>>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.

>>Well, thank you, everyone. And welcome to winning the roadmap race. How? Toe work with tech vendors to get the features that you need. We're here today with representatives or RBC Capital Markets. We will share some of their best practices for collaborating with technology vendors. I am Ada Mancini, solution architect here at Mirant us. And we're joined by Tina Bustamante, senior production manager, RBC Capital Markets and Minnows Agarwal, head of capital markets. Compute and data fabric. Um, RBC has been using docker since about 2016 and you've been closely involved with that effort. What moved you to begin, contain arising applications. >>Okay, uh, higher that. Thank you for having us. Um, back in 2016 when we started our journey one off our major focus, Syria was measuring develops capabilities And what we, uh what we found was it was challenging. Toe adopt develops across applications with different shapes and sizes, different text tax. And as the financial industry, we do have, um, a large presence of rental applications. So making it making that work was challenging. This is where containers were appealing. Tow us. In those early days, we started looking at containers as a possible solution to create a standardization across across different applications to have a consistent format. Other than that, we also saw containers as a potential technology that could be adopted across across enterprise, not just a small subset of applications. Uh, so that that was very interesting. Interesting. Tow us. In addition to that, uh, containers came with schedulers like kubernetes or swarm, which were, uh, which we're doing a lot more than all, which would do a lot more than the traditional traditional schedulers. As an example, resource management fell over management or scaling up and down, depending on a application or business requirements. So all those things were very appealing. It looked like a solutions to a number of problems that are number of challenges that we're facing. So that's when we got started with containers. >>So what subsequently motivated you to start utilizing swarm and then kubernetes? >>Yeah, other than resource management, the follower Management Aziz, you can imagine managing followers. D are Those are never difficult, Never easy on with containers. We saw that, as the container schedule is, we saw that it's a kind of becomes a manage manage service for us. Um, other aspect We are heavily regulated industry in capital markets, especially so creating an audit trail off events. Who did what? When? Uh, that's important. And containers seem to provide all those all those aspects tow us out of the box. Um, the another thing that we saw with containers under the schedulers, we could simplify our risk management. We could control what, what application on which container gets deployed, where, how they run on when they run. So all all those aspects of schedule er they simplify are seen to simplify at that time a lot off a lot off the traditional challenges, and that that's what was very appealing to us. >>Eso what kind of changes were required in the development culture and in operations in order to enable these new this new platform in this new delivery method? >>Yeah, that that's a good question, and any change obviously requires a lot of education. And this was not just a change across our developers or operations, but it was the change across throughout the change, starting with project managers, business analyst developers, Q A, uh, Cuba and our support personal. In addition, I talked about the risk and security Management so it it is. It is a change across the organization. It's, uh it's a cultural change. So the collaboration other than education collaboration was extremely, extremely important. So across those two, we started first with internal education, using something like internal lunch and learns. We did some external workshops or some hands on workshops. So a lot of those exercises were done in collaboration across all those all those things. The next item that we focused on is how do we get our high end developers the awareness of this technology on, uh, make sure they can. They can see, uh, the use cases. Or they can identify the use cases that can benefit from this technology. So we picked high end developers, noticed application and kind of try before you buy type of scenarios. So we ran through some applications to make sure they get their hands study. They feel comfortable with it on. Then they can broadcast that message. The broader organization, the next thing we did it waas getting the management buying. So obviously any change is going to require investment on uh, making sure there's a value proposition that's clear to our management as well as our business was critical very early on in in container option face. So that that that was that was another item that we focused heavily on. And the last thing I would say is a clearly defining strategy benefits so defining a roadmap off how we will proceed, How do we go from our low risk to high risk application or low risk medium risk applications? And what other strategy benefits are these purely operational? Are these purely cause best benefit? Or it's a modernization of the underlying technical facts. So if the containers do check all those three boxes So that that was that was our fourth item on the left that, uh, that, I would say, changed, um, in a container adoption journey. >>So as as people are getting onto the container ization process and as this is starting to gain traction, what things did your developers embrace as the real tangible benefits, um, of moving the containers of container platforms? >>It's interesting. The benefits are not just for developers. And the way I will answer this question is not from development operations. But let me answer it from the operations to developers. So operationally the moment developers saw that application can be deployed with containers relatively quickly without without having them on the collar without them writing a long release notes. They started seeing that benefit right away, but I don't need to be there late in the evening. I don't need to be there on call to create the environment or deploying, uh, deploying Q A versus production versus the are to them because, like do it right one on then repeat that success factor of different environments. So that was that. That was a big eye opening, um, eye opening for them. And they started realizing that Say, Look, I can free up my time now I can focus. I can focus on my core development, and I don't need to deal with the traditional traditional operational operational issues. So that's what that what? That was quite eye opening for all of us, not just for developers. And we started seeing those, uh, that are very early on. Another thing, I would say the developers talked about waas. Hey, I can validate this application on my laptop. I don't need to be I don't need to be on, uh, on on servers. I don't need all these servers. I don't need to share my service. I don't need to depend on infrastructure teams or other teams to get their check is done. Before I kept start my work, I can validate on my on my laptop. That was that was another very powerful feature. Um, that that empowered them. The last thing I would say is that the software defined aspect, uh, aspect off, um, off technology as an example, Network or storage. Although a lot of these traditional things that something Democrats have to call someone they have to wait on, then they have to deal with tickets. Now, they can do a lot of these things themselves. They can define it themselves, and that's very empowering. So they are perspective. Our move towards left, Um, s o the more control developers have, the better the product is. The better the quality of the product. The time to market improves on just the overall experience on the business benefits. They also start to They all start toe, um improved last part. One extra point. I would like to make here the success success of this waas so interesting, uh, to the development community even our developers from business. They they came along and they have shown interest in adopting containers. Whether it's, uh, the development developers from the quartz are the data science developers. They all started realizing the value value proposition of containers. So it was It was quite eye opening, I would have to say. >>And so while this while this process is happening while you're moving to container platforms, um, you started looking for new ways to try and deliver some of the benefits of containers and distributed systems orchestration more widely across the organization. And I think you identified a couple areas where, um, the doctor Enterprise kubernetes service wasn't meeting the features that you anticipated or it hadn't planned on integrating the features that you required. Um, can you tell us about that situation? >>Certainly. Haida. Thanks for having us again. Um, from the product management perspective, I would say products are always evolving and the capabilities can We have different stages of maturity. So when we reviewed what our application teams what are businesses looking to dio? One area that stood out was definitely the state of science space. Um, are quantum data science is really wanted to expand our risk analysis models. Um, they were looking for larger scales, uh, to compute like a lot more computing power. And we tried to see, um, come up with a way to be ableto facilitate their needs. Um, one thing, and it really, really came from like an early concept was the idea of being able to leverage GPU. Um, we stood up like a small R and D team, trying to see if there was something that would be feasible for our on our end. Um, but based on different factors and considerations and, you know, technical thinking involved in this we just realized that the complexity that it would bring to our you know, our overall technical back is not something, um that we would be, um, best suitable, I would say to do it on our own. So we reached out Thio Tim Aransas and brought forth, like, the concept of being able to scale the kubernetes pods on GPS. We relied on there authorities on their engineers Thio, you know, think about being able to expand, uh, kubernetes there kubernetes offering to be able to scale and potentially support running the pods and GPS um, definitely was not something that came from one day to the next that it did involve a number of conversations. Um, but, you know, I'm happy to say I was saying the recent months it has become part of the KUBERNETES product offering. >>Yeah, I believe that that effort, um, did take ah, while took a ah lot of engineering effort. Um, and I think initially all had done some internal r and D to try to work on those features, but ultimately, you decided to go with a different strategy and rely on the vendor to produce those assed part of the vendors product. Um, can you elaborate on the things that you found in that internal R and D? >>Well, we definitely saw the potential for there was definitely potential there. But, you know, the longevity of actually maintaining that GPU, uh, scaling using communities on our own was just not 100% like, in our expertise, expertise of something that we wanted to collaborate more closely with the vendor. Um, you know, technology is always evolving, So it's just the longevity of keeping up with, like, the the up to date features or capabilities testing que involved was just not something that we thought it would be. Something that we should be taking on on our own. >>Okay, So, like spending the time and engineering effort, focusing on the data science, the quantity of analysis parts I see. Um, and then ultimately, um, working with the vendor produced a release and where these features are now available. Um, how what did that engagement look like? Um, with RBC s involvement, >>I would say the engagement started off with, you know, discussing bringing it forth, being very open, you know, having transparency. So that delivery was always a little bit was the focus. Um, but it definitely, um, started office, you know, discussing what it would be like the business case. Why we would require the feature. Definitely the representative. Those and others engaged from them. A ransom side had their own, Um, you know, thoughts and opinions. Um, it had to be being able to run the work clothes, um, on GPU would be something that they would ultimately, as I mentioned, have to support on their end. Um, so we did work with them very closely. There was a very much a willingness collaborate we held a number of meetings. We discuss how the CPU support would would actually evolved. So it wasn't something that came about within like one sprint. No, that was never like our expectation. It did take a couple weeks to be able to see, like a beta product opine on it, see a demo, review it, discuss it further. Um, as you know, sometimes there might be a relief where this capability maybe offered, but there are delays. It's just, you know, part of off of our industry in a cent. Um, we're very much risk versus the nose mentioned, you know, >>when >>you are a financial institution. So we just wanted to make sure it was a viable product, that it was definitely available off the shelf, and then we would be able to leverage it. Um, but yeah, the key point, I would say, in terms of being able to bring the feature forward with definitely constant communication with Miranda, >>that's excellent. I'm glad that were ableto help bring that feature forward. I think that it's something that a lot of people have been asking for and like you said, it enables ah, whole new class of uh, problem solving. Okay. Uh, Meno je Tina, Thank you for your time today. It's been wonderful talking to you again. Uh, that is our session on working with your vendors. I want to thank everyone who's watching this for taking the time Thio contribute to our conference. Uh, awesome. Thank you, kitty.

>> live from New York. It's the Q covering AWS Global Summit 2019 brought to you by Amazon Web service is >> Welcome back. I'm stupid like co host Cory Quinn. And we're here at the end of a summit in New York City, where I'm really happy to welcome to the program first time guests, but somebody that has a nap, it's on my phone. So, Andy thing, who's the CEO of Door Dash, gave a great presentation this morning. Thanks so much for joining us. >> Absolutely happy to be here, guys. >> All right, so, you know, before we dig into the kind of your Amazon stack, bring us back. You talked about 2013. You know, your mission of the company will help empower local businesses. I think most people know, you know, door dash delivery from my local businesses. Whether that is a small place or, you know, chipotle o r like there. And I love little anecdote that you said the founders actually did the first few 100 deliveries, but it gives a little bit of the breath of the scope of the business now. >> Absolutely. I mean, when we started in 2013 you know, we started out of Ah dorm on Stanford campus and, like you said, we're doing the first couple 100 deliveries ourselves. But, you know, fast forwarding to today you were obviously at a much, much different level of scale. And I think one thing that I mentioned about it, Mikey No, a cz just We've been trying to keep up pays and more than doubling as a business every year. And it's a really fascinating industry that were in in the on demand delivery space in particular, I mean, Dara, the CEO of uber himself, said in May, which is a month and 1/2 ago. He said that you know, the food delivery industry may become bigger than a ride hailing industry someday. >> So just just one quick question on kind of food delivery. Because when I think back when I was in college, I worked at a food truck. It was really well known on campus, and there are people that 20 years later they're like stew. I remember you serving me these sandwiches, and I loved it in the community and we gather and we talk today on campus. Nobody goes to that place anymore, you know, maybe I know my delivery person more than I know the person that's making it. So I'm just curious about the relationship between local businesses and the people. How that dynamic changing the gig? Economy? I mean, yeah, you guys were right in the thick of it. No, it's a >> great question. I think. You know, for merchants, a lot of the things that we talk to them about it is you're actually getting access to customers who wouldn't even walk by your store in the first place. And I think that's something that they find to be very captivating. And it shows in the store sales data when they start partnering with the door dash. But we've also tried to building our products to really get customers to interact with the physical neighborhoods. Aaron the most concrete example of that as we launch a product called In Store in Star Pickup Chronic, where you can order online, skip the line and pick up the order yourself in the store, and I think a way we can build the AB experience around that, you know, you're gonna actually start building kind of a geospatial. Browse experience for customers with the door dash app, which means that they can get a little bit more familiarity with what's around them, as opposed to just kind of looking at it on their phones themselves. All right, >> so the logistics of this, you know, are not trivial. You talked about 325% order growth. You know, your database is billions of rose. You know, just the massive scale massive transaction. Therefore, you know, as a you know, your nap on. You know the scale you're at technology is pretty critical to your environment. So burgers inside that a little? >> Yeah. I mean, we're fortunate enough, and you and I are talking before the show. I mean, we're kind of born on the cloud way started off, actually on Roku. Uh, back in 2013 we adopted eight of us back in 2015. And there's just so many different service is that Amazon Web services has been able to provide us and they've added more overtime. I think the one that I talked about, uh was one that actually came out only in early 2018 which is the Aurora Post product. Um, we've been able to sail our databases scale up our analytics infrastructure. We've also used AWS for things like, you know, really time data streaming. They have the cloudwatch product where it gives us a lot of insight into the kind of our servers are behaving. And so the eight of us ecosystem in of itself is kind of evolving, and we feel like we've grown with them and they're growing with us. So it's been a great synergy over the past couple of years >> as you take a look at where you started and where you've wound up. Can you use that to extrapolate a little bit further? As far as what shortcomings you seeing today? That, ideally, would be better met by a cloud provider or at this point is it's such a simpatico relationship is you just alluded to where you just see effectively your continued to grow in the same simple directions just out of, I guess, happenstance. Yeah, it is a >> good question. I think there are some shortcomings. For example, eight of us just recently launched and chaos, which is their in house coffin solution. We're looking for something that's kind of a lot more vetted, right? So we're considering Do we adopt eight of us version or do we try to do it in house, or do we go with 1/3 party vendor? That's >> confidence. Hard to say no to these days. >> Yeah, exactly. And I think, you know, we want to make sure that we are building our infrastructure in a way that way, feel confident in can scale. I mean, with Aurora Post Chris, it's done wonders for us, but we've also kind of been the Pi. One of the pioneers were eight of us for scaling that product, and I think we got kind of lucky in some ways they're in terms of how it's been ableto pan out. But we want to make sure the stakes are a lot higher for us now. And so you know, when we have issues, millions of people face issues, so we want to make sure that we're being more thoughtful about it. Eight of us certainly has matured a lot over the past couple of years, but we're keeping our options open and we want to do what's best for our customers. Eight of us more often than not has a solution, but sometimes we have the you consider other solutions and consider the back that AWS may or may not. So some of the future problems. >> Oh yeah, it's, I think, that it's easy to overlook. Sometimes with something like a food delivery service. It's easy to make jokes about it about what you're too lazy to cook something. And sure, when I was younger, absolutely then I had a child. And when she wasn't going to sleep when she was a baby, I only had one hand. How do I How do I feed myself? There's an accessibility story. People aren't able to easily leave the house, so it's not just people aren't able to get their wings at the right time. This starts becoming impacting for people. It's an important need. >> Yeah, and I think it's been awesome to see just how quickly it's been adopted. And I think another thing about food delivery that you know people don't necessarily remember about today is it was Premier Li, just the very dense urban area phenomenon, like obviously in Manhattan, where we are today who delivers existed forever. But the suburbs is where the vast, vast majority of the growth of the industry has been and you know It's just awesome to see how this case has flourished with all different kinds of people. >> I have to imagine there's a lot of analytics that are going on for some of these. You said. In the rural areas, the suburban areas you've got, it's not as dense. And how do you make sure you optimize for people that are doing so little? So what are some of the challenges you're facing their in house technology helping? >> Exactly? Yeah. I mean, with our kind of a business, it's really important for us again to the lowest level of detail, right? Just cause we're going through 100 25% year on year in 2019 maybe we're growing faster in certain parts of the United States and growing slower and others, and that's definitely the case. And so, uh, one of the awesome things that we've been able to leverage from our cloud infrastructure is just the ability to support riel, time data access and our business operators across Canada. In the United States, they're constantly trying to figure out how are we performing relative to the market in our particular locality, meaning not just, you know, the state of New York. But Manhattan, in which district in Manhattan. Um, all that matters with a business like ours. Where is this? A hyper local economy? And so I think the real time infrastructure, particularly with things like with Aurora the faster up because we're able to actually get a lot of Reed. It's too these red because because it's not affecting our right volume. So that's been really powerful. And it's allowed our business operators to just really run in Sprint. >> So, Andy, I have to