(upbeat music) >> Welcome, everyone, to theCUBE's presentation of the AWS Startup Showcase. And this episode two of season two is called "Data as Code," the ongoing series covering exciting new startups in the AWS ecosystem. I'm John Furrier, your host of theCUBE. Today, we're excited to be joined by Jon Dahl, who is the co-founder and CEO of MUX, a hot new startup building cloud video for developers, video with data. John, great to see you. We did an interview on theCube Conversation. Went into big detail of the awesomeness of your company and the trend that you're on. Welcome back. >> Thank you, glad to be here. >> So, video is everywhere, and video for pivot to video, you hear all these kind of terms in the industry, but now more than ever, video is everywhere and people are building with it, and it's becoming part of the developer experience in applications. So people have to stand up video into their code fast, and data is code, video is data. So you guys are specializing this. Take us through that dynamic. >> Yeah, so video clearly is a growing part of how people are building applications. We see a lot of trends of categories that did not involve video in the past making a major move towards video. I think what Peloton did five years ago to the world of fitness, that was not really a big category. Now video fitness is a huge thing. Video in education, video in business settings, video in a lot of places. I think Marc Andreessen famously said, "Software is eating the world" as a pretty, pretty good indicator of what the internet is actually doing to the economy. I think there's a lot of ways in which video right now is eating software. So categories that we're not video first are becoming video first. And that's what we help with. >> It's not obvious to like most software developers when they think about video, video industries, it's industry shows around video, NAB, others. People know, the video folks know what's going on in video, but when you start to bring it mainstream, it becomes an expectation in the apps. And it's not that easy, it's almost a provision video is hard for a developer 'cause you got to know the full, I guess, stack of video. That's like low level and then kind of just basic high level, just play something. So, in between, this is a media stack kind of dynamic. Can you talk about how hard it is to build video for developers? How is it going to become easier? >> Yeah, I mean, I've lived this story for too long, maybe 13 years now, when I first build my first video stack. And, you know, I'll sometimes say, I think it's kind of a miracle every time a video plays on the internet because the internet is not a medium designed for video. It's been hijacked by video, video is 70% of internet traffic today in an unreliable, sort of untrusted network space, which is totally different than how television used to work or cable or things like that. So yeah, so video is hard because there's so many problems from top to bottom that need to be solved to make video work. So you have to worry about video compression encoding, which is a complicated topic in itself. You have to worry about delivering video around the world at scale, delivering it at low cost, at low latency, with good performance, you have to worry about devices and how every device, Android, iOS, web, TVs, every device handles video differently and so there's a lot of work there. And at the end of the day, these are kind of unofficial standards that everyone's using. So one of the miracles is like, if you want to watch a video, somehow you have to get like Apple and Google to agree on things, which is not always easy. And so there's just so many layers of complexity that are behind it. I think one way to think about it is, if you want to put an image online, you just put an image online. And if you want to put video online, you build complex software, and that's the exact problem that MUX was started to help solve. >> It's interesting you guys have almost creating a whole new category around video infrastructure. And as you look at, you mentioned stack, video stack. I'm looking at a market where the notion of a media stack is developing, and you're seeing these verticals having similar dynamics with cloud. And if you go back to the early days of cloud computing, what was the developer experience or entrepreneurial experience, you had to actually do a lot of stuff before you even do anything, provision a server. And this has all kind of been covered in great detail in the glory of Agile and whatnot. It was expensive, and you had that actually engineer before you could even stand up any code. Now you got video that same thing's happening. So the developers have two choices, go do a bunch of stuff complex, building their own infrastructure, which is like building a data center, or lean in on MUX and say, "Hey, thank you for doing all that years of experience building out the stacks to take that hard part away," but using APIs that they have. This is a developer focused problem that you guys are solving. >> Yeah, that's right. my last company was a company called Zencoder, that was an API to video encoding. So it was kind of an API to a small part of what MUX does today, just one of those problems. And I think the thing that we got right at Zencoder, that we're doing again here at MUX, was building four developers first. So our number one persona is a software developer. Not necessarily a video expert, just we think any developer should be able to build with video. It shouldn't be like, yeah, got to go be a specialist to use this technology, because it should become just of the internet. Video should just be something that any developer can work with. So yeah, so we build for developers first, which means we spend a lot of time thinking about API design, we spend a lot of time thinking about documentation, transparent pricing, the right features, great support and all those kind of things that tend to be characteristics of good developer companies. >> Tell me about the pipe lining of the products. I'm a developer, I work for a company, my boss is putting pressure on me. We need video, we have all this library, it's all stacking up. We hired some people, they left. Where's the video, we've stored it somewhere. I mean, it's a nightmare, right? So I'm like, okay, I'm cloud native, I got an API. I need to get my product to market fast, 'cause that is what Agile developers want. So how do you describe that acceleration for time to market? You mentioned you guys are API first, video first. How do these customers get their product into the market as fast as possible? >> Yeah, well, I mean the first thing we do is we put what we think is probably on average, three to four months of hard engineering work behind a single API call. So if you want to build a video platform, we tell our customers like, "Hey, you can do that." You probably need a team, you probably need video experts on your team so hire them or train them. And then it takes several months just to kind of to get video flowing. One API call at MUX gives you on-demand video or live video that works at scale, works around the world with good performance, good reliability, a rich feature set. So maybe just a couple specific examples, we worked with Robin Hood a few years ago to bring video into their newsfeed, which was hugely successful for them. And they went from talking to us for the first time to a big launch in, I think it was three months, but the actual code time there was like really short. I want to say they had like a proof of concept up and running in a couple days, and then the full launch in three months. Another customer of ours, Bandcamp, I think switched from a legacy provider to MUX in two weeks in band. So one of the big advantages of going a little bit higher in the abstraction layer than just building it yourself is that time to market. >> Talk about this notion of video pipeline 'cause I know I've heard people I talk about, "Hey, I just want to get my product out there. I don't want to get stuck in the weeds on video pipeline." What does that mean for folks that aren't understanding the nuances of video? >> Yeah, I mean, it's all the steps that it takes to publish video. So from ingesting the video, if it's live video from making sure that you have secure, reliable ingest of that live feed potentially around the world to the transcoding, which is we talked a little bit about, but it is a, you know, on its own is a massively complicated problem. And doing that, well, doing that well is hard. Part of the reason it's hard is you really have to know where you're publishing too. And you might want to transcode video differently for different devices, for different types of content. You know, the pipeline typically would also include all of the workflow items you want to do with the video. You want to thumbnail a video, you want clip, create clips of the video, maybe you want to restream the video to Facebook or Twitter or a social platform. You want to archive the video, you want it to be available for downloads after an event. If it's just a, if it's a VOD upload, if it's not live in the first place. You have all those things and you might want to do simulated live with the video. You might want to actually record something and then play it back as a live stream. So, the pipeline Ty typically refers to everything from the ingest of the video to the time that the bits are delivered to a device. >> You know, I hear a lot of people talking about video these days, whether it's events, training, just want peer to peer experience, video is powerful, but customers want to own their own platform, right? They want to have the infrastructure as a service. They kind of want platform as a service, this is cloud talk now, but they want to have their own capability to build it out. This allows them to get what they want. And so you see this, like, is it SaaS? Is it platform? People want customization? So kind of the general purpose video solution does it really exist or doesn't? I mean, 'cause this is the question. Can I just buy software and work or is it going to be customized always? How do you see that? Because this becomes a huge discussion point. Is it a SaaS product or someone's going to make a SaaS product? >> Yeah, so I think one of the most important elements of designing any software, but especially when you get into infrastructure is choosing an abstraction level. So if you think of computing, you can go all the way down to building a data center, you can go all the way down to getting a colo and racking a server like maybe some of us used to do, who are older than others. And that's one way to run a server. On the other extreme, you have just think of the early days of cloud competing, you had app engine, which was a really fantastic, really incredible product. It was one push deploy of, I think Python code, if I remember correctly, and everything just worked. But right in the middle of those, you had EC2, which was, EC2 is basically an API to a server. And it turns out that that abstraction level, not Colo, not the full app engine kind of platform, but the API to virtual server was the right abstraction level for maybe the last 15 years. Maybe now some of the higher level application platforms are doing really well, maybe the needs will shift. But I think that's a little bit of how we think about video. What developers want is an API to video. They don't want an API to the building blocks of video, an API to transcoding, to video storage, to edge caching. They want an API to video. On the other extreme, they don't want a big application that's a drop in white label video in a box like a Shopify kind of thing. Shopify is great, but developers don't want to build on top of Shopify. In the payments world developers want Stripe. And that abstraction level of the API to the actual thing you're getting tends to be the abstraction level that developers want to build on. And the reason for that is, it's the most productive layer to build on. You get maximum flexibility and also maximum velocity when you have that API directly to a function like video. So, we like to tell our customers like you, you own your video when you build on top of MUX, you have full control over everything, how it's stored, when it's stored, where it goes, how it's published, we handle all of the hard technology and we give our customers all of the flexibility in terms of designing their products. >> I want to get back some use case, but you brought that up I might as well just jump to my next point. I'd like you to come back and circle back on some references 'cause I know you have some. You said building on infrastructure that you own, this is a fundamental cloud concept. You mentioned API to a server for the nerds out there that know that that's cool, but the people who aren't super nerdy, that means you're basically got an interface into a server behind the scenes. You're doing the same for video. So, that is a big thing around building services. So what wide range of services can we expect beyond MUX? If I'm going to have an API to video, what could I do possibly? >> What sort of experience could you build? >> Yes, I got a team of developers saying I'm all in API to video, I don't want to do all that transit got straight there, I want to build experiences, video experiences on my app. >> Yeah, I mean, I think, one way to think about it is that, what's the range of key use cases that people do with video? We tend to think about six at MUX, one is kind of the places where the content is, the prop. So one of the things that use video is you can create great video. Think of online courses or fitness or entertainment or news or things like that. That's kind of the first thing everyone thinks of, when you think video, you think Netflix, and that's great. But we see a lot of really interesting uses of video in the world of social media. So customers of ours like Visco, which is an incredible photo sharing application, really for photographers who really care about the craft. And they were able to bring video in and bring that same kind of Visco experience to video using MUX. We think about B2B tools, videos. When you think about it, all video is, is a high bandwidth way of communicating. And so customers are as like HubSpot use video for the marketing platform, for business collaboration, you'll see a lot of growth of video in terms of helping businesses engage their customers or engage with their employees. We see live events obviously have been a massive category over the last few years. You know, we were all forced into a world where we had to do live events two years ago, but I think now we're reemerging into a world where the online part of a conference will be just as important as the in-person component of a conference. So that's another big use case we see. >> Well, full disclosure, if you're watching this live right now, it's being powered by MUX. So shout out, we use MUX on theCUBE platform that you're experiencing in this. Actually in real time, 'cause this is one application, there's many more. So video as code, is data as code is the theme, that's going to bring up the data ops. Video also is code because (laughs) it's just like you said, it's just communicating, but it gets converted to data. So data ops, video ops could be its own new category. What's your reaction to that? >> Yeah, I mean, I think, I have a couple thoughts on that. The first thought is, video is a way that, because the way that companies interact with customers or users, it's really important to have good monitoring and analytics of your video. And so the first product we ever built was actually a product called MUX video, sorry, MUX data, which is the best way to monitor a video platform at scale. So we work with a lot of the big broadcasters, we work with like CBS and Fox Sports and Discovery. We work with big tech companies like Reddit and Vimeo to help them monitor their video. And you just get a huge amount of insight when you look at robust analytics about video delivery that you can use to optimize performance, to make sure that streaming works well globally, especially in hard to reach places or on every device. That's we actually build a MUX data platform first because when we started MUX, we spent time with some of our friends at companies like YouTube and Netflix, and got to know how they use data to power their video platforms. And they do really sophisticated things with data to ensure that their streams well, and we wanted to build the product that would help everyone else do that. So, that's one use. I think the other obvious use is just really understanding what people are doing with their video, who's watching what, what's engaging, those kind of things. >> Yeah, data is definitely there. You guys mentioned some great brands that are working with you guys, and they're doing it because of the developer experience. And I'd like you to explain, if you don't mind, in your words, why is the MUX developer experience so good? What are some of the results you're seeing from your customers? What are they saying to you? Obviously when you win, you get good feedback. What are some of the things that they're saying and what specific develop experiences do they like the best? >> Yeah, I mean, I think that the most gratifying thing about being a startup founder is when your customers like what you're doing. And so we get a lot of this, but it's always, we always pay attention to what customers say. But yeah, people, the number one thing developers say when they think about MUX is that the developer experience is great. I think when they say that, what they mean is two things, first is it's easy to work with, which helps them move faster, software velocity is so important. Every company in the world is investing and wants to move quickly and to build quickly. And so if you can help a team speed up, that's massively valuable. The second thing I think when people like our developer experience is, you know, in a lot of ways that think that we get out of the way and we let them do what they want to do. So well, designed APIs are a key part of that, coming back to abstraction, making sure that you're not forcing customers into decisions that they actually want to make themselves. Like, if our video player only had one design, that that would not be, that would not work for most developers, 'cause developers want to bring their own design and style and workflow and feel to their video. And so, yeah, so I think the way we do that is just think comprehensively about how APIs are designed, think about the workflows that users are trying to accomplish with video, and make sure that we have the right APIs, make sure they're the right information, we have the right webhooks, we have the right SDKs, all of those things in place so that they can build what they want. >> We were just having a conversation on theCUBE, Dave Vellante and I, and our team, and I'd love to get you a reaction to this. And it's more and more, a riff real quick. We're seeing a trend where video as code, data as code, media stack, where you're starting to see the emergence of the media developer, where the application of media looks a lot like kind of software developer, where the app, media as an app. It could be a chat, it could be a peer to peer video, it could be part of an event platform, but with all the recent advances, in UX designers, coders, the front end looks like an emergence of these creators that are essentially media developers for all intent and purpose, they're coding media. What's your reaction to that? How do you see that evolving? >> I think the. >> Or do you agree with it? >> It's okay. >> Yeah, yeah. >> Well, I think a couple things. I think one thing, I think this goes along through saying, but maybe it's disagreement, is that we don't think you should have to be an expert at video or at media to create and produce or create and publish good video, good audio, good images, those kind of things. And so, you know, I think if you look at software overall, I think of 10 years ago, the kind of DevOps movement, where there was kind of a movement away from specialization in software where the same software developer could build and deploy the same software developer maybe could do front end and back end. And we want to bring that to video as well. So you don't have to be a specialist to do it. On the other hand, I do think that investments and tooling, all the way from video creation, which is not our world, but there's a lot of amazing companies out there that are making it easier to produce video, to shoot video, to edit, a lot of interesting innovations there all the way to what we do, which is helping people stream and publish video and video experiences. You know, I think another way about it is, that tool set and companies doing that let anyone be a media developer, which I think is important. >> It's like DevOps turning into low-code, no-code, eventually it's just composability almost like just, you know, "Hey Siri, give me some video." That kind of thing. Final question for you why I got you here, at the end of the day, the decision between a lot of people's build versus buy, "I got to get a developer. Why not just roll my own?" You mentioned data center, "I want to build a data center." So why MUX versus do it yourself? >> Yeah, I mean, part of the reason we started this company is we have a pretty, pretty strong opinion on this. When you think about it, when we started MUX five years ago, six years ago, if you were a developer and you wanted to accept credit cards, if you wanted to bring payment processing into your application, you didn't go build a payment gateway. You just probably used Stripe. And