>>From around the globe, it's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Oh, great. To have you with us here on the cube, as we continue our coverage of AWS reinvent 2020, doing it virtually of course, uh, out of a necessity as I'm sure all of you can appreciate we're joined now by Alex Sanchez, who is the head of cross GDC networks and Fujitsu and Fujitsu provider of global it services and solutions. And so their footprint, um, again, is, is around the world. Uh, Alex, thanks for joining us here on the cube. We appreciate your time. And, uh, I'd like to hear a little bit more about your role first off before we jump in and tell us a little bit about Fujitsu for those who might not be familiar with it. >>Thank you very much, Sean. I really appreciate it. Uh, well, uh, first, uh, let me start by providing some background on Fujitsu. We're a global it digital transformation company offering a full range of technology products, solutions, and services. Uh, we exist to keep our customer's business running and we strive to give the best possible experience across every customer touch point. My role as head of cross CDC networks, uh, makes me in charge of standardizing technology networks across our global delivery centers. And for the past couple of years, I have been working on the standardization of our contact center platform across all of our global delivery centers. >>Yeah, yeah. I mean, you mentioned global delivery centers, so let's, let's jump into that. Uh, first off, what are they, um, you know, how have you structured your business in that respect and, um, ultimately what kind of service or a solution are they providing to your customers? >>Absolutely. So our global delivery centers are interconnected, integrated global teams. Uh, we deliver a broad portfolio of standardized services, which includes cybersecurity workplace and much more. We're based out of, uh, eight different key countries. We serve customers in over 100 and uh, different countries and we provide support in over 40 different languages. Uh, we enabled, uh, those CDCs enabled us to consistently and resilient provide services to our customers, uh, 24 seven 365 days of the year. Uh, the service, uh, that we offer, uh, as, uh, for you to global delivery teams are constructed from fully standardized components. Uh, it allows us to, uh, be configured to meet our customer needs and deliver a flawless global consistency services. >>You just, you were just talking about multiple languages, right? You've got to deal with countries, uh, environments, uh, continents, uh, businesses with different needs of, of all, you know, all over the, over the map. If you might say that, um, how do you balance that? Or how do you approach that when you do have so many customers in a wide variety of venues with a wide variety of needs and yet, you know, you want to provide for them that exemplary service that they expect when they come to Fujitsu? >>Uh, well, yes, as I mentioned, uh, we strive to evolve our contact centers so that it meets that global need that global expansion. And we adapt to our customers' needs. Uh, we have our GDCs with teams that are engaged and enabled so that we can provide customers with, uh, the best customer experience we like to help our customers reimagine their employee experience. >>Yeah. You mentioned, uh, you're talking about the contact centers and I know that you're going through this major transformation right now, in terms of, of, uh, how they're operating, um, before we get into that and, and, and jump a little bit deeper into what you've already touched on, what was the problem before, or, you know, there's always a problem, right? We're always trying to solve something, make something better, put a little finer point on that in terms of, of what you were doing before, you know, where were we? >>Well, uh, if we get to this global delivery organization, uh, tries to build trust at every opportunity we aim to deepen our customer relationships by adding a value of mix, uh, of rock, solid delivery, innovation and collaboration. However, some of our previous systems, the net always offer us the functionality and flexibility that we needed to provide a diverse range of, uh, services to our customers and what they required. So that is the basis of our, uh, challenges and, uh, what we were striving to overcome. >>So you've, you've turned AWS, um, uh, again, Amazon connect, I know that, uh, that you've got widely deployed. What was it that, that attracted you to that in terms of finding the value in it, and then what kind of efficiencies and what kinds of improvement in your operations is, is connect providing you >>Well, uh, being able to, uh, think about the art of the possible adding value to our customers. Introducing next generation features, uh, our road with AWS connected started as a two month proof of concept, uh, with over 150 different agents initially supported out of one of those global delivery centers, providing support and services to, uh, one of the regions. So, uh, we started as a way to innovate and provide next generation functionality. >>Yeah. Proof of concept periods are always interesting, aren't they? Because you, you think you're going to find out some thing and, and you might, but then you sometimes find out something else, right. That, that you're like, okay, well, the, uh, there's another application here. There's another service here. There's another layer here. Um, what was it in that period of time for you then, as far as your takeaways that convinced you that, you know, this is right, this is good. We need this. And, and so we're going to jump in. Absolutely. So, >>Uh, I would say that one of those things is that we made marked improvements in our customer experience. We were able to rapidly onboard new agents and provide automated features, such as call recording sentiment analysis, integrated callback features. We were able to help our customers faster while simultaneously improving the service quality. >>Yeah. COVID, uh, has been, um, certainly wreaking havoc in, in every facet of life. Right. Um, no question personally, professionally unit, multiple industries. So how about the impact on your, in your world first off, just from, from COVID-19, uh, how you've had to assess what your client's needs are, how you, what your needs are and, and first off, how you've, how have you balanced that >>In the past year? Yes, well, uh, Fujitsu was able to move, uh, 95% of our contact survey agents to remote work environment, equipped with the tools that they needed to provide, uh, services while remaining safe and productive. Our contact center agents and operations was not able to persist, but actually thrive during the COVID 19 pandemic and provide the much needed support that our customers were expecting and, uh, provided from, from us. How fast >>Was it, you know, I guess it required, what, how quickly did you have to respond? Cause, uh, you know, I mean, this certainly has caught a lot of, or caught a lot of people by surprise back in early March and April. Um, and I assume that that Fujitsu's no different, right? All of a sudden you have, uh, a pandemic on your hands and you've got to move nimbly and quickly. So just talk about that, if you would, that, that quick transformation that you had to make and in terms of responding to the >>Absolutely. So with AWS connect, we were able to automate and simplify the complex contact center flows that we had previously, a product of this is it's ability to now make ad hoc changes in seconds while avoiding multiple vendors to actually get those implemented. One example of this is that for you to help one of our customers move from 4,500 QS to less than 400 by actually doing call tagging attributes, instead of just creating independent flows for each one of those countries. And this mainly because of the needs from the operation to be able to quickly create reports based on countries and languages. Yeah. >>And I know you were involved or, and, and, and I might still be, I'm not sure a beta testing, uh, with some of the new, um, AWS connect features that were announced recently, you know, here at, uh, during re-invent what, what is, um, what's got you going there, you know, what, what, uh, what's caught your attention and what are you excited about seeing I go into practice on a, on a wider basis? >>Well, John, I would to say that introduction of ado list tasks has greatly helped us improve our agent productivity. We were able to see improvements of around 30% and we expect refine our customer experience even further by adding additional AWS integrations. >>Now, you mentioned, mentioned further, there's always a next step, right? Isn't there Alex. I mean, there's always, it's as good as you are now. You can't afford to sit still. I mean, that's the competitive nature of your landscape. So where do you see yourself in, in terms of rollouts in the future, or if there's an area that you think this is the next, uh, challenge for us, uh, in the, in the short term, what would that be? >>Well, that AC very good question for you to provide, uh, contact center services to around 300 diverse customers with agents speaking dozens of different languages. And we are continually looking to improve those services and experience for our customers, as well as our employees. We believe that if our employees are happy and safe and they have the tools that they need to do their work, that would result in an M in a much more improved, uh, service to our customers as such, uh, for you to source invest money, invest in heavily in the of transformation. Some of those elements would include a location agnostic delivery. This would actually allow us to create virtual teams with so employees working from Fujitsu offices while some will continue working from home. This approach will offer, uh, significantly and greater flexibility for our employees, as well as an improved efficiency of our services. >>Uh, the ability to introduce self service and automation by introducing, uh, virtual assistants, uh, robotics, uh, voice recognition, speech to text conversion, sentiment analysis. It will help us reduce the time it takes for agents or staff in repetitive tasks, allowing them to focus on the more important, uh, improvement, adding value to our customers. Being able to add, uh, tasks such as technology upgrades, uh, knowledge and data management, uh, that analytics business recommendations from our customers. This would then, uh, tied into what we're doing with improved planning, uh, as situation changes. And definitely COVID has been one example of that. Uh, Fujitsu needs to respond rapidly to ensure that we continue to provide support to all of our customers, uh, wrote a planning system, provides insights recommendations to help us deal with those changes as well as offering a level of flexibility for employees to align with their personal needs. And, uh, finally, and tying this up with those innovations that we're looking into, uh, being able to take those into employee engagement. We're introducing a proof of concept with gamification on some of our contact center, uh, desks to provide employees with a rewarding environment that offers an increase, uh, find while also doing the work reinforcing behaviors and enhancing customer satisfaction while there's certainly, um, a new >>Order, a new world, right? In, in terms of how we have to operate in a business environment. And I think you hit a key word there it's flexibility, right? Ultimately giving your employees the flexibility to still do their jobs in a very productive environment and a safe environment is critical. And it seems like Fujitsu is committed to doing that. So congratulations on that and thank you for the time today. We really appreciate it. >>Thank you very much, Sean. And thank you for the opportunity.

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi and welcome to the cube Studios for another cube conversation where we go in-depth with thought leaders driving innovation across the tech industry I'm your host Peter Burris one of the dominant considerations that every business faces today is how do they work through the complex outcomes associated with cybersecurity as they find new ways to use their data and apply it to new classes of customer and market problems this is not a small problem especially given that so many bad actors out there are now also seeing a company's data as a potential enormous source of value now to see what businesses are doing to try to achieve those complex outcomes while at the same time lowering their overall security risk we've got a great conversation first off welcoming back Eric Herzog who's the chief marketing officer and vice president of worldwide storage channels from IBM storage Eric welcome back to the queue yeah thank you love to come and Eric you bought with you a really distinguished individual kaeleen Sanchez as vice president of IBM worldwide Systems lab service and some technical universities Colleen welcome back to the cube thank you so let's get the quick update where are we in this world of the outcomes of businesses seeking let's start with you what what our business is trying to do with cybersecurity today protect data and ensure that we provide a certain level of security levels to enable the overall end-to-end protection holistically so it's really important that we enable a full stack hence the strong partnership with Eric and I too and also the global team to pull together solutions then enable data protection so let's talk about the reasons why it's becomes that much more acute or that needs to become that much more acute because we've got we've got the reality that everybody's going to get penetrated in the next year of any size that it takes a long time often to figure out that you have been penetrated you've got new types of attacks the old ones of just kind of know phishing and whatnot while still prevalent so a problem now we've got ransomware and we've got a lot of new types of actions that bad people are taking what are some of the things that we're trying to protect ourselves from these days so the B's thing is you mentioned ransomware it's like this idea that we want to protect and also act as a worm so to speak to provide an abstraction layer to enable protection holistically of any given solution because data can be everywhere nowhere so yeah there's discussions about it's the new oil it's not the new oil necessarily it's pervasive it's everywhere so our data from our perspective can be in any device any media type and we need to figure out how to protect it at its core and so you see it as a full stack that just means we have to go lower in layers in order to protect the overall data so kind of what you're saying is that the more the security is closer to the data the more the data itself is secure the less reliant we are on policy which can lead to human error or human mistakes which could allow folks come in I got that right you're correct it's smart data it's this idea that it's multiple pieces and multiple owners of a maker checker policy that keeps the overall solution accountable that doesn't diminish the need for policy but Eric it certainly raises the specter or the spectrum of the fact that increasingly the smart folks within a business that are insuring or trying to diminish risk and ensuring assurance of the data need to start looking at how storage or the role that storage plays in this overall security framework I got that right yeah if you think about a traditional company their approach is we need to get security software to keep the bad guys out and they mean it's the Chiricahua for when we are breached to track them down talking to several CIOs at even midsize companies let alone the fortune 500 is sometimes it takes them days even weeks as you said to know they've even been training penetrated yet track it down while they're doing that imagine someone coming into your house and the police don't show up for 10 minutes even though your alarm went off and by the time the police go your house is totally empty and IBM stores you make sure that that doesn't happen it's as if everything is bolted down everything is locked and if they do steal something for example it's say write once read many technology they can't really use it right because it's wormed they can't change it so it's almost as if your TV required a fingerprint and even if they stole it they couldn't use your TV and that's the kind of thing I want to do is be pervasive and get enterprises as well as even small and mean courts to realize an overall cyber resiliency and security strategy involves keeping the bad guys out email will track them down but when they are in the house making sure everything is secure and essentially nothing can be stolen or utilized of your incredibly valuable data so using your metaphor of making sure the TV is bolted down or whatever is is bolted down that's however doesn't diminish the business's ability to move the TV if they want to if they have the rights and privileges to do so so let's talk about how the new tooling of storage is being bought together with some of the new services approaches to achieve these complex outcomes how is IBM looking at storage and storage related technologies as a as a foundation for achieving the new outcomes that the businesses want so for my services perspective we go in and partner with our core technologies within the storage portfolio to enable like something like bare metal to enable the armor around the overall solution we work to with the client to understand their pain points etc and how we optimize the solution to substantiate that we provide highly resilient flexible access to data but at the same time it's protected now this is a fast changing world and it's there's there's an enormous expertise both on the good side and the bad side obviously you've got you've got a development background talk a little bit about how IBM is relying on customers relying on universities other sources of deep knowledge about security issues and then translating that into IP that then finds itself into places like Eric storage portfolio so so we have processes like for instance the technical universities so we have discussions with an extended set of worldwide engineers and scientists to talk about specific important pain points related to cyber security so when we obtain that data we provide the training we collect information and then we provide or funnel that back into Eric's portfolio from an IBM storage perspective so Eric look you've you're an old man for an act as amaya as am i and so that is one area where security has not been an afterthought it's not been that separate how to what degree has that relationship between security and data and storage of permeated the way that you think about solutions solution directions and engaging your customers with your value propositions so one of the big things we've done is make sure that our security is across the entire portfolio primary data flash disk secondary data disk or tape and in fact as you know IBM is known for its hybrid multi cloud storage technology capable of easily and transparently tearing out to multiple public cloud providers when that data is in flight sure site better be encrypted so we've made sure that where this ransom where malware protection data encryption rest across the entire portfolio right once read many technology things like FIPS 140 - - which is a very important federal specification around security malware and ransomware protection with air gapping both to tape but also to cloud so we've made sure that the security aspect of storage is pervasive primary storage secondary storage cloud storage whatever you're doing your storage will always be secure so when they do breach the wall and they track the bad guy down as they're rooting around your file your block your object storage it's secure and they can't get anybody from the data you still can but they can't steal that data from you and that's a critical capability of spreading it beyond just the mainframe we have great technology with our new safeguard copy product we brought out last year that does incredible things to secure data but in fact we make sure that all sorts of security and resiliency technologies from an IBM perspective are spread even into our lowest end product our store wise 50 10 e has full data rest encryption encryption and flight so all those technologies everyone from the very entry products all the way up to our high-end product the DES family and everything in between yeah well one of the things about digital business is we're discovering new ways of leveraging data and unanticipated avenues to try to generate additional business and one of the things we've seen as we talk to customers is that increasingly that means that the weakest link in your security chain is going to be it's going to be instrumental at defining your overall security policy so treating security is an option is because you can secure something over here is increasingly difficult as you find new ways of integrating data so how is IBM helping to get customers to see that so I would say two points from lab services perspective as well as our business partners we we take on a consultative discussion or partnership so we learn from our clients and partners and users as much as they learn from us and we provide offerings to really explore that full stack to make that data smarter as we discussed before so digital business is happening it's transforming very rapidly IBM talks about the rise of the incumbents as they bring some of these digital native capabilities into their business I'm going to ask each of you for the one thing that you think is going to be most important for customers to think about this relationship between data storage and security Eric I'll start with you what do you think is the one thing that people need to start thinking more about over the course next year storage is not an afterthought in your secure strategy killing how about you co-create with our end users to enable the full and and prediction as you mentioned before and as you co-create don't forget that storage is intrinsic to whether or not it's secure or not great conversation thank you both for being here Eric Herzog's the chief marketing officer and vice president worldwide storage channels at IBM storage Colleen Sanchez is the vice president IBM worldwide Systems lab services and technical universities once again thank you both for being here and talking about this crucially important area thank you for having us thank you alright and once again I'm Peter Burroughs and until next time this has been a cube conversation [Music] you

