>>Hey, everyone. Welcome to the AWS partner, showcase women in tech. I'm Lisa Martin from the cube. And today we're gonna be looking into the exciting evolution of women in the tech industry. I'm going to be joined by Danielle GShock, the ISP PSA director at AWS. And we have the privilege of speaking with some wicked smart women from Teradata NetApp. JFI a 10th revolution group, company and honeycomb.io. We're gonna look at some of the challenges and biases that women face in the tech industry, especially in leadership roles. We're also gonna be exploring how are these tech companies addressing diversity, equity and inclusion across their organizations? How can we get more young girls into stem earlier in their careers? So many questions. So let's go ahead and get started. This is the AWS partner showcase women in tech. Hey, everyone. Welcome to the AWS partner showcase. This is season one, episode three. And I'm your host, Lisa Martin. I've got two great guests here with me to talk about women in tech. Hillary Ashton joins us the chief product officer at Terry data. And Danielle Greshaw is back with us, the ISV PSA director at AWS ladies. It's great to have you on the program talking through such an important topic, Hillary, let's go ahead and start with you. Give us a little bit of an intro into you, your background, and a little bit about Teradata. >>Yeah, absolutely. So I'm Hillary Ashton. I head up the products organization. So that's our engineering product management office of the CTO team. Um, at Teradata I've been with Terra data for just about three years and really have spent the last several decades. If I can say that in the data and analytics space, um, I spent time, uh, really focused on the value of, of analytics at scale, and I'm super excited to be here at Teradata. I'm also a mom of two teenage boys. And so as we talk about women in tech, I think there's, um, uh, lots of different dimensions and angles of that. Um, at Teradata, we are partnered very deeply with AWS and happy to talk a little bit more about that, um, throughout this discussion as well. >>Excellent. A busy mom of two teen boys. My goodness. I don't know how you do it. Let's now look, Atter data's views of diversity, equity and inclusion. It's a, the, it's a topic that's important to everyone, but give us a snapshot into some of the initiatives that Terra data has there. >>Yeah, I have to say, I am super proud to be working at Teradata. We have gone through, uh, a series of transformations, but I think it starts with culture and we are deeply committed to diversity, equity and inclusion. It's really more than just a statement here. It's just how we live our lives. Um, and we use, uh, data to back that up. Um, in fact, we were named one of the world's most ethical companies for the 13th year in a row. Um, and all of our executive leadership team has taken an oath around D E and I that's available on LinkedIn as well. So, um, in fact, our leadership team reporting into the CEO is just about 50 50, um, men and women, which is the first time I've worked in a company where that has been the case. And I think as individuals, we can probably appreciate what a huge difference that makes in terms of not just being a representative, but truly being on a, on a diverse and equitable, uh, team. And I think it really, uh, improves the behaviors that we can bring, um, to our office. >>There's so much value in that. It's I impressive to see about a 50 50 at the leadership level. That's not something that we see very often. Tell me how you, Hillary, how did you get into tech? Were you an engineering person by computer science, or did you have more of a zigzaggy path to where you are now? >>I'm gonna pick door number two and say more zigzaggy. Um, I started off thinking, um, that I started off as a political science major or a government major. Um, and I was probably destined to go into, um, the law field, but actually took a summer course at Harvard. I did not go to Harvard, but I took a summer course there and learned a lot about multimedia and some programming. And that really set me on a trajectory of how, um, data and analytics can truly provide value and, and outcomes to our customers. Um, and I have been living that life ever since. Um, I graduated from college, so, um, I was very excited and privileged in my early career to, uh, work in a company where I found after my first year that I was managing, um, uh, kids, people who had graduated from Harvard business school and from MIT Sloan school. Um, and that was super crazy, cuz I did not go to either of those schools, but I sort of have always had a natural knack for how do you take technology and, and the really cool things that technology can do, but because I'm not a programmer by training, I'm really focused on the value that I'm able to help, um, organizations really extract value, um, from the technology that we can create, which I think is fantastic. >>I think there's so much value in having a zigzag path into tech. You bring Danielle, you and I have talked about this many times you bring such breadth and such a wide perspective. That really is such a value. Add to teams. Danielle, talk to us from AWS's perspective about what can be done to encourage more young women to get and under and underrepresented groups as well, to get into stem and stay. >>Yeah, and this is definitely a challenge as we're trying to grow our organization and kind of shift the numbers. And the reality is, especially with the more senior folks in our organization, unless you bring folks with a zigzag path, the likelihood is you won't be able to change the numbers that you have. Um, but for me, it's really been about, uh, looking at that, uh, the folks who are just graduating college, maybe in other roles where they are adjacent to technology and to try to spark their interest and show that yes, they can do it because oftentimes it's really about believing in themselves and, and realizing that we need folks with all sorts of different perspectives to kind of come in, to be able to help really, um, provide both products and services and solutions for all types of people inside of technology, which requires all sorts of perspectives. >>Yeah, the diverse perspectives. There's so much value and there's a lot of data that demonstrates how much value revenue impact organizations can make by having diversity, especially at the leadership level. Hillary, let's go back to you. We talked about your career path. You talked about some of the importance of the focus on de and I at Tarana, but what are, what do you think can be done to encourage, to sorry, to recruit more young women and under groups into tech, any, any carrot there that you think are really important that we need to be dangling more of? >>Yeah, absolutely. And I'll build on what Danielle just said. I think the, um, bringing in diverse understandings, um, of, of customer outcomes, I mean, I, the we've really moved from technology for technology's sake and I know AWS and entirety to have had a lot of conversations on how do we drive customer outcomes that are differentiated in the market and really being customer centric and technology is wonderful. You can do wonderful things with it. You can do not so wonderful things with it as well, but unless you're really focused on the outcomes and what customers are seeking, um, technology is not hugely valuable. And so I think bringing in people who understand, um, voice of customer who understand those outcomes, and those are not necessarily the, the, the folks who are PhD in mathematics or statistics, um, those can be people who understand a day in the life of a data scientist or a day in the life of a citizen data scientist. And so really working to bridge the high impact technology with the practical kind of usability, usefulness of data and analytics in our cases, I think is something that we need more of in tech and sort of demystifying tech and freeing technology so that everybody can use it and having a really wide range of people who understand not just the bits and bites and, and how to program, but also the value in outcomes that technology through data and analytics can drive. >>Yeah. You know, we often talk about the hard skills, but this, their soft skills are equally, if not more important that even just being curious, being willing to ask questions, being not afraid to be vulnerable, being able to show those sides of your personality. I think those are important for, for young women and underrepresented groups to understand that those are just as important as some of the harder technical skills that can be taught. >>That's right. >>What do you think about from a bias perspective, Hillary, what have you seen in the tech industry and how do you think we can leverage culture as you talked about to help dial down some of the biases that are going on? >>Yeah. I mean, I think first of all, and, and there's some interesting data out there that says that 90% of the population, which includes a lot of women have some inherent bias in their day, day behaviors when it comes to to women in particular. But I'm sure that that is true across all kinds of, of, um, diverse and underrepresented folks in, in the world. And so I think acknowledging that we have bias and actually really learning how, what that can look like, how that can show up. We might be sitting here and thinking, oh, of course I don't have any bias. And then you realize that, um, as you, as you learn more about, um, different types of bias, that actually you do need to kind of, um, account for that and change behaviors. And so I think learning is sort of a fundamental, um, uh, grounding for all of us to really know what bias looks like, know how it shows up in each of us. >>Um, if we're leaders know how it shows up in our teams and make sure that we are constantly getting better, we're, we're not gonna be perfect anytime soon. But I think being on a path to improvement to overcoming bias, um, is really, is really critical. And part of that is really starting the dialogue, having the conversations, holding ourselves and each other accountable, um, when things aren't going in, in a, in a Coptic way and being able to talk openly about that, that felt, um, like maybe there was some bias in that interaction and how do we, um, how do we make good on that? How do we change our, our behavior? Fundamentally of course, data and analytics can have some bias in it as well. And so I think as we look at the, the technology aspect of bias, um, looking at at ethical AI, I think is a, a really important, uh, additional area. And I'm sure we could spend another 20 minutes talking about that, but I, I would be remiss if I didn't talk more about sort of the bias, um, and the over the opportunity to overcome bias in data and analytics as well. >>Yeah. The opportunity to overcome it is definitely there you bring up a couple of really good points, Hillary. It, it starts with awareness. We need to be aware that there are inherent biases in data in thought. And also to your other point, hold people accountable ourselves, our teammates, that's critical to being able to, to dial that back down, Daniel, I wanna get your perspective on, on your view of women in leadership roles. Do you think that we have good representation or we still have work to do in there? >>I definitely think in both technical and product roles, we definitely have some work to do. And, you know, when I think about, um, our partnership with Teradata, part of the reason why it's so important is, you know, Teradata solution is really the brains of a lot of companies. Um, you know, the what, how, what they differentiate on how they figure out insights into their business. And it's, it's all about the product itself and the data and the same is true at AWS. And, you know, we really could do some work to have some more women in these technical roles, as well as in the product, shaping the products. Uh, just for all the reasons that we just kind of talked about over the last 10 minutes, um, in order to, you know, move bias out of our, um, out of our solutions and also to just build better products and have, uh, better, you know, outcomes for customers. So I think there's a bit of work to do still. >>I agree. There's definitely a bit of work to do, and it's all about delivering those better outcomes for customers at the end of the day, we need to figure out what the right ways are of doing that and working together in a community. Um, we've had obviously a lot had changed in the last couple of years, Hillary, what's your, what have you seen in terms of the impact that the pandemic has had on this status of women in tech? Has it been a pro is silver lining the opposite? What are you seeing? >>Yeah, I mean, certainly there's data out there that tells us factually that it has been, um, very difficult for women during COVID 19. Um, women have, uh, dropped out of the workforce for a wide range of, of reasons. Um, and, and that I think is going to set us back all of us, the, the Royal us or the Royal we back, um, years and years. Um, and, and it's very unfortunate because I think we we're at a time when we're making great progress and now to see COVID, um, setting us back in, in such a powerful way. I think there's work to be done to understand how do we bring people back into the workforce. Um, how do we do that? Understanding work life balance, better understanding virtual and remote, working better. I think in the technology sector, um, we've really embraced, um, hybrid virtual work and are, are empowering people to bring their whole selves to work. >>And I think if anything, these, these zoom calls have, um, both for the men and the women on my team. In fact, I would say much more. So for the men on my team, I'm seeing, I was seeing more kids in the background, more kind of split childcare duties, more ability to start talking about, um, other responsibilities that maybe they had, uh, especially in the early days of COVID where maybe daycares were shut down. And, um, you had, you know, maybe a parent was sick. And so we saw quite a lot of, um, people bringing their whole selves to the office, which I think was, was really wonderful. Um, uh, even our CEO saw some of that. And I think, um, that that really changes the dialogue, right? It changes it to maybe scheduling meetings at a time when, um, people can do it after daycare drop off. >>Um, and really allowing that both for men and for women makes it better for, for women overall. So I would like to think that this hybrid working, um, environment and that this, um, uh, whole view into somebody's life that COVID has really provided for probably for white collar workers, if I'm being honest for, um, people who are in a, at a better point of privilege, they don't necessarily have to go into the office every day. I would like to think that tech can lead the way in, um, you know, coming out of the, the old COVID. I don't know if we have a new COVID coming, but the old COVID and really leading the way for women and for people, um, to transform how we do work, um, leveraging data and analytics, but also, um, overcoming some of the, the disparities that exist for women in particular in the workforce. >>Yeah, I think there's, there's like we say, there's a lot of opportunity there and I like your point of hopefully tech can be that guiding light that shows us this can be done. We're all humans at the end of the day. And ultimately if we're able to have some sort of work life balance, everything benefits, our work or more productive, higher performing teams impacts customers, right? There's so much value that can be gleaned from, from that hybrid model and embracing for humans. We need to be able to, to work when we can, we've learned that you don't have to be, you know, in an office 24, 7 commuting, crazy hours flying all around the world. We can get a lot of things done in a ways that fit people's lives rather than taking command over it. Wanna get your advice, Hillary, if you were to talk to your younger self, what would be some of the key pieces of advice you would say? And Danielle and I have talked about this before, and sometimes we, we would both agree on like, ask more questions. Don't be afraid to raise your hand, but what advice would you give your younger self and that younger generation in terms of being inspired to get into tech >>Oh, inspired and being in tech? You know, I think looking at technology as, in some ways, I feel like we do a disservice to, um, inclusion when we talk about stem, cuz I think stem can be kind of daunting. It can be a little scary for people for younger people. When I, when I go and talk to folks at schools, I think stem is like, oh, all the super smart kids are over there. They're all like maybe they're all men. And so, um, it's, it's a little, uh, intimidating. Um, and stem is actually, you know, especially for, um, people joining the workforce today. It's actually how you've been living your life since you were born. I mean, you know, stem inside and out because you walk around with a phone and you know how to get your internet working and like that is technology right. >>Fundamentally. And so demystifying stem as something that is around how we, um, actually make our, our lives useful and, and, and how we can change outcomes. Um, through technology I think is maybe a different lens to put on it. So, and there's absolutely for, for hard sciences, there's absolutely a, a great place in the world for folks who wanna pursue that and men and women can do that. So I, I don't want to be, um, uh, setting the wrong expectations, but I, I think stem is, is very holistic in, um, in the change that's happening globally for us today across economies, across global warming, across all kinds of impactful issues. And so I think everybody who's interested in, in some of that world change can participate in stem. It just may be through a different, through a different lens than how we classically talk about stem. >>So I think there's great opportunity to demystify stem. I think also, um, what I would tell my younger self is choose your bosses wisely. And that sounds really funny. That sounds like inside out almost, but I think choose the person that you're gonna work for in your first five to seven years. And it might be more than one person, but be, be selective, maybe be a little less selective about the exact company or the exact title. I think picking somebody that, you know, we talk about mentors and we talk about sponsors and those are important. Um, but the person you're gonna spend in your early career, a lot of your day with a lot, who's gonna influence a lot of the outcomes for you. That is the person that you, I think want to be more selective about, um, because that person can set you up for success and give you opportunities and set you on course to be, um, a standout or that person can hold you back. >>And that person can put you in the corner and not invite you to the meetings and not give you those opportunities. And so we're in an economy today where you actually can, um, be a little bit picky about who you go and work for. And I would encourage my younger self. I actually, I just lucked out actually, but I think that, um, my first boss really set me, um, up for success, gave me a lot of feedback and coaching. Um, and some of it was really hard to hear, but it really set me up for, for, um, the, the path that I've been on ever since. So it, that would be my advice. >>I love that advice. I it's brilliant. I didn't think it choose your bosses wisely. Isn't something that we primarily think about. I think a lot of people think about the big name companies that they wanna go after and put on a resume, but you bring up a great point. And Danielle and I have talked about this with other guests about mentors and sponsors. I think that is brilliant advice and also more work to do to demystify stem. But luckily we have great family leaders like the two of you helping us to do that. Ladies, I wanna thank you so much for joining me on the program today and talking through what you're seeing in de and I, what your companies are doing and the opportunities that we have to move the needle. Appreciate your time. >>Thank you so much. Great to see you, Danielle. Thank you Lisa, to see you. >>My pleasure for my guests. I'm Lisa Martin. You're watching the AWS partner showcase season one, episode three. Hey everyone. Welcome to the AWS partner showcase. This is season one, episode three, with a focus on women in tech. I'm your host, Lisa Martin. I've got two guests here with me, Sue Peretti, the EVP of global AWS strategic alliances at Jefferson Frank, a 10th revolution group company, and Danielle brushoff. One of our cube alumni joins us ISV PSA director, ladies. It's great to have you on the program talking about a, a topic that is near and dear to my heart at women in tech. >>Thank you, Lisa. >>So let's go ahead and start with you. Give the audience an understanding of Jefferson Frank, what does the company do and about the partnership with AWS? >>Sure. Um, so let's just start, uh, Jefferson Frank is a 10th revolution group company. And if you look at it, it's really talent as a service. So Jefferson Frank provides talent solutions all over the world for AWS clients, partners and users, et cetera. And we have a sister company called revelent, which is a talent creation company within the AWS ecosystem. So we create talent and put it out in the ecosystem. Usually underrepresented groups over half of them are women. And then we also have, uh, a company called rubra, which is a delivery model around AWS technology. So all three companies fall under the 10th revolution group organization. >>Got it. Danielle, talk to me a little bit about from AWS's perspective and the focus on hiring more women in technology and about the partnership. >>Yes. I mean, this has definitely been a focus ever since I joined eight years ago, but also just especially in the last few years we've grown exponentially and our customer base has changed. You know, we wanna have, uh, an organization interacting with them that reflects our customers, right. And, uh, we know that we need to keep pace with that even with our growth. And so we've very much focused on early career talent, um, bringing more women and underrepresented minorities into the organization, sponsoring those folks, promoting them, uh, giving them paths to growth, to grow inside of the organization. I'm an example of that. Of course I benefit benefited from it, but also I try to bring that into my organization as well. And it's super important. >>Tell me a little bit about how you benefited from that, Danielle. >>Um, I just think that, um, you know, I I've been able to get, you know, a seat at the table. I think that, um, I feel as though I have folks supporting me, uh, very deeply and wanna see me succeed. And also they put me forth as, um, you know, a, represent a representative, uh, to bring more women into the organization as well. And I think, um, they give me a platform, uh, in order to do that, um, like this, um, but also many other, uh, spots as well. Um, and I'm happy to do it because I feel that, you know, if you always wanna feel that you're making a difference in your job, and that is definitely a place where I get that time and space in order to be that representative to, um, bring more, more women into benefiting from having careers in technology, which there's a lot of value there, >>A lot of value. Absolutely. So back over to you, what are some of the trends that you are seeing from a gender diversity perspective in tech? We know the, the numbers of women in technical positions, uh, right. There's so much data out there that shows when girls start dropping up, but what are some of the trends that you are seeing? >>So it's, that's a really interesting question. And, and Lisa, I had a whole bunch of data points that I wanted to share with you, but just two weeks ago, uh, I was in San Francisco with AWS at the, at the summit. And we were talking about this. We were talking about how we can collectively together attract more women, not only to, uh, AWS, not only to technology, but to the AWS ecosystem in particular. And it was fascinating because I was talking about, uh, the challenges that women have and how hard to believe, but about 5% of women who were in the ecosystem have left in the past few years, which was really, really, uh, something that shocked everyone when we, when we were talking about it, because all of the things that we've been asking for, for instance, uh, working from home, um, better pay, uh, more flexibility, uh, better maternity leave seems like those things are happening. >>So we're getting what we want, but people are leaving. And it seemed like the feedback that we got was that a lot of women still felt very underrepresented. The number one thing was that they, they couldn't be, you can't be what you can't see. So because they, we feel collectively women, uh, people who identify as women just don't see enough women in leadership, they don't see enough mentors. Um, I think I've had great mentors, but, but just not enough. I'm lucky enough to have a pres a president of our company, the president of our company, Zoe Morris is a woman and she does lead by example. So I'm very lucky for that. And Jefferson, Frank really quickly, we put out a hiring a salary and hiring guide a career and hiring guide every year and the data points. And that's about 65 pages long. No one else does it. Uh, it gives an abundance of information around, uh, everything about the AWS ecosystem that a hiring manager might need to know. But there is what, what I thought was really unbelievable was that only 7% of the people that responded to it were women. So my goal, uh, being that we have such a very big global platform is to get more women to respond to that survey so we can get as much information and take action. So >>Absolutely 7%. So a long way to go there. Danielle, talk to me about AWS's focus on women in tech. I was watching, um, Sue, I saw that you shared on LinkedIn, the Ted talk that the CEO and founder of girls and co did. And one of the things that she said was that there was a, a survey that HP did some years back that showed that, um, 60%, that, that men will apply for jobs if they only meet 60% of the list of requirements. Whereas with females, it's far, far less, we've all been in that imposter syndrome, um, conundrum before. But Danielle, talk to us about AWS, a specific focus here to get these numbers up. >>I think it speaks to what Susan was talking about, how, you know, I think we're approaching it top and bottom, right? We're looking out at what are the, who are the women who are currently in technical positions and how can we make AWS an attractive place for them to work? And that's all a lot of the changes that we've had around maternity leave and, and those types of things, but then also, um, more flexible working, uh, can, you know, uh, arrangements, but then also, um, early, how can we actually impact early, um, career women and actually women who are still in school. Um, and our training and certification team is doing amazing things to get, um, more girls exposed to AWS, to technology, um, and make it a less intimidating place and have them look at employees from AWS and say like, oh, I can see myself in those people. >>Um, and kind of actually growing the viable pool of candidates. I think, you know, we're, we're limited with the viable pool of candidates, um, when you're talking about mid to late career. Um, but how can we, you know, help retrain women who are coming back into the workplace after, you know, having a child and how can we help with military women who want to, uh, or underrepresented minorities who wanna move into AWS, we have a great military program, but then also just that early high school, uh, career, you know, getting them in, in that trajectory. >>Sue, is that something that Jefferson Frank is also able to help with is, you know, getting those younger girls before they start to feel there's something wrong with me. I don't get this. Talk to us about how Jefferson Frank can help really drive up that in those younger girls. >>Uh, let me tell you one other thing to refer back to that summit that we did, uh, we had breakout sessions and that was one of the topics. What can cuz that's the goal, right? To make sure that, that there are ways to attract them. That's the goal? So some of the things that we talked about was mentoring programs, uh, from a very young age, some people said high school, but then we said even earlier, goes back to you. Can't be what you can't see. So, uh, getting mentoring programs, uh, established, uh, we also talked about some of the great ideas was being careful of how we speak to women using the right language to attract them. And some, there was a teachable moment for, for me there actually, it was really wonderful because, um, an African American woman said to me, Sue and I, I was talking about how you can't be what you can't see. >>And what she said was Sue, it's really different. Um, for me as an African American woman, uh, or she identified, uh, as nonbinary, but she was relating to African American women. She said, your white woman, your journey was very different than my journey. And I thought, this is how we're going to learn. I wasn't offended by her calling me out at all. It was a teachable moment. And I thought I understood that, but those are the things that we need to educate people on those, those moments where we think we're, we're saying and doing the right thing, but we really need to get that bias out there. So here at Jefferson, Frank, we're, we're trying really hard to get that careers and hiring guide out there. It's on our website to get more women, uh, to talk to it, but to make suggestions in partnership with AWS around how we can do this mentoring, we have a mentor me program. We go around the country and do things like this. We, we try to get the education out there in partnership with AWS. Uh, we have a, a women's group, a women's leadership group, uh, so much that, that we do, and we try to do it in partnership with AWS. >>Danielle, can you comment on the impact that AWS has made so far, um, regarding some of the trends and, and gender diversity that Sue was talking about? What's the impact that's been made so far with this partnership? >>Well, I mean, I think just being able to get more of the data and have awareness of leaders, uh, on how <laugh>, you know, it used to be a, a couple years back, I would feel like sometimes the, um, uh, solving to bring more women into the organization was kind of something that folks thought, oh, this is Danielle is gonna solve this. You know? And I think a lot of folks now realize, oh, this is something that we all need to solve for. And a lot of my colleagues who maybe a couple years ago, didn't have any awareness or didn't even have the tools to do what they needed to do in order to improve the statistics on their, or in their organizations. Now actually have those tools and are able to kind of work with, um, work with companies like Susan's work with Jefferson Frank in order to actually get the data and actually make good decisions and feel as though, you know, they, they often, these are not lived experiences for these folks, so they don't know what they don't know. And by providing data and providing awareness and providing tooling and then setting goals, I think all of those things have really turned, uh, things around in a very positive way. >>And so you bring up a great point about from a diversity perspective, what is Jefferson Frank doing to, to get those data points up, to get more women of, of all well, really underrepresented minorities to, to be able to provide that feedback so that you can, can have the data and gleamy insights from it to help companies like AWS on their strategic objectives. >>Right? So as I, when I go back to that higher that, uh, careers in hiring guide, that is my focus today, really because the more data that we have, I mean, the, and the data takes, uh, you know, we need people to participate in order to, to accurately, uh, get a hold of that data. So that's why we're asking, uh, we're taking the initiative to really expand our focus. We are a global organization with a very, very massive database all over the world, but if people don't take action, then we can't get the right. The, the, the data will not be as accurate as we'd like it to be. Therefore take better action. So what we're doing is we're asking people all over the, all over the world to participate on our website, Jefferson frank.com, the se the high, uh, in the survey. So we can learn as much as we can. >>7% is such a, you know, Danielle and I we're, we've got to partner on this just to sort of get that message out there, get more data so we can execute, uh, some of the other things that we're doing. We're, we're partnering in. As I mentioned, more of these events, uh, we're, we're doing around the summits, we're gonna be having more ed and I events and collecting more information from women. Um, like I said, internally, we do practice what we preach and we have our own programs that are, that are out there that are within our own company where the women who are talking to candidates and clients every single day are trying to get that message out there. So if I'm speaking to a client or one of our internal people are speaking to a client or a candidate, they're telling them, listen, you know, we really are trying to get these numbers up. >>We wanna attract as many people as we can. Would you mind going to this, uh, hiring guide and offering your own information? So we've gotta get that 7% up. We've gotta keep talking. We've gotta keep, uh, getting programs out there. One other thing I wanted to Danielle's point, she mentioned, uh, women in leadership, the number that we gathered was only 9% of women in leadership within the AWS ecosystem. We've gotta get that number up, uh, as well because, um, you know, I know for me, when I see people like Danielle or, or her peers, it inspires me. And I feel like, you know, I just wanna give back, make sure I send the elevator back to the first floor and bring more women in to this amazing ecosystem. >>Absolutely. That's not that metaphor I do too, but we, but to your point to get that those numbers up, not just at AWS, but everywhere else we need, it's a help me help use situation. So ladies underrepresented minorities, if you're watching go to the Jefferson Frank website, take the survey, help provide the data so that the woman here that are doing this amazing work, have it to help make decisions and have more of females and leadership roles or underrepresented minorities. So we can be what we can see. Ladies, thank you so much for joining me today and sharing what you guys are doing together to partner on this important. Cause >>Thank you for having me, Leah, Lisa, >>Thank you. My pleasure for my guests. I'm Lisa Martin. You're watching the cubes coverage of the AWS partner showcase. Thanks for your time. Hey everyone. Welcome to the AWS partner showcase season one, episode three women in tech. I'm your host, Lisa Martin. We've got two female rock stars here with me next. Stephanie Curry joins us the worldwide head of sales and go to market strategy for AWS at NetApp and Danielle GShock is back one of our QM ISV PSA director at AWS. Looking forward to a great conversation, ladies, about a great topic, Stephanie, let's go ahead and start with you. Give us an overview of your story, how you got into tech and what inspired you. >>Thanks so much, Lisa and Danielle. It's great to be on this show with you. Um, thank you for that. Uh, my name's Stephanie cur, as Lisa mentioned, I'm the worldwide head of sales for, uh, AWS at NetApp and run a global team of sales people that sell all things AWS, um, going back 25 years now, uh, when I first started my career in tech, it was kind of by accident. Um, I come from a different background. I have a business background and a technical background from school, um, but had been in a different career and I had an opportunity to try something new. Um, I had an ally really that reached out to me and said, Hey, you'd be great for this role. And I thought, I'd take a chance. I was curious. Um, and, uh, it, it turned out to be a 25 year career, um, that I'm really, really excited about and, and, um, really thankful for that person, for introducing me to the, to the industry >>25 years in counting. I'm sure Danielle, we've talked about your background before. So what I wanna focus on with you is the importance of diversity for high performance. I know what a machine AWS is, and Stephanie'll come back to you with the same question, but talk about that, Danielle, from your perspective, that importance, um, for diversity to drive the performance. >>Yeah. Yeah. I truly believe that, you know, in order to have high performing teams, that you have to have people from all different types of backgrounds and experiences. And we do find that oftentimes being, you know, field facing, if we're not reflecting our customers and connecting with them deeply, um, on, on the levels that they're at, we, we end up missing them. And so for us, it's very important to bring people of lots of different technical backgrounds experiences. And of course, both men, women, and underrepresented minorities and put that forth to our customers, um, in order to make that connection and to end up with better outcomes. So >>Definitely it's all about outcomes, Stephanie, your perspective and NetApp's perspective on diversity for creating highly performant teams and organizations. >>I really aligned with Danielle on the comment she made. And in addition to that, you know, just from building teams in my, um, career know, we've had three times as many women on my team since we started a year ago and our results are really showing in that as well. Um, we find the teams are stronger, they're more collaborative and to Danielle's point really reflective, not only our partners, but our customers themselves. So this really creates connections, which are really, really important to scale our businesses and, and really, uh, meet the customer where they're at as well. So huge proponent of that ourselves, and really finding that we have to be intentional in our hiring and intentional in how we attract diversity to our teams. >>So Stephanie let's stay with you. So a three X increase in women on the team in a year, especially the kind of last year that we've had is really incredible. I, I like your, I, your thoughts on there needs to be a, there needs to be focus and, and thought in how teams are hired. Let's talk about attracting and retaining those women now, especially in sales roles, we all know the number, the percentages of women in technical roles, but what are some of the things that, that you do Stephanie, that NetApp does to attract and retain women in those sales roles? >>The, the attracting part's really interesting. And we find that, you know, you, you read the stats and I'd say in my experience, they're also true in the fact that, um, a lot of women would look at a job description and say, I can't do a hundred percent of that, that, so I'm not even going to apply with the women that we've attracted to our team. We've actually intentionally reached out and targeted those people in a good way, um, to say, Hey, we think you've got what it takes. Some of the feedback I've got from those women are, gosh, I didn't think I could ever get this role. I didn't think I had the skills to do that. And they've been hired and they are doing a phenomenal job. In addition to that, I think a lot of the feedback I've got from these hires are, Hey, it's an aggressive sales is aggressive. Sales is competitive. It's not an environment that I think I can be successful in. And what we're showing them is bring those softer skills around collaboration, around connection, around building teams. And they do, they do bring a lot of that to the team. Then they see others like them there and they know they can be successful cuz they see others like them on the team, >>The whole concept of we can't be what we can't see, but we can be what we can't see is so important. You said a couple things, Stephanie, that really stuck with me. And one of them was an interview on the Cub I was doing, I think a couple weeks ago, um, about women in tech. And the stat that we talked about was that women will apply will not apply for a job unless they meet 100% of the skills and the requirements that it's listed, but men will, if they only meet 60. And I, that just shocked me that I thought, you know, I, I can understand that imposter syndrome is real. It's a huge challenge, but the softer skills, as you mentioned, especially in the last two years, plus the ability to communicate, the ability to collaborate are incredibly important to, to drive that performance of any team of any business. >>Absolutely. >>Danielle, talk to me about your perspective and AWS as well for attracting and retaining talent. And, and, and particularly in some of those challenging roles like sales that as Stephanie said, can be known as aggressive. >>Yeah, for sure. I mean, my team is focused on the technical aspect of the field and we definitely have an uphill battle for sure. Um, two things we are focused on first and foremost is looking at early career women and that how we, how can we bring them into this role, whether in they're in support functions, uh, cl like answering the phone for support calls, et cetera, and how, how can we bring them into this organization, which is a bit more strategic, more proactive. Um, and then the other thing that as far as retention goes, you know, sometimes there will be women who they're on a team and there are no other women on that team. And, and for me, it's about building community inside of AWS and being part of, you know, we have women on solution architecture organizations. We have, uh, you know, I just personally connect people as well and to like, oh, you should meet this person. Oh, you should talk to that person. Because again, sometimes they can't see someone on their team like them and they just need to feel anchored, especially as we've all been, you know, kind of stuck at home, um, during the pandemic, just being able to make those connections with women like them has been super important and just being a, a long tenured Amazonian. Um, that's definitely one thing I'm able to, to bring to the table as well. >>That's so important and impactful and spreads across organizations in a good way. Daniel let's stick with you. Let's talk about some of the allies that you've had sponsors, mentors that have really made a difference. And I said that in past tense, but I also mean in present tense, who are some of those folks now that really inspire you? >>Yeah. I mean, I definitely would say that one of my mentors and someone who, uh, ha has been a sponsor of my career has, uh, Matt YK, who is one of our control tower GMs. He has really sponsored my career and definitely been a supporter of mine and pushed me in positive ways, which has been super helpful. And then other of my business partners, you know, Sabina Joseph, who's a cube alum as well. She definitely has been, was a fabulous partner to work with. Um, and you know, between the two of us for a period of time, we definitely felt like we could, you know, conquer the world. It's very great to go in with a, with another strong woman, um, you know, and, and get things done, um, inside of an organization like AWS. >>Absolutely. And S I've, I've agreed here several times. So Stephanie, same question for you. You talked a little bit about your kind of, one of your, uh, original early allies in the tech industry, but talk to me about allies sponsors, mentors who have, and continue to make a difference in your life. >>Yeah. And, you know, I think it's a great differentiation as well, right? Because I think that mentors teach us sponsors show us the way and allies make room for us at the table. And that is really, really key difference. I think also as women leaders, we need to make room for others at the table too, and not forget those softer skills that we bring to the table. Some of the things that Danielle mentioned as well about making those connections for others, right. And making room for them at the table. Um, some of my allies, a lot of them are men. Brian ABI was my first mentor. Uh, he actually is in the distribution, was in distribution, uh, with advent tech data no longer there. Um, Corey Hutchinson, who's now at Hashi Corp. He's also another ally of mine and remains an ally of mine, even though we're not at the same company any longer. Um, so a lot of these people transcend careers and transcend, um, um, different positions that I've held as well and make room for us. And I think that's just really critical when we're looking for allies and when allies are looking for us, >>I love how you described allies, mentors and sponsors Stephanie. And the difference. I didn't understand the difference between a mentor and a sponsor until a couple of years ago. Do you talk with some of those younger females on your team so that when they come into the organization and maybe they're fresh outta college, or maybe they've transitioned into tech so that they can also learn from you and understand the importance and the difference between the allies and the sponsors and the mentors? >>Absolutely. And I think that's really interesting because I do take, uh, an extra, uh, approach an extra time to really reach out to the women that have joined the team. One. I wanna make sure they stay right. I don't want them feeling, Hey, I'm alone here and I need to, I need to go do something else. Um, and they are located around the world, on my team. They're also different age groups, so early in career, as well as more senior people and really reaching out, making sure they know that I'm there. But also as Danielle had mentioned, connecting them to other people in the community that they can reach out to for those same opportunities and making room for them >>Make room at the table. It's so important. And it can, you never know what a massive difference and impact you can make on someone's life. And I, and I bet there's probably a lot of mentors and sponsors and allies of mine that would be surprised to know, uh, the massive influence they've had Daniel back over. Let's talk about some of the techniques that you employ, that AWS employees to make the work environment, a great place for women to really thrive and, and be retained as Stephanie was saying. Of course that's so important. >>Yeah. I mean, definitely I think that the community building, as well as we have a bit more programmatic mentorship, um, we're trying to get to the point of having a more programmatic sponsorship as well. Um, but I think just making sure that, um, you know, both everything from, uh, recruit to onboard to ever boarding that, uh, they they're the women who come into the organization, whether it's they're coming in on the software engineering side or the field side or the sales side that they feel as that they have someone, uh, working with them to help them drive their career. Those are the key things that were, I think from an organizational perspective are happening across the board. Um, for me personally, when I run my organization, I'm really trying to make sure that people feel that they can come to me at any time open door policy, make sure that they're surfacing any times in which they are feeling excluded or anything like that, any challenges, whether it be with a customer, a partner or with a colleague. Um, and then also of course, just making sure that I'm being a good sponsor, uh, to, to people on my team. Um, that is key. You can talk about it, but you have to start with yourself as well. >>That's a great point. You you've got to, to start with yourself and really reflect on that. Mm-hmm <affirmative> and look, am I, am I embodying what it is that I need? And not that I know they need that focused, thoughtful intention on that is so importants, let's talk about some of the techniques that you use that NetApp uses to make the work environment a great place for those women are marginalized, um, communities to really thrive. >>Yeah. And I appreciate it and much like Danielle, uh, and much like AWS, we have some of those more structured programs, right around sponsorship and around mentorship. Um, probably some growth there, opportunities for allies, because I think that's more of a newer concept in really an informal structure around the allies, but something that we're growing into at NetApp, um, on my team personally, I think, um, leading by example's really key. And unfortunately, a lot of the, um, life stuffs still lands on the women, whether we like it or not. Uh, I have a very, uh, active husband in our household, but I still carry when it push comes to shove it's on me. Um, and I wanna make sure that my team knows it's okay to take some time and do the things you need to do with your family. Um, I'm I show up as myself authentically and I encourage them to do the same. >>So it's okay to say, Hey, I need to take a personal day. I need to focus on some stuff that's happening in my personal life this week now, obviously to make sure your job's covered, but just allowing some of that softer vulnerability to come into the team as well, so that others, um, men and women can feel they can do the same thing. And that it's okay to say, I need to balance my life and I need to do some other things alongside. Um, so it's the formal programs, making sure people have awareness on them. Um, I think it's also softly calling people out on biases and saying, Hey, I'm not sure if you know, this landed that way, but I just wanted to make you aware. And usually the feedback is, oh my gosh, I didn't know. And could you coach me on something that I could do better next time? So all of this is driven through our NetApp formal programs, but then it's also how you manifest it on the teams that we're leading. >>Absolutely. And sometimes having that mirror to reflect into can be really eye-opening and, and allow you to, to see things in a completely different light, which is great. Um, you both talked about, um, kind of being what you, uh, can see, and, and I know both companies are upset customer obsessed in a good way. Talk to me a little bit, Danielle, go back over to you about the AWS NetApp partnership. Um, some of that maybe alignment on, on performance on obviously you guys are very well aligned, uh, in terms of that, but also it sounds like you're quite aligned on diversity and inclusion. >>Well, we definitely do. We have the best partnerships with companies in which we have these value alignments. So I think that is a positive thing, of course, but just from a, from a partnership perspective, you know, from my five now plus years of being a part of the APN, this is, you know, one of the most significant years with our launch of FSX for NetApp. Um, with that, uh, key key service, which we're making available natively on AWS. I, I can't think of a better Testament to the, to the, um, partnership than that. And that's doing incredibly well and it really resonates with our customers. And of course it started with customers and their need for NetApp. Uh, so, you know, that is a reflection, I think, of the success that we're having together. >>And Stephanie talk to, uh, about the partnership from your perspective, NetApp, AWS, what you guys are doing together, cultural alignment, but also your alignment on really bringing diversity into drive performance. >>Yeah, I think it's a, a great question. And I have to say it's just been a phenomenal year. Our relationship has, uh, started before our first party service with FSX N but definitely just, um, uh, the trajectory, um, between the two companies since the announcement about nine months ago has just taken off to a, a new level. Um, we feel like an extended part of the family. We worked together seamlessly. A lot of the people in my team often say we feel like Amazonians. Um, and we're really part of this transformation at NetApp from being that storage hardware company into being an ISV and a cloud company. And we could not do this without the partnership with AWS and without the, uh, first party service of Fs XM that we've recently released. Um, I think that those joint values that Danielle referred to are critical to our success, um, starting with customer obsession and always making sure that we are doing the right thing for the customer. >>We coach our team teams all the time on if you are doing the right thing for the customers, you cannot do anything wrong. Just always put the customer at the, in the center of your decisions. And I think that there is, um, a lot of best practice sharing and collaboration as we go through this change. And I think a lot of it is led by the diverse backgrounds that are on the team, um, female, male, um, race and so forth, and just to really, uh, have different perspectives and different experiences about how we approach this change. Um, so we definitely feel like a part of the family. Uh, we are absolutely loving, uh, working with the AWS team and our team knows that we are the right place, the right time with the right people. >>I love that last question for each of you. And I wanna stick with you Stephanie advice to your younger self, think back five years. What advice would you seen what you've accomplished and maybe the thet route that you've taken along the way, what would you advise your youngest Stephanie self. >>Uh, I would say keep being curious, right? Keep being curious, keep asking questions. And sometimes when you get a no, it's not a bad thing, it just means not right now and find out why and, and try to get feedback as to why maybe that wasn't the right opportunity for you. But, you know, just go for what you want. Continue to be curious, continue to ask questions and find a support network of people around you that wanna help you because they are there and they, they wanna see you be successful too. So never be shy about that stuff. >><laugh> absolutely. And I always say failure does not have to be an, a bad F word. A no can be the beginning of something. Amazing. Danielle, same question for you. Thinking back to when you first started in your career, what advice would you give your younger self? >>Yeah, I think the advice I'd give my younger self would be, don't be afraid to put yourself out there. Um, it's certainly, you know, coming from an engineering background, maybe you wanna stay behind the scenes, not, not do a presentation, not do a public speaking event, those types of things, but back to what the community really needs, this thing. Um, you know, I genuinely now, uh, took me a while to realize it, but I realized I needed to put myself out there in order to, um, you know, allow younger women to see what they could be. So that would be the advice I would give. Don't be afraid to put yourself out there. >>Absolutely. That advice that you both gave are, is so fantastic, so important and so applicable to everybody. Um, don't be afraid to put yourself out there, ask questions. Don't be afraid of a, no, that it's all gonna happen at some point or many points along the way. That can also be good. So thank you ladies. You inspired me. I appreciate you sharing what AWS and NetApp are doing together to strengthen diversity, to strengthen performance and the advice that you both shared for your younger selves was brilliant. Thank you. >>Thank you. >>Thank you >>For my guests. I'm Lisa Martin. You're watching the AWS partner showcase. See you next time. Hey everyone. Welcome to the AWS partner showcase season one, episode three women in tech. I'm your host, Lisa Martin. I've got two female rock stars joining me. Next Vero Reynolds is here engineering manager, telemetry at honeycomb, and one of our cube alumni, Danielle Ock ISV PSA director at AWS. Join us as well. Ladies. It's great to have you talking about a very important topic today. >>Thanks for having us. >>Yeah, thanks for having me. Appreciate it. >>Of course, Vera, let's go ahead and start with you. Tell me about your background and tech. You're coming up on your 10th anniversary. Happy anniversary. >>Thank you. That's right. I can't believe it's been 10 years. Um, but yeah, I started in tech in 2012. Um, I was an engineer for most of that time. Uh, and just recently as a March, switched to engineering management here at honeycomb and, um, you know, throughout my career, I was very much interested in all the things, right. And it was a big FOMO as far as trying a few different, um, companies and products. And I've done things from web development to mobile to platforms. Um, it would be apt to call me a generalist. Um, and in the more recent years I was sort of gravitating more towards developer tool space. And for me that, uh, came in the form of cloud Foundry circle CI and now honeycomb. Um, I actually had my eye on honeycomb for a while before joining, I came across a blog post by charity majors. >>Who's one of our founders and she was actually talking about management and how to pursue that and whether or not it's right, uh, for your career. And so I was like, who is this person? I really like her, uh, found the company. They were pretty small at the time. So I was sort of keeping my eye on them. And then when the time came around for me to look again, I did a little bit more digging, uh, found a lot of talks about the product. And on the one hand they really spoke to me as the solution. They talked about developers owning their coding production and answering questions about what is happening, what are your users seeing? And I felt that pain, I got what they were trying to do. And also on the other hand, every talk I saw at the time was from, uh, an amazing woman <laugh>, which I haven't seen before. Uh, so I came across charity majors again, Christine Y our other founder, and then Liz Jones, who's our principal developer advocate. And that really sealed the deal for me as far as wanting to work here. >>Yeah. Honeycomb is interesting. This is a female founded company. You're two leaders. You mentioned that you like the technology, but you were also attracted because you saw females in the leadership position. Talk to me a little bit about what that's like working for a female led organization at honeycomb. >>Yeah. You know, historically, um, we have tried not to over index on that because there was this, uh, maybe fear awareness of, um, it taking away from our legitimacy as an engineering organization, from our success as a company. Um, but I'm seeing that, uh, rhetoric shift recently because we believe that with great responsibility, uh, with great power comes great responsibility, and we're trying to be more intentional as far as using that attribute of our company. Um, so I would say that for me, it was, um, a choice between a few offers, right. And that was a selling point for sure, because again, I've never experienced it and I've really seen how much they walk that walk. Um, even me being here and me moving into management, I think were both, um, ways in which they really put a lot of trust and support in me. And so, um, I it's been a great ride. >>Excellent. Sounds like it. Before we bring Danielle in to talk about the partnership. I do wanna have you there talk to the audience a little bit about honeycomb, what technology it's delivering and what are its differentiators. >>Yeah, absolutely. Um, so honeycomb is an observability tool, uh, that enables engineers to answer questions about the code that runs in production. And, um, we work with a number of various customers. Some of them are Vanguards, slack. Hello, fresh, just to name a couple, if you're not familiar with observability tooling, it's akin to traditional application performance monitoring, but we believe that observability is succeeding APM because, uh, APM tools were built at the time of monoliths and they just weren't designed to help us answer questions about complex distributed systems that we work with today, where things can go wrong anywhere in that chain. And you can't predict what you're gonna need to ask ahead of time. So some of the ways that we are different is our ability to store and query really rich data, which we believe is the key to understanding those complex systems. >>What I mean by rich data is, um, something that has a lot of attributes. So for example, when an error happens, knowing who it happened to, which user ID, which, um, I don't know, region, they were in, um, what, what, what they were doing at the time and what was happening at the rest of your system. And our ingest engine is really fast. You can do it in as little as three seconds and we call data like this. I said, kind of rich data, contextual data. We refer it as having high ality and high dimensionality, which are big words. But at the end of the day, what that means is we can store and we can query the data. We can do it really fast. And to give you an example of how that looks for our customers, let's say you have a developer team who are using comb to understand and observe their system. >>And they get a report that a user is experiencing a slowdown or something's wrong. They can go into comb and figure out that this only happens to users who are using a particular language pack with their app. And they operated their app last week, that it only happens when they are trying to upload a file. And so it's this level of granularity and being able to zoom in and out, um, under your data that allows you to understand what's happening, especially when you have an incident going on, right. Or your really important high profile customer is telling you that something's wrong. And we can do that. Even if everything else in your other tools looks fine, right? All of your dashboards are okay. You're not actually getting paged on it, but your customers are telling you that something's wrong. Uh, and we believe that's where we shine in helping you there. >>Excellent. It sounds like that's where you really shine that real time visibility is so critical these days. Danielle, Danielle, wanna bring you into the conversation. Talk to us a little bit about the honeycomb partnership from the AWS lens. >>Yeah. So excuse me, observability is obviously a very important, uh, segment in the cloud space, very important to AWS, um, because a lot of all of our customers, uh, as they build their systems distributed, they need to be able to see where, where things are happening in the complex systems that they're building. And so honeycomb is a, is an advanced technology partner. Um, they've been working with us for quite some time and they have a, uh, their solution is listed on the marketplace. Um, definitely something that we see a lot of demand with our customers and they have many integrations, uh, which, you know, we've seen is key to success. Um, being able to work seamlessly with the rest of the services inside of the AWS platform. And I know that they've done some, some great things with people who are trying to develop games on top of AWS, uh, things in that area as well. And so, uh, very important partner in the observa observability market that we have >>Back to you, let's kind of unpack the partnership, the significance that honeycomb ha is getting from being partners with an organization as potent and pivotal as AWS. >>Yeah, absolutely. Um, I know this predates me to some extent, but I know for a long time, AWS and honeycomb has really pushed the envelope together. And, um, I think it's a beneficial relationship for both ends. There's kind of two ways of looking at it. On the one side, there is our own infrastructure. So honeycomb runs on AWS and actually one of our critical workloads that supports that fast query engine that I mentioned uses Lambda. And it does so in a pretty Orthodox way. So we've had a longstanding conversation with the AWS team as far as drawing outside those lines and kind of figuring out how to use this technology in a way that works for us and hopefully will work for other customers of theirs as well. Um, that also allows us to ask for early access for certain features when they become available. >>And then that way we can be sort of the Guinea pigs and try things out, um, in a way that migrates our system and optimizes our own performance, but also allows again, other customers of AWS to follow in that path. And then the other side of that partnership is really supporting our customers who are both honeycomb users and AWS users, because it's, as you imagine, quite a big overlap, and there are certain ways in which we can allow our customers to more easily get their data from AWS to honeycomb. So for example, last year we built a tool, um, based on the new Lambda extension capability that allowed our users who run their applications in Lambdas to get that telemetry data out of their applications and into honeycomb. And it man was win, win. >>Excellent. So I'm hearing a lot of synergies from a technology perspective, you're sticking with you, and then Danielle will bring you in, let's talk about how honeycomb supports D and I across its organization. And how is that synergistic with AWS's approach? Yeah, >>Yeah, absolutely. So I sort of alluded to that hesitancy to over index on the women led aspect of ourselves. Um, but again, a lot of things are shifting, we're growing a lot. And so we are recognizing that we need to be more intentional with our DEI initiatives, and we also notice that we can do better and we should do better. And to that, and we're doing a few things differently, um, that are pretty recent initiatives. We are partnering with organizations that help us target specific communities that are underrepresented in tech. Um, some examples would be after tech hu Latinas in tech among, um, a number of others. And another initiative is DEI head start. That's something that is an internal, um, practice that we started that includes reaching out to underrepresented applicants before any new job for honeycomb becomes live. So before we posted to LinkedIn, before it's even live on our job speech, and the idea there is to kind of balance our pipeline of applicants, which the hope is will lead to more diverse hires in the long term. >>That's a great focus there. Danielle, I know we've talked about this before, but for the audience, in terms of the context of the honeycomb partnership, the focus at AWS for D E and I is really significant, unpack that a little bit for us. >>Well, let me just bring it back to just how we think about it, um, with the companies that we work with, but also in, in terms of, you know, what we want to be able to do, excuse me, it's very important for us to, you know, build products that reflect, uh, the customers that we have. And I think, you know, working with, uh, a company like honeycomb that is looking to differentiate in a space, um, by, by bringing in, you know, the experiences of many different types of people I genuinely believe. And I'm sure Vera also believes that by having those diverse perspectives, that we're able to then build better products for our customers. Um, and you know, it's one of, one of our leadership principles, uh, is, is rooted in this. I write a lot, it asks for us to seek out diverse perspectives. Uh, and you can't really do that if everybody kind of looks the same and thinks the same and has the same background. So I think that is where our de and I, um, you know, I thought process is rooted and, you know, companies like honeycomb that give customers choice and differentiate and help them, um, to do what they need to do in their unique, um, environments is super important. So >>The, the importance of thought diversity cannot be underscored enough. It's something that is, can be pivotal to organizations. And it's very nice to hear that that's so fundamental to both companies, Barry, I wanna go back to you for a second. You, I think you mentioned this, the DEI head start program, that's an internal program at honeycomb. Can you shed a little bit of light on that? >>Yeah, that's right. And I actually am in the process of hiring a first engineer for my team. So I'm learning a lot of these things firsthand, um, and how it works is we try to make sure to pre-load our pipeline of applicants for any new job opening we have with diverse candidates to the best of our abilities, and that can involve partnering with the organizations that I mentioned or reaching out to our internal network, um, and make sure that we give those applicants a head start, so to speak. >>Excellent. I like that. Danielle, before we close, I wanna get a little bit of, of your background. We've got various background in tag, she's celebrating her 10th anniversary. Give me a, a short kind of description of the journey that you've navigated through being a female in technology. >>Yeah, thanks so much. I really appreciate, uh, being able to share this. So I started as a software engineer, uh, back actually in the late nineties, uh, during the, the first.com bubble and, uh, have, have spent quite a long time actually as an individual contributor, um, probably working in software engineering teams up through 2014 at a minimum until I joined AWS, uh, as a customer facing solutions architect. Um, I do think spending a lot of time, hands on definitely helped me with some of the imposter syndrome, um, issues that folks suffer from not to say I don't at all, but it, it certainly helped with that. And I've been leading teams at AWS since 2015. Um, so it's really been a great ride. Um, and like I said, I'm very happy to see all of our engineering teams change, uh, as far as their composition. And I'm, I'm grateful to be part of it. >>It's pretty great to be able to witness that composition change for the better last question for each of you. And we're almost out of time and Danielle, I'm gonna stick with you. What's your advice, your recommendations for women who either are thinking about getting into tech or those who may be in tech, maybe they're in individual positions and they're not sure if they should apply for that senior leadership position. What do you advise them to do? >>I mean, definitely for the individual contributors, tech tech is a great career, uh, direction, um, and you will always be able to find women like you, you have to maybe just work a little bit harder, uh, to join, have community, uh, in that. But then as a leader, um, representation is very important and we can bring more women into tech by having more leaders. So that's my, you just have to take the lead, >>Take the lead, love that there. Same question for you. What's your advice and recommendations for those maybe future female leaders in tech? >>Yeah, absolutely. Um, Danielle mentioned imposter syndrome and I think we all struggle with it from time to time, no matter how many years it's been. And I think for me, for me, the advice would be if you're starting out, don't be afraid to ask, uh, questions and don't be afraid to kind of show a little bit of ignorance because we've all been there. And I think it's on all of us to remember what it's like to not know how things work. And on the flip side of that, if you are a more senior IC or, uh, in a leadership role, also being able to model just saying, I don't know how this works and going and figuring out answers together because that was a really powerful shift for me early in my career is just to feel like I can say that I don't know something. >>I totally agree. I've been in that same situation where just ask the question because you I'm guaranteed, there's a million outta people in the room that probably has the, have the same question and because of imposter syndrome, don't wanna admit, I don't understand that. Can we back up, but I agree with you. I think that is, um, one of the best things. Raise your hand, ask a question, ladies. Thank you so much for joining me talking about honeycomb and AWS, what you're doing together from a technology perspective and the focus efforts that each company has on D E and I, we appreciate your insights. Thank you so much for having us great talking to you. My pleasure, likewise for my guests, I'm Lisa Martin. You're watching the AWS partner showcase women in check. Welcome to the AWS partner showcase I'm Lisa Martin, your host. This is season one, episode three, and this is a great episode that focuses on women in tech. I'm pleased to be joined by Danielle Shaw, the ISV PSA director at AWS, and the sponsor of this fantastic program. Danielle, it's great to see you and talk about such an important topic. >>Yes. And I will tell you, all of these interviews have just been a blast for me to do. And I feel like there has been a lot of gold that we can glean from all of the, um, stories that we heard on these interviews and good advice that I myself would not have necessarily thought of. So >>I agree. And we're gonna get to set, cuz advice is one of the, the main things that our audience is gonna hear. We have Hillary Ashton, you'll see from TETA there, Reynolds joins us from honeycomb, Stephanie Curry from NetApp and Sue Paris from Jefferson Frank. And the topics that we dig into are first and foremost, diversity equity and inclusion. That is a topic that is incredibly important to every organization. And some of the things Danielle that our audiences shared were really interesting to me. One of the things that I saw from a thematic perspective over and over was that like D Reynolds was talking about the importance of companies and hiring managers and how they need to be intentional with de and I initiatives. And that intention was a, a, a common thing that we heard. I'm curious what your thoughts are about that, that we heard about being intentional working intentionally to deliver a more holistic pool of candidates where de I is concerned. What are your, what were some of the things that stuck out to you? >>Absolutely. I think each one of us is working inside of organizations where in the last, you know, five to 10 years, there's been a, you know, a strong push in this direction, mostly because we've really seen, um, first and foremost, by being intentional, that you can change the, uh, the way your organization looks. Um, but also just that, you know, without being intentional, um, there was just a lot of, you know, outcomes and situations that maybe weren't great for, um, you know, a healthy, um, and productive environment, uh, working environment. And so, you know, a lot of these companies have made a big investments and put forth big initiatives that I think all of us are involved in. And so we're really excited to get out here and talk about it and talk about, especially as these are all partnerships that we have, how, you know, these align with our values. So >>Yeah, that, that value alignment mm-hmm <affirmative> that you bring up is another thing that we heard consistently with each of the partners, there's a cultural alignment, there's a customer obsession alignment that they have with AWS. There's a D E and I alignment that they have. And I, I think everybody also kind of agreed Stephanie Curry talked about, you know, it's really important, um, for diversity on it, on, on impacting performance, highly performant teams are teams that are more diverse. I think we heard that kind of echoed throughout the women that we talked to in >>This. Absolutely. And I absolutely, and I definitely even feel that, uh, with their studies out there that tell you that you make better products, if you have all of the right input and you're getting all many different perspectives, but not just that, but I can, I can personally see it in the performing teams, not just my team, but also, you know, the teams that I work alongside. Um, arguably some of the other business folks have done a really great job of bringing more women into their organization, bringing more underrepresented minorities. Tech is a little bit behind, but we're trying really hard to bring that forward as well to in technical roles. Um, but you can just see the difference in the outcomes. Uh, at least I personally can just in the adjacent teams of mine. >>That's awesome. We talked also quite a bit during this episode about attracting women and underrepresented, um, groups and retaining them. That retention piece is really key. What were some of the things that stuck out to you that, um, you know, some of the guests talked about in terms of retention? >>Yeah. I think especially, uh, speaking with Hillary and hearing how, uh, Teradata is thinking about different ways to make hybrid work work for everybody. I think that is definitely when I talk to women interested in joining AWS, oftentimes that might be one of the first, uh, concerns that they have. Like, am I going to be able to, you know, go pick my kid up at four o'clock at the bus, or am I going to be able to, you know, be at my kids' conf you know, conference or even just, you know, have enough work life balance that I can, um, you know, do the things that I wanna do outside of work, uh, beyond children and family. So these are all very important, um, and questions that especially women come and ask, but also, um, you know, it kind of is a, is a bellwether for, is this gonna be a company that allows me to bring my whole self to work? And then I'm also gonna be able to have that balance that I need need. So I think that was something that is, uh, changing a lot. And many people are thinking about work a lot differently. >>Absolutely. The pandemic not only changed how we think about work, you know, initially it was, do I work from home or do I live at work? And that was legitimately a challenge that all of us faced for a long time period, but we're seeing the hybrid model. We're seeing more companies be open to embracing that and allowing people to have more of that balance, which at the end of the day, it's so much better for product development for the customers, as you talked about there's, it's a win-win. >>Absolutely. And, you know, definitely the first few months of it was very hard to find that separation to be able to put up boundaries. Um, but I think at least I personally have been able to find the way to do it. And I hope that, you know, everyone is getting that space to be able to put those boundaries up to effectively have a harmonious, you know, work life where you can still be at home most of the time, but also, um, you know, have that cutoff point of the day or at least have that separate space that you can feel that you're able to separate the two. >>Yeah, absolutely. And a lot of that from a work life balance perspective leads into one of the next topics that we covered in detail with, and that's mentors and sponsors the differences between them recommendations from, uh, the women on the panel about how to combat imposter syndrome, but also how to leverage mentors and sponsors throughout your career. One of the things that, that Hillary said that I thought was fantastic, advice were mentors and sponsors are concerned is, is be selective in picking your bosses. We often see people, especially younger folks, not necessarily younger folks. I shouldn't say that that are attracted to a company it's brand maybe, and think more about that than they do the boss or bosses that can help guide them along the way. But I thought that was really poignant advice that Hillary provided something that I'm gonna take into consideration myself. >>Yeah. And I honestly hadn't thought about that, but as I reflect through my own career, I can see how I've had particular managers who have had a major impact on helping me, um, with my career. But, you know, if you don't have the ability to do that, or maybe that's not a luxury that you have, I think even if you're able to, you know, find a mentor for a period of time or, um, you know, just, just enable for you to be able to get from say a point a to point B just for a temporary period. Um, just so you can grow into your next role, have a, have a particular outcome that you wanna drive, have a particular goal in mind find that person who's been there and done that and can really help you get through. If you don't have the luxury of picking your manager mentor, who can help you get to the next step. >>Exactly. That, that I thought that advice was brilliant and something that I hadn't really considered either. We also talked with several of the women about imposter syndrome. You know, that's something that everybody, I think, regardless of gender of your background, everybody feels that at some point. So I think one of the nice things that we do in this episode is sort of identify, yes, imposter syndrome is real. This is, this is how it happened to me. This is I navigated around or got over it. I think there's some great advice there for the audience to glean as well about how to dial down the imposter syndrome that they might be feeling. >>Absolutely. And I think the key there is just acknowledging it. Um, but also just hearing all the different techniques on, on how folks have dealt with it because everybody does, um, you know, even some of the smartest, most confident men I've, I've met in, uh, industry still talk to me about how they have it and I'm shocked by it oftentimes, but, um, it is very common and hopefully we, we talk about some good techniques to, to deal with that. >>I think we do, you know, one of the things that when we were asking the, our audience, our guests about advice, what would they tell their younger selves? What would they tell young women or underrepresented groups in terms of becoming interested in stem and in tech and everybody sort of agreed on me, don't be afraid to raise your hand and ask questions. Um, show vulnerabilities, not just as the employee, but even from a leadership perspective, show that as a leader, I, I don't have all the answers. There are questions that I have. I think that goes a long way to reducing the imposter syndrome that most of us have faced at some point in our lives. And that's just, don't be afraid to ask questions. You never know, oh, how can people have the same question sitting in the room? >>Well, and also, you know, for folks who've been in industry for 20, 25 years, I think we can just say that, you know, it's a, it's a marathon, it's not a sprint and you're always going to, um, have new things to learn and you can spend, you know, back to, we talked about the zing and zagging through careers, um, where, you know, we'll have different experiences. Um, all of that kind of comes through just, you know, being curious and wanting to continue to learn. So yes, asking questions and being vulnerable and being able to say, I don't know all the answers, but I wanna learn is a key thing, uh, especially culturally at AWS, but I'm sure with all of these companies as well, >>Definitely I think it sounded like it was really ingrained in their culture. And another thing too, that we also talked about is the word, no, doesn't always mean a dead end. It can often mean not right now or may, maybe this isn't the right opportunity at this time. I think that's another important thing that the audience is gonna learn is that, you know, failure is not necessarily a bad F word. If you turn it into opportunity, no isn't necessarily the end of the road. It can be an opener to a different door. And I, I thought that was a really positive message that our guests, um, had to share with the, the audience. >>Yeah, totally. I can, I can say I had a, a mentor of mine, um, a very, uh, strong woman who told me, you know, your career is going to have lots of ebbs and flows and that's natural. And you know that when you say that, not right now, um, that's a perfect example of maybe there's an ebb where it might not be the right time for you now, but something to consider in the future. But also don't be afraid to say yes, when you can. <laugh> >>Exactly. Danielle, it's been a pleasure filming this episode with you and the great female leaders that we have on. I'm excited for the audience to be able to learn from Hillary Vera, Stephanie Sue, and you so much valuable content in here. We hope you enjoy this partner showcase season one, episode three, Danielle, thanks so much for helping >>Us with it's been a blast. I really appreciate it >>All audience. We wanna enjoy this. Enjoy the episode.

