(gentle music) >> Narrator: "TheCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Hello everybody, welcome back to day four of "theCUBE's" coverage of MWC '23. We're here live at the Fira in Barcelona. Wall-to-wall coverage, John Furrier is in our Palo Alto studio, banging out all the news. Really, the whole week we've been talking about the disaggregation of the telco network, the new opportunities in telco. We're really excited to have AT&T and Snowflake here. Dave Whittington is the AVP, at the Chief Data Office at AT&T. Roddy Tranum is the Assistant Vice President, for Channel Performance Data and Tools at AT&T. And Phil Kippen, the Global Head Of Industry-Telecom at Snowflake, Snowflake's new telecom business. Snowflake just announced earnings last night. Typical Scarpelli, they beat earnings, very conservative guidance, stocks down today, but we like Snowflake long term, they're on that path to 10 billion. Guys, welcome to "theCUBE." Thanks so much >> Phil: Thank you. >> for coming on. >> Dave and Roddy: Thanks Dave. >> Dave, let's start with you. The data culture inside of telco, We've had this, we've been talking all week about this monolithic system. Super reliable. You guys did a great job during the pandemic. Everything shifting to landlines. We didn't even notice, you guys didn't miss a beat. Saved us. But the data culture's changing inside telco. Explain that. >> Well, absolutely. So, first of all IoT and edge processing is bringing forth new and exciting opportunities all the time. So, we're bridging the world between a lot of the OSS stuff that we can do with edge processing. But bringing that back, and now we're talking about working, and I would say traditionally, we talk data warehouse. Data warehouse and big data are now becoming a single mesh, all right? And the use cases and the way you can use those, especially I'm taking that edge data and bringing it back over, now I'm running AI and ML models on it, and I'm pushing back to the edge, and I'm combining that with my relational data. So that mesh there is making all the difference. We're getting new use cases that we can do with that. And it's just, and the volume of data is immense. >> Now, I love ChatGPT, but I'm hoping your data models are more accurate than ChatGPT. I never know. Sometimes it's really good, sometimes it's really bad. But enterprise, you got to be clean with your AI, don't you? >> Not only you have to be clean, you have to monitor it for bias and be ethical about it. We're really good about that. First of all with AT&T, our brand is Platinum. We take care of that. So, we may not be as cutting-edge risk takers as others, but when we go to market with an AI or an ML or a product, it's solid. >> Well hey, as telcos go, you guys are leaning into the Cloud. So I mean, that's a good starting point. Roddy, explain your role. You got an interesting title, Channel Performance Data and Tools, what's that all about? >> So literally anything with our consumer, retail, concenters' channels, all of our channels, from a data perspective and metrics perspective, what it takes to run reps, agents, all the way to leadership levels, scorecards, how you rank in the business, how you're driving the business, from sales, service, customer experience, all that data infrastructure with our great partners on the CDO side, as well as Snowflake, that comes from my team. >> And that's traditionally been done in a, I don't mean the pejorative, but we're talking about legacy, monolithic, sort of data warehouse technologies. >> Absolutely. >> We have a love-hate relationship with them. It's what we had. It's what we used, right? And now that's evolving. And you guys are leaning into the Cloud. >> Dramatic evolution. And what Snowflake's enabled for us is impeccable. We've talked about having, people have dreamed of one data warehouse for the longest time and everything in one system. Really, this is the only way that becomes a reality. The more you get in Snowflake, we can have golden source data, and instead of duplicating that 50 times across AT&T, it's in one place, we just share it, everybody leverages it, and now it's not duplicated, and the process efficiency is just incredible. >> But it really hinges on that separation of storage and compute. And we talk about the monolithic warehouse, and one of the nightmares I've lived with, is having a monolithic warehouse. And let's just go with some of my primary, traditional customers, sales, marketing and finance. They are leveraging BSS OSS data all the time. For me to coordinate a deployment, I have to make sure that each one of these units can take an outage, if it's going to be a long deployment. With the separation of storage, compute, they own their own compute cluster. So I can move faster for these people. 'Cause if finance, I can implement his code without impacting finance or marketing. This brings in CI/CD to more reality. It brings us faster to market with more features. So if he wants to implement a new comp plan for the field reps, or we're reacting to the marketplace, where one of our competitors has done something, we can do that in days, versus waiting weeks or months. >> And we've reported on this a lot. This is the brilliance of Snowflake's founders, that whole separation >> Yep. >> from compute and data. I like Dave, that you're starting with sort of the business flexibility, 'cause there's a cost element of this too. You can dial down, you can turn off compute, and then of course the whole world said, "Hey, that's a good idea." And a VC started throwing money at Amazon, but Redshift said, "Oh, we can do that too, sort of, can't turn off the compute." But I want to ask you Phil, so, >> Sure. >> it looks from my vantage point, like you're taking your Data Cloud message which was originally separate compute from storage simplification, now data sharing, automated governance, security, ultimately the marketplace. >> Phil: Right. >> Taking that same model, break down the silos into telecom, right? It's that same, >> Mm-hmm. >> sorry to use the term playbook, Frank Slootman tells me he doesn't use playbooks, but he's not a pattern matcher, but he's a situational CEO, he says. But the situation in telco calls for that type of strategy. So explain what you guys are doing in telco. >> I think there's, so, what we're launching, we launched last week, and it really was three components, right? So we had our platform as you mentioned, >> Dave: Mm-hmm. >> and that platform is being utilized by a number of different companies today. We also are adding, for telecom very specifically, we're adding capabilities in marketplace, so that service providers can not only use some of the data and apps that are in marketplace, but as well service providers can go and sell applications or sell data that they had built. And then as well, we're adding our ecosystem, it's telecom-specific. So, we're bringing partners in, technology partners, and consulting and services partners, that are very much focused on telecoms and what they do internally, but also helping them monetize new services. >> Okay, so it's not just sort of generic Snowflake into telco? You have specific value there. >> We're purposing the platform specifically for- >> Are you a telco guy? >> I am. You are, okay. >> Total telco guy absolutely. >> So there you go. You see that Snowflake is actually an interesting organizational structure, 'cause you're going after verticals, which is kind of rare for a company of your sort of inventory, I'll say, >> Absolutely. >> I don't mean that as a negative. (Dave laughs) So Dave, take us through the data journey at AT&T. It's a long history. You don't have to go back to the 1800s, but- (Dave laughs) >> Thank you for pointing out, we're a 149-year-old company. So, Jesse James was one of the original customers, (Dave laughs) and we have no longer got his data. So, I'll go back. I've been 17 years singular AT&T, and I've watched it through the whole journey of, where the monolithics were growing, when the consolidation of small, wireless carriers, and we went through that boom. And then we've gone through mergers and acquisitions. But, Hadoop came out, and it was going to solve all world hunger. And we had all the aspects of, we're going to monetize and do AI and ML, and some of the things we learned with Hadoop was, we had this monolithic warehouse, we had this file-based-structured Hadoop, but we really didn't know how to bring this all together. And we were bringing items over to the relational, and we were taking the relational and bringing it over to the warehouse, and trying to, and it was a struggle. Let's just go there. And I don't think we were the only company to struggle with that, but we learned a lot. And so now as tech is finally emerging, with the cloud, companies like Snowflake, and others that can handle that, where we can create, we were discussing earlier, but it becomes more of a conducive mesh that's interoperable. So now we're able to simplify that environment. And the cloud is a big thing on that. 