(soft instrumental music) >> Welcome back everyone, to theCUBE's coverage of AWS re:Invent 2021. I'm John Furrier, your host of theCUBE. We're here in person at a live physical event with real people. Of course, it's a hybrid event. Great stuff online. Check it out on the Amazon site, as well as theCUBE zone. We've got great guests, talking about the cloud vision for getting talent in to the marketplace, in being productive and for society Accenture always great content. Denise Reese, Managing Director of the South Market Unit Lead at Accenture, AABG, which stands for "Accenture Area Business Group" and Gina Gina Fratarcangeli who is also the managing director of Midwest sales leader. Ladies, thanks for coming, I appreciate you coming on and talking about the vision of talent. >> I guess >> Thanks for having us. >> Yes, absolutely. It's a pleasure to be here. >> So, Amazon's got this dangerous goal, to train 29 million people. Maureen Lonergan came on yesterday, who I've known for a long time, doing a great job. It's hard to get the talent in. First of all, it sounds harder than it really is, that's my opinion. You know, you get some training certifications and you're up and running. So, talent's a big thing. What do you guys do? Give us the overview. >> Sure. Well, we're having a lot of activity at Accenture trying to get talent in. Across the entire country we're spending a tremendous amount of effort to do that. A couple of critical things we're doing in the Midwest is bringing in and searching for different talent streams that we haven't typically done in the past. For instance, one thing that we're doing is, we set up an apprentice program where we're reaching out into the market to find diverse talent, who aren't coming through the critical normal college path and bringing folks in like that. And we've got 1200 people that we've brought in that way, just in the Midwest. Which has been a phenomenal new talent stream for us. And supporting our inclusion and diversity. One of the other exciting things is what we call "The Mom Project", where we're intentionally working with an organization called the Mom Project, to bring women back into the workplace who may have left while they were taking care of their families and helping them get certified in all the new cloud technology and getting back to work. >> I love how you guys are going after this whole places that not everyone's looking at, because what I love about Cloud is that, it's a level up kind of opportunity where you don't really have to have that pedigree, or that big-big school. Of course, I went to a different school. So, I have a little chip on my shoulder. I didn't go to MIT, wasn't North-east but still good school. But, I mean, you could really level up from anywhere. >> Gina: That's right. >> And the opportunities with Cloud are so great. This is like a huge thing. No I'm surprised no one knows about it. >> Absolutely. I would add to that. So, we've in the South, in Georgia in particular. We've just launched an initiative with the technical college system of Georgia and AWS. So, it's a public-private partnership, where we're actually helping to set the curriculum for those students that are going through programs, through the technical colleges. It's one of the largest parts of the university system of Georgia. And, we're actually helping to frame the curriculum. And, giving folks what they need, to your point. It is an opportunity to level up. It's a great way to get talent in non-traditional spaces. It helps us to achieve our inclusion and diversity roles or goals, rather. But, then it also allows us to really continue to fill that pipeline with folks that we may not have had access to otherwise. >> Is there a best practice that you see developing in the acquisition of talent? Or enticing people to come in? Because that's just economics you know, Maureen was telling me that it was this person she was unemployed, and she got certified and she's making six figures. >> Both: Yeah. >> She's like oh my God, this is great. So, that's the Cloud growth. Is there a way to entice people? Is there a pattern? Is it more economic? Is it more, hey, be part of something. What's the data showing? >> There's definitely a war for talent out there. And so in this space we continuously hear from our clients that they can't hire enough people. So in the past, in the technology space, a lot of clients were hiring their own teams and here they just can't get the skills fast enough. So we're spending a tremendous amount of time being proactive. We started a women in Cloud organization where we're proactively reaching out to the community to bring women in, let them know that we will help them get those certifications and partnering with organizations like Women in Cloud, which is a global organization to create new funnels of talent. >> I think the women angle is great. The mom network coming out of the work for back into the workforce, because things change. Like we were talking about how Amazon just changed over the past five years now that this architectural approach is changing. So that's cool. Also we were involved in the women in data science, out of Stanford University, they have that great symposium. This is power technical women. >> Yes >> And it's got a global following. So the women networks that are developing are phenomenal. So that's not just an Accenture thing, right? That's outside of Accenture. >> I think it's a combination because I think we do a really good job inside of Accenture to create opportunities for women of various ethnicities lived experiences to be able to come together to network internally, but then also to pour some of that talent that they have into the communities where we live and we all do business as well. So I think I'm seeing definitely a two-pronged approach there. >> Let me ask you a question, I don't mean to put you on the spot, but I kind of will, Accenture's known as a pretty great firm. So working at Accenture is kind of a big deal. Does that scare people? Because if you could work at a Accenture I mean, that's good pedigree right there. So like, when you're trying to get people coming into the cloud, do they get the Accenture mojo or does it work for them? And can you share your experiences on that? >> I've been here five years and it's been a phenomenal ride for me. I've really enjoyed the fact having a female CEO, I think, and having a CEO who is so committed to diversity on all aspects, right? Her commitment is 50% diversity parody by 2025 at every level of our organization. And that doesn't happen without really intentional efforts at the entry-level and everywhere through the process to ensure that women are not only promoted, but really given the support network among all of our leaders and mentorship to be successful. And it's not just words, it's something that we're really spending a lot of time doing with intention. And that word is out in the space now, as women come in, they're loving it and they're recruiting their other women into the organization and diverse groups as well as what I'm seeing. >> And so I actually just started at Accenture in March. So I've been around eight months. I actually joined from AWS, interestingly enough. And I can tell you from my own experience, the intentionality that Gina spoke to you is it's evident at all levels. I feel like the way that I was courted to the firm was nothing short of amazing. That's another story for another day, but I feel like my being where I am, being hired in as a managing director, as an experienced hire, I think my presence is a testament to the focus that Accenture has on inclusion diversity and the equity component as well. And then also in Atlanta, we are exceptionally fortunate. We have close to 30 black and Latin X managing directors and senior managing directors out of the Atlanta office. So what we're doing there is pretty magical and it's something that I've never experienced in my 25 years. >> It's contagious I hope, the magic is contagious. >> Yeah. >> Yes, absolutely. >> And it's exciting because we're known as a management consulting business, right? So our product is the people >> That's right. >> And so there is intention from day one as to what you want from your career and setting your career plan. So everyone is given those career counselors and the expectation that someone is thinking about your business and your personal business, and what is your role today and what should your role be in two years, and what skills do you need to get there? Which is awesome, it's a lot of fun. >> It's also walking the talk too, right? I mean, Amazon here, they had a 50% women on stage. I don't know if you noticed on the keynote, they was two men and two women, 50%. Of course the United Airlines, it's got to be three. We got to get a 51%,, 'cause technically 51% So it should be three to one, but yeah, like, okay, that was cute notice but that's good. But this is real, I've been a big proponent of software development. Customers are women too that's 51%. So I think this whole representation thing has to be more real and more intentional. And so I want to ask you, how would you share the best practice of making that real from the essential playbook? What could people learn and what mistakes should they avoid? I think people who do want to try with it, but they don't know what to do. >> You know, I think get started, right. Do the work. I feel like since I started in technology, we've been having this conversation about diversity and inclusion and bringing more people into the space. And now it's time for us to just do that. And I feel like Accenture is doing that in spades. I think also again, I've been using this word. I was on a breakout panel yesterday talking about our partnership with AWS and intentionality keeps coming up. But I think also it helps to have a CEO who's creating diversity as an imperative at the most senior levels of the firm and folks are being incentivized as a result. So you've got to put the mechanisms in place to ensure that folks understand that this is not just lip service. >> That's a great point. It's not only just the people, but the mechanisms. And one of the things that I've been saying early on in the top of the interview was Cloud is an instant leveler there, because if you can be so capable so fast. So like when you start thinking about getting people in the market, producing talent, this notion of meritocracy isn't lip service, because if you have the capabilities and the people side lineup, then it truly can be like that. 'Cause your game does the talking, right. >> And we're doing it with intention at every level in the organization so much though, that every people leader, one of their metrics is the diversity. And as we look at the promotions, making sure that that parody is there, but every person who's managing people has diversity as a metric that they're being measured on. And so I think that's really critical as well as having the people who are being the advocates and being the allies and really asking the questions as the teams are getting put together. You know, my job is to review all the deals in the Midwest. And when the teams come forward, I say, "Great where are the women on the team? Who are we putting it?" We're all talking about the diversity. So when we're going to a client meeting, where are the women who are you're taking to that meeting? And if the answer is well, there's not one who's technical yet, the most senior, the most technical, well, great bring her on and use this as a training opportunity. We need to walk the walk and talk the talk and show that to our clients. >> I think that's really good. You guys are senior leaders, one can do that, demonstrate that, but also you're in the field for Accenture. You're in front of your customers. What are you seeing out there and what excites you about being in these industry? >> Yeah, I love the fact that there are so many more women in this space. I love that we're having so many women out there with intention. We've had six female CEOs do women in Cloud panel discussions with us and with our team. So you made the comment early about cloud moving so fast. That's the most exciting thing for me and the fact that it is moving at such a pace that no one client is going to be able to get the skills fast enough. They need companies like Accenture. They need companies like AWS to help them where we're leveraging all the knowledge from our own other clients and bringing that together so we can help them accelerate their development. What about you? >> Absolutely. Now I would echo that as we used to say at AWS plus one to that. But I'm really hopeful because what I'm seeing is the number of folks with my lived experience better at senior executive levels, not only within Accenture and AWS, but in our customers. And I think going back to the point that you were making earlier regarding Cloud being a level up and giving folks opportunity, folks have to be able to see a path, right? It's one thing to just get a certification and tick a box, that's great. But if you don't see a pathway to being able to utilize that in a way that allows you to move up and seeing where we are now, just as a firm, just really, really excites me that every time I get onto a call and I see another strong, amazing woman, I'm like, man, this is amazing. And it's something that... I think it's a phenomenon that I've started to see maybe within the last like five years or so. And probably even within the last two to three years, I've started to see that even more so, so that really excites me. >> Well, first of all, you guys are great. You're contagious, okay? Which is good, a good thing. I love how you brought the whole path thing because path finders was a big part of Adam's Leslie's keynote, and it must be really fun to see people taking the path that you guys are pioneering- >> We're ploughing, we're ploughing >> Yes we are. We're ploughing and you know what else we're doing? We're lifting, as we climb. That is important. I would say that, we don't have all of these amazing opportunities and blessings just to talk about what we have, but if you're not actually bringing somebody else along and giving those opportunities to folks, then it's all for not. >> You got people and the Cloud, to get them people, which is, we're humans and the mechanisms software to bring it together, magic. >> Absolutely >> Congratulations. Thanks for coming on theCUBE. >> Both: Thanks for having us. >> Okay this is theCUBE, I'm John Furrier, host of theCUBE. You're watching theCUBE, the leader in global tech coverage from re:Invent 2021 AWS web services. Thanks for watching (soft instrumental music)

>>We're seeing green here at Vemo in 2022, you're watching the cube, Dave ante and David Nicholson wrapping up our second day of coverage. Dave, good show. Good to be, you know, again, good to be back. This is our third show in a row. We're a Cuban as well. So the cube is, is out there, but same every, every show we go to so far has been most of the people here haven't been out in two plus years. Yeah. Right. And, and, and they're like, Hey, let's go. Let's hug. Let's shake. I got my red band on cuz we've been on a lot of shows or just being careful <laugh> um, you know, Hey, but it's great to see people back, uh, >>Absolutely >>Such a different vibe than virtual virtual sucks. Everybody hates it now, but now it's going hybrid. People are trying to figure that out. Yeah. Uh, but it's, it's in your view, what's different. What's the same >>In terms of, uh, in person versus hybrid kind of what's happened since what's >>Different being here now versus say 2019, not that you were here in 2019, but a show in 2019. >>I, I think there's right now, there's a certain sense of, uh, of appreciation for the ability to come and do this. Mm-hmm <affirmative> um, >>As opposed to on we or oh, another show, right? >>Yeah. Yeah, exactly. And, and, uh, a personal opinion is that, um, I think that the hybrid model moving forward is going to end up being additive. I don't know that I don't, you know, people say we'll never go back to having in person the way we did before. Um, I'm holding out hope that that's not the case because I, I think so there's so much value to the kinds of conversations that we have, not only here on the set with folks in person, but just the hallway conversations, uh, the dinner conversations, um, those are so critical, uh, not only with between vendors and customers, but between different business units. Um, you know, I, I, I came into this thinking, you know, I know Veeam very well. I've known them since the beginning. Um, but you think I'm going to a conference to talk about backup software and it wasn't like that at all. I mean, this is, this is an overarching, very, very interesting subject to cover. So how is it different? I think people are appreciative. I wouldn't say we're backed full throttle a hundred percent, um, uh, back in the game yet. But, uh, but we we're getting there. Some >>Of the highlights Veeam now, number one, statistical tie for first place in revenue. There aren't a lot of segments, especially in storage where Dell is not number one, I guess technically Dell is like, I don't know, half a percentage point ahead, but Veeam's gonna blow by that. Unless Dell gets its data, >>Protect me as the luxury of focus, they can focus >>Like a laser on it focus. Right? That, that we, we saw this in the P PC where focused, we saw Dell's ascendancy cuz they were focused on PCs, right? Yeah. We saw Seagate on dis drives Intel and microprocesors Oracle on databases and, and, and Veeam applied that model to what they call modern data protection. Um, and, and the, so the reason why we think they're gonna go past is they growing at 20 plus percent each year. And, and I can almost guarantee Dell's data protection business isn't although it's been in a, I, I sense a downward slope lately, they don't divulge that data. Um, but if they were growing nicely, they would be talking about it. So I think they've been kind of hiding that ball, but Dell, you know, you can't count those guys out they're baby. >>No, you can't. And there's always >>A, they don't like to lose. They get that EMC DNA still in >>There. Yeah. You take, you can, you might take your eye off the ball for a little while to focus on other things. But uh, I think it'll be healthy for the industry at large, as Veeam continues to take market share. There's definitely gonna be pushback from, from others in the field, but >>The pure software play. Um, and you know that no hardware agenda thing and all that I think is, is clearly in Veeam's favor. Uh, but we'll see. I mean, Dell's got other, other strengths as do others. I mean, this is, this is, let's not forget this, this, this market is crowded and getting kind. I mean, you got, you got other players, new, new entrants, like cohesive in Rubrik Rubic, by the way is the one I was kind of referring to. That seems to be, you go to their LinkedIn, they seem to be pivoting to security. I was shocked when I saw that. I'm like, wow, is that just like a desperation move? Is that a way to get your valuation up? Is that, is there something I'm missing? I, I don't know. I haven't talked to those guys in a little bit, need to get, get there, but cause he and Rubrik couldn't get to IPO prior to, uh, you know, the, the, the, the, the tech sell off the tech lash. >>If you will Veeam, didn't need toves. We have 30% EBITDA and, and has had it for a while. So they've been, they caught lightning in a bottle years ago, and then now they got the inside capital behind them. Um, you got new entrance, like, like Kuo, you got com. Vault is out there. You still got, you know, Veritas is still out there competing and you know, a number of other, you get you got is wherever HP software landed in, in the MicroStrategy, uh, micro strategy. <laugh> um, no not micro strategy anyway, in that portfolio of companies that HP sold its software business to, you know, they're still out there. So, you know, a lot of ways to, to buy backup and recovery software, but these guys being the leader is no surprise. >>Yeah. You know, it's, I, I, I have to say it to me. It's a classic story of discipline >>Microfocus, sorry, >>Microfocus. Yeah, that's right. That's right. You know, it's funny. I, I, I could see that logo on a, I know I've got a notebook at home. Um, but, but theme is a classic example of well disciplined growth where you're not playing the latest buzzword game and trying to create adjacent businesses that are really, that might sound sexy, but have nothing to do with your core. They've been very, very disciplined about their approach, starting with, you know, looking at VMFS and saying, this is what we're gonna do, and then branching out from there in a logical way. So, so they're not out ahead of the tips of their skis in a way that some others have have gotten. And those, you know, sometimes swinging for the fence is great, but you can strike out that way also. And they've been hitting, you know, you could say they've been hitting singles and doubles just over and over and over again for years now. Well, that's been a great strategy. >>You've seen this a lot. I mean, I, I think you watched this at EMC when you were there as you, it was acquisitions to try to keep the growth up. It was, it was great marketing. I mean, unbelievable marketing cloud meets big data. Oh yeah. And you'd hear on CNBC. AMC is the cloud company. You're like, eh, fucking have a cloud. So, so you, you you've seen companies do that to your point about getting ahead of your skis. VMs never done that EMS like, eh, this is the product that works great. Yeah. Customers love it. They buy it, you know, we got the distribution channel set up and so that's always been, been, been part of their DNA. Um, and I think the other piece is putting meat on the bone of the tagline of modern data protection. When I first heard that I'm like, mm, okay. >>But then when you peel the onion on that, the core is back up in recovery, a lot of focus on recovery. And then the way they, I remember it was there in the audience when they announced, you know, support for bare metal, people went crazy. I'm like, wow, okay. They cuz they used to say, oh, never virtualization forever. Okay. So they beat that drum and you never say never in this business, do you, and then moving on to cloud and hybrid and containers and we're hearing about super cloud now, and maybe there'll be an edge use case there it's still unclear what that pattern is. You've talked about that with Zs, but it's not clear to me where you put your muscle yet in, um, in edge, but really being able to manage all that data that is people talk about data management that starts to be data management. And they've got a footprint that enables 'em to do that. >>Yeah. And, and I'd like to see that same discipline approach. That's gotten them here to continue no need to get on board a hype cycle. Um, what I really love from a business execution perspective from Veeam is the fact that they know their place in terms of the, their strategic advisory role for end user customers and their places largely in partnership with folks in the channel partners, large and small, um, in a couple of the conversations we had over the last few days, we talked about this idea that there are fewer and fewer seats at the table. Uh, working with customers, customers can't have 25 strategic vendor partners and a lot of smaller niche players that focus on something even as important as backup will pretend that they are, that they hold the same sort of strategic weight as a hyperscale cloud provider. Does they pretend that they're gonna be there in the CX O meetings? Um, when they're not Veeam knows exactly how to best leverage what they do with customers and that's through partners in the channel. >>The other thing is, um, new CEO, a non Eron, uh, the fifth CEO, I think I'm correct. Is that right at, at VE yes. Um, so two founders, uh, and then when Peter McKay came on, he was co CEO. Um, and then, um, yep. And let's see, I think yep. You the fifth. Okay. So each of the CEOs kind of had their own mark. Right. Um, and we asked an on in the analyst thing, what do you want your legacy to be? And I, I loved his answer. He's like, this is a fragmented business with a lot of adjacencies and we are the leader in revenue, but we only have 12% revenue share. I want to take that to 25%, 40%. That's like EMC at 30 plus percent of the storage market, Cisco of 60% of the networking market. Wow. If anybody could ever get there, but so 25 to 30% of a market that's that's big. Yeah. I liked his demeanor thought he had a really good style philosophy. Well-spoken well spoken. So new leadership, obviously insight brought him in to take them to the next level. Um, and, and really drive. I gotta believe get ready for IPO. We kind of admitted that. >>Yeah. And I, and IPO for them, one thing he mentioned is that, um, in this case, this is not an IPO let's high five and go to Vegas and get table service because now we finally have money. Uh, they're not doing, you know, obviously an injection in capital from an IPO is always a good thing or should be a good thing if handled properly, but that's not their primary driver. So it'll be very interesting to see if they can hit the timing. Right. Um, how that, how that works out >>Well and, and bill large is his was predecessor. Uh, he, he, he took over, uh, once the company, excuse me, went private. Um, >>Yeah, that phone backed up. >>I still good in the mic once the company went private, uh, well, no, they were always private. Once they got acquired for five plus billion dollars from inside capital, um, they, they put bill in charge, perfect choice for the transition. And it was like, okay, bill. It's like, when you, my brother's a sailor. He says, Hey, take, take the wheel, see that lighthouse or see that tree go for it, keep it on track. And that's what bill did. Perfect. And he knew the company knew where all the skeletons were buried and, and was perfect. Perfect transition for that. Now they're bringing in somebody who they feel can take it to the next level. They're at a billion. He said he could see 5 billion and, and beyond. So that's kind of cool. Um, the other thing was ecosystem as companies got a really robust ecosystem, all the storage array vendors came on. >>The, the, the backup appliance companies, you know, came on to the cube and had a presence here. Why? Because this is where all the customers are. This is the leader in backup in recovery. Yeah. They all want to partner with that leader. Now they're at out the other shows as well, uh, for the Veeam competitors, but frankly, Veeam, Veeam competitors. They don't have, like you said, they're pure play. Many of them don't have a show like this, or it's a smaller event. Um, and so they gotta be here. Uh, and I think the, the, the other thing was the ransomware study. What I really liked about Veeam is they not only just talked about it, they not only talked about their solution. They sh they did deep dive surveys and shared a ton of data with guys that knew data. Um, Dave Russell and Jason Buffington, both former analysts, Russell was a Gartner very well respected top Gartner analyst for years. Jason buff, Buffington at ESG who those guys did always did some really good, still do deep research. So you had them representing that data, but sharing it with the community, of course, it's, it's gonna be somewhat self-serving, but it wasn't as blatant. It that wasn't nearly as blatant as I often see with these surveys, gender surveys, I'll look at 'em. I can tell within like, seconds, whether it's just a bunch of marketing, you know, what, or there's real substance. Yeah. And this one had real substance to >>It. Yeah. And it's okay. When substance supports your business model. >>Yeah. Cool. >>It's great. Good >>Marketing. But yeah, as an best marketing, I'm not gonna use it. The whole industry can use this and build on it. Yeah. I think there were a lot of unanswered questions. I, what I love about Vema is they're going back and they, they did it in February. They, they updated it just recently. Now they're going back and doing more cuz they want to get it by country. So they're making investments. And then they're sharing that with the industry. I love that. >>It'll be interesting to see if they continue it over time, how things change if things change. Um, one of the things that we really didn't talk a lot about is, uh, and you know, it's, I know it's talked about behind closed doors, um, this idea of, uh, stockpiling day zero exploits, and the fact that a lot of these, these >>Things, >>A lot of these problems arguably could have been headed off, had our taxpayer funded organizations, shared information with private industry in a more timely fashion. Um, um, we had, um, uh, uh, was it, uh, Gina from AWS who gave the example of, uh, the not Petia, uh, experience in the hospital environment. And that came directly out of frankly a day zero exploit that the NSA had identified years earlier within Microsoft's operating system. And, uh, somehow others got ahold of that and used it for nefarious means. So the intent to stockpile and hang onto these things is always, um, noble, but sometimes the result is, uh, less than desirable. So that's, it'll be an interesting conversation. >>We'd be remiss if we didn't mention the, the casting acquisition, the, the, the container data protection, small piece of the business today. Uh, but strategic in the sense that, yeah, absolutely. If you want to appeal to developers, if, if, if, if, if you want to be in the cloud, you know, you better be able to talk containers generally in Kubernetes specifically. So they gotta play there as well. >>Well, they, they, they hit virtualization cloud containers. Maybe I'm missing something in between, but they seem to be >>Ransomware >>Catching waves effectively. Yeah. Ransomware, uh, catching waves effectively, uh, again, not in an artificial buzzword driven way, but in a legitimate disciplined business growth approach that, uh, that's impressive. >>And I, and I think Danny mentioned this, we, he said we've been a PLG product led growth company. Um, and I think they're evolving now. We talked about platforms versus product. We still got still a product company. Uh, but they're bill wants to build out a Supercloud. So we're watching that very closely. I, I think it is a thing. You got a lot of grief for the term, super cloud. Some people wince at it, but it's, there's something brewing. There's something different. That's not just cloud public cloud, not hybrid cloud, not private cloud it's across cloud it's super cloud. All right, Dave, Hey, it was a pleasure working with you this week. Always kind of funny. I mean, we're, the crew was out in, uh, in Valencia, Spain. Yeah. Uh, they'll in fact, they'll be broadcasting, I believe all the way through Friday. Uh, that's an early morning thing for the, uh, for the west coast and, but east coast should be able to catch that easily. >>Of course you can all check out all the replays on the cube.net, also YouTube, youtube.com/silicon angle go to wikibon.com. There's some, you know, research there I publish every week and, and others do, uh, as well, maybe not as frequently, but, uh, we have a great relationship with ETR. I'm gonna poke into some data protection stuff in their survey. See if I can find some interesting, uh, data there. And don't forget to go to Silicon an angle.com, which is all the news. This is the cube, our flagship production we're out at VEON 2022. Thanks for watching.