>>from around the globe. It's the Cube with digital coverage of HP Green Lake day made possible by Hewlett Packard Enterprise. Okay, we're not gonna dive right into some of the news and get into the Green Lake Announcement details. And with me to do that is Keith White is the senior vice president and general manager for Green Lake Cloud Services and Hewlett Packard Enterprise. Keith, thanks for your time. Great to see you. >>Hey, thanks so much for having me. I'm really excited to be here. >>You're welcome. And so listen, before we get into the hard news, can you give us an update on just Green Lake and the business? How's it going? >>You bet. No, it's fantastic. And thanks, you know, for the opportunity again. And hey, I hope everyone's at home staying safe and healthy. It's been a great year for HP Green Lake. There's a ton of momentum that we're seeing in the market place. Uh, we've booked over $4 billion of total contract value to date, and that's over 1000 customers worldwide, and frankly, it's worldwide. It's in 50 50 different countries, and this is a variety of solutions. Variety of workloads. So really just tons of momentum. But it's not just about accelerating the current momentum. It's really about listening to our customers, staying ahead of their demands, delivering more value to them and really executing on the HB Green Lake. Promise. >>Great. Thanks for that and really great detail. Congratulations on the progress, but I know you're not done. So let's let's get to the news. What do people need to know? >>Awesome. Yeah, you know, there's three things that we want to share with you today. So first is all about it's computing. So I could go into some details on that were actually delivering new industry work clothes, which I think will be exciting for a lot of the major industries that are out there. And then we're expanding RHP capabilities just to make things easier and more effective. So first off, you know, we're excited to announce today, um, acceleration of mainstream as adoption for high performance computing through HP Green Lake. And you know, in essence, what we're really excited about is this whole idea of it's a. It's a unique opportunity to write customers with the power of an agile, elastic paper use cloud experience with H. P s market. See systems. So pretty soon any enterprise will be able to tackle their most demanding compute and did intensive workloads, power, artificial intelligence and machine learning initiatives toe provide better business insights and outcomes and again providing things like faster time to incite and accelerated innovation. So today's news is really, really gonna help speed up deployment of HPC projects by 75% and reduced TCO by upto 40% for customers. >>That's awesome. Excited to learn more about the HPC piece, especially. So tell us what's really different about the news today From your perspective. >>No, that's that's a great thing. And the idea is to really help customers with their business outcomes, from building safer cars to improving their manufacturing lines with sustainable materials. Advancing discovery for drug treatment, especially in this time of co vid or making critical millisecond decisions for those finance markets. So you'll see a lot of benefits and a lot of differentiation for customers in a variety of different scenarios and industries. >>Yeah, so I wonder if you could talk a little bit mawr about specifically, you know exactly what's new. Can you unpack some of that for us? >>You bet. Well, what's key is that any enterprise will be able to run their modeling and simulation work clothes in a fully managed because we manage everything for them pre bundled. So we'll give folks this idea of small, medium and large H p e c h piece services to operate in any data center or in a cold a location. These were close air, almost impossible to move to the public cloud because the data so large or it needs to be close by for Leighton see issues. Oftentimes, people have concerns about I p protection or applications and how they run within that that local environment. So if customers are betting their business on this insight and analytics, which many of them are, they need business, critical performance and experts to help them with implementation and migration as well as they want to see resiliency. >>So is this a do it yourself model? In other words, you know the customers have toe manage it on their own. Or how are you helping there? >>No, it's a great question. So the fantastic thing about HP Green Lake is that we manage it all for the customer. And so, in essence, they don't have to worry about anything on the back end, we can flow that we manage capacity. We manage performance, we manage updates and all of those types of things. So we really make it. Make it super simple. And, you know, we're offering these bundled solutions featuring RHP Apollo systems that are purpose built for running things like modeling and simulation workloads. Um, and again, because it's it's Green Lake. And because it's cloud services, this provides itself. Service provides automation. And, you know, customers can actually, um, manage however they want to. We can do it all for them. They could do some on their own. It's really super easy, and it's really up to them on how they want to manage that system. >>What about analytics? You know, you had a lot of people want to dig deeper into the data. How are you supporting that? >>Yeah, Analytics is key. And so one of the best things about this HPC implementation is that we provide unopened platform so customers have the ability to leverage whatever tools they want to do for analytics. They can manage whatever systems they want. Want to pull data from so they really have a ton of flexibility. But the key is because it's HP Green Lake, and because it's HP es market leading HPC systems, they get the fastest they get the it all managed for them. They only pay for what they use, so they don't need to write a huge check for a large up front. And frankly, they get the best of all those worlds together in order to come up with things that matter to them, which is that true business outcome, True Analytics s so that they could make the decisions they need to run their business. >>Yeah, that's awesome. You guys clearly making some good progress here? Actually, I see it really is a game changer for the types of customers that you described. I mean, particularly those folks that you like. You said You think they can't move stuff into the cloud. They've got to stay on Prem. But they want that cloud experience. I mean, that's that's really exciting. We're gonna have you back in a few minutes to talk about the Green Lake Cloud services and in some of the new industry platforms that you see evolving >>awesome. Thanks so much. I look forward to it. >>Yeah, us too. So Okay, right now we're gonna check out the conversation that I had earlier with Pete Ungaro and Addison Snell on HPC. Let's watch welcome everybody to the spotlight session here green. Late day, We're gonna dig into high performance computing. Let me first bring in Pete Ungaro, Who's the GM for HPC and Mission Critical solutions, that Hewlett Packard Enterprise. And then we're gonna pivot Addison Snell, who is the CEO of research firm Intersect 3. 60. So, Pete, starting with you Welcome. And really a pleasure to have you here. I want to first start off by asking you what is the key trends that you see in the HPC and supercomputing space? And I really appreciate if you could talk about how customer consumption patterns are changing. >>Yeah, I appreciate that, David, and thanks for having me. You know, I think the biggest thing that we're seeing is just the massive growth of data. And as we get larger and larger data sets larger and larger models happen, and we're having more and more new ways to compute on that data. So new algorithms like A. I would be a great example of that. And as people are starting to see this, especially they're going through a digital transformations. You know, more and more people I believe can take advantage of HPC but maybe don't know how and don't know how to get started on DSO. They're looking for how to get going into this environment and many customers that are longtime HBC customers, you know, just consume it on their own data centers. They have that capability, but many don't and so they're looking at. How can I do this? Do I need to build up that capability myself? Do I go to the cloud? What about my data and where that resides. So there's a lot of things that are going into thinking through How do I start to take advantage of this new infrastructure? >>Excellent. I mean, we all know HPC workloads. You're talking about supporting research and discovery for some of the toughest and most complex problems, particularly those that affecting society. So I'm interested in your thoughts on how you see Green Lake helping in these endeavors specifically, >>Yeah, One of the most exciting things about HPC is just the impact that it has, you know, everywhere from, you know, building safer cars and airplanes. Thio looking at climate change, uh, to, you know, finding new vaccines for things like Covic that we're all dealing with right now. So one of the biggest things is how do we take advantage event and use that to, you know, benefit society overall. And as we think about implementing HPC, you know, how do we get started? And then how do we grow and scale as we get more and more capability? So that's the biggest things that we're seeing on that front. >>Yes. Okay, So just about a year ago, you guys launched the Green Lake Initiative and the whole, you know, complete focus on as a service. So I'm curious as to how the new Green Lake services the HPC services specifically as it relates to Greenlee. How do they fit in the H. P s overall high performance computing portfolio and the strategy? >>Yeah, great question. You know, Green Lake is a new consumption model for eso. It's a very exciting We keep our entire HPC portfolio that we have today, but extend it with Green Lake and offer customers you know, expanded consumption choices. So, you know, customers that potentially are dealing with the growth of their data or they're moving toe digital transformation applications they can use green light just easily scale up from workstations toe, you know, manage their system costs or operational costs, or or if they don't have staff to expand their environment. Green Light provides all of that in a manage infrastructure for them. So if they're going from like a pilot environment up into a production environment over time, Green Lake enables them to do that very simply and easily without having toe have all that internal infrastructure people, computer data centers, etcetera. Green Lake provides all that for them so they can have a turnkey solution for HBC. >>So a lot easier entry strategies. A key key word that you use. There was choice, though. So basically you're providing optionality. You're not necessarily forcing them into a particular model. Is that correct? >>Yeah, 100%. Dave. What we want to do is just expand the choices so customers can buy a new choir and use that technology to their advantage is whether they're large or small. Whether they're you know, a startup or Fortune 500 company, whether they have their own data centers or they wanna, you know, use a Coehlo facility whether they have their own staff or not, we want to just provide them the opportunity to take advantage of this leading edge resource. >>Very interesting, Pete. It really appreciate the perspective that you guys have bring into the market. I mean, it seems to me it's gonna really accelerate broader adoption of high performance computing, toe the masses, really giving them an easier entry point I want to bring in now. Addison Snell to the discussion. Addison. He's the CEO is, I said of Intersect 3 60 which, in my view, is the world's leading market research company focused on HPC. Addison, you've been following the space for a while. You're an expert. You've seen a lot of changes over the years. What do you see is the critical aspect in the market, specifically as it relates toward this as a service delivery that we were just discussing with Pete and I wonder if you could sort of work in their the benefits in terms of, in your view, how it's gonna affect HPC usage broadly. Yeah, Good morning, David. Thanks very much for having me, Pete. It's great to see you again. So we've been tracking ah lot of these utility computing models in high performance computing for years, particularly as most of the usage by revenue is actually by commercial endeavors. Using high performance computing for their R and D and engineering projects and the like. And cloud computing has been a major portion of that and has the highest growth rate in the market right now, where we're seeing this double digit growth that accounted for about $1.4 billion of the high performance computing industry last year. But the bigger trend on which makes Green like really interesting is that we saw an additional about a billion dollars worth of spending outside what was directly measured in the cloud portion of the market in in areas that we deemed to be cloud like, which were as a service types of contracts that were still utility computing. But they might be under a software as a service portion of the budget under software or some other managed services type of contract that the user wasn't reported directly is cloud, but it was certainly influenced by utility computing, and I think that's gonna be a really dominant portion of the market going forward. And when we look at growth rate and where the market's been evolving, so that's interesting. I mean, basically, you're saying this, you know, the utility model is not brand new. We've seen that for years. Cloud was obviously a catalyst that gave that a boost. What is new, you're saying is and I'll say it this way. I'd love to get your independent perspective on this is so The definition of cloud is expanding where it's you know, people always say it's not a place, it's an experience and I couldn't agree more. But I wonder if you could give us your independent perspective on that, both on the thoughts of what I just said. But also, how would you rate H. P. E s position in this market? Well, you're right, absolutely, that the definition of cloud is expanding, and that's a challenge when we run our surveys that we try to be pedantic in a sense and define exactly what we're talking about. And that's how we're able to measure both the direct usage of ah, typical public cloud, but also ah more flexible notion off as a service. Now you asked about H P E. In particular, And that's extremely relevant not only with Green Lake but with their broader presence in high performance computing. H P E is the number one provider of systems for high performance computing worldwide, and that's largely based on the breath of H. P s offerings, in addition to their performance in various segments. So picking up a lot of the commercial market with their HP apology and 10 plus, they hit a lot of big memory configurations with Superdome flex and scale up to some of the most powerful supercomputers in the world with the HP Cray X platforms that go into some of the leading national labs. Now, Green Light gives them an opportunity to offer this kind of flexibility to customers rather than committing all it wants to a particular purchase price. But if you want to do position those on a utility computing basis pay for them as a service without committing to ah, particular public cloud. I think that's an interesting role for Green Lake to play in the market. Yeah, it's interesting. I mean earlier this year, we celebrated Exa scale Day with support from HP, and it really is all about a community and an ecosystem is a lot of camaraderie going on in the space that you guys are deep into, Addison says. We could wrap. What should observers expect in this HPC market in this space over the next a few years? Yeah, that's a great question. What to expect because of 2020 has taught us anything. It's the hazards of forecasting where we think the market is going. When we put out a market forecast, we tend not to look at huge things like unexpected pandemics or wars. But it's relevant to the topic here because, as I said, we were already forecasting Cloud and as a service, models growing. Any time you get into uncertainty, where it becomes less easy to plan for where you want to be in two years, three years, five years, that model speaks well to things that are cloud or as a service to do very well, flexibly, and therefore, when we look at the market and plan out where we think it is in 2020 2021 anything that accelerates uncertainty actually is going. Thio increase the need for something like Green Lake or and as a service or cloud type of environment. So we're expecting those sorts of deployments to come in over and above where we were already previously expected them in 2020 2021. Because as a service deals well with uncertainty. And that's just the world we've been in recently. I think there's a great comments and in a really good framework. And we've seen this with the pandemic, the pace at which the technology industry in particular, of course, HP specifically have responded to support that your point about agility and flexibility being crucial. And I'll go back toe something earlier that Pete said around the data, the sooner we can get to the data to analyze things, whether it's compressing the time to a vaccine or pivoting our business is the better off we are. So I wanna thank Pete and Addison for your perspectives today. Really great stuff, guys. Thank you. >>Yeah, Thank you. >>Alright, keep it right there from, or great insights and content you're watching green leg day. Alright, Great discussion on HPC. Now we're gonna get into some of the new industry examples and some of the case studies and new platforms. Keith HP, Green Lake It's moving forward. That's clear. You're picking up momentum with customers, but can you give us some examples of platforms for industry use cases and some specifics around that? >>You know, you bet, and actually you'll hear more details from Arwa Qadoura she leads are green like the market efforts in just a little bit. But specifically, I want to highlight some examples where we provide cloud services to help solve some of the most demanding workloads on the planet. So, first off in financial services, for example, traditional banks are facing increased competition and evolving customer expectations they need to transform so that they can reduce risk, manage cop and provided differentiated customer experience. We'll talk about a platform for Splunk that does just that. Second, in health care institutions, they face the growing list of challenges, some due to the cove in 19 Pandemic and others. Years in the making, like our aging population and rise in chronic disease, is really driving up demands, and it's straining capital budgets. These global trance create a critical need for transformation. Thio improve that patient experience and their business outcomes. Another example is in manufacturing. They're facing many challenges in order to remain competitive, right, they need to be able to identify new revenue streams run more efficiently from an operation standpoint and scale. Their resource is so you'll hear more about how we're optimizing and delivery for manufacturing with S. A P Hana and always gonna highlight a little more detail on today's news how we're delivering supercomputing through HP Green Lake It's scale and finally, how we have a robust ecosystem of partners to help enterprises easily deploy these solutions. For example, I think today you're gonna be talking to Skip Bacon from Splunk. >>Yeah, absolutely. We sure are. And some really great examples there, especially a couple industries that that stood out. I mean, financial services and health care. They're ripe for transformation and maybe disruption if if they don't move fast enough. So Keith will be coming back to you a little later today to wrap things up. So So thank you. Now, now we're gonna take a look at how HP is partnering with Splunk and how Green Lake compliments, data rich workloads. Let's watch. We're not going to dig deeper into a data oriented workload. How HP Green Lake fits into this use case and with me, a Skip Bacon vice president, product management at Splunk Skip. Good to see >>you. Good to see you as well there. >>So let's talk a little bit about Splunk. I mean, you guys are a dominant player and security and analytics and you know, it's funny, Skip, I used to comment that during the big data, the rise of big data Splunk really never positioned themselves is this big data player, and you know all that hype. But But you became kind of the leader in big data without really, even, you know, promoting it. It just happened overnight, and you're really now rapidly moving toward a subscription model. You're making some strategic moves in the M and a front. Give us your perspective on what's happening at the company and why customers are so passionate about your software. >>Sure, a great, great set up, Dave. Thanks. So, yeah, let's start with the data that's underneath big data, right? I think I think it is usual. The industry sort of seasons on a term and never stops toe. Think about what it really means. Sure, one big part of big data is your transaction and stuff, right? The things that catch generated by all of your Oracle's USC Cheops that reflect how the business actually occurred. But a much bigger part is all of your digital artifacts, all of the machine generated data that tells you the whole story about what led up to the things that actually happened right within the systems within the interactions within those systems. That's where Splunk is focused. And I think what the market is the whole is really validating is that that machine generated data those digital artifacts are a tely least is important, if not more so, than the transactional artifacts to this whole digital transformation problem right there. Critical to showing I t. How to get better developing and deploying and operating software, how to get better securing these systems, and then how to take this real time view of what the business looks like as it's executing in the software right now. And hold that up to and inform the business and close that feedback loop, right? So what is it we want to do differently digitally in order to do different better on the transformation side of the house. So I think a lot of splints. General growth is proof of the value crop and the need here for sure, as we're seeing play out specifically in the domains of ICTs he operations Dev, ops, Cyber Security, right? As well as more broadly in that in that cloak closing the business loop Splunk spin on its hair and growing our footprint overall with our customers and across many new customers, we've been on its hair with moving parts of that footprints who and as a service offering and spawn cloud. But a lot of that overall growth is really fueled by just making it simpler. Quicker, faster, cheaper, easier toe operates Plunkett scale because the data is certainly not slowing down right. There's more and more and more of it every day, more late, their potential value locked up in it. So anything that we can do and that our partners conducive to improve the cost economics to prove the agility to improve the responsiveness of these systems is huge. That that customer value crop and that's where we get so excited about what's going on with green life >>Yeah, so that makes sense. I mean, the digital businesses, a data business. And that means putting data at the core. And Splunk is obviously you keep part of that. So, as I said earlier, spunk your leader in this space, what's the deal with your HP relationship? You touched on that? What should we know about your your partnership? And what's that solution with H h p E? What's that customer Sweet spot. >>Yep. Good. All good questions. So we've been working with HP for quite a while on on a number of different fronts. This Green lake peace is the most interesting and sort of the intersection of, you know, purist intersection of both of these threads of these factories, if you will. So we've been working to take our core data platform deployed on an enterprise operator for kubernetes. Stick that a top H P s green like which is really kubernetes is a service platform and go prove performance, scalability, agility, flexibility, cost economics, starting with some of slugs, biggest customers. And we've proven, you know, alot of those things In great measure, I think the opportunity you know, the ability to vertically scale Splunk in containers that taught beefy boxes and really streamline the automation, the orchestration, the operations, all of that yields what, in the words of one of our mutual customers, literally put it as This is a transformational platform for deploying and operating spot for us so hard at work on the engineering side, hard at work on the architectural referencing, sizing, you know, capacity planning sides, and then increasing really rolling up our sleeves and taking the stuff the market together. >>Yeah, I mean, we're seeing the just the idea of cloud. The definition of cloud expanding hybrid brings in on Prem. We talked about the edge and and I really We've seen Splunk rapidly transitioning its pricing model to a subscription, you know, platform, if you will. And of course, that's what Green Lakes all about. What makes Splunk a good fit for Green Lake and vice versa? What does it mean for customers? >>Sure, So a couple different parts, I think, make make this a perfect marriage. Splunk at its core, if you're using it well, you're using it in a very iterative discovery driven kind of follow you the path to value basis that makes it a little hard to plan the infrastructure and decides these things right. We really want customers to be focused on how to get more data in how to get more value out. And if you're doing it well, those things, they're going to go up and up and up over time. You don't wanna be constrained by size and capacity planning, procurement cycles for infrastructure. So the Green Lake model, you know, customers got already deployed systems already deployed, capacity available in and as the service basis, very fast, very agile. If they need a next traunch of capacity to bring in that next data set or run, that next set of analytics right it's available immediately is a service, not hey, we've got to kick off the procurement cycle for a whole bunch more hardware boxes. So that flexibility, that agility or key to the general pattern for using Splunk and again that ability to vertically scale stick multiple Splunk instances into containers and load more and more those up on these physical boxes right gives you great cost economics. You know, Splunk has a voracious appetite for data for doing analytics against that data less expensive, we can make that processing the better and the ability to really fully sweat, you know, sweat the assets fully utilize those assets. That kind of vertical scale is the other great element of the Green Lake solution. >>Yes. I mean, when you think about the value prop for for customers with Splunk and HP green, that gets a lot of what you would expect from what we used to talk about with the early days of cloud. Uh, that that flexibility, uh, it takes it away. A lot of the sort of mundane capacity planning you can shift. Resource is you talked about, you know, scale in a in a number of of use cases. So that's sort of another interesting angle, isn't it? >>Yeah. Faster. It's the classic text story. Faster, quicker, cheaper, easier, right? Just take in the whole whole new holy levels and hold the extremes with these technologies. >>What do you see? Is the differentiators with Splunk in HP, Maybe what's different from sort of the way we used to do things, but also sort of, you know, modern day competition. >>Yeah. Good. All good. All good questions. So I think the general attributes of splinter differentiated green Laker differentiated. I think when you put them together, you get this classic one plus one equals three story. So what? I hear from a lot of our target customers, big enterprises, big public sector customers. They can see the path to these benefits. They understand in theory how these different technologies would work together. But they're concerned about their own skills and abilities to go building. Run those and the rial beauty of Green Lake and Splunk is this. All comes sort of pre design, pre integrated right pre built HP is then they're providing these running containers as a service. So it's taking a lot of the skills and the concerns off the customers plate right, allowing them to fast board to, you know, cutting edge technology without any of the wrist. And then, most importantly, allowing customers to focus their very finite resource is their peoples their time, their money, their cycles on the things that are going to drive differentiated value back to the business. You know, let's face facts. Buying and provisioning Hardware is not a differentiating activity, running containers successfully, not differentiating running the core of Splunk. Not that differentiating. He can take all of those cycles and focus them instead on in the simple mechanics. How do we get more data in? Run more analytics on it and get more value out? Right then you're on the path to really delivering differentiated, you know, sustainable competitive basis type stuff back to the business, back to that digital transformation effort. So taking the skills out, taking the worries out, taking the concerns about new tech, out taking the procurement cycles, that improving scalability again quicker, faster, cheaper. Better for sure. >>It's kind of interesting when you when you look at the how the parlance has evolved from cloud and then you had Private Cloud. We talk a lot about hybrid, but I'm interested in your thoughts on why Splunk and HP Green Light green like now I mean, what's happening in the market that makes this the right place and in the right time, so to speak. >>Yeah, again, I put cloud right up there with big data is one of those really overloaded terms. Everything we keep keep redefining as we go if we define it. One way is as an experience instead of outcomes that customers looking for right, what does anyone of our mutual customers really want Well, they want capabilities that air quick to get up and running that air fast, to get the value that are aligned with how the price wise, with how they deliver value to the business and that they can quickly change right as the needs of the business and the operation shift. I think that's the outcome set that people are looking thio. Certainly the early days of cloud we thought were synonymous with public cloud. And hey, the way that you get those outcomes is you push things out. The public cloud providers, you know, what we saw is a lot of that motion in cases where there wasn't the best of alignment, right? You didn't get all those outcomes that you were hoping for. The cost savings weren't there or again. These big enterprises, these big organizations have a whole bunch of other work clothes that aren't necessarily public cloud amenable. But what they want is that same cloud experience. And this is where you see the evolution in the hybrid clouds and into private clouds. Yeah, any one of our customers is looking across the entirety of this landscape, things that are on Prem that they're probably gonna be on Prem forever. Things that they're moving into private cloud environments, things that they're moving into our growing or expanding or landing net new public cloud. They want those same outcomes, the same characteristics across all of that. That's a lot of Splunk value. Crop is a provider, right? Is we can go monitor and help you operate and developed and secure exactly all of that, no matter where it's located. Splunk on Green Lake is all about that stack, you know, working in that very cloud native way even where it made sense for customers to deploy and operate their own software. Even if this want, they're running over here themselves is hoping the modern, secure other work clothes that they put into their public cloud environments. >>Well, it Z another key proof point that we're seeing throughout the day here. Your software leader, you know, HP bring it together. It's ecosystem partners toe actually deliver tangible value. The customers skip. Great to hear your perspective today. Really appreciate you coming on the program. >>My pleasure. And thanks so much for having us take care. Stay well, >>Yeah, Cheers. You too. Okay, keep it right there. We're gonna go back to Keith now. Have him on a close out this segment of the program. You're watching HP Green Lake Day on the Cube. All right, We're So we're seeing some great examples of how Green Lake is supporting a lot of different industries. A lot of different workloads we just heard from Splunk really is part of the ecosystem. Really? A data heavy workload. And we're seeing the progress. HPC example Manufacturing. We talked about healthcare financial services, critical industries that are really driving towards the subscription model. So, Keith, thanks again for joining us. Is there anything else that we haven't hit that you feel are audience should should know about? >>Yeah, you bet. You know, we didn't cover some of the new capabilities that are really providing customers with the holistic experience to address their most demanding workloads with HP Green Lake. So first is our Green Lake managed security services. So this provides customers with an enterprise grade manage security solution that delivers lower costs and frees up a lot of their resource is the second is RHP advisory and Professional Services Group. So they help provide customers with tools and resource is to explore their needs for their digital transformation. Think about workshops and trials and proof of concepts and all of that implementation. Eso You get the strategy piece, you get the advisory piece, and then you get the implementation piece that's required to help them get started really quickly. And then third would be our H. P s moral software portfolio. So this provides customers with the ability to modernize their absent data unify, hybrid cloud and edge computing and operationalized artificial intelligence and machine learning and analytics. >>You know, I'm glad that you brought in the sort of machine intelligence piece in the machine learning because that's, ah, lot of times. That's the reason why people want to go to the cloud at the same time you bring in the security piece a lot of reasons why people want to keep things on Prem. And, of course, the use cases here. We're talking about it, really bringing that cloud experience that consumption model on Prem. I think it's critical critical for companies because they're expanding their notion of cloud computing really extending into hybrid and and the edge with that similar experience or substantially the same experience. So I think folks are gonna look at today's news as real progress. We're pushing you guys on some milestones and some proof points towards this vision is a critical juncture for organizations, especially those look, they're looking for comprehensive offerings to drive their digital transformations. Your thoughts keep >>Yeah, I know you. You know, we know as many as 70% of current and future APS and data are going to remain on Prem. They're gonna be in data centers, they're gonna be in Colo's, they're gonna be at the edge and, you know, really, for critical reasons. And so hybrid is key. As you mentioned, the number of times we wanna help customers transform their businesses and really drive business outcomes in this hybrid, multi cloud world with HP Green Lake and are targeted solutions. >>Excellent. Keith, Thanks again for coming on the program. Really appreciate your time. >>Always. Always. Thanks so much for having me and and take Take care of. Stay healthy, please. >>Alright. Keep it right there. Everybody, you're watching HP Green Lake day on the Cube

