(upbeat music) (logo shimmers) >> Good afternoon everyone, and welcome back to AWS re Invent 2022. We are live here from the show floor in Las Vegas, Nevada, we're theCUBE, my name is Savannah Peterson, joined by John Furrier, John, are you excited for the next segment? >> I love the innovation story, this next segment's going to be really interesting, an example of ecosystem innovation in action, it'll be great. >> Yeah, our next guests are actually award-winning, I am very excited about that, please welcome Alan and Becky from IBM. Thank you both so much for being here, how's the show going for ya? Becky you got a, just a platinum smile, I'm going to go to you first, how's the show so far? >> No, it's going great. There's lots of buzz, lots of excitement this year, of course, three times the number of people, but it's fantastic. >> Three times the number of people- >> (indistinct) for last year. >> That is so exciting, so what is that... Do you know what the total is then? >> I think it's over 55,000. >> Ooh, loving that. >> John: A lot. >> It's a lot, you can tell by the hallways- >> Becky: It's a lot. >> John: It's crowded, right. >> Yeah, you can tell by just the energy and the, honestly the heat in here right now is pretty good. Alan, how are you feeling on the show floor this year? >> Awesome, awesome, we're meeting a lot of partners, talking to a lot of clients. We're really kind of showing them what the new IBM, AWS relationship is all about, so, beautiful time to be here. >> Well Alan, why don't you tell us what that partnership is about, to start us off? >> Sure, sure. So the partnership started with the relationship in our consulting services, and Becky's going to talk more about that, right? And it grew, this year it grew into the IBM software realm where we signed an agreement with AWS around May timeframe this year. >> I love it, so, like you said, you're just getting started- >> Just getting started. >> This is the beginning of something magic. >> We're just scratching the surface with this right? >> Savannah: Yeah. >> But it represents a huge move for IBM to meet our clients where they are, right? Meet 'em where they are with IBM technology, enterprise technology they're used to, but with the look and feel and usage model that they're used to with AWS. >> Absolutely and so to build on that, you know, we're really excited to be an AWS Premier Consulting Partner. We've had this relationship for a little over five years with AWS, I'd say it's really gone up a notch over the last year or two as we've been working more and more closely, doubling down on our investments, doubling down on our certifications, we've got over 15,000 people certified now, almost 16,000 actually- >> Savannah: Wow. >> 14 competencies, 16 service deliveries and counting. We cover a mass of information and services from Data Analytics, IoT, AI, all the way to Modernization, SAP, Security Services, right. So it's pretty comprehensive relationship, but in addition to the fantastic clients that we both share, we're doing some really great things around joint industry solutions, which I'll talk about in a few minutes and some of those are being launched at the conference this year, so that's even better. But the most exciting thing to me right now is that we just found out that we won the Global Innovator Partner of the Year award, and a LATAM Partner of the Year award. >> Savannah: Wow. >> John: That's (indistinct) >> So, super excited for IBM Consulting to win this, we're honored and it's just a great, exciting part to the conference. >> The news coming out of this event, we know tomorrow's going to be the big keynote for the new Head of the ecosystem, Ruba. We're hearing that it's going to be all about the ecosystem, enabling value creation, enabling new kinds of solutions. We heard from the CEO of AWS, this nextGen environment's upon us, it's very solution-oriented- >> Becky: Absolutely. >> A lot of technology, it's not an either or, it's an and equation, this is a huge new shift, I won't say shift, a continuation for AWS, and you guys, we've been covering, so you got the and situation going on... Innovation solutions and innovation technology and customers can choose, build a foundation or have it out of the box. What's your reaction to that? Do you think it's going to go well for AWS and IBM? >> I think it fits well into our partnership, right? The the thing you mentioned that I gravitate to the most is the customer gets to choose and the thing that's been most amazing about the partnership, both of these companies are maniacally focused on the customer, right? And so we've seen that come about as we work on ways the customer to access our technology, consume the technology, right? We've sold software on-prem to customers before, right, now we're going to be selling SaaS on AWS because we had customers that were on AWS, we're making it so that they can more easily purchase it by being in the marketplace, making it so they can draw down their committed spin with AWS, their customers like that a lot- [John] Yeah. >> Right. We've even gone further to enable our distributor network and our resellers, 'cause a lot of our customers have those relationships, so they can buy through them. And recently we've enabled the customer to leverage their EDP, their committed spend with AWS against IBM's ELA and structure, right, so you kind of get a double commit value from a customer point of view, so the amazing part is just been all about the customers. >> Well, that's interesting, you got the technology relationship with AWS, you mentioned how they're engaging with the software consumption in marketplace, licensed deals, there's all kinds of new business model innovations on top of the consumption and building. Then you got the consulting piece, which is again, a big part of, Adam calls it "Business transformation," which is the result of digital transformation. So digital transformation is the process, the outcome is the business transformation, that's kind of where it all kind of connects. Becky, what's your thoughts on the Amazon consulting relationships? Obviously the awards are great but- >> They are, no- >> What's the next step? Where does it go from here? >> I think the best way for me to describe it is to give you some rapid flyer client examples, you know, real customer stories and I think that's where it really, rubber meets the road, right? So one of the most recent examples are IBM CEO Arvind Krishna, in his three key results actually mentioned one of our big clients with AWS which is the Department of Veterans Affairs in the US and is an AI solution that's helped automate claims processing. So the veterans are trying to get their benefits, they submit the claims, snail mail, phone calls, you know, some in person, some over email- >> Savannah: Oh, it gives me all the feels hearing you talk about this- >> It's a process that used to take 25 to 30 days depending on the complexity of the claims, we've gotten it down with AWS down to within 24 hours we can get the veterans what they need really quickly so, I mean, that's just huge. And it's an exciting story that includes data analytics, AI and automation, so that's just one example. You know, we've got examples around SAP where we've developed a next generation SAP for HANA Platform for Phillips Carbon Black hosted on AWS, right? For them, it created an integrated, scalable, digital business, that cut out a hundred percent the capital cost from on-prem solutions. We've got security solutions around architectures for telecommunications advisors and of course we have lots of examples of migration and modernization and moving workloads using Red Hat to do that. So there's a lot of great client examples, so to me, this is the heart of what we do, like you said, both companies are really focused on clients, Amazon's customer-obsessed, and doing what we can for our clients together is where we get the impact. >> Yeah, that's one of the things that, it sounds kind of cliche, "Oh we're going to work backwards from the customer," I know Amazon says that, they do, you guys are also very customer-focused but the customers are changing. So I'd love to get your reaction because we're now in that cloud 2.0, I call that 2.0 or you got the Amazon Classic, my word, and then Next Gen Cloud coming, the customers are different, they're transforming because IT's not a department anymore, it's in the DevOps pipeline. The developers are driving a lot of IT but security and on DataOps, it's the structural change happening at the customer, how do you guys see that at IBM? I know we cover a lot of Red Hat and Arvind talks to us all the time, meeting the customer where they are, where are they? Where are the customers? Can you share your perspective on where they are? >> It's an astute observation, right, the customer is changing. We have both of those sets of customers, right, we still have the traditional customer, our relationship with Central IT, right, and driving governance and all of those things. But the folks that are innovating many times they're in the line of business, they're discovering solutions, they're building new things. And so we need our offerings to be available to them. We need them to understand how to use them and be convenient for these guys and take them through that process. So that change in the customer is one that we are embracing by making our offerings easy to consume, easy to use, and easy to build into solutions and then easy to parlay into what central IT needs to do for governance, compliance, and these types of things, it's becoming our new bread and butter. >> And what's really cool is- >> Is that easy button- >> We've been talking about- >> It's the easy button. >> The easy button a lot on the show this week and if you just, you just described it it's exactly what people want, go on Becky. >> Sorry about that, I was going to say, the cool part is that we're co-creating these things with our clients. So we're using things like the Amazon Working Backward that you just mentioned.` We're using the IBM garage methodology to get innovative to do design working, design thinking workshops, and think about where is that end user?, Where is that stakeholder? Where are they, they thinking, feeling, doing, saying how do we make the easier? How do we get the easy button for them so that they can have the right solutions for their businesses. We work mostly with lines of business in my part of the organization, and they're hungry for that. >> You know, we had a quote on theCUBE yesterday, Savannah remember one of our guests said, you know, back in the, you know, 1990s or two 2000s, if you had four production apps, it was considered complex >> Savannah: Yeah. >> You know, now you got hundreds of workloads, thousands of workloads, so, you know, this end-to-end vision that we heard that's playing out is getting more complex, but the easy button is where these abstraction layers and technology could come in. So it's getting more complex because there's more stuff but it's getting easier because- >> Savannah: What is the magnitude? >> You can make it easier. This is a dynamic, share your thoughts on that. >> It's getting more complex because our clients need to move faster, right, they need to be more agile, right, so not only are there thousands of applications there are hundreds of thousands microservices that are composing those applications. So they need capabilities that help them not just build but govern that structure and put the right compliance over that structure. So this relationship- >> Savannah: Lines of governance, yeah- >> This relationship we built with AWS is in our key areas, it's a strategic move, not a small thing for us, it covers things like automation and integration where you need to build that way. It covers things like data and AI where you need to do the analytics, even things like sustainability where we're totally aligned with what AWS is talking about and trying to do, right, so it's really a good match made there. >> John: It really sounds awesome. >> Yeah, it's clear. I want to dig in a little bit, I love the term, and I saw it in my, it stuck out to me in the notes right away, getting ready for you all, "maniacal", maniacal about the customer, maniacal about the community, I think that's really clear when we're talking about 24 days to 24 hours, like the veteran example that you gave right there, which I genuinely felt in my heart. These are the types of collaborations that really impact people's lives, tell me about some of the other trends or maybe a couple other examples you might have because I think sometimes when our head's in the clouds, we talk a lot about the tech and the functionality, we forget it's touching every single person walking around us, probably in a different way right now than we may even be aware- >> I think one of the things that's been, and our clients have been asking us for, is to help coming into this new era, right, so we've come out of a pandemic where a lot of them had to do some really, really basic quick decisions. Okay, "Contact Center, everyone work from home now." Okay, how do we do that? Okay, so we cobbled something together, now we're back, so what do we do? How do we create digital transformation around that so that we are going forward in a really positive way that works for our clients or for our contact center reps who are maybe used to working from home now versus what our clients need, the response times they need, and AWS has all the technology that we're working with like Amazon Connect to be able to pull those things together with some of our software like Watson Assistant. So those types of solutions are coming together out of that need and now we're moving into the trend where economy's getting tougher, right? More cost cutting potentially is coming, right, better efficiencies, how do we leverage our solutions and help our clients and customers do that? So I think that's what the customer obsession's about, is making sure we really understand where their pain points are, and not just solve them but maybe get rid of 'em. >> John: Yeah, great one. >> Yeah. And not developing in a silo, I mean, it's a classic subway problem, you got to be communicating with your community if you want to continue to serve them. And IBM's been serving their community for a very long time, which is super impressive, do you think they're ready for the challenge? >> Let's do it. >> So we have a new thing on theCUBE. >> Becky: Oh boy. >> We didn't warn you about this, but here we go. Although you told, Alan, you've mentioned you're feeling very cool with the microphone on, so I feel like, I'm going to put you in the hot seat first on this one. Not that I don't think Becky's going to smash it, but I feel like you're channeling the power of the microphone. New challenges, treat it like a 32nd Instagram reel-style story, a hot take, your thought leadership, money clip, you know, this is your moment. What is the biggest takeaway, most important thing happening at the show this year? >> Most important thing happening at the show? Well, I'm glad you mentioned it that way, because earlier you said we may have to sing (presenters and guests all laughing) >> So this is much better than- >> That's actually part of the close. >> John: Hey, hey. >> Don't worry, don't worry, I haven't forgotten that, it's your Instagram reel, go. (Savannah laughs) >> Original audio happening here on theCUBE, courtesy of Alan and IBM, I am so here for it. >> So what my takeaway and what I would like for the audience to take away, out of this conversation especially, but even broadly, the IBM AWS relationship is really like a landmark type of relationship, right? It's one of the biggest that we've established on both sides, right- >> Savannah: It seems huge, okay you are too monolith in the world of companies, like, yeah- >> Becky: Totally. >> It's huge. And it represents a strategic change on both sides, right? With that customer- >> Savannah: Fundamentally- >> In the middle right? >> Savannah: Yeah. >> So we're seeing things like, you know, AWS is working with us to make sure we're building products the way that a AWS client likes to consume them, right, so that we have the right integration, so they get that right look and feel, but they still get the enterprise level capabilities they're used to from IBM, right? So the big takeaway I like for people to take, is this is a new IBM, it's a new AWS and IBM relationship, and so expect more of that goodness, more of those new things coming out of it. [John] Excellent, wow. >> That was great, well done, you nailed it. and you're going to finish with some acapella, right? (Alan laughs) >> You got a pitch pipe ready? (everyone laughs) >> All right Becky, what about you? Give us your hot take. >> Well, so for me, the biggest takeaway is just the way this relationship has grown so much, so, like you said, it's the new IBM it's the new AWS, we were here last year, we had some good things, this year we're back at the show with joint solutions, have been jointly funded and co-created by AWS and IBM. This is huge, this is a really big opportunity and a really big deal that these two companies have come together, identified joint customer needs and we're going after 'em together and we're putting 'em in the booth. >> Savannah: So cool. And there's things like smart edge for welding solutions that are out there. >> Savannah: Yes. >> You know, I talked about, and it's, you know you wouldn't think, "Okay, well what's that?" There's a lot to that, a lot of saving when you look at how you do welding and if you apply things like visual AI and auditory AI to make sure a weld is good. I mean, I think these are, these things are cool, I geek out on these things- >> John: Every vertical. >> I'm geeking out with you right now, just geeking- >> Yeah, yeah, yeah, so- >> Every vertical is infected. >> They are and it's so impactful to have AWS just in lockstep with us, doing these solutions, it's so different from, you know, you kind of create something that you think your customers like and then you put it out there. >> Yeah, versus this moment. >> Yeah, they're better together. >> It's strategic partnership- >> It's truly a strategic partnership. and we're really bringing that this year to reinvent and so I'm super excited about that. >> Congratulations. >> Wow, well, congratulations again on your awards, on your new partnership, I can't wait to hear, I mean, we're seven months in, eight months in to this this SaaS side of the partnership, can't wait to see what we're going to be talking about next year when we have you back on theCUBE. >> I know. >> and maybe again in between now and then. Alan, Becky, thank you both so much for being here, this was truly a joy and I'm sure you gave folks a taste of the new IBM, practicing what you preach. >> John: Great momentum. >> And I'm just, I'm so impressed with the two companies collaborating, for those of us OGs in tech, the big companies never collaborated before- >> Yeah. >> John: Yeah. Joint, co-created solutions. >> And you have friction between products and everything else. I mean's it's really, co-collaboration is, it's a big theme for us at all the shows we've been doing this year but it's just nice to see it in practice too, it's an entirely different thing, so well done. >> Well it's what gets me out of the bed in the morning. >> All right, congratulations. >> Very clearly, your energy is contagious and I love it and yeah, this has been great. Thank all of you at home or at work or on the International Space Station or wherever you might be tuning in from today for joining us, here in Las Vegas at AWS re Invent where we are live from the show floor, wall-to-wall coverage for three days with John Furrier. My name is Savannah Peterson, we're theCUBE, the source for high tech coverage. (cheerful upbeat music)

>>Good evening everyone and welcome back to Sparkly Sin City, Las Vegas, Nevada, where we are here with the cube covering AWS Reinvent for the 10th year in a row. John Furrier has been here for all 10. John, we are in our last session of day one. How does it compare? >>I just graduated high school 10 years ago. It's exciting to be, here's been a long time. We've gotten a lot older. My >>Got your brain is complex. You've been a lot in there. So fast. >>Graduated eight in high school. You know how it's No. All good. This is what's going on. This next segment, wrapping up day one, which is like the the kickoff. The Mondays great year. I mean Tuesdays coming tomorrow big days. The announcements are all around the kind of next gen and you're starting to see partnering and integration is a huge part of this next wave cuz API's at the cloud, next gen cloud's gonna be deep engineering integration and you're gonna start to see business relationships and business transformation scale a horizontally, not only across applications but companies. This has been going on for a while, covering it. This next segment is gonna be one of those things that we're gonna look at as something that's gonna happen more and more on >>Yeah, I think so. It's what we've been talking about all day. Without further ado, I would like to welcome our very exciting guest for this final segment, trust from single store. Thank you for being here. And we also have him on from IBM Data and ai. Y'all are partners. Been partners for about a year. I'm gonna go out on a limb only because their legacy and suspect that a few people, a few more people might know what IBM does versus what a single store does. So why don't you just give us a little bit of background so everybody knows what's going on. >>Yeah, so single store is a relational database. It's a foundational relational systems, but the thing that we do the best is what we call us realtime analytics. So we have these systems that are legacy, which which do operations or analytics. And if you wanted to bring them together, like most of the applications want to, it's really a big hassle. You have to build an ETL pipeline, you'd have to duplicate the data. It's really faulty systems all over the place and you won't get the insights really quickly. Single store is trying to solve that problem elegantly by having an architecture that brings both operational and analytics in one place. >>Brilliant. >>You guys had a big funding now expanding men. Sequel, single store databases, 46 billion again, databases. We've been saying this in the queue for 12 years have been great and recently not one database will rule the world. We know that. That's, everyone knows that databases, data code, cloud scale, this is the convergence now of all that coming together where data, this reinvent is the theme. Everyone will be talking about end to end data, new kinds of specialized services, faster performance, new kinds of application development. This is the big part of why you guys are working together. Explain the relationship, how you guys are partnering and engineering together. >>Yeah, absolutely. I think so ibm, right? I think we are mainly into hybrid cloud and ai and one of the things we are looking at is expanding our ecosystem, right? Because we have gaps and as opposed to building everything organically, we want to partner with the likes of single store, which have unique capabilities that complement what we have. Because at the end of the day, customers are looking for an end to end solution that's also business problems. And they are very good at real time data analytics and hit staff, right? Because we have transactional databases, analytical databases, data lakes, but head staff is a gap that we currently have. And by partnering with them we can essentially address the needs of our customers and also what we plan to do is try to integrate our products and solutions with that so that when we can deliver a solution to our customers, >>This is why I was saying earlier, I think this is a a tell sign of what's coming from a lot of use cases where people are partnering right now you got the clouds, a bunch of building blocks. If you put it together yourself, you can build a durable system, very stable if you want out of the box solution, you can get that pre-built, but you really can't optimize. It breaks, you gotta replace it. High level engineering systems together is a little bit different, not just buying something out of the box. You guys are working together. This is kind of an end to end dynamic that we're gonna hear a lot more about at reinvent from the CEO ofs. But you guys are doing it across companies, not just with aws. Can you guys share this new engineering business model use case? Do you agree with what I'm saying? Do you think that's No, exactly. Do you think John's crazy, crazy? I mean I all discourse, you got out of the box, engineer it yourself, but then now you're, when people do joint engineering project, right? They're different. Yeah, >>Yeah. No, I mean, you know, I think our partnership is a, is a testament to what you just said, right? When you think about how to achieve realtime insights, the data comes into the system and, and the customers and new applications want insights as soon as the data comes into the system. So what we have done is basically build an architecture that enables that we have our own storage and query engine indexing, et cetera. And so we've innovated in our indexing in our database engine, but we wanna go further than that. We wanna be able to exploit the innovation that's happening at ibm. A very good example is, for instance, we have a native connector with Cognos, their BI dashboards right? To reason data very natively. So we build a hyper efficient system that moves the data very efficiently. A very other good example is embedded ai. >>So IBM of course has built AI chip and they have basically advanced quite a bit into the embedded ai, custom ai. So what we have done is, is as a true marriage between the engineering teams here, we make sure that the data in single store can natively exploit that kind of goodness. So we have taken their libraries. So if you have have data in single store, like let's imagine if you have Twitter data, if you wanna do sentiment analysis, you don't have to move the data out model, drain the model outside, et cetera. We just have the pre-built embedded AI libraries already. So it's a, it's a pure engineering manage there that kind of opens up a lot more insights than just simple analytics and >>Cost by the way too. Moving data around >>Another big theme. Yeah. >>And latency and speed is everything about single store and you know, it couldn't have happened without this kind of a partnership. >>So you've been at IBM for almost two decades, don't look it, but at nearly 17 years in how has, and maybe it hasn't, so feel free to educate us. How has, how has IBM's approach to AI and ML evolved as well as looking to involve partnerships in the ecosystem as a, as a collaborative raise the water level together force? >>Yeah, absolutely. So I think when we initially started ai, right? I think we are, if you recollect Watson was the forefront of ai. We started the whole journey. I think our focus was more on end solutions, both horizontal and vertical. Watson Health, which is more vertically focused. We were also looking at Watson Assistant and Watson Discovery, which were more horizontally focused. I think it it, that whole strategy of the world period of time. Now we are trying to be more open. For example, this whole embedable AI that CICE was talking about. Yeah, it's essentially making the guts of our AI libraries, making them available for partners and ISVs to build their own applications and solutions. We've been using it historically within our own products the past few years, but now we are making it available. So that, how >>Big of a shift is that? Do, do you think we're seeing a more open and collaborative ecosystem in the space in general? >>Absolutely. Because I mean if you think about it, in my opinion, everybody is moving towards AI and that's the future. And you have two option. Either you build it on your own, which is gonna require significant amount of time, effort, investment, research, or you partner with the likes of ibm, which has been doing it for a while, right? And it has the ability to scale to the requirements of all the enterprises and partners. So you have that option and some companies are picking to do it on their own, but I believe that there's a huge amount of opportunity where people are looking to partner and source what's already available as opposed to investing from the scratch >>Classic buy versus build analysis for them to figure out, yeah, to get into the game >>And, and, and why reinvent the wheel when we're all trying to do things at, at not just scale but orders of magnitude faster and and more efficiently than we were before. It, it makes sense to share, but it's, it is, it does feel like a bit of a shift almost paradigm shift in, in the culture of competition versus how we're gonna creatively solve these problems. There's room for a lot of players here, I think. And yeah, it's, I don't >>Know, it's really, I wanted to ask if you don't mind me jumping in on that. So, okay, I get that people buy a bill I'm gonna use existing or build my own. The decision point on that is, to your point about the path of getting the path of AI is do I have the core competency skills, gap's a big issue. So, okay, the cube, if you had ai, we'd take it cuz we don't have any AI engineers around yet to build out on all the linguistic data we have. So we might use your ai but I might say this to then and we want to have a core competency. How do companies get that core competency going while using and partnering with, with ai? What you guys, what do you guys see as a way for them to get going? Because I think some people probably want to have core competency of >>Ai. Yeah, so I think, again, I think I, I wanna distinguish between a solution which requires core competency. You need expertise on the use case and you need expertise on your industry vertical and your customers versus the foundational components of ai, which are like, which are agnostic to the core competency, right? Because you take the foundational piece and then you further train it and define it for your specific use case. So we are not saying that we are experts in all the industry verticals. What we are good at is like foundational components, which is what we wanna provide. Got it. >>Yeah, that's the hard deep yes. Heavy lift. >>Yeah. And I can, I can give a color to that question from our perspective, right? When we think about what is our core competency, it's about databases, right? But there's a, some biotic relationship between data and ai, you know, they sort of like really move each other, right? You >>Need, they kind of can't have one without the other. You can, >>Right? And so the, the question is how do we make sure that we expand that, that that relationship where our customers can operationalize their AI applications closer to the data, not move the data somewhere else and do the modeling and then training somewhere else and dealing with multiple systems, et cetera. And this is where this kind of a cross engineering relationship helps. >>Awesome. Awesome. Great. And then I think companies are gonna want to have that baseline foundation and then start hiring in learning. It's like driving the car. You get the keys when you're ready to go. >>Yeah, >>Yeah. Think I'll give you a simple example, right? >>I want that turnkey lifestyle. We all do. Yeah, >>Yeah. Let me, let me just give you a quick analogy, right? For example, you can, you can basically make the engines and the car on your own or you can source the engine and you can make the car. So it's, it's basically an option that you can decide. The same thing with airplanes as well, right? Whether you wanna make the whole thing or whether you wanna source from someone who is already good at doing that piece, right? So that's, >>Or even create a new alloy for that matter. I mean you can take it all the way down in that analogy, >>Right? Is there a structural change and how companies are laying out their architecture in this modern era as we start to see this next let gen cloud emerge, teams, security teams becoming much more focused data teams. Its building into the DevOps into the developer pipeline, seeing that trend. What do you guys see in the modern data stack kind of evolution? Is there a data solutions architect coming? Do they exist yet? Is that what we're gonna see? Is it data as code automation? How do you guys see this landscape of the evolving persona? >>I mean if you look at the modern data stack as it is defined today, it is too detailed, it's too OSes and there are way too many layers, right? There are at least five different layers. You gotta have like a storage you replicate to do real time insights and then there's a query layer, visualization and then ai, right? So you have too many ETL pipelines in between, too many services, too many choke points, too many failures, >>Right? Etl, that's the dirty three letter word. >>Say no to ETL >>Adam Celeste, that's his quote, not mine. We hear that. >>Yeah. I mean there are different names to it. They don't call it etl, we call it replication, whatnot. But the point is hassle >>Data is getting more hassle. More >>Hassle. Yeah. The data is ultimately getting replicated in the modern data stack, right? And that's kind of one of our thesis at single store, which is that you'd have to converge not hyper specialize and conversation and convergence is possible in certain areas, right? When you think about operational analytics as two different aspects of the data pipeline, it is possible to bring them together. And we have done it, we have a lot of proof points to it, our customer stories speak to it and that is one area of convergence. We need to see more of it. The relationship with IBM is sort of another step of convergence wherein the, the final phases, the operation analytics is coming together and can we take analytics visualization with reports and dashboards and AI together. This is where Cognos and embedded AI comes into together, right? So we believe in single store, which is really conversions >>One single path. >>A shocking, a shocking tie >>Back there. So, so obviously, you know one of the things we love to joke about in the cube cuz we like to goof on the old enterprise is they solve complexity by adding more complexity. That's old. Old thinking. The new thinking is put it under the covers, abstract the way the complexities and make it easier. That's right. So how do you guys see that? Because this end to end story is not getting less complicated. It's actually, I believe increasing and complication complexity. However there's opportunities doing >>It >>More faster to put it under the covers or put it under the hood. What do you guys think about the how, how this new complexity gets managed or in this new data world we're gonna be coming in? >>Yeah, so I think you're absolutely right. It's the world is becoming more complex, technology is becoming more complex and I think there is a real need and it's not just from coming from us, it's also coming from the customers to simplify things. So our approach around AI is exactly that because we are essentially providing libraries, just like you have Python libraries, there are libraries now you have AI libraries that you can go infuse and embed deeply within applications and solutions. So it becomes integrated and simplistic for the customer point of view. From a user point of view, it's, it's very simple to consume, right? So that's what we are doing and I think single store is doing that with data, simplifying data and we are trying to do that with the rest of the portfolio, specifically ai. >>It's no wonder there's a lot of synergy between the two companies. John, do you think they're ready for the Instagram >>Challenge? Yes, they're ready. Uhoh >>Think they're ready. So we're doing a bit of a challenge. A little 32nd off the cuff. What's the most important takeaway? This could be your, think of it as your thought leadership sound bite from AWS >>2023 on Instagram reel. I'm scrolling. That's the Instagram, it's >>Your moment to stand out. Yeah, exactly. Stress. You look like you're ready to rock. Let's go for it. You've got that smile, I'm gonna let you go. Oh >>Goodness. You know, there is, there's this quote from astrophysics, space moves matter, a matter tells space how to curve. They have that kind of a relationship. I see the same between AI and data, right? They need to move together. And so AI is possible only with right data and, and data is meaningless without good insights through ai. They really have that kind of relationship and you would see a lot more of that happening in the future. The future of data and AI are combined and that's gonna happen. Accelerate a lot faster. >>Sures, well done. Wow. Thank you. I am very impressed. It's tough hacks to follow. You ready for it though? Let's go. Absolutely. >>Yeah. So just, just to add what is said, right, I think there's a quote from Rob Thomas, one of our leaders at ibm. There's no AI without ia. Essentially there's no AI without information architecture, which essentially data. But I wanna add one more thing. There's a lot of buzz around ai. I mean we are talking about simplicity here. AI in my opinion is three things and three things only. Either you use AI to predict future for forecasting, use AI to automate things. It could be simple, mundane task, it would be complex tasks depending on how exactly you want to use it. And third is to optimize. So predict, automate, optimize. Anything else is buzz. >>Okay. >>Brilliantly said. Honestly, I think you both probably hit the 32nd time mark that we gave you there. And the enthusiasm loved your hunger on that. You were born ready for that kind of pitch. I think they both nailed it for the, >>They nailed it. Nailed it. Well done. >>I I think that about sums it up for us. One last closing note and opportunity for you. You have a V 8.0 product coming out soon, December 13th if I'm not mistaken. You wanna give us a quick 15 second preview of that? >>Super excited about this. This is one of the, one of our major releases. So we are evolving the system on multiple dimensions on enterprise and governance and programmability. So there are certain features that some of our customers are aware of. We have made huge performance gains in our JSON access. We made it easy for people to consume, blossom on OnPrem and hybrid architectures. There are multiple other things that we're gonna put out on, on our site. So it's coming out on December 13th. It's, it's a major next phase of our >>System. And real quick, wasm is the web assembly moment. Correct. And the new >>About, we have pioneers in that we, we be wasm inside the engine. So you could run complex modules that are written in, could be C, could be rushed, could be Python. Instead of writing the the sequel and SQL as a store procedure, you could now run those modules inside. I >>Wanted to get that out there because at coupon we covered that >>Savannah Bay hot topic. Like, >>Like a blanket. We covered it like a blanket. >>Wow. >>On that glowing note, Dre, thank you so much for being here with us on the show. We hope to have both single store and IBM back on plenty more times in the future. Thank all of you for tuning in to our coverage here from Las Vegas in Nevada at AWS Reinvent 2022 with John Furrier. My name is Savannah Peterson. You're watching the Cube, the leader in high tech coverage. We'll see you tomorrow.

