>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>We're back in Miami. Welcome everybody. You watching the cube, the leader in live tech coverage. We're here at the IBM data and AI forum. Wow. What a day. 1700 customers. A lot of hands on labs sessions. What used to be the IBM analytics university is sort of morphed into this event. Now you see the buzz is going on. At least the Galean is here. She's the vice president of global sales for IBM data and AI. Welcome to the cube. Thank you for coming on. So this event is buzzing the double from last year almost. And uh, congratulations. >>Well, thank you very much. We have con, uh, lots of countries represented here. We have customers from small to large, every industry represented. And a, it's a, I can see a marked difference in the conversations in just a year around our, how customers want to figure out how to embark on this journey to AI. >>So yeah. So why are they come here? What's the, what's the primary motivation? >>Well, I think one IBM is recognized as the leader in AI and we just came out in the IDC survey as the three time w you know, leader, a recognized leader in AI. And when they come here they know they're going to hear from other clients who have embarked on similar journeys. They know they're going to have access to experts, hands on labs, and we bring our entire IBM team that's focused on data and AI to this event. So it's intimate, it's high skilled, it's high energy and they are learning a ton while they're. >>Yeah, a lot of content and you're educating but you're also trying to inspire people. I mean a raise. I was the hub this morning, he wrote this book, but he's this extreme, extreme, extreme like ultra marathoner. Uh, which I thought was a great talk this morning. And then you did a, I thought a good job of sort of connecting, you know, his talk of anything's possible to now bringing AI into the equation. What are you hearing from customers in terms of what they want to make possible and, and what's that conversation like in the field? >>Well, it's interesting because there is a huge recognition that every client that I talked to you, and they all want to understand this, that they have to be transforming their businesses on this journey to AI. So they all recognize that they need to start. Now. What I find when I talk to clients is that they're all coming in at different entry points. There's a maturity curve. So some are figuring out, you know, how do I move away from just Excel spreadsheets? I'm still running my business on Excel, right? And these are no banks in major that are operating on Excel spreadsheets and they're looking at niche competitors, you know, digital banks that are entering the scene. And if they don't change the way they operate, they're not going to survive. So a lot of companies are coming in knowing that they're low on the maturity curve and they better do something to move up that curve pretty fast. >>Some are in almost the second turn of the crank where they've invested in a lot of the AI technologies, they've built data science platforms, and now they're figuring out how do they get that next rev of productivity improvement? How do they come up with that next business idea that's going to give them that competitive advantage? So what I find is every client is embarking on this journey, which is a big difference where I think we were even a year, 18 months ago, where they were sort of just, okay, this is interesting. Now there I better do something. >>Okay, so you're a resource, you know, as the head of global sales for this group. So when you talk to customers that are immature, if I hear you right, they're saying, help us get started because we're going to fall behind. Uh, we're inefficient right now. We're drowning in spreadsheets, data. Our data quality is not where it needs to be. Help. Where do we start? What do you tell them? >>Well, one, we have a formula that we've proven works with clients. Um, we bring them into our garages where we will do design thinking, architectural workshops, and we figure out a use case because what we try not to do with our clients is boil the ocean. We want them to sh to have something that they can prove success around very quickly, create that minimal viable product, bring it back to the business so that the business can see, Oh, I understand. And then evolve that use case. So we will bring technical specialists, we will bring folks that are our own data scientists to these garage environments and we will work with them on building out this first use case. >>Explain the garage a little bit more. Is that those, those are sort of centers of excellence around the world or how do I tap them as a customer? Is it, is it a freebie? Is it for pay? Isn't it like the data science elite team? How does it all work? >>Well, it is. There are a number of physical locations and it's open to all clients. We have created these with co-leadership from across the entire IBM company. So our services organization, our cloud cognitive organization, all play a role in these garages. So we have a formal structure where a team can engage through a request process into the garages. We will help them define the use case they want to bring into the garage. We will bring them in for a period of time and provide the resources and capabilities and skills and that's not charged to the client. So we're trying to get them started now that they'll take that back to their company and then they will look at follow on opportunities and those may, you