thanks Peter I'm here with James kabila Sawicki bonds lead analyst for data science Jim been hearing a lot about data science and how machine learning is coming into this environment give it give us a little bit of a guidance as to how that this this whole space fits into data science you know how does that that infrastructure fit in with data science today yeah well stew data science is a set of practices for building and training statistical models often known as machine learning models to be deployed into applications to do things like predictive analysis automating next best offers and marketing and so forth so what machine learning is all about is the statistical model and those are built by a category of professionals known as data scientists but data scientists operated in teams there are data engineers who manage your data lake there are data modelers who build the models themselves there are there are professionals who specialize in training the models and deploying them trainings like Quality Assurance so what it's all about is really these part these functions are increasingly being combined into workflows they have to conform with DevOps practices because this is an important set of application development capabilities that are absolutely essential to deploy machine learning into AI in AI is really the secret sauce of so many apps nowadays all right Jim is we've looked at data center Ops walk us through the tech the process and the people okay data center else really is data science ops or often well wiki bomb we've referred to as DevOps for data science and really what we start with the the start with the people I've already been to sketch those up so in terms of the people the professionals involved in building and training and deploying and evaluating and iterating machine learning models there are the data scientists who are this justjust iskele modelers you might call them the algorithm jockeys though that may be regarded as a pejorative but nonetheless these are the high-powered professionals who who build who know which algorithm is correct for what the challenge they build the models on there are the data engineers who not only manage your data lakes the data lakes is where the training data is maintained the data for building the model and for training the models are maintained in data lakes the data engineers manage that they also manage data preparation data transformation data cleansing to get the data clean and correct so that it can be used to build high quality models there are other functions that are absolutely essential there are as what some call ml architour machine learning architects I like to think of them as subject-matter experts who work with the data scientist to build what are called the feature sets the predictors that need to be built into machine learning models for those models to do therefore perform their function correctly whether it be a prediction or like face recognition or natural language processing for your ear your chat BOTS and so forth you need the subject matter experts with you to provide guidance to the data scientists as to what variables to build into these models there was also coders there's a lot of coding that's done in data science and ml ops that's done in Python and Java and Scala and a variety of other languages and there's other functions as well but these are the core functions that need to be performed in a team environment really in a workflow and that is where the process comes in the workflow for data science in teams is DevOps it's really the continuous integration of different data sets as well as different models as well as different features into the building and training of AI so these need to be pretty nice functions need to be performed in a workflow that's highly structured where there's checkpoints and there's governance and there's a transparency and auditability so it really all this needs to be performed in a DevOps environment where you have the data lake which is the source of the the data of course we also have a source repository for managing the current and past versions of the models themselves where you also do governance on the code builds that are with each of the models that are deployed into your application environment so that's the process site at all and then the platform our tech side is really revolves around with some colleague data science workbench or a data science platform there's a variety of terms for it but essentially it is a development environment that enables a high degree of automation and all across all these functions because automation is absolutely essential for speed and consistency in terms of how models are built and entrained there's also a need for our collaboration capability strong ones within these platforms so these different human roles can work together in a cohesive fashion and really like a well-oiled machine screaming there's a need for repositories - like I said managed in govern the current versions of all the artifacts be they data be they models be they code bills and so forth that are essential so all of these people processes and Texas and there is building high-quality AI yeah so Jim I noticed you call it DevOps for data science so yes there's a real emphasis there on how we get all of these new things aligned with the process for DevOps and maybe help us put a point on you know why that's so important well because DevOps is how applications are built and deployed now everywhere which is essentially it so it's a workflow it's a standard workflow that involves a scaleable organization where you have code that is built and managed and governed according to a standard workflow standard repositories with checkpoints and transparency as a way of consensual e ensuring that high quality code is deployed into working applications according to essentially a factory-style automation or an industrialized workflow so data science is a development discipline data science needs to as a as a workflow needs to conform with the established DevOps practices that your application developers your coders have already established in fact most AI applications most machine learning applications involve code involved machine learning models but also involve containers and kubernetes and increasingly serverless interfaces and so forth so data science is not separated from the other aspects of the DevOps workflow itting and christie is a unified and integrated piece of your operations and they needs to be managed as such all right well Jim appreciate you going through the evolution on that I know you've written quite a bit about this topic on the wiki bond website and Peter will send it back to you

thanks Peter we're here with Patrick Osbourne who's the vice president and general manager of big data and secondary storage with HPE Patrick help us put into context what we've been talking about Emel ops AI and HP strategy in this area yeah thanks to I you know as you as you all have reported in the past and we've worked with with with you your HP is very strong in infrastructure solutions we've had you know quite a bit of success in AI and now deep learning cookbook which we released last year and so you know we're definitely helping customers along the maturity curve for AI and ml you see that we've got a number of advisory services I think one of the big things that we could get called out from customers is that there's a skills gap in operationalizing and and putting AI and ml workloads into production as well as you know we are a thought leader and have quite a bit of research with HP Labs on memory driven computing Gen Z and being able to squit you know scale those workloads within the enterprise so those are things that we're building off of in addition to some pretty high-profile and very valuable software acquisitions first and last year first around blue data which we talked about today in the context of ml ops and then most recently map R which is a very powerful scaleable persistent data layer for analytics so for us it's AI is a is a very is a top priority for us at HPE it's part of our corporate narrative and helping customers along that maturity curve is definitely where we're focused on great so how are HP and its partners helping customers along their journey that they're going on with AI yes so I think at the end of the day HP is very focused on our customers especially from a go-to markets perspective so we are we're in the phase now where we're helping customers not just explore but to operationalize AI and ml so whether it's cookbooks and our a specific products like machine learning operations which helps you scale from you know a data scientist or a danger engineer developing an algorithm on you know laptop to be able to running that at scale in the data center so for us that journey is is very important especially around the the outcome from the technology perspective technology partner perspective we have a number of really high profile and new relationships that were building for this new ecosystem around AI and ml and DL and so folks like data iku h2o on the hardware side Intel and NVIDIA we are bringing that to our customers to provide you know a complete solution so being able to take those ISVs and run them and containerized stateful you know deployment and then be able to you know partner with all of our hardware vendors and software vendors and then for the channel we feel that this is a huge you know this is like this is a great opportunity for them to certainly move up stack in how they talk to customers about their business outcomes so I think it's part of a three prong strategy and we're really kind of focused on those key areas yeah no doubt an area that's getting attention from all sectors of the marketplace so those that are watching HPE what should we be expecting to look to see from them in the form near future yeah so I think you know from our perspective we've got a number of releases that are coming up over the next year and pretty excited about that in addition to machine learning operations I think that the world you know will continue to be moving towards containers for more than just stateless applications we're starting now with AI and ml and I think there's a big you know future for other applications whether they're cloud native or you know those applications are refactored certainly living within a world of kubernetes right is becoming more of a reality from a deployment perspective so you know for us we're very you know focused on the customer outcome I think the other area too is that that HP has been very famous for lately is around consumption based services right so we're able to bring that vetted ecosystem the containerized deployment model and platform this the your accelerators compute networking and storage and even a persistent data layer and even you know even the the cloud experience to the customer as a biz outcome in a consumption experience their Green Lake is something that you know we think is very valuable for our customers all right well thank you patrick for helping us to put all of that into context Peter I'm going to send it back to you for the wrap