>> Announcer: From around the globe, it's theCUBE! With digital coverage of IBM Think 2021. Brought to you by IBM. >> Well welcome everyone as theCUBE continues our IBM Think series. It's a pleasure to have you with us here on theCUBE. I'm John Walls, and we're joined today by Brian Loveys who is the Director of Offering Management for Customer and Employee Care Applications at IBM in the Data and AI Division. So, Brian, thanks for joining us from Ottawa, Canada. Good to see you today. >> Yeah, great to be here, John. And looking forward to the session today. >> Which, by the way, I've learned Ottawa are the home of the world's largest ice skating rink. I doubt we get into that today, but it is interesting food for thought. So, Brian, first off, let's just talk about the AI landscape right now. I know IBM obviously very heavily invested in that. Just in terms of how you see this currently in terms of enterprise adoption, what people are doing with it, and just how you would talk about the state of the industry right now. >> You know, it's a really interesting one, right? I think if you look at it, you know, different companies, different industries, frankly, are at different stages of their AI journey, right? I think for me personally, what was really interesting was, and we're all going through the pandemic right now, but last year with COVID-19 in the March timeframe, it was really interesting to see the impact, frankly, in the space that I play predominantly in around customer care, right? When the pandemic hit, immediately call centers, contact centers got flooded with calls, right? And so it created a lot of problems for organizations. But what was interesting to me is it accelerated a lot of adoption of AI to organizations that typically lag in technology, right? So if you think about public sector, right, that was one area that got hit very, very hard with questions and those types of things, and trying to, you know, communicate out information. So it was really interesting to see those organizations, frankly, accelerate really, really quickly, right? And if you actually, you know, talk to those organizations now, I think one of the most interesting things to me in thinking about it and talking to them now is like, hey, you know, we can do this, right? AI is really not that complicated. It can be simplified, we can take advantage of it and all of those types of things, right? So I think for me, you know, I kind of see different industries at sort of different levels, but I think with COVID in particularly, you know, and frankly not just COVID, but even digital transformation alongside COVID is really driving a lot of AI in an accelerated manner. The other thing that I'll kind of talk to a little bit here is I still think we're very much in the early innings of this, right? There's a tremendous opportunity to innovate in this space. And I think we all know that, you know, data is continually being created every single day. And as more people become even more digitalized, there's more and more data being created. Like it's how do you start to harness that data more effectively, right, in your business every day. And frankly, I think we're just scratching the surface on it. And I think tremendous amount of opportunity as we move forward. >> Yeah, you really raised an interesting point which I hadn't thought about in terms of, we think about disruptors, we think about technology being a disruptor, right, but in this case it was purely, or really largely environment, you know, that was driving this disruption, right, forcing people to make these adoption moves and transitions maybe a little quicker than they expected. Well, so because of that, because maybe somebody had to speed up their timetable for deployments and what have you, what kind of challenges have they run into then, where, because as you describe it, it's not been the more organic kind of decision-making that might be made sometimes, situation dictated it. So what have you seen in terms of challenges, you know, barriers, or just a little more complexity, perhaps, for some people who're just now getting into the space because of the environment you were talking about? >> I think a lot of this is like, you know, people don't know where to get started, right, a lot of the time, or how AI can be applied. So a lot of this is going to be about education in terms of what it can and cannot do. And then it all depends on the use cases you're talking about, right? So if I think about, you know, building out machine learning models and those types of things, right, you know, the set of challenges that people will typically face in these types of things are, you know, how do I, you know, collect all the data that I need to go build these models, right? How do I organize that data? You know, how do I get the skillsets needed to ultimately, you know, take advantage of all of that data to actually then apply to where I need it in my business, right? So a lot of this is, you know, people need to understand those concepts or those pieces to ultimately be successful with AI. And you know, what IBM is doing right here, and I'll kind of, this will be a key theme throughout this conversation today is, you know, how do you sort of lower the time to value to get there across that spectrum, but also, you know, frankly, the skills required along the way as well? But a lot of it is like, people don't know what they don't know at the end of the day. >> Well, let me ask you about your AI play then. A lot of people involved in this space, as you well know, competition's pretty fierce and pretty widespread. There's a deep bench here. In terms of IBM though, what do you see as kind of your market differentiator then? You know, what do you think sets you apart in terms of what you're offering in terms of AI deployments and solutions? >> No, that's a great question. I think it's a multifaceted answer, frankly. The first thing I'll kind of talk through a little bit, right, is really around our platform and our framework, right? We kind of refer to as our AI ladder, but it's really an integrated, you know, sort of cohesive platform for companies around the journey to AI, right? So kind of what I was mentioning a bit earlier, right? If you think about, you know, AI is really about supplying the right data into AI, and then being able to infuse it to where you need it to go, right? So to do that, you need a lot of the underlying information architecture to do that, right? So you need the ability to collect the data. You need the ability to organize the data. You need the ability to build out these models or analyze the data, right? And then of course you need to be able to infuse that AI wherever you need it to be, right? And so we have a really nice integrated platform that frankly can be deployed on any cloud, right, so we get the flexibility of that deployment model with that integrated platform. And if you think about it, we also have built, right, you know, sort of these industry-leading AI applications that sit on top of that platform and that underlying infrastructure, right? So Watson Assistant, right, our conversational AI which we'll talk probably a little bit more on this conversation, right? Watson Discovery focused on, you know, intelligent document processing, right, AI search type applications. We've got these sort of market-leading applications that sit on top, but there's also other things, right? Like we have a very, very strong research arm, right, that continues to invest and funnel innovations into our product platform and into our product portfolio, right? I think many people are aware of Project Debater we took on some of the top debaters in the world, right? But research ultimately is very much tied, right, and even, you know, some of the teams that I work with on the ground, we've got them tied directly into the squads that build these products, right? So we have this really big strong research arm that continues to bring innovation around AI and around other aspects into that product portfolio. But it's not just- >> I'm sorry go ahead, please. >> Go ahead, sorry. >> No, no, you go, (laughs) I interrupted, you go ahead. >> Don't worry, I was just going to say, the other two things I'll say like, you know, I'm saying this right, but we've got a lot of sort of proof points in around it, right, so if you talk about the scale, right, the number of customers, the number of case studies, the number of references across the board, right, in around AI at IBM it is significant, right? And not only that, but we've got a lot of, sort of I'll say industry and third-party industry recognition, right? So think about most people are aware of sort of Gartner Magic Quadrants, right, and we're the leader almost across the board, right, or a leader across the board. So, you know, cloud AI developer service, insight engines, machine learning, go down the line. So, you know, if you don't trust me, there's certainly a lot of third party validation around that as well, if that makes sense. >> Yeah, sure does. You know, we hear a lot about conversational AI and, you know, with online chat bots and voice assistance, and a myriad applications in that respect. Let's talk about conversational right now. Some people think is a little narrow, but yet there appears to be a pretty broad opportunity at the same time. So let's talk about that conversational AI element to what you're talking about at IBM and how that is coming into play. And perhaps is a pretty big growth sector in this space. >> Yeah, I think, again, I talk about scratching the surface, early innings, you'll see that theme a lot too. And I think this is another area around that, right? So, listen, let's talk about the broader side. Let's first talk about where conversational AI is typically applied, right? So you see it in customer service. That's the obvious place where I've seen the most deployments in. But if you think about, it's not just really around customer service, right? There's use cases around sales and marketing. You can think about, you know, lead qualification for example, right. You know, I'm on a website, how can I get information about a product or service? How can I automate some of that information collection, answering questions, how can I schedule console? All those things can be automated using, right, conversational AI, but organizations don't want these sort of points solutions across the customer journey. What they're ultimately looking for is a single assistant to kind of, you know, front that particular customer. So what if I do come on from a lead qual perspective, but really I'm not there for lead qual, I'm actually a customer, and I want to get a question answered, right? You don't want to have these awkward starts and stops with organizations, right? So on the customer side where we see the conversational AI going is really sort of covering that whole gambit in terms of that customer journey, right? And it's not just the customer journey, but you also want to be across channels, right? So you can imagine not just, you know, the website and the chat on the website, but also, right, across your messaging channels, across your phone, right? And not just that, but you also want to be able to have a really nice experience around, hey maybe I'm on a phone call with some automation, but I need to be able to hand them off to a digital play, right? Maybe that's easier to sign up for a particular offer, or do some authentication, or whatever it might be, right? So to sort of be able to switch between the channels is really, really going to become more important in terms of a seamless experience as you do kind of go through it, right- >> So let's talk about customers- >> Oh, go ahead sir. >> Yeah, you talked about customers a little bit, and you mentioned case studies, but I hope we can get into some specifics, if you can give us some examples about people, companies with whom you've worked and some success that you've had in that respect. And I think maybe the usual suspects come to mind. I think about finance, I think about healthcare, but you said, "Hey buddy, but customer call issues, you know, service centers, that kind of thing would certainly come into play," but can you give us an idea or some examples of deployments and how this is actually working today? >> Oh, absolutely, right? So I think you were kind of mentioning, you were talking about sort of industries that are relevant, right? So, you know, the ones that I think are most relevant that we've seen are the ones with the biggest sort of consumer side of it, right? So clearly in financial services, banks, insurance are clearly obvious ones. Telecommunication, retail, healthcare, these are all sort of big industries with a lot of sort of customers coming in, right? And so you'll see different use cases in those industries as well, right? So the obvious one, we've got a really good client, Royal Bank of Scotland, they've now changed their name to NatWest in Scotland. So they started out with customer service, right? So dealing with personal banking questions through their website. What's interesting, and you'll see this with a lot of these use cases is they will start small, right, with a single use case, but they'll start to expand from there. So for example, NatWest, right, they're starting with personal banking, but they're now expanding to other areas of the business across that customer journey, right? So that's a great example of where we've seen it. Cardinal Health, right, because we're not dealing with customers in terms of external customers, but dealing with internal customers, right, from an IT help desk standpoint. So it's not always external customers. Oftentimes, frankly, it can be employees, right? So they are using it through an IDR system, right? So through over the phone, right, so I can call, instead of getting that 1-800 number, I'm going to get a nice natural language experience over the phone to help employees with common problems that they have with their help desk. So, and they started really, really small, right? They started with, you know, simple things like password resets, but that represented a tremendous amount of volume that ultimately hit at their call centers. So NatWest is a great example. CIBC, another bank in Canada, Toronto, is a great example. And the nice thing about what CIBC is doing and they're a big, you know, we have four big banks here in Canada. What CIBC do is really focusing a lot on the transactional side. So making it really easy to do interact transfers or send money, or all those types of things, or check your balance or whatever it might be. So putting a nice, simple interface on some of those common, transactional things that you would do with a bank as well. >> You know, before I let you go, I'd like to hit just a buzzword we hear a lot of these days, natural language processing, NLP. All right, so NLP, define that in terms of how you see it and how is it being applied today? Why does NLP matter, and what kind of differences is it making? >> Wow, natural language processing is a loaded term as a buzzword, I completely agree. I mean, listen, at the 50,000 foot level, natural language processing is really about understanding language, right? So what do I mean by that? So let's use the simple conversational example we just talked about. If somebody's asking about, you know, "I'd like to reset my password," right? You have to be able to understand, well what is the intent behind what that user is trying to do, right? They're trying to reset a password, right? So being able to understand that inquiry that user has that's coming in and being able to understand what the intent is behind it. That's sort of one key aspect of natural language processing, right? What is the intent or the topic around that paragraph or whatever it might be. The other sort of key thing around natural language processing, the importance of extracting certain things that you need to know. And again, using the conversational AI side, just for a minute, to give a simple example. If I said, "You know what, I need to reset my password." I know what the intent is, I want to reset a password, but, right, I don't know which password I'm trying to reset. Right, and so this is where sort of you have to be able to extract objects, and we call them entities a lot of the time and sort of the (indistinct) or lingo. But you got to be able to extract those elements. So, you know, I want to reset my ATM password. Great, right, so I know what they're trying to do, but I also need to extract that it's the ATM password that I'm trying to do. So that's one sort of key angle, natural language processing, and there's a lot of different AI techniques to be able to do those types of things. I'll also tell you though, there's a lot around the content side of the fence as well. So you can imagine how like a contract, right, and there were thousands of these contracts, and some of your terms may change. You know, how do you know, out of those thousands of contracts where the problems are, where I need to start looking, right? So another sort of key area of natural language processing is looking at the content itself, right? Can I look at these contracts and automatically understand that this is an indemnity clause, right? Or this is an obligation, right? Or those types of things, right, and being able to sort of pick those things out, so that I can help deal with those sort of contract-processing things. So that's sort of a second dimension. The third dimension I'll kind of give around this is really around, you can think about extracting things like sentiment, right? So we talked about, you know, extracting objects and nouns, and those types of things, but maybe I want to know in an analytics use case with customers, you know, what is the sentiment and, you know, analyzing social media posts or whatever it might be, what's the sentiment that people have around my product or service. So natural language process, if you think about it at the real high level is really about how do I understand language, but there's a variety of sort of ways to do that, if that makes sense. >> Yeah, no sure, and I think there are a lot of people out there saying, "Yeah, the sooner we can identify exasperation (laughs) the better off we're going to be, right, in handling the problems." So, it's hard work, but it's to make our lives easier, and congratulations for your fine work in that space. And thanks for joining us here on theCUBE. We appreciate the time today, Brian. >> Thank you very much. >> You bet, Brian Loveys, he's talking to us from IBM, talking about conversational AI and what it can do for you. I'm John Walls, thanks for joining us here on theCUBE. (upbeat music) ♪ Dah, deeah ♪ ♪ Dah, dee ♪ (chimes ringing)

>>from >>Around the globe. It's the cube with digital coverage of IBM think 2021 brought to you by IBM >>Well welcome everyone is the cube continues or IBM Thanks series. It's a pleasure to have you with us here on the cube. I'm john walls and we're joined today by brian loves who is the director of offering management for customer and employee care applications in the at IBM in the data and AI division. So brian, thanks for joining us from Ottawa Canada, good to see you today. >>Yeah, great to be here john I'm looking forward to the session today >>which by the way I've learned Ottawa is the home of the world's largest ice skating rink. I doubt we'll get into that today, but it is interesting food for thought. Uh so brian first off, let's just talk about um the Ai landscape right now. I know IBM obviously very heavily invested in that uh just in terms of how you see this currently as in terms of enterprise adoption, what people are doing with it and and just how you would talk about the state of the industry right now, >>you know, it's a really interesting one, right? I think if you look at it, you know different companies, different industries frankly are at different stages of their Ai journey, right? Um I think for me personally what was really interesting was, and we're all going through the pandemic right now, but last year with covid 19 in the March timeframe, it was really interesting to see the impact, frankly in the space that I played predominantly in around customer care, right? When the pandemic hit immediately call centers, contact centres got flooded with calls, right? And so it created a lot of problems for organizations. But it was interesting to me is it accelerated a lot of adoption of ai to organizations that typically lag and technology. Right? So if you think about public sector, right, that was one area that got hit very, very hard with questions and those types of things and trying to communicate and communicate out information. So it was really interesting to see those organizations frankly accelerate really, really quickly, right? And if you actually talk to those organizations now, I think one of the most interesting things to me and thinking about it and talking to them now is like, hey, you know, we can do this right, AI is really not that complicated, it can be simplified, we can take advantage of it and all of those types of things. Right? So I think for me, you know, I kind of see different industries that sort of different levels, but I think with Covid in particularly, you know, and frankly not just Covid, but even digital transformation alongside Covid is really driving a lot of ai in an accelerated manner. The other thing I'll kind of I'll kind of talk to a little bit here is I still think we're very much in the early innings of this, right, there is a tremendous opportunity innovating in the space and I think we all know that you know data is continually being created every single day and as more people become even more digitalized, there's more and more data being created. Like how do you start to harness that data more effectively, right in your business every day? And frankly I think we're just scratching scratching the surface on it and I think tremendous amount of opportunity as we move forward. >>Yeah, he really is really raised an interesting point which I hadn't thought about in terms of, we think about disruptors, we think about technology being a disrupter, right? But in this case it was purely really, largely environment that was driving this disruption, right, forcing people to to make these adoption moves and transitions maybe a little quicker than they expected. So because of that, because maybe somebody had to speed up their timetable for deployments and what have you what what kind of challenges have they run into them? Where because, as you describe it, it's not been the more organic kind of decision making that might be made, sometimes situation dictated it. So what have you seen in terms of challenges, barriers or just a little more complexity perhaps for some people who are just not getting into the space because of the environment you were talking about? >>I think a lot of this is like people don't know where to get started, right, a lot of the time or how ai can be applied. So a lot of this is going to be a bad education in terms of what it can and cannot do, and then it all depends on the use cases you're talking about, right? So if I think about, you know, building a machine learning models and those types of things right? You know, this set of challenges that people will typically face in these types of things are, you know, how do I collect all the data that I need to go build these models? Right? How do I organize that data? Um you know, how do I get the skill sets needed to ultimately, you know, take advantage of all that data to actually then apply to where I needed in my business? Right, So a lot of this is, you know, people need to understand, you know, those concepts are those pieces um to ultimately be successful with AI and you know what IBM is doing right here and I'll kind of this will be a key theme through this conversation today, is how do you sort of lower the time to value, to get there across that spectrum, but also, you know, frankly the skills >>required along the way as >>well, but a lot of it is like people don't know what they don't know at the end of the day. Mhm. >>Well, let me ask you about about your AI play then, a lot of people involved in this space, as you well know, you know, competitions pretty fierce and pretty widespread, there's a deep bench here um in terms of IBM know, what do you see is kind of your market different differentiator then, you know, what what do you think set you apart in terms of what you're offering in terms of AI deployments and solutions? >>No, that's a great question. I think it's a multifaceted answer, frankly. Um the first thing I'll kind of talk through a little bit right, is really around our platform and our our framework, right? We could refer to as our air ladder, um but it's really an integrated, you know, sort of cohesive platform for companies around the journey to AI, right? So kind of what I was mentioning earlier, right? If you think about, you know, AI is really about supplying the right data into A I. And then being able to infuse it to where you needed to go. Right? So to do that, you need a lot of the underlying information architecture to do that, Right? So you need the ability to collect the data, you need the ability to organize the data, you need the ability to to build out these models, right? Or analyze the data and then of course you need to be able to infuse that ai wherever you need it to be. Right. And so we have a really nice integrated platform that frankly can be deployed on any cloud. Right? So we got the flexibility that deployment model with that in greater platform. And you think about it? We also have built right, you know, sort of these industry leading Ai applications that sit on top of that platform and that underlying infrastructure. Right? So Watson assistant, Right. Our conversational AI, which we'll talk probably a little bit more on this conversation. Right, Watson discovery focus on, you know, intelligent document processing, right. AI search type applications. We've got these sort of market leading applications that sit on top, but there's also other things, right? Like we have a very, very strong research arm right, that continues to invest and funnel innovations into our product platform and into our product portfolio. Right? I think many people are aware of project debater, we took on some of the top debaters in the world, right? But research ultimately is very much tied, right? And even some of the teams that I work with on the ground, we've got them tied directly into the squads that build these products, Right? So we have this really big strong research arm that continues to bring innovation around AI and around other aspects into that product portfolio. But it's not just go ahead, >>Please go ahead. three. No, no. You know, I interrupted you. Go ahead. >>No, I was just gonna say that the other two things, I'll say it like, you know, I'm saying this right, but we've got a lot of sort of proof points and around it. Right? So, if you talk about the scale right? The number of customers, the number of case studies, a number of references across the board, right? In around AI AT IBM It is significant, Right? Um, and not only that, but we've got a lot of sort of, I'll say industry and third party industry recognition. Right? So think about most people are aware of sort of Gartner magic quadrants, right? And we're the leader almost across the board, Right? Or a leader across the board. So cloudy I developer service inside engines, machine learning go down the line. So, you know, if you don't trust me, there's certainly a lot of third party validation around that as well. That makes sense. >>Yeah, it sure does. You know, we're hearing a lot about conversational AI and, you know, with online chat bots and voice assistance and a myriad applications in that respect. Let's talk about conversational right now. Some people think it's little narrow, but, but yet there appears to be a pretty broad opportunity at the same time. So let's talk about that conversational AI um, uh, element um, to what you're talking about at IBM and how that is coming into play and, and perhaps is a pretty big growth sector in this space. >>Yeah, I think again, I talked about scratching the surface early innings. You'll see that theme a lot too. And I think this is another area around that. So listen, let's talk about the broader side. Let's first talk about where conversation always typically applied. Right? So you see it in customer service, that's the obvious place we're seeing the most appointments in. But if you think about, it's not just really around customer service, right? There's use cases around sales and marketing. If you think about, you know, lead qualification, for example, right? How can, you know, I'm on a website, how can I get information about a product or service? How can I automate some of that information collection, answering questions? How can I schedule console? All those things can be automated using great conversationally. I, the organizations don't want these sort of point solutions across the customer journey. What we're ultimately looking for is a single assistant to kind of, you know, front right, that particular customer. So what if I do come on from a legal perspective, but really I'm not here for legal. I'm actually a customer and I want to get a question answered, right? You don't want to have these awkward starts and stops with organizations, Right? So on the customer side where we see the conversation like, hey, I going and it's really kind of covering that full gambit in terms of that customer journey, right? And it's not just the customer journey, but you also want to be across channels, right? So you can imagine right now, not just, you know, the website and the chat on the website, but also right across their messaging channels, right across your phone. Right. And not just that, but you also want to be a really nice experience around, hey, maybe I'm on a phone call with some automation, but I need to be able to hand them off to a digital play. Right? Maybe that's easier to sign up for a particular offer or do some authentication or whatever might be, right. So to sort of be able to sort of switch between the channels, it's really, really going to become more important in this sort of sort of seamless experience as you just kind of go through it. Right? >>So you're coming by customers. Yeah. >>You talked about customers a little bit and you mentioned case studies, but can we get, I hope we can get into some specifics. You can give us some examples about people, companies with whom you've worked and and some success that you've had that respect. And I think maybe the usual suspects come to mind about finance. I might health care, but you said anybody with customer call issues, service centers, that kind of thing would certainly come into play. But can you give us an idea or some examples of deployments and how this is actually working today? >>Oh, absolutely. Right. So I think you kind of mentioned you become sort of industries that are relevant. Right? So, you know, the ones that I think are most relevant that we've seen are the ones with the biggest sort of consumer sort of side to it. Right? So clearly in financial services, banks, insurance, and clearly obvious ones telecommunications, retail, healthcare, these are all sort of big industries with a lot of sort of customers coming in. Right? So you'll see different use cases in those industries as well. Right. So the obvious one, we've got a really good client, Royal Bank of Scotland, they've now changed their name to natwest Open Scotland. Um So they started out with customer service. Right? So dealing with personal banking questions through their website, what's interesting and you'll see this with a lot of these use cases is they will start small, right with a single use case that they'll start to expand from there. So, for example, >>natwest right there, starting with they started with personal banking, but they're not expanding to other areas of the business across that customer journey. Right. So it's a great example of where we've seen it. Cardinal Health Right. We're not dealing with customers in terms of external customers but dealing with internal customers right from the help that standpoint. So it's not always external customers. Oftentimes frankly it can be employees. Right? So they are using it right through an I. V. R. System. Right? So through over the phone. Right. So I can call instead of getting that 1 800 number. I'm going to get a nice natural language experience over the phone to help employees with common problems that they have with their health does so. And they started really, really small, right? They started with simple things like password resets but that represented a tremendous amount of volume but ultimately headed their cost cost centers. So not West is a great example. C I B C. Another bank in Canada Toronto is a great example and the nice thing about what CNBC is doing and there are big, you know, we have four big banks here in Canada, what have you seen do is really focusing a lot on the transactional side. So making it really easy to do interact transfers or send money or over those types of things or check your balance or whatever it might be. So putting a nice simple interface on some of those common transactional things that you >>would do with the bank as well, >>you know, before I let you go, uh I'd like to hit this of buzz where we hear a lot of these days natural language processing. NLP Alright, so, so NLP define that in terms of how you see it and and how is it being applied today? Why why does NLP matter? And what kind of difference is it making? >>Wow, that's a loaded natural language processing. There's a loaded term in a buzzword. I completely agree. I mean listen, at the 50,000 ft level, natural language processing is really about understanding length, Right? So what do I mean by that? So let's use the simple conversational example. We just talked about if somebody is asking about, I'd like to reset my password right? You have to be able to understand what is the intent behind what that user is trying to do right there? Trying to reset a password, right? So being able to understand that inquiry that the user has that's coming in and being able to understand what the intent is behind it. >>That's sort of one, you know, aspect of natural language processing, right? What is the intent or the topic around that paragraph or whatever it might be. The other sort of key thing around natural language processing the importance, extracting certain things that you need to know. And again using the conversational ai side, just for a minute to give a simple example if I said you know what I need to reset my password, I know what the intent is. I want to reset a password but Right I don't know which password I'm trying to reset. Right? So this is where you have to be able to extract objects and we call them entities a lot of time in sort of the ice bake or lingo but you've got to be able to extract those elements. So you know I want to reset my A. T. M. Password. Great. Right so I know what they're trying to do but I also need to extract that it's the A. T. M. Password that I'm trying to do. So that's one sort of key angle of natural language processing and there's a lot of different techniques to be able to do those types of things. I'll also tell you though there's a lot around the content side of the fence as well, right? So you can imagine having a contract, right? And there are thousands of these contracts and some of your terms may change. How do you know, out of those thousands of contracts where the problems are, where I need to start looking, Right? So another sort of keep key area of natural language processing is looking at the content itself. Can I look at these contracts and automatically understand that this is an indemnity clause, Right? And this is an obligation, right? Or those types of things, right? And be able to sort of pick pick those things out so that I can help deal with those sort of contract processing things. That's sort of a second dimension. The third dimensional kind of kind of give around this is really around. You can think about extracting things like sentiment, right? So we talked about, you know, extracting objects and downs and those types of things. But maybe I want to know and analytics use case with customers. Um you know, what is the sentiment and you know, analyzing social media posts or whatever it might be. What's the sentiment that people have around my product or service? So naturally this process, if you think about it, the real high level is really about how do I understand language? But there's a variety of sort of ways to do that if that makes sense? >>Yeah, sure. And I think there's a lot of people out there saying, yeah, the sooner we can identify exasperation, the better off we're going to be right and handling the problems. But it's hard work but it's to make our lives easier and congratulations for your fine work in that space. And thanks for joining us here on the cube. We appreciate the time. Today, brian, >>thank very much. >>You bet BRian Levine is talking to us from IBM talking about conversational Ai and what it can do for you. I'm john Walsh, thanks for joining us here on the cube. Mhm. >>Mhm.