>> Live from San Francisco. Its The Cube. Covering IBM Think 2019. Brought to you by, IBM. >> Okay, welcome back everyone, live here in The Cube here in San Francisco, exclusive coverage of IBM Think 2019. I'm John Furrier and Stu meeting next guest is Calline Sanchez, Vice President of IBM Systems Labs Services. New role for you, welcome back to the cube. >> Yes. Thank you for asking me back. >> So the new role, Vice President of the Systems Lab Services. Sounds super cool, sounds like you got a little lab in there, a little experimentation >> yeah think of it as a sandbox for geeks worldwide. And what that means is we enable high performance computing deployments as well as what we do with blockchain and also artificial intelligence. >> So its a play ground for people that want to do some big things, solve big problems, what are some of the things that you offer, just take us through how it works. Do I just jump in online, is it a physical location? What's it like ? In 2018 9000 plus engagements worldwide in 123 countries. So to net it out is, it's not necessarily a single lab or a single garage, we have multiple locations and skills worldwide to enable these engagements. >> How big is the organization roughly? Its over a thousand folks, consultants who are smart and capable. >> We had a conversation yesterday with Jamie Thomas, talking about, from a super computer stand point, now IBM's reclaimed the top couple of positions there and from a research stand point, David Floyer from our team has been talking for years about how HPC architectures are really going to permeate what happens in the industry and I think about distributed architectures, it all seems to go back to what people in the HPC environment lived in. You've got background in that, you worked for one of the big labs, explain how this has come from something some government lab used to do to something that now many more companies around the globe are leveraging. >> Before IBM I worked at Sandia National Laboratories and the reason why I chose to work with these awesome skills worldwide in lab services is that I wanted to be part of the cool group, so to speak. So they were doing work in deployments with Oak Ridge National Laboratories and also Laurence Lilvermore. So you'll hear (inaudible) with Laurence Livermore speak on stage about some of the relevance associated with high performance computing and why were number 1. So, to get to our question it's cool to be back online with what I could say, high performance computing deployment. We are the mechanics so to speak in this organization. Similar to what we do with formula 1, people who put on the tires, add the air and also enable the cars to move around. Well without them, guess what? Things don't move around. >> So you guys work on the high performance systems, you got quantum coming around the corner, you got AI front and center so you guys are like the hot shots. You come in, you build solutions with what's in the tool chest, if you will with IBM, is that right ? >> correct You're 100% correct. I will say it in my mind, we make things real. We deploy and implement strategic technologies worldwide for the benefit of our end users and we do that also with our partners. >> Give an example of an engagement you guys have had that's notable, that's worth sharing. >> Recently, this was a really exciting area a Smarter Cities with Kazakhstan. And so heres this independent city that works on basically AI for filming things whether its a security thing recognizing certain faces, deployments associated with weapons etc. And they were able to secure safety based on the film, films that they've taken, those assets. Now the other aspect is managing safer traffic. So even the president of Kazakhstan felt it was extremely relevant that we helped him deploy and he comes back to one of our European leaders saying, hey we need more of this and we want it to be extensive, we want to scale this opportunity. >> Talk about the philosophy's you guys are deploying because it sounds like its a... you said sandbox, when I think sandbox I think you do prototypes, I'm thinking about cool stuff, building solutions and that kind of brings this whole entrepreneurial creation mindset. Do you guys have like a design thinking methodology, is there things you're bringing to the table what else is involved besides the sandbox? >> You are correct. We have a very key component of design thinking. There's a CTO that reports to me directly who leads our overall design thinking and so that's a key component of what we do worldwide. Now as far as... We also enable incubation of technologies. So it's like what we intend to do with IBM Cube, What we intend to do with blockchain on system Z. So with these things we have garages worldwide to deploy or incubate the technology. >> What's the coolest thing you've worked on so far? Or the team's worked on? >> That's really hard to say 'cause there's so much. >> It's like picking a favorite child. >> Yeah, it's like I have way too many. So I was - >> You mention blockchain. I like blockchain. Blockchain, are you in healthcare, is it more, is there certain industries that are popping out for you guys? >> So healthcare is an example but I have seen it in the telecom area as well as other industries in general. So we have 11 industries in which we serve. >> How about AI? We're always trying to understand where customers are, how they're really moving things forward, to understand that that HPC architecture is a foundational layer for many customers to help deploy AI. Where are customers starting to make progress ? Give us some of the vibe you're feeling from customers out there. >> So its exciting with AI right now because we have Power Vision that allows us as any of us to actually exploit, utilize and play with, so to speak. So from my perspective that is what's nice, is that you can enable opportunities with the consumer market and learn. Similar to what we do with, and for instance, I am jumping around here, IMB Cube. Where users can actually become a user and start evaluating algorithms in order to enable this really amazing technology as in IB Cube. >> That was always the promise of big date, is that we should be able to leverage our data and get the average business user to do it. So it sounds like AI will continue that trend. >> Correct. So in prior rule, I talked to all of you about big data storage, right and replication. So now what's amazing about the conversations is that they've transcended. Its like, here you're looking to manage these large data warehouses, when, what do you do with the data? How's it monetized, how is it used in order to solution what's possible. >> What is the goal of the organization, next 6 months, year, what's the charter, what's your key performance indicators, how do you guys measure success, client engagements, onboarding people, what is the business objectives? >> So we look at the number of engagements, we also look at educational services worldwide for instance I will be in Cairo, Egypt next week to work on specific things that are going on in Mia in order to enable this next growth market so to speak. What in addition we do to measure ourselves, utilization, classic services organization view of the world. So we also evaluate what we can do with revenue, profit and our understanding of growth and we really believe the focus is these growth technologies. >> Is there a criteria if I wanted to get involved, just say I am a customer, prospect, wow, I really want to get into this design thinking, got these labs, coolest labs services, I want to play with the cutting edge technologies, how do I get involved? Is there a criteria open to all or how does it work? >> In addition to IBM Systems Labs Services, I have technical universities and we actually run technical universities worldwide for end users, clients as well as what we do with partners and IBMers. And this is important because we're able to then discuss, talk, collaborate with SME's across multiple areas of technology. So its a very good question and very important that I mention the technical universities. >> Are there certifications along that line? What are some of the hot skill sets that people are looking to learn about ? >> It circles right back to your last question, AI. With regards to how we certify folks as well as we, in essence, they get enough training in boot camps in order to get badges. >> So their certification, they just pass the touring test and then they're okay. >> correct. Well. (laughs) I don't know about the touring test so to speak. >> So is there a website on IBM.com, is there like a URL as in like labservices.ibm.com? >> I personally like the look at twitter where you can do a search on IBM Lab Services or Tech U. >> Tech U. And screening, how big is that focus, used a lot of video, is it collaborative tooling is it face to face, virtual, how do you guys do the training, all the above? >> Unfair, I was going to say all of the above. (laughs) It depends. (laughs) Giving that classic response, our favorite is video blogs. What we can do in social media with the YouTube channels etc. to get our opinions or our voice out with regards to key technologies. >> Well great, make sure you let us know what those channels are and we'll promote them, get that metadata out there, of course The Cube loves to collaborate. And thanks for coming on and sharing. >> I appreciate it and I will definitely take a sticker and put it on my laptop. >> Calline Sanchez, Vice President of the new IBM Systems Lab Services. A lot of opportunities to get in the worldwide sandbox and put the sluices together from blockchain to cutting edge AI. Your live coverage here at San Francisco at IBM Think, I'm (inaudible) stay with us for more coverage after this short break. (lively music)

>> (Announcer) Live, from Las Vegas it's the Cube. Covering VM World 2018. Brought to you by VM Ware and it's ecosystem partners. >> Welcome back to the Cube's continuing coverage of VM World 2018, I'm Lisa Marin, with Dave Vellante >> Hey, Lisa. >> Dave, day three, we have had tremendous guests the last couple of days. And we're- a lot of alumni, a lot of new guests, another alumni joining us, Calline Sanchez, vice president of IMB Enterprise System Storage. Welcome back, Calline, it's great to have you here. >> No, thank you very much for letting me be here. >> And I want to congratulate Calline, because she was just named for the Tucson Hispanic Chamber of Commerce, 2018 Businesswoman of the Year. Just a few weeks ago. Amazing. >> Lovin' Tucson, by the way. >> Thank you. >> U of A. >> Yes. >> Bear Down. >> I appreciate the Wildcats reference, so, >> Haha. >> No doubt. And so, this Saturday, oh, I'm sorry. This Saturday, the first game, so- >> My daughter is a freshman at U of A, Hi, Pilar, I love you, baby. Good luck. You're going to crush it, I know you are. >> Haha. >> Dad of the year going on here. So, just before we get into all the storage stuff- >> Yeah. >> They're doing a, they're honoring you, just in about a month and a half or so, with this- >> Yeah. Yes, and I'm very excited about that. Just like you were saying with the community aspect, it's a high-touch award, and I was very thankful for it, because they gave me specific examples of, what I've done in Southern Arizona, in Tucson in particular, that they'll name. For instance, Excite for Girls, and things like that. >> That's awesome. >> Girls in STEM, right? >> Congratulations, that's fantastic. >> We need more inspiration, so, it's great that we, >> Ah, thank you. >> Now count you as one of our distinguished alumni. So, let's talk about what going on at IMB. Here we are at VM World 2018, we're hearing Dave, numbers of upwards of 21,000 people that have been here the last few days. 100,000 more engaging with, expecting to engage with the live streaming and the on demand experiences. What's going on with IBM, you know, from a revenue perspective, a growth perspective? What is exciting you about where you are today? >> So, I will talk in particular about storage. I'm really, really proud about this, being that we work in partnership with, like, Ed Walsh, and then also Eric Herzog. They've inspired me to get closer to building solutions with our end users. So we meet and work with our clients to build up cloud deployment solutions, in partnership with VM Ware, and we enable things like, okay, so there's tape, and then there's cloud-to-tier, so there's fundamental solutions out there in the marketplace that we as developers want to go and play with. It's almost like a great big sandbox. So to speak. >> So, I've got to ask you, because, I mean, everybody in storage says, well, Tape, tape is dead. And every time I see you we talk about tape. We talk about FLAPE. We talk about innovations that are coming to tape. You're a technologist. Right, you just said, as a developer we love to- dot-dot-dot. So, what is it about things like tape, things like mainframe, DS8000, these technologies that have, tried, true, running businesses, what is about those that excite you as a developer? >> Everything old is new again, >> Yeah, right. >> If we just go back to the basics of like, table stakes, right? Security is table stakes, right? Delivering on-time quality releases with optimizers like, tier-to-cloud, things like that. That's fundamental for us. Now, as it relates to tape, so, everything old is new again, like I mentioned a moment ago. Tape was the first device to fully encrypt. So every drive, if it fell off the truck, it was fully encrypted. So, tape is actually training the rest of our portfolio in similar skills, on how we do the end-to-end encryption elements. So, right now with DS8000, we're working in partnership with system Z, to deliver pervasive encryption. >> I got to ask you, so as a development executive, I see you at a lot of these shows. You like coming here? A lot of times, development execs want to, sort of, stay in the lab. But you're out and about, talking to customers. What are you learning? What is that about you that draws you to these shows? >> I was afraid that WE as a lab team would not be relevant unless we have conversations with end users, partners. You know, to really substantiate what's possible from being innovative. So, I would say, number one is relevance, and I felt like, I wanted to more social, because, I'm definitely in some cases, an introvert, though I'm looking above my shoes. That's I'm wearing- >> That's the definition, of an introvert and an extrovert in the tech world. You know that the difference is, right? >> I don't. >> An introvert looks at his or her own shoes, an extrovert looks at your shoes. >> Well, there you go. I've been looking at some shoes- >> Alright, so you're, you're extrovert oriented out here, what are you learning at VM World? what are the customers saying? What are they asking you for? What are you going to take back to the lab? >> A single pane of glass associated with what we intend with like, v-stream, or some of the aspects of automation, with regards to cloud deployment, to make it, like, completely- connected. If that, so to speak. And what I think is really great about all of that is I hate to put it this way, it's very iTunes like. Where it's like, sticky, and it's easy to use, or and, by the way, it's not so expensive, at least to start up. So, a lot of the discussions we've been having are with the various vendors on the expo floor, that they want to build solutions. IBM solutions associate with the cloud, and then the AWS guys, we meet with them. And they're like, well, how are, how can we ensure that we live in an interconnected data-centric world? And so that's what I think is very exciting is that, it's this idea of coopetition. Let's all be well connected, and do it well. >> Let's talk about the customer collaboration, as you mentioned, everything old is new again, we see that, in every aspect of life. Tape, mainframe, but you talked about we need to be relevant, but also need to developing solutions that you end user customers need to solve their business problems. How are you collaborating with customers to stay relevant, and to ensure that their businesses are able to take advantage of the super powers that Pat Gelsinger talked about on Monday, AI, machine learning, emerging technologies, what's that collaboration like? >> I would say the biggest collaborations that I've been participating recently is with cloud servers providers. And they appreciate the economics of physical media, or tape. And so, they think to themselves or they know the data, it's like, okay, less than a half-cent per gig, that's a big deal, right? So, and then we have discussions about total cost of ownership, aspects like that. So the partnership is also, how do we serve the data? And really having discussions about the data. And then, if evaluating the various work streams where, we would want to serve appropriately