'Cause you could not do this on-prem with on-prem technologies. It would be just too cost prohibitive, and too heavy of lifting, going back and forth, and managing the data. The simplicity the cloud brings with a smaller set of tools, and I'll say in the data space specifically, really allows us, maybe not a single instance of data for all use cases, but a greatly reduced ecosystem. And when you simplify your ecosystem, you simplify speed to market and data management. >> So I'm going to ask you, I know it's kind of internal organizational plumbing, but it'll inform my next question. So, Dave, you're with the Chief Data Office, and Roddy, you're kind of, you all serve in the business, but you're really serving the, you're closer to those guys, they're banging on your door for- >> Absolutely. I try to keep the 130,000 users who may or may not have issues sometimes with our data and metrics, away from Dave. And he just gets a call from me. >> And he only calls when he has a problem. He's never wished me happy birthday. (Dave and Phil laugh) >> So the reason I asked that is because, you describe Dave, some of the Hadoop days, and again love-hate with that, but we had hyper-specialized roles. We still do. You've got data engineers, data scientists, data analysts, and you've got this sort of this pipeline, and it had to be this sequential pipeline. I know Snowflake and others have come to simplify that. My question to you is, how is that those roles, how are those roles changing? How is data getting closer to the business? Everybody talks about democratizing business. Are you doing that? What's a real use example? >> From our perspective, those roles, a lot of those roles on my team for years, because we're all about efficiency, >> Dave: Mm-hmm. >> we cut across those areas, and always have cut across those areas. So now we're into a space where things have been simplified, data processes and copying, we've gone from 40 data processes down to five steps now. We've gone from five steps to one step. We've gone from days, now take hours, hours to minutes, minutes to seconds. Literally we're seeing that time in and time out with Snowflake. So these resources that have spent all their time on data engineering and moving data around, are now freed up more on what they have skills for and always have, the data analytics area of the business, and driving the business forward, and new metrics and new analysis. That's some of the great operational value that we've seen here. As this simplification happens, it frees up brain power. >> So, you're pumping data from the OSS, the BSS, the OKRs everywhere >> Everywhere. >> into Snowflake? >> Scheduling systems, you name it. If you can think of what drives our retail and centers and online, all that data, scheduling system, chat data, call center data, call detail data, all of that enters into this common infrastructure to manage the business on a day in and day out basis. >> How are the roles and the skill sets changing? 'Cause you're doing a lot less ETL, you're doing a lot less moving of data around. There were guys that were probably really good at that. I used to joke in the, when I was in the storage world, like if your job is bandaging lungs, you need to look for a new job, right? So, and they did and people move on. So, are you able to sort of redeploy those assets, and those people, those human resources? >> These folks are highly skilled. And we were talking about earlier, SQL hasn't gone away. Relational databases are not going away. And that's one thing that's made this migration excellent, they're just transitioning their skills. Experts in legacy systems are now rapidly becoming experts on the Snowflake side. And it has not been that hard a transition. There are certainly nuances, things that don't operate as well in the cloud environment that we have to learn and optimize. But we're making that transition. >> Dave: So just, >> Please. >> within the Chief Data Office we have a couple of missions, and Roddy is a great partner and an example of how it works. We try to bring the data for democratization, so that we have one interface, now hopefully know we just have a logical connection back to these Snowflake instances that we connect. But we're providing that governance and cleansing, and if there's a business rule at the enterprise level, we provide it. But the goal at CDO is to make sure that business units like Roddy or marketing or finance, that they can come to a platform that's reliable, robust, and self-service. I don't want to be in his way. So I feel like I'm providing a sub-level of platform, that he can come to and anybody can come to, and utilize, that they're not having to go back and undo what's in Salesforce, or ServiceNow, or in our billers. So, I'm sort of that layer. And then making sure that that ecosystem is robust enough for him to use. >> And that self-service infrastructure is predominantly through the Azure Cloud, correct? >> Dave: Absolutely. >> And you work on other clouds, but it's predominantly through Azure? >> We're predominantly in Azure, yeah. >> Dave: That's the first-party citizen? >> Yeah. >> Okay, I like to think in terms sometimes of data products, and I know you've mentioned upfront, you're Gold standard or Platinum standard, you're very careful about personal information. >> Dave: Yeah. >> So you're not trying to sell, I'm an AT&T customer, you're not trying to sell my data, and make money off of my data. So the value prop and the business case for Snowflake is it's simpler. You do things faster, you're in the cloud, lower cost, et cetera. But I presume you're also in the business, AT&T, of making offers and creating packages for customers. I look at those as data products, 'cause it's not a, I mean, yeah, there's a physical phone, but there's data products behind it. So- >> It ultimately is, but not everybody always sees it that way. Data reporting often can be an afterthought. And we're making it more on the forefront now. >> Yeah, so I like to think in terms of data products, I mean even if the financial services business, it's a data business. So, if we can think about that sort of metaphor, do you see yourselves as data product builders? Do you have that, do you think about building products in that regard? >> Within the Chief Data Office, we have a data product team, >> Mm-hmm. >> and by the way, I wouldn't be disingenuous if I said, oh, we're very mature in this, but no, it's where we're going, and it's somewhat of a journey, but I've got a peer, and their whole job is to go from, especially as we migrate from cloud, if Roddy or some other group was using tables three, four and five and joining them together, it's like, "Well look, this is an offer for data product, so let's combine these and put it up in the cloud, and here's the offer data set product, or here's the opportunity data product," and it's a journey. We're on the way, but we have dedicated staff and time to do this. >> I think one of the hardest parts about that is the organizational aspects of it. Like who owns the data now, right? It used to be owned by the techies, and increasingly the business lines want to have access, you're providing self-service. So there's a discussion about, "Okay, what is a data product? Who's responsible for that data product? Is it in my P&L or your P&L? Somebody's got to sign up for that number." So, it sounds like those discussions are taking place. >> They are. And, we feel like we're more the, and CDO at least, we feel more, we're like the guardians, and the shepherds, but not the owners. I mean, we have a role in it all, but he owns his metrics. >> Yeah, and even from our perspective, we see ourselves as an enabler of making whatever AT&T wants to make happen