from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape all right buddy welcome to this cute conversation my name is Dave Ville on time or the co-host of the cube and we're gonna have a conversation to really try to explore does infrastructure matter you hear a lot today I've ever since I've been in this business I've heard Oh infrastructure is dead hardware is dead but we're gonna explore that premise and with me is Randy Arsenault and Steve Kenaston they're both global market development execs at IBM guys thanks for coming in and let's riff thanks for having us Dave so here's one do I want to start with the data we were just recently at the MIT chief data officer event 10 years ago that role didn't even exist now data is everything so I want to start off with you here this bro my data is the new oil and we've said you know what data actually is more valuable than oil oil I can put in my car I can put in my house but I can't put it in both data is it doesn't follow the laws of scarcity I can use the same data multiple times and I can copy it and I can find new value I can cut cost I can raise revenue so data in some respects is more valuable what do you think right yeah I would agree and I think it's also to your point kind of a renewable resource right so so data has the ability to be reused regularly to be repurposed so I would take it even further we've been talking a lot lately about this whole concept that data is really evolving into its own tier so if you think about a traditional infrastructure model where you've got sort of compute and network and applications and workloads and on the edge you've got various consumers and producers of that data the data itself has those pieces have evolved the data has been evolving as well it's becoming more complicated it's becoming certainly larger and more voluminous it's better instrumented it carries much more metadata it's typically more proximal with code and compute so the data itself is evolving into its own tier in a sense so we we believe that we want to treat data as a tier we want to manage it to wrap the services around it that enable it to reach its maximum potential in a sense so guys let's we want to make this interactive in a way and I'd love to give you my opinions as well as links are okay with that but but so I want to make an observation Steve if you take a look at the top five companies in terms of market cap in the US of Apple Google Facebook Amazon and of course Microsoft which is now over a trillion dollars they're all data companies they've surpassed the bank's the insurance companies the the Exxon Mobil's of the world as the most valuable companies in the world what are your thoughts on that why is that I think it's interesting but I think it goes back to your original statement about data being the new oil the and unlike oil Ray's said you can you can put it in house what you can't put it in your car you also when it's burnt it's gone right but with data you you have it around you generate more of it you keep using it and the more you use it and the more value you get out of it the more value the company gets out of it and so as those the reason why they continue to grow in value is because they continue to collect data they continue to leverage that data for intelligent purposes to make user experiences better their business better to be able to go faster to be able to new new things faster it's all part of part of this growth so data is one of the superpowers the other superpower of course is machine intelligence or what everybody talks about as AI you know it used to be that processing power doubling every 18 months was what drove innovation in the industry today it's a combination of data which we have a lot of it's AI and cloud for scaling we're going to talk about cloud but I want to spend a minute talking about AI when I first came into this business AI was all the rage but we didn't have the amount of data that we had today we don't we didn't have the processing power it was too expensive to store all this data that's all changed so now we have this emerging machine intelligence layer being used for a lot of different inks but it's sort of sitting on top of all these workloads that's being injected into databases and applications it's being used to detect fraud to sell us more stuff you know in real time to save lives and I'm going to talk about that but it's one of these superpowers that really needs new hardware architectures so I want to explore machine intelligence a little bit it really is a game changers it really is and and and tying back to the first point about sort of the the evolution of data and the importance of data things like machine learning and adaptive infrastructure and cognitive infrastructure have driven to your point are a hard requirement to adapt and improve the infrastructure upon which that lives and runs and operates and moves and breathes so we always had Hardware evolution or development or improvements and networks and the basic you know components of the infrastructure being driven again by advances in material science and silicon etc well now what's happening is the growth and importance and and Dynamis city of data is far outpacing the ability of the physical sciences to keep pace right that's a reality that we live in so therefore things like you know cognitive computing machine learning AI are kind of bridging the gap almost between the limitations we're bumping up against in physical infrastructure and the immense unlocked potential of data so that intermediary is really where this phenomenon of AI and machine learning and deep learning is happening and you're also correct in pointing out that it's it's everywhere I mean it's imbuing every single workload