if you wanted to send text messages, you didn't build your own SMS gateway, you probably used Twilio. But if you were a developer and you wanted to stream video, you built your own video gateway, you built your own video application, which was really complex. Like we talked about, you know, probably three, four months of work to get something basic up and running, probably not live video that's probably only on demand video at that point. And you get no benefit by doing it yourself. You're no better than anyone else because you rolled your own video stack. What you get is risk that you might not do a good job, maybe you do worse than your competitors, and you also get distraction where you've just taken, you take 10 engineers and 10 sprints and you apply it to a problem that doesn't actually really give you differentiated value to your users. So we started MUX so that people would not have to do that. It's fine if you want to build your own video platform, once you get to a certain scale, if you can afford a dozen engineers for a VOD platform and you have some really massively differentiated use case, you know, maybe, live is, I don't know, I don't have the rule of thumb, live videos maybe five times harder than on demand video to work with. But you know, in general, like there's such a shortage of software engineers today and software engineers have, frankly, are in such high demand. Like you see what happens in the marketplace and the hiring markets, how competitive it is. You need to use your software team where they're maximally effective, and where they're maximally effective is building differentiation into your products for your customers. And video is just not that, like very few companies actually differentiate on their video technology. So we want to be that team for everyone else. We're 200 people building the absolute best video infrastructure as APIs for developers and making that available to everyone else. >> John, great to have you on with the showcase, love the company, love what you guys do. Video as code, data as code, great stuff. Final plug for the company, for the developers out there and prospects watching for MUX, why should they go to MUX? What are you guys up to? What's the big benefit? >> I mean, first, just check us out. Try try our APIs, read our docs, talk to our support team. We put a lot of work into making our platform the best, you know, as you dig deeper, I think you'd be looking at the performance around, the global performance of what we do, looking at our analytics stack and the insight you get into video streaming. We have an emerging open source video player that's really exciting, and I think is going to be the direction that open source players go for the next decade. And then, you know, we're a quickly growing team. We're 60 people at the beginning of last year. You know, we're one 50 at the beginning of this year, and we're going to a add, we're going to grow really quickly again this year. And this whole team is dedicated to building the best video structure for developers. >> Great job, Jon. Thank you so much for spending the time sharing the story of MUX here on the show, Amazon Startup Showcase season two, episode two, thanks so much. >> Thank you, John. >> Okay, I'm John Furrier, your host of theCUBE. This is season two, episode two, the ongoing series cover the most exciting startups from the AWS Cloud Ecosystem. Talking data analytics here, video cloud, video as a service, video infrastructure, video APIs, hottest thing going on right now, and you're watching it live here on theCUBE. Thanks for watching. (upbeat music)

>>Hey everyone. Welcome back to the cubes. Continuous coverage of AWS reinvent 2021 live. Yes. Live in Las Vegas, Lisa Martin, with Dave Nicholson. David's great to co-host with you. How you doing >>Fantastic. Great to be here with >>You, Lisa, as always, we're going to have a great conversation. Next to Cuba actually is two lifestyles, two remote studios. We've got over a hundred guests on the program talking about the next decade and cloud innovation and Dave and I are pleased to welcome Scott Carter, the CTO of TSS to the program. Scott. Welcome. >>Thank you. It's really, really great to be here. Really >>This a little bit. Great to have you on the program. Talk to us a little bit about, about TCIs and let's talk about your kind of journey to the cloud and your relationship with AWS. >>Absolutely. Um, you know, TCIs, we've been around as a company for about 40 years. We specialize in, uh, payment products specifically on the issuing side. So card issuing, we've worked with some of the largest financial brands in the world and retailers as well. Uh, and, and a lot of, you know, what I always tell people is if you have a card in your wallet today, uh, you could probably pull it out. And at least one of those cards is something that we manage and service for our customers. And, and we, uh, do everything full lifecycle of those payment products for our customers around the globe >>On behalf of being a cardholder. Thank you. Talk to me a little bit about the AWS partnership here we are at re-invent. >>Yeah, well, we started a very special, uh, partnership with AWS about 18 months ago. We're about 18 months into the journey, uh, and really our goal and our vision is to build out a financial services cloud for all of our clients and our retailers and fintechs. Uh, we're really focused right now on migrating some of our key products to the AWS cloud environment. We built we've used us a variety of AWS technology by some on-premise and in the cloud environment to migrate our processing platforms and all of our customer servicing systems. So we're in the middle of that journey. Uh, we've had a lot of successes so far. AWS is helping us out. Our engineering team is working side by side with the AWS engineering team to produce what we believe is going to be the next generation of payments, especially on the card issuing side, >>Next gen that's, that's important as a consumers, consumer life business life. We have that expectation that we're going to be able to transact whatever we want anytime day or night, >>Absolutely choice is key, uh, virtual physical, no matter where you are, we want to be able to facilitate your payment and make sure you have everything you need to support you through the full card life cycles, the life cycle of your account. >>So you talk about those cards being in our wallets and handbags. I know there's one that's actually smoking. It's so hot from use in my co-hosts handbag, but, >>Uh, we appreciate that >>Talk, talk, talk about this journey from the perspective of someone who, um, I assume like me is not just out of college, right? You've working, you've been working in this business for a while. And so you're going through the transition from the world of what some will refer to as legacy it into the world of cloud. Uh, talk about the challenges there. How do you go after the low hanging fruit versus the high hanging fruit? How do you evaluate something from an ROI perspective? Talk about that. >>Yeah, and I, you know, uh, I get that quite a similar question a lot. I get, you know, people are, are interested in the journey and especially CTOs and CEOs who were starting journeys at their own. I get a chance to talk with a lot of banks and retailers about their individual like modernization and transformation journeys. Um, and you know, the, the basics are true about the journey. And I had somebody tell me years ago that it's, it's, it's psychology, it's not technology. Uh, you've really got to address the people's side of the equation. First, you've got to focus on training and upskilling, make sure that the team comes along on the journey. And then you've gotta be a really good recruiter. You've got to go out and get the talent, the skills you need to build a good foundation. You gotta have the right partners. >>You know, we have partners like PWC and, and, uh, AWS and others that are really helping us with the journey. So that part of it's really, really important. The key is, and I think for us, uh, we really started building our talent pool, uh, probably more than five years ago. And so we were able to bring in some skill sets in dev ops and some skill sets. And, you know, nowadays AI we'd do a lot with ML and AI skill sets. Uh, but we were able to build in a lot of cloud skills and start to build out our development environments first, very, very early on. That's what we did. And we used those development environments for our engineers to cut their teeth and really get comfortable in the cloud. Um, I remember probably about three years ago, we installed our first Kubernetes cluster. Um, and we did it with a small team. >>And then over time we really incented the team by allowing them to get more and more certifications and grow their skills. And we really built up a really large team around just our on-premise cloud first. And then later that helped us with the migration, the journey into the actual public cloud for those same services. Um, and we use that, that same team as there today, we really invest in our people. We think it's important to have a staff that's there. We insource our staff. We really believe in that. Um, that's super important, even though we have partners that we really value, we make sure that we've got a core group of people that are really passionate about the journey and about cloud. And so that >>You mentioned that, that kind of cultural aspect. Yeah. And you mentioned bringing in a team starting years ago with a specific focus. What about the transition of folks who have been it practitioners for maybe decades making that transition? How has, how has that worked out culturally? Have you adopted a policy where you're basically saying, look, if you have experience with this stuff, great, stay with it. Yeah. But we're hiring net new people for the new stuff. Is that the strategy or is it >>Look like I've seen some do that? I personally don't feel that that works because you need some subject matter experts. You need people who really know your products and your company and your solutions and your customers. You really need those people to come along the journey. So what we've done internally is we created, for example, a digital boot camps where our team members could sign up that could come in. We actually construct the boot boot camps on about a six week schedule. Uh, we do two week sprints. So we do three sprints. We, we get them sort of inculcated and agile from the very beginning, we have demos at the end of each sprint. So they're working in an agile way as they're going through their training course. And then of course we, that gives us a chance to identify people who are really high potential to move into some of our cloud teams and our dev ops teams. >>And so that's been really, really beneficial for us. And I would tell you that today we've got people that have a broad range of skills just because of that digital bootcamp. So they may have started their career doing assembler or COBOL or something like that. But now they've tacked on some dev ops and some cloud skills. Uh, we have some that know dynamo DB, and they also know DB too. And we like that. So they have a broad range and those people bring a lot of deep expertise that you're not going to necessarily get with somebody that you're bringing, you know, new, you know, sometimes straight out of college into your company. You've got to grow those people too, but you need the experience, people there to help develop them. >>No, we often talk about people, process and technology, and it's kind of a phrase that's thrown around right. At every event with every vendor. But I really admire the focus on the people, part that you're talking about there and how it's really essential to enable, to enable the people, how you started very strategically starting with the people in the focus and the training on-prem then making the decision that they've, they've got the foundation. Now we need to migrate to the cloud. I'm curious the why AWS, you have a lot of choice course here we are at reinvent. But talk to me about why AWS is that strategic partner. >>We've, we've looked at a number of different cloud platforms for our business. And in fact, uh, global payments is a large company. So TCIs is sort of the issuing part of that. And so we have really great relationships with GCP and other cloud platforms, even some Azure in certain pockets of the company for the issuing side of the business, we went through a thorough evaluation and we felt like the tools, the technology, the platforms, really the, the maturity of that platform. And then the scale, you know, scale matters in our business. And a lot of businesses, it matters, uh, you know, the locations of all of the, uh, uh, availability zones and the regions that was really important to us. We were able to align all of the different AWS regions to where our customer locations are. And that's becoming more and more important as we, you know, we try to be more flexible now about where we, uh, you know, deploy our products around the globe. We want to make sure that whoever we partner with has a point of presence in those markets and that we can do that very, very quickly. We can stand up a new environment when we need to. And so that's what that's been really beneficial that we made that choice with AWS. Um, you know, there's a lot of cloud platforms out out there there's a lot of choice, but we just felt like AWS was the best for us. >>AWS is also very, very, very customer focused, but they probably would say customer obsessed, really that customer flywheel that generates everything that we'd even heard this morning in the keynote culturally, is TCIs similar to AWS in that respect. And can you share a little bit about that? >>Very much. So our reputation as a business is based on the relationships that we built with our customers, and we're known for that in financial services, the TCIs brand and the way that we think about our customers and the way that we partner with them. Um, you know, we, when we taught with the AWS team, we, we try to explain, you know, our history is, you know, w we're kind of the cloud for our customers. So they have a number of products and services. We support those, we manage those products. We, we build on top of, of those products for them. And so we really understand that it's important, not only that you're building a platform, but that platform has got to be able to support all the different things that our customers do every day. And we want that to be broad. We don't want it to be narrow. It's not just focused in one area. If our customers come to us and they say, well, you know, I need to build a data and an analytics platform, or I need some really specific fraud capabilities. We want to be able to support that on demand with our customers. And that's really the journey that we've taken with AWS. AWS is enabling that for us. >>And on-demand is key. I think we've one of the things that's been in short supply during the last 22 months is patients, right? That's >>Right. Absolutely. >>So describe the role of a CTO in that process. What does that look like? Because this isn't, you're not making unilateral decisions here, obviously you're working with the team, but talk about the CTO's perspective as you make decisions about whether AWS is the right fit for a part of your environment or GCP or something else. >>Yeah. I think, you know, um, we, we have, uh, a long history of supporting our own solutions and supporting our systems. And we run some of the world's largest like authorizations platforms, which those are the platforms where when you go into the store and you swipe your card, you, you have to get a response back from us. Like we have to give you that and we have to give it, we have a really specific amount of time. We have to give that back to you. And so we really understand operations and support and how to scale, uh, applications and systems and, and, and how to build really, really reliable solutions. We really understand that part of the business. So whoever we partner with, and, and you asked about my decision to CTO, it was really a group decision. You know, I have to partner with our business team, I have to get their buy-in. Um, they have to support the decision, whatever we do, it's a big investment, we're making the move to the cloud. And so, um, but we have to make sure that we, we cover off the basis. They've gotta be able to at least whatever, whoever our partner is, they've got to be able to at least provide the operational support and the reliability that we're able to give our customers today. So it's just a spreadsheet that's right. Technical qualifier, >>And whoever has the most boxes checked wins. That's right. You're taking into consideration all of those cultural aspects and the goals of the business. That's right. So as a chief technology officer, it's not just about the technology, it's about the business >>That's right, right. So I have a very, very close relationship with the president of our business, Galen, Jowers, um, and, and we built a team and we have on, on the, uh, the actual modernization or transformation team, we have members that represent that from a business perspective there I report into, uh, directly into the business teams. And then we have, uh, people from my, from my side of the, of the company. And we work every single day together and we're driving this forward. So the important part of that is at some point, we, we go to our customers and we show them, Hey, for this particular product or service that we're offering, we're going to be moving that to cloud on this kind of a schedule. And we're there together as a unified front and a unified communication with our customer to explain that journey. And we think that's really important that we do it that way and not do it. You know, like I've seen some companies they'll segment it and sort of technology, or it goes off and they kind of do their own sort of cloud initiative to us that wouldn't work for our business. It's gotta be together and enjoy it with the business. >>You sound like a very much a transformational CTO to me versus a traditional CTO and working at a legacy company that's been around for 40 years. That's impressive that the company is that forward in thinking, first of all, about its people, but also about that business, it partnership. But that has to be in lock step. We talk about that all the time, but it's hard to facilitate that, but you really sound like you guys have done a phenomenal job with some key strategic foresight is not the word. Um, I liked, like Dave was saying, it's not a spreadsheet. It's a checklist of technology requirements that people element is absolutely. >>Absolutely. And you have to, you have to, you have to be all in together on it because you know that as you go on the journey, you're going to have some failure. You're going to experience some challenges. Your customers might not be happy with every decision you make. So you have to be in it together. You're going to have to make that commitment as a company. And that's what we decided early earlier on is that we were going to do that and it's worked out well for us. >>What are some of the things that are going to be happening next for TCIs as we hopefully round out the year 2021 and go into a much better 20, 22, >>We've got a, we've got some really big things on the horizon. One of the things that we're working on right now is, um, we've, since we've been at this for 18 months, we're starting to get to a point where we have certain solutions that are ready to go. We're ready. We're going to be able in 2022 to make some key announcements around some parts of our platform, they're going to be available in AWS as a, as an offering. So we're excited about that. A lot of our customer servicing and some of the things that we do outside of our core processing platform are already cloud native. We run them in a cloud environment on our premise and some of those services, we're going to be able to go ahead and launch into the AWS in 2022. So we're really excited about that. We're right now in the throws of building an onboarding team, that's going to be working with both our customers and with our internal teams to make that shift and start migrating those applications out to the environment. >>So big, big things underway there. We've got a couple of, uh, really key strategic relationships that we've built over the last 12 months or so, um, that are all in, on our cloud journey. And so we're going to be able to announce some of those, uh, pretty soon as some of our customers and prospects, uh, that really want to be on the journey with us. So we're pretty excited about that. And I don't want to spoil any surprises there, so we'll wait and let that come out with the, with the schedule. But yeah, we've got a lot of great things ahead and we're very, very excited for where we're going. >>Awesome, Scott, great stuff. I love how transformational you are, the focus that you guys have on the people, as well as the technologies and the processes. Exciting. Congratulations on your, on your 18 month journey. And we'll have to have you back on so we can hear some of those, those, uh, you know, little, uh, Easter eggs that you just dropped. >>I'd love to, I'd love to be back on. This has been great. All right. >>And how did you know I have a credit card in my wallet running a whole. >>I've been feeling bad about saying that the whole time. He's not going to go well when we're done here, >>Wherever in Vegas, we hope you've enjoyed this. Like for Dave Nicholson, I'm Lisa Martin. You're watching the cube, the global leader in a live chat coverage.

