time. So the actual the key challenge is taking the data which is already available, which is already logged by the medical devices, integrating it and creating predictive models. And if we dive a little bit more into the research challenges, this is one of the Challenges. How to integrate diverse data sources, especially how to automate the costly process of data provisioning and cleaning? But also, once you have the data, let's say, how to create these models that can predict failures and the degradation of performance of a single medical device? Once you have these models and alerts, another challenge is how to automatically recommend service actions based on the probabilistic information on these possible failures? And once you have the insights even if you can recommend action still recommending an action should be done with the goal of planning, maintenance, for generating value. That means balancing costs and benefits, preventing unplanned downtimes without of course scheduling and unnecessary interventions because every intervention, of course, is a disruption for the clinical schedule. And there are many more applications that can be built off such as the optimal management of spare parts supplies. So how do you approach this problem? Our approach was to collect into one database Vertica. A large amount of historical data, first of all historical data coming from the medical devices, so event logs, parameter value system configuration, sensor readings, all the data that we have at our disposal, that in the same database together with records of failures, maintenance records, service work orders, part replacement contracts, so basically the evidence of failures and once you have data from the medical devices, and data from the failures in the same database, it becomes possible to correlate event logs, errors, signal sensor readings with records of failures and records of part replacement and maintenance operations. And we did that also with a specific approach. So we, we create integrated teams, and every integrated team at three figures, not necessarily three people, they were actually multiple people. But there was at least one business owner from a service organization. And this business owner is the person who knows what is relevant, which use case are relevant to solve for a particular type of product or a particular market. What basically is generating value or is worthwhile tackling as an organization. And we have data scientists, data scientists are the one who actually can manipulate data. They can write the queries, they can write the models and robust statistics. They can create visualization and they are the ones who really manipulate the data. Last but not least, very important is subject matter experts. Subject Matter Experts are the people who know the failure modes, who know about the functioning of the medical devices, perhaps they're even designed, they come from the design side, or they come from the service innovation side or even from the field. People who have been servicing the machines in real life for many, many years. So, they are familiar with the failure models, but also familiar with the type of data that is logged and the processes and how actually the systems behave, if you if you if you if you allow me in, in the wild in the in the field. So the combination of these three secrets was a key. Because data scientist alone, just statisticians basically are people who can all do machine learning. And they're not very effective because the data is too complicated. That's why you more than too complex, so they will spend a huge amount of time just trying to figure out the data. Or perhaps they will spend the time in tackling things that are useless, because it's such an interesting knows much quicker which data points are useful, which phenomenon can be found in the data or probably not found. So the combination of subject matter experts and data scientists is very powerful and together gathered by a business owner, we could tackle the most useful use cases first. So, this teams set up to work and they developed three things mainly, first of all, they develop insights on the failure modes. So, by looking at the data, and analyzing information about what happened in the field, they find out exactly how things fail in a very pragmatic and quantitative way. Also, they of course, set up to develop the predictive model with associated alerts and service actions. And a predictive model is just not an alert is just not a flag. Just not a flag, only flag that turns on like a like a traffic light, you know, but there's much more than that. It's such an alert is to be interpreted and used by highly skilled and trained engineer, for example, in a in a call center, who needs to evaluate that error and plan a service action. Service action may involve the ordering a replacement of an expensive part, it may involve calling up the customer hospital and scheduling a period of downtime, downtime to replace a part. So it has an impact on the clinical practice, could have an impact. So, it is important that the alert is coupled with sufficient evidence and information for such a highly skilled trained engineer to plan the service session efficiently. So, it's it's, it's a lot of work in terms of preparing data, preparing visualizations, and making sure that old information is represented correctly and in a compact form. Additionally, These teams develop, get insight into the failure modes and so they can provide input to the R&D organization to improve the products. So, to summarize these graphically, we took a lot of historical data from, coming from the medical devices from the history but also data from relational databases, where the service, work orders, where the part replacement, the contact information, we integrated it, and we set up to the data analytics. From there we don't have value yet, only value starts appearing when we use the insights of data analytics the model on live data. When we process live data with the module we can generate alerts, and the alerts can be used to plan the maintenance and the maintenance therefore the plant maintenance replaces replacing downtime is creating value. To give an idea of the, of the type of I cannot show you the details of these modules, all of these predictive models. But to give you an idea, this is just a picture of some of the components of our medical device for which we have models for which we have, for which we call the failure modes, hard disk, clinical grade monitoring, monitors, X ray tubes, and so forth. This is for MRI machines, a lot of custom hardware and other types of amplifiers and electronics. The alerts are then displayed in a in a dashboard, what we call a Remote monitoring dashboard. We have a team of remote monitoring engineers that basically surveyors the install base, looks at this dashboard picks up these alerts. And an alert as I said before is not just one flag, it contains a lot of information about the failure and about the medical device. And the remote monitor engineer basically will pick up these alerts, they review them and they create cases for the markets organization to handle. So, they see an alert coming in they create a case. So that the particular call center in in some country can call the customer and schedule and make an appointment to schedule a service action or it can add it preventive action to the schedule of the field service engineer who's already supposed to go to visit