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE! Now, here's your host, Stu Miniman! (bubbly music) >> Hi, I'm Stu Miniman and you're watching theCUBE's Boston area studio. Happy to welcome back to the program Randy Arseneau and Steve Kenniston. Gentlemen. >> We're back! (Stu laughs) >> Absolutely. >> It sees like only minutes ago we were here. >> How can I miss you if you don't go away, oh wow. Gentlemen, thank you so much. We've been talking storage and SDI, which of course, is software defined infrastructure essentials. We're gonna dig inside and Steve, let's start with, you know, sometimes we argue over definitional things, and when you hear software defined, oh it's about software, and especially when you talk about the storage world, it's like wait, there's always been software when we talk about storage. So explain why it's a little bit different now, then what we were doing, you know, even five years ago. >> Sure thing Stu, I think one of the number one things that we run into a lot of that we hear, conversationally, it's storage and it's software. And now we're hearing a lot more about data services, right? The ability to connect data, so forget the physical storage for a second. Connect the data to the people, right? Because as we've been talking all along today, right, this evolutionary platform is being able to provide more people access to more data than they've ever had before in a very secure way, right? Such that, they can actually not only get their jobs done, get 'em done better, get 'em done stronger, get 'em done faster, without all the, as my boss likes to say bouja bouja, (laughs) dealing with having to get at that data. So, if I look at before and after, right? If I look at the before aspect that dealt with, you know, how do I get to my data, right? Or how do I get access to that data if I'm a developer of five years ago, right? It ends up being, and you can attest to this, and please do, right? A conversation between the developer and IT. And then it becomes a myriad of questions back and forth about why do you need it. What do you need it for? How much do you need? Where does it need to live? How fast does it need to be? All of the things that you can get today programmed, right? Into a programmable infrastructure, and just say click and then we provide that to you, right? So before it was all these conversations. Then I gotta buy it, then I gotta procure it, then I gotta secure it, then I gotta know how many LUNs you need, and, how fast it is, and then I gotta provision that out to you, right? And you go okay, and then you say well now you need that. And then the next conversation is well I can't get to it with this application which is on this server which is, now it's another whole, you know, before a developer can start working, it could be a month. Whereas afterwards, it should be, if you have a good programmable infrastructure, I have my application like a Chef or a Puppet or an Ansible, and I push a button and it says, okay I need this particular data set, and it needs to have this set of services. It needs to be this available, it needs to perform this fast, I need to be able to make these types of copies, click go, right? That's kinda where we're at today with what we're hoping the software can do for you. >> Yeah, I always worry, sometimes we try to oversimplify things and we miss, kinda the why, so. One of my favorite jokes we all had is, you know, there is no cloud, it's just a computer somewhere else, because, and it was like no, no, no, wait, there's still gear underneath it. But I missed the: well why does that matter? It's like, oh wait, I've got an order of magnitude more, you know, that I can access for short periods of time, and therefore I can do things that I couldn't do before. And when I think about data it's, you know, you know, big data, you know, massive data, things like no, no, no, no, no, it's not just storing bunch of bits somewhere in case I need them for a regulatory thing, because I've gotta do governance compliance, blah, blah, blah, and everything, but, it's like: Wow! You know, data is, you know, a driver for business, and therefore, you know, data services that allow me to protect and secure and access all those data, is so super important. >> Absolutely, and there's another analog I think is it's, if you think about data services, as we're talking about it right now, it's becoming more of, and the self service metaphor is a very important one I think because, to Steve's earlier point, in the old world, you know, you used to have to go through this complicated workflow and checkpoints and sign-offs and all these procedural, you know, loopholes that you'd have to jump through in order to provision something which, by the time it became available it was probably outdated, right? So, you're constantly behind, right? Now, at the pace of global business and digital business and the importance of data as a driver of global business, you just don't have that luxury anymore, you can't afford to be waiting on the availability of that piece of information so, not only does the immediacy of it improve the actual value of the data, because there's always a temporal element of value for that data, and every second you waste is potentially value that's diminished from data. So not only do we now reduce that kind of latency, we also provide much better reuse, so we talk about this idea of incremental value, right? So you can take the same data element or data construct and now instantaneously repurpose it in multiple ways to extract additional maybe unforeseen value from it, right? So we've gotten away from the concept of these kinda siloed systems that are, you know, this is my production system, this is my OLAP system, my reporting system, we now have this convergence, cross pollination of data, that flows between and among all these systems, which can now be made available via something like a data services platform or a, you know, fill in the blank as a service type platform in the self service mode, where it can be used by any number of different applications and users for any number of different purposes. >> Yeah and what I like about it, what I hear from customers, it used to be, you had to have the budget of a nation or a team of PhDs to try to figure this out. And as you say, with this cross pollination, when I get the data versus when I'm gonna need the data, yes there's the temporal piece, but sometimes it's, I might be doing it for a different purpose, or I'm not sure and things are changing all the time. So that, going back to our initial conversation, that agility and flexibility, being able to access and, you know, tie into that data, and have a range of services is so critically important. >> Well that's a good comment, right? You need to have the budget of a nation in order to do this, and then along came all the cloud providers and said: no you don't, swipe your credit card and start working, right? The tricky part for me, the consumer, of that, great, now I've got the processing power I need, but I need my data to build my particular application. I love, sorta to finish that outright. In order to build the right application, to make sure it works for what I'm gonna do because at some point, right, and I know Dave likes to talk about this a lot, I need to be able to integrate it into my existing systems, and if the performance isn't the same and that's what, I'm gonna probably run into a lot of buggy stuff over here. So I need to be careful with that. What I think is interesting is, and I love to use the analogy, think of the first bank that came up with the application where I could snap a picture of my check, right? I bet the CIO of the competitive company went to his development team the next morning after seeing that commercial and said I want one of those. Do you think, I mean, if you broke, and I don't like always like to use particular verticals, because I think this conversation extends across all applications. But do you, if you had to break down what does one banking customer cost the bank over time, right? And then you said for every day I'm late I lose five customers, and you had to go through that whole lengthy process just to get started for the development team to start working on something like that, with their data, right? I've gotta make sure that the data fits into how does a deposit work, how does it transact, how does it show, when does it show? All those stuff matter to my data, right? I need to put that underneath. But now I can do it, if I can do it programmatically, or provide the infrastructure as a service, hit a button, and I don't have to be a rocket scientist, right, I can just do it for my application, now I'm up and running. >> Well, and flipping it, and looking at it from the providers perspective. So if I'm the consumer of those data services, it's great for me. If I'm the provider of those services it's also very beneficial to me, because now, having that elasticity and that fractional consumption model where I can offer you exactly as much compute or storage or, you know, analytic horsepower you need for your particular use-case and environment for as long as you need it. That gives me a tremendous value proposition that I can then provide to you, so it's really, mutually beneficial on both the supply and demand side, if you think about it. >> Actually, so David Floyer from our team has done lots of research talking about just, real business value that can be driven back when I can like leverage that data. It's like, oh wait, now I have things like Flash that allow me, you know, very fast to make snaps, wait, I have real, this is the actual data, and then I can test on that and then how fast can I get that back into production if need be. There's a lot of things we can do now that just those enablers in the new technologies at scale. >> Well, and I also think it's pretty interesting, we talked earlier about our three patterns, right? Modernize, transform, and the next gen. If you think about the next gen, right? That's, now what I wanna do as a corporation is I wanna bring on new people and I wanna do some data analytics. That data analytics is gonna allow me to learn stuff about my business, and I'm gonna wanna start to do stuff in a new way. I'm gonna wanna start to do stuff in a new way, today. Right? I don't wanna wait and say okay now that I know what I think I wanna do is X, and wait six months for the infrastructure to be there to start programming against it. No, I wanna make real time decisions today, I wanna do real time things today. That's how that evolution starts to happen and it needs to be faster for those people. >> Yeah well one of the promises is, you know, we're at cloud computing, when I need it, it's there, I don't need to worry about what's available. Talk to me about what is scale, and you know, that speed mean to your customers? What kinda architectures do they need to go to to be able to, you know, have that kind of experience no matter where they are? >> Yeah I think you're starting to boil it down into products and while that's good, right new technologies, and new capabilities like Flash and NVME and that sorta thing, that's the raw performance, that's the engine of the car, right? But you start thinking about all the telemetry data that racers collect to then tweak the car, not just the engine, the car, the foils, and that sorta thing, in order to get the maximum amount of speed out of the car, that's really the performance stuff that we're talking about and that's all the instrumentation around the different products and that sorta thing that sit within the portfolio, that enable things like self service, it enables things like, which is speed in its own way, right? And it's data protection, and it's faster RPOs and RTOs, different types of protections sets of services. It's disaster recovery, faster replication, replication to the cloud, lower costs, right, replication into the cloud, maybe not necessarily in on the data center. All of those thins in the portfolio equal speed. Whether speed be raw performance, getting from A to B, or speed of business, which means I can be, I can be doing whatever that thing is that makes me more competitive, quicker than the other guy can do it. >> Yeah, and when you have these services which are much more encapsulated and kind of, you know, to use a very old term, kinda self documenting in a way. If you think about taking a data element or a data structure or some piece of knowledge or information that we're gonna do some kind of processing on, and you load that with as many definitional characteristics as you can, without A: slowing it down, or B: making it too expensive, then you inherently improve the value of that thing, whatever that is, right? So, you know, great, there's a million examples, the picture of the check is a good one, you know, the telemetry coming from delivery trucks, for FedEx or UPS, there's a million examples where the ability to gather data, which would be gathered anyway, and used for some other purpose, but now you layer on some of these additional service characteristics and dimensions to it, and it becomes a whole new entity that now has a whole other set of values that can be expanded upon. So it's really this multiplicative effect that we see, that allows you to take your data, which is your most valuable asset typically, and leverage it across multiple use-cases and in multiple dimensions. >> Yeah, so, Steve, when I think about data services, you know, if I think the old world was rather fixed, and the new world is, you know where are we today, and what's kinda the near future look like? Help us walk through that a little bit. >> Yeah, I think you painted it very well in the beginning, right, we always like to look out front and say this is utopia, this is where we're going. Where are people today? I think there're a lot of technologies out there that, if you're starting to modernize, and I think we're in that modernization trend right now, where a lot of the newer technologies, or even some of the older technologies that you might have installed in your environment, are building out a robust API set. Because the new stuff is all API driven, so if I'm an incumbent and I'm in a data center and I wanna maintain my hold, my footprint, I need to start working with other things. Newer versions of a lot of the incumbent technology is building in restful APIs. Now you're bringing in newer technologies, maybe a Chef or Puppet sitting on top of your infrastructure that has restful APIs. The trick for the infrastructure, for the IT team, is to slowly evolve into that infrastructure developer that we talked about. Now they're learning how to connect those two, right? And as I'm learning how to connect those two, I'm also learning about how to make that data available in other locations, or how to make those applications talk in other locations, that they'll impact my production, right? So where are we today? I think we're slowly starting to understand what these API connectivities are, and if there isn't that connectivity, I think folks are really starting to look at what do I replace that incumbent with to make sure that I'm getting that out of what I'm gonna need for the future, so, I'd say we're 20% down the road, right? But things are moving fast, I mean, as time goes forward it goes faster and faster and faster, and, you know, a year from today we might be at 50%, right? Or 60. >> Yeah, and I would just add to that that the level of integration that exists between the products in the spectrum portfolio is very foundational and very, you know, it's a very intricate structure, right? So, as we evolve products and solutions, you know, we just had an announcement this week of the new Flash platform on the hardware side, so there's, as these things become available, they start to then elevate the value and improve the capabilities of other parts of the portfolio as well. So there's this kind of platform story that you start to be able to tell, and that's really what these, this series has been about and what the follow on sessions will be about drilling into specific solutions at a lower level of detail, is how do we build, you know, the information platform of the future for our clients? >> Yeah, great. And I know there's more coming in the future, but the last thing I wanted to ask you here is we had a while that we were saying well I'm just gonna simplify everything. Public cloud is cheap and easy, you know, hyper converge is gonna boil everything down, and it's just like this one box. Well, and if you look at both of those spaces, they've evolved and now the line I've used I think if I was going to, you know, build compute in Amazon, or go buy a server from pick your favorite OEM of choice, you know, the cloud probably has more options and is more complicated to buy. You know, we'll figure out how the pricing is depending on whether you buy the three years reserved instances or anything like that. But, you know, customers, the paradox in choices is really tough for people as they do so. How do you balance that flexibility, but still try to make it easier, because you know, staffing, you know, I can't have, you know engineers dedicated to, you know, helping trying to figure this out. Oh, the next release comes out in a month, and everything I learned is already old. >> I'd appreciate your input and feedback on that, because what I'm about to say is, is I think easy is relative, right? And it's relative to the person who needs to access the systems or the data or the equipment or that sorta thing right, so, if I may, if I'm someone who's graduated from college and looking to join the IT workforce today or in the next few years, right? To me, simple means, right, a myriad of things, right? And I might've been trained on and educated on, but I'm gonna stay in that world. Why do you continually buy an iPhone? You don't switch over to Android, why? >> They're from different ecosystems, yeah exactly. >> Right, or why does someone go the other way, right? It doesn't matter, you pick one, and that's what you know, and that becomes easy for you, right? And then, as I'm learning, so lets say I pick AWS, right? As I start to continue to learn, and they come out with new things, and that's what I pay attention to, I find these new things that plug in, right? And it's only when vendors come to you and talk about not just hey I've got this new fidget spinner, (chuckles) right, or wiz bang technology, it's, it's I know how to integrate with your platforms to make your life easier. Those are the conversations that actually pick peoples head up and go, oh okay that does make my life easier, right? >> And that's exactly what I was just gonna say, we talked earlier about this whole concept of integrate and automate verus rip and replace. You know, innovate as opposed to institutionalized, so this is exactly that. This is, we as trusted advisors and integrators at a factor are able to go to our clients and say look you have typically a complex environment that has multiple different platforms and stacks that you're working with. You may be able to standardize on a common model or a common model portfolio structure for everything. Not likely though, you're probably gonna continue to have, you know, different pieces of the puzzle. We, it's our job and our sellers job and our partners job to develop an integration strategy and an automation strategy that exploits each of those that are in place to the best of their current ability, and provides a path forward, so to your point, eventually will all things live in the cloud, and will the cloud become, you know, so self aware and so sophisticated that's it's able to provision itself and manage itself and write your apps for you, perhaps. You know, probably not in our lifetimes. So, in the mean time, large organizations and small organizations still have to get from point A to point B. They have to run their business, they can't afford to spend, you know, a king's ransom on IT. But they also have to be secure and reliable and perform, etc. So, we provide a portfolio of solutions that plugs into exiting infrastructure, augments it, maybe replaces it but not necessarily, and helps our clients get between here and there and provides them the headroom to grow into the future as well. >> What's your answer to the simple and easy? >> Yeah, well, first of all right. I think, just on the definitional piece, you know, we'll all be living in the cloud when we've just redefined that means it's, it's hooked up to the internet. (Steve and Randy laughing) It means that it's cloud because everything is. And, here's the challenge of the day, no one can keep up on everything, you know, I've had the pleasure to talk to some of the smartest dang people in this industry, and, >> Thank you. (laughs) even the ones, you know, absolutely, but the people that, you know, are creating new stuff, and you know, whatever it is, they're like, I can't keep up with my own firm. >> How do you have time to learn? >> You know, it's like, they say, you know, the doctor would need, you know, every week would need a thousand hours to read up on everything in their specialty. But, it doesn't mean that we're out of jobs, actually we've got lots of new jobs because we love, we've done some events with MIT, where it's, you know, racing with machines, it's people plus machines, you know, automation does not get rid of your job, what it hopefully gets rid of is the crap you didn't wanna deal with anyway. We saw for a while, we wanted to get rid of undifferentiated heavy lifting, well, let's hope that, you know, as, if you talk to the IT people, it's like the thing that you look at every month and you're like oh God I have to do that, or can't you automate that piece of it? >> And it actually raises a really interesting point, and we haven't touched on it, I don't think, up til this point, but, this is another one of the areas where IBM is uniquely positioned in this discussion, and in this space, to bring to bare a level of sophistication and advanced artificial intelligence, cognitive capability, machine learning, we are, today, delivering the worlds most sophisticated, powerful, capable solutions in that space, and not surprisingly that technology is imbuing everything that we do. So our entire portfolio is interspersed with very sophisticated AI capabilities, analytic capabilities for self adaptation, you know, self learning, self healing. So, it gives us again a competitive advantage I think, as we take these solutions to market that they are imbued with this very sophisticated level of advanced processing. >> Yeah and, the other thing I'd say, I know Steve you brought it up in one of our discussions there, IBM has a lot of partners. I never look at IBM saying we are the only one, we are the be all and end all, we'll have everything, no. The CIs and the MSPs and the CSPs and, you know, software partners and everything like that. You mentioned competitive environment, I think the first time I heard the word cooperative, it was almost always about IBM. Because, yes, IBM probably has a product that does something along those lines, but they know they're not the only ones and they'll continue to partner to make sure that customers get the solutions they need. Alright, any final words you wanna leave on this segment, gentleman? >> I wanna thank you very much for hosting us for this event. >> Indeed. Yeah, thank you for your insight, Stu, we appreciate it, you know it's always important for us to not read our own press clippings too much, it's important to get the external viewpoint and get the outside perspective, so we appreciate your input. >> Well hey, and thank you so much for bringin', both of you, we've worked with you for many years, always appreciate your viewpoints, and look forward to continuing the conversation. Alright, thank you so much as always. Give us any feedback if you have, check out theCUBE.net for all the websites, Randy Arseneau, Steve Kenniston, I'm Stu Miniman, thanks for watching theCUBE. (bubbly music)

>> From the Silicon Angle media office, in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. >> I'm Stu Miniman, and you're watching theCUBE's Boston area studio, where we're talking about storage, and SDI essentials. And of course, storage, and infrastructure, really are there for the data in the application. To help me dig into this, Rob Coventry, and Steve Kenniston, thank you so much, gentlemen. >> Thanks, Stu. >> Alright, so, yeah, when we talk about one of the only constant in the industry, Steve, you said in one of our other interviews, is change. The role of all of this infrastructure stuff is to run your applications, and of course the application's, you know, the really critical piece of everything we're doing, is the data. So, Rob, maybe talk to us a little bit about your viewpoint, what you're hearing from customers, help set up this conversation. >> Well, one of the biggest changes that's going on these days, is the move towards cloud. And I often kinda want to reset the definition of what we mean with, when we say cloud, 'cause it means so many different things to so many different people. To me, cloud is all about, not a place, not somewhere where you're running computing. While it may have started out that way, when Amazon launched AWS back in, what was it, '02 or '03? Or salesforce.com, and when they were running everything in the cloud. But, it's really evolved more to a style of computing, distinct and different from your traditional computing. It has certain attributes, those attributes are what distinguish cloud computing from traditional computing, more than anything. And so, basically now, storage has gotta evolve, and support that, just as like we did with virtualization. >> Yeah, absolutely. You know, when we did it, in the industry, we spent so much time arguing over definitions, and we went, "Hybrid, public, multi, composable, "composite, everything like that." Well, you know, when I talk to customers, most of them do have a cloud strategy, but, number one is, the ink's still drying on what that strategy is, and the pieces that make up that strategy are definitely changing over time, as they grow and mature. But, they absolutely know that no matter where it is, their data is one of the biggest assets that they have, outside of people, and therefore how can they leverage, how can they get more out of data? The whole wave of big data that we were well into, and the next wave of AI, is all data at the center of it. >> Yeah, I think, I like the way Rob kind of positioned this. We've, we talked about, you hear a lot of folks talk about cloud. You know, a big part of what we're trying to do is have our sellers, as well as the community, understand that cloud isn't a place, right, it's a thing. And you've kind of alluded to what I want to do, specific types of either development, or programming, or provide assets to the world, whether it'd be data, or whether it'd be things like websites, or that sort of thing. It's got to live somewhere. And where that lives is becoming more cloudy. Now, whether that place is on-prem, or it's in a cloud, or it's in a remote data center someplace, at the end of the day the functions that you want to be able to deliver on, behave in a cloud-like behavior, and I think that's becoming more the trend of what people want. And really, it's the consumption model of where it lives, and how you pay for it, is really the bigger part of how things evolve. >> Yeah, applications are changing a lot. You used to say, the era of shrink-wrapped software is mostly over now. It's, talk a lot about microservices now, and when I'm building things, you mention functions, which catch into functions, and services, and serverless. You know, a whole new area that's changing. What's needed for this world, you look at it, you've got, you know, most customers have hundreds if not thousands of applications. Most of those aren't ready for that brave new world of cloud-native. There's usually some stuff, so, maybe Rob give us a little bit on that spectrum, and where your customers... >> So, look, I think we recognize that people have the vast majority of their infrastructures running, or applications are running, on traditional infrastructure, right? And so, they've got a couple different choices. They've either got to modernize what they've got, and the modernization is, you know, it, I was sharing with Steve last week, you know, we're modernizing our house, because we built the house back in '01, it was golden oak, it was gold handles everywhere, and so now we're getting rid of all the gold, we're painting all the golden oak, and repainting the whole house, right? So, that's a modernization. It's not a complete refurbish, remodel, that's what we would refer as refactoring, right. That's a much bigger, heavy-duty thing. And so, businesses are going to have to look at those traditional applications, and decide which of them should be just simply modernized, and then adapted, or modernized to work, and orchestrated, with that bigger cloud-like environment, and which of them need to be refactored to operate with the underlying cloud infrastructure. Which, by the way, expectation is that it's completely virtualized, it's automated, it's policy-driven, it's orchestrated, it's got all those types of cloudy-like, you know, pay on demand types of characteristics, that people learned and love from AWS, and from Google, but now they're getting on-prem as well. >> Yeah, and let me poke at one thing, because you said, you know, virtualized, and I think you don't mean just a hyper-visor, but we have things like containerization, you know, bare metal's back, you know, it's so funny, what's old is new again. Remember, it was like, "Oh, we're gonna go 100% virtual," except for containers and everything else, now, so now we've got lots of flexibility into how it's deployed. And there's that modernizing the platform, and modernizing the applications, and sometimes you do one before the other, depending on how you're doing it. >> Great point. I mean, not everybody understands the distinction, right, between containers and VMs, right. But the way I look at it, containers, one of the first things that they were really trying to attack is, a more efficient way to do virtualization than what we had with VMs in the past, right? And one of the things that they learned, is if they break those applications into smaller functional microservices, then they get another benefit, and that is continuous development. That's critical to the flexibility and agility that the business needs, to be able to constantly evolve those applications. And the third factor is, what I call asynchronous scale. So, each little function can consume however much memory, storage, and compute that it needs, independent of all the other functions in there, whereas when it was operating as a monolithic application, the traditional approach, well you were kinda stuck with however much the largest footprint was required. Now, you get a lot more efficiency out of it, you get a lot more availability, and you get continuous development. That's what you get out of containerization. >> And if you bring that up even one more step now, right, and I like to use this analogy when I'm presenting to clients, and maybe this is helpful, is, if you look at our, just take two of our product. We'd take Spectrum Protect if you take Spectrum Protect Plus, right. Spectrum Protect, you know, 25 years in the industry, number two in the world, everything, right, millions of lines of code, might even be tens of millions of lines of code. Any time you have to do anything to that code, like I want it to support X, all ten million lines of code need to kinda make sure it's adaptable to that thing, and it needs to be able to lift and shift. And we were talking about agile development, which we do now, but you were also talking about the release trains, and all that stuff, right, and what ends up going in and out. Versus, look at Spectrum Protect Plus, built on an agile development, built on microservices. I want to put in a service, I can just grab that service and plug it in pretty easily. I don't have to kind of drag all that code kicking and screaming, so to speak, along with it. But, um, now I want to ask you a question, Stu. Because I tend to think the analysts, as well as kinda the thought leaders in any company that are trying to think about helping sellers sell, and that sort of thing, we're about 12 to 18 months ahead of the customer. We have to be, because we gotta kinda see what's out there. What are you hearing around this containerization, refactoring? I think we have an opinion, it'd be interesting to hear an outside view of what you think is happening. >> Sure, Steve. And in the last few years, I spent a lot of time going to the cloud shows, I go to CubeCon, going to my second year of doing serverless comps, so, look, yeah, serverless functions, as a service, we're still in the early adopter phase. Some cool startups out there, I'm excited to talk to real customers that are doing some cool things. But even I asked Andy Jassy if, you know, the CEO of AWS, he had made some comment, you know, if we had said a couple years ago, "If Amazon was built today, it would be built on AWS." And he had made this, "If Amazon was built today, "it would be built on Lambda Serverless." And I was like, come on, really? He's like, "Well, no, I mean, what I mean is, "that's the direction we're going, "but no, we're not there yet, because we can't run one "of the biggest global companies on this yet." So, look, we understand, what could be done today, and what can't, when we talk servers? Containers, containers are doing phenomenal, we're now, containers have been around over a decade, you know, Google's been talking for many years of how many billions of containers they spin up and down. But, I've talked to much smaller companies than, you know, the Googles and Yahoos of the world, that like containers, are moving in that environment. I'm not sure we've completely crossed the chasm to the majority, but most people have heard of Docker, they're starting to play with these things. You know, companies like IBM and everyone else have lots of offerings that leverage and use containers, because a lot of these things, it just gets baked in under the hood. When you talked to, you brought up virtualization, it's like, oh. It's, you know, we watched this wave from the last 15 years of virtualization, it's just for basic, we don't even think about, sure there's environments that aren't virtualized for a certain reason, if it's containers. But, you know, when you've seen Microsoft get up on stage, and talk about how they've embraced Linux. And a lot of the reason that they've embraced Linux is to do more with containers, that's there. So, containerization is going strong, but, when you're talking of the spectrum of applications, yeah, we're still early because, the long pole in the tent, at least customers like to, it's those applications. If I've been running a company for 20 years, and I have my database that keeps everything running, making a change is really hard. If I'm a brand new application, oh, I'm doing some cool, you know, no sequel, my sequel, you know, cool applications. So, it's a spectrum as we've been talking about, Steve, but, um, yeah, the progress is definitely happening faster than it ever has, but, you take those applications, there's a lot of them that I need to either start with a lift-and-shift and then talk about refactoring things, because making change in the application's tough. APIs, we haven't talked about yet, though, is a critical piece into this. As worry about, okay, we're just gonna have API sprawl just like we have with every other thing in IT. >> I definitely want to get to API, but one more, just one more piece of color. When you're at these conferences, and the users are there, listening to the folks, but one more piece of color is, do they have, applications run the business. But it has to sit on top of something, so there's the infrastructure piece. What are the questions around refactoring, and containerization, that happen around infrastructure? I'm trying to to think about how to get from A to B, what do I think about the underlying infrastructure, or is that even a conversation, because a lot of the stuff is cloud-native, right, I mean, or can be cloud-native. >> Yeah, and the nice thing about containers is, it just lives on top of Linux, so, you know, if I've got the skillset, and I understand that, it's relatively easy to move up that way. Yeah, for a lot of the developers, when they say, "The nice thing, if I do containers, if I do Kubernetes." I really don't care, the answer is yes. Am I gonna have stuff in my data center, yeah, of course. Am I gonna do stuff in the public cloud? Yes, and that's if I can have the same Linux image. We've been talking for years about, how much of the stack do I need to make sure is the same both places to make it work, because that was always the last mile of, "Okay, it's tested, my vendor said it's good, "but I get an okay, what about my application, "my configuration, and what I did?" When I use Salesforce, I don't need to worry about it. I can pull up on any device, well, the mobile is a little bit different than the browser, but for the most part, I'm anywhere in the world, or I work for any company, it's relatively the same look and feel. So, a little bit long answer on this, but when it comes to containers, what we've been trying to do, and what I found really interesting, is, the Nirvana's always been, "I don't want to worry about what's underneath the stack." And when I said, I mentioned the cool new thing, serverless-- >> The reason for that, is the business, with containers, gets that continuous development, and continuous availability, and scalability, all in that infrastructure. The infrastructure enables that, right? So, in my mind, the reason people want to do it is, they know, the speed of change in their business is never gonna get any slower. And this platform enables that speed of change. >> So, the one thing, those of us that live through the virtualization wave, virtualization, great, I don't need to worry about what server, or how many servers, or anything. Yeah, but, the storage and networking stuff, oh wait, that kind of all broke. And we spent a decade fixing that, and trust me, when containerization first went, I had, like three years ago, went into a conference, someone is like, "It's so much faster than virtualization, "it's this and this and this." And I got off, I'm like, "Hey, uh, "we've all got the wounds, and, y'know. "You know, less hair, now that we've gone through a decade "fixing all of these issues, what about this?" Docker did a great service to the industry, helped make containers available broadly, and have done a lot. I'd say networking is a little bit further along than storages, most of the the things, you know we talk serverless, it's mostly stateless today. When we talk containers, okay, where's my repository on the side, that I do things, so, state is still something we need to worry about. >> That said, you know we've made a big investment in our ability for a block storage, and by the way, all of our file storage offerings, to be able to work with both Kubernetes and OpenShift, so, those are two of the predominant, prevalent container-based systems out there. So, I think that, at least it gives that ability to attach anything that needs persistence to our storage. >> So, what I'd love to get your perspective on, because we talk about, boy these changes that are happening on the infrastructure side. For a while it used to be, okay, business needs a new application, let's go build a temple for it. So, the business people says app, and then the infrastructure comes, team's set in, okay, I've got the building specs, give me a million bucks and 18 months, and now I'll build it. Well, today, you don't have as much money, you don't have as much time, but that relationship between infrastructure and application, they've gotta be working so much closer, so, how do they, you know, when I'm building this, who is that that builds it, and how do they work even closer? >> Well, that's this, we can talk about the infrastructure developer, I guess, too. Because really, this is the role that kind of is an evolution from what maybe was in the past a storage administrator, right? It's somebody who is setting up a set of policies, and templates, and classes of storage, that abstracts the physical from the logical, so that the application developer, who is going into Kubernetes, or into OpenShifts, says, I need a class B usage for storage, that has backed-up, and maybe replicated. Or, I need a class C, that is backup, replicated, and highly available. And the storage administrator, in that case, is setting up those templates, and just simply making sure that he's monitoring all this, so that when the additional demand comes, he just plugs it in and starts to continue to add more. >> Okay, so, I've talked a lot to developers, I haven't run across an infrastructure developer, before, as a term, so, where do they come from, what's their skill-set, maybe help flesh that out a little bit for me. >> I was gonna say, I think in a number of customer presentations I've given over the course of this last year, it's come up a number of times. So, I think, and granted in a larger companies. And it typically comes across in a chart that shows, not the number of people are changing, but the skillset in the different organizations I have are changing. So, today where I spend a lot of time doing administration, five years from now I'm not gonna be doing that much administration. So, what I want are capabilities, well, first of all I need to program the infrastructure, so that it is programmatic, to either the application, connect through API so maybe I have a chef or puppet doing dev-ops, but when I make that call, as a developer in the company, to chef or puppet because I want this, to Rob's point, everything underneath that-- >> It's orchestrated underneath there. There's a set of policies that are set, that says, this is how much compute, how much network, what kind of storage you're gonna get. That's the infrastructure developer, who sent, using APIs that are in the infrastructure, and at the higher-level platforms like Kubernetes and OpenShift, that basically allow that developer that just says, "I need some of that, I need some of that." The experience is not a lot different than what they get with Amazon Web Services, or Google App Server. It's a similar kind of experience, but you can do this on premises now. >> Yeah, and, it's very similar, as you said, and it makes a lot of sense to me. Because, for sure, chef puppet, been hearing lots of people talk, that's the people, it's like, you're not configuring luns anymore, I don't need to do all the old masking, and all the configurations. The network people, it's like, no, you've got a different job, and it's shifted, that whole vision of infrastructure as code is starting to come to fruition. >> And we talk a lot, or at least I do, right around, IT, and technology, and infrastructure's made up of three things. People, process, and technology. And the people are evolving just as fast as the infrastructure needs to evolve. So, tomorrow, I want to be building a programmatic infrastructure today, so that my people can be focused on, like you said, where is the future, I don't know, but I constantly need to be thinking like the analysts think. I need to be 12 to 18 months ahead of the company, so that I can continuously evolve that infrastructure, and help them get there, but I don't disrupt the flow of the people that need access to the data, or the applications, or that sort of thing. It's gotta be constant, and that's how that skillset is changing. >> Okay, so, is that, what's that infrastructure developer's role in helping with the app modernization? How do I figure out, you know, what do I just build new, what do I move over, how do I start pulling things apart? >> Yeah, I think it definitely starts by looking at the different applications that they have, I think you made a good example where, okay so now I want to modernize as much as I can, and now I want to start drilling into by taking a break, gaining some knowledge and some insights about containerization, and APIs, and that of sort of thing, and figuring out which applications in my stack today, I can refactor, which makes sense to build out of microservices, you know, refactor into microservices and that sort of thing. Start doing that, get that done, and then start looking at, ahead of that, what's next? So, getting that infrastructure programmed and plumbed ready, so that anyone who needs to access it can, so it's more hands-off. Think of the younger generation coming into technology today, right? I want to use my iPhone, I want to do this, I want that piece of storage, I want it to be a click of a button. I, as an infrastructure developer, need to help set that up and make that happen, so that as we move forward, I'm doing other new things. Would you agree? >> Absolutely, absolutely. So, at the end of the day, those guys are basically taking advantage of those large pool of services, whether it be storage, networking, or computing, creating APIs, or leveraging APIs, in that infrastructure, and wiring it up so that the end-user developers can go and access them at will, without waiting. >> Yeah. Last thing I want to ask in this segment, is, you know, change is tough. And when I look at my application portfolio, it can be a little bit daunting, so what sort of things should they be doing, to make sure that they're ready for the modernization, the transformation, to get along that journey a little bit faster? >> Well, the first thing is, is that you've gotta have a software to find infrastructure to be able to do any of this. And basically what that software to find infrastructure has, is has a number of attributes. The first of which is, an actual separation between the physical and the software. It has policies, it has the ability to, APIs that allow you to control that, that are either through command-line interfaces or rust interfaces, such that it can be orchestrated, and then you take advantage of all those all policies, such that you can automate it, monitor it, and manage it centrally. That is the base definition of software-defined infrastructure, and we've had it with CPUs for a long time, we've had it with networking, people have been doing network separation of software and hardware, and it's really IBM that is unique in this business, that has a set of software-defining capabilities that I think is different than the rest of the marketplace. >> Yeah, I mentioned it earlier, but I think I'll close on it too, is, you know, lots of customers, gotta modernize the platform, and that really sets you up to be able to modernize the application. Alright, Rob and Steve, thanks so much for joining us, helping us walk through the data, and the applications. Alright, thank you so much, I'm Stu Miniman, and, appreciate you watching theCUBE.

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE! Now here's your host, Stu Miniman! (bubbly music) >> I'm Stu Miniman and this is theCUBE's Boston area studio, we're talking about storage and SDI solutions. But before we get into STI and all the industry buzz, we're gonna talk a little bit about some of the real business drivers. And joining me for this segment, happy to welcome back, Randy Arseneau and Steve Kenniston, gentleman, great to see you. >> Thanks for bein' here Stu. >> Thanks Stu great to be here. >> Thank you! Alright, so, talkin' about transformation, customers are going through transformations, IBM's going through transformation, everything's going in some kind of journey. But, let's talk about, you know, it used to be IT sat on the side, Randy we talked about in the intro, you know, IT in the business, you know, wait, they actually need to talk, communicate, work together. What are some of the key drivers that you're hearing from customers? >> So, it's a good question, and we talked a little bit about it on the previous segment. But, I think what's really happening now is that a lot of the terms that our industry has kind of overused and commoditized, have sorta become devalued, right? So, they no longer really mean anything significant. Terms like agility and flexibility and IT business alignment and transformation, which we hear a million times everyday, they've become just kind of background noise, but the reality is, especially now, in this era where, you know, information and data and analytics are driving businesses, and they're no longer, you know, nice things to have for the super advanced very sophisticated companies, they're table stakes, I mean, they're needed to survive in today's global economy. So they've taken on a whole different meaning, so when we talk about agility, for instance, agility means something very specific in the context of IT business alignment, and our solution stacks in particular. Generally speaking, the kinda the way I like to think of it is, I, I overuse sports analogies, but I think this one's relevant. So, a good quarterback is able to read and react. So, as the defense is shifting and making pre-snap adjustments, the quarterback views the field, sees what's happening, and is able to very quickly develop or institute a new offensive game, play, to take advantage of that situation. So that whole read and react idea is something that's very important for a business, especially now. So businesses are under constant pressure, competitive pressure, market pressure, compliance pressure, to be able to exploit their own IP, and their own data, and their own information, very very quickly. So that's number one. By using things like integration and automation within their IT organization as opposed to the old, you know, kind of vertical method of doing things. IT organizations are now able to respond to those rapid course corrections much more effectively. Same thing with flexibility, so when an organization needs to be flexible, or wants to be flexible, to adapt to a very rapidly changing environment. Things like, and this is really where Steve's product line is particularly relevant, things like data reuse, right? So we've got organizations that are running their business on this data, which is their most important asset. We're helping them develop new and creative ways to repurpose that data, efficiently, quickly, cost-effectively, so they can expand the value. So any given piece of data can now have a multiplicative value compared to its original form. >> Yeah, I think it's actually pretty important. When you think about, we're out there talking about products, right? And a lot of vendors are doing this, right? Buy my products you'll get agility or you'll get flexibility or that sorta thing, or maybe even more importantly, in a lot of the enablement we use to educate people on, we'll say, you know, this product enables data reuse. Well what does that really mean, right? What does that mean for business, right? And, when you say okay well it makes the business more agile well, how do you do that? Then it encompasses a whole breadth of solution sets around making that data available for the user, things like software defined storage, things like particular technologies, that can do data reuse. So, it kinda boils itself down in the stack, but to Randy's point, it's been so commoditized, the words, that we don't really understand what they mean, and I think part of what we're trying to do is, make sure when we talk agility, flexibility, or even our three patterns that we talked about, modernize and transform. What do they mean to us? What do they mean to you, the user? What do they mean--? Because that's very important to connect those two. >> Yeah, and I love, 'cause for a while we used to say, it used to be well, you know, do I get it faster, better, or cheaper? Or maybe I can give you some combination, and there're certain customers you talk to and it's like look, if you can just go faster, faster, faster, that's what I need. But, it's not speed alone, like the differentiation for things like agility is, number one: we are all horrible at predicting. It's like, okay, I'm gonna buy this, I'm gonna use this for the next three to five years, and six months into it, I either greatly over or underestimated, or everything changed, we made an acquisition, competition came in. I need to be able to adjust to that, so that was, I love the sports analogy, we love sports analogies on theCUBE. >> Well, you know. >> So that, you know, if I planned for, you know, this was the plan of attack, and what do ya know, they traded for a player the day before or their star quarterback went down, and the backup, who I didn't train against, all of a sudden their offense is different and we get torn apart, because we didn't plan, we couldn't react to it, you run back at halftime and try to adjust, but, you know, you need to be able to change. >> And again, I think another, from my perspective, from and IT business alignment, another, another metaphor that works well, is, you know, kinda what I call the DevOps-ification of business, right? So what's happening now, and it's interesting I think, is that you're seeing some of the practices around DevOps and agile development, which by the way, IBM uses for our own products. You're seeing that push upstream to the business, so the business is actually adopting DevOps-like methodologies for prototyping, you know, testing hypothesis, they're doing interesting things that kinda grew out of that world. So if you think about, even 10 years ago, that would've been kind of unimaginable, you would always have the business applying pressure, and projecting it's requirements onto IT, now you're seeing much more of a collaborative approach to attacking the market, gaining competitive advantage, and succeeding financially. >> Yeah, and if people aren't really familiar with DevOps, the thing that, you know, I really like about it is, number one it's no longer, you know, we used to be on these release trains. Okay, everybody on board the 18 to 24 month release train, we're gonna plan, oh wait, we didn't get this feature in, it'll be in the next one, we'll do a patch in six or eight months, no, no, no. There's the term CICD, continuous, you know, integration, and continuous deployment. It's, you know, push. Often. You know, daily, if not hourly, if not more, and, it's like wait what about security, what about all these things? No, no, no. If we actually plan and have a culture that buys in and understands and communicates, and you've got proper automation. You know, it's a game changer, all of those things that you used to be like: ugh, I couldn't do it. Now it's like no, we can do it, so. >> The only thing constant in business these days is change. >> Absolutely. >> So, if you know that, and you have to be able to plan and articulate and be ready for change, how do you make sure that the underlying infrastructure is ready to kind of adapt to whatever request you may have of it, right? It's now alive, right, it's like a person, I wanna ask it a question, and I need it to help respond quickly. >> And a lot of the focus of this series, as we talked about in the intro section, is our software defined infrastructure portfolio, which in many ways is kinda the fabric upon which a lot of these things are being woven now, right? So, we talk about DevOps, we talk about this rapid cycle, and this continuous pace of change and adaptability, adaptation. We're delivering solutions to market that really accelerate and enable that, right, so, one of the things we wanna make sure we communicate, you know, both internally and externally is the connective tissue that exists between solutions, products, technology capabilities on the software defined infrastructure side, and how that affects the business, and how that allows the business to be more agile, to be more flexible, to transform the way it thinks about taking solutions to market, competing, opening up new markets, you know, seizing opportunities in the marketplace. >> Yeah, it's, if you think about when you talk about strategy, smart companies, they've got feedback loops, and strategy is something you revisit often and come back leads to, when you talk about modernizing an environment, I always used to, you poke fun at marketing, oh we're going to make you future ready! Well when can I be in the future? Well, the future will be soon, well, then when I get to there am I now out of date because the future's not now? So, what is modernize, what does that really mean, and, you know, how does that fit in? >> Yeah, and it's a great point, and I think, we look at modernization as kind of the the constant retooling, right? So, IT is constantly looking for ways to be more responsive, to be more agile, be more closely aligned than a lockstep with the business, and align business. And again, we're trying to deliver solutions to market that enable them to do that effectively, cost effectively, quickly, you know, get up to speed rapidly. There's another, so we talked a little bit in the intro section about the C-level survey, the study that was done globally by IBM, it's done every year, the 2018 one was introduced recently, or published recently. Another one of the themes that was very important is that it's this concept of innovate don't institutionalize and the idea is that old companies, slow moving companies, more traditional companies, have a tendency to solve a problem or introduce and implement a system of some sort and be wed to it, because they adapt all of the ancillary work flows and everything around it, to fit that model. Which may make sense the day that it implements and goes live, but it almost immediately becomes obsolete or gets phased out, so, you need to have the ability to integrate, automate, innovate, like constantly be changing and adapting. >> Yeah, I love that, actually, in the innovation communities they say you don't want best practices, you want next practices, because I always need to be able to look at how I can do, right, learn what works and share that information, but, you're right not, this is the way we're doing it, this is the way it must always be, so let it be written, so let it be done. You know, no, we need to move and adjust. >> And I think, if you think about these things as in, in the beginning of the year when IBM launched global refining was, when we launched kind of our educational context for our sellers in the beginning of the year, it was really three patterns, right? There was modernize and transform, next gen applications, and then application refactoring. And in the beginning when we started to talk about, which I think is where 90% of the clients fall into, it's this modernize and transform, right? Easy to say, but what does it really mean? So, if you break it down into that fact that we know what clients have today, right? We know, you know, VMware's big, KVM is big, you know, Sequel is big, Oracle is big, right? If that's foundationally who you're talking to on an everyday basis, how do you help them take that solution set, and, don't start refactoring today, right, but take them to a point where when they start to do the refactoring, they're well positioned to do that simply and easily, right? So it's a long journey, but to get there you really need to kind of free up and shake loose some of that, some of the bolts, so that it's a lot more flexible over here. >> Yeah, so, talking about things that are changing all the time, so tell me, transformation, it's not about an angle, it's, you know, it's about journeys and being ready, so, you know, help us close the loop on that. >> Yeah, so we talk a lot about that internally, and again, transformation is another one of those kinda buzzwords that we're, we're trying to sorta demystify it, because it can be applied in a million different ways, and they're all relevant and valid, right, so transformation is a very broadly applicable term. When we talk about transformation, we're specifically talking about kind of the structural transformation of the infrastructure itself, so how are we making the storage and the compute more cloud like, more flexible, more easily provisioned, more self-service. So there's kind of a foundational level piece at the infrastructure level. We talk about transformation at the workflow level, so things like DevOps, like continuous development and integration. How do we provide our clients with the material they need, the raw materials, whether it's software, technology, education, best practices, all of the above, to be able to implement these new ways of doing business? And then there's really transformation of the business itself, now, a couple of those, the first two, are kind of happening within IT, but they are being driven by the transformation that the business is undergoing, so, the business is constantly, again, if they're still around and they're prospering, they're constantly looking for new markets to reach into, looking for ways to compete more effectively, looking for ways to gain and sustain competitive advantage in this very very dynamic environment. So transformation touches all of those, they're all equally valid, from our perspective, specifically as IBM, we're trying to tackle the sorta foundational level, and then kinda, by using assets like this, you know, research that we do at the C-level, we're trying to kinda build the connective tissue between the ground level IT stuff, and how the business is changing. >> Think, think, I mean, really as importantly, right, we're trying to build the foundation such that as we're thinking about the business, think taxis transforming to I wanna be more Uber like, or think even automotive industries wanting to be more Uber like, right? I read an interesting article about, you know, auto manufacturers today are thinking about no more buying of cars, right? That's a transformation of my business, right? How do I do that? Now a lot of it is, you know, I gotta set up the infrastructure, I gotta set up, you know, people, and process to do some of this, but the infrastructure has to adapt as well, right? And we gotta cause, and that's not gonna happen tomorrow, to your point Stu, like I wanna design for tomorrow, then the next day, then the next day, then the future, when is the future, right? But I need to have an infrastructure that can evolve with me as my business evolves and I get to this goal. >> And the shifts are now happening, they're no longer kinda tectonic shifts, they're seismic, right, they're not gradual, incremental, I mean they are in some cases, but they're more often seismic changes, and that's a great example. Uber burst onto the scene and fundamentally changed the way humans transport themselves from one place to another. And there's a million examples of that right? There's been genomic research, and even in media and entertainment, there's lots of ways and lots of places in which this shift towards more seismic change in the industry, or in a particular use-case is happening everyday. >> Yeah, so I love your insight, when you talk about your partners, you know, the old days were great where you used to just say hey, you've got a problem, I've got a product that will solve what you need, transaction, box, done. Now, it's like, we've been saying, when are we gonna have that silver belt in security, it's like, never. It's like, security is, you know, it's a practice, or, you know, it's a general theme that you have to do, it's like DevOps isn't a product, it's something we need to do. I heard a great line it was like, you know, oh, this whole AI stuff, well can I have a box and a data scientist and I can solve this stuff? No, no, no, this is going to be an initiative, we're gonna go through lots of iterations, and there's lots of pieces, so. It's a different world today, how do you help people through this as to, you know, what the relationship is now? >> No, it's very interesting, and to your point, can I buy a box that does that, right? We were at Think this year, and our security team, or actually I think it was our blockchain team was up, and I'm very interested in blockchain and what is it gonna do for the community as we kinda grow and that sorta thing. And up on the, on a chart they put this slide that had a million different, I mean thousands of different partners that we partner with, and we also enable to kinda deliver stuff, and in some cases we're competitive, in some cases it solves security, in some cases it does this. Now all of a sudden, it's not one thing anymore, it's like how does it fit into our infrastructure, but, back to your point about partnerships. I think IBM is constantly looking to its partners because they have really that trusted value and trusted relationship with the client, and at the end of the day, as much as we can come in and say oh this box will solve your problem, we don't really know what their problems are, right? It's the people who have those relationships that know where they're going along that evolutionary scale, that we really need to work and tie in with closely, to make sure that the solutions that we deliver on the underlying side are meeting their needs, which then in tern meet our clients needs, I think that's where we're goin'. >> And actually the blockchain is a great example of kinda building these vibrant ecosystems, right? Which is something else that large companies like IBM sometimes struggle with kinda building these very dynamic, very vibrant ecosystems, but I think IBM's very good at it, and I think we've demonstrated that in a number of different places, blockchain being a recent example, but there are many others. And the STI portfolio is no different right, we've got strong partnerships across the board with other software providers, other go-to-market partners, you know, other content providers, there's a million different angles that we are able to, to introduce into the conversation. So we think all of those things taken together allow our sellers and our partners to bring a solution to their clients, regardless of their industry or their size or their particular use-case, that helps them optimize their performance in this new world of super agile, constantly changing, continuous transformation, and do so, we think, better than anyone else in the industry >> Constantly changing, distributed in nature, sounds just like the blockchain itself. (both laugh) Alright, Steve, Randy, thank you so much for helpin' us demystify some of these key business drivers that we're going to. Lots more we'll be covering, I'm Stu Miniman, thanks for watching theCUBE. (bubbly music)