>>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

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi everybody this is David on tape with the cube and welcome to the special digital presentation we're really digging into how IBM is operationalizing and automating the AI and data pipeline not only for its clients but also for itself and with me is Julie Lochner who looks after offering management and IBM's data and AI portfolio Julie great to see you again okay 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 in 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 anytime we deliver a product from the data AI team to the market so think about all of the release cycle management pricing product management discipline etc the other roles that I play is really making sure that um 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 and so let's let's start with data ops I mean I think you know a lot of people are familiar with DevOps not maybe not everybody's familiar with the data Ops what do we need to know about data well I mean you bring up the point that everyone knows DevOps and and then in fact I think you know what data Ops really does is bring a lot of the benefits that DevOps did for application development to the data management organizations so when we look at what is data ops it's a data management it's a it's a data management set of principles that helps organizations bring business ready data to their consumers quickly it takes it borrows from DevOps similarly where you have a data pipeline that associates a business value requirement I have this business initiative it's gonna drive this much revenue or this much 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 dated management practices deployed a waterfall like methodology or implementation methodology and what that meant is all the data pipeline projects were implemented serially and it was dawn based on potentially a first-in first-out program management office with a DevOps 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 to validate its quality to create those data integration pipelines and then present it to the dashboard like if it's a Cognos dashboard for a operational process or even a data science team that whole end-to-end process gets streamlined through what we're calling data ops methodology so I mean as you well know we've been following this market since the early days of a dupe and people struggle with their data pipelines it's complicated for them there's a raft of tools and and and they spend most of their time wrangling data preparing data improving data quality different roles within the organization so it sounds like you know to borrow from from DevOps data OPS's is all about REME lining that data pipeline helping people really understand and communicate across end to 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 cost to automate a 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 its quality like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fast so 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 we're 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 through workflows and just cutting through all of the political barriers that often happens when these data's cross different organizational boundaries yeah so speed time to insights is critical but to in and then you know with DevOps you're really bringing together 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 a 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 and last you know six seven 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've captured the metadata you 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 to 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 you know dashboards operational processes okay and and we're gonna talk about some examples and one of them of course is ibm's own internal example but 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 opera there's an opportunity here to quickly define a small project align it to a 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 DevOps type programming skills to automate an instrument a lot of the technology a subject matter expert who understands the data sources and its meaning a line of business executive who can 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 that 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 datasets 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 in 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 filling in gaps kick off the next project and iterate 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 all right that's awesome I want to now get into some examples I mean or you're we're both massachusetts-based normally you'd be in our studio and we'd be sitting here face-to-face obviously with kovat 19 in this crisis we're all sheltering in place you're up in somewhere in New England I happen to be in my studio believe it but I'm the only one here so relate this to kovat how would data ops or maybe you have a concrete example in in terms of how it's helped inform or actually anticipate and keep up-to-date with what's happening with building 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 coded pandemic crisis 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 to you know act upon a data-driven decision so part of 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 the 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 catalog so I use that as the context for now we're faced with a pandemic crisis and I'm being asked in my business unit to respond very quickly how can we prioritize the offerings that are gonna help those in critical need so that we can get those products out to market we can offer a you know 90-day free use for governments and Hospital agencies so in order for me to do that as a operations lead for 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 you know 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 for governments hospitals state agencies etc that's a that's a perfect example now to to set the stage back to the corporate global chief data office organization they implemented some technology that allowed us to ingest 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 partnered with a corporate central organization and took advantage of the automated set of capabilities without having to invest in any additional resources or headcount 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 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 anyways it is it machine intelligence an AI that allows us to do this that's exactly right and as you know our organization is data and AI so we happen to have the a 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 that augmentation means it's automation through AI so with years of experience and natural language understanding we can comb through a lot of the metadata that's available electronically we can comb through unstructured data 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 the AI is had 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 lives everywhere you might have your supply chain data and your ERP maybe that sits on Prem you might have some sales data that's sitting in the SAS store in a cloud somewhere you might have you know a weather data that you want to bring in in theory anyway the more data that you have the better insights that you can gather assuming you've got the right data quality but so let me start with like where the data is right so so it sits anywhere you don't know where it's gonna be but you know you need it so that that's part of this right is being able to read it 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 a cloud so the data location actually changes while we're in the middle of projects we have many or coming even during this this pandemic crisis we have many organizations that are using this as 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 its meaning it might change how you connect to it um 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 was 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 workflows 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 oughta mating these workflows so that you can accommodate when the data decides to move because of corporate policy change from on-prem to cloud then that's a big part of what data Ops offers it's the same thing by the way for DevOps they've had to accommodate you know 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're 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 in order to scale with automation that's another key part of data Ops is it not it's well it's a big part of the value proposition and a lot of a part of the business base 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 query 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 under Snatcher Alang guaa Jing to do a lot of the heavy lifting for you and a big part of data ops workflows and building these pipelines is to do what we call management-by-exception so if your algorithms say you know 80% confident that this is a phone number and your organization has a you know 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 percent sure this is an email address right and you I 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 now that's awesome I mean you're right the no schema on right said I throw it into a data leg the data link becomes the data swap 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 I mean it seems like you 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 a you've got a data quality component before you can actually implement and get yet to insight so you know where our customers on the 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 it 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 take advantage of the benefits data ops delivers so what we did 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 second one is and you trust it like can you trust its quality can you trust its meeting and the third one is can you put it to use so if you really think about it when you begin with what data do you know right 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 departments 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 they're they're governed processes but for these new initiatives and when you identify I'm in my example with the Kovach crisis 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 its 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 workflows to remediate and then when you get to putting it to use there's several methods for making data available you know one is simply making a data Mart available to a small set of users that's what most people do well first they make a 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 ETL tool for data integration but Nirvana is when you really get to that self-service data prep where users can query 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 you know data pipeline and I hit go and it automatically generates that output for data science use cases for a Cognos 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 observation to bring organizations to well Julie I think there's no question that this kovat crisis is accentuated the importance of digital you know we talk about digital transformation a lot and it's it's certainly real although I would say a lot of people that we talk to will say well you know not on my watch or I'll be retired before that all happens will 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 your data you know story together and your act together then you're gonna you're not going to be able to compete and data ops really is a key aspect of that so you know give us a parting word all right 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 write 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 iterate access that data know it trust it put it to use so that we can respond to those in need when they need it Julie Locker your incredible practitioner really hands-on really appreciate you coming on the Kuban and sharing your knowledge with us thank you okay thank you very much it was a pleasure to be here all right and thank you for watching everybody this is Dave Volante for the cube and we will see you next time [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. We're here at the Intercontinental Hotel. You're watching the Cube? The leader and I live tech covered set. Daubert is here. He's the vice president of data and I and a I and the chief data officer of cloud and cognitive software. And I'd be upset too. Good to see you again. >>Good. See, Dave, thanks for having me >>here. The data in a I form hashtag data. I I It's amazing here. 1700 people. Everybody's gonna hands on appetite for learning. Yeah. What do you see out in the marketplace? You know what's new since we last talked. >>Well, so I think if you look at some of the things that are really need in the marketplace, it's really been around filling the skill shortage. And how do you operationalize and and industrialize? You're a I. And so there's been a real need for things ways to get more productivity out of your data. Scientists not necessarily replace them. But how do you get more productivity? And we just released a few months ago, something called Auto A I, which really is, is probably the only tool out there that automates the end end pipeline automates 80% of the work on the Indian pipeline, but isn't a black box. It actually kicks out code. So your data scientists can then take it, optimize it further and understand it, and really feel more comfortable about it. >>He's got a eye for a eyes. That's >>exactly what is a eye for an eye. >>So how's that work? So you're applying machine intelligence Two data to make? Aye. Aye, more productive pick algorithms. Best fit. >>Yeah, So it does. Basically, you feed it your data and it identifies the features that are important. It does feature engineering for you. It does model selection for you. It does hyper parameter tuning and optimization, and it does deployment and also met monitors for bias. >>So what's the date of scientists do? >>Data scientist takes the code out the back end. And really, there's some tweaks that you know, the model, maybe the auto. Aye, aye. Maybe not. Get it perfect, Um, and really customize it for the business and the needs of the business. that the that the auto A I so they not understand >>the data scientist, then can can he or she can apply it in a way that is unique to their business that essentially becomes their I p. It's not like generic. Aye, aye for everybody. It's it's customized by And that's where data science to complain that I have the time to do this. Wrangling data >>exactly. And it was built in a combination from IBM Research since a great assets at IBM Research plus some cattle masters at work here at IBM that really designed and optimize the algorithm selection and things like that. And then at the keynote today, uh, wonderment Thompson was up there talking, and this is probably one of the most impactful use cases of auto. Aye, aye to date. And it was also, you know, my former team, the data science elite team, was engaged, but wonderment Thompson had this problem where they had, like, 17,000 features in their data sets, and what they wanted to do was they wanted to be able to have a custom solution for their customers. And so every time they get a customer that have to have a data scientist that would sit down and figure out what the right features and how the engineer for this customer. It was an intractable problem for them. You know, the person from wonderment Thompson have prevented presented today said he's been trying to solve this problem for eight years. Auto Way I, plus the data science elite team solve the form in two months, and after that two months, it went right into production. So in this case, oughta way. I isn't doing the whole pipeline. It's helping them identify the features and engineering the features that are important and giving them a head start on the model. >>What's the, uh, what's the acquisition bottle for all the way as a It's a license software product. Is it assassin part >>of Cloudpack for data, and it's available on IBM Cloud. So it's on IBM Cloud. You can use it paper use so you get a license as part of watching studio on IBM Cloud. If you invest in Cloudpack for data, it could be a perpetual license or committed term license, which essentially assassin, >>it's essentially a feature at dawn of Cloudpack for data. >>It's part of Cloudpack per day and you're >>saying it can be usage based. So that's key. >>Consumption based hot pack for data is all consumption based, >>so people want to use a eye for competitive advantage. I said by my open that you know, we're not marching to the cadence of Moore's Law in this industry anymore. It's a combination of data and then cloud for scale. So so people want competitive advantage. You've talked about some things that folks are doing to gain that competitive advantage. But the same time we heard from Rob Thomas that only about 4 to 10% penetration for a I. What? What are the key blockers that you see and how you're knocking them >>down? Well, I think there's. There's a number of key blockers, so one is of access to data, right? Cos have tons of data, but being able to even know what data is, they're being able to pull it all together and being able to do it in a way that is compliant with regulation because you got you can't do a I in a vacuum. You have to do it in the context of ever increasing regulation like GDP R and C, C, P A and all these other regulator privacy regulations that are popping up. So so that's that's really too so access to data and regulation can be blockers. The 2nd 1 or the 3rd 1 is really access to appropriate skills, which we talked a little bit about. Andi, how do you retrain, or how do you up skill, the talent you have? And then how do you actually bring in new talent that can execute what you want on then? Sometimes in some cos it's a lack of strategy with appropriate measurement, right? So what is your A II strategy, and how are you gonna measure success? And you and I have talked about this on Cuban on Cube before, where it's gotta measure your success in dollars and cents right cost savings, net new revenue. That's really all your CFO is care about. That's how you have to be able to measure and monitor your success. >>Yes. Oh, it's so that's that Last one is probably were where most organizations start. Let's prioritize the use cases of the give us the best bang for the buck, and then business guys probably get really excited and say Okay, let's go. But to up to truly operationalize that you gotta worry about these other things. You know, the compliance issues and you gotta have the skill sets. Yeah, it's a scale. >>And sometimes that's actually the first thing you said is sometimes a mistake. So focusing on the one that's got the most bang for the buck is not necessarily the best place to start for a couple of reasons. So one is you may not have the right data. It may not be available. It may not be governed properly. Number one, number two the business that you're building it for, may not be ready to consume it right. They may not be either bought in or the processes need to change so much or something like that, that it's not gonna get used. And you can build the best a I in the world. If it doesn't get used, it creates zero value, right? And so you really want to focus on for the first couple of projects? What are the one that we can deliver the best value, not Sarah, the most value, but the best value in the shortest amount of time and ensure that it gets into production because especially when you're starting off, if you don't show adoption, people are gonna lose interest. >>What are you >>seeing in terms of experimentation now in the customer base? You know, when you talk to buyers and