>>from around the globe. It's the Cube covering >>space and cybersecurity. Symposium 2020 hosted by Cal Poly >>Over On Welcome to this Special virtual conference. The Space and Cybersecurity Symposium 2020 put on by Cal Poly with support from the Cube. I'm John for your host and master of ceremonies. Got a great topic today in this session. Really? The intersection of space and cybersecurity. This topic and this conversation is the cybersecurity workforce development through public and private partnerships. And we've got a great lineup. We have Jeff Armstrong's the president of California Polytechnic State University, also known as Cal Poly Jeffrey. Thanks for jumping on and Bang. Go ahead. The second director of C four s R Division. And he's joining us from the office of the Under Secretary of Defense for the acquisition Sustainment Department of Defense, D O D. And, of course, Steve Jake's executive director, founder, National Security Space Association and managing partner at Bello's. Gentlemen, thank you for joining me for this session. We got an hour conversation. Thanks for coming on. >>Thank you. >>So we got a virtual event here. We've got an hour, have a great conversation and love for you guys do? In opening statement on how you see the development through public and private partnerships around cybersecurity in space, Jeff will start with you. >>Well, thanks very much, John. It's great to be on with all of you. Uh, on behalf Cal Poly Welcome, everyone. Educating the workforce of tomorrow is our mission to Cal Poly. Whether that means traditional undergraduates, master students are increasingly mid career professionals looking toe up, skill or re skill. Our signature pedagogy is learn by doing, which means that our graduates arrive at employers ready Day one with practical skills and experience. We have long thought of ourselves is lucky to be on California's beautiful central Coast. But in recent years, as we have developed closer relationships with Vandenberg Air Force Base, hopefully the future permanent headquarters of the United States Space Command with Vandenberg and other regional partners, we have discovered that our location is even more advantages than we thought. We're just 50 miles away from Vandenberg, a little closer than u C. Santa Barbara, and the base represents the southern border of what we have come to think of as the central coast region. Cal Poly and Vandenberg Air force base have partner to support regional economic development to encourage the development of a commercial spaceport toe advocate for the space Command headquarters coming to Vandenberg and other ventures. These partnerships have been possible because because both parties stand to benefit Vandenberg by securing new streams of revenue, workforce and local supply chain and Cal Poly by helping to grow local jobs for graduates, internship opportunities for students, and research and entrepreneurship opportunities for faculty and staff. Crucially, what's good for Vandenberg Air Force Base and for Cal Poly is also good for the Central Coast and the US, creating new head of household jobs, infrastructure and opportunity. Our goal is that these new jobs bring more diversity and sustainability for the region. This regional economic development has taken on a life of its own, spawning a new nonprofit called Reach, which coordinates development efforts from Vandenberg Air Force Base in the South to camp to Camp Roberts in the North. Another factor that is facilitated our relationship with Vandenberg Air Force Base is that we have some of the same friends. For example, Northrop Grumman has has long been an important defense contractor, an important partner to Cal poly funding scholarships and facilities that have allowed us to stay current with technology in it to attract highly qualified students for whom Cal Poly's costs would otherwise be prohibitive. For almost 20 years north of grimness funded scholarships for Cal Poly students this year, their funding 64 scholarships, some directly in our College of Engineering and most through our Cal Poly Scholars program, Cal Poly Scholars, a support both incoming freshman is transfer students. These air especially important because it allows us to provide additional support and opportunities to a group of students who are mostly first generation, low income and underrepresented and who otherwise might not choose to attend Cal Poly. They also allow us to recruit from partner high schools with large populations of underrepresented minority students, including the Fortune High School in Elk Grove, which we developed a deep and lasting connection. We know that the best work is done by balanced teams that include multiple and diverse perspectives. These scholarships help us achieve that goal, and I'm sure you know Northrop Grumman was recently awarded a very large contract to modernized the U. S. I. C B M Armory with some of the work being done at Vandenberg Air Force Base, thus supporting the local economy and protecting protecting our efforts in space requires partnerships in the digital realm. How Polly is partnered with many private companies, such as AWS. Our partnerships with Amazon Web services has enabled us to train our students with next generation cloud engineering skills, in part through our jointly created digital transformation hub. Another partnership example is among Cal Poly's California Cybersecurity Institute, College of Engineering and the California National Guard. This partnership is focused on preparing a cyber ready workforce by providing faculty and students with a hands on research and learning environment, side by side with military, law enforcement professionals and cyber experts. We also have a long standing partnership with PG and E, most recently focused on workforce development and redevelopment. Many of our graduates do indeed go on to careers in aerospace and defense industry as a rough approximation. More than 4500 Cal Poly graduates list aerospace and defense as their employment sector on linked in, and it's not just our engineers and computer sciences. When I was speaking to our fellow Panelists not too long ago, >>are >>speaking to bang, we learned that Rachel sins, one of our liberal arts arts majors, is working in his office. So shout out to you, Rachel. And then finally, of course, some of our graduates sword extraordinary heights such as Commander Victor Glover, who will be heading to the International space station later this year as I close. All of which is to say that we're deeply committed the workforce, development and redevelopment that we understand the value of public private partnerships and that were eager to find new ways in which to benefit everyone from this further cooperation. So we're committed to the region, the state in the nation and our past efforts in space, cybersecurity and links to our partners at as I indicated, aerospace industry and governmental partners provides a unique position for us to move forward in the interface of space and cybersecurity. Thank you so much, John. >>President, I'm sure thank you very much for the comments and congratulations to Cal Poly for being on the forefront of innovation and really taking a unique progressive. You and wanna tip your hat to you guys over there. Thank you very much for those comments. Appreciate it. Bahng. Department of Defense. Exciting you gotta defend the nation spaces Global. Your opening statement. >>Yes, sir. Thanks, John. Appreciate that day. Thank you, everybody. I'm honored to be this panel along with President Armstrong, Cal Poly in my long longtime friend and colleague Steve Jakes of the National Security Space Association, to discuss a very important topic of cybersecurity workforce development, as President Armstrong alluded to, I'll tell you both of these organizations, Cal Poly and the N S. A have done and continue to do an exceptional job at finding talent, recruiting them in training current and future leaders and technical professionals that we vitally need for our nation's growing space programs. A swell Asare collective National security Earlier today, during Session three high, along with my colleague Chris Hansen discussed space, cyber Security and how the space domain is changing the landscape of future conflicts. I discussed the rapid emergence of commercial space with the proliferations of hundreds, if not thousands, of satellites providing a variety of services, including communications allowing for global Internet connectivity. S one example within the O. D. We continue to look at how we can leverage this opportunity. I'll tell you one of the enabling technologies eyes the use of small satellites, which are inherently cheaper and perhaps more flexible than the traditional bigger systems that we have historically used unemployed for the U. D. Certainly not lost on Me is the fact that Cal Poly Pioneer Cube SATs 2020 some years ago, and they set the standard for the use of these systems today. So they saw the valiant benefit gained way ahead of everybody else, it seems, and Cal Poly's focus on training and education is commendable. I especially impressed by the efforts of another of Steve's I colleague, current CEO Mr Bill Britain, with his high energy push to attract the next generation of innovators. Uh, earlier this year, I had planned on participating in this year's Cyber Innovation Challenge. In June works Cal Poly host California Mill and high school students and challenge them with situations to test their cyber knowledge. I tell you, I wish I had that kind of opportunity when I was a kid. Unfortunately, the pandemic change the plan. Why I truly look forward. Thio feature events such as these Thio participating. Now I want to recognize my good friend Steve Jakes, whom I've known for perhaps too long of a time here over two decades or so, who was in acknowledge space expert and personally, I truly applaud him for having the foresight of years back to form the National Security Space Association to help the entire space enterprise navigate through not only technology but Polly policy issues and challenges and paved the way for operational izing space. Space is our newest horrifying domain. That's not a secret anymore. Uh, and while it is a unique area, it shares a lot of common traits with the other domains such as land, air and sea, obviously all of strategically important to the defense of the United States. In conflict they will need to be. They will all be contested and therefore they all need to be defended. One domain alone will not win future conflicts in a joint operation. We must succeed. All to defending space is critical as critical is defending our other operational domains. Funny space is no longer the sanctuary available only to the government. Increasingly, as I discussed in the previous session, commercial space is taking the lead a lot of different areas, including R and D, A so called new space, so cyber security threat is even more demanding and even more challenging. Three US considers and federal access to and freedom to operate in space vital to advancing security, economic prosperity, prosperity and scientific knowledge of the country. That's making cyberspace an inseparable component. America's financial, social government and political life. We stood up US Space force ah, year ago or so as the newest military service is like the other services. Its mission is to organize, train and equip space forces in order to protect us and allied interest in space and to provide space capabilities to the joint force. Imagine combining that US space force with the U. S. Cyber Command to unify the direction of space and cyberspace operation strengthened U D capabilities and integrate and bolster d o d cyber experience. Now, of course, to enable all of this requires had trained and professional cadre of cyber security experts, combining a good mix of policy as well as high technical skill set much like we're seeing in stem, we need to attract more people to this growing field. Now the D. O. D. Is recognized the importance of the cybersecurity workforce, and we have implemented policies to encourage his growth Back in 2013 the deputy secretary of defense signed the D. O d cyberspace workforce strategy to create a comprehensive, well equipped cyber security team to respond to national security concerns. Now this strategy also created a program that encourages collaboration between the D. O. D and private sector employees. We call this the Cyber Information Technology Exchange program or site up. It's an exchange programs, which is very interesting, in which a private sector employees can naturally work for the D. O. D. In a cyber security position that spans across multiple mission critical areas are important to the d. O. D. A key responsibility of cybersecurity community is military leaders on the related threats and cyber security actions we need to have to defeat these threats. We talk about rapid that position, agile business processes and practices to speed up innovation. Likewise, cybersecurity must keep up with this challenge to cyber security. Needs to be right there with the challenges and changes, and this requires exceptional personnel. We need to attract talent investing the people now to grow a robust cybersecurity, workforce, streets, future. I look forward to the panel discussion, John. Thank you. >>Thank you so much bomb for those comments and you know, new challenges and new opportunities and new possibilities and free freedom Operating space. Critical. Thank you for those comments. Looking forward. Toa chatting further. Steve Jakes, executive director of N. S. S. A Europe opening statement. >>Thank you, John. And echoing bangs thanks to Cal Poly for pulling these this important event together and frankly, for allowing the National Security Space Association be a part of it. Likewise, we on behalf the association delighted and honored Thio be on this panel with President Armstrong along with my friend and colleague Bonneau Glue Mahad Something for you all to know about Bomb. He spent the 1st 20 years of his career in the Air Force doing space programs. He then went into industry for several years and then came back into government to serve. Very few people do that. So bang on behalf of the space community, we thank you for your long life long devotion to service to our nation. We really appreciate that and I also echo a bang shot out to that guy Bill Britain, who has been a long time co conspirator of ours for a long time and you're doing great work there in the cyber program at Cal Poly Bill, keep it up. But professor arms trying to keep a close eye on him. Uh, I would like to offer a little extra context to the great comments made by by President Armstrong and bahng. Uh, in our view, the timing of this conference really could not be any better. Um, we all recently reflected again on that tragic 9 11 surprise attack on our homeland. And it's an appropriate time, we think, to take pause while the percentage of you in the audience here weren't even born or babies then For the most of us, it still feels like yesterday. And moreover, a tragedy like 9 11 has taught us a lot to include to be more vigilant, always keep our collective eyes and ears open to include those quote eyes and ears from space, making sure nothing like this ever happens again. So this conference is a key aspect. Protecting our nation requires we work in a cybersecurity environment at all times. But, you know, the fascinating thing about space systems is we can't see him. No, sir, We see Space launches man there's nothing more invigorating than that. But after launch, they become invisible. So what are they really doing up there? What are they doing to enable our quality of life in the United States and in the world? Well, to illustrate, I'd like to paraphrase elements of an article in Forbes magazine by Bonds and my good friend Chuck Beans. Chuck. It's a space guy, actually had Bonds job a fuse in the Pentagon. He is now chairman and chief strategy officer at York Space Systems, and in his spare time he's chairman of the small satellites. Chuck speaks in words that everyone can understand. So I'd like to give you some of his words out of his article. Uh, they're afraid somewhat. So these are Chuck's words. Let's talk about average Joe and playing Jane. Before heading to the airport for a business trip to New York City, Joe checks the weather forecast informed by Noah's weather satellites to see what pack for the trip. He then calls an uber that space app. Everybody uses it matches riders with drivers via GPS to take into the airport, So Joe has lunch of the airport. Unbeknownst to him, his organic lunch is made with the help of precision farming made possible through optimized irrigation and fertilization, with remote spectral sensing coming from space and GPS on the plane, the pilot navigates around weather, aided by GPS and nose weather satellites. And Joe makes his meeting on time to join his New York colleagues in a video call with a key customer in Singapore made possible by telecommunication satellites. Around to his next meeting, Joe receives notice changing the location of the meeting to another to the other side of town. So he calmly tells Syria to adjust the destination, and his satellite guided Google maps redirects him to the new location. That evening, Joe watches the news broadcast via satellite. The report details a meeting among world leaders discussing the developing crisis in Syria. As it turns out, various forms of quote remotely sensed. Information collected from satellites indicate that yet another band, chemical weapon, may have been used on its own people. Before going to bed, Joe decides to call his parents and congratulate them for their wedding anniversary as they cruise across the Atlantic, made possible again by communications satellites and Joe's parents can enjoy the call without even wondering how it happened the next morning. Back home, Joe's wife, Jane, is involved in a car accident. Her vehicle skids off the road. She's knocked unconscious, but because of her satellite equipped on star system, the crash is detected immediately and first responders show up on the scene. In time, Joe receives the news books. An early trip home sends flowers to his wife as he orders another uber to the airport. Over that 24 hours, Joe and Jane used space system applications for nearly every part of their day. Imagine the consequences if at any point they were somehow denied these services, whether they be by natural causes or a foreign hostility. And each of these satellite applications used in this case were initially developed for military purposes and continue to be, but also have remarkable application on our way of life. Just many people just don't know that. So, ladies and gentlemen, now you know, thanks to chuck beans, well, the United States has a proud heritage being the world's leading space faring nation, dating back to the Eisenhower and Kennedy years. Today we have mature and robust systems operating from space, providing overhead reconnaissance to quote, wash and listen, provide missile warning, communications, positioning, navigation and timing from our GPS system. Much of what you heard in Lieutenant General J. T. Thompson earlier speech. These systems are not only integral to our national security, but also our also to our quality of life is Chuck told us. We simply no longer could live without these systems as a nation and for that matter, as a world. But over the years, adversary like adversaries like China, Russia and other countries have come to realize the value of space systems and are aggressively playing ketchup while also pursuing capabilities that will challenge our systems. As many of you know, in 2000 and seven, China demonstrated it's a set system by actually shooting down is one of its own satellites and has been aggressively developing counter space systems to disrupt hours. So in a heavily congested space environment, our systems are now being contested like never before and will continue to bay well as Bond mentioned, the United States has responded to these changing threats. In addition to adding ways to protect our system, the administration and in Congress recently created the United States Space Force and the operational you United States Space Command, the latter of which you heard President Armstrong and other Californians hope is going to be located. Vandenberg Air Force Base Combined with our intelligence community today, we have focused military and civilian leadership now in space. And that's a very, very good thing. Commence, really. On the industry side, we did create the National Security Space Association devoted solely to supporting the national security Space Enterprise. We're based here in the D C area, but we have arms and legs across the country, and we are loaded with extraordinary talent. In scores of Forman, former government executives, So S s a is joined at the hip with our government customers to serve and to support. We're busy with a multitude of activities underway ranging from a number of thought provoking policy. Papers are recurring space time Webcast supporting Congress's Space Power Caucus and other main serious efforts. Check us out at NSS. A space dot org's One of our strategic priorities in central to today's events is to actively promote and nurture the workforce development. Just like cow calling. We will work with our U. S. Government customers, industry leaders and academia to attract and recruit students to join the space world, whether in government or industry and two assistant mentoring and training as their careers. Progress on that point, we're delighted. Be delighted to be working with Cal Poly as we hopefully will undertake a new pilot program with him very soon. So students stay tuned something I can tell you Space is really cool. While our nation's satellite systems are technical and complex, our nation's government and industry work force is highly diverse, with a combination of engineers, physicists, method and mathematicians, but also with a large non technical expertise as well. Think about how government gets things thes systems designed, manufactured, launching into orbit and operating. They do this via contracts with our aerospace industry, requiring talents across the board from cost estimating cost analysis, budgeting, procurement, legal and many other support. Tasker Integral to the mission. Many thousands of people work in the space workforce tens of billions of dollars every year. This is really cool stuff, no matter what your education background, a great career to be part of. When summary as bang had mentioned Aziz, well, there is a great deal of exciting challenges ahead we will see a new renaissance in space in the years ahead, and in some cases it's already begun. Billionaires like Jeff Bezos, Elon Musk, Sir Richard Richard Branson are in the game, stimulating new ideas in business models, other private investors and start up companies. Space companies are now coming in from all angles. The exponential advancement of technology and microelectronics now allows the potential for a plethora of small SAT systems to possibly replace older satellites the size of a Greyhound bus. It's getting better by the day and central to this conference, cybersecurity is paramount to our nation's critical infrastructure in space. So once again, thanks very much, and I look forward to the further conversation. >>Steve, thank you very much. Space is cool. It's relevant. But it's important, as you pointed out, and you're awesome story about how it impacts our life every day. So I really appreciate that great story. I'm glad you took the time Thio share that you forgot the part about the drone coming over in the crime scene and, you know, mapping it out for you. But that would add that to the story later. Great stuff. My first question is let's get into the conversations because I think this is super important. President Armstrong like you to talk about some of the points that was teased out by Bang and Steve. One in particular is the comment around how military research was important in developing all these capabilities, which is impacting all of our lives. Through that story. It was the military research that has enabled a generation and generation of value for consumers. This is kind of this workforce conversation. There are opportunities now with with research and grants, and this is, ah, funding of innovation that it's highly accelerate. It's happening very quickly. Can you comment on how research and the partnerships to get that funding into the universities is critical? >>Yeah, I really appreciate that And appreciate the comments of my colleagues on it really boils down to me to partnerships, public private partnerships. You mentioned Northrop Grumman, but we have partnerships with Lockie Martin, Boeing, Raytheon Space six JPL, also member of organization called Business Higher Education Forum, which brings together university presidents and CEOs of companies. There's been focused on cybersecurity and data science, and I hope that we can spill into cybersecurity in space but those partnerships in the past have really brought a lot forward at Cal Poly Aziz mentioned we've been involved with Cube set. Uh, we've have some secure work and we want to plan to do more of that in the future. Uh, those partnerships are essential not only for getting the r and d done, but also the students, the faculty, whether masters or undergraduate, can be involved with that work. Uh, they get that real life experience, whether it's on campus or virtually now during Covic or at the location with the partner, whether it may be governmental or our industry. Uh, and then they're even better equipped, uh, to hit the ground running. And of course, we'd love to see even more of our students graduate with clearance so that they could do some of that a secure work as well. So these partnerships are absolutely critical, and it's also in the context of trying to bring the best and the brightest and all demographics of California and the US into this field, uh, to really be successful. So these partnerships are essential, and our goal is to grow them just like I know other colleagues and C. S u and the U C are planning to dio, >>you know, just as my age I've seen I grew up in the eighties, in college and during that systems generation and that the generation before me, they really kind of pioneered the space that spawned the computer revolution. I mean, you look at these key inflection points in our lives. They were really funded through these kinds of real deep research. Bond talk about that because, you know, we're living in an age of cloud. And Bezos was mentioned. Elon Musk. Sir Richard Branson. You got new ideas coming in from the outside. You have an accelerated clock now on terms of the innovation cycles, and so you got to react differently. You guys have programs to go outside >>of >>the Defense Department. How important is this? Because the workforce that air in schools and our folks re skilling are out there and you've been on both sides of the table. So share your thoughts. >>No, thanks, John. Thanks for the opportunity responded. And that's what you hit on the notes back in the eighties, R and D in space especially, was dominated by my government funding. Uh, contracts and so on. But things have changed. As Steve pointed out, A lot of these commercial entities funded by billionaires are coming out of the woodwork funding R and D. So they're taking the lead. So what we can do within the deal, the in government is truly take advantage of the work they've done on. Uh, since they're they're, you know, paving the way to new new approaches and new way of doing things. And I think we can We could certainly learn from that. And leverage off of that saves us money from an R and D standpoint while benefiting from from the product that they deliver, you know, within the O D Talking about workforce development Way have prioritized we have policies now to attract and retain talent. We need I I had the folks do some research and and looks like from a cybersecurity workforce standpoint. A recent study done, I think, last year in 2019 found that the cybersecurity workforce gap in the U. S. Is nearing half a million people, even though it is a growing industry. So the pipeline needs to be strengthened off getting people through, you know, starting young and through college, like assess a professor Armstrong indicated, because we're gonna need them to be in place. Uh, you know, in a period of about maybe a decade or so, Uh, on top of that, of course, is the continuing issue we have with the gap with with stamps students, we can't afford not to have expertise in place to support all the things we're doing within the with the not only deal with the but the commercial side as well. Thank you. >>How's the gap? Get? Get filled. I mean, this is the this is again. You got cybersecurity. I mean, with space. It's a whole another kind of surface area, if you will, in early surface area. But it is. It is an I o t. Device if you think about it. But it does have the same challenges. That's kind of current and and progressive with cybersecurity. Where's the gap Get filled, Steve Or President Armstrong? I mean, how do you solve the problem and address this gap in the workforce? What is some solutions and what approaches do we need to put in place? >>Steve, go ahead. I'll follow up. >>Okay. Thanks. I'll let you correct. May, uh, it's a really good question, and it's the way I would. The way I would approach it is to focus on it holistically and to acknowledge it up front. And it comes with our teaching, etcetera across the board and from from an industry perspective, I mean, we see it. We've gotta have secure systems with everything we do and promoting this and getting students at early ages and mentoring them and throwing internships at them. Eyes is so paramount to the whole the whole cycle, and and that's kind of and it really takes focused attention. And we continue to use the word focus from an NSS, a perspective. We know the challenges that are out there. There are such talented people in the workforce on the government side, but not nearly enough of them. And likewise on industry side. We could use Maura's well, but when you get down to it, you know we can connect dots. You know that the the aspect That's a Professor Armstrong talked about earlier toe where you continue to work partnerships as much as you possibly can. We hope to be a part of that. That network at that ecosystem the will of taking common objectives and working together to kind of make these things happen and to bring the power not just of one or two companies, but our our entire membership to help out >>President >>Trump. Yeah, I would. I would also add it again. It's back to partnerships that I talked about earlier. One of our partners is high schools and schools fortune Margaret Fortune, who worked in a couple of, uh, administrations in California across party lines and education. Their fifth graders all visit Cal Poly and visit our learned by doing lab and you, you've got to get students interested in stem at a early age. We also need the partnerships, the scholarships, the financial aid so the students can graduate with minimal to no debt to really hit the ground running. And that's exacerbated and really stress. Now, with this covert induced recession, California supports higher education at a higher rate than most states in the nation. But that is that has dropped this year or reasons. We all understand, uh, due to Kobe, and so our partnerships, our creativity on making sure that we help those that need the most help financially uh, that's really key, because the gaps air huge eyes. My colleagues indicated, you know, half of half a million jobs and you need to look at the the students that are in the pipeline. We've got to enhance that. Uh, it's the in the placement rates are amazing. Once the students get to a place like Cal Poly or some of our other amazing CSU and UC campuses, uh, placement rates are like 94%. >>Many of our >>engineers, they have jobs lined up a year before they graduate. So it's just gonna take key partnerships working together. Uh, and that continued partnership with government, local, of course, our state of CSU on partners like we have here today, both Stephen Bang So partnerships the thing >>e could add, you know, the collaboration with universities one that we, uh, put a lot of emphasis, and it may not be well known fact, but as an example of national security agencies, uh, National Centers of Academic Excellence in Cyber, the Fast works with over 270 colleges and universities across the United States to educate its 45 future cyber first responders as an example, so that Zatz vibrant and healthy and something that we ought Teoh Teik, banjo >>off. Well, I got the brain trust here on this topic. I want to get your thoughts on this one point. I'd like to define what is a public private partnership because the theme that's coming out of the symposium is the script has been flipped. It's a modern error. Things air accelerated get you got security. So you get all these things kind of happen is a modern approach and you're seeing a digital transformation play out all over the world in business. Andi in the public sector. So >>what is what >>is a modern public private partnership? What does it look like today? Because people are learning differently, Covert has pointed out, which was that we're seeing right now. How people the progressions of knowledge and learning truth. It's all changing. How do you guys view the modern version of public private partnership and some some examples and improve points? Can you can you guys share that? We'll start with the Professor Armstrong. >>Yeah. A zai indicated earlier. We've had on guy could give other examples, but Northup Grumman, uh, they helped us with cyber lab. Many years ago. That is maintained, uh, directly the software, the connection outside its its own unit so that students can learn the hack, they can learn to penetrate defenses, and I know that that has already had some considerations of space. But that's a benefit to both parties. So a good public private partnership has benefits to both entities. Uh, in the common factor for universities with a lot of these partnerships is the is the talent, the talent that is, that is needed, what we've been working on for years of the, you know, that undergraduate or master's or PhD programs. But now it's also spilling into Skilling and re Skilling. As you know, Jobs. Uh, you know, folks were in jobs today that didn't exist two years, three years, five years ago. But it also spills into other aspects that can expand even mawr. We're very fortunate. We have land, there's opportunities. We have one tech part project. We're expanding our tech park. I think we'll see opportunities for that, and it'll it'll be adjusted thio, due to the virtual world that we're all learning more and more about it, which we were in before Cove it. But I also think that that person to person is going to be important. Um, I wanna make sure that I'm driving across the bridge. Or or that that satellites being launched by the engineer that's had at least some in person training, uh, to do that and that experience, especially as a first time freshman coming on a campus, getting that experience expanding and as adult. And we're gonna need those public private partnerships in order to continue to fund those at a level that is at the excellence we need for these stem and engineering fields. >>It's interesting People in technology can work together in these partnerships in a new way. Bank Steve Reaction Thio the modern version of what a public, successful private partnership looks like. >>If I could jump in John, I think, you know, historically, Dodi's has have had, ah, high bar thio, uh, to overcome, if you will, in terms of getting rapid pulling in your company. This is the fault, if you will and not rely heavily in are the usual suspects of vendors and like and I think the deal is done a good job over the last couple of years off trying to reduce the burden on working with us. You know, the Air Force. I think they're pioneering this idea around pitch days where companies come in, do a two hour pitch and immediately notified of a wooden award without having to wait a long time. Thio get feedback on on the quality of the product and so on. So I think we're trying to do our best. Thio strengthen that partnership with companies outside the main group of people that we typically use. >>Steve, any reaction? Comment to add? >>Yeah, I would add a couple of these air. Very excellent thoughts. Uh, it zits about taking a little gamble by coming out of your comfort zone. You know, the world that Bond and Bond lives in and I used to live in in the past has been quite structured. It's really about we know what the threat is. We need to go fix it, will design it says we go make it happen, we'll fly it. Um, life is so much more complicated than that. And so it's it's really to me. I mean, you take you take an example of the pitch days of bond talks about I think I think taking a gamble by attempting to just do a lot of pilot programs, uh, work the trust factor between government folks and the industry folks in academia. Because we are all in this together in a lot of ways, for example. I mean, we just sent the paper to the White House of their requests about, you know, what would we do from a workforce development perspective? And we hope Thio embellish on this over time once the the initiative matures. But we have a piece of it, for example, is the thing we call clear for success getting back Thio Uh, President Armstrong's comments at the collegiate level. You know, high, high, high quality folks are in high demand. So why don't we put together a program they grabbed kids in their their underclass years identifies folks that are interested in doing something like this. Get them scholarships. Um, um, I have a job waiting for them that their contract ID for before they graduate, and when they graduate, they walk with S C I clearance. We believe that could be done so, and that's an example of ways in which the public private partnerships can happen to where you now have a talented kid ready to go on Day one. We think those kind of things can happen. It just gets back down to being focused on specific initiatives, give them giving them a chance and run as many pilot programs as you can like these days. >>That's a great point, E. President. >>I just want to jump in and echo both the bank and Steve's comments. But Steve, that you know your point of, you know, our graduates. We consider them ready Day one. Well, they need to be ready Day one and ready to go secure. We totally support that and and love to follow up offline with you on that. That's that's exciting, uh, and needed very much needed mawr of it. Some of it's happening, but way certainly have been thinking a lot about that and making some plans, >>and that's a great example of good Segway. My next question. This kind of reimagining sees work flows, eyes kind of breaking down the old the old way and bringing in kind of a new way accelerated all kind of new things. There are creative ways to address this workforce issue, and this is the next topic. How can we employ new creative solutions? Because, let's face it, you know, it's not the days of get your engineering degree and and go interview for a job and then get slotted in and get the intern. You know the programs you get you particularly through the system. This is this is multiple disciplines. Cybersecurity points at that. You could be smart and math and have, ah, degree in anthropology and even the best cyber talents on the planet. So this is a new new world. What are some creative approaches that >>you know, we're >>in the workforce >>is quite good, John. One of the things I think that za challenge to us is you know, we got somehow we got me working for with the government, sexy, right? The part of the challenge we have is attracting the right right level of skill sets and personnel. But, you know, we're competing oftentimes with the commercial side, the gaming industry as examples of a big deal. And those are the same talents. We need to support a lot of programs we have in the U. D. So somehow we have to do a better job to Steve's point off, making the work within the U. D within the government something that they would be interested early on. So I tracked him early. I kind of talked about Cal Poly's, uh, challenge program that they were gonna have in June inviting high school kid. We're excited about the whole idea of space and cyber security, and so on those air something. So I think we have to do it. Continue to do what were the course the next several years. >>Awesome. Any other creative approaches that you guys see working or might be on idea, or just a kind of stoked the ideation out their internship. So obviously internships are known, but like there's gotta be new ways. >>I think you can take what Steve was talking about earlier getting students in high school, uh, and aligning them sometimes. Uh, that intern first internship, not just between the freshman sophomore year, but before they inter cal poly per se. And they're they're involved s So I think that's, uh, absolutely key. Getting them involved many other ways. Um, we have an example of of up Skilling a redeveloped work redevelopment here in the Central Coast. PG and e Diablo nuclear plant as going to decommission in around 2020 24. And so we have a ongoing partnership toe work on reposition those employees for for the future. So that's, you know, engineering and beyond. Uh, but think about that just in the manner that you were talking about. So the up skilling and re Skilling uh, on I think that's where you know, we were talking about that Purdue University. Other California universities have been dealing with online programs before cove it and now with co vid uh, so many more faculty or were pushed into that area. There's going to be much more going and talk about workforce development and up Skilling and Re Skilling The amount of training and education of our faculty across the country, uh, in in virtual, uh, and delivery has been huge. So there's always a silver linings in the cloud. >>I want to get your guys thoughts on one final question as we in the in the segment. And we've seen on the commercial side with cloud computing on these highly accelerated environments where you know, SAS business model subscription. That's on the business side. But >>one of The >>things that's clear in this trend is technology, and people work together and technology augments the people components. So I'd love to get your thoughts as we look at the world now we're living in co vid um, Cal Poly. You guys have remote learning Right now. It's a infancy. It's a whole new disruption, if you will, but also an opportunity to enable new ways to collaborate, Right? So if you look at people and technology, can you guys share your view and vision on how communities can be developed? How these digital technologies and people can work together faster to get to the truth or make a discovery higher to build the workforce? These air opportunities? How do you guys view this new digital transformation? >>Well, I think there's there's a huge opportunities and just what we're doing with this symposium. We're filming this on one day, and it's going to stream live, and then the three of us, the four of us, can participate and chat with participants while it's going on. That's amazing. And I appreciate you, John, you bringing that to this this symposium, I think there's more and more that we can do from a Cal poly perspective with our pedagogy. So you know, linked to learn by doing in person will always be important to us. But we see virtual. We see partnerships like this can expand and enhance our ability and minimize the in person time, decrease the time to degree enhanced graduation rate, eliminate opportunity gaps or students that don't have the same advantages. S so I think the technological aspect of this is tremendous. Then on the up Skilling and Re Skilling, where employees air all over, they can be reached virtually then maybe they come to a location or really advanced technology allows them to get hands on virtually, or they come to that location and get it in a hybrid format. Eso I'm I'm very excited about the future and what we can do, and it's gonna be different with every university with every partnership. It's one. Size does not fit all. >>It's so many possibilities. Bond. I could almost imagine a social network that has a verified, you know, secure clearance. I can jump in, have a little cloak of secrecy and collaborate with the d o. D. Possibly in the future. But >>these are the >>kind of kind of crazy ideas that are needed. Are your thoughts on this whole digital transformation cross policy? >>I think technology is gonna be revolutionary here, John. You know, we're focusing lately on what we call digital engineering to quicken the pace off, delivering capability to warfighter. As an example, I think a I machine language all that's gonna have a major play and how we operate in the future. We're embracing five G technologies writing ability Thio zero latency or I o t More automation off the supply chain. That sort of thing, I think, uh, the future ahead of us is is very encouraging. Thing is gonna do a lot for for national defense on certainly the security of the country. >>Steve, your final thoughts. Space systems are systems, and they're connected to other systems that are connected to people. Your thoughts on this digital transformation opportunity >>Such a great question in such a fun, great challenge ahead of us. Um echoing are my colleague's sentiments. I would add to it. You know, a lot of this has I think we should do some focusing on campaigning so that people can feel comfortable to include the Congress to do things a little bit differently. Um, you know, we're not attuned to doing things fast. Uh, but the dramatic You know, the way technology is just going like crazy right now. I think it ties back Thio hoping Thio, convince some of our senior leaders on what I call both sides of the Potomac River that it's worth taking these gamble. We do need to take some of these things very way. And I'm very confident, confident and excited and comfortable. They're just gonna be a great time ahead and all for the better. >>You know, e talk about D. C. Because I'm not a lawyer, and I'm not a political person, but I always say less lawyers, more techies in Congress and Senate. So I was getting job when I say that. Sorry. Presidential. Go ahead. >>Yeah, I know. Just one other point. Uh, and and Steve's alluded to this in bonded as well. I mean, we've got to be less risk averse in these partnerships. That doesn't mean reckless, but we have to be less risk averse. And I would also I have a zoo. You talk about technology. I have to reflect on something that happened in, uh, you both talked a bit about Bill Britton and his impact on Cal Poly and what we're doing. But we were faced a few years ago of replacing a traditional data a data warehouse, data storage data center, and we partner with a W S. And thank goodness we had that in progress on it enhanced our bandwidth on our campus before Cove. It hit on with this partnership with the digital transformation hub. So there is a great example where, uh, we we had that going. That's not something we could have started. Oh, covitz hit. Let's flip that switch. And so we have to be proactive on. We also have thio not be risk averse and do some things differently. Eyes that that is really salvage the experience for for students. Right now, as things are flowing, well, we only have about 12% of our courses in person. Uh, those essential courses, uh, and just grateful for those partnerships that have talked about today. >>Yeah, and it's a shining example of how being agile, continuous operations, these air themes that expand into space and the next workforce needs to be built. Gentlemen, thank you. very much for sharing your insights. I know. Bang, You're gonna go into the defense side of space and your other sessions. Thank you, gentlemen, for your time for great session. Appreciate it. >>Thank you. Thank you. >>Thank you. >>Thank you. Thank you. Thank you all. >>I'm John Furry with the Cube here in Palo Alto, California Covering and hosting with Cal Poly The Space and Cybersecurity Symposium 2020. Thanks for watching.