know, work out to be different services opportunities as they move forward. But we're on that get started phase. >>Yeah. Yeah. I mean you're a fraud for profit company, so it's great to have a loss leader, but the line outside the door at the garage must be huge for people that want to get in. Hi. How are you managing the dominion? >>Yes, well we're increasing obviously our capacity around the garages. Um, and we're still making customers aware of the garages. So there's still, because it's a commitment on their side, like they just can't come in and kick the tires. We ask them to bring their line of business along with their technical teams into the garages because that's where you get the best product coming out of it. When you know you've got something that's going to solve a business problem, but you have to have buy in from both sides. >>I want to ask you about the AI ladder. You know, Rob Thomas has been using this construct for awhile. It didn't just come out of thin air. I'm sure there was a lot of customer input and a lot of debate about what should be on the ladder. We went, when I first heard of the day AI ladder, it was, there was data in IAA analytics, ML and AI, sort of the building, the technical or technology building blocks. It's now become verbs, which I love, which is collect, organize, analyze and infuse, which is all about operationalizing and scaling. How is that resonating with customers and how do they fit into that methodology or framework? >>Well, I'll tell you, I use that framework with every single client and I described that there is a set of steps and you know, obviously to the ladder that every customer has to embark upon. And it starts with some very basic principles and as soon as you start with the very basic principles, every client is like, of course like it seems so obvious that first and foremost you have to date as the foundation, right? AI is not created out of, you know, someone in a back room. The foundation to AI is, is information and data. Yet every customer, every customer struggles with that data is coming from multiple systems, multiple sources that they can't get to the data fast enough. They're shipping data around an organization. It's not managed. And yet that they know that in five years, the data they think they need today is going to be completely different. >>It could be 12 months, but certainly in the future. So how do you build out that architecture that allows them to build that now, but have the agility to grow as the requirements change? You start with that basic discussion and they're like, well of course. So that's collect and then you bring it up and you talk about how do you govern that data? How do you know where that data originated? Who is the owner? How do you know what that data means? What system did it come from? What's the, you know, who has access to it? How do you create that set of govern data? And we'll of course every client recognizes they have that set of issues. So I could continue working my way up the ladder and every client realizes that, okay, I re I'm, here's where I am today. What you just painted for me is absolutely what I need to focus on and address. Now help me get from a to B. >>So I'm really interested in this discussion because it sounds like you're a very disciplined sales leaders and you said you use the ladder with virtually every client and I presume your sales teams use the ladder. So you train your salespeople how to converse the ladder. And then the other observation I'd love your thoughts on this is every step of the ladder has these questions. So you're asking customers questions and I'm sure it catalyzes conversation, the, the answers to which you have solutions presumably from any of them. But I wonder if you could talk about that. >>Well, let me tell about the ladder and how we're using it with our Salesforce because it was a unifying approach, not just within our own team, our data and AI team, but outside of data and AI. Because not only did we explain it to clients this way, but to the rest of IBM, our business partners, our whole ecosystem. So unifying in that we started every single conversation with our sales team on enabling them on how do they talk to their clients, our materials, our use cases, our references, our marketing campaigns. We tied everything to this unified approach and it's made a huge difference in how we communicate our value to clients and explain this journey to AI in in comprehensive steps that everyone could understand and relate to. >>Love it. How is the portfolio evolving to map into that framework? And what can we expect going forward? What can you share with us at least? >>Well, the other amazing feat I'll call it that we produced around this is I'll talk to a client and I'll describe these capabilities and then I will say to a customer, you don't have to do every one of these things that I've just described, but you can implement what you need when you need it. Because we have built all of this into a unified platform called cloud pack for data and it's a modern data platform. It's built on an open infrastructure built on red hat OpenShift so that you can run it on your own premises as a private cloud or on public clouds, whether that be IBM or Amazon or Zohre. It allows you to have a framework, a platform built on this open modern infrastructure with access to all these capabilities I've just described as services and you decide completely open what services you need to deploy when you grow the platform as you need