you talk about, you know, you look at the I T. Spending service. People are concerned about tariffs. The trade will hurt the 2020 election. They're being a little bit cautious. But in the last two or three years have been a lot of experimentation going on. And a big part of that is a I and machine learning. What are you seeing in terms of that experimentation turning into actually production project that we can learn from and maybe do some new experiments? >>Yeah, and I think it depends on how you're doing the experiments. There's, I think there's kind of academic experimentation where you have data science, Sistine Data science teams that come work on cool stuff that may or may not have business value and may or may not be implemented right. They just kind of latch on. The business isn't really involved. They latch on, they do projects, and that's I think that's actually bad experimentation if you let it that run your program. The good experimentation is when you start identity having a strategy. You identify the use cases you want to go after and you experiment by leveraging, agile to deliver these methodologies. You deliver value in two weeks prints, and you can start delivering value quickly. You know, in the case of wonderment, Thompson again 88 weeks, four sprints. They got value. That was an experiment, right? That was an experiment because it was done. Agile methodologies using good coding practices using good, you know, kind of design up front practices. They were able to take that and put it right into production. If you're doing experimentation, you have to rewrite your code at the end. And it's a waste of time >>T to your earlier point. The moon shots are oftentimes could be too risky. And if you blow it on a moon shot, it could set you back years. So you got to be careful. Pick your spots, picked ones that maybe representative, but our lower maybe, maybe lower risk. Apply agile methodologies, get a quick return, learn, develop those skills, and then then build up to the moon ship >>or you break that moon shot down its consumable pieces. Right, Because the moon shot may take you two years to get to. But maybe there are sub components of that moon shot that you could deliver in 34 months and you start delivering knows, and you work up to the moon shot. >>I always like to ask the dog food in people. And I said, like that. Call it sipping your own champagne. What do you guys done internally? When we first met, it was and I think, a snowy day in Boston, right at the spark. Some it years ago. And you did a big career switch, and it's obviously working out for you, But But what are some of the things? And you were in part, brought in to help IBM internally as well as Interpol Help IBM really become data driven internally? Yeah. How has that gone? What have you learned? And how are you taking that to customers? >>Yeah, so I was hired three years ago now believe it was that long toe lead. Our internal transformation over the last couple of years, I got I don't want to say distracted there were really important business things I need to focus on, like gpr and helping our customers get up and running with with data science, and I build a data science elite team. So as of a couple months ago, I'm back, you know, almost entirely focused on her internal transformation. And, you know, it's really about making sure that we use data and a I to make appropriate decisions on DSO. Now we have. You know, we have an app on her phone that leverages Cognos analytics, where at any point, Ginny Rometty or Rob Thomas or Arvin Krishna can pull up and look in what we call E P M. Which is enterprise performance management and understand where the business is, right? What what do we do in third quarter, which just wrapped up what was what's the pipeline for fourth quarter? And it's at your fingertips. We're working on revamping our planning cycle. So today planning has been done in Excel. We're leveraging Planning Analytics, which is a great planning and scenario planning tool that with the tip of a button, really let a click of a button really let you understand how your business can perform in the future and what things need to do to get it perform. We're also looking across all of cloud and cognitive software, which data and A I sits in and within each business unit and cloud and cognitive software. The sales teams do a great job of cross sell upsell. But there's a huge opportunity of how do we cross sell up sell across the five different businesses that live inside of cloud and cognitive software. So did an aye aye hybrid cloud integration, IBM Cloud cognitive Applications and IBM Security. There's a lot of potential interplay that our customers do across there and providing a I that helps the sales people understand when they can create more value. Excuse me for our customers. >>It's interesting. This is the 10th year of doing the Cube, and when we first started, it was sort of the beginning of the the big data craze, and a lot of people said, Oh, okay, here's the disruption, crossing the chasm. Innovator's dilemma. All that old stuff going away, all the new stuff coming in. But you mentioned Cognos on mobile, and that's this is the thing we learned is that the key ingredients to data strategies. Comprised the existing systems. Yes. Throw those out. Those of the systems of record that were the single version of the truth, if you will, that people trusted you, go back to trust and all this other stuff built up around it. Which kind of created dissidents. Yeah. And so it sounds like one of the initiatives that you you're an IBM I've been working on is really bringing in the new pieces, modernizing sort of the existing so that you've got sort of consistent data sets that people could work. And one of the >>capabilities that really has enabled this transformation in the last six months for us internally and for our clients inside a cloud pack for data, we have this capability called IBM data virtualization, which we have all these independent sources of truth to stomach, you know? And then we have all these other data sources that may or may not be as trusted, but to be able to bring them together literally. With the click of a button, you drop your data sources in the Aye. Aye, within data. Virtualization actually identifies keys across the different things so you can link your data. You look at it, you check it, and it really enables you to do this at scale. And all you need to do is say, pointed out the data. Here's the I. P. Address of where the data lives, and it will bring that in and help you connect it. >>So you mentioned variances in data quality and consumer of the data has to have trust in that data. Can you use machine intelligence and a I to sort of give you a data confidence meter, if you will. Yeah. So there's two things >>that we use for data confidence. I call it dodging this factor, right. Understanding what the dodging this factor is of the data. So we definitely leverage. Aye. Aye. So a I If you have a date, a dictionary and you have metadata, the I can understand eight equality. And it can also look at what your data stewards do, and it can do some of the remediation of the data quality issues. But we all in Watson Knowledge catalog, which again is an in cloudpack for data. We also have the ability to vote up and vote down data. So as much as the team is using data internally. If there's a data set that had a you know, we had a hive data quality score, but it wasn't really valuable. It'll get voted down, and it will help. When you search for data in the system, it will sort it kind of like you do a search on the Internet and it'll it'll down rank that one, depending on how many down votes they got. >>So it's a wisdom of the crowd type of. >>It's a crowd sourcing combined with the I >>as that, in your experience at all, changed the dynamics of politics within organizations. In other words, I'm sure we've all been a lot of meetings where somebody puts foursome data. And if the most senior person in the room doesn't like the data, it doesn't like the implication he or she will attack the data source, and then the meeting's over and it might not necessarily be the best decision for the organization. So So I think it's maybe >>not the up, voting down voting that does that, but it's things like the E PM tool that I said we have here. You know there is a single source of truth for our finance data. It's on everyone's phone. Who needs access to it? Right? When you have a conversation about how the company or the division or the business unit is performing financially, it comes from E. P M. Whether it's in the Cognos app or whether it's in a dashboard, a separate dashboard and Cognos or is being fed into an aye aye, that we're building. This is the source of truth. Similarly, for product data, our individual products before me it comes from here's so the conversation at the senior senior meetings are no longer your data is different from my data. I don't believe it. You've eliminated that conversation. This is the data. This is the only data. Now you can have a conversation about what's really important >>in adult conversation. Okay, Now what are we going to do? It? It's >>not a bickering about my data versus your data. >>So what's next for you on? You know, you're you've been pulled in a lot of different places again. You started at IBM as an internal transformation change agent. You got pulled into a lot of customer situations because yeah, you know, you're doing so. Sales guys want to drag you along and help facilitate activity with clients. What's new? What's what's next for you. >>So really, you know, I've only been refocused on the internal transformation for a couple months now. So really extending IBM struck our cloud and cognitive software a data and a I strategy and starting to quickly implement some of these products, just like project. So, like, just like I just said, you know, we're starting project without even knowing what the prioritized list is. Intuitively, this one's important. The team's going to start working on it, and one of them is an aye aye project, which is around cross sell upsell that I mentioned across the portfolio and the other one we just got done talking about how in the senior leadership meeting for Claude Incognito software, how do we all work from a Cognos dashboard instead of Excel data data that's been exported put into Excel? The challenge with that is not that people don't trust the data. It's that if there's a question you can't drill down. So if there's a question about an Excel document or a power point that's up there, you will get back next meeting in a month or in two weeks, we'll have an e mail conversation about it. If it's presented in a really live dashboard, you can drill down and you can actually answer questions in real time. The value of that is immense, because now you as a leadership team, you can make a decision at that point and decide what direction you're going to do. Based on data, >>I said last time I have one more questions. You're CDO but you're a polymath on. So my question is, what should people look for in a chief data officer? What sort of the characteristics in the attributes, given your >>experience, that's kind of a loaded question, because there is. There is no good job, single job description for a chief date officer. I think there's a good solid set of skill sets, the fine for a cheap date officer and actually, as part of the chief data officer summits that you you know, you guys attend. We had were having sessions with the chief date officers, kind of defining a curriculum for cheap date officers with our clients so that we can help build the chief. That officer in the future. But if you look a quality so cheap, date officer is also a chief disruption officer. So it needs to be someone who is really good at and really good at driving change and really good at disrupting processes and getting people excited about it changes hard. People don't like change. How do you do? You need someone who can get people excited about change. So that's one thing. On depending on what industry you're in, it's got to be. It could be if you're in financial or heavy regulated industry, you want someone that understands governance. And that's kind of what Gardner and other analysts call a defensive CDO very governance Focus. And then you also have some CDOs, which I I fit into this bucket, which is, um, or offensive CDO, which is how do you create value from data? How do you caught save money? How do you create net new revenue? How do you create new business models, leveraging data and a I? And now there's kind of 1/3 type of CDO emerging, which is CDO not as a cost center but a studio as a p N l. How do you generate revenue for the business directly from your CDO office. >>I like that framework, right? >>I can't take credit for it. That's Gartner. >>Its governance, they call it. We say he called defensive and offensive. And then first time I met Interpol. He said, Look, you start with how does data affect the monetization of my organization? And that means making money or saving money. Seth, thanks so much for coming on. The Cube is great to see you >>again. Thanks for having me >>again. All right, Keep it right to everybody. We'll be back at the IBM data in a I form from Miami. You're watching the Cube?

(upbeat music) >> Announcer: Live from New York, it's the Cube, covering AWS Global Summit 2019. Brought to you by Amazon Web Services. >> Welcome back, we're here in New York City at AWS Summit, one of the regional summits. Over 10,000 people in attendance. I'm Stu Miniman. My cohost is Corey Quinn and happen to welcome Slalom to the program for the first time. So, Slalom, like Amazon themselves, is based in Seattle, yet, also has a presence here in New York City. And representing that, to my right, we have Parul Patel, who is the managing director of middle market for Slalom, based here in New York City. >> That's right. >> World Trade Center, I believe that's where your office is. >> That's right. >> That's excellent. And Joe Berg is the managing director with Slalom based out of Seattle. Thank you so much for joining us. >> Yeah, thank you. >> All right, so, I did walk by the booth this morning. Build as a service is the big takeaway. But for our audience that might not be familiar with Slalom, give us the bumper sticker. >> Yeah, so, the way we like to tell the story is Slalom is a modern consulting company focused on strategy, technology and business transformation. As a part of the technology work that we've been doing for clients for the last couple decades, we started to see a shift in that really with the advent of cloud and companies like AWS. Really changing the technology landscape and what was really possible. You mentioned the build as a service tagline. That's really what the operating model that we built to serve those customers at the scale and at the velocity that they're starting to execute on their most mission critical digital initiatives today. So build as a service is really how we dig in and leverage platforms like AWS and provide value for customers. All right, so, Parul, one of the things we like about these regional summits is it's not just little bit rinse and repeat when you go to the environments-- >> Parul: Right. >> But they do speak to the local market. So when you look at that, some of the customers in the keynotes, you expect to see some financial services-- >> Parul: Right. >> Being here in New York City. A startup like Door Dash, where they were here. Give us your viewpoint, what is special or unique about the greater metropolitan region here in New York City that you see with your customers. >> Sure. So I think as we think about New York as a market, a lot of industries, a lot of companies that are based here. Certainly financial service is one of the big ones. But the buzz in the market is all about cloud. What are we going to do, how are we going to get into the cloud? The question we like to ask our customers is, why. Why do you want to be in the cloud? And what we're seeing, especially in financial services, is a lot of innovation. So as we think about what Joe does from a build as a service perspective, we have a client in financial services who they wanted to figure out, how do we generate more revenue? So we built them, with our build as a service capability, an AWS platform that helped them bring data together and figure out how to monetize that data across different business units and innovate. And so I think it's things like that that we ask that question of why. We can leverage cloud to really do that transformation. >> That's great. We always talk about IT can't be the organization of no. Or, as a friend of mine, Alan Cohen, said, that there's the triangle of no and slow and we need to move up to the top, which is go. >> Right. >> So how does cloud help with that move forward. That love story you talk about, how do I monetize data, how do I move that forward. There's been that promise of that but how do I turn it from a lofty goal into actual reality? >> Yeah, maybe Joe, I'll let you answer that with a little bit of how we bring it to life with our build as a service. >> Honestly, we look at cloud, it's not just an enabler of business today. It's almost fueling business today. And the reality is, the customer consumer demand out there for digital experiences is exponentially growing, right? Organizations are trying to transform themselves into these modern technology companies. Doesn't matter what industry, financial services-- >> Parul: That's right. >> Or otherwise, they're really trying to transform themselves. And cloud is really allowing them to get out of the procurement game, out of the infrastructure game, out of the data center game and really start to lean into, how do I just make use of this in a meaningful way that's going to translate into those revenue streams that Parul talked about. >> It's deceptively complex. Sorry, it's deceptively simple, I suppose, to take a look at what cloud represents, of, okay, now whatever you want instead of buying it, waiting six weeks for it to show up, if you're lucky, and then racking it. Suddenly, it's an API call away. The technology piece is interesting but how does that impact the cultural change, the processes, the governance story about it? The cost control, speaking as a cloud economist, how do you find that this is revolutionizing these companies as they are migrating into this brave new world and transforming? >> Joe: Yeah. >> When I think about cloud, so to me, it isn't a technology play at all across a business. It's about changing your business that starts with changing your mindset. So, being in the cloud, and leveraging cloud, is about how do I do things different? And that means, I'm looking at my fundamental operating model. I'm looking at who my customers are and then changing the mindset of my people. And culturally, we're going to become faster, we're going to iterate a lot more. And having things like cloud, which I can spin up instances at the click of a button, makes it easier for me to do that. But it comes with, I've got to think about my people, right? And I always tell our clients, explain to your people why this is important to them and why it's important to the business because they're going to be able to learn new skills. They're going to be able to do more and become more marketable out there. And so, to me it's a company transformation, not necessarily a technology play at all. >> Yeah, and I'll just maybe piggyback on the back of that. When you take your strategy and you start to think about translating how we're going to do things in a digital business environment and you start to think about the demands that consumer base has on how fast you release features, how quickly you are procuring new experiences for them. It is absolutely about an operating model