>> From Gillette Stadium in Foxboro, Massachusetts, if the queue covering Vita Winter warmer, twenty nineteen brought to you by Silicon Angle media. >> I'm stupid. And this is the cubes coverage of the V tug Winter warmer twenty nineteen here at Gillette Stadium, home of the New England Patriots, and is my distinct pleasure to welcome to the program. Two time Super Bowl champion number fifty Patriots alumnae Rob Ninkovich. Rob may think you are doing great. Thanks for having awesome. You know, the team's a little bit of flutter. We brought, you know, one of the people here that help support the Patriots. The last two rings the Patriots had and you were on those teams, So yeah, >> it was, uh, you know, privileged and blessed to be on a couple Super Bowl winning teams. So >> did I hear right? Was the last one actually on your birthday to know that was the first >> one? That was really, really, really, really nice. >> Capable ring on your palm. My birthday. So your birthday's coming up here on February first, so >> I'll be the Big thirty five. Yeah, we'll have to sell me. I can't believe it. I'm almost seventy after seventy. >> Congratulations. Looking good and thank you. Feel good. Probably look out >> running climbing mountains. I'm gonna climb Mount Kilimanjaro with Chris Long here pretty soon in a month off. So I'll be in Africa raising money for water boys. So we'll be drilling for clean water, so it'll be cool. >> That's great. Yeah. I mean, let's hit on that. You know, you were you were drafted by the same. Believe you met your wife. Yeah. They're Southern Southern girls. Both of you are, you know, giving back to the community. Do a lot of charitable work. Would love to hear a little bit about that. >> Yeah. I mean, I think that the platform that athletes have is is tremendous. So if you can give back to your community, you know, that's that's one of the best things that I think an athlete can give back. And that's, you know, people that are in need and people that aren't as fortunate. So you know, for me, I work with Matt Light and the Light Foundation. So him on his board of directors and that basically brings kids in from troubled areas and backgrounds and they go into a camp and it's a four year program. So they start. And then they graduate, so to speak. When they get through high school and they're going into their college years, but it's a great program for trying to develop your skills. And you know, a lot of kids that don't have, you know, maybe a great family background that you know is a healthy background to where it's trying to bring kids together and show them some different things that could help them moving forward. And, you know, life skills that everyone needs. >> Yeah, this conference actually talk a lot about skills in career because and the technology field things were changing a lot. Now I've watched football. Most of my career were actually my season tickets. I can see across the field here and, you know, in your career, your eleven year career in the NFL changed a lot. I think you came into the NFL as an outside linebacker. And when you're here in the Patriots who switched the defensive end? No, you know what kind of things do you learn? And you know, how do you kind of have the mindset to say, like, Okay, well, this is the job and the skills and the things I'm looking to do. And now, like a weight, I need toe, you know, have a hand down and be facing off against some really big guys. >> Yes. So, I mean, I think the Toby the chameleon, so to speak and be able to change and adapt to your environment. I mean, that's what makes not just an athlete, but, you know, every every business person that can change with time and in with the trends of technology and how things were adapting over overtime, that's what's gonna continue your success. So if you stay, if you just one thing and you never want to change and you never adapt, you're goingto be overtaken by somebody else. So you have to have that mindset. When I arrived here with the Patriots, I knew that you had to be multiple. He had to continue toe, do different things with your career in position or else, you know, really, you don't stick around as long. So you know, for me, I was a defensive lineman, linebacker, special teams. You do it all, and it helps Not only your team, but it helps your career and, you know, have a long career that, >> yeah, not only do your job, but when you're called to do multiple jobs, you you're going to step in and do that. It kind of seems to be the Patriot. >> You have to. You have to. If you can't do the multiple job thing, it's This might not be the place. >> Yeah, So, you know, we just had, you know, one of the most amazing games and Patriot history that I think I've seen. I'm curious us now, an observer rather than player. You know what your thought looking at a game like that? You know, I know heart rates were a little bit high for those of us in New England, but, you know, it's really amazing win like that. >> Yeah. I mean, it goes to show you that the mental toughness and the that just never quit mentality is one of the main characteristics of this team this year in their story, you can't look at previous years or just you can't look att history. When it comes to football, it comes down. Who's going to play the best football in that particular day? And, you know, you look at what the defense has done so far. It's been tremendous going against the Chargers, who are a great team and you know, everyone was making the excuse of, you know, they travelled a lot in the time change was tough, but then them going on >> the road, >> which hadn't been a strong point form this year and getting a win and shutting out a team in the first half. The Chiefs that were really powerful and really explosive in one of the best in the NFL. It just goes to show you that, you know, in the playoffs, it's a completely different season. It's a new season. You've gotto just forget about what happened in the regular season. You know, moving on to this next game. I think that they just need to continue that high momentum and playing the way that they are playing, which is running the football, being tough and playing physical for four quarters. Being physical. That's that's what's going to win in this next one >> sixty minutes. And if it goes in over time, a little bit longer, >> you got it. Whatever it takes. >> Rob Ninkovich really appreciate you spending some time with that. Well, best of luck on Kilimanjaro this year. Exciting. Yeah, I know you do some social media. They're so sure people can follow along >> Yeah, I'll be on there. I got Instagram Nick five o. So And then I'm Niko. Five o for Twitter. So I'm out there. All right, all right. >> Thank you so much. And we, of course, are out there all the time. Go to the cube dot net to catch all the videos. Find me on Twitter. I'm just stew s to you and, uh, super pleasure to be able to have Rob Ninkovich. And thanks so much for the veto >> booth.

live from Gillette Stadium in Foxboro Massachusetts extracting the signal from the noise it's the kue covering Vitas New England winter warmer 2016 now your host Stu minimum welcome back to the cube I'm Stu miniman with Wikibon com we are here at the 2016 v tug winter warmer at Gillette Stadium home of the New England Patriots and very excited to have a patriot Hall of Famer three-time Super Bowl champion number 80 Troy brown Troy thank you so much for stopping by oh man thank you for having me on I appreciate it alright so so so Troy you know we got a bunch of geeks here and they they they we talked about you know their jobs are changing a lot and you know the question I have for you is you did so many different jobs when you're on the Patriot you know how do you manage that how do you go about that from a mindset i mean i think so many of the job you did we're so specialized never spent years doing it yet you know you excelled in a lot of different positions i think first of all i think the coach bill belichick you know I think he does a good job of evaluating is his people and his players and the people that work for them and think about him he never asked an individual to do more than they can handle and I think I was one of those individuals that he saw that could you know didn't get her out about too many different things that didn't get seemed like I was overwhelmed at any moment with the job that I was at already asked to do and if I had to do multiple jobs then I would probably be one of those guys that could handle that type of situation so it started with him and in me I guess it was just my personality and my work havoc and my work ethic and just never letting the opponent know that I was a little bit shaken a little bit weary a little bit tired at times and I just continue to chip away and be my job and not you know and I took a lot of pride in being able to manage and do a lot of different things at one time and and then really accelerate yeah so you saw the transformation in the Patriot organization I mean you know it great organization here in New England but you know we were living in a phenomenal time for the Patriots over the last 20 years it and what do you attribute that that transformation to well I think it started you know you look at when Robert crab bought the team in 94 which I was here year before he bought the team in 93 I was glad to be true Bledsoe and parcels are the first year and that really Parcells really kind of got people around here excited about football I think for the first time they were having you know capacity crowds at training camp out at Bryant college you know something they never did before I mean you're talking about a team that won two games the year prior they were two and 14 and things got so lucky winning those two games in 1992 so you bringing a guy that's you know when a couple super bowls with the Giants high-profile guy gets everybody excited about the possibility of winning and I think things started to change then and then you bring in a hands-on owner because I believe James awethu wine was the previous owner that he bought the team from and lived in st. Louis it can't be hands-on when you you know live you know half the country away from from here so he bought the team and bought the local guy and again that the enthusiasm goes through the roof and expectations in through the roof we make the playoffs in 1994 and you know the things happen they don't get along and then when you go through another coach Pete Carroll for three years and you bring in Belo check and he drives a young quarterback by the name of Tom Brady and you know those types of things those people those guys able to handle different things and different jobs as well you know and you couple that with you surround them with good people like myself david patten Antwone Smith I laws or the lawyer milloy Rodney Harrison guys that kind of embody the Patriot Way and you get what you have today and it all started with the fact that mr. Kraft and Bill Belichick now been together with 15 16 years and I think you look across the NFL across any sport you don't see the type of longevity and the type of continuity that those who have and you throw on Tom Brady into that mixers been along for that entire ride as well you just think you're not going to find out in any other sport any other team maybe a couple here you notice end Antonio Spurs no in longevity I believe it is the key and you have to build that you know see you see too many owners that throwing the town were too quick yeah you know what the young coast is trying to build a team in the system yeah so I have to ask you if you had to choose one for 15 years pray to your Belichick for 15 years yeah 15 years that maybe Brady because you know it eventually will come to an end you know Bella chikan probably coach I want to know one only known for longer than 15 years we had to choose one for 15 years I guess I'll go with Brady but you know I don't think I know if one works not the other you know so that's kind of how to be a question that people be asking for many many years to come yeah so personally for you when you look back at your career you know any favorite moments that they have that mean there's so many to so many the franchise for yourself i mean i could think of all the ones that i had the pleasure to say that was a big punt return against the pittsburgh starters yeah AFC championship no well botas me start up the scoring for us yeah that was a big moment that the strip in 06 in the superbowl that year it was a big play yeah able to get us into the AFC championship game this all the Super Bowls that we were part of and then were able to win and all those moments are just so treasured and value about me that is kind of hard to place a place one over the other but you know it was all a lot of great and fantastic moments for us all right so last question I have for you looking at the Patriots today what's your prediction for the Patriots you