the customer for example. This is a picture and high-level picture of the overall data person architecture. On the bottom we have install base install base is formed by all our medical devices that are connected to our Philips and more service network. Data is transmitted in a in a secure and in a secure way to our enterprise infrastructure. Where we have a so called Data Lake, which is basically an archive where we store the data as it comes from, from the customers, it is scrubbed and protected. From there, we have a processes ETL, Extract, Transform and Load that in parallel, analyze this information, parse all these files and all this data and extract the relevant parameters. All this, the reason is that the data coming from the medical device is very verbose, and in legacy formats, sometimes in binary formats in strange legacy structures. And therefore, we parse it and we structure it and we make it magically usable by data science teams. And the results are stored in a in a vertica cluster, in a data warehouse. In the same data warehouse, where we also store information from other enterprise systems from all kinds of databases from SQL, Microsoft SQL Server, Tera Data SAP from Salesforce obligations. So, the enterprise IT system also are connected to vertica the data is inserted into vertica. And then from vertica, the data is pulled by our predictive models, which are Python and Rscripts that run on our proprietary environment helps with insights. From this proprietary environment we generate the alerts which are then used by the remote monitoring application. It's not the only application this is the case of remote monitoring. We also have applications for particular remote service. So whenever we cannot prevent or predict we cannot predict an issue from happening or we cannot prevent an issue from happening and we need to react on a customer call, then we can still use the data to very quickly troubleshoot the system, find the root cause and advice or the best service session. Additionally, there are reliability dashboards because all this data can also be used to perform reliability studies and improve the design of the medical devices and is used by R&D. And the access is with all kinds of tools. So Vertica gives the flexibility to connect with JDBC to connect dashboards using Power BI to create dashboards and click view or just simply use RM Python directly to perform analytics. So little summary of the, of the size of the data for the for the moment we have integrated about 500 terabytes worth of data tables, about 30 trillion data points. More than eighty different data sources. For our complete connected install base, including our customer relation management system SAP, we also have connected, we have integrated data from from the factory for repair shops, this is very useful because having information from the factory allows to characterize components and devices when they are new, when they are still not used. So, we can model degradation, excuse me, predict failures much better. Also, we have many years of historical data and of course 24/7 live feeds. So, to get all this going, we we have chosen very simple designs from the very beginning this was developed in the back the first system in 2015. At that time, we went from scratch to production eight months and is also very stable system. To achieve that, we apply what we call Exhaustive Error Handling. When you process, most of people attending this conference probably know when you are dealing with Big Data, you have probably you face all kinds of corner cases you feel that will never happen. But just because of the sheer volume of the data, you find all kinds of strange things. And that's what you need to take care of, if you want to have a stable, stable platform, stable data pipeline. Also other characteristic is that, we need to handle live data, but also be able to, we need to be able to reprocess large historical datasets, because insights into the data are getting generated over time by the team that is using the data. And very often, they find not only defects, but also they have changed requests for new data to be extracted to distract in a different way to be aggregated in a different way. So basically, the platform is continuously crunching data. Also, components have built-in monitoring capabilities. Transparent transparency builds trust by showing how the platform behaves. People actually trust that they are having all the data which is available, or if they don't see the data or if something is not functioning they can see why and where the processing has stopped. A very important point is documentation of data sources every data point as a so called Data Provenance Fields. That is not only the medical device where it comes from, with all this identifier, but also from which file, from which moment in time, from which row, from which byte offset that data point comes. This allows to identify and not only that, but also when this data point was created, by whom, by whom meaning which version of the platform and of the ETL created a data point. This allows us to identify issues and also to fix only the subset of when an issue is identified and fixed. It's possible then to fix only subset of the data that is impacted by that issue. Again, this grid trusts in data to essential for this type of applications. We actually have different environments in our analytic solution. One that we call data science environment is more or less what I've shown so far, where it's deployed in our Philips private cloud, but also can be deployed in in in public cloud such as Amazon. It contains the years of historical data, it allows interactive data exploration, human queries, therefore, it is a highly viable load. It is used for the training of machine learning algorithms and this design has been such that we it is for allowing rapid prototyping and for large data volumes. In other environments is the so called Production Environment where we actually score the models with live data from generation of the alerts. So this environment does not require years of data just months, because a model to make a prediction does not need necessarily years of data, but maybe some model even a couple of weeks or a few months, three months, six months depending on the type of data on the failure which has been predicted. And this has highly optimized queries because the applications are stable. It only only change when we deploy new models or new versions of the models. And it is designed optimized for low latency, high throughput and reliability is no human intervention, no human queries. And of course, there are development staging environments. And one of the characteristics. Another characteristic of all this work is that what we call Data Driven Service Innovation. In all this work, we use the data in every step of the process. The First business case creation. So, basically, some people ask how did you manage to find the unlocked investment to create such a platform and to work on it for years, you know, how did you start? Basically, we started with a business case and the business case again for that we use data. Of course, you need to start somewhere you need to have some data, but basically, you can use data to make a quantitative analysis of the current situation and also make it as accurate as possible estimate quantitative of value creation, if you have that basically, is you can justify the investments and you can start building. Next