>> Announcer: From around the globe, it's theCUBE, with digital coverage of IBM Think 2021, brought to you by IBM. >> Hey, welcome to theCUBE's coverage of IBM Think, the digital event experience. I'm your host, Lisa Martin, welcoming back to the program one of our CUBE alumn. Dominique Dubois joins me. She's the Global Strategy and Offerings Executive in the Business Transformation Services of IBM. Dominique, it's great to talk to you again. >> Hi Lisa, great to be with you today. >> So we're going to be talking about the theme of this interview. It's going to be the ROI of AI for business. We've been talking about AI emerging technologies for a long time now. We've also seen a massive change in the world. I'd love to talk to you about how organizations are adopting these emerging technologies to really help transform their businesses. And one of the things that you've talked about in the past, is that there's these different elements of AI for business. One of them is trust, right, the second is ease of use, and then there's this importance of data in all of these important emerging technologies that require so much data. How do those elements of AI come together to help IBM's clients be able to deliver the products and services that their customers are depending on? >> Yeah. Thank you, Lisa. So, when we look at AI and AI solutions with our clients, I think how that comes together is in the way in which we don't look at AI, or AI application solution, independently, right. We're looking at it and we're applying it within our customer's operations with respect to the work that it's going to do, with respect to the part of the operations and the workflow and the function that it sits in, right. So the idea around trust and ease of use and the data that can be leveraged in order to kind of create that AI and allow that AI to be self-learning and continue to add value really is fundamental around how we design and how we implement it within the workflow itself. And how we are working with the employees, with the actual humans, that are going to be touching that AI, right, to help them with new skills that are required to work with AI, to help them with what we call the new ways of working, right, 'cause it's that adoption that really is critical to get the use of AI in enterprises at scale. >> That adoption that you just mentioned, that's critical. That can be kind of table stakes. But what we've seen in the last year is that we've all had to pivot, multiple times, and be reactionary, or reactive, to so many things out of our control. I'm curious what you've seen in the last year in terms of the appetite for adoption on the employees front. Are they more willing to go, all right, we've got to change the way we do things, and it's probably going to be, some of these are going to be permanent? >> Yeah. Lisa, we've absolutely seen a huge rise in the adoption, right, or in the openness, the mindset. Let's just call it the mindset, right. It's more of an open mindset around the use of technology, the use of technology that might be AI backed or AI based, and the willingness to, and I will say, the willingness to try is really then what starts that journey of trust, right. And we're seeing that open up in spades. >> That is absolutely critical. It's just the willingness, being open-minded enough to go, all right, we've got to do this, so we've got to think about this. We don't really have any other choices here. Things are changing pretty quickly. So talk to me, in this last year of change, we've seen massive disruptions and some silver linings for sure, but I'd love to know what IBM and the state of Rhode Island have done together in its challenging time. >> Yeah, so, really interesting partnership that we started with the state of Rhode Island. Obviously, I think this year, there's been lots of things. One of them has been speed, so everything that we had to do has been with haste, right, with urgency. And that's no different than what we did with the state of Rhode Island. The governor there, Gina Raimondo, she took very swift action, right, when the pandemic started. And one of the actions she took was to partner with private firms, such as IBM and others, to really help get her economy back open. And that required a lot of things. One of them, as you mentioned, trust, right, was a major part of what the governor there needed with her citizenships, with her citizens, excuse me, in order to be able to open back up the economy, right. And so, a key pillar of her program, and with our partnership, was around the AI-backed solutions that we brought to the state of Rhode Island, so inclusive of contact tracing, inclusive of work that we had provided around AI-based analytics that allowed really the governor to speak to citizens with hard facts quickly, almost real time, right, and start to build that trust, but also competence, and competence was the main, one of the main things that was required during this pandemic time. And so, there were, through this, the AI-based solutions that we provided, which were, there were many pillars, we were able to help Rhode Island not only open their economy, but they were one of the only states that had their schools open in the fall, and as a parent, I always see that as a litmus, if you will, of how our state is doing, right. And so they opened in the fall, and they, as far as I know, have stayed open. And I think part of that was from the AI-based contact tracing, the AI-backed virtual, sorry, AI analytics, the analytics suite around infections and predictions and what we were able to provide the governor in order to make swift decisions and take action. >> That's really impressive. That's one of the challenges I've had living in California, is you (mumbles) you are going to be data-driven than actually be data-driven, but the technology, living in Silicon Valley, the technology is there to be able to facilitate that, yet there was such a disconnect, and I think that's, you bring up the word confidence, and customers need confidence, citizens need confidence, knowing that what we've seen in the last year has shown in a lot of examples that real time isn't a nice-to-have anymore, it's a requirement. I mean, this is clearly life-and-death situations. That's a great example of how a state came to IBM to partner and say, how can we actually leverage emerging technologies like AI to really and truly make real-time data-driven decisions that affect every single person in our state. >> Mm-hmm. Absolutely, absolutely! Really, really, I think, a great example of the public-private partnerships that are really popping up now, more and more so because of that sense of urgency and that need to build greater ecosystems to create better solutions. >> So that's a great example in healthcare, one that our government in public health, and I think everybody, it will resonate with everybody here, but you've also done some really interesting work that I want to talk about with AI-driven insights into supply chain. We've also seen massive changes to supply chain, and so many organizations having to figure out, whether they were brick-and-mortar only, changing that, or really leveraging technology to figure out where do we need to be distributing products and services, where do we need to be investing. Talk to me about Bestseller India, and what it is that you guys have done there with intelligent workflows to really help them transform their supply chain. >> Yeah, Bestseller India, really great, hugely successful fashion forward company in India, and that term fashion forward always is mind boggling to me because basically, these are clothing retailers who go from runway to store within a matter of days, couple of weeks, which always is just hugely impressive, right, just what goes into that. And when you think about what happens in a supply chain to be able to do that, the requirements around demand forecasting, what quantities, of what style, what design, to what stores, and you think about the India market, which is notoriously a difficult market, lots of micro-segments, and so very difficult to serve. And then you couple that what's been happening from an environmental sustainability perspective, right. I think every industry has been looking more about how they can be more environmentally sustainable, and the clothing industry is no different. And when, and there is a lot of impact, right, so a stat that really has hit home with me, right: 20% of all the clothes that are made globally goes unsold. That's all a lot of clothing, that's a lot of material, and that's a lot of environmental product that goes into creating it. And so, Bestseller India really took it to heart to become not only more environmentally sustainable, but to help itself and be digitally ready for things like the pandemic that ultimately hit. And they were in a really good position. And we worked with them to create something called Fabric AI. So Fabric AI is India's only, first and only, AI-based platform that drives their supply chain, so it drives not only their decisions on what design should they manufacture, but it also helps to improve the entire workflow of what we call design to store. And the AI-based solution is really revolutionary, right, within India, but I think it's pretty revolutionary globally, right, globally as well. And it delivered really big impact, so, reductions in the cost, right, 15-plus reduction in cost. It helped their top line, so they saw a 5% plus top line, but it also reduced their unsold inventory by 5% and more, right. They're continuing to focus on that environmental sustainability that I think is a really important part of their DNA, right, the Bestseller India's DNA. >> And it's one that so many companies and other industries can learn from. I was reading in that case study on Bestseller India on the IBM website that I think it was 40 liters of water to make a cotton shirt. And to your point about the percentage of clothing that actually goes unsold and ends up in landfills, you see there the opportunity for AI to unlock the visibility that companies in any industry need to determine what is the demand that we should be filling, where should it be distributed, where should we not be distributing things. And so I think it was an interesting kind of impetus that Bestseller India had about one of their retail lines or brands was dropping in revenue, but they had been able to apply this technology to other areas of the business and make a pretty big impact. >> Yeah, absolutely. So they had been been very fortunate to have 11 years of growth, right, in all of their brands. And then one of their brands kind of hit headwinds, but the CIO and head of supply chain at that time really had the foresight to be able to say, you know what, we're hitting a problem, one of our brands, but this really is indicative of a more systemic problem. And that problem was lack of transparency, lack of data-driven, predictive, and automation to be able to drive a more effective and efficient kind of supply chain in the end, so, really had the forethought to dive into that and fix it. >> Yeah. And now talk to me about IBM Garage Band, and how's that, how did that help in this particular case? >> Yeah. So, in order to do this, right, it was, they had no use of AI, no use of automation, at the time that we started this. And so to really not only design and build and execute on Fabric AI, but to actually focus on the adoption, right, of AI within the business, we really needed to bring together the leaders across many lines of businesses, IT and HR, right. And when you think about pulling all of these different units together, we used our IBM Garage approach, which really is, there are many attributes and many facets of the IBM Garage, but I think one of the great results of using our IBM Garage approach is being able to pull from across all those different businesses, all of which may have some different objectives, right, they're coming from a different lens, from a different space, and pulling them together around one focus mission, which for here was Fabric AI. And we were able to actually design and build this in less than six months, which I think is pretty dramatic and pretty incredible from a speed and acceleration perspective. But I think even more so was the adoption, was the way in which we had, through all of it, already been working with the employees 'cause it's really touched almost every part of Bestseller India, so really being able to work with them and all the employees to make sure that they were ready for these new ways of working, that they had the right skills, that they had the right perspective, and that it was going to be adopted. >> That, we, if we unpack that, if we had time, that can be a whole separate conversation because the important, the most important thing about adoption is the cultures of these different business units have to come together. You said you rolled this out in a very short period of time, but you also were taking the focus on the employees. They need to understand the value in it. why they should be adopting it. And changing that culture, that's a whole other separate conversation, but that's an, that's a very interesting and very challenging thing to do. I wish we had more time to talk about that one. >> Yeah. It really is an, that the approach of bringing everyone together, it makes it just very dynamic, which is what's needed when you have all of those different lenses coming together, so, yeah. >> It is, 'cause you get a little bit of thought diversity as well when we're using AI. Well, Dominic, thank you for joining me today. Talked to me about what you guys are doing with many different types of customers, how you're helping them to integrate emerging technologies to really transform their business and their culture. We appreciate your time. >> Well, thank you, Lisa. Thanks >> For Dominique Dubois, I'm Lisa Martin. You're watching theCUBE's coverage of IBM Think, the digital event. (upbeat music)

(serene music) >> From around the globe, it's theCUBE, with digital coverage of IBM Think 2021, brought to you by IBM. >> Hey, welcome to theCUBE's coverage of IBM Think, the digital event experience. I'm your host, Lisa Martin, welcoming back to the program one of our CUBE alumn. Dominique Dubois joins me. She's the Global Strategy and Offerings Executive in the Business Transformation Services of IBM. Dominique, it's great to talk to you again. >> Hi Lisa, great to be with you today. >> So we're going to be talking about the theme of this interview. It's going to be the ROI of AI for business. We've been talking about AI emerging technologies for a long time now. We've also seen a massive change in the world. I'd love to talk to you about how organizations are adopting these emerging technologies to really help transform their businesses. And one of the things that you've talked about in the past, is that there's these different elements of AI for business. One of them is trust, right, the second is ease of use, and then there's this importance of data in all of these important emerging technologies that require so much data. How do those elements of AI come together to help IBM's clients be able to deliver the products and services that their customers are depending on? >> Yeah. Thank you, Lisa. So, when we look at AI and AI solutions with our clients, I think how that comes together is in the way in which we don't look at AI, or AI application solution, independently, right. We're looking at it and we're applying it within our customer's operations with respect to the work that it's going to do, with respect to the part of the operations and the workflow and the function that it sits in, right. So the idea around trust and ease of use and the data that can be leveraged in order to kind of create that AI and allow that AI to be self-learning and continue to add value really is fundamental around how we design and how we implement it within the workflow itself. And how we are working with the employees, with the actual humans, that are going to be touching that AI, right, to help them with new skills that are required to work with AI, to help them with what we call the new ways of working, right, 'cause it's that adoption that really is critical to get the use of AI in enterprises at scale. >> That adoption that you just mentioned, that's critical. That can be kind of table stakes. But what we've seen in the last year is that we've all had to pivot, multiple times, and be reactionary, or reactive, to so many things out of our control. I'm curious what you've seen in the last year in terms of the appetite for adoption on the employees front. Are they more willing to go, all right, we've got to change the way we do things, and it's probably going to be, some of these are going to be permanent? >> Yeah. Lisa, we've absolutely seen a huge rise in the adoption, right, or in the openness, the mindset. Let's just call it the mindset, right. It's more of an open mindset around the use of technology, the use of technology that might be AI backed or AI based, and the willingness to, and I will say, the willingness to try is really then what starts that journey of trust, right. And we're seeing that open up in spades. >> That is absolutely critical. It's just the willingness, being open-minded enough to go, all right, we've got to do this, so we've got to think about this. We don't really have any other choices here. Things are changing pretty quickly. So talk to me, in this last year of change, we've seen massive disruptions and some silver linings for sure, but I'd love to know what IBM and the state of Rhode Island have done together in its challenging time. >> Yeah, so, really interesting partnership that we started with the state of Rhode Island. Obviously, I think this year, there's been lots of things. One of them has been speed, so everything that we had to do has been with haste, right, with urgency. And that's no different than what we did with the state of Rhode Island. The governor there, Gina Raimondo, she took very swift action, right, when the pandemic started. And one of the actions she took was to partner with private firms, such as IBM and others, to really help get her economy back open. And that required a lot of things. One of them, as you mentioned, trust, right, was a major part of what the governor there needed with her citizenships, with her citizens, excuse me, in order to be able to open back up the economy, right. And so, a key pillar of her program, and with our partnership, was around the AI-backed solutions that we brought to the state of Rhode Island, so inclusive of contact tracing, inclusive of work that we had provided around AI-based analytics that allowed really the governor to speak to citizens with hard facts quickly, almost real time, right, and start to build that trust, but also competence, and competence was the main, one of the main things that was required during this pandemic time. And so, there were, through this, the AI-based solutions that we provided, which were, there were many pillars, we were able to help Rhode Island not only open their economy, but they were one of the only states that had their schools open in the fall, and as a parent, I always see that as a litmus, if you will, of how our state is doing, right. And so they opened in the fall, and they, as far as I know, have stayed open. And I think part of that was from the AI-based contact tracing, the AI-backed virtual, sorry, AI analytics, the analytics suite around infections and predictions and what we were able to provide the governor in order to make swift decisions and take action. >> That's really impressive. That's one of the challenges I've had living in California, is you (mumbles) you are going to be data-driven than actually be data-driven, but the technology, living in Silicon Valley, the technology is there to be able to facilitate that, yet there was such a disconnect, and I think that's, you bring up the word confidence, and customers need confidence, citizens need confidence, knowing that what we've seen in the last year has shown in a lot of examples that real time isn't a nice-to-have anymore, it's a requirement. I mean, this is clearly life-and-death situations. That's a great example of how a state came to IBM to partner and say, how can we actually leverage emerging technologies like AI to really and truly make real-time data-driven decisions that affect every single person in our state. >> Mm-hmm. Absolutely, absolutely! Really, really, I think, a great example of the public-private partnerships that are really popping up now, more and more so because of that sense of urgency and that need to build greater ecosystems to create better solutions. >> So that's a great example in healthcare, one that our government in public health, and I think everybody, it will resonate with everybody here, but you've also done some really interesting work that I want to talk about with AI-driven insights into supply chain. We've also seen massive changes to supply chain, and so many organizations having to figure out, whether they were brick-and-mortar only, changing that, or really leveraging technology to figure out where do we need to be distributing products and services, where do we need to be investing. Talk to me about Bestseller India, and what it is that you guys have done there with intelligent workflows to really help them transform their supply chain. >> Yeah, Bestseller India, really great, hugely successful fashion forward company in India, and that term fashion forward always is mind boggling to me because basically, these are clothing retailers who go from runway to store within a matter of days, couple of weeks, which always is just hugely impressive, right, just what goes into that. And when you think about what happens in a supply chain to be able to do that, the requirements around demand forecasting, what quantities, of what style, what design, to what stores, and you think about the India market, which is notoriously a difficult market, lots of micro-segments, and so very difficult to serve. And then you couple that what's been happening from an environmental sustainability perspective, right. I think every industry has been looking more about how they can be more environmentally sustainable, and the clothing industry is no different. And when, and there is a lot of impact, right, so a stat that really has hit home with me, right: 20% of all the clothes that are made globally goes unsold. That's all a lot of clothing, that's a lot of material, and that's a lot of environmental product that goes into creating it. And so, Bestseller India really took it to heart to become not only more environmentally sustainable, but to help itself and be digitally ready for things like the pandemic that ultimately hit. And they were in a really good position. And we worked with them to create something called Fabric AI. So Fabric AI is India's only, first and only, AI-based platform that drives their supply chain, so it drives not only their decisions on what design should they manufacture, but it also helps to improve the entire workflow of what we call design to store. And the AI-based solution is really revolutionary, right, within India, but I think it's pretty revolutionary globally, right, globally as well. And it delivered really big impact, so, reductions in the cost, right, 15-plus reduction in cost. It helped their top line, so they saw a 5% plus top line, but it also reduced their unsold inventory by 5% and more, right. They're continuing to focus on that environmental sustainability that I think is a really important part of their DNA, right, the Bestseller India's DNA. >> And it's one that so many companies and other industries can learn from. I was reading in that case study on Bestseller India on the IBM website that I think it was 40 liters of water to make a cotton shirt. And to your point about the percentage of clothing that actually goes unsold and ends up in landfills, you see there the opportunity for AI to unlock the visibility that companies in any industry need to determine what is the demand that we should be filling, where should it be distributed, where should we not be distributing things. And so I think it was an interesting kind of impetus that Bestseller India had about one of their retail lines or brands was dropping in revenue, but they had been able to apply this technology to other areas of the business and make a pretty big impact. >> Yeah, absolutely. So they had been been very fortunate to have 11 years of growth, right, in all of their brands. And then one of their brands kind of hit headwinds, but the CIO and head of supply chain at that time really had the foresight to be able to say, you know what, we're hitting a problem, one of our brands, but this really is indicative of a more systemic problem. And that problem was lack of transparency, lack of data-driven, predictive, and automation to be able to drive a more effective and efficient kind of supply chain in the end, so, really had the forethought to dive into that and fix it. >> Yeah. And now talk to me about IBM Garage Band, and how's that, how did that help in this particular case? >> Yeah. So, in order to do this, right, it was, they had no use of AI, no use of automation, at the time that we started this. And so to really not only design and build and execute on Fabric AI, but to actually focus on the adoption, right, of AI within the business, we really needed to bring together the leaders across many lines of businesses, IT and HR, right. And when you think about pulling all of these different units together, we used our IBM Garage approach, which really is, there are many attributes and many facets of the IBM Garage, but I think one of the great results of using our IBM Garage approach is being able to pull from across all those different businesses, all of which may have some different objectives, right, they're coming from a different lens, from a different space, and pulling them together around one focus mission, which for here was Fabric AI. And we were able to actually design and build this in less than six months, which I think is pretty dramatic and pretty incredible from a speed and acceleration perspective. But I think even more so was the adoption, was the way in which we had, through all of it, already been working with the employees 'cause it's really touched almost every part of Bestseller India, so really being able to work with them and all the employees to make sure that they were ready for these new ways of working, that they had the right skills, that they had the right perspective, and that it was going to be adopted. >> That, we, if we unpack that, if we had time, that can be a whole separate conversation because the important, the most important thing about adoption is the cultures of these different business units have to come together. You said you rolled this out in a very short period of time, but you also were taking the focus on the employees. They need to understand the value in it. why they should be adopting it. And changing that culture, that's a whole other separate conversation, but that's an, that's a very interesting and very challenging thing to do. I wish we had more time to talk about that one. >> Yeah. It really is an, that the approach of bringing everyone together, it makes it just very dynamic, which is what's needed when you have all of those different lenses coming together, so, yeah. >> It is, 'cause you get a little bit of thought diversity as well when we're using AI. Well, Dominic, thank you for joining me today. Talked to me about what you guys are doing with many different types of customers, how you're helping them to integrate emerging technologies to really transform their business and their culture. We appreciate your time. >> Well, thank you, Lisa. Thanks >> For Dominique Dubois, I'm Lisa Martin. You're watching theCUBE's coverage of IBM Think, the digital event. (upbeat music)

>>Yeah, yeah. >>Welcome back, everyone. And let's get ready for session number two, which is all around picking the right use cases. We're going to take a look at how to make the most of your data driven journey through the lens of some instructive customer examples. So today we're joined by thought squads David Copay, who is a director of business value consulting like Daniel, who's a customer success manager and then engagement manager. Andrea Frisk, who not so long ago was actually a product manager. Canadian Tire, who are one of our customers. And she was responsible for the thoughts. What implementation? So we figured Who better to get involved? But yeah, let's Let's take it away, David. >>Thanks, Gina. Welcome, everybody. And Andrea Blake looking forward to this session with you. A zoo. We all know preparation early is key to success on Duin. Any project having the right team on sponsorship Thio, build and deploy. Ah, use case is critical being focused on three outcome that you have in mind both the business deliverables and then also the success criteria of how you're going to manage, uh, manage and define success. When you get there, Eyes really critical to to set you up in the right direction initially. So, Andrea, as as we mentioned, uh, you came from an organization that quite several use cases on thoughts about. So maybe you can talk us through some of those preparation steps that, yeah, that you went through and and share some insights on how folks can come prepare appropriately. >>Eso having the right team members makes such a difference. Executive support really helped the Canadian tire adoption spread. It gave the project presence and clout in leadership meetings and helped to drive change from the top down. We had clear goals and success criteria from our executive that we used to shape the go forward plan with training and frame the initial use case roadmap. One of the other key benefits over executive sponsor was that the reporting team for our initial use case rolled up by underhand. So there was a very clear directive for a rapid phase out of the old tools once thought Spot supported the same data story. And this is key because as you start to roll through use cases, you wanna realize the value. And if you're still executing the old the same time as the new. That's not gonna happen. As we expanded into areas where we were unfamiliar with the data in business utilization, we relied on the data experts and and users to inform what success would look like in the new use cases. We learned early on that those who got volunteer old and helping didn't always become the champions. That would help you drive value from the use case. Using the thoughts about it meant tables. We started to seek out users who are consistently logging in after an initial training, indicating their curiosity and appetite to learn more. We also looked for activities outside of just pin board views toe identify users that had the potential to build and guide new users as subject matter experts, not just in a data but in thought spot. This helps us find the right people to cultivate who were already excited about the potential of thought spot and could help us champion a use case. >>That's really helpful, great, great insight for someone who's been there and done that. Blake is as a customer success manager. Obviously, you approach many of the same situations, anything you'd like to add that >>I still along with the right team. My first question with any use cases. Why Why are we doing this? You've gathered all this data and now we want to use it. But But what for? When you get that initial response on Why this use case? Don't stop there. Keep asking Why keep digging? Keep digging. Keep digging. So what you're essentially trying to get at is what does the decision is that we will be made or potentially be made because of this use case. For example, let's say that we're looking at an expenses use case. What will be done with the insides gathered with this use case? Are those insights going? Thio change the expense approval process Now, Once you have that, why defined now it becomes a lot easier to define the success criteria. Success criteria they use. Face can sometimes be difficult to truly defined. But when you understand why it becomes much easier, so now you can document that success criteria. And the hard part at that point is to actually track that success over time, track the success of the use case, which is something that is easily miss but It's something that is incredibly useful to the overall initiative. >>Right measure. Measure the outcomes. You can't manage what you what? You can't what you don't measure right? As the old adage goes, and you know it's part of the business consulting team. That's really where we come in. Is helping customers really fundamentally define? How are we going to measure a success? Aziz. We move forward. Andi, I think you know, I think we've alluded to this a little bit in terms of that sort of ongoing nature of This is, you know, after the title of the session, eyes choosing the right news cases in the plural right? So it's very important to remember that this is not a single point in time event that happens once. This is a constant framework or process, because most organizations will find that there's many use cases, potentially dozens of use cases that thoughts what could be used for, and clearly you can't move forward with all of them. At the same time, eso. Another thing that our team