imagine just data is one of the most important things of your business. How do you look at that as an asset is their, You know, new things. That new service is that you could be putting out there both for the merchants as well for the customers. Absolutely. I think one of >> the biggest ones we try to do is you know, we never give merchant direct access to the customer data because we want to protect the customer's information, but we do give them inside. That's how they can increase their sales and target customers. I haven't used them before, So one of the biggest programs we launched over the past few years is what we call Try me free so merchants can actually target customers who've never place an order from their store before and offer them a free delivery for their order from that store. So that's a great way for merchants acquire new customers. And it's simple concept for them to understand. And over time we definitely want to be able to personalize the ability to target the sort of promotions on. So we have a lot of data to do that on. We also have data in terms of what customers like what they don't like in terms of their order behavior in terms of how they're raiding the food, the restaurant. So that kind of dynamic is something that is pretty interesting Data set for us to have. You know, you look at a other local companies out there like Yelp, Google Maps. They don't actually have verified transaction information, whereas we d'oh. So I think it's really powerful. Merchants actually have that make decisions. >> It's a terrific customer experience. It almost seems to some extent to be aligned with the Amazons Professor customer obsession leadership principle to some extent, and the reason I bring that up is you mentioned you started on Hiroko and then in 2015 migrated off to AWS. Was it a difficult decision for you to decide first to eventually go all in on a single provider? And secondly, to pick AWS as that provider It wasn't >> a hard decision for us to go to. Ah, no cloud provider. That was, you know, ready to like showtime. It's a hero is more of a student project kind of scale at that time. I don't know what they're doing today. Um, but I think a doubt us at the time was still very, very dominant and that we're considering Azure and G C P. I think was kind of becoming a thing back then made of us. It was always the most mature, and they've done a great job of keeping their lead in this space. Uh, Google, an azure have cropped up. Obviously, Oracle clouds coming up Thio and were considered I mean, we consider the capabilities of something like Google Cloud their machine. Learning soft service is a really powerful. They actually have really sophisticated, probably more so than a W s kubernetes service is actually more sophisticated. I guess it's built in house at Google. That makes sense. But, you know, we've considered landscape out there, but AWS has served a lot of our knees up to this point. Um, and I think it's gonna be a very dynamic industry with the cloud space. And there's so much at stake for all these different companies. It's fascinating to just be a part of it and kind of leverage. It >> s o nd I'm guessing, you know, when you look at some of your peers out there and you know, when a company files in s one and every goes, Oh, my God, Look at their cloud, Bill. You know, how do you look at that balance? You send your keynote this morning. You know, you like less than a handful of engineers working on the data infrastructure. So you know that line Item of cloud you know, I'm guessing is nontrivial from your standpoint. So how do you look at that? Internally is how do you make sure you keep control and keep flexibility and your options Yet focus on your core business and you know not, you know, that the infrastructure piece >> of it that was such a great question, because it's something that way we think about that trade off a lot. Obviously. In the early days, what really mattered ultimately is Do we have product market bid? Do we have? Do we have something that people will care about? Right. So optimizing around costs obviously was not prudent earlier on. Now we're in a such a large scale, and obviously the bills very big, uh, that, you know, optimizing the cost is very real thing, um, and part of what keeps, you know, satisfied with staying on one provider is kind of a piece of set up. And what you already have figured there? Um and we don optimization is over the years wear folks on financing now who basically looking at Hey, where are areas were being extremely inefficient. Where are areas that we could do? Bookspan, this is not just on AWS with is on all our vendors. Obviously eight of us is one of our biggest. I'm not the biggest line item there. Um, and we just kind of take it from there, and there's always trade offs you have to make. But I know there's companies out there that are trying to sell the value proposition of being ableto optimize your cloud span, and that is definitely something that there's a lot of. I'm sure there's a lot of places to cut costs in that we don't know about. And so, yeah, I think that's something that way we're being mindful of. >> Yeah, it's a challenge to you See across the board is that there's a lot of things you can do programmatically with a blind assessment of the bill. But without business inside, it becomes increasingly challenging. And you spoke to it yourself. Where you're not going to succeed or fail is a business because the bill winds up getting too high. Unless you're doing something egregious, it's a question of growth. It's about ramping, and you're not gonna be able to cost optimize your way to your next milestone unless something is very strange with your business. So focusing on it in due course is almost always the right answer. >> Yeah, I mean, when I think about increasing revenue or deep recent costs nine times out of 10 we're trying to provide more value, right, so increasing revenues, usually they go to option for us, but they're sometimes where it's obvious. Hey, there's a low hanging fruit and cutting costs, and if it's relatively straightforward to do, then let's do it. I think with all the cloud infrastructure that we've been able to build on top of, we've been able to focus a lot of our energy and efforts on innovating, building new things, cementing our industry position. And, yeah, I think it's been awesome. On top >> of what? Want to give you the final word? Any addressing insights in your business? You know, it's like I like food and I like eating out and, you know, it feels like, you know, we've kind of flatten the world in lot is like, You know, I think it was like, uh, like, 556 years ago. The first time I went white and I got addressed to Pok. Everybody in California knows, okay, but I live on the East Coast now. I've got, like, three places within half an hour of me that I could get it. So you know those kind of things. What insight to you seeing you know what's changing in the marketplace? What? What's exciting you these >> days? Yeah, I mean, for us, we've definitely seen phenomenon where different food trans kind of percolate across different areas. I'm going to start in one region and then spread out across the entire United States or even Canada. I would say I don't way try to have as much emergence election on a platform. It's possible so that no matter what the new hot hottest trend is that more likely than not, we're gonna have what you want on the platform. And I think what's really exciting to us over the next couple years is you know, last year we actually started way started satisfying grocery delivery. So, uh, in fact, we power a lot of grocery deliveries for Walmart today, which is exciting, and a lot of other grocers lined up as well. We're gonna see how far we can take our logistics capabilities from that standpoint, But really, we want to want to have as many options as possible for our customers. >> Anything. Thanks so much for joining us. Congressional Congratulations on the progress with your death for Cory Quinn. I'm stupid and we'll be back here with more coverage from eight of US summit in New York City. 2019. Thanks is always watching. Cute

>> Live from San Francisco, California, it's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at San Francisco. We're here at Fisherman's Wharf covering the IBM Chief Data Officer event #IBMCDO. This is the tenth year of this event. They tend to bookend them both in San Francisco and in Boston, and you're watching theCUBE, the leader in live tech coverage. My name is Dave Valante. John Thomas is here, Cube alum and distinguished engineer, Director of Analytics at IBM, and somebody who provides technical direction to the data science elite team. John, good to see you again. Steve Aliouk is back. He is the Vice President of Deep Learning in the Global Chief Data Office, thanks for comin' on again. >> No problem. >> Let's get into it. So John, you and I have talked over the years at this event. What's new these days, what are you working on? >> So Dave, still working with clients on implementing data science and AI data use cases, mostly enterprise clients, and seeing a variety of different things developing in that space. Things have moved into broader discussions around AI and how to actually get value out of that. >> Okay, so I know one of the things that you've talked about is operationalizing machine intelligence and AI and cognitive and that's always a challenge, right. Sounds good, we see this potential but unless you change the operating model, you're not going to get the type of business value, so how do you operationalize AI? >> Yeah, this is a good question Dave. So, enterprises, many of them, are beginning to realize that it is not enough to focus on just the coding and development of the models, right. So they can hire super-talented Python TensorFlow programmers and get the model building done, but there's no value in it until these models actually are operationalized in the context of the business. So one aspect of this is, actually we know, we are thinking of this in a very systematic way and talking about this in a prescriptive way. So, you've got to scope your use cases out. You got to understand what is involved in implementing the use case. Then the steps are build, run, manage, and each of these have technical aspects and business aspects around, right. So most people jump right into the build aspect, which is writing the code. Yeah, that's great, but once you build the code, build the models by writing code, how do you actually deploy these models? Whether that is for online invocation or back storing or whatever, how do you manage the performance of these models over time, how do you retrain these models, and most importantly, when these models are in production, how do I actually understand the business metrics around them? 'Cause this goes back to that first step of scoping. What are the business KPI's that the line of business cares about? The data scientist talks about data science metrics, position and recall and Area Under the ROC Curve and accuracy and so on. But how do these relate to business KPI's. >> All right, so we're going to get into each of those steps in a moment, but Steve I want to ask you, so part of your charter, Inderpal, Global Chief Data Officer, you guys have to do this for IBM, right, drink your own champagne, dog footing, whatever you call it. But there's real business reasons for you to do that. So how is IBM operationalizing AI? What kind of learnings can you share? >> Well, the beauty is I got a wide portfolio of products that I can pull from, so that's nice. Like things like AI open to Watson, some of the hardware components, all that stuffs kind of being baked in. But part of the reason that John and I want to do this interview together, is because what he's producing, what his thoughts are kind of resonates very well for our own practices internally. We've got so many enterprise use cases, how are we deciding, you know, which ones to work on, which ones have the data, potentially which ones have the biggest business impact, all those KPI's etcetera, also, in addition to, for the practitioners, once we decide on a specific enterprise use case to work on, when have they reached the level where the enterprise is having a return on investment? They don't need to keep refining and refining and refining, or maybe they do, but they don't know these practitioners. So we have to clearly justify it, and scope it accordingly, or these practitioners are left in this kind of limbo, where they're producing things, but not able to iterate effectively for the business, right? So that process is a big problem I'm facing internally. We got hundreds of internal use cases, and we're trying to iterate through them. There's an immense amount of scoping, understanding, etcetera, but at the same time, we're building more and more technical debt, as the process evolves, being able to move from project to project, my team is ballooning, we can't do this, we can't keep growing, they're not going to give me another hundred head count, another hundred head count, so we're definitely need to manage it more appropriately. And that's where this mentality comes in there's-- >> All right, so I got a lot of questions. I want to start unpacking this stuff. So the scope piece, that's we're setting goals, identifying the metrics, success metrics, KPI's, and the like, okay, reasonable starting point. But then you go into this, I think you call it, the explore or understanding phase. What's that all about, is that where governance comes in? >> That's exactly where governance comes in. Right, so because it is, you know, we all know the expression, garbage in, garbage out, if you don't know what data you're working with for your machine learning and deep learning enterprise projects, you will not have the resource that you want. And you might think this is obvious, but in an enterprise setting, understanding where the data comes from, who owns the data, who work on the data, the lineage of that data, who is allowed access to the data, policies and rules around that, it's all important. Because without all of these things in place, the models will be questioned later on, and the value of the models will not realized, right? So that part of exploration or understanding, whatever you want to call it, is about understanding data that has to be used by the ML process, but then at a point in time, the models themselves need to be cataloged, need to be published, because the business as a whole needs to understand what models have been produced out of this data. So who built these models? Just as you have lineage of data, you need lineage of models. You need to understand what API's are associated with the models that are being produced. What are the business KPI's that are linked to model metrics? So all of that is part of this understand and explore path. >> Okay, and then you go to build. I think people understand that, everybody wants to start there, just start the dessert, and then you get into the sort of run and manage piece. Run, you want a time to value, and then when you get to the management phase, you really want to be efficient, cost-effective, and then iterative. Okay, so here's the hard question here is. What you just described, some of the folks, particularly the builders are going to say, "Aw, such a waterfall approach. Just start coding." Remember 15 years ago, it was like, "Okay, how do we "write better software, just start building! "Forget about the requirements, "Just start writing code." Okay, but then what happens, is you have to bolt on governance and security and everything else so, talk about how you are able to maintain agility in this model. >> Yeah, I was going to use the word agile, right? So even in each of these phases, it is an agile approach. So the mindset is about agile sprints and our two week long sprints, with very specific metrics at the end of each sprint that is validated against the line of business requirements. So although it might sound waterfall, you're actually taking an agile approach to each of these steps. And if you are going through this, you have also the option to course correct as it goes along, because think of this, the first step was scoping. The line of business gave you a bunch of business metrics or business KPI's they care about, but somewhere in the build phase, past sprint one or sprint 2, you realize, oh well, you know what, that business KPI is not directly achievable or it needs to be refined or tweaked. And there is that circle back with the line of business and a course correction as it was. So it's a very agile approach that you have to take. >> Are they, are they, That's I think right on, because again, if you go and bolt on compliance and governance and security after the fact, we know from years of experience, that it really doesn't work well. You build up technical debt faster. But are these quasi-parallel? I mean there's somethings that you can do in build as the scoping is going on. Is there collaboration so you can describe, can you describe that a little bit? >> Absolutely, so for example, if I know the domain of the problem, I can actually get started with templates that help me accelerate the build process. So I think in your group, for example, IBM internally, there are many, many templates these guys are using. Want to talk a little bit about that? >> Well, we can't just start building up every single time. You know, that's again, I'm going to use this word and really resonate it, you know it's not extensible. Each project, we have to get to the point of using templates, so we had to look at those initiatives and invest in those initiatives, 'cause initially it's harder. But at least once we have some of those cookie-cutter templates and some of them, they might have to have abstractions around certain parts of them, but that's the only way we're ever able to kind of tackle so many problems. So no, without a doubt, it's an important consideration, but at the same time, you have to appreciate there's a lot of projects that are fundamentally different. And that's when you have to have very senior people kind of looking at how to abstract those templates to make them reusable and consumable by others. >> But the team structure, it's not a single amoeba going through all these steps right? These are smaller teams that are, and then there's some threading between each step? >> This is important. >> Yeah, that's tough. We were just talking about that concept. >> Just talking about skills and >> The bind between those groups is something that we're trying to figure out how to break down. 'Cause that's something he recognizes, I recognize internally, but understanding that those peoples tasks, they're never going to be able to iterate through different enterprise problems, unless they break down those borders and really invest in the communication and building those tools. >> Exactly, you talk about full stack teams. So you, it is not enough to have coding skills obviously. >> Right. What is the skill needed to get this into a run environment, right? What is the skill needed to take metrics like not metrics, but explainability, fairness in the moderates, and map that to business metrics. That's a very different skill from Python coding skills. So full stack teams are important, and at the beginning of this process where someone, line of business throws 100 different ideas at you, and you have to go through the scoping exercise, that is a very specific skill that is needed, working together with your coders and runtime administrators. Because how do you define the business KPI's and how do you refine them later on in the life cycle? And how do you translate between line of business lingo and what the coders are going to call it? So it's a full stack team concept. It may not necessarily all be in one group, it may be, but they have to work together across these different side loads to make it successful. >> All right guys, we got to leave it there, the trains are backing up here at IBM CDO conference. Thanks so much for sharing the perspectives on this. All right, keep it right there everybody. You're watchin' "theCUBE" from San Francisco, we're here at Fisherman's Wharf. The IBM Chief Data Officer event. Right back. (bubbly electronic music)

