>> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE conversation. >> Hey, welcome back everybody Jeff Frick here with the cube. This is a special week it's Grace Hopper week, and Grace Hopper is the best name in tech conferences. The celebration of women in computing, and we've been going there for years we're not there this year, but one of the themes that comes up over and over at Grace Hopper is women and girls need to see women in positions that they can envision themselves being in someday. That is a really important piece of the whole diversity conversation is can I see people that I can role model after and I just want to bring up something from a couple years back from 2016 when we were there, we were there with Mimi Valdez, Christina Deoja and Dr. Jeanette Epps, Dr. Jeanette Epps is the astronaut on the right. They were there talking about "The Hidden Figures" movie. If you remember it came out 2016, it was about Katherine Johnson and all the black women working at NASA. They got no credit for doing all the math that basically keep all the astronauts safe and they made a terrific movie about it. And Janet is going up on the very first Blue Origin Space Mission Next year. This was announced a couple of months ago, so again, phenomenal leadership, black lady astronaut, going to go into space and really provide a face for a lot of young girls that want to get into that and its clearly a great STEM opportunity. So we're excited to have four terrific women today that well also are the leaders that the younger women can look up to and follow their career. So we're excited to have them so we're just going to go around. We got four terrific guests, our first one is Annabel Chang, She is the Head of State Policy and Government Regulations at Waymo. Annabel great to see you, where are you coming in from today? >> from San Francisco >> Jeff: Awesome. Next up is Inamarie Johnson. She is the Chief People and Diversity Officer for Zendesk Inamarie, great to see you. Where are you calling in from today? >> Great to be here. I am calling in from Palos Verdes the state >> Jeff: awesome >> in Southern California. >> Jeff: Some of the benefits of a virtual sometimes we can, we couldn't do that without the power of the internet. And next up is Jennifer Cabalquinto she is the Chief Financial Officer of the Golden State Warriors. Jennifer, great to see you Where are you coming in from today? >> Well, I wish I was coming in from the Chase Center in San Francisco but I'm actually calling in from Santa Cruz California today. >> Jeff: Right, It's good to see you and you can surf a lot better down there. So that's probably not all bad. And finally to round out our panelists, Kate Hogan, she is the COO of North America for Accenture. Kate, great to see you as well. Where are you coming in from today? >> Well, it's good to see you too. I am coming in from the office actually in San Jose. >> Jeff: From the office in San Jose. All right, So let's get into it . You guys are all very senior, you've been doing this for a long time. We're in a kind of a crazy period of time in terms of diversity with all the kind of social unrest that's happening. So let's talk about some of your first your journeys and I want to start with you Annabel. You're a lawyer you got into lawyering. You did lawyering with Diane Feinstein, kind of some politics, and also the city of San Francisco. And then you made this move over to tech. Talk about that decision and what went into that decision and how did you get into tech? 'cause we know part of the problem with diversity is a pipeline problem. You came over from the law side of the house. >> Yes, and to be honest politics and the law are pretty homogenous. So when I made the move to tech, it was still a lot of the same, but what I knew is that I could be an attorney anywhere from Omaha Nebraska to Miami Florida. But what I couldn't do was work for a disruptive company, potentially a unicorn. And I seized that opportunity and (indistinct) Lyft early on before Ride Hailing and Ride Sharing was even a thing. So it was an exciting opportunity. And I joined right at the exact moment that made myself really meaningful in the organization. And I'm hoping that I'm doing the same thing right now at Waymo. >> Great, Inamarie you've come from one of my favorite stories I like to talk about from the old school Clorox great product management. I always like to joke that Silicon Valley needs a pipeline back to Cincinnati and Proctor and Gamble to get good product managers out here. You were in the classic, right? You were there, you were at Honeywell Plantronics, and then you jumped over to tech. Tell us a little bit about that move. Cause I'm sure selling Clorox is a lot different than selling the terrific service that you guys provide at Zendesk. I'm always happy when I see Zendesk in my customer service return email, I know I'm going to get taken care of. >> Oh wow, that's great. We love customers like you., so thank you for that. My journey is you're right from a fortune 50 sort of more portfolio type company into tech. And I think one of the reasons is because when tech is starting out and that's what Zendesk was a few five years back or so very much an early stage growth company, two things are top of mind, one, how do we become more global? And how do we make sure that we can go up market and attract enterprise grade customers? And so my experience having only been in those types of companies was very interesting for a startup. And what was interesting for me is I got to live in a world where there were great growth targets and numbers, things I had never seen. And the agility, the speed, the head plus heart really resonated with my background. So super glad to be in tech, but you're right. It's a little different than a consumer products. >> Right, and then Jennifer, you're in a completely different world, right? So you worked for the Golden State Warriors, which everybody knows is an NBA team, but I don't know that everyone knows really how progressive the Warriors are beyond just basketball in terms of the new Chase Center, all the different events that you guys put on it. And really the leadership there has decided we really want to be an entertainment company of which the Golden State Warrior basketball team has a very, very important piece, you've come from the entertainment industry. So that's probably how they found you, but you're in the financial role. You've always been in the financial role, not traditionally thought about as a lot of women in terms of a proportion of total people in that. So tell us a little bit about your experience being in finance, in entertainment, and then making this kind of hop over to, I guess Uber entertainment. I don't know even how you would classify the warriors. >> Sports entertainment, live entertainment. Yeah, it's interesting when the Warriors opportunity came up, I naturally said well no, I don't have any sports background. And it's something that we women tend to do, right? We self edit and we want to check every box before we think that we're qualified. And the reality is my background is in entertainment and the Warriors were looking to build their own venue, which has been a very large construction project. I was the CFO at Universal Studios Hollywood. And what do we do there? We build large attractions, which are just large construction projects and we're in the entertainment business. And so that sort of B to C was a natural sort of transition for me going from where I was with Universal Studios over to the Warriors. I think a finance career is such a great career for women. And I think we're finding more and more women entering it. It is one that you sort of understand your hills and valleys, you know when you're going to be busy and so you can kind of schedule around that. I think it's really... it provides that you have a seat at the table. And so I think it's a career choice that I think is becoming more and more available to women certainly more now than it was when I first started. >> Yeah, It's interesting cause I think a lot of people think of women naturally in human resources roles. My wife was a head of human resources back in the day, or a lot of marketing, but not necessarily on the finance side. And then Kate go over to you. You're one of the rare birds you've been at Accenture  for over 20 years. So you must like airplanes and travel to stay there that long. But doing a little homework for this, I saw a really interesting piece of you talking about your boss challenging you to ask for more work, to ask for a new opportunity. And I thought that was really insightful that you, you picked up on that like Oh, I guess it's incumbent on me to ask for more, not necessarily wait for that to be given to me, it sounds like a really seminal moment in your career. >> It was important but before I tell you that story, because it was an important moment of my career and probably something that a lot of the women here on the panel here can relate to as well. You mentioned airplanes and it made me think of my dad. My father was in the air force and I remember him telling stories when I was little about his career change from the air force into a career in telecommunications. So technology for me growing up Jeff was, it was kind of part of the dinner table. I mean it was just a conversation that was constantly ongoing in our house. And I also, as a young girl, I loved playing video games. We had a Tandy computer down in the basement and I remember spending too many hours playing video games down there. And so for me my history and my really at a young age, my experience and curiosity around tech was there. And so maybe that's, what's fueling my inspiration to stay at Accenture for as long as I have. And you're right It's been two decades, which feels tremendous, but I've had the chance to work across a bunch of different industries, but you're right. I mean, during that time and I relate with what Jennifer said in terms of self editing, right? Women do this and I'm no exception, I did this. And I do remember I'm a mentor and a sponsor of mine who called me up when I'm kind of I was at a pivotal moment in my career and he said you know Kate, I've been waiting for you to call me and tell me you want this job. And I never even thought about it. I mean I just never thought that I'd be a candidate for the job and let alone somebody waiting for me to kind of make the phone call. I haven't made that mistake again, (laughing) but I like to believe I learned from it, but it was an important lesson. >> It's such a great lesson and women are often accused of being a little bit too passive and not necessarily looking out for in salary negotiations or looking for that promotion or kind of stepping up to take the crappy job because that's another thing we hear over and over from successful people is that some point in their career, they took that job that nobody else wanted. They took that challenge that really enabled them to take a different path and really a different Ascension. And I'm just curious if there's any stories on that or in terms of a leader or a mentor, whether it was in the career, somebody that you either knew or didn't know that was someone that you got kind of strength from kind of climbing through your own, kind of career progression. Will go to you first Annabel. >> I actually would love to talk about the salary negotiations piece because I have a group of friends about that we've been to meeting together once a month for the last six years now. And one of the things that we committed to being very transparent with each other about was salary negotiations and signing bonuses and all of the hard topics that you kind of don't want to talk about as a manager and the women that I'm in this group with span all types of different industries. And I've learned so much from them, from my different job transitions about understanding the signing bonus, understanding equity, which is totally foreign to me coming from law and politics. And that was one of the most impactful tools that I've ever had was a group of people that I could be open with talking about salary negotiations and talking about how to really manage equity. Those are totally foreign to me up until this group of women really connected me to these topics and gave me some of that expertise. So that is something I strongly encourage is that if you haven't openly talked about salary negotiations before you should begin to do so. >> It begs the question, how was the sensitivity between the person that was making a lot of money and the person that wasn't? And how did you kind of work through that as a group for the greater good of everyone? >> Yeah, I think what's really eye opening is that for example, We had friends who were friends who were on tech, we had friends who were actually the entrepreneurs starting their own businesses or law firm, associates, law firm partners, people in PR, so we understood that there was going to be differences within industry and frankly in scale, but it was understanding even the tools, whether I think the most interesting one would be signing bonus, right? Because up until a few years ago, recruiters could ask you what you made and how do you avoid that question? How do you anchor yourself to a lower salary range or avoid that happening? I didn't know this, I didn't know how to do that. And a couple of women that had been in more senior negotiations shared ways to make sure that I was pinning myself to a higher salary range that I wanted to be in. >> That's great. That's a great story and really important to like say pin. it's a lot of logistical details, right? You just need to learn the techniques like any other skill. Inamarie, I wonder if you've got a story to share here. >> Sure. I just want to say, I love the example that you just gave because it's something I'm super passionate about, which is transparency and trust. Then I think that we're building that every day into all of our people processes. So sure, talk about sign on bonuses, talk about pay parody because that is the landscape. But a quick story for me, I would say is all about stepping into uncertainty. And when I coach younger professionals of course women, I often talk about, don't be afraid to step into the role where all of the answers are not vetted down because at the end of the day, you can influence what those answers are. I still remember when Honeywell asked me to leave the comfort of California and to come to the East coast to New Jersey and bring my family. And I was doing well in my career. I didn't feel like I needed to do that, but I was willing after some coaching to step into that uncertainty. And it was one of the best pivotal moment in my career. I didn't always know who I was going to work with. I didn't know the challenges and scope I would take on, but those were some of the biggest learning experiences and opportunities and it made me a better executive. So that's always my coaching, like go where the answers aren't quite vetted down because you can influence that as a leader. >> That's great, I mean, Beth Comstock former vice chair at GE, one of her keynotes I saw had a great line, get comfortable with being uncomfortable. And I think that its a really good kind of message, especially in the time we're living in with accelerated change. But I'm curious, Inamarie was the person that got you to take that commitment. Would you consider that a sponsor, a mentor, was it a boss? Was it maybe somebody not at work, your spouse or a friend that said go for it. What kind of pushed you over the edge to take that? >> It's a great question. It was actually the boss I was going to work for. He was the CHRO, and he said something that was so important to me that I've often said it to others. And he said trust me, he's like I know you don't have all the answers, I know we don't have this role all figured out, I know you're going to move your family, but if you trust me, there is a ton of learning on the other side of this. And sometimes that's the best thing a boss can do is say we will go on this journey together. I will help you figure it out. So it was a boss, but I think it was that trust and that willingness for him to stand and go alongside of me that made me pick up my family and be willing to move across the country. And we stayed five years and really, I am not the same executive because of that experience. >> Right, that's a great story, Jennifer, I want to go to you, you work for two owners that are so progressive and I remember when Joe Lacob came on the floor a few years back and was booed aggressively coming into a franchise that hadn't seen success in a very long time, making really aggressive moves in terms of personnel, both at the coaches and the players level, the GM level. But he had a vision and he stuck to it. And the net net was tremendous success. I wonder if you can share any of the stories, for you coming into that organization and being able to feel kind of that level of potential success and really kind of the vision and also really a focus on execution to make the vision real cause vision without execution doesn't really mean much. If you could share some stories of working for somebody like Joe Lacob, who's so visionary but also executes so very, very effectively. >> Yeah, Joe is, well I have the honor of working for Joe, for Rick Welts to who's our president. Who's living legend with the NBA with Peter Guber. Our leadership at the Warriors are truly visionary and they set audacious targets. And I would say from a story the most recent is, right now what we're living through today. And I will say Joe will not accept that we are not having games with fans. I agree he is so committed to trying to solve for this and he has really put the organization sort of on his back cause we're all like well, what do we do? And he has just refused to settle and is looking down every path as to how do we ensure the safety of our fans, the safety of our players, but how do we get back to live entertainment? And this is like a daily mantra and now the entire organization is so focused on this and it is because of his vision. And I think you need leaders like that who can set audacious goals, who can think beyond what's happening today and really energize the entire organization. And that's really what he's done. And when I talked to my peers and other teams in there they're talking about trying to close out their season or do these things. And they're like well, we're talking about, how do we open the building? And we're going to have fans, we're going to do this. And they look at me and they're like, what are you talking about? And I said, well we are so fortunate. We have leadership that just is not going to settle. Like they are just always looking to get out of whatever it is that's happening and fix it. So Joe is so committed His background, he's an epidemiologist major I think. Can you imagine how unique a background that is and how timely. And so his knowledge of just around the pandemic and how the virus is spread. And I mean it's phenomenal to watch him work and leverage sort of his business acumen, his science acumen and really think through how do we solve this. Its amazing. >> The other thing thing that you had said before is that you basically intentionally told people that they need to rethink their jobs, right? You didn't necessarily want to give them permission to get you told them we need to rethink their jobs. And it's a really interesting approach when the main business is just not happening, right? There's just no people coming through the door and paying for tickets and buying beers and hotdogs. It's a really interesting talk. And I'm curious, kind of what was the reception from the people like hey, you're the boss, you just figure it out or were they like hey, this is terrific that he pressed me to come up with some good ideas. >> Yeah, I think when all of this happened, we were resolved to make sure that our workforce is safe and that they had the tools that they needed to get through their day. But then we really challenged them with re imagining what the next normal is. Because when we come out of this, we want to be ahead of everybody else. And that comes again from the vision that Joe set, that we're going to use this time to make ourselves better internally because we have the time. I mean, we had been racing towards opening Chase Center and not having time to pause. Now let's use this time to really rethink how we're doing business. What can we do better? And I think it's really reinvigorated teams to really think and innovate in their own areas because you can innovate anything, right?. We're innovating how you pay payables, we're all innovating, we're rethinking the fan experience and queuing and lines and all of these things because now we have the time that it's really something that top down we want to come out of this stronger. >> Right, that's great. Kate I'll go to you, Julie Sweet, I'm a big fan of Julie Sweet. we went to the same school so go go Claremont. But she's been super aggressive lately on a lot of these things, there was a get to... I think it's called Getting to 50 50 by 25 initiative, a formal initiative with very specific goals and objectives. And then there was a recent thing in terms of doing some stuff in New York with retraining. And then as you said, military being close to your heart, a real specific military recruiting process, that's formal and in place. And when you see that type of leadership and formal programs put in place not just words, really encouraging, really inspirational, and that's how you actually get stuff done as you get even the consulting businesses, if you can't measure it, you can't improve it. >> Yeah Jeff, you're exactly right. And as Jennifer was talking, Julie is exactly who I was thinking about in my mind as well, because I think it takes strong leadership and courage to set bold bold goals, right? And you talked about a few of those bold goals and Julie has certainly been at the forefront of that. One of the goals we set in 2018 actually was as you said to achieve essentially a gender balance workforce. So 50% men, 50% women by 2025, I mean, that's ambitious for any company, but for us at the time we were 400,000 people. They were 500, 6,000 globally. So when you set a goal like that, it's a bold goal and it's a bold vision. And we have over 40% today, We're well on our path to get to 50%, I think by 2025. And I was really proud to share that goal in front of a group of 200 clients the day that it came out, it's a proud moment. And I think it takes leaders like Julie and many others by the way that are also setting bold goals, not just in my company to turn the dial here on gender equality in the workforce, but it's not just about gender equality. You mentioned something I think it's probably at as, or more important right now. And that's the fact that at least our leadership has taken a Stand, a pretty bold stand against social injustice and racism, >> Right which is... >> And so through that we've made some very transparent goals in North America in terms of the recruitment and retention of our black African American, Hispanic American, Latinex communities. We've set a goal to increase those populations in our workforce by 60% by 2025. And we're requiring mandatory training for all of our people to be able to identify and speak up against racism. Again, it takes courage and it takes a voice. And I think it takes setting bold goals to make a change and these are changes we're committed to. >> Right, that's terrific. I mean, we started the conversation with Grace Hopper, they put out an index for companies that don't have their own kind of internal measure to do surveys again so you can get kind of longitudinal studies over time and see how you're improving Inamarie, I want to go to you on the social justice thing. I mean, you've talked a lot about values and culture. It's a huge part of what you say. And I think that the quote that you use, if I can steal it is " no culture eats strategy for breakfast" and with the social injustice. I mean, you came out with special values just about what Zendesk is doing on social injustice. And I thought I was actually looking up just your regular core mission and value statement. And this is what came up on my Google search. So I wanted to A, you published this in a blog in June, taking a really proactive stand. And I think you mentioned something before that, but then you're kind of stuck in this role as a mind reader. I wonder if you can share a little bit of your thoughts of taking a proactive stand and what Zendesk is doing both you personally, as well as a company in supporting this. And then what did you say as a binder Cause I think these are difficult kind of uncharted waters on one hand, on the other hand, a lot of people say, hello, this has been going on forever. You guys are just now seeing cellphone footage of madness. >> Yeah Wow, there's a lot in there. Let me go to the mind reader comments, cause people are probably like, what is that about? My point was last December, November timing. I've been the Chief People Officer for about two years And I decided that it really was time with support from my CEO that Zendesk have a Chief Diversity Officer sitting in at the top of the company, really putting a face to a lot of the efforts we were doing. And so the mind reader part comes in little did I know how important that stance would become, in the may June Timing? So I joked that, it almost felt like I could have been a mind reader, but as to what have we done, a couple of things I would call out that I think are really aligned with who we are as a company because our culture is highly threaded with the concept of empathy it's been there from our beginning. We have always tried to be a company that walks in the shoes of our customers. So in may with the death of George Floyd and the world kind of snapping and all of the racial injustice, what we said is we wanted to not stay silent. And so most of my postings and points of view were that as a company, we would take a stand both internally and externally and we would also partner with other companies and organizations that are doing the big work. And I think that is the humble part of it, we can't do it all at Zendesk, we can't write all the wrongs, but we can be in partnership and service with other organizations. So we used funding and we supported those organizations and partnerships. The other thing that I would say we did that was super important along that empathy is that we posted space for our employees to come together and talk about the hurt and the pain and the experiences that were going on during those times and we called those empathy circles. And what I loved is initially, it was through our mosaic community, which is what we call our Brown and black and persons of color employee resource group. But it grew into something bigger. We ended up doing five of these empathy circles around the globe and as leadership, what we were there to do is to listen and stand as an ally and support. And the stories were life changing. And the stories really talked about a number of injustice and racism aspects that are happening around the world. And so we are committed to that journey, we will continue to support our employees, we will continue to partner and we're doing a number of the things that have been mentioned. But those empathy circles, I think were definitely a turning point for us as an organization. >> That's great, and people need it right? They need a place to talk and they also need a place to listen if it's not their experience and to be empathetic, if you just have no data or no knowledge of something, you need to be educated So that is phenomenal. I want to go to you Jennifer. Cause obviously the NBA has been very, very progressive on this topic both as a league, and then of course the Warriors. We were joking before. I mean, I don't think Steph Curry has ever had a verbal misstep in the history of his time in the NBA, the guy so eloquent and so well-spoken, but I wonder if you can share kind of inside the inner circle in terms of the conversations, that the NBA enabled right. For everything from the jerseys and going out on marches and then also from the team level, how did that kind of come down and what's of the perception inside the building? >> Sure, obviously I'm so proud to be part of a league that is as progressive and has given voice and loud, all the teams, all the athletes to express how they feel, The Warriors have always been committed to creating a diverse and equitable workplace and being part of a diverse and equitable community. I mean that's something that we've always said, but I think the situation really allowed us, over the summer to come up with a real formal response, aligning ourselves with the Black Lives Matter movement in a really meaningful way, but also in a way that allows us to iterate because as you say, it's evolving and we're learning. So we created or discussed four pillars that