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hi, everybody. This is Dave Volante with Cuban. Welcome to the special digital presentation. We're really digging into how IBM is operational izing and automating the AI and data pipeline not only for its clients, but also for itself. And with me is Julie Lockner, who looks after offering management and IBM Data and AI portfolio really great to see you again. >>Great, great to be here. Thank you. Talk a >>little bit about the role you have here at IBM. >>Sure, so my responsibility in offering >>management and the data and AI organization is >>really twofold. One is I lead a team that implements all of the back end processes, really the operations behind any time we deliver a product from the Data and AI team to the market. So think about all of the release cycle management are seeing product management discipline, etcetera. The other role that I play is really making sure that I'm We are working with our customers and making sure they have the best customer experience and a big part of that is developing the data ops methodology. It's something that I needed internally >>from my own line of business execution. But it's now something that our customers are looking for to implement in their shops as well. >>Well, good. I really want to get into that. So let's let's start with data ops. I mean, I think you know, a lot of people are familiar with Dev Ops. Not maybe not everybody's familiar with data ops. What do we need to know about data? >>Well, I mean, you bring up the point that everyone knows Dev ops. And in fact, I think you know what data ops really >>does is bring a lot of the benefits that Dev Ops did for application >>development to the data management organizations. So when we look at what is data ops, it's a data management. Uh, it is a data management set of principles that helps organizations bring business ready data to their consumers. Quickly. It takes it borrows from Dev ops. Similarly, where you have a data pipeline that associates a business value requirement. I have this business initiative. It's >>going to drive this much revenue or this must cost >>savings. This is the data that I need to be able to deliver it. How do I develop that pipeline and map to the data sources Know what data it is? Know that I can trust it. So ensuring >>that it has the right quality that I'm actually using, the data that it was meant >>for and then put it to use. So in in history, most data management practices deployed a waterfall like methodology. Our implementation methodology and what that meant is all the data pipeline >>projects were implemented serially, and it was done based on potentially a first in first out program management office >>with a Dev Ops mental model and the idea of being able to slice through all of the different silos that's required to collect the data, to organize it, to integrate it, the validate its quality to create those data integration >>pipelines and then present it to the dashboard like if it's a Cognos dashboard >>or a operational process or even a data science team, that whole end to end process >>gets streamlined through what we're pulling data ops methodology. >>So I mean, as you well know, we've been following this market since the early days of Hadoop people struggle with their data pipelines. It's complicated for them, there's a a raft of tools and and and they spend most of their time wrangling data preparing data moving data quality, different roles within the organization. So it sounds like, you know, to borrow from from Dev Ops Data offices is all about streamlining that data pipeline, helping people really understand and communicate across. End the end, as you're saying, But but what's the ultimate business outcome that you're trying to drive? >>So when you think about projects that require data to again cut costs Teoh Artemia >>business process or drive new revenue initiatives, >>how long does it take to get from having access to the data to making it available? That duration for every time delay that is spent wasted trying to connect to data sources, trying to find subject matter experts that understand what the data means and can verify? It's quality, like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fat, though the business value of data ops is always associated with something that the business is trying to achieve but with a time element so if it's for every day, we don't have this data to make a decision where either making money or losing money, that's the value proposition of data ops. So it's about taking things that people are already doing today and figuring out the quickest way to do it through automation or work flows and just cutting through all the political barriers >>that often happens when these data's cross different organizational boundaries. >>Yes, sir, speed, Time to insights is critical. But in, you know, with Dev Ops, you really bringing together of the skill sets into, sort of, you know, one Super Dev or one Super ops. It sounds with data ops. It's really more about everybody understanding their role and having communication and line of sight across the entire organization. It's not trying to make everybody else, Ah, superhuman data person. It's the whole It's the group. It's the team effort, Really. It's really a team game here, isn't it? >>Well, that's a big part of it. So just like any type of practice, there's people, aspects, process, aspects and technology, right? So people process technology, and while you're you're describing it, like having that super team that knows everything about the data. The only way that's possible is if you have a common foundation of metadata. So we've seen a surgeons in the data catalog market in the last, you know, 67 years. And what what the what? That the innovation in the data catalog market has actually enabled us to be able >>to drive more data ops pipelines. >>Meaning as you identify data assets you captured the metadata capture its meaning. You capture information that can be shared, whether they're stakeholders, it really then becomes more of a essential repository for people don't really quickly know what data they have really quickly understand what it means in its quality and very quickly with the right proper authority, like privacy rules included. Put it to use >>for models, um, dashboards, operational processes. >>Okay. And we're gonna talk about some examples. And one of them, of course, is IBM's own internal example. But help us understand where you advise clients to start. I want to get into it. Where do I get started? >>Yeah, I mean, so traditionally, what we've seen with these large data management data governance programs is that sometimes our customers feel like this is a big pill to swallow. And what we've said is, Look, there's an operator. There's an opportunity here to quickly define a small project, align into high value business initiative, target something that you can quickly gain access to the data, map out these pipelines and create a squad of skills. So it includes a person with Dev ops type programming skills to automate an instrument. A lot of the technology. A subject matter expert who understands the data sources in it's meeting the line of business executive who translate bringing that information to the business project and associating with business value. So when we say How do you get started? We've developed A I would call it a pretty basic maturity model to help organizations figure out. Where are they in terms of the technology, where are they in terms of organizationally knowing who the right people should be involved in these projects? And then, from a process perspective, we've developed some pretty prescriptive project plans. They help you nail down. What are the data elements that are critical for this business business initiative? And then we have for each role what their jobs are to consolidate the data sets map them together and present them to the consumer. We find that six week projects, typically three sprints, are perfect times to be able to a timeline to create one of these very short, quick win projects. Take that as an opportunity to figure out where your bottlenecks are in your own organization, where your skill shortages are, and then use the outcome of that six week sprint to then focus on billing and gaps. Kick off the next project and iterating celebrate the success and promote the success because >>it's typically tied to a business value to help them create momentum for the next one. >>That's awesome. I want to get into some examples, I mean, or we're both Massachusetts based. Normally you'd be in our studio and we'd be sitting here for face to face of obviously with Kobe. 19. In this crisis world sheltering in place, you're up somewhere in New England. I happened to be in my studio, but I'm the only one here, so relate this to cove it. How would data ops, or maybe you have a, ah, a concrete example in terms of how it's helped, inform or actually anticipate and keep up to date with what's happening with both. >>Yeah, well, I mean, we're all experiencing it. I don't think there's a person >>on the planet who hasn't been impacted by what's been going on with this Cupid pandemic prices. >>So we started. We started down this data obscurity a year ago. I mean, this isn't something that we just decided to implement a few weeks ago. We've been working on developing the methodology, getting our own organization in place so that we could respond the next time we needed to be able todo act upon a data driven decision. So part of the step one of our journey has really been working with our global chief data officer, Interpol, who I believe you have had an opportunity to meet with an interview. So part of this year Journey has been working with with our corporate organization. I'm in a line of business organization where we've established the roles and responsibilities we've established the technology >>stack based on our cloud pack for data and Watson knowledge padlock. >>So I use that as the context. For now, we're faced with a pandemic prices, and I'm being asked in my business unit to respond very quickly. How can we prioritize the offerings that are going to help those in critical need so that we can get those products out to market? We can offer a 90 day free use for governments and hospital agencies. So in order for me to do that as a operations lead or our team, I needed to be able to have access to our financial data. I needed to have access to our product portfolio information. I needed to understand our cloud capacity. So in order for me to be able to respond with the offers that we recently announced and you'll you can take a look at some of the examples with our Watson Citizen Assistant program, where I was able to provide the financial information required for >>us to make those products available from governments, hospitals, state agencies, etcetera, >>that's a That's a perfect example. Now, to set the stage back to the corporate global, uh, the chief data office organization, they implemented some technology that allowed us to, in just data, automatically classify it, automatically assign metadata, automatically associate data quality so that when my team started using that data, we knew what the status of that information >>was when we started to build our own predictive models. >>And so that's a great example of how we've been partnered with a corporate central organization and took advantage of the automated, uh, set of capabilities without having to invest in any additional resources or head count and be able to release >>products within a matter of a couple of weeks. >>And in that automation is a function of machine intelligence. Is that right? And obviously, some experience. But you couldn't you and I when we were consultants doing this by hand, we couldn't have done this. We could have done it at scale anyway. It is it is it Machine intelligence and AI that allows us to do this. >>That's exactly right. And you know, our organization is data and AI, so we happen to have the research and innovation teams that are building a lot of this technology, so we have somewhat of an advantage there, but you're right. The alternative to what I've described is manual spreadsheets. It's querying databases. It's sending emails to subject matter experts asking them what this data means if they're out sick or on vacation. You have to wait for them to come back, and all of this was a manual process. And in the last five years, we've seen this data catalog market really become this augmented data catalog, and the augmentation means it's automation through AI. So with years of experience and natural language understanding, we can home through a lot of the metadata that's available electronically. We can calm for unstructured data, but we can categorize it. And if you have a set of business terms that have industry standard definitions through machine learning, we can automate what you and I did as a consultant manually in a matter of seconds. That's the impact that AI is have in our organization, and now we're bringing this to the market, and >>it's a It's a big >>part of where I'm investing. My time, both internally and externally, is bringing these types >>of concepts and ideas to the market. >>So I'm hearing. First of all, one of the things that strikes me is you've got multiple data, sources and data that lives everywhere. You might have your supply chain data in your er p. Maybe that sits on Prem. You might have some sales data that's sitting in a sas in a cloud somewhere. Um, you might have, you know, weather data that you want to bring in in theory. Anyway, the more data that you have, the better insights that you could gather assuming you've got the right data quality. But so let me start with, like, where the data is, right? So So it's it's anywhere you don't know where it's going to be, but you know you need it. So that's part of this right? Is being able >>to get >>to the data quickly. >>Yeah, it's funny. You bring it up that way. I actually look a little differently. It's when you start these projects. The data was in one place, and then by the time you get through the end of a project, you >>find out that it's moved to the cloud, >>so the data location actually changes. While we're in the middle of projects, we have many or even during this this pandemic crisis. We have many organizations that are using this is an opportunity to move to SAS. So what was on Prem is now cloud. But that shouldn't change the definition of the data. It shouldn't change. It's meaning it might change how you connect to it. It might also change your security policies or privacy laws. Now, all of a sudden, you have to worry about where is that data physically located? And am I allowed to share it across national boundaries right before we knew physically where it waas. So when you think about data ops, data ops is a process that sits on top of where the data physically resides. And because we're mapping metadata and we're looking at these data pipelines and automated work flows, part of the design principles are to set it up so that it's independent of where it resides. However, you have to have placeholders in your metadata and in your tool chain, where we're automating these work flows so that you can accommodate when the data decides to move. Because the corporate policy change >>from on prem to cloud. >>And that's a big part of what Data ops offers is the same thing. By the way, for Dev ops, they've had to accommodate building in, you know, platforms as a service versus on from the development environments. It's the same for data ops, >>and you know, the other part that strikes me and listening to you is scale, and it's not just about, you know, scale with the cloud operating model. It's also about what you were talking about is you know, the auto classification, the automated metadata. You can't do that manually. You've got to be able to do that. Um, in order to scale with automation, That's another key part of data office, is it not? >>It's a well, it's a big part of >>the value proposition and a lot of the part of the business case. >>Right then you and I started in this business, you know, and big data became the thing. People just move all sorts of data sets to these Hadoop clusters without capturing the metadata. And so as a result, you know, in the last 10 years, this information is out there. But nobody knows what it means anymore. So you can't go back with the army of people and have them were these data sets because a lot of the contact was lost. But you can use automated technology. You can use automated machine learning with natural, understand natural language, understanding to do a lot of the heavy lifting for you and a big part of data ops, work flows and building these pipelines is to do what we call management by exception. So if your algorithms say 80% confident that this is a phone number and your organization has a low risk tolerance, that probably will go to an exception. But if you have a you know, a match algorithm that comes back and says it's 99% sure this is an email address, right, and you have a threshold that's 98%. It will automate much of the work that we used to have to do manually. So that's an example of how you can automate, eliminate manual work and have some human interaction based on your risk threshold. >>That's awesome. I mean, you're right, the no schema on write said. I throw it into a data lake. Data Lake becomes a data swamp. We all know that joke. Okay, I want to understand a little bit, and maybe you have some other examples of some of the use cases here, but there's some of the maturity of where customers are. It seems like you've got to start by just understanding what data you have, cataloging it. You're getting your metadata act in order. But then you've got you've got a data quality component before you can actually implement and get yet to insight. So, you know, where are customers on the maturity model? Do you have any other examples that you can share? >>Yeah. So when we look at our data ops maturity model, we tried to simplify, and I mentioned this earlier that we try to simplify it so that really anybody can get started. They don't have to have a full governance framework implemented to to take advantage of the benefits data ops delivers. So what we did is we said if you can categorize your data ops programs into really three things one is how well do you know your data? Do you even know what data you have? The 2nd 1 is, and you trust it like, can you trust it's quality? Can you trust it's meeting? And the 3rd 1 is Can you put it to use? So if you really think about it when you begin with what data do you know, write? The first step is you know, how are you determining what data? You know? The first step is if you are using spreadsheets. Replace it with a data catalog. If you have a department line of business catalog and you need to start sharing information with the department's, then start expanding to an enterprise level data catalog. Now you mentioned data quality. So the first step is do you even have a data quality program, right. Have you even established what your criteria are for high quality data? Have you considered what your data quality score is comprised of? Have you mapped out what your critical data elements are to run your business? Most companies have done that for there. They're governed processes. But for these new initiatives And when you identify, I'm in my example with the covert prices, what products are we gonna help bring to market quickly? I need to be able to >>find out what the critical data elements are. And can I trust it? >>Have I even done a quality scan and have teams commented on it's trustworthiness to be used in this case, If you haven't done anything like that in your organization, that might be the first place to start. Pick the critical data elements for this initiative, assess its quality, and then start to implement the work flows to re mediate. And then when you get to putting it to use, there's several methods for making data available. One is simply making a gate, um, are available to a small set of users. That's what most people do Well, first, they make us spreadsheet of the data available, But then, if they need to have multiple people access it, that's when, like a Data Mart might make sense. Technology like data virtualization eliminates the need for you to move data as you're in this prototyping phase, and that's a great way to get started. It doesn't cost a lot of money to get a virtual query set up to see if this is the right join or the right combination of fields that are required for this use case. Eventually, you'll get to the need to use a high performance CTL tool for data integration. But Nirvana is when you really get to that self service data prep, where users can weary a catalog and say these are the data sets I need. It presents you a list of data assets that are available. I can point and click at these columns I want as part of my data pipeline and I hit go and automatically generates that output or data science use cases for it. Bad news, Dashboard. Right? That's the most mature model and being able to iterate on that so quickly that as soon as you get feedback that that data elements are wrong or you need to add something, you can do it. Push button. And that's where data obscurity should should bring organizations too. >>Well, Julie, I think there's no question that this covert crisis is accentuated the importance of digital. You know, we talk about digital transformation a lot, and it's it's certainly riel, although I would say a lot of people that we talk to we'll say, Well, you know, not on my watch. Er, I'll be retired before that all happens. Well, this crisis is accelerating. That transformation and data is at the heart of it. You know, digital means data. And if you don't have data, you know, story together and your act together, then you're gonna you're not gonna be able to compete. And data ops really is a key aspect of that. So give us a parting word. >>Yeah, I think This is a great opportunity for us to really assess how well we're leveraging data to make strategic decisions. And if there hasn't been a more pressing time to do it, it's when our entire engagement becomes virtual like. This interview is virtual right. Everything now creates a digital footprint that we can leverage to understand where our customers are having problems where they're having successes. You know, let's use the data that's available and use data ops to make sure that we can generate access. That data? No, it trust it, Put it to use so that we can respond to >>those in need when they need it. >>Julie Lockner, your incredible practitioner. Really? Hands on really appreciate you coming on the Cube and sharing your knowledge with us. Thank you. >>Thank you very much. It was a pleasure to be here. >>Alright? And thank you for watching everybody. This is Dave Volante for the Cube. And we will see you next time. >>Yeah, yeah, yeah, yeah, yeah