to that data is used to decide where to focus your efforts. In this case, we decided to focus on the use cases that had the maximum estimated business impact, with business impact meaning here, customer value, as well as value for the company. So we want to reduce unplanned downtime, we want to give value to our customers. But it would be not sustainable, if for creating value, we would start replacing, you know, parts without any consideration for the cost of it. So it needs to be sustainable. Also, then we use data to analyze the failure modes to actually do digging into the data understanding of things fail, for visualization, and to do reliability analysis. And of course, then data is a key to do feature engineering for the development of the predictive models for training the models and for the validation with historical data. So data is all over the place. And last but not least, again, these models is architecture generates new data about the alerts and about the how good the alerts are, and how well they can predict failures, how much downtime is being saved, how money issues have been prevented. So this also data that needs to be analyzed and provides insights on the performance of this, of this models and can be used to improve the models found. And last but not least, once you have performance of the models you can use data to, to quantify as much as possible the value which is created. And it is when you go back to the first step, you made the business value you you create the first business case with estimates. Can you, can you actually show that you are creating value? And the more you can, have this fitness feedback loop closed and quantify the better it is for having more and more impact. Among the key elements that are needed for realizing this? So I want to mention one about data documentation is the practice that we started already six years ago is proven to be very valuable. We document always how data is extracted and how it is stored in, in data model documents. Data Model documents specify how data goes from one place to the other, in this case from device logs, for example, to a table in vertica. And it includes things such as the finish of duplicates, queries to check for duplicates, and of course, the logical design of the tables below the physical design of the table and the rationale. Next to it, there is a data dictionary that explains for each column in the data model from a subject matter expert perspective, what that means, such as its definition and meaning is if it's, if it's a measurement, the use of measure and the range. Or if it's a, some sort of, of label the spec values, or whether the value is raw or or calculated. This is essential for maximizing the value of data for allowing people to use data. Last but not least, also an ETL design document, it explains how the transformation has happened from the source to the destination including very important the failure and the strategy. For example, when you cannot parse part of a file, should you load only what you can parse or drop the entire file completely? So, import best effort or do all or nothing or how to populate records for which there is no value what are the default values and you know, how to have the data is normalized or transform and also to avoid duplicates. This again is very important to provide to the users of the data, if full picture of all the data itself. And this is not just, this the formal process the documents are reviewed and approved by all the stakeholders into the subject matter experts and also the data scientists from a function that we have started called Data Architect. So to, this is something I want to give about, oh, yeah and of course the the documents are available to the end users of the data. And we even have links with documents of the data warehouse. So if you are, if you get access to the database, and you're doing your research and you see a table or a view, you think, well, it could be that could be interesting. It looks like something I could use for my research. Well, the data itself has a link to the document. So from the database while you're exploring data, you can retrieve a link to the place where the document is available. This is just the quick summary of some of the of the results that I'm allowed to share at this moment. This is about image guided therapy, using our remote service infrastructure for remotely connected system with the right contracts. We can achieve we have we have reduced downtime by 14% more than one out of three of cases are resolved remotely without an engineer having to go outside. 82% is the first time right fixed rate that means that the issue is fixed either remotely or if a visit at the site is needed, that visit only one visit is needed. So at that moment, the engineer we decided the right part and fix this straightaway. And this result on average on 135 hours more operational availability per year. This therefore, the ability to treat more patients for the same costs. I'd like to conclude with citing some nice testimonials from some of our customers, showing that the value that we've created is really high impact and this concludes my presentation. Thanks for your attention so far. >> Thank you Morrow, very interesting. And we've got a number of questions that we that have come in. So let's get to them. The first one, how many devices has Philips connected worldwide? And how do you determine which related center data workloads get analyzed with protocols? >> Okay, so this is just two questions. So the first question how many devices are connected worldwide? Well, actually, I'm not allowed to tell you the precise number of connected devices worldwide, but what I can tell is that we are in the order of tens of thousands of devices. And of all types actually. And then, how would we determine which related sensor gets analyzed with vertica well? And a little bit how I set In the in the presentation is a combination of two approaches is a data driven approach and the knowledge driven approach. So a knowledge driven approach because we make maximum use of our knowledge of the failure modes, and the behavior of the medical devices and of their components to select what we think are promising data points and promising features. However, from that moment on data science kicks in, and it's actually data science is used to look at the actual data and come up with quantitative information of what is really happening. So, it could be that an expert is convinced that the particular range of value of a sensor are indicative of a particular failure. And it turns out that maybe it was too optimistic on the other way around that in practice, there are many other situations situation he was not aware of. That could happen. So thanks to the data, then we, you know, get a better understanding of the phenomenon and we get the better modeling. I bet I answered that, any question? >> Yeah, we have another question. Do you have plans to perform any analytics at the edge? >> Now that's a good question. So I can't disclose our plans on this right now, but at the edge devices are certainly one of the options we look at to help our customers towards Zero Unplanned Downtime. Not only that, but also to facilitate the integration of our solution with existing and future hospital IT infrastructure. I mean, we're talking about advanced security, privacy and guarantee that the data is always safe remains. patient data and clinical data remains does