it's transforming every industry and a fairly blistering pace IBM's been front and center around artificial intelligence in cognitive computing since the beginning we have a really interesting perspective on it and I think we bring that to a lot of the solutions that we offer as well Ginni Rometty a couple years ago actually use the term incumbent disruptors and when I think of that I think about artificial intelligence and I think about companies like the ones I mentioned before that are very valuable they have data at their core most incumbents don't they have data all over the place you know they might have a bottling plant at the core of the manufacturing plant or some human process at the core so to close that gap artificial intelligence from the incumbents the appointees they're gonna buy that from companies like IBM they're gonna you know procure Watson or other AI tools and you know or maybe you know use open-source AI tools but they're gonna then figure out how to apply those to their business to do whatever fraud detection or recommendation engines or maybe even improve security and we're going to talk about this in detail but Steve this there's got to be new infrastructure behind that we can't run these new workloads on infrastructure that was designed 30 40 years ago exactly I mean I think I am truly fascinated by with this growth of data it's now getting more exponential and why we think about why is it getting more exponential it's getting more exponential because the ease at which you can actually now take advantage of that data it's going beyond the big financial services companies the big healthcare companies right we're moving further and further and further towards the edge where people like you and I and Randi and I have talked about the maker economy right I want to be able to go in and build something on my own and then deliver it to either as a service as a person a new application or as a service to my infrastructure team to go then turn it on and make something out of that that infrastructure it's got to come down in cost but all the things that you said before performance reliability speed to get there intelligence about data movement how do we get smarter about those things all of the underlying ways we used to think about how we managed protect secure that it all has evolved and it's continuing to evolve everybody talks about the journey the journey to cloud why does that matter it's not just the cloud it's also the the componentry underneath and it's gonna go much broader much bigger much faster well and I would just add just amplify what Steve said about this whole maker movement one of the other pressures that that's putting on corporate IT is it's driving essentially driving product development and innovation out to the end to the very edge to the end user level so you have all these very smart people who are developing these amazing new services and applications and workloads when it gets to the point where they believe it can add value to the business they then hand it off to IT who is tasked with figuring out how to implement it scale it protect it secured debt cetera that's really where I believe I um plays a key role or where we can play a key role add a lot of values we understand that process of taking that from inception to scale and implementation in a secure enterprise way and I want to come back to that so we talked about data as one of the superpowers an AI and the third one is cloud so again it used to be processor speed now it's data plus AI and cloud why is cloud important because cloud enables scale there's so much innovation going on in cloud but I want to talk about you know cloud one dot o versus cloud two dot o IBM talks about you know the new era of cloud so what was cloud one dot o it was largely lift and shift it was taking a lot of crap locations and putting him in the public cloud it was a lot of tests in dev a lot of startups who said hey I don't need to you know have IT I guess like the cube we have no ID so it's great for small companies a great way to experiment and fail fast and pay for you know buy the drink that was one dot o cloud to dot all to datos is emerging is different it's hybrid it's multi cloud it's massively distributed systems distributed data on Prem in many many clouds and it's a whole new way of looking at infrastructure and systems design so as Steve as you and I have talked about it's programmable so it's the API economy very low latency we're gonna talk more about what that means but that concept of shipping code to data wherever it lives and making that cloud experience across the entire infrastructure no matter whether it's on Prem or in cloud a B or C it's a complicated problem it really is and when you think about the fact that you know the big the big challenge we started to run into when we were talking about cloud one always shadow IT right so folks really wanted to be able to move faster and they were taking data and they were actually copying it to these different locations to be able to use it for them simply and easily well once you broke that mold you started getting away from the security and the corporate furnance that was required to make sure that the business was safe right it but it but it but following the rules slowed business down so this is why they continued to do it in cloud 2.0 I like the way you position this right is the fact that I no longer want to move data around moving data it within the infrastructure is the most expensive thing to do in the data center so if I can move code to where I need to be able to work on it to get my answers to do my AI to do my intelligent learning that all of a sudden brings a lot more value and a lot more speed and speed as time as money