>> (vocalizing) >> Narrator: From theCube studios in Palo Alto and Boston, it's theCube. Covering IBM Think, brought to you by IBM. >> Hi everybody, this is Dave Vallante and this is our continuing coverage of Think 2020, the digital event experience. This is the post-thing, the sort of halo effect, the afterthoughts, and joining me is Rob Thomas, he's back. The Senior Vice president of Cloud and Data Platform. Rob, thanks for taking some time to debrief on Think. >> Absolutely Dave, great to be here, good to see you again. >> Yeah, so you have a great event, you guys put it together in record time. I want to talk about sort of your innovation agenda. I mean, you are at the heart of innovation. You're talking cloud, data, AI, really the pillars of innovation, I could probably add in edge to extend the cloud. But I wonder if you could talk about your vision for the innovation agenda and how you're bringing that to customers. I mean, we heard from PayPal, you talked about Royal Bank of Scotland, Credit Mutual, a number of customer examples. How are you bringing innovation forward with the customer? >> I wouldn't describe innovation, maybe I'd give it two different categories. One is, I think the classic term would be consumerization, and you're innovating by making interiorized technology really easy to use. That's why we built out a huge design capability, it's why we've been able to get products like Watson Assistant to get companies live in 24 hours. That's the consumerization aspect, just making enterprise products really easy to use. The second aspect is even harder, which is, how do you tap into an institution like IBM Research, where we're doing fundamental invention. So, one of our now strengths in the last couple of months was around taking technology out of IBM Debater, project Debater, the AI system that could debate humans and then putting that into enterprised products. And, you saw companies like PayPal that are using Watson Assistant and now they have access to that kind of language capability. There's only two aspects here, there's the consumerization and then there's about fundamental technology that really changes how businesses can operate. >> I mean, the point you made about speed and implementation in your key note was critical, I mean really, within 24 hours, very important during this pandemic. Talk about automation, you know, you would think by now right, everything's automation. But, now you're seeing a real boom in automation and it really is driven by AI, all this data, so there's seems to be a next wave, almost a renaissance, if you will, in automation. >> There is and I think automation, when people hear first of the term, it's sometimes a scary term. Because people are like hey, is this going to take my job? Gain a lot of momentum for automation is a difficult, repetitive tasks that nobody really wanted to do in the first place. Whether it's things like data matching, containerizing an application. All these are really hard things and the output's great, but nobody really wants to do that work, they just want the outcome. And, as we've started to demonstrate different use cases for automation that are in that realm, a lot of momentum has taken off, that we're seeing. >> I want to come back to this idea of consumerization and simplification. I mean, when you think about what's been happening over the last several years. And, you and I have talked about this a lot, AI for consumer versus AI for business and enterprise. And really, one of the challenges for the encumbrance, if you will, is to really become data driven, put data at the core and apply machine intelligence to that, just to that data. Now the good news is, they don't have to invent all this stuff, because guys like you are doing that and talk about how you're making that simple. I mean, cloud packs is an example of that, simplification, but talk about how customers are going to be able to tap into AI without having to be AI inventors. >> Well, the classic AI problem actually is a data problem, and the classic data problem is data slide over, which is a company has got a lot of data but it's spread across a hundred or a thousand or tens of thousands different repositories or locations. Our strategy when we say a hybrid cloud is about how do we unify those data storage. So, it's called PaaS, on red hat open shift. We do a lot of things like data virtualization, really high performance. So, we take what is thousands of different data sources and we have that packed like a single fluid item. So then, when you're training models, you can train your models in one place and connect to all your data. That is the big change that's happening and that's how you take something like hybrid cloud, and it actually starts to impact your data architecture. And once you're doing that, then AI becomes a lot easier, because the biggest AI challenge that I described is, where's the data? Is the data in a usable form? >> A lot of times in this industry, you know, we go whale hunting, there are a lot of big companies out there, a lot of times they take priority. You know, at the same time though, a lot of the innovations are coming from companies, you know, we've never even heard of that could be multi-billion dollar companies by the end of the decade. So, how can, you know, small companies and mid-sized companies tap into this trend? Is it just for the big whales or could the small guys participate? >> The thing that's pretty amazing about modern cloud and data technology, I'll call it, is it's accessible to companies of any size. When we talked about, you know, the hundred or so clients that have adopted Watson Assistant since COVID-19 started, many of those are very small institutions with no IT staff or very limited IT staff. Though, we're making this technology very accessible. when you look at something like data, now a small company may not have a hundred different repositories, which is fine, but what they do have is they do want to make better predictions, they do want to automate, they do want to optimize the business processes that they're running in their business. And, the way that we've transformed our model consumption base starting small, it's really making technology available to, you know, from anywhere from the local deli to the Fortune 50 Company. >> So, last question is, What are your big takeaways from Think? I would ask that question normally when we're in a live event. It's a little different with the digital event, but there are still takeaways. What was your reaction and what do to leave people with? >> Even as we get back to doing physical events, which I'm positive will happen at some point. What we learned is there is something great about an immersive digital experience. So, I think the future of events is probably higher than this. Meaning, a big digital experience, to complement the physical experience. That's one big takeaway because the reaction was so positive to the content and how people could access it. Second one is the, all the labs that we did. So, for developers, builders, those were at capacity, meaning we didn't even take any more. So, there's definitively a thirst in the market for developing new applications, developing new data products, developing new security products. That's clear just by the attendance that we saw, that's exciting. Now, I'd say third, that is that AI is now moving into the mainstream, that was clear from the customer examples, whether it was with Tansa or UPS or PayPal that I mentioned before, that was talking with me. AI is becoming accessible to every company, that's pretty exciting. >> Well, the world is hybrid, oh you know the lab, the point you're making about labs is really important. I've talked to a number of individuals saying, "Hey I'm using this time to update my skills. I'm working longer hours, maybe different times of the day, but I'm going to skill up." And you know, the point about AI, 37 years ago, when I started in this business AI was all the buzz and it didn't happen. It's real this time and I'm really excited Rob, that you're at the heart of all this innovation, so really, I appreciate you taking the time. And, best of luck, stay safe, and hopefully we'll see you face to face. >> Offscreen Man: Sure. >> Thanks Dave, same to you and the whole team at theCube, take care. >> Thank you Rob, and thank you for watching everybody, this is Dave Vellante for theCube and our coverage of IBM Think 2020, the digital event experience and the post-event. We'll see you next time. (music)

>>From the cube studios in Palo Alto in Boston. It's the cube covering the IBM thing brought to you by IBM. We're back and this is Dave Vellante and you're watching the cube and we're covering wall-to-wall the IBM 2020 I think digital experience. Rob Thomas is here. He's the senior vice president of clouds and data. Right. Warm rub. Always a pleasure to see you. I wish you were face to face, but Hey, we're doing the best we can. As you say, doing the best we can. Great to see you Dave. Hope family safe, healthy, happy as best you can be. Yeah. Ditto. You back out your Robin. Congratulations on on the new role, you and the cube. We've been riding this data wave for quite some time now. It's really been incredible. It really is. And last year I talked to you about how clients, we're slowly making progress on data strategy, starting to experiment with AI. >>We've gotten to the point now where I'd say it's game on for AI, which is exciting to see and that's a lot of what the theme of this year's think is about. Yeah, and I definitely want to dig into that, but I want to start by asking you sort of moves that you saw you're in there seeing your clients make with regard to the cobot night covert 19 crisis. Maybe how you guys are helping them in very interested in what you see as sort of longterm and even, you know, quasi permanent as a result of this. I would first say it this way. I don't, I'm not sure the crisis is going to change businesses as much as it's going to be accelerating. What would have happened anyway, regardless of the industry that you're in. We see clients aggressively looking at how do we get the digital faster? >>How do we automate more than we ever have before? There's the obvious things like business resiliency and business continuity, managing the distributed workforce. So to me, what we've seen is really about, and acceleration, not necessarily in a different direction, but an acceleration on. The thing is that that we're already kind of in the back of their minds or in the back of their plans now that as we'll come to the forefront and I'm encouraged because we see clients moving at a rate and pace that we'd never seen before that's ultimately going to be great for them, great for their businesses. And so I'm really happy to see that you guys have used Watson to really try to get, you know, some good high fidelity answers to the citizens. I wonder if you could explain that initiative. Well, we've had this application called Watson assistant for the last few years and we've been supporting banks, airlines, retailers, companies across all industries and helping them better interact with our customers and in some cases, employees. >>We took that same technology and as we saw the whole covert 19 situation coming, we said, Hey, we can evolve Watson assistant to serve citizens. And so it started by, we started training the models, which are intent based models in Watson assistant on all the publicly available data from the CDC as an example. And we've been able to build a really powerful virtual agent to serve really any citizen that has questions about and what they should be doing. And the response has been amazing. I mean, in the last two weeks we've gone live with 20 organizations, many of which are state and local governments. Okay. Also businesses, the city of Austin children's healthcare of Atlanta. Mmm. They local governments in Spain and Greece all over the world. And in some instances these clients have gotten live in less than 24 hours. Meaning they have a virtual agent that can answer any question. >>They can do that in less than 24 hours. It's actually been amazing to see. So proud of the team that built this over time. And it was kind of proof of the power of technology when we're dealing with any type of a challenge. You know, I had a conversation earlier with Jamie Thomas about quantum and was asking her sort of how your clients are using it. The examples that came up were financial institutions, pharmaceutical know battery manufacturers, um, airlines. And so it strikes me when you think about uh, machine intelligence and AI, the type of AI that you're yeah, at IBM is not consumer oriented AI. It's really designed for businesses. And I wonder if you could sort of add some color to that. Yeah, let's distinguish the difference there. Cause I think you've said it well consumer AI is smart speakers things in our home, you know, music recommendations, photo analysis and that's great and it enriches all of our personal lives. >>AI for business is very different. This is about how do you make better predictions, how do you optimize business processes, how do you automate things that maybe your employees don't want to do in the first time? Our focus in IBM as part of, we've been doing with Watson is really anchoring on three aspects of AI language. So understanding language because the whole business world is about communication of language, trust meaning trusted AI. You understand the models, you understand the data. And then third automation and the whole focus of what we're doing here in the virtual think experience. It's focused on AI for automation. Whether that's automating business processes or the new announcement this week, which is around automating AI opera it operations for a CIO. You, you've talked the years about this notion of an AI ladder. You actually, I actually wrote a book on it and uh, but, but it's been hard for customers to operationalize AI. >>Mmm. We talked about this last year. Thanks. What kind of progress, uh, have we made in the last 12 months? There's been a real recognition of this notion that your AI is only as good as your data. And we use the phrase, there's no AI without IAA, meaning information architecture, it's all the same concept, which is that your data, it has to be ready for AI if you want to too get successful outcomes with AI and the steps of those ladders around how you collect data, how you organize data, how you analyze data, how you infuse that into your business processes. seeing major leaps forward in the last nine months where organizations are understanding that connection and then they're using that to really drive initiatives around AI. So let's talk about that a little bit more. This notion of AI ops, I mean it's essentially the take the concept of dev ops and apply it to the data pipeline if you will. >>Everybody, you know, complains, you know, data scientists complained that all, they spent all their time wrangling data, improving data quality, they don't have line of sight across their organization with regard to other data specialists, whether it's data engineers or even developers. Maybe you could talk a little bit more about that announcement and sort of what you're doing in that area. Sure. So right. Let me put a number on it because the numbers are amazing. Every year organizations lose 2016 point $5 billion of revenue because of outages in it system. That is a staggering number when you think about it. And so then you say, okay, so how do you break down and attack that problem? Well, do you have to get better at fixing problems or you have to get better at avoiding problems altogether. And as you may expect, a little bit of both. You, you want to avoid problems obviously, but in an uncertain world, you're always going to deal with unforeseen challenges. >>So the also the question becomes how fast can you respond and there's no better use of AI. And then to do, I hope you like those tasks, which is understanding your environment, understanding what the systems are saying through their data and identifying issues become before they become outages. And once there is an outage, how do you quickly triage data across all your systems to figure out where is the problem and how you can quickly address it. So we are announcing Watson AI ops, which is the nervous system for a CIO, the manager, all of their systems. What we do is we just collect data, log data from every source system and we build a semantic layer on top that. So Watson understands the systems, understands the normal behavior, understands the acceptable ranges, and then anytime something's not going like it should, Watson raises his hand and says, Hey, you should probably look at this before it becomes a problem. >>We've partnered with companies like Slack, so the UI for Watson AI ops, it's actually in Slack so that companies can use and employees can use a common collaboration tool too. Troubleshoot or look at either systems. It's, it's really powerful. So that we're really proud of. Well I just kind of leads me to my next question, which I mean, IBM got the religion 20 years ago on openness. I mean I can trace it back to the investment you made and Lennox way back when. Um, and of course it's a huge investment last year in red hat, but you know, open source company. So you just mentioned Slack. Talk about open ecosystems and how that it fits into your AI and data strategy. Well, if you think about it, if we're going to take on a challenge this grand, which is AI for all of your it by definition you're going to be dealing with full ecosystem of different providers because every organization has a broad set of capabilities we identified early on. >>That means that our ability to provide open ecosystem interoperability was going to be critical. So we're launching this product with Slack. I mentioned with box, we've got integrations into things like PagerDuty service now really all of the tools of modern it architecture where we can understand the data and help clients better manage those environments. So this is all about an open ecosystem and that's how we've been approaching it. Let's start, it's really about data, applying machine intelligence or AI to that data and about cloud for scale. So I wonder what you're seeing just in terms of that sort of innovation engine. I mean obviously it's gotta be secure. It's, it seems like those are the pillars of innovation for the next 10 plus years. I think you're right. And I would say this whole situation that we're dealing with has emphasized the importance of hybrid deployment because companies have it capabilities on public clouds, on private clouds, really everywhere. >>And so being able to operate that as a single architecture, it's becoming very important. You can use AI to automate tasks across that whole infrastructure that makes a big difference. And to your point, I think we're going to see a massive acceleration hybrid cloud deployments using AI. And this will be a catalyst for that. And so that's something we're trying to help clients with all around the world. You know, you wrote in your book that O'Reilly published that AI is the new electricity and you talked about problems. Okay. Not enough data. If your data is you know, on prem and you're only in the cloud, well that's a problem or too much data. How you deal with all that data, data quality. So maybe we could close on some of the things that you know, you, you talked about in that book, you know, maybe how people can get ahold of it or any other, you know, so the actions you think people should take to get smart on this topic. >>Yeah, so look, really, really excited about this. Paul's the capitalists, a friend of mine and a colleague, we've published this book working with a Riley called the a ladder and it's all the concepts we talked about in terms of how companies can climb this ladder to AI. And we go through a lot of different use cases, scenarios, I think. Yeah. Anybody reading this is going to see their company in one of these examples, our whole ambition was to hopefully plant some seeds of ideas for how you can start to accelerate your journey to AI in any industry right now. Well, Rob, it's always great having you on the cube, uh, your insights over the years and you've been a good friend of ours, so really appreciate you coming on and, uh, and best of luck to you, your family or wider community. I really appreciate it. Thanks Dave. Great to be here and again, wish you and the whole cube team the best and to all of our clients out there around the world. We wish you the best as well. All right. You're watching the cubes coverage of IBM think 20, 20 digital, the vent. We'll be right back right after this short break. This is Dave Volante.