helps customers walk through is what's the impact, the potential value, other particular use case. You know, you, Blake, you mentioned some of those outcomes, is it? Changing the expense processes it around? Reducing customer churn is an increasing speed toe insight and speak the market on defining those measurable outcomes that define the vertical axis here. The strategic importance off that use case. Um, but that's not the only dimension that you're gonna look at the East to deploy factors into that you could have the most valuable use case ever. But if it's going to take you to three years to get it implemented for various reasons, you're not really gonna start with that one, right? So the combination of east to deploy, aligned with the strategic importance or business value really gives you that road map of where to focus to prioritize on use cases. Eso again, Andrea, you've been through this, um, in your prior time at Canadian time. Maybe you can share some thoughts on how you approach that. >>Yeah. So our initial use case was a great launching platform because the merchandizing team had a huge amount across full engagement. So once we had the merchants on board, we started to plan or use case roadmap looking for other areas, and departments were thought spot had already started to spread by word of mouth and we where we felt there was a high strategic importance. As we started to scope these areas, the ease of deployment started to get more complicated. We struggled to get the right people engaged and didn't always have the top down support for resources in the new use case area. We wanted to maintain momentum with the adoption, but it was starting to feel like we were stalling out on the freeway. Then the strategic marketing team reached out and was really excited about getting into thought spot. This was an underserved team where when it came to data, they always had someone else running it for them, and they'd have to request reports and get the information in. Um, and our initial roadmap focused on the biggest impact areas where we could get the most users, and this team was not on the radar. But when we started to engage with them, we realized that this was gonna be an easy deployment. We already had the data and thought spot to support their needs, and it turned into such a great win because as a marketing team, they were so thrilled to have thought spot and to get the data when they needed it and wanted it. They continued to spread the word and let everyone know. But it also gave the project team a quick win to put some gas in the tank and keep us moving. So you want to plan your use case trajectory, but you also need to be willing to adapt to keep the momentum going. >>Yeah, no, that's a That's a really great point. So So Blake is a customer success manager. I'm sure you lived through some integration of this all the time. So any anything you wanted to add that >>Yes. So to Andrew's point, continuous delivery is key for technical folks out there were talking and agile methodology mindset versus a waterfall. So to show value, there's many different factors that air at play. You need to look at the overall business initiatives. We need to look at financial considerations. We need to look at different career objectives and also resource limitations. So when you start thinking about all those different factors, this becomes a mixture of art and science. So, for example, at the beginning of a project when thought spot is has just been purchased or whatever tool has just been purchased. You want to show immediate value to justify that purchase. So in order to show immediate value, you might want to look at a project or a use case that is tightly aligned to a business objective. Therefore, it shows value, and it has data that is ready to go without many different transformations. But as you move forward, you have to come up with a plan that is going to mix together these difficult use cases with the easier use cases and high business values cases versus the lower. So in order to do that, my most successful customers are evaluating those different business factors and putting those into place with an overall use case development plan. >>Really good feedback. That's great. Thank you. Thanks, Blake. Um, I think s a little bit of a reality check here. Right. So I think we all recognize that any technology implementation, um, is gonna have her bumps in the road. It's not gonna be smooth sailing all along the way. You know, we talk about people, process and technology. The technology wrote wrote roadblocks can be infrastructure related there could be some of the data quality issues that you're alluding to there. Like Onda, people in process fall into the sort of the cultural, uh, cultural cultural side of it. Blake, maybe you can spend a couple minutes going through. What? What if some of those bigger roadblocks that people may face on that, um, technical side on how they could both prepare for them and then address them as they come along? >>Yeah. So the most intimidating part of any business intelligence or analytics initiative is that it's going to put the data directly into the hands of the business users. And this is especially true with ocelot. So why this is intimidating is because it's going toe, lay bare and expose any data issues that exist. So this is going to lead to the most common objective that I hear to starting. Any new use case or any FBI initiative overall, which is our data isn't ready. And essentially that is fear of failure. So when data isn't ready and companies aren't ready to start these projects, what happens is to get around those data issues. There's a lot of patchwork that's happening, you know, this patchwork is necessary just to keep the wheels in motion just to keep things going. So what I mean by the patchwork is extracting the data from a source doing some manual manipulation, doing some manipulation directly within the within the database in order to satisfy those business users request. So this keeps things going, but it's not addressing the key issues that are in place now. While it's intimidating to start these initiatives, the beauty of starting these B I initiatives is it's going to force your company to address and fix these issues. And this, to me, is somewhere where thoughts what is a gigantic benefit? It's not something that we talk about necessarily or market, but thought Spot is really good at helping fix these data issues. And I say this for two reasons. One his data quality. So, with thoughts about you can run, searches directly against your most granular level data and find where those data issues exist, and now, especially with embrace, you're running it directly against the source. So thats what is going to really help you figure out those data quality issues. So as you develop a use case, we can uncover those data quality issues and address them accordingly. And second is data governance. So especially again with embrace and our cloud, our cloud structure is you are going to be bringing Companies are going to be bringing data sources from all over the place all into one source and into one logical view. And so traditionally, the problem with that is that your data and source a might be the theoretically the same data and source B. But the numbers are different. And so you have different versions of the truth. So what thoughts about helps you do is when you bring those sources together. Now you're gonna identify those issues, and now you're gonna be forced to address them. You're gonna be forced to address naming convention issues, business logic issues, which business logic translates to the technical logic toe transform that data and then also security and access. Who was actually able to see this data across these different data sources. So overall, the biggest objective eye here is our data isn't ready. But I challenge that. And I say that by taking on this initiative with thought spot, you were going to be directly addressing that issue and thoughts. What's going to help you fix it? >>Yeah, that's Ah, I'd love that observation that, you know, data quality issues. They're not gonna go away by themselves. And if thoughts, thoughts what could be part of the solution, then even better. So that's a That's a really great observation. Eso Andrea, looking at the sort of the cultural side of things the people in process, Um, what are some of the challenges that you've seen there that folks in the audience could that could learn from? >>Yeah. So think about the last time you learned a new system or tool. How long did it take you to get adjusted and get the performance you wanted from it? Maybe you hit the ground running, but maybe you still feel like you're not quite getting the most out of it. Everyone deals with change differently, and sometimes we get stuck in the change curve and never fully adapt. Companies air no different. Ah, lot of the roadblocks you may face are not only from individual struggling to get on board, but can be the result of an organizational culture that may not be used to change or managing it. Their external impacts on how we accept change such as Was there a clear message about the upcoming changes and impacts? Was there a communication channel for questions and concerns? Did individuals feel like their input was sought after and valued? Where there are multiple mediums, toe learn from was their time to learn? Organizational change is hard. And if there isn't a culture that allocates time and resources to training, then realizing success is gonna be an uphill battle. It will be harder to move people forward if they don't have the time to get comfortable and feel acclimated to the new way of doing things. Without the training and change support from the organization, you'll end up running the old and the new simultaneously, which we talked about not in our live supporting users, in both eyes going to negate that value. There were times at Canadian Tire where we really struggled to get key stakeholders engaged or to get leadership by it on the time of the resources that we're gonna be needed and committed Thio to make a use case successful. So gauging where people and the organization are in the change curve is the first step in moving them along the path towards acceptance and integration. So you'll wanna have an action plan to address the concerns and resistance and a way to solicit and channel feedback. >>Yeah, that's Zo great feedback. And I particularly like what you talked about sort of the old and the new because, you know, we've talked about success and measurement on value quite a bit in this session, and ultimately that's that's the goal, right? Is to live a Value s o. This is a framework that we found really helpful visit. Value Team is defining those success criteria really actually falls into two categories on the right hand side. Better decisions. Um, that's ultimately what you're looking to drive with thoughts about right. You're looking to get newer inside faster to be able to drive action and outcomes based on decisions that do. Maybe we're using your gut for previously on the words under that heading. They're going to change by organizations. So you know, those don't get too caught up on those, but it's really around defining, you know, one. Are those better decisions that you're looking to drive, Who what's the persona is gonna be making them one of their actually looking to accomplish when inside. So they're looking to get one of what are the actions they're going to take on those insights? And then how do we measure Thean pact of those actions that then provides us with the the foundation of a business case in our I, um, in parallel to that, it's important to remember that this use case is not just operating in a vacuum, right? Every organization has a Siri's off strategic transformational initiatives move to the cloud democratized data, etcetera. And to the extent that you can tie particular use cases into those key strategic initiatives, really elevates the importance off that use case outside of its own unique business case. In our calculation on Bazzaz several purposes, right, it raises the visibility project. It raises the visibility of the person championing project on. Do you know reality here is that every idea organization has tons of projects have taken invest in, but the ones they're gonna be more likely to invest in other ones that are tied to those strategic initiatives. So it increases the likelihood of getting the support and funding that you need to drive this forward um, that's really around defining the success success criteria upfront. Um, and >>what >>we find is a lot of organizations do that pretty well, and they've got a solid, really solid business case to move forward. But then over time, they kind of forget about that on. Do you know, a year down the line two years down the line, Maybe even, you know, three months, six months down the line. Maybe people have rotated through the business. People have come and gone, and you almost forget the benefit that you're driving, right? And so it's really important to not do that and keep an eye on and track Onda, look back and analyze and realize the value that use cases have driven on. Obviously, the structure of that and what you measure is gonna very significantly by escape. But it's really important there Thio to make sure that you're counting your success and measuring your success. Um, Andrea, I don't any any thoughts on that from from your past experience. >>Yeah, um, success will be different For each use case, 1 may be focused on reducing the time to insights in a fast competitive market, while another may be driven by a need to increase data fluency to reduce risk. The weighting of each of these criterias will shift and and the value perception should as well. Um, but one thing that we don't want to forget is to share your personal successes. So be proud of the work that you've done in the value it's created. Um, if you're a user who has taken advantage of thought spot and managed to grab a competitive edge by having faster in depth access to data, share that in your business reviews. If you're managing the adoption at your company, share your use case winds and user adoption stories. Your customer success team is here to help you articulate the value and leverage the great work being done in and because of thought spot. >>Yeah, long story short here. This benefits everybody. This is something that's easily overlooked and something that it ZZ not to do this to track adoption to define the r o I, but it benefits those benefits. Start spot benefits of customers. Everybody wins. When we do this, >>that's Ah, that's a great point. So, um, so if we talk about you know, as we wrap the session up. You know what can what can folks in the audience dio right now to start making some of this stuff happened? You know, you're Blake again, coming back to you in customer success. How have you and your role help customers take that next step and start executing on some of the things that we've talked about? >>Yeah. So to start off with, I would just say for each use case as much as possible, define the why and to find the success criteria. Just start off with those two, those two elements and over time that that process we'll get more and more refined and our goal within the CSCE or within within thoughts. But overall, not just the C s order is to enable all of our all of our customers to be able to do all these things on their own. And to be a successful, it's possible to be able to pick the right use cases to be able to execute those right use cases as effectively as possible. So we are here to help with that. CS is here to help with that. Your account executives here to help with that, we have use case workshops. We have our professional services team that can get in and help develop use cases. So lots of options available in goal. We all mutually benefit when we try to track towards thes best possible use cases. >>All right, that we're here to help. That's Ah, that's a great way. Thio, wrap up the session there. Thanks, Blake. For all of your thoughts and Andrea to hope everyone in the audience got some valuable insights here on how to choose the right news case and be successful with thoughts about, um, with that being, I'll hand it back over to you. >>Amazing. That was an awesome session. Thank you so much, guys. So our third session is up next, and we're going to be going Global s. Oh, hang on tight as we explore best practices from the extended ecosystem of cloud based analytics. >>Yeah,

>>from the Cube Studios in Palo Alto and Boston. It's the Cube covering empowering the autonomous enterprise brought to you by Oracle Consulting. Welcome back, everybody to this special digital event coverage. The Cube is looking into the rebirth of Oracle Consulting. Janet George is here. She's group VP Autonomous for Advanced Analytics with machine learning and artificial intelligence at Oracle on she joined by Grant Gibson is VP of growth and strategy. Folks, welcome to the Cube. Thanks so much for coming on. I want to start with you because you get strategy in your title start big picture. What is the strategy with Oracle specifically as it relates to autonomous and also consulting? >>Sure. So I think you know, Oracle has a deep legacy of strength and data and over the company's successful history, it's evolved what that is from steps along the way. If you look at the modern enterprise Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology and people know that they need to take advantage of it. It's the how that's really tricky and that most enterprises, in order to really get an enterprise level, are rely on AI investment. Need to engage in projects of significant scope, and going from realizing there's an opportunity realizing there's a threat to mobilize yourself to capitalize on it is a daunting task. Certainly one that's anybody that's got any sort of legacy of success has built in processes as building systems has built in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs as well as the data science needs. >>So there's about five or six things that I want to follow up with you there, so this is a good conversation. Ever since I've been in the industry, we were talking about a sort of start stop start stopping at the ai Winter, and now it seems to be here. I almost feel like the technology never lived up to its promise you didn't have the horsepower compute power data may be so we're here today. It feels like we are entering a new era. Why is that? And how will the technology perform this time? >>So for AI to perform is very reliant on the data. We entered the age of Ai without having the right data for AI. So you can imagine that we just launched into Ai without our data being ready to be training sex for AI. So we started with big data. We started the data that was already historically transformed. Formatted had logical structures, physical structures. This data was sort of trapped in many different tools. And then suddenly Ai comes along and we see Take this data, our historical data we haven't tested to see if this has labels in it. This has learning capability in it. Just trust the data to AI. And that's why we saw the initial wave of ai sort of failing because it was not ready to fully ai ready for the generation of ai if >>you will. And part of I think the leap that clients are finding success with now is getting novel data types and you're moving from zeros and ones of structured data, too. Image language, written language, spoken language You're capturing different data sets in ways that prior tools never could. So the classifications that come out of it, the insights that come out of it, the business process transformation comes out of it is different than what we would have understood under the structure data formats. So I think it's that combination of really being able to push massive amounts of data through a cloud product processes at scale. That is what I think is the combination that takes it to the next plateau, for >>sure. The language that we use today, I feel like it's going to change. And you just started to touch on some of it, sensing our senses and visualization on the the auditory. So it's it's sort of this new experience that customers are seeing a lot of this machine intelligence behind. >>I call it the autonomous and price right, the journey to be the autonomous enterprise, and when you're on this journey to be the autonomous enterprise, you need really the platform that can help you be cloud is that platform which can help you get to the autonomous journey. But the Thomas journey does not end with the cloud. It doesn't end with the Data Lake. These are just infrastructures that are basic necessary necessities for being on that on that autonomous journey. But at the end, it's about how do you train and scale at, um, very large scale training that needs to happen on this platform for AI to be successful. And if you are an autonomous and price, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components ai and machine learning to derive business, intelligence and business value. >>So I want to get into a little bit of Oracle's role. But to do that, I want to talk a little bit more about the industry. So if you think about the way that the industry seems to be restructuring around data, historically, industries had their own stack value chain and if you were in in in the finance industry, you were there for life. >>So when you think about banking, for example, highly regulated industry think about our culture. These are highly regulated industries there. It was very difficult to destruct these industries. But now you look at an Amazon, right? And what does an Amazon or any other tech giants like Apple have? They have incredible amounts of data. They understand how people use for how they want to do banking. And so they've come up with a lot of cash or Amazon pay. And these things are starting to eat into the market. Right? So you would have never thought and Amazon could be a competition to a banking industry just because of regulations. But they're not hindered by the regulations because they're starting at a different level. And so they become an instant threat in an instant destructive to these highly regulated industries. That's what data does, right when you use data as your DNA for your business and you are sort of born in data or you figure out how to be autonomous. If you will capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be like the food industry can be the cloud industry, the book industry, you know, different industries. So you know that that's what I see happening with the tech giants. >>So great, there's a really interesting point that the Gina is making that you mentioned. You started off with a couple of industries that are highly regulated, harder to disrupt, use it got disrupted. Publishing got disrupted. But you've got these regulated businesses. Defense. Automotive actually hasn't been surely disrupted yet. Tesla. Maybe a harbinger. And so you've got this spectrum of disruption. But is anybody safe from disruption? >>I don't think anyone's ever say from it. It's It's changing evolution, right? That you whether it's, you know, swapping horseshoes for cars are TV for movies or Netflix are any sort of evolution of a business. You're I wouldn't coast on any of it. And I think t earlier question around the value that we can help bring the Oracle customers is that you know, we have a rich stack of applications, and I find that the space between the applications, the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company, but it's trapped from both a technology and a business perspective. And that's where I think really any company can take advantage of knowing it's data better and changing itself to take advantage of what's already there. >>Yet powerful people always throw the bromide of the data is the new oil. And we've said no data is far more valuable because you can use it in a lot of different places where you can use once, and it's follow the laws of scarcity data, if you can unlock it. And so a lot of the incumbents they have built a business around whatever factory, our process and people, a lot of the trillion are starting us that become millionaires. You know, I'm talking about data is at the core data company. So So it seems like a big challenge for your incumbent customers. Clients is to put data at the core, be able to break down those silos. How do they do that? >>Grading down silos is really super critical for any business. It was okay to operate in a silo, for example. You would think that Oh, you know, I could just be payroll, inexpensive falls, and it wouldn't matter matter if I get into vendor performance management or purchasing that can operate as asylum. But anymore, we are finding that there are tremendous insights. But in vendor performance management, I expensive for these things are all connected, so you can't afford to have your data sits in silos. So grading down that silo actually gives the business very good performance right insights that they didn't have before. So that's one way to go. But but another phenomena happens When you start to great down the silos, you start to recognize what data you don't have to take your business to the next level. That awareness will not happen when you're working with existing data so that Obama's comes into form. When you great the silos and you start to figure out you need to go after a different set of data to get you to a new product creation. What would that look like? New test insights or new Catholics avoidance that that data is just you have to go through the iteration to be able to figure that out. >>Stakes is what you're saying. So this notion of the autonomous enterprise. I help me here cause I get kind of autonomous and automation coming into I t I t ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >>I think when is a technology problem? The company? Is it a loss? AI has to be a business problem. AI has to inform the business strategy. Ai has been companies the successful companies that have done so. 90% of my investments are going towards state. We know that most of it going towards ai this data out there about this, right? And so we look at what are these? 90 90% of the companies investments where he's going and whose doing this right who's not doing this right? One of the things we're seeing as results is that the companies that are doing it right have brought data into the business strategy. They've changed their business model, right? So it's not like making a better taxi, but coming up with global, right? So it's not like saying Okay, I'm going to have all these. I'm going to be the drug manufacturing company. I'm gonna put drugs out there in the market this is I'm going to do connected help, right? And so how does data serves the business model of being connected? Help rather than being a drug company selling drugs to my customers, right? It's a completely different way of looking at it. And so now you guys informing drug discovery is not helping you just put more drugs to the market. Rather, it's helping you come up with new drugs that would help the process of connected games. There's a >>lot of discussion in the press about, you know, the ethics of a and how far should we take a far. Can we take it from a technology standpoint, Long room there? But how far should we take it? Do you feel as though public policy will take care of that? A lot of that narrative is just kind of journalists looking for, You know, the negative story. Well, that's sort itself out. How much time do you spend with your customers talking about that >>we in Oracle, we're building our data science platform with an explicit feature called Explained Ability. Off the model on how the model came up with the features what features they picked. We can rearrange the features that the model picked. Citing Explain ability is very important for ordinary people. Trust ai because we can't trust even even they decided this contrast right to a large extent. So for us to get to that level where we can really trust what AI is picking in terms of a modern, we need to have explain ability. And I think a lot of the companies right now are starting to make that as part of their platform. >>We're definitely entering a new era the age of of AI of the autonomous enterprise folks. Thanks very much for great segment. Really appreciate it. >>Yeah. Pleasure. Thank you for having us. >>All right. And thank you and keep it right there. We'll be back with our next guest right after this short break. You're watching the Cube's coverage of the rebirth of Oracle consulting right back. Yeah, yeah, yeah, yeah, yeah, yeah