rate if I can get it done faster I get more valuable and then just you know people often talk about moving data but you're right on you the last thing you want to do is move data in just think about how long it takes to back up the first time you ever backed up your iPhone how long it took well and that's relatively small compared to all the data in a data center there's another subtext here from a standpoint of cloud 2.0 and it involves the edge the edge is a new thing and we have a belief inside of wiki bond and the cube that we talk about all the time that a lot of the inference is going to be done at the edge what does that mean it means you're going to have factory devices autonomous vehicles a medical device equipment that's going to have intelligence in there with new types of processors and we'll talk about that but a lot of the the inference is that conclusions were made real-time and and by the way these machines will be able to talk to each other so you'll have a machine to machine communication no humans need to be involved to actually make a decision as to where should I turn or you know what should be the next move on the factory floor so again a lot of the data is gonna stay in place now what does that mean for IBM you still have an opportunity to have data hubs that collect that data analyze it maybe push it up to the cloud develop models iterate and push it back down but the edge is a fundamentally new type of approach that we've really not seen before and it brings in a whole ton of new data yeah that's a great point and it's a market phenomenon that has moved and is very rapidly moving from smartphones to the enterprise right so right so your point is well-taken if you look in the fact is we talked earlier that compute is now proximal to the data as opposed to the other way around and the emergence of things like mesh networking and you know high bandwidth local communications peer-to-peer communications it's it's not only changing the physical infrastructure model and the and the best practices around how to implement that infrastructure it's also fundamentally changing the way you buy them the way you consume them the way you charge for them so it's it's that shift is changing and having a ripple effect across our industry in every sense whether it's from the financial perspective the operational perspective the time to market perspective it's also and we talked a lot about industry transformation and disruptors that show up you know in an industry who work being the most obvious example and just got an industry from the from the bare metal and recreate it they are able to do that because they've mastered this new environment where the data is king how you exploit that data cost-effectively repeatably efficiently is what differentiates you from the pack and allows you to create a brand new business model that that didn't exist prior so that's really where every other industry is going you talking about those those those big five companies in North America that are that are the top top companies now because of data I often think about rewind you know 25 years do you think Amazon when they built Amazon really thought they were going to be in the food service business that the video surveillance business the drone business all these other book business right maybe the book business right but but their architecture had to scale and change and evolve with where that's going all around the data because then they can use these data components and all these other places to get smarter bigger and grow faster and that's that's why they're one of the top five this is a really important point especially for the young people in the audience so it used to be that if you were in an industry if you were in health care or you were in financial services or you were in manufacturing you were in that business for life every industry had its own stack the sales the marketing the R&D everything was wired to that industry and that industry domain expertise was really not portable across businesses because of data and because of digital transformations companies like Amazon can get into content they can get into music they can get it to financial services they can get into healthcare they can get into grocery it's all about that data model being portable across those industries it's a very powerful concept that you and I mean IBM owns the weather company right so I mean there's a million examples of traditional businesses that have developed ways to either enter new markets or expand their footprint in existing markets by leveraging new sources of data so you think about a retailer or a wholesale distributor they have to very accurately or as accurately as possible forecast demand for goods and make sure logistically the goods are in the right place at the right time well there are million factors that go into that there's whether there's population density there's local cultural phenomena there's all sorts of things that have to be taken into consideration previously that would be near impossible to do now you can sit down again as an individual maker I can sit down at my desk and I can craft a model that consumes data from five readily available public api's or data sets to enhance my forecast and I can then create that model execute it and give it to two of my IT guy to go scale-out okay so I want to start getting into the infrastructure conversation again remember the premise of this conversation it doesn't read for structure matter we want to we want to explore that oh I start at the high level with with with cloud multi-cloud specifically we said cloud 2.0 is about hybrid multi cloud I'm gonna make a statements of you guys chime in my my assertion is that multi cloud has largely