not go outside the parameters of the hospital of course, while we want to enhance our functionality provides more value with our services. Yeah, so edge definitely very interesting area of innovation. >> Another question, what are the most helpful vertica features that you rely on? >> I would say, the first that comes to mind, to me at this moment is ease of integration. Basically, with vertica, we will be able to load any data source in a very easy way. And also it really can be interfaced very easily with old type of ions as an application. And this, of course, is not unique to vertica. Nevertheless, the added value here is that this is coupled with an incredible speed, incredible speed for loading and for querying. So it's basically a very versatile tool to innovate fast for data science, because basically we do not end up another thing is multiple projections, advanced encoding and compression. So this allows us to perform the optimizations only when we need it and without having to touch applications or queries. So if we want to achieve high performance, we Basically spend a little effort on improving the projection. And now we can achieve very often dramatic increases in performance. Another feature is EO mode. This is great for for cloud for cloud deployment. >> Okay, another question. What is the number one lesson learned that you can share? >> I think that would my advice would be document control your entire data pipeline, end to end, create positive feedback loops. So I hear that what I hear often is that enterprises I mean Philips is one of them that are not digitally native. I mean, Philips is 129 years old as a company. So you can imagine the the legacy that we have, we will not, you know, we are not born with Web, like web companies are with with, you know, with everything online and everything digital. So enterprises that are not digitally native, sometimes they struggle to innovate in big data or into to do data driven innovation, because, you know, the data is not available or is in silos. Data is controlled by different parts of the organ of the organization with different processes. There is not as a super strong enterprise IT system, providing all the data, you know, for everybody with API's. So my advice is to, to for the very beginning, a creative creating as soon as possible, an end to end solution, from data creation to consumption. That creates value for all the stakeholders of the data pipeline. It is important that everyone in the data pipeline from the producer of the data to the to the consumers, basically in order to pipeline everybody gets a piece of value, piece of the cake. When the value is proven to all stakeholders, everyone would naturally contribute to keep the data pipeline running, and to keep the quality of the data high. That's the students there. >> Yeah, thank you. And in the area of machine learning, what types of innovations do you plan to adopt to help with your data pipeline? >> So, in the error of machine learning, we're looking at things like automatically detecting the deterioration of models to trigger improvement action, as well as connected with active learning. Again, focused on improving the accuracy of our predictive models. So active learning is when the additional human intervention labeling of difficult cases is triggered. So the machine learning classifier may not be able to, you know, classify correctly all the time and instead of just randomly picking up some cases for a human to review, you, you want the costly humans to only review the most valuable cases, from a machine learning point of view, the ones that would contribute the most in improving the classifier. Another error is is deep learning and was not working on it, I mean, but but also applications of more generic anomaly detection algorithms. So the challenge of anomaly detection is that we are not only interested in finding anomalies but also in the recommended proper service actions. Because without a proper service action, and alert generated because of an anomaly, the data loses most of its value. So, this is where I think we, you know. >> Go ahead. >> No, that's, that's it, thanks. >> Okay, all right. So that's all the time that we have today for questions. I want to thank the audience for attending Mauro's presentation and also for your questions. If you weren't able to, if we weren't able to answer your question today, I'd ask let we'll let you know that we'll respond via email. And again, our engineers will be at the vertica, on the vertica quorums awaiting your other questions. It would help us greatly if you could give us some feedback and rate the session before you sign off. Your rating will help us guide us as when we're looking at content to provide for the next vertica BTC. Also, note that a replay of today's event and a PDF copy of the slides will be available on demand, we'll let you know when that'll be by email hopefully later this week. And of course, we invite you to share the content with your colleagues. Again, thank you for your participation today. This includes this breakout session and hope you have a wonderful day. Thank you. >> Thank you

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Welcome back to the Cube. We are in Las Vegas, Lisa Martin with John Wall's. I'm very excited that we're kind of color coordinated >>way. Didn't compare notes to begin with, but certainly the pink thing. It's worth it if >>you like. You complete me. >>Oh, thank you. Really, Joe, I don't hear that very often. My wife says that >>you tell that we're at the end of day one of the coverage of A W s three in bed. Good day, though. Yes, it has been very excited. We have a couple of guests joining us for our final segment on this. Please welcome. We have Bill Gerard CTO of Digital Transformation and Scale solutions at Intel Bill, welcome to our show. >>Thank you very much. Happy to be here >>And one of our friends. That's no stranger to the Cube. One of our former host, Bobby Allyn, the CEO of Cloud Generate. Bobby. >>Thank you. Thank you for having us. >>Guys, here we are. This there has not been a lull in the background noise all day. Not reinvent day one. But Bobby want to start with you. Talk to her audience about cloud genera. Who are you guys? What do you do? And what's different about what you're delivering? >>One of the first things is different about Claude Generous where we're located. So we're in Charlotte, which I call Silicon South. So we're kind of representing the East Coast, and we're a company that focuses, focuses on helping with workload, placement and transformation. So where you don't know whether something should go on from off grim. If you put it in Amazon, which service's should have consumed licensing models? Pricing models way help you make data driven decisions, right? So you're not just going based on opinion, you're going based on fact. >>And that's challenging because, you know, in the as, as John Ferrier would say, No Cloud Wanda Otto, which was compute network storage, it was the easy I shouldn't say easy, but the lift and shit applications that enterprises do are these workloads should go to the cloud. Now we have you know what's left over, and that's challenging for organization. Some of the legacy once can't move. How do you help from a Consul Tatum's down point that customers evaluate workloads? What data are they running? What the value that data has and if they are able to move some of those more challenging applications. >>So