we wanted to work around. And that was really around wallet, heart, beat, and then tongue or voice. And Wallet is really around putting our money where our mouth is, right? And supporting organizations and groups that aligned with the values that we were trying to move forward. Heart is around engaging our employees and our fan base really, right? And so during this time we actually launched our employee resource groups for the first time and really excited and energized about what that's doing for our workforce. This is about promoting real action, civic engagement, advocacy work in the community and what we've always been really focused in a community, but this really hones it around areas that we can all rally around, right? So registration and we're really focused on supporting the election day results in terms of like having our facilities open to all the electorate. So we're going to have our San Francisco arena be a ballot drop off, our Oakland facilities is a polling site, Santa Cruz site is also a polling location, So really promoting sort of that civic engagement and causing people to really take action. heart is all around being inclusive and developing that culture that we think is really reflective of the community. And voice is really amplifying and celebrating one, the ideas, the (indistinct) want to put forth in the community, but really understanding everybody's culture and really just providing and using the platform really to provide a basis in which as our players, like Steph Curry and the rest want to share their own experiences. we have a platform that can't be matched by any pedigree, right? I mean, it's the Warriors. So I think really getting focused and rallying around these pillars, and then we can iterate and continue to grow as we define the things that we want to get involved in. >> That's terrific. So I have like pages and pages and pages of notes and could probably do this for hours and hours, but unfortunately we don't have that much time we have to wrap. So what I want to do is give you each of you the last word again as we know from this problem, right? It's not necessarily a pipeline problem, it's really a retention problem. We hear that all the time from Girls in Code and Girls in Tech. So what I'd like you to do just to wrap is just a couple of two or three sentences to a 25 year old, a young woman sitting across from you having coffee socially distanced about what you would tell her early in the career, not in college but kind of early on, what would the be the two or three sentences that you would share with that person across the table and Annabel, we'll start with you. >> Yeah, I will have to make a pitch for transportation. So in transportation only 15% of the workforce is made up of women. And so my advice would be that there are these fields, there are these opportunities where you can make a massive impact on the future of how people move or how they consume things or how they interact with the world around them. And my hope is that being at Waymo, with our self driving car technology, that we are going to change the world. And I am one of the initial people in this group to help make that happen. And one thing that I would add is women spend almost an hour a day, shuttling their kids around, and we will give you back that time one day with our self driving cars so that I'm a mom. And I know that that is going to be incredibly powerful on our daily lives. >> Jeff: That's great. Kate, I think I might know what you're already going to say, but well maybe you have something else you wanted to say too. >> I don't know, It'll be interesting. Like if I was sitting across the table from a 25 year old right now I would say a couple of things first I'd say look intentionally for a company that has an inclusive culture. Intentionally seek out the company that has an inclusive culture, because we know that companies that have inclusive cultures retain women in tech longer. And the companies that can build inclusive cultures will retain women in tech, double, double the amount that they are today in the next 10 years. That means we could put another 1.4 million women in tech and keep them in tech by 2030. So I'd really encourage them to look for that. I'd encouraged them to look for companies that have support network and reinforcements for their success, and to obviously find a Waymo car so that they can not have to worry where kids are on for an hour when you're parenting in a few years. >> Jeff: I love the intentional, it's such a great word. Inamarie, >> I'd like to imagine that I'm sitting across from a 25 year old woman of color. And what I would say is be authentically you and know that you belong in the organization that you are seeking and you were there because you have a unique perspective and a voice that needs to be heard. And don't try to be anything that you're not, be who you are and bring that voice and that perspective, because the company will be a better company, the management team will be a better management team, the workforce will be a better workforce when you belong, thrive and share that voice. >> I love that, I love that. That's why you're the Chief People Officer and not Human Resources Officer, cause people are not resources like steel and cars and this and that. All right, Jennifer, will go to you for the wrap. >> Oh my gosh, I can't follow that. But yes, I would say advocate for yourself and know your value. I think really understanding what you're worth and being willing to fight for that is critical. And I think it's something that women need to do more. >> Awesome, well again, I wish we could go all day, but I will let you get back to your very, very busy day jobs. Thank you for participating and sharing your insight. I think it's super helpful. And there and as we said at the beginning, there's no better example for young girls and young women than to see people like you in leadership roles and to hear your voices. So thank you for sharing. >> Thank you. >> All right. >> Thank you. >> Okay thank you. >> Thank you >> All right, so that was our diversity panel. I hope you enjoyed it, I sure did. I'm looking forward to chapter two. We'll get it scheduled as soon as we can. Thanks for watching. We'll see you next time. (upbeat music)

(upbeat instrumental jazz music) >> Announcer: From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE conversation. >> Hello and welcome to theCUBE Studios in Palo Alto, California for another CUBE Conversation where we go in-depth with thought leaders driving innovation across the tech industry. I'm your host, Peter Burris. Everybody's talking about digital business and the transformation to digital business, and the promise that it heralds for better customer experience, new levels of business productivity, and, quite frankly, the types of changes that are going to save humanity in certain respects. But in those conversations, we too often focus on the technology, the applications, what we're going to do with A.I., what we're going to do with machine learning, and not enough about the people. In fact, often it's presumed that we're going to dislocate or displace a whole lot of people, but the simple reality is every system features work by people using systems to improve their productivity. And it's time that we focused more attention on how we're going to improve the productivity of people as they use technology to undertake more complex work that is uniquely required, or that uniquely requires human capacities. Now, big topic, but we've got a great conversation. We're joined by Dejan Deklich, who's a Chief Product Officer of 8x8 to talk about this. Dejan, welcome to the cube. >> Thank you, Peter, pleasure. I'm delighted to see your amazing studio over here. >> Well thank you, Dejan. So let's start, 8x8, tell us a little bit about 8x8. >> So 8x8 has been in business for a really long time. We have been public for 30 years now, give or take. We are in the communication and collaboration business. We are now close to 2000 people worldwide. And I have to tell you, I have offices all over the world, and I have remote employees all over the world, and I can tell you a lot about learning how to deal with people, at scale, in remote locations with different languages and different laws surrounding them. >> Well let's jump into it. So I mentioned up front this notion that we have to move more attention to the people side of the equation. Certainly in the U.S., despite all these significant improvements in technology, we still face an employment issue. We are at full employment. So clearly, we're using people to do things, but the question, especially to use your scenario, 2000 people company, offices all around the world, serious people doing serious work. What can we do, ultimately, to improve the productivity of how those people work together from a variety of different perspectives? >> Right, so that's a problem that I struggle with on the daily basis. Imagine, 20 years ago, you had everybody sitting in the cube next to each other, talking to each other, going for lunch together. Then maybe 10-15 years ago, we moved to remote teams, everybody spun up a team in Asia, spun up a team in Europe. Now you are in the world where you have remote individuals working literally all over the world. How do you get them all together is really difficult. One thing that we tried to do at 8x8 was literally fly in all the newbies that we hire every month into San Jose, and introduce them to the company. Works great, but the cost is enormous. So we have now shifted to a much more video-based, enabling of individuals and teams. So what we did at 8x8, is we started this new product line, 8x8 Video Meetings, with exactly this goal in mind. I wanted to have a way to reach out any individual, wherever in the world they might be, with minimum amount of drama, with minimum amount of impact on their day-to-day work. I just want the teams to collaborate and communicate. And I think we have seen in the past that there were plenty of research studies, Frost and Sullivan is one of them, Hanover Group is another one, which say that the impact of video communications on the teams, on the decision making, on simplification of day-to-day business, is huge. And, to me, that is the key for the next ten to twenty years in our industry. >> So, Dejan, I've also had some experience with large teams on a global basis. And each time that some new technology came out, folks flocked to that new technology, whether it was email, and then text, or then, you know, collaboration, some sort of video collaboration. But it always seemed to me, as though, those silos, those became silos. They became independent channels for how you work with people, and the choice that you made about how to set the meeting up really constrained what you could do in the meeting. It seems as though it's time to think about how all these different communication mechanisms can come together in a common platform so that you can choose what you need to use at the time that you're trying to effect the communication. Have I got that right? >> I think you stole my words, they are perfect. Look, think of it also, it's more complex than that. You also have people from lots of different generations. You have millennials, who first thing they will do is whip their phone and start texting somebody. You have, you and me, who are not necessarily millennials anymore, who will likely start the conversation with a phone call. And you go, my job as a Chief Products Officer of 8x8 is to figure out, how do I get all these people working together? And we have seen that there is an enormous value in having a unified platform which allows everybody to choose whichever mode of communication they want to engage in. So, if you know, you are a bit older millennial like myself, I will start with a phone call, and then I will hop into a video meeting. For the younger kids in the company, they will probably start it with chat, text, and then go possibly to the phone if the conversation becomes too long, and then eventually into a meeting. To me, the key for every enterprise and mid-market customer out there is how do you put all of this information together so you really know what your employees are doing, and what your customers are doing. >> Yeah, I'll give you a great example of that, some of the customers that I'm working with these days, is this whole notion of evidence-based management, which is in many respects, the manifestation of A.I., M.L., and some of this other stuff, analytics into how business managers actually operate. It's very difficult to communicate findings from some of these models using text, or even using voice. You need images, you need pictures, but you don't want to just send a static file. You would rather be explaining something to, you know, the finding of the model, the outcome of the model, to your executive. Observe whether or not they look confused. How do you envision some of these new application styles that we're building for some of these new digital approaches, pulling video into the conversation that much more? >> Exactly, look, I think what you said is the key. We are humans, and we evolved through interactions with each other. If I look at you and I see you are smiling, probably my presentation is going to go slightly differently than if I see you go really upset at whatever I'm showing. Putting a solution together that allows you to share the screen, talk in really high definition audio and video, as well as see the face of the person you are talking with is the key to me. And then, as you think about it going forward, starting to actually record the conversation, start to extract the knowledge out of this conversation. A lot of times in the meeting, somebody will say something really, really smart. Mostly, by the end of the meeting, it's 45 minutes later, you forgot about it. If you have a recording, if you have a transcript, you can actually do something with that information. So to me, it's all about, remove the barriers, extract as much information from this conversation as possible, and then, if possible, provide the enterprise with the API where they can get all the information in some form of digital download. So, I personally, I'm a huge believer of M.L. and A.I. that you opened up with, and I believe that getting a lot of this information together will really change how we think about operations, and how we think about running remote teams and local teams. >> I think that one of the important things, and I think you mentioned, I can kind of pull this together, is that video by itself is often difficult to search. But when you combine video and text through transcriptions, translations, et cetera, now you've got something that's searchable, but you still are able to retain the power of the video. Is 8x8 looking at this as part of a unified platform? And if you are, it suggests that these are not things that you regard as wholly distinct, but as part of the fundamental challenge of, how do you improve communication inside a business. >> Exactly, so when I started two and a half years ago, the first thing I did is I started the journey on the fully unified analytics platform. I want to have all the text messages, all the phone calls, all the transcriptions of all the meetings, all the contact center information, I want everything in one place, so I can then start deploying my M.L. and A.I. models across the data. I tend to believe we are uniquely positioned to do that, because not only do we have the actual product lines, but we also have the captive audience, in form of a customer on the phone, or in the meeting, or calling a support team in the contact center. Putting all of that together, and getting the insights that drive human behavior, to me, is the absolute key for the industry. If I can know what problems you are facing, and if I have the context of your problem, I can probably solve your issue much, much faster than if it is the first conversation of a type, please give me your mother's maiden name, and last four digits of your Social Security Number. >> Well, and I want to build on that 'cause, here at theCUBE, we obviously use video pretty extensively. And how we turn the conversations we're having into concepts, or knowledge, or artifacts that users can use to make decisions. We've found, and this is what I want to test with you, that something really interesting happens, there's a lot of research to support this. You, as you mentioned, we are humans with bicameral vision. Most of the information we get, we come in through our eyes. It just is that way. We're tuned for that. And so, when you're looking at a face or you're having a conversation with someone, and that face is available to you, as part of the interaction, you just listen better, you retain better, you focus harder, you pay more attention. And it seems as though video is an absolute essential feature, or it must be an essential feature, of how we improve communication, especially if we're going to ask people to take on more challenging tasks, perform more challenging work that feature higher risks. What do you think about that? >> I agree, and I think there is one more little point before all of that. The usage of the product has to be super simple, and it has to be incredibly intuitive. You do not, my regular example is I'm always two minutes late to a start of the meeting, and then if I get asked, "Oh, now please download some plugin so you can start the meeting, blah, blah, blah," the time has gone by, now I'm fifteen minutes late to a a meeting. Then people yell at me, generally, because I'm late. Well, with 8x8 meetings there is no need to download any plugins, and you remove this barrier to entry into the conversation. To me, that is almost the key to the whole thing. Just like the phone is, by now, intuitive for everybody, just like texting, video has to become exactly the same, where, we need to communicate, well let's just hop in it, let's talk it through, let's see how we each react to it, and then we go move forward. >> You know, I think it's a great point. If the technology generates stress in the conversation, you've diminished the productivity of the conversation. One of the biggest challenges that CIOs face today is the business is applying, is going after all these new opportunities with technology in mind, but if you don't get the enterprise to adopt the technology, it fails. And so you really have a challenge of abandonment. It's not just that that individual phone call loses productivity, but the entire approach to how you conduct business gets abandoned, and you don't want that. So by doing it more simply, you get better results. So what kind of experience have your customers been enjoying as they use 8x8, advance some of these new technologies, and what do you anticipate for their use of video? >> So, the way I see it, there are almost two categories of customers that videollate at 8x8. There is the relatively simple customer, the small mid-market customer, and then when you enter the enterprise, all hell breaks loose. The complexity starts exploding. We have customers that have deployed us at 4000 locations worldwide. Imagine operating a system at that scale. And you go, you are not only talking different locations, you are talking different legal jurisdictions, you are talking different geographies, different continents. Putting all of that together, and simplifying this communication is the key for the customers. And I have seen again and again, CIOs try to force their workforce onto a platform of choice, right? And, one of my friends who is a CIO here in the valley, says the easiest way for a CIO to get fired is to force sales and engineering on the same text messaging or video meeting solutions. One group will get you fired. So you go, if you go with 8x8, suddenly you can have everybody on the same platform. The firing concept goes away, which is always good, and you enable massive gains in scale and in performance. You reduce the barriers to entry for all these people. >> But, let me explore why that is because I think it's an interesting concept. And I think what you're saying is that sales people typically use different workflows, require different classes of information, that can be rendered in different mechanisms, text, or whatever else it might be. Engineering is showing different workflows, different classes of people, different kinds of information. So trying to make engineering give up some of what they need, or sales give up some of what they need to try to make both happy, that's the prescription for failure, and you're saying that by being able to support all of those workflows, roles, and information forms, you get a more complete system? >> Exactly, you get a more complete system and, for you as a CIO who is deploying 8x8 or a similar tool, you suddenly get to see how your employees are actually interacting with each other, as well as how they're interacting with the end customer. To me, it is fascinating how much the computer science is changing the way people communicate with each other. I know who you are, I have a lot of information from the web around you, maybe I can tailor this communication specifically for you. To me, that is the path forward to the future. Using all of this data about you as a person in the context of the enterprise is the key. >> So the right tool for the right conversation and the right roles, but, still with the opportunity to do derivative analysis as you bring all that information together later. >> Exactly, the analysis is the key. So we have seen all sorts of really interesting things happen at 8x8 as we are putting more and more of our internal employees on these tools. You start seeing inefficiencies in support. You start seeing inefficiencies on the sales side. And you go, "Well, before I had no idea, I did not know that my sales people are not calling, following people in sales force." Well, now I can see it. I can actually do something about it, and I don't need analysts who will write me reports and build Tableau data sheets and whatnot. I can see day-to-day what is going on with my labor force and employees. >> Excellent! Dejan, thanks very much for being on theCUBE. >> Thank you so much! This was a lot of fun. >> So, Dejan Deklich from 8x8, Chief Product Officer, thanks again for joining us, for another CUBE conversation. I'm Peter Burris, see you next time. (upbeat instrumental jazz music)

>> From Santa Clara, in the heart of Silicon Valley it's theCUBE, covering the Churchills, 2019. Brought to you by SiliconANGLE Media. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in Santa Clara, California at the Churchills. It's the ninth annual awards banquet put on by the Churchills Club, and this year is all about leadership. We're excited to be joined by our next guest who knows a little bit about leadership. He's Carl Guardino, the president and CEO of the Silicon Valley Leadership Group. Carl, great to see you. >> Great to see you, too, Jeff. >> So what is the Silicon Valley Leadership Group all about? >> The Silicon Valley Leadership Group is an association of about 360, primarily innovation economy employers that want to make a positive, proactive difference here in the region, as well as in our state and across the United States. >> What are some of the hot topics that are on top of the plate right now? Because there is a lot of craziness kind of going on here in Silicon Valley. >> There is. But what we try to do is impact those issues that are as important to families in their living rooms as they are to CEOs in their board rooms. And here in the bay area, we call those THEE issues. An acronym, T-H-E-E. The T, traffic; H, housing affordability; E, education; and the fourth E, the economy. And we try to bring together diverse points of view for those areas that unite us, where we can actually solve some of those challenges. >> Right, and those are big, big challenges. And you work both with public as well as private groups to try to bring them together to make movement on those things. >> We're a bridge. And the first thing about a bridge is that you try to bring folks together to cross the bridge and work together. The second most important thing about a bridge is that you build them, you don't burn them down. And that's the role that we try to play with 360 highly engaged CEOs and c-suite officers. >> And it's only appropriate, because tonight you'll be sitting down in a conversation with the mayor of San Jose, Sam Liccardo, to kind of get into some of these issues. San Jose seems to be on a roll right now, a positive roll. A lot of positive news coming out of San Jose. >> Yes, and that always starts with leadership rather than luck. San Jose mayor Sam Liccardo, 10th largest city in the United States, has been able to strike that balance of being pro-innovation economy, while also caring deeply about his citizens, the residents of San Jose, 1,053,000, and how we make sure that we have a strong and vibrant economy, but also a great quality of life. >> Right. So how do you even begin to - we'll start with traffic. The T in the THEE. To address that issue, it's so multifaceted, right, it's so tied to jobs, it's tied to housing, it's tied to the growth of the economy, you know, unfortunately freeways are slow to build, public transportation's expensive, but we continue to see growth there. How do you kind of eat that elephant, one bite at a time, with something like traffic? >> Well the role of the leadership group is, again, by bringing people together to solve complex problems in a democracy with winning solutions. So we'd rather win than whine. And when it comes to traffic, one of our core competencies is actually to lead and run ballot initiatives to fund transportation improvements throughout the region and the state. In fact, in the last 30 years alone, I've had the pleasure of going on loan from the leadership group to run ballot campaigns for transportation improvements that have totaled 30 billion dollars in revenue through those measures, approved by voters to reach into our own wallets, rather than our neighbors, to build improvements that, this Christmas, in time to go into your stocking, we'll be bringing BART to San Jose, and working on the electrification of Caltrain, linking transit and better road improvements, making it better for all of us trying to travel throughout this region. >> Right. Good, we need it. >> We do. >> And on the housing, you know, because the housing is also very closely tied to traffic, and we see that the old days of single-family homes on big pieces of dirt, those are going away. They just can't support it in higher density areas like San Fransisco, San Jose, to bring those jobs next to that. So we're seeing a huge transformation in the housing space as well. >> And we need a huge transformation, both in transportation and in housing. And it's really the flip side to the same coin. T, for tails, or transportation; H, for head, or housing. And you have to make sure that you keep those linked. In fact, one of our initiatives right now is to work with all six, fixed rail transit operators throughout the nine bay area counties. What are the current and future uses of those half miles around every fixed rail transit stop that you have? How do we maximize those uses? Here's a great example. What Google wants to do in downtown San Jose, at the Diridon SAP station, is only because of the Silicon Valley Leadership Group's work to bring BART to that station, electrify Caltrain, light rail is there, Amtrak, ACE, et cetera, and they want to have 20 to 25 thousand future Google employees there within the next 10 to 12 years. Why? Because it is a sustainable location that doesn't rely on you and I slogging through traffic in our single-occupant cars. >> Right. I can't wait to see what you guys do to El Camino. That's the next one that's going to - as somebody once said in one of these traffic things, it's just a bunch of old retail stores with empty parking lots, just placed by Microsoft. Or excuse me, by Amazon. So I think we'll see a big transformation with housing and jobs, you know, along that quarter, which happens to parallel the Caltrain, and is near and dear to my heart. So a lot of good opportunities I think to make improvements. >> Jeff, there is. And as hard as transportation and traffic solutions are to put into place, housing is even tougher. And while Bay Area residents think housing is the bigger crisis, the solutions are tougher to come about, because the community isn't as united on those solutions. So the role that a group of employers like ours play, is how do we bring people together around solutions that make sure that we build homes, that are good for everyone in our society. >> Well Carl, I like your positive attitude, a lot of winning and no whining, so I wish you nothing but success. And we'll be watching. >> Thank you, Jeff. >> You're welcome. He's Carl, I'm Jeff, you're watching theCUBE. We're at the Churchills in Santa Clara, California. Thanks for watching. We'll see you next time.