in terms of the key products and officers' trade-in offers, trade-in programs, all that requires this data infrastructure, and managing reps and agents, and what they do from a channel performance perspective. We still ourselves see ourselves as key enablers of that. And we've got to be flexible, and respond quickly to the business. >> I always had empathy for the data engineer, and he or she had to service all these different lines of business with no business context. >> Yeah. >> Like the business knows good data from bad data, and then they just pound that poor individual, and they're like, "Okay, I'm doing my best. It's just ones and zeros to me." So, it sounds like that's, you're on that path. >> Yeah absolutely, and I think, we do have refined, getting more and more refined owners of, since Snowflake enables these golden source data, everybody sees me and my organization, channel performance data, go to Roddy's team, we have a great team, and we go to Dave in terms of making it all happen from a data infrastructure perspective. So we, do have a lot more refined, "This is where you go for the golden source, this is where it is, this is who owns it. If you want to launch this product and services, and you want to manage reps with it, that's the place you-" >> It's a strong story. So Chief Data Office doesn't own the data per se, but it's your responsibility to provide the self-service infrastructure, and make sure it's governed properly, and in as automated way as possible. >> Well, yeah, absolutely. And let me tell you more, everybody talks about single version of the truth, one instance of the data, but there's context to that, that we are taking, trying to take advantage of that as we do data products is, what's the use case here? So we may have an entity of Roddy as a prospective customer, and we may have a entity of Roddy as a customer, high-value customer over here, which may have a different set of mix of data and all, but as a data product, we can then create those for those specific use cases. Still point to the same data, but build it in different constructs. One for marketing, one for sales, one for finance. By the way, that's where your data engineers are struggling. >> Yeah, yeah, of course. So how do I serve all these folks, and really have the context-common story in telco, >> Absolutely. >> or are these guys ahead of the curve a little bit? Or where would you put them? >> I think they're definitely moving a lot faster than the industry is generally. I think the enabling technologies, like for instance, having that single copy of data that everybody sees, a single pane of glass, right, that's definitely something that everybody wants to get to. Not many people are there. I think, what AT&T's doing, is most definitely a little bit further ahead than the industry generally. And I think the successes that are coming out of that, and the learning experiences are starting to generate momentum within AT&T. So I think, it's not just about the product, and having a product now that gives you a single copy of data. It's about the experiences, right? And now, how the teams are getting trained, domains like network engineering for instance. They typically haven't been a part of data discussions, because they've got a lot of data, but they're focused on the infrastructure. >> Mm. >> So, by going ahead and deploying this platform, for platform's purpose, right, and the business value, that's one thing, but also to start bringing, getting that experience, and bringing new experience in to help other groups that traditionally hadn't been data-centric, that's also a huge step ahead, right? So you need to enable those groups. >> A big complaint of course we hear at MWC from carriers is, "The over-the-top guys are killing us. They're riding on our networks, et cetera, et cetera. They have all the data, they have all the client relationships." Do you see your client relationships changing as a result of sort of your data culture evolving? >> Yes, I'm not sure I can- >> It's a loaded question, I know. >> Yeah, and then I, so, we want to start embedding as much into our network on the proprietary value that we have, so we can start getting into that OTT play, us as any other carrier, we have distinct advantages of what we can do at the edge, and we just need to start exploiting those. But you know, 'cause whether it's location or whatnot, so we got to eat into that. Historically, the network is where we make our money in, and we stack the services on top of it. It used to be *69. >> Dave: Yeah. >> If anybody remembers that. >> Dave: Yeah, of course. (Dave laughs) >> But you know, it was stacked on top of our network. Then we stack another product on top of it. It'll be in the edge where we start providing distinct values to other partners as we- >> I mean, it's a great business that you're in. I mean, if they're really good at connectivity. >> Dave: Yeah. >> And so, it sounds like it's still to be determined >> Dave: Yeah. >> where you can go with this. You have to be super careful with private and for personal information. >> Dave: Yep. >> Yeah, but the opportunities are enormous. >> There's a lot. >> Yeah, particularly at the edge, looking at, private networks are just an amazing opportunity. Factories and name it, hospital, remote hospitals, remote locations. I mean- >> Dave: Connected cars. >> Connected cars are really interesting, right? I mean, if you start communicating car to car, and actually drive that, (Dave laughs) I mean that's, now we're getting to visit Xen Fault Tolerance people. This is it. >> Dave: That's not, let's hold the traffic. >> Doesn't scare me as much as we actually learn. (all laugh) >> So how's the show been for you guys? >> Dave: Awesome. >> What're your big takeaways from- >> Tremendous experience. I mean, someone who doesn't go outside the United States much, I'm a homebody. The whole experience, the whole trip, city, Mobile World Congress, the technologies that are out here, it's been a blast. >> Anything, top two things you learned, advice you'd give to others, your colleagues out in general? >> In general, we talked a lot about technologies today, and we talked a lot about data, but I'm going to tell you what, the accelerator that you cannot change, is the relationship that we have. So when the tech and the business can work together toward a common goal, and it's a partnership, you get things done. So, I don't know how many CDOs or CIOs or CEOs are out there, but this connection is what accelerates and makes it work. >> And that is our audience Dave. I mean, it's all about that alignment. So guys, I really appreciate you coming in and sharing your story in "theCUBE." Great stuff. >> Thank you. >> Thanks a lot. >> All right, thanks everybody. Thank you for watching. I'll be right back with Dave Nicholson. Day four SiliconANGLE's coverage of MWC '23. You're watching "theCUBE." (gentle music)

>> Announcer: Live, from San Francisco. It's The Cube, covering Oracle Open World 2016. Brought to you by Oracle. Now, here's your host, John Furrier and Peter Burris. >> Hey, welcome back everyone, we are live here in San Francisco. This is SiliconANGLE Media's The Cube. It's our flagship program, we go out to the events and extract the signal from the noise. I'm John Furrier, the CEO of SiliconANGLE Media, joined by co-host Peter Burris all week. Three days of wall-walk of day three. He's the head of research at SiliconeANGLE Media Inc., as well as the general manager of Wikibon research. Our next guest is Roddy Martin, VP of SC Supply Chain Cloud Product Marketing at Oracle. Welcome to The Cube. >> Thank you very much for the opportunity. I look forward to the discussion. >> Thanks for coming on. Really want to hear your thought leadership around the supply chain transformation, because it might be a little bit bumpy depending upon your perspective. But is a huge opportunity going on in every single theater of where software used to be a point solution. The cloud is now an opportunity for customers to think differently, and is a catalyst for essentially a business model change as well as a fundamental data-driven change. Your thoughts on this? What do you see going on? What are the key inflection points? >> So a very interesting part of my background is I came out of the brewing industry in South Africa. and then I led the supply chain practice at AMR