been a symptom of multi-vendor shadow IT different developers different workloads different lines of business saying hey we want to we want to do stuff in the cloud this happened so many times in the IT business all and then I was gonna govern it how is this gonna be secure who's got access control on and on and on what about compliance what about security then they throw it over to IT and they say hey help us fix this and so itea said look we need a strategy around multi cloud it's horses for courses maybe we go for cloud a for our collaboration software cloud B for the cognitive stuff cloud C for the you know cheap and deep storage different workloads for different clouds but there's got to be a strategy around that so I think that's kind of point number one and I T is being asked to kind of clean up this stuff but the future today the clouds are loosely coupled there may be a network that connects them but there's there's not a really good way to take data or rather to take code ship it to data wherever it lives and have it be a consistent well you were talking about an enterprise data plane that's emerging and that's kind of really where the opportunity is and then you maybe move into the control plane and the management piece of it and then bring in the edge but envision this mesh of clouds if you will whether it's on pram or in the public cloud or some kind of hybrid where you can take metadata and code ship it to wherever the data is leave it there much smaller you know ship five megabytes of code to a petabyte of data as opposed to waiting three months to try to ship you know petabytes to over the network it's not going to work so that's kind of the the spectrum of multi cloud loosely coupled today going to this you know tightly coupled mesh your guys thoughts on that yeah that's that's a great point and and I would add to it or expand that even further to say that it's also driving behavioral fundamental behavioral and organizational challenges within a lot of organizations and large enterprises cloud and this multi cloud proliferation that you spoke about one of the other things that's done that we talked about but probably not enough is it's almost created this inversion situation where in the past you'd have the business saying to IT I need this I need this supply chain application I need this vendor relationship database I need this order processing system now with the emergence of this cloud and and how easy it is to consume and how cost-effective it is now you've got the IT guys and the engineers and the designers and the architects and the data scientists pushing ideas to the business hey we can expand our footprint and our reach dramatically if we do this so you've get this much more bi-directional conversation happening now which frankly a lot of traditional companies are still working their way through which is why you don't see you know 100% cloud adoption but it drives those very productive full-duplex conversations at a level that we've never seen before I mean we encounter clients every day who are having these discussions are sitting down across the table and IT is not just doesn't just have a seat at the table they are often driving the go-to-market strategy so that's a really interesting transformation that we see as well in addition to the technology so there are some amazing things happening Steve underneath the covers and the plumbing and infrastructure and look at we think infrastructure matters that's kind of why we're here we're infrastructure guys but I want to make a point so for decades this industry is marked to the cadence of Moore's law the idea that you can double processing speeds every 18 months disk drive processors disk drives you know they followed that curve you could plot it out the last ten years that started to attenuate so what happened is chip companies would start putting more cores on to the real estate well they're running out of real estate now so now what's happening is we've seen this emergence of alternative processors largely came from mobile now you have arm doing a lot of offload processing a lot of the storage processing that's getting offloaded those are ARM processors in video with GPUs powering a lot of a lot of a is yours even seeing FPGAs they're simple they're easy them to spin up Asics you know making a big comeback so you've seen these alternative processes processors powering things underneath where the x86 is and and of course they're still running applications on x86 so that's one sort of big thing big change in infrastructure to support this distributed systems the other is flash we saw flash basically take out spinning disk for all high-speed applications we're seeing the elimination of scuzzy which is a protocol that sits in between the the the disk you know and the rest of the network that's that's going away you're hearing things like nvme and rocky and PCIe basically allowing stores to directly talk to the so now a vision envision this multi-cloud system where you want to ship metadata and code anywhere these high speed capabilities interconnects low latency protocols are what sets that up so there's technology underneath this and obviously IBM is you know an inventor of a lot of this stuff that is really gonna power this next generation of workloads your comments yeah I think I think all that 100% true and I think the one component that we're fading a little bit about it even in the infrastructure is the infrastructure software right there's hardware we talked a lot talked about a lot of hardware component that are definitely evolving to get us better stronger faster more secure more reliable and that sort of thing and then there's also infrastructure software so not just the application databases