>> from Santa Clara, California It's the Cube covering techno ovation World Pitch Summit 2019 Brought to you by Silicon Angle Media Now here's Sonia to Gari >> Hi and welcome to the Cube. I'm your host, Sonia to Gari. And we're here at Oracle's Agnew's campus in Santa Clara, California covering techno vacations World Pitch Summit 2019. Ah, pitch competition in which girls from around the world developed mobile lapse in order to create positive change in the world with us. Today we have Team Coco from Kazakhstan. Welcome. The members are, um Dilma as camel Over and Mallika Bree by Ava Uh, Donna Ulanova and Lube of do Chen Kuo Welcome. And congratulations on being finalists. Thank you. So your app is called tech Go. Can you tell us more about it? >> Yes. Uh so so techo in three d mobile application, which has a minute reality and as connected to the hardware which has dedicated for the behavioral change of people for so that they can become more conscious and like a friendly. >> And can you tell us more about how it works? Yes, >> of course there is. Luba, who can explain this? Okay. S >> o r application is about an astronaut who needs to save the planet. S O Firstly is there is a game in which a person needs to save your hair. Virtual airs by selling some ecological problems in it so that he or she wrote, be educated to both real life scenarios. And I also have a step counter which tracks your carbon footprint and encourages people to trust Morgan Friend the transportation options And that's a rare make really impact is that we connect our application with a special trash boxes in our city. All those locations are shown on the map, and coming to this place is user received trash box. And since Rosa Garbage and then because he has restaurants carriage here, she will get some points and your impact will be realized in the eventuality. Yeah, >> So what impact in society do you hope that this app will help change >> Rapids three t mobile application and it's a game. That is why Gamification and theater magic reality, which is a r which is inside this game a cz more visually in psychological attractive to people and those challenges that we provide a game are intensified so that most of the people. When they accomplish their goals, they might get, like, have a certain profit out of it so that they can become worker friendly and gain benefits. This is how we want to make sure that people might gain my changed a behavior for the sake of ecology. >> That's awesome. So you're using essentially a game incentivize people to make better choices in their everyday lives. That's great. And so how >> did you >> come up with this idea? >> So look, I will explain >> this. Actually, there were before some eco trash boxes in our school because like the thing off, ecological problems and recycling is one of the most talked about topics in Kazakhstan nowadays. And like in our school, the students try. Thio make this echo charge boxes, but they were always empty because students wasn't incent ified to recycle the garbage. And we tested our up in our school and we already launched it in our school and this ups incentivize our students. And now this I could trash boxes with our hard way always full. So >> that's awesome. See, you already found some success with your app. Thank you. Do you think that that this is a problem in the bigger community. >> Oh, maybe Donna Comptel. >> So we're saying that we started locally, but we got to go globally within that, uh, a pollution, like a pollution global problem and we trying to solve all over the world. So in our game, we have the whole world that you become an astronaut. So you should be aware for hold the problem that was happening in the earth. So we are trying to engage and educate people to be more global on to be more responsible for our final for our home. >> It sounds like everyone in the world should download that app. Yes, I do hope Thio uh, expand if you get the funding. >> Yes, um, we plan to expand not only in our country, Kazakhstan on only locally, but also globally. And we would like to create the eco friendly community across Central Asia since we want to make sure that consciousness is global in our area. >> And what struggles have you faced trying to create this app? >> Um, probably there were some struggles and off course in the realization and, uh, the realization of technical part of this project and creating a business model, since we are not very experienced in this kind of things. But since we have participated in techno vacation and we were immersed in this protest and were modified Thio motivated. Yeah, and we're motivated to learn all this things and acquire those skills. And this is why we became more experienced in this stuff. So right now, uh, those struggles that we face before not longer problem for us. So yeah, this what we faced? >> So techno vacation has definitely helped. Do you improve your app and yes, right houses. Tech innovation Helped you? >> Yeah, Um, probably someone else wants to ask you this question. >> How is SECNAV ation help? You were What skills have you learned from this journey? For >> example, one of the most important skills, I guess iss a teamwork. Like after we started to work on the one project, we started to listen each other excavation actually helped us too. Um, I understand the opinions off other people and like to understand the problems in our society. We start to dream bigger to think bigger, wider kind of that >> That's amazing. And also take Novation helping us >> to explore new companies to be more like open a person to come to The company's asked about the help on not like B just like see the problems and trying to solve trying to find a solution and be the people of the world and be responsible for our planet for what's happening in our local community on be aware of everything. >> And, um So I heard you guys had an amazing week. Um, you you went to whoever You went some other places. So can you tell us more about your week >> you want? So we went to amazing places in a Silicon Valley in a San Francisco San Jose and we so, like it'd, for example, Golden Gate Bridge. And also the Alcatraz so were so impressed by their architecture by the people by the nature on DDE. We just expected a lot of Onda. We just got this old expectations come to the reality on dhe. We hope that that kind of dream will come true in our future, and we gonna to work in a one of the big companies that were located here. I know all the universities. So >> how is it like going to the different tech companies and seeing it in real life. >> So we >> visited Uber Company and Google Ventures, and both we I have seen people who work is there, and we're really impressive on. And we really like it. It? Yeah. And, uh, I think so. Before, like in my childhood, I dreaming to be to be in Silicon Valley, to goes there and, like, meet people who are work already working you And now, like my dream came through. >> That's awesome. And you get to see California And you you might be able to win today. So thank you so much for being on. I wish you all the best. And I hope you haven't amazing pitch tonight. Thank you. This has been Team Coco from Kazakhstan. I'm your host, Sonia to Garey. This is the Cube. Stay tuned for more

>> Live from Orlando, Florida its theCUBE covering Enterprise Connect 2019. Brought to you by Five9. >> Hello from Orlando, Lisa Martin with Stu Miniman theCUBE. We are live, day three at Enterprise Connect 2019. We have been in Five9's booth all week and we're very excited to welcome to the program for the first time Harry Moseley the CIO of Zoom Video Communications. Harry thanks so much for joining Stu and me on The CUBE today. >> Lisa, Stu its a pleasure to be here, thank you for having me. >> And you're a hall of famer, you have been inducted into the CIO Magazine's hall of fame and recognized as one of the world's top 100 CIO's be Computer World >> Yes that's right >> So we're in the presence of a VIP >> (chuckles) Well thank you for that it's, as I say its all credit back to the wonderful people that have supported me throughout my career. And I've worked with some amazing people and leaders and, who have supported me and the visions that I've created for their organizations. And so, I understand its about me but it's also about the great teams that I've worked with in my past. I can't make this stuff up, yep. >> Harry, we love talking to CIO's especially one with such a distinguished career as yours 'cause the role of CIO has gone through a lot of changes. IT has gone through a lot of changes. You know we've been doing this program for nine years. Remember reading Nick Carr's IT, does IT matter? And you know, we believe IT matters more than ever Not just IT, the business, the relationship maybe give us a little more of your view point as to the role of the CIO and technology, at a show like this. We hear about the CMO and the business and IT all working together. >> Yeah so its actually, in my opinion, there's never been a better time to be a CIO, irrespective of the company you are in, whether its a tech company like where I'm, you know Zoom Video Communications or any one of the prior companies I worked for, professional services, financial services. But even when you think about it like trucking, You think about trucking as an industry, you think about trucking as a company, its like it was a very sort of brick and mortars? But now its all about digital, right? A friend of mine runs a shipping container company and to think that they load five miles of wagons every day. And so I said to him, "how long does it take to load a wagon on a truck?" "It takes four minutes, and you know what Harry, "we're working that down to three. "And that'll increase our revenue by 20 to 25 percent.' And so its just fantastic. And the pace of change, you know it's just growing exponentially. It's just fascinating, the things that we can actually do today we only dreamed about them a year ago. And you think about it sort of' I can't wait to be back here next year, 'cause we're going to just lift the roof off this place in terms of the capabilities. And so its fantastic, yeah it's just absolutely fantastic. >> So looking at, a lot of us know Zoom for video conferencing and different things like that, but you said something very interesting in your fireside chat this morning that I hadn't thought about, and that is when, either going from audio to video, when you're on a video chat you really can't or shouldn't multi-task. So in terms of capturing peoples attention, enabling meetings to happen maybe more on time, faster, more productive. Thought that was an interesting realization, I thought, you're right. >> It just clicks, it just works. You know mobile, you know when I go back to my you know sort of' going back and again, thank you for the recognition from the key note. But when I go back earlier in my career it's like dialing that number, dialing that ten digit number, misdialing that number, what happened? I got to' hang up, I got to' get a dial tone, I got to' dial the numbers again. Now I'm like two minutes late and I know I'm late more often than I'd like, but when its late because of something like that, that's frustrating. That's really frustrating. And so the notion that you can just click on your mobile device, you can click on your laptop, I have no stress anymore, in joining meetings anywhere. I love telling the story about how I had a client meeting, I was in O'Hare Airport and I joined the client prospect meeting. I joined the prospect meeting on my phone using the free wifi service at O'Hare Airport. Put up my virtual background on my phone I just showed you this Stu, with our logo shared the content off of my phone 18 minutes into this 30 minutes call, the person I was talking to, the CIO for this firm called a halt to the meeting. This is what exactly what happened. Enough, I've heard enough. (announcement in background) >> Keep going. >> Keep going, okay. Enough, I didn't know what enough meant. And so I was a little spooked by that if you will. He goes, "you're on a phone, you're in O'Hare Airport, "you've got a virtual background, "you're sharing content, its all flawless. "Its like this is an amazing experience "that we can't get from all the technology "investment we've done in this space "for our company. "So guys, enough. "We're starting a proof of concept on Monday. "No more discussions about it. "Harry, looking forward to being a business partner." >> Does it get better than that? >> It doesn't get better than that. Its like you know, you hop through security, you get on a plane, and its cruisin' all the way home. >> Yeah I mean Harry, I do have to say, you know disclaimer, we are Zoom customers I'm actually a Zoom admin and its that simplicity that you've built into it is the experience, makes it easy. >> And then when you, and Stu, sorry to interrupt you but I got really excited about this stuff as you can tell. But, and then you look at the enterprise. So you're admin? You get into the enterprise management portal and its like Stu, I had a really bad experience. Oh let me look that up, oh yeah, okay. Where were you? You know, I was in outer Mongolia Ah okay, about five minutes into the call you had some packet loss, its like yeah it wasn't. But it still maintains the connection, right? So you can actually, so our Enterprise Management Portal is awesome. >> Yeah so that actually where I was going with the question, is you know I remember back, I actually worked for Lucent right after they spun out from AT&T. And we had videos talking about pervasive video everywhere, in my home in the business. Feels like we're almost there but still even when I have a team get together my folks that live in Silicon Valley, their connectivity's awful. You know when they have their, and its like oh well my computer or my phone don't have the cycles to be able to run. Maybe we have to turn off some of the video Are we getting there, will 5G solve some of these issues? Will the next generation of phones and computers keep up with it? Because it's, I'm sure you can guess we're big fans of video. It's a lot of what we do. >> Because video is the new voice, right. We like video. If I can only hear you and I can't see you, then when I make a statement I can't see you nodding. If I say something you like, you nod. So we get that concurrency of the experience Again it comes back Stu, where were we a year ago? The capabilities we had, where will we be a year from today? Whether its AI, whether its the power in the device in front of us whether its the network, you know, 5G is becoming a reality. It's going to take some time to get there but you've got sort of great technologies and capabilities, that you know, you look at the introduction of our real-time transcription services. I mean how cool is that? I'm sure there's lots of questions, so lots of people would ask about that real-time transcription in terms of, well what's next? I'm not going to talk about what's next. But as they say in life, watch this space. >> Yeah, just you made some announcements at the show with some partners I actually believe Otter AI is one of the ones you mentioned there. I got a demo of their thing, real time, a little bit of AI built in there. Can you talk about some of those partnerships? >> Yeah so we have great, we love our partnerships right? Whether its on the AI space, with Apple and Siri and Amazon and Otter. We also love our partnerships with Questron and Logitek and HP, and Polly of course. Again its the notion of, we have terrific software. You guys realize that, right? Its terrific software, proprietary QOS proprietary capabilities, its like its a fantastic experience every time on our software. These partners have great technologies too. But they're more on the hardware side, we are software engineers at our core. As Andreson said, I think it was about ten years go, "software is the easing thing in the world "so you take terrific software "you imbed it in terrific hardware "with terrific partners and what happens "is you get exceptional experiences." And that's what we want to deliver to people. So its not about the technology, its about the people. Its about making people happy, making easy, taking stress off the table. You go to the meeting, you light it up, you share the content, you record it, you can watch it later, its just terrific. >> So the people, the experiences you about we've been hearing that thematically for the last three days. As we know as consumers, the consumer behavior is driving so much of this change that has to happen, for companies to not just digitally transform, but to be competitive. We're in Five9's booth and they've mentioned they've got five billion minutes of recorded customer conversations. You guys can record, but its not just about the recording of the voice and the video and the transcription. Tell us about what you're doing to enable the context, so that the data and the recordings have much more value. >> Yeah so , I mean its the notion of being able to sort of rewind and replay. I'll give you another example if I may. Coming out of an office in Palo Alto jumped in the Uber, going back to San Jose for a client meeting. I'm a New Yorker as we talked about a few minutes ago and, I don't know the traffic patterns in Southern, in the Valley. And its about 5:00 o'clock, 5:15. San Jose meetings 5:45. Normally it would be fine, but its rush hour, what do I know about rush hour? I know a lot more now than then. I realize I'm not going to be able to make it on time. Put up the client logo, virtual background on the phone, in the Uber, client gets on the call, Harry where are you? I'm in the back of an Uber. Again, the same sort of experience. Then he asks the question, "well with this recording capability, "can I watch it at 35,000 feet?" Of course you can. And that was it. That was the magic moment for this particular client, because he said "I'm client facing all the time. "I don't get it in time, "I don't always make my management meetings "so I won't have to ask my colleagues what happened "and get their interpretation of the meeting. "I can actually watch the meeting "when I'm at 35,000 feet on a plane, going to Europe." So that's what this is all about. >> Alright, well Harry obviously this space excites you a bunch. Can you bring us back a little bit? This brought you out of retirement and the chase, the space is changing so fast. We come a year from now, what kind of things do we think we'll be talking about, and what's going to keep you excited going forward? >> So lets talk about the first part first and then sort of' break it into two. So yes I had a fantastic career and I retired and so when I met Eric and I met the leadership team at Zoom and I dug into the technology and I understood sort of' A, the culture of the company which is amazing. When I understood the product capability and how this was built as video first, and how we would have this maniacal focus if you will on sort of being a software company at our core. And how it was all about the people. That was sort of a very big part of my decision. So that was one. Two is, look we have a labor shortage right? We can't hire enough people, we can't hire the people, we have more jobs than we have people. So and so, retaining talent is really important. Giving them the technology and the studies that have been done, if you make an investment in the technology, that helps with retention. That helps with profit. It helps with, product innovation. So investment in the people. And the ability to collaborate. It's very hard to work if you don't collaborate, right? It just makes it really, very lumpy if you will. So the ability to collaborate locally, nationally, and globally, and people say, well what's collaborating locally? It's kind of like we can just walk down the corridor. Yeah, well if you're in two different buildings how do you get there? And then it gives us, a foot of snow between you, its makes it really hard. So collaborating locally, nationally, and globally is super important. So you put all that together that was the, what convinced me to say okay you know what, retirement, we're just going to put a pause button on that. And we're going to gave some fun over here. And that really has been, so I've, over a year now and its been absolutely amazing. So yes, big advances. What's in the the future? I think the future, you know there's been a lot of discussion around AI. We hear that its like, all the time. And we've seen from a variety of different providers this week in terms of their, their thoughts around how they're going to leverage AI. Its not about the technology, its about the end of the its about the user experience. And you look at the things that we started to do, we talked about real-time transcriptions a few moments ago, you look at the partnership that we have with Linkedin where you can hover over the name and their Linkinin profile pops up. You're going to see this, I just see this as an exponential change in these abilities. Because you have these building blocks today that you can grow on an exponential basis. So, the world is our oyster, is how I fundamentally think about it. And the art of the possible is now possible, And so lets, I think the future is going to' be absolutely amazing. Who would have, sorry Lisa, who would have thought a year ago, you could get on a plane using facial recognition? Let me just throw that out there. I mean, that's pretty amazing. Who would have thought a year ago that when you rent a car, you can just look at the camera on the way out and you're approved to go? Who would have thought that? >> So with that speed I'm curious to get your take on how Zoom is facilitating adoption. You mentioned some great customers examples where your engagement with them via Zoom Video Conference basically sold the POC in and of itself, with you at an airport >> That's a great questions. >> I guess O'Hare has pretty good wifi. >> What's that? >> O'Hare has pretty good wifi. >> A little choppy but, but it worked. >> It worked. >> Because of our great software, yeah. >> There you go, but in terms of adoption so as customers understand, alright our consumers are so demanding, we have to be able to react, and facilitate collaboration internally and externally. How, what are some of the tools and the techniques that Zoom delivers to enable those guys and gals to go I get it, I'm going to use it, And I'm actually going to actually use it successfully? >> This is a question, I don't know how many clients, CIOs, CTOs, C suite execs I talk to, and they all say, they all ask me similar sorts of questions. Like we're not a video first culture. Its like video, its kind of like we're a phone culture. And then I, so I throw that right back at them and I say and why is that? Because we don't have a good video platform. Aha. Now, when you have good video, when it just works when its easy, when its seamless, when its platform agnostic. IOS, Andriod, Mac, Windows, Linux, VDI, web. When you have this sort of, this platform when you're agnostic to the platform, and its a consistent high quality experience, you use it. So its the notion of, Lisa, it's the notion of would we rather get into a room and, would we rather get into a room and have a face to face meeting? Absolutely. So why would you get on a call and not like to see the people you're talking to. You like to see the people. Why, because its a video first. >> Unless its just one of those meetings that's on my calender and I didn't want to be there and I'm not going to listen. But I totally agree with you Harry. So, another hot button topic that I think we're at the center of here and that I'm sure you have an opinion on. Remote workers. So we watched some really big companies I think really got back in the dialogue a coupla' years ago when Yahoo was like okay, everybody's got to' come in work for us and we've seen some very large public companies that said you need to be in your workforce. and as I said, I'm sure you've got some pretty strong opinions on this >> I don't know what's going on here, quite honestly Stu but its like I think you're reading my brain because these are things I love talking about. So yeah, its. Sorry repeat the question? >> Remote workers. >> Remote workers, yeah. So first of all, I was at an event recently we talked about remote work. We didn't like the term. Its a distributed workforce. >> Yes. Because if you say you're a remote worker its kind like, that doesn't give you that warm feeling of being part of the organization. So we call it, so we said, we should drop calling people remote workers and we should call them a distributed work force. So that's one. Two is, I'm in New york, I'm in Orlando, I'm in Chicago, I'm in Atlanta, I'm in Denver. I'm on planes, I'm in an Uber. I don't feel disconnected at all. Why? Because I can see my colleagues, and its immersive. They share content with me. I'm walking down Park Avenue and I've got my phone and they're sharing content and I'm zooming in and I can see them and I can hear them and I'm giving feedback and I'm marking up on my phone, as I'm walking. So I don't feel, and then when I go to, its fascinating, and then I go to San Jose and I'm walking around the office and I'm seeing people physically. It doesn't feel like I haven't seen them, its really funny. I was in San Jose last week, Wednesday and Thursday in San Jose, took the red-eye back. Hate the red-eye but, I don't like flying during the day, I think it's inefficient, a waste of time. Took the red-eye back, now I'm on calls Friday morning from my office at home with my green screen, Zoom background and everybody's got, it's like I'm talking to the same people I was talking to yesterday but they were in the flesh, now they're on video. It's like Harry where are you, why didn't you come to the room? Well I'm back in New York. It's just just that simple, yep. >> That simple and really it sounds like Harry, what Zoom is delivering is a cultural transformation for some of these newer or older companies who, there is no reason not to be a video culture. We thank you so much for taking some time >> Thank you, thank you >> To stop by theCUBE and chat with Stu and me about all of the exciting things that brought you back into tech. and I'm excited to dial up how I'm using Zoom. >> Well we can take five minutes after this and I can show you some cool tricks >> Wow, from the CIO himself. Harry Moseley, thank you so much for your time. >> Thank you, thank you >> Great to have you on the program. For Stu Miniman, I'm Lisa Martin and you're watching theCUBE (upbeat tune)