based on whatever specific cloud infrastructure. And then, also, taking a step back, we have to be interconnected. There's no question. So, I would say the number one set of skills our end users are working with right now happen to be the cloud service providers. >> What are some of the big business benefits that they're achieving, we think, new business models, new revenue streams, market expansion. What are some of the things that you're proud of that IBM storage solutions are helping your customers to deliver? >> Going to tape, it's the economics, yes. It's the security based on encryption, yes. And then also, the other aspect of, is, we're serving big data. I mean, it's like we're having discussions about they're going to grow to, zettabytes by 2020, things like that. I never thought in my life, especially as an engineering student, or in computer science, I would ever be talking about this big of data. And now we're here. And so, we're learning how to enable in partnership with clients, what would be the right, or appropriate solution. >> So, I'm searching our video library, because somebody said this week something that was really interesting to me and I wanted to get your perspective from a development mind, someone who's technical. We're hearing a lot about migrating to the cloud. And how easy that is. And then, I think it was Pat Gelsinger said, there's three laws. There's the law of physics, the laws of a company, and the law of the land. And, those are immutable, generally. But I want to ask you about the laws of physics. So, in terms of just moving data into the cloud, we talk about petabyte, exabytes, there's so much data. How feasible is it for a customer to move data, and just stuff it all into the cloud, and what are you doing to either help them do that, or bring the cloud experience to their data? >> Depending on the client interests of on-prem, off-prem, or hybrid, right? We work to evaluate APIs in collaborations, so we enable a streamline, so it's not only just understanding the components of the cloud deployment, but it's also partnering with all elements of the entire ecosystem's stack. So, it depends but we really start with the client's end use case. What do you want? What kind of security do you want? Are you okay with off-prem, public clouds? Or, maybe it's specific data, how do we go about managing the data so we secure it, like, we bucket-ize it. So those are some of the discussions we've been having on the floor, here, at VM world, but also, within our labs, and also with the clients directly. >> You know what I love about that answer? I'll translate it. It's not a biz- it's not a technical problem, Dave, it's a business problem, >> Yes. >> Is really what you're tell me. >> And that's a fundament- you asked the question before. That's fundamentally why I am here. >> Right. >> I don't believe we can live in this world anymore, where it's like, we build it, and then they come. >> Field of Dreams does not exist anymore. >> Yeah. And so, now we've got to have conversations with our end users, to develop, what we've going to put on the roadmap. And so I always felt like, okay, well, when I'd see the roadmap in the lab, I'm like, okay, well, who wants this? Who asked for this, right? And those ended up becoming some of my fundamental questions. So then, I started to come here, or conferences like this, because I could have those conversations with the end users and partners. >> That's interesting, who wants this? Who needs this? What problems does it solve? Why us, why now? Those are the kinds of things you're asking. >> Let's talk about why us? IBM has been around for a very long time. What do you think, again, in this got to be relevant, we need it to be really defined by customer needs and uses. Everything old is new again. What, in your opinion, makes, why should a customer go, in my VM environment, IBM. >> I'm going to start with why I even personally want to remain with IBM. It's a great big candy store. >> Haha. >> And what I have to remind myself is, just don't eat too much, right? And, by the way, I still eat way too much. But what's great about it is, it's a sandbox, so, I can talk to you software engineers one day, who are telling me about certain APIs they're building in Python. Then, oh, by the way, I meet with a mechanical set of engineers, cuz they want to enable robot arms. Oh, and by the way, should we have a discussion on microcode and firmware for the entire stack. So I take a step back, and I'm thinking, Wow- the only set of conversation I really prior was not having, is about services. And to me, services is like the wrapping paper, for a present that you're about to receive. And really understanding the overall, end-to-end stack infrastructure. So, I believe from an IBM perspective, it's the ecosystem. It's a great big candy store. Just don't eat too much. >> Haha. So, how do you spend your time? Do you spend your time thinking, collaborating with team on, architecture, on, vision, on, northstar, writing code. How do you spend your time day-to-day? >> Can I say, all of the above? And, the vast majority, right now, really just making sure we're relevant in the marketplace, so that we re-fresh the right amount of cycles. So, right now, what we're going to be doing in 2019, we're going to be talking about it right now. Architecting what the future looks like. And that's part of the reason why I'm here at VM World 2018, is I'm wanting to verify my roadmap. Am I taking the right approach with the extended team? Cuz it is team, and I work with them. These engineers and scientists are so right, and have great ideas. Let's just make sure they're great ideas that will keep us relevant and keep us paid. >> So, have you gotten that validation, in the last few days at VM World? >> Give me one more day. >> Haha. Well, Calline, thanks so much for stopping by and sharing. Not only what IBM is doing to continue to innovate and stay relevant, but also what's exciting to you- >> Yeah. >> About working for IBM, and again, Congrats on getting the award. >> Yes, and thank you very much for highlighting that, cuz it's, I'm very excited as just an individual, it's like, it was unexpected. >> Well, you're representing women in tech, women in STEM, it's awesome, congratulations. >> Thank you very much. >> We're really happy. >> And, by the way, I'll definitely reach out to your daughter at some point. >> Oh, great. >> Say, hey, let's go to a tailgate. >> Love it. >> I won't corrupt. >> Haha. Fantastic, Calline, thank you so much for your time. I'm Lisa Martin with Dave Vellante. We want to thank you for watching the Cube, we are in day three of our continue coverage from VM World 2018. Stick around, we'll be right back with our next guest.

>> Announcer: Live from Las Vegas, it's theCUBE. Covering IBM Think 2018. Brought to you by IBM. >> Welcome back to IBM Think 2018. My name is Dave Vellante and you're watching theCUBE, the leader in live tech coverage. Day three of our wall-to-wall coverage of IBM Think. IBM took a number of conferences, Interconnect, World of Watson, Edge, which was the infrastructure conference, brought them together. We're here to talk to Calline Sanchez, who is the Vice President of IBM Enterprise System Storage. Edge was your show? >> Yes. >> Dave: Welcome to the new world. >> Great! No, it's been exciting to be a part of the Think conference. >> Yeah. >> And what I think is great about it is we're talking solutions and the full stack. The full stack based on hardware, MinuteWare applications, software, all of the feeders associated with delivering end users a solution. >> Well, I was talking to Ed Walsh earlier actually yesterday he came on, we weren't talking a lot of speeds and feeds, even though he's capable of it. But he's was talking more about the adjacencies in IBM's businesses and Cloud, and artificial intelligence that are helping, sort of, uplift the storage business. I have observed that having been an observer of the storage business for years I've been hearing from big systems companies for decades that they're going to do that. They've had trouble succeeding but it seems like it's finally taking hold. What's your perspective? >> I would agree. So the good comments associated with Ed is, he's built a great team, we enjoy working together, he is fair, pragmatic in general. So we work to build collaboration within the IBM company to deliver solid solutions to end users, so, he's done a great job. >> So, you guys have reported four straight quarters of growth, not just like, half a percent growth either, some high single digit growth in some cases. What are the factors that are driving that? You mentioned, sort of, teamwork, culture, leadership. I'm sure there's some product stuff too. From your perspective. >> Yes. >> What's driving that? >> So. I actually, within our, my portfolio I partner with Jeff Barber on is, like, DS8000 Enterprise storage and we see significant growth in that area based on our focus on flash and our investment with regards to flash optimization. The other aspect to really highlight is, what we're doing in tape, and I know we've talked about tape before. >> Tape? >> Yeah, I know. >> Come on. Alright let's talk about tape. >> Alright, well there's two components in that tactically we're about to deliver a drive that's about the size of my hand that is called the LTO8, it's part of the LTO line. 12 terabytes for rawest capacity. >> Yeah so tape is interesting. I mean the investment that used to be, you know back in the 80s, disc drive investments, all the VCs were pouring money into disc drives and heads and media and a lot of those investments have dried up. You're not seeing the same types of investments. Tape, it's easier to do sort of funky things. Multiple heads, drive super high bandwidths, you know do some sort of anticipatory indexing. >> Calline: Yeah. >> Where do you see the use cases for tape? It got blown out of backup. Where is it being used today? Is it archiving? Is it media? You know the NAB show's coming up. Probably see a lot of tape there, but where are you seeing momentum for tape? >> So you are correct from a media and entertainment perspective in A/V, that's a great industry we partner with. A few years back for LTFS, now Spectrum Archive rebranded as part of the Spectrum family, we won an Emmy. That's like... >> No kidding. I didn't know that. >> Yeah we won an Emmy so it's great in partnership with media and entertainment. We're relevant there and our technology was relevant there. Now the other area for significant growth, which helped feed those four quarters you referenced before is what we're doing with cloud service providers. We're relevant from a hardware infrastructure perspective based on tape. Tape is cool again and there's a lot of companies worldwide who really believe that because it's all about big data storage for the right economic price as well as energy efficiency. >> Well the gap between cost per bit for disc and tape is still enormous. >> Calline: It is. >> Tape is much, much, much cheaper and that's not going to change any time soon right? >> That is correct. It is much cheaper. So I'll give you an example. So basically less than a cent per gig per year. Now, I would actually even say it's less than a half cent. So it's just the economies of it. So a lot of what we do in talking about tape is the value from a cost perspective and the value you can provide a client where it's like hey they have big data, we can help serve it and we do that with tape. >> But is it, Calline, is it the sleep at night factor? Like okay, I'm going to put it in tape. Hopefully I never have to recover from it but it's my last, my media of last resort. I'm in compliance if I put it there. Is that right? Or are people actually recovering from tape? >> Yes, both. >> Yes, okay. >> So we're recovering from tape based on worth fundamental tertiary storage for some of these enterprise clients where I have to discuss like tier management across primary, secondary, and tertiary storage. So people think tape classically is an archive. Well actually there's use cases that are fed by tape that can attach all the components of tier management so I think it's more, it's more than just archive. It's big data. >> Now let's talk about cloud. I thought cloud was going to take the on prem business and wipe it out. What happened? >> Well it depends. That's what I like about IBM's perspective is hybrid. So we can serve both private as well as public clouds. And we also focus on optimizers. And what do I mean by an optimizer? For example, DS8000 in 2017, we delivered transparent cloud tiering which allows you to basically take a primary device and treat every other storage component as a target to like push data. Oh, by the way, you can push data to whatever cloud store in the sky that would be public or in some cases private. Based on security requirements associated with enterprise clients. >> So the criterion is largely security not performance is that right? Or both? >> Both, it's a combination. And it really depends on the use case that a client comes to bear or talks to us about. >> So I forget what you call it, but you guys had, early on, you had some automated capabilities and did some magic heuristics to match data and device characteristics to put the right data on the right device. And you've extended that to cloud is that right? So it's like policy based. >> Yeah. See, you are correct so what you were referring to is easy tier management. >> Easy tier, right. >> So easy tier allowed you to move data to like a hotspot. Think of it as like a temperature reading. If it's hot data, it stays on flash or media types like that. If it's cold data, it goes off to ship off to cheap disc or possibly tape. Now our extension to that is transparent cloud tiering. >> I remember when you guys first announced easy tier. I'm thinking about it now. I talked to some customers and they said eh, you know I want some knobs to be able to turn. I like to be able to manually move things around. And then this sort of machine intelligence wave comes through and people whose primary expertise was loan management realizing that that's probably not the best career path for them. So have you seen customers become much more comfortable with that automation? >> Yes. There is an autopilot mode with regards to data management. But for some enterprise clients, I'm going to steal your word. They have to feel comfortable. They have to see that the right data was moved to the next tier and it's being managed appropriately. So some people like to like for instance your temperature reading in your house. Some people like that your dial is like 72.3, right. And you just know that temperature, right. Which is mental, right. Though so clients were like that before, but with this idea of efficiency, and we talked about flash efficiency based on one of our last interviews is that it gives you more time. More time to think about other things. And so easy tier provided us the capability, especially if you go autopilot. Those end users can think about something different within their data centers to manage things differently, more efficiently. So it gives you time. And all I know is every Christmas, I pray to the lord that I want 25 hours in a day. >> Yeah. So hear hear. So the storage industry, for years, has been famous for doing more with less. You know constantly taking cost out. Guys are whipping boys of customers and just squeezing every dime out of you as possible. You made, IBM's made a lot of statements about Moore's Law, Moore's Law is you know waning, it can't be as aggressive anymore. Got to play different tricks. How has that applied to storage? How do you keep wringing costs out of storage? >> So I fundamentally believe everything old is new again. So we have to pay attention the history or the legacy to really determine what the future roadmap is. And so what's nice that we partner with Ed Walsh on is talking about our building materials across our entire solutions set. And insuring we provide for exceptional efficiency. We definitely want, within IBM, to be the Toyota production system for storage. >> So, reminds me you say everything old is new. Or new is old. I remember a head of IBM storage one time who didn't know anything about storage. He admitted I don't know anything about storage, but I know this. It needs to be lightning fast, rock solid, and dirt cheap. Has that changed? And what's new in storage? >> So no it has not changed, right. Though what we've been talking about is some really dirt cheap technology with regards to like tape, right? And last I checked, less than a cent per gig per year for storage management? That's huge right? So that helps the wallet. But at the same time, there's some new future items like we're wanting to play in the nanotechnology space. Specifically to partnering with Sony, Sony Media with regards to sputter media. So what people can go out and see when they have time is watch YouTube videos about what sputter media is about. Now, some of the deployment associated with sputter media was 220 terrabytes for a single drive. That's our goal. So when clients come to us and say hey we want to serve or be served with data capabilities of like two x per year, we're at a point where we're going to blow their socks off because we're going to have an offering on the table tactically to be north of 220 terrabytes per drive? Pretty exceptional. >> What are some of the other kind of cool techs that we should be watching? I mean we've seen advancements in file systems, obviously saw the Hadoop and big data craze. Flash has completely changed not only storage, but application development. You really couldn't be doing all this AI stuff without flash storage. NVME, NVME over fabric is coming in hot. You guys have done things like cappy to get sort of atomic rights. >> Yes. >> And capabilities like that. Again, game changing geeky things that have business outcomes that completely change the application development paradigm. What should we be watching for from IBM, some of the cool tech? >> So the other aspect that you've asked me in a prior conversation is about quantum computing. So we just need enough bits so they store those bits on us. So those are some of the early discussions about how IBM storage is going to play in quantum. >> Yeah, you've got some cool demos here on quantum. It's kind of blow your mind demos so check those out. Calline I'll give you last word. IBM Think, put a bumper sticker on it. >> So, tape is not dead, it's sexy. And then also this other aspect of, I don't know, we can grow and so IBM storage is where it's at. And that's the reason why I remain here. >> Tape is sexy. Tape is big and sexy. >> I know, big and sexy. >> Calline thanks very much for coming back on theCUBE. >> Thank you. >> It's great to see you again. >> It's great to see you. >> Alright keep it right there everybody. We'll be back after this short break. (upbeat music)