(upbeat music) >> Announcer: From the cube Studios in Palo Alto and Boston, it's the cube! Covering IBM Think. Brought to you by IBM. >> Hi everybody, this is Dave Vellante of theCUBE, and you're watching our coverage of the IBM digital event experience. A multi-day program, tons of content, and it's our pleasure to be able to bring in experts, practitioners, customers, and partners. Sriram Raghavan is here. He's the Vice President of IBM Research in AI. Sriram, thanks so much for coming on thecUBE. >> Thank you, pleasure to be here. >> I love this title, I love the role. It's great work if you're qualified for it.(laughs) So, tell us a little bit about your role and your background. You came out of Stanford, you had the pleasure, I'm sure, of hanging out in South San Jose at the Almaden labs. Beautiful place to create. But give us a little background. >> Absolutely, yeah. So, let me start, maybe go backwards in time. What do I do now? My role's responsible for AI strategy, planning, and execution in IBM Research across our global footprint, all our labs worldwide and their working area. I also work closely with the commercial parts. The parts of IBM, our Software and Services business that take the innovation, AI innovation, from IBM Research to market. That's the second part of what I do. And where did I begin life in IBM? As you said, I began life at our Almaden Research Center up in San Jose, up in the hills. Beautiful, I had in a view. I still think it's the best view I had. I spent many years there doing work at the intersection of AI and large-scale data management, NLP. Went back to India, I was running the India lab there for a few years, and now I'm back here in New York running AI strategy. >> That's awesome. Let's talk a little bit about AI, the landscape of AI. IBM has always made it clear that you're not doing consumer AI. You're really tying to help businesses. But how do you look at the landscape? >> So, it's a great question. It's one of those things that, you know, we constantly measure ourselves and our partners tell us. I think we, you've probably heard us talk about the cloud journey . But look barely 20% of the workloads are in the cloud, 80% still waiting. AI, at that number is even less. But, of course, it varies. Depending on who you ask, you would say AI adoption is anywhere from 4% to 30% depending on who you ask in this case. But I think it's more important to look at where is this, directionally? And it's very, very clear. Adoption is rising. The value is more, it's getting better appreciated. But I think more important, I think is, there is broader recognition, awareness and investment, knowing that to get value out of AI, you start with where AI begins, which is data. So, the story around having a solid enterprise information architecture as the base on which to drive AI, is starting to happen. So, as the investments in data platform, becoming making your data ready for AI, starts to come through. We're definitely seeing that adoption. And I think, you know, the second imperative that businesses look for obviously is the skills. The tools and the skills to scale AI. It can't take me months and months and hours to go build an AI model, I got to accelerate it, and then comes operationalizing. But this is happening, and the upward trajectory is very, very clear. >> We've been talking a lot on theCUBE over the last couple of years, it's not the innovation engine of our industry is no longer Moore's Law, it's a combination of data. You just talked about data. Applying machine technology to that data, being able to scale it, across clouds, on-prem, wherever the data lives. So. >> Right. >> Having said that, you know, you've had a journey. You know, you started out kind of playing "Jeopardy!", if you will. It was a very narrow use case, and you're expanding that use case. I wonder if you could talk about that journey, specifically in the context of your vision. >> Yeah. So, let me step back and say for IBM Research AI, when I think about how we, what's our strategy and vision, we think of it as in two parts. One part is the evolution of the science and techniques behind AI. And you said it, right? From narrow, bespoke AI that all it can do is this one thing that it's really trained for, it takes a large amount of data, a lot of computing power. Two, how do you have the techniques and the innovation for AI to learn from one use case to the other? Be less data hungry, less resource hungry. Be more trustworthy and explainable. So, we call that the journey from narrow to broad AI. And one part of our strategy, as scientists and technologists, is the innovation to make that happen. So that's sort of one part. But, as you said, as people involved in making AI work in the enterprise, and IBM Research AI vision would be incomplete without the second part, which is, what are the challenges in scaling and operationalizing AI? It isn't sufficient that I can tell you AI can do this, how do I make AI do this so that you get the right ROI, the investment relative to the return makes sense and you can scale and operationalize. So, we took both of these imperatives. The AI narrow-to-broad journey, and the need to scale and operationalize. And what of the things that are making it hard? The things that make scaling and operationalizing harder: data challenges, we talked about that, skills challenges, and the fact that in enterprises, you have to govern and manage AI. And we took that together and we think of our AI agenda in three pieces: Advancing, trusting, and scaling AI. Advancing is the piece of pushing the boundary, making AI narrow to broad. Trusting is building AI which is trustworthy, is explainable, you can control and understand its behavior, make sense of it and all of the technology that goes with it. And scaling AI is when we address the problem of, how do I, you know, reduce the time and cost for data prep? How do I reduce the time for model tweaking and engineering? How do I make sure that a model that you build today, when something changes in the data, I can quickly allow for you to close the loop and improve the model? All of the things, think of day-two operations of AI. All of that is part of our scaling AI strategy. So advancing, trusting, scaling is sort of the three big mantras around which the way we think about our AI. >> Yeah, so I've been doing a little work in this around this notion of DataOps. Essentially, you know, DevOps applied to the data and the data pipeline, and I had a great conversation recently with Inderpal Bhandari, IBM's Global Chief Data Officer, and he explained to me how, first of all, customers will tell you, it's very hard to operationalize AIs. He and his team took that challenge on themselves and have had some great success. And, you know, we all know the problem. It's that, you know AI has to wait for the data. It has to wait for the data to be cleansed and wrangled. Can AI actually help with that part of the problem, compressing that? >> 100%. In fact, the way we think of the automation and scaling story is what we call the "AI For AI" story. So, AI in service of helping you build the AI that helps you make this with speed, right? So, and I think of it really in three parts. It's AI for data automation, our DataOps. AI used in better discovery, better cleansing, better configuration, faster linking, quality assessment, all of that. Using AI to do all of those data problems that you had to do. And I called it AI for data automation. The second part is using AI to automatically figure out the best model. And that's AI for data science automation, which is, feature engineering, hyperparameter optimization, having them all do work, why should a data scientist take weeks and months experimenting? If the AI can accelerate that from weeks to a matter of hours? That's data science automation. And then comes the important part, also, which is operations automation. Okay, I've put a data model into an application. How do I monitor its behavior? If the data that it's seeing is different from the data it was trained on, how do I quickly detect it? And a lot of the work from Research that was part of that Watson OpenScale offering is really addressing the operational side. So AI for data, AI for data science automation, and AI to help automate production of AI, is the way we break that problem up. >> So, I always like to ask folks that are deep into R&D, how they are ultimately are translating into commercial products and offerings? Because ultimately, you got to make money to fund more R&D. So, can you talk a little bit about how you do that, what your focus is there? >> Yeah, so that's a great question, and I'm going to use a few examples as well. But let me say at the outset, this is a very, very closed partnership. So when we, the Research part of AI and our portfolio, it's a closed partnership where we're constantly both drawing problem as well as building technology that goes into the offering. So, a lot of our work, much of our work in AI automation that we were talking about, is part of our Watson Studio, Watson Machine Learning, Watson OpenScale. In fact, OpenScale came out of Research working Trusted AI, and is now a centerpiece of our Watson project. Let me give a very different example. We have a very, very strong portfolio and focus in NLP, Natural Language Processing. And this directly goes into capabilities out of Watson Assistant, which is our system for conversational support and customer support, and Watson Discovery, which is about making enterprise understand unstructurally. And a great example of that is the Working Project Debater that you might have heard, which is a grand challenge in Research about building a machine that can do debate. Now, look, we weren't looking to go sell you a debating machine. But what did we build as part of doing that, is advances in NLP that are all making their way into assistant and discovery. And we actually just talked about earlier this year, announced a set of capabilities around better clustering, advanced summarization, deeper sentiment analysis. These made their way into Assistant and Discovery but are born out of research innovation and solving a grand problem like building a debating machine. That's just an example of how that journey from research to product happens. >> Yeah, the Debater documentary, I've seen some of that. It's actually quite astounding. I don't know what you're doing there. It sounds like you're taking natural language and turning it into complex queries with data science and AI, but it's quite amazing. >> Yes, and I would encourage you, you will see that documentary, by the way, on Channel 7, in the Think Event. And I would encourage you, actually the documentary around how Debater happened, sort of featuring back of the you know, backdoor interviews with the scientist who created it was actually featured last minute at Copenhagen International Documentary Festival. I'll invite viewers to go to Channel 7 and Data and AI Tech On-Demand to go take a look at that documentary. >> Yeah, you should take a look at it. It's actually quite astounding and amazing. Sriram, what are you working on these days? What kind of exciting projects or what's your focus area today? >> Look, I think there are three imperatives that we're really focused on, and one is very, you know, just really the project you're talking about, NLP. NLP in the enterprise, look, text is a language of business, right? Text is the way business is communicated. Within each other, with their partners, with the entire world. So, helping machines understand language, but in an enterprise context, recognizing that data and the enterprises live in complex documents, unstructured documents, in e-mail, they live in conversations with the customers. So, really pushing the boundary on how all our customers and clients can make sense of this vast volume of unstructured data by pushing the advances of NLP, that's one focus area. Second focus area, we talked about trust and how important that is. And we've done amazing work in monitoring and explainability. And we're really focused now on this emerging area of causality. Using causality to explain, right? The model makes this because the model believes this is what it wants, it's a beautiful way. And the third big focus continues to be on automation. So, NLP, trust, automation. Those are, like, three big focus areas for us. >> sriram, how far do you think we can take AI? I know it's a topic of conversation, but from your perspective, deep into the research, how far can it go? And maybe how far should it go? >> Look, I think we are, let me answer it this way. I think the arc of the possible is enormous. But I think we are at this inflection point in which I think the next wave of AI, the AI that's going to help us this narrow-to-broad journey we talked about, look, the narrow-to-broad journey's not like a one-week, one-year. We're talking about a decade of innovation. But I think we are at a point where we're going to see a wave of AI that we like to call "neuro-symbolic AI," which is AI that brings together two sort of fundamentally different approaches to building intelligence systems. One approach of building intelligence system is what we call "knowledge driven." Understand data, understand concept, logically, reasonable. We human beings do that. That was really the way AI was born. The more recent last couple of decades of AI was data driven, Machine learning. Give me vast volumes of data, I'll use neural techniques, deep learning, to to get value. We're at a point where we're going to bring both of them together. Cause you can't build trustworthy, explainable systems using only one, you can't get away from not using all of the data that you have to make them. So, neuro-symbolic AI is, I think, going to be the linchpin of how we advance AI and make it more powerful and trustworthy. >> So, are you, like, living your childhood dream here or what? >> Look, for me I'm fascinated. I've always been fascinated. And any time you can't find a technology person who hasn't dreamt of building an intelligent machine. To have a job where I can work across our worldwide set of 3,000 plus researchers and think and brainstorm on strategy with AI. And then, most importantly, not to forget, right? That you talked about being able to move it into our portfolios so it actually makes a difference for our clients. I think it's a dream job and a whole lot of fun. >> Well, Sriram, it was great having you on theCUBE. A lot of fun, interviewing folks like you. I feel a little bit smarter just talking to you. So thanks so much for coming on. >> Fantastic. It's been a pleasure to be here. >> And thank you for watching, everybody. You're watching theCUBE's coverage of IBM Think 2020. This is Dave Vellante. We'll be right back right after this short break. (upbeat music)

(relaxing music) >> Announcer: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering IBM Think, brought to you by IBM. >> Welcome back to theCUBE, I'm Stu Miniman, and this is theCUBEs coverage of IBM Think 2020, the digital experience, we're getting to talk to the IBM executive, the customers, and their partners Where they are around the globe, really happy to bring back the program, one of our people online. Michelle Peluso, she is the senior vice president of digital sales and chief marketing officer for all of IBM. Michelle, thanks so much for joining us. >> Thank you so much. It's great to have you as we get ready for Think 2020. >> boy, Michelle, you know, working for a big company like IBM, I can only imagine how much current global activities have impacting you, anybody If you turn on TV, you know that the ads that you're seeing are obviously have a very different manner than what we were seeing before this happened. And, you know, the focus of Think, of course, you know, really centers around what is happening, how you're helping IBM customers in part through there. So give us a little bit of insight as to, you know, how much the team has had the, you know, rapidly move towards the new reality? >> Well, look our company has been very focused on a couple of major priorities. First of all, our people keeping them safe and healthy and thinking about what are we learning from all this? How do we use new tools in different ways? How do we work in agile ways that will outlast even this current crisis? Secondly, of course, our clients we have pivoted hard to the essential offerings for recovery and transformation our clients need most right now. Things like business continuity, things like enabling Watson to engage all your customers virtually, things like supply chain resiliency, things like increased agility on the cloud, health and human Services. These are new offerings, new bundles that we know our clients need most right now, and so we've been pivoting hard. Third thing, as a marketer, of course, I've been very focused on how does the brand show up in this moment? How do we think about this cadre of events we used to do in person? How do we transform and think about generating demand in a virtual world, really improving the end to end digital experiences of everything we do? And of course, lastly, it's about how do we help create a cure? How do we help make sure that we speed this process along so we've done a lot from you know, taking super computing power and really applying it to the fight to find cures and find vaccines. We have donated things like Watson Assistant so that governments can get access to free chatbots to help their customers with knowledge and information about COVID-19. So, lots of things we're doing across all those friends. It's certainly been a time of really rapid transformation and the most important thing we can do is listen and pivot quickly. >> Yeah, really important points Michelle, listening to customers. I'm curious, you know, what are you hearing from customers? Obviously, you know, they have lots of challenges. And therefore, it probably changed a little bit how they think about who they partner with, you know, who they go to, to be a trusted, you know, partner in these times. So, you know, what feedback Are you getting from customers? How do they look at the relationship with IBM in your ecosystem, that might be a little different than before? >> Well, we're talking to customers more than ever, as you can imagine. And I think we have seen seven offerings, seven things that our clients really are learning going through this experience and need help with. And those range as I mentioned earlier, from supply chain continuity and resiliency to the new cybersecurity landscape. There's so many different and unique cyber risks right now. Virtual teaming, virtual work from home. Business continuity and resiliency, increased agility on the cloud things like, you know, making sure that we're supporting the health and human Services of our people. So those are some of the examples of what matters most to clients right now virtually engaging with customers with Watson. So those are the things that we have pivoted hard to make sure that we help our clients with the essential process of recovery and transformation. Because there isn't going where, there's no back to normal. We were very convinced that this is a rethink and Think 2020 is coming at the perfect time, as businesses start to slowly reopen their doors. You know, it's going to be a very important conversation with our clients on how we accelerate recovery and transformation. And transformation is important because we have learned a lot. And there are some things that we need to go back and improve. And there's some lessons we've learned that we can, you know, take with us into this sort of new world. So it's a challenging time for sure. But it's also one that is ripe with opportunities. And I've seen so much creativity and so much dedication. As we, you know, we had to remake Think in 60 days, a totally new platform, you know, new capabilities, new content, and at three x the volume. So the teams have done a remarkable job. And I'm excited for the conversation. >> What I'm curious, what you're hearing, is customers that are, you know, starting are in the midst of that journey, is the global pandemic, is it accelerating what they're doing? Is it stalling them? They're not definitely finding, >> you know, and I think it's really two things. One is, how does the team operate and you know, I've been very passionate for my entire career about agile as a discipline, small cross functional teams aligned on a mission, shared values, really have an incredible ability using the agile rituals to prioritize and to move quickly and to optimize that is more important than ever before. That is what is enabling kind of this more rapid, you know, cycles we're seeing and then I think are critical. >> What should we be taking as lessons and, you know, new practices that will continue in the future? >> Well, from a client perspective, I think we're going to see where digital has always sort of been, you know, mission critical. I think there's going to be incredible and continued, you know, rapid acceleration to a digital environment. And that's not just outside in what, you know, do we have a good mobile app? Do we have a good web experience that's inside out. How do we digitize the, you know, the call center so that customers can get virtual answers with chatbots? How do we digitize and use AI to improve HR, supply chain apart from fundamental, you know, manufacturing operational procedures. So that's one thing I think will be a permanent change. Secondly, I think we're going to see the same thing on the cloud, I think clients that had you know, three to five year journeys on their roadmaps of how they think about their cloud architecture in what workloads are we going to move to the public cloud? Almost all of them are saying that now has to be compressed. So I think we're going to see more rapid acceleration and adoption and journey to cloud. I think there's some new things that we'll see in terms of blockchain and cybersecurity and others that will also reimagine the landscape of our clients. On the people side, you know, we're adjusting, right? We're going to have to figure out this new way of being, this new way of normal, which might be a bit more hybrid than we're used to. Sometime in the office, sometime at home. I fundamentally believe more agile teams truly agile is a mission. So I think these are just some of the areas that we're going to see a reimagination of how work gets done, and what work gets done to make us more resilient, you know, stronger, and to emerge from what has been an immensely challenging period for so many, and personally so, for so many. And how do we take some lessons from this? So we emerged stronger >> All right. So Michelle, I was looking back at when we first had you on theCUBE. And when you were, you know, just coming on IBM as the CMO. And you know, you talk then about how you've always worked for digital companies, so here in 2020, the global pandemic, of course, you know, is on everyone's mind, but when people leave Think, how should they be thinking about IBM? if, you know, what is different, you know, and what is the same, over 100 year old company, one of the most trusted brands in the industry, but new leadership with Arvind, And how do you want people to think of IBM going forward? >> I think times of great challenge, are actually meant for the IBM brand. I think that our clients are looking more than ever for partners they can trust who can help them find the world's most innovative technology, with deep expertise and understanding of how work actually happens across these industries and with a blanket of Kind of security and likely trusted, responsible stewardship that matters more. So I hope our clients and our business partners because we have an immensely rich agenda for our business partners, I hope they emerge knowing that IBM is their essential partner for recovery and for transformation. And there is simply nothing we won't do to help them make their business stronger and in so doing to build a stronger more resilient world. >> Well, Michelle Peluso congratulations and the team on everything to make Think 2020 Digital come and really appreciate being able to participate with you. >> Thanks for I really appreciate it. >> Stay tuned for lots more coverage from the cube. I'm Stu Miniman. Thanks for watching. (upbeat music)

>>Yeah, >>from the Cube Studios in Palo Alto and Boston. It's the Cube covering IBM. Think brought to you by IBM. >>Welcome back to IBM. Think 2020. The global experience. My name, Stupid man. And happy to welcome to the program. Aaron Bobick, who is the vice president and chief design officer for the IBM Cloud Data and AI portfolios. Thank you so much for joining us. >>Thank you, Steven. Great to >>be here. Alright. So I always love talking to design people. My background is engineering. I said on the Cube a couple of times I feel they didn't really teach us in school enough about design. We all know on the consumer side, when you have >>a >>phenomenal technology and beautiful designed together, it's an amazing experience. So you've got a brought purview. You've had a very diverse background. Help us understand. You know what a chief design office they're across, you know, cloud and Data and ai is responsible for >>so in a in a just my job is to really ensure that we design and develop usable and meaningful experiences for our users. Finds customers and partners in the little mawf cloud in the eye both evolving technologies. Um, adoption challenges here and there, and our job is to simplify >>the complex and the network. Okay, that's awesome. You know, I think back, you know, early web days, you know, we were happy if we just had a u I let alone Didn't think about the ux experience there. So you know, what are some of the important things? You know, what? What's IBM looking at? To make sure that that user interface is something that is Yeah. >>So I'll take a step back. And question is doing Say that, you know, in the sounding times while we're still figuring out new ways stood up So to get work done and really get the essence off being more productive design is there to help figure out a solution to these human, because at the end of it, design is really an expression of intent and intend to help solve the problem and overcome everyday challenges. So, you know, be at IBM is basically focusing on helping our users and partners and customers be more productive. And the feeling is that design has become really important to IBM, not just IBM does. Other landed companies are having great advantages. So if I just call it a few studies in a recent guard from the study found that 89% of companies that they would focus and you extend them apart. So this is about differentiation by design the second Forrester Little study, and they found that 70% of projects fail because of poor us, and that's a huge number. There's also city by the GM of the Design Management Institute that says that design that companies are poor home S and P 500 by 20. So all in all this is that design is now a very important aspect of how we go to market, and it's essential. The good news. IBM has always been part of Indiana money for ponderous Thomas. What Jr said, Good design is good business, though We're in it for the long run. >>Yeah, obviously a long history. There are over 100 years of focus on that. So one of the big themes we've heard the last couple of years, you know, see X. That's about that customer experience and not only the external customers but the internal customers we're talking about, you know, support agents and the like. So how is IBM making sure that it is on the leading edge for the >>great questions to over the last? I would say a good 10 years. We really work hard to develop a culture off designing, design, thinking and close by IBM. Whether it's product development, the services we offer support. We work with customers pretty much every touch point of the user has with us. Design has had an influence in it. To get to where we are today, we had to go hire a whole bunch of formally trained designers. We're working across more than 50 plus global design studio to bring in diversity and part of an idea. And at the end of the day, it's not about this confidence in craft. It's also what the baby work. So we had to hire designers, but we also changing the way IBM offers across organizations work. The level of the strain were called the Enterprise Design Thinking Framework, which is essentially our take a human centered design. Build a scale for the enterprise, so the enterprise is a key element here. The practices we've developed using those frameworks helps our team collaborate better keeping the users and their need at the center of everything we do. But it's not just for us. We also developed it for generally everyone. So if anyone wants to take it up, they could try IBM dot com slash design thinking and give it a shot. And through all of these, we have managed to see some incredible progress internally across organizations with alignment and go to market. But we've also seen some great progress that internally as well, case in point over 20 international designer words for design in the Enterprise. But with the last two years across the portfolio, So it's been a fun ride and our focus for customer experience because the endpoints, all the touchpoints has really given us >>a lot of minutes. Well, congratulations on the award is there. We know enterprises are particular and challenging there. They're not necessarily the first to deploy something new. But one of the big discussions we've had for years when you talk about Cloud and AI is a skill set and training. So what are some of the unique challenges that you have from a design stand point in the enterprise? >>I think the answer to your question is in your question, and it comes down to the enterprise. Enterprise is unique in many different ways, right? First of all, it's about mission critical needs, and second is about productivity. Our minds and the users are coming to us to help them solve these massive, complex challenges and problems, from data management to automation to modernization, to being on the cloud or adopting AI. They're really looking detained, the way they work and at scale. This means that we, as designers and at IBM, have to really take the time to understand the users, to see what their pain points are detected environments and the context of the working so that IBM can ultimately >>help solve the conflict. >>No, that's one part second because it's in the enterprise but also dealing with the fact that technology is evolving at a very rapid pace. Thinking about containers, ai Blockchain, you name it and we know that in order to meet the needs of this modern day age workers, we really need to think out of the box and be a little bit ahead of the curve designed for collaboration and the adoption of these emerging technologies without adding a huge learning curve, but that's a challenge as well. How do we adopt technologies without adding learning curves? So as a profession in design, we have to keep up with it, adopt and constantly lead with innovation. In essence, you know, designing for the enterprise brings interesting and unique challenges, and IBM is >>up for it. Well, you know, it sounds great to talk about just having a design that is super easy. And people get, um I'm wondering if you have any, any tips that you could have out there because, you know, I know myself. I'm always Frank, talk to other people, understand what they're doing. And sometimes it's like, Oh, well, today I learned this, and I wish I had learned this two years ago because, boy, you saved me, you know, an hour, a week of my time when I did this. And it's one of things I enjoy doing is trying to help people with short cuts or new ways of doing things. So we get set in our ways when we learn a new technology that tends to be where it fossilized in our brain, and it's upto look at something with fresh eyes and say, Oh, I got an update G. Maybe I should press that button and or float over and to understand what it does. Is there any any guidance that you can have? Is how do you make it simple and intuitive yet overcoming all of the legacy that we have when when we come into it with what interfaces were used? >>I do think that designers have this unique talent of being able to connect the dots, and that's our superpower. So in terms of tips I would take get to know your users get to know them really, really well, think about what exactly are their blockers and then think about technology and see how it can solve that over to connect the dots. So just to give an example. And I was talking about sort of design being broader than this interface design, you know in IBM started reacting to over 19. We need a lot of things. One of the things we did was we kinda defined solution to improve human computer interaction, very using sort of AI technologies like Watson Assistant and Children's Hospitals to help answer the huge number of questions coming in around 19. So from that standpoint, design is about beyond interfaces. And I feel if we take a step back and figure out, what problem are we trying to solve here? And how do we ensure that the users mental model off the things that they used to using in the everyday use, like 20 maps? How can you bring in those innovations back in the enterprise? That issue? >>Okay, you mentioned technologies are changing so fast, you know, AI containers loud. How's your team keeping up with all of this? You know, the pace of change and stop for a drop. You know, we're in S T I C D model these days. So what's the role of the designer in both? Keeping up with the new things and making sure that you know you're helping the user along the way. >>Fortunately, IBM we have a few advantages in having a broader organization called IBM Research. And IBM Research is a little bit forward facing, and they try to predict the uptake of technology that we have a little bit of a heads up on stage now that is a quantum computing, and such as Well, we got enough up there to as a designer. The inherent trade for designers to be curious and Barbara curiosity is to make sure that we learned, and we can combine them and instead of you bring in a sponge. And I think the fact that designers have this golden acid of empathy is very tender and used, and these superpowers to work with designers in other parts of the business, depending the doctor. But how can we not only solve? The problem is we see it but also solve the problems that are not visible. So the later needs of users. So I feel in a lot of different ways. Designers, you know, >>I >>have to be curious there to solve complex problems, and they have to keep up with technology. It's decimated. >>Yeah, I'm curious. It's exciting times. What excites you about the field of design these days? >>I had no Let me take a step back. Your question at the heart of it. I believe that I'm a designer because I believe we can design solutions that impacts people's lives. So in some ways we are adding to a value of human life, and that's what you mean to design and especially in enterprise design, is about that complexity if the messiness off, complex infrastructure and business use cases and localization and globalization is a really hairy problem. So I feel from an intellectual standpoint, this gives me a way to use my that are curious mind as well as my expertise to help solve this problem. So that's what drew me into >>delight. Excellent. Well, so much going on at IBM Think this week I want to give you the final word. What message do you want to share with IBM users, customers and business partners? >>Thank you. Stupid opportunity. Of course. I want to say thank you. Thank you for believing in us for being a North Star. You are The reason why we've invested so much in design and user experience really make our lives better and your willingness to sort of work alongside us every step of the way. It's really appreciate it. I mean, we tend to really feel that you see with us, so help us innovate, help us bring in great experiences that help you get your business are so on that note. If I could do a little shout out to want to be for our customers and prospects here who are listening in the joining on the user experience program. So we can co create experiences with you to solve your problems and hopefully build solutions that you love. Check out the link IBM that based on these experiences, the easy sign up and the second thing that popped a little bit of a user research like invite you to join in on the research about your journey here is that it's still involving field. I understand we're all going to challenges in adopting AI. Let's all learn, share and help each other and infusing AI in your enterprise. Thank you for being >>part of our innovation journey. Excellent. Well, thank you so much for sharing with our community. This update love the fusion of technology and design co creations. One of our favorite words when we talk about this part of the model that we do on the Cube. So thank you so much for joining us. Thank you. All right. Lots more coverage from IBM. Think 2020 The global experience. I'm stupid, man. And thank you for watching the Cube. >>Yeah, Yeah, yeah, yeah

so at the starting at the Google level we have data centers in four continents so we're in North America South America Asia and Europe of course we have a probably one of the world's largest global private networks with you know 13 undersea cables that are our own and hundreds of thousands of miles of dark fiber and and lit fiber that we you know we operate like I said probably one of the world's largest networks we have in in Europe were in five countries in Europe we're in two countries in Asia were in one country in South America and that's at the Google and in North America of course we have many many many sites across all of North America that's at the Google level now cloud has 19 regions that they operate in and 58 zones so each region of course has multiple zones in it you know we cover Google has presence in over 200 countries worldwide so really it is truly global operations so AccuWeather has been running an API service for the past ten years and we have lots of enterprise clients but we started to realize we are missing a whole business opportunity so we partnered with a eg and we created a new self-serve API developer portal that allows developers to go in there sign up on their own and get started and it's been great for us as far as like basically unlocking new revenue opportunities with api's because as you said everything is api's we also say everything is impacted by the weather so why not have everyone use a cue other api's to fulfill their weather needs I think if you look at what's going on and I talk to a lot of customers and developers and IT teams and clearly I think they are overwhelmed with the different things which are going on in this space so how do you make it simple how do you make it open how do you make it hybrid so you have flexibility of choices becoming top of the mind for many of the users now the lock in which many vendors currently provide it becomes very difficult for many of this users people moving around and meet the business requirements so I think having a solution and technology stack which is really understanding that complexity around that and making it simple in after dock I think is important so the focus well there's a theme in a couple different levels the broad theme is a cloud like no other because we've introduced a lot of new different features and products and programs we introduced anthos this morning which was really revolutionary way of using containers broadly Multi cloud hybrid cloud so it's from a product standpoint but it's also a cloud like no other because it's about the community that's here and it's truly a partnership with our customers and our partners about building this cloud together and we see the community as a really key part of that it's really core to Google's values around openness open-source technology and really embracing the broader community to build the cloud together well you know I think it continues to be continues to cooperate in the technical community very well and a couple of data points right one is around kubernetes that started what four or five years ago and that's going really strong but more importantly you know as the industry matures there are what I would call special interest groups that are starting to emerge in the kubernetes community one thing that we are playing very close attention to is the storage sake which is the ability to federated storage across multiple clouds and how do you do it seamlessly within the framework of googan IDs as opposed to trying to create a hack or a one-off that some vendors attempted to do so we try to take a very holistic view of it and make sure I mean the industry we are in it's time to drive volumes and volumes drive standards so I think we play very very close I think one of the biggest things that I'm seeing in this entire conference to date has been almost a mind shift change I mean this is conferences called Google Next and for a long time that's been one of their biggest problems they're focusing on what's next rather than what is today and they're inventing the future - almost at the expense of the present I think the big messaging today was both about reassuring enterprises that they're serious about this and also building a narrative where they're now talking about coming at this from a position of being able to embrace customers where they are and speak their language I think that that's transformative for Google and it's something I don't think that we've seen them do seriously at least not for very long I think that there's no question that this is a data game and we said early on John and the cube that big data war was going to be one in the cloud the data was going to reside in the cloud and having now machine intelligence applied to that data is what's giving companies competitive advantage and scale and economics I was struck by the stats that Google gave at the beginning of the keynote today Google in the last three years has spent 47 billion dollars on capital expenditures this year to date alone they've spent 13 billion dollars in capex and data centers 13 billion it would take IBM three and a half years to spend that much in capex it would take Oracle six years so from an economic standpoint in a scale standpoint Google Microsoft and Amazon are gonna win that game there's no question in my mind I am a student of AI I did my masters and PhD in that and I went through that change in my career because we had to collect the data match it and now analyze it and actually make a decision about it and we had a lot of false positives in some cases know something of which you don't want that either and what happened is our modeling capabilities became much better and we with this rich data and you actually tap into that data like you can go in there the data is there and disparate data we can pull in data from different sources and actually remove the outliers and make our decision real time right there we didn't have the processing capability we didn't have a place like pops up where global can scan and bring in data at hundreds of gigabytes of data that's messaging that you want to deal with at scale no matter where it is and process that that wasn't available for us now it's a real it's like a candy shop for technologists all the technologies in our hands and we want all these things so if you look at that category of that repetitive work AI can play a really amazing role in helping alleviate that mundane repetitive work and so you know great example of that as smart composed which hopefully you've used yep and so what we look at is things like say a salutation in an email where you have to think about who are you addressing how do you want to address them how do you spell their name we can alleviate that and make your composition much faster so the exciting announcement that we had today was that we are leveraging the Google assistant so the assistant that you're used to using at home via your home devices or on your phone and we're connecting that to your Google Calendar and so you'll be able to ask your assistant what you have on your schedule you know know what's ahead of you during your day and be able to do that on the go so you know I think in general one of the unique opportunities that we have with G suite is not only AI for taking these products that consumers know in love and bringing them into the enterprise and so we see that that helps people adopt and understand the products if it also just brings that like consumer grade simplicity and elegance in the design into the enterprise which brings joy to the workplace yeah so we've been working we've been hard at work over the last eight months since our last next can you believe that it's only been eight months and we last last year we were here announcing gk on prem this year we've rebranded CSP to anthos and enlarged it and we've moved it to GA so that's the big announcements in our spotlight we actually walk through all the pieces and gave three live demos as well as had two customers on stage and really the big difference in the eight months is while we're moving to GA now we've been working throughout this time with a set of customers we saw unprecedented demand for what we announced last year and we've had that privilege of working with customers to build a product which is what's unique really yeah and so we had two of those folks up on stage talking about the transformation that anthos is creating in their companies yeah absolutely I think particularly most of the larger enterprise accounts tend to have a multi vendor strategy for almost every category right including cloud which typically is one of the largest pens and you know it's it's typically what we see is people looking at certain classes or workloads running on particular clouds so it may be transactional systems running on AWS you know a lot of their more traditional enterprise workloads that were running on Windows servers potentially running on this year we see a lot of interest in data intensive sorts of analytics workloads potentially running on GCP and so I think larger companies tend to kind of look at it in terms of what's the best platform for the use case that they have in mind but in general you know I they are looking at multiple cloud vendors [Music] you