part of the framework for us, Lisa, is we want to make sure we understand what people are willing and able to change right, because sometimes it's not just about lower costs. Sometimes it's about agility, flexibility, deploying a different region. So what we often start with his wit is better look like you would assist us with life for your organization. And so then, based on that, we analyze the applications with an objective, data driven framework and then make sure the apse land where they're supposed to go. We're not selling any skewer product. We're selling advice to give you inside about what you should do, >>Bobby, I think. And maybe Bill to you could chime in here on this. If you give people a choice, What does this look like? What you know, What do you want? I don't want to do anything right. I want to stay put, right? But that obviously that's not an option, But you I'm sure you do get pushed back quite a bit from these almost the legacy mindset. And we've talked a lot about this whole transformation versus transition. Some people don't want to go, period. So how do you cajole them? Persuade them bring them along on this journey? Because it's gonna be a long trip. Yeah, I think you gotta pack a lunch. >>It's a good point. I think what we've seen, most of them have data experience that this is a tried and elements didn't get the results that they expected. This is where you know, the partnership that we have with call General. Really? You know that data driven, intelligent, based planning is super important, right? We want to really fundamentally health organizations move the right workloads, make sure they get the right results and not have to redo it. Right? And so part of that, you know, move when you're either past scars or not used to what you're doing. Give him the data and the information to be able to do that intelligently and make that as fast as they can. And you know, at the right, you know, experience in performance from a capability perspective. >>So so many businesses these days, if they're not legacy if they're not looking in the rear view mirror, what is the side mirror site? Objects are closer than they appear, even for Amazon. Right? For all of these companies, there are smaller organizations that might be born in a cloud compared to the legacy two words. And if they're not looking at, we have to transform from the top down digitally, truly transform. Their business may not be here in a year or two, so the choice and I think they need to pack a lunch and a hip flask for this because it's quite the journey. But I'm curious with the opportunity that cloud provides. When you have these consultation conversations, what are This? Could be so transformative not just to a business, but to a do an entire industry. Bill talked to us from your perspective about some of the things that you've seen and how this next generation of cloud with a I machine learning, for example, can can really transfer like what's the next industry that you think is prime to be really flipped upside down? >>Well, the good news is I think most of the industries in the segment that we talked to have realized they need to some level of transformation. So doing the business as usual really isn't an option to really grow and drive in the future. But I do think the next evolution really does center on what's happening in a I and analytics. Whether it's, you know, moving manufacturing from video based defect detection, supply chain integrity. You know what's happening from a retail was really the first in that evolution, but we see it in health care in Federal Data Center modernization, and it's really moving at a faster pace and adopting those cloud technologies wherever they needed, both in their data center in the public, cloud out of the edge. And we'll start to see a real shift from really consolidation in tow. Large hyper converts, data centers to distributed computing where everything again. And that's where we're excited about the work we're doing with the Amazon, the work we're doing with Eyes V partners to be at the capability where they need it, but I think it will be really the next. Evolution of service is everywhere. >>Never talk us through an example or use case of a customer that you're working with, a cloud genera with intel and and a W S. What does that trifecta look like for, say, a retailer or financial service is organization >>so that that looks like this? ELISA. When we when we talk about workload placement, we think that most companies look at that as a single question. It's at least a five fold question. Right there is the venue. There's the service. There's the configuration, the licensing model and the pricing model. You need to look at all five of those things. So even if you decided on a DBS is your strategic partner, we're not done yet. So we have a very large financialservices customer that I can't name publicly. But we've collaborated with them to analyze tens of thousands of workloads, some that go best off from some that go best on for him. And they need guidance and coaching on things like, Are you paying for redhead twice your pay for licensing on him? Are you also paying for that in the cloud? There are things that maybe should be running an RT s database as a service. Here's your opportunity to cut down on labor and shift some of the relationships tohave, toe re index and databases is not glamorous or differential to value for your business. Let's take advantage of what a TBS does well and make this better for your company. One of the things that I want to kind of introduce to piggyback on your question. We lean on people process technology as kind of the three, the three legged horse in the Enterprise. I want to change that people process product or people process problem. We're falling in love with the tech and getting lazy. Technology should be almost ubiquitous or under the covers to make a product better or to solve a problem for the customer. >>Well, maybe on that, I mean automation concern to come in and make a big play here because we're taking all these new tasks if you could automate them that you free your people, your developers to do their thing right. So you raise an interesting point on that about being lazy and relying on things. But yet you do want off put our offload some of these nasty not to free up that creativity and free up the people to do what they're supposed to be doing. It's a delicate balance, though, isn't it? It is. It is. This >>is where I think the data driven, you know, informed decisions important. We did a lot of research with Cloud Jenner and our customers, and there's really four key technical characteristics when evaluating workload. The 1st 1 of course, is the size of the data. Where is the created words They use Words that consumed the 2nd 1? Is the performance right? Either performance not only to other systems around it or the end user, but the performance of the infrastructure. What do you need out of the capability? The level of integration with other systems? And then, of course, security. We hear that time and again, right? Regulatory needs. What are we having from top secret data to company sensitive data? Really Getting that type of information to drive those workload placement decision becomes at the forefront of that on getting, you know, using cloud gender to help understand the number of interfaces in and out the sides of the data. The performance utilization of the system's really helps customers understand