>> Narrator: Live from Las Vegas It's The Cube Covering VMworld 2017. Brought to you by VMware and its ecosystem partners. >> Hey, welcome back to The Cube. Continuing coverage of VMworld 2017. Day two of the event, lots of exciting conversations that we've had so far. I'm Lisa Martin with my cohost Dave Vellante-- >> Hey. >> Hey! We're excited to be cohosting together, right Dave? >> That's right. >> Of course! And we have Cube alumni Calline Sanchez, Vice President of IBM Enterprise Storage Systems. Welcome back to The Cube. >> Thank you for inviting me. It's always great to have discussions with you. >> Yeah. So, talk to us, we're at VMworld day two, what's new with IBM and VMware? >> So what was great about working, or walking through the expo floor, is hearing conversations about data backup, like as they say with the IBM Backup Bar-- >> It's hot! >> They have, and also this idea that we work to optimize data within the entire stack. So yeah, you have your base infrastructure, but you layer on top of that things that support the digital experience. >> Why is backup so hot? Why now? >> Well, so my favorite reason is because of tape. Tape allows you to cheaply store data, so it's like about a cent per gig. That's a big deal. And I don't know, I suspect you like really good deals on shoes, bags, et cetera, I know I do. So that's what's great about tape, is it's cost effective as well as it's a high performer, high capacity element that we intend to deliver. >> OK, so I buy that. I've always been a fan of the economic argument for tape. Let me ask you another question, and see if you see this, Calline. It seems like when virtualization came into vogue, people had to re-architect their backup for a variety of reasons, less physical resources, et cetera. Is cloud affecting the way in which people think about backup and if so, how? >> So we support cloud service providers. As they say with tape, if you're cost effective and you can meet certain performance and capacity requirements well, you usually are part of the stack associated with the delivery into the cloud service provider data centers worldwide. So all I'm saying is that it's relevant, it's important that we continue to innovate, associate with what's required with regards to tape. >> Well, while we're on the subject of tape, let's carry that through. The conventional wisdom from the spinning disk and now the flash guys, oh, tape, tape is dead, I've been hearing tape is dead since I've been in this business, which is now quite a long time. What's kept tape alive, it's obviously the economics, but it's got to be more than that. It's got to be easier to use, it's got to be functional, what kind of innovations have occurred around tape to make it continue to be viable? >> So I would say our focus on enhancing spectrum archive. It used to be called a linear tape file system, and really it's this idea of a USB for file access or data access. So we keep working on focusing and delivering data access patterns that are actually efficient for our clients, simple to use, and we enable automation, which has been something that's great based on Ed Walsh's focus or strategy for our storage portfolio, and I know you've just heard that we had two awesome growth quarters within IBM Storage and our goal is to continue that through modernizing our entire portfolio. >> Three would make a trend, I told Ed. And he's like, "Come on, gimme a break." No, but it is awesome to see IBM's storage business growing again and hopefully that can continue. >> So speaking of innovation, and you talked about tape and people think tape's been dead for a long time, but you're talking about it as a core component of cloud strategies for businesses. How has IBM evolved your messaging, your positioning, as technologies have evolved and customers are now going, "We have to keep a ton of data," Michael Dowd talked about the importance of data today being at the CEO agenda level. Talk to us about how some of the innovations IBM is doing to help customers understand the relevance of different types of storage according to data growth but also going from data centers to centers of data. >> Great question. So, one thing that's really interesting, being that I'm from the lab, we have delivered, or our intent is to accelerate the entire roadmap as it relates to tape, so that we stay ahead of the delivery path and meet the requirements based on clients worldwide whether they're scientific clients based on some of the advanced data that is required, as well as cloud service providers. They say, "Hey, we're expecting you to innovate "and deliver as quickly as possible." And sometimes it's like the requests are quite interesting and fascinating based on just even the digital or the analytics of measuring like, temperatures in data centers. And what we're doing with Rocky interface based on ethernet interfaces. The clients are pushing us with regards to improving overall and delivering to meet the cloud economics that they require, as well as the attributes of. >> What's changed at IBM, if anything, I'm inferring something's changed because I've always said, one of the criticisms I've had of IBM storage is the pace with which it was able to get products out of engineering and to the marketplace, and that pace has accelerated quite dramatically. I don't know if it's new leadership, you mentioned Ed Walsh before, or there's been a change in the philosophy, am I dreaming or have I noticed-- >> No, you're completely accurate. So when we're talking about development or delivery, we're so much more agile that we really work to reduce the complexity of delivery, and we're delivering major functions or complex things to more simple, and getting client input sooner, and partner input sooner than later. Where as previously, it was like we worked for over a year sometimes on technologies or advancements and it would take a while for those clients to then adopt. Now, we have to deliver something a heck of a lot faster than we had done before. >> And are customers part of that innovation process? It sounds like-- >> They are. >> That's been a big change-- >> So we're big. Historically, we always talked about betas. Now we're talking about alphas, and some of these original demos in order to grow our understanding of the use case in the very early phases. And usually we did not have this type of discussions prior, at least in my experience, but now it's like it's a requirement. So, with new leadership is a component, as we discussed, but also this idea of really focused agility. Delivering to the marketplace faster, listening to our clients, so that means improvement based on how we go to market as well. Because it's important that we deliver value to our clients or we're not relevant. >> We were talking earlier to another guest, a competitive company, and we were talking about the anatomy of a transaction, and we were going through it and at one point he said that it hits a mainframe in an associated database and he said, "And that's OK." So we know the mainframe, alive and well, we've done a bunch of Cube activities, we were there at the Z13 launch at the Jazz at Lincoln, which was a great event. >> That's awesome. >> And so, give us the update on what's happening there. You guys have made some new announcements there, new DS8000 class systems, new Z systems, what's going on at that transaction world? >> So I would say two, or actually three major things that are part of that announcement to collaborate with Z is improvement based on modernizing our service support structure, which is like remote code load, things like that, so that we can have experts remotely, via a control center, help clients load latest levels of code as well as new feature function. The second element that I would say is, lead with flash. So we've optimized flash storage that complements specifically some of the ZOS, the System Z workload, which is significant for us to deliver to the marketplace as well. And then third, is this idea of Z hyperlink. Z hyperlink is this idea of, like, synch iO. It's a different structure that, yes, it'll take a while for adoption, we have a number of our alphas that are working in partnership with us to solution. Well, we're going to be doing replication, and also some of the iO streams differently than we had in the past. >> Question for you on the alphas. >> Yeah. >> From a business perspective, since so much has changed, lots of announcements just in the last 36 hours, as technology changes rapidly and stop-run tech companies are, like you said, poised to deliver agility faster, when you're talking with alphas, as you said, kind of in the nascent stages of a use case being developed, what are some of the key business metrics that your alpha clients are articulating to you that, when we get to x stage of this alpha, we need to be able to demonstrate x, y, z back to the business, thinking of cost reductions, resource allocations, faster time to market, what are some of those business KPI's that you're hearing from your clients? >> Yeah. So I would say it's price performance as well as capacity based on the amount of data growth. So those three things are fundamental components that come up quite often. Now, it usually is made very clear to us that things like security, like quality, that's job one. That's table stakes. Like, if we want to have fine dining, we'll just assume there's going to be this nice handkerchief as well as tablecloth. Well, security and quality are just fundamental. So they want to think about those things less. Because they're just naturally being delivered via whatever technology we're putting out or delivering from the lab. >> Alright, let's bring it back to VMworld. We're here. VMware, VMworld, what do you guys got going here, what's the relevance of all the activity that you have going to this event? >> So what's great about the event is we have the data backup bar that's associated with what we're doing with Spectrum Protect Plus. What I personally like and love about the Spectrum Protect Plus is simplicity. It's delivering this idea of usability. Which is important because we received feedback from our clients in very early stages on how we deliver. So we have a data backup bar to discuss some of that technology and actually run through specific downloads which I think is great, cause you get feedback out on the floor immediately to ensure that we're improving. The other aspect of our booths is discussing things, some of the fundamental infrastructure just like we talked previously on tape, as well as DS8000, cause DS8000 is not only a mainframe attach, but it's attachment agnostic. So we support aspects of distributive storage as well. For instance, we have some of the VMware enhancements that will allow us to more efficiently capture or reclaim data in thin provision volumes, and VMware has been fundamental in partnering with us to deliver. >> So continue to go to market approaches with VMware on the backup side, also on the cloud foundation side for IBM? >> Yes. >> Excellent. Thank you so much for stopping by The Cube again and sharing your thoughts and what's going on with the industry and how IBM is moving forward with respect to innovation and working with clients together. >> Right. Wonderful. Thank you. >> For my cohost Dave Vellante, I'm Lisa Martin. >> Stick around, you're watching day two of The Cube's coverage of VMworld 2017, we'll be right back. [Upbeat Synth Music]

(upbeat techno music) >> Announcer: Live from Las Vegas, it's theCUBE covering InterConnect 2017, brought to you by IBM. >> Okay, welcome back, everyone. We are here live in Las Vegas for IBM InterConnect 2017. This is theCUBE's coverage of IBM InterConnect. I'm John Furrier, my cohost, Dave Vellante. We have Calline Sanchez, Vice President of IBM Enterprise, Storage Development at IBM. We had an interview at VMworld last year about tape, making tape cool. Great to see you again. >> Thank you. Thank you for welcoming me back, so I guess I wasn't too bad last time. >> No, you're good. >> Calline: Right? >> We love tapes. The tape culture out there, there's a tape community. >> Calline: Yes! >> Tape has been dead forever. It's going to die this year is what everyone always predicted. They're going to die next year. It never dies. Tape it always around, and Dave and I, you know, we see this all the time. >> Calline: Yeah, it's back. >> It's cool. It's relevant, and it's the east expensive storage... >> That is correct. >> Out there. So what's the update? What's cool about tape this year? >> So, I think when I was speaking to you earlier, you talked about flape, what we're doing with Flash and actual tape. So in partnership with our micro-coders, our engineers and scientists we partner with in Tucson, Arizona, with a team in Zurich in research, to really figure out what we're doing with flape. And by the way, flape is a cool name, right? It's a very developer name. >> Well, you know, Wikibon coined that term. That was David Floyer. >> Oh, really. >> The Flash plus tape. Yes. But the premise was that there's not a lot of innovation going on in disc drive heads. >> Calline: Correct. >> And they're hermetically sealed, whereas in tape, you can do a lot more, more bandwidth, and you can do some cool stuff with search, right? >> Yes. >> And new tape formats. >> Calline: Right. >> Right, so that's all coming together, and are you... Is there software now associated with that index so you can more quickly search? >> So we have created a management layer that supports what we intend with flape, and also across the tape portfolio to really consume applications at a higher level to enable what we need to do with our consumability, not only from a tape perspective, but also with Flash. >> Right, so the economics really still favor tape. Flape, Flash supports the speed, so it starts to encroach on some of that long-term archiving. >> Which is important based on archive, 'cause, well, aren't we all data hoarders? We like to keep our data and archive it and stage it off, whatever it is. It could be based on what we're doing with tape and also, you know, hard disc drives. Some clients that I work with substantiate archive data to cheap drives as well. So hopefully, eventually, the transition will be to enable what we want to do with Flash, once Flash of course is a cheaper or a stronger price competitive thing. So, bridging though to, from our last conversation, believe me, tape is sexy, so I'm telling the audience here, it's like, hello, if you're not talking about tape, well, where have you been? But at the same time, I want to talk about Flash, and what we do with DS8000. So we have an enterprise monolithic system that covers like six nines of availability, that substantiates what we do in the Flash market, and we just recently announced, from enterprise down to entry, so mid-range as well as entry devices, that are all Flash. So we care more, from an IBM perspective, on what we're doing associated with the Flash investment. Friends of mine like Erik Herzog, I'm sure, get on stage with you to talk about like, IBM is focusing on Flash. It's relevant to us. It's relevant to our clients. >> And the software, too. Very software-driven. Flash is key, but you're bringing this capacity at this... What was it, six nines? >> Yep. >> I mean... >> It's reliability. >> I mean that's just like, just dump all the data. That's a perfect scenario. >> Yes, and it's a beautiful thing. In addition to what Flash does, from an engineering perspective... Forgive me, I'm going to be a geek for a moment, is that it allows us to in the lab to focus on other things, so basically, that latency or the chase for performance equals more, more meaning that we can focus more on what it means to develop optimizers, like, for instance, EasyTier, et cetera, to really enable a better benefit. Also some of those engineers and scientists allow us to focus more on flape as well. >> So explain that concept. Okay, latency equals more. What specifically do you mean, like the latency on the devices, \the data movement, just double down on that for a second. >> So, from a performance perspective, we have to work around bottlenecks. That was where our focus was, but now, with Flash, we worry less about those individual components from a reliability perspective as well as chasing latency or performance measurements based on IOPS, and in order to do that, we don't have to worry about it as much anymore from an engineering standpoint. So it allows us the time to really focus on what matters next, like the value that we could think of that we could benefit clients with regards to advanced technologies, technologies of value. >> Like what? What does that free you? It liberates both creativity... >> Calline: Data and analytics. >> Really, okay. >> Calline: So like, for instance, the expo floor associated with Interconnect, you meet all these people that talk about how data matters. Well, it's the intelligence around data, and so we want to figure out how to harness that data and drive out the intelligence so that the smarts associated with the data, and that's what it allows us to talk about. >> Yeah, so let's keep on that theme of business impact, we were talking about latency before. Everybody knows Flash is fast. You're implying, or I'm inferring from your statements that it's still more expensive. >> Calline: Correct. >> However, you got data reduction technologies and you have this data sharing notion. In other words, I can share much more data with the same copy out of a Flash. That has an impact on developer productivity, which ripples through on innovation. >> Calline: Yes, correct. >> Are you seeing that have business impacts within your client base? >> Yes. So, for instance, at first, we started to talk about, from an engineering perspective, compression and deduplication, how to be much more efficient with that data storage. And then afterwards, we started to talk about, well, you know, we had to move quickly to serve our clients associated with those feature functions, and now we're about talking about how we harness or archive, you know, how we enable big data, and also the IT aspect, the intelligence of the data, and how we can translate that to improving client value. For example, I just saw on the expo floor, partnered with Glassbeam, and with the... I did basically a meeting with Glassbeam on the floor to talk about what we've done with them in partnership to harness the power of data. >> Dave: What is Glassbeam? >> So Glassbeam is basically makes sense of the mess of data that we just have out there and makes it much more intelligent. So, it allows their system, their algorithms, to ingest data and better understand where we're at with that data, no different than what we do with Watson as well. So, that, from a Watson perspective, you ingest the data and you can provide additional smartness about that data as well as the intelligence of. >> And what kind of data are we talking about here? Structured data, unstructured data? >> Structured data, and specifically associated with Glassbeam, it was all about really bringing in the plumbing of data for our clients worldwide. So, clients experience our systems worldwide associated DS8000, and we wanted to better be in a position to serve our clients adequately, and what I mean by that is they could have an error that occurred or we want to be proactive with them based on Call Home, and also some of the heartbeat information we get based on the systems, and we want to adequately share with them that. So, you as a client could, I could send you a really beautiful, simple email or communication, maybe it's a tweet that basically says, hey, there's something we're worried about, and we've got to proactively address it, ASAP. >> Well, and there's all kinds of metrics buried in those files, right? There's utilization data, there's data on the effectiveness of thin provisioning, you mentioned compression, deduplication... >> Calline: Yes. >> I mean, I don't know what else is in there. It's probably a ton more stuff, obviously problems that occur. So have you been able to get to the point where you could be anticipatory and head off, you know, front run some of those problems? >> So our end goal is to build an autonomic system, an autonomic system that has the brain to self-heal, and that's what we want to focus on in the future. Now, are we there yet? No, we're not, but what we're doing with Watson or Glassbeam or some of these optimizers, these tools, to build better systems, it something that we're doing associated with building the future of an autonomic system. >> I mean, one of the things John and I have been talking about with this, you know, Jenny was talking about cognitive to the core this morning... >> Yep. >> And this cognitive world we live in. >> Just a whole new set of metrics emerging and KPIs. I mean, you mentioned self-healing. We still, to this day track, availability, and okay, the light on the server versus the application, things like that. >> Calline: Yeah. >> We see, and I wonder if you could comment on this, a whole new set of KPIs emerging from the infrastructure standpoint of, you know, what percent of the problems were self-healed... >> Calline: Yes. >> How can we affect that and increase that and what are we doing with that free time? Are you hearing from that clients, that they're changing or adding to the metrics, KPIs that they're entering? >> Yes, so first, am I hearing from clients on that? Yes. So it's always these questions of like, okay, so from a cognition perspective, cognitive focus, what are you going to do to help us to self-heal as well as how do you build in the intelligence based on artificial intelligence to really self-heal, and that's one of the focuses we're working on. >> So what's the coolest thing happening now, 'cause last time, I loved the conscious we had about capacity and stuff that I learned was all the engineering, just to squeeze more out of... 