know going on in the playoffs here going to the AFC champ I think it a bit difficult task Denver's not been a friendly place for the Patriots over the history of this franchise not just now but it is specifics as to why it's so tough to find there I don't know I don't know what it is I mean you could say the altitude but we've been out then we played well at times even there's team this year they played well the first time they went out there had an unfortunate drop punt you know that kind of changed the complexity of the game and things just changed I mean it's that's the kind of luck that we have the last time I played out there was I think 05 I think of something in the divisional round and I fumbled Kevin Faulk fumble Tom Brady threw a pick-six basically and it was like you threw your most dependable players that turned the football over and didn't play well you know how often that would that happen so Rob Gronkowski gets hit in the knee this year so and then lose him for a couple games and his season starts to turn so just so many unfortunate things that happen out there but you have to give Denver a lot of credit as well because you know they come out and they play hard to have a really good defense quarterback that can be really good you know he's a game manager at this point in his career that's a great job of doing it you know and it seemed to rally behind his presence on the field so it'll be a tough task for the Patriots even though I think the Patriots do have the better football team overall it's just been a difficult place for the New England Patriots to get wins yeah in the past I said you have a matchup for the Super Bowl that you're picking I'm picking the Patriots for sure and from what I saw from Carolina last week I got to go with Carolina playing at home against Arizona I think the defense is just too tough and Cam Newton and that run game and that offensive line has just been been pretty remarkable and surprising after losing probably the best offensive weapon in Kelvin Benjamin so yeah well you know a little something about a Carolina versa you know New England Super Bowl so hopefully things will turn out like it did last time try really appreciate you stopping by thank you so much for trying to save the program will be right back here with a wrap-up of the cubes coverage of the V tug 2016 winter warmer thanks so much for watching you

>> From Gillette Stadium, in Foxborough, Massachusetts, it's theCUBE covering VTUG Winter Warmer 2019, brought to you by SiliconANGLE Media. >> Hi I'm Stu Miniman and this is theCUBE at VTUG Winter Warmer 2019 at Gillette Stadium, home of the New England Patriots, AFC Champions week out from going to Super Bowl 53. Joining me is a user from the great state of Maine, Dawnna Pease, who is the Director of Computing Infrastructure and Services for the state of Maine, thank you for joining us. >> Yes, thank you. >> Alright so Dawnna, you've been to a few VTUGs, of course the Summer Fest, which is, you know it might not be quite as big, as the winter one, but it is known even broader, I've known people come from out of the country because there's a giant lobster bake at the end of the day. I've been a few times, but you know tell us, you've been to VTUG before, yes? >> We have, so I have been to many, especially in Maine. And this is probably our fourth or fifth one that I've broughten the team from the state of Maine here and I feel it's really crucial and important because it allows them to network, to talk with their peers and to look at the technologies of how we can provide services for the constituents of the state of Maine and for our services that we offer within our office. >> Yeah so we always love talking to the users, we love to be able to help you share with your peers what you've been learning and actually I've had lots of great government discussions over the last few years, even attended, I attended a public sector show in the cloud space last year, and it's always fascinating because people have a misconception when it comes to what it's like to be IT in government, so let's dig into that a little bit. Tell us a little bit about your role, your group, what's kind of under your purview. >> Sure, I've been in state government going on 33 years as a public servant, very proud of that. I have a great group and I am the Director of Computing Infrastructure Services and it's really directory services, Microsoft stack. We have VMWare environment that we been probably nine years now and we're just implementing SimpliVity our hyperconverged, and after extensive research on that, we really solidified and selected HPE SimpliVity because in state government we had a lot of aging servers that needed to be replaced as well as our VM environment which was 44 nodes and it was a huge investment so not only on the licensing, hardware, storage, the compute part as well. So lookin' at the hyperconverged that was just one of many of our technologies that we looked at. >> So Dawnna take us back, how long ago did you start looking at that initiative? >> Oh 18 months. >> Okay, and was it a single location, multiple locations, can you give us any, how many you know servers or VMs or locations that this solution was going to span? >> For me it was actually spannin' and takin' on many of our on-prem solutions that we have. Like our SQL environment, our application hostin', the one offs, we're bringin' into that. As well as upgrading our existing VM cluster. So it's really taken on and morphed even more. We have a lot of net new as that want to participate in this environment so for us it is literally like a cloud solution, but it's for within our own private cloud solution on that. >> And these were critical business productivity applications that you're talking about? >> Absolutely >> This wasn't a new project to do, you know, early days of hyper converged, it was like oh I'm doing desktop virtualization, let me roll this out. I mean you're talking about databases and applications. >> Absolutely so we run close to, little over 600 servers for virtual and physical, so when all said and done within our hyperconverged our goal is to really be under 60 physicals left within state government. And currently today we have probably over 400 in our virtual environment today. So we're really expanding that more and bringing the services all into one knowing that we're going to have compute network and everything in our storage will all be in this environment. Plus we have a legacy storage environment, so when you're thinking of your legacy storage environment and you're looking at your refreshment of hardware and all the licenses around that our return on investment was huge for the state of Maine. So it was literally the wise choice for us to do within state government for tax payers, saving money. Also for the state as a whole. >> I have to imagine in addition to kind of the Capex piece if you're saying going from 900 to 400 and looking to get down to 60, operationally hopefully it makes the jobs of you know you and your team, a little bit easier once things are up and running. And that's one of the promises of hyperconverged, is it should be that cloud layer, it should be almost invisible when you talk about, it's just a pool that my virtualization lives on but I don't need to touch and rack and stack stuff the way that I might have in the past. >> Exactly, exactly, good point on that. Also on that we've really taken a broad look at how we can leverage the cloud so from a disaster recovery aspect and not only havin' the site resilience between two data centers, but how we can leverage the cloud for that continuity aspect. So we're really broadening that and the team's doing a fabulous, excellent job at that. >> Are you doing the Cloud DR today or is that a future plan? >> That is future. >> Okay, going to leverage a public cloud as that Are you far enough down? >> Government. So we have Azure today and we have a government tenant on that so we will use that aspect within the government tenant as well. >> Great so primarily Microsoft applications, you've moved into hyperconverged and you leveraged the Azure government certified cloud pieces. >> Correct >> Okay, awesome, when you started going down this path did you have in your mind hyperconverged or is that, how did you end up on that type of solution? >> So no, we didn't. Doin' the research on that and lookin' at all options, and really doin' the research with that, hyperconverged was more of makin' sense from the return on investment and also from a ... I want to say the simplified fashion, like you said it's simple you want to make it not so complex, it provided everything within that environment, and it was really based on how we were structured today, the investment that we would need to do if didn't go down this path. And taking in, so we did go with the hyperconverged. >> In your previous environment were you using HPE for the servers or the storage? >> So we were HPE, we are an HPE shop. And we have VMC, we have Pure Storage, we have different aspects of our storage today that exist so lookin' at that as well, we had an investment that we either needed to upgrade, replace, and, or invest. >> What I was poking at a little bit is were you HPE before, was that part of the decision to buy SimpliVity which is part of the HPE family or was that not a major factor? >> It was not a major factor, I mean we were ... We have always been a HPE shop, however we had criteria we were lookin' at, so you know after doing the research and we had 15, we were lookin' at 15 vendors at the time. We narrowed it down to like eight, and out of that we really narrowed it down to two that were in the quadrant, in the Gartner quadrant. And in doing our own research and study and bringin' all the vendors in and everything and what we had already invested what we currently had, it really came out to SimpliVity as the choice. >> And your 18 months into this, you've got some Cloud DR in the future, how are things going? What have you learned so far, is there anything you would have done differently or any advice you'd give to your peers if they're starting to go down this path? >> Do the research, do the research, be very thorough in what you're lookin' at for your requirements. And you know not only the research but look at what you've already invested in and take that into consideration and what your return on investment, what you're looking for your return on investment because you need to look just past not only your hosting environment but it really goes into can your network support that environment? Do you need to upgrade your network, your storage aspects, licensing aspects of that as well? So it's a huge investment, however look at the money they already pay in. >> Yeah licensing, one of those things when you talk about that great reduction of servers, are you today or do you expect in the future some of those licensing costs from the database, the virtualization, will those actually be able to be scaled down? >> Absolutely, and that was part of our ROI as well. By a lot, you know and that is one of the benefits of the hyperconverged as well. Once you set that up and purchase the proper licenses, I mean like data center licenses, you can put in as many VMs as you need within that environment and that's important. So you're really just looking at your compute at that, what you need for storage and compute. >> Yeah, I'm curious just spoke, cause we have, we've worked with clients for years on that and often times I've got a ELA or I've got a multi-year contract there and I have to renegotiate it, has that gone smoothly? Have there been any bumps along the road or is it pretty straightforward that licensing can be a huge chunk of your budget and like oh great, I'm two years later and I'm going to save myself a lot of money. >> So I actually am the administrator of our enterprise agreement with Microsoft, had been for many years, so I know what we have. And so I work very closely with that and I as far as the licensing and what we have, so for the renewals, I will say it gets easier. I found that being consolidated because when the agencies own their IT, at the time, we had many enterprise agreements and that was more complex so if you can actually consolidate and go into one, we have one enterprise agreement, or under the three I would say, it's much more manageable on that. So I don't find that that's a show stopper on that, it's gotten easier over the years. Simplified, it's more simplified. >> It's great to hear that and actually Microsoft has made great strides, Microsoft today is not the Microsoft of fives years ago or 10 years ago. >> Correct, I would agree. >> So, Dawnna Pease, pleasure talking to you. Thank you so much for sharing your experiences and be sure to check out thecube.net for all the recordings from the VTUG Winter Warmer 2019 as well as all of the other shows. I'm Stu Miniman and thank you for watching theCUBE.