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE! Now, here's your host, Stu Miniman! >> Hi, I'm Stu Miniman, and you're watching theCUBE here in our Boston area studio. We're gonna be talking about storage and SDI essentials. Happy to welcome back to the program Randy Arsenault and welcome to the program for the first time Rob Coventry. Gentlemen, thanks so much for joining us. >> Thank you. >> So, on theCUBE, we've been documenting it, so many shows and so many interviews, digital transformation, how everyone's going through various changes in the industry, and we're gonna do a series of interviews here from our studio talking about what IBM's doing about transformation and enablement. So why don't we start there? Randy, we'll start with you. We've discussed many of these things with you. You're back to IBM, so give us what brought you back and what's changing at Big Blue. >> Yeah, thanks Stu, it's great to be back. So it's been an interesting, so, I came back late last year, December last year, so I've had an opportunity to kind of come into this process that Rob and his team have been working on for a while, so I kinda got dropped in midstream and it gave me sort of an interesting perspective on how things have changed, first of all, so there's been a fairly significant change in the way products are brought to market, the way we message, the way we communicate, the way we enable our sellers and partners, so it's been really interesting to kind of dive right in and get right into the meat of it. And I think Rob and his team have done a really good job of building a programmatic and systematic approach to delivering enablement and education to our sellers and our partners. So there's a whole process, a formalized process, around how we create it, how we deliver it. I'll let Rob expand on that a little bit, but from my perspective, it's been interesting to be able to participate right from the beginning in kind of adding an outside end view since I'm sort of coming at it with fresh eyes. So I think it's been a really good collaboration, working with the two teams coming together. So, the process, I think Rob and his team, again, have really perfected the engine that we use to produce these things. >> Yeah. Rob, love to get your viewpoint. I mean, industry watchers, I think back to even when I did my MBA, or when I worked on the vendor side, and as I've been an analyst, IBM's one of the companies that breaks the rules as to everything changes all the time, you have companies come and go. Living here in Massachusetts, we've seen lots of brands come and go. IBM's stalwart in the industry, so give us a little insight as to what you're working on. >> So yeah, IBM is the survivor in that dinosaur game that we seem to manage to evolve constantly. The evolution is an important aspect of it, and so one of the things that we did about a year and a half ago is we evolved our sales force and storage. We went from several different discrete roles to a single storage sales role. We knew what we had to do in that regard is to bring everybody up to speed to at least a minimum level in order to sell our entire portfolio, and what its strengths bring to the table, instead of just the product they were familiar with, that they were comfortable with, and that they had success with in the past. So one of the things that we did was, we kinda observed, how did we do transformation or education in the past? And it was predominately what I call a bottoms-up approach. Have a product, it solves a set of problems, here's how to do it, here's how to sell it, here's what it does, here's how it competes, works great in the industry. When you've got a large number of products, you cannot educate people in the right amount of time following that bottoms-up because by the time that you learn it, we'll have moved on the the next set of products and our competitors will outpace us. So what did was we said, let's take a tops-down approach and ask the question, what kind of conversations do our customers wanna have that lead into the various solutions that we're trying to sell, that'll give you an opportunity to have some credibility, solve the problems on your way in, and let the conversation dictate which product it is. So we created a set of five conversation that focused on things like dev ops, modernization, resiliency, life cycle management, you know, the kind of things that every IT department does, and then go from there, and it's worked pretty well. But one of the things that we observed was, we assumed a certain base knowledge when we put that enablement together, and we realized there's a set of terms that I think that they're lacking that we need to help them with. >> Yeah, so that was kinda my first project when I came back, was getting involved in the creation of these kinda streams and assets for this education in January, and it was delivered and it was successful and it was fairly detailed and pretty explicit, but it introduced a lot of terminology that we sort of presumed people were already familiar with, and we found out that wasn't necessarily always the case. So the real goal of this session or this series, is to kinda set the stage a little bit so we almost think of this as kind of a prequel, like this is really meant to functionally proceed what we did in January, so the goal is that once this is put together, folks will be able to go through this and then go reconsume or reintroduce themselves to the January content, and have a much better sense for what the terminology is. Hopefully we can demystify some of the buzzwords and some of the industry lingo that they're hearing from clients, and really provide a better framework in which to have the conversations that Robert was talking about. >> Yeah, it's fascinating. We talk sometimes the analogies we use is, right, are you talking in the right language? For me, I think food comes to mind. It's like okay, if I go to a foreign country and even if I don't speak the language, they can point me towards, here's the meat, here's the fish, here's the vegetable, and then I kinda know what I'd like, but it's kinda nice if you know, okay, well, Portuguese food, I'm kinda going to be looking at this, so getting some of the basic down to an understand and then participate and get deeper involved. >> And the other challenge is that a lot of the terminology that we use has become very commoditized, right? The words that every vendor in our space uses and uses and overuses, things like agility and transformation and modernization and refactoring and containerization, these are all terms that our sellers and our clients and our partners see a million times every day. So not only do we need to understand that a fairly baseline foundational level, what do those actually mean, but what do they mean specifically in the context of our solution portfolios? So as we go talk to clients, we can now translate from the very abstract sort of idea of refactoring, for instance, into a specific set of best practices and solutions that our clients can capitalize upon and use to achieve that goal. >> Rob, I have to think that we've seen this transformation from the customers too. IT is not this silo that just waits for the business to come, and well now, I can't do that, or it'll take me six months. No, IT needs to be with the business, driving the business, so your sellers need to be aligned with that and helping, is we're all in this journey together. >> No doubt about it. In fact, one of our primary goals here is the give the sellers context of, I call it the explain the hard candy shell that IT needs to look like, and don't worry about how everything inside of it works. The business looks at IT as that hard candy shell. They just wanna consume simple things like flexibility and agility, so that they can turn around and deliver the business that they need to deliver in the very competitive world that they live in, and we need to explain IT in all these terms in the context from the businessperson's perspective, and from that, then I think what it'll do is help them better use that in the context of their sales efforts. >> Yeah, and a lot of this is being driven by, so IBM every year does a sea level global survey, which is a pretty big deal for the company. So this year, 2018, first one was published recently, with a population of almost 13,000 sea level clients from around the world, so this is a pretty robust piece of research, and a lot of the findings, probably not surprisingly this year, are focused on these concepts of agility, these concepts of rapid prototyping. There's three very specific best practices that are called out: interrogate your environment, so constantly be on the lookout for opportunities, changes, threats, both from a business outside-in perspective, and also from an IT perspective in support of the business goals. Commit with frequency, so constantly be evaluating where you're investing, how you're prioritizing, where you're focusing your scarce IT resources. And experiment deliberately, so do lots of pilot programs, lots of prototypes. Introduce things like dev ops and rapid development, which by the way, IBM has done, so one of the interesting things that's changed since I was here last time is internally within the spectrum portfolio, we now have a fully agile workflow, which is one of the reasons why the portfolio was so dramatically transformed over the last five years that I was elsewhere. So I find it interesting that we're litting it internally, but we're also able to communicate that to the outside world as well. >> Excellent, I'll take a large box of ready for the future. I have no idea what it will be, and I can't ask you for for it, but I'll take three. >> Alright. >> You know, you might say that if you're a big company, but we recognize that some of the sounds very big, very large enterprise, and it may not apply to somebody that's small, and one of the things that I observed in this CXO study is I think it's applicable to no matter who you are, in the value chain of some of these very large companies, because there's a recognition that you have to operate in that orchestrated world that works with the supply chain that you're part of, and if you don't continually reinvent, continually evolve your IT to enable your business to keep pace with the expectations of that orchestrated business, then you're not going to be relevant in the future either. So we think that there's applicability here whether you're a large company or you're a small company, and one of the things that we're gonna try to do here is try to help our sellers understand that. >> Absolutely, great point is no matter whether you're big or small, everybody is being affected by a lot of these-- >> That's right. >> Stressors, it's just order of magnitude for some of them. Alright, Randy and Rob, thank you so much for helping us kick off the series. >> The best, thanks Stu. >> Looking forward into digging into much more of it. >> Thank you Stu, I appreciate it. >> Alright, and I'm Stu Miniman, thanks so much for watching theCUBE.

(bright, upbeat music) >> Hi, I'm Peter Burris, and welcome to another CUBE Conversation. We've got another great guest this week, Ed Bugnion, who's a professor of computer science at EPFL, a leading technical university in Switzerland. Ed, welcome to theCUBE. >> Thanks for having me. >> So Ed, you do at EPFL, you are leading research on the future of the data center. What I want to do, is I want to talk about the near term of the data center, 'cause a lot of people have questions about what's going to happen over the next few years. Let's posit that the data center's not going to go away any time soon, and instead talk about inside the data center. What's going to happen with the organization of technology inside data centers? >> Well it's always been a chase about how to reduce complexity. You always start with basically having a number of moving parts and then the business requirements keep increasing, and at some point, the complexity just overwhelms the operational model. So I was involved in virtualization. I've been working virtualization for close to 20 years. Right, virtualization was about reducing the complexity for the servers, and basically moved from having to manage servers one by one, separate the physical from logical and sort of solving that problem. Now what we actually did, as a side effect, is we actually pushed the remaining aspects of that complexity elsewhere. The servers were mobile, they were flexible, they could v motion across a cluster, they had to be stored on a storage area network so as a result, we ended up having this entire operational complexity around the management of storage area networks for very large amounts of data and as the increase in virtualization became more and more important, that became bigger, more of an issue. So then I actually got involved into into networking and networking was about the fact that a decade or so ago there was a proliferation of incompatible networks inside the data center. I was involved at Cisco in the pure storage, unified conversion networking with the UCS product so we could both do storage and regular TCPIP networking on the same on the line firework. This was about reducing complexity, but we didn't address all the complexity problems, we created other bottlenecks so it's always this ever shifting issue with dealing with scale. >> So as we virtualized the servers, we virtualize the storage and now we're virtualizing the network, that suggests that we can start bringing these things together in new novel ways have I got that right? >> Yeah so we first virtualized the network access, right, the storage access and the SANs and then now we're obviously with hyperconvergence we're about disaggregating storage and rethinking storage because of these new requirements. That's solves a number of the problems, right? It's actually now proving out to be sort of an industry-wide accepted model that we move away from storage arrays into hyperconverged models and hyperconvergence alone if the only thing you're doing is moving blocks around is again only solving part of the problem, you still need to worry about DR, you still need to worry about backup, you still need to worry about offsite. You still need to worry about locality, right, because having completely filed storage is a gross violation of the locality principal and the locality principal actually does come back and matter at some point in time. So it's really about finding the balance between the space and feeds, what needs to be co-located and what can be disaggregated and then what use-case must be addressed. >> And I presume, how much control can be bought from a single point of presence, console, onto the underlying infrastructures, is that how the rest are worried about? >> Yeah so I think there's, you're going to have to separate two things. One is the physical building blocks and the other one are the operational consoles, right, and the physical building blocks, the number of people providing these physical building blocks is small and if anything, shrinking. If you think about the operational console, the different panels, right? If you think about the different software companies providing technology, they actually themselves offer different panels to different constituencies. The silos have not completely disappeared in the IT operation model today, they're, communication is much better, tools facilitate this communication but silos not completely gone. So you still have these different panels, they can come from one vendor, different vendors, the same vendor can actually provide multiple capabilities but the theme is do you actually want to move away from having to deal with the complexity of having completely different universes into having much more coherent elements to talk to each other? >> So if we have this more coherence, presumably that means these more coherent elements can actually support each other in providing, as you said earlier, some of the crucial features of what a complex, large, scalable system needs to perform. You mentioned backup restore for example. How do you anticipate that the requirements of what constitute as systems, before it was scale compute and now we're actually worried about making sure that all those other issues from an automation from a business requirement standpoint and increasing impinging upon what we regard as design, like, having data protection. How do those new constraints start to impact folks to think about what to buy, what to use now? >> Yeah it's actually fascinating that tape, right, as we know it and as we knew it which largely has not changed, right, is actually still present. Tape obviously is a sequential approach, it's not by any stretch not the most easy way technology to operate and yet it still has sort of a presence so moving away from this, and the interesting observation is and you can now move away from these classic approaches of backup to object-based solutions. These object-based solutions, provide that you have the appropriate kind of connectivity assumptions can either be offsite or onsite and it's a very fluid and transparent model. And these object-based solutions are actually now designed into scale and can be used to either store primary data and stream data also to store backups of data and so this convergence between using object storage between what is backup and what is live data is one of the interesting themes. >> So we're talking about convergence of the hardware elements, but now we're also talking about convergence of the services and the capabilities associated, all within the same console, all within the same platform, utilizing specialization where it makes sense, have I got that right? >> Yes I mean you obviously have different use cases right? One of the things that is always goes back to the question of what is the API right? If you have an API and it is really you know gets and sets on an object model, that is designed to operate transactional objects right, you effectively are in a particular mindset. If you actually want to guarantee retention, you actually want a different set of APIs right, one of the things that's really important is to make sure that the data is actually safe and that the API won't prevent a catastrophic misuse and deletion of the data, for example. >> So there's one bit of advice you can offer someone who's sitting in a data center today and thinking about what they should be doing to increase the returns on their data assets and what they provide to the business, what would that kind of one thing that you'd leave them with be? >> Almost depends on where you start from, right? >> Peter: Okay good point. >> But having said that, there are sort of two general approaches, one is sort of the incremental approach which is you try to catch up with the technology trends and the other one is to say, okay what are actually my problems that I'm trying to solve purely from an infrastructure perspective and how do I actually solve these problems in a reasonable timeframe? It's actually if you think about the pros and cons between the two approaches, the first approach is this pragmatic, it's going to be better this quarter than last quarter, but you may never be able to catch up the other approach requires a little bit more thinking, sometimes process re-engineering, sometimes thinking about things differently. Changing the operational model, how your teams operate within the IT organization, sometimes it actually delivers the right solution. >> And we do have a model for how to do this, the big hyper-scalers are doing just that second approach and it's having a consequential impact on the industry isn't it? >> Yeah well storage, the storage industry has always been a fascinating industry, it was static for a few years, it's now extremely dynamic industry, there's a lot of companies that went public in the storage space over the last few years as we all know. They went because there was new technology, right? Flash sort of was transforming to the landscape. Now object and hyperconverged and post-hyperconverged solutions are actually also completely transforming the landscape because now, we think about storage different because it's not, the paradigm is no longer the same. >> Thinking about computing entirely differently. Storage plus everything else. >> Well at the end of the day, this is purely, this is infrastructure right? >> Right. >> And infrastructure is never for infrastructure's sake. Infrastructure is to deliver a new capabilities, new applications. The combination of you know phenomenal increases in primary memory, in Flash memory, and NVME, all these technologies are sort of transforming our expectation with respect to responsiveness and access to data. And then the changes on the compute side and the huge specialization going on in hardware in A-six that we know how to process data in much more efficient way and this is, we haven't talked about AI yet but fundamentally when you think about all these AI-based improvements, it is about being able to put massive amount of computational capabilities onto mass amounts of data. >> So you've been part VMware, you've been part of Neva, you've been part of a lot of different companies, if you look out, what types of foci, what types of centers of innovation amongst, in the valley do you look to for leadership? (laughing) >> The nice thing is, I was in the valley, i was in the industry and now I'm. >> And now you're out. (laughing) >> So I actually don't have to take a position. It's actually nice to be able to look at it much more from a principal perspective rather than to look at is as to which of the existing players are, the agenda they're trying to push. They each have legitimate agendas because they're driving their business and the evolution of their business for their customer and trying to deliver value to their customer. Obviously the customers have to choose. When I look at it sort of from my perspective both academically and so simply from an IT perspective as I operate a fair amount of IT EDPFL, it's really this notion of what problems are we trying to solve? And whether the boundaries that we traditionally had between the classic large vendors still make sense in this sort of hyperconverged environment. >> Alright well, Ed Bugnion, Professor of computer science at EDPFL, thanks again for being on theCUBE and this is Peter Burris and once again, great CUBE conversation and hope to see you soon. (bright upbeat music)

>> Hi, I'm Peter Burris, and welcome to Wikibon Action Item Quick Take. Big Data SV is one of our important shows where we bring thought leadership around big data to the Cube and have great conversations about what's happening in the Big Data universe. John Furrier, what are we looking for in the next couple of weeks? >> Big Data Silicon Valley known as Big Data SV as we have NYC for New York City, two events that we co-produce in conjunction with Strata Conference going on side-by-side where we do the following. We have three days: Tuesday, Wednesday, and Thursday, the sixth, seventh, and eighth. We're going to be in San Jose. And we have a great lineup. And it's pretty much sold out, but we added Thursday for more live interviews, where we extract the signal from the noise. So we have more opportunities to interview more people, and also, we're opening up more sponsorship slots. So if you want to get your company's name out there, get above the noise, and get those thought leadership interviews out, we have just released extra sponsorship opportunities for Thursday for live interviews and conversations on the Cube, a new format. As you know, it's proven as a conversational great way to get the word out in an informative, inspirational way. Of course, that's the Cube mission, Peter, as you know. And we love doing what we do. We love the support of our sponsors. So if you want to be a sponsor and have that conversation with us, we'd love to entertain that opportunity on Thursday, March 8th. >> Alright, John Furrier, your Cube host, who actually is going to be hosting Big Data SV. This has been a Wikibon Action Item Quick Take.

(uptempo orchestral music) >> Welcome to theCubeConversations here in Palo Alto, California. theCube Studios, I'm John Furrier. The co-host of theCube, and co-founder of SiliconANGLE Media. Junaid Islam is the present CTO of Vidder that supports the public sector as well as the defense community as well as other criminalist oriented security paradigms. Expert in the field. Also part of coming Vidder that's doing a lot of work in the area. Thanks for sharing your time here with us. >> Well thanks for having me. >> We had a segment earlier on cybersecurity and the government. So that was phenomenal and also, we talked on the impact of hacking on business. So the number one issue on the boardroom agenda is security. Data, security, it's a big data problem. It's an AI opportunity, some things that are coming out. Embryonic, it's an early shift. Security is a challenge. The old model, the firewall, a moot, doors, access, you get in then you're done. It's over, it's a criminalist world. People can get access to these networks. Security is screwed right now. And we generally feels that. So the question for you is the Enterprise and in business as we're looking to show up security. Isn't it a do-over? >> Yeah, yeah, I think like other industries. Whether you talk about the PBS. Yes, yes, where you talk about computers shifting to the data center and then the cloud. I think last year or this year, Gartner said 100 billion will be spent on security. I cannot believe anybody who was involved in that 100 billion dollar expenditure is happy. In fact we have something interesting. Security expenditure has risen consistently over the past five or six years, and cyber attacks have also risen consistently. So that's not the kind of correlation you want. >> And they're buying anything that moves basically, they're desperate so it seems like they're like drunken sailors. Just like give me something. They're thirsty for solutions. So they're groping for something. >> Yeah and what we're seeing is a couple of things. One is the attackers have gotten much more sophisticated. And they basically can bypass all of the existing security appliances. So what we need is a new approach or a new security stack that really fits both the architectural environment of American companies where they use Clouds and data centers, and they have employees and contractors. But also cyber attacks which have gone much more sophisticated. The classic cyber attack used to be connecting to the Server remotely or stealing a password. We still have the classics but we have some new ones where we have malware that can actually go from the user's device to inside the network. And you find that existing security products just don't work well in this environment. >> What are some of the do over ideas? >> Absolutely malware, we see it ransomware, super hot, the HBO example recently. They didn't given, who knows what they actually did. They weren't public about it but actually they maybe get a little bit in but these are organized businesses. They're targeting with the Sony hacks well documented but again businesses, I'm not always funded this. And then you got the move to the clouds. Couple dynamics. Cloud computing. Amazon has done extremely well, they're leading. Now getting a lot more of the Enterprise. They won the CIA deal a few years ago over IBM. And you see a lot of government Cloud rocking and rolling, and then you've got the on premise data center challenges. That's the situation of the customer then now you have potentially an understaffed security force. >> Well actually so, I think let's start with that point. In terms of our theme of a do over. Talk about that first and then let's talk the techno part. I think one do over that America needs is security has to move out of the IT department, and become a stand alone department reporting ideally to the executive staff and not being on it. I think one of the unfortunate things is because security is a cost center within IT, it competes with other IT expenditures such as new applications, which are revenue generating. It's very hard to be a cost center asking for money when there's a guy sitting next to you who's doing something to make money. But unfortunately, unless security is properly funded and staffed, it never happens. And this unfortunately is a chronic issue through all US companies. One of the things we've seen that has worked for example in the financial world is most financial institutions, probably all now security is a pure organization to IT, that helps a lot. This is actually not a new idea, this was something the intelligence community probably started-- >> Cost structures, it's just the cost structures. Reduce the cost is the optimization behavior. What you're saying is just like applications are tied to top line in revenue, which gives them top line mojo. You got to think of security as a money saving table stake. >> That's right. >> People are losing money. The cost are now becoming obvious, in some cases crippling. >> Yeah so I think people need to think of security as fundamental to the life of a company, number one. I think the other thing that needs to happen from a security perspective. Now that we've broken off this entity is it security needs to become a threat based or risk based. Too much of security in the United States is based on compliance models. Unfortunately cyber attackers do not follow that model when they want to attack us. They basically work outside the model and come up with creative ways to get inside of organizations. >> Basically blindside. >> That's right. >> The company. >> I can't tell you how many meetings probably all where I meet the security team and they're totally busy just going through the list of 20 or 50 things that they're are supposed to do. So when you talk about attack vectors. They say you know that's really great and I know it's important but we can't get to it. So this is another important shift organizationally. First you break it out. Second, get focus on something that's important. once we have that we get to the next part which is technologies. And right now what happens is people buy a security point product for different networks. One for data center or one for Cloud. And this doesn't work so I think we have to move to security solutions that can work across hybrid environments, and can also work across different roles. I think that is critical and unless we get that in technically. >> Yeah, this is the thing with Cloud and (indistinct talking). I want to bring this up. I had multiple change to sit down with Andy Jassy. The CEO of Amazon web services. Fantastic executive, built a great business there. On his mind, what's been important for him for many years has been security, and Amazon has done an amazing job with security. But that's in the Cloud. Now Andy Jassy and Amazon thinks everyone should be in the public Cloud. Now they have a deal with VMware but they're just powering VMware's on prem in their Cloud. It's not really a VMware issue but Amazon's world is raising the public Cloud. But they've done really, really good on security, but yet most of the buyers would say hey, the Cloud is unsecure I can't trust it. So you have the dynamic between the data center on premise resource. So people default to the behavior of and leaving here with the on premise. Or I'll put a little bit in the cloud, a little bit of workloads here. A little bit in the Microsoft. Google's got some, I'll keep the tire on Google. But they never really leaving the home base of the data center. But yet some are arguing and Dave Vellante, my co-host on theCube talks about this all the time. There's actually more scale in the Cloud. More data sharing going in the cloud and that the cloud actually got better security. So how do you see that resolving because this is a key architectural opportunity and challenge for Enterprise. >> Actually I think there's an optimal model which is if you think about what the data center gives you. It gives you a lot of visibility and physical control as in with your hands. The problem is when you put everything in the data center. You don't have enough people to manage it all properly. The Cloud on the other hand gives you a lot of scale but you can't actually touch the Cloud. So the optimal mix is imagine your encryption and access control solutions live in your data center. But what they control access to is to Cloud resources. So you can actually, if you're just open your mind conceptually. >> So it's like saying, it's like segmenting a network. You're segmenting feasibility. >> That's right, so now you don't need a gigantic data center because what's in your data center which can be a lot smaller now are things like your identity-based access management solutions. You can keep your cryptographic elements. You can have your HSM, things that generate random numbers and search there. But now this is actually can be very tiny. It can just be a rack of gear. But through that rack of gear, you can have very fine control of people accessing Cloud resources. And I think this idea of building, it's not so much a hybrid network, but it's a notion that a small physically locked down asset can control a lot of virtual assets. It's gaining mind share in the banking world. In fact, just this summer, there was bank that implemented such an architecture where the control elements were the Cloud were their FFIC data center. And it include, it basically managed access to Amazon VPC and it worked well. >> So interlocking is a strategy. I can see that, by the way I see that playing out pretty well. So I got to ask the next question which comes to mind is that sounds great on paper. Or actually in certain situations it might perfect. But what about the geo-political landscape? because Amazon has people that develop on the Cloud that aren't US citizens. So the government might say wait a minute, you got to only employ Americans so they got to carve out and do some whatever weird doings with the numbers to get that certification. But they need data centers in Germany because the German government wants certain things. So you have geo-political issues now on the companies. How does that affect security? Because now a Cloud like Amazon or a multinational company has two things going on. I have multiple offices and I'm operating in multiple geo-political landscapes with these regional centers. The regional clouds, or at Amazon they're called regions. >> So actually Amazon has actually done a great job. They basically have their global market, but they also have data centers now which are only opened to US persons in US companies like Globe Cloud. As well as well as they support the C2S which is the intelligence communities Black Cloud, which is basically off net so I think now-- >> John: So they're doing a good job? >> Yeah, they're doing a good job but the key thing is how you use that resource is really still up to the enterprise. And that's where enterprises have to get good at creating the architecture and policies to be able to harness Amazon's compute capacity. Amazon, is the foundation but you really have to finish off the solution and the other thing going back full circle to your first question. Unless the security team has their freedom and the mandate to do that, they'll actually never get there. >> So it's staffing and architecture. >> That's right. >> Well they both architecture. It's one's organizational architecture. Debt funding and one is more of a hardcore virtual and physical touching. >> And you know what I put in the middle? I'd say know your risks and develop counter measures to them. because if you go to that security team and you say you have to build a counter measure for every attack. That's not going to work either. A company has to be realistic is what is really important? Maybe it's the data of our customers. >> So the answer to the first question then obviously is yes a security do over is needed. But there is no silver bullet and you can't buy an application, it's an architectural framework holistically >> Junaid: That's right. >> That everyone has to do, okay cool. So the question I have on the Amazon, I want to get your thoughts 'cause it's a debate we have all the time on theCube is. And certainly Amazon has competitors that say, Amazon is really not winning in the enterprise. They've got thousand of Enterprise customers. They are winning in the Enterprise so Oracle is catching up, barely in fourth place. But trying to get there and they're actually making that transformation. Looking pretty good, what more now assume that Oracle will (indistinct talking). But Amazon has one great gov Cloud deals. So they're convinced the government that they could do it. >> Junaid: Yeah. >> So to me that's, my argument is if the government is winning with Amazon. It should be a no brainer for the Enterprises so this comes back down to the number one question that's been holding back Cloud growth. Whoa, security, I don't want to put it in the Cloud. How real is that objection now? 'Cause the knee jerk reaction is you know what, I got an on prem, I don't trust the Cloud. But it seems like the Cloud is getting more trust. What's your thoughts on that objection? >> So one of the things as even though when we use the word Cloud, generically or Amazon generically. Amazon has evolved a lot in the last three to four years that I've been working on it. The number of embedded tools in Amazon is vast now. If we were having this conversation two years ago. The notion that granular encryption modules would be there and Amazon is apart of an offering. It would have been science fiction or the fact that-- >> More that S3 and AC2, what else could there be? >> That's right or they have things like virtual HSM. They have embedded identity and access control tools all there so I think first of all. All of the building blocks that you would want are there. Now unfortunately there's no short cuts. Amazon is not going to do the work for you, you still need a staff that knows how to use digital certificates. You still need your own identity based access control system to manage access of your employees and contractors and people in India to these assets in the Cloud. But having said that, we now actually have a model that is much cheaper than the classic data center model. That's basically usable. >> I'm smirking some people think I'm an Amazon web services fan boy but besides the fact that I love the company. They've done well and there's so many new services, and they literally have been skating rings around the competition. If you look at the complexity that they have been dealing with and the innovations. So the outputs put that out there. I'm a little biased 'cause I think they're doing a great job. But now, the game start to shift as Amazon continues to add more services. Welcome to the big leagues called the Enterprise and government, which they're doing some business in now. So the question is besides Amazon, those other guys. Verizon, the Telecos have really trying to figure out what to do with over the top for years. Now they're also powering a lot of multi tenet workloads as well, including their own stuff. So telecos and service providers out there, what are they doing because they're still critical infrastructure around the world. >> Actually, I think if we just use Amazon as a reference point or example. Amazon initially didn't worry about security but then over the last few years, worked hard to integrate security into their offering. We're now in the early stages of seeing that from for example carriers like Verizon. Where in the past Verizon was saying first secure yourself then in the last two years. Version okay, here's some products and services you can buy. But now where we're heading is they're trying to make the network inherently secure. A lot of the basic components like device matching to identity matching basically making that apart of the underlying fabric. So I think the good news is as-- >> So they're making advances there. They have networks. They know networking. >> So the good news is as bleak as it all seems as we are making significant progress as an industry and as a country. Having said that, my only and warning is you still need an executive team. A security team that knows how to leverage all of these components and pull them together. And that goes back to having a risk based approach and protecting the most important things. I think you can do that, I think the tool set that's come out now is actually pretty sophisticated. >> So final question, I want to get your thoughts and we can end the segment and then we'll take a little bit about Vidder, your company. But I asked Pat Gelsinger, CEO of VMware at VM World just recently about the security duo 'cause Dave Vellante asked him years ago. He said absolutely it's going to be (indistinct talking) so Pat Gelsinger has it right again. The guy is like Nostradamus when it comes to tech trend. He's a wave guy from Intel, so he gets the waves. But I asked him about that question again this year and I'll send the tip out on Twitter. I'll put it out on Twitter, I'll make a link to it. He said that 5G is going to be the big kahuna of the next 30 years. He thinks that as 5G starts to get out, it's going to develop 10X number of antennas, 100X of bandwidth, new spectrum allocations, 100X new devices, they're all going to be connected as well. As you mentioned we're a connected world. This brings up the edges of the network where he says, "Next thirty years is going to be massive build out." So okay, 5G is coming. Industrial IoT, IoT internet of things is happening. How is this going to change a security game because now you have networking and you see VMware. We're doing NSX and Cisco has been trying to the Enterprise figuring out the virtualization of network level. Everything comes back down to the network. Is that where the action is because it seems to me that the network guys have to figure this out. And that seems to be the point of reference in terms of opportunity. Or is it a challenge or is it moving up the stack. How does all the networking changes happen? >> So for IoT, we really need two things to happen. I think one is we actually don't have a security standard for IoT devices. And specifically the issue is malware. IoT devices and softwares made worldwide and I think one of the biggest policy weaknesses we have right now is there's no minimum standard. This needs to be solved, otherwise we're in a lot of problem but in parallel to that. There is a lot of technical development. One of the things that's happening in the networking world is for the past 20 years. We were driven by what's called a network VPN of Layer 3 VPN, it's your classic VPN, that connects a device to a server. The problem with that is if you have malware on the device it gets through. So there's this new kind of VPN which is an application VPN or we call it a Layer 4, which is basically a softer process in the device tool. A softer process in a server. So that's the new model, which is-- >> They're making them as dumb as possible and go up the stat. >> Not so much-- >> There were guys that are going to roll-- >> I could have used different terms. I could have say make the app network application aware so that it only lets the applications get through. Not any kind of connection, so I think that is something. >> Well the networks have to smarter and enable the smartness. >> So smarter networks are happening and it's an area that I worked in. It's very excited. >> John: I don't mean to offend you by saying dumb network-- >> But the application but to be clear though that's just one piece of the puzzle. The other piece of the puzzle which unfortunately is a little bit lacking is there's no standards for IoT software today. And unless we have concepts like secure boot, that is the software can't be tampered with. I think I've unfortunately there's a bit of risk but I'm hopeful-- >> And then IoT for folks watching, there might be any inside baseball. It's a surface area problem. There's more points of attack vectors, so we talk about the compliance thing. >> Not only are there more attacks, by and large IoT devices are made outside of the United States. Physically they are made in China and a lot of the software comes from India. And there's nothing wrong with that, but the global supply chain provides plenty of opportunities for cyber attackers to inject in their code. And this is something we need to watch very carefully and then like I said-- >> So this is actually one of those weird derivative results of outsourcing that American companies have realized that's a problem. >> Yeah so. >> Is that right? >> Yeah so it's something we need to watch carefully. >> Okay, thanks for coming on the theCube. Really appreciate you sharing your perspectives. Talk about Vidder, you're the president and CTO. You guys in the security business. Obviously you're an expert with (indistinct talking). We'll have you back and multiple times. I'd love to get your company as we follow all the security trends. We have a cyber connect conference with Centrified coming up in New York. We're covering gov Cloud AWS and other players out there. What's Vidder doing? What's the company do for products? How do you guys sell? Who's your customers and what are the cool things you're doing? >> We've developed a access control solution based on a new standard called software defined parameter. And there's two things that are unique about it. First with technology like software defined parameter. We work in the Cloud in the data center, but more importantly, we're able to stop existing attacks and emerging attacks. So things like password theft, credential theft of server exploitation we stop because we don't want to allow connections from unknown devices or people. The other thing is say you're known, and you connect with server. We basically look inside your laptop and only allow the authorized process to connect to the server. So if there's malware on the device, it can't actually make it through. >> John: So it shuts down the malware. >> That's right. >> John: So you're trying sneak through. >> That's right, the malware. We can't stop the malware from getting on the device, but we can make sure it doesn't get to the other side. >> So it doesn't cross pollinate. It doesn't go viral. >> That right so a lot of the stuff we do is very important. We work with a range of-- >> You have government, obviously contracts. I'm sure you have that can't talk about but you do right? >> Yeah we do a little bit of work with the government and we're just start working with Verizon, which is public. Where they wish to create services where malware actually can't go through the connections. So we're doing exciting stuff and we're-- >> Enterprise customers at all? >> Yeah, yeah we have banks. >> Who are on high alert. >> That's right. >> You guys do tier one or it's the houses are burning down, you're there. So we do banks and we're just started doing some work in a hospital were again it's (indistinct talking) compliant, and they need to make sure that data doesn't leave the hospital. >> So what's the number one thing that you guys have as ransomware something that you solve. What areas do you guys being called in? What's the big fire bell, if you will? They ring the bell when do you come in? What the thing, just in general? >> Our number one reason for existing is stopping attacks on application servers or service that old data. That's our focus. So if you have data or an application that someone is after. We will make sure nobody gets to that data. In fact, we'll even make sure if there's a spy, or insider attack, who comes into your organization. They'll only be able to what their allowed to do and won't be able to do anything else. >> So on the weekly Fox that was big. Would you guys have helped there is they were a customer or is that just different thing? >> I know we could have helped because one of the things that happened is they used their server exploit to basically propagate through their data center. So we probably wouldn't have done much on the initial exploit, but we would have kept it from going deeper into the system. >> And they hid for four months and they were poking around so you would have detected. >> Yeah and we certainly would have stopped all the poking around. Because we basically, you can think of us as an identity based access control mechanism. So based on your identity, you can only do very specific things. And in their case, they had the identity of the user. We wouldn't have let them do anything except maybe just go to one website. >> Yeah you would have shut them down. They should have been doing business with Vidder. Jay thank you for coming on theCube here for theCubeConversation in Palo Alto, California. I'm Jon Furrier with theCubeConversation. Thanks for watching. (slow orchestral music)