that can translate strategy and do initiative and budgeting planning into execution very quickly. It's also, then, about when they move into the actual execution. IT organizations were not built to build technology products. They were there to build technology projects. And the confluence of those events of this becoming mission critical and part of their external facing strategy has really required that transformation and cultural shift as well, in terms of how do we build things very fastly and quickly-- >> Parul: That's right. >> Get them out to market in a iterative way that has impact and benefit and value to consumer. And I think that is the holistic complexity that organizations are dealing with, with something that is making technology very simple but the actual then motion of getting that technology to be useful is complex. >> Yeah, and it becomes very challenging to get to a point of people who are used to the old way of doing things. they're seeing the skillset that's required continue to evolve. And it's very challenging for a large-scale company to say, okay, I'm going to go out this week and hire 2,000 new people who are all up to speed on a cloud provider. >> Parul: Yeah. >> That's something that's almost impossible for people to do. So there has to be a bridge. There has to be a story that isn't, well, we're going to replace you with a younger version. >> Right. >> There has to be something that opens a door and a way to get there. And doing that both culturally and on an individual level seems like it's something a lot of companies are struggling with right now. Is that something you're seeing in your customer base? >> Absolutely. I love that question because it gives me an invitation to talk about build as a service. >> That's right. >> And build as a service, we're playing on the as a service language that companies like AWS establish, right? And the idea about build as a service is it's instantly available. You've got idea, you need to go start executing quickly. Maybe competitor A has already built an experience out there that is surpassing you in the marketplace. You don't time to think about, how do I pull all these things together? How do I upskill my resources in terms of skillsets and capabilities to then get to the point where I can execute? I need to do that now. But, I'm also on this journey of transforming my internal culture and my people and my skillsets. So how do I get a jumpstart in that. We have built a model to help our customers instantly tap into that. And these multidisciplinary teams that really holistically are bringing solution to customer, but we're also doing this in what we call a co-creation model. How do we help them learn and adopt those same principles that are going to help them build modern technology, software and products when we're gone and they're becoming self-dependent. And I think that is part of the journey of how you can leverage a company like Slalom. >> And that's why I would add, Joe, as we think about our offering, it is about getting to velocity in the software engineering space in the cloud. But this co-creation concept, I think, is one that we've heard from our clients that not a lot of people do. It's easy for partners to come in and say, here, we'll just do it for you. And our model is, we want to do it with you to the point where, when you have an agile team, we've got a mixed team of Slalom team members and client team members where we're helping the client team members learn along the way because these are all new technologies that are evolving so fast that it's hard to keep up, for anyone. >> It give me hope to hear what you're saying here, 'cause we all have the scars of listing through. It's like, okay I did a big rollout. Oh how'd it go? Well, you know, it was six to 12 months later than we thought and we all did the corporate mandated training. Yet, a month later, we're all lookin' at each other sayin, oh my gosh, how do we deal with what we have? And of course, it is no longer just waterfall and throw things over there. It is constantly changing. Therefore, co-creation is a term we love-- >> Joe: Yeah. >> And help us walk through. How long is an engagement like this? How much is there the ramp up? And then, as a service, so I'm assuming there is maintenance and you're staying engaged as after we are through some of those milestones. >> Sure, sure. Well, I always kind of start with, we moved from, as I said earlier, a project mindset into a product mindset. So each of these we consider its own piece of software. And product really starts way out here on the ideation site. So Parul talks a lot with customers about the strategy of what new revenue streams you need be thinking about. how do you engage with experiences? Once we move into this I know what I want to build. Now I just don't know how I'm going to get there to the finish line, as you were talking about earlier, Stu. That's really where we enter in with this build as a service model. And we start with a short four to six week discovery phase. So we can start to establish the foundation of what we're going to build together with our customers. That's where co-creation starts, right? What are other priorities? What are the features? How do we do agile together, which is usually a term companies use but it's not a term they know how to use, or a motion they know how to exercise well with. And so, how do we establish those things that we're going to create together? And then we scale into what we would call an MVP release cycle. Our whole idea is that we help you get to an MVP. We help you get to more viable product and then you start to become the owners of those future releases. That's that co-creation piece, where we can bring you alongside us, establish culture, actually create business value by actually getting something out the door. And then, you start to own it yourself. Depending on the competency and the abilities of the customers we work with, that can vary in terms of when those transitions happen. But we look at that as typically anywhere from a 10 to 14 week exercise to get that first iteration out. And then we start to iterate faster than that. >> Are most of your customers, are they just dealing with the people that they have in house? Or are they having to bring in new people to help with that transition along the way? I'm assuming it's a bit of a mix. >> I think it is a bit of a mixed bag and I think one of the keys, what I like about our philosophy, is that, we're all about how do we get you working software as quickly as possible. While we can do a four to six week discovery, we have client in a startup in the healthcare space, where we got them through discovery within four weeks. We do two week sprints. After three sprints, we had software up and running. And so, within 10 weeks, we said, here's what you need, and here's some working software. That I think, in a lot of ways, people say, hey, we're agile, we work fast. People typically are not delivering software in 10 weeks. And that to me, is the differentiator for the way we approach our problems, is we want to get to that working software as fast as possible. >> Right, at some point it almost feels like agile stopped meaning agility and started meaning we have a lot of meetings every morning. >> Parul: Yes. >> Joe: That's right. >> And that doesn't work. >> Yeah. >> That's right. That's a great way to say it, yeah. >> All right, a lot of customers here. Tell us what are some of the top things you're hearing from people. What bring them to your booth? What are some of those things that kind of set off the, oh this is a good fit for working with Slalom. >> Sure, well, I get asked all the time, what industries do you guys work in? Where is this most relevant, especially when you're talking about build as a service. And the reality is, it just slices horizontally right through every industry. Because, I don't know of an industry, whether it be healthcare, financial services, retail, manufacturing, I don't know of one that isn't on that journey. They're at different places on that journey, and the adoption curve, but usually we seem them coming. I think there's a stat out there that says 80% of the enterprise customers have adopted cloud. But only about 10% of the work clothes are on cloud, right? >> Parul: That's right. >> So they're coming to us with saying, hey we know we're on this journey of moving to the cloud, but we're stuck in really getting the most value out of the cloud and how can you help us accelerate the value that we believe is there with a platform like AWS? And that's where we're really entering in and finding those critical experiences that are going to create value, not only internally in terms of momentum, but externally in terms of their business. >> Yeah. And I would say that as we think about when companies look at us and why they picked Slalom as an organization to work with, one of the key differentiators is we like to work with people that we enjoy working with. So we truly want to partner with our clients and so companies say, you know what? We want people that we enjoy working with. we want people that are going to challenge us and be innovative. And that's what you're going to get. When you get Slalom, if you're lookin' for someone to be innovative and challenging a little bit, we're probably not the best fit for your company, right? That's just being honest out there. But I think the other piece of it is that we want to accelerate your journey and enable you to do it. So, we're not in the business. While we have long-term capabilities, like as a service, etc, we're not in the business of taking over your business or being in the outsourcing space. And so, our mindset is all about how do we make you better? And help you realize your vision? And I think that's why we work across a lot of different industries and a lot of different types of companies. >> Joe and Perul, really appreciate you helping share how you're helping customers through that journey, through great adoption in the cloud. Thanks for sharin' and all the updates on Slalom. >> Thank for having us! >> Yeah, thanks for havin' us. >> All right. for Corey Quinn, >> Take care. >> I'm Stu Minimann. We'll be back here with lots more coverage from AWS Summit in New York City. Thanks for watchin' the Cube. (upbeat music)

>> Live from Las Vegas. It's theCUBE. Covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back, everyone to theCUBE's live coverage of the AWS Executive Summit here in Las Vegas. I'm your host, Rebecca Knight. We have today, Mahmoud El Assir, he is the CTO and Senior Vice President of Global Technology Services at Verizon. And Annette Rippert, Senior Managing Director, Accenture Technology, North America. Thank you so much for coming on theCUBE. >> Great to be here. >> Thanks for having us. >> So we are talking today about Verizon's migration to the cloud, but Verizon is a company that many people have familiarity with, Mahmoud. Just lay out a few facts and figures for our viewers here. >> Sure, I'll say Verizon is Fortune 16 company. Last year we made $126 billion dollars from our, kind of, loyal customers. We are, today we deployed, we were the first people to deploy 5G. And we have 98% coverage in U.S., so we are America's fastest and most reliable wireless service. >> So it's a company that touches so many of our lives. >> Yup. >> Earlier this year, Verizon selected AWS as its preferred cloud provider. What was, one, what was the impetus for moving to the cloud? And two why AWS? >> Yeah, that's a great question. But I'd like to zoom out a little but more and talk about what is Verizon? What's our mission and how kind of tackling it? So when you think about Verizon, our mission is to deliver the promise of the digital world, right? Enable, deploy 5G and enable the 4th Industrial Revolution. And as part of this, it's all about empowering humans to do more, right? And in global technology solutions our winning aspiration is to develop products and services that our customers and employees love. And then we, and also to be the destination for world class technology talent. And be the investment innovation center for the company. So when it comes to digital transformation we look at the enables and where we want to invest our energy and how do you want to leverage the right partners. So the heart of our technology transformation is the public cloud. When you think about what the public cloud, it's like where you want us. It will allow us to spend more of our energy building solutions and for our customers. And creating value for our customers. Also public cloud will allow us, and or business, to experiment faster, better and cheaper. In technology our focus is to always save on efficiency, speed and innovation. So that is our kind of model and at the heart of this, public cloud is a key kind of element for our journey. >> Well I want to get into that journey a little bit more, but Annette, I want to bring you into the conversation here. So, Verizon is one of the leading communications companies that is migrating to the cloud at this scale. >> Yes. >> What are some of the lessons, as you have helped and observed and also helped this partnership grow, what are some of the key takeaways that you would say? >> Well, I think there is a couple you know, if you take a look at some of the lessons that our clients learn. You know when at Accenture we go into the market really helping our clients think about how do we leverage technology for achieving business outcomes. You just talked about some extraordinary business outcomes that you're looking to achieve and you'll do that through a variety of things, including leveraging technology. And so, just like that we encourage our clients to be thinking about what is the business innovation? What is the outcome? The disruption that we're looking to achieve through leveraging technologies like AWS, right? I think secondly, if you think a little bit about the importance in that journey of communicating that vision. Of what will it mean to be able to leverage that kind of technology? You just communicated a very strong vision. And that's so important to the change journey that many of these organizations go on. You know there is the importance of the investment strategy, but ultimately, the innovation that the organization itself the engineers within the organization are a part of delivering, you know, the kind of innovation that you'll be delivering is really, it will not only make such a big impact on those in your enterprise, delivering that. But, you know, to all of us who are consumers of your business strategy which will be fabulous. And I think, in the end, you know, one of the most exciting things, and it's really sitting Alexis, as we were talking a little bit about some of what Verizon is doing earlier in the day, one of the most important things is really thinking about how this provides an opportunity for the enterprise to change. So, you know, moving to be a much more agile enterprise, being able to respond to market changes, and certainly in the business that you're in, the market is changing everyday. And so by leveraging innovative products like AWS' platform, you know it really provides the opportunity to constantly leverage new technology in that environment. >> And that, as you said, the market is changing everyday and customers, they're demanding things and companies are providing customers with things they don't even know that they want until they have them in their hands. How, at a time when customer differentiation is such a key competitive advantage, how are you staying ahead of the game and making sure that you know you're sort of getting inside the heads of your customers? And then you're also delivering what they want and expect. >> Customers comes first at Verizon, right? So it's at the heart of our technology is also leveraging emerging technology. So cloud is one, scaling AIML is another one. One of the big programs we're doing is, how do you move personalization to one-on-one personalization? How do you make every customer feel they have their own network, our network. Like their own network that's personalized for their needs. There own experience, their own plans. Their own recognition. So that's key. So today when you think about most companies do segmentation or personalization at the cluster level. So one of the biggest things is we're shifting now from systems of engagement, and systems of records. We're inserting systems of insights. A system of insight allows to build the DNA for every customer and will allow us to personalize the customer experience for every customer at the customer level based on all the data, kind of, we know about them, from the data they use with us, and will allow us to personalize their experience at every touch point. >> So what, how would that look like? What will a personalized customer experience at Verizon look like in the future going forward? What are some of your goals and aspirations? >> Imagine you're like a, you've bought every iPhone, since iPhone one through like iPhone ten, right? >> I can imagine that. >> So you're an iPhone enthusiast, right? So, when you come up on our website recommend, like the iPhone, the next iPhone say, the next iPhone is up, the next iPhone red is up or so. So we know more about you and your history and we recommend right accessories, we recommend and so we tell you, hey this stuff is coming. So you feel we're watching out for you. You're like we know, we know you. We know you better than anybody. So at any touch point when you come to us we kind of tell you what's the next thing for you. And then even when you don't know we, like from a network kind of performance from everything we proactively, kind of cater for you. That's a big one. The other one, how do you, when you want to talk to us, how do you get leverage technology like Chatbots and conversationally AVRs and stuff. And make sure you feel you're like, we know you. If you have a different accent, we recognize the accent, then you say, hey do you want to speak in that language? >> (laughs) >> So imagine the power of doing that. Versus today you have to do, like you have Spanish AVR, you have to have a, or have a Spanish kind of call center. Imagine through a IML and Chatbots and stuff, you can recognize all the stuff and personalize the experience. Today at Verizon, we are known of our network superiority. And we have great customer experience but we want to be known also for our experience the same way we are known for our network. And we believe that at Verizon, there is always a higher gear. So we all aspire for the higher gear and aspire our customers to feel they have a Verizon for every customer. >> So this, that's from the customer experience. And as you said, the goal is to have the customer feel that the company empathizes with them and really gets them. What about the workforce changes? I mean Annette was talking about the importance of change management and the cultural shift that these kinds of transformations entail. Have you come up against any challenges at Verizon in terms of this migration? >> Sure I would say, at the heart of our kind of transformation, there are four main pillars. The first pillar is, enabling all these modern technologies. This is like cloud, Cloud Native, API, AI, ML. And especially go back to cloud, the time of enabling cloud was very important to get everybody on board at beginning of the journey. So one of our biggest thing is to get like the security team on board, as early in the process as possible, and make sure security team is a development team, not just a kind of a controls team. So having an engineering team on the security side is a big one to kind of automate all this kind of, all the security controls we need in the cloud so we have the right guardrails and have everything automated. Another