>> From Gillette Stadium in Foxboro, Massachusetts, it's the CUBE. Covering VTUG Winter Warmer 2019. Brought to you by SiliconANGLE Media. >> I'm Stu Miniman, and this is theCUBE's coverage of the VTUG Winter Warmer 2019. Just had Rob Ninkovich from the New England Patriots on the program. And, happy to bring on the program, one of the co-leaders of this VTUG event, Chris Williams. Whose day job is as a cloud architect with GreenPages, but is co-leader here at VTUG, does some user groups, and many other things, and actually a CUBE alum, even. Back four years ago, the first year-- >> That's right. >> -that we did this, we had you on the program, but a few things have changed, you know... You have a little less hair. >> This got a little longer. A little less here. >> More gray hair. Things like that. We were talking, >> Funny how that works out. you know, Rob was, you know, talking about how he's 35, and we were, like, yeah, yeah, 35, I remember 35. >> A child. (laughing) >> Things like that. Just wait til you hit your 40's and stuff starts breaking. >> Oh, so much to look forward to. >> So, Chris, first of all, thank you. We love coming to an event like this. I got to talk to a few users on-air, and I talked to, you know, get a, just, great pulse of what's going on in the industry. Virtualization, cloud computing, and beyond. So, you know, we know these, you know, local events are done, you know, a lot of it is the passion of the people that do it, and therefore we know a lot goes into it. >> I appreciate it, thanks for having me on. >> Alright, so bring people up to speed. What's your life like today? What do you do for work? What do you do for, you know, the passion projects? >> Ah, so the passion projects recently have been a lot of, we're doing a Python for DevOp series on vBrownBag. For the AWS Portsmouth User Group, we're also doing a machine learning and robotics autonomous car driving project, using Python as well. And for VTUG, we're looking at a couple of different tracks, also with the autonomous driving, and some more of the traditional, like, VMware to CAS Cloud Hybrid training kind of things. >> Excellent, so in the near future, the robots will be replacing the users here, and we'll have those running around. >> I have my Skynet t-shirt on underneath here. >> Ah, yes, Skynet. (laughing) You know if you Tweet that out, anything about Skynet, there's bots that respond to you with, like, things from The Terminator movies. >> I built one of them. >> Did you? (laughter) Well, thank you. They always make me laugh, and if there's not a place for snark on Twitter, then, you know, all we have left is kind of horrible politics, so. >> That's true, that's true. >> Great, so, yeah, I mean, Cloud AI, robotics, you know, what's the pulse? When you talk to users here, you know, they started out, you know, virtualization. There's lots of people that are, "I'm rolling out my virtualization, "I'm expanding what use-cases I can use it on, "I might be thinking about how cloud fits into that, "I'm looking at, you know, VmMare and Amazon especially, "or Microsoft, how all those fit together." You know, what are you hearing, what drives some of those passion projects other than, you know, you're interested in 'em? >> So, a lot of what my passion projects are driven, it's kind of a confluence of a couple of different events. I'm passionate about the things that I work on, and when I get into a room with customers, or whatever like that, or with the end users, getting together and talking about, you know, what's the next step? So, we as users, as a user group and as a community, we're here to learn about not just what today is... what's happening today, but, what's going to keep us relevant in the future, what are the new things coming down the pipe. And, a lot of that is bending towards the things that I'm interested in, fortuitously. Learning how to take my infrastructure knowledge and parlay that into a DevOps framework. Learning how to take Python and some of the stuff that I'm learning from the devs on the AWS side, and teaching them the infrastructure stuff. So, it's a bi-directional learning thing, where we all come together to that magical DevOps unicorn in the middle, that doesn't really exist, but... >> Yeah, I tell you, we've had this conversation a few times here, and many times over the last few years especially, is that, there's lots of opportunities to learn. And, you know, >> Too many. >> is your job threatened? And, the only reason your job should be threatened, is if you think you can keep doing, year after year, what you were doing before, because chances are either you will be disrupted in the job, or if not, the people you're working for might be disrupted, because if they're not pushing you along those tracks, and the tools and the communities to be able to learn stuff is, I can learn stuff at a fraction of the cost in faster times. >> Yep. >> Might not learn as much, but I'm saying I can pick up new skills, I can start getting into cloud. You know, it's not $1000 and six months to get the first piece of it. >> Exactly. >> It might be 40 to 60 hours online. >> Yep. >> And, you know, cost you 30 to 100 bucks, so, it's... >> Yeah, the lift in training, is a lot easier because, you're basically swiping your credit card, and with AWS, you have a free tier for 12 months, that you can play with and just, you know, doodle around, and then... And figure things out. You don't have to buy a home lab, you don't have to buy NFR license, or get NFR licenses from Vmware. But, the catch to that is, you do have to do it. There's a... remember Charlie and the Chocolate Factory? >> Of course. >> Remember the dad was doing the toothpaste tubes, he was the guys screwing the toothpaste tubes onto the machines. At the end of the story, he got, you know, automated out of a job, because they had a machine screwing the toothpaste tubes on. And then, at the end, he was the guy fixing the machine that was screwing the toothpaste tubes on. >> Right. >> So, in our world, that infrastructure guy, who's been deploying manually virtual machines, there's a piece of code, there's an infrastructure code, that will do that for them now. They've got to know how to modify and refactor that piece of code, and get good. And, get good at that. >> Yeah, you know, I've talked to a couple of people, we talk about, you know, there's big, you know, vendor shows, and then there's, you know, regional user groups and meet-up's, and the like. Give us a little insight into, you know, let's start with VTUG specifically, and, you know, what you're doin' up in the Portland area. Would love to hear some of the dynamics now, you know, it feels like there's just been a ground swell for many years now, to drive those, you know, local, and many times, more specialized events, as opposed to bigger, broader events. >> Yeah, it's interesting, because we like the bigger, broader events, because it gets everybody together to talk about, things across a broad spectrum. So, here we have the infrastructure guys, and we have the DevOps guys, and we have a couple of Developers, and stuff like that. And so, getting that group think, that mind share, into one room together, gets everybody's creative juices flowing. So, people are starting to learn from each other, that the Dev's, are getting some ideas about how infrastructure works, the infrastructure guys are getting some ideas about, you know, how to, how to automate a certain piece of their job. To make that, you know, minimize and maximize a thousand times, you know, go away. So, I love... I love the larger groups because of that. The smaller groups are more specialized, more niche. So, like, when you get into a smaller version, then, it's mostly infrastructure guys, or mostly Dev's, or some mixture thereof. So, they both definitely have their place, and that's why I love doing both of them. >> Yeah, and, you know, what can you share, kind of, speeds and feeds of this show here. I know, it's usually over a thousand people >> Yep. >> You know, had, you know, bunch of keynotes going on. You know, we talked about The Patriots, in, you know, quite a number of, you know, technology companies, people that are the, kind of, SI's or VAR's in the mix. >> Yeah, so, we had, I think, 35 sponsers. We had, six different keynotes, or six general sessions. We talked about everything from Azure to AWS, to Vmware. We covered the gamate of the things that the users are interested in. >> You had... don't undersell the general sessions there. (laughing) There was one that was on, like, you know, Blockchain and Quantum computing, I heard. >> Yep, yep. >> There was, an Amazon session, that was just, geekin' out on the database stuff, I think, there. >> Yes, yeah, Graph tier, yep. >> So, I mean, you know, it's not just marketing slideware up there, I saw a bunch of code in many of the sessions. >> Oh yeah, yeah. >> You know, this definitely is, you know, I was talkin' with the Amazon... Randell earlier, here on the program, and said that-- >> The Amazon Randall. (laughing) >> Yeah, yeah, sorry, Randall from Amazon, here. >> He's a very large weber. >> Gettin' at the end of the day, I've done a few of these, but, you know, remember like, four years ago, the first, like, cloud 101 session here? >> Yeah, yep. >> And, I was like, you know, I probably could have given that session, but, everybody here was like, "Oh, my gosh", you know, I just found out about that electricity. >> Right. >> You know, that, this is amazing. And, today, most people, understand a little bit more of... We've gotten the 101, so, you know, I'm getting into more of the pieces of it, but. >> Yeah, it was really gratifiying because, the one that he gave was, all of the service, all of the new services, of which, there were like, more than 100, in 50 minutes or less. And, he talks really, really fast. And, everybody was riveted, we... I mean, people were coming in, even up until the last minute. And, they all got it. It wasn't like, what am I do... what am I going to do with this? It's, this is what I need to know, and this is valuable information. >> Yeah, we were having a lunch conversation, about, like, when you listen to a Podcast, what speed do you listen on? So, I tend to listen at about one and a half speed, normally. >> Me too, yep. >> You know, Frappe was sayin', he listens at 2x, normally. >> Does he really? >> Somebody like, Randall, I think I would, put the video up, and you can actually go into YouTube, and things like that, and adjust the speed settings, I might hit, put him down to 0.75, or something like that, >> Yeah, absolutely. >> Because absolutely, you know, otherwise, you can listen to it at full speed, and just like, pause and rewind, and then things like that. But, definitely, someone... I respect that, I'm from New Jersey, originally, I tend to talk a little faster, on camera I try to keep a steady pace, so that, people can keep up with my excitement. >> I do, I speed up too. He actually, does this everyday. He flies to a new city, does it once a day. So, he's, he's gotten... This is like rapid fire now. >> Alright, want to give you the final word, you know, VTUG, you know, I think, people that don't know it, you go to VTUG.com, A Big Winter Warmer, here. There's The Big Summer one, >> The Summer Slam. >> With the world famous, you know, Lobster Bake Fest, there, I've been to that one a few times. I know people that fly from other countries, to come to that one. What else should we know about? >> So, we're about to revamp the website, we've got some new and interesting stuff coming up on there. Now that, we also have our slack channel, everybody communicates on the backhand through that. We're going to start having some user content, for the website. So, people can start posting blog articles, and things of that nature, there. I'm going to start doing, like a little, AW... like learn AWS, on the VTUG blog, so, people can start, you know, ramping up on some of the basics and everything. And, and if, that gains traction, then, we'll maybe get into some more advanced topics, from Azure, and AwS, and Vmware of course, Vmware is always going to be there, that's... Some of the stuff that Cody is doing, Cody Jarklin is doing, over at Vmware, like the CAS stuff, where it's the shim layer, and the management of all the different clouds. That's some really, really cool stuff. So, I'm excited to showcase some of that on the website. >> Alright, wow. Chris Williams, really appreciate you coming. And, as always, appreaciate the partnership with the VTUG, to have us here. >> Thanks for havin' me. >> Alright, and thank you as always for watching. We always love to bring you the best community content, we go out to all the shows, help extract the signal for the noise. I'm Stu Miniman, thanks for watchin' The CUBE. (energetic music) (energetic music) (energetic music)

>> From Gillette Stadium in Foxboro, Massachusetts, if the queue recovering Vita Winter warmer, twenty nineteen Brought to you by Silicon Angle media. >> Hi. I'm stupid men. And this is the cubes coverage of V tug Winter warmer. Twenty nineteen here. A Gillette Stadium, home of the New England Patriots. Happy to welcome to the program. A community member, Someone I've known for many years at this point. Jonathan Frappe here. Who's with V Brown bag? Thanks so much for joining us from >> Thanks for having me. >> All right, so, you know, I watched this event, and when it started, it was, you know, originally the V mug for New England. And then it became vey tug And one, there's some of the politics stuff which we don't need to go into, but part of it was virtual ization and cloud. And what's the interaction there and what will users have to do? Different. And part of that is jobs. And one of the reasons I really wanted to bring you on is, you know, you started out heavy in that virtual ization base and you've been going through those machinations. So maybe just give our audience a little bit about, you know, your background, some of the things skill sets. You've got lots of acronyms on your on your you know, resume as it is for certification. You've done. So let's start there. >> Sure. So my background. I started this help desk. I did Windows two thousand Active Directory, administration and Exchange Administration all on site and moved into Mohr server administration. And when the empire started to become a thing, I was like, Wow, this is This is a game changer and I need to sort of shift my skill set. I understand the applications of music. I've been supporting him. But virtualization is going to change change That so started to shift there and saw a similar thing with Public Cloud and automation a cz, That same sort of next step beyond infrastructure management. >> All right. And you've had a bunch of certification. The real off a few. You know what? Where are you today? What? What have you added gives a little bit of a timeline. >> My first certification was a plus which come to you seemingly has come around and joined the ranks of posting toe linked in for everybody. So a plus was my first one. EMC PM, CSC on Windows two thousand. Took a little bit of a break in back into it. Bcp five era so four, five years ago. Cem Cem. Other of'em were Certs NSX Cloud see Emma and most recently, the solution's architect associate for a Ws. >> OK, great, in when you look at the kind of virtual ization and cloud, it's not like you thirst, which one day and said, Okay, I no longer need the VM were stuff. I'm going to do the cloud tell us a little bit about you know what led you to start doing the cloud and you know how you you know how your roles that you've had and you know the skill set that you want to have for your career. You know how you look at those. >> So for me, it is about being able to support what my business is doing. And sometimes the right answer's going to be VM, where sometimes it's going to be physical. Sometimes it's going to be containers or public cloud, or, you know, new fancy buzzwords like server lists. And I've always in my career tried to support what where, what application we're delivering to get the business, the information they need. So for me to do that properly, I need to be well versed across all of that infrastructure so that when when it's time to deliver something in public cloud or time to deliver something in the container, I'm ready to go when you do that. >> Yeah. What? What? What's the push and pull for some of the training bin? Is this something that you've seen? You said, like Veum, where you saw it, like, Oh, my gosh, I need to hop on that. You know, I remember back to those early days I remember engineers I worked with that were just like, this thing is amazing. That was like preview motion, even. Yeah, but you know, just what? That that impact we've seen over the last, you know, ten to fifteen years of that growth has there been times where the business is coming said, Hey, can you go learn this? Kaixian orders have been you driving most about yourself. Uh, >> it's it's been both. There are times when the business has come and said, Hey, we would really like to take advantage of