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Okay. Welcome back, everyone. We're live here in San Francisco for the cubes. Exclusive coverage of IBM. Think twenty nineteen for a student in our next two guests are great. Glad Co senior vice president, general manager of the mainframe division Broadcom Only with CIA and acquired Clayton Donley, head of security and immigration. Broadcom, both formerly of CIA. Big acquisition. Big value guys. Welcome to the Cube. Good to see you. >> Thanks a lot for having us. >> So we just talked before we came on camera here, IBM. Think a lot of cars here and software a icloud systems and software working together. Kind of thesis of the Broadcom. See a acquisition that that murder, that move was a big one. A lot of analysts liked it. Your thoughts. Now that's playing out here. Yeah, I think it was >> really interesting. If you look at what Broadcom has gone after in the marketplace, is they're They're not looking for the flash in the pan or trying to chase the next new thing. They're looking for core businesses or components. Software products that they believe are, you know, have real staying power and will be around for a decade or Mohr into the future, and then they want to invest in those and nurture they really want to be. Even if it's in a new space, they want to invest in something where they'll be number one or two in the marketplace. >> It's interesting. You're looking at the mark with cloud, you see scale. You see data, all this stuff we talked about for years and years, but it really comes back down. A systems and software working together clouds one big, complex distributed system. So all from distractions could maybe tract away. Those complexities isn't doing its end of the day. It's software plus large scale systems. This kind of was playing out. This is in the wheelhouse of what you guys do it. Can you guys had some color that trend and what needs to happen to make it more and more viable? Mohr performance easier to use? >> Yeah, I mean, I think that, you know, what we see is that customers are having a lot of problems with individual pieces of software. They're having problems when they put all this together. All right, So if you look at even to your question about Brock, come a moment ago. They sort of came in in price software through through the data center because they're providing everything. May I chips, too? Fibre channel networks and other kinds of things that are running peoples, you know, networks of the very largest scale and what their realizes when you get into the enterprise software level customers have such challenges because, you know, they don't get to cherry pick just cool things or the easy things to go integrate. They've got everything from mainframe client server to interior to whatever they picked up over the years. That stuff has to work together seamlessly to get kind of value. That makes sense. And that's why I think when you start looking at, you kind of are focus. It's on helping customers bring that together, get that value. >> It's all about the hybrid environment, because what we're getting at here is I got to make the legacy work with the new. But the beautiful thing about cloud native of some of these new micro services and containers is you don't have to kill the old Bring in the new. There's a great abstraction around software now that's making them work together. But yet the new stuff and work really great. This is the kind of the new architecture. Your thoughts on this? >> Yeah. I mean, obviously you're here a lot about that here it think right there, talking all about hybrid I T or multi cloud. I think there's a stat out there that, you know, seventy five percent of the large enterprises in the world say they'll be have multi cloud or hybrid environments by twenty twenty. I think they all have it today, Right? You think mobile mainframe, right? There's not workloads that work in isolation. You pick up your phone and you go to check your balance. It's gonna kick off a transaction that's going to go toe edge device or an edge server that's going to go through a network and maybe hit another server. And eventually it's going to go back to a mainframe to check the balance or to transfer funds or something like that. So they're having to deal with it already today. And and there's two kind of sides of the coin. You want that interaction for the developers to be common across those platforms, yet you want them to be ableto leverage on the power the strength, the security of the underlying platform without having to know all the gory details, which is, you know why. It makes a lot of sense for us mainframe and distributed. If you look across what is the CIA Technologies portfolio that Broadcom acquired, A lot of the capabilities that we have are the same capabilities that work across those environments so that the enterprise customers can interact with it one way. >> Clayton it. When I hear this environment, there's certain things that I need to worry about everywhere. It's, you know, my data. How do I protect my data? And, of course, security is one of those areas where there are lots of different environments, and unfortunately, there's lots of different considerations. Depending on which clouds I have which environment, you know, mainframe X eighty six power. You all have different considerations. The mantra I've heard that that seems to resonate is security is everyone's responsibility, you know, up and down the stack from the chip level all the way through the application. So explain where you know CIA. Now Broadcom fits in tow this picture and lives in this, you know, even more. Header. Genus world and by the way, totally agree. Multi cloud is what customers have today. Yeah, >> I mean, if you if you look at it, I mean, customers. They're building out, you say new mobile applications and and, you know, building them, his services in the cloud and so forth. But what we're finding is that the transactions and other kinds of things, they're still happening in some of these other environments. Maybe those environments still live in a data center. Maybe they've been moved to a private cloud. Maybe they're in a public cloud. Writing on my ass or some other kind of bank is a service. What we're finding is that each of you that transaction has to be protected. The guy that gives you the ability to call that transaction from a mobile app needs to be protected. All of these things need to be protected. But then you need to be able to orchestrate that. Make sure that you're laying down those based protecting those bits. Same way every time testing them the same way every time. And I think that if you look at what we're looking at and our values really in digital infrastructure management, right, but you're you're bringing all these pieces in cloud, multi cloud mainframe, All of these environments you have, You have a way to operate it, Manage it as well as for security. >> Yeah. So, Greg, you know, when I look back my career, there's something that's been repeating a lot. It's I go back to find it here, go back to the nineties. It was like, Okay, what was some of the reasons why the excess piece failed? It was like, Well, it was networking security, you know, in cloud happened. It was, well, security and management, howto like, you know, figure out some of these management of a hetero genius environment has typically been a downfall in it. It's something that we struggled at as an industry. So why will now be different? How how is the industry helping to solve that issue? And, you know, simple is something that we keep trying to hear. But, you know, actually, achieving it is pretty >> challenge. I think it's fundamentally realizing that the core large enterprises in the world today are using mainframes, right, and some of them have tried to migrate. Something's off, and it's not about complexity of migrating it off. It's about whether or not you can land somewhere that has that same security throughput, resiliency, all that kind of stuff. But if you recognize that you're gonna have these systems interacting and you recognize that we have to make it easier for people whether they're coming out of university or they're coming from a background of distributed or open source, you want to make it easier to interact. It's what's informing everything we do in our strategy and mainframe. So we talk about open, frictionless and optimized. So it's all about the idea of that mainframe system and those processes that were running, whether it's Dev ops, whether it's, you know, databases and tools, whatever we're doing, the security, the analytics that we're doing that has to be open and be able to interact with other people's tools as well as other people's platforms. Frictionless is all about the idea of you got to make it easy to do that interaction somebody that comes at this from a non mainframe context that maybe knows I calm the cartoon characters of open source. You know, get your gold for Jenkins or whatever, right that they can use that to interact with the mainframe and leverage it, and then you want to be optimized. You want to make it for the real deep technical professional to get the most out of it and focus where the expertise is, or for the novice cannot really have to need training wheels, but to be able to ride that bike right away and perform the things. So all these things you can see how their kind of informed and setting that tone of thinking about, ah, hybrid environment and connecting that mainframe in, across, not sitting as an island unto itself, >> I mean, you bring up a good point. A couple points, One is distributed. Computing has been around for a while. Mainframes. I mean, I'm old enough to remember that I was private client server way. We see the point of the main finger. >> You're gonna be >> dead soon. Most of all, kind of went away that, but it never died, right? We all know, but there's a renaissance. Rumors of my death are greatly, exactly. A lot of them didn't go down, but they were, but they were really died. But but here's the thing. There's a renaissance and mainframe because of cloud computing and cloud operations. If everything's cloud operationalized, then essentially you have a big one. Big distribute computer call resource and edges that are subsystem. So the notion of buying a mainframe isn't a platform decision. It's a right tool. The right job kind of decision, so people are not looking at mainframes was a bad decision. If it fits right, that's not like everyone should buy made friends. But if you need it, the horse power, the question is begs. The question is, why is there a renaissance and mainframes? What's the reason why people are buying them? Is it because it fits into a certain position? Is that certain scale? Is it because they could plug right into the cloud and be a big resource? >> I think there's I think there's also, ah, realization, you know, think about if you're the the newer CEO, our CTO, and you start looking at your state and you realize that you know this mainframe thing thatyou're spending twenty percent of your budget on is actually doing seventy percent of your process that you kind of look at it and you go. We'll work really cost effective. So then you start looking at? Well, where is it most cost effective. And does it make sense to use, Use it there. And then when you could tie it into everything else, when you can can get the same types of security tools and lock it down and locked the interaction down you say, Hey, this might, But this might make sense for me to do it. And I think it just ends up being dollars and cents and then the resiliency, right? I mean, when people aren't having that downtime >> plate, you're going to run your business. You want up time. If you're any commerce, you want high stamp your systems. So it really is the right tool for the world, like a thing for the right job. Is this happening? Give us the update on our people, buying more reason because it's just it's better. >> I think part of it also is, you know, why fix what isn't broken, right? The main friends running there, It's up. It's provided transactions. I think he used to have used to have this impediment to getting access, to need to find some old global guy, you need to find all this other stuff because you had your business, >> Cobol programmers. But now it runs analytics. >> It's like a It's like a foreign language to some people, right? You say Kobol was like, after one Chinese. So what? We've done those We've made it. So you don't have to learn. Cobell. You don't have to learn some specialized thing. You can come in with a prize. You come in with the technology, they teach kids and, you know, elementary school t use Java script and other kinds of things to come in access. So same things that are now in the mean >> it's basically a big iron and the old expression, big horsepower, >> horsepower, high throughput, high resiliency. >> Greg, I heard you talk about things like Dev ops that you fit in this environment. Absolutely. We've attracted. I remember, you know, when you nosy lennix on mainframe rolled out fifteen years ago. You want to do the cool new dock? Er, you know things? Absolutely. But if I look at the death ofthe people, people that are going to pay for this a lot of times they say, Well, I'm used to more that cloud model. How do I get? You know, they moved to an off ex model. We're still early in that trend, but, you know, Dizzy Syria's mainframe. Will it fit into the new modern paradigm? From a CFO standpoint, >> I definitely think so. If you if you look at a lot of the stuff that's going on in the marketplace and even concepts that we're testing with clients today around, you know you can refer to it as consumption based pricing or value based pricing, you know, looking at how much you're actually using and then charging for that with a known, you know, Hey, if I grow my capacity this much, how much am I going to pay or if I go down? I'm not going to be able to redeploy those dollars elsewhere. All those constructs are stuff that we're working with customers today on. So it is very much the idea of a cloud like environment that can either be delivered on creme through you buying your own hardware or, you know there's IBM that as easy cloud, there's folks like in so no its center that have clouds that have mainframe up in them today. >> And the developer environment clearly is going towards infrastructures code, which is the abstraction away? Just programmable infrastructure. They don't care where it was fast, right? Doesn't matter. Does it really matter >> how I look? Way contributed, too. Zoe Bright side, right? That was the command line interface, and everybody was like, Oh, my God, You know, they thought maybe we had some executives that were sitting back that had this brilliant idea. We were actually using out agile methodologies in our development, and we gave in each programming increment. We gave the engineers time to do what they wanted to do. You know, one sprint per cycle. And some of our young developers said, You know what I wish I could use, Get Jenkins and gulp and tie them into Endeavor or these other Dev ops tools or it stops tools. They developed it as an internal use tool for a command line, and we've stumbled over on accent. We said, Oh my God, this is thing We think something we think customers would want. And then, as we got talking with Rocket with IBM, Rocket had a Web interface, IBM at the mediation layer and we said, Holy cow, You know, this is something. If we got together, we contributed. We could really start a renaissance around mainframe, and a lot of people are going What? Why you've got proprietary tools and software. Why would you open up? Because the reality is we want our customers to find it easier to work with the mainframe and look out compete on the differentiation of my underlying lying product, whether it be price or function. But I want my customers to be able to tie in my software with IBM with rockets with rooms out whomever and picked because of where the value is, not because they feel locked in >> its You're going about one >> of the gripes about mainframe, right? People thought they were locked in >> lock and proprietary weird interfaces freshen, You take the friction away >> and that's not that's your father's >> mainframe. That's not today's May in front of that was exactly the old. The only kind of perception, right? We bring Lennox and all these tools and infrastructures. Code is just another resource on the network. Guys. Thanks for the insight. Appreciate Left home My mainframe. My God made my day here. So I'm free clad world final. Give a plug quickly for Broadcom. What, you guys working on. What's the big news here for you guys? Give a quick. >> Hey, I'll tell you for me, Broadcom acquiring the mainframe business is all about investment. And, I mean, we're a software business, So more than ninety percent of my expenses people, if I'm not hiring, I'm full of it. I'm not investing. I'm hiring were posted like crazy. We're hiring, We're expanding to the team, and the idea is all about there's customers have used core products for many years, and they want to count on him for many years to come. Were making those investments, and we're going to continue to invest in the new capabilities dealt, make more efficient, effective on the platform. >> Your thoughts, >> you know, I >> mean, I think that you know that, you know, it's interesting. You look a broad, calm, and a lot of people don't know. You know what's the focus right there? They're not traditionally the software space, and so >> on. They are the >> first thing you well, they are now. And one of the things that we're doing this if you look at our investment rate in R and D in general, it's up there. I mean, world class. If you look at the largest your most successful cloud players forget about, you know, your large cap. Take protect companies to sell in terms of percentage of our percentage of revenue. They spend it R and B. We're far above that. We're at a very high level. We're going to continue to invest in a lot of innovation, you know? Aye, aye. Machine Learning Dev. Ops, of course. You know, curious security is >> a cultural shift. We could see vinyl records. We're gonna come back now. You got mainframe back. How much back can I get a mainframe for? If I want to be the new cool kid on the block, you >> got to go to IBM >> for the hardware. But I could talk to you about yourself or to help you with it. You gotta mean faith for the Cube. Just have one in our house. Thanks, guys. I appreciate it. Thanks. Pleasure. You covered your talking mean freeze and IBM Think software. Lynn Nix, The new World Cloud Data. I I'm John's First Amendment back with more coverage after this short break

live from Orlando Florida it's the cube coverage conf 18 got to you by Splunk hey welcome back to Splunk kampf 18 conf 8 hashtag Splunk conf 18 my name is Dave Volante I'm here with my co-host a minimun you watching the cube the leader and live tech coverage there's two days of wall-to-wall coverage is our seventh year stew at conf we're seeing the evolution of Splunk from kind of analyzing log files to having deep business impact across the organization and doing more with data Jim Nichols is here is the DevOps manager in Improv odda healthcare company good to see it thanks for coming to the cube again thank you for having me thank you so tell us about M privada and then love the the title DevOps in the title we'll get into that sure first the company yep so in providers the healthcare IT security company and we provide health court healthcare organizations around the world with secure Identity Management multi-factor authentication and enable just ubiquitous access to whatever sort of medical systems that they need to get into and we really try to enable healthcare by establishing trust between the medical providers the patient's the data and do that all securely and seamlessly so that we're not Security's not a part of their workflow it's just in there and they don't have to think about it and they just get access to what they need when they need it so I hear yeah on your website trust between people technology and information reminds me a little bit of a certain software company that branding is all around us today that is there seems like there's a line up between what Splunk does in your company's mission oh they're there absolutely is and you know like Splunk in privada has a very strong on premises in the data center footprint and we're expanding that into the cloud and that's where most of my work is is kind of managing those cloud systems that kind of complement the on-premise appliance and we're looking at how that's going to move into the cloud and what that means and it's very similar to like what Splunk is done with Splunk enterprise and now moving into Splunk cloud and we're actually a customer's point cloud everything that we do that we could possibly do is out in the cloud not in the data center in you you've got DevOps two new titles maybe bring us inside you know what that means that improv odda you usually think about you know moving fast things are changing all the time it's themes that we heard in the keynote this morning so explain that a little bit yeah so the way the DevOps model that we follow at improv odd is really like kind of a consultant model where we've got a small team of a very senior very expert DevOps folks and they kind of get assigned out to the agile teams and they're a team member that gets planned into the Sprint's plan and what we're going to be dealing and really kind of make sure that those deployment events or the DevOps work that we need to do is planned in as part of the normal development work and that consultancy model is really good in regards to Splunk because we run the Splunk infrastructure we do all the training we do some of the basic dashboard work and make sure that no matter what the team products onshore offshore wherever they are we're all looking at data exactly the same way exact same dashboards and it really kind of forces the knowledge to get shared throughout the organization across products and how we think about things and so Splunk you know DevOps isn't like a tool or a thing or whatever but Splunk is definitely a great like enabling forcing function to make sure that we are sharing metrics how the system works what we're learning on and all that stuff in a really consistent way so you know the t-shirt met tricks I've seen that I have what do you think that means oh so it's like the same old same old man metrics so huh what does that mean to you guys you have new metrics do you have a sort of new set of KPIs that you're using ourselves so I think the metrics part is that it's maybe 10 years ago the IT industry figured out how to get every single metric about CPU memory disk ram and all the tool there are a lot of different tools for doing it you know Splunk zabbix data dog others I don't know if it's okay to talk about other products or whatever but you know when you get like a CPU alert that goes off all right the CPU usage is 92% is that good or is that bad it sounds kind of high and you get that alert you look at that CPU chart and it's like there's no context there's no information and you know you might be designing your system to run at 90% if it's doing some batch processing or something so it's like metrics it's like you need to get the alert you need to know what's going on but you really need to like get the insight into what it is and that's why a lot of this stuff that they show this morning at the keynote was really exciting where you've got the metrics in one place the logs in one place it's all in one place so you get that alert and you can look at it and then see what else is going on without having to like jump into a bunch of different systems and how about DevOps your DevOps in the title what is how do you guys look at DevOps what is DevOps to you and where did it come from and where is it going I think that I've been doing DevOps my entire career since I got out of college and I came out of WPI and was studying like performance evaluation and it's like how do you measure systems get the insight how do you make sure they're running efficiently and I think that what I was kind of doing on the performance engineering side kind of intersected with like the agile movement and folks get into agile development teams and trying to integrate that knowledge and the metrics and how you're gonna run it in production into that sort of product building process so I feel like I've been doing DevOps for a long time and called it different things over the years you know for for us at improv Adi it's really about enabling our developers to deliver functionality to our customers as fast and as safely as possible so you know we're in the healthcare industry and you know the the systems that we build and integrate and support support life right like these are doctors that are using these systems they have to work a hundred percent all the time and that adds some interesting wrinkles where you wouldn't really think about doing continuous deployment for the system that you know somebody's going to get logged into to get into their medical records you might want to be able to move that quickly if you need to if there's an emergency bug fix but the level of safety and testing that we need to put in before it actually gets into production that's really where we spend a lot of our time in DevOps is making sure that that's a fish but that's fast and then when it goes from going from like a test environment into production if it takes an hour for office is not that big of a deal we're