it. And, Oh, by the way, if you don't have the red hat OpenShift environment set up, we'll package that in a system and I will roll in the system to you and allow you to have access to the capabilities in ours. >>How's the red hat conversation going? I would imagine a lot of the traditional IBM customers are stoked. He just picked up red hat, you know, a very innovative company, open source mindset. Um, at the same time I would imagine a lot of red hat customers saying, is IBM really gonna? Let them keep their culture. How's that conversation going in the field? >>Well, I will tell you we've been a hundred percent consistent in terms of everything that you've heard Jenny and Arvin Krishna talk about in the fact that we are going to maintain their culture, keep them as that separate entity inside of IBM. It's absolutely perpetrated throughout the entire IBM company. Um, we have a lot to learn from, from them as I'm sure they have to learn from us, but it truly is operating and I see it in the clients that I'm working with as a real win-win. >>If you had to take one thing away from this event that you want customers to, to remember, what would it be? >>Start now. Um, because if you don't begin on this journey to AI, you will find yourselves, you know, fighting against new competitors, uh, increasing costs, you know, you have to improve productivity. Every client is embarking on this journey to AI start now. >>And when you were talking about, uh, the maturity model and, and one of those levels was folks that had started already and they wanted to get to the next level, when you go into those clients, do you discern a different sort of attitude? We've started, we're down the path. Did they have more of a spring in their step? Are they like chomping at the bit to really go faster and extend their lead relative to the competition competition? What's the dynamic like in those accounts? >>That's a great question because I was with a client this afternoon, um, a large manufacturer of, uh, of goods and they are at this turning point where they did kind of phase one, they implemented cloud pack for data and they did it to just join some of their disparate systems. Now, I mean, I, I barely got a word in because he was so excited cause he's, now what I'm going to do is I'm going to figure out where my factories should go based on where my products are selling. So he's now looking at how he can change his whole distribution process as a result of getting access to this data and analytics that he never had before. Um, and I was like, okay, well just tell me how I can help you. And he was like, no way ahead. >>So this was the big kickoff day. I know yesterday there was sort of deep learning hands on stuff, the big keynotes. Today we're only here for one day. What are we going to miss? What's, what's happening tomorrow? >>Well, it's a bit of a repeat of today. So we'll have another keynote tomorrow from Beth Smith who runs our Watson, uh, business for IBM. We'll have more hands on labs. We have a lot of customer presentations where they're sharing their best practices. Um, lots of fun. >>Where do you want to see this event go? And what kind of, what's next in an IBM event land? >>Well, the feedback from last year this year says we have to do this again next year. It's, it's, it will be bigger because I think this year approves that it's already doubled and we'll probably see a similar dynamic. Um, so I fully expect us to be here. Well, maybe not here. We're sort of outgrowing this hotel. Um, but doing this event again next year, >>AI machine learning automation, uh, I'll throw in cloud. These are the hottest topics going. Elise, thanks very much for coming to the cube was great to have you. >>It's great. It's great meeting with you. >>It. Thank you for watching everybody. That's a wrap from Miami. Go to siliconangle.com check out all the news of the cube.net is where you'll find all these videos and follow the, uh, the Twitter handles at the cube at the cube three 65. I'm Dave Volante. We're out. We'll see you next time.

>> from Austin, Texas. It's Theo Cube, covering pure storage. Accelerate 2019. Brought to you by pure storage. >> Welcome back to the Cube. Lisa Martin Day Volante is my co host were a pure accelerate 2019 in Austin, Texas. A couple of guests joining us. Next. Please welcome Barack elected director product management for slunk. Welcome back to the Cube. Thank you. And guess who's back. Von Stewart. V. P. A. Technology from pure Avon. Welcome back. >> Hey, thanks for having us guys really excited about this topic. >> We are too. All right, so But we'll start with you. Since you're so excited in your nice orange pocket square is peeking out of your jacket there. Talk about the Splunk, your relationship. Long relationship, new offerings, joint value. What's going on? >> Great set up. So Splunk impure have had a long relationship around accelerating customers analytics The speed at which they can get their questions answered the rate at which they could ingest data right to build just more sources. Look at more data, get faster time to take action. However, I shouldn't be leading this conversation because Split Split has released a new architecture, a significant evolution if you will from the traditional Splunk architectural was built off of Daz and a shared nothing architecture. Leveraging replicas, right? Very similar what you'd have with, like, say, in H D. F s Work it load or H c. I. For those who aren't in the analytic space, they've released the new