(energetic music) >> Live from Orlando Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back everyone, to theCUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We're joined by Lynn Lucas. She is the CMO of Cohesity. Thanks so much for coming on the program, Lynn. >> Oh, just so excited to be here with you guys and host you in the Cohesity booth for the first time at Microsoft Ignite. >> It's been a lot of fun. There's a lot of buzz around here, and it's fun to be right, to be your neighbor. Exactly. >> Great. >> So today, there's been a lot of news, some new exciting announcements of integrations with Microsoft. I wonder if you can walk our viewers a little bit through what Cohesity announced today. >> Absolutely. So, we have been partners with Microsoft for some time, and today, we announced extensions to our capabilities with Microsoft Azure and Office 365. So Cohesity now extends data protection and backup for Office 365, including granular recovery of mailboxes and granular search for discovery purposes. We also have extended our integration with the Azure data box, and we also are increasing our DR capabilities for our customers with Azure so we now have fail back from the Azure Cloud for disaster recovery purposes. So, just continuing to see tremendous growth, hundreds of Microsoft customers with Cohesity, and these new capabilities are going to expand the possibilities for them. >> Lynn, it's an interesting conversation these days 'cause, you know, in our research, and we've talked about this, data's at the center of everything, and the challenge for customers is data's everywhere. You look here at the Microsoft show, well, I've got all my traditional stuff, I've got my SaaS stuff, my PubliCloud stuff, now Edge with the data box things there. Microsoft plays across there, and it sounds like Cohesity is playing in all of these areas, too. >> Absolutely, and I thought, you know, Sacha did such a good job in the keynote yesterday of really laying out the imperative for digital transformation, data being at the heart of it, but also laying out one of the key challenges which he pointed out, which is the data silos. And, I think Cohesity is right smack in the center of that conversation because we've always been about consolidating secondary data silos. And, you know, our partnership with Microsoft, really, I think, reinforces what they've been talking about, which is also a hybrid strategy that the bulk of customers that we talk to see that their data is going to be on premise, it's going to be in the cloud, and increasingly, it's goinna at the Edge, and we span all of those locations to create this one operating environment so that things like the new open data initiative, I think, will be much easier for customers because they won't be wondering, well, is my data all in one place to be operated on? >> So, talk about the problem of the data silos, because, as you said, it's one of the biggest challenges that companies face today. They are data rich and yet, this data's here and this data's here. Can you describe a little bit about what kind of problems this is for companies, and why this matters? >> So, I think it's just something folks are starting to really get a handle on. As I talked to individual folks here at the show, you'd be surprised at how many aren't even really sure, maybe, how many islands they have, you know, so, even mapping where is all my data, I think, is a capability that many organizations are still getting their arms around. And the challenge, of course, is that in today's world, it's very expensive to move large data sets, and so you want to bring compute to the data, which is what a hyper-convergence in Cohesity is about. And, when you look at the imperatives at the board level, the CEO level, they increasingly see that data becomes really the true competitive advantage for most organizations, and yet, if they can't operate or bring compute to that data and do something with it, they're really at a handicap. We call, you know, some of the newer companies are kind of data-centric or data natives, the Air BNB's, the, maybe, Netflixes of the world, not everyone aspires to be them. As well, not everyone has the resources that those companies may have had or just stay short period of time. Most organizations have the benefit of years of data. We want to level the playing field and allow them to become competitive with their data by providing that single foundation. >> Yeah, Lynn, it's a big show here. They said thirty thousand people and a really diverse ecosystem. What really surprised me is the spectrum of customers that you have here. I mean, we know Microsoft has a long history in higher education. We spoke to one of your customers, Brown University, and of course, long history they have with Microsoft. What are some of the things that you're hearing from customers, maybe, what's different at this show than some of the other, cloud and kind of younger shows that we might go to. This show's been around about almost thirty years now, so. >> Yeah, you know, isn't it, you know, I hate to give our ages but, I think we've been doing this for a while now, right? And Microsoft has been part of the IT ecosystem in a major way, and it's great to see the vibrancy here and how they're talking about AI and ML and moving forward with it. You know, what strikes me here is that a lot of the organizations here are now really understanding the pragmatism of having a hybrid strategy of what makes sense in the cloud as well as what may continue to be on prem for them. I think we complement that well. I'm really excited, too, about the idea that we are going to be using machine learning to be doing a lot more that humans simply can't keep up with in terms of the data growth and then doing something productive with that. And I think that's a conversation that we're just tapping the surface of here at this show. >> Yeah, you've said something that really resonated with me. You know, we have people that have been in the industry a while and, I look at you, your founder, Mohit, and this isn't his first rodeo. He'd been looking at data back from a couple of generations of solutions, and people are very excited. Machine learning, as you said, we used to talk about automation and intelligence around this environment. Now, I lived in the storage industry for quite a while, and we've talked about it but it feels more real when I talk to the architects and the people building this stuff. They are just so excited about what we will be able to do today that we talked about a decade or so ago but now really can make reality for customers. >> No, absolutely, and I think, you know, we have our own investment in that. Helios, which we announced just last month, you know, provides that machine learning capability because what we hear from our customers is what they love is the ability to have simplicity because, let's face it, IT environments continue to grow in complexity. They're looking for ways to subtract that complexity so they can apply their talents to solving the primary mission, as I call it, of their organization, whether that be public sector or private sector, adoing that in a simpler way. You know, look, one of the great stories that one of our customers is talking about here is how Cohesity helped him with a standard thing that most IT organizations have, which is, we're going to do a power shut down and we've got to perform a DR failover, and this particular organization, University of Pennsylvania Annenberg, had a set of twelve websites which, the professors and the students rely on, and it was going to take them literally almost a month to try to move them, and they didn't have that kind of time, and with Cohesity, with our DR capabilities, he was able to do that literally with a few clicks, kept the community of professors and students happy, and didn't spend, more importantly, twenty days trying to rebuild websites for a standard IT event, right? That's the kind of real life story in terms of what IT gets back that they can invest in other more important focus areas for their business. >> Well, for their business and also, just for their lives giving people their time back, their weekends back, their time at night >> Weekends and nights, right? >> With their families, yeah. >> We all need that. >> Satya Nadella is such a proponent of an improving workplace productivity, even five percent, he says, can make this big difference. Can you talk a little bit about how you view that workplace productivity at Cohesity and your approach to giving people either time to concentrate on more value for their companies or just their lives? >> So, again, a super story that we have from another customer that is here at Microsoft, and is an Azure customer, and a Cohesity customer. HKS, one of the world's most respected architectural firms, designed AT&T Stadium, there's a new major pediatric hospital going in in Dubai. They operate in ninety-four countries with remote designers and architects, and because of their inefficient backup processes and archive processes, they literally were having their associates have to work weekends as well as losing time on their projects, and time is money, and they, you know, in some cases, are penalized if they don't make certain dates. And so, I think, these are really pragmatic examples. On average here, pulling some of the folks here, I've heard that they can get a day a week back, sometimes for their administrator who now doesn't have to do repetitive manual tasks anymore. >> One of the things we always love digging into is, you talk about people's jobs and some of the new careers that are happening. We talked to one guest earlier this week. He said, if you're a customer and you learn Azure as what you're doing, like, you're resume is gold. We've talked to, and the really early Edge, like site reliability engineering, he said, don't put SRE on your resume or every recruiter will be calling you up and you won't even be able to answer your phone. Cohesity, you're doing a bit of hiring also. Maybe you could talk about- >> We are! >> What are you seeing from customers and what are you looking for internally? >> We have tremendous good fortune, we grew three hundred percent in revenues year over year, we're hiring in our RTP offices, in our San Jose, in India, around the globe. You know, we look for the best and the brightest, a lot of engineering talent, marketing talent as well, really, across the board but, you know, I think to the point you just made for the IT folks that are here, looking forward as to how you are going to help your business with your data infrastructure or data flows throughout their organization is, to me, where some of the career movement is happening when you hear the talk about how important it is to so many aspects of the business. >> And what are the sort of challenges that you're having with hiring, or are you? I mean, you're a red hot company, but, are you finding it difficult to find the kind of skills, the kind of talent that you want? I mean, what is, what's the candidate pool like? >> You know, so, I think what's really interesting, we are red hot, we have a lot of applicants so, I'd say, in general, no, we're very blessed that way. I think, though, more businesses, including ours, are finding it's difficult to get, say, those data scientists, right? Some of these also front end or back end developers, you know, it's not just the technical companies that are recruiting for that anymore. It's not just the Cohesitys and the Microsofts that are looking for that talent, but it's now also the Netflixes or, you know, the eBays, et cetera, right? They are all looking for the type of talent that we are and so, in general, I think that this bodes well for young people or folks really anywhere in their career watching about, thinking about, where the talent needs are, and there's a lot of activity and interest in people with those kinds of skills. >> You know, let me just follow up on that. So, Cohesity is a Silicon Valley-based company but, as you mentioned, you've got an RTP location. We've seen quite a lot of Silicon Valley-based companies that are starting to do a lot more hiring outside 'cause it's, I'm going to be honest, really expensive to live in the valley these days. So, any commentary on that dynamic? >> Well, you know, I think you're in Boston, not the lowest cost market either in the country. >> True, it's true! >> Yeah, you know, I think with a lot of the technology that's out there, you know, people don't have to be co-located, and we certainly also look to develop and invest in other communities around the globe, so we're not looking solely in San Jose but also in RTP, we've got headquarters in Europe as well as, of course, in India. So we look for talent everywhere, and, my own personal team, you know, I have folks basically around the US as well as across parts of the globe because talent, in many cases, is what matters and where you are physically, you know, some of the great technology that's out there can help break down those barriers of time and distance. >> Finally, this conference, it's thirty thousand people from five thousand different companies around the world. What is going to be, I mean, we're only on day two, but, what's been your big take-away so far? What's the vibe you're getting here at Ignite? >> You know, the vibe has been one of energy, of excitement. I've talked to a lot of folks from around the globe. I've been actually, pretty amazed at some of the people from different countries around the globe that are here, which is fantastic to see that draw in, and I feel like there's a general sense of excitement that technology and what Microsoft's doing can help solve some of the bigger challenges that are here, in the world, and for their own businesses, and we really look forward to Cohesity helping them lay that great data infrastructure foundation, consolidate their silos and help them build a foundation for, you know, doing more with their data. >> Great. Lynn Lucas, thank you so much for coming on theCube. It was great, great talking to you. >> Thank you. >> I'm Rebecca Knight for Stu Miniman. We will have more from Microsoft Ignite and theCube's live coverage coming up in just a little bit. (electronic music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCube. Covering Datawork Summit 2018. Brought to you by Hortonworks. >> Welcome back to theCube's live coverage of Dataworks here in San Jose, California. I'm your host, Rebecca Knight along with my cohost James Kobielus. We have with us Vimal Endiran. He is the Global Business Data Group Ecosystem Lead, at Accenture. He's coming to us straight from the Motor City. So, welcome Vimal. >> Thank you, thank you Rebecca. Thank you Jim. Looking forward to talk to you for the next ten minutes. >> So, before the cameras were rolling we were talking about how data veracity and how managers can actually know that the data that they're getting, that they're seeing, is trustworthy. What's your take on that right now? >> So, in the today's age the data is coming at you in a velocity that you never thought about, right. So today, the organizations are gathering data probably in the magnitude of petabytes. This is a new normal. We used to talk about gigs and now it's in petabytes. And the data coming in the form of images, video files, from the edge, you know edge devices, sensors, social media and everything. So, the amount of data, this is becoming the fuel for the new economy, right. So that companies, who can find a way to take advantage and figure out a way to use this data going to have a competitive advantage over their competitors. So, for that purpose, even though it's coming at that volume and velocity doesn't mean it's useful. So the thing is if they can find a way to make the data can be trustworthy, by the organization, and at the same time it's governed and secured. That's what's going to happen. It used to be it's called data quality, we call it when the structure it's okay, everything is maintained in SAP or some system. It's good it's coming to you. But now, you need to take advantage of the tools like machine learning, artificial intelligence, combining these algorithms and tool sets and abilities of people's mind, putting that in there and making it somewhat... Things can happen to itself at the same time it's trustworthy, we have offerings around that Accenture is developing place... It differs from industry to industry. Given the fact if the data coming in is something it's only worth for 15 seconds. After that it has no use other than understanding how to prevent something, from a sense of data. So, we have our offerings putting into place to make the data in a trustworthy, governed, secured, for an organization to use it and help the organization to get there. That's what we are doing. >> The standard user of your tools is it a data steward in the traditional sense or is it a data scientist or data engineer who's trying to, for example, compile a body of training data for use in building and training machine learning models? Do you see those kinds of customers for your data veracity offerings, that customer segment growing? >> Yes. We see both sides pretty much all walk of customers in our life. So, you hit the nail on the head, yes. We do see that type of aspects and also becoming, the data scientists you're also getting another set of people, the citizen data scientist. The people--- >> What is that? That's a controversial term. I've used that term on a number of occasions and a lot of my colleagues and peers in terms of other analysts bat me down and say, "No, that demeans the profession of data science by calling it..." But you tell me what how Accenture's defining that. >> The thing is, it's not demeaning. The fact is to become a citizen data scientist you need the help of data scientists. Basically, every time you need to build a model. And then you feed some data to learn. And then have an outcome to put that out. So you have a data scientist creating algorithms. What a citizen data scientist means, say if I'm not a data scientist, I should be able to take advantage of a model built for my business scenario, feed something data in, whatever I need to feed in, get an output and that program, that tool's going to tell me, go do this or don't do this, kind of things. So I become a data scientist by using a predefined model that's developed by an expert. Minds of many experts together. But rather than me going and hiring hundred experts, I go and buy a model and able to have one person maintain or tweak this model continuously. So, how can I enable that large volume of people by using more models. That's what-- >> If a predictive analytics tool that you would license from whatever vendor. If that includes prebuilt machine learning models for a particular tasks in it does that... Do you as a user of that tool, do you become automatically a citizen data scientist or do you need to do some actual active work with that model or data to live up to the notion of being a citizen data scientist? >> It's a good question. In my mind, I don't want to do it, my job is something else. To make something for the company. So, my job is not creating a model and doing that. My job is, I know my sets of data, I want to feed it in. I want to get the outcome that I can go and say increase my profit, increase my sales. That's what I want to do. So I may become a citizen data scientist without me knowing. I won't even be told that I'm using a model. I will take this set of data, feed it in here, it's going to tell you something. So, our data veracity point of view, we have these models built into some of platforms. That can be a tool from foreign works, taking advantage of the data storage tool or any other... In our own algorithms put in that helps you to create and maintain the data veracity to a scale of, if you say one to five, one is being low, five is being bad, to maintain at the five level. So that's the objective of that. >> So you're democratizing the tools of data science for the rest of us to solve real business problems. >> Right. >> So the data veracity aside, you're saying the user of these tools is doing something to manage, to correct or enhance or augment the data that's used to feed into these prebuilt models to achieve these outcomes? >> Yes. The augmented data, the feed data and the training data it comes out with an outcome to say, go do something. It tells you to perform something or do not perform. It's still an action. Comes out with an action to achieve a target. That's what it's going to be. >> You mention Hortonworks and since we are here at Dataworks and the Hortonworks show, tell us a little bit about your relationship with that company. >> Definitely. So Hortonworks is one of our premiere strategic partners. We've been the number one implementers, the partners for last two years in a row, implementing their technology across many of our clients. From partnership point of view, we have jointly developed offerings. What Accenture is best at, we're very good at industry knowledge. So with our industry knowledge and with their technology together what we're doing is we're creating some offerings that you can take to market. For example, we used to have data warehouses like using Teradata and older technology data warehouses. They're still good but at the same time, people also want to take the structured, unstructured data, images files and able to incorporate into the existing data warehouses. And how I can get the value out of the whole thing together. That's where Hortonworks' type of tools comes to play. So we have developed offerings called Modern Data Warehouse, taking advantage of your legacy systems you have plus this new data coming together and immediately you can create an analytics case, used case to do something. So, we have prebuilt programs and different scripts that take in different types of data. Moving into a data lake, Hortonworks data lake and then use your existing legacy data and all those together help you to create analytics use cases. So we have that called data modernization offering, we have one of that. Then we have-- >> So that's a prebuilt model for a specific vertical industry requirements or a specific business function, predictive analytics, anomaly detection and natural language processing, am I understanding correctly? >> Yes. We have industry based solutions as well but also to begin with, the data supply chain itself. To bring the data into the lake to use it. That's one of the offerings we play-- >> ...Pipeline and prepackaged models and rules and so forth. >> Right, prepackaged data ingestion, transformation, that prepackaged to take advantage with the new data sets along with your legacy data. That's one offering called data modernization offering. That to cloud. So, we can take to cloud. Hortonworks in a cloud it can be a joure, WS, HP, any cloud plus moving data. So that's one type of offering. Today actually we announced another offering jointly with Hortonworks, Atlas and Grainger Tool to help GDPR compliance. >> Will you explain what that tool does specifically to help customers with GDPR points. Does it work out of the box with Hortonworks data stewards studio? >> Well, to me I can get your answers from my colleagues who are much more technical on that but the fact is I can tell you functionally what the tool does is. >> Okay, please. >> So you, today the GDPR is basically, there's account regulations about you need to know about your personal data and you have your own destiny about your personal data. You can call the company and say, "Forget about me." If you are an EU resident. Or say, "Modify my data." They have to do it within certain time frame. If not they get fined. The fine can be up to 4% of the company's... So it's going to be a very large fine. >> Total revenue, yeah. >> So what we do is, basically take this tool. Put it in, working with Hortonworks this Atlas and Granger tool, we can go in and scan your data leak and we can scan at the metadata level and come into showcase. Then you know where is your personal data information about a consumer lies and now I know everything. Because what used to be in a legacy situation, the data originated someplace, somebody takes it and puts a system then somebody else downloads to an X file, somebody will put in an access data base and this kind of things. So now your data's pulling it across, you don't know where that lies. In this case, in the lake we can scan it, put this information, the meta data and the lineage information. Now, you immediately know where the data lies when somebody calls. Rebecca calls and says, "No longer use my information." I exactly know it's stored in this place in this table, in this column, let me go and take it out from here so that Rebecca doesn't exist anymore. Or whoever doesn't exist anymore. So that's the idea behind it. Also, we can catalog the entire data lake and we know not just personal information, other information, everything about other dimensions as well. And we can use it for our business advantage. So that's what we announced today. >> We're almost out of time but I want to finally ask you about talent because this is a pressing issue in Silicon Valley and beyond in really the tech industry, finding the right people, putting them in the right jobs and then keeping them happy there. So recruiting, retaining, what's Accenture's approach? >> This area, talent is the hardest one. >> Yes! >> Thanks to Hortonworks and Hortonworks point of view >> Send them to Detroit where the housing is far less expensive. >> Not a bad idea. >> Exactly! But the fact is-- >> We're both for Detroiters. >> What we did was, Hortonworks, Accenture has access to Hortonworks University, all their educational aspects. So we decided we're going to take that advantage and we going to enhance our talent by bringing the people from our... Retraining the people, taking the people to the new. People who know the legacy data aspects. So take them to see how we take the new world. So then we have a plan to use Hortonworks together the University, the materials and the people help, together we going to train about 500 people in different geos, 500 per piece and also our the development centers in India, Philippines, these places, so we have a larger plan to retrain the legacy into new. So, let's go and get people from out of the college and stuff, start building them from there, from an analyst to a consultant to a technical level and so that's the best way we are doing and actually the group I work with. Our group technology officer Sanjiv Vohra, he's basically in charge of training about 90,000 people on different technologies in and around that space. So the magnet is high but that's our approach to go and try and people and take it to that. >> Are you training them to be well rounded professionals in all things data or are you training them for specific specialties? >> Very, very good question. We do have this call master data architect program, so basically in the different levels after these trainings people go through specially you have to do so many projects, come back have an interview with a panel of people and you get certified, within the company, at certain level. At the master architect level you go and help a customer transform their data transformation, architecture vision where do you want to go to, that level. So we have the program with a university and that's the way we've taken it step by step to people to that level. >> Great. Vimal, thank you so much for coming on theCube. >> Thank you. >> It was really fun talking to you. >> Thank you so much, thank you for having me. Thank you. >> I'm Rebecca Knight for James Kobielus we will have more, well we actually will not be having any more coming up from Dataworks. This has been the Dataworks show. Thank you for tuning in. >> Signing off for now. >> And we'll see you next time.