from the cube studios in Palo Alto in Boston it's the cube covering empowering the autonomous enterprise brought to you by Oracle consulting welcome back everybody to this special digital event coverage that the cube is looking into the rebirth of Oracle consulting Janet George is here she's group vp autonomous for advanced analytics with machine learning and artificial intelligence at oracle and she's joined by grant gibson is a group vp of growth and strategy at oracle folks welcome to the cube thanks so much for coming on thank you thank you great I want to start with you because you get strategy in your title like just start big picture what is the strategy with Oracle specifically as it relates to autonomous and also consulting sure so I think you know Oracle has a deep legacy of strengthened data and over the company's successful history it's evolved what that is from steps along the way if you look at the modern enterprise of Oracle client I think there's no denying that we've entered the age of AI that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward and while generally it's acknowledge that it's a transformative technology and people know that they need to take advantage of it it's the how that's really tricky and that most enterprises in order to really get an enterprise level ROI on an AI investment need to engage in projects of significant scope and going from realizing there's an opportunity to realize and there's a threat to mobilizing yourself to capitalize on it is a is a daunting task for an enemy certainly one that's you know anybody that's got any sort of legacy of success has built-in processes that's built in systems has built in skillsets and making that leap to be an autonomous enterprise is is challenging for companies to wrap their heads around so as part of the rebirth of Oracle consulting we've developed a practice around how to both manage the the technology needs for that transformation as well as the human needs as well as the data science needs to it so rather there's about five or six things that I want to followup with you there so there's gonna be good conversations Janet so ever since I've been in the industry we're talking about AI in sort of start stop start stop we had the AI winter and now it seems to be here it's almost feel like that the the technology never lived up to its promise you didn't have the horsepower a compute power you know enough data maybe so we're here today feels like we are entering a new era why is that and and how will the technology perform this time so for AI to perform it's very reliant on the data we entered the age of AI without having the right data for AI so you can imagine that we we just launched into AI without our data being ready to be training sex for AI so we started with bi data or we started the data that was already historically transformed formatted had logical structures physical structures this data was sort of trapped in many different tools and then suddenly AI comes along and we say take this data our historical data we haven't tested to see if this has labels in it this has learning capability in it we just thrust the data to AI and that's why we saw the initial wave of AI sort of failing because it was not ready to fall AI ready for the generation of AI and part of I think the leap that clients are finding success with now is getting the Apple data types and you're moving from the zeros and ones of structured data to image language written language spoken language you're capturing different data sets in ways that prior tools never could and so the classifications that come out of it the insights that come out of it the business process transformation comes out of it is different than what we would have understood under the structured data format so I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale that is what I think is the combination that takes it to the next plateau for sure the language that we use today I feel like is going to change and you just started to touch on some of them you know sensing you know they're our senses and you know the visualization and the the the the auditory so it's it's sort of this new experience that customers are saying a lot of this machine intelligence behind them I call it the autonomous enterprise right the journey to be the autonomous enterprise and when you're on this journey to be the autonomous enterprise you need really the platform that can help you be cloud is that platform which can help you get to the autonomous journey but the autonomous journey does not end with the cloud right or doesn't end with the dead lake these are just infrastructures that are basic necessary necessities for being on that on that autonomous journey but at the end it's about how do you train and scale at a very large scale training that needs to happen on this platform for AI to be successful and if you are an autonomous enterprise then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value if you will so you've got the platform you've got the data and now you're actually tapping into the autonomous components AI and machine learning to derive business intelligence and business value so I want to get into a little bit of Oracle's role but to do that I want to talk a little bit more about the industry so if you think about the way this the industry seems to be restructuring around data there historically Industries had their own stack or value chain and if you were in the finance industry you were there for life you know so when you think about banking for example highly regulated industry think about our geek culture these are highly regulated industries they're come it was very difficult to disrupt these industries but now you look at an Amazon right and what does an Amazon or any other tech giant like Apple have they have incredible amounts of data they understand how people use or how they want to do banking and so they've cut off the tap of cash or Amazon pay and these things are starting to eat into the market right so you would have never thought an Amazon could be a competition to your banking industry just because of regulations but they are not hindered by the regulations because they're starting at a different level and so they become an instant threat and an instant destructor to these highly regulated industries that's what data does right then you use data as you DNA for your business and you are sort of born in data or you figured out how to be autonomous if you will capture value from that data in a very significant manner then you can get into industries that are not traditionally your own industry it can be like the food industry it can be the cloud industry the book industry you know different industries so you know that that's what I see happening with the tech giants so great this is a really interesting point that Gina is making that you mentioned you started off with like a couple of industries that are highly regulated harder to disrupt you know music got disrupted publishing got disrupted but you've got these regulated businesses you know defense automotive actually hasn't been truly disrupted yet so I'm Tesla maybes a harbinger and so you've got this spectrum of disruption but is anybody safe from disruption okay I don't think anyone's ever safe from it it's it's changed in evolution right that you whether it's you know swapping horseshoes for cars or TV for movies or Netflix or any sort of evolution of a business you I wouldn't coast on any of them and I think to earlier question around the value that we can help bring to Oracle customers is that you know we have a rich stack of applications and I find that the space between the applications the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company but it's trapped from both a technology and a business perspective and that's where I think really any company can take advantage of knowing its data better and changing itself to take advantage of what's already there yet powerful bit people always throw the bromide out the data is the new oil and we've said no data is far more valuable because you can use it in a lot of different places or you can use once and it's has to follow laws of scarcity data if you can unlock it and so a lot of the incumbents they have built a business around whatever a factory or you know process and people a lot of the the trillion-dollar start in us that they're become trillionaires you know I'm talking about data is at the core their data company so so it seems like a big challenge for you you're incumbent customers clients is to put data hit the core be able to break down those silos how do they do that grading down silos is really super critical for any business it was okay to operate in a silo for example you would think that oh you know I could just be payroll in expense reports and it wouldn't man matter if I get into vendor performance management or purchasing that can operate as a silo but anymore we are finding that there are tremendous insights between vendor performance management I expensive all these things are all connected so you can't afford to have your data set in silos so grading down that silo actually gives the business very good performance right insights that they didn't have before so that's one way to go but but another phenomena happens when you start to great down the silos you start to recognize what data you don't have to take your business to the next level right that awareness will not happen when you're working with existing data so that awareness comes into form when you great the silos and you start to figure out you need to go after different set of data to get you to new product creation what would that look like new test insights or new capex avoidance then that data is just you have to go through the eye tration to be able to figure that out which takes is what you're saying happy so this notion of the autonomous under president help me here because I get kind of autonomous and automation coming into IT IT ops I'm interested in how you see customers taking that beyond the technology organization into the enterprise I think when AI is a technology problem the company is it at a loss ai has to be a business problem ai has to inform the business strategy ai has two main companies the successful companies that have done so 90 percent of our investments are going towards data we know that and and most of it going towards AI data out there about this right and so we looked at what are these ninety cup ninety percent of the company's investments where are these going and who is doing this right and who's not doing this right one of the things we are seeing as results is that the companies that are doing it right have brought data into their business strategy they've changed their business model right so it's not like making a better taxi but coming up with uber right so it's not like saying okay I'm going to have all these I'm going to be the drug manufacturing company I'm going to put drugs out there in the market versus I'm going to do connected health right and so how does data serve the business model of being connected health rather than being a drug company selling drugs to my customers right it's a completely different way of looking at it and so now I is informing drug discovery AI is not helping you just put more drugs to the market rather it's helping you come up with new drugs that will help the process of connected game there's a lot of discussion in the press about you know the ethics of AI and how far should we take AI and how far can we take it from a technology standpoint long roadmap there but how far should we take it do you feel as though public policy will take care of that a lot of that narrative is just kind of journalists looking for you know the negative story well that's sort itself out how much time do you spend with your customers talking about that we in Oracle we're building our data science platform with an explicit feature called explain ability off the model on how the model came up with the features what features it picked we can rearrange the features that the model picked so I think explain ability is very important for ordinary people to trust AI because we can't trust AI even even data scientists contrast AI right to a large extent so for us to get to that level where we can really trust what AI is picking in terms of a model we need to have explained ability and I think a lot of the companies right now are starting to make that as part of their platform well we're definitely entering a new era the the age of AI of the autonomous enterprise folks thanks very much for a great segment really appreciate it yeah our pleasure thank you for having us thank you alright and thank you and keep it right there we're right back with our next guest for this short break you're watching the cubes coverage of the rebirth of Oracle consulting right back you [Music]

>>Yeah, yeah, >>yeah! >>Welcome back, everybody. To this special digital event coverage, the Cube is looking into the rebirth of Oracle Consulting. Janet George is here. She's group VP Autonomous for Advanced Analytics with machine learning and artificial intelligence at Oracle. And she's joined by Grant Gibson Group VP of growth and strategy at Oracle. Folks, welcome to the Cube. Thanks so much for coming on. Great. I want to start with you because you get strategy in your title like this. Start big picture. What is the strategy with Oracle specifically as it relates to autonomous and also consulting? >>Sure. So I think you know, Oracle has a deep legacy of strength and data and, uh uh, over the company's successful history. It's evolved what that is from steps along the way. And if you look at the modern enterprise Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology and people know that they need to take advantage of it, it's the how that's really tricky and that most enterprises, in order to really get an enterprise level, are rely on AI investment. Need to engage in projects of significant scope, and going from realizing there's an opportunity of realizing there's a threat to mobilize yourself to capitalize on it is a daunting task or certainly one that's, you know, Anybody that's got any sort of legacy of success has built in processes as building systems has built in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs as well as the data science needs. >>So there's about five or six things that I want to follow up with you there. So this is a good conversation. Ever since I've been in the industry, we were talking about a sort of start stop start stop at the Ai Winter, and now it seems to be here is almost feel like the technology never lived up to its promise. If you didn't have the horsepower compute power data may be so we're here today. It feels like we are entering a new era. Why is that? And how will the technology perform this time? >>So for AI to perform it's very remind on the data we entered the age of Ai without having the right data for AI. So you can imagine that we just launched into Ai without our data being ready to be training sex for AI. So we started with B I data or we started the data that was already historically transformed. Formatted had logical structures, physical structures. This data was sort of trapped in many different tools. And then suddenly Ai comes along and we see Take this data, our historical data we haven't tested to see if this has labels in it. This has learning capability in it. Just trust the data to AI. And that's why we saw the initial wave of ai sort of failing because it was not ready to full ai ready for the generation of Ai, if you will. >>So, to me, this is I always say, this was the contribution that Hadoop left us, right? I mean, the dupe everybody was crazy. It turned into big data. Oracle was never that nuts about it is gonna watch, Setback and wash obviously participated, but it gathered all this data created Chief Data Lakes, which people always joke turns into data swamps. But the data is often times now within organizations least present. Now it's a matter of what? What what's The next step is >>basically about Hadoop did to the world of data. Was her dupe freed data from being stuck in tools it basically brought forth. This concept of a platform and platform is very essential because as we enter the age of AI and be entered, the better wide range of data. We can't have tools handling all of the state of the data needs to scale. The data needs to move, the data needs to grow. And so we need the concept of platforms so we can be elastic for the growth of the data, right, it can be distributed. It can grow based on the growth of the data, and it can learn from that data. So that is that's the reason why Hadoop sort of brought us into the platform board, >>right? A lot of that data ended up in the cloud. I always say, You know, for years we marched to the cadence of Moore's law. That was the innovation engine in this industry and fastest, you could get a chip in, you know, you get a little advantage, and then somebody would leapfrog. Today it's got all this data you apply machine intelligence and cloud gives you scale. It gives you agility of your customers. Are they taking advantage of the new innovation cocktail? First of all, do you buy that? How do you see them taking >>advantage of? Yeah, I think part of what James mentioned makes a lot of sense is that at the beginning, when you know you're taking the existing data in an enterprise and trying to do AI to it, you often get things that look a lot like what you already knew because you're dealing with your existing data set in your existing expertise. And part of I think the leap that clients are finding success with now is getting novel data types, and you're moving from, uh, zeros and ones of structured data, too. Image language, written language, spoken language. You're capturing different data sets in ways that prior tools never could. And so the classifications that come out of it, the insights that come out of it, the business process transformation comes out of it is different than what we would have understood under the structure data format. So I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale. That is what I think is the combination that takes it to the next plateau for sure. >>So you talked about sort of. We're entering a new era Age of a AI. You know, a lot of people, you know, kind of focus on the cloud is the current era, but it really does feel like we're moving beyond that. The language that we use today, I feel like it's going to change, and you just started to touch on some of it. Sensing, you know, there are senses and you know the visualization in the the auditory. So it's It's sort of this new experience that customers are seeing a lot of this machine intelligence behind. >>I call it the autonomous and a price right. The journey to be the autonomous enterprise. And then you're on this journey to be the autonomous enterprise you need. Really? The platform that can help you be cloud is that platform which can help you get to the autonomous journey. But the autonomous journey does not end with the cloud or doesn't end with the data lake. These are just infrastructures that are basic necessary necessities for being on that on that autonomous journey. But at the end, it's about how do you train and scale at, um, very large scale training that needs to happen on this platform for AI to be successful. And if you are an autonomous and price, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components ai and machine learning to derive business, intelligence and business value. >>So I want to get into a little bit of Oracle's role. But to do that I want to talk a little bit more about the industry. So if you think about the way that the industry seems to be restructuring around data. Historically, industries had their own stack value chain, and if you were in in in the finance industry, you were there for life. We had your own sales channel distribution, etcetera. But today you see companies traversing industries, which has never happened before. You know, you see apple getting into content and music, and there's so many examples are buying whole foods data is sort of the enabler. There you have a lot of organizations, your customers, that are incumbents that they don't wanna get disrupted your part big party roles to help them become that autonomous and press so they don't get disrupted. I wonder if you could maybe maybe comment on How are you doing? >>Yeah, I'll comment and then grant you China, you know. So when you think about banking, for example, highly regulated industry think about RG culture. These are highly regulated industries there. It was very difficult to destruct these industries. But now you look at an Amazon, right? And what is an Amazon or any other tech giants like Apple have? They have incredible amounts of data. They understand how people use for how they want to do banking. And so they've come up with Apple cash or Amazon pay, and these things are starting to eat into the market, right? So you would have never thought and Amazon could be a competition to a banking industry just because of regulations. But they're not hindered by the regulations because they're starting at a different level. And so they become an instant threat in an instant destructive to these highly regulated industries. That's what data does, right when you use data as your DNA for your business and you are sort of born in data or you figured out how to be autonomous. If you will capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be like the food industry can be the cloud industry, the book industry, you know, different industries. So you know that that's what I see happening with the tech giants. >>So great, there's a really interesting point that the Gina is making that you mentioned. You started off with a couple of industries that are highly regulated, the harder to disrupt use, it got disrupted, publishing got disrupted. But you've got these regulated businesses. Defense or automotive actually hasn't been truly disrupted yet. Some Tesla, maybe a harbinger. And so you've got this spectrum of disruption. But is anybody safe from disruption? >>Kind of. I don't think anyone's ever say from it. It's It's changing evolution, right? That you whether it's, you know, swapping horseshoes for cars are TV for movies or Netflix are any sort of evolution of a business You're I wouldn't coast on any of them. And I think to the earlier question around the value that we can help bring the Oracle customers is that you know, we have a rich stack of applications, and I find that the space between the applications, the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company. But it's trapped from both a technology and a business perspective. Uh, and that's where I think really any company can take advantage of knowing it's data better and changing itself to take advantage of what's already there. >>Yet powerful people always throw the bromide out. The data is the new oil, and we've said. No data is far more valuable because you can use it in a lot of different places. Oil you can use once and it's follow the laws of scarcity data if you can unlock it. And so a lot of the incumbents they have built a business around, whatever a factory or a process and people, a lot of the trillion are starting us that have become billionaires. You know, I'm talking about Data's at the core. They're data companies. So So it seems like a big challenge for your incumbent customers. Clients is to put data at the core, be able to break down those silos. How do they do that? >>Grading down silos is really super critical for any business. It was okay to operate in a silo, for example. You would think that, Oh, you know, I could just be payroll and expense reports and it wouldn't matter matter if I get into vendor performance management or purchasing that can operate as a silo. But any movie of finding that there are tremendous insights between vendor performance management I expensive for these things are all connected, so you can't afford to have your data sits in silos. So grading down that silo actually gives the business very good performance, right? Insights that they didn't have before. So that's one way to go. But but another phenomena happens when you start to great down the silos, you start to recognize what data you don't have to take your business to the next level, right. That awareness will not happen when you're working with existing data so that a Venice comes into form when you great the silos and you start to figure out you need to go after a different set of data to get you to a new product creation. What would that look like? New test insights or new cap ex avoidance that that data is just you have to go through the iteration to be able to figure that out. >>It becomes it becomes a business problem, right? If you got a process now where you can identify 75% of the failures and you know the value of the other 25% of failures, that becomes a simple investment. How much money am I willing to invest to knock down some portion that 25% and it changes it from simply an I t problem or expense management problem to you know, the cash problem. >>But you still need a platform that has AP eyes that allows you to bring in those data sets that you don't have access to this enable an enabler. It's not the answer. It's not the outcome in and of itself, but it enables. And >>I always say, you can't have the best toilet if you're coming, doesn't work. You know what I mean? So you have to have your plumbing. Your plumbing has to be more modern. So you have to bring in modern infrastructure distributed computing that that you cannot. There's no compromise there, right? You have to have the right equal system for you to be able to be technologically advanced on a leader in that >>table. Stakes is what you're saying. And so this notion of the autonomous enterprise I would help me here cause I get kind of autonomous and automation coming into I t I t ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >>Yeah, this is this is such a great question, right? This is what I've been talking about all morning. Um, I think when AI is a technology problem, the company is that at a loss AI has to be a business problem. AI has to inform the business strategy. AI has to been companies. The successful companies that have done so. 90% of my investments are going towards state. We know that and most of it going towards AI. There's data out there about this, right? And so we look at what are these? 90 90% of the company's investments. Where are these going and whose doing this right? Who's not doing this right? One of the things we're seeing as results is that the companies that are doing it right have brought data into their business strategy. They've changed their business model, right? So it's not like making a better taxi, but coming up with a bow, right? So it's not like saying Okay, I'm going to have all these. I'm going to be the drug manufacturing company. I'm gonna put drugs out there in the market forces. I'm going to do connected help, right? And so how does data serve the business model of being connected? Help rather than being a drug company selling drugs to my customers, right? It's a completely different way of looking at it. And so now you guys informing drug discovery is not helping you just put more drugs to the market. Rather, it's helping you come up with new drugs that would help the process of connected games. There's a >>lot of discussion in the press about, you know, the ethics of AI, and how far should we take? A far. Can we take it from a technology standpoint, Long road map there? But how far should we take it? Do you feel as though of public policy will take care of that? A lot of that narrative is just kind of journalists looking for, You know, the negative story. Well, that's sort itself out. How much time do you spend with your customers talking about that and is what's Oracle's role there? I mean, Facebook says, Hey, the government should figure this out. What's your point? >>I think everybody has a role. It's a joint role, and none of us could give up our responsibilities as data scientists. We have heavy responsibility in this area on. We have heavy responsibility to advise the clients on the state area. Also, the data we come from the past has to change. That is inherently biased, right? And we tend to put data signs on biased data with the one dimensional view of the data. So we have to start looking at multiple dimensions of the data. It's got to start examining. I call it a responsible AI when you just simply take one variable or start to do machine learning with that because that's not that's not right. You have to examine the data. You got to understand how much biases in the data are you training a machine learning model with the bias? Is there diversity in the models? Is their diversity in the data? These are conversations we need to have. And we absolutely need policy around this because unless our lawmakers start to understand that we need the source of the data to change. And if we look at this, if we look at the source of the data and the source of the data is inherently biased or the source of the data has only a single representation, we're never going to change that downstream. AI is not going to help us. There so that has to change upstream. That's where the policy makers come into into play. The lawmakers come into play, but at the same time as we're building models, I think we have a responsibility to say can be triangle can be built with multiple models. Can we look at the results of these models? How are these feature's ranked? Are they ranked based on biases, sex, HP II, information? Are we taking the P I information out? Are we really looking at one variable? Somebody fell to pay their bill, but they just felt they they build because they were late, right? Voices that they don't have a bank account and be classified. Them is poor and having no bank account, you know what I mean? So all of this becomes part of response >>that humans are inherently biased, and so humans or building algorithms right there. So you say that through iteration, we can stamp out, the buyers >>can stamp out, or we can confront the bias. >>Let's make it transparent, >>make transparent. So I think that even if we can have the trust to be able to have the discussion on, is this data the right data that we're doing the analysis on On start the conversation day, we start to see the change. >>We'll wait so we could make it transparent. And I'm thinking a lot of AI is black box. Is that a problem? Is the black box you know, syndrome an issue or we actually >>is not a black box. We in Oracle, we're building our data science platform with an explicit feature called Explained Ability. Off the model on how the model came up with the features what features they picked. We can rearrange the features that the model picked, citing Explain ability is very important for ordinary people. Trust ai because we can't trust even even they designed This contrast ai right to a large extent. So for us to get to that level, where we can really trust what ai speaking in terms of a modern, we need to have explain ability. And I think a lot of the companies right now are starting to make that as part of their platform. >>So that's your promise. Toe clients is that your AI will be a that's not everybody's promised. I mean, there's a lot of black box and, you know, >>there is, if you go to open source and you start downloading, you'll get a lot of black boss. The other advantage to open source is sometimes you can just modify the black box. You know they can give you access, and you could modify the black box. But if you get companies that have released to open, source it somewhat of a black box, so you have to figure out the balance between you. Don't really worry too much about the black box. If you can see that the model has done a pretty good job as compared to other models, right if I take if I triangulate the results off the algorithm and the triangulation turns out to be reasonable, the accuracy on our values and the Matrix is show reasonable results. Then I don't really have to brief one model is to bias compared to another moderate. But I worry if if there's only one dimension to it. >>Well, ultimately much too much of the data scientists to make dismay, somebody in the business side is going to ask about cause I think this is what the model says. Why is it saying that? And you know, ethical reasons aside, you're gonna want to understand why the predictions are what they are, and certainly as you're going to examine those things as you look at the factors that are causing the predictions on the outcomes, I think there's any sort of business should be asking those responsibility questions of everything they do, ai included, for sure. >>So we're entering a new era. We kind of all agree on that. So I want to just throw a few questions out, have a little fun here, so feel free to answer in any order. So when do you think machines will be able to make better diagnoses than doctors? >>I think they already are making better diagnosis. And there's so much that I found out recently that most of the very complicated cancel surgeries are done by machines doctors to standing by and making sure that the machines are doing it well, right? And so I think the machines are taking over in some aspects. I wouldn't say all aspects. And then there's the bedside manners. You really need the human doctor and you need the comfort of talking to >>a CIO inside man. Okay, when >>do you >>think that driving and owning your own vehicle is going to be the exception rather than the rule >>that I think it's so far ahead. It's going to be very, very near future, you know, because if you've ever driven in an autonomous car, you'll find that after your initial reservations, you're going to feel a lot more safer in an autonomous car because it's it's got a vision that humans don't. It's got a communication mechanism that humans don't right. It's talking to all the fleets of cars. Richardson Sense of data. It's got a richer sense of vision. It's got a richer sense of ability to react when a kid jumps in front of the car where a human will be terrified, not able to make quick decisions, the car can right. But at the same time we're going to have we're gonna have some startup problems, right? We're going to see a I miss file in certain areas, and junk insurance companies are getting gearing themselves up for that because that's just but the data is showing us that we will have tremendously decreased death rates, right? That's a pretty good start to have AI driving up costs right >>believer. Well, as you're right, there's going to be some startup issues because this car, the vehicle has to decide. Teoh kill the person who jumped in front of me. Or do I kill the driver killing? It's overstating, but those are some of the stories >>and humans you don't. You don't question the judgment system for that. >>There's no you person >>that developed right. It's treated as a one off. But I think if you look back, you look back five years where we're way. You figure the pace of innovation and the speed and the gaps that we're closing now, where we're gonna be in five years, you have to figure it's I mean, I don't I have an eight year old son. My question. If he's ever gonna drive a car, yeah, >>How about retail? Do you think retail stores largely will disappear? >>I think retail. Will there be a customer service element to retail? But it will evolve from where it's at in a very, very high stakes, right, because now, with our if I did, you know we used to be invisible as we want. We still aren't invisible as you walk into a retail store, right, Even if you spend a lot of money in in retail. And you know now with buying patterns and knowing who the customer is and your profile is out there on the Web, you know, just getting a sense of who this person is, what their intent is walking into the store and doing doing responsible ai like bringing value to that intent right, not responsible. That will gain the trust. And as people gain the trust and then verify these, you're in the location. You're nearby. You normally by the sword suits on sale, you know, bring it all together. So I think there's a lot of connective tissue work that needs to happen. But that's all coming. It's coming together, >>not the value and what the what? The proposition of the customers. If it's simply there as a place where you go and buy, pick up something, you already know what you're going to get. That story doesn't add value. But if there's something in the human expertise and the shared felt, that experience of being in the store, that's that's where you'll see retailers differentiate themselves. I >>like, yeah, yeah, yeah, >>you mentioned Apple pay before you think traditional banks will lose control of payment systems, >>They're already losing control of payment systems, right? I mean, if you look at there was no reason for the banks to create Siri like assistance. They're all over right now, right? And we started with Alexa first. So you can see the banks are trying to be a lot more customized customer service, trying to be personalized, trying to really make it connect to them in a way that you have not connected to the bank before. The way we connected to the bank is you know, you knew the person at the bank for 20 years or since when you had your first bank account, right? That's how you connect with the banks. And then you go to a different branch, and then all of a sudden you're invisible, right? Nobody knows you. Nobody knows that you were 20 years with the bank. That's changing, right? They're keeping track of which location you're going to and trying to be a more personalized. So I think ai is is a forcing function in some ways to provide more value. If anything, >>we're definitely entering a new era. The age of of AI of the autonomous enterprise folks, thanks very much for great segment. Really appreciate it. >>Yeah. Pleasure. Thank you for having us. >>All right. And thank you and keep it right there. We'll be back with our next guest right after this short break. You're watching the Cube's coverage of the rebirth of Oracle consulting right back. Yeah, yeah, yeah, yeah.