'Cause the tape is a great thing, but reliability is killer. You got some great reliability, so it's a good solution, but there's always the engineering side of it that's science. What's going on that you guys are kind of digging away at, pounding away at for tech that people might not know about for tape? >> So, using the cognitive systems or AI as the foundation, we're thinking about how to build in intelligence within our systems, and the way to do that is the reason why I keep focusing on this word, autonomic. How do we build a true autonomic system? It's almost like a system that has its own brain, right? And that chip set that exists inside associated with DS8000 is like power devices, right, whether it's six-core, eight-core, whatever, how big of a brain do you want is kind of a discussion to have, but what's important about that is we really want to figure out how to be smart enough to self-heal, and we don't know how to do that just yet, and it's going to be, just like you had mentioned, all this information and pulling it in to really determine how we go about doing so. >> So that's kind of near-term, those are sort of... Maybe in the binoculars you can start to see how you can utilize analytics and cognitive to do some of that self-healing. I wanted to ask you a sort of telescope question. We heard Jenny talk today about quantum. What are your thoughts on that in terms of the implications for storage? >> My thoughts on quantum. So first of all, let's figure out how to harness the science of quantum computing, right? So that's the first fundamental step, like, I don't know, first step of the twelve-step problem, realizing you have a challenge, right? (Dave laughs) So, from that, it's like really realize that and recognize that, and IBM is working on what we're doing with quantum computing. As far as how it relates specifically to storage, so, we think it could be a benefit with relates to DS8000 tape as well, because think about it. Tape, as far as the library side, that's what we did is we built out infrastructure that really harnessed this aspect of data and did it in the cheapest way possible, energy-efficient way possible, so I think quantum, from our perspective, is like a leapfrog into the future of what we enable with some of our thinking there. And Jenny and team as well as her senior leadership are influencing how we should think about quantum computing as it relates to storage. So, I say the next time that we meet, you should probably ask that question of me again, like how far along are you? >> Dave: Deal. >> Step one and a half or two of the twelve-step program? >> I would say one and a half. >> Dave: Go ahead, sorry. >> No, go ahead. >> I wanted to ask you about when Ed Walsh took over. >> By the way, I like the two of you competing on questions. (all laugh) >> We both like to talk. >> We can't get enough tape. (all laugh) >> We have tape everywhere, look at it. Taping down the lights... >> So, here's my question, Calline. So when Ed Walsh took over the GM of the Storage Division, I asked him this. IBM's always had a rich heritage of R&D and development. However, my comment was, sometimes it was sort of development for development's sake, and I feel like, and he sort of said this. One of my missions is to get, you know, align engineering with, you know, go to market, get stuff out of the pipeline, into the market sooner. From an engineering perspective, have you guys begun to do that? What changes have you affected? Are you seeing the effects of that sort of initiative? >> So, when have an agility process within IBM Development that was, basically Ed Walsh was a huge advocate for that, supported it, and his intent is for us the push all of this wonderful IP that we build in-house to the marketplace as quickly as possible. So I say at this moment, we're there. I just, right now, he's, in the nicest way possible, and the most charming way, telling me, it's like, you're not fast enough. (men laugh) Right? And that's a good thing. That means that there's more innovation, more intellectual property we can put into the marketplace, faster, quicker whatever that means, in larger increments, versus it being me... Previously, I would tell you, it's like, so DS8000, I may deliver that to you, target-wise, 12 months from now. That's not good enough anymore. >> So Ed's coming on tomorrow, so we'll ask him how Calline's doing maybe. (all laugh) We'll put him on the spot and you on the spot at the same time, if you don't mind. >> Oh yeah, no problem. >> Calline, it's always great to chat with you, love these conversations, thanks for coming on theCUBE, sharing the insights on the tape, the DS8000. Appreciate it. >> Thank you very much. >> And it's theCUBE live here in Las Vegas for IBM InterConnect. I'm John Furrier with Dave Vellante. You're watching theCUBE. Stay with us, we've got more great interviews for the rest of the day and all day tomorrow. We'll be right back. (upbeat techno music)

>>Um, Jamie Sharath with Liga data, I'm primarily on the delivery side of the house, but I also support our new business teams. I'd like to spend a minute really just kind of telling you about, uh, uh, legal data where basically a Silicon valley startup, uh, started in 2014 and, uh, our lead iron, our executive team, basically where the data officers at Yahoo before this, uh, we provide managed data services and we provide products that are focused on telcos. So we have some experience in non telco industry, but our focus for the last seven years or so is specifically on telco. So again, something over 200 employees, we have a global presence in north America, middle east Africa, Asia, and Europe. And we have folks in all of those places. Uh, I'd like to call your attention to the, uh, the middle really of the screen there. >>So here is where we have done some partnership with Cloudera. So if you look at that, you can see we're in Holland and, uh, Jamaica, and then a lot to throughout Africa as well. Now the data fabric is the product that we're talking about. And the data fabric is basically a big data type of data warehouse with a lot of additional functionality involved. The data fabric is comprised of, uh, some something called flare, which we'll talk about admitted below there, and then the Cloudera data platform underneath. So this is how we're partnering together. We, uh, we, we have this tool and it's, uh, it's functioning and delivering in something over and up. Oops. So flare now, flare is a piece of that. It's legal data IP. The rest is Cloudera. And what flare does is that basically pulls in data and integrates it to an event streaming, uh, platform. >>It, uh, it is the engine behind the data fabric. Uh, it's also a decisioning platform. So in real time, we're able to pull in data. We're able to run analytics on it and we're able to alert our, do whatever is needed in a real-time basis. Of course, a lot of clients at this point are still sending data in batch. So it handles that as well, but we call that a cut off picture Sanchez. Now Sacho is a very interesting app. It's an AI analytics app for executives. What it is is it runs on your mobile phone. It ties into your data. Now this could be the data fabric, but it couldn't be a standalone product. And basically it allows you to ask, you know, human type questions to say, how are my gross ads last week? How are they comparing against same time last week before that? >>And even the same time 60 days ago. So as an executive or as an analyst, I can pull it up and I can look at it instantly in a meeting or anywhere else without having to think about queries or anything like that. So that's pretty much for us legal data. Now, it really does set the context of where we are. So this is a traditional telco environment. So you see the systems of record and you see the cloud, you see OSS and BSS day. So one of the things that the next step above which calls we call the system of intelligence of the data fabric does, is it mergers that BSS and OSS data. So the longer we have any silos or anything that's separated, it's all coming into one area to allow business, to go in or allow data scientists go in and do that. >>So if you look at the bottom line, excuse me, of the, uh, of the system of intelligence, you can see that flare is the tool that pulls in the data. So it provides even screening capabilities, it preserves entity states, so that you can go back and look at it to the state at any time. It does stream analytics that is as the data is coming in, it can perform analytics on it. And it also allows real-time decisioning. So that's something that, uh, that's something that business users can go in and create a system of, uh, if them's, it looks very much like a graph database where you can create a product that will allow the user to be notified if a certain condition happens. So for instance, a bundle, so a real-time offer or user is fixing to run out of is ongoing and an offer can be sent to him right on the fly. >>And that's set up by the business user as opposed to programmers a data infrastructure. So the fabric has really three areas. That data is persistent, obviously there's the data lake. So the data lake stores that level of granularity that is very deep years and years of history, data scientists love that. And, uh, you know, for a historical record keeping and requirements from the government, that data would be stored there. Then there's also something we call the business semantics layer and the business semantics layer contains something over 650 specific telco KPIs. These are initially from PM forum, but they also are included in, uh, various, uh, uh, mobile operators that we've delivered at. And we've, we've grown that. So that's there for business. The data lake is there for data scientists, analytical stores, uh, they can be used for many different reasons. There are a lot of times RDBMS is, are still there. >>So these, this, this basically platform, this cloud they're a platform can tie into analytical data stores as well via flair access and reporting. So graphic visualizations, API APIs are a very key part of it. A third-party query tools, any kind of grid jewels can be used. And those are the, of course, the, uh, the ones that are highly optimized and allow, you know, search of billions of records. And then if you look at the top, it's the systems of engagement, then you might vote this use cases. So telco reporting, hundreds of KPIs that are, that are generated for users, segmentation, basically micro to macro segmentation, segmentation will play a key role in a use case. We talk about in a minute monetizations. So this helps telco providers monetize their specific data, but monetize it in, okay, how to do they make money off of it, but also how might you leverage this data to, in, in dates with another client? >>So for instance, in some cases where it's allowed a DPI is used and the, uh, fabric tracks exactly where each person goes each, uh, we call it a subscriber, goes within his, uh, um, uh, internet browsing for 5g and, uh, all that data is stored. Uh, whereas you can tell a lot of things where the segment, the profile that's being used and, you know, what are they propensity to buy? Did they spend a lot of time on the Coca-Cola page? There are buyers out there that find that information very valuable, and then there's sideshow. And we spoke briefly about Sacha before that sits on top of the fabric or it's it's alone. >>So, so the story really that we want to tell is, is one, this is, this is one case out of it. This is a CVM type of case. So there was a mobile operator out there that was really offering, you know, packages, whether it's a bundle or whether it's a particular tool to subscribers, they, they were offering kind of an abroad approach that it was not very focused. It was not depending on the segments that were created around the profiling earlier, uh, the subscriber usage was somewhat dated and this was causing a lot of those. Uh, a lot of those offers to be just basically not taken and not, not, uh, uh, there was limited segmentation capabilities really before the, uh, before the, uh, fabric came in. Now, one of the key things about the fabric is when you start building segments, you can build that history. >>So all of that data stored in the data lake can be used in terms of segmentation. So what did we do about that? The, the, the MDNO, the challenge, uh, we basically put the data fabric in and the data fabric was running Cloudera data platform and that, uh, and that's how we team up. Uh, we facilitated the ability to personalize campaign. So what that means is, uh, the segments that were built and that user fell within that segment, we knew exactly what his behavior most likely was. So those recommendations, those offers could be created then, and we enable this in real time. So real-time ability to even go out to the CRM system, again, their further information about that, all of these tools, again, we're running on top of the cloud data platform, uh, what was the outcome? Willie, uh, outcome was that there was a much more precise offer given to the client that is, that was accepted, you know, increase in cross sell and upsell subscriber retention. >>Uh, our clients came back to us and pointed out that, uh, it was 183% year on year revenue increase. Uh, so this is a, this is probably one of the key use cases. Now, one thing to really mention is there are hundreds and hundreds of use cases running on the fabric. And, uh, I would even say thousands. A lot of those have been migrated. So when the fabric is deployed, when they bring the, uh, Cloudera and the legal data solution in there's generally a legacy system that has many use cases. So many of those were, were migrated virtually all of them in pen, on put on the cloud. Uh, another issue is that new use cases are enabled again. So when you get this level of granularity and when you have campaigns that can now base their offers on years of history, as opposed to 30 days of history, the campaigns campaign management response systems, uh, are, are, uh, are enabled quite a bit to do all, uh, to be precise in their offers. Yeah. >>Okay. So this is a technical slide. Uh, one of the things that we normally do when we're, when we're out there talking to folks, is we talk and give an overview and that last little while, and then we give a deep technical dive on all aspects of it. So sometimes that deep dive can go a couple of hours. I'm going to do this slide and a couple of minutes. So if you look at it, you can see over on the left, this is the, uh, the sources of the data. And they go through this tool called flare that runs on the cloud. They're a data platform, uh, that can either be via cues or real-time cues, or it can be via a landing zone, or it can be a data extraction. You can take a look at the data quality that's there. So those are built in one of the things that flare does is it has out of the box ability to ingest data sources and to apply the data quality and validation for telco type sources. >>But one of the reasons this is fast to market is because throughout those 10 or 12 opcos that we've done with Cloudera, where we have already built models, so models for CCN, for air for, for most mediation systems. So there's not going to be a type of, uh, input that we haven't already seen are very rarely. So that actually speeds up deployment very quickly. Then a player does the transformation, the, uh, the metrics, continuous learning, we call it continuous decisioning, uh, API access. Uh, we, uh, you know, for, for faster response, we use distributed cash. I'm not going to go too deeply in there, but the layer and the business semantics layer again, are, are sitting top of the Cloudera data platform. You see the cough, but flu, uh, Q1 on the right as well. >>And all of that, we're calling the fabric. So the fabric is Cloudera data platform and the cloud and flair and all of this runs together. And by the way, there've been many, many, many, many hundreds of hours testing flare with Cloudera and, uh, and the whole process, the results, what are the results? Well, uh, there are, there are four I'm going to talk about, uh, we saw the one for the, it was called my pocket pocket, but it's a CDM type, uh, use case. Uh, the subscribers of that mobile operator were 14 million plus there was a use case for a 24 million plus a year on year revenue was 130%, uh, 32 million plus for 38%. These are, um, these are different CVM pipe, uh, use cases, as well as network use cases. And then there were 44%, uh, telco with 76 million subscribers. So I think that there are a lot more use cases that we could talk about, but, but in this case, this is the ones we're looking at again, 183%. This is something that we find consistently, and these figures come from our, uh, our actual end client. So how do we unlock the full potential of this? Well, I think to start is to arrange a meeting and, uh, it would be great to, to, uh, for you to reach out to me or to Anthony. Uh, we're working in conjunction on this and we can set up a, uh, we can set up a meeting and we can go through this initial meeting. And, uh, I think that's the very beginning. Uh, again, you can get additional information from Cloudera website and from the league of data website, Anthony, that's the story. Thank you. >>Oh, that's great. Jeremy, thank you so much. It's a, it's, it's wonderful to go deep. And I know that there are hundreds of use cases being deployed in MTN, um, but great to go deep on one. And like you said, it can, once you get that sort of architecture in place, you can do so many different things. The power of data is tremendous, but it's great to be able to see how you can, how you can track it end to end from collecting the data, processing it, understanding it, and then applying it in a commercial context and bringing actual revenue back into the business. So there is your ROI straightaway. Now you've got a platform that you can transform your business on. That's, that's, it's a tremendous story, Jimmy, and thank you for your partnership. So, um, that's, uh, that's, that's our story for today, like Jamie says, um, please do fleet, uh, feel free to reach out to us. Um, the, the website addresses are there and our contact details, and we'd be delighted to talk to you a little bit more about some of the other use cases, perhaps, um, and maybe about your own business and, uh, and how we might be able to make it, make it perform a little better.