doing like you know multi week to week release cycles or even longer and so as far as like DevOps a lot of the movement has been around like continuous delivery and deployment and we kind of use that to optimize like the test build and debug cycle and that way when we know when we get to production that's going to go smoothly and that there aren't going to be any unanticipated how do security fit into this conversation sometimes you know the the buzzword term you know dev sac ops is you know how to how to Splunk in your practice look at security well so you know where a security company you know you know and we wouldn't really ever call anything dev sack offs because security is ingrained in a part of every single thing that we do walking into the building every day when we badge in I think about it our security people like is the building's secure all the way into like what we're ending up doing in the system so obviously Splunk is a huge supporter of that so we've got audit trail information on all the systems and we can know not only what you are system administrators and DevOps users are doing but like what docker is doing what commands it runs and really get at a very very low level of detail and we literally have everything that ever happens on those systems is audited and we've built a whole set of alerts around things that we know about things that we think might be a problem and we use kind of our expertise in the healthcare security space and then apply that to all our cloud systems so it's like we never have a team called dev sack ops it's like it's it's just what we do it's the first most important thing that we think about every day is security so that's why it's a little bit different for us but we like some of the ideas and I've you know we've started doing some work around automated security testing on the application code you know running like static analysis dynamic analysis integrating web scanning tools into our CI CD pipelines so that it just makes it that much easier you know and not wait till the end before you ship it or whatever we have it right in the development process what's the regime for your organization you know the classic development and operations throw it over the fence and okay DevOps brings those together but you still got a spectrum of skills and presumably you've got people on you know some kind of maturity model where you've got sort of newer folks maybe guys coming out of college like you were several years ago and you're training them and sort of you're one unified team at the same time you you might have some degrees of specialization so what have you found is the right regime for the DevOps team well I think the consultant model that we've established works really well and we've got a very senior DevOps person that's on the agile team and they may do some of the really tricky bits but once we get out of the part that only us as DevOps can do we really try to get the developers to do it so a lot of that's like Splunk training how do you build a dashboard here's maybe a simple example dashboard now you do the next panel that sort of thing to try to level everybody up and get everybody on the same page you know turned in terms of this divide between like Devon Ops when I actually joined and provided DevOps was NIT it was managed as part of like our SAS management offering along with like a lot of the other applications that IT managed and one of the very first things that our senior vice-president did was like they get to be in development they can't Oregon is a we were working together we're all on the same teams we're all doing all that stuff but just mentally organizationally get rid of the divide put them in engineering and report to the VP of engineering just like the developers development managers and architects and that's the way we've just get rid of any organizational or thought divide between the between the groups Jim you mention alerts just now and we've heard a few times you get alerts and you know I imagined the beeper in the old days now you get an alert on your mobile phone where are we in terms of being able to take action on those alerts have the machines take action for us is that an objective that you have is that just too damn scary your thoughts yeah so my first my first impression is that it's a little scary we do have some problems that occur with some frequency right so losing an Amazon ec2 instance happens you know 10 times out of 100 instances in the cloud on a given month so there's certain types of those failures that we've automated around just because you have to as a part of just doing business in the cloud so why do the Amazon like auto scaling groups all that stuff we've got a couple of you know issues that happen that we want to just resolve faster and repair faster they don't impact customer experience or user experience but we just want to get on top of those sooner so we've started to automate some of the very thin small carefully controlled controlled use cases so that if the alert were to go up spurious lis I know it's not gonna then take down a system that was running and finding good false positives exactly so only were places where false positives can be tolerated is where we're looking to do that yeah you don't want to take the humans out of the equation just yet or maybe ever for some of the simple things we we have and we can and we will but some of the complicated things it's like just stop and look at it and think about it for 90 seconds and then make the action we're to come up with how to program that 90 seconds of thought is like maybe talk about it be complete about it off oh this way okay let me explain it to somebody a second time and make sure it's right and then go and do a quake like just philosophically that's where I have to get a sheen to do that so Jim you're wearing the revolution a word shirt my understanding in privada is now one of two two-time Award winners if I got it right you're a commander Award winner maybe you could explain what that means and what it means to you and your company sure so the commander award is really about getting you know other folks in your organization using Splunk looking you know either looking at a dashboard at a report or digging down into the data and you know so why I won the award was really around like our use of docker containers so it was really important to me that developers people in DevOps people and support don't really have like a strong like network operations function but those types of folks that they're all looking at the exact same thing all the exact same tools all the exact same data so kind of as part of that mission it's just I hold trainings I hold office hours I've got one of my DevOps folks down here today or at the conference to then kind of spread the Splunk gospel show people how to use it if they've got questions all that sort of stuff and then the other part of that is really just showing people what we can do and advocating for the making decisions based on the data we have it in data you know I have it in spunk let's look at that to make the decision so that's really what that commander Awards kind of all about so if you're doing the doctor stuff you're a bit of a trailblazer so we were only a few years into this container initiative I was walking the show floor I even saw some companies looking at like the serverless technology you know what what led you to kind of put these pieces together and you know it tell us a little bit about kind of the community that you lean on to learn these things yes so the the technology trend around containers was very strong and very fast like with Amazon's especially like that when they came out with their ECS orchestration it was really fast and very strong and really the the technology trend kind of led me into it and then the developers being like we're gonna use docker we're gonna have to figure you're gonna have to figure out how to Splunk it so really from the very beginning I've gone through each and every sort of possible way to get data out of a dr. container in this Splunk and part of that is you know networking with the Splunk folks pretty good relationship with the with the fella that wrote the logging driver that went into the dock or open source project and like looked at the code reviews and all that and then it's really just trying it out trying things out and eventually kind of got to the sweet spot now where I've got the developers are all using local docker compose and that's configured a certain way then when we run in Amazon it's using Amazon ECS where I've also been working on kubernetes for a while and the way that you configure your docker in each of those environments is totally different the code running in the is exactly the same so we've realized that vision but the runtime environment is totally different so kind of where we're at now the config may be totally different on the logging drivers but in the end when you load up Splunk and you look at it and Splunk it's exactly the same whether it's your local laptop and amazon in production staving staging or whatever and I think my kind of favorite part in terms of like the Splunk commander award and getting folks using Splunk is that the way that I have it set it up set up now there is literally no local log file for the developers on their laptop it just doesn't exist it all goes out to Splunk so you can do a lot with grep and text pad and stuff on your local local laptop and I get that but now that they're in Splunk and it's just it's been a great way to get folks on board with what its gonna look like in production I know what it looks like in dev so I can make sure that my logs are good I'm logging enough and not too much and all that stuff so that's really where docker is really software is the same now we've got the logging the same the tools are all the same but then the runtime bits those are a little bit different and that's abstracted away hopefully Jim what does a DevOps guy want from a vendor you got a lot of open source stuff that you're working with you got a lot of different tooling what do you look for in a vendor what's what's the thumbs-up and positives and what what stuff really kind of ticked you off well so you know we're we're a key trusted vendor for a lot of healthcare organizations so I can kind of talk about how I we prison if a customer or a user comes up comes to us with a problem doesn't matter what it is it's our problem and we go through exhaustive lengths to identify where the problem actually is and so that may be in our code that maybe in another vendors code some third party some open source thing doesn't matter we're after the evidence we're after the facts we don't care if it's not in our code we're gonna help our customer be successful and that's what we would want from any vendor right so if we contact them with a support case we've got a problem we don't want any of this uh looks like a firewall problem or something like get to the data get to the facts and if you can prove if the vendor can prove that the problem is somewhere else great but we want a reproducible test case we want this whole finger-pointing thing is like it's horrible inside of an organization in terms of like running operational systems but then when you've got like as your Google Cloud Amazon Cloud Salesforce service now all these things all working together like you can't people just going to own the problem basically and that's what that's what we do right so if the customer comes to us with an issue it's our problem and then we go from there and figure it out and that's really what any vendor that we work with especially like a production operational sort of system that's really what we look for so you look for collaboration and focus on solving the problem not not the finger-pointing you know a virtual single throat to choke if you will yeah exactly hm well thanks very much for joining us on the cube is great to have you yeah thank you thank you very much appreciate I keep right - everybody stew and I'll be back hashtag Splunk conf 18 you're watching the cube right back [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)

>> From the Palace of Fine Arts in San Francisco, it's theCUBE, covering AT&T Spark. Now here's Jeff Frick. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at San Francisco, at the historic Palace of Fine Arts, it's a beautiful spot, it's redone, they moved Exploratorium out a couple years ago, so now it's in a really nice event space, and we're here for the AT&T Spark Event, and the conversation's all around 5G. But we're excited to have our first guest, and he's working on something that's a little bit tangential to 5G-related, but not absolutely connected, but really important work, it's Chris Sambar, he is the SVP of FirstNet at AT&T, Chris, great to see you. >> Thanks Jeff, great to be here, I appreciate it. >> Yeah, so you had a nice Keynote Presentation, talking about FirstNet. So for people I've missed, that aren't familiar, what is AT&T FirstNet? >> Sure, I'll give a quick background. As I was mentioning up there, tomorrow is the 17-year Anniversary of 9/11. So 17 years ago tomorrow, a big problem in New York City. Lots of first responders descended on the area. All of them were trying to communicate with each other, they were trying to use their radios, which they're you know, typically what you see a first responder using, the wireless networks in the area. Unfortunately challenges, it wasn't working. They were having trouble communicating with each other, their existing wireless networks were getting congested, and so the 9/11 Commission came out with a report years later, and they said we need a dedicated communications network, just for First Responders. So they spun all this up and they said, we're going to dedicate some Spectrum, 20 megahertz of D-Class Spectrum, which is really prime Spectrum. Seven billion dollars and we're going to set up this Federal entity, called the FirstNet Authority, and they're going to create a Public Safety Network across America. So FirstNet Authority spent a few years figuring out how to do it, and they landed on what we have today, which was a Public/Private Partnership, between AT&T, and Public Safety throughout America, and we're building them a terrific network across the country. It is literally a separate network so when I, I think of wireless in America, I think of four main commercial carriers, AT&T, Verizon, T-Mobile, Sprint. This is the 5th carrier, this is Public Safety's Wireless Network just for them. >> So when you say an extra network, so it's a completely separate, obviously you're leveraging infrastructure, like towers and power and those types of things. But it's a completely separate network, than the existing four that you mentioned. >> Yeah, so if you walk into our data centers throughout the country, you're going to see separate hardware, physical infrastructure that is just for FirstNet, that's the core network just for this network. On the RAN, the Radio Access Network, we've got antennas that have Band 14 on them, that's Public Safety's Band, dedicated just for them when they need it. So yeah, it's literally a physically separate network. The SIM card that goes into a FirstNet device, is a different SIM card than our commercial users would use, because it's separate. >> So one of the really interesting things about 5G, and kind of the evolution of wireless is, is taking some of the load that has been taken by like WiFi, and other options for fast, always on connectivity. I would assume radio, and I don't know that much about radio frequencies that have been around forever with communications in, in First Responders. Is the vision that the 5G will eventually take over that type of communication as well? >> Yeah, absolutely. If you look at the evolution of First Responder, and Public Safety Communications, for many years now they've used radios. Relatively small, narrow Spectrum bands for Narrow Band Voice, right, just voice communications. It really doesn't do data, maybe a little bit, but really not much. Now they're going to expand to this Spectrum, the D-Class, the D-Block Spectrum, excuse me, which is 700 megahertz, it's a low-band Spectrum, that'll provide them with Dedicated Spectrum, and then the next step, as you say, is 5G, so take the load off as Public Safety comes into the, the new Public Safety Communications space, that they've really been wanting for years and years, they'll start to utilize 5G as well on our network. >> So where are you on the development of FirstNet, where are you on the rollout, what's the sequence of events? >> The first thing we did, the award was last year in March 2017. The first thing we did was we built out the core network. When I talked about all that physical infrastructure, that basically took a year to build out, and it was pretty extensive, and about a half a billion dollars so, that was the first thing we did, that completed earlier this year. >> Was that nationwide or major metro cities or how-- >> Nationwide, everywhere in the country. >> Nationwide, okay. >> So now what we're doing is, we are putting the Spectrum that we were given, or I should say we were leased for 25 years, we're putting that Spectrum up across our towers all over the country so, that will take five years, it's a five-year build-out, tens of thousands of towers across America, will get this Public Safety Spectrum, for Public Safety, and for their use. >> Right. Will you target by GEO, by Metro area, I mean, how's it going to actually happen? That's a huge global rollout, five years is a long time. How you kind of prioritize, how are you really going to market with this? >> The Band 14 Spectrum is being rolled out in the major, the major dense areas across the country. I will tell you that by the end of the rollout, five years from now, 99% of the population of America, will have Band 14 Spectrum, so the vast majority of the population. Other areas where we don't roll it out, rural areas for example, all of the features that Public Safety wants, we call them (mumbles) and priority, which is the features to allow them to always have access to the network whenever they need it. Those features will be on our regular commercial Spectrum. So if Band 14 isn't there, the network will function exactly as if it were there for them. >> Right. Then how do you roll it out to the agencies, all the First Responders, the Fire, the Police, the EMTs, et cetera? How do they start to take advantage of this opportunity? >> Sure, so we started that earlier this year. We really started in a March-April timeframe in earnest, signing up agencies, and the uptake's been phenomenal. It's over 2500 Public Safety Agencies across America, over 150,00, and that number grows by thousands every week. That's actually a pretty old number but, they are signing up in droves. In fact, one of the problems we were having initially is, handling the volume of First Responders that wanted to sign up, and the reason is they're seeing that, whether it's a fire out in Oregon, and they need connectivity in the middle of nowhere, in a forest where there's no wireless connectivity at all, we'll bring a vehicle out there, put up an antenna and provide them connectivity. Whether it's a Fourth of July show, or a parade, or an active shooter, wherever large groups of people, combined together and the network gets congested, they're seeing that wow, my device works no matter what. I can always send a text message, I can send a video, it just works. Where it didn't work before. So they love it, and they're really, they're really signing up in droves, it's great. >> It's really interesting because it's, it's interesting that this was triggered, as part of the post 9/11 activity to make things better, and make things safer. But there was a lot of buzz, especially out here in the West with, with First Responders in the news, who were running out of band width. As you said, the Firefighters, the fire's been burning out here, it seems like forever, and really nobody thinking about those, or obviously they're probably roaming on their traditional data plan, and they're probably out there, for weeks and weeks at a time, that wasn't part of their allocation, when they figured out what plan they should be. So the timing is pretty significant, and there's clearly a big demand for this. >> Absolutely. So that example that you sight is a really good one. Two weeks ago, there was a lot in the news about a fire agency in the West, that said they were throttled by their carrier. It was a commercial carrier, and commercial carriers have terms and conditions, that sometimes they need to throttle usage, if you get to a certain level. That's how commercial networks work. >> Right, right. >> FirstNet was built with not only different technology, hardware, software, but with different terms and conditions. Because we understand that, when a First Responder responds to your house, we don't want that to be the minute in time, when their network, their plan got maxed out, and now they're getting throttled. >> Right. >> So we don't have any throttling on the FirstNet Network. So it's not only the hardware, software, technical aspects of the network, but the terms and conditions are different. It's what you would expect that a First Responder would have and want on their device, and that's what we're providing for them. >> Right, and the other cool thing that you mentioned is, we see it all the time, we go to a lot of conferences. A lot of people probably experience it at, at big events right, is that still today, WiFi and traditional LTE, has hard times in super-dense environments, where there's just tons and tons and tons of bodies I imagine, absorbing all that signal, as much as anything else, so to have a separate Spectrum in those type of environments which are usually chaotic when you got First Responders, or some of these mass events that you outlined, is a pretty important feature, to not get just completely wiped out by everybody else happening to be there at the same time. >> Exactly. I'll give you two quick examples, that'll illustrate what you just said. The first one is, on the Fourth of July, in downtown Washington D.C. You can imagine that show. It's an awesome show, but there are hundreds of thousands of people that gather around that Washington Monument, to watch the show. And the expectation is at the peak of the show, when all those people are there, you're not really going to be sending text messages, or calling people, the network's probably just not going to work very well. That's, we've all gotten used to that. >> Right, right. >> This year, I had First Responders, who were there during the event, sending me videos of the fireworks going off. Something that never would've been possible before, and them saying oh my gosh. The actually works the way it's supposed to work, we can use our phones. Then the second example, which is a really sad example. There was a recent school shooting down in Florida. You had Sheriffs, Local Police, Ambulances. You even had some Federal Authorities that showed up. They couldn't communicate with each other, because they were on different radio networks. Imagine if they had that capability of FirstNet, where they could communicate with each other, and the network worked, even though there were thousands of people that were gathering around that scene, to see what was going on. So that's the capability we're bringing to Public Safety, and it's really good for all of us. >> Do you see that this is kind of the, the aggregator of the multi-disparate systems that exist now, as you mentioned in, in your Keynote, and again there's different agencies, they've got different frequencies, they've got Police, Fire, Ambulance, Federal Agencies, that now potentially this, as just kind of a unified First Responder network, becomes the defacto way, that I can get in touch with anyone regardless of of where they come from, or who they're associated with? >> That is exactly the vision of FirstNet. In major cities across America, Police, Fire, Emergency Medical typically, are on three different radio networks, and it's very difficult for them to communicate with each other. They may have a shared frequency or two between them, but it's very challenging for them. Our goal is to sign all of them up, put them on one LTE network, the FirstNet Network, customized for them, so they can all communicate with each other, regardless of how much congestion is on the network. So that's the vision of FirstNet. >> Then that's even before you get into the 5G impacts, which will be the data impacts, whereas I think again, you showed in some of your examples, the enhanced amount of data that they can bring to bear, on solving a problem, whether it's a layout of a building for the Fire Department or drone footage from up above. We talked to Menlo Park Fire, they're using drones more and more to give eyes over the fire to the guys down on the ground. So there's a lot of really interesting applications that you can get more better data, to drive more better applications through that network, to help these guys do their job. >> Yeah, you've got it, the smart city's cameras, don't you want that to be able to stream over the network, and give it to First Responders, so they know what they're going to encounter, when they show up to the scene of whatever issue's going on in the city, of course you do, and you need a really reliable, stable network to provide that on. >> Well Chris, this is not only an interesting work, but very noble, and an important work, so appreciate all of the efforts that you're putting in, and thanks for stopping by. >> I appreciate it Jeff, it's been great talking with you. >> Alright, he's Chris, I'm Jeff, you're watching theCUBE, we're in San Francisco at the Palace of Fine Arts, at AT&T Spark. Thanks for watching, we'll see you next time. (bright music)