virtual ization or public cloud. And it from a technology perspective, there may have been other factors that would impact the ability to do that. So that's why for me. I tried to sort of stay ahead of it when, you know virtual ization was taking off and everything I had was on physical servers. I knew I needed to have the VM where peace in my pocket so that when the business was ready and when other things like compliance, we're ready for it. We could move forward and sort of advanced that same thing with Public Cloud. Now that that's Mohr prevalent and sort of accepted in the industry a lot more cos they're moving in that direction. >> Yeah, and you know, what tips would you give your Pierre if they're a virtual ization person? You know, how are the waters in the cloud world is there are a lot of similarities. Is it? You know, do I have to go relearn and, oh, my gosh, I need to go learn coding for two years before I understand how to do any of this stuff. >> I think it's helpful. Tto learn some level of coding, but do it in an environment that you're comfortable in today. So if you're of'em were admin today, you know there's power, see Ally and be realized orchestrator and and even if you're on via Mars Cloud platform there's there's some basic power shell on bass scripting you could do in the cloud Automation. Get comfortable with the environment, you know. And then as that comfort grows when you move Oh, look, there's power shell commandments for a ws. If that's the route, you go so oh, already understand the format and how I how I glue those things together so you could get comfortable in the environment you're in today and sort of get ready for whatever that next step is. >> Yeah, I've always found I find it interesting. Look at these ecosystems and see where the overlaps and where two things come together. You know, I actually worked with Lennox for about twenty years. So I you know, back when I worked at Emcee the storage company and I supported the Lenox Group and Lennox was kind of this side thing. And then you kind of saw that grow over time and Lennox and virtual ization. We're kind of parallel, but didn't overlap is much. And then when we get to the cloud, it feels like everybody ended up in that space and there were certain skill sets that clinics people had that made it easy to do cloud in certain things that the fertilization people had that made it easy do there. But we're kind of all swimming in the same pools. We see that now in the, you know, core bernetti space. Now I see people I know from all of those communities on, but it's kind of interesting. Curious if you have anything you've seen in kind of the different domains and overlapping careers. >> Yes, you. For me. I think what's help is focusing on how the applications the business uses consumed, what some of the trends are around, how you know whether finance or marketing teams are interacting with those applications. If I know how the application works and what I need to do something to support it, the concepts aren't going to be vastly different. If I know how Exchange's install their sequel servers install, there's some custom application is insult. I could do that across the VM, where environment native US environment and should it supported into Docker by leveraging Cooper Netease. >> All right, so you've mentioned about the time the application, can you? How has it changed your relationship with kind of the application owners as you go from, you know, physical, virtual, the cloud. >> I don't think it should change much. The problem probably the biggest shift that you have is that at some point now, things are out of your control. So when I've got a server sitting in my data center that I can walk down the hallway to if something's not working, I have access to it. If there's an application down in the public cloud, or there's an A Ws outage or any public cloud provider outage, I have to wait. And that sort of I think the thing that I've seen business struggle with the most like, well, it's down, go fix it. It's like, I can't get to it right now, and I'm probably not driving to Virginia, Oregon to go reboot that server for Amazon. >> Whoever absolutely big shift we've seen right is, you know a lot of what I is. It I am managing is now things that aren't in my environment. You know, there was my data centers. My might have had hosted data centers where I'd call somebody up, you know, you know, tell the Rex paper person to reboot the servers or it's right, it's in the public cloud. In which case it's like, OK, what tools. What can I trouble shoot myself? Or is there some, you know, out of that I'm not aware of, you know, is affecting me. Yeah, >> it's Ah, it's a good shift to have for a infrastructure person because we're really getting to the point now. I think the tails, the scales have tipped to focusing more on delivering business value versus delivering infrastructure. The CFO doesn't necessarily think or care that spinning up a new V m faster is cool. They care about getting their application to their team so that they could do their work. So I think taking, you know, going to public cloud or going to other platforms where that's removed it sort of forces you to move to supporting supporting those business applications. >> So I'm curious it every time we have one of these generational shift time. Time is like, Oh, my gosh, I'm going to be out of a job on the server ID men Virtualization is going to get rid of me. I'm a virtual ization Had been cloud's going to get rid of me. This whole server listing will probably just get rid of all the infrastructure people I've read article yesterday was called the Creeping Apocalypse a CZ what they called it. But, you know, you know what you saying is there general fear in your peers or, you know, do you just, you know, dive in and understand it and learn it? If you could stay, you know, up with or a little bit ahead of the curve, you know you're going to keep employed. >> I would say that there's a mix there. Some people, even just a few months ago, some some folks I talked to and they were just sort of breaking into automation and like how they can automate deploying their applications in their legitimate concern, was I won't have a job anymore and sort of the way I looked at that was my job's going to change. I don't spend my entire day administering Windows two thousand active directory boxes any more. So I need Yes, I need to shift that and start thinking about what's next. If I can automate the routine task, you know, deploying an application, patching and application, bringing things up and down when there's some sort of failure than I, uh, I'm going to naturally grow my career in that way by getting rid of the boring stuff. >> Yeah, and I've been here in this argument against automation for decades now, and the question I always put two people is like, Look, if I could give you an extra hour a day or an extra day a week, do you have other projects that you could be doing or things that the business is asking for? That would be better. And I've yet to find somebody that didn't say, Yeah, of course, on DH. What are the things that you're doing that it would be nice to get rid of, You know, other people is like I love the serenity of racking and stacking cabling stuff. And nothing gets people more excited than beautiful cables in Iraq. I thought yesterday I saw people like going off about here's this data center with these beautiful, you know, rack, you know? So with the cable ties and everything, but I'm like, really, you know, there's more value you can add absolutely out there. So >> automate yourself into your next job. It is sort of the way I think I like to think about it. It's not a meeting, >> so let's you know, just look forward a little bit, you know? There's all these waves, you know, Cloud been a decade data was talking to keep downs in this morning on the Cube on we said, you know, when he talks to users, it's their data that super important applications absolutely is what drives, uh, you know, my infrastructure, but it's the data that's the super important piece. So you know, whether it be, you know, you're a I or, you know, you figure various buzz word of the day I ot You know, data is in the center. So what do you looking forward to is? Are there new search or new training that that are exciting? You are areas that you think you're Pierre should be poking out to help try to stay ahead of the curve. >> Yeah, and back to my earlier point about leveraging the thing you know today and how to sort of grow your career. And that next skill set is how I can look at data and make. I understand what's going on around that. So maybe maybe today that's taking some stats from any SX. I hosted an application and correlating that data together on help. You underst Yes. And you know what that means for the applicator action before or use their calls in. And that's going to help you grow into sort of this new realm of like, machine learning and big data. And in analytics, which I think is really the next thing that we're going to need to start doing as Mohr and more of that infrastructure shifted away into surveillance platforms and things that were not worried about How can I understand? How can I take that data? Transform it, use it, correlated together to, you know, help make decisions. >> Alright, on final thing, give us update on our friends at V Brown bag. So, you know, we talked Well, I always say, you know, when we go to V m world, it's like we're there. I'm trying to help kind of balance between the business and the technology. You want to go a little deeper and really geek out and understand some of these things. That's where you know the V brown bag. You know, people are going to be able to dig in with the community in the ecosystem. There was the V and V brown bag for virtual ization. But he brown bags doing much more than just traditional virtualization today. You know what? What? What's on the docket? >> Eso upcoming This year, we're gonna have some episodes around Python so helping add men's get to know Python start to get comfortable with it, Which would be a great language to a automate things that maybe you're doing today in your application, but also to be able to take data and and use Python, too. Manage that data extract value out of that data so that you can help make decisions. So look for the throughout this year and, you know, learn new things. >> All right, Jonathan, from pure pleasure to talk with you on camera after talking to off camera for many years. Thanks so much for joining us. All right. And we appreciate you joining us at this virtual ization and cloud user event. Ve tug Winter warmer. Twenty nineteen on student a minute. Thanks for watching the cue

>> From Gillette Stadium in Foxboro, Massachusetts, if the queue covering Vita Winter warmer, twenty nineteen brought to you by Silicon Angle media. Hi. >> I'm Jackie Sampson here with stew Minutemen wrapping up the show today. Ah, we're here. Gillette. >> So to this's the fifty year Vito, what's changed? >> Yeah, Jackie, so much has changed. So I've actually been coming to show for about >> eight years, and it was known as the >> New England V Mug back then. So when it switched for the tug, number one is a little bit more independent than a V M, where users group >> itself so broader on virtual >> station. But they actually made a conscious effort to expand beyond >> virtualization and talk about cloud computing. And four years ago, cloud computing while it had been gone, gone for about five years, most people coming to this show really didn't understand much beyond. I'd heard a cloud computing. I might have seen it on, like commercials from Microsoft, you know, to the cloud or some stuff like that. But they really didn't understand it. So I loved an event like this that brought in. They brought an >> Amazon. Microsoft had them give presentations, and they were breaking out from the ecosystem. This ecosystems >> gone through a maturation. Most of the vendors, I believe there's about >> five vendors here have a basic organization but have grown in decline. So we see in the users the ecosystem of the show. Make sure it's still over a thousand people here every year, and it's one that I was loved. >> That's awesome. So I was wondering. >> There are a lot of interesting guests that were on the Cube today. So what were the calm >> Dan's in virtualization >> space that you think company should >> start paying closer attention to twenty nineteen? >> Eso a common thing when I look back to twenty eighteen and continue here in twenty nineteen share >> really defines our industry today. So when we talk about going from virtual ization to cloud, we understand that that's gonna have to some disruption. We're at a user conference here, love talking to these users, and I talkto one user talked about the their hyper converge roll out, and they're going to be extending that for d R to the >> clouds I had a guest >> on today. Actually, the first one I've done it, Vito. He used to do virtualization, but in his day job today, all he does is a ws, and he does coding with PHP and he helps build out. Actually, Jackie, you gotta listen to this one because they're company does hair in massage, but for senior citizens on Lee. So it's really interesting based out of Cleveland. He's based locally. But you know, it's a nice niche and understanding the technology underneath that helps them at all of their location to do that. So you know, the common theme is, you know, it's a great time to be in technology. There's a lot of change going on, and there's great opportunities at events like this and training material for people tto learn and grow and keep themselves relevant and keep their business moving. >> That's pretty cool. So, >> speaking of relevance, who are some >> of the key players in >> space over some of the key players and talk? Teo? >> Yeah, so, >> you know, look, my first two guests were probably >> the two that have >> the biggest market share in the most relevant. So that >> is somewhere, you know, dominant in the virtual ization place and Amazon. Think clear Leader came for stuffed services going beyond actually supposed to have a guest on from microphone >> soft. Unfortunately, she was sick today. And look, it is not a winner. Take all. There is broad ecosystem and a lot of diversity out there in the ecosystem. So look, there's lots of virtual ization that isn't VM, where there's lots of cloud activity that's happening, both of them. What they've done really well in our balancing is their ecosystem. So a lot of change going on there. Neither of those companies is nearly as >> don't say the New England Patriots were going to their third Super Bowl in a row on DH talking. Did you know I'm a little excited about being here? A. Gillette? I wore my season ticket pin here. They just turned the lights on for us. Behind here, I >> can see my season ticket here. I was here. >> Wade. Rob Ninkovich on the program so way didn't talk to rob about too much. But, you know, even he was talking about the charitable works it does on new technologies. >> The underpinning he was actually telling me off camera, he's like, you know, Helen, I'm not doing football is like I should be in tech. You know, text. There's a lot going on. It's really interesting. And you know, that's the analogy we always have with the Cube is you know, one of the earliest clients said, where the pen attack. Let's give independent coverage, you know, help understand. Watch those waves and change justice in sports. If you want them long enough, things do change. You know, the NFL today. There's a very past happy league, and I think backto, when I was much younger, it was like, you know, defense running wins game today, you know, I mean, cloud computing is all the rage and rightly so, and there's still a lot of growth there. But, you know, virtual ization >> important. And there's >> so many different areas for people to be able to dig in. And that keeps >> us hopping from show to show on Keeps me excited. Teo. Find ofthe community people on technologists, users that >> will share their experiences. >> That's pretty cool. So did you have any favorite interview today? Or interviews? Plural. >> Yeah, you know, Jackie, >> it's always tough for me to, you know, choose a choose a favorite. >> So no right way has taught leadership pieces. You know where you talked about it? We talked about >> career with some computer people we talked to use, or so >> I hate to say it always liked to be like, Yeah, yeah, thiss one. But you know, overall, it was really good. I'm really happy to be able, Teo, participate. Even It's tough when I look back. In the years >> that I've been doing this, >> it's just the diversity of the new things that we get to learn your aunt and that keep >> me excited. You know, from year to year, >> it's awesome. So, Stew, thank you so much for wrapping up the show today. >> And, Jackie, I really appreciate you helping me. You know, wrap this up. You know, you're No, >> you know that. Love to say that. Thank you, everyone. I'm Jackie with student. Thanks >> for watching.