(light orchestral music) >> Hello, everyone. Welcome to special CUBEConversation here in theCUBE studio in Palo Alto, California. I'm John Furrier, the co-founder of SiliconANGLE Media and also the co-host of theCUBE. We're here with Junaid Islam, who is the President and CTO of a company called Vidder. Also supports the public sector and the defense community. Teaches a class on cyber intelligence and cyber warfare. Junaid, thank you for coming in. >> Well, thanks for having me, it's great to be here. >> Now, you see, we've been doing a lot of coverage of cyber in context to one, the global landscape, obviously >> Yeah >> And in our area of enterprise and emerging tech you see the enterprises are all shaking in their boots. But you now have new tools like IoT which increases the service area of attacks. You're seeing AI being weaponized for bad actors. But in general, it's just that it's really a mess right now. >> Yeah >> And security is changing. So, I'd like to get your thoughts on it and also talk about some of the implications around the cyber warfare that's going on. Certainly the election's on everyone's mind, you see fake news. But really, it's a complete new generational shift that's happening. With all the good stuff going on, block chain and everything else, and AI, there's also bad actors. Fake news is not just fake content. There's an underlying infrastructure, a critical infrastructure, involved. >> Yeah, you're 100% right. And I think what you have hinted on is something that is only, now, people are getting awareness of. That is, as America becomes a more connected society, we become more vulnerable to cyber attacks. For the past few years, really, cyber attacks were driven by people looking to make twenty bucks, or whatever, but now you really have state actors moving into the cyber attack business. And actually subsidizing attackers with free information. And hoping to make them more lethal attackers against the United States. And this really is completely new territory. When we think about cyber threats almost all of the existing models, don't capture the risks involved here. And it affects every American. Everybody should be worried about what's going on. >> And, certainly, the landscape has changed in security and tech with cloud computing, but more importantly, we have Trump in the office and all this brouhaha over just that in itself. But in concern to that, you're seeing the Russians, we're seeing them involved in the election, you're seeing China putting blocks and everything, and changing how the rules, again. It's a whole global economy. So I got to ask you the question that's on everyone's mind is cyber war is real. We do not have a West Point, Navy SEALs for cyber yet. There's some stuff at Berkeley that's pretty interesting to me. That Michael Grimes at Morgan Stanley is involved with. A bunch of other folks as well. Where a new generation of attacks is happening. >> Junaid: Yeah. >> In the US of A right now. Could you comment and share your thoughts and reactions to what's happening now that's different in the US from a cyber attack standpoint and why the government is trying to move quickly why companies are moving quickly. What's different now? Why is the attacks so rampant? What's changed? >> I think the biggest difference we have now is what I would call direct state sponsorship of cyber attack tools. A great example of that is the Vault 7 disclosure on WikiLeaks. Typically, when you've had intelligence agencies steal one thing from another country, they would keep it a secret. And, basically, use those vulnerabilities during a time of an attack or a different operation. In this case, we saw something completely different. We think the Russians might have stolen, but we don't know. But whoever stole it, immediately puts it back into the public domain. And why do they do that? They want those vulnerabilities to be known by as many attackers as possible, who then, in turn, will attack the United States at across not only public sector organizations, but as private. And one of the interesting outcomes that you've seen is the malware attacks or cyber attacks we saw this year were much more lethal than ever before. If you look at the WannaCry attack and then the NotPetya attack. NotPetya attack started with the Russians attacking the Ukraine. But because of the way that they did the attack, they basically created malware that moved by itself. Within three days, computers in China that were 20 companies away from the original target were losing their data. And this level of lethality we've never seen. And it is a direct result of these state actors moving into the cyber warfare domain. Creating weapons that basically spread through the internet at very high velocity. And the reason this is so concerning for the United States is we are a truly connected society. All American companies have supply chain partners. All American companies have people working in Asia. So we can't undo this and what we've got to do, very quickly, is develop counter measures against this. Otherwise, the impacts will just get worse and worse. >> So in the old days, if I get this right, hey I attack you, I get to see a backdoor to the US. And spy on spy kind of thing. >> Junaid: Yeah. >> Right, so now, you're saying is, there's a force multiplier >> That's right out there with the crowd. So they're essentially democratizing the tools. We used to call it kiddie scripts. Now they're not kiddie scripts anymore, they're real weapons of cyber weaponry that's open to people who want to attack or motivated to attack the US. Is that kind of, am I getting that right? >> That's right. I mean, if you look at what happened in WannaCry, you had people looking for $200 payout, but they were using tools that could have easily wiped out a country. Now, the reason this works for America's enemies, as it were, or adversaries, is in the short run, they get to test out weapons. In the long run, they're really learning about how these attacks propagated. And make no mistake, if there's a political event and it's in their interest to be able to shut down US computers. It's just something we need to worry about and be very conscious of. Of specifically, these new type of attack vectors. >> Now to put my fear mongering hat on because as a computer scientist, myself, back in the day, I could only imagine how interesting this is to attack the United States. What is the government doing? What is the conversations that you're hearing? What are some of the things going on in the industry around? OK, we're seeing so sophisticated, so orchestrated. At many levels, state actors, democratizing the tools for the bad guys, if you will, but we've seen fraud and cyber theft be highly mafia driven or sophisticated groups of organized, black market companies. Forms, I mean, really well funded, well staffed. I mean, so the HBO hack just a couple weeks ago. I mean, it's shaking them down with ransomware. Again, many, many different things. This has got to scare the cyber security forces of the United States. What are they doing? >> So I think, one thing I think Americans should feel happy about is within the defense and intelligence community, this has become one of the top priorities. So they are implementing a huge set of resources and programs to mitigate this. Unfortunately, they will, they need to take care of themselves first. I think it's still still up to enterprises to secure their own systems against these new types of attacks. I think we can certainly get direction from the US government. And they've already begun outreach programs. For example, the FBI actually has a cyber security branch, and they actually assign officers to American companies who are targets. And typically that's actually, I think, started last year. >> John: Yeah. But they'll actually come meet you ahead of the attack and introduce themselves. So that's actually pretty good. And that's a fantastic program. I know some of the people there. But you still have to become aware. You still have to look at the big risks in your company and figure out how to protect them. That is something that no law enforcement person can help you at. Because that has to be pro-active. >> You know we everyone who watches my Silicon Valley podcast knows that I've been very much, talk a lot about Trump, and no one knows if I voted for him or not or actually, didn't vote for him, but that's a different point. We've been critical of Trump. But also at the same time, the whole wall thing is kind of funny, in itself, building wall is ridiculous, but that's take that to the firewall problem. >> Junaid: Yeah. >> Let's talk about tech. The old days, you have a firewall. Right? The United States really has no firewall because the perimeters or the borders, if you will, are not clear. So in the industry they call it "perimeter-less". There's no more moat, there's no more front door. There's a lot of access points into networks in companies. This is changing the security paradigm. Not only at the government level, but the companies who are creating value but also losing money on these attacks. >> Junaid: Yeah. >> So what is the security paradigm today? Is it people putting their head in the sand? Are there new approaches? >> Junaid: Well, yeah. >> Is there a do over, is there a reset? Security is the number one thing. >> So I >> What are companies and governments doing? >> So I think, well first of all, there's a lot of thinking going on but I think there's two things that need to happen. I think one, we certainly need new policies and laws. I think just on the legal side, whether you look at the most recent Equifax breach we need to update laws on people holding assets that they need to become liable. We also need more policies that people need to lock down national critical infrastructure. Like power systems. And then the third thing is the technical aspect. I'd bring it. We actually in the United States actually do have technologies that are counter measures to all of these attacks and we need to bring those online. And I think as daunting as it looks like protecting the country, actually, it's a solvable problem. For example, there's been a lot of press that you know foreign governments are scanning US power infrastructure. And, you know, from my perspective as a humble networking person, I've always wondered why do we allow basically connectivity from outside the United States to power plants which are inside the United States. I mean, you could easily filter those at the peering points. And I know some people might say that's controversial, you know, are we going to spy on >> John: And ports too. >> Yeah. >> Like, you know, ports of New Orleans. I was talking to the CTO there. He's saying maritimes are accessing the core network. >> Yeah, so from my perspective as a technical, I'm not a politician, but I >> (laughs) That's good, thank God! We need more of you out there. >> I would and I've worked on this problem a little bit I would certainly block in-bound flows from outside the United States to critical infrastructure. There is no value or reason, logical reason, you would give a why someone from an external country should be allowed to scan a US asset. And that is technically quite simple for us to do. It is something that I and others have talked about you know, publically and privately. I think that's a very simple step we could do. Another very simple step we could do across the board is basically authenticated access. That is, if you are accessing a US government website, you need to sign in and there will be an MFA step-up. And I think that makes >> What's an MFA step-up. >> Well like some kind of secondary >> OK. >> Say your accessing the IRS portal and you just want to check on something you know, that you're going to sign in and we're going to send a message to your phone to make sure you are you. I know a lot of people will feel, hey, this is an invasion of privacy. But you know, I'll tell you what's an invasion of privacy. Someone stealing 140 million IDs or your backgrounds, and having everything. >> John: That just happened. >> That's a bigger >> John: That's multifactor authentication. >> So I think that >> Unless they hack your cell phone which the bitcoin guys have already done. >> Yeah >> So, it's easy for hackers to hack one system. It's harder for hackers to hack multiple systems. So I think at the national security level, there are a number of simple things we can do that are actually not expensive. That I think we as a society have to really think about doing. Because having a really governments which are very anti-American destabilizing us by taking all of our data out doesn't really help anyone. So that's the biggest loss. >> And there's no risk for destabilizing America enemies out there. They what's the disincentive. Are they going to get put in jail? There's no real enforcement. >> Junaid: Yeah. I mean, cyber is a great leverage. >> So one of the things that I think that most people don't understand is the international laws on cyber attacks just don't exist anymore. They have a long way to catch up. Let me give a counter-example, which is drugs. There are already multilateral agreements on chasing drug traffickers as they go from country to country. And there's a number of institutions that monitor and enforce that. That actually works quite well. We also have new groups focusing on human trafficking. You know, it's slowly happening but in the area of cyber we haven't even started a legal framework on what would constitute a cyber attack. And, sadly, one of the reasons that it's not happening, is America's enemies don't want it to happen. But this is where I think, as a nation, first you have to take care of yourself. And then on a multi-lateral perspective the US should start pushing a cyber security framework world wide, so that if you start getting emails from that friendly prince, who's actually a friend of mine How about you know about putting in some we can actually go back to that country and say hey, you know, we don't want to send you any more money anymore. >> John: Yeah, yeah exactly. Everyone's going to make 18 million dollars if they give them their username, password and social security number. Alright, final question on this segment, around the cyber security piece. What's the action, going forward? I would say it's early days and hardcore days right now. It's really the underbelly of the internet. Globally is attacking, we see that. The government doesn't have enough legal framework yet in place. They need to do that. But there's a lot of momentum around creating a Navy SEALs. You need a version of land, air and sea. Or multidisciplinary combat. >> Junaid: Yeah. >> Efforts out there there's been conversations certainly in some of our networks that we talk about. What's the young generation. I mean, you've got a lot of gamers out there that would love to be part of a new game if you will called cyber defense. What's going on? Is there any vision around how to train young people. Is there an armed forces concept? Is there something like this happening? What's the next what do we need to do as a government? >> So you've actually touched on a very difficult issue. Because if you think about security in the United States it's really been driven by a compliance model. Which is here's these set of things to memorize and this is what you do to become secure. And all of our cyber security training courses are based on models. If there's one thing we learned about cyber attackers is that these people are creative and do something new every time. And go around the model. So, I think one of the most difficult things is actually to develop training courses that almost don't have any boundaries. Because the attackers don't confine themselves to a set of attack vectors. Yet we, in our training do, we say, this is what you need to do. And time and time again people just do something that's completely different. So that's one thing we have to understand. The other thing we have to understand, which is related to that, is that all of US's cyber security plans are public and conferences. All of our universities are open. So we actually have. >> John: The playbook is out there. >> We actually, so one of the things that does happen is if you go to any large security conference you see a lot of people from the countries that are attacking us showing up everywhere. Actually going to universities and learning the course. I think there are two things. One we really need to think deeper about just how attacks are being done which are unbounded. And, two, which is going to be a bit more difficult we have to rethink how we share information on a worldwide basis of our solutions. >> John: Mmm-hmm. >> So probably not the easy answer you wanted. But I think >> Well, it's complex and required unstructured thinking that's not tied up. It's like the classic frog in boiling water dies and you put a frog in boiling water and it jumps out. We're in this false sense of security with these rules. >> Junaid: Yeah. >> Thinking we're secure And we're, people are killing us with this security >> Yeah >> It's scary >> And like I say, it's even worse when we figure out a solution the first thing we do is we tell everybody including our enemies, giving them all a lot of chance to figure out how to attack us. So I think >> So don't telegraph, don't be so open Be somewhat secretive in a ways, is actually helpful. >> I think, sadly, I think we've come to the very unfortunate position now where I think we need to, especially in the area of cyber rethink our strategies because as an open society we just love telling everybody what we do. >> John: So the final question. Final, final question. Is just, again, to end this segment. So cyber security is real or not real. How real is this? Can you just share some color for the folks watching who might say hey, you know I think it's all smoke and mirrors. I don't believe the New York Times. I don't believe this. Trump's saying this. And is this real problem? And how big is it? >> I think it is real. I think we have this calendar year, twenty seventeen, we have moved from the classic, you know, kind of like cyber, attack you know like someone's being fished to really a, the beginning of a cyber warfare. And unlike kinetic warfare where someone blows something up this is a new face that's long and drawn out. And I think one of the things that makes us very vulnerable as a society is we are an open society, we're interlinked with every other global economy. And I think we have to think about this seriously because unfortunately there's a lot of people who don't want to see America succeed. They're just like that. Even though we're nice people >> John: Yeah >> But, it's pretty important. >> It requires some harmony, it requires some data sharing. Junaid Islam, President and CTO of Vidder. Talking about the cyber security cyber warfare dynamic that's happening. It's real. It's dangerous. And our countries and other countries need to get their act together. Certainly, I think, a digital West Point, a digital Navy SEALs needs to happen. And I think this is a great opportunity for us to kind of do some good here and keep an open society while maintaining security. Junaid, thanks for sharing your thoughts. I'm John Furrier with theCUBE, here in Palo Alto. Thanks for watching. (dramatic orchestral music)

(perky music) >> Hello everyone. Welcome to a special CUBE Conversation here in the CUBE studio in Palo Alto, California. I'm John Furrier the co-founder of SiliconANGLE Media and also the co-host of the CUBE. We're here with Junaid Islam who's the president and CEO of a company called Vidder. Also supports the public sector and the defense community, teaches a class on cyber intelligence and cyber warfare. Junaid thank you for coming in. >> Well thanks for having me. It's great to be here. >> Okay, you know we've been doing a lot of coverage of cyber in context to one, the global landscape obviously. >> Yeah. >> In our area of enterprise and emerging tech, you see the enterprises are all, you know, shaking in their boots. But you now have new tools like IOT which increases the service area of attacks. You're seeing AI being weaponized for bad actors. But in general it's just really a mess right now. >> Yeah. >> And security is changing, so I'd like to get your thoughts on and also talk about, you know, some of the implications around the cyber warfare that's going on. Certainly the election is on everyone's mind. You see fake news. But really it's a complete new generational shift that's happening. With all the good stuff going on, block chain and everything else and AI, there's also bad actors. You know, fake news is not just fake content. There's an underlying infrastructure, critical infrastructure involved. >> Yeah, you're 100% right and I think what you have hinted on is something that is only now people are getting awareness of. As that is as America becomes a more connected society we become more vulnerable to cyber attacks. For the past few years really cyber attacks were driven by people looking to make $20 or whatever, but now you really have state actors moving into the cyber attack business and actually subsidizing attackers with free information and hoping to make them more lethal attackers against the United States. And this really is completely new territory. When we think about cyber threats almost all of the existing models don't capture the risks involved here and it affects every American. Everybody should be worried about what's going on. >> And certainly the landscape has changed in security and tech (mumble) cloud computing, but more importantly we have Trump in the office and there's all this brouhaha over just that in itself, but in concert to that you're seeing the Russians, we're seeing them involved in the election, you're seeing, you know, China putting, you know, blocks on everything and changing how the rules (mumble). It's a whole global economy. So I got to ask the question that's on everyone's mind, is cyber war is real? We do not have a West Point, Navy Seals for cyber yet. I know there's some stuff at Berkeley that's pretty interesting to me that Michael Grimes at Morgan Stanley's involved in with a bunch of other folks as well, where a new generation of attacks is happening. >> Junaid Islam: Yeah. >> In the US of A right now. Could you comment and share your thoughts in reaction to what's happening now that's different in the US from a cyber attack standpoint and why the government is trying to move quickly, why companies are moving quickly, what's different now? Why is the attacks so rampant? What's changed? >> I think the biggest difference we have now is what I would call direct state sponsorship of cyber attack tools. A great example of that is the Vault 7 disclosure on WikiLeaks. Typically when you've had intelligence agencies steal one thing from another country they would keep it a secret and basically use those vulnerabilities during a time of an attack or a different operation. In this case we saw something completely different. We think the Russians might has stolen it but we don't know. But whoever stole it immediately puts it back into the public domain. And why do they do that? They want those vulnerabilities to be known by as many attackers as possible who then in turn will attack the United States at across not only a public sector organizations but as private, and one of the interesting outcomes you've seen is the malware attacks, or the cyber attacks we saw this year were much more lethal than ever before. If you look at the Wannacry attack and then the NotPetya attack. NotPetya started with the Russians attacking the Ukraine but because of the way they did the attack they basically created malware that moved by itself. Within three days computers in China that were 20 companies away from the original target were losing their data. And this level of lethality we've never seen and it is a direct result of these state actors moving into the cyber warfare domain, creating weapons that basically spread through the internet at very high velocity and the reason this is so concerning for the United States is we are a truly connected society. All American companies have supply chain partners. All American companies have people working in Asia. So we can't undo this and what we've got to do very quickly is develop counter-measures against this. Otherwise the impacts will just get worse and worse. >> So the old days, if I get this right, hey, I attack you, I get to see a back door to the US and spy on spy kind of thing- >> Junaid Islam: Yeah. >> So now you're saying is there's a force multiplier out there- >> That's right. >> John Furrier: With the crowd, so they're essentially democratizing the tools, not, we used to call it kiddie scripts. >> Junaid Islam: Yeah. Now they're not kiddie scripts any more. They're real weapons of cyber weaponry that's open to people who want to attack, or motivated to attack, the US. Is that kind of, am I getting that right? >> That's right. I mean if you look at what happened in WannaCry, you had people looking for a $200 payout but they were using tools that could have easily wiped out a country. Now the reason this works for America's enemies as it were, or adversaries, is in the short run they get to test out weapons. In the long run they're really learning about how these attacks propagated and, you know, make no mistake, if there's a political event and it's in their interests to be able to shut down US computers it's just something I think we need to worry about and be very conscious of specifically these new type of attack vectors. >> Now to put my fear mongering hat on, because, you know, as a computer scientist myself back in the day, I can only imagine how interesting this is to attack the United States. What is the government doing? What's the conversations that you're hearing? What are some of the things going on in the industry around okay, we're seeing something so sophisticated, so orchestrated at many levels. You know, state actors, democratizing the tools for the bad guys, if you will, but we've seen fraud and cyber theft be highly mafia-driven or sophisticated groups of organized, you know, under the, black market companies. Forms, I mean really well-funded, well-staffed, I mean so the HBO hack just a couple weeks ago, I mean, shaking them down with ransom-ware. Again there's many, many different things. This has got to scare the cyber security forces of the United States. What are they doing? >> So I think, one thing I think Americans should feel happy about is within the defense and intelligence community this has become one of the top priorities. So they are implementing a huge set of resources and programs to mitigate this. Unfortunately, you know, they need to take care of themselves first. I think it's still up to enterprises to secure their own systems against these new types of attacks. I mean I think we can certainly get direction from the US government and they've already begun outreach programs, for example, the FBI actually has a cyber security branch and they actually assign officers to American companies who are targets and typically that's actually, I think it started last year, but they'll actually come meet you ahead of the attack and introduce themselves so that's actually pretty good. And that's a fantastic program. I know some of the people there. But you still have to become aware. You still have to look at the big risks in your company and figure out how to protect them. That is something that no law enforcement person can help you at because that has to be proactive. >> You know everyone who watches my silicon valley podcast knows that I've been very much, talk a lot about Trump and no one knows if I voted for him or not. I actually didn't vote for him but that's a different point. We've been critical of Trump but also at the same time, you know, the whole wall thing's kind of funny in and of itself. I mean, building a wall's ridiculous. But let's take that to the firewall problem. >> Junaid Islam: Yeah. >> Let's talk about tech. The old days, you had a firewall, all right? The United States really has no firewall because the perimeters or the borders, if you will, are not clear. So in the industry they call it perimeter-less. There's no more mote. There's no more front door. There's a lot of access points into networks and companies. This is changing the security paradigm not only at the government level but the companies who are creating value but also losing money on these attacks. >> Junaid Islam: Yeah. >> So what is the security paradigm today? Is it people putting their head in the sand? Are there new approaches? >> Junaid Islam: Well, yeah. >> Is it a do-over? Is there a reset? Security is a number one thing. What are companies and governments doing? >> So I think, well first of all there's a lot of thinking going on, but I think there's two things that need to happen. I think one, we certainly need new policies and laws. I think just on the legal side, whether if you look at the most recent Equifax breach, we need to update laws on people holding assets that they need to become liable. We also need more policies that people need to lock down national, critical infrastructure like power systems and then the third thing is the technical aspect (mumble). We actually, in the United States we actually do have technologies that are counter measures to all of these attacks and we need to bring those online. And I think as daunting as it looks like protecting the country, actually it's a solvable problem. For example, there's been a lot of press that, you know, foreign governments are scanning US power infrastructure. And, you know, from my perspective as a humble networking person, I've always wondered why do we allow basically connectivity from outside the United States to power plants which are inside the United States? I mean, you could easily, you know, filter those at the peering points and I know some people might say that's controversial, you know. Are we going to spy on- >> John Furrier: Yeah, and ports, too. Like- >> Yeah. >> John Furrier: You know, ports of New Orleans. I was talking to the CTO there. He's saying maritimes are accessing the core network. >> Yeah and so from my perspective as a technical, I'm not a politician, but- >> That's good! Thank God! >> But I- >> We need more of you out there. >> And I've worked on this problem a little bit. I would certainly block inbound flows from outside the United States to critical infrastructure. There is no value or reason, logical reason, you would give of why someone from an external country should be allowed to scan a US asset. And that is technically quite simple for us to do. It is something that I and others have talked about, you know, publicly and privately. I think that's a very simple step we could do. Another very simple step we could do across the board is basically authenticated access. That is if you are accessing a US government website you need to sign in and there will be an MFA step up. And I think this makes sense- >> What's an MFA step up? >> Well like some kind of secondary- >> Okay, yeah. >> So say you're accessing the IRS portal and you want to just check on something, you know, that you're going to sign in and we're going to send a message to your phone to make sure you are you. I know a lot of people will feel, hey, this is an invasion of privacy but you know I tell you what's an invasion of privacy: someone stealing 140 million IDs or your backgrounds and having everything. >> John Furrier: Which just happened. >> That's a bigger- >> So MFA multi- >> That's right, factor. Yeah, yeah. >> John Furrier: Multifactor Authentication. >> Yeah, so I think, again- >> John Furrier: Unless they hack your cellphone which the BitCoin guys have already done. >> Yeah. But, so it's easier for hackers to hack one system. It's hard for hackers to hack multiple systems. So I think at the national security level there are a number of simple things we could do that are actually not expensive that I think we as a society have been, have to really think about doing because having really governments which are very anti-American destabilizing us by taking all of our data out doesn't really help anyone, so that's the biggest loss. >> And it's no risk for the destabilizing America enemies out there. What's the disincentive? They're going to get put in jail? There's no real enforcement, I mean, cyber is great leverage. >> So one of the things that I think most people don't understand is the international laws on cyber attacks just don't exist anymore. They have a long way to catch up. Let me give a counter example which is drugs. There are already multilateral agreements on chasing drug traffickers as they go from country to country. And there's a number of institutions that monitor, that enforce that. That actually works quite well. We also have new groups focusing on human trafficking. You know, slowly happening. But in the area of cyber, we haven't even started a legal framework on what would constitute a cyber attack and sadly one of the reasons it's not happening is America's enemies don't want it to happen. But this is where I think as a nation first you have to take care of yourself and then on a multilateral perspective the US should start pushing a cyber security framework worldwide so that if you start getting emails from that friendly prince who's actually a friend of mine about, you know, putting in some, you know, we can actually go back to that country and say, hey, you know, we don't want to send you any more money anymore. >> John Furrier: Yeah, yeah, exactly. Everyone's going to make $18 million if they give up their user name, password, social security number. >> Junaid Islam: Yeah. >> All right, final question on this segment around, you know, the cyber security piece. What's the action going forward? I would say it's early days and hardcore days right now. It's really the underbelly of the internet globally is attacking. We see that. The government is, doesn't have a legal framework yet in place. They need to do that. But there's a lot of momentum around creating a Navy Seals, you know, the version of land, air, and sea, or multi-disciplinary combat. >> Junaid Islam: Yeah. >> Efforts out there. There's been conversations certainly in some of our networks that we talk about. What's the young generation? I mean, you got a lot of gamers out there that would love to be part of a new game, if you will, called cyber defense. What's going on, I mean, is there any vision around how to train young people? Is there an armed forces concept? Is there something like this happening? What's the next, what do we need to do as a government? >> So you actually touched on a very difficult issue because if you think about security in the United States it's really been driven by a compliance model, which is here's the set of things to memorize and this is what you do to become secure. And all of our cyber security training courses are based on models. If there's one thing we've learned about cyber attackers is these people are creative and do something new every time. And go around the model. So I think one of the most difficult things is actually to develop training courses that almost don't have any boundaries. Because the attackers don't confine themselves to a set of attack vectors, yet we in our training do. We say, well this is what you need to do and time and time again people just do something that's completely different. So that's one thing we have to understand. The other thing we have to understand which is related to that is that all of US's cyber security plans are public in conferences. All of our universities are open so we actually have, there's been- >> John Furrier: The playbook is out there. >> We actually, so one of the things that does happen is if you go to any large security conference you see a lot of people from the countries that are attacking us showing up everywhere. Actually going to universities and learning the course, so I think there's two things. One, we really need to think deeper about just how attacks are being done which are unbounded. And two, which is going to be a little bit more difficult, we have to rethink how we share information on a worldwide basis of our solutions and so probably not the easy answer you wanted but I think- >> It's complex and requires unstructured thinking that's not tied up. I mean- >> Yeah. >> It's like the classic, you know, the frog in boiling water dies and they put a frog in boiling water it jumps out. We're in this false sense of security with these rules- >> Yeah. >> Thinking we're secure, and people are killing us with this. >> Junaid Islam: Yeah and like I say, it's even worse when we figure out a solution. The first thing we do is we tell everybody including our enemies. Giving them a lot of chance to- >> John Furrier: Yeah. >> Figure out how to attack us. So I think, you know, we do have some hard challenges. >> So don't telegraph, don't be so open. Be somewhat secretive in a way is actually helpful. >> I think sadly, I think we've come to the very unfortunate position now where I think we need to, especially in the area of cyber. Rethink our strategies because as an open society we just love telling everybody what we do. >> John Furrier: Yeah, well so the final question, final, final question is just to end the segment. So cyber security is real or not real, I mean, how real is this? Can you just share some color for the folks watching who might say, hey, you know, I think it's all smoke and mirrors? I don't believe The New York Times, I don't believe this, Trump's saying this and is this real problem and how big is it? >> I think it is real. I think we have this calendar year 2017, we have moved from the classic, you know, kind of like cyber attack, you know, like someone's being phished for too, really the beginning of the cyber warfare and unlike kinetic warfare where somebody blows something up, this is a new phase that's long and drawn out and I think one of the things that makes us very vulnerable as a society is we are an open society. We are interlinked with every other global economy. And I think we have to think about this seriously because unfortunately there's a lot of people who don't want to see America succeed. They're just like that. Even though we're nice people. >> John Furrier: Yeah. >> But and so it's pretty important. >> It requires some harmony, it requires some data sharing. Junaid Islam, president and CTO of Vidder talking about the cyber security, cyber warfare dynamic that's happening. It's real. It's dangerous. And our country and other countries need to get their act together. Certainly I think a digital West Point, a digital Navy Seals needs to happen and I think this is a great opportunity for us to kind of do some good here and keep an open society while maintaining security. Junaid thanks for sharing your thoughts. I'm John Furrier with the CUBE here in Palo Alto. Thanks for watching.