>> Narrator: Live from Miami, Florida, it's The Cube. Covering IBM's Data in AI Forum, brought to you by IBM. >> Welcome back to Miami, everybody. You're watching The Cube, the leader in live tech coverage. We're here covering the IBM Data and AI Forum. Scott Buckles is here to my right. He's the business unit executive at IBM and long time Cube alum, Daniel Hernandez is the Vice President of Data and AI group. Good to see you guys, thanks for coming on. >> Thanks for having us. >> Good to see you. >> You're very welcome. We're going to talk about data ops, kind of accelerating the journey to AI around data ops, but what is data ops and how does it fit into AI? Daniel, we'll start with you. >> There's no AI without data. You've got data science to help you build AI. You've got dev ops to help you build apps. You've got nothing to basically help you prepare data for AI. Data ops is the equivalent of dev ops, but for delivering AI ready data. >> So, how are you, Scott, dealing with this topic with customers, is it resonating? Are they leaning into it, or are they saying, "what?" >> No, it's absolutely resonating. We have a lot of customers that are doing a lot of good things on the data science side. But, trying to get the right data at the right people, and do it fast, is a huge problem. They're finding they're spending too much time prepping data, getting the data into the models, and they're not spending enough time failing fast with some of those models, or getting the models that they need to put in production into production fast enough. So, this absolutely resonates with them because I think it's been confusing for a long time. >> So, AI's scary to a lot of people, right? It's a complicated situation, right? And how do you make it less scary? >> Talk about problems that can be solved with it, basically. You want a better customer experience in your contact center, you want a similarly amazing experience when they're interacting with you on the web. How do you do that? AI is simply a way to get it done, and a way to get it done exceptionally well. So, that's how I like to talk about it. I don't start with here's AI, tell me what problems you can solve. Here are the problems you've got, and where appropriate, here's where AI can help. >> So what are some of your favorite problems that you guys are solving with customers. >> Customer and employee care, which, basically, is any business that does business has customers. Customer and employee care are huge a problem space. Catching bad people, financial crimes investigation is a huge one. Fraud, KYC AML as an example. >> National security, things like that, right? >> Yeah. >> You spend all your time with customers, what else? >> Well, customer experience is probably the one that we're seeing the most. The other is being more efficient. Helping businesses solve those problems quicker, faster. Try to find new avenues for revenue. How to cut costs out of their organization, out of their run time. Those are the ones that we see the most. >> So when you say customer experience, immediately chat bots jumps into my head. But I know we're talking more than, sort of a, transcends chat bots, but double click on customer experience, how are people applying machine intelligence to improve customer experience? >> Well, when I think of it, I think about if you call in to Delta, and you have one bad experience, or your airline, whatever that airline may be, that that customer experience could lead to losing that customer forever, and there used to be an old adage that you have one bad experience and you tell 10 people about it, you have a good one, and you tell one person, or two peoples. So, getting the right data to have that experience is where it becomes a challenge and we've seen instances where customers, or excuse me, organizations are literally trying to find the data on the screen while the customer is on hold. So, they're saying, "can I put you on hold?" and they're trying to go out and find it. So, being able to automate finding that data, getting it in the right hands, to the right people, at the right time, in moment's notice, is a great opportunity for AI and machine learning, and that's an example of how we do it. >> So, from a technical standpoint, Daniel, you guys have this IBM Cloud Pak for Data that's going to magic data virtualization thing. Let's take an example that Scott just gave us, think of an airline. I love my mobile app, I can do everything on my mobile app, except there are certain things I can't do, I have to go to the website. There are certain things I have to do with e-commerce that I have to go to the website that I can't do. Sometimes watching a movie, I can't order a movie from the app, I have to go to website, the URL, and order it there and put it on my watch list. So, I presume that there's some technical debt in each of those platforms, and there's no way to get the data from here, and the data from here talking to each other. Is that the kind of problem that you're solving? >> Yes, and in this particular case, you're actually touching on what we mean by customer and employee care everywhere. The interaction you have on your phone should be the same as the interaction and the kind of response on the web, which should be the same, if not better, when you're talking to a human being. How do you have the exceptional customer and employee care, all channels. Today, say the art is, I've got a specific experience for my phone, a specific experience for my website, a specific, different experience in my contact center. The whole work we're doing around Watson Assistant, and it as a virtual assistant, is to be that nervous system that underpins all channels, and with Cloud Pak for Data, we can deliver it anywhere. You want to run your contact center on an IBM Cloud? Great. You want to run it on Amazon, Azure, Google, your own private center, or everything in between, great. Cloud Pak for Data is how you get Watson Assistant, the rest of Watson and our data stack anywhere you want, so you can deliver that same consistent, amazing experience, all channels, anywhere. >> And I know the tone of my question was somewhat negative, but I'm actually optimistic, and there's a couple examples I'll give. I remember Bill Belichick one time said, "Agh, the weather, it can't ever get the weather right," this is probably five, six years ago. Actually, they do pretty well with the weather compared to 10 or 15 years ago. The other is fraud detection. In the last 10 years, fraud detection has become so much better in terms of just the time it takes to identify a fraud, and the number of false positives. Even in the last, I'd say, 12 to 18 months, false positives are way down. I think that's machine intelligence, right? >> I mean, if you're using business rules, they're not way down. They're still way up. If you're using more sophisticated techniques, that are depending upon the operational data to be trained, then they should be way down. But, there is still a lot of these systems that are based on old school business rules that can't keep up. They're producing alerts that, in many cases, are ignored, and because they're ignored, you're susceptible to bad issues. With, especially AI based techniques for fraud detection, you better have good data to train this stuff, which gets back to the whole data ops thing, and training those with good data, which data ops can help you get done. >> And a key part to data ops is the people and the process. It's not just about automating things and automating the data to get it in the right place. You have to modernize those business processes and have the right skills to be able to do that as well. Otherwise, you're not going to make the progress. You're not going to reap the benefits. >> Well, that was actually my next question. What about the people and the process? We were talking before, off camera, about our PA, and he's saying "pave the cow path." But sometimes you actually have to re-engineer the process and you might not have the skill set. So it's people and process, and then technology you lay in. And we've always talked about this, technology is always going to change. Smart technologists will figure it out. But, the people and the process, that's the hardest part. What are you seeing in the field? >> We see a lot of customers struggling with the people and process side, for a variety of reasons. The technology seems to be the focus, but when we talk to customers, we spend a lot of time saying, "well, what needs to change in your business process "when this happens? "How do those business rules need to change "so you don't get those false positives?" Because it doesn't matter at the end of the day. >> So, can we go back to the business rules thing? So, it sounds like the business rules are sort of an outdated, policy based, rigid sort of structure that's enforced no matter what. Versus machine intelligence, which can interpret situations on the fly, but can you add some color to that and explain the difference between what you call sort of business rules based versus AI based. >> So the AI based ones, in this particular case, probably classic statistical machine learning techniques, to do something like know who I am, right? My name is Danny Hernandez, if you were to Google Danny Hernandez, the number one search result is going to be a rapper. There is a rapper that actually just recently came out, he's not even that good, but he's a new one. A statistical machine learning technique would be able to say, "all right, given Daniel "and the context information I know about him, "when I look for Daniel Hernandez, "and I supplement the identity with that "contextual information, it means it's one of "the six that work at IBM." Right? >> Not the rapper. >> Not the rapper. >> Not the rapper. >> Exactly. I don't mind being matched with a rapper, but match me with a good rapper. >> All you've got to do is search Daniel Hernandez and The Cube and you'll find him. >> Ha, right. Bingo. Actually that's true. So, in any case, the AI based techniques basically allow you to isolate who I am, based on more features that you know about me, so that you get me right. Because if you can't even start there, with whom are you transacting, you're not going to have any hope of detecting fraud. Either that, or you're going to get false positives because you're going to associate me with someone that I'm not, and then it's just going to make me upset, because when you should be transacting with me, you're not because you're saying I'm someone I'm not. >> So, that ties back to what we were saying before, know you're customer and anti money laundering. Which, of course, was big, and still is, during the crypto craze. Maybe crypto is not as crazy, but that was a big deal when you had bitcoin at whatever it was. What are some practical applications for KYC AML that you're seeing in the field today? >> I think that what we see a lot of, what we're applying in my business is automating the discovery of data and learning about the lineage of that data. Where did it come from? This was a problem that was really hard to solve 18 months ago, because it took a lot of man power to do it. And as soon as you did it once, it was outdated. So, we've recently released some capabilities within Watson Knowledge Catalog that really help automate that, so that as the data continues to grow, and continues to change, as it always does, that rather than having two, three hundred business analysts or data stewards trying to go figure that out, machine learning can go do that for you. >> So, all the big banks are glomming on to this? >> Absolutely. >> So think about any customer onboarding, right? You better know who your customer is, and you better have provisions around anti money laundering. Otherwise, there's going to be some very serious downside risk. It's just one example of many, for sure. >> Let's talk about some of the data challenges because we talked a lot about digital, digital business, I've always said the difference between a business and a digital business is how they use data. So, what are some of the challenging issues that customers are facing, and particularly, incumbents, Ginni Rometty used the term a couple of events ago, and it might have even been World of Watson, incumbent disruptors, maybe that was the first think, which I thought was a very poignant term. So, what are some of the data challenges that these incumbents are facing, and how is IMB helping solve them? >> For us, one of them that we see is just understanding where their data is. There is a lot of dark data out there that they haven't discovered yet. And what impact is that having on their analytics, what opportunities aren't they taking advantage of, and what risks are they being exposed to by that being out there. Unstructured data is another big part of it as well. Structured data is sort of the easy answer to solving the data problem, >> [Daniel Hernandez] But still hard. >> But still hard. Unstructured data is something that almost feels like an afterthought a lot of times. But, the opportunities and risks there are equally, if not greater, to your business. >> So yeah, what you're saying it's an afterthought, because a lot of times people are saying, "that's too hard." >> Scott Buckles: Right. >> Forget it. >> Scott Buckles: Right. Right. Absolutely. >> Because there's gold in them there hills, right? >> Scott Buckles: Yeah, absolutely. >> So, how does IBM help solve that problem? Is it tooling, is it discovery tooling? >> Well, yeah, so we recently released a product called InstaScan, that helps you to go discover unstructured data within any cloud environment. So, that was released a couple months ago, that's a huge opportunity that we see where customers can actually go and discover that dark data, discover those risks. And then combine that with some of the capabilities that we do with structured data too, so you have a holistic view of where your data is, and start tying that together. >> If I could add, any company that has any operating history is going to have a pretty complex data environment. Any company that wants to employ AI has a fundamental choice. Either I bring my AI to the data, or I bring my data to the AI. Our competition demand that you bring your data to the AI, which is expensive, hard, often impossible. So, if you have any desire to employ this stuff, you had better take the I'm going to bring my AI to the data approach, or be prepared to deal with a multi-year deployment for this stuff. So, that principle difference in how we think about the problem, means that we can help our customers apply AI to problem sets that they otherwise couldn't because they would have to move. And in many cases, they're just abandoning projects all together because of that. >> So, now we're starting to get into sort of data strategy. So, let's talk about data strategy. So, it starts with, I guess, understanding the value of your data. >> [Daniel Hernandez] Start with understanding what you got. >> Yeah, what data do I have. What's the value of that data? How do I get to that data? You just mentioned you can't have a strategy that says, "okay, move all the data into some God box." >> Good luck. >> Yeah. That won't work. So, do customers have coherent data strategies? Are they formulating? Where are we on that maturity curve? >> Absolutely, I think the advent of the CDO role, as the Chief Data Officer role, has really helped bring the awareness that you have to have that enterprise data strategy. >> So, that's a sign. If there's a CDO in the house. >> There's someone working on enterprise, yeah, absolutely. >> So, it's really their role, the CDO's role, to construct the data strategy. >> Absolutely. And one of the challenges that we see, though, in that, is that because it is a new role, is like going back to Daniel's historical operational stuff, right? There's a lot of things you have to sort out within your data strategy of who owns the data, right? Regardless of where it sits within an enterprise, and how are you applying that strategy to those data assets across the business. And that's not an easy challenge. That goes back to the people process side of it. >> Well, right. I bet you if I asked Jim Cavanaugh what's IBM's data strategy, I bet you he'd have a really coherent answer. But I bet you if I asked Scott Hebner, the CMO of the data and AI group, I bet you I'd get a somewhat different answer. And so, there's multiple data strategies, but I guess it's (mumbles) job to make sure that they are coherent and tie in, right? >> Absolutely. >> Am I getting this? >> Absolutely. >> Quick study. >> So, what's IBM's data strategy? (laughs) >> Data is good. >> Data is good. Bring AI to the data. >> Look, I mean, data and AI, that's the name of the business, that's the name of the portfolio that represents our philosophy. No AI without data, increasingly, not a lot of value of data without AI. We have to help our customers understand this, that's a skill, education, point of view problem, and we have to deliver technology that actually works in the wild, in their environment, not as we want them to be, but as they are. Which is often messy. But I think that's our fun. It's the reason we've been here for a while. >> All right, I'll give you guys a last word, we got to run, but both Scott and Daniel, take aways from the event today, things that you're excited about, things that you learned. Just give us the bumper sticker. >> For me, you talk about whether people recognize the need for a data strategy in their role. For me, it's people being pumped about that, being excited about it, recognizing it, and wanting to solve those problems and leverage the capabilities that are out there. >> We've seen a lot of that today. >> Absolutely. And we're at a great time and place where the capabilities and the technologies with machine learning and AI are applicable and real, that they're solving those problems. So, I think that gets everybody excited, which is cool. >> Bring it home, Daniel. >> Excitement, a ton of experimentation with AI, some real issues that are getting in the way of full-scale deployments, a methodology data ops, to deal with those real hardcore data problems in the enterprise, resonating, a technology stack that allows you to implement that as a company is, through Cloud Pak for Data, no matter where they want to run is what they need, and I'm happy we're able to deliver it to them. >> Great. Great segment, guys. Thanks for coming. >> Awesome. Thank you. >> Data, applying AI to that data, scaling with the cloud, that's the innovation cocktail that we talk about all the time on The Cube. Scaling data your way, this is Dave Vellante and we're in Miami at the AI and Data Forum, brought to you by IBM. We'll be right back right after this short break. (upbeat music)

>> Narrator: Live from Miami, Florida. It's theCUBE. Covering IBM's data and AI forum. Brought to you by IBM. >> Welcome back to the port of Miami everybody. This is theCube, the leader in live tech coverage. We're here covering the IBM AI and data forum. Of course, the centerpiece of IBM's AI platform is Watson. Beth Smith is here, she's the GM of IBM Watson. Beth, good to see you again. >> You too. Always good to be with theCUBE. >> So, awesome. Love it. So give us the update on Watson. You know, it's beyond Jeopardy. >> Yeah, yeah. >> Oh, wow. >> That was a long time ago now. (laughs) >> Right, but that's what a lot of people think of, when they think of Watson. What, how should we think about Watson today? >> So first of all, focus Watson on being ready for business. And then, a lot of people ask me, "So what is it?" And I often describe it as a set of tools, to help you do your own AI and ML. A set of applications that are AI applications. Where we have prebuilt it for you, around a use case. And there is examples where it gets embedded in a different application or system that may have existed already. In all of those cases, Watson is here, tuned to business enterprise, how to help people operational-wise, AI. So they can get the full benefit, because at the end of the day it's about those business outcomes. >> Okay, so the tools are for the super geeks, (Beth laughs) who actually want to go in and build the real AI. >> (laughs) That's right, that's right. >> The APPS are, okay. It's prebuilt, right? Go ahead and apply it. >> That's right. >> And the embedded is, we don't even know we're using it, right? >> That's right, or you may. Like, QRadar with Watson has an example of using Watson inside of it. Or, OpenPages with Watson. So sometimes you know you're using it. Sometimes you don't. >> So, how's the mix? I mean, in terms of the adoption of Watson? Are there enough like, super techies out there, who are absorbing this stuff? Or is it mostly packaged APPS? Is it a mix? >> So it is a mix, but we know that data science skills are limited. I mean, they're coveted, right? And so those are the geeks, as you say, that are using the tool chain as a part of it. And we see that in a lot of customers and a lot of industries around the world. And then from a packaged APP standpoint, the biggest use case of adoption is really around customer care, customer service, customer engagement. That kind of thing. And we see that as well. All around the world, all different industries. Lots of great adoption. Watson Assistant is our flagship in that. >> So, in terms of, if you think about these digital initiatives, we talked about digital transformation, >> Yup. >> Last few years, we kind of started in 2016 in earnest, it's real when you talk to customers. And there was a ton of experimentation going on. It was almost like spaghetti. Throw against the wall and see what sticks. Are you seeing people starting to place their bets on AI, Narrowing their scope, and really driving you know, specific business value now? >> Beth: Yeah. >> Or is it still kind of all over the place? >> Well, there's a lot of studies that says about 51% or so still stuck in experimentation. But I would tell you in most of those cases even, they have a nice pilot that's in production, that's doing a part of the business. So, 'cause people understand while they may be interested in the sexiness of the technology, they really want to be able to get the business outcomes. So yes, I would tell 'ya that things have kind of been guided, focused towards the use cases and patterns that are the most common. You know, and we see that. Like I mentioned, customer care. We see it in, how do you help knowledge workers? So you think of all those business documents, and papers and everything that exists. How do you assist those knowledge workers? Whether or not it's an attorney or an engineer, or a mortgage loan advisor. So you see that kind of use case, and then you see customers that are building their own. Focused in on, you know, how do they optimize or automate, or predict something in a particular line of business? >> So you mentioned Watson Assistant. So tell us more about Watson Assistant, and how has that affected adoption? >> So Watson Assistant as I said, it is our flagship around customer care. And just to give you a little bit of a data point, Watson Assistant now, through our public cloud, SaaS version, converses with 82 million end users a month. So it's great adoption. And this is, this is enabling customers. Customers of our customers, to be able to get self-service help in what they're doing. And Watson Assistant, you know, a lot of people want to talk about it being a chat bot. And you can do simple chat bots with it. But it's to sophisticated assistance as well. 'Cause it shows up to do work. It's there to do a task. It's to help you deal with your bank account, or whatever it is you're trying to do, and whatever company you're interacting with. >> So chat bots is kind of a, (laughs) bit of a pejorative. But you're talking about digital systems, it's like a super chat bot, right? >> Beth: Yeah. I saw a stat the other day that there's going to be, by I don't know, 2025, whatever. There's going to be more money spent on chat bot development, or digital assistance, than there is on mobile development. And I don't know if that's true or not, >> Beth: Mhm, wow. But it's kind of an interesting thing. So what are you seeing there? I mean, again I think chat bots, people think, oh, I got to talk into a bot. But a lot of times you don't know you're, >> Beth: That's right. >> so they're getting, they're getting better. I liken it to fraud detection. You know, 10 years ago fraud detection was like, six months later you'll, >> Right. >> you'll get a call. >> Exactly. >> And so chat bots are just going to get better and better and better, and now there's this super category that maybe we can define here. >> That's right. >> What is that all about? >> That's right. And actually I would tell you, they kind of, they can become the brain behind something that's happening. So just earlier today I was, I was with a customer and talking about their email CRM system, and Watson Assistant is behind that. So chat bots aren't just about what you may see in a little window. They're really about understanding user intent, guiding the user through what they're trying to either find out or do, and taking the action as a part of it. And that's why we talk about it being more than chat bots. 'Cause it's more than a FAQ interchange. >> Yes, okay. So it's software, >> Beth: Yes. >> that actually does, performs tasks. >> Beth: Yes. >> Probably could call other software, >> Beth: Absolutely. >> to actually take action. >> That's right. >> I mean, I see. We think of this as systems of agency, actually. Making, sort of, >> That's right. >> decisions and then I guess, the third piece of that is, having some kind of human interaction, where appropriate, right? >> That's right. >> What do you see in terms of, you know, infusing humans into the equation? >> So, well a couple of things. So one of the things that Watson Assistant will do, is if it realizes that it's not the expert on whatever it is, then it will pass over to an expert. And think of that expert as a human agent. And while it's doing that, so you may be in the queue, because that human person is tied up, you can continue to do other things with it, while you're waiting to actually talk to the person. So that's a way that the human is in the loop. I would tell you there's also examples of how the agents are being assisted in the background. So they have the interaction directly with the user, but Watson Assistant is helping them, be able to get to more information quicker, and narrow in on what the topic is. >> So you guys talk about the AI ladder, >> Beth: Mhm. >> Sort of, Rob talked about that this morning. My first version of the AI ladder was building blocks. It was like data and AI analytics, ML, and then AI on top of that. >> Beth: Yup. >> I said AI. Data and IA. >> Beth: Yup. >> Information Architecture. Now you use verbs. Sort of, to describe it. >> Beth: Yup. Which is actually more powerful. Collect, organize, analyze and infuse. Now infuse is like the Holy Grail, right? 'Cause that's operationalizing and being able to scale AI. >> Beth: That's right. >> What can you tell us about how successful companies are infusing AI, and what is IBM doing to help them? >> So, I'm glad you picked up first of all, that these are verbs and it's about action. And action leads to outcome, which is, I think, critical. And I would also tell you yes, infuse is, you know, the Holy Grail of the whole thing. Because that's about injecting it into business processes, into workflows, into how things are done. So you can then see examples of how attorneys may be able to get through their legal prep process in just a few minutes, versus 10, 15 hours on certain things. You can see conversion rates of, from a sales standpoint, improve significantly. A number of different things. We've also got it as a part of supply chain optimization, understanding a little bit more about both inventory, but also where the goods are along the way. And particularly when you think about a very complicated thing, there could be a lot of different goods in various points of transit. >> You know, I was sort of joking. Not joking, but mentioning Jeopardy at first. 'Cause a lot of people associate Watson with Jeopardy. >> Beth: Right. >> I can't remember the first time I saw that. It had to be the mid part of the last decade. What was it? >> Beth: February of 2011. >> 2011, okay I thought I even saw demos before that. I'm actually sure I did. Like in, back in some lab in IBM. And of course, the potential like, blew your mind. >> Right. >> I suspect you guys didn't even know what you had at the time. You were like, "Okay, we're going to go change the world." And you know, when you drive up and down 101 in Silicone Valley, it's like, "Oh, Watson this, Watson that." You know, you get the consumer guys, doing facial recognition, ad serving. You know, serving up fake news, you know. All kinds of applications. But IBM started to do something different. You're trying to really change business. Did you have any clue as to what you had at the time? And then how much of a challenge you were taking on, and then bring us to where we are now, and what do you see as a potential for the next 10 years? >> So, of course we had a clue. So let me start there. (Dave laughs) But with that, I think the possibilities of it weren't completely understood. There's no question in my mind about that. And what the early days were, were understanding, okay, what is that business application? What's the pattern that's going to come about as a part of it? And I think we made tremendous progress on that along the way. I would tell you now, you mentioned operationalizing stuff, and you know, now it's about, how do we help companies have it more throughout their company? Through different lines of business, how does it tie to various things that are important to us? And so that brings in things like trust, explainablity, the ethics of what it's doing. Bias detection and mitigation. And I actually believe a lot of that, and the operationalizing it within the processes, is where we're going to head, going forward. Of course there'll continue to be advancements on the features and the capabilities, but it's going to be about that. >> Alright, I'm going to ask you the it's depends question. (Beth laughs) So I know that's your answer, but at the macro, can machines make better diagnosis than doctors today, and if not, when will they be able to, in your view? >> So I would actually tell you that today they cannot, but what they can do is help the doctor make a better diagnosis than she would have done by herself. And because it comes back to this point of, you know, how the machine can process so much information, and help the expert, in this case the doctor's the expert, it could be an attorney, it could be an engineer, whatever. Help that expert be able to augment the knowledge that he or she has as a part of it. So, and that's where I think it is. And I think that's where it will be for my lifetime. >> So, there's no question in your mind that machines today, AI today, is helping make better diagnosis, it's just within augmented or attended type of approach. >> Absolutely. >> And I want to talk about Watson Anywhere. >> Beth: Okay, great. >> So we saw some discussion in the key notes and some demos. My understanding is, you could bring Watson Anywhere, to the data. >> That's right. >> You don't have to move the data around. Why is that important? Give us the update on Watson Anywhere. >> So first of all, this is the biggest requirement I had since I joined the Watson team, three and a half years ago. Was please can I have Watson on-prem, can I have Watson in my company data center, etcetera. And you know, we needed to instead, really focus in on what these patterns and use cases were, and we needed some help in the platform. And so thanks to Cloud Pak for data, and the underlying Red Hat OpenShift and container platform, we now are enabled to truly take Watson anywhere. So you can have it on premise, you can have it on the other public clouds, and this is important, because like you said, it's important because of where your data is. But it's also important because the workloads of today and tomorrow are very complex. And what's on cloud today, may be on premise tomorrow, may be in a different cloud. And as that moves around, you also want to protect the investment of what you're doing, as you have Watson customize for what your business needs are. >> Do you think you timed it right? I mean, you kind of did. All this talk about multicloud now. You really didn't hear much about it four or five years ago. For awhile I thought you were trying to juice your cloud business. Saying, "You want, if you want Watson, you got to go to the IBM cloud." Was there some of that, or was it really just, "Hey, now the timing's right." Where clients are demanding it, and hybrid and multicloud and on-prem situations? >> Well look, we know that cloud and AI go hand in hand. So there was a lot of positive with that. But it really was this technology point, because had I taken it anywhere three and a half years ago, what would've happened is, every deployment would've been a unique environment, a unique stack. We needed to get to a point that was a modern day, you know, infrastructure, if you will. And that's what we get now, with a container based platform. >> So you're able to scale it, such that every instance isn't a snowflake, >> That's right. >> that requires customization. >> That's right. So then I can invest in the enhancements to the actual capabilities it is there to do, not supporting multiple platform instantiations, under the covers. >> Well, okay. So you guys are making that transparent to the customer. How much of an engineering challenge is that? Can you share that with us? You got to run on this cloud, on that cloud, or on forever? >> Well, now because of Cloud Pak for data, and then what we have with OpenShift and Kubernetes and containers, it becomes, well, you know, there's still some technical work, my engineering team would tell you it was a lie. But it's simple now, it's straightforward. It's a lot of portability and flexibility. In the past, it would've been every combination of whatever people were trying to do, and we would not have had the benefit of what that now gives you. >> And what's the technical enable there? Is it sort of open API's? Architecture that allows for the interconnectivity? >> So, but inside of Watson? Or the overall platform? >> The overall platform. >> So I would say, it's been, at it's, at it's core it's what containers bring. >> Okay, really. So it's that, it's that. It's the marriage of your tech, >> Yeah. >> with the container wave. >> That's right. That's right. Which is why the timing was critical now, right? So you go back, yes they existed, but it really hadn't matured to a point of broad adoption. And that's where we are now. >> Yeah, the adoption of containers, Kubernetes, you know, micro services. >> Right, exactly. Now it's on a very steep curve. >> Exactly. >> Alright, give your last word on, big take away, from this event. What do you hearing, you know, what are you, some of the things you're most excited about? >> So first of all, that we have all of these clients and partners here, and all the buzz that you see. And that we've gotten. And then the other thing that I would tell you is, the great client examples. And what they're bragging on, because they are getting business outcomes. And they're getting better outcomes than they thought they would achieve. >> IBM knows how to throw an event. (Beth laughs) Beth, thanks so much for coming to theCUBE. >> Thank you, good to >> Appreciate it. >> see you again. >> Alright, great to see you. Keep it right there everybody, we'll be back. This is theCUBE live, from the IBM Data Forum in Miami, we'll be right back. (upbeat instrumental music)