>> From Gillette Stadium in Foxboro, Massachusetts, if the queue covering Vita Winter warmer, twenty nineteen brought to you by Silicon Angle media. >> Hi, I'm stew Minutemen. And this is the Cube Worldwide Leader and live tech coverage. >> We're on the ground here at the V Tug winter warmer, and it is twenty nineteen. It's actually, the thirteenth year of this event was one of the original, if not the original Veum, where user groups covers virtual ization, cloud computing and even Mohr, always great to be able to get back to the community, get some good interviews and no better person helped me start with my first interview at a show of the year. But good friend of the program, Keith Towns and he is the CTO advisor. And he's also now a slew front architect with the M. Where Keith. Thanks for joining >> us. Thanks for having me on the cute. >> Yeah. So, Keith, I mean, you were host of our program for a number of years. You're now, you know, back working on the vendor side. But you know, you know this community. You know what I always say in my career, There, certain communities, an ecosystem where there's just love to be a part of it. And the virtual ization group. You know, I've been part of it for a long time. You know, Veum, wear and beyond, though, you know people that you know, they get excited, They geek out on the technology and they love to share. And that's why we come to events like this. >> Yeah, it is amazing. Just, you know, the every every show is getting smaller, but maybe with the session of a Ws re event, but I don't think the intensity has shrunk at all. You get around friends, you know, we're just at a desk and one of the ten days, actually, how did I get a job doing X? And the community was like, Oh, you just talk to the people at this table. So it is. It is a great, great commute. >> Yeah, it's an interesting dynamic you talk about. You know, we've seen the huge growth in Meetups in user groups and regional shows. You know, vm Where does Veum World but the VM world being where forums around the globe. I'm sure you probably have to go for a few of those they've been doing well. I'm right back in my emcee Daisy M. C. Did a number of those. So we see you. Amazon Reinvent is growing, but oh, my God, they're regional shows are ridiculous. I I've said some of those regional shows either different communities or different localities can actually be even better than some of the big shows on. You know, we love Keith. We're happy to welcome you here to the home of the NFC Championship. New England Patriots ur >> First off, Congratulations. The wait went a little better for you to bare sand and say, You know what? Tom Brady won't play forever, so enjoy it. This is amazing backdrop through him. Little finish that you've not involved. Invited me to a veto before now. >> Oh. Oh, I'm sorry, Keith. It's It's a community thing that absolutely got to come. Absolutely. I've had friends. Most of them. It is local. I'm talking to users from Maine and Massachusetts, Rhode Island and Connecticut and like so you gave a keynote this morning and you didn't True fashion. You did a block post about reality check leading in, and I thought it was a great way for us to start is, You know, there's so much change in the industry, uh, those of us that are technologies that you know, we're super excited because there's so much new stuff. It's not like Oh, jeez, you know, twenty nineteen is probably going to be just like twenty eighteen. It's like, Oh, my gosh, what did I do in twenty eighteen? What do I have to change? How do I keep up? How do I manage it? I would love to get your viewpoint. You know what's going on with Keith? And you're talking about a lot of users, so you know how help share, You know, what is the reality? Check that everybody's going >> to know. We're talking about a pre recording in the banter. Just, you know, whether it's, you know, Vienna where we're hip Theo and all the stuff that Casey Kelsey Hightower is going out with Cooper Netease. Then as you spent spent out to serve earless, uh, infrastructures Cole scripting it centre. There's much to learn that you're a bit overwhelmed and we're seeing this out. You know, as I'm talking to executive CTO CEOs, VP of infrastructure, they're filling the same kind of excitement at the same time. Overwhelmed this Like what? What's what's really You know, we had the big cloud movements over a few years ago where I think we're at the height cycle where organizations are starting to understand that. You know, Cloud isn't the destination is part of a strategy, and everyone seems to be in the throes of figuring out what that means for us. We're just on the crowd chat, talking about multi Cloud and the drivers around. Multi Cloud. You guys did a great job hosting that cloud shit chat, nothing. We saw the gambit off where people are. You know, uh, there's not really a business rationality people who are really in the throes of trying to figure it out. >> Yeah, actually, I love to comment friend of ours that we've had on the program before, Bobby Allen from Cloud General said when he's working with companies, if they ask for a three year strategy plan, he said, I will not do it unless we guarantee that we will go revisit it every six months because I looked back. You know, Clay Christensen, you no way talks about strategy is strategy is a point in time thing, not something that you write it in stone. I've been saying for a couple of years cloud strategies that companies today is, they wrote it in ink and the ink still drying. And, you know, you're probably going to need toe, you know, go through it and change it because it is changing fast and therefore, you know, huge. Out I started Deploy something. Oh, wait, what about the next thing? Or there's some new practice or something to do it. So it is challenging because I need to run my business. Today. I got to set my budget for the year, usually, um and it's I need to be agile. But, you know, I can't constantly be tearing everything up and you're not going to be throwing it out or re training and skills. I mean, there's so many challenges. >> So still, you might remember when when I was on the other side of the the table. I, uh it was meant at somewhat of a D that Veum where moves at the speed of the aisle, and it was picked up as Maury compliment. But >> it was a >> big I'll be honest that it was a dig. And what I've learned the past few months is that Veum, where has to move at the speed of the CIA, is no longer and It's not just being wherever the community has and the CIA always faced with that we could do a few years ago. A cloud strategy, and that thing can sit on the desk for a year, and it would still be valid. But the bobbies point, if you're going to do a strategy and three year strategy, got to revisit that every six months and this agility that were not accustomed to previously in the industry, we have to now become super agile and figure out how do we keep the lights on and innovate at the pace That business, these witches? Pretty good chance. >> Yeah, it's attorney were beginning the year I made a comment personally said, You know, I'm not a big believer in, you know, setting. You know, Resolutions. Mohr. You know, let's set goals Your runner, I do some biking and it's like, Okay, you know, I've got a big race I want to do this year. I'm gonna work myself, you know, towards that goal and raise the money. You've got a certain target and something that you could do over the year. It's and there's no way that you do that, cos you know they've got goals that they need to accomplish and business. And it's great to say, Oh, well, we need to be more efficient. We need to do some down something different. But, you know, reality is, you know, it's not just digital transformation of modernizing. It was, you know. Oh, okay. Do I need to transform my backup? You know, data protection, you know, huge activity going on in the marketplace right now, you know? So, what >> is sixty million noon investment in one >> week? Exactly. You know, the wave of hyper convergence is one that really changed a lot of architectures and had people change. You know, we've talked cloud computing. They're what are some of the, You know, some of the big, you know, movements that you see, you know, will you? Tracking the industry? It was kind of the the intel refunds for a cycle, and, you know, Oh, well, it's the next version of Microsoft or, you know, Veum, where operating system would be one of those big, you know, kind of ticked. Talks of what? What are some of the big commonalities that you're seeing Al? So they're actually moving people to >> new things without a doubt. There is one conversation that customers cannot get the enough of. And I had Ah, on my little vlog. I had game being from Vienna, where V P off the Storch and Business availability unit and I challenged her on the via Where? Vision around this. But customers cannot get enough of having a conversation around data. What they What do they do with data? And how does a move data? How did they get compute closest to data? How did we get data they're closest to? They're re sources. We talked about it on the multi cloud conversation, but by far conversations are around. Howto they extract value from data had really protect data, and howto they make sure their compliant with the data is something that that's driving a lot of innovation and a lot of conversation. A lot of interest. >> Yeah, Keith, it's a great one. When I look at you know, our research team, that wicked bond data is that the center of everything. In many ways, the failings of big data was talking about, You know, the challenges. I have infrastructure. No, the growth and the variety and blah, blah, blah and everything that's not what important to the business they don't care about, You know, it's like, Oh, well, there's a storage problem in a network problem. It's the business says there's data, you know? Do I protect my bird business to make sure that I'm not a risk? You know, all the things like DDP are coming And can I livered value? Do I Can I get new lines of business? Can I generate revenue out of that? And I've seen early signs that we've learned this whole, You know, a I m l movement. You know, data, Really? At the center. All right, we've seen enough storage. We went from talking about storing data to about, you know, that data ecosystem, Andi, even computing and I ot data where data needs to be, how I work it. Absolutely a center. So, yeah, it's great to hear that. Customers are identifying that. We've been doing like, chief data officer events for many years. You know, where does data live? Is that a CEO Thing? Is that a different part of the business? I don't know if you've got anything you're seeing from, you know, your customers is Tau, >> who owns the Data initiative, So it's really interesting. I had a conversation with a major bank, and it was a one on one with the CDO and what I thought was the most tricky part of the conversation is that here, Not only does he report directly into the CIA, which you know is to be expected, but he meets regularly with the board of directors. So data were seen. I've seen these seedy old rolls being popped up, and it's not just about the technology as you mentioned. It's about the whole approach about this asset that we have. It's so critical that worth creating a sea level position that today might reporting to the CEO but is most definitely accountable to the border director. >> Well, yeah, Keith, it's that the trend we've been watching for a while, as it used to be, it was a cost center. And, you know, it's kind of, you know, that's what it was considered today. If it isn't in, you know, direct relationship, working with the business, the business will go find somebody else to do it. The whole stealthy movement. You know, I can go find an answer for what I'm doing. I think about project I've worked on in my career and been like, I wish it was easy. You know, fifteen years ago, it was today to do those. But we see security's a board level discussion data as a board level discussion is excellent. And all of those things that traditionally you would think that own them. Having awareness and visibility and information communication flow between the board in the C suite is great progress. You >> know, it's interesting. I was a big proponent of this prior to coming on The vendor side is that vendors have to start having conversations outside of it. So traditional infrastructure of injustice, his goal. Hurry, right saw and where the whole the Dale emcee Dale Technologies they have to skill up and have conversations with CIA moles. Seo's CEO Ole's H R directors because the these buying centers now have power to go out and buy solutions. You know, talked about in my no keynote this morning. You know how many people have worked day? How many people have salesforce applications? They had nothing to do when I had no nothing to do with the procurement of off these solutions. The ball is moving outside of just traditional for court technology is starting to get to the point where regular users can consume business users can consume these massive, massive solutions based on technology and just happens to be a label. The technology, whether sales Force worked in >> Sochi, thought on this this whole point there want to ask you, In my career, there's often been groups inside a business that didn't get along. And we, you know, built silos. You know, the storage in the network team don't get along cloud and traditional I t You know what we're fighting? You know who owns it? Turf wars Managing that, You know, have we built silos in multi cloud today? Is everybody holding hands and, you know, pointing the business in the same direction, you could kind of give us the good the bad. So what? We need to work on going forward. >> I think the good is that you know that the umbrella of infrastructure starting to work as a single. Uh, you So you have storage, compu networking, even configuration man groups that were kind of confrontational before and territorial. Those groups are starting. Tio. Come on. Their one senior manager or one senior executive looking at? How do you provide services as a group and providing those services? I think we're we're starting to see Silos is actually the developer versus the infrastructure group is developers just wantto FBI, too. A set of services. They want infrastructure to get away. Developers themselves. Haven't you know, kind of katende enough of the scars from heaven have to do operations, So there's a different view off the world. And, uh, today I think developers haven't yet getting the budget power off operations. But the business wants solutions, and they're going out there competing with traditional Teo get the dollars to run the services in the cloud or or wherever, however they consumed them, whether it's, you know, just saw Chick fil a's deploying two thousand ten points to run six thousand containers at the edge. Is that something that's run by tears? That something wrong? Run by developers? I don't know. Check feeling well enough to know about. This is what we're seeing in >> industry. Yeah. All right. Well, keep towns. And always a pleasure to catch up with you. Thanks so much for joining us. Be sure to check him out see Teo advisor on Twitter, check out his blogged. And of course, thank you so much for watching. We'll be back. Uh, lots more coverage here at V tug. Winter warmer, twenty nineteen. Thanks for watching.