>>Hi today, I'm going to talk about network analytics and what that means for, for telecommunications as we go forward. Um, thinking about, uh, 5g, what the impact that's likely to have on, on network analytics and the data requirement, not just to run the network and to understand the network a little bit better. Um, but also to, to inform the rest of the operation of the telecommunications business. Um, so as we think about where we are in terms of network analytics and what that is over the last 20 years, the telecommunications industry has evolved its management infrastructure, uh, to abstract away from some of the specific technologies in the network. So what do we mean by that? Well, uh, in the, in the initial, uh, telecommunications networks were designed, there were management systems that were built in, um, eventually fault management systems, uh, assurance systems, provisioning systems, and so on were abstracted away. >>So it didn't matter what network technology had, whether it was a Nokia technology or Erickson technology or Huawei technology or whatever it happened to be. You could just look at your fault management system, understand where false, what happened as we got into the last sort of 10, 15 years or so. Telecommunication service providers become became more sophisticated in terms of their approach to data analytics and specifically network analytics, and started asking questions about why and what if in relation to their network performance and network behavior. And so network analytics as a, as a bit of an independent function was born and over time, more and more data began to get loaded into the network analytics function. So today just about every carrier in the world has a network analytics function that deals with vast quantities of data in big data environments that are now being migrated to the cloud. >>As all telecommunications carriers are migrating as many it workloads as possible, um, to the cloud. So what are the things that are happening as we migrate to the cloud that drive, uh, uh, enhancements in use cases and enhancements and scale, uh, in telecommunications network analytics? Well, 5g is the big thing, right? So 5g, uh, it's not just another G in that sense. I mean, in some cases, in some senses, it is 5g means greater bandwidth, lower latency and all those good things. So, you know, we can watch YouTube videos with less interference and, and less sluggish bandwidth and so on and so forth. But 5g is really about the enterprise and enterprise services. Transformation, 5g is more secure, kind of a network, but 5g is also a more pervasive network 5g, a fundamentally different network topology than previous generations. So there's going to be more masts and that means that you can have more pervasive connectivity. >>Uh, so things like IOT and edge applications, autonomous cars, smart cities, these kinds of things, um, are all much better served because you've got more masks that of course means that you're going to have a lot more data as well. And we'll get to that. The second piece is immersive digital services. So with more masks, with more connectivity, with lower latency with higher man, the potential, uh, is, is, is, is immense for services innovation. And we don't know what those services are going to be. We know that technologies like augmented reality, virtual reality, things like this have great potential. Um, but we, we have yet to see where those commercial applications are going to be, but the innovation and the innovation potential for 5g is phenomenal. Um, it certainly means that we're going to have a lot more, uh, edge devices, um, uh, and that again is going to lead to an increase in the amount of data that we have available. >>And then the idea of pervasive connectivity when it comes to smart, smart cities, uh, autonomous, autonomous currents, um, uh, integrated traffic management systems, um, all of this kind of stuff, those of those kind of smart environments thrive where you've got this kind of pervasive connectivity, this persistent, uh, connection to the network. Um, again, that's going to drive, um, um, uh, more innovation. And again, because you've got these new connected devices, you're going to get even more data. So this rise, this exponential rise in data is really what's driving the change in, in network analytics. And there are four major vectors that are driving this increase in data in terms of both volume and in terms of speed. So the first is more physical elements. So we said already that 5g networks are going to have a different apology. 5g networks will have more devices, more and more masks. >>Um, and so with more physical elements in the network, you're going to get more physical data coming off those physical networks. And so that needs to be aggregated and collected and managed and stored and analyzed and understood when, so that we can, um, have a better understanding as to why things happened the way they do, why the network behaves in which they do in, in, in, in ways that it does and why devices that are connected to the network. And ultimately of course, consumers, whether they be enterprises or retail customers, um, behave in the way they do in relation to their interaction within our edge nodes and devices, we're going to have a, uh, an explosion in terms of the number of devices. We've already seen IOT devices with your different kinds of trackers and, uh, and, and sensors that are hanging off the edge of the network, whether it's to make buildings smarter car smarter, or people smarter, um, in, in terms of having the, the, the measurements and the connectivity and all that sort of stuff. >>So the numbers of devices on the agent beyond the age, um, are going to be phenomenal. One of the things that we've been trying to with as an industry over the last few years is where does the telco network end, and where does the enterprise, or even the consumer network begin. You used to be very clear that, you know, the telco network ended at the router. Um, but now it's not, it's not that clear anymore because in the enterprise space, particularly with virtualized networking, which we're going to talk about in a second, um, you start to see end to end network services being deployed. Um, uh, and so are they being those services in some instances are being managed by the service provider themselves, and in some cases by the enterprise client, um, again, the line between where the telco network ends and where the enterprise or the consumer network begins, uh, is not clear. >>Uh, so, so those edge, the, the, the proliferation of devices at the age, um, uh, in terms of, um, you know, what those devices are, what the data yield is and what the policies are, their need to govern those devices, um, in terms of security and privacy, things like that, um, that's all going to be really, really important virtualized services. We just touched on that briefly. One of the big, big trends that's happening right now is not just the shift of it operations onto the cloud, but the shift of the network onto the cloud, the virtualization of network infrastructure, and that has two major impacts. First of all, it means that you've got the agility and all of the scale, um, uh, benefits that you get from migrating workloads to the cloud, the elasticity and the growth and all that sort of stuff. But arguably more importantly for the telco, it means that with a virtualized network infrastructure, you can offer entire networks to enterprise clients. >>So if you're selling to a government department, for example, is looking to stand up a system for certification of, of, you know, export certification, something like that. Um, you can not just sell them the connectivity, but you can sell them the networking and the infrastructure in order to serve that entire end to end application. You could sentence, you could offer them in theory, an entire end-to-end communications network, um, and with 5g network slicing, they can even have their own little piece of the 5g bandwidth that's been allocated against the carrier, um, uh, and, and have a complete end to end environment. So the kinds of services that can be offered by telcos, um, given virtualize network infrastructure, uh, are, are many and varied. And it's a, it's a, it's a, um, uh, an outstanding opportunity. But what it also means is that the number of network elements virtualized in this case is also exploding. >>That means the amount of data that we're getting on, uh, informing us as to how those network elements are behaving, how they're performing, um, uh, is, is, is going to go up as well. And then finally, AI complexity. So on the demand side, um, while historically, uh, um, network analytics, big data, uh, has been, has been driven by, um, returns in terms of data monetization, uh, whether that's through cost avoidance, um, or service assurance, uh, or even revenue generation through data monetization and things like that. AI is transforming telecommunications and every other industry, the potential for autonomous operations, uh, is extremely attractive. And so understanding how the end-to-end telecommunication service delivering delivery infrastructure works, uh, is essential, uh, as a training ground for AI models that can help to automate a huge amount of telecommunications operating, um, processes. So the AI demand for data is just going through the roof. >>And so all of these things combined to mean big data is getting explosive. It is absolutely going through the roof. So that's a huge thing that's happening. So as telecommunications companies around the world are looking at their network analytics infrastructure, which was initially designed for service insurance primarily, um, and how they migrate that to the cloud. These things are impacting on those decisions because you're not just looking at migrating a workload to operate in the cloud that used to work in the, in the data center. Now you're looking at, um, uh, migrating a workload, but also expanding the use cases in that work and bear in mind, many of those, those are going to need to remain on prem. So they'll need to be within a private cloud or at best a hybrid cloud environment in order to satisfy a regulatory jurisdictional requirements. So let's talk about an example. >>So LGU plus is a Finastra fantastic service provider in Korea. Um, huge growth in that business over the last, uh, over the last 10, 15 years or so. Um, and obviously most people will be familiar with LG, the electronics brand, maybe less so with, uh, with LG plus, but they've been doing phenomenal work. And we're the first, uh, business in the world who launch commercial 5g in 2019. And so a huge milestone that they achieved. And at the same time they deploy the network real-time analytics platform or in rep, uh, from a combination of Cloudera and our partner calmer. Now, um, there were a number of things that were driving, uh, the requirement for it, for the, for the analytics platform at the time. Um, clearly the 5g launch was that was the big thing that they had in mind, but there were other things that re so within the 5g launch, um, uh, they were looking for, for visibility of services, um, and service assurance and service quality. >>So, you know, what services have been launched? How are they being taken up? What are the issues that are arising, where are the faults happening? Um, where are the problems? Because clearly when you launch a new service, but then you want to understand and be on top of the issues as they arise. Um, so that was really, really important. The second piece was, and, you know, this is not a new story to any telco in the world, right. But there are silos in operation. Uh, and so, um, taking advantage of, um, or eliminating redundancies through the process, um, of, of digital transformation, it was really important. And so particular, the two silos between wired and the wireless sides of the business come together so that there would be an integrated network management system, um, for, uh, for LGU plus, as they rolled out 5g. So eliminating redundancy and driving cost savings through the, the integration of the silos is really, really important. >>And that's a process and the people thing every bit, as much as it is a systems and a data thing. So, um, another big driver and the fourth one, you know, we've talked a little bit about some of these things, right? 5g brings huge opportunity for enterprise services, innovation. So industry 4.0 digital experience, these kinds of use cases, um, are very important in the south Korean marketing and in the, um, in the business of LGU plus. And so, uh, um, looking at AI and how can you apply AI to network management? Uh, again, there's a number of use cases, really, really exciting use cases that have gone live now, um, in LG plus since, uh, since we did this initial deployment and they're making fantastic strides there, um, big data analytics for users across LGU plus, right? So it's not just for, um, uh, it's not just for the immediate application of 5g or the support or the 5g network. >>Um, but also for other data analysts and data scientists across the LGU plus business network analytics, while primarily it's primary it's primary use case is around network management, um, LGU plus, or, or network analytics, um, has applications across the entire business, right? So, um, you know, for customer churn or next best offer for understanding customer experience and customer behavior really important there for digital advertising, for product innovation, all sorts of different use cases and departments within the business needed access to this information. So collaboration sharing across the network, the real-time network analytics platform, um, it was very important. And then finally, as I mentioned, LG group is much bigger than just LG plus it's because the electronics and other pieces, and they had launched a major group wide digital transformation program in 2019, and still being a part of that was, well, some of them, the problems that they were looking to address. >>Um, so first of all, the integration of wired and wireless data service data sources, and so getting your assurance data sources, your network, data sources, uh, and so on integrated with is really, really important scale was massive for them. Um, you know, they're talking about billions of transactions in under a minute, uh, being processed, um, and hundreds of terabytes per day. So, uh, you know, phenomenal scale, uh, that needed to be available out of the box as it were, um, real time indicators and alarms. And there was lots of KPIs and thresholds set that, you know, w to make, make it to meet certain criteria, certain standards, um, customer specific, real time analysis of 5g, particularly for the launch root cause analysis, an AI based prediction on service, uh, anomalies and service service issues was, was, was a core use case. Um, as I talked about already the provision of service of data services across the organization, and then support for 5g, uh, served the business service, uh, impact, uh, was extremely important. >>So it's not just understand well, you know, that you have an outage in a particular network element, but what is the impact on the business of LGU plus, but also what is the impact on the business of the customer, uh, from an outage or an anomaly or a problem on, on, on the network. So being able to answer those kinds of questions really, really important, too. And as I said, between Cloudera and Kamarck, uh, uh, and LGU plus, uh, really themselves an intrinsic part of the solution, um, uh, this is, this is what we, we ended up building. So a big complicated architecture space. I really don't want to go into too much detail here. Um, uh, you can see these things for yourself, but let me skip through it really quickly. So, first of all, the key data sources, um, you have all of your wireless network information, other data sources. >>This is really important because sometimes you kind of skip over this. There are other systems that are in place like the enterprise data warehouse that needed to be integrated as well, southbound and northbound interfaces. So we get our data from the network and so on, um, and network management applications through file interfaces. CAFCA no fire important technologies. And also the RDBMS systems that, uh, you know, like the enterprise data warehouse that we're able to feed that into the system. And then northbound, um, you know, we spoke already about me making network analytics services available across the enterprise. Um, so, uh, you know, uh, having both the file and the API interface available, um, for other systems and other consumers across the enterprise is very important. Um, lots of stuff going on then in the platform itself to petabytes and persistent storage, um, Cloudera HDFS, 300 nodes for the, the raw data storage, um, uh, and then, uh, could do for real time storage for real-time indicator analysis, alarm generation, um, uh, and other real time, um, processes. >>Uh, so there, that was the, the core of the solution, uh, spark processes for ETL key quality indicators and alarming, um, and also a bunch of work done around, um, data preparation, data generation for transferal to, to third party systems, um, through the northbound interfaces, um, uh, Impala, API queries, um, for real-time systems, uh, there on the right hand side, and then, um, a whole bunch of clustering classification, prediction jobs, um, through the, uh, the, the, the, the ML processes, the machine learning processes, uh, again, another key use case, and we've done a bunch of work on that. And, um, I encourage you to have a look at the Cloudera website for more detail on some of the work