thing, same thing like with the other teams. Get them on board in the journey have an advisory kind of board with the other team and security team and legal teams and everybody is onboarding on the journey. So that's I'd say key and pay lots of dividends investment upfront but pays lots of dividends so you can move faster. It's like more of a slow down to speed up. So that's a big one. The second one is, technology is one thing, but you need the culture. So you need to have sustainable momentum in this kind of movement. So the proxy we wanted to have is like have AWS certifications. Because you need 10% believers to have momentum. So our proxy to believers is AWS certifications. So we put a program in place we call it: Verizon Cloud Train. And that train basically is like a 12-week, six sprints, and we help our teams prepare for their certification. So last year we did more than a thousand, we have more than 1800 people probably right now certified with AWS. >> That is incredible. >> At the same time, we set up a dojo's, which are like emergent centers. So we have like 40, 50 seats in different cities and with like five six coaches. So if you are a team who wants to come in and move your application to the cloud, we help you do it. If you want to decompartmentalize your application to microservices we help you. If you want to do ABI's, we help you. So we helped you build deep expertise into these technologies we are doing. So that is like, transforming the teams, and up scaling, I would call it up scaling the talent, is key. Hiring great talent in key rolls is also key. The third pillar is changing the way we work from, what you call a project based, to outcome based. And this beyond agile. Agile is an enabler for this, but how do you change the model where everything is outcome based? Where you have the business and the technology team working together to move an outcome. If I want to increase my kind of video-on-demand revenue per customer, everybody making all the changes, experimenting, and making sure that's a need, is moving. It's not like I did my code, I delivered my, I did my testing, I deployed my app. It's what's a business and what's a customer kind of expectation. And fourth one is, how do you establish internal kind of communities and get out of a like the thiefdoms and stuff. And get a culture of kind of sharing and cheering for others. So we have like Dev Ops days internally within the company, bring in external, internal speakers. We have internal kind of intersourcing for some piece of code. So you have to fire on all cylinders I would say. And get as many kind of parties included as early in the process. And have also an objective to have everything as code. And it's a journey, so you have to always keep on exercising new muscles and more muscles and the more muscles you exercise, the faster you can go. >> So Mahmoud, Annette already shared with us her key learnings from your experience and your journey. What would you say, I mean you're hear at AWS reInvent, it's not your first rodeo, you've been to this conference many times before. When you're talking with other CTO's, CIO's and they're saying, hey, so how's it going for you? What's your advice for a company that is really just starting this, this process? >> Sure, I would say the movement to the public cloud is not just a cost play. I mean, cost needs to be, efficiency needs to be there, but that shouldn't be the primary kind of objective. The primary objective should be speed and innovation. At the same time, deliver a cost. Lots of people say, oh do I, is the same, you can't compare it same-for-same. Because it's different. On prem you can do like A, B testing. In the cloud you can do A to Z testing for much cheaper. You don't need everything you have on prem. You can experiment, so think about it as accelerating the speed of innovation. That's the key one. And I said it before, but I'll say it again. It's like all about having the right kind of, from like a security perspective, people will argue, oh public cloud is insecure? I would argue, public cloud can be more secure than on prem because you have all the tools to kind of automatically, kind of protect and detect and recover. And you have more tooling to allow you to be more secure. It's having the right kind of guardrails and the right controls, right automation and right teams. So it's, you have to build muscle across all these fronts. And have them as a front as possible. >> Great, and great note to end on. Thank you so much Mahmoud and Annette. >> Thank you. >> I appreciate it. >> Very good. >> Been really fun having you on the show. >> Thank you. >> Thank you for having us. >> We will have more >> Thanks, Ann. >> from theCUBE's live coverage of the AWS Executive Summit, coming up in just a little bit. (upbeat music)

>> Live, from Times Square, in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI. Brought to you by IBM. >> Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage, and we're here covering a special presentation of IBM's Change the Game: Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50-60 analysts here. They've got a partner summit going on, and then tonight, at Terminal 5 of the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreesha Rao who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. >> Thank you, Dave. >> Well, thanks Dave for having us. >> Yes, always a pleasure Seth. We've known each other for a while now. I think we met in the snowstorm in Boston, sparked something a couple years ago. >> Yep. When we were both trapped there. >> Yep, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bhandari, and you guys are doing inside of IBM. I want to talk a little bit more about your other half which is working with clients and the Data Science Elite Team, and we'll get into what you're doing with Niagara Bottling, but let's start there, in terms of that side of your role, give us the update. >> Yeah, like you said, we spent a lot of time talking about how IBM is implementing the CTO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM, and we found a consistent theme with all the clients, in that, they needed help learning how to implement data science, AI, machine learning, whatever you want to call it, in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise, so we felt really strongly that it was important for the future of IBM that all of our clients become successful at it because what we don't want to do is we don't want in two years for them to go "Oh my God, this whole data science thing was a scam. We haven't made any money from it." And it's not because the data science thing is a scam. It's because the way they're doing it is not conducive to business, and so we set up this team we call the Data Science Elite Team, and what this team does is we sit with clients around a specific use case for 30, 60, 90 days, it's really about 3 or 4 sprints, depending on the material, the client, and how long it takes, and we help them learn through this use case, how to use Python, R, Scala in our platform obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open-source, if they're not happy with what the product looks like, they can take their toys and go home afterwards. It's on us to prove the value as part of this, but there's a key point here. My team is not measured on sales. They're measured on adoption of AI in the enterprise, and so it creates a different behavior for them. So they're really about "Make the enterprise successful," right, not "Sell this software." >> Yeah, compensation drives behavior. >> Yeah, yeah. >> So, at this point, I ask, "Well, do you have any examples?" so Shreesha, let's turn to you. (laughing softly) Niagara Bottling -- >> As a matter of fact, Dave, we do. (laughing) >> Yeah, so you're not a bank with a trillion dollars in assets under management. Tell us about Niagara Bottling and your role. >> Well, Niagara Bottling is the biggest private label bottled water manufacturing company in the U.S. We make bottled water for Costcos, Walmarts, major national grocery retailers. These are our customers whom we service, and as with all large customers, they're demanding, and we provide bottled water at relatively low cost and high quality. >> Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. I always observed, really got into a lot of organizations. I was always observed that it was really the heads of Application that drove AI because they were the glue between the business and IT, and that's really where you sit in the organization, right? >> Yes. My role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. >> So take us the through the project if you will. What were the drivers? What were the outcomes you envisioned? And we can kind of go through the case study. >> So the current project that we leveraged IBM's help was with a stretch wrapper project. Each pallet that we produce--- we produce obviously cases of bottled water. These are stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper, and this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around a pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. >> And over breakfast we were talking. You guys produce 2833 bottles of water per second. >> Wow. (everyone laughs) >> It's enormous. The manufacturing line is a high speed manufacturing line, and we have a lights-out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods, pallets of water, going out. It's called pellets to pallets. Pellets of plastic coming in through one end and pallets of water going out through the other end. >> Are you sitting on top of an aquifer? Or are you guys using sort of some other techniques? >> Yes, in fact, we do bore wells and extract water from the aquifer. >> Okay, so the goal was to minimize the amount of material that you used but maintain its stability? Is that right? >> Yes, during transportation, yes. So if we use too much plastic, we're not optimally, I mean, we're wasting material, and cost goes up. We produce almost 16 million pallets of water every single year, so that's a lot of shrink wrap that goes around those, so what we can save in terms of maybe 15-20% of shrink wrap costs will amount to quite a bit. >> So, how does machine learning fit into all of this? >> So, machine learning is way to understand what kind of profile, if we can measure what is happening as we wrap the pallets, whether we are wrapping it too tight or by stretching it, that results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. >> I.e. too much material, right? >> Too much material is conservative, and aggressive is too little material, and so we can achieve some savings if we were to alternate between the profiles. >> So, too little material means you lose product, right? >> Yes, and there's a risk of breakage, so essentially, while the pallet is being wrapped, if you are stretching it too much there's a breakage, and then it interrupts production, so we want to try and avoid that. We want a continuous production, at the same time, we want the pallet to be stable while saving material costs. >> Okay, so you're trying to find that ideal balance, and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share with that. >> Yes, so each pallet takes about 16-18 wraps of the stretch wrapper going around it, and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight which is the amount of plastic that goes around each of the pallet. >> So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data that was, "if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? >> Yes. >> So it's a truly predictive and iterative model that we've built with them. >> And, you're obviously injecting data in terms of the trip to the store as well, right? You're taking that into consideration in the model, right? >> Yeah that's mainly to make sure that the pallets are stable during transportation. >> Right. >> And that is already determined how much containment force is required when your stretch and wrap each pallet. So that's one of the variables that is measured, but the inputs and outputs are-- the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. >> And the data comes from where? Is it observation, maybe instrumented? >> Yeah, the instruments. Our stretch-wrapper machines have an ignition platform, which is a Scada platform that allows us to measure all of these variables. We would be able to get machine variable information from those machines and then be able to hopefully, one day, automate that process, so the feedback loop that says "On this profile, we've not had any breaks. We can continue," or if there have been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. >> Yeah, so think of it as, it's kind of a traditional manufacturing production line optimization and prediction problem right? It's minimizing waste, right, while maximizing the output and then throughput of the production line. When you optimize a production line, the first step is to predict what's going to go wrong, and then the next step would be to include precision optimization to say "How do we maximize? Using the constraints that the predictive models give us, how do we maximize the output of the production line?" This is not a unique situation. It's a unique material that we haven't really worked with, but they had some really good data on this material, how it behaves, and that's key, as you know, Dave, and probable most of the people watching this know, labeled data is the hardest part of doing machine learning, and building those features from that labeled data, and they had some great data for us to start with. >> Okay, so you're collecting data at the edge essentially, then you're using that to feed the models, which is running, I don't know, where's it running, your data center? Your cloud? >> Yeah, in our data center, there's an instance of DSX Local. >> Okay. >> That we stood up. Most of the data is running through that. We build the models there. And then our goal is to be able to deploy to the edge where we can complete the loop in terms of the feedback that happens. >> And iterate. (Shreesha nods) >> And DSX Local, is Data Science Experience Local? >> Yes. >> Slash Watson Studio, so they're the same thing. >> Okay now, what role did IBM and the Data Science Elite Team play? You could take us through that. >> So, as we discussed earlier, adopting data science is not that easy. It requires subject matter, expertise. It requires understanding of data science itself, the tools and techniques, and IBM brought that as a part of the Data Science Elite Team. They brought both the tools and the expertise so that we could get on that journey towards AI. >> And it's not a "do the work for them." It's a "teach to fish," and so my team sat side by side with the Niagara Bottling team, and we walked them through the process, so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? So it's side by side with their team. Our team sat there and walked them through it. >> For how many weeks? >> We've had about two sprints already, and we're entering the third sprint. It's been about 30-45 days between sprints. >> And you have your own data science team. >> Yes. Our team is coming up to speed using this project. They've been trained but they needed help with people who have done this, been there, and have handled some of the challenges of modeling and data science. >> So it accelerates that time to --- >> Value. >> Outcome and value and is a knowledge transfer component -- >> Yes, absolutely. >> It's occurring now, and I guess it's ongoing, right? >> Yes. The engagement is unique in the sense that IBM's team came to our factory, understood what that process, the stretch-wrap process looks like so they had an understanding of the physical process and how it's modeled with the help of the variables and understand the data science modeling piece as well. Once they know both side of the equation, they can help put the physical problem and the digital equivalent together, and then be able to correlate why things are happening with the appropriate data that supports the behavior. >> Yeah and then the constraints of the one use case and up to 90 days, there's no charge for those two. Like I said, it's paramount that our clients like Niagara know how to do this successfully in their enterprise. >> It's a freebie? >> No, it's no charge. Free makes it sound too cheap. (everybody laughs) >> But it's part of obviously a broader arrangement with buying hardware and software, or whatever it is. >> Yeah, its a strategy for us to help make sure our clients are successful, and I want it to minimize the activation energy to do that, so there's no charge, and the only requirements from the client is it's a real use case, they at least match the resources I put on the ground, and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. >> So you've got to have skin in the game obviously, an IBM customer. There's got to be some commitment for some kind of business relationship. How big was the collective team for each, if you will? >> So IBM had 2-3 data scientists. (Dave takes notes) Niagara matched that, 2-3 analysts. There were some working with the machines who were familiar with the machines and others who were more familiar with the data acquisition and data modeling. >> So each of these engagements, they cost us about $250,000 all in, so they're quite an investment we're making in our clients. >> I bet. I mean, 2-3 weeks over many, many weeks of super geeks time. So you're bringing in hardcore data scientists, math wizzes, stat wiz, data hackers, developer--- >> Data viz people, yeah, the whole stack. >> And the level of skills that Niagara has? >> We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it, and that's the whole journey that we are in, in trying to quantify with the help of data, and be able to connect our systems with data, systems and models that help us analyze what happened and why it happened and what to do before it happens. >> Your team must love this because they're sort of elevating their skills. They're working with rock star data scientists. >> Yes. >> And we've talked about this before. A point that was made here is that it's really important in these projects to have people acting as product owners if you will, subject matter experts, that are on the front line, that do this everyday, not just for the subject matter expertise. I'm sure there's executives that understand it, but when you're done with the model, bringing it to the floor, and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some guy or lady sitting up in the ivory tower saying "thou shalt." >> Now you don't know the outcome yet. It's still early days, but you've got a model built that you've got confidence in, and then you can iterate that model. What's your expectation for the outcome? >> We're hoping that preliminary results help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process. And through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plants. And if the plants decide, hey they have a subjective preference for one profile versus another with the data we are capturing we can measure when they deviated from what we specified. We have a lot of learning coming from the approach that we're taking. You can't control things if you don't measure it first. >> Well, your objectives are to transcend this one project and to do the same thing across. >> And to do the same thing across, yes. >> Essentially pay for it, with a quick return. That's the way to do things these days, right? >> Yes. >> You've got more narrow, small projects that'll give you a quick hit, and then leverage that expertise across the organization to drive more value. >> Yes. >> Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing. >> Thank you. >> Congratulations. You must be really excited. >> No. It's a fun project. I appreciate it. >> Thanks for having us, Dave. I appreciate it. >> Pleasure, Seth. Always great talking to you, and keep it right there everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel. cubenyc #cubenyc Check out the ibm.com/winwithai Change the Game: Winning with AI Tonight. We'll be right back after a short break. (minimal upbeat music)