from Gillette Stadium in Foxborough Massachusetts it's the cube covering Vita winter warmer 2019 brought to you by silicon angle media hi I'm Stu minimun and this is the cube at V tug winter warmer 2019 at Gillette Stadium home of the New England Patriots the AFC Championship team going to the Super Bowl third year in a row yet again Randall right yeah paying it's my Los Angeles Rams oh so happy to welcome to the program Randall hunt who's a software engineer with AWS did a keynote this morning I believe it was a hundred AWS features in 50 minutes and felt like you we added a couple more than 100 and went a little over 50 minutes but I think we probably hit 57 minutes that was what the slide counter said but yeah I added a couple of the updates since reinvent you know reinvent is not the end of our innovation we continued releasing new stuff after that all right so our program we're not going to be showing JavaScript we're gonna take a deep breath and slow down a little bit because you know our audience absolutely knows Amazon I tell you this show remember like four years ago first time AWS presented me at Microsoft and AWS here and people heard cloud 101 and I was like come on I could have given this presentation and they were walking around like oh my god I just you know found out that you know who you know horseless carriages and I can do that do them and things like this so you know cloud we've been there for a decade but we're still I believe you know day zero day one is what Amazon always likes this is day one it's always day one so there's no way we can shove the entire reinventing keynote into this discussion so you know want to start first Tulsa rent a little bit about yourself your role what you work on and what customers you talk to sure so I studied physics and then I found out physicists don't really make any money so I became a software engineer and I worked at NASA I worked at SpaceX and worked with this company called MongoDB back then it was called Tianjin and then I am an Amazon I was my second time around in Amazon I'm a software engineer there but I'm also a Technical Evangelist and what that means is I get to travel around the world and make make all of the demos and chat with all of our customers and kind of solicit feedback from them and then kind of try to act as the voice of the customer for the service teams whenever I can get them to listen yeah so probably not going to go into open source versus software licensing of things with you because we want to make sure that we can publish I tell you space is one of those things I love it when I've interviewed people that have been in space I've talked to lots of companies that have our code in space Amazon you have I loved you know robotics and space are hard and we make it easy and I kind of laugh cuz I was an engineer as an undergrad I mean I studied a little bit of you know what it takes to break gravity and understand I always love watching you know all the shows about space and track SpaceX would you work for and things like that give me a break you haven't made space easy well I think space as a whole is getting easier this industry is becoming more approachable one of the things that we launched to reinvent this year was a ground station and this is something where if you have an S band or UHF you know satellite and leo which is low Earth orbit or mio which is medium Earth orbit you can basically down stream that data to one of these ground stations which is you know essentially attach to a region you know in this case us East 2 which is in a like Ohio area and you can go and say hey just stream this data into s3 for me or you know let me access this from my V PC which is pretty gnarly if you think about it you know you have a you have an IP address which is a satellite in space yeah I love I worked on replication technology 15 years ago and it was like okay can the application take the ping off the satellite or you know how do we do this so look we're leveraging satellites a little bit more I understand it's a great tagline to make those useful and more readily just you know it's amazing you think about when you think about my availability zones and regions it's now you know that things aren't just on the Terra Firma well I'm looking forward to the first availability zone on the on the moon or on Mars that that'll be you know when we have utopia planitia 1a that'll be the really cool AZ alright we heard the first blue origins working to Mars no well the latency you know if you have 300,000 and fit three hundred fifty thousand kilometers on average between the Earth and the moon so you know you can go around the earth it would speed of light 7.5 times every second to go to the moon is a fool I hang it's like six seven seconds or so so the latency requirements become a little bit harder there I roll more my wrong pin I have I have the Grace Hopper nanosecond which is the wit which is you know curled up and if you follow the white thing it's how long light would take to travel that and it does it in two nanoseconds so you got me I'm a physics lover and love space as does a lot of our audience so bring it down to the thing one of the things that amazon has done really well is I don't need to be a physics geek to be able to use this technology we're having arguments as to you know if I'm starting out or if I want to restart my career today do I go code or heck you know let me just use lambda and all these wonderful things that Amazon have and I might not even need to know traditional coding I mean when I learned programming you know it was you learned logic and wrote lines of code and then when you went to coding it's pulling pieces and modifying things and in the future it's it seems like serverless goes even further along that spectrum I definitely think there's opportunities for folks who have just you know I don't want to say modest coding abilities but people who were kind of you know industry adjacent scientists you know data scientists folks like that who may not necessarily be software engineers or have the they couldn't recite in Big O notation for mergesort and things like that from scratch you know but they know how to write basic code there's a lot of opportunity now for those developers and I'll call them developers to go and write a lambda function and just have it accomplishing a large portion of their business logic for their whole company I think the you know you have a spectrum of compute options you have you know ec2 on the one side and then you have containers and then as you move towards service you get this this you know spectrum between Fargate and lambda and lambda being the the chief level of abstraction but I I think in a couple cases you can you know even go further than that with things like amplify which is a service that well it's an open source project that we launched and it's also a service that we launched and it takes together a bunch of different AWS services things like app sank and kognito and lambda and it merges them all together with one CLI call you can go and say hey spin up a static site for me like a Hugo static site or something and it'll build the code pipeline build all that stuff for you without you having to you know worry about all the stuff and if developers are starting new today you know I remember when I started I really had to go deep on some of the networking stuff you know I had to learn all these different routers and like how to program them and these like the industry router so you know the million dollar ones and having to rack and stack this stuff and the knowledge is not really needed to operate of large-scale enterprise you know if you if you know a Ralph's table and you you you know V pcs you know you can run you know a multi-billion dollar company if you want yeah it's been interesting to watch too and you know I think the last five years the proliferation of services in AWS got to a point where is like oh my gosh if I wanted to kind of configure a server for my datacenter or configure an equivalent something that I wanted at AWS there was more choices in the public cloud than there was there and people like oh my gosh how do I learn it how do I do this but what we start to see is it's more don't need to do that because what do I want to do if there's an application that I can run where services that will help make it easier for me to do that because the whole it's not let me replicate what I was doing here and do it there but I have to kind of start with a clean sheet of paper and say okay well what what's the goal what data do I need what applications do I need to build and start there I'm curious what you see and how do you help companies through that so that this is a really common scenario so I this is a kind of key point here is enterprises and companies have existed since before the cloud was really around so why do we keep seeing so much uptick why do we keep seeing so many customers moving into the cloud and how do we make it easier for customers to get into the cloud with their existing workloads so along that same spectrum if you have greenfield projects if I were running my own company and I were doing everything I would absolutely start in the cloud and I would build everything as kind of cloud native and if you want to migrate these existing workloads that's part of the one of the things that we launched this year in partnership with VMware is VMware kind of interface for AWS so you can use your native vCenter and vSphere kind of control plane to access EBS to access route 53 and ec2 and all the other kind of underlying stuff that you are interested in run it you can even do RDS on VMware in my environment so that line is definitely blurring between my stuff and my stuff somewhere else and when people are talking about migrating workloads right you know you can take the lowest hanging fruit the most orthogonal piece of your infrastructure and you can say hey let me take this piece as an experimental proof of concept workload and what kind of lift and shift it into the cloud and then let me build the accoutrement the glue and all the other stuff that kind of is associated with that workload cloud native and you'll get additional agility your you know 1:1 ops person can manage this whole suite of things across 19 20 regions of AWS and you know there's kind of global availability and all this kind of good stuff that typically comes with the cloud and in addition to that as you keep moving more and more workloads over it's not like it's a static thing you know you can evolve you can adjust the application you can add new features and you can build new stuff as your move these applications over to the cloud yeah and it's interesting because just the dynamics are changing so much so there's been there's still so much movement to the cloud and then oh well some people I'm pulling stuff back and then you see you have a WS outposts so later 2019 we expect to Amazon to have you know footprint in people's environments and then you know Jeff just to make things even more complicated well the whole edge computing IOT and the like which you know everything from snowball and these pieces so the answer is it gets even more complicated but you know your your AWS I know is trying to help simplify this for use right the board I think I can say anything at all about AWS it's that if a customer is asking us to build something we are gonna do our best to make that customer happy we take customer feedback so incredibly seriously in all of our meetings all of our service team meetings you know we that voice of the customer is very strong and so if people are saying hey I want a AWS in my own datacenter you know that's kind of the genesis of outpost and it's this idea that well we have this control plane we have this hardware let's figure out how we can get it to more customers and customers are saying hey I want into my data center I want to just be able to plug in some fiber and plug in some power and I want it to work and that's the idea right we're gonna when I think of every company that I've watched there's usually something that people will gripe about and what I've been very impressed with Amazon Amazon absolutely listens and moves pretty fast to be able to address things and if you see you know if I'm a competitor of Amazon I'm like oh well you know this is the way that we get in there you know where we think we have an advantage chances are that Amazon is addressing it looking to you know move past it and you know absolutely the Amazon of 2019 is sure not the Amazon of 2018 or you know when you thought about it you know 2015 and it's big challenge for people as to because usually I think of something and you never get a second chance to make a first impression but it changes so much right everything changes that you know I need to revisit it it's like oh well this is the way I do things well Amazon has five different ways you can do that now um you know which one fits you best and I think that's important is different applications gonna have different characteristics that you want to be able to pull in and run in different ways yeah you know honestly I'm a huge fan of service I I think service is where a ton of different workloads are going to move into the future and I just see more and more companies migrating their existing you know everything from elastic Beanstalk applications so like vdq you know VMware images into the service environment and I like seeing that kind of uptick and someone recently I I can't remember who it was someone sent me a screenshot of their console with their ec2 instances in 2010 and maybe it was part of this 10-year challenge thing on Twitter where it's 2009 versus 2019 but they sent me you know they're in one large and the screenshot of the console from back then and they sent me a screenshot of 2019 and Wow things really have changed and you don't really notice it as much when you're using it every day but I can imagine you know their their Ops teams where they haven't logged into the console in three years because you know everything is done kind of in an automated fashion they set up their auto scaling group you know three years ago and then the only time they ever log in is to update to new instance types or something for the cost savings and I get messages on Twitter sometimes from people who are like whoa console got an update this is so cool and then sometimes we we get messages from people where you know we changed the EBS volume snapshotting things we had somebody who had it was like 130,000 EBS snapshots or something and they were like hey you removed my ability for me to select multiple snapshots it what it's like well you have a hundred and thirty thousand so we went in into the UI and we added a little icon that works better for large groups of snapshots you know if there's a customer pain point we will do everything we can to address it all right Randall Hunt really appreciate you sharing with us your experience what's going on with customers and absolutely that 10-year challenge we know things change fast we used to measure in decades I say now it's usually more like you know 18 to 24 months before between everything AWS in 2029 it's gonna be crazy and I can't I can't imagine what its gonna look like then all right well the cube we started broadcasting from in 2010 we appreciate you staying with us through 2019 check out the cube net for all of our programming I'm Stu minimun and thanks so much for watching the key