(upbeat music) >> Hi, I'm Peter Burris and welcome to another CUBE Conversation from our wonderful studios in Palo Alto, California. Great conversation today, we got Christian Rodatus, who is the CEO of Datameer, here to talk about some of the trends within the overall analytic space. One of the most important things happening in technology today. Christian, welcome back to theCube! >> Good morning, Peter, thanks for having me today. >> It's great to have you here. Hey, let's start with, kind of some of the preliminaries. What's happening at Datameer? >> Well we've been around for nine years now, which is a lot of time in a very agile technology space. And I actually just came back from an Investiere offsite that was arranged from one of our biggest investors. And everything is centering around the cloud, right? We were trotting along within the Hadoop ecosystem, the big data ecosystem over the past couple years and since, 12, 15 months, the transition and the analytics market and how it's transforming from on premise to the cloud in a hybrid way as well has been stunning, right? And we're faced with a challenge in innovating in those spaces and making our product relevant for on premise deployment, for cloud deployments, and various different cloud platforms, and in a hybrid fashion as well. And we've been traditionally working with customers that have been laggards in terms of cloud adoption because we do a lot of business and financial services, and insurance, healthcare, telecommunications, but even in those industries over the past year, it has been stunning how they are accelerate cloud adoption, how they move analytic workloads to the cloud. >> Well, actually, they all sound like sometimes leaders in the analytics world, even if they're laggards in the cloud. And there's something of a relationship there. People didn't want to do a lot of their analytics because they were doing analytics in some of the most strategic, sensitive data, and they felt pressured to not give that off to a company that they felt perhaps, or an industry that's a little bit less ready from infrastructure standpoint. But our research shows pretty strongly that we're seeing a push to adoption, precisely because so much of that ecosystem got wrapped up in the infrastructure and never got to the possible value of analytics. So is that helping to force this along, do you think, the idea of-- >> Absolutely, right, if you look at the key drivers, and there was some other analyst research that I read this week. Why are people being moderated moving analytic workloads into the cloud? It's really less cost, it's really business agility. How do they become independent from IT and procure services across the organization in a very simple, easy, and fast fashion? And then there's a lot of fears associated with it. It's data governance, it's security, it's data privacy, is what these industries that we predominately work in are concerned with, right, and we provide a solution framework that actually helps them to transition those on premise analytic workloads into the cloud and still get the enterprise grade features that they're used to from an on premise solution deployment. >> Yeah, so in other words, a lot of businesses confuse failure to deal with big data infrastructure as failure to do big data. >> That's correct. >> I want to build on something you've just said, specifically the governance issue, because I think you're absolutely right. There's an enormous lack of understanding about what really constitutes data governance. It used to be, oh, data governance is what the data administrator does when they do modeling, and who gets to change the model, and who owns the model, and who gets to, all that other stuff. We're talking about something fundamentally different as we embed more deeply some of these analytics directly into high value business activities that are being utilized or performed by high cost business executives. >> Absolutely. >> How does data governance play out, and I'm going to ask you specifically, what are you guys doing that makes data governance more accessible, more manageable, within Datameer customers? >> So I think there's two key features to a solution that's important. So number one, we have very much a self-service aspect to it, so we're pushing abilities to model and create views on the big data assets that are persisting in the data lakes, towards a business user, right? But we do this in a very governed way, right? We can provide barefold data lineage, we can audit every single step, how the data's being sourced, how it's being manipulated on the way, and provide an audit trail, which is very important for many of the customers that we work with. And we really bring this into the hands of the business users without much IT interference. They don't have to work on models to be built and so on and so forth, and this is really what helps them build rapid analytic applications that provide a lot of value and benefits for their business processes. >> So you talked about how you're using governance, or the ability to provide a manageable governance regime, to open up the aperture on the utilization of some of these high value analytics frameworks by broader numbers of individuals within the organization. That seems to me to be a pretty significant challenge for a lot of businesses. It's not enough to just have a ivory tower group of data scientists be good at crafting data, understanding data, and then advising people what actions to take based on that data. It seems it has to be more broadly diffused within the organization, what do you think? >> So this is clearly the trend, and as these analytics services move to the cloud, you will see this even more so, right? You will have created data assets and you provide access control for certain using groups that can see and work with this data, but then you need to provide a solution framework that enables these customers to consume this in a very seamless and an easy way. This is basically what we are doing. We're going to push it down to the end user and give them the ability to work on complex analytical problems using our framework in a governed way, in a fast way, in a very iterative analytic workflow. A lot of our customers say they have analytic, or they pursue analytic problems that are of investigative nature, and you cannot do this if you rely on IT to build new new models to delay the process-- >> Or if you only rely on IT. >> And only rely on IT, right? They want to do this on their own and create their own views, depending on their analytic workflow in a very rapid, rapid way. And so we support this in a highly governed way that can do this in a very fast and rapid fashion, and as it moves to the cloud, it provides some of the even more opportunities to do so. >> So as CEO of Datameer, you're spending a lot time with customers. Are there some patterns that you're seeing customers, in addition to buy Datameer, but are there some patterns in addition to what you just described that the successful companies are utilizing to facilitate this fusion? Are they training people more? >> Yep. Are they embedding this more deeply into other types of applications or workflows? What are some of those patterns of success that you're seeing amongst your customers? >> So that's a very interesting question, right, because a lot of big data initiatives within companies fail for the lack of an option. So they build these big data lakes and ramp up cloud services, and they never really see adoption. And so the successful customers we work with, they have a couple of things they do differently than others. They have a centralized, serious type of organization, usually, that facilitates and promotes and educates people on number one, the data assets being available through the organization, about the tool sets that are being used, and amongst one of them, obviously, is Datameer within our customers, and they facilitate constant education and experience sharing across the organization for the user of big data assets throughout the organization. And these companies, they see adoption, right? And it spreads throughout the organization. It has increasing momentum and adoption across various business departments from many eye value use cases. >> So we've done a lot of research. I myself have spent a lot of time on questions of technology adoption, questions within the large enterprises. And you actually described it fails to adopt, and from adoption standpoint, it's called they abandon. >> Absolutely true. >> One of the things that often catalyzes whether or not someone continues to adopt, or a group determines to abandon, is a lack of understanding of what the returns are, what kind of returns these changes of behavior are initiating or instantiating. I've always been curious why a lot of these software tools don't do a good job of actually utilizing data about utilization, from a big data standpoint, to improve the adoption of big data. Are you seeing any effort made by companies to use Datameer to help businesses better adopt Datameer? >> Well, I haven't seen that yet. I see this more with our OEN customers. So we've got OEN customers that analyze the cloud consumption with their customers and provide analytics on users across the organization. I see these things, and from our standpoint, we facilitate this process by providing use case discovery workshops, so we have a services organization that helps our customers to see the light, literally, right, to understand what's the nature of the data assets available. How can they leverage for specific use case, high value use case, implementations, experience sharing, what other customers are doing, what kind of high value application are they going after in a specific industry, and things like this. We do lunch and learns with our customers. We just recently did one with a big healthcare provider and the interest is definitely there. You get 200 people in a room for a lunch and learn meeting, and everyone's interesting, how they can make their life easier and make better business decisions based on the data assets that are available throughout the organization. >> That's amazing, when a lunch and learn meeting goes from 20 people to 200 people, it really becomes much more focused on learn. One of the questions I have related to this is that you've got a lot of experience in the analytics space, more than big data, and how the overall analytics space has evolved over the years. We have some research, pretty strong to suggest that it's time to start thinking about big data not as a thing unto itself, but as part of an aggregate approach to how enterprises should think about analytics. What do you think? How do you think an enterprise should start to refashion its understanding of the role that big data plays in a broader understanding of analytics? >> Back in the earlier days, when my career come from the EDW road, and then all the large enterprises had EDWs and they tried to build a centralized repository of data assets-- >> Highly modeled. >> Highly modeled, a lot of work to set up, structured, highly modeled, extreme complex to modify and service a new application regressed from business users, and then came the Hadoop data lake base, big data approach there. It said dump the data in, and this is where we were a part, within where we became very successful in providing a tool framework that allows customers to build virtue of use into these data assets in a very rapid fashion, driven by the business user community. But to some extent, these data lakes have also had issues in servicing the bread and butter BI user community throughout the organization, and the EDW never really went away, right, so now we have EDWs, we have data lakes that service different analytic application requirements throughout the organization. >> And new reporting systems. >> And even reporting systems. And now the third wave is coming by moving workloads into the cloud, and if you look into the cloud, the wealth of available solutions to a customer becomes even more complex, as cloud vendors themselves build out tons of different solutions to service different analytical needs. The marketplaces offer hundreds of solutions of third party vendors, and the customers try to figure out how all these things can be stitched together and provide the right services for the right business user communities throughout the organization. So what we see moving forward will be a hybrid approach that will retain some of the on premise EDW and data lake services, and those will be combined with multi-cloud services. So there always will not be a single cloud service, and we're already seeing this today. One of our customers is Sprint Pinsight, the advertising business of the Sprint. Telecommunications companies say they have a massive Hadoop on premise data lake, and then they do all the preprocessing of the ATS data from their network, with Datameer on premise, and we condensed down the data assets from a daily volume of 70 terabytes to eight, and this gets exposed to a secret cloud base dataware service for BI consumption throughout the organization. So you see these hybrid, very agile services emerging throughout our customer base, and I believe this will be the future-- >> Yeah, one of the things we like about the concept, or the approach of virtual view, is precisely that. It focuses in on the value that the data's creating, and not the underlying implementation, so that you have greater flexibility about whether you treat it as a big data approach, or EDW approach, or whether you put it here, or whether you put it there. But by focusing on the outcome that gets delivered, it allows a lot of flexibility in the implementation you employed. >> Absolutely, I agree. >> Phenomenal, Christian Rodatus, CEO of Datameer, thanks again for being on theCUBE! >> Thanks so much. I appreciate it, thanks, peter. >> We'll be back.

I won't mind that either live from San Francisco it's the queue covering Red Hat summit 2018 brought to you by Red Hat and welcome back to the cube we're here live in San Francisco on day two of our coverage of red hat summit 2018 I'm John Troyer I'm here with Larry soccer Larry is the hi Larry weõll area the global lead for infrastructure growth and strategy at Accenture that's great though and welcome as a first timer to the cube you remember the Cuban member the cube alums Ozzy awesome so one of the themes here that we've noticed here on day two of the conference is the reality of hybrid cloud multi-cloud the demos up on stage have been real production workloads from real companies at a global scale and the the theme it's been a lot about open shift open and an open ship and that as a bridge for the right with the rest of Red Hat's stack so Accenture and global si you know work with very big companies very complicated problems and enabling them hybrid cloud is that important for you and your customers absolutely Accenture actually got out very aggressively about four or five years of work go with our cloud first strategy and it was very public centric you know how do we you know how do you start to take advantage of the innovation of the hyper scalers the AWS as the answer is to really start to innovate drive drive agile application development and get out there very quickly however if you take a look at our clients you know they're typically large complex global 2000 companies and for variety of reasons whether it's regulatory reasons gxb compliance if you go to the pharmaceutical industry HIPAA for health care you know PCI they they've really you know they continue to invest in their data centers I mean other reasons toko clouds an interesting one it's a proximity thing it's the thing that actually connects the the public providers and and if he's getting built on that performance you know if you know I start to look with sa P driving a terabyte Hana you know where do you start to deploy that so you know and then even investment say a lot of our clients have significant investments in their data centers and infrastructure so what we've been doing over the last probably six to eight months is really taking a look at a lot of the innovation that we saw from those hyper scalars and bringing it to the to the data center and really trying to create industrialized private clouds with the same kind of standardization that you haven't you know in the world of Amazon and as you're in a you know same automation the cloud operating model and really start to do that not just in the datacenter a private cloud but also the rest of infrastructure and ultimately our clients are going to end up with with hybrid environment so we're you know we've been using our extension cloud platform to integrate you know the public providers now in the person and the private side you know with open shifts the you know the VMware's of the world and even back into the legacy infrastructure well that's that's fascinating and also I think really grounded in reality I mean that the tech industry there's you know we all there's all these pendulums and hype cycles and a few years ago it's right right we we were talking a lot about public there was a lot of innovation and it and maybe it's taken a few years for the private stack and the hybrid stack to catch up to give you that advantage in terms of agility and in terms of speed to market speed to production can you talk a little bit about maybe what that relationship with with openshift you say you you're seeing we saw a like I said we've seen a lot of open ship in production are you saying that as well yeah yeah we did we definitely are I mean you know we have a lot of our clients who they're looking ok hey I this look I want to start getting to more service architectures I want to start adopting the new technologies agile development you know start to really embrace DevOps at the same time it's you know either for data gravity or for compliance reasons there's certain applications they just can't move into the public environments ASAP say you know has been challenging to do it particularly as we start to get HANA so you know they've been starting to look and say ok well open ship becomes a very attractive alternative to start developing applications that I can then you know right in a private environment as well as bring up into Amazon and as yours so a few years ago for better or worse one of the terms people were using was lift and shift right and people were taking their you know or legacy that there's a lot of years of battle-tested infrastructure and do you just hoist it into the cloud do I have to rewrite it can i containerize it I mean what are people doing and how are you going back to the scale of our clients you know a lot our plants will have anywhere from two thousand to over twenty thousand workloads and applications so the the notion of lift and shift or or modernization it's not a binary problem so what we actually did was we took our app modernization practice which is part about technology business we coupled it with our infrastructure migration teams as a part of our Accenture Operations Group and we created an integrated cloud factory and then we actually took we had two different two sets of tools we combined them into one accelerate toolkit and and what that does is it allows us to do the upfront application portfolio assessment we figure out the dispositions of the applications you know what what needs to stay together you know we determine which ones need to be refactored or remediated or modernized and that's our technology organization and then for those that we need to just migrate or so you know a few minor changes we then had you know do all the planning the migrations of that and we're able to do this at you know at scale with the factory leveraging a combination of onshore and offshore and these tools to do all the automation and do the you know the wave planning keeping dependencies and moving data around and and we're able to do you know anywhere at one climb we doing over 1200 workloads a month that's amazing I mean that the scale and