(upbeat electronic music) >> Live from San Jose, in the heart of Silicon Valley, it's theCUBE. Covering Dataworks Summit 2018, brought to you by Hortonworks. (electronic whooshing) >> Welcome back to theCUBE's live coverage of Dataworks, here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have two guests in this segment, we have Steve Wooledge, he is the VP of Product Marketing at Arcadia Data, and Satya Ramachandran, who is the VP of Engineering at Neustar. Thanks so much for coming on theCUBE. >> Our pleasure and thank you. >> So let's start out by setting the scene for our viewers. Tell us a little bit about what Arcadia Data does. >> Arcadia Data is focused on getting business value from these modern scale-out architectures, like Hadoop, and the Cloud. We started in 2012 to solve the problem of how do we get value into the hands of the business analysts that understand a little bit more about the business, in addition to empowering the data scientists to deploy their models and value to a much broader audience. So I think that's been, in some ways, the last mile of value that people need to get out of Hadoop and data lakes, is to get it into the hands of the business. So that's what we're focused on. >> And start seeing the value, as you said. >> Yeah, seeing is believing, a picture is a thousand words, all those good things. And what's really emerging, I think, is companies are realizing that traditional BI technology won't solve the scale and user concurrency issues, because architecturally, big data's different, right? We're on the scale-out, MPP architectures now, like Hadoop, the data complexity and variety has changed, but the BI tools are still the same, and you pull the data out of the system to put it into some little micro cube to do some analysis. Companies want to go after all the data, and view the analysis across a much broader set, and that's really what we enable. >> I want to hear about the relationship between your two companies, but Satya, tell us a little about Neustar, what you do. >> Neustar is an information services company, we are built around identity. We are the premiere identity provider, the most authoritative identity provider for the US. And we built a whole bunch of services around that identity platform. I am part of the marketing solutions group, and I head the analytics engineering for marketing solutions. The product that I work on helps marketers do their annual planning, as well as their campaign or tactical planning, so that they can fine tune their campaigns on an ongoing basis. >> So how do you use Arcadia Data's primary product? >> So we are a predictive analytics platform, the reporting solution, we use Arcadia for the reporting part of it. So we have multi terabytes of advertising data in our values, and so we use Arcadia to provide fast taxes to our customers, and also very granular and explorative analysis of this data. High (mumbles) and explorative analysis of this data. >> So you say you help your customers with their marketing campaigns, so are you doing predictive analytics? And are you during churn analysis and so forth? And how does Arcadia fit into all of that? >> So we get data and then they build an activation model, which tells how the marketing spent corresponds to the revenue. We not only do historical analysis, we also do predictive, in the sense that the marketers frequently done what-if analysis, saying that, what if I moved my budget from page search to TV? And how does it affect the revenue? So all of this modeling is built by Neustar, the modeling platform is built by the Neustar, but the last mile of taking these reports and providing this explorative analysis of the results, that is provided by the reporting solution, which is Arcadia. >> Well, I mean, the thing about data analytics, is that it really is going to revolutionize marketing. That famous marketing adage of, I know my advertising works, I just don't know which half, and now we're really going to be able to figure out which half. Can you talk a little bit about return on investment and what your clients see? >> Sure, we've got some major Fortune 500 companies that have said publicly that they've realized over a billion dollars of incremental value. And that could be across both marketing analytics, and how we better treat our messaging, our brand, to reach our intended audience. There's things like supply chain and being able to more realtime analyze what-if analysis for different routes, it's things like cyber security and stopping fraud and waste and things like that at a much grander scale than what was really possible in the past. >> So we're here at Dataworks and it's the Hortonworks show. Give us a sense of the degree of your engagement or partnership with Hortonworks and participation in their partner ecosystem. >> Yeah, absolutely. Hortonworks is one of our key partners, and what we did that's different architecturally, is we built our BI server directly into the data platforms. So what I mean by that is, we take the concept of a BI server, we install it and run it on the data nodes of Hortonworks Data Platform. We inherit the security directly out of systems like Apache Ranger, so that all that administration and scale is done at Hadoop economics, if you will, and it leverages the things that are already in place. So that has huge advantages both in terms of scale, but also simplicity, and then you get the performance, the concurrency that companies need to deploy out to like, 5,000 users directly on that Hadoop cluster. So, Hortonworks is a fantastic partner for us and a large number of our customers run on Hortonworks, as well as other platforms, such as Amazon Web Services, where Satya's got his system deployed. >> At the show they announced Hortonworks Data Platform 3.0. There's containerization there, there's updates to Hive to enable it to be more of a realtime analytics, and also a data warehousing engine. In Arcadia Data, do you follow their product enhancements, in terms of your own product roadmap with any specific, fixed cycle? Are you going to be leveraging the new features in HDP 3.0 going forward to add value to your customers' ability to do interactive analysis of this data in close to realtime? >> Sure, yeah, no, because we're a native-- >> 'Cause marketing campaigns are often in realtime increasingly, especially when you're using, you know, you got a completely digital business. >> Yeah, absolutely. So we benefit from the innovations happening within the Hortonworks Data Platform. So, because we're a native BI tool that runs directly within that system, you know, with changes in Hive, or different things within HDFS, in terms of performance or compression and things like that, our customers generally benefit from that directly, so yeah. >> Satya, going forward, what are some of the problems that you want to solve for your clients? What is their biggest pain points and where do you see Neustar? >> So, data is the new island, right? So, marketers, also for them now, data is the biggest, is what they're going after. They want faster analysis, they want to be able to get to insights as fast as they can, and they want to obviously get, work on as large amount of data as possible. The variety of sources is becoming higher and higher and higher, in terms of marketing. There used to be a few channels in '70s and '80s, and '90s kind of increased, now you have like, hundreds of channels, if not thousands of channels. And they want visibility across all of that. It's the ability to work across this variety of data, increasing volume at a very high speed. Those are high level challenges that we have at Neustar. >> Great. >> So the difference, marketing attribution analysis you say is one of the core applications of your solution portfolio. How is that more challenging now than it had been in the past? We have far more marketing channels, digital and so forth, then how does the state-of-the-art of marketing attribution analysis, how is it changing to address this multiplicity of channels and media for advertising and for influencing the customer on social media and so forth? And then, you know, can you give us a sense for then, what are the necessary analytical tools needed for that? We often hear about a social graph analysis or semantic analysis, or for behavioral analytics and so forth, all of this makes it very challenging. How can you determine exactly what influences a customer now in this day and age, where, you think, you know, Twitter is an influencer over the conversation. How can you nail that down to specific, you know, KPIs or specific things to track? >> So I think, from our, like you pointed out, the variety is increasing, right? And I think the marketers now have a lot more options than what they have, and that that's a blessing, and it's also a curse. Because then I don't know where I'm going to move my marketing spending to. So, attribution right now, is still sitting at the headquarters, it's kind of sitting at a very high level and it is answering questions. Like we said, with the Fortune 100 companies, it's still answering questions to the CMOs, right? Where attribution will take us, next step is to then lower down, where it's able to answer the regional headquarters on what needs to happen, and more importantly, on every store, I'm able to then answer and tailor my attribution model to a particular store. Let's take Ford for an example, right? Now, instead of the CMO suite, but, if I'm able to go to every dealer, and I'm able to personal my attribution to that particular dealer, then it becomes a lot more useful. The challenge there is it all needs to be connected. Whatever model we are working for the dealer, needs to be connected up to the headquarters. >> Yes, and that personalization, it very much leverages the kind of things that Steve was talking about at Arcadia. Being able to analyze all the data to find those micro, micro, micro segments that can be influenced to varying degrees, so yeah. I like where you're going with this, 'cause it very much relates to the power of distributing federated big data fabrics like Hortonworks' offers. >> And so it's streaming analytics is coming to forward, and it's been talked about for the past longest period of time, but we have real use cases for streaming analytics right now. Similarly, the large volumes of the data volumes is, indeed, becoming a lot more. So both of them are doing a lot more right now. >> Yes. >> Great. >> Well, Satya and Steve, thank you so much for coming on theCUBE, this was really, really fun talking to you. >> Excellent. >> Thanks, it was great to meet you. Thanks for having us. >> I love marketing talk. >> (laughs) It's fun. I'm Rebecca Knight, for James Kobielus, stay tuned to theCUBE, we will have more coming up from our live coverage of Dataworks, just after this. (upbeat electronic music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE. Covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back everyone, you are watching day two of theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight. I'm coming at you with my cohost Jame Kobielus. We're joined by Partha Seetala, he is the Chief Technology Officer at Robin Systems, thanks so much for coming on theCUBE. >> Pleasure to be here. >> You're a first timer, so we promise we don't bite. >> Actually I'm not, I was on theCUBE- >> Oh! >> At DockerCon in 2016. >> Oh well excellent, okay, so now you're a veteran, right. >> Yes, ma'am. >> So Robin Systems, as before the cameras were rolling, we were talking about it, it's about four years old, based here in San Jose, venture backed company. Tell us a little bit more about the company and what you do. >> Absolutely. First of all, thanks for hosting me here. Like you said, Robin is a Silicon Valley based company. Our focus is in allowing applications, such as big data, databases, no sequel and AI ML, to run within the Kubernetes platform. What we have built is a product that converges storage, complex storage, networking, application workflow management, along with Kubernetes to create a one click experience where users can get managed services kind of feel when they're deploying these applications. They can also do one click life cycle management on these apps. Our thesis has initially been to, instead of looking at this problem from an infrastructure up into application, to actually look at it from the applications down and then say, "Let the applications drive the underlying infrastructure to meet the user's requirements." >> Is that your differentiating factor, would you say? >> Yeah, I think it is because most of the folks out there today are looking at is as if it's a competent based play, it's like they want to bring storage to Kubernetes or networking to Kubernetes but the challenges are not really around storage and networking. If you talk to the operations folk they say that, "You know what? Those are underlying problems but my challenge is more along the lines of, okay, my CIO says the initiative is to make my applications mobile. They want go across to different Clouds. That's my challenge." The line of business user says, "I want to get a managed source experience." Yes, storage is the thing that you want to manage underneath, but I want to go and click and create my, let's say, an Oracle database or distributions log. >> In terms of the developer experience here, from the application down, give us a sense for how Robin Systems tooling your product enables that degree of specification of the application logic that will then get containerized within? >> Absolutely, like I said, we want applications to drive the infrastructure. What it means is that we, Robin is a software platform. We later ourselves on top of the machines that we sit on whether it is bare metal machines on premises, our VMs, or even an Azure, Google Cloud as well as AWs. Then we make the underlying compute, storage, network resources almost invisible. We treat it as a pool of resources. Now once you have this pool of resources, they can be attached to the applications that are being deployed as can inside containers. I mean, it's a software place, install on machines. Once it's installed, the experience now moves away from infrastructure into applications. You log in, you can see a portal, you have a lot of applications in that portal. We ship support for about 25 applications of some such. >> So these are templates? >> Yes. >> That the developer can then customize to their specific requirements? Or no? >> Absolutely, we ship reference templates for pretty much a wide variety of the most popular big data, no sequel, database, AI ML applications today. But again, as I said, it's a reference implementation. Typically customers take the reference recommendation and they enhance it or they use that to onboard their custom apps, for example, or the apps that we don't ship out of the box. So it's a very open, extensible platform but the goal being that whatever the application might be, in fact we keep saying that, if it runs somewhere else, it's runs on Robin, right? So the idea here is that you can bring anything, and we just, the flip of switch, you can make it a one click deploy, one click manage, one click mobile across Clouds. >> You keep mentioning this one click and this idea of it being so easy, so convenient, so seamless, is that what you say is the biggest concern of your customers? Is this ease and speed? Or what are some other things that are on their minds that you want to deliver? >> Right, so one click of course is a user experience part but what is the real challenge? The real challenges, there are a wide variety of tools being used by enterprises today. Even the data analytic pipeline, there's a lot across the data store, processor pipeline. Users don't want to deal with setting it up and keeping it up and running. They don't want that, they want to get the job done, right? Now when you only get the job done, you really want to hide the underlying details of those platforms and the best way to convey that, the best way to give that experience is to make it a single click experience from the UI. So I keep calling it all one click because that is the experience that you get to hide the underlying complexity for these apps. >> Does your environment actually compile executable code based on that one click experience? Or where does the compilation and containerization actually happen in your distributed architecture? >> Alright, so, I think the simplest- >> You're a prem based offering, right? You're not in the Cloud yourself? >> No, we are. We work on all the three big public clouds. >> Oh, okay. >> Whether it is Azure, AWS or Google. >> So your entire application is containerized itself for deployment into these Clouds? >> Yes, it is. >> Okay. >> So the idea here is let's simplify it significantly, right? You have Kubernetes today, it can run anywhere, on premises, in the public Cloud and so on. Kubernetes is a great platform for orchestrating containers but it is largely inaccessible to a certain class of data centric applications. >> Yeah. >> We make that possible. But our take is, just onboarding those applications on Kubernetes does not solve your CXO or you line of business user's problems. You ought to make the management, from an application point of view, not from a container management point of view, from an application point of view, a lot easier and that is where we kind of create this experience that I'm talking about, one click experience. >> Give us a sense for how, we're here at DataWorks and it's the Hortonworks show. Discuss with us your partnership with Hortonworks and you know, we've heard the announcement of HDP 3.0 and containerization support, just give us a rough sense for how you align or partner with Hortonworks in this area. >> Absolutely. It's kind of interesting because Hortonworks is a data management platform, if you think about it from that point of view and when we engaged with them first- So some of our customers have been using the product, Hortonworks, on top of Robin, so orchestrating Hortonworks, making it a lot easier to use. >> Right. >> One of the requirements was, "Are you certified with Hortonworks?" And the challenge that Hortonworks also had is they had never certified a container based deployment of Hortonworks before. They actually were very skeptical, you know, "You guys are saying all these things. Can you actually containerize and run Hortonworks?" So we worked with Hortonworks and we are, I mean if you go to the Hortonworks website, you'll see that we are the first in the entire industry who have been certified as a container based play that can actually deploy and manage Hortonworks. They have certified us by running a wide variety of tests, which they call the Q80 Test Suite, and when we got certified the only other players in the market that got that stamp of approval was Microsoft in Azure and EMC with Isilon. >> So you're in good company? >> I think we are in great company. >> You're certified to work with HTP 3.0 or the prior version or both? >> When we got certified we were still in the 2.X version of Hortonworks, HTP 3.0 is a more relatively newer version. But our plan is that we want to continue working with Hortonworks to get certified as they release the program and also help them because HTP 3.0 also has some container based orchestration and deployment so you want to help them provide the underlying infrastructure so that it becomes easier for beyond to spin up more containers. >> The higher level security and governance and all these things you're describing, they have to be over the Kubernetes layer. Hortonworks supports it in their data plane services portfolio. Does Robin Systems solutions portfolio tap in to any of that, or do you provide your own layer of sort of security and metadata management so forth? >> Yeah, so we don't want- >> In context of what you offer? >> Right, so we don't want to take away the security model that the application itself provides because might have step it up so that they are doing governance, it's not just logging in and auto control and things like this. Some governance is built into. We don't want to change that. We want to keep the same experience and the same workflow hat customers have so we just integrate with whatever security that the application has. We, of course, provide security in terms of isolating these different apps that are running on the Robin platform where the security or the access into the application itself is left to the apps themselves. When I say apps, I'm talking about Hortonworks. >> Yeah, sure. >> Or any other databases. >> Moving forward, as you think about ways you're going to augment and enhance and alter the Robin platform, what are some of the biggest trends that are driving your decision making around that in the sense of, as we know that companies are living with this deluge of data, how are you helping them manage it better? >> Sure. I think there are a few trends that we are closely watching. One is around Cloud mobility. CIOs want their applications along with their data to be available where their end users are. It's almost like follow the sun model, where you might have generated the data in one Cloud and at a different time, different time zone, you'll basically want to keep the app as well as data, moving. So we are following that very closely. How we can enable the mobility of data and apps a lot easier in that world. The other one is around the general AI ML workflow. One of the challenges there, of course, you have great apps like TensorFlow or Theano or Caffe, these are very good AI ML toolkits but one of the challenges that people face, is they are buying this very expensive, let's say NVIDIA DGX Box, this box costs about $150,000 each, how do you keep these boxes busy so that you're getting a good return on investment? It will require you to better manage the resources offered with these boxes. We are also monitoring that space and we're seeing that how can we take the Robin platform and how do you enable the better utilization of GPUs or the sharing of GPUs for running your AI ML kind of workload. >> Great. >> Those are, I think, two key trends that we are closely watching. >> We'll be discussing those at the next DataWorks Summit, I'm sure, at some other time in the future. >> Absolutely. >> Thank you so much for coming on theCUBE, Partha. >> Thank you. >> Thank you, my pleasure. Thanks. >> I'm Rebecca Knight for James Kobielus, We will have more from DataWorks coming up in just a little bit. (techno beat music)

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks Summit here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Rob Bearden. He is the CEO of Hortonworks. So thanks so much for coming on theCUBE again, Rob. >> Thank you for having us. >> So you just got off of the keynote on the main stage. The big theme is really about modern data architecture. So we're going to have this modern data architecture. What is it all about? How do you think about it? What's your approach? And how do you walk customers through this process? >> Well, there's a lot of moving parts in enabling a modern data architecture. One of the first steps is what we're trying to do is unlock the siloed transactional applications, and to get that data into a central architecture so you can get real time insights around the inclusive dataset. But what we're really trying to accomplish then within that modern data architecture is to bring all types of data whether it be real time streaming data, whether it be sensor data, IoT data, whether it be data that's coming from a connected core across the network, and to be able to bring all that data together in real time, and give the enterprise the ability to be able to take best in class action so that you get a very prescriptive outcome of what you want. So if we bring that data under management from point of origination and out on the edge, and then have the platforms that move that through its entire lifecycle, and that's our HDF platform, it gives the customer the ability to, after they capture it at the edge, move it, and then have the ability to process it as an event happens, a condition changes, various conditions come together, have the ability to process and take the exact action that you want to see performed against that, and then bring it to rest, and that's where our HDP platform comes into play where then all that data can be aggregated so you can have a holistic insight, and have real time interactions on that data. But then it then becomes about deploying those datasets and workloads on the tier that's most economically and architecturally pragmatic. So if that's on-prem, we make sure that we are architected for that on-prem deployment or private cloud or even across multiple public clouds simultaneously, and give the enterprise the ability to support each of those native environments. And so we think hybrid cloud architecture is really where the vast majority of our customers today and in the future, are going to want to be able to run and deploy their applications and workloads. And that's where our DataPlane Service Offering gives them the ability to have that hybrid architecture and the architectural latitude to move workloads and datasets across each tier transparently to what storage file format that they did or where that application is, and we provide all the tooling to match the complexity from doing that, and then we ensured that it has one common security framework, one common governance through its entire lifecycle, and one management platform to handle that entire lifecycle data. And that's the modern data architecture is to be able to bring all data under management, all types of data under management, and manage that in real time through its lifecycle til it comes at rest and deploy that across whatever architecture tier is most appropriate financially and from a performance on-cloud or prem. >> Rob, this morning at the keynote here in day one at DataWorks San Jose, you presented this whole architecture that you described in the context of what you call hybrid clouds to enable connected communities and with HDP, Hortonworks Data Platform 3.0 is one of the prime announcements, you brought containerization into the story. Could you connect those dots, containerization, connected communities, and HDP 3.0? >> Well, HDP 3.0 is really the foundation for enabling that hybrid architecture natively, and what's it done is it separated the storage from the compute, and so now we have the ability to deploy those workloads via a container strategy across whichever tier makes the most sense, and to move those application and datasets around, and to be able to leverage each tier in the deployment architectures that are most pragmatic. And then what that lets us do then is be able to bring all of the different data types, whether it be customer data, supply chain data, product data. So imagine as an industrial piece of equipment is, an airplane is flying from Atlanta, Georgia to London, and you want to be able to make sure you really understand how well is that each component performing, so that that plane is going to need service when it gets there, it doesn't miss the turnaround and leave 300 passengers stranded or delayed, right? Now with our Connected platform, we have the ability to take every piece of data from every component that's generated and see that in real time, and let the airlines make that real time. >> Delineate essentially. >> And ensure that we know every person that touched it and looked at that data through its entire lifecycle from the ground crew to the pilots to the operations team to the service. Folks on the ground to the reservation agents, and we can prove that if somehow that data has been breached, that we know exactly at what point it was breached and who did or didn't get to see it, and can prevent that because of the security models that we put in place. >> And that relates to compliance and mandates such as the Global Data Protection Regulation GDPR in the EU. At DataWorks Berlin a few months ago, you laid out, Hortonworks laid out, announced a new product called the Data Steward Studio to enable GDPR compliance. Can you give our listeners now who may not have been following the Berlin event a bit of an update on Data Steward Studio, how it relates to the whole data lineage, or set of requirements that you're describing, and then going forward what does Hortonworks's roadmap for supporting the full governance lifecycle for the Connected community, from data lineage through like model governance and so forth. Can you just connect a few dots that will be helpful? >> Absolutely. What's important certainly, driven by GDPR, is the requirement to be able to prove that you understand who's touched that data and who has not had access to it, and that you ensure that you're in compliance with the GDPR regulations which are significant, but essentially what they say is you have to protect the personal data and attributes of that data of the individual. And so what's very important is that you've got to be able to have the systems that not just secure the data, but understand who has the accessibility at any point in time that you've ever maintained that individual's data. And so it's not just about when you've had a transaction with that individual, but it's the rest of the history that you've kept or the multiple datasets that you may try to correlate to try to expand relationship with that customer, and you need to make sure that you can ensure not only that you've secured their data, but then you're protecting and governing who has access to it and when. And as importantly that you can prove in the event of a breach that you had control of that, and who did or did not access it, because if you can't prove any breach, that it was secure, and that no one breached it, who has or access to this not supposed to, you can be opened up for hundreds of thousands of dollars or even multiple millions of dollars of fines just because you can't prove that it was not accessed, and that's what the variety of our platforms, you mentioned Data Studio, is part of. DataPlane is one of the capabilities that gives us the ability. The core engine that does that is Atlas, and that's the open source governance platform that we developed through the community that really drives all the capabilities for governance that moves through each of our products, HDP, HDF, then of course, and DataPlane and Data Studio takes advantage of that and how it moves and replicates data and manages that process for us. >> One of the things that we were talking about before the cameras were rolling was this idea of data driven business models, how they are disrupting current contenders, new rivals coming on the scene all the time. Can you talk a little bit about what you're seeing and what are some of the most exciting and maybe also some of the most threatening things that you're seeing? >> Sure, in the traditional legacy enterprise, it's very procedural driven. You think about classic Encore ERP. It's worked very hard to have a very rigid, very structural procedural order to cash cycle that has not a great deal of flexibility. And it takes through a design process, it builds product, that then you sell product to a customer, and then you service that customer, and then you learn from that transaction different ways to automate or improve efficiencies