>> From Tavern on the Green in Central Park, New York, it's theCUBE, covering Veritas Vision Solution Day. Brought to you by Veritas. (robotic music) >> We're back in the heart of Central Park. We're here at Tavern on the Green. Beautiful location for the Veritas Vision Day. You're watching theCUBE, my name is Dave Vellante. We go out to the events, we extract the signal from the noise, we got the CEO of Veritas here, Greg Hughes, newly minted, nine months in. Greg, thanks for coming on theCUBE. >> It's great to be here Dave, thank you. >> So let's talk about your nine. What was your agenda your first nine months? You know they talk about the 100 day plan. What was your nine month plan? >> Yeah, well look, I've been here for nine months, but I'm a boomerang. So I was here from 2003 to 2010. I ran all of global services, during that time and became the chief strategy officer after that. Was here during the merger by Semantic. And then ran the Enterprise Product Group. So I had all the products and all the engineering teams for all the Enterprise products. And really my starting point is the customer. I really like to hear directly from the customer. So I've spent probably 50% of my time out and about, meeting with customers. And at this point, I've met with a 100 different accounts all around the world. And what I'm hearing, makes me even more excited to be here. Digital transformation is real. These customers are investing a lot in digitizing their companies. And that's driving an explosion of data. That data all needs to be available and recoverable and that's where we step in. We're the best at that. >> Okay, so that was sort of alluring to you. You're right, everybody's trying to get digital transformation right. It changes the whole data protection equation. It kind of reminds me, in a much bigger scale, of virtualization. You remember, everybody had to rethink their backup strategies because you now have less physical resources. This is a whole different set of pressures, isn't it? It's like you can't go down, you have to always have access to data. Data is-- >> 24 by seven. >> Increasingly valuable. >> Yup. >> So talk a little bit more about the importance of data, the role of data, and where Veritas fits in. >> Well, our customers are using new, they're driving new applications throughout the enterprise. So machine learning, AI, big data, internet of things. And that's all driving the use of new data management technologies. Cassandra, Hadoop, Open Sequel, MongoDB. You've heard all of these, right? And then that's driving the use of new platforms. Hyper-converged, virtual machines, the cloud. So all this data is popping up in all these different areas. And without Veritas, it can exist, it'll just be in silos. And that becomes very hard to manage and protect it. All that data needs to be protected. We're there to protect everything. And that's really how we think about it. >> The big message we heard today was you got a lot of different clouds, you don't want to have a different data protection strategy for each cloud. So you've got to simplify that for people. Sounds easy, but from an R&D perspective, you've got a large install base, you've been around for a long, long time. So you've got to put investments to actually see that through. Talk about your R&D and investment strategy. >> Well, our investment strategy's very simple. We are the market share leader in data protection and software-defined storage. And that scale, gives us a tremendous advantage. We can use that scale to invest more aggressively than anybody else, in those areas. So we can cover all the workloads, we can cover wherever our customers are putting their data, and we can help them standardize on one provider of data protection, and that's us. So they don't have to have the complexity of point products in their infrastructure. >> So I wonder if we could talk, just a little veer here, and talk about the private equity play. You guys are the private equity exit. And you're seeing a lot of high profile PE companies. It used to be where companies would go to die, and now it's becoming a way for the PE guys to actually get step-ups, and make a lot of money by investing in companies, and building communities, investing in R&D. Some of the stuff we've covered. We've followed Syncsort, BMC, Infor, a really interesting company, what's kind of an exit from PE, right? Dell, the biggest one of all. Riverbed, and of course Veritas. So, there's like a new private equity playbook. It's something you know well from your Silver Lake days. Describe what that dynamic is like, and how it's changed. >> Oh look, private equity's been involved in software for 10 or 15 years. It's been a very important area of investment in private equity. I've worked for private equity firms, worked for software companies, so I know it very well. And the basic idea is, continue the investment. Continue in the investment in the core products and the core customers, to make sure that there is continued enhancement and innovation, of the core products. With that, there'll be continuity in customer relationships, and those customer relationships are very valuable. That's really the secret, if you will, of the private equity playbook. >> Well and public markets are very fickle. I mean, they want growth now. They don't care about profits. I see you've got a very nice cash flow, you and some of the brethren that I mentioned. So that could be very attractive, particularly when, you know, public markets they ebb and flow. The key is value for customers, and that's going to drive value for shareholders. >> That's absolutely right. >> So talk about the TAM. Part of a CEOs job, is to continually find new ways, you're a strategy guy, so TAM expansion is part of the role. How do you look at the market? Where are the growth opportunities? >> We see our TAM, or our total addressable market, at being around $17 billion, cutting across all of our areas. Probably growing into high single digits, 8%. That's kind of a big picture view of it. When I like to think about it, I like to think about it from the themes I'm hearing from customers. What are our customers doing? They're trying to leverage the cloud. Most of our customers, which are large enterprises. We work with the blue-chip enterprises on the planet. They're going to move to a hybrid approach. They're going to on-premise infrastructure and multiple cloud providers. So that's really what they're doing. The second thing our customers are worried about is ransomware, and ransomware attacks. Spearfishing works, the bad guys are going to get in. They're going to put some bad malware in your environment. The key is to be resilient and to be able to restore at scale. That's another area of significant investment. The third, they're trying to automate. They're trying to make investments in automation, to take out manual labor, to reduce error rate. In this whole world, tape should go away. So one of the things our customers are doing, is trying to get rid of tape backup in their environment. Tape is a long-term retention strategy. And then finally, if you get rid of tape, and you have all your secondary data on disc or in the cloud, what becomes really cool, is you can analyze all that data. Out of bound, from the primary storage. That's one of the bigger changes I've seen since I've returned back to Veritas. >> So $17 billion, obviously, that transcends backup. Frankly, we go back to the early days of Veritas, I always thought of it as a data management company and sort of returned to those roots. >> Backup, software defined storage, compliance, all those areas are key to what we do. >> You mentioned automation. When you think about cloud and digital transformation, automation is fundamental, we had NBCUniversal on earlier, and the customer was talking about scripts and how scripts are fragile and they need to be maintained and it doesn't scale. So he wants to drive automation into his processes as much as possible, using a platform, a sort of API based, modern, microservices, containers. Kind of using all those terms. What does that mean for you guys in terms of your R&D roadmap, in terms of the investments that you're making in those types of software innovations? >> Well actually one of the things we're talking about today is our latest release of NetBackup 812, which had a significant investment in APIs and that allow our customers to use the product and automate processes, tie it together with their infrastructure, like ServiceNow, or whatever they have. And we're going to continue full throttle on APIs. Just having lunch with some customers just today, they want us to go even further in our APIs. So that's really core to what we're doing. >> So you guys are a little bit like the New England Patriots. You're the leader, and everybody wants to take you down. So you always start-- >> Nobody's confused me for Tom Brady. Although my wife looks... I'll stack her up against Giselle anytime, but I'm no Tom Brady. >> So okay, how do you maintain your leadership and your relevance for customers? A lot of VC money coming into the marketplace. Like I said, everybody wants to take the leader down. How do you maintain your leadership? >> We've been around for 25 years. We're very honored to have 95% of the Fortune 100, are our customers. If you go to any large country in the world it's very much like that. We work with the bluest of blue-chips, the biggest companies, the most complex, the most demanding (chuckling), the most highly regulated. Those are our customers. We steer the ship based on their input, and that's why we're relevant. We're listening to them. Our customer's extremely relevant. We're going to help them protect, classify, archive their data, wherever it is. >> So the first nine months was all about hearing from customers. So what's the next 12 to 18 months about for you? >> We're continuing to invest, delighted to talk about partnerships, and where those are going, as well. I think that's going to be a major emphasis of us to continue to drive our partnerships. We can't do this alone. Our customers use products from a variety of other players. Today we had Henry Axelrod, from Amazon Web Services, here talking about how we're working closely with Amazon. We announced a really cool partnership with Pure Storage. Our customers that use Pure Storage's all-flash arrays, they know their data's backed up and protected with Veritas and with NetBackup. It's continually make sure that across this ecosystem of partners, we are the one player that can help our large customers. >> Great, thank you for mentioning that ecosystem is a key part of it. The channel, that's how you continue to grow. You get a lot of leverage out of that. Well Greg, thanks very much for coming on theCUBE. Congratulations on your-- >> Dave, thank you. >> On the new role. We are super excited for you guys, and we'll be watching. >> I enjoyed it, thank you. >> All right. Keep it right there everybody we'll be back with our next guest. This is Dave Vellante, we're here in Central Park. Be right back, Veritas Vision, be right back. (robotic music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> Announcer: Live from Gillette Stadium in Foxboro, Massachusetts, it's theCUBE, covering #VTUG's New England Winner Warmer 2017. Now, your host, Stu Miniman. >> And we're back, with SiliconANGLE Media's presentation of theCUBE, we're the worldwide leader in enterprise tech coverage, happy to welcome back to the program, Steve Pao, who's the CMO of Igneous Systems, Steve flew out from Seattle here to, welcome to the home of the New England Patriots. >> Oh my gosh. Number 12, number 12! >> The 12 man representing here, you've got, I have to say, I almost canceled my season tickets when Pete Carroll was our coach, so, luckily he's worked out better for you than he did for us. My wife's a Brown fan, she says the same thing about Bill Belichick. So, it's the coaching fraternity is kind of like the tech world, it's a small group, you all kind of get to know each other and move around, so, thanks for joining us. >> Yes, well