architecture that's disaggregated based off of cashing and an object store construct called Smart Store, which Broth is the product manager for? >> All right, tell us about that. >> So we release a smart for the future as part of spunk Enterprise. $7 to about a near back back in September Timeframe. Really Genesis or Strong Smart Strong goes back to the key customer problem that we were looking to solve. So one of our customers, they're already ingesting a large volume of data, but the need to retain the data for twice, then one of Peter and in today's architecture, what it required was them to kind of lean nearly scale on the amount of hardware. What we realized it. Sooner or later, all customers are going to run into this issue. But if they want in just more data or reading the data for longer periods, of time, they're going to run into this cost ceiling sooner or later on. The challenge is that into this architecture, today's distributes killer dark picture that we have today, which of all, about 10 years back, with the evolution of the Duke in this particular architecture, the computer and story Jacqui located. And because computer storage acqua located, it allows us to process large volumes of data. But if you look at the demand today, we can see that the demand for storage or placing the demand for computer So these are, too to directly opposite trans that we're seeing in the market space. If you need to basically provide performance at scale, there needs to be a better model. They need a better solution than what we had right now. So that's the reason we basically brought Smart store on denounced availability last September. What's Marceau brings to the table is that a D couples computer and storage, So now you can scale storage independent of computers, so if you need more storage or if you need to read in for longer periods of time, you can just kill independent on the storage and with level age, remote object stores like Bill Flash bid to provide that data depository. But most of your active data said still decides locally on the indexers. So what we did was basically broke the paradigm off computer storage location, and we had a small twist. He said that now the computer stories can be the couple, but you bring comfort and stories closer together only on demand. So that means that when you were running a radio, you know, we're running a search, and whenever the data is being looked for that only when we bring the data together. The other key thing that we do is we have an active data set way ensure that the smart store has ah, very powerful cash manager that allows that ensures that the active data set is always very similar to the time when your laptop, the night when your laptop has active data sets always in the cash always on memory. So very similar to that smarts for cash allows you to have active data set always locally on the index. Start your search performance is not impact. >> Yes, this problem of scaling compute and storage independently. You mentioned H. D. F s you saw it early on there. The hyper converged guys have been trying to solve this problem. Um, some of the database guys like snowflakes have solved it in the cloud. But if I understand correctly, you're doing this on Prem. >> So we're doing this board an on Prem as well as in Cloud. So this smart so feature is already available on tramp were also already using a host all off our spun cloud deployments as well. It's available for customers who want obviously deploy spunk on AWS as well. >> Okay, where do you guys fit in? So we >> fit in with customers anywhere from on the hate say this way. But on the small side, at the hundreds of terabytes up into the tens and hundreds of petabytes side. And that's really just kind of shows the pervasiveness of Splunk both through mid market, all the way up through the through the enterprise, every industry and every vertical. So where we come in relative to smart store is we were a coat co developer, a launch partner. And because our object offering Flash Blade is a high performance object store, we are a little bit different than the rest of the Splunk s story partner ecosystem who have invested in slow more of an archive mode of s tree right, we have always been designed and kind of betting on the future would be based on high performance, large scale object. And so we believe smart store is is a ah, perfect example, if you will, of a modern analytics platform. When you look at the architecture with smart store as brush here with you, you want to suffice a majority of your queries out of cash because the performance difference between reading out a cash that let's say, that's NAND based or envy. Emmy based or obtain, if you will. When you fall, you have to go read a data data out of the Objects store, right. You could have a significant performance. Trade off wean mix significantly minimized that performance drop because you're going to a very high bandwith flash blade. We've done comparison test with other other smart store search results have been published in other vendors, white papers and we show Flash blade. When we run the same benchmark is 80 times faster and so what you can now have without architecture is confidence that should you find yourself in a compliance or regulatory issue, something like Maybe GDP are where you've got 72 hours to notify everyone who's been impacted by a breach. Maybe you've got a cybersecurity case where the average time to find that you've been penetrated occurs 206 days after the event. And now you gotta