the speed that time-to-market even in the demos here on stage has been actually pretty pretty surprising to me because it means that it's real as our people shift as people are shifting their portfolios into a hybrid stance some workloads here some workloads in a multi cloud can you talk a little bit about how you're approaching multi cloud and now you're approaching maybe the multi cloud over time well you know I mean we made a big bet on our Accenture cloud platform so which which is really a cmp started very public focused you know how do I provision and manage optimize my workloads across the public providers we've now started to integrate in the private side much more aggressively we're always doing it at our clients but it was a very custom one-off as we start to industrialize and standardize on the private side it now gives a seamless hybrid cloud management we're actually extending that to go to legacy we've still got a number of clients like insurance companies where they've got significant business logic trapped in their mainframes and our app modernization guys are starting to wrap those with micro services starting to do front-end development in OpenShift it's example and get closer to the users for you know for better customer experience much more agile delivery while still maintaining that frame and and what we find is as you've got these distributed applications based on micro services you now need to manage across that hybrid environment and it's it's public it's private but it's also legacy infrastructure yeah and that's got to be complicated one of the other themes of this show probably coming out of Red Hat's own culture of openness and a we had a great I love the the keynote this morning talking about well you know planning is great but you know eventually the plan is going to hit the battlefield and you've got to be adaptive and you've got to be agile so when you are talking with the CIO and when you're talking with these leads of business and their IT leads you know what are some of the things that you're preparing them with and what are maybe some of the signals that they're up there ready to do this versus maybe not ready to do this yeah you know very good question what's interesting is when I talk to most of our of the CIOs I think they've got a pretty good handle on the technologies I mean it's and not to trivialize it's not simple technology but I think most have focused a lot of their energy on that I think their biggest challenges are the culture and and the operating model so you know if you look thinking well how the hyper scalars do it I mean firstly standardize which I think that's you know these CIOs are typically do want you know they they're not driving standard t-shirt sizes they don't have that discipline to have a standardized service catalog what you need to audit traditionally the enterprise everything most custom everything was bespoke exactly so it's not in their DNA to go to that Christian you know standardization and I mean think about the hyper scalars I mean a well Amazon innovates an incredible pace they still have a discrete set of services and if you can automate and do real cloud operating model you really need to have that level of standardization the whole operate the business and operational transformation is very difficult you know it's interesting now the apps guys have typically done a reasonably good job I mean getting out there and using agile development you know they're embedded in the be used doing their sprints etcetera still some work to be done for the infrastructure guys you know if you if you start to take a look at it you could have an app team doing - you know two-week sprints they're ready to drop code all of a sudden have to wait 12 weeks for the infrastructure to catch up so we've been spending a lot of time looking at how do we enable software to find infrastructure how do we start to even do you know infrastructure is code with similar Sprint's and embedded into the be used groans etc talyn's a huge issue I mean they are all struggling it's very hard to get people with native cloud skills you know it sorts them in the market so most of our clients are really struggling I mean it's good for us as an integrator and bring me how to bring those skills but but they too need to develop those skills as well and that all in some way solves over itself over time as standardization happens right yeah as kubernetes becomes more ubiquitous you will have more people trained up in kubernetes same thing with some of the infrastructure layer maybe can you drill down maybe a little bit more into the infrastructure and how are you helping so do you say the infrastructure folks become more agile you know at some point you've got mainframes they're not moving so you kind of have to wall them off with some agile layers we'd be big proponents of software-defined infrastructure or I think VMware has actually done a pretty good job getting the market up to speed on software-defined data centers so how do you how do you first use virtualization techniques like you know if you think about VMware is NSX or Cisco's ACI part how do you deploy those two to provide the vehicle to do the automation and then grit you know severe you know just very intense automation now if I have to standardize first but then I start to automate so whether it's V you know VMware would be realize it's you know ansible so I mean we've seen red have to do some great work around ansible and doing that automation we use chef in our in our central cloud platform but but it but really starting to drive that similar type standardization and automation but but you have to chain change how you operate to do that and I think that's where a lot of people struggle so they you know they may have automation projects acceptor but they they haven't really fundamentally shifted how they do it so at one of our clients a life sciences client we actually were doing we were implemented a software-defined datacenter we had service now as as the the front end portal you know V realized automation integrated with a GXP compliance system and we just kept iterating through in two-week Sprint's we would incremental II deliver a you know first minimal Viable Product and compute and storage then up to t-shirts we got into you know more database-as-a-service eventually even as being able to spend up s a PE basis instances and we were able to leverage a lot of the automation including the network which is oftentimes a long pole in order to accomplish that right alright so starting with bite-sized pieces and exactly incrementally improvement and that's the great thing about agile right I mean it and to put the problems and the apps guys have known it for a while as infrastructure guys with a little new so we've actually taken out Accenture DevOps platform and we've created an infrastructure exclude plugin you know that uses github and jerry' to now deliver drop releases of infrastructure as good well that's great I mean you mentioned a lot of different tools and platforms here a lot of them open source right we're here at Red Hat summit I think one of the again one of the signals of this week they were you know announcement with Microsoft announcement with IBM you know very serious and you all have been working with them very serious enterprise ready ecosystem here do you get any pushback about the open source nature of some of these things you know less and less and a number of years ago there was clearly you know because of particularly licensing an tabria Enterprise great applications I think that you know I think people become much more comfortable with open source I mean what when it one thing I often look at is Kafka and you looking at me I see so much Kafka getting deployed right now it's you know open source model it's you know I'm seeing it used in so many different uses you know you pet use cases and development and so I think I think a lot of and thanks to Red Hat I you know give them credit for bringing open source to mainstream and to the enterprise market I'm putting you know licensing around it so I think no I don't see the same kind of pushback anymore and I think the walls changed it's kind of the bearable right it's either both at the cloud layer and then at the infrastructure layer in the automation everything like that you know maybe talk a little bit more about some of a Accenture what I would I would have been gathering here right there's a bunch of open source tools you're using but you have your own tool sets too right and and and the eccentric cloud can you talk a little bit so the extension cloud platform is I mean we do use a lot of third-party technologies we're not gonna go reinvent the wheel we're gonna pull in the best of products that we can me and it says and we started off I mean it's been out there for about five years you know to be you know we have an orchestration platform that's built into it we do use a lot of shaft to do you know the provisioning of the environments have a but you know and we keep evolving it we've changed out building optimization engines and now are very focused on how do we push it into the private world so that brings in new tools and capabilities to do that automation so so as we continue to push that the the next big step that we're focused on is the application and infrastructure management so one of the emerging problems is we start to see micro services get adopted and you're gonna get applications that might have a front-end running service and Amazon you know with lambda you know distributed private cloud with a CouchDB you know data right yeah and then a mainframe reservation system so this is one of our you know one of our clients has that environment how do you manage and troubleshoot across that environment so the ability to first look at what I'll call the application or service topology you know up in the tools like I just saw dynaTrace presentation app DS of the world but then go you know the east-west apology then mapping north south into virtualized and physical infrastructure and this to me is gonna be one of our you know more difficult challenges because that at the you know at the same time you've got that complexity it's getting more complicated you know I think containers become much more dynamic you software-defined networking it becomes a lot harder to sexualize and troubleshoot that so we starting to look at the assurance or service management side and really start to innovate you know more there yeah that's that's amazing and I think that's going to become more and more necessary right we you know with big companies global you know distributed all over the world distributed on multiple platforms with private with private components all these services mixed together with a service bus you know you know when that blows up it's gonna blow up spectacular exactly and we've all been on those calls with 50 people that we can't afford to do you know and it's everybody I'm a network guy everyone points at us I really do want the tooling and instrumentation I mean the other big change that's interesting is the operator is gonna change I mean I think there's two major elements to that it's obviously you know DevOps you know development and operations getting cut you know much tighter together asre is a great example that and I think we you know if I look at DevOps right now I feel it's still very dev centric I mean we've grade on CI CD pipelines not quite as good on the op side I think we've got some room to to change there oh there's a lot of there's a lot of growth and journey and I love that the community like we can all learn together and I think open source and all these pieces are a big piece of it but I look at on the infrastructure side in the infrastructure operation side one things we're looking at now is how do we transform both our clients operators and our own operators when we do the outsourcing so how do we take them from what was traditionally eyes on glass looking at consoles and now write the next you know data ingestion scripts the the analytic algorithms of visualizations you know write the next automations to streamline something and over time tune the AI engines as we start to adopt AI to particularly around performance optimization you know how do we start to incorporate that absolutely I think yeah we're all facing that I mean it sounds like I really enjoyed learning about how all everything that Accenture is bringing to the table on this enterprise journey to the cloud Larry thanks for joining us Larry said Larry soccer Global lead for infrastructure growth and strategy at Accenture thanks for being on the deck enjoy it I think we are here we're just wrapping up here we are live here for two days at Red Hat summit in San Francisco we're closing up our second day we'll be joining you in the morning tomorrow as we finish off the conference that's all what you can always count here live on the cube

Live from San Juan, Puerto Rico. It's the Cube. Covering Blockchain Unbound. Brought to you by, Blockchain Industries. >> Welcome back everyone. This is the Cube's exclusive coverage in Puerto Rico. I'm John Furrier, the co-host of the Cube, co-founder of SiliconANGLE Media. In Puerto Rico for Blockchain Unbound, this is a global conference. Going to the next level in industry migration up and growth, and blockchain, decentralized internet and obviously cryptocurrency, changing the world up and down the stack. I have an industry veteran here. My next guest Salim is founding CEO, Singularity University and author of the best-selling book, Exponential Organizations. He's seen many waves, friend, known him for years. Haven't seen you in a while, you look great. You haven't changed. >> (laughs) The hair has changed a lot. >> (laughs) I've still got mine. Hey great to see you. Bumping into you in Puerto Rico is really compelling because you have a nose for the future, and I've always respected that about you. You have the ability to understand at the root level what's going on but also pull back and see the big picture. Puerto Rico is the center of all the action because the killer wrap in this is money. So money is driving a lot of change, but there's some fundamental infrastructure, stack upgrades going on. Blockchain has been highly discussed, crypto is highly hyped, ICO's are-- Scammers out there but now some legits. What's your take? What's your view right now on the current situation? >> Well I think what's happening with a place like Puerto Rico is. When you get kind of wiped out of the old, you have the chance to leap-frog. When you think about any of our traditional environments, laying down Blockchain technologies, et cetera. It's really, really hard because you have to get the Supreme Court, the Constitution to approve blockchain based land titles, and then you build a stack there from a legal perspective. Here they can basically start from scratch and do it completely from the ground up. Which is what's exciting for everybody here. >> The top story that we've been reporting here is that Puerto Rico is rebooting. The hurricane obviously, I won't say a forcing function, but in general when you get wiped out, that is certainly an opportunity to rebuild. If there's any kind of silver lining in that. >> There's a long history of that. Japan got wiped out during World War II, so did Germany and they rebounded incredibly. We've seen that recently with Rwanda. We do a lot of work in Medillin, in Colombia, and that's just been one of the worst cities in the world, is now the most innovative city in the world. So this is the transition that we've seen a pattern for. >> One of the things I'm really excited about decentralization and blockchain is all the conversations have the same pattern. Efficiency is getting wired into things. So if you see slack in the system or inefficiencies, entrepreneurs are feeling the void. The entrepreneurial eye of the tiger goes that to that opportunity to reset, reduce steps, save time and make things easier. Classic value proposition in these new markets. You run a great university but also author of Exponential Organizations. A lot of people are scared, they're like, "Whoa, hold on. Slow down, this is bullshit, "we're not going to prove it." And then the other half saying, "No this is the future." So you have two competing forces colliding. You have the new guard saying, "We got to do this, this is the future." Old guard saying, "Blocks, Road blocks, blockers" You covered this in your book in a way, so how do you win, who wins? How do you create a win win? >> You can create a win win. What you have to do is leap-frog to the newest, fast as possible. The only question is, how can you get to the new? And the problem that you have is, as you rightly pointed out is. When you try disruptive innovation in any large organization or institution, the immune system attacks. I saw this at Yahoo running Brickhouse. Yahoo is supposedly a super advanced organization, and yet the minute you try to do something really radical, you spend all your time fighting the mother ship. So I've been focusing a lot of time the last few years focused on that particular problem, and we're pretty excited, we believe we've cracked it. >> How does someone crack that code? If I'm Puerto Rico, obviously the government officials are here at Blockchain Unbound. This is not just a tech conference. It's like a tech conference, investor conference, kind of world economic form rolled into one. >> Sure >> There's some serious players here. What's your advice to them? >> So what we do, and let me describe what we do in the private sector and what we do in the public sector. A couple of years ago, the global CI of Procter & Gamble came to me and said, "Hey, we'd like to work with you." And what we typically see is, some executive from a big company will come to Singularity. They'll go back headquarters with their hair on fire going, "Oh my god!" If they're from BMW for example. They go back going, "Drones, autonomous cars, hyperloop, VR." Back in Munich, they'll be given a white coat and some medicine and be put in a corner. "You're too crazy, now stand over there." And that's the tension that you are talking about. And then somebody else will come six months later then they'll do the Silicon Valley tour, then they'll have one of our people go over there, and it takes about three years for the big company to get up to speed, just the C-Suite to get up to speed. Forget transmitting that down. So I was talking to Linda Clement-Holmes and I said, "Look we're about to start this three year dance "I've been thinking about this, "let's shrink it to 10 weeks." So we designed what we now call an ExO Sprint. Which is how you get a leadership, culture and management thinking of a legacy organization, three years ahead in a 10 week process. And the way we do it is, we're in an opening workshop, that's really shock and awe. Freaks out all the incumbent management. And then young leaders and future lieutenants of the business do the thinking of what should come next. And they report back. Some thing about that opening workshop suppresses the immune system, and when the new ideas arrive they don't attack them in the same way. >> It's like a transplant if you will. >> It's like when you do a kidney transplant. You suppress the immune system, right? It's that same idea. So we've now run that like a dozen times. We just finished TD Ameritrade, HP, Visa, Black & Decker, et cetera. We're open-sourcing it. We're writing a manual on how to do it so that anybody can self-provision that process and run it. Because, every one of the Global 5000 has to go through that process with or without us. So then we said, "Okay, could we apply it to the public sector?" Where the existing policy is the immune system. You try and update transportation and you're fighting the taxis. Or education and you're fighting the teacher's unions. We have a 16 week process that we run in cities. We do it through a non-profit called the Fastrack Institute based out of Miami. We've run it four times in Medillin, in Colombia and we just finished four months with the mayor of Miami on the future of transportation. We're talking to the officials here about running a similar process here in Puerto Rico. >> Are they serious about that? Because they throw money at projects, it kind of sits on the vine, dies on the vine. Because there is an accelerated movement right now. I mean, exponential change is here. I'll give you an example. We're seeing and reporting that this digital nation trend is on fire. Suddenly everyone wants digital cities, IoT is out there. But now what cryptocurrency, the money being the killer app. It's flowing everywhere, out of Colombia, out of everywhere. Every country is moving money around with crypto it's easier, faster. So everyone is trying to be the crypto, ICO city. Saw it on Telegram today, France wants to be, Paris wants to be the ICO city. Puerto Rico, Bahrain, Armenia, Estonia. U.K. just signed a deal with Coinbase. What the hell is going on? How do you rationalize this and what do you see as a future of state here? >> Well I think, couple of thoughts. And you're hitting into some of the things I've been thinking about a lot recently. Number one is, that when you have a regulatory blockage, it's a huge economic developing opportunity for anybody that can leap-frog it. Nevada authorized autonomous cars early and now a lot of testing is done there. So the cities that have appreciated-- >> So you're saying regulatory is an opportunity to have a competitive advantage? >> Huge, because look at Zug in Switzerland. Nobody had ever heard of the place. You pass through there on the way to Zermatt. But now it's like a destination that everybody needs to get to because they were earlier. This is the traditional advantage of places like Hong Kong or Dubai or whatever. They're open and they're hungry. So we're going to see a lot of that going on. I think there's a bigger trend though, which is that we're seeing more and more action happen at the city level and very, very little happen at the national or global level. The world is moving too fast today for a big country to keep up. It's all going to happen this next century at the city level. >> Or smaller countries. >> Or small countries. >> So what's going on here at Blockchain Unbound for you? Why are you here? What are you doing? What's your story? >> I have this kind of sprint that we run in the private sector and in the public sector and then a community of about 200 consultants. And I have to pay 200 people in 40 countries and it's and unholy mess. Withholding taxes and concerns around money transfer costs-- >> It's a hassle. >> It's a nightmare. And so I've been thinking about an internal cryptocurrency just to pay our network. All of a sudden now, three or four countries have said, "Hey we want to buy that thing, "to have access to your network." So I've got all this demand over here, and I need to figure out how to design this thing properly. So