or that sort of thing but but software to manage all this and I think in a hybrid multi cloud world you know you've got these multiple clauses for all practical purposes there's no way around it right marketing gets more value out of the Google analytic tools and Google's cloud and developers get more value out of using the tools in AWS they're gonna continue to use that at the end of the day I as a business though need to be able to extract the value from all of those things in order to make different business decisions to be able to move faster and surface my clients better there's hardware that's gonna help me accomplish that and then there are software things about managing that whole consetta component tree so that I can maximize the value across that entire stack and that stack is multiple clouds plus the internal clouds external clouds everything yeah so it's great point and you're seeing clear examples of companies investing in custom hardware you see you know Google has its own ship Amazon its own ship IBM's got you know power 9 on and on but none of this stuff works if you can't manage it so we talked before about programmable infrastructure we talked about the data plane and the control plane that software that's going to allow us to actually manage these multiple clouds as at least a quasi single entity you know something like a logical entity certainly within workload classes and in Nirvana across the entire you know network well and and the principal or the principle drivers of that evolution of course is containerization right so the containerization phenomenon and and you know obviously with our acquisition of red hat we're now very keenly aware and acutely plugged into the whole containerization phenomenon which is great we're you're seeing that becoming almost the I can't think of us a good metaphor but you're seeing containerization become the vernacular that's being spoken in multiple different types of reference architectures and use case environments that are vastly different in their characteristics whether they're high throughput low latency whether they're large volume whether they're edge specific whether they're more you know consolidated or hub-and-spoke models containerization is becoming the standard by which those architectures are being developed and with which they're being deployed so we think we're very well-positioned working with that emerging trend and that rapidly developing trend to instrument it in a way that makes it easier to deploy easier to instrument easier to develop so that's key and I want to sort of focus now on the relevance of IBM one side one thing that we understand because that that whole container is Asian think back to your original point Dave about moving data being very expensive and the fact that the fact that you want to move code out to the data now with containers microservices all of that stuff gets a lot easier development becomes a lot faster and you're actually pushing the speed of business faster well and the other key point is we talked about moving code you know to the data as you move the code to the data and run applications anywhere wherever the data is using containers the kubernetes etc you don't have to test it it's gonna run you know assuming you have the standard infrastructure in place to do that and the software to manage it that's huge because that means business agility it means better quality and speed alright let's talk about IBM the world is complex this stuff is not trivial the the more clouds we have the more edge we have the more data we have the more complex against IBM happens to be very good at complex three components of the innovation cocktail data AI and cloud IBM your customers have a lot of data you guys are good with data it's very strong analytics business artificial intelligence machine intelligence you've invested a lot in Watson that's a key component business and cloud you have a cloud it's not designed to compete not knock heads and the race to zero with with the cheap and deep you know storage clouds it's designed to really run workloads and applications but you've got all three ingredients as well you're going hard after the multi cloud world for you guys you've got infrastructure underneath you got hardware and software to manage that infrastructure all the modern stuff that we've talked about that's what's going to power the customers digital transformations and we'll talk about that in a moment but maybe you could expand on that in terms of IBM's relevance sure so so again using the kind of maker the maker economy metaphor bridging from that you know individual level of innovation and creativity and development to a broadly distributed you know globally available work loader or information source of some kind the process of that bridge is about scale and reach how do you scale it so that it runs effectively optimally is easily managed Hall looks and feels the same falls under the common umbrella of services and then how do you get it to as many endpoints as possible whether it's individuals or entities or agencies or whatever scale and reach iBM is all about scale and reach I mean that's kind of our stock and trade we we are able to take solutions from small kind of departmental level or kind of skunkworks level and make them large secure repeatable easily managed services and and make them as turnkey as possible our services organizations been doing it for decades exceptionally well our product portfolio supports that you talk about Watson and kind of the cognitive computing story we've been a thought leader in this space for decades I mean we didn't just arrive on the scene two years ago when machine learning and deep learning and IO ste started to become prominent and say