>> Live from San Diego, California It's the queue covering Sisqo live US 2019 Tio by Cisco and its ecosystem barters >> Welcome back. We're here, Cisco Live San Diego. You're watching the Cubans to minimum. My co host is Dave Volante and happy to welcome to the program. First of all, I have to tell Valente, no relation was the vice president of product management who are Cloud Platform in Solutions group at Cisco. And joining us is also Santana Dasgupta, who's a distinguished systems engineer at Cisco. We're gonna be talking about service Friday. Gentlemen, thanks so much for joining us. Of course. Alright, so jail let let's start the service. Freida Group, Of course. You know, we've heard for a long time how important service fighters are out there. Everything from service writers were going to become the new channel. A Sze Yu know customers less unless they're building their own data centers. You know, service fighters become a bigger environment. Tell us a little bit about you know your organ the latest What's going on in your customers? >> Yeah, So you know what? Cisco Obviously they are trying to help Ray in the transformation to actually multi cloud leveraging. Actually, the cloud benefits not only for enterprises and public sectors, but also for the service providers so that they can also reaped the benefit off the new actually trans technologies coming out, including five g on in that context. Obviously, if you really want to take advantage of Far Gina proper way going forward, starting actually with an evolution of architectures, you really have to look at the clouds and specifically what we call the telco cloud. >> Yeah, so the Espy market is going through a mass killed transformation, transformation in the business model and architecture and how you take the services to the market on one key. And it blew up the transformation that we believe is virtual elation, adopting the whole notion of telco cloud very virtualized your core functions for enabling the delivery of services in a more agile fashion into the market. But also it's all about transforming the court services construct itself. How do we push on the services element into the age of the net for being closer to the proximity of the Indians so that it enables much? Lord didn't see a new monitor visible applications, which is where service order to have a lot of open right now. >> So if I could just dig in on that for 1st 2nd you talk about services. So we watched that wave of network functions virtual ization, NFI where before it was I just had lots of appliances and rolling out each service individually, as opposed to what people want is they want, you know, the basically, you know, at market for the enterprise. And, you know, I just want to be able to get my services. You know, when I'm a consumer and you know, I want to do things well, I've got the Internet and I get those things. I need a similar environment from the service fighters going out to the Enterprise. Do I have that kind of high level, right? >> Yes, actually, we had on that bath. I mean, they're completely years as an industry were on the journey to actually get there on go. We initially talked about most of the core functions, like think ofthe armory packet corner policy or some some value added engine at the back end. But the world is evolving faster. To actually also think through that how we can add more consumer facing applications and services on top of it, like augmented reality, virtual reality, cloud gaming and all that sort >> of stuff. Dale, this is a real imperative for telcos, and it's a complicated situation, right, because they've got decades and decades of infrastructure built up. Don Tapscott famously said one time that God may have created the world in six days, but he didn't have an install base. And so the telcos they have of kind of a fossilized, hard installed based built around making sure it's up and not necessarily agile. Now you got all these over the top players coming in, and all these value other services on top of dumb pipe, the price is air coming down. The demand for data is going up, so they gotta change. That's right, right? So what? What do they have to do and what role this Cisco play? >> So again, it told about that software defined transformation and win that is required. And they, you know, we talked already a bit more about the record, an example that was actually even showcase briefly this morning because certainly, obviously it's a greenfield operator, so it's a bit of difference, but We think that there is a lot off applique ability to brown field as well tow the legacy. You have to actually chuck into the different domains what, that service provider environment and really start looking at how you can offer both consumer services and business services at a price point at a level of automation and agility that makes sense. And that is pretty much comparable to a large extent to what the cloud providers of the week. Um, you know, there are advantages the service providers hive in terms ofthe. Obviously, the services they deliver today thie assets that they own, the proximity, the locations as well, that they have the relationships. But really, there is a, as we said earlier, Nassif transformation that start with the network, but also with those pockets where you need to Software eyes will turn to software many of those assets >> essentially talking about a specialized telco cloud, if you will. So how is that different from you know, the clouds that we know the private clouds, the hybrid clouds, the public clouds, one of the attributes that are different in how do people get on the company's getting telcos? Get in that journey. >> Yeah, well, I mean, if you look at, uh, the telco industry in general, including ourselves, like the vendors. I mean, I call myself for ourselves as, like, you know, coming back from the era of dinosaurs, right? So, I mean, if you look at the access technology for last three decades, what have changed? Nothing way have been moving from one G Tito Tito treaty to 40. Now we're talking a five g without talking off. A fundamentally destructive are differentiated architecture. So that's something which is actually being coming up all in the front front at the moment on, that's changing the way the networks can be built. How you can build on how you can break the monolithic supplication and adopt a more decomposed, desegregated our conjecture and also, at the same time, drive all the services and applications in a more distributed manner with a flexible placement capability, so that you can enable all sort of new applications and services. And again, I mean at the other. And given the fact that this is mostly a brown Fillon moment, it is largely all about culture transformation, given the fact that you know, unless the people process on, the culture revolves. This would be a very tough journey. Moving for >> one of the point back to your question is wellies. Though there are nuances big ones between a 90 cloud, uh, today in the cloud that are generally club general purpose Cloud that offered, you know, buy are obviously partners ws Microsoft, Google it and really a telco clan based on the nature ofthe those network functions. The workload on the nature of this were close. The traffic demand that they have the understanding or cliff There are how the hardware itself or the underpinning the infrastructure needs to have some specific attributes to make this work at scale. But we're trying to mimic as much as possible the scaling capabilities, the flexibility, agility, the elasticity of a cloud so that service providers can read the prophet off pretty much a general cloud >> involvement. Conceptually, there are a lot of similar out similarities. I presume that from a developer standpoint, there's a Dev ops analog, analog, maybe a cloud native, maybe serve earless. Something like server list functions absolutely in Telco cloud. >> Absolutely, absolutely. So what we see is the idea under Telco World are actually coming together because I need a lot ofthe telco expertise were also at the same time. I need a lot of expertise because that's what exact exactly right now happening. I mean, there's some fundamental differences between a standard righty private, our hybrid Claude and tell the cloud like I deploy our thousands or hundreds of locations are set a few locations. The applications are different. It's highly Io intensive. You're dealing with a lot of packets like millions of packets which are mostly are transiting function going in and out. But having said that while this initial deployment wave is being targeted for mostly for those delicate type of obligations, we're seeing a very clear demand on a journey towards a common goal of setting up our one unified cloud, right so that you can host it and telco all in the same cloud on that's exactly what they want sexually takes a reality. >> Well, in one of the things I'm surprised we haven't touched on yet is EJ Computing is, you know, critical for these environment. And I can't just have bespoke solutions for all of them. From my corrida edge toe, you know, Telco, there need to be communications amongst all of these because data is going to flow between them and therefore, it can't be. You know, Moz, in between them, I need to be able to pass data and have my applications access these various pieces. >> Absolutely. In fact, the way we have he'll concede some of the systems is a unified architecture that is distributed as a Delco plowed. So that actually from the new service managers or the new ways says B. S s. They see, actually, one unified cloud with placement capabilities based on constraints where you can actually put the workload where they need to be based on Soleil is based on the requirements in technical resources that are available, you know, from forage to a central DC and all the way to actually a public cloud because we're starting seeing some of the customers around the world. It's really a massive transformation that is global. Some of them are starting to look at how they can leverage the public cloud for bursting purposes, for disaster recovery, or even for other functions for specific applications that maybe less demanding, actually on the side. >> Well, since I know you were talking about how that one of the differences that hell cozier more distributed, you know, greater io intensity. My question is, can we learn from the telco clouds from a security model standpoint? Because normally if they go tell coz we're kind of behind traditional i t. But from a security model astounds maybe more challenging. And you always hear the traditional i t. So we it's going to the edge, the telcos already there. So is the security model actually more advanced than what can we learn from that? And how is it >> evolving? Yeah, the security model is still evolving. So in fact, I would say for the total cloud which is being done at the more Court Central Data Center location, the security model is pretty advanced. But when things go towards the edge, especially its computing, which is huge, the security model is actually evolving. And we see a lot of promises with things such as, you know, secure chain of trust, or even block Chinn actually coming there and trying to play a huge role. So I think that's one area which we expected you all over the next few years. It's a lot of challenge but also you know, it's very exciting in that particular space. >> And actually those. This is a very key point because that infrastructure from service providers is actually usually many of the country's part of the national assets the cyber securities. The agencies in those countries work actually with Cisco Security Trust officer letters to really make sure that we do have a level of security that goes beyond maybe even the boundaries of what we've seen on enterprise. So yes, to your point, there is a lot of advances in that area as well. >> All right, so jail, half the shows I've been to this year have had a breakout for Telco. There's there's no denying that there's a lot of growth and a lot of change happening in that environment. What differentiates Cisco's approach from the rest of the people looking at the multi cloud and software pieces >> so more people are murky? Pool area is first. Obviously we have these murky cloud or this hybrid cloud view in which we have worked with the best out there. The Web scale providers, the cloud providers. In fact, if I look at racket and others there are even mimicking this notion off a sorry the Google approach to, you know, really the reliability enginering the transformation off those class cloud in a very specific way. Theater aspect is we're doing it. We have a holistic view at the Telco Cloud. It's not just the infrastructure, it's the automation. The automation is absolutely critical that there is absolutely no touch from humans to be able actually to manage of that scale even more so if you deploy it in 1,000 of edge points, it has to be completely actually automated. So the aspect ofthe automation, the aspect of security, the aspect of people transformation, organizational as well is something that, between the service component to this other solution and the products is very unique. And what we do, it's Cisco. >> Yeah, if I may just add one thing on top of that, just chill said right. So if you look at our playing the Espy or telco market, we have a comprehensive solution. We are solutions right from routing Optical Jacinto Compute Telco, Claude Watch television automation, melodic or being gcm. Here's a bunch of stuff, right? But what becomes very interesting is if you look at 55 g and we all are talking up. The five G is going to be all about enterprise services now. Think about it for a while, right? Who is the number one dominant player in the market for a better price, with the deepest portfolio absolution and the farthest reaching there? No price market that Cisco. So that's what we believe, that we can actually really, you know, creator right confluence of border side of the technology to create the right offer for our customers and held them to take to the market. >> In fact, we've taken a number off our very large enterprise customers that journey to understand, from their point of view as well how they could leverage five g wife like six in the context off a mobile first cloud first type environment. And it's across permeates, actually, obviously what those service providers need to offer to grow again beyond customer services, which is not where, actually the you know, the hyper growth will be as faras Service school sir, >> Well, jail in Santa Ana. Thank you so much for sharing the updates. What happened? Tell Cho service provider space. Thanks so much for joining us. Everybody alright, We'll be back with lots more water wall coverage here at Cisco alive. San Diego 2019 for David Dante on stew Minimum. And thank you for what? Thank you.

>> Live from Bellevue, Washington, it's theCUBE, covering Smartsheet ENGAGE '18. Brought to you by Smartsheet. >> Welcome back to theCUBE's continuing coverage of Smartsheet ENGAGE 2018 from Bellevue, Washington, I'm Lisa Martin, and I'm sitting here with a couple of Smartsheeters. We've got Steven Branstetter, the VP of customer and partner success. And, Mike Andrews, you are the VP of strategic accounts. Guys, welcome! >> Thanks for having us. >> You're Smartsheeters! >> That's right. >> We are. >> I have to say, I was very scared to say that on the air, and I did it twice now, and I'm going to stop, 'cause I didn't mess it up. So, Steven, running customer and partner success. I want to start there, because customer success as a term can mean different things to different companies. Something that I read that you wrote recently was customers' feedback saying, "Guys at Smartsheet, you need to be operating a different playbook for customer success." So, first question: How do you define and deliver customer success at Smartsheet? >> Right, so, first of all, customer success is often looked at as a single department, and it's not. It is a whole company effort. You've talked with our product folks, talking with sales, everyone in the organization is part of that customer success. What they're telling us, what the customers are telling us is customer success primarily is about change management. We're going through a transformation that has a lot to do with your product, not everything to do with your product. But, we need help with that transformation. And, what you saw on the keynote was you saw three folks standing up who said, "I, at my organization, signed up "to do this really hard thing." And, we didn't have a playbook as to how to do that thing. What we try to do as a customer success organization, as a company, is make sure we're standing behind that person. So, when that person comes out and says, I can accomplish that thing, that unsolvable thing for our organization, and I can do that on Smartsheet, we want to make sure that person is successful. And so, sometimes, that's the customer success team. Sometimes, that's the training team. Sometimes, that's our consulting team. Sometimes, it's elements of product helping to come alongside them, showing them what's possible. So, customer success at Smartsheet is holistic. It's not meant to be a single department. This is a company effort, so that when folks do raise that hand and take on that impossible task, that we're with them to make sure they can accomplish that. And, that creates the stories that you heard earlier today. >> And, what Steven's talking about is, during the general session this morning, the CEO, Mark Mader, actually went down to the audience and just randomly asked several, maybe three customers to talk about how Smartsheet is empowering them. And, it was really interesting how articulate they were, being put on the spot. But, how they were able to speak so eloquently to how they are facilitating this transformation. You mentioned change management. That's a hard thing to do. >> It is. When you're looking at an enterprise that has a ton of applications, and, Mike, you know this well, being a sales leader, they're comfortable with certain applications, yet companies grow organically by acquisition, and there's a lot of different tools that some groups are married to. Other groups are, eh, I'm not so sure. To transform digitally, cultural transformation is probably step one. So, how are you seeing, and, this is the second part question to you, Mike. How have you evolved CS in Smartsheet to be facilitators of that change management, not only for customers, but for you guys as well? >> So, one of the things we thought early on was, we tried this new thing, it was called Office Hours, and we did it at one of our largest customers, and it was a huge success. Literally, the first day we do it, 400 people show up on this webcast, and it was fantastic. And so, I talk with Mike, and we talked with organizations saying, we have this new thing, Ii's going to be amazing. The feedback was fantastic. We go to that next organization to roll out the same thing, and four people show up instead of 400. >> Wow. >> And so, one of the things that's been really important for us is understanding not all organizations are the same, especially in the enterprise. That, as we create that playbook, there's certain elements that absolutely resonate at, maybe, our tech customers, that don't resonate at all in the manufacturing space or organizations, and that each of those organizations are different. So, we've built a lot of that playbook with an understanding that different elements of it are going to be applicable at different organizations. And, that's the way we've approached it, which has been really successful, where we know there are elements that have to happen. We know there are elements where we need to have scalable programs. Not everything can be one-on-one. But, at most organizations, there has to be some level of one-on-one connection as well. And, whether that's a big Smartsheet day which we'll run, which folks will fly their own folks into, it's almost like a mini ENGAGE conference at their own organization. Or, whether that's all over the Web. So, we'll go to some customers. We'll show up in person, and there's a big meeting room, there's only four people there. And, they tell us, well, there's actually 200 people watching this. And so, it's figuring out that motion, at least at the enterprise, that's different for every organization. But, as you also know, we have a long tail through our organization as well. So, while we have those really large customers, we also have this long tail where we need to meet those customers at scale. We need to provide programs. So, our Center of Excellence is a good example of that. Our Webinar series is good example of that, where we provide these motions that at a scaled element, so even our smallest customer can take advantage of it. >> Awesome, so, Mike, transitioning over to you. So, I love stats. Geeky, very geeky, but I admit it freely. I was looking at Smartsheet, 75000 customers. Here, you have about 1100 companies represented over 20 countries. You guys have presence in half the Fortune 500, 90% of the Fortune 100, lot of customers, pan industry. Some of the things that they were hearing from you guys, or, rather, you're hearing from them is, we want you to build for scale, as you were talking about, Steven. We want you to teach us how to phish. And, they want you, also, to help them do it right and do it fast. How are you helping customers do it right and do it fast? Can you do both at the same time? >> Absolutely, we're proving that. And, I think, something that's really unique about how we go to market, and really the basis of our ethos as a business, is we're obsessed with keeping the software easy to use. And, as we add functionality to not get it heavy and put friction in place. So, when we think about engaging with the biggest companies in the world, we have the benefit of starting from organic adoption, where individuals and teams are using the software. They're experiencing value, they're sharing. They're collaborating. And what we see happen, the dynamic we see happening is, as individuals share and go to directors or VPs, we start from sort of work execution, project management, task tracking, and the next step is often these line of business solutions, whether MNA or product planning or employee engagement. Literally every function in the business can benefit from the ability to configure the software. And, keep in mind, we've already taken off the table the biggest issue. I've been in enterprise software for 30 years. I've sat with a lot of CIOs who've written seven figure checks. And, when they're honest with me, the biggest thing they worry about is: Is this software going to get used? We take that issue off the table. We turn it on its head. And, that ability to have that basis of adoption, to have raving fans who love using the software, and then the added benefit of being able to go higher in an organization with senior leaders who want transparency. They want speed. They want accountability. That configurability to solve bigger and bigger, more complex, more strategic flows is a huge advantage for us. It's, frankly, what fuels us, sort of our passion around serving our customers, because we get such great feedback. >> That configurability that you mentioned, Mike, kind of seems to be how customer success is set up. To be configurable, sort of modular, to be able to adjust it with the agility that's needed to deliver what these customers are needing. So, sounds like, maybe, land and expand. I know we've got a gentleman from the office of the CIO at PayPal who's going to be on shortly with us, really helping the C-Suite at PayPal, which everyone uses to be able to see things more clearly, have that transparency in terms of managing projects. >> Absolutely. >> So, I know Cisco's a customer as well. So, is it pretty typical to start with a function within marketing, for example, where there's a team that, hey, this is innovative. This is going to integrate with Jira and Slack, and all these things. Is that kind of a common sales conversation? >> Absolutely. We practice the principles of the challenge your sale and challenge your customer. And, one of the key elements of the challenge your customer is this idea of a mobilizer. And, the mobilizer does two things. They drive change, and they build consensus. And, what we find is those individuals who are change agents often times love our software, because they can do things that they wouldn't otherwise, they'd have to depend on a consultant or IT. So, we find those individuals and we work with them, and they coach us up on: what are the priorities, who are the key players?" And, that becomes a common play we run to get higher in the organization. The other thing that's happening now, I'm seeing it, really, over the past year, is organizations are starting to choose to sort of play offense with us. So, we'll continue to have that bottoms-up organic growth. But now, we're seeing VPs of marketing or CMOs, or CFOs or COOs realize, hey, you know what? I love the fact I have this base of users who love the software, and I can do things, I can enable priorities or initiatives that span the organization, get away from side-load apps, and have the kind of visibility and speed that's been unheard of. And, we're starting to see that our customers wanting to play offense with us. >> That speed to value element has just been critical. So, you heard in the stories this morning, we have MOD Pizza. Their first solution, the gentleman probably built that in a day. And, that was just to roll out one store, and then they rolled out eight the next year. And, I'm sure they made some modifications there. And then, they need to go from eight to around 200 in a year. And, they were able to do that very quickly. They were able to take an existing solution and make the modifications, add in one more element, which is control center for us, to make it that much more scalable. So, when you talk about the land and expand motion, it's both within the customer as a whole, but on a solution as well, where we have story after story where someone starts a new initiative. They don't know whether it's going to work out. It works out really well, and that effort they put into the initial solution isn't lost. They don't have to switch over to a different application, because it's now gotten too big, or some element like that. The software and the application is able to grow with their growth as a business, which eliminates a lot of those things that often happens in business, where you have to pause something that's growing to replace a software. >> Right, so, in terms of the feedback loop, you obviously, as you were describing, Steven, the customer success program you're running here is very cross-functional, very collaborative. It's product management. It's marketing, it's sales, it's IT. It's all these groups that need to come together. What is the process like, maybe from both of your perspectives, Steven, starting with you, of getting customer feedback when they're engaging with their customer success manager, for example, and they want a feature that is not quite there yet, How do you take that feedback from the customers, from the field, and start to really prioritize that internally? >> So, let me start. So, one of the things we've introduced this year is, as we've grown the field organization, is we're using our own software, and we've built these territory hubs. So, the account exec, the SC, the CSM, the SSR, the internal team, everyone is on the same page, as it relates to what we're doing in the account. And, we run weekly meetings. We check off on priorities and to-dos. So, you have that visibility by use of our own platform. So, everybody's on the same page. And, that idea of signal that we talk about, that Gina Mark talked about, it starts with that team that is right there with the customer, and then we feed it. Often times, I'll let Steven take the hand off. So, we have that signal. We have the pulse right with the customer with these field teams, and then that gets fed. And, I'll let Steven talk about how we drive it here sort of in Bellevue. >> Yeah, so, there's two elements of getting that signal, and I'm sure there's more, if you think about it. But, one is from the internal team, and one is the feedback from the customer. And, we, not surprisingly, have used the Smartsheet application to do that. But, any time we're getting a customer signal. That could be from our community, that could be coming in from a support ticket, that could be a conversation with a customer success manager, could be from any site. That feedback then goes into a Smartsheet form, and that goes directly to the product management team. And, anyone who has submitted that from a support rep perspective, for example, gets visibility to where that stands in the progress. So, is it something we're looking into? Is it in progress? If there's a date to it, what does that look like? So, we get all that. And then, the other element is we are huge users of Smartsheet internally. And, Mark likes to talk about that he is the biggest user of the mobile application across our whole customer base, and he probably is. But, we absolutely eat our own dog food there, or drink our own champagne. >> I like that one better. >> Probably a better one. And, that motion really helps us understand how to use the application, so Dynamic View, which was launched this week. We're going to be one of the biggest users of that right out of the gate. For the example that I just brought up, what Dynamic View allows us to do is it allows us to provide a view of all of those submissions of request, and the right view to the right company, or the right internal stakeholders, so they know exactly what that status is. So, those are two ways that we get that feedback back into our producting. >> Mike, you said you've been in sales for a long time. How helpful in a sales situation is the fact that you do drink your own champagne? >> Huge, it's huge. >> On Smartsheet, I imagine, a lot of companies don't show that. >> It's a really big deal; anybody who's, really anybody in the company. Anybody's who's touching the customer, When I hire people, the ability to have that confidence and understand how to use and speak from personal experience that fuels passion, it fuels credibility, and it's authentic, which is one of our core values. And then, so much of it is the art of the possible on the whiteboard with the customer. This ability to move from an idea, we've literally mapped out processes, and within 30 minutes, the essay's in there, and we've prototyped a solution. And, not only is it a quality solution, but the customer's blown away by the speed with which we've done it. But, that starts with that deep understanding of the platform and all the functionality, and what you can do with it. >> Right, I'm sure that breeds that authenticity that Gene actually talked about. Well, we're almost out of time, but I want to quickly, Steven, talk about the Partner Success Program. You guys partner with Amazon, Oracle, NetSuite, Salesforce, Slack, Google, I'm probably leaving out a few. Talk to us a little bit about the partner evolution as you compete with some of these partners as well. >> Well, I'm going to switch that a little. So, we have two elements of partners. So, we have those technology partners that you're speaking to. And then, we have the solution provider partners and resellers; that's more in my world. But, what's been really exciting about those folks and, we had a big partner day yesterday, so I'm kind of coming off the high of talking with all these folks. And, one of the things that we hear over and over again is whatever their focus is. So, sometimes, that's a geography focus. Sometimes, that's an industry focus. They tell us how much we're missing already. So, they'll say, if I'm focused on the accounting industry, they'll say, you guys don't even know how great your off the shelf application is in the accounting world. And, what they're so excited about is being able to configure it, being able to build the applications on top of Smartsheet. That then, they can bring to that world, so that, from a scale perspective, we don't have to be experts in accounting. We don't have to be experts in any of those different verticals or in those geographies. We can leverage those partners, their expertise, their relationships, in order to bring that to market in each of those areas. >> Any feedback, I know we're out of time. But, any feedback on some of the announcements that came out today from some of your key partners, besides two thumbs way up? >> They were extremely excited about Dynamic View and seeing what's possible from a new solution perspective. They were just like the rest of the customers. So, when there was the final slide showing all the new features we're bringing, all the phones came out to take pictures. It was a great scene, and they were definitely in that mix. >> Excellent, well, Steven and Mike, thanks so much for stopping by theCUBE and sharing with us how you're transforming, how the customers are able to evolve and transform with your technology. We know you have a lot of meetings to get to, so we'll let you go to that. >> Thank you very much. >> Thank you. >> We want to thank you for watching theCUBE. I'm Lisa Martin live at Smartsheet ENGAGE 2018. Stick around, I'll be right back with my next guest. (techno music)