that we did here. Um, so this is some pretty cool stuff. Um, and then finally, just the upstream services, some of these there's lots more than, than, than simply these ones, but service assurance is really, really important. So SQM cm and SED grade. So the service quality management customer experience, autonomous controllers, uh, really, really important consumers of, of the, of the real-time analytics platform, uh, and your conventional service assurance, um, functions like faulted performance management. Uh, these things are as much consumers of the information and the network analytics platform as they are providers of data to the network, uh, analytics >>Platform. >>Um, so some of the specific use cases, uh, that, uh, have been, have been stood up and that are delivering value to this day and lots of more episodes, but these are just three that we pulled out. Um, so first of all, um, uh, sort of specific monitoring and customer quality analysis, Karen response. So again, growing from the initial 5g launch and then broadening into broader services, um, understanding where there are the, where there are issues so that when people complaining, when people have an issue, um, that, um, uh, that we can answer the, the concerns of the client, um, in a substantive way, um, uh, AI functions around root cause analysis or understanding why things went wrong when they went wrong. Um, uh, and also making recommendations as to how to avoid those occurrences in the future. Uh, so we know what preventative measures can be taken. Um, and then finally the, uh, the collaboration function across LGU plus extremely important and continues to be important to this day where data is shared throughout the enterprise, through the API Lira through file interfaces and other things, and through interface integrations with, uh, with upstream systems. >>So, um, that's kind of the, the, uh, real quick run through of LGU plus the numbers are just stave staggering. Um, you know, we've seen, uh, upwards of a billion transactions in under 40 seconds being, um, uh, being tested. Um, and, and we've gone beyond those thresholds now, already, um, and we're started and, and, and, and this isn't just a theoretical sort of a benchmarking test or something like that. We're seeing these kinds of volumes of data and not too far down the track. So, um, with those things that I mentioned earlier with the proliferation of, of, um, of network infrastructure, uh, in the 5g context with virtualized elements, with all of these other bits and pieces are driving massive volumes of data towards the, uh, the, the, the network analytics platform. So phenomenal scale. Um, this is just one example we work with, with service providers all over the world is over 80% of the top 100 telecommunication service providers run on Cloudera. >>They use Cloudera in the network, and we're seeing those customers, all migrating legacy cloud platforms now onto CDP onto the Cloudera data platform. Um, they're increasing the, the, the jobs that they do. So it's not just warehousing, not just ingestion ETL, and moving into things like machine learning. Um, and also looking at new data sources from places like NWTF the network data analytics function in 5g, or the management and orchestration layer in, in software defined networks, network, function, virtualization. So, you know, new use cases coming in all the time, new data sources coming in all the time growth in, in, in, in the application scope from, as we say, from edge to AI. Um, and so it's, it's really exciting to see how the, the, the, the footprint is growing and how, uh, the applications in telecommunications are really making a difference in, in facilitating, um, network transformation. And that's covering that. That's me covered for today. I hope you found that helpful, um, by all means, please reach out, uh, there's a couple of links here. You can follow me on Twitter. You can connect to the telecommunications page, reach out to me directly at Cloudera. I'd love to answer your questions, um, uh, and, uh, and talk to you about how big data is transforming networks, uh, and how network transformation is, is accelerating telcos, uh, throughout >>Jamie Sharath with Liga data, I'm primarily on the delivery side of the house, but I also support our new business teams. I'd like to spend a minute really just kind of telling you about the legal data, where basically a Silicon valley startup, uh, started in 2014, and, uh, our lead iron, our executive team, basically where the data officers at Yahoo before this, uh, we provide managed data services, and we provide products that are focused on telcos. So we have some experience in non telco industry, but our focus for the last seven years or so is specifically on telco. So again, something over 200 employees, we have a global presence in north America, middle east Africa, Asia, and Europe. And we have folks in all of those places, uh, I'd like to call your attention to the, uh, the middle really of the screen there. So here is where we have done some partnership with Cloudera. >>So if you look at that and you can see we're in Holland and Jamaica, and then a lot to throughout Africa as well. Now, the data fabric is the product that we're talking about. And the data fabric is basically a big data type of data warehouse with a lot of additional functionality involved. The data fabric is comprised of, uh, some something called a flare, which we'll talk about in a minute below there, and then the Cloudera data platform underneath. So this is how we're partnering together. We, uh, we, we have this tool and it's, uh, it's functioning and delivering in something over 10 up. So flare now, flare is a piece of that legal data IP. The rest is there. And what flare does is that basically pulls in data, integrates it to an event streaming platform. It's, uh, it is the engine behind the data fabric. >>Uh, it's also a decisioning platform. So in real time, we're able to pull in data. We're able to run analytics on it, and we're able to alert are, do whatever is needed in a real-time basis. Of course, a lot of clients at this point are still sending data in batch. So it handles that as well, but we call that a CA picture Sanchez. Now Sacho is a very interesting app. It's an AI analytics app for executives. What it is is it runs on your mobile phone. It ties into your data. Now this could be the data fabric, but it couldn't be a standalone product. And basically it allows you to ask, you know, human type questions to say, how are my gross ads last week? How are they comparing against same time last week before that? And even the same time 60 days ago. So as an executive or as an analyst, I can pull it up and I can look at it instantly in a meeting or anywhere else without having to think about queries or anything like that. >>So that's pretty much for us at legal data, not really to set the context of where we are. So this is a traditional telco environments. So you see the systems of record, you see the cloud, you see OSS and BSS data. So one of the things that the next step above which calls we call the system of intelligence of the data fabric does, is it mergers that BSS and OSS data. So the longer we have any silos or anything that's separated, it's all coming into one area to allow business, to go in or allow data scientists go in and do that. So if you look at the bottom line, excuse me, of the, uh, of the system of intelligence, you can see that flare is the tools that pulls in the data. So it provides even streaming capabilities. It preserves entity states, so that you can go back and look at it state at any time. >>It does stream analytics that is as the data is coming in, it can perform analytics on it. And it also allows real-time decisioning. So that's something that, uh, that's something that business users can go in and create a system of, uh, if them's, it looks very much like the graph database, where you can create a product that will allow the user to be notified if a certain condition happens. So for instance, a bundle, so a real-time offer or user is succinct to run out of is ongoing, and an offer can be sent to him right on the fly. And that's set up by the business user as opposed to programmers, uh, data infrastructure. So the fabric has really three areas. That data is persistent, obviously there's the data lake. So the data lake stores that level of granularity that is very deep years and years of history, data, scientists like that, uh, and, uh, you know, for a historical record keeping and requirements from the government, that data would be stored there. >>Then there's also something we call the business semantics layer and the business semantics layer contains something over 650 specific telco KPIs. These are initially from PM forum, but they also are included in, uh, various, uh, uh, mobile operators that we've delivered at. And we've, we've grown that. So that's there for business data lake is there for data scientists, analytical stores, uh, they can be used for many different reasons. There are a lot of times RDBMS is, are still there. So these, this, this basically platform, this cloud they're a platform can tie into analytical data stores as well via flair access and reporting. So graphic visualizations, API APIs are a very key part of it. A third-party query tools, any kind of grid tools can be used. And those are the, of course, the, uh, the ones that are highly optimized and allow, you know, search of billions of records. >>And then if you look at the top, it's the systems of engagement, then you might vote this use cases. So teleco reporting, hundreds of KPIs that are, that are generated for users, segmentation, basically micro to macro segmentation, segmentation will play a key role in a use case. We talked about in a minute monetization. So this helps teleco providers monetize their specific data, but monetize it in. Okay, how to, how do they make money off of it, but also how might you leverage this data to engage with another client? So for instance, in some where it's allowed a DPI is used, and the fabric tracks exactly where each person goes each, uh, we call it a subscriber, goes within his, uh, um, uh, internet browsing on the, on the four or 5g. And, uh, the, all that data is stored. Uh, whereas you can tell a lot of things where the segment, the profile that's being used and, you know, what are they propensity to buy? Do they spend a lot of time on the Coca-Cola page? There are buyers out there that find that information very valuable, and then there's signs of, and we spoke briefly about Sanchez before that sits on top of the fabric or it's it's alone. >>So, so the story really that we want to tell is, is one, this is, this is one case out of it. This is a CVM type of case. So there was a mobile operator out there that was really offering, you know, packages, whether it's a bundle or whether it's a particular tool to subscribers, they, they were offering kind of an abroad approach that it was not very focused. It was not depending on the segments that were created around the profiling earlier, uh, the subscriber usage was somewhat dated and this was causing a lot of those. A lot of those offers to be just basically not taken and, and not, not, uh, audited. Uh, there was limited segmentation capabilities really before the, uh, before the, uh, fabric came in. Now, one of the key things about the fabric is when you start building segments, you can build that history. >>So all of that data stored in the data lake can be used in terms of segmentation. So what did we do about that? The, the, the envy and, oh, the challenge this, uh, we basically put the data fabric in and the data fabric was running Cloudera data platform and that, uh, and that's how we team up. Uh, we facilitated the ability to personalize campaign. So what that means is, uh, the segments that were built and that user fell within that segment, we knew exactly what his behavior most likely was. So those recommendations, those offers could be created then, and we enable this in real time. So real-time ability to even go out to the CRM system and gather further information about that. All of these tools, again, we're running on top of the Cloudera data platform, uh, what was the outcome? Willie, uh, outcome was that there was a much more precise offer given to the client that is, that was accepted, no increase in cross sell and upsell subscriber retention. >>Uh, our clients came back to us and pointed out that, uh, it was 183% year on year revenue increase. Uh, so this is a, this is probably one of the key use cases. Now, one thing to really mention is there are hundreds and hundreds of use cases running on the fabric. And I would even say thousands. A lot of those have been migrated. So when the fabric is deployed, when they bring the Cloudera and the legal data solution in there's generally a legacy system that has many use cases. So many of those were, were migrated virtually all of them in pen, on put on the cloud. Uh, another issue is that new use cases are enabled again. So when you get this level of granularity and when you have campaigns that can now base their offers on years of history, as opposed to 30 days of history, the campaigns campaign management response systems, uh, are, are, uh, are enabled quite a bit to do all, uh, to be precise in their offers. Okay. >>Okay. So this is a technical slide. Uh, one of the things that we normally do when we're, when we're out there talking to folks, is we talk and give an overview and that last little while, and then we give a deep technical dive on all aspects of it. So sometimes that deep dive can go a couple of hours. I'm going to do this slide and a couple of minutes. So if you look at it, you can see over on the left, this is the, uh, the sources of the data. And they go through this tool called flare that runs on the cloud. They're a data platform, uh, that can either be via cues or real-time cues, or it can be via a landing zone, or it can be a data extraction. You can take a look at the data quality that's there. So those are built in one of the things that flare does is it has out of the box ability to ingest data sources and to apply the data quality and validation for telco type sources. >>But one of the reasons this is fast to market is because throughout those 10 or 12, uh, opcos that we've done with Cloudera, where we have already built models, so models for CCN, for air for, for most mediation systems. So there's not going to be a type of, uh, input that we haven't already seen are very rarely. So that actually speeds up deployment very quickly. Then a player does the transformations, the, uh, the metrics, continuous learning, we call it continuous decisioning, uh, API access. Uh, we, uh, you know, for, for faster response, we use distributed cash. I'm not going to go too deeply in there, but the layer in the business semantics layer again, are, are sitting on top of the Cloudera data platform. You see the Kafka CLU, uh, Q1, the right as well. >>And all of that, we're calling the fabric. So the fabric is Cloudera data platform and the cloud and flair and all of this runs together. And, and by the way, there've been many, many, many, many hundreds of hours testing flare with Cloudera and, uh, and the whole process, the results, what are the results? Well, uh, there are, there are four I'm going to talk about, uh, we saw the one for the, it was called my pocket pocket, but it's a CDM type, a use case. Uh, the subscribers of that mobile operator were 14 million plus there was a use case for 24 million plus that a year on year revenue was 130%, uh, 32 million plus for 38%. These are, um, these are different CVM pipe, uh, use cases, as well as network use cases. And then there were 44%, uh, telco with 76 million subscribers. So I think that there are a lot more use cases that we could talk about, but, but in this case, this is the ones we're looking at, uh, again, 183%. This is something that we find consistently. And these figures come from our, uh, our actual end client. How do we unlock the full potential of this? Well, I think to start is to arrange a meeting and, uh, it would be great to, to, uh, for you to reach out to me or to Anthony. Uh, we're working at the junction on this, and we can set up a, uh, we can set up a meeting and we can go through this initial meeting. And, uh, I think that's the very beginning. Uh, again, you can get additional information from Cloudera website and from the league of data website, Anthony, that's the story. Thank you. >>No, that's great. Jeremy, thank you so much. It's a, it's, it's wonderful to go deep. And I know that there are hundreds of use cases being deployed in MTN, um, but great to go deep on one. And like you said, it can, once you get that sort of architecture in place, you can do so many different things. The power of data is tremendous, but it's great to be able to see how you can, how you can track it end to end from collecting the data, processing it, understanding it, and then applying it in a commercial context and bringing actual revenue back into the business. So there is your ROI straight away. Now you've got a platform that you can transform your business on. That's, that's, it's a tremendous story, Jamie, and thank you for your part. Sure. Um, that's a, that's, that's our story for today. Like Jamie says, um, please do flee, uh, feel free to reach out to us. Um, the, the website addresses are there and our contact details, and we'd be delighted to talk to you a little bit more about some of the other use cases, perhaps, um, and maybe about your own business and, uh, and how we might be able to make it, make it perform a little better. So thank you.