(the Cube jingle) >> Hello, welcome to the CUBEConversation here in Palo Alto, California in theCUBE Studios. I'm John Furrier, the co-host of the CUBE and co-founder of SiliconANGLE Media. Junaid Islam is president and CTO of Vidder, supports the public sector as well as the defense community as well as other perimeterless oriented security paradigms, expert in the field, also part of up and coming Vidder that's doing a lot of work in the area. Thanks for sharing your time here with us. >> Well thanks for having me. >> We had a segment earlier on cyber security in the government so that was phenomenal but also we talked about the impact of hacking on business. So the number one issue on the board room agenda is security. >> Yeah. >> Data, security, it's all, it's a big data problem, it's a AI opportunity. Some things that are coming out is embryonic early shifts. Security is a challenge. The old model of the firewall, a mode, doors, access, you get in, then you're done. It's over, it's a perimeterless world. People can get access to these networks. Security is screwed right now. Everyone kind of generally feels that. So the question for you is in the enterprise and in businesses who are looking to sure up security, is it a do-over? >> Yeah, yeah, I think, like other industries, whether you talk about-- >> Yeah, so that's a yes? >> The PBS-- Yes, yes. >> Yes, it's a do-over. >> This is where you're talking about computers shifting to the data center and then the cloud, I think last year, or I think this year, Gardner said 100 billion will be spent on security. I cannot believe anybody who is involved in that 100 billion dollar expenditure is happy. In fact, we have something interesting. Security expenditure has risen consistently over the past five or six years. And cyber attacks have also risen consistently. That's not the kind of correlation you want. >> Yeah, they'll buy anything that moves basically. They're desperate-- >> That's correct. >> So it seems like they're like drunken sailors. Just like, "Give me something." They're like thirsty for solution so they're groping for something. >> Yeah, what we're seeing is a couple of things. One is the attackers have gotten much more sophisticated and they basically can by-pass all of the existing security appliances. So what we need is a new approach or a new security stack that really fits both the architectural environment of American companies where they use clouds and data centers, and they have employees and contractors, but also cyber attacks which have gotten much more sophisticated. And the classic cyber attack used to be connecting to the server remotely or stealing a password. We still have the classics but we have some new ones where we have malware that can actually go from the users device to inside the network. And you find that existing security products just don't work well in this environment. And so it'-- >> So what is in the do-over ideas. Obviously malware, we see it. Ransomware is super hot, the HBO example recently. They didn't give in, who knows what they actually did. They weren't public about it but I'm sure they did maybe give a little bit in. But these are organized businesses. >> Yeah. >> Right? They're targeting... The Sony hack's well documented, but again, businesses have not always funded this. And then you got the move to the clouds. Couple dynamics. Cloud computing. Amazon's done extremely well, they're leading now getting a lot more in the enterprise. They won the CIA deal a few years ago over IBM. >> Yeah. >> And you've seen a lot, GovCloud rockin' and rollin'. And then you got the on-premise data center challenges. So that's the situation of the customer. But then now you have potentially an understaffed security force. >> Well, actually it's so. I think let's start with that point in terms of our theme of a do-over. Talk about that first-- >> Yeah, all right. >> Then let's talk the techno part. I think one do-over that America needs is security has to move out of the IT department and become a standalone department reporting ideally to the executive staff, if not being on it. I think one of the unfortunate things is because security is a cost center within IT it competes with other IT expenditures such as new applications which are revenue generating. It's very hard to be a cost center asking for money when there's a guy sitting next to you who's doing something to make money. But unfortunately, unless security is properly funded and staffed, it never happens. And this unfortunately is a chronic issue through all U.S. companies. One of the things we've seen that has worked, for example, in the financial world, is most financial institution, probably all, now security is a pure organization to IT and that helps a lot. This is actually not a new idea. This was something the intelligence community probably started 15 years ago. >> And the cost structure-- >> Yeah. >> Is just a cost structure. >> Reduce the cost as-- >> Yeah. >> As the optimization behavior. What you're saying is just like Apple cases are tied to top line revenue, which gives them power-- >> Yeah. >> And mojo, you got-- >> Security. >> You got to think of security as a money saving table stake. >> That's right. >> People are losing money. The costs are now becoming obvious. >> Yeah. >> And in some cases crippling. >> Yes, so I think people need to think of security as fundamental to the life of a company, number one. I think the other thing that needs to happen from a security perspective, now that we've broken off this entity, is that security needs to become threat based or risk based. Too much of a merit security in the United States is based on compliance models. Unfortunately cyber attackers do not follow that model when they want to attack us. They basically work outside the model and come up with creative ways to get inside-- >> Yeah. >> Of organization. >> And basically blindside-- >> That's right. >> bleeding the companies. >> Yeah, so I can't tell you how many meetings, probably all, where I meet the security team and they're totally busy just going through this list of 20 or 50 things they're supposed to do. So when you talk about attack vectors, they say, "You know that's really great and I know "it's important but we can't "get to it." So this is another important shift organizationally. First break it out, second get focus on something that's important. >> Yeah. >> Once we have that, we get to the next part, which is technologies, and right now what happens is people buy a security point product for different networks; one for data center, one for cloud, and this doesn't work. So I think we have to move to security solutions that can work across hybrid environments and can also work across different roles. I think that is kind of critical. Unless we get that in technically, I-- (laughs) >> Yeah, and this is the dynamic with cloud and the data center. I want to bring this up. I had a multiple chance to sit down with Andy Jassy who's the CEO of Amazon Web services. Fantastic executive, built a great business there. What's on his mind and what's been important for him for many years has been security. And Amazon has done an amazing job with security. But that's in the cloud. Now, Andy Jassy and Amazon thinks everyone should be in the public cloud. >> Yeah. >> Now they have a deal with VMware but they're just powering VMware's OnPrem in their cloud. It's not really their... VMware issue, but Amazon's world is everything's in the public cloud. But they've done really, really good on security. But yet most of the buyers would say, "Hey, the cloud "is unsecure, I can't trust it." So you have the dynamic between the data center on premise resource. So people kind of default to the behavior of I'm leaving everything on premise or I'm only putting a little on the cloud, a little bit of work loads here, a little bit in the Microsoft. Google's got some, I'll keep the tires on Google. But they're never really leaving the home base of the data center. >> Yeah. >> But yet some are arguing, and Dave Vellante my co-host on theCUBE talks about this all the time, there's actually more scale in the cloud, more data sharing going on in the cloud-- >> Yeah. >> And that the cloud actually has got better security. >> Yeah. >> So how do you see that resolving because this is a key architectural opportunity and challenge for enterprises. >> So I actually, I think there's an optimal model which is if you think about what the data center gives you, it gives you a lot of visibility and physical control, as in with your hands. The problem is when you put everything in the data center you don't have enough people to manage it all properly. The cloud on other hand gives you a lot of skill but you can't actually touch the cloud. So the optimal mix is, imagine your encryption and access control solutions live in your data center but what they control access to is to cloud resources. So you can actually... If you just open your mind conceptually, as-- >> So instead of saying... It's like segmenting a network, you're segmenting capability. >> That's right. So now you don't need a gigantic data center because what's in your data center which can be a lot smaller now, are things like your identity based access management solution, you can keep your cryptographic elements, you can have your HSM, things that generate random numbers and certs there. But now this is, actually can be very tiny. It could just be a rack of year. >> Yeah. >> But through that rack of year, you can have very fine control of people accessing cloud resources. And I think this idea of building, it's not so much a hybrid network, but it's a notion that a small physically locked down asset can control a lot of virtual assets is gaining a mind share in the banking world. In fact, just this summer there was a bank that implemented such an architecture where the control elements for the cloud when their FFIAC data center and it include... It basically managed access to Amazon DPCs and it worked well. >> So interlocking is a strategy, I can see that playing-- >> Yeah. >> And by the way I can see that playing very well. So I got to ask the next question which kind of comes to mind as, that sounds great-- >> Yeah. >> On paper, or actually in certain situations, it might be perfect. But what about the geopolitical landscape because Amazon has people that develop on the cloud that aren't U.S. citizens. >> Yeah. >> So the government might say, "Wait a minute. "You got to only employ Americans." So they got to carve out and do some whatever weird things with the numbers to get the certification. But they need data centers in Germany because the German government wants certain things. So you have geopolitical issues now on the companies. How does that affect security because now a cloud like Amazon or a multi-national company has two things going on. I had multiple offices and I've been operating in multiple geopolitical landscapes with these regional centers, the regional cloud, or on Amazon they're called regions. >> Yeah. >> Or zones. >> So actually Amazon actually has done a great job. They basically have their global market but they also have data centers now which are only open to U.S. persons and U.S. companies like GovCloud as well as the support C2S which is the intelligence community's black cloud, which is basically off net. So I think now-- >> So they're doing a good job, you think? >> Yeah, they're doing a good job. But the key thing is how you use that resource is really still up to the enterprise. And that's where enterprises have to get good at creating the architecture and policies to be able to harness Amazon's kind of compute capacity. Amazon can, it's kind of the foundation but you really have to finish off the solution. And the other thing, going back full circle to your first question, unless the security team has the freedom and the mandate to do that, they'll actually never get there. >> So it's staffing and architecture-- >> That's right. >> Well they're both architectural. It's just one's organizational architecture and funding and one is more of a hard core virtual and physical touching and understanding. >> Yeah, and you know what I'd put in the middle? I'd say know your risks and then develop counter measures to them. Because if you go to that security team and you say you have to build a counter measure for every attack, that's not going to work either. A company has to be realistic is what is really important (laughs) and maybe it's the data of our customers. (laughs) >> So the answer to the first question then, obviously is yes. >> Yeah. >> A security do-over is needed but there's no silver bullet. You can't buy an application. It's an architectural framework, wholistically. >> That's right. >> That everyone has to do. Okay, cool. So the question I have on the Amazon, I want to get your thoughts on this because the debate we have all the time on theCUBE is, and certainly Amazon has competitors that say, "Oh, Amazon's really not winning in the enterprise." They got thousands of enterprise customers. They are winning in the enterprise so Oracle's catching up, barely in fourth place, but trying to get there. And they're actually making that transformation, looking pretty good, we'll have more analysis on that Oracle open role. But Amazon has won great GovCloud deals. >> Yes. >> So they've kind of convinced the government that they could do it. >> Yeah. >> To me that's... My argument is if the government's winning with Amazon, it should be a no brainer (laughs) for the enterprises. So this comes back down to the number one question that's been, quote, holding back cloud growth. Whoa, security, I don't want to put it in the cloud. How real is that objection now? 'Cause knee jerk reaction is, "You know what, "I got it OnPrem, I don't trust the cloud." But it seems like the cloud is getting more trust. What's your thoughts on that on changing? >> Yeah, actually, so one of the things is even though we use the word cloud kind of generically or Amazon generically, Amazon has evolved a lot in the last three to four years that I've been working on it. The number of embedded tools on Amazon is vast now. If we were having this conversation two years ago the notion that granular encryption modules would be there in Amazon as a part of an offering, it would've been science fiction. Or the fact that-- >> More than S3 and EC2. What else could they do? (laughs) >> That's right, or they have things like virtual HSM, they have embedded identity access control tools all there. So I think, first of all, all of the building blocks that you would want are there. Now unfortunately there's no short cuts. Amazon's not going to do the work for you. You still need a staff that knows how to use digital certificates. You still need your own identity based access control system to manage access of your employees and contractors and people in India to these assets in the cloud. But having said that, we now actually have a model that is much cheaper than the classic data center model that's basically usable. >> I'm smirking because some people think I'm an Amazon Web services fan boy but besides the fact that I love the company, they've done well and there's so many new services. >> Yeah. >> And they've literally been skating rings around the competition. >> Yeah. >> If you look at the complexity that they've been dealing with and the innovation, so I'll put that out there, a little bit biased because I think they're doing a great job, but now the game starts to shift. As Amazon continues to add more services welcome to the big leagues called the enterprise in government which they're doing some business in now. So the question is, besides Amazon, there's other guys. >> Yeah. >> Verizon, the Telco's have been really trying to figure out what to do with over the top for years. (laughs) Now they're also powering a lot of multi-tenant workloads as well including their own stuff. >> Yeah. >> So Telco and service providers out there, what are they doing because they're still critical infrastructure around the world? >> So actually I think if we just use Amazon as a reference point or example, Amazon initially didn't worry about security but then over the last few years, worked hard to integrate security into their offering. We're now in the early stages of seeing that from, for example carriers like Verizon, where in the past Verizon was saying first secure yourself then in the last two years, Verizon said, "Okay, here's "some products and services you can buy." Now where we're heading is what they're trying to make the network inherently secure. A lot of the basic components like device matching to identity matching, basically-- >> Yeah. >> Making that a part of the underlying fabric. So I think the good news is as-- >> So they're making advances there? >> Yeah. >> Well they have networks. >> Yeah. >> They know networking. >> Yeah, so the good news is as bleak as this all seems, we are making significant progress as an industry and as a country. Having said that, my only warning is you still need an executive team, a security team that knows how to leverage all of these components and pull them together. And that goes back to having a risk based approach and protecting the most important things. And I think if you can do that, I think the tool set that's come out now is actually pretty sophisticated. >> So final question, I want to get your thoughts and we can end this segment and then we'll talk a little bit about Vidder and your company. But I asked Pat Gelsinger, CEO of VMware, at VMworld just recently about the security do-over. Because Dave Vellante asked him years ago. >> Yeah. >> He said, "Absolutely, there's going to be a do-over!" So Pat Gelsinger is right again. The guy's like Nostradamus when it comes to tech trends. He's a wave guy from Intel so he gets the waves. But I asked him about that question again this year and I'll send the clip on Twitter. I'll put it out on Twitter, I'll make a link to it. He said that 5G is going to be the big kahuna of the next 30 years and he thinks that 5G starts to get out it's going to deliver 10 X number of antennas, 100 extra bandwidth, new spectrum allocations, 100 X new devices, that are all going to be connected as well. As you mentioned we're a connected world. This brings up the edge of the network he says, "Next five years is going to... "Next 30 years is going to be a massive build out." >> Yeah. >> So okay, 5G is coming. Industrial IOT, IOT, the Internet of Things is happening. How is this going to change the security game? Because now you have networking and you see VMware doing NSX and Cisco's been trying to get to the enterprise figuring out the virtualization on a network level. Everything comes back down to the network. Is that where the action is because it seems to me that the network guys have to figure this out and that seems to be the point of reference of the terms of opportunity or is it a challenge or is it moving up the stack? How does all the networking changes happen? >> So for IOT we really need two things to happen. I think one is we actually don't have a security standard for IOT devices and specifically the issue is malware. IOT devices and their software is made worldwide. And I think one of the biggest policy weaknesses we have right now is there's no minimum standard. This needs to be solved otherwise we're in a lot problem. But in parallel to that, there is a lot of technical development. One of the things that's happening in the networking world is for the past 20 years we were driven by what's called a network VPN, or layer three VPN, it's your classic VPN that connects a device to a server. The problem with that is if you have malware on the device it gets through. So there's this new kind of VPN which is an application VPN, or we call it a layer four, which is basically a softer process in the device to a softer process in a server. So that's kind of the new model which is-- >> So make the network as dumb as possible and go up the stack and attack it? >> Yeah, well not so much-- >> Well I'm over simplifying-- >> Or reaction-- >> The network guys are going to roll in the-- >> I was going to use a different term. I was going to say make the-- >> The dumb pipes. >> Make the network application aware so that it only lets applications get through not any kind of connection. So I think that is something happening. >> Well the networks have to be smarter. >> Yeah, so-- >> That enable the smartness. >> So smarter networks are happening and it's an area that I work in, it's very excited. >> I don't mean to offend you by saying dumb network. >> Yeah, but the application... To be clear though, that's just one piece of the puzzle. The other piece of the puzzle, which unfortunately is a little bit lacking, is there's no standards for IOT software today. >> Yeah. >> And unless we have concepts like secure boot that is the software can't be tampered with, I think unfortunately there's a bit of risk. But I'm hopeful-- >> And then IOT, for the folks watching that might not be in the inside baseball know it's a surface area problem. There's more points of attack-- >> Yeah. >> Vectored. So we're talking about the compliance thing. >> Not only are there more attacks, by and large IOT devices are made outside the United States. Physically they're made in China and a lot of the software comes from India and there's nothing wrong with that but the global supply chain provides plenty of opportunities for cyber attackers to inject in their code. >> Yeah. >> And this is something we need to watch very carefully and then like I said-- >> So this is actually one of those weird derivative results of outsourcing. >> Yeah. >> That American companies have realized that it's a problem. >> Yeah. So it's-- >> Is that right? >> Yeah so it's something we need to watch carefully. >> Okay, thanks for coming on theCUBE. >> Thank you. >> We really appreciate you sharing your perspectives. Tell me what Vidder, your president and CTO, you guys are in the security business, obviously you're an expert. With great call we'll have you back on multiple times. We'd love to get your commentary as we follow all the security trends. We have a Cyber Connect Conference with Centrify-- >> Yeah. >> Coming up in New York. We're covering GovCloud, AWS, and all the other players out there. What's Vidder doing? What's the company do for products? How do you guys sell, who's your customers, and what are the cool things you're doing? >> We've developed a access control solution based on a new standard called Software Defined Perimeter. And there's two things that are unique about it. First with a name like, technology is like Software Defined Perimeter, we work in the cloud in the data center but more importantly we're able to stop existing attacks and emerging attacks. So things like password theft, credential theft, or server exploitation, we stop because we don't allow connections from unknown devices or people. But the other thing is say you're known and you connect to a server, we basically look inside your laptop and only allow the authorized process to connect to the server. So if there's malware on the device it can actually make it through. >> So it's just on the malware? >> That's right. >> If you want to sneak through-- >> That's right. >> You're going to shut that down. >> We can't stop the malware from getting on the device but we can make sure it doesn't get to the other side. >> So it doesn't cross-pollinate. >> Yeah, yeah. >> It doesn't go viral. >> That's right. So a lot of the stuff we do is very important. We work with a range of big-- >> You have government, obviously, contracts. >> Yeah, we-- >> I'm sure you have, that you can't talk about, but you do, right? >> We do a little bit of work with the government and we're just working with Verizon which is public, where they wish to create services where malware actually can't go through the connections. So we're doing exciting stuff and we're-- >> Enterprise customers at all? >> Yeah, yeah. We have banks-- >> People who are on high alert. >> That's right, yeah. >> You guys are the tier one. >> That's right. >> Where if the houses are burning down-- >> Yeah. >> You're there. >> So we do banks and we just started doing work at a hospital where, again, it's HIPAA compliant and they need to make sure that data doesn't leave the hospital. So what's the number one thing that you guys have? Is Ransomware something that you solve? What areas do you guys... Being called in? What's the big fire bell, if you will, they ring the bell, when do you come in? What's the thing? Just in general or? >> Our number one reason for existing is stopping attacks on application servers or servers that hold data. That's kind of our focus so if you have data or an application that someone is after, we will make sure that nobody gets to that data. In fact we'll even make sure if there's a spy or insider attacker who comes into your organization they'll only be able to do what they're allowed to do and won't be able to do anything else. >> So on the Equifax news that was big, would you guys help there if they were a customer or is that just a different thing? >> No, we could've helped because one of the things that happened is they used a server exploit to basically propagate through their data center. So we probably wouldn't have done much on the initial exploit but we would've kept it from going deeper into the system. >> And they hid for four months and they were poking around so you would've detected them as well. >> Yeah, we certainly would've stopped all the poking around because we basically... You can think of us as identity based access control mechanism so based on your identity you can only do very specific things. And in their case, they had the identity of the user. We wouldn't have let them do anything except maybe just go to one website. >> Yeah, you would shut them down manually. >> That's right. >> They should've been doing business with Vidder. Junaid thank you for coming on theCUBE here for the CUBEConversation. In Palo Alto, California I'm John Furrier with the CUBEConversation. Thanks for watching. (the Cube jingle)

(gentle music) >> Hello everyone. Welcome to this CUBE conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got a great remote guest coming in. Joseph Nelson, co-founder and CEO of RoboFlow hot startup in AI, computer vision. Really interesting topic in this wave of AI next gen hitting. Joseph, thanks for coming on this CUBE conversation. >> Thanks for having me. >> Yeah, I love the startup tsunami that's happening here in this wave. RoboFlow, you're in the middle of it. Exciting opportunities, you guys are in the cutting edge. I think computer vision's been talked about more as just as much as the large language models and these foundational models are merging. You're in the middle of it. What's it like right now as a startup and growing in this new wave hitting? >> It's kind of funny, it's, you know, I kind of describe it like sometimes you're in a garden of gnomes. It's like we feel like we've got this giant headstart with hundreds of thousands of people building with computer vision, training their own models, but that's a fraction of what it's going to be in six months, 12 months, 24 months. So, as you described it, a wave is a good way to think about it. And the wave is still building before it gets to its full size. So it's a ton of fun. >> Yeah, I think it's one of the most exciting areas in computer science. I wish I was in my twenties again, because I would be all over this. It's the intersection, there's so many disciplines, right? It's not just tech computer science, it's computer science, it's systems, it's software, it's data. There's so much aperture of things going on around your world. So, I mean, you got to be batting all the students away kind of trying to get hired in there, probably. I can only imagine you're hiring regiment. I'll ask that later, but first talk about what the company is that you're doing. How it's positioned, what's the market you're going after, and what's the origination story? How did you guys get here? How did you just say, hey, want to do this? What was the origination story? What do you do and how did you start the company? >> Yeah, yeah. I'll give you the what we do today and then I'll shift into the origin. RoboFlow builds tools for making the world programmable. Like anything that you see should be read write access if you think about it with a programmer's mind or legible. And computer vision is a technology that enables software to be added to these real world objects that we see. And so any sort of interface, any sort of object, any sort of scene, we can interact with it, we can make it more efficient, we can make it more entertaining by adding the ability for the tools that we use and the software that we write to understand those objects. And at RoboFlow, we've empowered a little over a hundred thousand developers, including those in half the Fortune 100 so far in that mission. Whether that's Walmart understanding the retail in their stores, Cardinal Health understanding the ways that they're helping their patients, or even electric vehicle manufacturers ensuring that they're making the right stuff at the right time. As you mentioned, it's early. Like I think maybe computer vision has touched one, maybe 2% of the whole economy and it'll be like everything in a very short period of time. And so we're focused on enabling that transformation. I think it's it, as far as I think about it, I've been fortunate to start companies before, start, sell these sorts of things. This is the last company I ever wanted to start and I think it will be, should we do it right, the world's largest in riding the wave of bringing together the disparate pieces of that technology. >> What was the motivating point of the formation? Was it, you know, you guys were hanging around? Was there some catalyst? What was the moment where it all kind of came together for you? >> You know what's funny is my co-founder, Brad and I, we were making computer vision apps for making board games more fun to play. So in 2017, Apple released AR kit, augmented reality kit for building augmented reality applications. And Brad and I are both sort of like hacker persona types. We feel like we don't really understand the technology until we build something with it and so we decided that we should make an app that if you point your phone at a Sudoku puzzle, it understands the state of the board and then it kind of magically fills in that experience with all the digits in real time, which totally ruins the game of Sudoku to be clear. But it also just creates this like aha moment of like, oh wow, like the ability for our pocket devices to understand and see the world as good or better than we can is possible. And so, you know, we actually did that as I mentioned in 2017, and the app went viral. It was, you know, top of some subreddits, top of Injure, Reddit, the hacker community as well as Product Hunt really liked it. So it actually won Product Hunt AR app of the year, which was the same year that the Tesla model three won the product of the year. So we joked that we share an award with Elon our shared (indistinct) But frankly, so that was 2017. RoboFlow wasn't incorporated as a business until 2019. And so, you know, when we made Magic Sudoku, I was running a different company at the time, Brad was running a different company at the time, and we kind of just put it out there and were excited by how many people liked it. And we assumed that other curious developers would see this inevitable future of, oh wow, you know. This is much more than just a pedestrian point your phone at a board game. This is everything can be seen and understood and rewritten in a different way. Things like, you know, maybe your fridge. Knowing what ingredients you have and suggesting recipes or auto ordering for you, or we were talking about some retail use cases of automated checkout. Like anything can be seen and observed and we presume that that would kick off a Cambrian explosion of applications. It didn't. So you fast forward to 2019, we said, well we might as well be the guys to start to tackle this sort of problem. And because of our success with board games before, we returned to making more board game solving applications. So we made one that solves Boggle, you know, the four by four word game, we made one that solves chess, you point your phone at a chess board and it understands the state of the board and then can make move recommendations. And each additional board game that we added, we realized that the tooling was really immature. The process of collecting images, knowing which images are actually going to be useful for improving model performance, training those models, deploying those models. And if we really wanted to make the world programmable, developers waiting for us to make an app for their thing of interest is a lot less efficient, less impactful than taking our tool chain and releasing that externally. And so, that's what RoboFlow became. RoboFlow became the internal tools that we used to make these game changing applications readily available. And as you know, when you give developers new tools, they create new billion dollar industries, let alone all sorts of fun hobbyist projects along the way. >> I love that story. Curious, inventive, little radical. Let's break the rules, see how we can push the envelope on the board games. That's how companies get started. It's a great story. I got to ask you, okay, what happens next? Now, okay, you realize this new tooling, but this is like how companies get built. Like they solve their own problem that they had 'cause they realized there's one, but then there has to be a market for it. So you actually guys knew that this was coming around the corner. So okay, you got your hacker mentality, you did that thing, you got the award and now you're like, okay, wow. Were you guys conscious of the wave coming? Was it one of those things where you said, look, if we do this, we solve our own problem, this will be big for everybody. Did you have that moment? Was that in 2019 or was that more of like, it kind of was obvious to you guys? >> Absolutely. I mean Brad puts this pretty effectively where he describes how we lived through the initial internet revolution, but we were kind of too young to really recognize and comprehend what was happening at the time. And then mobile happened and we were working on different companies that were not in the mobile space. And computer vision feels like the wave that we've caught. Like, this is a technology and capability that rewrites how we interact with the world, how everyone will interact with the world. And so we feel we've been kind of lucky this time, right place, right time of every enterprise will have the ability to improve their operations with computer vision. And so we've been very cognizant of the fact that computer vision is one of those groundbreaking technologies that every company will have as a part of their products and services and offerings, and we can provide the tooling to accelerate that future. >> Yeah, and the developer angle, by the way, I love that because I think, you know, as we've been saying in theCUBE all the time, developer's the new defacto standard bodies because what they adopt is pure, you know, meritocracy. And they pick the best. If it's sell service and it's good and it's got open source community around it, its all in. And they'll vote. They'll vote with their code and that is clear. Now I got to ask you, as you look at the market, we were just having this conversation on theCUBE in Barcelona at recent Mobile World Congress, now called MWC, around 5G versus wifi. And the debate was specifically computer vision, like facial recognition. We were talking about how the Cleveland Browns were using facial recognition for people coming into the stadium they were using it for ships in international ports. So the question was 5G versus wifi. My question is what infrastructure or what are the areas that need to be in place to make computer vision work? If you have developers building apps, apps got to run on stuff. So how do you sort that out in your mind? What's your reaction to that? >> A lot of the times when we see applications that need to run in real time and on video, they'll actually run at the edge without internet. And so a lot of our users will actually take their models and run it in a fully offline environment. Now to act on that information, you'll often need to have internet signal at some point 'cause you'll need to know how many people were in the stadium or what shipping crates are in my port at this point in time. You'll need to relay that information somewhere else, which will require connectivity. But actually using the model and creating the insights at the edge does not require internet. I mean we have users that deploy models on underwater submarines just as much as in outer space actually. And those are not very friendly environments to internet, let alone 5g. And so what you do is you use an edge device, like an Nvidia Jetson is common, mobile devices are common. Intel has some strong edge devices, the Movidius family of chips for example. And you use that compute that runs completely offline in real time to process those signals. Now again, what you do with those signals may require connectivity and that becomes a question of the problem you're solving of how soon you need to relay that information to another place. >> So, that's an architectural issue on the infrastructure. If you're a tactical edge war fighter for instance, you might want to have highly available and maybe high availability. I mean, these are words that mean something. You got storage, but it's not at the edge in real time. But you can trickle it back and pull it down. That's management. So that's more of a business by business decision or environment, right? >> That's right, that's right. Yeah. So I mean we can talk through some specifics. So for example, the RoboFlow actually powers the broadcaster that does the tennis ball tracking at Wimbledon. That runs completely at the edge in real time in, you know, technically to track the tennis ball and point the camera, you actually don't need internet. Now they do have internet of course to do the broadcasting and relay the signal and feeds and these sorts of things. And so that's a case where you have both edge deployment of running the model and high availability act on that model. We have other instances where customers will run their models on drones and the drone will go and do a flight and it'll say, you know, this many residential homes are in this given area, or this many cargo containers are in this given shipping yard. Or maybe we saw these environmental considerations of soil erosion along this riverbank. The model in that case can run on the drone during flight without internet, but then you only need internet once the drone lands and you're going to act on that information because for example, if you're doing like a study of soil erosion, you don't need to be real time. You just need to be able to process and make use of that information once the drone finishes its flight. >> Well I can imagine a zillion use cases. I heard of a use case interview at a company that does computer vision to help people see if anyone's jumping the fence on their company. Like, they know what a body looks like climbing a fence and they can spot it. Pretty easy use case compared to probably some of the other things, but this is the horizontal use cases, its so many use cases. So how do you guys talk to the marketplace when you say, hey, we have generative AI for commuter vision. You might know language models that's completely different animal because vision's like the world, right? So you got a lot more to do. What's the difference? How do you explain that to customers? What can I build and what's their reaction? >> Because we're such a developer centric company, developers are usually creative and show you the ways that they want to take advantage of new technologies. I mean, we've had people use things for identifying conveyor belt debris, doing gas leak detection, measuring the size of fish, airplane maintenance. We even had someone that like a hobby use case where they did like a specific sushi identifier. I dunno if you know this, but there's a specific type of whitefish that if you grew up in the western hemisphere and you eat it in the eastern hemisphere, you get very sick. And so there was someone that made an app that tells you if you happen to have that fish in the sushi that you're eating. But security camera analysis, transportation flows, plant disease detection, really, you know, smarter cities. We have people that are doing curb management identifying, and a lot of these use cases, the fantastic thing about building tools for developers is they're a creative bunch and they have these ideas that if you and I sat down for 15 minutes and said, let's guess every way computer vision can be used, we would need weeks to list all the example use cases. >> We'd miss everything. >> And we'd miss. And so having the community show us the ways that they're using computer vision is impactful. Now that said, there are of course commercial industries that have discovered the value and been able to be out of the gate. And that's where we have the Fortune 100 customers, like we do. Like the retail customers in the Walmart sector, healthcare providers like Medtronic, or vehicle manufacturers like Rivian who all have very difficult either supply chain, quality assurance, in stock, out of stock, anti-theft protection considerations that require successfully making sense of the real world. >> Let me ask you a question. This is maybe a little bit in the weeds, but it's more developer focused. What are some of the developer profiles that you're seeing right now in terms of low-hanging fruit applications? And can you talk about the academic impact? Because I imagine if I was in school right now, I'd be all over it. Are you seeing Master's thesis' being worked on with some of your stuff? Is the uptake in both areas of younger pre-graduates? And then inside the workforce, What are some of the devs like? Can you share just either what their makeup is, what they work on, give a little insight into the devs you're working with. >> Leading developers that want to be on state-of-the-art technology build with RoboFlow because they know they can use the best in class open source. They know that they can get the most out of their data. They know that they can deploy extremely quickly. That's true among students as you mentioned, just as much as as industries. So we welcome students and I mean, we have research grants that will regularly support for people to publish. I mean we actually have a channel inside our internal slack where every day, more student publications that cite building with RoboFlow pop up. And so, that helps inspire some of the use cases. Now what's interesting is that the use case is relatively, you know, useful or applicable for the business or the student. In other words, if a student does a thesis on how to do, we'll say like shingle damage detection from satellite imagery and they're just doing that as a master's thesis, in fact most insurance businesses would be interested in that sort of application. So, that's kind of how we see uptick and adoption both among researchers who want to be on the cutting edge and publish, both with RoboFlow and making use of open source tools in tandem with the tool that we provide, just as much as industry. And you know, I'm a big believer in the philosophy that kind of like what the hackers are doing nights and weekends, the Fortune 500 are doing in a pretty short order period of time and we're experiencing that transition. Computer vision used to be, you know, kind of like a PhD, multi-year investment endeavor. And now with some of the tooling that we're working on in open source technologies and the compute that's available, these science fiction ideas are possible in an afternoon. And so you have this idea of maybe doing asset management or the aerial observation of your shingles or things like this. You have a few hundred images and you can de-risk whether that's possible for your business today. So there's pretty broad-based adoption among both researchers that want to be on the state of the art, as much as companies that want to reduce the time to value. >> You know, Joseph, you guys and your partner have got a great front row seat, ground floor, presented creation wave here. I'm seeing a pattern emerging from all my conversations on theCUBE with founders that are successful, like yourselves, that there's two kind of real things going on. You got the enterprises grabbing the products and retrofitting into their legacy and rebuilding their business. And then you have startups coming out of the woodwork. Young, seeing greenfield or pick a specific niche or focus and making that the signature lever to move the market. >> That's right. >> So can you share your thoughts on the startup scene, other founders out there and talk about that? And then I have a couple questions for like the enterprises, the old school, the existing legacy. Little slower, but the startups are moving fast. What are some of the things you're seeing as startups are emerging in this field? >> I think you make a great point that independent of RoboFlow, very successful, especially developer focused businesses, kind of have three customer types. You have the startups and maybe like series A, series B startups that you're building a product as fast as you can to keep up with them, and they're really moving just as fast as as you are and pulling the product out at you for things that they need. The second segment that you have might be, call it SMB but not enterprise, who are able to purchase and aren't, you know, as fast of moving, but are stable and getting value and able to get to production. And then the third type is enterprise, and that's where you have typically larger contract value sizes, slower moving in terms of adoption and feedback for your product. And I think what you see is that successful companies balance having those three customer personas because you have the small startups, small fast moving upstarts that are discerning buyers who know the market and elect to build on tooling that is best in class. And so you basically kind of pass the smell test of companies who are quite discerning in their purchases, plus are moving so quick they're pulling their product out of you. Concurrently, you have a product that's enterprise ready to service the scalability, availability, and trust of enterprise buyers. And that's ultimately where a lot of companies will see tremendous commercial success. I mean I remember seeing the Twilio IPO, Uber being like a full 20% of their revenue, right? And so there's this very common pattern where you have the ability to find some of those upstarts that you make bets on, like the next Ubers of the world, the smaller companies that continue to get developed with the product and then the enterprise whom allows you to really fund the commercial success of the business, and validate the size of the opportunity in market that's being creative. >> It's interesting, there's so many things happening there. It's like, in a way it's a new category, but it's not a new category. It becomes a new category because of the capabilities, right? So, it's really interesting, 'cause that's what you're talking about is a category, creating. >> I think developer tools. So people often talk about B to B and B to C businesses. I think developer tools are in some ways a third way. I mean ultimately they're B to B, you're selling to other businesses and that's where your revenue's coming from. However, you look kind of like a B to C company in the ways that you measure product adoption and kind of go to market. In other words, you know, we're often tracking the leading indicators of commercial success in the form of usage, adoption, retention. Really consumer app, traditionally based metrics of how to know you're building the right stuff, and that's what product led growth companies do. And then you ultimately have commercial traction in a B to B way. And I think that that actually kind of looks like a third thing, right? Like you can do these sort of funny zany marketing examples that you might see historically from consumer businesses, but yet you ultimately make your money from the enterprise who has these de-risked high value problems you can solve for them. And I selfishly think that that's the best of both worlds because I don't have to be like Evan Spiegel, guessing the next consumer trend or maybe creating the next consumer trend and catching lightning in a bottle over and over again on the consumer side. But I still get to have fun in our marketing and make sort of fun, like we're launching the world's largest game of rock paper scissors being played with computer vision, right? Like that's sort of like a fun thing you can do, but then you can concurrently have the commercial validation and customers telling you the things that they need to be built for them next to solve commercial pain points for them. So I really do think that you're right by calling this a new category and it really is the best of both worlds. >> It's a great call out, it's a great call out. In fact, I always juggle with the VC. I'm like, it's so easy. Your job is so easy to pick the winners. What are you talking about its so easy? I go, just watch what the developers jump on. And it's not about who started, it could be someone in the dorm room to the boardroom person. You don't know because that B to C, the C, it's B to D you know? You know it's developer 'cause that's a human right? That's a consumer of the tool which influences the business that never was there before. So I think this direct business model evolution, whether it's media going direct or going direct to the developers rather than going to a gatekeeper, this is the reality. >> That's right. >> Well I got to ask you while we got some time left to describe, I want to get into this topic of multi-modality, okay? And can you describe what that means in computer vision? And what's the state of the growth of that portion of this piece? >> Multi modality refers to using multiple traditionally siloed problem types, meaning text, image, video, audio. So you could treat an audio problem as only processing audio signal. That is not multimodal, but you could use the audio signal at the same time as a video feed. Now you're talking about multi modality. In computer vision, multi modality is predominantly happening with images and text. And one of the biggest releases in this space is actually two years old now, was clip, contrastive language image pre-training, which took 400 million image text pairs and basically instead of previously when you do classification, you basically map every single image to a single class, right? Like here's a bunch of images of chairs, here's a bunch of images of dogs. What clip did is used, you can think about it like, the class for an image being the Instagram caption for the image. So it's not one single thing. And by training on understanding the corpora, you basically see which words, which concepts are associated with which pixels. And this opens up the aperture for the types of problems and generalizability of models. So what does this mean? This means that you can get to value more quickly from an existing trained model, or at least validate that what you want to tackle with a computer vision, you can get there more quickly. It also opens up the, I mean. Clip has been the bedrock of some of the generative image techniques that have come to bear, just as much as some of the LLMs. And increasingly we're going to see more and more of multi modality being a theme simply because at its core, you're including more context into what you're trying to understand about the world. I mean, in its most basic sense, you could ask yourself, if I have an image, can I know more about that image with just the pixels? Or if I have the image and the sound of when that image was captured or it had someone describe what they see in that image when the image was captured, which one's going to be able to get you more signal? And so multi modality helps expand the ability for us to understand signal processing. >> Awesome. And can you just real quick, define clip for the folks that don't know what that means? >> Yeah. Clip is a model architecture, it's an acronym for contrastive language image pre-training and like, you know, model architectures that have come before it captures the almost like, models are kind of like brands. So I guess it's a brand of a model where you've done these 400 million image text pairs to match up which visual concepts are associated with which text concepts. And there have been new releases of clip, just at bigger sizes of bigger encoding's, of longer strings of texture, or larger image windows. But it's been a really exciting advancement that OpenAI released in January, 2021. >> All right, well great stuff. We got a couple minutes left. Just I want to get into more of a company-specific question around culture. All startups have, you know, some sort of cultural vibe. You know, Intel has Moore's law doubles every whatever, six months. What's your culture like at RoboFlow? I mean, if you had to describe that culture, obviously love the hacking story, you and your partner with the games going number one on Product Hunt next to Elon and Tesla and then hey, we should start a company two years later. That's kind of like a curious, inventing, building, hard charging, but laid back. That's my take. How would you describe the culture? >> I think that you're right. The culture that we have is one of shipping, making things. So every week each team shares what they did for our customers on a weekly basis. And we have such a strong emphasis on being better week over week that those sorts of things compound. So one big emphasis in our culture is getting things done, shipping, doing things for our customers. The second is we're an incredibly transparent place to work. For example, how we think about giving decisions, where we're progressing against our goals, what problems are biggest and most important for the company is all open information for those that are inside the company to know and progress against. The third thing that I'd use to describe our culture is one that thrives with autonomy. So RoboFlow has a number of individuals who have founded companies before, some of which have sold their businesses for a hundred million plus upon exit. And the way that we've been able to attract talent like that is because the problems that we're tackling are so immense, yet individuals are able to charge at it with the way that they think is best. And this is what pairs well with transparency. If you have a strong sense of what the company's goals are, how we're progressing against it, and you have this ownership mentality of what can I do to change or drive progress against that given outcome, then you create a really healthy pairing of, okay cool, here's where the company's progressing. Here's where things are going really well, here's the places that we most need to improve and work on. And if you're inside that company as someone who has a preponderance to be a self-starter and even a history of building entire functions or companies yourself, then you're going to be a place where you can really thrive. You have the inputs of the things where we need to work on to progress the company's goals. And you have the background of someone that is just necessarily a fast moving and ambitious type of individual. So I think the best way to describe it is a transparent place with autonomy and an emphasis on getting things done. >> Getting shit done as they say. Getting stuff done. Great stuff. Hey, final question. Put a plug out there for the company. What are you going to hire? What's your pipeline look like for people? What jobs are open? I'm sure you got hiring all around. Give a quick plug for the company what you're looking for. >> I appreciate you asking. Basically you're either building the product or helping customers be successful with the product. So in the building product category, we have platform engineering roles, machine learning engineering roles, and we're solving some of the hardest and most impactful problems of bringing such a groundbreaking technology to the masses. And so it's a great place to be where you can kind of be your own user as an engineer. And then if you're enabling people to be successful with the products, I mean you're working in a place where there's already such a strong community around it and you can help shape, foster, cultivate, activate, and drive commercial success in that community. So those are roles that tend themselves to being those that build the product for developer advocacy, those that are account executives that are enabling our customers to realize commercial success, and even hybrid roles like we call it field engineering, where you are a technical resource to drive success within customer accounts. And so all this is listed on roboflow.com/careers. And one thing that I actually kind of want to mention John that's kind of novel about the thing that's working at RoboFlow. So there's been a lot of discussion around remote companies and there's been a lot of discussion around in-person companies and do you need to be in the office? And one thing that we've kind of recognized is you can actually chart a third way. You can create a third way which we call satellite, which basically means people can work from where they most like to work and there's clusters of people, regular onsite's. And at RoboFlow everyone gets, for example, $2,500 a year that they can use to spend on visiting coworkers. And so what's sort of organically happened is team numbers have started to pull together these resources and rent out like, lavish Airbnbs for like a week and then everyone kind of like descends in and works together for a week and makes and creates things. And we call this lighthouses because you know, a lighthouse kind of brings ships into harbor and we have an emphasis on shipping. >> Yeah, quality people that are creative and doers and builders. You give 'em some cash and let the self-governing begin, you know? And like, creativity goes through the roof. It's a great story. I think that sums up the culture right there, Joseph. Thanks for sharing that and thanks for this great conversation. I really appreciate it and it's very inspiring. Thanks for coming on. >> Yeah, thanks for having me, John. >> Joseph Nelson, co-founder and CEO of RoboFlow. Hot company, great culture in the right place in a hot area, computer vision. This is going to explode in value. The edge is exploding. More use cases, more development, and developers are driving the change. Check out RoboFlow. This is theCUBE. I'm John Furrier, your host. Thanks for watching. (gentle music)