>> Narrator: Live from Midtown Manhattan it's theCUBE! Covering Big Data, New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsor. >> Okay, welcome back everyone. Live here in New York this is theCUBE's coverage of Big Data NYC, our event. We've been doing it for five years, it's our event in conjunction with Strata Data, which is the O'Reilly Media that we run, it's a separate event. But we've been covering the Big Data for eight years since 2010, Hadoop World. This is theCUBE. Of course theCUBE is never going to change, they might call it Strata AI next year, whatever trend that they might see. But we're going to keep it theCUBE. This is in New York City, our eighth year of coverage. Guys, welcome to theCUBE. Our next two guests is Andrew Burt, Chief Privacy Officer and Andrew Gillman, Chief Customer Officer and CMO. It's a start-up so you got all these fancy titles, but you're on the A-team from Immuta. Hot start-up. Welcome to theCUBE. Great to see you again. >> Thanks for having us, appreciate it. >> Okay, so you guys are the start-up feature here this week on theCUBE, our little segment here. I think you guys are the hottest start-up that is out there and that people aren't really talking a lot about. So you guys are brand new, you guys have got a really good reputation. Getting a lot of props inside the community. Especially in the people who know data, data science, and know some of the intelligence organizations. But respectful people like Dan Hutchin says you guys are rockstars and doing great. So why all the buzz inside the community? Now you guys are just starting to go to the market? What's the update on the company? >> So great story. Founded in 2014, (mumbles) Investment, it was announced earlier this year. And the team, group of serial entrepreneurs sold their last company CSC, ran the public sector business for them for a while. Really special group of engineers and technologists and data scientists. Headquartered out of D.C. Customer success organization out of Columbus, Ohio, and we're servicing Fortune 100 companies. >> John: So Immuta, I-M-M-U-T-A. >> Immuta.com we just launched the new website earlier this week in preparation for the show. And the easiest way-- >> Immuta, immutable, I mean-- >> Immutable, I'm sure there's a backstory. >> Immutable, yeah. We do not ever touch the raw data. So we're all about managing risk and managing the integrity of the data. And so risk and integrity and security are baked into everything we do. We want our customers to know that their data will be immutable, and that in using us they'll never pose an additional risk to that underlying data. >> I think of blockchain when I think of immutability, like I'm so into blockchaining these dayS as you guys know, I've been totally into it. >> There's no blockchain in their technology. >> I know, but let's get down to why the motivation to enter the market. There's a lot of noisy stuff out there. Why do we need another unified platform? >> The big opportunity that we saw was, organizations had spent basically the past decade refining and upgrading their application infrastructure. But in doing so under the guise of digital transformation. We've really built that organization's people processes to support monolithic applications. Now those applications are moving to the cloud, they're being rearchitected in a microsurfaces architecture. So we have all this data now, how do we manage it for the new application, which we see is really algorithm-centric? The Amazons of the world have proven, how do you compete against anyone? How do you disrupt any industry? That's operationalize your data in a new way. >> Oh, they were developer-centric right? They were very focused on the developer. You guys are saying you're algorithm-centric, meaning the software within the software kind of thing. >> It's really about, we see the future enterprise to compete. You have to build thousands of algorithms. And each one of those algorithms is going to do something very specific, very precise, but faster than any human can do. And so how do you enable an application, excuse me, an algorithm-centric infrastructure to support that? And today, as we go and meet with our customers and other groups, the people, the processes, the data is everywhere. The governance folks who have to control how the data is used, the laws are dynamic. The tooling is complex. So this whole world looks very much like pre-DevOps IT, or pre-cloud IT. It takes on average between four to six months to get a data scientist up and running on a project. >> Let's get into the company. I wanted to just get that gist out, put some context. I see the problem you solve: a lot of algorithms out there, more and more open sources coming up to the scene. With the Linux Foundation, having their new event Rebrand the Open Source summit, shows exponential growth in open source. So no doubt about it, software's going to be new guys coming on, new gals. Tons of software. What is the company positioning? What do you guys do? How many employees? Let's go down by the numbers and then talk about the problem that you solve. >> Okay, cool. So, company. We'll be about 40 people by Q1. Heavy engineering, go to market. We're operating and working with, as I mentioned, Fortune 100 clients. Highly regulated industries. Financial services, healthcare, government, insurance, et cetera. So where you have lots of data that you need to operationalize, that's very sensitive to use. What else? Company positioning. So we are positioned as data management for data science. So the opportunity that we saw, again, managing data for applications is very different than managing data for algorithm development, data sciences. >> John: So you're selling to the CDO, Chief Data Officer? Are you selling to the analytics? >> In a lot of our customers, like in financial services, we're going right into the line of business. We're working with managing directors who are building next generation analytics infrastructure that need to unify and connect the data in a new way that's dynamic. It's not just the data that they have within their organization, they're looking to bring data in from outside. They want to also work collaboratively with governance professionals and lawyers who in financial services, they are, you know, we always jest in the company that different organizations have these cool new tools, like data scientists have all their new tools. And the data owners have flash disks and they have all this. But the governance people still have Microsoft Word. And maybe the newer tools are like Wikis. So now we can get it off of Word and make it shareable. But what we allow them to do is, and what Andrew Burt has really driven, is the ability for you to take internal logic, internal policies, external regulations, and put them into code that becomes dynamically enforceable as you're querying the data, as you're using it, to train algorithms, and to drive, mathematical decision-making in the enterprise. >> Let's jump into some of the privacy. You're the Chief Privacy Officer, which is codeword for you're doing all the governance stuff. And there's a lot of stuff business-wise that's going on around GDPR which is actually relevant. There's a lot of dollars on table for that too, so it's probably good for business. But there's a lot of policy stuff going on. What's going on with you guys in this area? >> So I think policy is really catching up to the world of big data. We've known for a very long time that data is incredibly important. It's the lifeblood of an increasingly large number of organizations, and because data is becoming more important, laws are starting to catch up. I think GDPR is really, it's hot to talk about. I think it is just the beginning of a larger trend. >> People are scared. People are nervous. It's like they don't know, this could be a blank check that they're signing away. The enforcement side is pretty outrageous. >> So I mean-- >> Is that right? I mean people are scared, or do you think? >> I think people are terrified because they know that its important, and they're also terrified because data scientists, and folks in IT have never really had to think very seriously about implementing complex laws. I think GDPR is the first example of laws, forcing technology to basically blend software and law. The only way, I mean one of our theses is, the only way to actually solve for GDPR is to invent laws within the software you're using. And so, we're moving away from this meetings and memos type approach to governing data, which is very slow and can take months, and we need it to happen dynamically. >> This is why I wanted to bring you guys in. Not only, Andrew, we knew each other from another venture, but what got my attention for you guys was really this intersection between law and society and tech. And this is just the beginning. You look at the tell-signs there. Peter Burris who runs research for Wikibon coined the term programming the real world. Life basically. You've got wearables, you've got IOT, this is happening. Self-driving cars. Who decides what side of the street people walk on now? Law and code are coming together. That's algorithm. There'll be more of them. Is there an algorithm for the algorithms? Who teaches the data set, who shares the data set? Wait a minute, I don't want to share my data set because I have a law that says I can't. Who decides all this stuff? >> Exactly. We're starting to enter a world where governments really, really care about that stuff. Just in-- >> In Silicon Valley, that's not in their DNA. You're seeing it all over the front pages of the news, they can't even get it right in inclusion and diversity. How can they work with laws? >> Tension is brewing. In the U.S. our regulatory environment is a little more lax, we want to see innovation happen first and then regulate. But the EU is completely different. Their laws in China and Russia and elsewhere around the world. And it's basically becoming impossible to be a global organization and still take that approach where you can afford to be scared of the law. >> John: I don't know how I feel about this because I get all kinds of rushes of intoxication to fear. Look at what's going on with Bitcoin and Blockchain, underbelly is a whole new counterculture going on around in-immutable data. Anonymous cultures, where they're complete anonymous underbellies going on. >> I think the risk-factors going up, when you mentioned IOTs, so its where you are and your devices and your home. Now think about 23 and Me, Verily, Freenome, where you're digitizing your DNA. We've already started to do that with MRIs and other operations that we've had. You think about now, I'm handing over my DNA to an organization because I want find out my lineage. I want to learn about where I came from. How do I make sure that the derived data off of that digital DNA is used properly? Not just for me, as Andrew, but for my progeny. That introduces some really interesting ethical issues. It's an intersection of this new wave of investment, to your point, like in Silicon Valley, of bringing healthcare into data science, into technology and the intersection. And the underpinning of the whole thing is the data. How do we manage the data, and what do we do-- >> And AI really is the future here. Even though machine-learning is the key part of AI, we just put out an article this morning on SiliconANGLE from Gina Smith, our new writer. Google Brain Chief: AI tops humans in computer vision, and healthcare will never be the same. They talk about little things, like in 2011 you can barely do character recognition of pictures, now you can 100%. Now you take that forward, in Heidelberg, Germany, the event this week we were covering the Heidelberg Laureate Forum, or HLF 2017. All the top scientists were there talking about this specific issue of, this is society blending in with tech. >> Absolutely. >> This societal impact, legal impact, kind of blending. Algorithms are the only thing that are going to scale in this area. This is what you guys are trying to do, right? >> Exactly, that's the interesting thing. When you look at training models and algorithms in AI, right, AI is the new cloud. We're in New York, I'm walking down the street, and there's the algorithm you're writing, and everything is Ernestine Young. Billboards on algorithms, I mean who would have thought, right? An AI. >> John: theCUBE is going to be an AI pretty soon. "Hey, we're AI! "Brought to you by, hey, Siri, do theCUBE interview." >> But the interesting part of the whole AI and the algorithm is you have n number of models. We have lots of data scientists and AI experts. Siri goes off. >> Sorry Siri, didn't mean to do that. >> She's trying to join the conversation. >> Didn't mean to insult you, Siri. But you know, it's applied math by a different name. And you have n number of models, assuming 90% of all algorithms are single linear regression. What ultimately drives the outcome is going to be how you prepare and manage the data. And so when we go back to the governance story. Governance in applications is very different than governance in data science because how we actually dynamically change the data is going to drive the outcome of that algorithm directly. If I'm in Immuta, we connect the data, we connect the data science tools. We allow you to control the data in a unique way. I refer to that as data personalization. It's not just, can I subscribe to the data? It's what does the data look like based on who I am and what those internal and external policies are? Think about this for example, I'm training a model that doesn't mask against race, and doesn't generalize against age. What do you think is going to happen to that model when it goes to start to interact? Either it's delivered as-- >> Well context is critical. And the usability of data, because it's perishable at this point. Data that comes in quick is worth more, but historically the value goes down. But it's worth more when you train the machine. So it's two different issues. >> Exactly. So it's really about longevity of the model. How can we create and train a model that's going to be able to stay in? It's like the new availability, right? That it's going to stay, it's going to be relevant, and it's going to keep us out of jail, and keep us from getting sued as long as possible. >> Well Jeff Dean, I just want to quote one more thing to add context. I want to ask Andrew over here about his view on this. Jeff Dean, the Google Brain Chief behind all of the stuff is saying AI-enabled healthcare. The sector's set to grow at an annual rate of 40% through 2021, when it's expected to hit 6.6 billion spent on AI-enabled healthcare. 6.6 billion. Today it's around 600 million. That's the growth just in AI healthcare impact. Just healthcare. This is going to go from a policy privacy issue, One, healthcare data has been crippled with HIPPA slowing us down. But where is the innovation going to come from? Where's the data going to be in healthcare? And other verticals. This is one vertical. Financial services is crazy too. >> I mean, honestly healthcare is one of the most interesting examples of applied AI, and it's because there's no other realm, at least now, where people are thinking about AI, and the risk is so apparent. If you get a diagnosis and the doctor doesn't understand why it's very apparent. And if they're using a model that has a very low level of transparency, that ends up being really important. I think healthcare is a really fascinating sector to think about. But all of these issues, all of these different types of risks that have been around for a while are starting to become more and more important as AI takes-- >> John: Alright, so I'm going to wrap up here. Give you guys both a chance, and you can't copy each other's answer. So we'll start with you Andrew over here. Explain Immuta in a simple way. Someone who's not in the industry. What do you guys do? And then do a version for someone in the industry. So elevator pitch for someone who's a friend, who's not in the industry, and someone who is. >> So Immuta is a data management platform for data science. And what that actually gives you is, we take the friction out of trying to access data, and trying to control data, and trying to comply with all of the different rules that surround the use of that data. >> John: Great, now do the one for normal people. >> That was the normal pitch. >> Okay! (laughing) I can't wait to hear the one for the insiders. >> And then for the insiders-- >> Just say, "It's magic". >> It's magic. >> We're magic, you know. >> Coming from the infrastructure role, I like to refer to it as a VMWare for data science. We create an abstraction layer than sits between the data and the data science tools, and we'll dynamically enforce policies based on the values of the organization. But also, it drives better outcomes. Because today, the data owners aren't confident that you're going to do with the data what you say you're going to do. So they try to hold it. Like the old server-huggers, the data-huggers. So we allowed them to unlock that and make it universally available. We allow the governance people to get off those memos, that have to be interpreted by IT and enforced, and actually allow them to write code and have it be enforced as the policy mandates. >> And the number one problem you solve is what? >> Accelerate with confidence. We allow the data scientists to go and build models faster by connecting to the data in a way that they're confident that when they deploy their model, that it's going to go into production, and it's going to stay into production for as long as possible. >> And what's the GDPR angle? You've got the legal brain over here, in policy. What's going on with GDPR? How are you guys going to be a solution for that? >> We have the most, I'd say, robust option of policy enforcement on data, I think, available. We make it incredibly easy to comply with GDPR. We actually put together a sample memo that says, "Here's what it looks like to comply with GDPR." It's written from a governance department, sent to the internal data science department. It's about a page and a half long. We actually make that very onerous process-- >> (mumbles) GDPR, you guys know the size of that market? In terms of spend that's going to be coming around the corner? I think it's like the Y2K problem that's actually real. >> Exactly, it feels the same way. And actually Andrew and his team have taken apart the regulation article by article and have actually built-in product features that satisfy that. It's an interesting and unique--- >> John: I think it's really impressive that you guys bring a legal and a policy mind into the product discussion. I think that's something that I think you guys are doing a little bit different than I see anyone out there. You're bringing legal and policy into the software fabric, which is unique, and I think it's going to be the standard in my opinion. Hopefully this is a good trend, hopefully you guys keep in touch. Thanks for coming on theCUBE, thanks for-- >> Thanks for having us. >> For making time to come over. This is theCUBE, breaking out the start-up action sharing the hot start-ups here, that really are a good position in the marketplace, as the generation of the infrastructure changes. It's a whole new ballgame. Global development platform, called the Internet. The new Internet. It's decentralized, we even get into Blockchain, we want to try that a little later, maybe another segment. It's theCUBE in New York City. More after this short break.

>> Welcome to the Intel AI Lounge. Today, we're very excited to share with you the Precision Medicine panel discussion. I'll be moderating the session. My name is Kay Erin. I'm the general manager of Health and Life Sciences at Intel. And I'm excited to share with you these three panelists that we have here. First is John Madison. He is a chief information medical officer and he is part of Kaiser Permanente. We're very excited to have you here. Thank you, John. >> Thank you. >> We also have Naveen Rao. He is the VP and general manager for the Artificial Intelligence Solutions at Intel. He's also the former CEO of Nervana, which was acquired by Intel. And we also have Bob Rogers, who's the chief data scientist at our AI solutions group. So, why don't we get started with our questions. I'm going to ask each of the panelists to talk, introduce themselves, as well as talk about how they got started with AI. So why don't we start with John? >> Sure, so can you hear me okay in the back? Can you hear? Okay, cool. So, I am a recovering evolutionary biologist and a recovering physician and a recovering geek. And I implemented the health record system for the first and largest region of Kaiser Permanente. And it's pretty obvious that most of the useful data in a health record, in lies in free text. So I started up a natural language processing team to be able to mine free text about a dozen years ago. So we can do things with that that you can't otherwise get out of health information. I'll give you an example. I read an article online from the New England Journal of Medicine about four years ago that said over half of all people who have had their spleen taken out were not properly vaccinated for a common form of pneumonia, and when your spleen's missing, you must have that vaccine or you die a very sudden death with sepsis. In fact, our medical director in Northern California's father died of that exact same scenario. So, when I read the article, I went to my structured data analytics team and to my natural language processing team and said please show me everybody who has had their spleen taken out and hasn't been appropriately vaccinated and we ran through about 20 million records in about three hours with the NLP team, and it took about three weeks with a structured data analytics team. That sounds counterintuitive but it actually happened that way. And it's not a competition for time only. It's a competition for quality and sensitivity and specificity. So we were able to indentify all of our members who had their spleen taken out, who should've had a pneumococcal vaccine. We vaccinated them and there are a number of people alive today who otherwise would've died absent that capability. So people don't really commonly associate natural language processing with machine learning, but in fact, natural language processing relies heavily and is the first really, highly successful example of machine learning. So we've done dozens of similar projects, mining free text data in millions of records very efficiently, very effectively. But it really helped advance the quality of care and reduce the cost of care. It's a natural step forward to go into the world of personalized medicine with the arrival of a 100-dollar genome, which is actually what it costs today to do a full genome sequence. Microbiomics, that is the ecosystem of bacteria that are in every organ of the body actually. And we know now that there is a profound influence of what's in our gut and how we metabolize drugs, what diseases we get. You can tell in a five year old, whether or not they were born by a vaginal delivery or a C-section delivery by virtue of the bacteria in the gut five years later. So if you look at the complexity of the data that exists in the genome, in the microbiome, in the health record with free text and you look at all the other sources of data like this streaming data from my wearable monitor that I'm part of a research study on Precision Medicine out of Stanford, there is a vast amount of disparate data, not to mention all the imaging, that really can collectively produce much more useful information to advance our understanding of science, and to advance our understanding of every individual. And then we can do the mash up of a much broader range of science in health care with a much deeper sense of data from an individual and to do that with structured questions and structured data is very yesterday. The only way we're going to be able to disambiguate those data and be able to operate on those data in concert and generate real useful answers from the broad array of data types and the massive quantity of data, is to let loose machine learning on all of those data substrates. So my team is moving down that pathway and we're very excited about the future prospects for doing that. >> Yeah, great. I think that's actually some of the things I'm very excited about in the future with some of the technologies we're developing. My background, I started actually being fascinated with computation in biological forms when I was nine. Reading and watching sci-fi, I was kind of a big dork which I pretty much still am. I haven't really changed a whole lot. Just basically seeing that machines really aren't all that different from biological entities, right? We are biological machines and kind of understanding how a computer works and how we engineer those things and trying to pull together concepts that learn from biology into that has always been a fascination of mine. As an undergrad, I was in the EE, CS world. Even then, I did some research projects around that. I worked in the industry for about 10 years designing chips, microprocessors, various kinds of ASICs, and then actually went back to school, quit my job, got a Ph.D. in neuroscience, computational neuroscience, to specifically understand what's the state of the art. What do we really understand about the brain? And are there concepts that we can take and bring back? Inspiration's always been we want to... We watch birds fly around. We want to figure out how to make something that flies. We extract those principles, and then build a plane. Don't necessarily want to build a bird. And so Nervana's really was the combination of all those experiences, bringing it together. Trying to push computation in a new a direction. Now, as part of Intel, we can really add a lot of fuel to that fire. I'm super excited to be part of Intel in that the technologies that we were developing can really proliferate and be applied to health care, can be applied to Internet, can be applied to every facet of our lives. And some of the examples that John mentioned are extremely exciting right now and these are things we can do today. And the generality of these solutions are just really going to hit every part of health care. I mean from a personal viewpoint, my whole family are MDs. I'm sort of the black sheep of the family. I don't have an MD. And it's always been kind of funny to me that knowledge is concentrated in a few individuals. Like you have a rare tumor or something like that, you need the guy who knows how to read this MRI. Why? Why is it like that? Can't we encapsulate that knowledge into a computer or into an algorithm, and democratize it. And the reason we couldn't do it is we just didn't know how. And now we're really getting to a point where we know how to do that. And so I want that capability to go to everybody. It'll bring the cost of healthcare down. It'll make all of us healthier. That affects everything about our society. So that's really what's exciting about it to me. >> That's great. So, as you heard, I'm Bob Rogers. I'm chief data scientist for analytics and artificial intelligence solutions at Intel. My mission is to put powerful analytics in the hands of every decision maker and when I think about Precision Medicine, decision makers are not just doctors and surgeons and nurses, but they're also case managers and care coordinators and probably most of all, patients. So the mission is really to put powerful analytics and AI capabilities in the hands of everyone in health care. It's a very complex world and we need tools to help us navigate it. So my background, I started with a Ph.D. in physics and I was computer modeling stuff, falling into super massive black holes. And there's a lot of applications for that in the real world. No, I'm kidding. (laughter) >> John: There will be, I'm sure. Yeah, one of these days. Soon as we have time travel. Okay so, I actually, about 1991, I was working on my post doctoral research, and I heard about neural networks, these things that could compute the way the brain computes. And so, I started doing some research on that. I wrote some papers and actually, it was an interesting story. The problem that we solved that got me really excited about neural networks, which have become deep learning, my office mate would come in. He was this young guy who was about to go off to grad school. He'd come in every morning. "I hate my project." Finally, after two weeks, what's your project? What's the problem? It turns out he had to circle these little fuzzy spots on these images from a telescope. So they were looking for the interesting things in a sky survey, and he had to circle them and write down their coordinates all summer. Anyone want to volunteer to do that? No? Yeah, he was very unhappy. So we took the first two weeks of data that he created doing his work by hand, and we trained an artificial neural network to do his summer project and finished it in about eight hours of computing. (crowd laughs) And so he was like yeah, this is amazing. I'm so happy. And we wrote a paper. I was the first author of course, because I was the senior guy at age 24. And he was second author. His first paper ever. He was very, very excited. So we have to fast forward about 20 years. His name popped up on the Internet. And so it caught my attention. He had just won the Nobel Prize in physics. (laughter) So that's where artificial intelligence will get you. (laughter) So thanks Naveen. Fast forwarding, I also developed some time series forecasting capabilities that allowed me to create a hedge fund that I ran for 12 years. After that, I got into health care, which really is the center of my passion. Applying health care to figuring out how to get all the data from all those siloed sources, put it into the cloud in a secure way, and analyze it so you can actually understand those cases that John was just talking about. How do you know that that person had had a splenectomy and that they needed to get that pneumovax? You need to be able to search all the data, so we used AI, natural language processing, machine learning, to do that and then two years ago, I was lucky enough to join Intel and, in the intervening time, people like Naveen actually thawed the AI winter and we're really in a spring of amazing opportunities with AI, not just in health care but everywhere, but of course, the health care applications are incredibly life saving and empowering so, excited to be here on this stage with you guys. >> I just want to cue off of your comment about the role of physics in AI and health care. So the field of microbiomics that I referred to earlier, bacteria in our gut. There's more bacteria in our gut than there are cells in our body. There's 100 times more DNA in that bacteria than there is in the human genome. And we're now discovering a couple hundred species of bacteria a year that have never been identified under a microscope just by their DNA. So it turns out the person who really catapulted the study and the science of microbiomics forward was an astrophysicist who did his Ph.D. in Steven Hawking's lab on the collision of black holes and then subsequently, put the other team in a virtual reality, and he developed the first super computing center and so how did he get an interest in microbiomics? He has the capacity to do high performance computing and the kind of advanced analytics that are required to look at a 100 times the volume of 3.2 billion base pairs of the human genome that are represented in the bacteria in our gut, and that has unleashed the whole science of microbiomics, which is going to really turn a lot of our assumptions of health and health care upside down. >> That's great, I mean, that's really transformational. So a lot of data. So I just wanted to let the audience know that we want to make this an interactive session, so I'll be asking for questions in a little bit, but I will start off with one question so that you can think about it. So I wanted to ask you, it looks like you've been thinking a lot about AI over the years. And I wanted to understand, even though AI's just really starting in health care, what are some of the new trends or the changes that you've seen in the last few years that'll impact how AI's being used going forward? >> So I'll start off. There was a paper published by a guy by the name of Tegmark at Harvard last summer that, for the first time, explained why neural networks are efficient beyond any mathematical model we predict. And the title of the paper's fun. It's called Deep Learning Versus Cheap Learning. So there were two sort of punchlines of the paper. One is is that the reason that mathematics doesn't explain the efficiency of neural networks is because there's a higher order of mathematics called physics. And the physics of the underlying data structures determined how efficient you could mine those data using machine learning tools. Much more so than any mathematical modeling. And so the second thing that was a reel from that paper is that the substrate of the data that you're operating on and the natural physics of those data have inherent levels of complexity that determine whether or not a 12th layer of neural net will get you where you want to go really fast, because when you do the modeling, for those math geeks in the audience, a factorial. So if there's 12 layers, there's 12 factorial permutations of different ways you could sequence the learning through those data. When you have 140 layers of a neural net, it's a much, much, much bigger number of permutations and so you end up being hardware-bound. And so, what Max Tegmark basically said is you can determine whether to do deep learning or cheap learning based upon the underlying physics of the data substrates you're operating on and have a good insight into how to optimize your hardware and software approach to that problem. >> So another way to put that is that neural networks represent the world in the way the world is sort of built. >> Exactly. >> It's kind of hierarchical. It's funny because, sort of in retrospect, like oh yeah, that kind of makes sense. But when you're thinking about it mathematically, we're like well, anything... The way a neural can represent any mathematical function, therfore, it's fully general. And that's the way we used to look at it, right? So now we're saying, well actually decomposing the world into different types of features that are layered upon each other is actually a much more efficient, compact representation of the world, right? I think this is actually, precisely the point of kind of what you're getting at. What's really exciting now is that what we were doing before was sort of building these bespoke solutions for different kinds of data. NLP, natural language processing. There's a whole field, 25 plus years of people devoted to figuring out features, figuring out what structures make sense in this particular context. Those didn't carry over at all to computer vision. Didn't carry over at all to time series analysis. Now, with neural networks, we've seen it at Nervana, and now part of Intel, solving customers' problems. We apply a very similar set of techniques across all these different types of data domains and solve them. All data in the real world seems to be hierarchical. You can decompose it into this hierarchy. And it works really well. Our brains are actually general structures. As a neuroscientist, you can look at different parts of your brain and there are differences. Something that takes in visual information, versus auditory information is slightly different but they're much more similar than they are different. So there is something invariant, something very common between all of these different modalities and we're starting to learn that. And this is extremely exciting to me trying to understand the biological machine that is a computer, right? We're figurig it out, right? >> One of the