>> Announcer: Live from New York, it's theCUBE covering Big Data New York City 2016. Brought to you by headline sponsors Cisco, IBM, Nvidia, and our ecosystem sponsors. Now, here are your hosts, Dave Vellante and Peter Burris. >> We're back. Goutham Belliappa is here. He's with Capgemini. He's the Big Data Integration and Analytics Leader at Capgemini. Welcome to theCUBE. >> Thank you. Happy to be here with you. >> So a lot going on this week at Big Data. You guys have one of the top SI's consultants in the world. What are you seeing as far as the transformation of organizations to become data driven? What are some of the drivers that you're seeing out there? >> It's a good question. So a couple of years ago, we started on this journey with Cloudera about four years ago. When we started this journey on LinkedIn, you saw the poster that said, "Big Data is like teenage sex - everybody talks about it, nobody does it." Right? The reality shifted considerably. So while the technology's evolved considerably over the last four years, the most important thing is most of our clients are feeling pressure from the disruptors in Silicon Valley. You see the AirBnb's and the Amazon's and the Google apply pressure's on traditional industries that didn't exist before. For example, a lot of our auto clients don't believe auto clients are the biggest threat. They believe Apple, and Google, and Amazon are the biggest threat. Right? Because what our clients are afraid of, the incumbents, the traditional companies are afraid of, is they don't want to become a commodity manufacturer of components for a software company. They don't want, for example, GM manufacturing a part that Apple is putting the wrapper on, selling and making the margin on. So, more and more tech is driving the industry to where GE made the announcement they no longer want to be known as an engine manufacturer, they want to be an IT company. >> Peter: Or a financial services firm. >> Or a financial services firm. And you see the same thing in pharma as well. We see the pharma companies don't want to be known as manufacturers of med devices, they want to own the service industry. Move up the value chain and secure the revenue stream. So that's what's changing the industry as a whole and then Big Data Central to the strategy of data-enabled transformation. >> So it's like the death, what was the article we saw yesterday? Who wrote that? "The Death of Tech". It was Rob Thomas, right? The death of tech companies is now the rebirth of... all companies are tech companies. >> All companies are tech companies and that's the future of all companies: to be a tech company and move from selling commodities to selling services and having a vested interest in the outcome that the clients receive at the end of the day. >> Yeah, I once wrote a piece many year ago that suggested that we would see more non-tech companies generate SAS and Cloud applications than tech companies themselves. And while it's still hasn't come true there's evidence on the horizon that it very well likely will be a major feature of how companies engage their customers through their own version of SAS or deploying their own Clouds for their own ecosystem. And you can go back, thirty years, thirty-five years and look at MAP/TOP for example and the promise of what it meant to define and deploy standards that could integrate whole industries around data. Hasn't happened, but we can see it actually happening on the horizon. What industry? I mean, you're still looking at things through an industry lenses, right? Where do you see it happening before it's happening elsewhere? >> So, the first place it happens naturally is tech because they're closest to it, right? To give you the classic example, I can go anywhere and buy an Office license today. I have to subscribe to Office, right? So, what it's done to Microsoft, it's changed the fundamentals of the balance sheet from selling perpetual licenses, getting revenue once and then having the prospect of not having a customer later, to selling it over a sustained period of time. So moving from one-time revenue hits to perpetual revenue. So tech is where it's starting off. And even in tech, we're actually pushing the boundaries by working some of our providers like Cloudera and some of the other providers out there to move from a perpetual license model to as-a-service model. So what this enables people like us to do is to offer as-a-service to our customers because our customers need to offer as-a-service to their end users as well, right? I gave you the example of GE because it's public knowledge. They want to move up the spectrum of not selling an engine but leasing an engine to an airplane manufacturer and then owning the services revenue on it, right? So when Delta, let's say, that's leasing the engine is no longer owning a commodity, they're becoming asset light, right? The companies like GE and other companies when they become tech, they need to become asset light as well, which means not being burdened by land, labor, and capital but, as they get paid for outcome, they want to pay for outcome as well. >> Somebody's got to own the asset eventually. This is not a game of musical chairs where the asset-owning music keeps playing and then it stops and somebody's got all the assets. >> Ghoutham: Exactly. >> So how do you see... the global sense of how organization, how is this going to get institutionalized? Are we just going to have a few companies with enormous assets and everybody else running software? How do you think it's going to play out? >> Good question. So Jeff Bezos was at a manufacturing company outside of Arland recently and he pointed at and antique generator sitting next to the plane and said, 'Back in the day, everybody had 'a generator sitting next to the 'company producing electricity.' But today we have a big distribution plan and we get it off the grid, right? So to your point, yes, we see the scale and the price reduction coming from a few companies owning those pieces of assets. For example, it's almost impossible to compete with the Amazon's and Google's of the world today because at the scale that they receive. And the customers get the benefit of that. Similarly, you'll see the software, right? So software, you see the software companies owning the assets and title and leasing it back to the customer. So to your point, yes, we're moving to a model where it's more scalable and the price efficiencies of them, they're passed on to the end consumer. >> Peter: So historically, in a more asset-oriented company, historically, if you take a look, for example, at Porter. Porter's competitive strategy. So Porter would say, 'Pick your industry' where an industry is a way of categorizing companies with similarly procured and deployed assets. Automobile had a collection of assets and hotelery had a collection of assets. So pick your industry based on your knowledge and what kind of returns you're likely to get. Pick your position in that industry and then decide what games you're going to play using the five-factor analysis you did. But it was all tied back to assets. So if the world's getting less asset-oriented, hard asset-oriented >> Ghoutham: Hard assets >> What does that do to competitive strategy? >> Good point. So the hard assets are getting commoditized. The value comes in what you can build on top of the hard assets, which is your IP, right? So the soft assets of IP and software is where the value's going to be. So there's a lot of pressure on hard-asset companies. You see many companies getting at the server market because they can't compete with the Amazon's and the Google's. They can wide-label and manufacture all their stuff. The differentiation is going to come in the software. That's the reason companies like GE and the other pharma companies and automobile companies want to become tech companies, because that's where the margin is, that's where the differentiation is. It's no longer in the tangible, hard-assets but it's in what you can do with them. >> Dave: Well, and it says data's going to be one of those differentiators. >> Yeah, yeah. >> And a big asset so what... Everybody in theory has to become data-driven, maybe in fact has to be- >> Data is their asset, is their differentiator. >> You've pointed out many times all this digitization is data. >> Peter: Well, yeah. >> Digital equals data. >> So our basic proposition is that increasingly the whole notion of being a digital business is about how you differentially use data to create and sustain customers. So let me build on that for a second and say that there's this term in economics known as "asset specificity" which essentially is the degree to which an asset is applied to a single or limited numbers of uses. Programmability reduces asset specificity so if we go back to the airline engine example, GE added programmability to an airplane engine and was able to turn it into a service. Uber was able to add programmability to a bunch of consumer cars and was able to turn it into a ride sharing capability. What does that say about the future of an industry-oriented approach to conducting business if I am now able to reconfigure my asset base very quickly and the industry's based on how my assets are reconfigured. What does that say about the future of industry? >> Ghoutham: So, in my opinion, I don't think the future of industry is going to change because you still going to have a specialization based on the domain you're selling to and the expertise that you have. >> Peter: So it's customer-focused industry definitions not asset-based industry definition. >> Ghoutham: The hard assets or going to get commoditized and get moved out to a few specialty players. But the differentiation is going to be on how you serve the customers and the type of customer that you serve. >> Dave: So what are the head winds you're seeing in terms of customers getting to this data nirvana? What are the challenges that they're facing? >> So, Peter Drucker. There's an attribute of Peter Drucker, regardless of who said it, 'Culture eats strategy for breakfast.' We work with retailers all the time who understand that they face an existential threat from Amazon, however their culture prevents them from being like Amazon. It prevents them from experimenting. It prevents them from failing fast. It prevents them from acting together. For example, a lot of customers want to have an OmniChannel strategy. It's a seamless commerce strategy but then they have a silo for the stores they have a silo for the call centers, they have a silo for the web, but they don't act together. So culture is one of the biggest barriers we see in enabling that journey. Tech, we know that tech works. Two years ago we're doing technical POC's. Today, we're not anymore. We know that tech works, right? So get over it. So it's a culture and the attitude and the ability to change how you go to market that's to me the biggest challenge. >> Peter: But isn't there also finance? Because hard assets still are associated with a rate of amortization, depreciation, and utilization. There's expertise and what not built up around that, and this becomes especially critical when you start thinking about the impedance mismatch between agile development and budgeting, for example. So how do you anticipate that not only culture has to change, but also the way we think about finance? Or is financing disciplines end up being a part of the culture? >> Ghoutham: So you're absolutely right. So, financing discipline has to be part of the culture. To give you an abstract example, back in the day when we did a data warehouse or a data project, we'd do a huge, let's say for lack of an argument, 10 million dollar project. Today we're doing 40, 50, 50k, 100k projects. So Agile has gone from fixed scope where you laid out a two-year project with an end in mind and by the time you achieve that end the requirements have changed and the business has moved on, to achieving small objectives. So we're consuming it in chunks. You're going from fixed scope to fixed budget. So I've got a certain allocation that I need to use and I prioritize it on a regular basis on how I want to consume that basis that I have. >> So it's almost a subscription? Are you going in basically almost subscription-basis? Going to a customer and saying, here's the outcome. We will achieve that outcome over a period of time. You'll sign up to achieve that outcome over a 12-month period and will consume that budget in 12-month increments? >> First and second, in any given period, you can re-prioritize the outcome that you want to achieve. During the journey for 12 months, if you realize something new, you have the flexibility to change. Let me take out this chunk of work and do something else so I have the flexibility. >> Peter: So you can redefine the outcomes? >> Yes. >> It's almost like, I don't know if you'd call it this, I'd be interested to know what you guys call it, but it's almost like a subscription-to-outcome business model. >> Ghoutham: Exactly. >> Dave: Service is a service. >> Ghoutham: We call it sprint as a service. >> Service is a service. >> We call it sprint as a service is our defined model of how to go to market around that is we know two sprints ahead what we're going to deliver. Everything else is indicative, right? Because not everything we do has to succeed. That's a mindset change that our customers need to realize. We believe the biggest reason clients fail is because failure is not an option. They put so much behind it, when they fail, it's catastrophic. >> Peter: Because careers fail- >> Yes >> Peter: And not the project fails. >> Exactly. >> Dave: You're not saying "failure equals fire" mentality. If that's the culture, then people refuse to fail and they end up failing. >> Until it's catastrophic. >> (Dave laughing) >> So I was having a conversation last week at Oracle OpenWorld when theCUBE was here, great show, and had a really good conversation with a competitor of yours who talked about how they were going to use machine-learning in the contracting process by sweeping up all kinds of data and that would help them actually define the characteristics of what they were going to deliver. How much work was going to take, how much labor, what other resources? And they were able to get rid of the 500 thousand to five million dollar part of the assessment or the assessment part of a deal, drive it down to 50 thousand dollars or less and in the process come up with contracts who are much more customer-friendly. What other types of changes are happening in the services business as we do a better job of packaging intellectual property whether it's this "service as a service" or "service subscription" or whatever you mentioned or even thinking about machine learning being applied to the contracting process. >> Dave: "Sprint as a service" >> That's correct. Sorry. Thank you. >> You've asked a number of questions so first thing >> I did. >> Let me talk about machine learning and human task automation. So one of the biggest things we're doing today is learning to understand and automate human tasks. One of the biggest things we've seen, supply chain companies for example, is they don't have enough planners, right? So you hire a bunch of planners. You have different variations and skills. So we're taking the top 5% of planners, automating what everybody else does and letting them handle exceptions. And workforce automation, in many of those areas, we're beginning to automate human tasks and letting the human handle exceptions that a machine cannot handle. So machine learning has becoming fundamental in everything, and not just contract negotiation, but actually enabling companies to scale in areas where they could never scale because they never had enough people to do it. We're not just doing it externally to our clients. One of the things we're doing internally is we don't have an Big Data developers so we're beginning to use machine learning to automate a lot of tasks that developers will do. Industrialize a lot of it so we can scale in our delivery approach as well. >> Peter: Excellent. >> Come back to this event. You guys are here, you're on the floor. We've been talking all week about, you know, Hadoop is kind of yesterday's news. >> Ghoutham: Yes, yes. >> What are you guys seeing? You got a big chunk of customers that said alright, we're going to invest in Hadoop. We have the skill sets. And then a big chunk of... I'm not going there. And now they're sort of looking at new ways. Whether it's Cloud, whether it's Spark. >> Peter: And a big chunk of customers will say I do want to go there, but I'm having problems getting there. >> Yeah, right. And I got some serious challenges. So what are you seeing there, and how is CapGemini helping them? >> So we did an analysis with Forrester and one thing we'll say that 100% of our clients are going to Hadoop. It's not 95%. So everybody's going to Hadoop in one way, shape, or form. Whether you go with the traditional distribution, go with an Amazon as your whatever, everybody's going to Hadoop in some way, shape, or form. To address the reluctance, we spoke about the Uberization of the industry, which is you have a contract, which is an outcome-based contract. So we go to our clients who have fears about moving to Hadoop and say, 'We'll take the risk'. Let's write an outcome-based contract to move you guys into the noob because you know you need to go there. You're afraid to go there so we'll take the risk, we'll shift the risk over to us and we'll move you onto Hadoop. The last piece is industrialization. So back two years ago, we designed code for every little thing that we needed to do. Today, we've automated a lot of our code generation from existing systems, from knowledge we've gained, including machine learning to we're able to mechanize a lot of the code. Frankly, we did it because we had a developer shortage. So we started industrializing a lot of our IPN, our assets, and our learnings, but this is also helping our customers move on to the new world. It's improved the quality of a delivery. It's improved the velocity of a delivery. It's reduced the price where we're much more competitive. To give you an example in the BPO space back in the day we did labor arbitrage. But more and more, like with our clients who use manual auditing, we're using machine learning to automate a lot of that. And that more than pays for the cost of Hadoop. So to answer your specific question, gone are the days of 'Hey, I want to get into Hadoop.' The question is what business value can I achieve? How fast can I achieve it, and if you're afraid, can I take the risk for you? >> And that business value, historically, if I can use that term on such a nascent industry, Has been... the ROI's been a Reduction on Investment. >> Ghoutham: Correct. I'm going to lower the cost of my enterprise data warehouse. >> Ghoutham: That was two years ago. >> Okay so what is it today? >> Today, it is 'How can I reduce your marketing span? 'How can I optimize your marketing span? 'How can I improve the accuracy 'of your supply chain planning?' So it's more in terms of directly delivering business value versus the cost reduction. Many of our clients say the cost reduction is irrelevant. Frankly, because the business case is so huge. To give you an example of one of our supply chain clients, their fill-rate for orders is 60% which means they're a big manufacturer, they're only to fill 60% of the orders that come through. That's because they're not able to plan where to deploy product and so on and so forth. So if you increase it by 5%, it's a 300 million dollar annual business case. My two million dollar data warehouse optimization, it's irrelevant. It's peanuts in a 300 million dollar annual business case. It's things like that that's helping machine learning and Hadoop evolve in the ecosystem. The cost-reduction play was just a way to slide the infrastructure in. You can do a lot more with it. >> And when you're selling to the CIO's and business leaders, that resonates. >> Ghoutham: Yeah. Absolutely. >> Great. We'll have to leave it there. Thanks very much for coming to theCUBE, Ghou. >> Ghoutham: My pleasure. My pleasure. >> Alright keep it right there everybody. We'll be back with our next guest. This is theCUBE. We're live at Big Data NYC. Be right back. (techno music)