(inspirational music) (music ends) >> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities, including data exploration business intelligence, natural language processing and machine learning directly within the fabric makes it faster and easier for organizations to gain new insights and power intelligence predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi, yeah, thank you so much for having me. And so for this demo, we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements, and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo, and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see, and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be, for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software, or adverse reaction warnings from a clinical risk grouping application, and so much more. So I'm really going to be simulating a patient logging in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here, and I'm going to be looking for information where the last name of this patient is Simmons, and their medical record number or their patient identifier in the system is 32345. And so as you can see, I have this single JSON payload that showed up here of, just, relevant clinical information for my patient whose last name is Simmons, all within a single response. So fantastic, right? Typically though, when we see responses that look like this there is an assumption that this service is interacting with a single backend system, and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture, we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here, we have our data fabric coordinator which is going to be in charge of this refinement and analysis, those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service, and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end, we do also support full life cycle API management within this platform. When you're dealing with APIs, I always like to make a little shout out on this, that you really want to make sure you have enough, like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what context. >> Can I just interrupt you for a second, Jess? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you could have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry, and API securities are like, really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So, there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So, the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product, and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So, that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of the security. >> And that's been designed in, it's not a bolt on as they like to say. >> Absolutely. >> Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly, each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like Fire. Interactions with a homegrown enterprise data warehouse for instance, may use SQL. For a cloud-based solutions managed by a vendor, they may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and applications. And I'm about to log out, so I'm going to (chuckles) keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources, and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is, it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST, or SOAP, or SQL, or FTP, regardless of that protocol, there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as in healthcare we have HL7, we have Fire, we have CCDs, across the industry, JSON is, you know, really hitting a market strong now, and XML payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel, I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example, communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR, I'm leveraging a standard healthcare messaging format known as Fire, which is a REST based protocol. And then when I'm working with my health record management system, I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly, and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN, and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out-of-the box or black box approach to be able to develop things that are specific to their data fabric, or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you not only get an opportunity to view how we're establishing these connections or how we're building out these processes, but you have the opportunity to inject your own kind of processing, your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out-of-the-box code that is provided in this data fabric platform from IRIS, combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out-of-the-box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. (laughs) >> It's a lot here. You know, actually- >> I can pause. >> If I could, if we just want to sort of play that back. So we went to the connect and the collect phase. >> Yes, we're going into refine. So it's a good place to stop. >> So before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know, the ability to bring in different dev tools. We heard about Fire, which of course big in healthcare. And that's the standard, and then SQL for traditional kind of structured data, and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely. And I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection, into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refinement. >> We're going into refinement. Exciting. (chuckles) So how do we actually do refinement? Where does refinement happen? And how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator, or stands for Smart Data Fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information, it's aggregating it, and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like. And as you can see, it follows a flow chart like structure. So there's a start, there is an end, and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce, or we make this data fabric a bit smarter, and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection, we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging, 'cause you need to be able to know, you know, if there was an issue, where did that issue happen in which connected process, and how did it affect the other processes that are related to it? In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric, to when data was sent back out from that smart data fabric. So I didn't record the time, but I bet if you recorded the time it was this time that we sent that request in and you can see my patient's name and their medical record number here, and you can see that that instigated four different calls to four different systems, and they're represented by these arrows going out. So we sent something to chart script, to our health record management system, to our clinical risk grouping application, into my EMR through their Fire server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems, and we bundle them together. And from my Fire lovers, here's our Fire bundle that we got back from our Fire server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping, or errors that were thrown, alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Sure, who did what when where, what did the machine do what went wrong, and where did that go wrong? Right at your fingertips. >> Right. And I'm a visual person so a bunch of log files to me is not the most helpful. While being able to see this happened at this time in this location, gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric, is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information it's transforming that data, in a format that your consumer's not going to understand. It's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? It all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well, we can keep going. I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this, but essentially if we go back to our coordinator here, we can see here's that original, that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here, which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric, but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at, and we're running it through a machine learning model that exists within the smart data fabric pipeline, and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world, is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL-like syntax to be able to construct and execute these predictions. So, it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge, right? Because it directly affects the cost for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment, or, you know, as an outpatient perhaps, to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day, which is what makes this so exciting. >> Awesome demo. >> Thank you! >> Jess, are you cool if people want to get in touch with you? Can they do that? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy, and we'd love to hear from you. I always love talking about this topic so we'd be happy to engage on that. >> Great stuff. Thank you Jessica, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment, we're going to dig into the use cases where data fabric is driving business value. Stay right there. (inspirational music) (music fades)

(intro music) >> Now, as we all know, businesses are awash with data, from financial services to healthcare to supply chain and logistics and more. Our activities, and increasingly, actions from machines are generating new and more useful information in much larger volumes than we've ever seen. Now, meanwhile, our data-hungry society's expectations for experiences are increasingly elevated. Everybody wants to leverage and monetize all this new data coming from smart devices and innumerable sources around the globe. All this data, it surrounds us, but more often than not, it lives in silos, which makes it very difficult to consume, share, and make valuable. These factors, combined with new types of data and analytics, make things even more complicated. Data from ERP systems to images, to data generated from deep learning and machine learning platforms, this is the reality that organizations are facing today. And as such, effectively leveraging all of this data has become an enormous challenge. So, today, we're going to be discussing these modern data challenges and the emergence of so-called "Smart Data Fabrics" as a key solution to said challenges. To do so, we're joined by thought leaders from InterSystems. This is a really creative technology provider that's attacking some of the most challenging data obstacles. InterSystems tells us that they're dedicated to helping customers address their critical scalability, interoperability, and speed-to-value challenges. And in this first segment, we welcome Scott Gnau, he's the global Head of Data Platforms at InterSystems, to discuss the context behind these issues and how smart data fabrics provide a solution. Scott, welcome. Good to see you again. >> Thanks a lot. It's good to be here. >> Yeah. So, look, you and I go back, you know, several years and, you know, you've worked in Tech, you've worked in Data Management your whole career. You've seen many data management solutions, you know, from the early days. And then we went through the hoop, the Hadoop era together and you've come across a number of customer challenges that sort of change along the way. And they've evolved. So, what are some of the most pressing issues that you see today when you're talking to customers and, you know, put on your technical hat if you want to. >> (chuckles) Well, Dave, I think you described it well. It's a perfect storm out there. You know, combined with there's just data everywhere and it's coming up on devices, it's coming from new different kinds of paradigms of processing and people are trying to capture and harness the value from this data. At the same time, you talked about silos and I've talked about data silos through my entire career. And I think, I think the interesting thing about it is for so many years we've talked about, "We've got to reduce the silos and we've got to integrate the data, we've got to consolidate the data." And that was a really good paradigm for a long time. But frankly, the perfect storm that you described? The sources are just too varied. The required agility for a business unit to operate and manage their customers is creating an enormous presser and I think ultimately, silos aren't going away. So, there's a realization that, "Okay, we're going to have these silos, we want to manage them, but how do we really take advantage of data that may live across different parts of our business and in different organizations?" And then of course, the expectation of the consumer is at an all-time high, right? They expect that we're going to treat them and understand their needs or they're going to find some other provider. So, you know, pulling all of this together really means that, you know, our customers and businesses around the world are struggling to keep up and it's forcing a real, a new paradigm shift in underlying data management, right? We started, you know, many, many years ago with data marts and then data warehouses and then we graduated to data lakes, where we expanded beyond just traditional transactional data into all kinds of different data. And at each step along the way, we help businesses to thrive and survive and compete and win. But with the perfect storm that you've described, I think those technologies are now just a piece of the puzzle that is really required for success. And this is really what's leading to data fabrics and data meshes in the industry. >> So what are data fabrics? What problems do they solve? How do they work? Can you just- >> Yeah. So the idea behind it is, and this is not to the exclusion of other technologies that I described in data warehouses and data lakes and so on, but data fabrics kind of take the best of those worlds but add in the notion of being able to do data connectivity with provenance as a way to integrate data versus data consolidation. And when you think about it, you know, data has gravity, right? It's expensive to move data. It's expensive in terms of human cost to do ETL processes where you don't have known provenance of data. So, being able to play data where it lies and connect the information from disparate systems to learn new things about your business is really the ultimate goal. You think about in the world today, we hear about issues with the supply chain and supply and logistics is a big issue, right? Why is that an issue? Because all of these companies are data-driven. They've got lots of access to data. They have formalized and automated their processes, they've installed software, and all of that software is in different systems within different companies. But being able to connect that information together, without changing the underlying system, is an important way to learn and optimize for supply and logistics, as an example. And that's a key use case for data fabrics. Being able to connect, have provenance, not interfere with the operational system, but glean additional knowledge by combining multiple different operational systems' data together. >> And to your point, data is by its very nature, you know, distributed around the globe, it's on different clouds, it's in different systems. You mentioned "data mesh" before. How do data fabrics relate to this concept of data mesh? Are they competing? Are they complimentary? >> Ultimately, we think that they're complimentary. And we actually like to talk about smart data fabrics as a way to kind of combine the best of the two worlds. >> What is that? >> The biggest thing really is there's a lot around data fabric architecture that talks about centralized processing. And in data meshes, it's more about distributed processing. Ultimately, we think a smart data fabric will support both and have them be interchangeable and be able to be used where it makes the most sense. There are some things where it makes sense to process, you know, for a local business unit, or even on a device for real-time kinds of implementations. There are some other areas where centralized processing of multiple different data sources make sense. And what we're saying is, "Your technology and the architecture that you define behind that technology should allow for both where they make the most sense." >> What's the bottom line business benefit of implementing a data fabric? What can I expect if I go that route? >> I think there are a couple of things, right? Certainly, being able to interact with customers in real time and being able to manage through changes in the marketplace is certainly a key concept. Time-to-value is another key concept. You know, if you think about the supply and logistics discussion that I had before, right? No company is going to rewrite their ERP operational system. It's how they manage and run their business. But being able to glean additional insights from that data combined with data from a partner combined with data from a customer or combined with algorithmic data that, you know, you may create some sort of forecast and that you want to fit into. And being able to combine that together without interfering with the operational process and get those answers quickly is an important thing. So, seeing through the silos and being able to do the connectivity, being able to have interoperability, and then, combining that with flexibility on the analytics and flexibility on the algorithms you might want to run against that data. Because in today's world, of course, you know, certainly there's the notion of predictive modeling and relational theory, but also now adding in machine learning, deep learning algorithms, and have all of those things kind of be interchangeable is another important concept behind data fabric. So you're not relegated to one type of processing. You're saying, "It's data and I have multiple different processing engines and I may want to interchange them over time." >> So, I know, well actually, you know, when you said "real time", I infer from that, I don't have a zillion copies of the data and it's not in a bunch of silos. Is that a correct premise? >> You try to minimize your copies of the data? >> Yeah. Okay. >> There's certainly, there's a nirvana that says, "There's only ever one copy of data." That's probably impossible. But you certainly don't want to be forced into making multiple copies of data to support different processing engines unnecessarily. >> And so, you've recently made some enhancements to the data fabric capability that takes it, you know, ostensibly to the next level. Is that the smart piece? Is that machine intelligence? Can you describe what's in there? >> Well, you know, ultimately, the business benefit is be able to have a single source of the truth for a company. And so, what we're doing is combining multiple technologies in a single set of software that makes that software agile and supportable and not fragile for deployment of applications. At its core, what we're saying is, you know, we want to be able to consume any kind of data and I think your data fabric architecture is predicated on the fact that you're going to have relational data, you're going to have document data, you may have key-value store data, you may have images, you may have other things, and you want to be able to not be limited by the kind of data that you want to process. And so that certainly is what we build into our product set. And then, you want to be able to have any kind of algorithm, where appropriate, run against that data without having to do a bunch of massive ETL processes or make another copy of the data and move it somewhere else. And so, to that end, we have, taking our award-winning engine, which, you know, provides, you know, traditional analytic capabilities and relational capabilities, we've now integrated machine learning. So, you basically can bring machine learning algorithms to the data without having to move data to the machine learning algorithm. What does that mean? Well, number one, your application developer doesn't have to think differently to take advantage of the new algorithm. So that's a really good thing. The other thing that happens is if you, you're playing that algorithm where the data actually exists from your operational system, that means the round trip from running the model to inferring some decision you want to make to actually implementing that decision can happen instantaneously, as opposed to, you know, other kinds of architectures, where you may want to make a copy of the data and move it somewhere else. That takes time, latency. Now the data gets stale, your model may not be as efficient because you're running against stale data. We've now taken all of that off the table by being able to pull that processing inside the data fabric, inside of the single source of truth. >> And you got to manage all that complexity. So you got one system, so that makes it, you know, cost-effective, and you're bringing modern tooling to the platform. Is that right? >> That's correct. >> How can people learn more and maybe continue the conversation with you if they have other questions? (both chuckle) >> Call or write. >> Yeah. >> Yeah, I mean, certainly, check out our website. We've got a lot of information about the different kinds of solutions, the different industries, the different technologies. Reach out: scottg@intersystems.com. >> Excellent. Thank you, Scott. Really appreciate it and great to see you again. >> Good to see you. >> All right, keep it right there. We have a demo coming up next. You want to see smart data fabrics in action? Stay tuned. (ambient music)

(upbeat music) >> Now, as we all know, businesses are awash with data, from financial services to healthcare to supply chain and logistics and more. Our activities, and increasingly, actions from machines are generating new and more useful information in much larger volumes than we've ever seen. Now, meanwhile, our data hungry society's expectations for experiences are increasingly elevated. Everybody wants to leverage and monetize all this new data coming from smart devices and innumerable sources around the globe. All this data, it surrounds us, but more often than not, it lives in silos, which makes it very difficult to consume, share, and make valuable. These factors combined with new types of data and analytics make things even more complicated. Data from ERP systems to images, to data generated from deep learning and machine learning platforms, this is the reality that organizations are facing today. And as such, effectively leveraging all of this data has become an enormous challenge. So today, we're going to be discussing these modern data challenges in the emergence of so-called smart data fabrics as a key solution to said challenges. To do so, we're joined by thought leaders from InterSystems. This is a really creative technology provider that's attacking some of the most challenging data obstacles. InterSystems tells us that they're dedicated to helping customers address their critical scalability, interoperability, and speed to value challenges. And in this first segment, we welcome Scott now. He's the global head of data platforms at InterSystems to discuss the context behind these issues and how smart data fabrics provide a solution. Scott, welcome, good to see you again. >> Thanks a lot. It's good to be here. >> Yeah, so look, you and I go back, you know, several years and you've worked in tech. You've worked in data management your whole career. You've seen many data management solutions, you know, from the early days. And then we went through the Hadoop era together. And you've come across a number of customer challenges that sort of changed along the way, and they've evolved. So what are some of the most pressing issues that you see today when you're talking to customers, and, you know, put on your technical hat if you want to? >> Well, Dave, I think you described it well. It's a perfect storm out there, you know, combined with, there's just data everywhere. And it's coming up on devices, it's coming from new different kinds of paradigms of processing and people are trying to capture and harness the value from this data. At the same time, you talked about silos, and I've talked about data silos through my entire career. And I think the interesting thing about it is for so many years we've talked about we've got to reduce the silos, and we've got to integrate the data, we've got to consolidate the data. And that was a really good paradigm for a long time. But frankly, the perfect storm that you described, the sources are just too varied. The required agility for a business unit to operate and manage their customers is creating an enormous pressure. And I think, ultimately, silos aren't going away. So there's a realization that, okay, we're going to have these silos, we want to manage them, but how do we really take advantage of data that may live across different parts of our business and in different organizations? And then, of course, the expectation of the consumer is at an all-time high, right? They expect that we're going to treat them and understand their needs, or they're going to find some other provider. So, you know, pulling all of this together really means that, you know, our customers and businesses around the world are struggling to keep up, and it's forcing a new paradigm shift in underlying data management, right? We started, you know, many, many years ago with data marts and then data warehouses, and then we graduated to data lakes where we expanded beyond just traditional transactional data into all kinds of different data. And at each step along the way, we help businesses to thrive and survive and compete and win. But with the perfect storm that you've described, I think those technologies are now just a piece of the puzzle that is really required for success. And this is really what's leading to data fabrics and data meshes in the industry. >> So what are data fabrics? What problems do they solve? How do they work? Can you just add- >> Yeah, so the idea behind it is, and this is not to the exclusion of other technologies that I described in data warehouses and data lakes and so on. But data fabrics kind of take the best of those worlds, but add in the notion of being able to do data connectivity with provenance as a way to integrate data versus data consolidation. And when you think about it, you know, data has gravity, right? It's expensive to move data. It's expensive in terms of human cost to do ETL processes where you don't have known provenance of data. So being able to play data where it lies and connect the information from disparate systems to learn new things about your business is really the ultimate goal. You think about in the world today, we hear about issues with the supply chain, and supply and logistics is a big issue, right? Why is that an issue? Because all of these companies are data driven. They've got lots of access to data. They have formalized and automated their processes. They've installed software. And all of that software is in different systems within different companies. But being able to connect that information together without changing the underlying system is an important way to learn and optimize for supply and logistics, as an example. And that's a key use case for data fabrics being able to connect, have provenance, not interfere with the operational system, but glean additional knowledge by combining multiple different operational systems' data together. >> And to your point, data is by its very nature, you're distributed around the globe, it's on different clouds, it's in different systems. You mentioned data mesh before. How do data fabrics relate to this concept of data mesh? Are they competing? Are they complimentary? >> Ultimately, we think that they're complimentary. And we actually like to talk about smart data fabrics as a way to kind of combine the best of the two worlds. >> What is that? I mean, the biggest thing really is there's a lot around data fabric architecture that talks about centralized processing. And in data meshes, it's more about distributed processing. Ultimately, we think a smart data fabric will support both and have them be interchangeable and be able to be used where it makes the most sense. There are some things where it makes sense to process, you know, for a local business unit, or even on a device for real time kinds of implementations. There are some other areas where centralized processing of multiple different data sources make sense. And what we're saying is your technology and the architecture that you define behind that technology should allow for both where they make the most sense. >> What's the bottom line business benefit of implementing a data fabric? What can I expect if I go that route? >> I think there are a couple of things, right? Certainly being able to interact with customers in real time and being able to manage through changes in the marketplace is certainly a key concept. Time to value is another key concept. You know, if you think about the supply and logistics discussion that I had before, right? No company is going to rewrite their ERP operational system. It's how they manage and run their business. But being able to glean additional insights from that data combined with data from a partner, combined with data from a customer, or combined with algorithmic data that, you know, you may create some sort of forecast and that you want to fit into. And being able to combine that together without interfering with the operational process and get those answers quickly is an important thing. So seeing through the silos and being able to do the connectivity being able to have interoperability, and then combining that with flexibility on the analytics and flexibility on the algorithms you might want to run against that data. Because in today's world, of course, certainly there's the notion of predictive modeling and relational theory, but also now adding in machine learning, deep learning algorithms, and have all of those things kind of be interchangeable is another important concept behind data fabrics. So you're not relegated to one type of processing. You're saying it's data, and I have multiple different processing engines and I may want to interchange them over time. >> So, I know, well actually, when you said real time, I infer from that I don't have a zillion copies of the data and it's not in a bunch of silos. Is that a correct premise? >> You try to minimize your copies of the data. There's a nirvana that says there's only ever one copy of data. That's probably impossible. But you certainly don't want to be forced into making multiple copies of data to support different processing engines unnecessarily. >> And so you've recently made some enhancements to the data fabric capability that takes it, you know, ostensibly to the next level. Is that the smart piece, is that machine intelligence? Can you describe what's in there? >> Well, you know, ultimately the business benefit is be able to have a single source of the truth for a company. And so what we're doing is combining multiple technologies in a single set of software that makes that software agile and supportable and not fragile for deployment of applications. At its core, what we're saying is, we want to be able to consume any kind of data, and I think your data fabric architecture is predicated on the fact that you're going to have relational data you're going to have document data, you may have key value store data, you may have images, you may have other things, and you want to be able to not be limited by the kind of data that you want to process. And so that certainly is what we build into our product set. And then you want to be able to have any kind of algorithm where appropriate run against that data without having to do a bunch of massive ETL processes or make another copy of the data and move it somewhere else. And so to that end, we have taken our award-winning engine, which, you know, provides traditional analytic capabilities and relational capabilities. We've now integrated machine learning. So you basically can bring machine learning algorithms to the data without having to move data to the machine learning algorithm. What does that mean? Well, number one, your application developer doesn't have to think differently to take advantage of the new algorithms. So that's a really good thing. The other thing that happens is if you're playing that algorithm where the data actually exists from your operational system, that means the roundtrip from running the model to inferring some decision you want to make to actually implementing that decision can happen instantaneously. As opposed to, you know, other kinds of architectures where you may want to make a copy of the data and move it somewhere else. That takes time, latency. Now the data gets stale. Your model may not be as efficient because you're running against stale data. We've now taken all of that off the table by being able to pull that processing inside the data fabric, inside of the single source of truth. >> And you got to manage all that complexity. So you got one system, so that makes it cost effective, and you're bringing modern tooling to the platform. Is that right? >> That's correct. How can people learn more and maybe continue the conversation with you if they have other questions? >> (Scott laughs) Call or write. Yeah, I mean, certainly check out our website. We've got a lot of information about the different kinds of solutions, the different industries, the different technologies. Reach out at scottg@intersystems.com. >> Excellent, thank you, Scott. Really appreciate it. And great to see you again. >> Good to see you. All right, keep it right there. We have a demo coming up next. If you want to see smart data fabrics in action, stay tuned. (upbeat music)

>> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities including data exploration, business intelligence natural language processing, and machine learning directly within the fabric, makes it faster and easier for organizations to gain new insights and power intelligence, predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi. Yeah, thank you so much for having me. And so for this demo we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software or adverse reaction warnings from a clinical risk grouping application and so much more. So I'm really going to be assimilating a patient logging on in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here and I'm going to be looking for information where the last name of this patient is Simmons and their medical record number their patient identifier in the system is 32345. And so as you can see I have this single JSON payload that showed up here of just relevant clinical information for my patient whose last name is Simmons all within a single response. So fantastic, right? Typically though when we see responses that look like this there is an assumption that this service is interacting with a single backend system and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here we have our data fabric coordinator which is going to be in charge of this refinement and analysis those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end we do also support full lifecycle API management within this platform. When you're dealing with APIs I always like to make a little shout out on this that you really want to make sure you have enough like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what contact. >> Can I just interrupt you for a second? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you can have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry and API securities are really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of security. >> And that's been designed in, >> Absolutely, yes. it's not a bolt-on as they like to say. Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like FIRE, interactions with a homegrown enterprise data warehouse for instance may use SQL for a cloud-based solutions managed by a vendor. They may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and and applications. And I'm about to log out so I'm going to keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST or SOAP or SQL or FTP regardless of that protocol there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as, in healthcare we have H7, we have FIRE we have CCDs across the industry. JSON is, you know, really hitting a market strong now and XML, payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR I'm leveraging a standard healthcare messaging format known as FIRE, which is a rest based protocol. And then when I'm working with my health record management system I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So let's, why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out of the box or black box approach to be able to develop things that are specific to their data fabric or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you cannot, you not only get an opportunity to view how we're establishing these connections or how we're building out these processes but you have the opportunity to inject your own kind of processing your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out of the box code that is provided in this data fabric platform from IRIS combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out of the box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. >> It's a lot here. You know, actually, if I could >> I can pause. >> If I just want to sort of play that back. So we went through the connect and the collect phase. >> And the collect, yes, we're going into refine. So it's a good place to stop. >> Yeah, so before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know the ability to bring in different dev tools. We heard about FIRE, which of course big in healthcare. >> Absolutely. >> And that's the standard and then SQL for traditional kind of structured data and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely, and I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refine. >> We're going into refinement, exciting. So how do we actually do refinement? Where does refinement happen and how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator or stands for smart data fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information it's aggregating it and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like and as you can see it follows a flow chart like structure. So there's a start, there is an end and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL Logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce or we make this data fabric a bit smarter and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging 'cause you need to be able to know, you know, if there was an issue, where did that issue happen, in which connected process and how did it affect the other processes that are related to it. In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric to when data was sent back out from that smart data fabric. So I didn't record the time but I bet if you recorded the time it was this time that we sent that request in. And you can see my patient's name and their medical record number here and you can see that that instigated four different calls to four different systems and they're represented by these arrows going out. So we sent something to chart script to our health record management system, to our clinical risk grouping application into my EMR through their FIRE server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems and we bundle them together. And for my FIRE lovers, here's our FIRE bundle that we got back from our FIRE server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping or errors that were thrown alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Etcher, who did what, when, where what did the machine do? What went wrong and where did that go wrong? >> Exactly. >> Right in your fingertips. >> Right, and I'm a visual person so a bunch of log files to me is not the most helpful. Well, being able to see this happened at this time in this location gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information, it's transforming that data, in a format that your consumer's not going to understand it's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? This all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well we can keep going. 'Cause right now, I mean we can, oh, we're at 18 minutes. God help us. You can cut some of this. (laughs) I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this but essentially if we go back to our coordinator here we can see here's that original that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at and we're running it through a machine learning model that exists within the smart data fabric pipeline and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL like syntax to be able to construct and execute these predictions. So it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge. >> Yes. >> Right, because it directly affects the cost of for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment or you know, as an outpatient perhaps to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely, absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day which is what makes this so exciting. >> Awesome demo. >> Thank you. >> Fantastic people, are you cool? If people want to get in touch with you? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy and we'd love to hear from you. I always love talking about this topic, so would be happy to engage on that. >> Great stuff, thank you Jess, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment we're going to dig into the use cases where data fabric is driving business value. Stay right there.