in their supply chain. But it's very procedural, very linear. And in the new world of connected data models, you want to bring transparency and real time understanding and connectivity between the enterprise, the customer, the product, and the supply chain, and that you can take real time best in practice action. So for example you understand how well your product is performing. Is your customer using it correctly? Are they frustrated with that? Are they using it in the patterns and the frequency that they should be if they are going to expand their use and buy more, and if they're not, how do we engage in that cycle? How do we understand if they're going through a re-review and another buying of something similar that may not be with you for a different reason. And when we have real time visibility to our customer's interaction, understand our product's performance through its entire lifecycle, then we can bring real time efficiency with linking those together with our supply chain into the various relationships we have with our customers. To do that, it requires the modern data architecture, bringing data under management from the point it originates, whether it's from the product or the customer interacting with the company, or the customer interacting potentially with our ecosystem partners, mutual partners, and then letting the best in practice supply chain techniques, make sure that we're bringing the highest level of service and support to that entire lifecycle. And when we bring data under management, manage it through its lifecycle and have the historical view at rest, and leverage that across every tier, that's when we get these high velocity, deep transparency, and connectivity between each of the constituents in the value chain, and that's what our platforms give them the ability to do. >> Not only your platform, you guys have been in business now for I think seven years or so, and you shifted from being in the minds of many and including your own strategy from being the premier data at rest company in terms of the a Hadoop platform to being one of the premier data in motion companies. Is that really where you're going? To be more of a completely streaming focus, solution provider in a multi-cloud environment? And I hear a lot of Kafka in your story now that it's like, oh yeah, that's right, Hortonworks is big on Kafka. Can you give us just a quick sense of how you're making that shift towards low latency real time streaming, big data, or small data for that matter, with embedded analytics and machine learning? >> So, we have evolved from certainly being the leader in global data platforms with all the work that we do collaboratively, and in through the community, to make Hadoop an enterprise viable data platform that has the ability to run mission critical workloads and apps at scale, ensuring that it has all the enterprise facilities from security and governance and management. But you're right, we have expanded our footprint aggressively. And we saw the opportunity to actually create more value for our customers by giving them the ability to not wait til they bring data under management to gain an insight, because in that case, they're happened to be reactive post event post transaction. We want to give them the ability to shift their business model to being interactive, pre-event, pre-conditioned. The way to do that we learned was to be able to bring the data under management from the point of origination, and that's what we used MiNiFi and NiFi for, and then HDF, to move it through its lifecycle, and your point, we have the intellect, we have the insight, and then we have the ability then to process the best in class outcome based on what we know the variables are we're trying to solve for as that's happening. >> And there's the word, the phrase asset which of course is a transactional data paradigm plan, I hear that all over your story now in streaming. So, what you're saying is it's a completely enterprise-grade streaming environment from n to n for the new era of edge computing. Would that be a fair way of-- >> It's very much so. And our model and strategy has always been bring the other best in class engines for what they do well for their particular dataset. A couple of examples of that, one, you brought up Kafka, another is Spark. And they do what they do really well. But what we do is make sure that they fit inside an overall data architecture that then embodies their access to a much broader central dataset that goes from point of origination to point of rest on a whole central architecture, and then benefit from our security, governance, and operations model, being able to manage those engines. So what we're trying to do is eliminate the silos for our customers, and having siloed datasets that just do particular functions. We give them the ability to have an enterprise modern data architecture, we manage the things that bring that forward for the enterprise to have the modern data driven business models by bringing the governance, the security, the operations management, ensure that those workflows go from beginning to end seamlessly. >> Do you, go ahead. >> So I was just going to ask about the customer concerns. So here you are, you've now given them this ability to make these real time changes, what's sort of next? What's on their mind now and what do you see as the future of what you want to deliver next? >> First and foremost we got to make sure we get this right, and we really bring this modern data architecture forward, and make sure that we truly have the governance correct, the security models correct. One pane of glass to manage this. And really enable that hybrid data architecture, and let them leverage the cloud tier where it's architecturally and financially pragmatic to do it, and give them the ability to leg into a cloud architecture without risk of either being locked in or misunderstanding where the lines of demarcation of workloads or datasets are, and not getting the economies or efficiencies they should. And we solved that with DataPlane. So we're working very hard with the community, with our ecosystem and strategic partners to make sure that we're enabling the ability to bring each type of data from any source and deploy it across any tier with a common security, governance, and management framework. So then what's next is now that we have this high velocity of data through its entire lifecycle on one common set of platforms, then we can start enabling the modern applications to function. And we can go look back into some of the legacy technologies that are very procedural based and are dependent on a transaction or an event happening before they can run their logic to get an outcome because that grinds the customer in post world activity. We want to make sure that we're bringing that kind of, for example, supply chain functionality, to the modern data architecture, so that we can put real time inventory allocation based on the patterns that our customers go in either how they're using the product, or frustrations they've had, or success they've had. And we know through artificial intelligence and machine learning that there's a high probability not only they will buy or use or expand their consumption of whatever that they have of our product or service, but it will probably to these other things as well if we do those things. >> Predict the logic as opposed to procedural, yes, AI. >> And very much so. And so it'll be bringing those what's next will be the modern applications on top of this that become very predictive and enabler versus very procedural post to that post transaction. We're little ways downstream. That's looking out. >> That's next year's conference. >> That's probably next year's conference. >> Well, Rob, thank you so much for coming on theCUBE, it's always a pleasure to have you. >> Thank you both for having us, and thank you for being here, and enjoy the summit. >> We're excited. >> Thank you. >> We'll do. >> I'm Rebecca Knight for Jim Kobielus. We will have more from DataWorks Summit just after this. (upbeat music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE, overing DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back everyone to day two of theCUBE's live coverage of DataWorks, here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host James Kobielus. We have two guests on this panel today, we have Tim Vincent, he is the VP of Cognitive Systems Software at IBM, and Steve Roberts, who is the Offering Manager for Big Data on IBM Power Systems. Thanks so much for coming on theCUBE. >> Oh thank you very much. >> Thanks for having us. >> So we're now in this new era, this Cognitive Systems era. Can you set the scene for our viewers, and tell our viewers a little bit about what you do and why it's so important >> Okay, I'll give a bit of a background first, because James knows me from my previous role as, and you know I spent a lot of time in the data and analytics space. I was the CTO for Bob running the analytics group up 'til about a year and a half ago, and we spent a lot of time looking at what we needed to do from a data perspective and AI's perspective. And Bob, when he moved over to the Cognitive Systems, Bob Picciano who's my current boss, Bob asked me to move over and really start helping build, help to build out more of a software, and more of an AI focus, and a workload focus on how we thinking of the Power brand. So we spent a lot of time on that. So when you talk about cognitive systems or AI, what we're really trying to do is think about how you actually couple a combination of software, so co-optimize software space and the hardware space specific of what's needed for AI systems. Because the act of processing, the data processing, the algorithmic processing for AI is very, very different then what you would have for traditional data workload. So we're spending a lot of time thinking about how you actually co-optimize those systems so you can actually build a system that's really optimized for the demands of AI. >> And is this driven by customers, is this driven by just a trend that IBM is seeing? I mean how are you, >> It's a combination of both. >> So a lot of this is, you know, there's a lot of thought put into this before I joined the team. So there was a lot of good thinking from the Power brand, but it was really foresight on things like Moore's Law coming to an end of it's lifecycle right, and the ramifications to that. And at the same time as you start getting into things like narrow NATS and the floating point operations that you need to drive a narrow NAT, it was clear that we were hitting the boundaries. And then there's new technologies such as what Nvidia produces with with their GPUs, that are clearly advantageous. So there's a lot of trends that were comin' together the technical team saw, and at the same time we were seeing customers struggling with specific things. You know how to actually build a model if the training time is going to be weeks, and months, or let alone hours. And one of the scenarios I like to think about, I was probably showing my age a bit, but went to a school called University of Waterloo, and when I went to school, and in my early years, they had a batch based system for compilation and a systems run. You sit in the lab at night and you submit a compile job and the compile job will say, okay it's going to take three hours to compile the application, and you think of the productivity hit that has to you. And now you start thinking about, okay you've got this new skill in data scientists, which is really, really hard to find, they're very, very valuable. And you're giving them systems that take hours and weeks to do what the need to do. And you know, so they're trying to drive these models and get a high degree of accuracy in their predictions, and they just can't do it. So there's foresight on the technology side and there's clear demand on the customer side as well. >> Before the cameras were rolling you were talking about how the term data scientists and app developers is used interchangeably, and that's just wrong. >> And actually let's hear, 'cause I'd be in this whole position that I agree with it. I think it's the right framework. Data science is a team sport but application development has an even larger team sport in which data scientists, data engineers play a role. So, yeah we want to hear your ideas on the broader application development ecosystem, and where data scientists, and data engineers, and sort, fall into that broader spectrum. And then how IBM is supporting that entire new paradigm of application development, with your solution portfolio including, you know Power, AI on Power? >> So I think you used the word collaboration and team sport, and data science is a collaborative team sport. But you're 100% correct, there's also a, and I think it's missing to a great degree today, and it's probably limiting the actual value AI in the industry, and that's had to be data scientists and the application developers interact with each other. Because if you think about it, one of the models I like to think about is a consumer-producer model. Who consumes things and who produces things? And basically the data scientists are producing a specific thing, which is you know simply an AI model, >> Machine models, deep-learning models. >> Machine learning and deep learning, and the application developers are consuming those things and then producing something else, which is the application logic which is driving your business processes, and this view. So they got to work together. But there's a lot of confusion about who does what. You know you see people who talk with data scientists, build application logic, and you know the number of people who are data scientists can do that is, you know it exists, but it's not where the value, the value they bring to the equation. And the application developers developing AI models, you know they exist, but it's not the most prevalent form fact. >> But you know it's kind of unbalanced Tim, in the industry discussion of these role definitions. Quite often the traditional, you know definition, our sculpting of data scientist is that they know statistical modeling, plus data management, plus coding right? But you never hear the opposite, that coders somehow need to understand how to build statistical models and so forth. Do you think that the coders of the future will at least on some level need to be conversant with the practices of building,and tuning, or training the machine learning models or no? >> I think it's absolutely happen. And I will actually take it a step further, because again the data scientist skill is hard for a lot of people to find. >> Yeah. >> And as such is a very valuable skill. And what we're seeing, and we are actually one of the offerings that we're pulling out is something called PowerAI Vision, and it takes it up another level above the application developer, which is how do you actually really unlock the capabilities of AI to the business persona, the subject matter expert. So in the case of vision, how do you actually allow somebody to build a model without really knowing what a deep learning algorithm is, what kind of narrow NATS you use, how to do data preparation. So we build a tool set which is, you know effectively a SME tool set, which allows you to automatically label, it actually allows you to tag and label images, and then as you're tagging and labeling images it learns from that and actually it helps automate the labeling of the image. >> Is this distinct from data science experience on the one hand, which is geared towards the data scientists and I think Watson Analytics among your tools, is geared towards the SME, this a third tool, or an overlap. >> Yeah this is a third tool, which is really again one of the co-optimized capabilities that I talked about, is it's a tool that we built out that really is leveraging the combination of what we do in Power, the interconnect which we have with the GPU's, which is the NVLink interconnect, which gives us basically a 10X improvement in bandwidth between the CPU and GPU. That allows you to actually train your models much more quickly, so we're seeing about a 4X improvement over competitive technologies that are also using GPU's. And if we're looking at machine learning algorithms, we've recently come out with some technology we call Snap ML, which allows you to push machine learning, >> Snap ML, >> Yeah, it allows you to push machine learning algorithms down into the GPU's, and this is, we're seeing about a 40 to 50X improvement over traditional processing. So it's coupling all these capabilities, but really allowing a business persona to something specific, which is allow them to build out AI models to do recognition on either images or videos. >> Is there a pre-existing library of models in the solution that they can tap into? >> Basically it allows, it has a, >> Are they pre-trained? >> No they're not pre-trained models that's one of the differences in it. It actually has a set of models that allow, it picks for you, and actually so, >> Oh yes, okay. >> So this is why it helps the business persona because it's helping them with labeling the data. It's also helping select the best model. It's doing things under the covers to optimize things like hyper-parameter tuning, but you know the end-user doesn't have to know about all these things right? So you're tryin' to lift, and it comes back to your point on application developers, it allows you to lift the barrier for people to do these tasks. >> Even for professional data scientists, there may be a vast library of models that they don't necessarily know what is the best fit for the particular task. Ideally you should have, the infrastructure should recommend and choose, under various circumstances, the models, and the algorithms, the libraries, whatever for you for to the task, great. >> One extra feature of PowerAI Enterprises is that it does include a way to do a quick visual inspection of a models accuracy with a small data sample before you invest in scaling over a cluster or large data set. So you can get a visual indicator as to the, whether the models moving towards accuracy or you need to go and test an alternate model. >> So it's like a dashboard, of like Gini coefficients and all that stuff, okay. >> Exactly it gives you a snapshot view. And the other thing I was going to mention, you guys talked about application development, data scientists and of course a big message here at the conference is, you know data science meets big data and the work that Hortonworks is doing involving the notion of container support in YARN, GPU awareness in YARN, bringing data science experience, which you can include the PowerAI capability that Tim was talking about, as a workload tightly coupled with Hadoop. And this is where our Power servers are really built, not for just a monolithic building block that always has the same ratio of compute and storage, but fit for purpose servers that can address either GPU optimized workloads, providing the bandwidth enhancements that Tim talked about with the GPU, but also day-to-day servers, that can now support two terrabytes of memory, double the overall memory bandwidth on the box, 44 cores that can support up to 176 threads for parallelization of Spark workloads, Sequel workloads, distributed data science workloads. So it's really about choosing the combination of servers that can really mix this evolving workload need, 'cause a dupe isn't now just map produced, it's a multitude of workloads that you need to be able to mix and match, and bring various capabilities to the table for a compute, and that's where Power8, now Power9 has really been built for this kind of combination workloads where you can add acceleration where it makes sense, add big data, smaller core, smaller memory, where it makes sense, pick and choose. >> So Steve at this show, at DataWorks 2018 here in San Jose, the prime announcement, partnership announced between IBM and Hortonworks was IHAH, which I believe is IBM Host Analytics on Hortonworks. What I want to know is that solution that runs inside, I mean it runs on top of HDP 3.0 and so forth, is there any tie-in from an offering management standpoint between that and PowerAI so you can build models in the PowerAI environment, and then deploy them out to, in conjunction with the IHAH, is there, going forward, I mean just wanted to get a sense of whether those kinds of integrations. >> Well the same data science capability, data science experience, whether you choose to run it in the public cloud, or run it in private cloud monitor on prem, it's the same data science package. You know PowerAI has a set of optimized deep-learning libraries that can provide advantage on power, apply when you choose to run those deployments on our Power system alright, so we can provide additional value in terms of these optimized libraries, this memory bandwidth improvements. So really it depends upon the customer requirements and whether a Power foundation would make sense in some of those deployment models. I mean for us here with Power9 we've recently announced a whole series of Linux Power9 servers. That's our latest family, including as I mentioned, storage dense servers. The one we're showcasing on the floor here today, along with GPU rich servers. We're releasing fresh reference architecture. It's really to support combinations of clustered models that can as I mentioned, fit for purpose for the workload, to bring data science and big data together in the right combination. And working towards cloud models as well that can support mixing Power in ICP with big data solutions as well. >> And before we wrap, we just wanted to wrap. I think in the reference architecture you describe, I'm excited about the fact that you've commercialized distributed deep-learning for the growing number of instances where you're going to build containerized AI and distributing pieces of it across in this multi-cloud, you need the underlying middleware fabric to allow all those pieces to play together into some larger applications. So I've been following DDL because you've, research lab has been posting information about that, you know for quite a while. So I'm excited that you guys have finally commercialized it. I think there's a really good job of commercializing what comes out of the lab, like with Watson. >> Great well a good note to end on. Thanks so much for joining us. >> Oh thank you. Thank you for the, >> Thank you. >> We will have more from theCUBE's live coverage of DataWorks coming up just after this. (bright electronic music)

>> Live, from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back everyone to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my cohost James Kobielus. We are joined by Mike McNamara, he is the Senior Product and Solutions Marketing at NetApp. Thanks so much for coming on theCUBE. >> Thanks for having me. >> You're a first timer, >> Yes, >> So this is very exciting! >> Happy to be here. >> Welcome. >> Thanks. >> So, before the cameras were rolling, we were talking about how NetApp has been in this space for a while, but is really just starting to be recognized as a player. So, talk a little bit about your company's evolution. >> Sure. So, in the whole analytic space, is something NetApp was in a long time ago, and then sort got out of it, and then over the last several years, we've gotten back in, and we recognize it's a huge opportunity for data storage, data management, if you look at IDC Data, massive, massive market, but, the opportunity for us, is like you know what, they're mainly using a direct attached storage model where compute and storage is tied together. And now, with data just exploding, and growing like crazy, it's always been growing, but now it seems like it's just growing like crazy now, that, and customers wanting to have data on-prem, but also being able to move it off to the cloud, we're like, hey this is a great opportunity for us to come in with a solution that's, external storage solution that can come in and show them the benefits of have a more reliable, have an opportunity to move their data off to the cloud, we've got great solutions with that, so it's gone well, but it's been a little bit different, like at this show, a lot of the people, the data scientists, data engineers, some who know us, some still don't like, so, NetApp, what do you guys do, and so it's a little bit of an education, 'cause it's not a traditional buyer, if you will, we look at them as influencers, but it's only one influence than we traditionally have sold to say Vice President of Infrastructure, as an example, or maybe a Director of Storage Admin, but most of those folks are not here, so we're, this is just kind of a new market for us that we're making inroads. >> How do data scientists, or do they influence the purchase of storage solutions, or data management solutions? >> Sure, so they want to have access to the data, they want to be able analyze it quickly and effectively, they want to make sure it's always available, you know, at their fingertips so to speak. We can help them by giving them very fast, very reliable solutions, and specially with our software, they want to do for example, do some virtual clone of that data, and just do some testing on that without impacting their production data, we can do that in a snap, so we can make their lives a lot easier, so we can show them how, hey, mister data scientist, we can make your life a little easier-- >> Or miss data scientist. >> Or miss, we were talking about that, >> There are a lot of women in this field. >> Yeah, yeah. >> More than we realize, and they're great. >> So we can help you do your job better, and then, that, him or her can then influence who's making the purchase decisions. >> Yeah, training sets, test sets, validation sets of data for the machine learning and analytics development pipeline, yes, you need a solid storage infrastructure to do it right. >> Absolutely. >> So, when you're getting inside the head of your potential buyer here, the VP of Infrastructure, or data admin, what is it that you're hearing from those people most, what are their concerns, what keeps them up at night, and where do you come in? >> Yeah, so one of the concerns is, often times, you're, hey, how do I, do you have a cloud storage, connected to the cloud, you know, I'm doing things on-prem now, but is there a path, so that's a big one. And we, NetApp, pride ourselves on being the most cloud-connected, all flash storage in the industry. So, that's a big focus, big push for us. If you saw our marketing, it shows data authority for the hybrid cloud, so we really honestly do, whether it's with Google, or Azure, or AWS, we know our software runs in those environments, it also runs on-premises, but because it's the same on-tap software, we can move data between those environments. So, we get a real good storage, so we can you know, boom, check the box, we got you covered if you want to utilize the cloud, and I think the next piece of that is just from a protecting, protecting the data, you know, again I said data is just growing so much, I want to make sure it's always available, and we can back it up and all that, and that's been a core, core strength, versus like a lot of these traditional solutions they've been using, these direct attached models, they just don't have anywhere near the enterprise-grade data protection that NetApp has always prided itself on, over many decades now. And so, we can help them do that, and quite honestly, a lot of people think, well you know, you guys are external storage, how do you compare versus direct attached storage from our total cost, that's another one. I can tell you definitively, and we've got data to back it up from a total cost of ownership point of view, because of the fact that, of the advantages we bring from, up-time, and you know from RAID, but you know, in a Hadoop environment, often times there's three copies of data. With our solution, a good piece of software, there's only one copy of your data, so have three versus one is a big saving, but even what we do with the data, compressing it, and compacting it, a lot of benefits. So, we do have honest to goodness, outwards to 50% better total cost of ownership, versus a DAS model. >> Do you use machine learning within your portfolio? I'm hearing of more stories, >> Great question, yeah. >> Incorporating machine learning to automate or facilitate more of the functions in the data protection or data management life-cycle. >> Yeah, that's a great question, and we do use, so we've got a piece of software which we call Active IQ, it was referred to as Ace Update, you may have, it may ring a bell, but to answer your question, so we've got thousands of thousands of NetApp systems out there, and those customers that allow us, we have, think of it as kind of a call home feature, where we're getting data back from all our installed customers, and then we will go and do predictive analytics, and do some machine learning on that data, so then we can go back to those customers and say, hey you know what, you've got this volume that's unprotected, you should protect this, or we can show them, if you were to move that data off into our cloud environment, here's maybe performance you would see, so we do do a lot of that predictive-- >> Predictive performance assessment, it sounds like there's anomaly detection in there as well. >> Anomaly as well, letting them know, hey, you know, it's time for this drive, it may fail on you, let's ship you out a new drive now before it happens, so yeah, a lot of, from an analytics, predictive analysis going on. And you know, it's a huge benefit to our customers. Huge benefit. >> I know you're also doing a push toward artificial intelligence, so I'd like to hear more about that, and then also, if there's any best practices that have emerged. >> Sure, sure, so yes. That is another big area, so it's kind of a logical progression from where we were, if you will, in the analytics space, data lakes, but now moving into artificial intelligence, which has always been around, but it's really taking more of a more prominent role, I mean just a quick fun fact, I read that, you know that at the royal wedding that recently happened, did you know that Amazon used artificial intelligence to help us, the TV viewer, identify who the guests were. >> Ooh. >> So, you know it's like, it's everywhere, right? And so for us, we see that trend, a ton of data that needs to be managed, and so we kind of look at it from the edge to the core, to the cloud, those three, not pillars, but directional ways, taking data from IOT centers at the edge, bring it into the core, doing training, and then if the customer so chooses, out to the cloud. So, yeah it is a big push for us now, and we're going a lot with Nvidia, is a key partner with us. >> Really? This is a bit futuristic, but I can see a role going forward for AI to look into large data volumes, like video objects, to find things like faces, and poses and gestures and so forth, and see, to use that intelligence to be able to reduce the data sets down to where it's reduced, to de-duplicate, so that you can use less storage and then you can re-construct the original video objects or whatever going forward, I mean as a potential use of AI within the storage efficiency. >> Yep, yeah you're right, and that again, like in the analytic space, how we roll our in-line efficiency capabilities and data protection, is you know, very important, and then being able to move the data off into the cloud, if the customer so chooses, or just wants to use the cloud. So yeah, some of the same benefits from cloud connectivity, performance and efficiency that analytics apply certainly to AI. You know, another fun fact too about AI, which might help us, you and I living in the Boston area, is that I've read IBM has a patent out to use AI in traffic signaling, so in conjunction with cameras, to get AI, so hopefully that, you know, that works well it could alleviate-- >> Lead them out of the Tip O'Neill tunnel easy. (laughing) >> You got it maybe worse in D.C. (laughing) >> I'd like to hear though, if you have any best practices that with this moving into AI, how are you experimenting with it, and how are you finding it used most efficiently and effectively. >> Yeah, so I think one way we are eating our own dog food, so to speak, in that we're using it internally, we're using it on our customers' data, as I was explaining to help look at trends, and do analysis. So that's one, and then it's other things, just you know, partnering with companies like Nvidia as well and coming out with a joint solution, so we're doing work with them on different solution areas. >> Great, great. Well, Mike thanks so much for coming on theCUBE, >> Thanks for having me! >> It was fun having you. >> You survived! >> Yes! (laughs) >> We'll look forward to many more CUBE conversations. >> Great to hear from NetApp, you're very much in the game. >> Indeed, indeed. >> Alright, thank you very much. >> I'm Rebecca Knight for James Kobielus, we will have more from theCUBE's coverage of DataWorks coming up in just a little bit. (electronic music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018. Brought to you by HortonWorks. >> We are wrapping up Day One of coverage of Dataworks here in San Jose, California on theCUBE. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have two guests for this last segment of the day. We have Sudhir Hasbe, who is the director of product management at Google and Ram Venkatesh, who is VP of Engineering at Hortonworks. Ram, Sudhir, thanks so much for coming on the show. >> Thank you very much. >> Thank you. >> So, I want to start out by asking you about a joint announcement that was made earlier this morning about using some Hortonworks technology deployed onto Google Cloud. Tell our viewers more. >> Sure, so basically what we announced was support for the Hortonworks DataPlatform and Hortonworks DataFlow, HDP and HDF, running on top of the Google Cloud Platform. So this includes deep integration with Google's cloud storage connector layer as well as it's a certified distribution of HDP to run on the Google Cloud Platform. >> I think the key thing is a lot of our customers have been telling us they like the familiar environment of Hortonworks distribution that they've been using on-premises and as they look at moving to cloud, like in GCP, Google Cloud, they want the similar, familiar environment. So, they want the choice to deploy on-premises or Google Cloud, but they want the familiarity of what they've already been using with Hortonworks products. So this announcement actually helps customers pick and choose like whether they want to run Hortonworks distribution on-premises, they want to do it in cloud, or they wat to build this hybrid solution where the data can reside on-premises, can move to cloud and build these common, hybrid architecture. So, that's what this does. >> So, HDP customers can store data in the Google Cloud. They can execute ephemeral workloads, analytic workloads, machine learning in the Google Cloud. And there's some tie-in between Hortonworks's real-time or low latency or streaming capabilities from HDF in the Google Cloud. So, could you describe, at a full sort of detail level, the degrees of technical integration between your two offerings here. >> You want to take that? >> Sure, I'll handle that. So, essentially, deep in the heart of HDP, there's the HDFS layer that includes Hadoop compatible file system which is a plug-able file system layer. So, what Google has done is they have provided an implementation of this API for the Google Cloud Storage Connector. So this is the GCS Connector. We've taken the connector and we've actually continued to refine it to work with our workloads and now Hortonworks has actually bundling, packaging, and making this connector be available as part of HDP. >> So bilateral data movement between them? Bilateral workload movement? >> No, think of this as being very efficient when our workloads are running on top of GCP. When they need to get at data, they can get at data that is in the Google Cloud Storage buckets in a very, very efficient manner. So, since we have fairly deep expertise on workloads like Apache Hive and Apache Spark, we've actually done work in these workloads to make sure that they can run efficiently, not just on HDFS, but also in the cloud storage connector. This is a critical part of making sure that the architecture is actually optimized for the cloud. So, at our skill and our customers are moving their workloads from on-premise to the cloud, it's not just functional parity, but they also need sort of the operational and the cost efficiency that they're looking for as they move to the cloud. So, to do that, we need to enable these fundamental disaggregated storage pattern. See, on-prem, the big win with Hadoop was we could bring the processing to where the data was. In the cloud, we need to make sure that we work well when storage and compute are disaggregated and they're scaled elastically, independent of each other. So this is a fairly fundamental architectural change. We want to make sure that we enable this in a first-class manner. >> I think that's a key point, right. I think what cloud allows you to do is scale the storage and compute independently. And so, with storing data in Google Cloud Storage, you can like scale that horizontally and then just leverage that as your storage layer. And the compute can independently scale by itself. And what this is allowing customers of HDP and HDF is store the data on GCP, on the cloud storage, and then just use the scale, the compute side of it with HDP and HDF. >> So, if you'll indulge me to a name, another Hortonworks partner for just a hypothetical. Let's say one of your customers is using IBM Data Science Experience to do TensorFlow modeling and training, can they then inside of HDP on GCP, can they use the compute infrastructure inside of GCP to do the actual modeling which is more compute intensive and then the separate decoupled storage infrastructure to do the training which is more storage intensive? Is that a capability that would available to your customers? With this integration with Google? >> Yeah, so where we are going with this is we are saying, IBM DSX and other solutions that are built on top of HDP, they can transparently take advantage of the fact that they have HDP compute infrastructure to run against. So, you can run your machine learning training jobs, you can run your scoring jobs and you can have the same unmodified DSX experience whether you're running against an on-premise HDP environment or an in-cloud HDP environment. Further, that's sort of the benefit for partners and partner solutions. From a customer standpoint, the big value prop here is that customers, they're used to securing and governing their data on-prem in their particular way with HDP, with Apache Ranger, Atlas, and so forth. So, when they move to the cloud, we want this experience to be seamless from a management standpoint. So, from a data management standpoint, we want all of their learning from a security and governance perspective to apply when they are running in Google Cloud as well. So, we've had this capability on Azure and on AWS, so with this partnership, we are announcing the same type of deep integration with GCP as well. >> So Hortonworks is that one pane of glass across all your product partners for all manner of jobs. Go ahead, Rebecca. >> Well, I just wanted to ask about, we've talked about the reason, the impetus for this. With the customer, it's more familiar for customers, it offers the seamless experience, But, can you delve a little bit into the business problems that you're solving for customers here? >> A lot of times, our customers are at various points on their cloud journey, that for some of them, it's very simple, they're like there's a broom coming by and the datacenter is going away in 12 months and I need to be in the cloud. So, this is where there is a wholesale movement of infrastructure from on-premise to the cloud. Others are exploring individual business use cases. So, for example, one of our large customers, a travel partner, so they are exploring their new pricing model and they want to roll out this pricing model in the cloud. They have on-premise infrastructure, they know they have that for a while. They are spinning up new use cases in the cloud typically for reasons of agility. So, if you, typically many of our customers, they operate large, multi-tenant clusters on-prem. That's nice for, so a very scalable compute for running large jobs. But, if you want to run, for example, a new version of Spark, you have to upgrade the entire cluster before you can do that. Whereas in this sort of model, what they can say is, they can bring up a new workload and just have the specific versions and dependency that it needs, independent of all of their other infrastructure. So this gives them agility where they can move as fast as... >> Through the containerization of the Spark jobs or whatever. >> Correct, and so containerization as well as even spinning up an entire new environment. Because, in the cloud, given that you have access to elastic compute resources, they can come and go. So, your workloads are much more independent of the underlying cluster than they are on-premise. And this is where sort of the core business benefits around agility, speed of deployment, things like that come into play. >> And also, if you look at the total cost of ownership, really take an example where customers are collecting all this information through the month. And, at month end, you want to do closing of books. And so that's a great example where you want ephemeral workloads. So this is like do it once in a month, finish the books and close the books. That's a great scenario for cloud where you don't have to on-premises create an infrastructure, keep it ready. So that's one example where now, in the new partnership, you can collect all the data through the on-premises if you want throughout the month. But, move that and leverage cloud to go ahead and scale and do this workload and finish the books and all. That's one, the second example I can give is, a lot of customers collecting, like they run their e-commerce platforms and all on-premises, let's say they're running it. They can still connect all these events through HDP that may be running on-premises with Kafka and then, what you can do is, in-cloud, in GCP, you can deploy HDP, HDF, and you can use the HDF from there for real-time stream processing. So, collect all these clickstream events, use them, make decisions like, hey, which products are selling better?, should we go ahead and give?, how many people are looking at that product?, or how many people have bought it?. That kind of aggregation and real-time at scale, now you can do in-cloud and build these hybrid architectures that are there. And enable scenarios where in past, to do that kind of stuff, you would have to procure hardware, deploy hardware, all of that. Which all goes away. In-cloud, you can do that much more flexibly and just use whatever capacity you have. >> Well, you know, ephemeral workloads are at the heart of what many enterprise data scientists do. Real-world experiments, ad-hoc experiments, with certain datasets. You build a TensorFlow model or maybe a model in Caffe or whatever and you deploy it out to a cluster and so the life of a data scientist is often nothing but a stream of new tasks that are all ephemeral in their own right but are part of an ongoing experimentation program that's, you know, they're building and testing assets that may be or may not be deployed in the production applications. That's you know, so I can see a clear need for that, well, that capability of this announcement in lots of working data science shops in the business world. >> Absolutely. >> And I think coming down to, if you really look at the partnership, right. There are two or three key areas where it's going to have a huge advantage for our customers. One is analytics at-scale at a lower cost, like total cost of ownership, reducing that, running at-scale analytics. That's one of the big things. Again, as I said, the hybrid scenarios. Most customers, enterprise customers have huge deployments of infrastructure on-premises and that's not going to go away. Over a period of time, leveraging cloud is a priority for a lot of customers but they will be in these hybrid scenarios. And what this partnership allows them to do is have these scenarios that can span across cloud and on-premises infrastructure that they are building and get business value out of all of these. And then, finally, we at Google believe that the world will be more and more real-time over a period of time. Like, we already are seeing a lot of these real-time scenarios with IoT events coming in and people making real-time decisions. And this is only going to grow. And this partnership also provides the whole streaming analytics capabilities in-cloud at-scale for customers to build these hybrid plus also real-time streaming scenarios with this package. >> Well it's clear from Google what the Hortonworks partnership gives you in this competitive space, in the multi-cloud space. It gives you that ability to support hybrid cloud scenarios. You're one of the premier public cloud providers and we all know about. And clearly now that you got, you've had the Hortonworks partnership, you have that ability to support those kinds of highly hybridized deployments for your customers, many of whom I'm sure have those requirements. >> That's perfect, exactly right. >> Well a great note to end on. Thank you so much for coming on theCUBE. Sudhir, Ram, that you so much. >> Thank you, thanks a lot. >> Thank you. >> I'm Rebecca Knight for James Kobielus, we will have more tomorrow from DataWorks. We will see you tomorrow. This is theCUBE signing off. >> From sunny San Jose. >> That's right.

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Stephanie McReynolds. She is the Vice President of Marketing at Alation. Thanks so much for, for returning to theCUBE, Stephanie. >> Thank you for having me again. >> So, before the cameras were rolling, we were talking about Kevin Slavin's talk on the main stage this morning, and talking about, well really, a background to sort of this concern about AI and automation coming to take people's jobs, but really, his overarching point was that we really, we shouldn't, we shouldn't let the algorithms take over, and that humans actually are an integral piece of this loop. So, riff on that a little bit. >> Yeah, what I found fascinating about what he presented were actual examples where having a human in the loop of AI decision-making had a more positive impact than just letting the algorithms decide for you, and turning it into kind of a black, a black box. And the issue is not so much that, you know, there's very few cases where the algorithms make the wrong decision. What happens the majority of the time is that the algorithms actually can't be understood by human. So if you have to roll back >> They're opaque, yeah. >> in your decision-making, or uncover it, >> I mean, who can crack what a convolutional neural network does, at a layer by layer, nobody can. >> Right, right. And so, his point was, if we want to avoid not just poor outcomes, but also make sure that the robots don't take over the world, right, which is where every like, media person goes first, right? (Rebecca and James laugh) That you really need a human in the loop of this process. And a really interesting example he gave was what happened with the 2015 storm, and he talked about 16 different algorithms that do weather predictions, and only one algorithm predicted, mis-predicted that there would be a huge weather storm on the east coast. So if there had been a human in the loop, we wouldn't have, you know, caused all this crisis, right? The human could've >> And this is the storm >> Easily seen. >> That shut down the subway system, >> That's right. That's right. >> And really canceled New York City for a few days there, yeah. >> That's right. So I find this pretty meaningful, because Alation is in the data cataloging space, and we have a lot of opportunity to take technical metadata and automate the collection of technical and business metadata and do all this stuff behind the scenes. >> And you make the discovery of it, and the analysis of it. >> We do the discovery of this, and leading to actual recommendations to users of data, that you could turn into automated analyses or automated recommendations. >> Algorithmic, algorithmically augmented human judgment is what it's all about, the way I see it. What do you think? >> Yeah, but I think there's a deeper insight that he was sharing, is it's not just human judgment that is required, but for humans to actually be in the loop of the analysis as it moves from stage to stage, that we can try to influence or at least understand what's happening with that algorithm. And I think that's a really interesting point. You know, there's a number of data cataloging vendors, you know, some analysts will say there's anywhere from 10 to 30 different vendors in the data cataloging space, and as vendors, we kind of have this debate. Some vendors have more advanced AI and machine learning capabilities, and other vendors haven't automated at all. And I think that the answer, if you really want humans to adopt analytics, and to be comfortable with the decision-making of those algorithms, you need to have a human in the loop, in the middle of that process, of not only making the decision, but actually managing the data that flows through these systems. >> Well, algorithmic transparency and accountability is an increasing requirement. It's a requirement for GDPR compliance, for example. >> That's right. >> That I don't see yet with Wiki, but we don't see a lot of solution providers offering solutions to enable more of an automated roll-up of a narrative of an algorithmic decision path. But that clearly is a capability as it comes along, and it will. That will absolutely depend on a big data catalog managing the data, the metadata, but also helping to manage the tracking of what models were used to drive what decision, >> That's right. >> And what scenario. So that, that plays into what Alation >> So we talk, >> And others in your space do. >> We call that data catalog, almost as if the data's the only thing that we're tracking, but in addition to that, that metadata or the data itself, you also need to track the business semantics, how the business is using or applying that data and that algorithmic logic, so that might be logic that's just being used to transform that data, or it might be logic to actually make and automate decision, like what they're talking about GDPR. >> It's a data artifact catalog. These are all artifacts that, they are derived in many ways, or supplement and complement the data. >> That's right. >> They're all, it's all the logic, like you said. >> And what we talk about is, how do you create transparency into all those artifacts, right? So, a catalog starts with this inventory that creates a foundation for transparency, but if you don't make those artifacts accessible to a business person, who might not understand what is metadata, what is a transformation script. If you can't make that, those artifacts accessible to a, what I consider a real, or normal human being, right, (James laughs) I love to geek out, but, (all laugh) at some point, not everyone is going to understand. >> She's the normal human being in this team. >> I'm normal. I'm normal. >> I'm the abnormal human being among the questioners here. >> So, yeah, most people in the business are just getting our arms around how do we trust the output of analytics, how do we understand enough statistics and know what to apply to solve a business problem or not, and then we give them this like, hairball of technical artifacts and say, oh, go at it. You know, here's your transparency. >> Well, I want to ask about that, that human that we're talking about, that needs to be in the loop at every stage. What, that, surely, we can make the data more accessible, and, but it also requires a specialized skill set, and I want to ask you about the talent, because I noticed on your LinkedIn, you said, hey, we're hiring, so let me know. >> That's right, we're always hiring. We're a startup, growing well. >> So I want to know from you, I mean, are you having difficulty with filling roles? I mean, what is at the pipeline here? Are people getting the skills that they need? >> Yeah, I mean, there's a wide, what I think is a misnomer is there's actually a wide variety of skills, and I think we're adding new positions to this pool of skills. So I think what we're starting to see is an expectation that true business people, if you are in a finance organization, or you're in a marketing organization, or you're in a sales organization, you're going to see a higher level of data literacy be expected of that, that business person, and that's, that doesn't mean that they have to go take a Python course and learn how to be a data scientist. It means that they have to understand statistics enough to realize what the output of an algorithm is, and how they should be able to apply that. So, we have some great customers, who have formally kicked off internal training programs that are data literacy programs. Munich Re Insurance is a good example. They spoke with James a couple of months ago in Berlin. >> Yeah, this conference in Berlin, yeah. >> That's right, that's right, and their chief data officer has kicked off a formal data literacy training program for their employees, so that they can get business people comfortable enough and trusting the data, and-- >> It's a business culture transformation initiative that's very impressive. >> Yeah. >> How serious they are, and how comprehensive they are. >> But I think we're going to see that become much more common. Pfizer has taken, who's another customer of ours, has taken on a similar initiative, and how do they make all of their employees be able to have access to data, but then also know when to apply it to particular decision-making use cases. And so, we're seeing this need for business people to get a little bit of training, and then for new roles, like information stewards, or data stewards, to come online, folks who can curate the data and the data assets, and help be kind of translators in the organization. >> Stephanie, will there be a need for a algorithm curator, or a model curator, to, you know, like a model whisperer, to explain how these AI, convolutional, recurrent, >> Yeah. >> Whatever, all these neural, how, what they actually do, you know. Would there be a need for that going forward? Another as a normal human being, who can somehow be bilingual in neural net and in standard language? >> I think, I think so. I mean, I think we've put this pressure on data scientists to be that person. >> Oh my gosh, they're so busy doing their job. How can we expect them to explain, and I mean, >> Right. >> And to spend 100% of their time explaining it to the rest of us? >> And this is the challenge with some of the regulations like GDPR. We aren't set up yet, as organizations, to accommodate this complexity of understanding, and I think that this part of the market is going to move very quickly, so as vendors, one of the things that we can do is continue to help by building out applications that make it easy for information stewardship. How do you lower the barrier for these specialist roles and make it easy for them to do their job by using AI and machine learning, where appropriate, to help scale the manual work, but keeping a human in the loop to certify that data asset, or to add additional explanation and then taking their work and using AI, machine learning, and automation to propagate that work out throughout the organization, so that everyone then has access to those explanations. So you're no longer requiring the data scientists to hold like, I know other organizations that hold office hours, and the data scientist like sits at a desk, like you did in college, and people can come in and ask them questions about neural nets. That's just not going to scale at today's pace of business. >> Right, right. >> You know, the term that I used just now, the algorithm or model whisperer, you know, the recommend-er function that is built into your environment, in similar data catalog, is a key piece of infrastructure to rank the relevance rank, you know, the outputs of the catalog or responses to queries that human beings might make. You know, the recommendation ranking is critically important to help human beings assess the, you know, what's going on in the system, and give them some advice about how to, what avenues to explore, I think, so. >> Yeah, yeah. And that's part of our definition of data catalog. It's not just this inventory of technical metadata. >> That would be boring, and dry, and useless. >> But that's where, >> For most human beings. >> That's where a lot of vendor solutions start, right? >> Yeah. >> And that's an important foundation. >> Yeah, for people who don't live 100% of their work day inside the big data catalog. I hear what you're saying, you know. >> Yeah, so people who want a data catalog, how you make that relevant to the business is you connect those technical assets, that technical metadata with how is the business actually using this in practice, and how can we have proactive recommendation or the recommendation engines, and certifications, and this information steward then communicating through this platform to others in the organization about how do you interpret this data and how do you use it to actually make business decisions. And I think that's how we're going to close the gap between technology adoption and actual data-driven decision-making, which we're not quite seeing yet. We're only seeing about 30, when they survey, only about 36% of companies are actually confident they're making data-driven decisions, even though there have been, you know, millions, if not billions of dollars that have gone into the data analytics market and investments, and it's because as a manager, I don't quite have the data literacy yet, and I don't quite have the transparency across the rest of the organization to close that trust gap on analytics. >> Here's my feeling, in terms of cultural transformations across businesses in general. I think the legal staff of every company is going to need to get real savvy on using those kinds of tools, like your catalog, with recommendation engines, to support e-discovery, or discovery of the algorithmic decision paths that were taken by their company's products, 'cause they're going to be called by judges and juries, under a subpoena and so forth, and so on, to explain all this, and they're human beings who've got law degrees, but who don't know data, and they need the data environment to help them frame up a case for what we did, and you know, so, we being the company that's involved. >> Yeah, and our politicians. I mean, anyone who's read Cathy's book, Weapons of Math Destruction, there are some great use cases of where, >> Math, M-A-T-H, yeah. >> Yes, M-A-T-H. But there are some great examples of where algorithms can go wrong, and many of our politicians and our representatives in government aren't quite ready to have that conversation. I think anyone who watched the Zuckerberg hearings you know, in congress saw the gap of knowledge that exists between >> Oh my gosh. >> The legal community, and you know, and the tech community today. So there's a lot of work to be done to get ready for this new future. >> But just getting back to the cultural transformation needed to be, to make data-driven decisions, one of the things you were talking about is getting the managers to trust the data, and we're hearing about what are the best practices to have that happen in the sense, of starting small, be willing to experiment, get out of the lab, try to get to insight right away. What are, what would your best advice be, to gain trust in the data? >> Yeah, I think the biggest gap is this issue of transparency. How do you make sure that everyone understands each step of the process and has access to be able to dig into that. If you have a foundation of transparency, it's a lot easier to trust, rather than, you know, right now, we have kind of like the high priesthood of analytics going on, right? (Rebecca laughs) And some believers will believe, but a lot of folks won't, and, you know, the origin story of Alation is really about taking these concepts of the scientific revolution and scientific process and how can we support, for data analysis, those same steps of scientific evaluation of a finding. That means that you need to publish your data set, you need to allow others to rework that data, and come up with their own findings, and you have to be open and foster conversations around data in your organization. One other customer of ours, Meijer, who's a grocery store in the mid-west, and if you're west coast or east coast-based, you might not have heard of them-- >> Oh, Meijers, thrifty acres. I'm from Michigan, and I know them, yeah. >> Gigantic. >> Yeah, there you go. Gigantic grocery chain in the mid-west, and, Joe Oppenheimer there actually introduced a program that he calls the social contract for analytics, and before anyone gets their license to use Tableau, or MicroStrategy, or SaaS, or any of the tools internally, he asks those individuals to sign a social contract, which basically says that I'll make my work transparent, I will document what I'm doing so that it's shareable, I'll use certain standards on how I format the data, so that if I come up with a, with a really insightful finding, it can be easily put into production throughout the rest of the organization. So this is a really simple example. His inspiration for that social contract was his high school freshman. He was entering high school and had to sign a social contract, that he wouldn't make fun of the teachers, or the students, you know, >> I love it. >> Very simple basics. >> Yeah, right, right, right. >> I wouldn't make fun of the teacher. >> We all need social contract. >> Oh my gosh, you have to make fun of the teacher. >> I think it was a little more formal than that, in the language, but that was the concept. >> That's violating your civil rights as a student. I'm sorry. (Stephanie laughs) >> Stephanie, always so much fun to have you here. Thank you so much for coming on. >> Thank you. It's a pleasure to be here. >> I'm Rebecca Knight, for James Kobielus. We'll have more of theCUBE's live coverage of DataWorks just after this.