I've been working with some of the folks like Brock and DNA and others to help think through it. But what I'm really excited about here is that, there's a-- You know what I love is the spectrum of dress. You got the radical, Burning Man, hippie guy, all the way to a three-piece suit. And that diversity is very, very rich and really, real creativity comes from it. This feels like the web in '96, '95. It's just starting, people know there's something really magical. They don't quite know what to do. >> Well what I'm impressed about is that there's no real bad vibe from either sets of groups. There's definitely some posturing, I've noticed some things. Obviously I'm wearing a jacket, so those guys aren't giving me hugs like they're giving Brock a hug. I get that, but the thing is, the coexistence is impressive. I'm not seeing any real mud-slinging, again I didn't like how Brock got handled with John Oliver. I thought that was unacceptable because he's done a lot of good work. I don't know him personally, I've never met him, but I like what he's doing, I like his message. His keynote here, at d10e, was awesome. Really the right messaging, I thought. That's something that I want to get behind and I think everyone should. But he just got trashed. Outside of that, welcoming culture. And they're like, "Hey if you don't like it, "just go somewhere else." They're not giving people a lot of shit for what they do. It's really accepting on all sides. >> Here's my take on the whole decentralization thing. We run the world today on a series of very top down hierarchical structures. The corporation, the military industrial complex, Judeo-Christian religions, et cetera. That are very hierarchical-- Designed for managing scarcity, right? We're moving the world very, very quickly to abundance. We now have an abundance of information, we'll soon have an abundance of energy, we'll soon have an abundance of money, et cetera. And when you do these new structures, you need very decentralized structures. Burning Man, the maker movement, the open-source movement, et cetera. It's a very nurturing, participatory, female type of archetype and we're moving very quickly to that. What we're seeing in the world today is the tension going from A to B. >> And also when you have that next level, you usually have entrepreneurs and sponsorships. People who sponsor entrepreneurs the promotion side of it, PR and that starts the industry. Then when it hits that level it's like, "Wow it's going to the next level." Then it gets capital markets to come in. Then you have new stake holders coming in now with government officials. This thing is just rocket-shipping big time. >> Yes >> And so, that's going to change the dynamics. Your thoughts and reaction to that dynamic. >> Completely, for example... When we do these public sprints we end up usually with a decentralized architecture that needs to built. For example, we're working with the justice system in Colombia. And the Supreme Court has asked us to come in and re-do the entire justice system. Now you think about all the court filings and court dates, and briefs, and papers all should be digitized and put on a blockchain type structure because it's all public filing. We have an opportunity to completely re-do that stack and then make that available to the rest of the world. I think that trend is irreversible for anything that previously had centered-- I mean, most government services are yes, ratifying this and ratifying that. They all disappear. >> Well Salim, I want to tap your brain for a second. Since you're here, get it out there, I want to throw a problem at you, quick real time riff with you. So one of the things that I've been thinking about is obviously look at what cloud computing did, no one saw Amazon web services early, except some of the insiders like us. Who saw it's easy to host and build a data center. "I have no money, I'm a start-up or whatever." You use AWS, EC2 and S3... They were misunderstood, now it's clear what they're doing. But that generated the DevOps movement. So question for you is, I want to riff with you on is, "Okay that created programmable infrastructure, "the notion of server-less now going mainstream." Meaning, I don't have to talk about the server, I need resource so I can just make software, make it happen. That's flipped around the old model, where it used to be the network would dictate to the applications what they could do. How is that DevOps ethos, certainly it's driven by open-source, get applied to this cryptocurrency? Because now you have blockchain, cryptocurrency, ICO is kind of an application if you will, capital market. How does that model get flipped? Is there a DevOps model, a blockchain ops model, where the decentralized apps are programming the blockchain? Because the plumbing is the moving chain right now. You got, Hashgraph's got traction, then you got Etherium, Lightning's just got 2.5 million dollars. I mean, anyone who's technical knows it's a moving train in the plumbing. But the business logic is pretty well-defined. I'm like, "I want to innovate this process. "I'm going to eliminate the efficiency." So this dynamic. Does the business model drive infrastructure? Does the plumbing drive the business model? Your thoughts on this new dynamic and how that plays out. >> I suspect you and in violent agreement here. It's always going to be lead by the business model because you need something to act as the power of pull to pull the thing along, right? The real reason for the success of Etherium right now is all the ICOs and it was a money driven thing. Today we're going to see these new stacks, now we're on version three of these new types of stacks coming along, and I think they're all looking for a business model. Once we find some new killer ops for this decentralized structure, then you'll see things happen. But the business model is where it's at. >> So basically I agree with you. I think we're on the same page here. But then advice would be to the entrepreneurs, don't fret about the infrastructure, just nail your business model because the switching cost might not be as high as you think. Where in the old days, when we grew up, you made a bad technical assess and you're out of business. So it's kind of flipped around. >> Yeah, just hearing about this term, atomic swaps. Where you can just, essentially once you have a tokenized structure, you can just move it to something else pretty quickly. Therefore, all the effort should be on that. I think finding the really compelling use cases for this world is going to be fascinating to see. >> So software-defined money, software-defined business, software defined society is coming. >> Yes >> Okay, software defined, that's the world Salim thanks for coming on, sharing your awesome expert opinon. Congratulations on your awesome book. How many countries is your book, Exponential Organizations-- >> It's now about a quarter of a million copies in 15 languages. >> Required reading in all MBA programs, and the C-Suite. Congratulations, it's like the TANEx Engineering that Mark Dandriso put out. A whole new paradigm of management is happening. Digital transformation. >> We now have the ability to scale an organization structure as fast as we can scale technology. >> Blockchain you know, the nature of the firm was all about having people in one spot. So centralized, you can manage stuff. Now with blockchain you have a decentralized organization. That's your new book, the Decentralized Organization. >> Although, I'm not sure I have another book in me. >> There's a book out there for somebody, Decentralized Organizations. Salim, thank you for joining us. The Cube here, I'm John Furrier the co-host. Day two coverage of Blockchain Unbound more coverage after this short break. (electronic music)

>> (lively music) (lively music) >> [Narrator] Live, from Fisherman's Wharf in San Francisco, it's theCUBE. Covering IBM Chief Data Officers Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back everybody. >> Jeff Flick here with theCUBE alongside Peter Burris, our chief research officer from Wikibon. We're at the IBM Chief Data Officers Strategy Summit Sprint 2017. It's a mouthful but it's an important event. There's 170 plus CDO's here sharing information, really binding their community, sharing best practices and of course, IBM is sharing their journey which is pretty interesting cause they're taking their own transformational journey, writing up a blue print and going to deliver it in October. Drinking their own champagne as they like to say. We're really excited to have CUBE alumni, many time visitor Seth Dobrin. He is the chief data officer of IBM Analytics. Seth welcome. >> Yeah, thanks for having me again. >> Absolutely, so again, these events are interesting. There's a series of them. They're in multiple cities. They're, now, going to go to multiple countries. And it's really intended, I believe, or tell me, it's a learning experience in this great, little, tight community for this, very specific, role. >> Yeah, so these events are, actually, really good. I've been participating in these since the second one. >> So, since the first one in Boston about 2 1/2 years ago. They're really great events because it's an opportunity for CDO's or de facto CDO's in organizations to have in depth conversations with their peers about struggles, challenges, successes. >> It really helps to, kind of, one piece says you can benchmark yourself, how are we doing as an organization and how am I doing as a CDO and where do I fit within the bigger community or within your industry? >> How have you seen it evolve? Not just the role, per say, but some of the specific challenges or implementation issues that these people have had in trying to deliver a value inside their company. >> Yeah, so when they started, three years ago, there, really, were not a whole lot of tools that CDO's could use to solve your data science problems, to solve your cloud problems, to solve your governance problem. We're starting to get to a place in the world where there are actual tools out there that help you do these things. So you don't struggle to figure out how do I find talent that can build the tools internally and deploy em. It's now getting the talent to, actually, start implementing things that already exist. >> Is the CDO job well enough defined at this point in time? Do you think that you can, actually, start thinking about tools as opposed to the challenges of the business? In other words, is every CDO different or are the practices, now, becoming a little bit more and the conventions becoming a little bit better understood and stable so you >> can outdo a better job of practicing the CDO role? >> Yeah, I think today, the CDO role is still very ill defined. It's, really, industry by industry and company by company even, CDO's play different roles within each of those. I've only been with IBM for the last four months. I've been spending a lot of that time talking to our clients. Financial services, manufacturing, all over the board and really, the CDO's in those people are all industry specific, they're in different places and even company by company, they're in different places. It really depends on where the company's are on their data and digital journey what role the CDO has. Is it really a defensive play to make sure we're not going to violate any regulations or is it an offensive play and how do we disrupt our industry instead of being disrupted because, really, every industry is in a place where you're either going to be the disruptor or you're going to be the distruptee. And so, that's the scope, the breadth of, I think, the role the CDO plays. >> Do you see it all eventually converging to a common point? Cause, obviously, the CFO and the CMO, those are pretty good at standardized functions over time that wasn't always that way. >> Well, I sure hope it does. I think CDO's are becoming pretty pervasive. I think you're starting to see, when this started, the first one I went to, there were, literally, 35 people >> and only 1/2 of then were called CDO's. We've progressed now where we've got 100 people over 170 some odd people that are here that are CDO's. Most of them have the CDO title even. >> The fact that that title is much more pervasive says that we're heading that way. I think industry by industry you'll start seeing similar responsibilities for CDO's but I don't think you'll start seeing it across the board like a CFO where a CFO does the same thing regardless of the industry. I don't think you'll see that in a CDO for quite some time. >> Well one of the things, certainly, we find interesting is that the role the data's playing in business involvement. And it, partly, the CDO's job is to explain to his or her peers, at that chief level, how using data is going to change the way that they do things from the way that they're function works. And that's part of the reason, I think, why you're suggesting that on a vertical basis that the CDO's job is different. Cause different industries are being impacted themselves by data differently. So as you think about the job that you're performing and the job the CDO's are performing, what part is technical? What part is organizational? What part is political? Et cetera. >> I think a lot of the role of a CDO is political. Most of the CDO's that I know have built their careers on stomping on people's toes. How do I drive change by infringing on other people's turf effectively? >> Peter: In a nice way. >> Well, it depends. In the appropriate way, right? >> Peter: In a productive way. >> In the appropriate way. It could be nice, it could not be nice >> depending on the politics and the culture of the organization. I think a lot of the role of a CDO, it's, almost, like chief disruption officer as much as it is data officer. I think it's a lot about using data >> but, I think, more importantly, it's about using analytics. >> So how do you use analytics to, actually, drive insights and next best action from the data? I think just looking at data and still using gut based on data is not good enough. For chief data officers to really have an impact and really be successful, it's how do you use analytics on that data whether it's machine learning, deep learning, operations research, to really change how the business operates? Because as chief data officers, you need to justify your existence a lot. The way you do that is you tie real value to decisions that your company is making. The data and the analytics that are needed for those decisions. That's, really, the role of a CDO in my mind is, how do I tie value of data based on decisions and how do I use analytics to make those decisions more effective? >> Were the early days more defensive and now, shifting to offensive? It sounds like it. That's a typical case where you use technology, initially, often to save money before you start to use it to create new value, new revenue streams. Is that consistent here? By answering that, you say they have to defend themselves sometimes when you would think it'd be patently obvious >> that if you're not getting on a data software defined train, you're going to be left behind. >> I think there's two types. There's CDO's that are there to protect freedom to operate and that's what I call, think of, as defensive. And then, there's offensive CDO's and that's really bringing more value out of existing processes. In my mind, every company is on this digital transformation journey and there's two steps to it. >> One is this data science transformation which is where you use data and analytics to accelerate your businesses current goals. How do I use data analytics to accelerate my businesses march towards it's current goals? Then there's the second stage which is the true digital transformation which is how do I use data and analytics to, fundamentally, change how my industry and my company operates? So, actually, changing the goals of the industry. For example, moving from selling physical products to selling outcomes. You can't do that until you've done this data transformation till you've started operating on data, till you've started operating on analytics. You can't sell outcomes until you've done that. It's this two step journey. >> You said this a couple of times and I want to test an idea on you and see what you think. Industry classifications are tied back to assets. So, you look at industries and they have common organization of assets, right? >> Seth: Yep. Data, as an asset, has very, very, different attributes because it can be shared. It's not scarce, it's something that can be shared. As we become more digital and as this notion of data science or analytics, the world of data places in asset and analytics plays as assets becomes more pervasive, does that start to change the notion of industry because, now, by using data differently, you can use other assets and deploy other assets differently? >> Yeah, I think it, fundamentally, changes how business operates and even how businesses are measured because you hit on this point pretty well which is data is reusable. And so as I build these data or digital assets, the quality of a company's margins should change. For every dollar of revenue I generate. Maybe today I generate 15% profit. As you start moving to a digital being a more digital company built on data and analytics, that percent of profit based on revenue should go up. Because these assets that you're building to reuse them is extremely cheap. I don't have to build another factory to scale up, I buy a little bit more compute time. Or I develop a new machine learning model. And so it's very scalable unlike building physical products. I think you will see a fundamental shift in how businesses are measured. What standards that investors hold businesses to. I think, another good point is, a mind set shift that needs to happen for companies is that companies need to stop thinking of data as a digital dropping of applications and start thinking of it as an asset. Cause data has value. It's no longer just something that's dropped on the table from applications that I built. It's we are building to, fundamentally, create data to drive analytics, to generate value, to build new revenue for a company that didn't exist today. >> Well the thing that changes the least, ultimately, is the customer. And so it suggests that companies that have customers can use data to get in a new product, or new service domains faster than companies who don't think about data as an asset and are locked into how can I take my core set up, my organization, >> my plant, my machinery and keep stamping out something that's common to it or similar to it. So this notion of customer becomes the driver, increasingly, of what industry you're in or what activities you perform. Does that make sense? >> I think everything needs to be driven from the prospective of the customer. As you become a data driven or a digital company, everything needs to be shifted in that organization from the perspective of the customer. Even companies that are B to B. B to B companies need to start thinking about what is the ultimate end user. How are they going to use what I'm building, for my business partner, my B to B partner, >> what is their, actual, human being that's sitting down using it, how are they going to use it? How are they going to interact with it? It really, fundamentally, changes how businesses approach B to B relationships. It, fundamentally, changes the type of information that, if I'm a B to B company, how do I get more information about the end users and how do I connect? Even if I don't come in direct contact with them, how do I understand how they're using my product better. That's a fundamental just like you need to stop thinking of data as a digital dropping. Every question needs to come from how is the end user, ultimately, going to use this? How do I better deploy that? >> So the utility that the customer gets capturing data about the use of that, the generation of that utility and drive it all the way back. Does the CDO have to take a more explicit role in getting people to see that? >> Yes, absolutely. I think that's part of the cultural shift that needs to happen. >> Peter: So how does the CDO do that? >> I think every question needs to start with what impact does this have on the end user? >> What is the customer perspective on this? Really starting to think about. >> I'm sorry for interrupting. I'd turn that around. I would say it's what impact does the customer have on us? Because you don't know unless you capture data. That notion of the customer impact measurement >> which we heard last time, the measureability and then drive that all the way back. That seems like it's going to become an increasingly, a central design point. >> Yeah, it's a loop and you got to start using these new methodologies that are out there. These design thinking methodologies. It's not just about building an Uber app. It's not just about building an app. It's about how do I, fundamentally, shift my business to this design thinking methodology to start thinking cause that's what design thinking is all about. It's all about how is this going to be used? And every aspect of your business you need to approach that way. >> Seth, I'm afraid they're going to put us in the chaffing dish here if we don't get off soon. >> Seth: I think so too, yeah. >> So we're going to leave it there. It's great to see you again and we look forward to seeing you at the next one of these things. >> Yeah, thanks so much. >> He's Seth, he's Peter, I'm Jeff. You're watching theCUBE from the IBM Chief Data Officers Strategy Summit Spring 2017, I got it all in in a mouthful. We'll be back after lunch which they're >> setting up right now. (laughs) (lively music) (drum beats)

>> 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)