go dig through your old data illegal discovery, you know, questions around, you know, customer purchases, purchases or credit card payments. Any time where you've got to go back in the history, we're gonna deliver those results and order of magnitude faster than any other object store in the market today. That translates from ours. Today's days, two weeks, and we think that falls into our advantage. Almost two >> orders of magnitude. >> Can this be Flash Player >> at 80%? Sorry, Katie. Time 80 x. Yes, that's what I heard. >> Do you display? Consider what flashlight is doing here. An accelerant of spunk, workloads and customer environment. >> Definitely, because the forward with the smart, strong cash way allow high performance at scale for data that's recites locally in the cash. But now, by using a high performance object store like your flash played. Customers can expect the same high performing board when data is in the cash as well as invented sin. Remorseful >> sparks it. Interesting animal. Um, yeah, you have a point before we >> subjects. Well, I don't want to cut you off. It's OK. So I would say commenting on the performance is just part of the equation when you look at that, UM, common operational activities that a splitting, not a storage team. But a Splunk team has to incur right patch management, whether it's at the Splunk software, maybe the operating system, like linen store windows, that spunk is running on, or any of the other components on side on that platform. Patch Management data Re balancing cause it's unequal. Equally distributed, um, hardware refreshes expansion of the cluster. Maybe you need more computer storage. Those operations in terms of time, whether on smart store versus the classic model, are anywhere from 100 to 1000 times faster with smart store so you could have a deployment that, for example, it takes you two weeks to upgrade all the notes, and it gets done in four hours when it's on Smart store. That is material in terms of your operational costs. >> So I was gonna say, Splunk, we've been watching Splunk for a long time. There's our 10th year of doing the Cube, not our 10th anniversary of our 10th year. I think it will be our ninth year of doing dot com. And so we've seen Splunk emerged very cool company like like pure hip hip vibe to it. And back in the day, we talked about big data. Splunk never used that term, really not widely in its marketing. But then when we started to talk about who's gonna own the big data, that space was a cloud era was gonna be mad. We came back. We said, It's gonna be spunk and that's what's happened. Spunk has become a workload, a variety of workloads that has now permeated the organization, started with log files and security kind of kind of cumbersome. But now it's like everywhere. So I wonder if you could talk to the sort of explosion of Splunk in the workloads and what kind of opportunity this provides for you guys. >> So a very good question here, Right? So what we have seen is that spunk has become the de facto platform for all of one structure data as customers start to realize the value of putting their trying to Splunk on the watch. Your spunk is that this is like a huge differentiate of us. Monk is the read only skim on reed which allows you to basically put all of the data without any structure and ask questions on the flight that allows you to kind of do investigations in real time, be more reactive. What's being proactive? We be more proactive. Was being reactive scaleable platform the skills of large data volumes, highly available platform. All of that are the reason why you're seeing an increase that option. We see the same thing with all other customers as well. They start off with one data source with one use case and then very soon they realize the power of Splunk and they start to add additional use cases in just more and more data sources. >> But this no >> scheme on writer you call scheme on Reed has been so problematic for so many big data practitioners because it just became the state of swamp. >> That didn't >> happen with Splunk. Was that because you had very defined use cases obviously security being one or was it with their architectural considerations as well? >> They just architecture, consideration for security and 90 with the initial use cases, with the fact that the scheme on Reid basically gives open subject possibilities for you. Because there's no structure to the data, you can ask questions on the fly on. You can use that to investigate, to troubleshoot and allies and take remedial actions on what's happening. And now, with our new acquisitions, we have added additional capabilities where we can talk, orchestrate the whole Anto and flow with Phantom, right? So a lot of these acquisitions also helping unable the market. >> So we've been talking about TAM expansion all week. We definitely hit it with Charlie pretty hard. I have. You know, I think it's a really important topic. One of things we haven't hit on is tam expansion through partnerships and that flywheel effect. So how do you see the partners ship with Splunk Just in terms of supporting that tam expansion the next 10 years? >> So, uh, analytics, particularly log and Alex have really taken off for us in the last year. As we put more focus on it, we want to double down on our investments as we go through the end of this year and in the next year with with a focus on Splunk um, a zealous other alliances. We think we are in a unique position because the rollout of smart store right customers are always