really fun things that Ray Chrisfall likes to talk about is, and it falls in the genre of biomimmicry, and how we actually replicate biologic evolution in our technical solutions so if you look at, and we're beginning to understand more and more how real neural nets work in our cerebral cortex. And it's sort of a pyramid structure so that the first pass of a broad base of analytics, it gets constrained to the next pass, gets constrained to the next pass, which is how information is processed in the brain. So we're discovering increasingly that what we've been evolving towards, in term of architectures of neural nets, is approximating the architecture of the human cortex and the more we understand the human cortex, the more insight we get to how to optimize neural nets, so when you think about it, with millions of years of evolution of how the cortex is structured, it shouldn't be a surprise that the optimization protocols, if you will, in our genetic code are profoundly efficient in how they operate. So there's a real role for looking at biologic evolutionary solutions, vis a vis technical solutions, and there's a friend of mine who worked with who worked with George Church at Harvard and actually published a book on biomimmicry and they wrote the book completely in DNA so if all of you have your home DNA decoder, you can actually read the book on your DNA reader, just kidding. >> There's actually a start up I just saw in the-- >> Read-Write DNA, yeah. >> Actually it's a... He writes something. What was it? (response from crowd member) Yeah, they're basically encoding information in DNA as a storage medium. (laughter) The company, right? >> Yeah, that same friend of mine who coauthored that biomimmicry book in DNA also did the estimate of the density of information storage. So a cubic centimeter of DNA can store an hexabyte of data. I mean that's mind blowing. >> Naveen: Highly done soon. >> Yeah that's amazing. Also you hit upon a really important point there, that one of the things that's changed is... Well, there are two major things that have changed in my perception from let's say five to 10 years ago, when we were using machine learning. You could use data to train models and make predictions to understand complex phenomena. But they had limited utility and the challenge was that if I'm trying to build on these things, I had to do a lot of work up front. It was called feature engineering. I had to do a lot of work to figure out what are the key attributes of that data? What are the 10 or 20 or 100 pieces of information that I should pull out of the data to feed to the model, and then the model can turn it into a predictive machine. And so, what's really exciting about the new generation of machine learning technology, and particularly deep learning, is that it can actually learn from example data those features without you having to do any preprogramming. That's why Naveen is saying you can take the same sort of overall approach and apply it to a bunch of different problems. Because you're not having to fine tune those features. So at the end of the day, the two things that have changed to really enable this evolution is access to more data, and I'd be curious to hear from you where you're seeing data come from, what are the strategies around that. So access to data, and I'm talking millions of examples. So 10,000 examples most times isn't going to cut it. But millions of examples will do it. And then, the other piece is the computing capability to actually take millions of examples and optimize this algorithm in a single lifetime. I mean, back in '91, when I started, we literally would have thousands of examples and it would take overnight to run the thing. So now in the world of millions, and you're putting together all of these combinations, the computing has changed a lot. I know you've made some revolutionary advances in that. But I'm curious about the data. Where are you seeing interesting sources of data for analytics? >> So I do some work in the genomics space and there are more viable permutations of the human genome than there are people who have ever walked the face of the earth. And the polygenic determination of a phenotypic expression translation, what are genome does to us in our physical experience in health and disease is determined by many, many genes and the interaction of many, many genes and how they are up and down regulated. And the complexity of disambiguating which 27 genes are affecting your diabetes and how are they up and down regulated by different interventions is going to be different than his. It's going to be different than his. And we already know that there's four or five distinct genetic subtypes of type II diabetes. So physicians still think there's one disease called type II diabetes. There's actually at least four or five genetic variants that have been identified. And so, when you start thinking about disambiguating, particularly when we don't know what 95 percent of DNA does still, what actually is the underlining cause, it will require this massive capability of developing these feature vectors, sometimes intuiting it, if you will, from the data itself. And other times, taking what's known knowledge to develop some of those feature vectors, and be able to really understand the interaction of the genome and the microbiome and the phenotypic data. So the complexity is high and because the variation complexity is high, you do need these massive members. Now I'm going to make a very personal pitch here. So forgive me, but if any of you have any role in policy at all, let me tell you what's happening right now. The Genomic Information Nondiscrimination Act, so called GINA, written by a friend of mine, passed a number of years ago, says that no one can be discriminated against for health insurance based upon their genomic information. That's cool. That should allow all of you to feel comfortable donating your DNA to science right? Wrong. You are 100% unprotected from discrimination for life insurance, long term care and disability. And it's being practiced legally today and there's legislation in the House, in mark up right now to completely undermine the existing GINA legislation and say that whenever there's another applicable statute like HIPAA, that the GINA is irrelevant, that none of the fines and penalties are applicable at all. So we need a ton of data to be able to operate on. We will not be getting a ton of data to operate on until we have the kind of protection we need to tell people, you can trust us. You can give us your data, you will not be subject to discrimination. And that is not the case today. And it's being further undermined. So I want to make a plea to any of you that have any policy influence to go after that because we need this data to help the understanding of human health and disease and we're not going to get it when people look behind the curtain and see that discrimination is occurring today based upon genetic information. >> Well, I don't like the idea of being discriminated against based on my DNA. Especially given how little we actually know. There's so much complexity in how these things unfold in our own bodies, that I think anything that's being done is probably childishly immature and oversimplifying. So it's pretty rough. >> I guess the translation here is that we're all unique. It's not just a Disney movie. (laughter) We really are. And I think one of the strengths that I'm seeing, kind of going back to the original point, of these new techniques is it's going across different data types. It will actually allow us to learn more about the uniqueness of the individual. It's not going to be just from one data source. They were collecting data from many different modalities. We're collecting behavioral data from wearables. We're collecting things from scans, from blood tests, from genome, from many different sources. The ability to integrate those into a unified picture, that's the important thing that we're getting toward now. That's what I think is going to be super exciting here. Think about it, right. I can tell you to visual a coin, right? You can visualize a coin. Not only do you visualize it. You also know what it feels like. You know how heavy it is. You have a mental model of that from many different perspectives. And if I take away one of those senses, you can still identify the coin, right? If I tell you to put your hand in your pocket, and pick out a coin, you probably can do that with 100% reliability. And that's because we have this generalized capability to build a model of something in the world. And that's what we need to do for individuals is actually take all these different data sources and come up with a model for an individual and you can actually then say what drug works best on this. What treatment works best on this? It's going to get better with time. It's not going to be perfect, because this is what a doctor does, right? A doctor who's very experienced, you're a practicing physician right? Back me up here. That's what you're doing. You basically have some categories. You're taking information from the patient when you talk with them, and you're building a mental model. And you apply what you know can work on that patient, right? >> I don't have clinic hours anymore, but I do take care of many friends and family. (laughter) >> You used to, you used to. >> I practiced for many years before I became a full-time geek. >> I thought you were a recovering geek. >> I am. (laughter) I do more policy now. >> He's off the wagon. >> I just want to take a moment and see if there's anyone from the audience who would like to ask, oh. Go ahead. >> We've got a mic here, hang on one second. >> I have tons and tons of questions. (crosstalk) Yes, so first of all, the microbiome and the genome are really complex. You already hit about that. Yet most of the studies we do are small scale and we have difficulty repeating them from study to study. How are we going to reconcile all that and what are some of the technical hurdles to get to the vision that you want? >> So primarily, it's been the cost of sequencing. Up until a year ago, it's $1000, true cost. Now it's $100, true cost. And so that barrier is going to enable fairly pervasive testing. It's not a real competitive market becaue there's one sequencer that is way ahead of everybody else. So the price is not $100 yet. The cost is below $100. So as soon as there's competition to drive the cost down, and hopefully, as soon as we all have the protection we need against discrimination, as I mentioned earlier, then we will have large enough sample sizes. And so, it is our expectation that we will be able to pool data from local sources. I chair the e-health work group at the Global Alliance for Genomics and Health which is working on this very issue. And rather than pooling all the data into a single, common repository, the strategy, and we're developing our five-year plan in a month in London, but the goal is to have a federation of essentially credentialed data enclaves. That's a formal method. HHS already does that so you can get credentialed to search all the data that Medicare has on people that's been deidentified according to HIPPA. So we want to provide the same kind of service with appropriate consent, at an international scale. And there's a lot of nations that are talking very much about data nationality so that you can't export data. So this approach of a federated model to get at data from all the countries is important. The other thing is a block-chain technology is going to be very profoundly useful in this context. So David Haussler of UC Santa Cruz is right now working on a protocol using an open block-chain, public ledger, where you can put out. So for any typical cancer, you may have a half dozen, what are called sematic variance. Cancer is a genetic disease so what has mutated to cause it to behave like a cancer? And if we look at those biologically active sematic variants, publish them on a block chain that's public, so there's not enough data there to reidentify the patient. But if I'm a physician treating a woman with breast cancer, rather than say what's the protocol for treating a 50-year-old woman with this cell type of cancer, I can say show me all the people in the world who have had this cancer at the age of 50, wit these exact six sematic variants. Find the 200 people worldwide with that. Ask them for consent through a secondary mechanism to donate everything about their medical record, pool that information of the core of 200 that exactly resembles the one sitting in front of me, and find out, of the 200 ways they were treated, what got the best results. And so, that's the kind of future where a distributed, federated architecture will allow us to query and obtain a very, very relevant cohort, so we can basically be treating patients like mine, sitting right in front of me. Same thing applies for establishing research cohorts. There's some very exciting stuff at the convergence of big data analytics, machine learning, and block chaining. >> And this is an area that I'm really excited about and I think we're excited about generally at Intel. They actually have something called the Collaborative Cancer Cloud, which is this kind of federated model. We have three different academic research centers. Each of them has a very sizable and valuable collection of genomic data with phenotypic annotations. So you know, pancreatic cancer, colon cancer, et cetera, and we've actually built a secure computing architecture that can allow a person who's given the right permissions by those organizations to ask a specific question of specific data without ever sharing the data. So the idea is my data's really important to me. It's valuable. I want us to be able to do a study that gets the number from the 20 pancreatic cancer patients in my cohort, up to the 80 that we have in the whole group. But I can't do that if I'm going to just spill my data all over the world. And there are HIPAA and compliance reasons for that. There are business reasons for that. So what we've built at Intel is this platform that allows you to do different kinds of queries on this genetic data. And reach out to these different sources without sharing it. And then, the work that I'm really involved in right now and that I'm extremely excited about... This also touches on something that both of you said is it's not sufficient to just get the genome sequences. You also have to have the phenotypic data. You have to know what cancer they've had. You have to know that they've been treated with this drug and they've survived for three months or that they had this side effect. That clinical data also needs to be put together. It's owned by other organizations, right? Other hospitals. So the broader generalization of the Collaborative Cancer Cloud is something we call the data exchange. And it's a misnomer in a sense that we're not actually exchanging data. We're doing analytics on aggregated data sets without sharing it. But it really opens up a world where we can have huge populations and big enough amounts of data to actually train these models and draw the thread in. Of course, that really then hits home for the techniques that Nervana is bringing to the table, and of course-- >> Stanford's one of your academic medical centers? >> Not for that Collaborative Cancer Cloud. >> The reason I mentioned Standford is because the reason I'm wearing this FitBit is because I'm a research subject at Mike Snyder's, the chair of genetics at Stanford, IPOP, intrapersonal omics profile. So I was fully sequenced five years ago and I get four full microbiomes. My gut, my mouth, my nose, my ears. Every three months and I've done that for four years now. And about a pint of blood. And so, to your question of the density of data, so a lot of the problem with applying these techniques to health care data is that it's basically a sparse matrix and there's a lot of discontinuities in what you can find and operate on. So what Mike is doing with the IPOP study is much the same as you described. Creating a highly dense longitudinal set of data that will help us mitigate the sparse matrix problem. (low volume response from audience member) Pardon me. >> What's that? (low volume response) (laughter) >> Right, okay. >> John: Lost the school sample. That's got to be a new one I've heard now. >> Okay, well, thank you so much. That was a great question. So I'm going to repeat this and ask if there's another question. You want to go ahead? >> Hi, thanks. So I'm a journalist and I report a lot on these neural networks, a system that's beter at reading mammograms than your human radiologists. Or a system that's better at predicting which patients in the ICU will get sepsis. These sort of fascinating academic studies that I don't really see being translated very quickly into actual hospitals or clinical practice. Seems like a lot of the problems are regulatory, or liability, or human factors, but how do you get past that and really make this stuff practical? >> I think there's a few things that we can do there and I think the proof points of the technology are really important to start with in this specific space. In other places, sometimes, you can start with other things. But here, there's a real confidence problem when it comes to health care, and for good reason. We have doctors trained for many, many years. School and then residencies and other kinds of training. Because we are really, really conservative with health care. So we need to make sure that technology's well beyond just the paper, right? These papers are proof points. They get people interested. They even fuel entire grant cycles sometimes. And that's what we need to happen. It's just an inherent problem, its' going to take a while. To get those things to a point where it's like well, I really do trust what this is saying. And I really think it's okay to now start integrating that into our standard of care. I think that's where you're seeing it. It's frustrating for all of us, believe me. I mean, like I said, I think personally one of the biggest things, I want to have an impact. Like when I go to my grave, is that we used machine learning to improve health care. We really do feel that way. But it's just not something we can do very quickly and as a business person, I don't actually look at those use cases right away because I know the cycle is just going to be longer. >> So to your point, the FDA, for about four years now, has understood that the process that has been given to them by their board of directors, otherwise known as Congress, is broken. And so they've been very actively seeking new models of regulation and what's really forcing their hand is regulation of devices and software because, in many cases, there are black box aspects of that and there's a black box aspect to machine learning. Historically, Intel and others are making inroads into providing some sort of traceability and transparency into what happens in that black box rather than say, overall we get better results but once in a while we kill somebody. Right? So there is progress being made on that front. And there's a concept that I like to use. Everyone knows Ray Kurzweil's book The Singularity Is Near? Well, I like to think that diadarity is near. And the diadarity is where you have human transparency into what goes on in the black box and so maybe Bob, you want to speak a little bit about... You mentioned that, in a prior discussion, that there's some work going on at Intel there. >> Yeah, absolutely. So we're working with a number of groups to really build tools that allow us... In fact Naveen probably can talk in even more detail than I can, but there are tools that allow us to actually interrogate machine learning and deep learning systems to understand, not only how they respond to a wide variety of situations but also where are there biases? I mean, one of the things that's shocking is that if you look at the clinical studies that our drug safety rules are based on, 50 year old white guys are the peak of that distribution, which I don't see any problem with that, but some of you out there might not like that if you're taking a drug. So yeah, we want to understand what are the biases in the data, right? And so, there's some new technologies. There's actually some very interesting data-generative technologies. And this is something I'm also curious what Naveen has to say about, that you can generate from small sets of observed data, much broader sets of varied data that help probe and fill in your training for some of these systems that are very data dependent. So that takes us to a place where we're going to start to see deep learning systems generating data to train other deep learning systems. And they start to sort of go back and forth and you start to have some very nice ways to, at least, expose the weakness of these underlying technologies. >> And that feeds back to your question about regulatory oversight of this. And there's the fascinating, but little known origin of why very few women are in clinical studies. Thalidomide causes birth defects. So rather than say pregnant women can't be enrolled in drug trials, they said any woman who is at risk of getting pregnant cannot be enrolled. So there was actually a scientific meritorious argument back in the day when they really didn't know what was going to happen post-thalidomide. So it turns out that the adverse, unintended consequence of that decision was we don't have data on women and we know in certain drugs, like Xanax, that the metabolism is so much slower, that the typical dosing of Xanax is women should be less than half of that for men. And a lot of women have had very serious adverse effects by virtue of the fact that they weren't studied. So the point I want to illustrate with that is that regulatory cycles... So people have known for a long time that was like a bad way of doing regulations. It should be changed. It's only recently getting changed in any meaningful way. So regulatory cycles and legislative cycles are incredibly slow. The rate of exponential growth in technology is exponential. And so there's impedance mismatch between the cycle time for regulation cycle time for innovation. And what we need to do... I'm working with the FDA. I've done four workshops with them on this very issue. Is that they recognize that they need to completely revitalize their process. They're very interested in doing it. They're not resisting it. People think, oh, they're bad, the FDA, they're resisting. Trust me, there's nobody on the planet who wants to revise these review processes more than the FDA itself. And so they're looking at models and what I recommended is global cloud sourcing and the FDA could shift from a regulatory role to one of doing two things, assuring the people who do their reviews are competent, and assuring that their conflicts of interest are managed, because if you don't have a conflict of interest in this very interconnected space, you probably don't know enough to be a reviewer. So there has to be a way to manage the conflict of interest and I think those are some of the keypoints that the FDA is wrestling with because there's type one and type two errors. If you underregulate, you end up with another thalidomide and people born without fingers. If you overregulate, you prevent life saving drugs from coming to market. So striking that balance across all these different technologies is extraordinarily difficult. If it were easy, the FDA would've done it four years ago. It's very complicated. >> Jumping on that question, so all three of you are in some ways entrepreneurs, right? Within your organization or started companies. And I think it would be good to talk a little bit about the business opportunity here, where there's a huge ecosystem in health care, different segments, biotech, pharma, insurance payers, etc. Where do you see is the ripe opportunity or industry, ready to really take this on and to make AI the competitive advantage. >> Well, the last question also included why aren't you using the result of the sepsis detection? We do. There were six or seven published ways of doing it. We did our own data, looked at it, we found a way that was superior to all the published methods and we apply that today, so we are actually using that technology to change clinical outcomes. As far as where the opportunities are... So it's interesting. Because if you look at what's going to be here in three years, we're not going to be using those big data analytics models for sepsis that we are deploying today, because we're just going to be getting a tiny aliquot of blood, looking for the DNA or RNA of any potential infection and we won't have to infer that there's a bacterial infection from all these other ancillary, secondary phenomenon. We'll see if the DNA's in the blood. So things are changing so fast that the opportunities that people need to look for are what are generalizable and sustainable kind of wins that are going to lead to a revenue cycle that are justified, a venture capital world investing. So there's a lot of interesting opportunities in the space. But I think some of the biggest opportunities relate to what Bob has talked about in bringing many different disparate data sources together and really looking for things that are not comprehensible in the human brain or in traditional analytic models. >> I think we also got to look a little bit beyond direct care. We're talking about policy and how we set up standards, these kinds of things. That's one area. That's going to drive innovation forward. I completely agree with that. Direct care is one piece. How do we scale out many of the knowledge kinds of things that are embedded into one person's head and get them out to the world, democratize that. Then there's also development. The underlying technology's of medicine, right? Pharmaceuticals. The traditional way that pharmaceuticals is developed is actually kind of funny, right? A lot of it was started just by chance. Penicillin, a very famous story right? It's not that different today unfortunately, right? It's conceptually very similar. Now we've got more science behind it. We talk about domains and interactions, these kinds of things but fundamentally, the problem is what we in computer science called NP hard, it's too difficult to model. You can't solve it analytically. And this is true for all these kinds of natural sorts of problems by the way. And so there's a whole field around this, molecular dynamics and modeling these sorts of things, that are actually being driven forward by these AI techniques. Because it turns out, our brain doesn't do magic. It actually doesn't solve these problems. It approximates them very well. And experience allows you to approximate them better and better. Actually, it goes a little bit to what you were saying before. It's like simulations and forming your own networks and training off each other. There are these emerging dynamics. You can simulate steps of physics. And you come up with a system that's much too complicated to ever solve. Three pool balls on a table is one such system. It seems pretty simple. You know how to model that, but it actual turns out you can't predict where a balls going to be once you inject some energy into that table. So something that simple is already too complex. So neural network techniques actually allow us to start making those tractable. These NP hard problems. And things like molecular dynamics and actually understanding how different medications and genetics will interact with each other is something we're seeing today. And so I think there's a huge opportunity there. We've actually worked with customers in this space. And I'm seeing it. Like Rosch is acquiring a few different companies in space. They really want to drive it forward, using big data to drive drug development. It's kind of counterintuitive. I never would've thought it had I not seen it myself. >> And there's a big related challenge. Because in personalized medicine, there's smaller and smaller cohorts of people who will benefit from a drug that still takes two billion dollars on average to develop. That is unsustainable. So there's an economic imperative of overcoming the cost and the cycle time for drug development. >> I want to take a go at this question a little bit differently, thinking about not so much where are the industry segments that can benefit from AI, but what are the kinds of applications that I think are most impactful. So if this is what a skilled surgeon needs to know at a particular time to care properly for a patient, this is where most, this area here, is where most surgeons are. They are close to the maximum knowledge and ability to assimilate as they can be. So it's possible to build complex AI that can pick up on that one little thing and move them up to here. But it's not a gigantic accelerator, amplifier of their capability. But think about other actors in health care. I mentioned a couple of them earlier. Who do you think the least trained actor in health care is? >> John: Patients. >> Yes, the patients. The patients are really very poorly trained, including me. I'm abysmal at figuring out who to call and where to go. >> Naveen: You know as much the doctor right? (laughing) >> Yeah, that's right. >> My doctor friends always hate that. Know your diagnosis, right? >> Yeah, Dr. Google knows. So the opportunities that I see that are really, really exciting are when you take an AI agent, like sometimes I like to call it contextually intelligent agent, or a CIA, and apply it to a problem where a patient has a complex future ahead of them that they need help navigating. And you use the AI to help them work through. Post operative. You've got PT. You've got drugs. You've got to be looking for side effects. An agent can actually help you navigate. It's like your own personal GPS for health care. So it's giving you the inforamation that you need about you for your care. That's my definition of Precision Medicine. And it can include genomics, of course. But it's much bigger. It's that broader picture and I think that a sort of agent way of thinking about things and filling in the gaps where there's less training and more opportunity, is very exciting. >> Great start up idea right there by the way. >> Oh yes, right. We'll meet you all out back for the next start up. >> I had a conversation with the head of the American Association of Medical Specialties just a couple of days ago. And what she was saying, and I'm aware of this phenomenon, but all of the medical specialists are saying, you're killing us with these stupid board recertification trivia tests that you're giving us. So if you're a cardiologist, you have to remember something that happens in one in 10 million people, right? And they're saying that irrelevant anymore, because we've got advanced decision support coming. We have these kinds of analytics coming. Precisely what you're saying. So it's human augmentation of decision support that is coming at blazing speed towards health care. So in that context, it's much more important that you have a basic foundation, you know how to think, you know how to learn, and you know where to look. So we're going to be human-augmented learning systems much more so than in the past. And so the whole recertification process is being revised right now. (inaudible audience member speaking) Speak up, yeah. (person speaking) >> What makes it fathomable is that you can-- (audience member interjects inaudibly) >> Sure. She was saying that our brain is really complex and large and even our brains don't know how our brains work, so... are there ways to-- >> What hope do we have kind of thing? (laughter) >> It's a metaphysical question. >> It circles all the way down, exactly. It's a great quote. I mean basically, you can decompose every system. Every complicated system can be decomposed into simpler, emergent properties. You lose something perhaps with each of those, but you get enough to actually understand most of the behavior. And that's really how we understand the world. And that's what we've learned in the last few years what neural network techniques can allow us to do. And that's why our brain can understand our brain. (laughing) >> Yeah, I'd recommend reading Chris Farley's last book because he addresses that issue in there very elegantly. >> Yeah we're seeing some really interesting technologies emerging right now where neural network systems are actually connecting other neural network systems in networks. You can see some very compelling behavior because one of the things I like to distinguish AI versus traditional analytics is we used to have question-answering systems. I used to query a database and create a report to find out how many widgets I sold. Then I started using regression or machine learning to classify complex situations from this is one of these and that's one of those. And then as we've moved more recently, we've got these AI-like capabilities like being able to recognize that there's a kitty in the photograph. But if you think about it, if I were to show you a photograph that happened to have a cat in it, and I said, what's the answer, you'd look at me like, what are you talking about? I have to know the question. So where we're cresting with these connected sets of neural systems, and with AI in general, is that the systems are starting to be able to, from the context, understand what the question is. Why would I be asking about this picture? I'm a marketing guy, and I'm curious about what Legos are in the thing or what kind of cat it is. So it's being able to ask a question, and then take these question-answering systems, and actually apply them so that's this ability to understand context and ask questions that we're starting to see emerge from these more complex hierarchical neural systems. >> There's a person dying to ask a question. >> Sorry. You have hit on several different topics that all coalesce together. You mentioned personalized models. You mentioned AI agents that could help you as you're going through a transitionary period. You mentioned data sources, especially across long time periods. Who today has access to enough data to make meaningful progress on that, not just when you're dealing with an issue, but day-to-day improvement of your life and your health? >> Go ahead, great question. >> That was a great question. And I don't think we have a good answer to it. (laughter) I'm sure John does. Well, I think every large healthcare organization and various healthcare consortiums are working very hard to achieve that goal. The problem remains in creating semantic interoperatability. So I spent a lot of my career working on semantic interoperatability. And the problem is that if you don't have