Research, which today is Gartner. And we did a lot of studies on, what are companies doing to lead this transformation? Because it's a transformation of the interim business operating model of a company. This is not stitching data together in the traditional supply chain system sense. So one of the very first foundations that is really fundamental, and Gartner has done a great job of carrying the search forward, is the idea that every company progresses to an interim operating model in five stages of capability, and every one of those builds on the other. So they're either reacting in stage one's problem and never saw the shortage coming and ran out of product. Stage two is I performance improve around projects. Stage three is I drive functional excellence. And stage four I start working as an engine outside an operating model. In other words, I'm driving the business from what's happening in the market and I'm making sure that supply is matching demand. So it's very interesting and it's very important to consider that as the base foundation for this whole discussion. >> So that outside is interesting, we've heard this before, a lot of people are going that way, but there's no shortcuts. Can you talk about, cause you talk about the endpoint is then outside-in. >> Right, when you're operating as a demand-driven interim supply channel operating model, you can't run out of supply, right? So if you saw a change happening in the marketplace but there's nothing to supply, you've really just messed up the business. And so, each of these stages builds on every other stage. So functional excellence is: Am I good at planning? Am I good at product management? Am I good at logistics? Because those are the foundations for operating in the interim business model. This is why the Oracle's blanching in the cloud, in fact all of Oracle's developments in the cloud are so important because you're effectively building a new process oriented operating model that spins the entire business. If I started off with ERP systems and then I put logistics in place and tied it together, there's all sorts of disconnects in the business. When you pick it up in cycle times, you pick it in disconnect sometimes, they don't see changes to the marketplace for weeks. So, this overarching end to end supply chain operating model in the cloud is a fundamental enabler. >> So how do you gauge a customer? First of all, I buy everything that you said, but I want to bring up a point, because it seems to me that the theme of Oracle OpenWorld that traditional applications and I won't say, I'll just say the word Silo just to use it as a point, has been a specific domain specific thing. But to be end to end and be outside-in, which is the end game, you have to know how to talk and integrate with other systems which might have been a problem if you built the most badass end to end system. >> That is a part of the challenge and in fact, a lot of companies that I've worked with over the 15 years I've been researching this, they get stuck for that very reason. In other words, this is a re-engineering of the whole IT infrastructure versus having a thousand consultants come in and tie all my data together over a question of four years and move 15 instances of whatever system you want to one. >> So, if I question on the journey thing, you mentioned thousands of consultants, which customers are now seeing. They want faster mile posts, they want to see faster agility but a lot of the customers actually outline the journey for the customer. So they're saying, here's your journey and they shorten the mile posts for the deliverables. But they're the one getting paid for it so is that the right model, should they be outlining the journey for the customer? >> And they are. It's been very interesting because I was a partner with a major global consulting company for four years and I've been mixing with them here, they suddenly recognizing that this path to the cloud is something they've better get on the bandwagon because they're not going to have a thousand consultants deploying whatever ERP system you talk about as the future of IT. So, what's happening is the business is having much more of a say in this fast deployment, fast time to value, putting these new-- >> So they're driving the journey for parameters? >> They are gearing up for this new journey, the consultants are. >> So, let's get to the fundamentals behind all this and ask a question about it. At the end of the day, digital technologies give customers an option to do their journeys very differently whether in a B2B sense or a consumer sense. And as they use digital technologies, they're also giving data up and so we have now a combination where customers are getting something out of digital, they are demanding it as part of the engagement model. They are giving up data along the way, and the technologies for sensing and doing something with that data in business are now, we're not figuring out how that impacts business design, process design, and offering design. >> So, that's stage 4S, what we talk about is people, process, and technology versus, in the past, when you had stage one, two, and three. People as one set of projects, process as another set of projects, and technology as another set of projects. >> Yeah, I may or may not take some middlings with the model you put out, but it does matter. At the end of the day, what is driving this increasingly is that it used to be that the dominant consideration in, I think, and I'm testing you, the dominant consideration was assets. Where is the physical asset, where are the materials, where is the machine, and we'll focus our returns on this things and then presume that there's a demand for it and now we're getting all this data about demand and that is having an impact on how we talk about arranging the assets. >> That is the inside-out to outside-in. So, let me give you an example without mentioning companies. A major retailer and a major pharmaceutical company. They share pollen data, they share weather data, they mine Facebook to find out what are people saying about allergies, let's say in New England. And the ragweed's busting and they say, do we have the right levels of inventory, and they're moving inventory to make sure that people who aren't on Facebook are saying we can't buy this particular product. They're moving inventory, that's the difference. >> So, they're sharing data amongst themselves. >> Yes, and they're collaborating between retailers. >> Arguably a similar example, and a retailer that's actually not moving inventory but moving pointers and offering new channel options so that someone decides may not, that they know somebody's going to come into the store, the size may not be there but they can still get it to them that day. >> So, it's very interesting, Procter and Gamble, who I did a lot of work with, and this is public domain information, the CEO drove two fundamental transformation messages in the business. And they called it the two moments of truth. He said, we will always have our product when we say we've got a product. So, if we promote a new product, the consumer goes to the shelf, it will be there. Moment of truth number two, we understand why consumers choose and use our products. And you don't fix number two until you fix number one because if I wanted a small tube of toothpaste and I went in and there were only big ones, it's the wrong buying signal. So, what you're seeing is that whole flip to measuring what the market's looking for and shaping their demand and then making sure that the assets and the supply system is geared to deliver. >> Right, I want to ask you a question. First of all, I love that point, I love your point about the data, but here's the question: cause supply chain has been very instrumentation drive, okay, and that certainly is transforming but now you mention Procter and Gamble. We are living in an era where, in the history of business, you can actually now potentially measure everything. So how does that impacting the reconfiguration of the business model? I mean, Procter and Gamble has those moments of truth, every company will have a moment of truth which is, everything is now measurable so, advertising to employee things and everything. >> So let's take the asset story versus the on shelf thing, right, so when I have assets and I'm getting all the data out of my assets, what am