>> from our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to this. Keep conversation here in Palo Alto, California. Keep studios. I'm John for the host of the Cube were with Cuba Lum nine. Special gas *** while the president of products and marking it in from Attica. I make great to see you has been a while, but a couple months. How's things good to be >> back has always >> welcome back. Okay, so in dramatic, a world's coming up. We have a whole segment on that, but we've been covering you guys for a long, long time. Data is at the center the value proposition. Again and again, it's Maur amplified. Now the fog is lifting. Show in the world is now seeing what we think we were told about four years ago with data. What's new? What's that? What's the big trends going on that you guys air doubling down on what's new? What's changed? Here's the update. Sure, >> I think we've been talking for the last couple of years. I think you're right. It is becoming more and more important. I think three things we see a lot one is. Obviously you saw this whole world of district transformation. I think that definitely has picked up so much steam. Now. I mean, every company's going digital and And that the officer, that creates a whole new paradigm shift for companies to come almost recreate themselves remained. And so that data becomes the new definition. And that's what we call the thing is you side and fanatical even before the data three dollar word. But data is the center of everything, right? And in basically see the volume of data growth, you know, the utilization of data to make decisions, whether it's, you know, a decision on the shop floor decisions basically related to a cyber security or whatever it is on the keel of your signal is different now. Is the hole e. I assisted data management. I mean the scale ofthe complexity, the scale of growth, you know, multi cloud, multi platform, all the stuff that's in front of us. It's very difficult to run the old way of doing things. So that's where we see the one thing that we see a whole lot is is becoming a lot more mainstream still early days. But it's assisting the whole ability for companies to what I call exploit data to really become a lot more transformative. >> You've been on this for a while again. We get what we had to go back to. The Cube archives were almost pullout clips from two years ago be relevant today. You know the data control understanding. You know that. You know, I understand where the date of governance is ours. So is the foundational thing. But you guys nailed the chat box. You've been doing a Iot of previous announcements. This is putting a lot of pressure on you. The president of products you got. Get this out there. What's new? What's happening inside in from Attica? He's pedaling as fast as you can. What are some of the updates? Give >> us the best example. I was just like the duck, right? You know, you're really selling your Felix comma the top and then you're really finally I think it's great for us. I think I look a tw ee eye ee eye. It's like this so much fun around machine learning. We look at it, it's two different ways. One is how we leverage machine learning Vidin our products to help our customers, making it easy for them to. As I said, so many different data types Think of I ot data instructor data streaming data. How do you bring all that stuff together and married with your existing transaction? It'LL make sense. So we're leveraging a lot of machine learning to make the internal products a lot more easier to consume. A lot more smarter, a lot more. Richard, The second thing is that we what we call his are a clear which we are. Really? If you remember a couple years ago and in America World, how guard then helps our customers make smarter decisions in the in the one of data signs and all these new data workbench is, you know, the old statistical models are only as good as they can never be. So we're leveraging, helping our customers take the value proposition of r B. I clear then what? I make things that, you know, find patterns that, you know, statistical models cannot. So, to me, I look att, both of those really leveraging ml to shape our products, which is married to a lot of innovation and then creating our eclair to that help customers make smarter decisions, easier decisions, complex decisions. Which would I kill the humans or the statistical models? >> Really Well, this is the balance between machines and humans working together. And you guys have nailed this before. And I think this was two years ago. I started to hear the words land adopt, expand from you guys. Write, which is you've got to get adoption, right? And so as you're iterating on this product, focus, you've got to get it working your >> butt looks big, maniacal focus of that. Let's talk about >> what? What you've learned there because that's a hard thing. You guys are doing well at it. We've got to get a doctor. Means you gotta listen to customers going do the course correction. What's the learning is coming out of that. That >> is actually such a good point. We made such. We were always a very customer centric company. But as you said like that, as the world shifted towards a new subscription cloud model, be really focused on helping our customers adopt our products. And you know, in this new world, customers are also struggling with new architectures and everything, so we double down on what we call customer success, making sure we can help our customers adopt the products. And whether it's it's, it's too will benefit. Our customers can value very quickly. And of course, we believe in what we call a customer for life. Our ability to then grow without customers and held them deliver value becomes a lot better, so we're really for So we have globally across the board customers, success managers, we really invest in a customer's. The moment we a customer, buys a product from us, we directly engage with them to help them understand forthis use case. How you >> implement its not just self serving. That's one thing which I appreciate because you know, how hard is it? Build products these days, especially with philosophy, have changed, but it's also we have in the large scale data. You need automation. You've gotta have machine learning. You gotta have these disciplines. Sure this both on your own, but also for the customer. Yes, any updates on the Clare and some customer learnings, and you're seeing that air turning into either use cases or best practices, >> many of them. So take a simple example, right? I mean, we think if we take these things for granted, right? I mean, taking over here to talk about I open these designs on all of these sensors. We were streaming data, right? Or even robots in the shop floor. Sort of. That data has no schema, no structure, nor definition. It's coming like Netflix data has to. And for customers, there's a lot of volume on it. None of it could be junk. Right? So how do you first think that volume of data creates some structure to it for you to do analytics? You You can only do analytics if you put some structure to it. Right. So first thing is that we leverage clear help customers create what are called scheme, and you can create some structure to it. Then what we do allow is basically clear through clear. It can naturally bring what we have. The data quality on top of it. Like how much of it is irrelevant? How much of it is noise? How much would it really make sense? So then what was you said? It signal from the noisy were helping customers get signal from the noise of data. That's where it becomes very handy because It's a very man will cumbersome, time consuming and something very difficult to do. So that's an area of every have leveraged, creating structure, adding data quality on top and finding rules that didn't probably naturally didn't exist, that you and he would be able to see machines are able to do it. And to your point, our belief is this is my one hundred percent believe we believe in the eye assisting the humans. We have given the value ofthe Claire, tow our users that it compliments you. And that's where we're trying to help our users get more productive and deliver more value faster. >> Productivity is multifold. It's like also efficiency. You don't want people wasting time on project that can be automated. You focus that valuable resource somewhere else. Yeah, okay, so let's shift gears on. Taking from Attica World coming up. Let's spend some time on that. What's the focus this year? The show. It's coming up right around the corner. What's going to focus on what's going to be the agenda? What's on the plate >> give you a quick sense of how it's the shape of its going to be our biggest in from Attica well, so it's twentieth year again. Back in Vegas, you know we love Vegas. Of course, we have obviously a couple of days line up over there and you guys will be there too Great sort of speakers. So obviously we'LL have mean stage speakers like so we'LL have some CEO of Google Cloud Thomas Korean is going to be there We'LL have on main stage with Neil We'LL have the CEO of dealer Breaks Ali with me We'LL also have the CMO off a ws ariel there. Then we have a couple of customers lined up Simon from Credit Suisse Daniels CD over Nissan. We also have the head of the eye salmon Guggenheimer from Microsoft, as well as the chief product officer of Tableau Francois on means. So we have a great lineup of speakers, customers and some of our very, very strategic partners with us. Remember last year we also had Scott country. That means too eighty plus session's pretty much a ninety percent led by customers. We have seventy to eighty customers. Presentable sessions, technical business. We have all kinds of tracks. We have hands on labs. We have learnings. Customers really want to come. Lana products. Talked to the experts someone to talk to the product manager. Someone talk to the engineers literally, so many hands on lab. So it's going to be a full blown a couple of days. What's >> the pitch for someone watching that has never been in from Attica world? Why should they come for the show? >> I always tell them three things. Number one is that it's a user conference for our customers to known all things about data management. And then, of course, in that context, they learned a lot about so they learned a lot about the industry. So Dave one we kicked around by market perspective giving Assessor the market is going, how everybody should be stepping back from the data and understanding. Where are these district transformation? E I? Where is the world of detail going? We have some great analysts coming, talking, some customers talking. We'LL be talking about futures over there. Then it is all about hands on learning, right, learning about the product hearing from some of these experts, right from the industry experts as well as our customers teaching what to do, what not to do and networking. It's always great to network writes a great place for people to learn from each other. So it's a great forum for for two of those three things. But the team this year is all around here. I talked about clear. In fact, our tagline Dissidents, clarity unleashed. I really want to, basically has been developing for the last couple of years. It's become becoming a lot who means stream for us in our offerings. And this year we really are taking it being stream. So it's kinda like unleashing it where everybody can genuinely use a truly use it from the data data management. Active >> clarity is a great team. I mean plays on Claire, But this is what we're starting to see. Some visibility into some clear economic benefits, business benefits, technical benefits, kind of all starting to come in. How would you categorize those three years? Because, you know, that's generally the consensus these days is that what was once a couple years ago was like foggy. When you see now you're starting to see that lift. You see economic, business and technical benefits. >> To me, it's all about economic and business. Anniversary technology plays a role in driving value for the business, my gramophone believing that right? And if you think about some of the trans today, right, ah, billion users are coming into play. That he be assisted by data is doubling every year. You know, the volume of data and and amount ofthe amount off. And I obviously business users today. I mean, when I run a business I want, I always say, tomorrow's data yesterday to make a decision. Today it's just in time, and that's where it comes into play. So our goal is to help organizations transformed themselves truly, you know, be more productive, produce operational cost by the government and compliance that's becoming such a mainstream topic. It's not just basically making analytical decisions. How do you make sure that your data is safe and secure? You don't want to get basically hit by any of these cyberattacks. They're all coming after data. So governance and compliance of data that's becoming but in the end got stored on the >> data thing. Yeah, I wanna get your reactions. You mention some shots like some stats here. Date explosion fifteen point three's added bytes per year in traffic, five million business data users and growing twenty billion connected devices. One billion workers will be assisted by learning. So no thanks for putting those stats, but I want to get your reactors. Some of these other points here, eighty percent of enterprises air that we're looking at multi cloud. They're really evaluating their where the data sits in that kind of equation short. And then the other thing is that the responsibility and role of the chief data? Yes, these air new dynamic. I think you guys will be addressing that. And because organizational stuff dynamics, skill, gaps are issues. But also you have multi clouds form. >> And that's a big thing. I mean, look thin. The old World John hatred Unite is always too large in the price is right, and it's going to stay here. In fact, I think it's not just cloud. Think of it this way, one promised. Ilya is not going away. It's producing in school. But then you have this multi cloud world sassafras pass halves infrastructure. If I'm a customer, I want to do all of it. But the biggest problem comes, you said, is that my data is everywhere. How do I make sense of it? And then how do I go on it like my customer data sitting somewhat in this *** up in that platform in this on prime application transaction after running hardware Connect three. And how do I make sense? It doesn't get. I can have a governance and control around it. That's where data management becomes more important but more complex. But that's where it comes into making it easier. One of the things we've seen a lot of you touched upon is the rise of the Sirio. In fact, we have Danielle from the Sanchez, a CD off Mr North America on Main Stage, talking about her rule and how they've leveraged data to transform themselves. That is something we're seeing a lot more because you know, the rule of the city or making sure there is, You know, not only a sense of governance and compliance, a sense of how to even understand the value of dude across an enterprise again. I see one of the things we're gonna talk about this. It's old system thinking around data. We call it system, thinking three daughter data is becoming a platform C. There was always that the hard way earlier, whether it is server or computer. We believe that data is becoming a platform in itself. Whether you think about it in terms of scary, in terms ofthe governance, in terms of e i times a privacy, you have to think of data as a platform. That's the that's the other. But >> I think that is very powerful statement, and I'd like to get your thoughts. You know, we've had many countries. Is on camera off camera around product. Silicon Valley Venture Capital. How come started to create value. One of the old adage is used to be build a platform. That's your competitive strategy. There were a platform company, and >> that was a >> strategic competitive advantage that is unique to the company. And they created enablement. Facebook's a great example. Monetize all the data from users. Look where they are short. If you think about platforms today, Charlie, it seems to be table stakes. Not as a competitive is more of a foundational element of all businesses, not just startups enterprises. This seems to be a common thread. Do you agree with that that platforms were becoming table stakes? Because if we have to think like systems people, whether it's an enterprise show supplier ballistically the platform becomes stable. States that could be on primary cloud. Your reactions >> are gonna agree that I'll say it slightly differently. Yes, I think I think platform is a critical competent for any enterprise when they think of their entire technology strategy because you can't do peace feels otherwise. You become a system integrated over your own right. But it's not easy to be a platform clear itself, right? Because it's a platform player. The responsibility of what you have to offer your customer becomes a lot bigger. So we always t have this intelligent in a platform. Uh, but the other thing is that the rule of the platform is different. It has to be very modeling and FBI driven. Nobody wants to buy a monolithic platform. I don't want as an enterprise it on my own. I'm gonna implement five years a platform you want. It's gonna be like a Lego block. Okay? You It builds by itself, not monolithic, very driven my micro services based And that's our belief that in the new World, yes, black form is very critical for youto accelerate your district transformation journeys or data driven district transformation journeys but the platform better be FBI driven micro services based, very nimble that it's not a precursor to value creation but creates value as you want. It's >> all kind of depends on the customer. Get up a thin, foundational data platform from you guys, for instance. And then what you're saying is composed off >> different continents. For example, you have a data integration platform, then you can do the quality on top. You do. You could do master data management on top. You can provide governance. You can provide privacy. You could do cataloging it all builds its not like Oh my gosh, I have to go do all these things over the course of five years. Then I'LL get value. You gotta create value all along. Today's customers want value like in two months. Three months. You don't wait for a year or >> two years. This is exactly why I think the kind of Operation Storm systems mindset that you're referring to. This is kind of enterprises. They're behaving others the way that you see on premise, thinking around data and cloud multi cloud emerging. It's a systems view of distributed computing with the right block Lego blocks >> that that's what I believe is. That's what we heard from customers. He r I spend most of my time traveling, talking to customers on my way to try to understand what customers want today. And you know some of this late and demand that they have it. They can't sometimes articulate my job. I always end up on the road most of the time just to hearing customers, and that's what they want. They want exactly appoint a platform that Bill's not monolithic, but they don't want the platform. They do want to make it easy for them not to do everything piecemeal. Every project is a data project, whether it's a customer experience project, whether it's the government's project, whether it is nothing else but an analytical. It's a data project, but you don't want to repeat it every time. That's what they want, >> but I know you got a hard stuff, but I want your thoughts on this because I've heard the word workload mentioned so many more times these in the past year. It was a tad cloud of all the cute conversation with a word workload was mentioned to be the biggest fund. Yes, work has been around for a while, but nice seeing more and more workloads coming on. Yeah, that's more important for day that we're close to being tied into the data absolutely, and then sharing data cross multiple workloads. That's a big focus. Perhaps you see that same thing. >> We absolutely see that, Onda. The unique thing that we see also that new work towards getting created and the old workloads are not going away, which is where the hybrid becomes very important. See, these serve large enterprises and their goal is to have an hybrid. So, you know, I'm running a old transaction workload over here. I want to have an experimental workload. I want to start a new book. I want all of them to talk to each other. I don't want them to become silos. And that's when they look to us to say connect the dots for me. You can be in the cloud as an example. Our cloud platform, you know, last time and fanatical will remember we talked about like it wasn't five trillion transactions a month, but it's double that it to pen trillion transaction a month growing like crazy. But our traditional workload is also still there. So we connect the dots for customers. >> I mean, thank you for coming on sharing the insights house. You guys doing well? You got three thousand developers, billions in revenue. Thanks for coming. Appreciate the insight. And looking for Adrian from Attica World. Thank you very much. Meanwhile, here inside the Cuban shot furry with cute conversation in Palo Alto. Thanks for watching.