>> Live from San Jose, in the heart of Silicon Valley, it's theCube. Covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back to day two of theCube's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight along with my co-host James Kobielus. James, it's great to be here with you in the hosting seat again. >> Day two, yes. >> Exactly. So here we are, this conference, 2,100 attendees from 32 countries, 23 industries. It's a relatively big show. They do three of them during the year. One of the things that I really-- >> It's a well-established show too. I think this is like the 11th year since Yahoo started up the first Hadoop summit in 2008. >> Right, right. >> So it's an established event, yeah go. >> Exactly, exactly. But I really want to talk about Hortonworks the company. This is something that you had brought up in an analyst report before the show started and that was talking about Hortonworks' cash flow positivity for the first time. >> Which is good. >> Which is good, which is a positive sign and yet what are the prospects for this company's financial health? We're still not seeing really clear signs of robust financial growth. >> I think the signs are good for the simple reason they're making significant investments now to prepare for the future that's almost inevitable. And the future that's almost inevitable, and when I say the future, the 2020s, the decade that's coming. Most of their customers will shift more of their workloads, maybe not entirely yet, to public cloud environments for everything they're doing, AI, machine learning, deep learning. And clearly the beneficiaries of that trend will be the public cloud providers, all of whom are Hortonworks' partners and established partners, AWS, Microsoft with Azure, Google with, you know, Google Cloud Platform, IBM with IBM Cloud. Hortonworks, and this is... You know, their partnerships with these cloud providers go back several years so it's not a new initiative for them. They've seen the writing on the wall practically from the start of Hortonworks' founding in 2011 and they now need to go deeper towards making their solution portfolio capable of being deployable on-prem, in cloud, public clouds, and in various and sundry funky combinations called hybrid multi-clouds. Okay, so, they've been making those investments in those partnerships and in public cloud enabling the Hortonworks Data Platform. Here at this show, DataWorks 2018 here in San Jose, they've released the latest major version, HDP 3.0 of their core platform with a lot of significant enhancements related to things that their customers are increasingly doing-- >> Well I want to ask you about those enhancements. >> But also they have partnership announcements, the deep ones of integration and, you know, lift and shift of the Hortonworks portfolio of HDP with Hortonworks DataFlow and DataPlane Services, so that those solutions can operate transparently on those public cloud environments as the customers, as and when the customers choose to shift their workloads. 'Cause Hortonworks really... You know, like Scott Gnau yesterday, I mean just laid it on the line, they know that the more of the public cloud workloads will predominate now in this space. They're just making these speculative investments that they absolutely have to now to prepare the way. So I think this cost that they're incurring now to prepare their entire portfolio for that inevitable future is the right thing to do and that's probably why they still have not attained massive rock and rollin' positive cash flow yet but I think that they're preparing the way for them to do so in the coming decade. >> So their financial future is looking brighter and they're doing the right things. >> Yeah, yes. >> So now let's talk tech. And this is really where you want to be, Jim, I know you. >> Oh I get sleep now and I don't think about tech constantly. >> So as you've said, they're really doing a lot of emphasis now on their public cloud partnerships. >> Yes. >> But they've also launched several new products and upgrades to existing products, what are you seeing that excites you and that you think really will be potential game changers? >> You know, this is geeky but this is important 'cause it's at the very heart of Hortonworks Data Platform 3.0, containerization of more... When you're a data scientist, and you're building a machine learning model using data that's maintained, and is persisted, and processed within Hortonworks Data Platform or any other big data platform, you want the ability increasingly for developing machine learning, deep learning, AI in general, to take that application you might build while you're using TensorFlow models, that you build on HDP, they will containerize it in Docker and, you know, orchestrate it all through Kubernetes and all that wonderful stuff, and deploy it out, those AI, out to increasingly edge computing, mobile computing, embedded computing environments where, you know, the real venture capital mania's happening, things like autonomous vehicles, and you know, drones, and you name it. So the fact is that Hortonworks has made that in many ways the premier new feature of HDP 3.0 announced here this week at the show. That very much harmonizes with what their partners, where their partners are going with containerization of AI. IBM, one of their premier partners, very recently, like last month, I think it was, announced the latest version of IBM, what do they call it, IBM Cloud Private, which has embedded as a core feature containerization within that environment which is a prem-based environment of AI and so forth. The fact that Hortonworks continues to maintain close alignment with the capabilities that its public cloud partners are building to their respective portfolios is important. But also Hortonworks with its, they call it, you know, a single pane of glass, the DataPlane Services for metadata and monitoring and governance and compliance across this sprawling hybrid multi-cloud, these scenarios. The fact that they're continuing to make, in fact, really focusing on deep investments in that portfolio, so that when an IBM introduces or, AWS, whoever, introduces some new feature in their respective platforms, Hortonworks has the ability to, as it were, abstract above and beyond all of that so that the customer, the developer, and the data administrator, all they need to do, if they're a Hortonworks customer, is stay within the DataPlane Services and environment to be able to deploy with harmonized metadata and harmonized policies, and harmonized schemas and so forth and so on, and query optimization across these sprawling environments. So Hortonworks, I think, knows where their bread is buttered and it needs to stay on the DPS, DataPlane Services, side which is why a couple months ago in Berlin, Hortonworks made a, I think, the most significant announcement of the year for them and really for the industry, was that they announced the Data Steward Studio in Berlin. Tech really clearly was who addressed the GDPR mandate that was coming up but really did a stewardship as an end-to-end workflow for lots of, you know, core enterprise applications, absolutely essential. Data Steward Studio is a DataPlane Service that can operate across multi-cloud environments. Hortonworks is going to keep on, you know... They didn't have a DPS, DataPlane Services, announcements here in San Jose this week but you can best believe that next year at this time at this show, and in the interim they'll probably have a number of significant announcements to deepen that portfolio. Once again it's to grease the wheels towards a more purely public cloud future in which there will be Hortonworks DNA inside most of their customers' environments going forward. >> I want to ask you about themes of this year's conference. The thing is is that you were in Berlin at the last big Hortonworks DataWorks Summit. >> (speaks in foreign language) >> And really GDPR dominated the conversations because the new rules and regulations hadn't yet taken effect and companies were sort of bracing for what life was going to be like under GDPR. Now the rules are here, they're here to stay, and companies are really grappling with it, trying to understand the changes and how they can exist in this new regime. What would you say are the biggest themes... We're still talking about GDPR, of course, but what would you say are the bigger themes that are this week's conference? Is it scalability, is it... I mean, what would you say we're going, what do you think has dominated the conversations here? >> Well scalability is not the big theme this week though there are significant scalability announcements this week in the context of HDP 3.0, the ability to persist in a scale-out fashion across multi-cloud, billions of files. Storage efficiency is an important piece of the overall announcement with support for erasure coding, blah blah blah. That's not, you know, that's... Already, Hortonworks, like all of their cloud providers and other big data providers, provide very scalable environments for storage, workload management. That was not the hugest, buzzy theme in terms of the announcements this week. The buzz of course was HDP 3.0. Containerization, that's important, but you know, we just came out of the day two keynote. AI is not a huge focus yet for a lot of the Hortonworks customers who are here, the developers. They're, you know, most of their customers are not yet that far along in their deep learning journeys and whatever but they're definitely going there. There's plenty of really cool keynote discussions including the guy with the autonomous vehicles or whatever that, the thing we just came out of. That was not the predominant theme this week here in terms of the HDP 3.0. I think what it comes down to is that with HDP 3.0... Hive, though you tend to take it for granted, it's been in Hadoop from the very start, practically, Hive is now a full enterprise database and that's the core, one of the cores, of HDP 3.0. Hive itself, Hive 3.0 now is its version, is ACID compliant and that may be totally geeky to the most of the world but that enables it to support transactional applications. So more big data in every environment is supporting more traditional enterprise application, transactional applications that require like two-phase commit and all that goodness. The fact is, you know, Hortonworks have, from what I can see, is the first of the big data vendors to incorporate those enhancements to Hive 3.0 because they're so completely tuned in to the Hive environment in terms of a committer. I think in many ways that is the predominant theme in terms of the new stuff that will actually resonate with the developers, their customers here at the show. And with the, you know, enterprises in general, they can put more of their traditional enterprise application workloads on big data environments and specifically, Hortonworks hopes, its HDP 3.0. >> Well I'm excited to learn more here at the on theCube with you today. We've got a lot of great interviews lined up and a lot of interesting content. We got a great crew too so this is a fun show to do. >> Sure is. >> We will have more from day two of the.

>> From San Jose, in the heart of Silicon Valley, it's theCube. Covering DataWorks Summit 2018. Brought to you by Hortonworks. (upbeat music) >> Welcome back to theCUBE's live coverage of Data Works here in sunny San Jose, California. I'm your host Rebecca Knight along with my co-host James Kobielus. We're joined by Pandit Prasad. He is the analytics, projects, strategy, and management at IBM Analytics. Thanks so much for coming on the show. >> Thanks Rebecca, glad to be here. >> So, why don't you just start out by telling our viewers a little bit about what you do in terms of in relationship with the Horton Works relationship and the other parts of your job. >> Sure, as you said I am in Offering Management, which is also known as Product Management for IBM, manage the big data portfolio from an IBM perspective. I was also working with Hortonworks on developing this relationship, nurturing that relationship, so it's been a year since the Northsys partnership. We announced this partnership exactly last year at the same conference. And now it's been a year, so this year has been a journey and aligning the two portfolios together. Right, so Hortonworks had HDP HDF. IBM also had similar products, so we have for example, Big Sequel, Hortonworks has Hive, so how Hive and Big Sequel align together. IBM has a Data Science Experience, where does that come into the picture on top of HDP, so it means before this partnership if you look into the market, it has been you sell Hadoop, you sell a sequel engine, you sell Data Science. So what this year has given us is more of a solution sell. Now with this partnership we go to the customers and say here is NTN experience for you. You start with Hadoop, you put more analytics on top of it, you then bring Big Sequel for complex queries and federation visualization stories and then finally you put Data Science on top of it, so it gives you a complete NTN solution, the NTN experience for getting the value out of the data. >> Now IBM a few years back released a Watson data platform for team data science with DSX, data science experience, as one of the tools for data scientists. Is Watson data platform still the core, I call it dev ops for data science and maybe that's the wrong term, that IBM provides to market or is there sort of a broader dev ops frame work within which IBM goes to market these tools? >> Sure, Watson data platform one year ago was more of a cloud platform and it had many components of it and now we are getting a lot of components on to the (mumbles) and data science experience is one part of it, so data science experience... >> So Watson analytics as well for subject matter experts and so forth. >> Yes. And again Watson has a whole suit of side business based offerings, data science experience is more of a a particular aspect of the focus, specifically on the data science and that's been now available on PRAM and now we are building this arm from stack, so we have HDP, HDF, Big Sequel, Data Science Experience and we are working towards adding more and more to that portfolio. >> Well you have a broader reference architecture and a stack of solutions AI and power and so for more of the deep learning development. In your relationship with Hortonworks, are they reselling more of those tools into their customer base to supplement, extend what they already resell DSX or is that outside of the scope of the relationship? >> No it is all part of the relationship, these three have been the core of what we announced last year and then there are other solutions. We have the whole governance solution right, so again it goes back to the partnership HDP brings with it Atlas. IBM has a whole suite of governance portfolio including the governance catalog. How do you expand the story from being a Hadoop-centric story to an enterprise data-like story, and then now we are taking that to the cloud that's what Truata is all about. Rob Thomas came out with a blog yesterday morning talking about Truata. If you look at it is nothing but a governed data-link hosted offering, if you want to simplify it. That's one way to look at it caters to the GDPR requirements as well. >> For GDPR for the IBM Hortonworks partnership is the lead solution for GDPR compliance, is it Hortonworks Data Steward Studio or is it any number of solutions that IBM already has for data governance and curation, or is it a combination of all of that in terms of what you, as partners, propose to customers for soup to nuts GDPR compliance? Give me a sense for... >> It is a combination of all of those so it has a HDP, its has HDF, it has Big Sequel, it has Data Science Experience, it had IBM governance catalog, it has IBM data quality and it has a bunch of security products, like Gaurdium and it has some new IBM proprietary components that are very specific towards data (cough drowns out speaker) and how do you deal with the personal data and sensitive personal data as classified by GDPR. I'm supposed to query some high level information but I'm not allowed to query deep into the personal information so how do you blog those queries, how do you understand those, these are not necessarily part of Data Steward Studio. These are some of the proprietary components that are thrown into the mix by IBM. >> One of the requirements that is not often talked about under GDPR, Ricky of Formworks got in to it a little bit in his presentation, was the notion that the requirement that if you are using an UE citizen's PII to drive algorithmic outcomes, that they have the right to full transparency. It's the algorithmic decision paths that were taken. I remember IBM had a tool under the Watson brand that wraps up a narrative of that sort. Is that something that IBM still, it was called Watson Curator a few years back, is that a solution that IBM still offers, because I'm getting a sense right now that Hortonworks has a specific solution, not to say that they may not be working on it, that addresses that side of GDPR, do you know what I'm referring to there? >> I'm not aware of something from the Hortonworks side beyond the Data Steward Studio, which offers basically identification of what some of the... >> Data lineage as opposed to model lineage. It's a subtle distinction. >> It can identify some of the personal information and maybe provide a way to tag it and hence, mask it, but the Truata offering is the one that is bringing some new research assets, after GDPR guidelines became clear and then they got into they are full of how do we cater to those requirements. These are relatively new proprietary components, they are not even being productized, that's why I am calling them proprietary components that are going in to this hosting service. >> IBM's got a big portfolio so I'll understand if you guys are still working out what position. Rebecca go ahead. >> I just wanted to ask you about this new era of GDPR. The last Hortonworks conference was sort of before it came into effect and now we're in this new era. How would you say companies are reacting? Are they in the right space for it, in the sense of they're really still understand the ripple effects and how it's all going to play out? How would you describe your interactions with companies in terms of how they're dealing with these new requirements? >> They are still trying to understand the requirements and interpret the requirements coming to terms with what that really means. For example I met with a customer and they are a multi-national company. They have data centers across different geos and they asked me, I have somebody from Asia trying to query the data so that the query should go to Europe, but the query processing should not happen in Asia, the query processing all should happen in Europe, and only the output of the query should be sent back to Asia. You won't be able to think in these terms before the GDPR guidance era. >> Right, exceedingly complicated. >> Decoupling storage from processing enables those kinds of fairly complex scenarios for compliance purposes. >> It's not just about the access to data, now you are getting into where the processing happens were the results are getting displayed, so we are getting... >> Severe penalties for not doing that so your customers need to keep up. There was announcement at this show at Dataworks 2018 of an IBM Hortonwokrs solution. IBM post-analytics with with Hortonworks. I wonder if you could speak a little bit about that, Pandit, in terms of what's provided, it's a subscription service? If you could tell us what subset of IBM's analytics portfolio is hosted for Hortonwork's customers? >> Sure, was you said, it is a a hosted offering. Initially we are starting of as base offering with three products, it will have HDP, Big Sequel, IBM DB2 Big Sequel and DSX, Data Science Experience. Those are the three solutions, again as I said, it is hosted on IBM Cloud, so customers have a choice of different configurations they can choose, whether it be VMs or bare metal. I should say this is probably the only offering, as of today, that offers bare metal configuration in the cloud. >> It's geared to data scientist developers and machine-learning models will build the models and train them in IBM Cloud, but in a hosted HDP in IBM Cloud. Is that correct? >> Yeah, I would rephrase that a little bit. There are several different offerings on the cloud today and we can think about them as you said for ad-hoc or ephemeral workloads, also geared towards low cost. You think about this offering as taking your on PRAM data center experience directly onto the cloud. It is geared towards very high performance. The hardware and the software they are all configured, optimized for providing high performance, not necessarily for ad-hoc workloads, or ephemeral workloads, they are capable of handling massive workloads, on sitcky workloads, not meant for I turned this massive performance computing power for a couple of hours and then switched them off, but rather, I'm going to run these massive workloads as if it is located in my data center, that's number one. It comes with the complete set of HDP. If you think about it there are currently in the cloud you have Hive and Hbase, the sequel engines and the stories separate, security is optional, governance is optional. This comes with the whole enchilada. It has security and governance all baked in. It provides the option to use Big Sequel, because once you get on Hadoop, the next experience is I want to run complex workloads. I want to run federated queries across Hadoop as well as other data storage. How do I handle those, and then it comes with Data Science Experience also configured for best performance and integrated together. As a part of this partnership, I mentioned earlier, that we have progress towards providing this story of an NTN solution. The next steps of that are, yeah I can say that it's an NTN solution but are the product's look and feel as if they are one solution. That's what we are getting into and I have featured some of those integrations. For example Big Sequel, IBM product, we have been working on baking it very closely with HDP. It can be deployed through Morey, it is integrated with Atlas and Granger for security. We are improving the integrations with Atlas for governance. >> Say you're building a Spark machine learning model inside a DSX on HDP within IH (mumbles) IBM hosting with Hortonworks on HDP 3.0, can you then containerize that machine learning Sparks and then deploy into an edge scenario? >> Sure, first was Big Sequel, the next one was DSX. DSX is integrated with HDP as well. We can run DSX workloads on HDP before, but what we have done now is, if you want to run the DSX workloads, I want to run a Python workload, I need to have Python libraries on all the nodes that I want to deploy. Suppose you are running a big cluster, 500 cluster. I need to have Python libraries on all 500 nodes and I need to maintain the versioning of it. If I upgrade the versions then I need to go and upgrade and make sure all of them are perfectly aligned. >> In this first version will you be able build a Spark model and a Tesorflow model and containerize them and deploy them. >> Yes. >> Across a multi-cloud and orchestrate them with Kubernetes to do all that meshing, is that a capability now or planned for the future within this portfolio? >> Yeah, we have that capability demonstrated in the pedestal today, so that is a new one integration. We can run virtual, we call it virtual Python environment. DSX can containerize it and run data that's foreclosed in the HDP cluster. Now we are making use of both the data in the cluster, as well as the infrastructure of the cluster itself for running the workloads. >> In terms of the layers stacked, is also incorporating the IBM distributed deep-learning technology that you've recently announced? Which I think is highly differentiated, because deep learning is increasingly become a set of capabilities that are across a distributed mesh playing together as is they're one unified application. Is that a capability now in this solution, or will it be in the near future? DPL distributed deep learning? >> No, we have not yet. >> I know that's on the AI power platform currently, gotcha. >> It's what we'll be talking about at next year's conference. >> That's definitely on the roadmap. We are starting with the base configuration of bare metals and VM configuration, next one is, depending on how the customers react to it, definitely we're thinking about bare metal with GPUs optimized for Tensorflow workloads. >> Exciting, we'll be tuned in the coming months and years I'm sure you guys will have that. >> Pandit, thank you so much for coming on theCUBE. We appreciate it. I'm Rebecca Knight for James Kobielus. We will have, more from theCUBE's live coverage of Dataworks, just after this.