I doing with all of that data, right? Because it's not connected to demand. What I got to know is what demand data do I really want to be able to move my assets to the right place. >> Peter: By the way, the shelf is an asset. >> Of course it is, yes. It's a sensing point and it's an asset. They own it, they replenish that shelf. So the point is, data is everywhere and now these, the consulting and the BPM organizations supporting and companies doing their own business process manner, they got to know what data is really important and what data from the outside-in is going to allow me to leverage a new operating model for my business and become digital. >> So, this is really awesome, I was talking with an Oracle executive last night at one of their customer parties and we had a conversation around this data sharing. This is a new, different behavior. This is a theme of the show that no one's really talking about but it's in plain sight which is there is a data sharing aspect of systems and vendors and companies. >> Roddy: That's why the cloud is so important. >> John: This is now impacting everything. >> Everything. >> How do companies go forward and do this? What are you seeing, is there a best practice, is there a starting point? Is there a five step process on that? >> Well, first of all, these transformations are being lead by the C level executive team in a business. This is now longer somebody who decides to buy a new IT system and plug it in to the business. So, the business is saying, how do we change the operating model of the way we work, right? So, and then, what are the capabilities, and this is where that five stage model comes in, what capabilities do we need to look at building over the next three years so that we can operate in this intent way because you can't wake up tomorrow and go from an inside-out asset driven business to an outside-in demand driven business in two weeks. It ain't going to happen. >> So what's the progression? What's the progress bar look like when you have that moment of an epiphany and say, you know, I'm the CEO-- >> What's the earning point of the business? If it's Procter and Gamble, I want X number of one billion dollars brands. If you're a pharmaceutical company, you want to launch brand new drugs and you want to do it at half the price and half the speed that you're used to. It's the business articulating, this is why the leadership teams are so fundamental, articulating what's the burning platform and then translating that back into the capabilities-- >> So you get a reverse engineer. >> Outside-In. >> Outside-In, I love it. >> The way our research says it, and it's very similar but I want to test this because it's, we say start with context. >> Yes. >> What are you going to do with your customer that you have to do better than everybody else? And then identify the community that you're going to do it with and identify the capabilities that are going to delight that community. So it's context, community, and capabilities. >> Now here's the context, further piece to context. If context changes, how quickly do I sense that change and how fast can I respond to that change? Because if I've got all my asset capabilities and my supply capabilities locked into one set of context and that changes and I now have to re-engineer my whole business, I may lose the whole show in the process. I got to see those changes as they are happening, literally in real time. This is where the internet of things, this is where demand shaping, demand sensing, retailers collaborating, supplies connected into supply chain, everybody sharing that information and the fact that not many people, they don't know how to do it. The culture of business is not yet at the points-- >> That's why the measurement thing I brought up, I mean Procter and Gamble, they used to say to their agencies, we know that 50% of our advertising is good, we don't know which half. So now they can measure it all just like in every other aspect so this is where the business model-- >> You also have to be careful about whether or not, again going back to context changes, measurements change, data can blow you away. You have to be very smart about how you do it so a lot of these intelligent things, machine learning, how the models get built, how the insides get delivered, all become very very important. Very quickly, I have two quick questions for you. One is really approximate to the conversation, one less so but the approximate one: IOT. IOT is, has many many applications. Certainly turning analogue data into digital data so you can build models is a crucial piece of it. But it also has another implication in how you enact the output of that model back into the real word. How does supply chain and IOT come together? >> So if you look at the studies that are being done by Oracle and Gartner et cetera on what's important to the supply chain, two things come up. One is visibility and the other is analytics. Right, so there's tons of data available, to your point just now. That data could cause massive noise to the business unless you know what you're looking at. I know companies that will say, 95% visibility of changes on their demand side is good enough but I'm good enough on the supply side to be able to adjust. But you got to know which data to look at. So I'm looking at on shelf. I'm looking at what consumers are choosing and using, I'm looking to see what of my contract manufacturers-- >> Peter: Analyze key constraints. >> Bingo, so it's not about, I think what we're all going to have to learn in the internet of things is we need, again, a cloud based internet of things platform that does the analytics. >> Because we can rewire things faster. >> Exactly, you can adjust the business to new scenarios based on what you're reading from the demand side and what you're reading from the supply side. >> So you're a great foil for my second question. My second question is you look back at the history, or the recent history let's call it, of strategy, very asset based, Porter said pick the industry that has the best returns, pick your position in that industry, then choose your games based on the five factor analysis that you want to play to get to that position. Very asset oriented, we're in control, that's going to dictate how things change. What you just suggested was a very very different way of thinking about strategy. >> Same fundamentals. It's the same fundamentals but it's allowing yourself to adjust those fundamentals based on what's happening in the market place. >> Peter: But you're not going to base it on just the assets. >> No, we're not going to base it on the assets unless you've focused on, like if you're an engineering company and that's all you make is machines, you can't suddenly start producing toothpaste, for example. There are, that's why I say it's a reconfiguration of those same principles but flexible enough to meet demand. >> So how does, how does the world of design and the world of strategy start to come together in C suite? >> Fundamentally, because it's the voice of the customer that starts to count. It's the voice of the customer that dictates the strategy. So if my customers don't want green Guinness for Saint Patrick's Day, don't make any, because it's going to hang around and get thrown away, right? So, the voice of the customer determines what's happening on the demand side and the supply side has to be agile enough to meet that need. >> So, I would suggest keep Guinness the way it is because it's damn good the way it is, so personally I would agree on the Guinness comment. No green Guinness. >> So, what's the South Africa beer? >> Castle Lager. Well, SAB, South African Brewery, has been bought by Anheuser-Busch InBrev, a massive big giant. >> We love beer and if there's any beer sponsors out there, we're happy looking for our Budweiser. We want a, maybe an IPA in there. Roddy, thanks for spending the time, coming in with you, appreciate it. Some thought leadership here on Reconfiguration and looking at some of the nuances that are really going to impact the buyers here on The Cube. Oracle Open will be back with more live coverage from SiliconANGLE's The Cube after this short break.