>>The cube at big data SV 2014 is brought to you by headline sponsors. When disco we make Hadoop invincible and Aptean accelerating big data, 2.0, >>Okay. We're back here, live in Silicon valley. This is big data. It has to be, this is Silicon England, Wiki bonds, the cube coverage of big data in Silicon valley and all around the world covering the strata conference. All the latest news analysis here in Silicon valley, the cube was our flagship program about the events extract the signal from noise. I'm John furrier, the founders of looking angle. So my co-host and co-founder of Wiki bond.org, Dave Volante, uh, George Matthew CEO, altruist on the cube again, back from big data NYC just a few months ago. Um, our two events, um, welcome back. Great to be here. So, um, what fruit is dropped into the blend or the change, the colors of the big data space this this time. So we were in new Yorkers. We saw what happened there. A lot of talk about financial services, you know, big business, Silicon valley Kool-Aid is more about innovation. Partnerships are being formed, channel expansion. Obviously the market's hot growth is still basing. Valuations are high. What's your take on the current state of the market? >>Yeah. Great question. So John, when we see this market today, I remember even a few years ago when I first visited the cave, particularly when it came to a deep world and strata a few years back, it was amazing that we talked about this early innings of a ballgame, right? We said it was like, man, we're probably in the second or third inning of this ball game. And what has progressed particularly this last few years has been how much the actual productionization, the actual industrialization of this activity, particularly from a big data analytics standpoint has merged. And that's amazing, right? And in a short span, two, three years, we're talking about technologies and capabilities that were kind of considered things that you play with. And now these are things that are keeping the lights on and running, you know, major portions of how better decision-making and analytics are done inside of organizations. So I think that industrialization is a big shift forward. In fact, if you've listened to guys like Narendra Mulani who runs most of analytics at Accenture, he'll actually highlight that as one of the key elements of how not only the transformation is occurring among organizations, but even the people that are servicing a large companies today are going through this big shift. And we're right in the middle of it. >>We saw, you mentioned a censure. We look at CSC, but service mesh and the cloud side, you seeing the consulting firms really seeing build-out mandates, not just POC, like let's go and lock down now for the vendors. That means is people looking for reference accounts right now? So to me, I'm kind of seeing the tea leaves say, okay, who's going to knock down the reference accounts and what is that going to look like? You know, how do you go in and say, I'm going to tune up this database against SAP or this against that incumbent legacy vendor with this new scale-out, all these things are on in play. So we're seeing that, that focus of okay, tire kicking is over real growth, real, real referenceable deployments, not, not like a, you know, POC on steroids, like full on game-changing deployments. Do you see that? And, and if you do, what versions of that do you seeing happening and what ending of that is that like the first pitch of the sixth inning? Uh, w what do you, how would you benchmark that? >>Yeah, so I, I would say we're, we're definitely in the fourth or fifth inning of a non ballgame now. And, and there's innings. What we're seeing is I describe this as a new analytic stack that's emerged, right? And that started years ago when particularly the major Hadoop distro vendors started to rethink how data management was effectively being delivered. And once that data management layer started to be re thought, particularly in terms of, you know, what the schema was on read what the ability to do MPP and scale-out was in terms of how much cheaper it is to bring storage and compute closer to data. What's now coming above that stack is, you know, how do I blend data? How do I be able to give solutions to data analysts who can make better decisions off of what's being stored inside of that petabyte scale infrastructure? So we're seeing this new stack emerge where, you know, Cloudera Hortonworks map are kind of that underpinning underlying infrastructure where now our based analytics that revolution provides Altrix for data blending for analytic work, that's in the hands of data analysts, Tableau for visual analysis and dashboarding. Those are basically the solutions that are moving forward as a capability that are package and product. >>Is that the game-changing feature right now, do you think that integration of the stack, or is that the big, game-changer this sheet, >>That's the hardening that's happening as we speak right now, if you think about the industrialization of big data analytics that, you know, as I think of it as the fourth or fifth inning of the ballgame, that hardening that ability to take solutions that either, you know, the Accentures, the KPMGs, the Deloitte of the world deliver to their clients, but also how people build stuff internally, right? They have much better solutions that work out of the box, as opposed to fumbling with, you know, things that aren't, you know, stitched as well together because of the bailing wire and bubblegum that was involved for the last few years. >>I got it. I got to ask you, uh, one of the big trends you saw in certainly in the tech world, you mentioned stacks, and that's the success of Amazon, the cloud. You're seeing integrated stacks being a key part of the, kind of the, kind of the formation of you said hardening of the stack, but the word horizontally scalable is a term that's used in a lot of these open source environments, where you have commodity hardware, you have open source software. So, you know, everything it's horizontally scalable. Now, that's, that's very easy to envision, but thinking about the implementation in an enterprise or a large organization, horizontally scalable is not a no brainer. What's your take on that. And how does that hyperscale infrastructure mindset of scale-out scalable, which is a big benefit of the current infrastructure? How does that fit into, into the big day? >>Well, I think it fits extremely well, right? Because when you look at the capabilities of the last, as we describe it stack, we almost think of it as vertical hardware and software that's factually built up, but right now, for anyone who's building scale in this world, it's all about scale-out and really being able to build that stack on a horizontal basis. So if you look at examples of this, right, say for instance, what a cloud era recently announced with their enterprise hub. And so when you look at that capability of the enterprise data hub, a lot of it is about taking what yarn has become as a resource manager. What HDFS has been ACOM as a scale-out storage infrastructure, what the new plugin engines have merged beyond MapReduce as a capability for engines to come into a deep. And that is a very horizontal description of how you can do scale out, particularly for data management. >>When we built a lot of the work that was announced at strata a few years ago, particularly around how the analytics architecture for Galerie, uh, emerged at Altryx. Now we have hundreds of, of apps, thousands of users in that infrastructure. And when we built that out was actually scaling out on Amazon where the worker nodes and the capability for us to manage workload was very horizontal built out. If you look at servers today of any layer of that stack, it is really about that horizontal. Scale-out less so about throwing more hardware, more, uh, you know, high-end infrastructure at it, but more about how commodity hardware can be leveraged and use up and down that stack very easily. So Georgia, >>I asked you a question, so why is analytics so hard for so many companies? Um, and you've been in this big data, we've been talking to you since the beginning, um, and when's it going to get easier? And what are you guys specifically doing? You know, >>So facilitate that. Sure. So a few things that we've seen to date is that a lot of the analytics work that many people do internal and external to organizations is very rote, hand driven coding, right? And I think that's been one of the biggest challenges because the two end points in analytics have been either you hard code stuff that you push into a, you know, a C plus plus or a Java function, and you push it into database, or you're doing lightweight analytics in Excel. And really there needs to be a middle ground where someone can do effective scale-out and have repeatability in what's been done and ease of use. And what's been done that you don't have to necessarily be a programmer and Java programmer in C plus plus to push an analytic function and database. And you certainly don't have to deal with the limitations of Excel today. >>And really that middle ground is what Altryx serves. We look at it as an opportunity for analysts to start work with a very repeatable re reasonable workflow of how they would build their initial constructs around an analytic function that they would want to deploy. And then the scale-out happens because all of the infrastructure works on that analyst behalf, whether that be the infrastructure on Hadoop, would that be the infrastructure of the scale out of how we would publish an analytic function? Would that be how the visualizations would occur inside of a product like Tableau? And so that, I think Dave is one of the biggest things that needs to shift over where you don't have the only options in front of you for analytics is either Excel or hard coding, a bunch of code in C plus plus, or Java and pushing it in database. Yeah. >>And you correct me if I'm wrong, but it seems to be building your partnerships and your ecosystem really around driving that solution and, and, and really driving a revolution in the way in which people think about analytics, >>Ease of use. The idea is that ultimately if you can't get data analysts to be able to not only create work, that they can actually self-describe deploy and deliver and deliver success inside of an organization. And scale that out at the petabyte scale information that exists inside of most organizations you fail. And that's the job of folks like ourselves to provide great software. >>Well, you mentioned Tableau, you guys have a strong partnership there, and Christian Chabot, I think has a good vision. And you talked about sort of, you know, the, the, the choices of the spectrum and neither are good. Can you talk a little bit more about that, that, that partnership and the relationship and what you guys are doing together? Yeah. >>Uh, I would say Tableau's our strongest and most strategic partner today. I mean, we were diamond sponsors of their conference. I think I was there at their conference when I was on the cube the time before, and they are diamond sponsors of our conference. So our customers and particular users are one in the same for Tablo. It really becomes a, an experience around how visual analysis and dashboard, and can be very easily delivered by data analysts. And we think of those same users, the same exact people that Tablo works with to be able to do data blending and advanced analytics. And so that's why the two software products, that's why the two companies, that's where our two customer bases are one in the same because of that integrated experience. So, you know, Tableau is basically replacing XL and that's the mission that thereafter. And we feel that anyone who wants to be able to do the first form of data blending, which I would think of as a V lookup in Excel, should look at Altryx as a solution for that one. >>So you mentioned your conference it's inspire, right? It >>Is inspiring was coming up in June, >>June. Yeah. Uh, how many years have you done inspire? >>Inspire is now in its fifth year. And you're gonna bring the >>Cube this year. Yeah. >>That would be great. You guys, yeah, that would be fun. >>You should do it. So talk about the conference a little bit. I don't know much about it, but I mean, I know of it. >>Yeah. It's very centered around business users, particularly data analysts and many organizations that cut across retail, financial services, communications, where companies like Walmart at and T sprint Verizon bring a lot of their underlying data problems, underlying analytic opportunities that they've wrestled with and bring a community together this year. We're expecting somewhere in the neighborhood of 550 600 folks attending. So largely to, uh, figure out how to bring this, this, uh, you know, game forward, really to build out this next rate analytic capability that's emerging for most organizations. And we think that that starts ultimately with data analysts. All right. We think that there are well over two and a half million data analysts that are underserved by the current big data tools that are in this space. And we've just been highly focused on targeting those users. And so far, it's been pretty good at us. >>It's moving, it's obviously moving to the casual user at some levels, but I ended up getting there not soon, but I want to, I want to ask you the role of the cloud and all this, because when you have underneath the hood is a lot of leverage. You mentioned integrates that's when to get your perspective on the data cloud, not data cloud is it's putting data in the cloud, but the role of cloud, the role of dev ops that intersection, but you're seeing dev ops, you know, fueling a lot of that growth, certainly under the hood. Now on the top of the stack, you have the, I guess, this middle layer for lack of a better description, I'm of use old, old metaphor developing. So that's the enablement piece. Ultimately the end game is fully turnkey, data science, personalization, all that's, that's the holy grail. We all know. So how do you see that collision with cloud and the big, the big data? >>Yeah. So cloud is basically become three things for a lot of folks in our space. One is what we talked about, which is scale up and scale out, uh, is something that is much more feasible when you can spin up and spin down infrastructure as needed, particularly on an elastic basis. And so many of us who built our solutions leverage Amazon being one of the most defacto solutions for cloud based deployment, that it just makes it easy to do the scale-out that's necessary. This is the second thing it actually enables us. Uh, and many of our friends and partners to do is to be able to bring a lower cost basis to how infrastructure stood up, right? Because at the end of the day, the challenge for the last generation of analytics and data warehousing that was in this space is your starting conversation is two to $3 million just in infrastructure alone before you even buy software and services. >>And so now if you can rent everything that's involved with the infrastructure and the software is actually working within days, hours of actually starting the effort, as opposed to a 14 month life cycle, it's really compressing the time to success and value that's involved. And so we see almost a similarity to how Salesforce really disrupted the market. 10 years ago, I happened to be at Salesforce when that disruption occurred and the analytics movement that is underway really impacted by cloud. And the ability to scale out in the cloud is really driving an economic basis. That's unheard of with that >>Developer market, that's robust, right? I mean, you have easy kind of turnkey development, right? Tapping >>It is right, because there's a robust, uh, economy that's surrounding the APIs that are now available for cloud services. So it's not even just at the starting point of infrastructure, but there's definite higher level services where all the way to software as industry, >>How much growth. And you'll see in those, in that, as that, that valley of wealth and opportunity that will be created from your costs, not only for the companies involved, but the company's customers, they have top line focus. And then the goal of the movement we've seen with analytics is you seeing the CIO kind of with less of a role, more of the CEO wants to the chief data officer wants most of the top line drivers to be app focused. So you seeing a big shift there. >>Yeah. I mean, one of the, one of the real proponents of the cloud is now the fact that there is an ability for a business analyst business users and the business line to make impacts on how decisions are done faster without the infrastructure underpinnings that were needed inside the four walls in our organization. So the decision maker and the buyer effectively has become to your point, the chief analytics officer, the chief marketing officer, right. Less so that the chief information officer of an organization. And so I think that that is accelerating in a tremendous, uh, pace, right? Because even if you look at the statistics that are out there today, the buying power of the CMO is now outstrip the buying power of the CIO, probably by 1.2 to 1.3 X. Right. And that used to be a whole different calculus that was in front of us before. So I would see that, uh, >>The faster, so yeah, so Natalie just kind of picked this out here real time. So you got it, which we all know, right. I went to the it world for a long time service, little catalog. Self-service, you know, Sarah's already architectures whatever you want to call it, evolve in modern era. That's good. But on the business side, there's still a need for this same kind of cataloguing of tooling platform analytics. So do you agree with that? I mean, do you see that kind of happening that way, where there's still some connection, but it's not a complete dependency. That's kind of what we're kind of rethinking real time you see that happen. >>Yeah. I think it's pretty spot on because when you look at what businesses are doing today, they're selecting software that enables them to be more self-reliant the reason why we have been growing as much among business analysts as we have is we deliver self-reliance software and in some way, uh, that's what tablet does. And so the, the winners in this space are going to be the ones that will really help users get to results faster for self-reliance. And that's, that's really what companies like Altrix Stanford today. >>So I want to ask you a follow up on that CMOs CIO discussion. Um, so given that, that, that CMOs are spending a lot more where's the, who owns the data, is that, is we, we talk, well, I don't know if I asked you this before, but do you see the role of a chief data officer emerging? And is that individual, is that individual part of the marketing organization? Is it part of it? Is it a separate parallel role? What are you, >>One of the things I will tell you is that as I've seen chief analytics and chief data officers emerge, and that is a real category entitled real deal of folks that have real responsibilities in the organization, the one place that's not is in it, which is interesting to see, right? Because oftentimes those individuals are reporting straight to the CEO, uh, or they have very close access to line of business owners, general managers, or the heads of marketing, the heads of sales. So I seeing that shift where wherever that chief data officer is, whether that's reporting to CEOs or line of business managers or general managers of, of, you know, large strategic business units, it's not in the information office, it's not in the CEO's, uh, purview anymore. And that, uh, is kind of telling for how people are thinking about their data, right? Data is becoming much more of an asset and a weapon for how companies grow and build their scale less. So about something that we just have to deal with. >>Yeah. And it's clearly emerging that role in certain industry sectors, you know, clearly financial services, government and healthcare, but slowly, but we have been saying that, >>Yeah, it's going to cross the board. Right. And one of the reasons why I wrote the article at the end of last year, I literally titled it. Uh, analytics is eating the world, is this exact idea, right? Because, uh, you have this, this notion that you no longer are locked down with data and infrastructure kind of holding you back, right? This is now much more in the hands of people who are responsible for making better decisions inside their organizations, using data to drive those decisions. And it doesn't matter the size and shape of the data that it's coming in. >>Yeah. Data is like the F the food that just spilled all over it spilled out from the truck and analytics is on the Pac-Man eating out. Sorry. >>Okay. Final question in this segment is, um, summarize big data SV for us this year, from your perspective, knowing what's going on now, what's the big game changer. What should the folks know who are watching and should take note of which they pay attention to? What's the big story here at this moment. >>There's definite swim lanes that are being created as you can see. I mean, and, and now that the bigger distribution providers, particularly on the Hadoop side of the world have started to call out what they all stand for. Right. You can tell that map are, is definitely about creating a fast, slightly proprietary Hadoop distro for enterprise. You can tell that the folks at cloud era are focusing themselves on enterprise scale and really building out that hub for enterprise scale. And you can tell Horton works is basically embedding, enabling an open source for anyone to be able to take advantage of. And certainly, you know, the previous announcements and some of the recent ones give you an indicator of that. So I see the sense swimlanes forming in that layer. And now what is going to happen is that focus and attention is going to move away from how that layer has evolved into what I would think of as advanced analytics, being able to do the visual analysis and blending of information. That's where the next, uh, you know, battle war turf is going to be in particularly, uh, the strata space. So we're, we're really looking forward to that because it basically puts us in a great position as a company and a market leader in particularly advanced analytics to really serve customers in how this new battleground is emerging. >>Well, we really appreciate you taking the time. You're an awesome guest on the queue biopsy. You know, you have a company that you're running and a great team, and you come and share your great knowledge with our fans and an audience. Appreciate it. Uh, what's next for you this year in the company with some of your goals, let's just share that. >>Yeah. We have a few things that are, we mentioned a person inspired coming up in June. There's a big product release. Most of our product team is actually here and we have a release coming up at the beginning of Q2, which is Altryx nine oh. So that has quite a bit involved in it, including expansion of connectivity, uh, being able to go and introduce a fair degree of modeling capability so that the AR based modeling that we do scales out very well with revolution and Cloudera in mind, as well as being able to package into play analytic apps very quickly from those data analysts in mind. So it's, uh, it's a release. That's been almost a year in the works, and we're very much looking forward to a big launch at the beginning of Q2. >>George, thanks so much. You got inspire coming out. A lot of great success as a growing market, valuations are high, and the good news is this is just the beginning, call it mid innings in the industry, but in the customers, I call the top of the first lot of build-out real deployment, real budgets, real deal, big data. It's going to collide with cloud again, and I'm going to start a load, get a lot of innovation all happening right here. Big data SV all the big data Silicon valley coverage here at the cube. I'm Jennifer with Dave Alonzo. We'll be right back with our next guest. After the short break.