on a different scale in terms of when they want to adopt a new architecture right. It is a significant decision that they have to make. And so we believe between the combination of flash array for the hot tear and flash played for the cold is a nice way for customers with classic Splunk architecture to modernize their platform. Leverage the benefits of data reduction to drive down some of the cost leverage. The benefits of Flash to increase the rate at which they can ask questions and get answers is a nice stepping stone. And when customers are ready because Flash Blade is one of the few storage platforms in the market at this scale out band with optimized for both NFS and object, they can go through a rolling nondestructive upgrade to smart store, have you no investment protection, and if they can't repurpose that flash rate, they can use peers of service to have the flesh raise the hot today and drop it back off just when they're done within tomorrow. >> And what about C for, you know, big workloads, like like big data workloads. I mean, is that a good fit here? You really need to be more performance oriented. >> So flash Blade is is high bandwith optimization, which really is designed for workload. Like Splunk. Where when you have to do a sparse search, right, we'll find that needle in the haystack question, right? Were you breached? Where were you? Briefed. How were you breached? Go read as much data as possible. You've gotta in just all that data, back to the service as fast as you can. And with beast Cloud blocked, Teresi is really optimized it a tear to form of NAND for that secondary. Maybe transactional data base or virtual machines. >> All right, I want more, and then I'm gonna shut up sick. The signal FX acquisition was very interesting to me for a lot of reasons. One was the cloud. The SAS portion of Splunk was late to that game, but now you're sort of making that transition. You saw Tableau you saw Adobe like rip the band Aid Off and it was somewhat painful. But spunk is it. So I wonder. Any advice that you spend Splunk would have toe von as pure as they make that transition to that sass model. >> So I think definitely, I think it's going to be a challenging one, but I think it's a much needed one in there in the environment that we are in. The key thing is to always because two more focus and I'm sure that you're already our customer focus. But the key is key thing is to make sure that any service is up all the time on make sure that you can provide that up time, which is going to be crucial for beating your customers. Elise. >> That's good. That's good guidance. >> You >> just wanted to cover that for you favor of keeping you date. >> So you gave us some of those really impressive stats In terms of performance. >> They're almost too good to be true. >> Well, what's customer feedback? Let's talk about the real world when you're talking to customers about those numbers. What's the reaction? >> So I don't wanna speak for Broth, so I will say in our engagements within their customer base, while we here, particularly from customers of scale. So the larger the environment, the more aggressive they are to say they will adopt smart store right and on a more aggressive scale than the smaller environments. And it's because the benefits of operating and maintaining the indexer cluster are are so great that they'll actually turn to the stores team and say, This is the new architecture I want. This is a new storage platform and again. So when we're talking about patch management, cluster expansion Harbor Refresh. I mean, you're talking for a large sum. Large installs weeks, not two or 3 10 weeks, 12 weeks on end so it can be. You can reduce that down to a couple of days. It changes your your operational paradigm, your staffing. And so it has got high impact. >> So one of the message that we're hearing from customers is that it's far so they get a significant reduction in the infrastructure spent it almost dropped by 2/3. That's really significant file off our large customers for spending a ton of money on infrastructure, so just dropping that by 2/3 is a significant driver to kind of move too smart. Store this in addition to all the other benefits that get smart store with operational simplicity and the ability that it provides. You >> also have customers because of smart store. They can now actually bursts on demand. And so >> you can think of this and kind of two paradigms, right. Instead of >> having to try to avoid some of the operational pain, right, pre purchase and pre provisional large infrastructure and hope you fill it up. They could do it more of a right sides and kind of grow in increments on demand, whether it's storage or compute. That's something that's net new with smart store um, they can also, if they have ah, significant event occur. They can fire up additional indexer notes and search clusters that can either be bare metal v ems or containers. Right Try to, you know, push the flash, too. It's Max. Once they found the answers that they need gotten through. Whatever the urgent issues, they just deep provisionals assets on demand and return back down to a steady state. So it's very flexible, you know, kind of cloud native, agile platform >> on several guys. I wish we had more time. But thank you so much fun. And Deron, for joining David me on the Cube today and sharing all of the innovation that continues to come from this partnership. >> Great to see you appreciate it >> for Dave Volante. I'm Lisa Martin, and you're watching the Cube?