how to move the right workload. What's involved and then how to put that in the right eight of us instance, and use the right ideas capabilities, >>and you and you both have hit on something here because the complexity of this decision, because it's multi dimensional, you talked about the five points a little bit ago. Now you talked about four other factors. Sue, this is not a static environment, No, and to me that as you're making a decision, that point is what's very difficult for, I would assume for the people that you're interfacing with on the company level. Yes, because it's a moving target for them, right? They just it's it's dynamic and changing your data flows exponentially. Increasing capabilities are changing. How do you keep them from just breaking down? >>I don't want to jump in on that, because again, I'm going to repeat this again. That my thesis is often technology is the easy part. We need to have conversations about what we want to do. And so I had a conversation earlier today. Think of Amazon like a chef. They could make anything I want, but I need to decide what I want to eat. If I'm a vegan and he wants steak. That's not Amazons fault. If they can't cook something, that's a mismatch of a bad conversation. We need to communicate. So what I'm finding is a lot of executives are worried about this. There were Then you're going to give me the right the wrong answer to the right question. The reality is you may have the wrong question. First of all right, the question is usually further upstream, so the worry that you're gonna give me the wrong answer to the right question. But often you need to worry that you're getting your starting with the wrong question. You're gonna get the right answer asked the right question first. And then you got a chance to get to the final destination. But >>and then he in this multi cloud world that many organizations live in, mostly not My strategy could be by Emma A could be bi developer preference for different solutions. A lot of Serios air telling us we've inherited a lot of this multi cloud and technical debt. Exactly. So does not just compound the problem because to your point, I mean you think of one way we hear so many different stats about the number of clouds that on average enterprises using is like 5 to 9. That whole world. That's a reality for organizations. So in terms of how the business can be transformed by what you guys are doing together, it seems like there's a tremendous opportunity there. But to your point, Bobby, where do you start? How do you help them understand what? That right first question is at the executive level so that those four technical points that Bill talked about Tek thee you know, the executive staff is all on board with Yes, this is the question we're asking then will understand it. The technology is right. Sold >>it. It's got to start with, Really? What? The company's business imperatives, right? It can't start with an I t objective. It's it's Are we moving into new markets? Do we need thio deploy capabilities faster? Are we doing a digital customer experience? Transformation? Are we deploying new factories, new products into new regions, and so really the first areas? What's the core company strategy, imperatives of the business objectives? And >>then how >>does I t really help them achieve that? In some cases, it may be we have to shift and reduce our data center footprints way have to move capabilities to where we have a new region. Deployments, right? We've got to get him over to Europe. We don't have capabilities in Europe. We're going to Asia. I've got a mobile sales force now where I need to get that customer, meet the customer where they're doing, you know, in the retail store, and >>that >>really then leads quite simply, too. What are the capabilities that we have in house that we're using? >>How are >>they being utilized? And he's using them, and then how do we get them to where they need to be? Some cases accost, imperative. Some cases and agility, Time to market and another's and we're seeing this more often is really what are the new sets of technologies? A. I service is training in forgetting that we're not experience to do and set up, and we don't want to spend the time to go train our infrastructure teams on the technology. So we'll put our data scientists in there figuring out the right set of workloads, the right set of technology, that we can then transform and move our applications to utilize it really starts, I think with the business conversation, or what's the key inflection point that they're experiencing? >>And have you seen that change in the last few years that now it's where you know, cloud not cloud. What goes on Cloud was an I t conversation to your point, Bill. And then the CEO got involved in a little bit later. But now we're we're seeing and hearing the CEO has got to be involved from a business imperative perspective. >>Share some data, right? Uh, so, you know, a couple of years ago, everybody was pursuing cloud largely for cost. Agility started to become primary, and that's still very important. A lot of the internal enterprise data modernizations were essentially stalled a bit because they were trying to figure how much do we move to the the public cloud, right. We want to take advantage of those modern service is at that time, we did a lot of research with our partners. He was roughly 56% of enterprise workload for in their own data center. You know, the rest of them Republic Cloud. And then we saw really the work, the intelligent workload discussion that says we've had some false starts. Organizations now really consistently realize they need both, you know, their own infrastructure and public cloud, and we've actually seen on increase of infrastructure modernization. While they're moving more and more stuff to the cloud, they're actually growing there on centre. It's now roughly 59% on Prem today for that same business, and that's largely because they're using more. Cloud service is that they're also even using Maur on premise, and they're realizing it's a balance and not stalling one or starving one and then committing to the other the committing to both and really just growing the business where it needs to go. >>Strategic reasons. All right? >>Yes, well, there should be four strategic reasons. There aren't always back to your question about which question asked. One of the questions I often ask is, What do you think the benefits will be if you go to cloud? And part of what happens is is not a cloud capability? Problem is an expectation problem. You're not gonna put your GOP system in the cloud and dropped 30% costs in a month, and so that's where we need to have a conversation on, You know, let's iterating on what this is actually gonna look like. Let's evolve the organization. Let's change our thinking. And then the other part of this and this were clouded or an intel come in. Let's model with simulation looks like. So we're gonna take those legacy work clothes unless model containers. Let's model Micro Service is so before you have to invest in transformation to may not make sense. Let's see what the outcome's look like through simulation through a through M l and understand. Where does it make sense to apply? The resource is, you know, to double click on that solution that will help the business. >>I was gonna finish my last question, Bobby, with you saying, Why, Cloud General? But I think you just answered that. So last question for you, though, from from