>>Mhm Yeah, this is Dave valentin. I'd like to welcome you back to the cubes coverage of couch base connect online with the theme of this event is modernized now and one of the big announcements is Capella which of course as you all undoubtedly know is the brightest star in the constellation Auriga, which is latin for charioteer, yep, you can find that in the constellation of that constellation of the night sky in late february, early March in the northern hemisphere. So with that little tidbit, I'd like to welcome in scott Anderson to the cube, who is the senior vice president of product management and business operations. That couch base scott welcome. Good to see you. >>Thank you very much. Thanks for having me. >>That's our pleasure. So you've launched couch based cappella if I understand correctly, it's built on couch based server seven which he launched just a few months ago in the middle of the summer. Can you give us an overview of Capella? >>Yeah, absolutely. So couch based Capellas are fully managed databases. Service for enterprise applications. One of the goals of launching Capella and our databases is service offering that we just announced today is about increasing the accessibility of couch base so it's about making it easy for a developer or an enterprise to get up and running in just a few clicks in a couple of minutes Um and about making it more affordable and accessible through the development phase through the test phase, the production phase. So really it's about ease of use having the right offerings aligned to the phase of development that customers in and eventually into the production of their enterprise application leveraging capella and couch based Server seven. >>So let me ask you, I I went pretty deep with ravi on the, on the technical side and I want to understand what makes Capella different from some of the competitive offerings. Is it the sort of the fundamentals that I learned from Ravi about how you guys have have have really done an awesome focus on on on sequel but been able to maintain acid compliance deal with distributed architectural challenges and then bringing that over to database as a service. Is that the fundamental, what are some of the other differentiators? >>Yeah, that that is the fundamental, we have an amazing platform that Roddy and our core engineering team built and we've talked about that and I think Robbie mentioned that the ease of sequel and applying that to a documented oriented database, then combining some of those capabilities with the ease of use, the ability that you can get up and running, signing up for our free trial couple minutes later you've got a database endpoint that is fully managed by couch base. And so we're doing the monitoring, we're doing alerting, we have calls to action based off what events are occurring within the database environment, ensuring it's always available as well as doing kind of the mundane tasks of backup and recovery, uh scaling the environment, upgrades and so forth. So it's really about ease of use, making it um leveraging are incredibly robust, broad platform um and then making that in different consumable model for our customers and developers and getting started really easily. The other thing that we have done is really leverage the best practices over the last 10 or 11 years of some of the largest enterprises in the world using couch based for the mission critical applications. So we've codified those best practices and that's how we keep that service, high performance, always on highly available. And that's one of the core value propositions that were able to bring with Capella. It's really about management capability, global visibility of your clusters coupled with what we believe is the best no sequel database in the marketplace today. >>What about what about cost, total cost of ownership as you scale a lot of times when you scale out, you get dis economies of scale, it's kind of like, you know, you get that negative curve, uh what are you seeing? >>Yeah, we've done a third party benchmark studies which have proven out how we are able to literally scale the environment uh and continue on that curve as you add notes, you're getting that incremental performance that you would expect. The other thing that we do that's really unique within couch bases are multidimensional scaling and this allows you to place our services, things like data index, query, full text search indexes and analytics, you can co locate those on single nodes within a cluster or you can have dedicated notes for each one of those services. The reason that is important is you get work line isolation for those specific services within our cluster. The other thing that you can do is you can match the compute infrastructure to the needs of each one of those services. So some services like query are much more core, compute intensive and that allows you to have a specific instance type that is optimized for that, reducing your costs, indexes where you want very fast performance, you may want to have a higher amount of memory relative to the number of course. So that ability to mix and match the infrastructure with an existing cluster allows us to lower overall costs. That coupled with their blazing fast performance with our in memory architecture allows people to get incredible performance at scale. Um, what we've proven out in the study that I mentioned earlier is we have that linear scalability and you're able to do more for less at the end of the day, you're getting more operations per second per dollar if you want to use that as a metric. >>Got it. Thank you for that. What do customers need to think about when they want to get started with Capela? How difficult is it for people to jump in? >>It is incredibly simple. It's as simple as going to couch base dot com clicking on start your free trial, You're going to that free trial, you provide a minimal set of information for us and it's literally a few clicks and you're going to have a database endpoint within three minutes and that's really been a foundation of, of what we've been focused on over the last 6-9 months is removing any friction we can in the process because our goal is to give a tremendous user experience and get people up and running as quickly as possible. So we're really, really proud of that. And then from a paid offering perspective, we have a number of offerings which are really aligned to the needs of each customer, some individuals who want a larger cluster and they want to be able to pay for that. We've optimized service levels around that in terms of level support and the features that we think are appropriate for a dev cycle, a test cycle and then into production and lastly we will be announcing a number of promotional starter pack bundles, really trying to couple the overall service that we have with Capella with some of our expertise, so helping new users get up and running in terms of things like index definitions, what's the best way to do document design and schema within within couch base. Our end goal is to match these services and bundles with the life cycle of application development. So in my development phase what's the offering for me as I move for production readiness, what services capabilities I need and then production and the ongoing if I expand my use. So we've been really focused on, how do we get people up up and running as quickly as possible and how do we get them to production as quickly as possible at the lowest total cost? >>That's nice. That's a nice accelerant for customers. Um, so as you heard upfront, I did a little research about the name Capella. How did you choose it and why? >>Well, one thing I learned early in my career is naming is not a strong suit of mine. I leave that to John or our chief marketing officer in the overall team. Um, we all have opinions, but I trust John and we went through, I think it was over 60 names, seven rounds of debate to come up with capella, but we want to name of strength. We like the alliteration couch, basic capella together. Um, one of the little facts may have tipped it over is I believe in latin, it means little goats. So we kind of played from the barriers. Always think to jerry rice goat, greatest of all time. So that was a nice play on that also. Um, but I leave it to them and really happy with the overall name, love the liberation, Love some of the hidden meanings within that. Um, and we're really, really excited about getting going. So you wouldn't want me to pick the name. Um, I get a vote. Um, but I would say my overall influence is a little bit lower than where john's is and matt cain, who I know you spoke with previously. >>I love it, jerry rice definitely is a little go because I'm from New England. So of course tom we think tom brady is the big goat. I >>know we've, I grew up in that joe Montana era, so maybe you can take that off line after this interview. We can have our own debate, but I guess super bowl trophies or the ultimate measure at the end of the day. >>Now I've got a little stat for you. So, so Capella is also one of the 88 modern constellations as adopted by the International Astronomical Union. I. E. Not one of the ancient constellations. Pretty clever. Right. >>Exactly. >>Scott is great to have you on the cube. Thanks so much. Really, >>thank you so much. >>All right. And thank you for watching. Thank you for watching. Our pleasure. Thank you much of the cubes coverage of couch based connect 2021. Keep it right there for more great content. Mm mhm