thanks for having me! >> Alright, so Steve, we've been talking to you guys since you were coming out of Stealth, why don't you give our audience, what's the update on Igneous? >> Okay, well for those of you who don't know us, what Igneous really does is we offer an onsite private cloud storage service, and that's our first offering, it's part of our greater mission of providing true cloud for local data, and what we basically offer today is an unstructured data store that's completely delivered as a service, we take our own equipment, we install it, we monitor it, we manage it, we even refresh it when necessary, and all the customer has to do is really subscribe, and that's it. It's all pay-as-you-go, and it's all zero touch for the customer. We launched back in October, as you recall, and one of the things that I think that it's been really great since launching is that we've really started to see how customers that didn't know us are actually really evaluating, really, I think, the convergence of two trends, one is there's this data growth, data trend that goes on, and pretty much everybody we talk to, citing data growth rates on the order of doubling every three years, where IT budgets are growing less than 5% a year, so there's this mismatch where, basically everybody's hitting this juncture that what they used to do can't work because the data's growing faster than the budget. And at the same time, there's this data growth that's actually happening, and the data growth is not from relational databases and structured data, but rather, a lot of new applications that are logging sensor data that are supporting machine learning, AI, really, it's machine-generated data being analyzed by machines, with humans really just training the AI and the machine learning. >> Yes, Steve, I want to unpack that a little bit, let's talk it, because many of us that watch storage, it's been like, well the storage industry, it needs to change, it's not about selling boxes, it's not about capacity, and even on unstructured data, it was kind of like, okay well, what's creating data and what's actually valuable? How much is it just, do I stick it on a cheap tier, what do I actually do with it, what's interesting you guys do, some of those use cases, throughout machine learning, machine data, things like sensors, every time I hear that word, that IoT buzzword kind of pops into our head, but maybe you could talk to some of those, what's bringing customers, what's that driving challenge that they have, that you're helping to solve, that's different from the way storage has been done for many years? >> Yeah, I think, that's a great question, and I think that there's just been a real transition, and I think the transition has been largely created by the kinds of data that we want to manage and that we want to curate, and as we're seeing these sort of large unstructured data sets, it starts with the data, so as an example, you take equipment that used to exist in the past, like let's say in scientific computing. You used to have flow cytometers, which were just time-series data, and then what's now happened is is associate with ever flow cytometers, now a real-time video feed. When you look at the old world of microscopy, what you used to do is you used to flash freeze a sample, and basically take a picture of it, and now what you can do with lattice light-sheet microscopy is you can actually look at cells in vivo, while they're alive, and you can, I've personally gotten to watch a T cell move through a collagen matrix, and that's all microscopy, but generating orders of magnitude more data. That is, we're looking at these very, very different data sets, we're looking at very, very different kinds of computing, and what that requires is very, very different kind of infrastructure. And so, the infrastructure has just had to get a lot more intelligent, and the architecture has had to get a lot different, and what we've noticed is is that, that a lot of the patterns that are actually being built in the public cloud as they've taken kind of a fresh look at the computing models, have really become appropriate for this new kind of computing, and we don't see that on the premises, and that's really what we set out to go do. >> Yeah, it's interesting, it's probably the wrong term, but it sounds like we're describing kind of object storage 2.0. 1.0, I remember this healthcare use cases, everybody, when I was doing radiology, when you're doing certain healthcare and sciences, I need metadata, I need to understand that, but now there's just orders of magnitude more data, and technologies are making, it's denser, prices have come down, so the idea has been around for a little bit while, but it sounds like the technology's matured to allow kind of an explosion-- >> Well, and it's just a computing model, it's like one of these things where we're really, because of the emergence of microservices, one of the things that we've seen is applications want a restful interaction with the storage layer, and so, so it turns out that that tends to be very, very perfect for a cloud-like implementation where you can actually implement high volume, unstructured data really, really well via a restful API, where in the old world of POSIX semantics and that kind of transactional model, you just lost scalability. Either you had a lot of proprietary hardware, with that VRAM, you had proprietary interconnects data with things like InfiniBand, and nowadays, being able to loosely couple distributed systems is really the name of the game, and that's ultimately what we aim to build at Igneous, and that's all the technology, in terms of our commercial offering, the customer doesn't care what's behind it, but fundamentally, what you're looking for is the scalability and resilience that the cloud offers by doing that on premises. >> Yeah, so Steve, we had a really interesting crowd chat about a month or so ago talking about hybrid cloud, and the thing I've been saying for the last, probably year, is, as customers try to figure out what goes where in the cloud environment, you know, I've got SaaS, I've got public out of, I've got my private cloud, it's follow the data and follow the applications. In the cloud, things like mobile and even some video streaming, I think we understand how to do that, but why does on-premises make sense for your customers, your workloads, and your solution? >> Yeah, absolutely, and so, first, a little bit on hybrid cloud, there are kind of two different definitions of hybrid cloud, one is kind of the AWS VMWare scheme where what you're really looking to do is run your old stuff that you were running on-prem, in the public cloud, and you call that hybrid. But there's another way to look at it, which is to say, hey, let's take a look at the computing patterns that are being run in the public cloud, how do I bring that down to the premises? And the reason that you might want to do that is, it's really twofold, one is the gravity of the data, so it might just be that the datasets are too big to move back and forth over very thin internet pipes, and so you want to actually keep the data close to its source. The other is something that we've seen, which is really more of a preference, which is that while I think that cloud technologies actually have a lot of capability for security, there are a lot more hoops for folks to run through to ensure that they're compliant with their own internal policies, and where they've already set out a set of policies for how they run the stuff behind the firewall, sometimes it's just simpler for them to actually keep all of the data on the premises, and not actually have to worry about some of the issues in tracking, and compliance issues associated with how you move the data around. >> Yeah. One of the things we've heard from users is when they use public cloud, one of the things they really like is, sometimes the CFO's not fully onboard, but buying things as a service, so, they want to understand predictability, but they want to buy it as a service, understand, how does your solution fit into that kind of paradigm? >> That's great, I think our solution fits into both trends really, really well, because what we're really offering, we talked a little bit about technology, but really fundamentally, we're offering a service, and so when Igneous goes to a customer, our interaction is as a service. Customers interact with our service via APIs, and they get a bill for a subscription, and so it's an as-a-service model, you don't buy hardware, you don't install software, you don't have systems to manage. At the same time, there is a predictability that's a little bit of the downside of the public cloud, because there's a fee, generally, to access your data at a storage, and often, when people don't actually understand their data access and their data movement patterns, the costs of running applications in public cloud become quite unpredictable, and you actually don't run into that unpredictability with a solution like Igneous, because our data is on your local area network, and we don't charge you to access the data that's on your own network. >> So, I've come to an event like this, if I'm thinking about my storage today, the conversation in the marketplace has been, well, the new choices out there is, there's, the HCI, the hyper-convergence infrastructure, and there's flash, the AFA devices out there. And of course, even the lines between those are blurring, because I can have an all-flash configuration of hyper-converged, and some of the all-flasher a things are getting converged and put into more things, how do you help customers as the, what's the bullet point as to, well, this is for this kind of application, this is for this solution, and hey, there's this whole new category that you need to be thinking about. >> Yeah, I think that's perfect, and I think the real trick here is is that there's a difference between your hot tier and your flash tier, and your capacity tier, and fundamentally, the flash tier is really good when Time To First Byte is very important, so that might be for your relational database applications and things of that sort, where there tends to be a lot of searching through an index, and so you've got a lot of low-latency requirements. And then on the other hand, what you have is a capacity tier, they may be your video surveillance, they may be your large, unstructured documents, they may be your censor data, and in those contexts, you don't necessarily need the Time To First Byte, what you really need is capacity throughput, and so the overhead of setting up, for example, a restful connection is not significant when compared to the amount of data that actually needs to go through the system, and that's actually where restful semantics actually gets superior to positive semantics, when you have very, very large, unstructured data sets. Hyper-converge is actually a little bit of a different world, and I think that while hyper-converge has worked out pretty well, I think, for virtualization workloads, we've really found that when it comes to these very, very large unstructured data sets, hyper-converge isn't necessarily always the way to go, you tend to find a utilization issue between your compute and your storage layers, where you have to actually think about how you're balancing all this stuff, and so, really, the world that we've really seen emerge as new applications come forward, is there's really a trend to write microservices that are stateless, and to have them talk to a stateful layer, that's why in the public cloud, there's a pattern of having things like elastic container services talking to an S3, and we definitely see on premises that same type of things that's going to emerge. There's going to be some time to get there, admittedly, as I was mentioning kind of at the beginning, we've seen this really interesting set of interest patterns, one is from the folks who are developing these new applications that are utilizing unstructured data, there's a lot of interest we're getting right now from IT folks that are just getting started with object storage to do secondary workloads, to do backups, to do archives, and it's been interesting that we've been getting a lot of interest in our service as a new way to approach some of these data protection workflows. >> Alright, so Steve, last question I've got for you, came out of Stealth Q4 last year, what do we look for in 2017 from Igneous? >> Yeah, so I think that you'll see it on both of those fronts, I think that one thing that's going to be seen in 2017 is a lot more development on our side around building up a tool chain for folks to use for a data protection tier, and so, we've got a new offering coming online, we're calling it Igneous Insights, which provides information about what's currently on your primary storage tiers, we've got a whole set of replication services, they're coming up to do backup, archive, things like replication to the cloud, but what we're also really moving forward with is a lot of what's needed in the tool chain to really support hybrid and multi-clouds, so how you facilitate the data movement in and out of the cloud, as well how you do the auditing and management of the data, no matter where it lives. >> Alright, Steve Pao, really appreciate you catching up, and if you want to find out more about this category, check out cube365.net/trueprivatecloud, that's C-U-B-E, number 365.net/trueprivatecloud, which has resources from the whole industry, including from Igneous, including from Wikibon and theCUBE, as to what's happening kind of this true private cloud, hybrid cloud environment. We'll be back with lots more coverage here, thanks for watching theCUBE. (electronic music) >> Announcer: Since the dawn of the cloud, theCUBE has been there.