an expectation perspective, give me one of your favorite examples of customer whatever kind of industry there and that you've come in and helped them really level, set their expectations and kick that door wide open. >>That's tough, many >>to choose from. >>Yeah, let me let me try to tackle that one quickly. Store's computer databases. Those are all things that people look at I think what people are struggling with the most in terms of kind of expectations is what they're willing and able to change. So this is kind of what I leave on. Bill and I talked about this earlier today. A product is good, a plan is better. A partnership is best. Because with the enterprises of saying is, we're overwhelmed. Either fix it for me or get in there with me and do it right. Be in this together. So what we've learned is it's not about were close applications. It's all kind of the same. We need help. We're overwhelmed. I want a partner in telling Claude Juncker the get in this thing with me. Help me figure this out because I told you this cloud is at best a teenager. They just learned how to drive is very capable, but it needs some guard rails. >>I love that. Thanks you guys So much for explaining with Johnny what you guys are doing together and how you're really flipping the model for what customers need to be evaluated and what they need to be asking. We appreciate your time. >>Thank you for having us >>our pleasure. Thank you. for John Wall's I'm Lisa Martin. You've been watching the Cube at Reinvent 19 from Vegas. Wants to go tomorrow.

>> Narrator: Live from Vancouver, Canada, it's theCUBE, covering OpenStack Summit North America 2018, brought to you by Red Hat, the OpenStack Foundation, and its ecosystem partners. >> The sun has come out, but we're still talking about a lot of the cloud here at the OpenStack Summit 2018 in Vancouver. I'm Stu Miniman with my co-host John Troyer. Happy to welcome to the program the 2018 Super User Award winner, George Mihaiescu, who's the senior cloud architect with the Ontario Institute for Cancer Research or OICR. First of all, congratulations. >> Thank you very much for having me. >> And thank you so much for joining us. So cancer research, obviously is, one of the things we talk about is how can technology really help us at a global standpoint, help people. So, tell us a little about the organization first, before we get into the tech of it? >> So OICR is the largest cancer research institution in Canada, and is funded by government of Ontario. Located in Toronto, we support about 1,700 researchers, trainees and clinician staff. It's focused entirely on cancer research, it's located in a hub of cancer research in downtown Toronto, with Princess Margaret Hospital, Sick Kids Hospital, Mount Sinai, very, very powerful research centers, and OICR basically interconnects all these research centers and tries to bring together and to advance cancer research in the province, in Canada and globally. >> That's fantastic George. So with that, sketch out for us a little bit your role, kind of the purview that you have, the scope of what you cover. >> So I was hired four years ago by OICR to build and design cloud environment, based on a research grant that was awarded to a number of principal investigators in Canada to build this cloud computing infrastructure that can be used by cancer researchers to do large-scale analysis. What happens with cancer, because the variety of limitations happening in cancer patients, researchers found that they cannot just analyze a few samples and draw a conclusion, because the conclusion wouldn't be actually valid. So they needed to do large-scale research, and the ICGC, which is International Cancer Genome Consortium, an organization that's made of 17 countries that are donating, collecting and analyzing data from cancer patients, okay, they decided to put together all this data and to align it uniformly using the same algorithm and then analyze it using the same workflows, in order to actually draw conclusion that's valid across multiple data sets. They are focusing on the 50 most common types of cancer that affect most people in this world, and for each type of cancer, at least two countries provide and collect data. So for brain cancer, let's say we have data sets from two countries, for melanoma, for skin, and this basically gives you better confidence that the conclusion you draw is valid, and then the more pieces of the puzzle you throw on the table, the easier to see the big picture that's this cancer. >> You know George, I mean, I'm a former academic, and you know, the more data you get right, the more infrastructure you're going to have to have. I'm just reading off the announcement, 2,600 cores, 18 terabytes of RAM, 7.3 petabytes of storage, right, that's a lot of data, and it's a lot of... accessed by a lot of different researchers. When you came in, was the decision to use OpenStack already made, or did you make that decision, and how was the cloud architected in that way? >> The decision was basically made to use open source. We wanted basically to spend the money on capacity, on hardware, on research and not on licensing and support. >> John: Good use of everybody's tax dollars. >> Exactly, so you cannot do that if you have to spend money for paying licensing, then you probably have only half of the capacity that you could. So that means less large analysis, and longer it takes, and more costly. So Ceph for storing the data sets and OpenStack for infrastructure as a service offering was a no-brainer. My specialty was in OpenStack and Ceph, I started OpenStack seven years ago, so I was hired to design and build, and I had a chance to actually do alignment, and invitation calling for some of the data sets, so I was able to monitor the kind of stress that this workflows put on the system, so when I design it, I knew what is important, and what to focus on. So it's a cloud environment, it's customized for cancer research. We have very good ratio of RAM per CPU, we have very large local discs for the VM, for the virtual machines to be able to download very large data sets. We built it so if one compute node fails, you only impact a few workflows running there, you don't impact single small points of failures. Another tuning that we applied to the system too. >> George, can walk us through a little bit of the stack? What do you use, do you build your own OpenStack, or do you get it from someone? >> So basically, we use community hardware, we just high-density chassis, currently from Super Micro, Ubuntu for the operating system, no licensing there, OpenStack from the VM packages. We focus more on stability, scalability and support costs, internal support costs, because it's just myself and I have a colleague Gerard Baker, who's a cloud engineer, and you have to support all this environment, so we try to focus on the features that are most useful to our users, as well as less strain on our time and support resources. >> I mean that's, let's talk about the scalability right? You said the team is you and a colleague. >> George: Yes. >> But mostly, right. And you know, in the olden days, right, you would be taking care of maybe a handful of machines, and maybe some disk arrays in the lab. Now you're basically servicing an entire infrastructure for all of Canada, right? At how many universities? >> Well basically, it's global, so we have 40 research projects from four continents. So we have from Australia, from Israel, from China, from