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen, brought to you by Del Technologies and its ecosystem partners. >> Welcome back, everyone to the cubes. Live coverage of Del Technologies World here at the Venetian fifteen thousand attendees. One of the biggest, most important tech conferences all year long. I'm Rebecca, not your host. Along with my co host, stew Minutemen. We're joined by Caitlin Gordon. She is the VP product marketing at Delhi Emcee. Thanks so much for coming back on the cute Kate. I >> know This is so nice. Maybe we'LL have to make it three days in a row. >> I would we would love that. All right, so the last year at this very comforted you lunch power, Max, what's Tet Walker viewers through Sort of. The new capability is the latest and greatest. What's going on with power Max this year? >> Yeah, My favorite thing to talk about his power, Max. So we couldn't miss that today. Yeah, So a couple of updates in the Power Mac's front couple on the software side and then on more on the hardware side as well. S o from ah software side. We've got a couple pieces, which is a lot of our customers, really starting with the largest of our customers, are looking to add more automation into their data centers, and storage is no exception. And how do I automate some of those storage work clothes? Teo, make things run more seamlessly, get into more of a cloud operating model. So we had a couple of announcements on that front. We have a new V R. Oh, plug in, um, to automate work clothes through the r o A. CZ. Well, as ants will play books coming this summer, a couple important automation hand spins and obviously a lot more to come there in the future. The other one in a similar vein, is that containers, right. We've seen the increase adoption of container. So, um, and that the container is being used in production applications means that external storage is actually become a reality in that world, and the support for a C. S. I plug in on power Max, is something that we're seeing more interest from. So we have announced that's coming this summer as well. >> So, Caitlyn, I remember a year ago when Power Mask got announced. I heard things like intelligence and automation. And I went to add non, you know who's been working on this kind of technology for decades? Is that non how we've been talking about this for decades? Tell me why it's different and he lit up like I hadn't seen him in awhile, told me, What's going on for I want you to connect now a year later is what's this mean for customers? What does that automation? You know, an intelligence mean, is there a certain KP eyes or hero metrics who have is two customers using this today that they couldn't have done? And with you, no last generation intelligence storage? >> Yeah. Hey, think about it. It's really about moving to this concept of the Autonomous Data center. And how does this become an autonomous storage system? So both the intelligence within the system that we talked about last year and the decisions that the system is making itself every single day all by itself, that's that has really changed. And it's a completely new evolution of its making billions of decisions a day for customers so they don't have to do that means you're gonna have fewer people managing storage and they can invest in other things. Then when you move that up the stack, some of that the bureau, the answer will play books really enables you to then automate more of the work flows within that so again gets you more into that operating model, and you can automate not just the storage infrastructure, but then get to this autonomous data center >> So way talk to Travis briefly about Dev ops and you're mentioning answerable playbooks. You know, for years we've been talking to customers and saying, Okay, we we need to get two more agile environments, you know, Dev ops there, but enterprise storage specifically, there's a little bit slowed up, so it sounds like we're starting to get to greater adoption. What? What, what what got us over that you know, Hurdle, and where our customers with it today? >> Yeah, and I think it's really the maturity of our largest global customers that have gotten to a place where, for the workloads that will continue to remain on these thes on from infrastructure on our purpose built storage on our high end arrays, they need to run that as efficiently as possible. Um, and a lot of the work we've done to build in a. I does part of that, but really, ultimately they're looking at in there. Three. Terek protector. How do they run things more smoothly? Um, and it's really our customers that have brought that us is a requirement, and we've been able to to support that. >> So how do you work with customers? Mean innovation is, of course, an underlying theme of this of this conference. Talk about how you collaborate with customers to to solve their problems and how you help them. Think ahead what their future needs are. >> Yeah, and certainly Travis, I myself, might our teams, as well as the engineering team, spend a lot of time with our customers in the briefing centre. A lot of in the field, um, really talking about their challenges and the privilege that we have, especially with something like a Power Max platform, is the customers we have. There are the ones that are constantly pushing the boundaries of what we can do for them today, so they always need the best performance. The best efficiency and what has changed is they also now we need that simplicity. They need that operational simplicity, even on their high resiliency. High performance systems. Um, and we spend a lot of time understanding those requirements on DH, the problems that they're trying to solve and how we can help them get there and that that could be automation that could be containers. But it could also be cloud right, And that's the other piece that we've we made a lot of investments across our portfolio is how do we support that cloud consumption cloud operating model, leveraging public cloud? Um, and and a lot of it really just comes from how do we help our oppressors continue to solve their problems? >> It's a competitive marketplace, and, as you said, customers, they want everything. They want efficiency. They want simplicity. They wanted to not cost them too much money. What what's your unique selling point? How do you message this is This is why our solution is >> that I mean, our overall strategy delancy from a storage perspective is that we're way. We'LL have a single product in each segment with which we've compete and each one will be architected for very specific requirements so that we can meet the combination of a price points and it features and capabilities across all these different perspectives and that each one of our platforms is designed to be industry leading in that category. Which is why we have power Max on the high end, the resiliency, that performance, the availability that you know, banks, hospitals, governments around the world expect. But the same time we have mid range pot for us. We have an entry platform that could be sold for under twenty five thousand dollars, right and has a different set of requirements. We have the unstructured business, which is supporting the data. Aaron. That data explosion in a file data, Um, so the The fact is the matter is this. That is all about having the right actor architecture's so customers can have the data in the right place at the right time with the right service level. Um, and