>>live from Miami, Florida. It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, Everybody. You're watching the Cube, the leader in live tech coverage. We're here covering the IBM data and a I form. Rob Thomas is here. He's the general manager for data in A I and I'd be great to see again. >>Right. Great to see you here in Miami. Beautiful week here on the beach area. It's >>nice. Yeah. This is quite an event. I mean, I had thought it was gonna be, like, roughly 1000 people. It's over. Sold or 17. More than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands on presentations. Tell us a little bit more about how this event has evolved. >>It started as a really small training event, like you said, which goes back five years. And what we saw those people, they weren't looking for the normal kind of conference. They wanted to be hands on. They want to build something. They want to come here and leave with something they didn't have when they arrived. So started as a little small builder conference and now somehow continues to grow every year, which were very thankful for. And we continue to kind of expand at sessions. We've had to add hotels this year, so it's really taken off >>you and your title has two of the three superpowers data. And of course, Cloud is the third superpower, which is part of IBMs portfolio. But people want to apply those superpowers, and you use that metaphor in your your keynote today to really transform their business. But you pointed out that only about a eyes only 4 to 10% penetrated within organizations, and you talked about some of the barriers that, but this is a real appetite toe. Learn isn't there. >>There is. Let's go talk about the superpower for a bit. A. I does give employees superpowers because they can do things now. They couldn't do before, but you think about superheroes. They all have an origin story. They always have somewhere where they started and applying a I an organization. It's actually not about doing something completely different. It's about extenuating. What you already d'oh doing something massively better. That's kind of in your DNA already. So we're encouraging all of our clients this week like use the time to understand what you're great at, what your value proposition is. And then how do you use a I to accentuate that? Because your superpower is only gonna last if it's starts with who you are as a company or as a >>person who was your favorite superhero is a kid. Let's see. I was >>kind of into the whole Hall of Justice. Super Superman, that kind of thing. That was probably my cartoon. >>I was a Batman guy. And the reason I love that movie because all the combination of tech, it's kind of reminds me, is what's happening here today. In the marketplace, people are taking data. They're taking a I. They're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's a There's an issue with the quality of data and and there's a there's a skills gap as well. So let's let's start with the data quality problem described that problem and how are you guys attacking it? >>You're a I is only as good as your data. I'd say that's the fundamental problem and organization we worked with. 80% of the projects get slowed down or they get stopped because the company has a date. A problem. That's why we introduce this idea of the A i ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports a I. So how they collect their data, organize their data, analyze their data and ultimately begin to infuse a I into business processes soap. Every organization needs to climb that ladder, and they're all different spots. So for someone might be, we gotta focus on organization a data catalogue. For others, it might be we got do a better job of data collection data management. That's for every organization to figure out. But you need a methodical approach to how you attack the data problem. >>So I wanna ask you about the Aye aye ladder so you could have these verbs, the verbs overlay on building blocks. I went back to some of my notes in the original Ai ai ladder conversation that you introduced a while back. It was data and information architecture at the at the base and then building on that analytics machine learning. Aye, aye, aye. And then now you've added the verbs, collect, organized, analyze and infused. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model, >>I would think of it as building blocks and the methodology, which is you got to decide. Do wish we focus on our data collection and doing that right? Is that our weakness or is a data organization or is it the sexy stuff? The Aye. Aye. The data science stuff. We just This is just a tool to help organizations organize themselves on what's important. I asked every company I visit. Do you have a date? A strategy? You wouldn't believe the looks you get when you ask that question, you get either. Well, she's got one. He's got one. So we got seven or you get No, we've never had one. Or Hey, we just hired a CDO. So we hope to have one. But we use the eye ladder just as a tool to encourage companies to think about your data strategy >>should do you think in the context I want follow up on that data strategy because you see a lot of tactical data strategies? Well, we use Data Thio for this initiative of that initiative. Maybe in sales or marketing, or maybe in R and D. Increasingly, our organization's developing. And should they develop a holistic data strategy, or should they trying to just get kind of quick wins? What are you seeing in the marketplace? >>It depends on where you are in your maturity cycle. I do think it behooves every company to say We understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities gonna be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable tohave an overarching data strategy, and I recommended companies think about a hub and spokes model for this. Have a centralized chief date officer, but your business units also need a cheap date officer. So strategy and one place execution in another. There's a best practice to going about this >>the next you ask the question. What is a I? You get that question a lot, and you said it's about predicting, automating and optimizing. Can we unpack that a little bit? What's behind those three items? >>People? People overreact a hype on topics like II. And they think, Well, I'm not ready for robots or I'm not ready for self driving Vehicles like those Mayor may not happen. Don't know. But a eyes. Let's think more basic it's about can we make better predictions of the business? Every company wants to see a future. They want the proverbial crystal ball. A. I helped you make better predictions. If you have the data to do that, it helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do you software to automate that there's about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far off thing. We're talking about basics better predictions, better automation, better optimization. >>Now interestingly, use the term black magic because because a lot of a I is black box and IBM is always made a point of we're trying to make a I transparent. You talk a lot about taking the bias out, or at least understanding when bias makes sense. When it doesn't make sense, Talk about the black box problem and how you're addressing. >>That starts with one simple idea. A eyes, not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box. With Watson, we have a few things. We've got tools for clients that want to build their own. Aye, aye, to think of it as a tool box you can choose. Do you want a hammer and you want a screwdriver? You wanna nail you go build your own, aye, aye. Using Watson. We also have applications, so it's basically an end user application that puts a I into practice things like Watson assistant to virtually no create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So, aye, aye, for Watson is about tools. You want to build your own applications if you want to consume an application, but we've also got in bed today. I capability so you can pick up Watson and put it inside of any software product in the >>world. He also mentioned that Watson was built with a lot of of of, of open source components, which a lot of people might not know. What's behind Watson. >>85% of the work that happens and Watson today is open source. Most people don't know that it's Python. It's our it's deploying into tensorflow. What we've done, where we focused our efforts, is how do you make a I easier to use? So we've introduced Auto Way. I had to watch the studio, So if you're building models and python, you can use auto. I tow automate things like feature engineering algorithm, selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist could do their job better >>so again come back to a adoption. We talked about three things. Quality, trust and skills. We talked about the data quality piece we talked about the black box, you know, challenge. It's not about skills you mention. There's a 250,000 person Gap data science skills. How is IBM approaching how our customers and IBM approaching closing that gap? >>So think of that. But this in basic economic terms. So we have a supply demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me, who helped a client get to their first big win with a I. It's that simple. We go in for 4 to 6 weeks. It's an elite team. It's not a long project we're gonna get you do for your success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto. Aye, aye. But we also do things like using a eye for building data catalogs, metadata creation data matching so making that data prep process automated through A. I can also help that supply demand. Miss Max. The way that you solve this is we put skills on the field, help clients, and we do a lot of automation in software. That's how we can help clients navigate this. So the >>data science elite team. I love that concept because way first picked up on a couple of years ago. At least it's one of the best freebies in the business. But of course you're doing it with the customers that you want to have deeper relationships with, and I'm sure it leads toe follow on business. What are some of the things that you're most proud of from the data science elite team that you might be able to share with us? >>The clients stories are amazing. I talked in the keynote about origin stories, Roll Bank of Scotland, automating 40% of their customer service. Now customer SATs going up 20% because they put their customer service reps on those hardest problems. That's data science, a lead helping them get to a first success. Now they scale it out at Wonderman Thompson on stage, part of big W P p big advertising agency. They're using a I to comb through customer records they're using auto Way I. That's the data science elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own. Once we left, we got countless examples where this team has gone in for very short periods of time. And clients don't talk about this because they have to talk about it cause they're like, we can't believe what this team did. So we're really excited by the >>interesting thing about the RVs example to me, Rob was that you basically applied a I to remove a lot of these mundane tasks that weren't really driving value for the organization. And an R B s was able to shift the skill sets. It's a more strategic areas. We always talk about that, but But I love the example C. Can you talk a little bit more about really, where, where that ship was, What what did they will go from and what did they apply to and how it impacted their businesses? A improvement? I think it was 20% improvement in NPS but >>realizes the inquiry's they had coming in were two categories. There were ones that were really easy. There were when they were really hard and they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said, we can automate all the easy stuff, we can put all of our people in the hardest things customer sat shot through the roof. Now what is a virtual agent do? Let's decompose that a bit. We have a thing called intent classifications as part of Watson assistant, which is, it's a model that understands customer a tent, and it's trained based on the data from Royal Bank of Scotland. So this model, after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent, because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software, and you can only do that with things like a I. >>And what is the role of the human element in that? How does it interact with that virtual agent. Is it a Is it sort of unattended agent or is it unattended? What is that like? >>So it's two pieces. So for the easiest stuff no humans needed, we just go do that in software for the harder stuff. We've now given the RVs, customer service agents, superpowers because they've got Watson assistant at their fingertips. The hardest thing for a customer service agent is only finding the right data to solve a problem. Watson Discovery is embedded and Watson assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff the humans don't want to do in the first place. >>I'm gonna shift gears a little bit. Talk about, uh, red hat in open shift. Obviously huge acquisition last year. $34 billion Next chapter, kind of in IBM strategy. A couple of things you're doing with open shift. Watson is now available on open shifts. So that means you're bringing Watson to the data. I want to talk about that and then cloudpack for data also on open shifts. So what has that Red had acquisition done for? You obviously know a lot about M and A but now you're in the position of you've got to take advantage of that. And you are taking advantage of this. So give us an update on what you're doing there. >>So look at the cloud market for a moment. You've got around $600 million of opportunity of traditional I t. On premise, you got another 600 billion. That's public clouds, dedicated clouds. And you got about 400 billion. That's private cloud. So the cloud market is fragmented between public, private and traditional. I t. The opportunity we saw was, if we can help clients integrate across all of those clouds, that's a great opportunity for us. What red at open shift is It's a liberator. It says right. Your application once deployed them anywhere because you build them on red hot, open shift. Now we've brought cloudpack for data. Our data platform on the red hot open shift certified on that Watson now runs on red had open shift. What that means is you could have the best data platform. The best Aye, Aye. And you can run it on Google. Eight of us, Azure, Your own private cloud. You get the best, Aye. Aye. With Watson from IBM and run it in any of those places. So the >>reason why that's so powerful because you're able to bring those capabilities to the data without having to move the date around It was Jennifer showed an example or no, maybe was tail >>whenever he was showing Burt analyzing the data. >>And so the beauty of that is I don't have to move any any data, talk about the importance of not having Thio move that data. And I want I want to understand what the client prerequisite is. They really take advantage of that. This one >>of the greatest inventions out of IBM research in the last 10 years, that hasn't gotten a lot attention, which is data virtualization. Data federation. Traditional federation's been around forever. The issue is it doesn't perform our data virtualization performance 500% faster than anything else in the market. So what Jennifer showed that demo was I'm training a model, and I'm gonna virtualized a data set from Red shift on AWS and on premise repositories a my sequel database. We don't have to move the data. We just virtualized those data sets into cloudpack for data and then we can train the model in one place like this is actually breaking down data silos that exist in every organization. And it's really unique. >>It was a very cool demo because what she did is she was pulling data from different data stores doing joins. It was a health care application, really trying to understand where the bias was peeling the onion, right? You know, it is it is bias, sometimes biases. Okay, you just got to know whether or not it's actionable. And so that was that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? >>Start using cloudpack for data. We've got something on the Web called cloudpack experiences. Anybody can go try this in less than two minutes. I just say go try it. Because cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call scheme a folding. So a skiing version of the schema from your source writing compact for data. This is like instant access to your data. >>It sounds like magic. OK, last question. One of the big takeaways You want people to leave this event with? >>We are trying to inspire clients to give a I shot. Adoption is 4 to 10% for what is the largest economic opportunity we will ever see in our lives. That's not an acceptable rate of adoption. So we're encouraging everybody Go try things. Don't do one, eh? I experiment. Do Ah, 100. Aye, aye. Experiments in the next year. If you do, 150 of them probably won't work. This is where you have to change the cultural idea. Ask that comes into it, be prepared that half of them are gonna work. But then for the 52 that do work, then you double down. Then you triple down. Everybody will be successful. They I if they had this iterative mindset >>and with cloud it's very inexpensive to actually do those experiments. Rob Thomas. Thanks so much for coming on. The Cuban great to see you. Great to see you. All right, Keep right, everybody. We'll be back with our next guest. Right after this short break, we'll hear from Miami at the IBM A I A data form right back.

>> Announcer: From Seattle, Washington it's The Cube covering AWS Imagine. Brought to you by Amazon Web Services. >> Welcome back everybody, Jeff Frick here with The Cube. We're here in downtown Seattle at the AWS Imagine Education Conference. It's the second year of the conference. It's really successful so much now they have another education conference, excuse me, Imagine Conference coming up for nonprofits, but this is the education one. About 800 people and we're excited to have, I think they had representatives from like 40 countries here. It's amazing, such a small conference with such great global representation. We've got our first guest, all the way from Valencia, Spain. He is Inaki Bilbao Estrada and the Vice Chancellor for Internationalization and Innovation at the CEU Universidad Cardenal Herrera. It's a mouthful, welcome. >> Thank you very much. >> So first off, impressions from the show, from the keynotes this morning. >> It was very impressive, the keynote by Andrew Co Intersession by Amazon. We were impressed, we were included in the keynote and we are very proud of having been included in the keynote for our Alexa skill. >> Great, so before we get into kind of what they talked about, let's back up a few steps in terms of what you are trying to accomplish as an institution. So give us a little bit of background on the college, how big it was, and kind of what was going on and what you wanted to really do differently. >> We are a Spanish University. We belong with CEU San Pablo Foundation which owns three universities in Spain, Barcelona, Madrid, and Valencia. We are a not for profit universities and in Valencia, in our case, we are very proud that we used to be a local university with only 300 international students eight years ago and right now we have reached 2500 international students which represents around 30-33% of the population of the university. We are right now 8000 undergraduate students and 3000 graduate students. >> So that's pretty amazing. So as you said, you were really kind of a regional university and you decided you wanted more international students. Why did you want more international students and then once you made that goal, what were some of the major objectives at the beginning of this process or problems that you had to overcome? >> It was a trend in higher education institutions but for us it was very important for two reasons, one the sustainability of the university, but also and I think the main reason is that we wanted to have our students to have a global experience. We wanted to become a global university based in Valencia, but we have right now more than 80 countries represented on campus. >> Wow, so what were some of the big hurdles that you saw that were going to get in the way of attracting more of these international students? >> So it was very important for us to adapt all our processes to our students. For this we have a very helpful firm partnered on campus. It was the IT department with Jose Roch in charge of this department and through technology we have been able to escalate and automate, get the automation of all of this process in order to reach bigger number of international students. So we have adapted all the processes to the needed of our international students, our new population of international students. >> Right, so you were highlighted today for a very specific thing, for a very specific device, which is Alexa, and voice as an interface and we saw some of the Alexa stuff last year, in terms of the kids asking it, you know, when is my test, is my homework due, these types of things, but you guys are actually taking it to the next level. Explain to the folks what you guys have done with Alexa. >> So we have used Alexa to introduce a virtual assistant for all our students national and international students and one other things that have been highlighted in the keynote is that is not only in English, but also in Spanish. Like this we are covering the two most speaked language on campus, English and Spanish. >> So it's bi, so you've got a bilingual Alexa in the room. >> Yeah, yeah, yeah. So for us it was very important as explained before that technologies had been asked to cover all the population of students, not only part of them. >> Right and using English is kind of universal language, regardless of what their native tongue is. >> Yeah, yeah. >> So did you have to build all this from scratch? How much was Amazon helping you to do the English to Spanish translation, was it written in Spanish, how did some of those logistics work out? >> So we began six months ago with the project with the help of Amazon, they were very, very, very helpful for us. With Ana Cabez and Juan Manuel Gomez from the UK team of Amazon and they guided us how to develop the Alexa skills for the goals that we set with them, what we wanted to achieve with the virtual assistant for our students. >> And yeah, so the skills are the things that you actually write, so how many different skills did you write especially for your students? >> So we, what we are doing is to build only one, but we are integrating all the services in one only skill. So we are integrating services related with what my next assignment on Blackboard, which are my grades, how can I book a room in the library or another space of the university, locations of the different services or professors of the university. We are integrating a lot of services, but in one skill because we don't want the students to have to switch between skills. >> Jeff: Right, right. >> So we're aiming to have one virtual assistant for the students in only one skill. >> So that's interesting, I didn't even think about all the integration points that you have. But you've got integration points in all these other systems. The room booking services, the library services, Blackboard and the other educational services. >> The learning management system. >> So how many points of integration are there? >> A lot we are working right now, we are focusing around five, seven integration points, because also we are integrating it with our CRM in order to have personalized message to different segments of our students, depending of if they are due to get some documentation to the registrar office. We think that integration with CRM allows us to give personalized message and notification to our students depending on the situations. >> Jeff: Right. >> So it's not a general notification for all of the students on campus. >> Right, that's awesome. Again, highlighted in the keynote really I think is the first kind of bilingual implementation of Alexa. So that's terrific. I want to shift gears a little bit about innovation and transformation. We go to a lot of tech shows, talk to a lot of big companies, everybody wants to digitally transform and innovate. Traditionally education hasn't been known as the most progressive industry in terms of transforming. You said right off the bat, that's your job is about transformation and innovation. Where's that coming from? Is that from the competitive world in which you live? Is that a top-down leadership directive? What's kind of pushing basically the investment in this innovation around your guys' school? >> So I believe that education can be disrupt in the next five, ten years. So what we think at the university is that we have to be closer to this disruption and in this sense we are working a lot to improve the students' experience of our students on campus because if not we think that it makes no sense to study on campus when you can go online. >> Jeff: Right. >> So that's why we're using technology to improve the students' experience on campus. So we are trying to avoid those things that have no value added for the students through technology and through the digital transformation. In order that we have more time for these value added interaction between the staff, academic and nonacademic staff, with the students. >> Right, and then how has the reception been with the staff, both the academic staff and the nonacademic staff because clearly the students are your customers, your primary customer, but they're a customer as well. So how have they embraced this and got behind it? >> So I seen all the institutions and you have a part of the institution that is not so in favor of these innovations, but the big number of professors and staff have seen the benefits of not to have to answer email Saturday night because the virtual assistant is 24 hours seven days a week. So they've seen the benefits of how technology can give them more time for these value added interaction with their students. For this in order to avoid only top-down decisions we have created digital ambassador programs which this program what we do is to share with our professors and with our nonacademic staff what we are planning and how they see the project. >> Jeff: Right, right. >> And we are integrating their opinions and their suggestions in the program. >> So you're six months into it you said since you launched it. >> Yeah. >> Okay, I'm just curious if you could share any stories, biggest surprises, things that you just didn't expect. I always like long and unintended consequences, you know, as you go through this process. >> So one of things is in Spain, Alexa was launched in November, last November so it's very new. >> Jeff: Very new. >> Very new in Spain. There's no voice assistant in the last nine months, it have exploded, but we didn't have before. So the students have been very impressed that the university were working at this level with the technology so new because it was even new for them, even if they are younger and they knew a lot about this technology. They were impressed that the university so quickly reacted to the introduction of the technology. The other point is through innovation, we are also using Alexa for the digital transformation of learning and teaching. We have launched an innovation program for quizzes for the students. And we have the huge amount of volunteers that they want to see how it works. >> Right, right, just curious too, to get your take on voice as an interface. You made an interesting comment before we turned the cameras on that email just doesn't work very well for today's kids. They don't use it. They're not used to using it. But voice still seems to really be lagging. I get an email from Google every couple of days saying, here ask your Google Assistant this or ask your Alexa this, you know, we still haven't learned it. From where you're sitting and seeing kind of this new way to interact and as you said get away from these emails in the middle of the night that ask, when's my paper due and I could ask the assistant. How do you see that evolving? Are you excited about it? Do you see voice as really the centerpieces of a lot of these new innovations or is it just one of many things that you're working on? >> So I think the difference is that usually higher education institutions would have use of email for communication with students with so massive amount of emails. I think what they feel with the voice assistants is that they have the freedom to choose what they want to know or not to know. So if they can ask voice, virtual assistant, as in one case, they have the freedom when they want the information. >> Jeff: Right. >> So I think its a big difference between emails, in an email you decide when you send the information to the students, with voice technologies, the student, it's the student who is asking when they want the information. >> Jeff: Right. >> So I think it's important for them. >> It's huge because they never ask for the email. >> No, they, and after they tell us that it wasn't important information that they didn't check the email. >> Right. >> They complain that they don't have the right information. >> Right, well Inaki, thank you for sharing your story and congratulations on this project. Sounds like you're just getting started, you've got a long ways to go. >> Thank you so much. >> All right, thank you. He's Inaki, I'm Jeff. You're watching the Cube, we're in downtown Seattle at AWS Imagine Education Conference. Thanks for watching. See you next time. 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