Europe, US, Canada. So approved cancer researchers that can access the data open up an account with us, and they get a quota, and they start their virtual machines, they download the data sets from the extra API of Ceph to their VMS, and they do analysis and we charge them for the time used, and because the use, everything is open source, and we don't pay any licensing fees, we are able to, and we don't run for profit, we charge them just what it costs us to be able to replenish the hardware when it fails. >> Nice, nice. And these are actually the very large machines, right? Because you have to have huge, thick data sets, you've got big data sets you have to compare all at once. >> Yeah, an average bandwidth of a file that has the normal DNA of the patient, and they need also the tumor DNA from the biopsy, an average whole genome sequence is about 150 gigabytes. So they need at least 300 gigabytes, and depending on the analysis, if they find mutations, then the output is usually five, 10 gigabytes, so much smaller. For other workflows, you have to actually align the data, so you input 150 gigabytes and the output is 150 or a bit more with metadata. And so nevertheless, you need very large storage for the virtual machines, and these are virtual machines that run very hard, in terms of you cannot do CPU over subscription, you cannot do memory over subscription, when you have a workflow that runs for four days, hundred percent CPU. So is different than other web scale environments, where you have website was running at 10%, or you can do 10 to one subscription, and then you go much cheaper or different solutions. Here you have to only provide what you have physically. >> John: That's great. >> George, you've said you participated in the OpenStack community for about seven years now. >> George: Yes. >> What kind of, do you actually contribute code, what pieces are you active in the community? >> Yeah, so I'm not a developer. My background is in networking, system administration and security, but I was involved in OpenStack since the beginning, before it was a foundation. I went to the first OpenStack public conference in Boston seven years ago, at the International Intercontinental Hotel and over time I was involved in discussions from the RAC channel, mailing list support, reporting backs. Even recently we had very interesting packet affected as well. The cloud package that is supposed to resize the disk of the VM as it boots, it was not using more than two terabytes because it was a bug, okay. So we reported this, and Scott Moffat, who's the maintainer of the cloud utils package, worked on the bug, and two days later, we had a fix, and they built a package, it's in the latest cloud Ubuntu image, and that happen, everybody else is going to use the same virtual Ubuntu package, so somebody who now has larger than two terabytes VMs, when they boot, they'll be able to resize and use the entire disk. And that's just an example of how with open source we can achieve things that would take much longer in commercial distribution, where even if you pay, doesn't necessarily mean that the response... >> Sure. Also George, any lessons learned? You've been with us a long time, right, and like Ceph. One thing we noticed today in the keynote, is actually a lot of the storage networking and compute wasn't really talked, those projects were maybe down focused a bit, as they talked about all the connectivity to everything else. So, I mean any lessons, so you... My point is, the infrastructure is stable of OpenStack, but any lessons learned along the journey? >> I think the lessons are that you can definitely build very affordable and useful and scalable infrastructure, but you have to get your expectations right. We only use from the open standard project that we consider are stable enough, so we can support them confidently without spending, like if a project adds 5% value to your offering, but eats 80% of your time debugging and trying to get it working, and doesn't have packages and missing documentation and so on, that's maybe not a good fit for your environment if you don't have the manpower to. And if it's not absolutely needed. Another very important lesson is that you have to really stay up to date, like go to the conferences, read the emails from the mailing list, be active in the community, because the OpenStack meetups in Toronto for 2018, we present there, we talk to other members. In these seven years I read tens of thousands of emails, so I learn from other users experiences, I try to help where I can. You have to be involved with the developers, I know the Ceph core developers, Sage and other people. So, you can't do this just by staying on the side and looking, you have to be involved. >> Good, George what are you looking for next from this community? You talked about the stability, are there pieces that you're hoping reach that maturity threshold for yourselves, or new functionalities that you're looking for down the road? >> I think what we want to provide to our researchers, 'cause they don't run web scale applications, so their needs are a little bit different. We want to add Magnum to our environment, to allow them deploy Kubernetes cluster easily. We want to add Octavia to expose the services, even though they don't run many web services, but you have to find a way to expose them when they run them. Maybe, Trove, database as a service, we'll see if we can deploy it safely and if it's stable enough. Anything that OpenStack comes up with, we basically look, is it useful, is it stable, can you do it, and we try it. >> George, last thing. Your group is the Super User of the Year. Can you just walk us through that journey, what led to the nomination, what does it mean to your team to win? >> I think we are a bit surprised, because we are a very small team, and our scale is not as big as T-Mobile or the other members, but I think it shows that again, for a big company to be able to deploy OpenStack at scale and make it work, it's maybe not very surprising 'cause yes, they have the resources, they have a lot of manpower and a lot of... But for a small institution or organization, or small company to be able to do it, without involving a vendor, without involving extra costs, I think that's the thing that was appreciated by the community and by the OpenStack Foundation, and yeah, we are pretty excited to have won it. >> All right, George, let me give you the final word, as somebody that's been involved with the community for a while. What would you say to people if they're, you know, still maybe looking from the outside or played with it a little bit. What tips would you give? >> I think we are living proof that it can be done, and if you wait until things are perfect, then they will never be, okay. Even Google has services in beta, Amazon has services in beta. You have to install OpenStack, it's much more performant and stable than when I started with OpenStack, where there was just a few projects, but definitely they will get help from the community, and the documentation's much better. Just go and do it, you won't regret it. >> George, as we know, software will eventually work, hardware will eventually fail. >> Absolutely. >> So, George Mihaiescu, congratulations to OICR on the Super User of the Year award, for John Troyer, I'm Stu Miniman, we're getting towards the end of day one of three days of wall to wall coverage here at OpenStack Summit 2018 in Vancouver. Thanks so much for watching theCUBE.