that's why we have this portfolio and within each portfolio that were leading in each one of those categories, That's kind of the bigger perspective we have on it. We do not just have a hammer. Not everything is a nail for us. Um, and that's an important part of how we can partner with with our customers to help themselves. Not one challenge, but all the challenges they have >> killing one of the interesting shifts we saw the show is clouds being talked at more than ever at this show. One of the earlier segments we had on we talked about the cloud enabled infrastructure. So things like power, Max, you know, I asked J. Crone, you know, tell me why this is cloud watching, and he gave me a good answer. What I want to ask you your angle on is when you talk to customers, you know how to storage fit into the overall discussion of their cloud strategy. You know what, some of the key business drivers and you know how how's Del technology? >> And I'm glad you said that because Jay and I have had this cloud washing conversation as well as I think that's the unfortunate thing in the reality in the market in the past, probably ten years is a lot of cloud washing, and where we're really focused today is, and we talked a little about this yesterday as well as they say. There's one piece of the how do we fit into overall Del technologies cloud strategy with the Del Tech Cloud. I'm in the VCF integration. We kind of covered that the other pieces that when we look at cloud enabled infrastructure, we're focused on solving really specific use cases that we hear our customers trying to solve today of connecting that data center into a public cloud. So that could be what we call cloud connected systems. The tearing of data from your own promises, infrastructure into the public cloud. Really, that's more of an archiving. This case, a kind of a tape replacement use case that could be dead, remain cloud tear, cloud tearing cloud pools. All the different pieces we have there could be CLO Data Services, right. Offering storage Data services is in a public cloud. Unity Cloud Edition will be one or the New Delhi emcee. Cloud storage services could be another one or even that cloud data insights piece of it. So it's really about solving that solving real challenges about disaster recovery Analytics in the cloud. How do you do that? In a really impactful way? That's simple and easy for customers. >> Yeah, the other Claude related thing wanted to get your take on is many of solutions. I heard on there is, you know, it's VX rail underneath. It's VX rail underneath. It's VX rail under >> you. Notice that >> I did, and you know a way. We had a number of people. V X ray. Lt's doing great, but, you know, if you talk about cloud and the infrastructure that I have in my data center, you know, we've talked Teo, talk to Dell for years. You know, the new power Max last year is underneath some of those admire. Where does that fit in? Kind of CIA and cloud, you know, infrastructure piece. >> Yeah, in a lot of different places. And for Roddy, for reasons, right? Some of us just the high value workloads you need. The scalability, the resiliency, the performance you need the ability to scale your computing your capacity separately. You want to be able to consolidate not just your applications, but actually all your file and sew something like unity or even power. Max, you can have your block workloads and your file workloads there. So we have a lot of customers looking to use traditional three tier architecture, but leverage that in a true cloud operating model from an automation standpoint, cloud consumption model, but also leveraging public cloud computing, right, leveraging the public cloud and really impactful ways, for example, for disaster recovery, eh? So it's really that combining what people love about our industry leading best of reed storage. Um, with that agility of the public cloud is a combination that we certainly hear a lot from our customers of How can I make the best use of clouds? Everyone walks in and say that club first strategy, but it's really about well, how do you actually think about data first and then how do you have a cloud strategy that supports that? >> So So let's talk about the future. I mean, ahs, You said This is what the customer is thinking about right now, but it's your job to think ahead and make sure that you are giving them solutions that fit their future need. So what are you thinking about the solutions that are available today that were really unimaginable five years ago. I think about ahead to twenty twenty five when there is enough data to fill the Empire State Building thirteen times over. How are you helping companies manage the tsunami of data? >> Yeah, and I think part of that is really about again the operations we talked about. Part of that really just comes back to having the right architecture for that type of workload. So this is where I salon actually well before the data era actually was designed for this specifically. So Iceland, created in the early two thousand's, was designed of one file system from terabytes and two petabytes. A single administrator can manage now up to fifty eight petabytes in a single file system. That's game changing when you think about the scale that we're seeing today. So the reason we went to that capacity isn't certainly just cause we thought we could. It was cause our customers were asking for it. Is these workloads in that data that we're talking about autonomous driving center that are just driving the scale? Ability limits, And they're asking for more and more in the most efficient floor print possible. And if you think about that, especially even in the cloud context, there's a There's a combination of How do you leverage that in the in the data center right? And physics means you can't get it up into the cloud necessarily. Um, but then also, there are use cases. They're like analytics of How do you leverage public cloud computing? But then you have that industry leading scale out now, as on the from the storage side so you can combine that. So you talk about something that we talked about here last year, and now we're talking about it a little bit more as well as our integration with Google Cloud platforms. So a lot of our customers are looking to use G. C p for compute for analytics workloads on DH. It's really almost rent your compute for analytics, but you have to have the right storage platform with the right architecture on the back end of that. So what we've done is fully integrated. Iceland, uh, platform and file system through G C P portal. So you could actually combine that public hug, compute and that file system that can support that type of scale. So it's a really unique combination that can help support not only the scale of that data, but also that some of the unique use cases and work loads that are coming out of that >> So Caitlin lot of products here that that would be talking about. Last thing I want to ask is customer customer conversation you have, you know, is data the center of the challenge and opportunity. They have something else that kind of bubbling up. As you look across the conversations you're having that you could have your audience. >> I think at the center of what I hear from customers, Data's in there, but they don't come in saying its data, right? They'll come in thinking about, you know, just trying to figure out how to use cloud properly there. Think about how Doe I simplify things. How do I, um, operate in a way to meet the service levels with a budget that's definitely not getting bigger? Um, and really be as efficient as possible. And it's not, um, some people are looking to go public. Cloud thinking. It's an easy button are there, but it's it's really about How do we change things? Teo run more efficiently and customers inherently to understand, right that the data is at the center of it, and that's increasingly the most valuable asset in the organization. And then they need to optimize their infrastructure to support that, so it really does come down to what? What can we help them to simplify? Optimize. Secure that so that they can truly unlock that. David Capital. >> Well, thank you so much, Caitlin, for coming back on the Cube. That's thanks for having me. Rebecca Knight for stew Minutemen. There is so much more coming up of the cubes. Live coverage of Del Technologies World in just a little bit.