well-defined, or self-defined data, and if you don't have well-defined and documented metadata, and you start operating on it, it's real easy to reach false conclusions and I can give you a classic example. It's well known, with hundreds of studies looking at when you give an antibiotic before surgery and how effective it is in preventing a post-op infection. Simple question, right? So most of the literature done prosectively was done in institutions where they had small sample sizes. So if you pool that, you get a little bit more noise, but you get a more confirming answer. What was done at a very large, not my own, but a very large institution... I won't name them for obvious reasons, but they pooled lots of data from lots of different hospitals, where the data definitions and the metadata were different. Two examples. When did they indicate the antibiotic was given? Was it when it was ordered, dispensed from the pharmacy, delivered to the floor, brought to the bedside, put in the IV, or the IV starts flowing? Different hospitals used a different metric of when it started. When did surgery occur? When they were wheeled into the OR, when they were prepped and drapped, when the first incision occurred? All different. And they concluded quite dramatically that it didn't matter when you gave the pre-op antibiotic and whether or not you get a post-op infection. And everybody who was intimate with the prior studies just completely ignored and discounted that study. It was wrong. And it was wrong because of the lack of commonality and the normalization of data definitions and metadata definitions. So because of that, this problem is much more challenging than you would think. If it were so easy as to put all these data together and operate on it, normalize and operate on it, we would've done that a long time ago. It's... Semantic interoperatability remains a big problem and we have a lot of heavy lifting ahead of us. I'm working with the Global Alliance, for example, of Genomics and Health. There's like 30 different major ontologies for how you represent genetic information. And different institutions are using different ones in different ways in different versions over different periods of time. That's a mess. >> Our all those issues applicable when you're talking about a personalized data set versus a population? >> Well, so N of 1 studies and single-subject research is an emerging field of statistics. So there's some really interesting new models like step wedge analytics for doing that on small sample sizes, recruiting people asynchronously. There's single-subject research statistics. You compare yourself with yourself at a different point in time, in a different context. So there are emerging statistics to do that and as long as you use the same sensor, you won't have a problem. But people are changing their remote sensors and you're getting different data. It's measured in different ways with different sensors at different normalization and different calibration. So yes. It even persists in the N of 1 environment. >> Yeah, you have to get started with a large N that you can apply to the N of 1. I'm actually going to attack your question from a different perspective. So who has the data? The millions of examples to train a deep learning system from scratch. It's a very limited set right now. Technology such as the Collaborative Cancer Cloud and The Data Exchange are definitely impacting that and creating larger and larger sets of critical mass. And again, not withstanding the very challenging semantic interoperability questions. But there's another opportunity Kay asked about what's changed recently. One of the things that's changed in deep learning is that we now have modules that have been trained on massive data sets that are actually very smart as certain kinds of problems. So, for instance, you can go online and find deep learning systems that actually can recognize, better than humans, whether there's a cat, dog, motorcycle, house, in a photograph. >> From Intel, open source. >> Yes, from Intel, open source. So here's what happens next. Because most of that deep learning system is very expressive. That combinatorial mixture of features that Naveen was talking about, when you have all these layers, there's a lot of features there. They're actually very general to images, not just finding cats, dogs, trees. So what happens is you can do something called transfer learning, where you take a small or modest data set and actually reoptimize it for your specific problem very, very quickly. And so we're starting to see a place where you can... On one end of the spectrum, we're getting access to the computing capabilities and the data to build these incredibly expressive deep learning systems. And over here on the right, we're able to start using those deep learning systems to solve custom versions of problems. Just last weekend or two weekends ago, in 20 minutes, I was able to take one of those general systems and create one that could recognize all different kinds of flowers. Very subtle distinctions, that I would never be able to know on my own. But I happen to be able to get the data set and literally, it took 20 minutes and I have this vision system that I could now use for a specific problem. I think that's incredibly profound and I think we're going to see this spectrum of wherever you are in your ability to get data and to define problems and to put hardware in place to see really neat customizations and a proliferation of applications of this kind of technology. >> So one other trend I think, I'm very hopeful about it... So this is a hard problem clearly, right? I mean, getting data together, formatting it from many different sources, it's one of these things that's probably never going to happen perfectly. But one trend I think that is extremely hopeful to me is the fact that the cost of gathering data has precipitously dropped. Building that thing is almost free these days. I can write software and put it on 100 million cell phones in an instance. You couldn't do that five years ago even right? And so, the amount of information we can gain from a cell phone today has gone up. We have more sensors. We're bringing online more sensors. People have Apple Watches and they're sending blood data back to the phone, so once we can actually start gathering more data and do it cheaper and cheaper, it actually doesn't matter where the data is. I can write my own app. I can gather that data and I can start driving the correct inferences or useful inferences back to you. So that is a positive trend I think here and personally, I think that's how we're going to solve it, is by gathering from that many different sources cheaply. >> Hi, my name is Pete. I've very much enjoyed the conversation so far but I was hoping perhaps to bring a little bit more focus into Precision Medicine and ask two questions. Number one, how have you applied the AI technologies as you're emerging so rapidly to your natural language processing? I'm particularly interested in, if you look at things like Amazon Echo or Siri, or the other voice recognition systems that are based on AI, they've just become incredibly accurate and I'm interested in specifics about how I might use technology like that in medicine. So where would I find a medical nomenclature and perhaps some reference to a back end that works that way? And the second thing is, what specifically is Intel doing, or making available? You mentioned some open source stuff on cats and dogs and stuff but I'm the doc, so I'm looking at the medical side of that. What are you guys providing that would allow us who are kind of geeks on the software side, as well as being docs, to experiment a little bit more thoroughly with AI technology? Google has a free AI toolkit. Several other people have come out with free AI toolkits in order to accelerate that. There's special hardware now with graphics, and different processors, hitting amazing speeds. And so I was wondering, where do I go in Intel to find some of those tools and perhaps learn a bit about the fantastic work that you guys are already doing at Kaiser? >> Let me take that first part and then we'll be able to talk about the MD part. So in terms of technology, this is what's extremely exciting now about what Intel is focusing on. We're providing those pieces. So you can actually assemble and build the application. How you build that application specific for MDs and the use cases is up to you or the one who's filling out the application. But we're going to power that technology for multiple perspectives. So Intel is already the main force behind The Data Center, right? Cloud computing, all this is already Intel. We're making that extremely amenable to AI and setting the standard for AI in the future, so we can do that from a number of different mechanisms. For somebody who wants to develop an application quickly, we have hosted solutions. Intel Nervana is kind of the brand for these kinds of things. Hosted solutions will get you going very quickly. Once you get to a certain level of scale, where costs start making more sense, things can be bought on premise. We're supplying that. We're also supplying software that makes that transition essentially free. Then taking those solutions that you develop in the cloud, or develop in The Data Center, and actually deploying them on device. You want to write something on your smartphone or PC or whatever. We're actually providing those hooks as well, so we want to make it very easy for developers to take these pieces and actually build solutions out of them quickly so you probably don't even care what hardware it's running on. You're like here's my data set, this is what I want to do. Train it, make it work. Go fast. Make my developers efficient. That's all you care about, right? And that's what we're doing. We're taking it from that point at how do we best do that? We're going to provide those technologies. In the next couple of years, there's going to be a lot of new stuff coming from Intel. >> Do you want to talk about AI Academy as well? >> Yeah, that's a great segway there. In addition to this, we have an entire set of tutorials and other online resources and things we're going to be bringing into the academic world for people to get going quickly. So that's not just enabling them on our tools, but also just general concepts. What is a neural network? How does it work? How does it train? All of these things are available now and we've made a nice, digestible class format that you can actually go and play with. >> Let me give a couple of quick answers in addition to the great answers already. So you're asking why can't we use medical terminology and do what Alexa does? Well, no, you may not be aware of this, but Andrew Ian, who was the AI guy at Google, who was recruited by Google, they have a medical chat bot in China today. I don't speak Chinese. I haven't been able to use it yet. There are two similar initiatives in this country that I know of. There's probably a dozen more in stealth mode. But Lumiata and Health Cap are doing chat bots for health care today, using medical terminology. You have the compound problem of semantic normalization within language, compounded by a cross language. I've done a lot of work with an international organization called Snowmed, which translates medical terminology. So you're aware of that. We can talk offline if you want, because I'm pretty deep into the semantic space. >> Go google Intel Nervana and you'll see all the websites there. It's intel.com/ai or nervanasys.com. >> Okay, great. Well this has been fantastic. I want to, first of all, thank all the people here for coming and asking great questions. I also want to thank our fantastic panelists today. (applause) >> Thanks, everyone. >> Thank you. >> And lastly, I just want to share one bit of information. We will have more discussions on AI next Tuesday at 9:30 AM. Diane Bryant, who is our general manager of Data Centers Group will be here to do a keynote. So I hope you all get to join that. Thanks for coming. (applause) (light electronic music)

>> live from Boston, Massachusetts. It's the Cube covering IBM Chief Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts. Day Volante and Stew Minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. Stillman and I have pleased to have Jean Kolker on a Cuba lem. Uh, he's IBM vice president and chief data officer of the Global Technology Services division. And Seth Dobrin who's the Director of Digital Strategies. That Monsanto. You may have seen them in the news lately. Gentlemen. Welcome to the Cube, Jean. Welcome back. Good to see you guys again. Thanks. Thank you. So let's start with the customer. Seth, Let's, uh, tell us about what you're doing here, and then we'll get into your role. >> Yes. So, you know, the CDO summit has been going on for a couple of years now, and I've been lucky enoughto be participating for a couple of a year and 1/2 or so, Um, and you know, really, the nice thing about the summit is is the interaction with piers, um, and the interaction and networking with people who are facing similar challenges from a similar perspective. >> Yes, kind of a relatively new Roland topic, one that's evolved, Gene. We talked about this before, but now you've come from industry into, ah, non regulated environment. Now what's happened like >> so I think the deal is that way. We're developing some approaches, and we get in some successes in regulated environment. Right? And now I feel with And we were being client off IBM for years, right? Using their technology's approaches. Right? So and now I feel it's time for me personally to move on something different and tried to serve our power. I mean, IBM clients respected off in this striking from healthcare, but their approaches, you know, and what IBM can do for clients go across the different industries, right? And doing it. That skill that's very beneficial, I think, for >> clients. So Monsanto obviously guys do a lot of stuff in the physical world. Yeah, you're the head of digital strategy. So what does that entail? What is Monte Santo doing for digital? >> Yes, so, you know, for as head of digital strategies for Monsanto, really? My role is to number one. Help Monsanto internally reposition itself so that we behave and act like a digital companies, so leveraging data and analytics and also the cultural shifts associated with being more digital, which is that whole kind like you start out this conversation with the whole customer first approach. So what is the real impact toe? What we're doing to our customers on driving that and then based on on those things, how can we create new business opportunities for us as a company? Um, and how can we even create new adjacent markets or new revenues in adjacent areas based on technologies and things we already have existing within the company? >> It was the scope of analytics, customer engagement of digital experiences, all of the above, so that the scope is >> really looking at our portfolio across the gamut on DH, seeing how we can better serve our customers and society leveraging what we're doing today. So it's really leveraging the re use factor of the whole digital concept. Right? So we have analytics for geospatial, right? Big part of agriculture is geospatial. Are there other adjacent areas that we could apply some of that technology? Some of that learning? Can we monetize those data? We monetize the the outputs of those models based on that, Or is there just a whole new way of doing business as a company? Because we're in this digital era >> this way? Talked about a lot of the companies that have CEOs today are highly regulated. What are you learning from them? What's what's different? Kind of a new organization. You know, it might be an opportunity for you that they don't have. And, you know, do you have a CDO yet or is that something you're planning on having? >> Yes, So we don't have a CDO We do have someone acts as an essential. he's a defacto CEO, he has all of the data organizations on his team. Um, it's very recent for Monsanto, Um, and and so I think, you know, in terms of from the regular, what can we learn from, you know, there there are. It's about half financial people have non financial people, are half heavily regulated industries, and I think, you know, on the surface you would. You would think that, you know, there was not a lot of overlap, but I think the level of rigor that needs to go into governance in a financial institution that same thought process. Khun really be used as a way Teo really enable Maur R and D. Mohr you know, growth centered companies to be able to use data more broadly and so thinking of governance not as as a roadblock or inhibitor, but really thinking about governance is an enabler. How does it enable us to be more agile as it enable us to beam or innovative? Right? If if people in the company there's data that people could get access to by unknown process of known condition, right, good, bad, ugly. As long as people know they can do things more quickly because the data is there, it's available. It's curated. And if they shouldn't have access it under their current situation, what do they need to do to be able to access that data? Right. So if I would need If I'm a data scientist and I want to access data about my customers, what can I can't? What can and can't I do with that data? Number one doesn't have to be DEA Nana Mayes, right? Or if I want to access in, it's current form. What steps do I need to go through? What types of approval do I need to do to do to access that data? So it's really about removing roadblocks through governance instead of putting him in place. >> Gina, I'm curious. You know, we've been digging into you know, IBM has a very multifaceted role here. You know how much of this is platforms? How much of it is? You know, education and services. How much of it is, you know, being part of the data that your your customers you're using? >> Uh so I think actually, that different approaches to this issues. My take is basically we need Teo. I think that with even cognitive here, right and data is new natural resource worldwide, right? So data service, cognitive za za service. I think this is where you know IBM is coming from. And the BM is, you know, tradition. It was not like that, but it's under a lot of transformation as we speak. A lot of new people coming in a lot off innovation happening as we speak along. This line's off new times because cognitive with something, really you right, and it's just getting started. Data's a service is really new. It's just getting started. So there's a lot to do. And I think my role specifically global technology services is you know, ah, largest by having your union that IBM, you're 30 plus 1,000,000,000 answered You okay? And we support a lot of different industries basically going across all different types of industries how to transition from offerings to new business offerings, service, integrated services. I think that's the key for us. >> Just curious, you know? Where's Monsanto with kind of the adoption of cognitive, You know what? Where are you in that journey? >> Um, so we are actually a fairly advanced in the journey In terms of using analytics. I wouldn't say that we're using cognitive per se. Um, we do use a lot of machine learning. We have some applications that on the back end run on a I So some form of artificial or formal artificial intelligence, that machine learning. Um, we haven't really gotten into what, you know, what? IBM defined his cognitive in terms of systems that you can interact with in a natural, normal course of doing voice on DH that you spend a whole lot of time constantly teaching. But we do use like I said, artificial intelligence. >> Jean I'm interested in the organizational aspects. So we have Inderpal on before. He's the global CDO, your divisional CDO you've got a matrix into your leadership within the Global Services division as well as into the chief date officer for all of IBM. Okay, Sounds sounds reasonable. He laid out for us a really excellent sort of set of a framework, if you will. This is interval. Yeah, I understand your data strategy. Identify your data store says, make those data sources trusted. And then those air sequential activities. And in parallel, uh, you have to partner with line of business. And then you got to get into the human resource planning and development piece that has to start right away. So that's the framework. Sensible framework. A lot of thought, I'm sure, went into it and a lot of depth and meaning behind it. How does that framework translate into the division? Is it's sort of a plug and play and or is there their divisional goals that are create dissonance? Can you >> basically, you know, I'm only 100 plus days in my journey with an IBM right? But I can feel that the global technology services is transforming itself into integrated services business. Okay, so it's thiss framework you just described is very applicable to this, right? So basically what we're trying to do, we're trying to become I mean, it was the case before for many industries, for many of our clients. But we I want to transform ourselves into trusted broker. So what they need to do and this framework help is helping tremendously, because again, there's things we can do in concert, you know, one after another, right to control other and things we can do in parallel. So we trying those things to be put on the agenda for our global technology services, okay. And and this is new for them in some respects. But some respects it's kind of what they were doing before, but with new emphasis on data's A service cognitive as a service, you know, major thing for one of the major things for global technology services delivery. So cognitive delivery. That's kind of new type off business offerings which we need to work on how to make it truly, you know, once a sense, you know, automated another sense, you know, cognitive and deliver to our clients some you value and on value compared to what was done up until recently. What >> do you mean by cognitive delivery? Explained that. >> Yeah, so basically in in plain English. So what's right now happening? Usually when you have a large systems  computer IT system, which are basically supporting lot of in this is a lot of organizations corporations, right? You know, it's really done like this. So it's people run technology assistant, okay? And you know what Of decisions off course being made by people, But some of the decisions can be, you know, simple decisions. Right? Decisions, which can be automated, can standardize, normalize can be done now by technology, okay and people going to be used for more complex decisions, right? It's basically you're going toe. It turned from people around technology assisted toa technology to technology around people assisted. OK, that's very different. Very proposition, right? So, again, it's not about eliminating jobs, it's very different. It's taken off, you know, routine and automata ble part off the business right to technology and given options and, you know, basically options to choose for more complex decision making to people. That's kind of I would say approach. >> It's about scale and the scale to, of course, IBM. When when Gerstner made the decision, Tio so organized as a services company, IBM came became a global leader, if not the global leader but a services business. Hard to scale. You could scare with bodies, and the bigger it gets, the more complicated it gets, the more expensive it gets. So you saying, If I understand correctly, the IBM is using cognitive and software essentially to scale its services business where possible, assisted by humans. >> So that's exactly the deal. So and this is very different. Very proposition, toe say, compared what was happening recently or earlier? Always. You know other. You know, players. We're not building your shiny and much more powerful and cognitive, you know, empowered mouse trap. No, we're trying to become trusted broker, OK, and how to do that at scale. That's an open, interesting question, but we think that this transition from you know people around technology assisted Teo technology around people assisted. That's the way to go. >> So what does that mean to you? How does that resonate? >> Yeah, you know, I think it brings up a good point actually, you know, if you think of the whole litany of the scope of of analytics, you have everything from kind of describing what happened in the past All that to cognitive. Um, and I think you need to I understand the power of each of those and what they shouldn't should be used for. A lot of people talk. You talk. People talk a lot about predictive analytics, right? And when you hear predictive analytics, that's really where you start doing things that fully automate processes that really enable you to replace decisions that people make right, I think. But those air mohr transactional type decisions, right? More binary type decisions. As you get into things where you can apply binary or I'm sorry, you can apply cognitive. You're moving away from those mohr binary decisions. There's more transactional decisions, and you're moving mohr towards a situation where, yes, the system, the silicon brain right, is giving you some advice on the types of decisions that you should make, based on the amount of information that it could absorb that you can't even fathom absorbing. But they're still needs really some human judgment involved, right? Some some understanding of the contacts outside of what? The computer, Khun Gay. And I think that's really where something like cognitive comes in. And so you talk about, you know, in this in this move to have, you know, computer run, human assisted right. There's a whole lot of descriptive and predictive and even prescriptive analytics that are going on before you get to that cognitive decision but enables the people to make more value added decisions, right? So really enabling the people to truly add value toe. What the data and the analytics have said instead of thinking about it, is replacing people because you're never going to replace you. Never gonna replace people. You know, I think I've heard people at some of these conferences talking about, Well, no cognitive and a I is going to get rid of data scientist. I don't I don't buy that. I think it's really gonna enable data scientist to do more valuable, more incredible things >> than they could do today way. Talked about this a lot to do. I mean, machines, through the course of history, have always replaced human tasks, right, and it's all about you know, what's next for the human and I mean, you know, with physical labor, you know, driving stakes or whatever it is. You know, we've seen that. But now, for the first time ever, you're seeing cognitive, cognitive assisted, you know, functions come into play and it's it's new. It's a new innovation curve. It's not Moore's law anymore. That's driving innovation. It's how we interact with systems and cognitive systems one >> tonight. And I think, you know, I think you hit on a good point there when you said in driving innovation, you know, I've run, you know, large scale, automated process is where the goal was to reduce the number of people involved. And those were like you said, physical task that people are doing we're talking about here is replacing intellectual tasks, right or not replacing but freeing up the intellectual capacity that is going into solving intellectual tasks to enable that capacity to focus on more innovative things, right? We can teach a computer, Teo, explain ah, an area to us or give us some advice on something. I don't know that in the next 10 years, we're gonna be able to teach a computer to innovate, and we can free up the smart minds today that are focusing on How do we make a decision? Two. How do we be more innovative in leveraging this decision and applying this decision? That's a huge win, and it's not about replacing that person. It's about freeing their time up to do more valuable things. >> Yes, sure. So, for example, from my previous experience writing healthcare So physicians, right now you know, basically, it's basically impossible for human individuals, right to keep up with spaced of changes and innovations happening in health care and and by medical areas. Right? So in a few years it looks like there was some numbers that estimate that in three days you're going to, you know, have much more information for several years produced during three days. What was done by several years prior to that point. So it's basically becomes inhuman to keep up with all these innovations, right? Because of that decision is going to be not, you know, optimal decisions. So what we'd like to be doing right toe empower individuals make this decision more, you know, correctly, it was alternatives, right? That's about empowering people. It's not about just taken, which is can be done through this process is all this information and get in the routine stuff out of their plate, which is completely full. >> There was a stat. I think it was last year at IBM Insight. Exact numbers, but it's something like a physician would have to read 1,500 periodic ALS a week just to keep up with the new data innovations. I mean, that's virtually impossible. That something that you're obviously pointing, pointing Watson that, I mean, But there are mundane examples, right? So you go to the airport now, you don't need a person that the agent to give you. Ah, boarding pass. It's on your phone already. You get there. Okay, so that's that's That's a mundane example we're talking about set significantly more complicated things. And so what's The gate is the gate. Creativity is it is an education, you know, because these are step functions in value creation. >> You know, I think that's ah, what? The gate is a question I haven't really thought too much about. You know, when I approach it, you know the thinking Mohr from you know, not so much. What's the gate? But where? Where can this ad the most value um So maybe maybe I have thought about it. And the gate is value, um, and and its value both in terms of, you know, like the physician example where, you know, physicians, looking at images. And I mean, I don't even know what the error rate is when someone evaluates and memory or something. And I probably don't want Oh, right. So, getting some advice there, the value may not be monetary, but to me, it's a lot more than monetary, right. If I'm a patient on DH, there's a lot of examples like that. And other places, you know, that are in various industries. That I think that's that's the gate >> is why the value you just hit on you because you are a heat seeking value missile inside of your organisation. What? So what skill sets do you have? Where did you come from? That you have this capability? Was your experience, your education, your fortitude, >> While the answer's yes, tell all of them. Um, you know, I'm a scientist by training my backgrounds in statistical genetics. Um, and I've kind of worked through the business. I came up through the RND organization with him on Santo over the last. Almost exactly 10 years now, Andi, I've had lots of opportunities to leverage. Um, you know, Data and analytics have changed how the company operates on. I'm lucky because I'm in a company right now. That is extremely science driven, right? Monsanto is a science based company. And so being in a company like that, you don't face to your question about financial industry. I don't think you face the same barriers and Monsanto about using data and analytics in the same way you may in a financial types that you've got company >> within my experience. 50% of diagnosis being proven incorrect. Okay, so 50% 05 0/2 summation. You go to your physician twice. Once you on average, you get in wrong diagnosis. We don't know which one, by the way. Definitely need some someone. Garrett A cz Individuals as humans, we do need some help. Us cognitive, and it goes across different industries. Right, technologist? So if your server is down, you know you shouldn't worry about it because there is like system, you know, Abbas system enough, right? So think about how you can do that scale, and then, you know start imagined future, which going to be very empowering. >> So I used to get a second opinion, and now the opinion comprises thousands, millions, maybe tens of millions of opinions. Is that right? >> It's a try exactly and scale ofthe data accumulation, which you're going to help us to solve. This problem is enormous. So we need to keep up with that scale, you know, and do it properly exactly for business. Very proposition. >> Let's talk about the role of the CDO and where you see that evolving how it relates to the role of the CIA. We've had this conversation frequently, but is I'm wondering if the narratives changing right? Because it was. It's been fuzzy when we first met a couple years ago that that was still a hot topic. When I first started covering this. This this topic, it was really fuzzy. Has it come in two more clarity lately in terms of the role of the CDO versus the CIA over the CTO, its chief digital officer, we starting to see these roles? Are they more than just sort of buzzwords or grey? You know, areas. >> I think there's some clarity happening already. So, for example, there is much more acceptance for cheap date. Office of Chief Analytics Officer Teo, Chief Digital officer. Right, in addition to CEO. So basically station similar to what was with Serious 20 plus years ago and CEO Row in one sentence from my viewpoint would be How you going using leverage in it. Empower your business. Very proposition with CDO is the same was data how using data leverage and data, your date and your client's data. You, Khun, bring new value to your clients and businesses. That's kind ofthe I would say differential >> last word, you know, And you think you know I'm not a CDO. But if you think about the concept of establishing a role like that, I think I think the name is great because that what it demonstrates is support from leadership, that this is important. And I think even if you don't have the name in the organization like it, like in Monsanto, you know, we still have that executive management level support to the data and analytics, our first class citizens and their important, and we're going to run our business that way. I think that's really what's important is are you able to build the culture that enable you to leverage the maximum capability Data and analytics. That's really what matters. >> All right, We'll leave it there. Seth Gene, thank you very much for coming that you really appreciate your time. Thank you. Alright. Keep it right there, Buddy Stew and I'll be back. This is the IBM Chief Data Officer Summit. We're live from Boston right back.