(upbeat synth music) >> Narrator: Live from San Francisco, it's The Cube, covering Oracle OpenWorld 2016. Brought to you by Oracle. Now, here's your hosts John Furrier and Peter Burris. >> Hey, welcome back, everyone. We are here live in San Francisco for Oracle Open World 2016. This is SilconANGLE Media. It's The Cube, our flagship program, where we go out to the events and extract the signal from the noise. I'm John Furrier, the co-CEO of SiliconANGLE Media, joined by my co-host this week, Peter Burris, head of research at SiliconANGLE Media as well as the General Manager of Wikibon Research. Our next guest is Hari Sankar, who's the group Vice President of Enterprise Performance. Welcome to The Cube. >> Thank you. >> Thanks for joining us today. So, one of the things that you're in is performance management but in a different way, kind of a CFO perspective. >> Hari: That's right. >> Which this show is all about, ROI, total cost of ownership. But Oracle has a lot of software, finance software. First, take a step back and spend a minute to describe what is performance management and your role at Oracle. >> So traditionally, performance management is really about how finance sort of manages the overall business performance of a company. It's about things like forward-looking things like planning, forecasting, and budgeting. It's about, sort of, backward-looking things like okay, our quarter is done, how do we close the books and how do we report the numbers both internally, for management recording purposes, and externally, to the street and various stakeholders. So there is the compliance side to it. There is a strategy side to it, and these things have been traditionally what is performance management. What we are seeing now is that kind of discipline is now going beyond finance into operating lines of businesses, sales and marketing and manufacturing and so on. >> The, the-- >> One of the things, sorry, John, I think one of the things that is really interesting, especially in light of this show, is as we go through a process of digital transformation, where data becomes one of the most important assets in the business, that means that the asset specificity, to use a finance term, the degree to which an asset has only one use, starts to go down because you can program it. So marketing, sales, all the assets, intellectual property, data-oriented, that they've been developing over the years now can be bought under the umbrella of Enterprise Performance Management. >> That is absolutely true. That is absolutely-- >> So how is that happening? >> So part of how this is happening is let's say you are a marketing organization. You are spending $50 million on digital marketing. Now, there is a desire from the part of the marketing department to sort of manage that spend more diligently with more discipline and drither, just like finance manages any other line item in the budget. There's more desire to provide transparency to the business, in terms of here's where we are spending it and here's where we are getting returns, here's where we are perhaps not getting returns. So that is the planning part of it, and then there is also the reporting part of it, where we are seeing the emergence of the concept of narrative reporting, where you are saying hey, look, I'm not just going to distribute numbers and charts to my stakeholders, whether it's inside the company or outside, I'm going to give them context, I'm going to give them commentary on these numbers. If there is a variance, I'm going to tell them why is this there. Do I expect this variance to be there next quarter? What am I doing about it? So, it sort of brings those numbers to life and avoids that back and forth that typically happens. >> How much is the Performance Management moving out of the CFO function, and I want to get your take on how the costs in IT is becoming not just a functional shared resource but IT is now integrated across the whole company. Mark Hurd had tweeted yesterday on Twitter, "As more CEOs and CFOs understand "the potential of the cloud, "CIOs are going to get a lot more help," implying Oracle is going to help them. But it brings up the point that the CIO now is brought into the CFO conversation, they always have in facilities and what not, but now from a business perspective their contribution is significant and now co-mingled is it. Do you see that trend happening and what does that mean for the software side of it? >> We're definitely seeing that trend happening. For example, the most important new term to come out in finance in some time is the notion of digital finance. >> John: The notion of what? >> Digital finance, right? So this is really about whether you call it digitalization or not, digital finance, digital marketing, digital sales. So this digital business idea sort of elevates this role of the CIO because, as you said, data becomes a very, very important asset in terms of how you fundamentally drive innovation in your business, and so that digital notion is sort of elevating the role of the CIO. And in the context of Performance Management, as you see this spread beyond finance into other lines of businesses, other lines of businesses are starting to be more disciplined and rigorous in how they sort of measure their performance, how they manage their performance. There's also a need to connect the dots across. You know, if I'm doing a marketing plan, which is an important element of my overall spend, if there is a fluctuation or change, a big change in my marketing spend, that needs to be reflected back in the finance budget. So connecting the dots and aligning the plans across different functions is becoming a big priority as well. So you're seeing a lot of important changes happening. >> You just said a few things that's just gotten me standing up and getting all excited. Peter and I looked at each other, digital business, digital assets, digitizing your business, these are the mega-- >> Data value. >> Data value, this comes back down to what we've been talking about all week here on The Cube and for the past year. This is now what was once a come together, have a meeting, share, cross-pollinate, somewhat automated but in the end manual, to fully integrated. This is probably the biggest business problem in digital transformation right now. How come we're not hearing more? This is a-- >> Yeah, I think that's a great point, John. At the end of the day and what we've been talking about is that so this is is a little bit of SiliconANGLE Media, Wikibon, we believe that digital business, full-stop, is how you use data to differentially create sustained customers. >> Absolutely. >> That is digital business. You can say all kinds of new channels and all this other stuff, but it all boils down to are you using data as an asset better than your competitors? >> Yep. >> So that as a basis, two things. First off, interesting that Mark Hurd, we talked about it earlier, this is a quick aside, Mark Hurd talking about how CIOs are going to get more help. Remember when we talked about how Oracle's going to have to bring a lot of the IT group forward in its new transformation. >> This is it right here. >> Absolutely, but I'm going to throw you a little bit of a curve ball. I hope I'm not going to throw you a curve ball but its a very, very important point. As the IT organization, or as increasingly, the methods that we use to create digital assets, and increasingly also products, they're iterative, they're empirical, they're opportunistic, they're agile. That the traditional, year-long budget that says you have a certain money to spend, and you spend it or it goes away and you better not fail with this money, comes under attack by Agile, and I know a lot of CIOs that I talk to are trying to reconcile the impedance mismatch between Agile and Sprints, and being opportunistic and recognizing when something isn't working, and the CFO who's still talking about annualized releases of money. So I've always felt that you could not reconcile those. You could not bring those two points of view forward without EPM. Are you seeing that as well and how are you helping it? >> Yeah, we're definitely seeing this because this older, you're absolutely right. The old notion of let's make a budget once a year, get it right, and execute on it for the rest of the year, we are seeing that seeing that fading really fast. What people are saying is, look, plans are made only to be changed. Let's not fixate on getting the perfect plan in place. Let's start with a reasonable plan with the assumption that it's going to tweak and iterate and change many, many times over the year. So the focus is now on, less on getting it right the first time, more on how do we make dynamic changes to it in an agile fashion, just to your point. >> And reflect those changes throughout the entire cost-- >> And into finances-- >> Back into finance. >> It all comes back to finance. >> It comes back to finance because at the end of the day, let's say, take a simple example of a manufacturing company-- >> Paul: Finance is the language of business. It still is. >> End of the day, your business performance is measured in dollars and cents. I mean, period, right? So, let's say, your product mix changes because your customer demand is changing. That needs to be reflected back into finance, in terms of, okay, are we making more money or less money? Is it more revenue or less revenue? That needs to be reflected back, and so we're definitely seeing, in fact, the tagline for Enterprise Performance Management that we use these days is enabling business agility. So two parts to that, driving agile decisions, to your point, the second is, once you drive those agile decisions. Let's say I decided to expand into a new business and I did an acquisition. Fast forward six months, you need to reflect the results of that combined entity into your financial results, do it quickly, do it in a way that is correct and you're confident about the results and that's the job of finance. So it's agility of operations, agility in decision making, those two have to sort of come together. >> So here's my question then. I love this conversation because I think this speaks to the full-closed loop of Cloud and DevOps and the innovation around Agile. How much flexibility is built into the software, and I'm kind of going with the database route for a second, systems of records, schemas in database 'cause business plans can say it once a year and it's failing, I agree, I can see that failing. But, also, fixed schemas, can fail too. Well, I don't want to add the new data in 'cause the database can't handle it. I've heard that from developers before. Again, it slows the things down, so as you move from systems of record, which can be fixed and tweaked, the engagement data is the business engagement gestures. So how is that factoring into your software? You guys see that and is this AI Bot revolution and the machine learning, the smart software after engagement. Can you thread that through and explain how that fits? >> Let's start simple and sort of get a little more sophisticated quickly. The first things is we are seeing a lot more people come into the planning process than before. The old model was finance did the planning for other people. Now, people are doing their own plans, then sort of feeding it into the overall plan. People intentionally are pushing that because they want plans and decisions to be made closer to the point of action. Secondly, there is a greatest emphasis on driving fact-based decisions. For instance, we are working with some large consumer goods companies where they are saying, look, don't come here and tell me that I'm going to spend 10% less on this large line item compared to last year, Throw the last year's budget out and do a zero-based budget. I mean, zero-based budgeting is not a new concept. It's been around, but it's getting a new lease of life because in industries where profits are on the squeeze, they are saying "Look, I don't want "to do the traditional budgeting. "I want to go to a zero-based budget." >> Because they get facts that are surfacing faster. Is that kind of the premise? >> Facts, but more over to the performance of the business. >> That is definitely true. The facts that are surfacing faster, and, therefore, I want to give the tools to make use of those facts to the people who are closer to where they are surfacing. >> John: This is a digitized business in that scenario. >> Definitely true. >> Everything's instrumented. >> Good value. >> Hari: Yeah, definitely true. >> We always say on The Cube, I mean, this is the first time in the history of business in the world that you can actually measure everything. >> That is absolutely true. >> If you want to measure everything, you actually can do it. >> That is absolutely true. >> Now the CFO, which was once the measurement system, has to get integrated in. Am I getting this right? >> You are getting this right. You are getting this right. And the other part of your question is about okay, how is intelligence coming into, so some of these decisions over time, if you see a pattern, they can be perhaps automated. Plan adjustments can be, maybe some elements of plan adjustments can be automated, but I don't see finance going that far. That may be taken as an input. Maybe a recommendation comes from automated intelligence, and people will sort of take a look at it and say, "Hey, I want to go with this because it makes sense, "or I'm going to override it this way "because this doesn't take into account "what I'm planning for in the next quarter." >> Yeah, what scares me, though, in the whole bot thing, I mean, this is not a dis on Larik, I love the vision, it's got me all excited, is if they try to get too AI before they actually build the building blocks, they really can get ahead of themselves. So, you can see that head room, for sure, but a lot of companies are kind of in that planing mode. Is that true? What's this progress bar of customers right now who are into this, are in the software? I mean, track bots are great for certain things, but you can't really automate AI yet and everything. Or can you? >> I think there is probably a class of decisions that can be automated, but when it comes to finance, and finance tends to be conservative and for good reason, they definitely see the value of recommendations based on data, based on real-time data, but they still want to have the controls. >> [John} Got It. >> So that's kind of the mindset that we have seen. >> So real options valuations could really, really be helped by AI. But at the end of the day, you have to be able to close the books, and you don't need AI to help you close the books. >> This is a fascinating conversation. >> If I can add one quick conversation, just a quick point, as Enterprise Performance Management starts to weave its way into other parts of the business, institutionally, does that mean we're going to see controllers start to end up in different functions? >> Hari: (laughs) IOD of controllers? >> As a human interface that goes along with the system so that it works together. >> It's a definite possibility, right? Because if you're planning as rigorously in marketing as in finance and if you aremeasuring and reporting as rigorously in sales as you're doing in finance, maybe there's a sales controller function that becomes a legitimate need. But at the end of the day, today, you focus so much attention on reporting your numbers to the street. You focus attention on precision and accuracy and confidence in all of that. Why is that not a requirement for internal Reporting? >> It's the same argument when we talk about the technology of a structure. You move the computer to where the data is. You could move the controller where the action is, to your point earlier. It's a fascinating conversation, Hari. Thanks for sharing the insight. Love to do a follow-up on this because I think this really connects the language of business and kind of validates the digital fabric of digitization. But quick, I want to give you the last minute to give an update on the business, how you guys are doing. This is a pretty big deal. How's your business results, what's down the roadmap, what's the sales going to be like next month? I'm only kidding, I know. (all laugh) >> Sure, sure. I think the cloud has been a really game changer in this business. What the cloud has done has lowered the bar where we're seeing many mid-sized businesses start using Performance Management best practices, just like larger companies. We are seeing divisions or functions inside of larger businesses using Performance Management software for the first time. So there's a big market expansion, and we are seeing an expansion across other lines of businesses outside of finance. We are certainly seeing that. We are seeing that, you know, we introduced our first Cloud software in Enterprise Performance Management about two and a half years ago. At that time, we were not sure how the market update was going to be because we said finance tends to be conservative. Are they going to be comfortable doing their aggregated planning in the cloud, or are they going to be comfortable doing, reporting things in the cloud? We've been sort of pleasantly surprised by the willingness of finance, helped in part by the success the companies have had in deploying HR software in the cloud or CRM software in the cloud and so on. So the cloud has taken off. We have well north of 1,000 customers that have picked up EPM software in the cloud. We are very happy to see 100, 150 deployments go live every quarter, and we are seeing use cases in marketing, we are seeing use cases in HR of strategic workforce planning or marketing spend planning happened using EPM-style software. So, happy to see mid-sized businesses see real value from planning. >> John: Good integration capabilities? >> Good integration, I'm glad you mentioned it. Very good integration back into, for example, if you have financials in the cloud and EPM in the cloud, there are nice linkages between the two. So four teams are very important to us. We are seeing pervasive use of EPM software. We are seeing agile operations helped by EPM software in the cloud. We are seeing connected operations, whether it's the backbone systems or across functions. And we are seeing people take a sort of a comprehensive view of this, whether it's across functions or across processes. >> This is fascinating. We could go another hour. This is a really interesting topic because I think it really highlights a fact that, what we always say in The Cube is, you can provision technology faster and you get time to value certainly as the customers start to be creative and implement it. They get to actually put it to work and get the data around and behind. So thanks so much for spending the time on the insights on the EPM. We appreciate it, thank you so much. >> Thank you, I enjoyed the conversation. >> Okay, you're watching The Cube, live coverage here in San Francisco at Oracle OpenWorld 2016. I'm John Ferrier with Peter Burris. Thanks for watching. (upbeat synth music)