this sounds interesting we're gonna plant our flag here we've been there we've been there for a long time so you know I kind of from an infrastructure perspective I kind of like to use the analogy that you know we are technology ethos is built on AI it's built on cognitive computing and and sort of adaptive computing every one of our portfolio products is imbued with that same capability so we use it internally we're kind of built from AI for AI so maybe that's the answer to this question of it so what do you say that somebody says well you know I want to buy you know my flash storage from pure AI one of my bi database from Oracle I want to buy my you know Intel servers from Dell you know whatever I want to I want to I want control and and and I gotta go build it myself why should I work with IBM do you do you get that a lot and how do you respond to that Steve I think I think this whole new data economy has opened up a lot of places for data to be stored anywhere I think at the end of the day it really comes down to management and one of the things that I was thinking about as you guys were we're conversing is the enterprise class or Enterprise need for things like security and protection that sort of thing that rounds out the software stack in our portfolio one of the things we can bring to the table is sure you can go by piece parts and component reform from different people that you want right and in that whole notion around fail-fast sure you can get some new things that might be a little bit faster that might be might be here first but one of the things that IBM takes a lot of pride was a lot of qual a lot of pride into is is the quality of their their delivery of both hardware and software right so so to me even though the infrastructure does matter quite a bit the question is is is how much into what degree so when you look at our core clients the global 2,000 right they want to fail fast they want to fail fast securely they want to fail fast and make sure they're protected they want to fail fast and make sure they're not accidentally giving away the keys to the kingdom at the end of the day a lot of the large vendor a lot of the large clients that we have need to be able to protect their are their IP their brain trust there but also need the flexibility to be creative and create new applications that gain new customer bases so the way I the way I look at it and when I talk to clients and when I talk to folks is is we want to give you them that while also making sure they're they're protected you know that said I would just add that that and 100% accurate depiction the data economy is really changing the way not only infrastructure is deployed and designed but the way it can be I mean it's opening up possibilities that didn't exist and there's new ones cropping up every day to your point if you want to go kind of best to breed or you want to have a solution that includes multi vendor solutions that's okay I mean the whole idea of using again for instance containerization thinking about kubernetes and docker for instance as a as a protocol standard or a platform standard across heterogeneous hardware that's fine like like we will still support that environment we believe there are significant additive advantages to to looking at IBM as a full solution or a full stack solution provider and our largest you know most mission critical application clients are doing that so we think we can tell a pretty compelling story and I would just finally add that we also often see situations where in the journey from the kind of maker to the largely deployed enterprise class workload there's a lot of pitfalls along the way and there's companies that will occasionally you know bump into one of them and come back six months later and say ok we encountered some scalability issues some security issues let's talk about how we can develop a new architecture that solves those problems without sacrificing any of our advanced capabilities all right let's talk about what this means for customers so everybody talks about digital transformation and digital business so what's the difference in a business in the digital business it's how they use data in order to leverage data to become one of those incumbent disruptors using Ginny's term you've got to have a modern infrastructure if you want to build this multi cloud you know connection point enterprise data pipeline to use your term Randy you've got to have modern infrastructure to do that that's low latency that allows me to ship data to code that allows me to run applet anywhere leave the data in place including the edge and really close that gap between those top five data you know value companies and yourselves now the other piece of that is you don't want to waste a lot of time and money managing infrastructure you've got to have intelligence infrastructure you've got to use modern infrastructure and you've got to redeploy those labor assets toward a higher value more productive for the company activities so we all know IT labor is a chop point and we spend more on IT labor managing Leung's provisioning servers tuning databases all that stuff that's gotta change in order for you to fund digital transformations so that to me is the big takeaway as to what it means for customer and we talked about that sorry what we talked about that all the time and specifically in the context of the enterprise data pipeline and within that pipeline kind of the newer generation machine learning deep learning cognitive workload phases the data scientists who are involved at various stages along the process are obviously kind of scarce resources they're very expensive so you can't afford for them to be burning cycles and managing environments you know spinning up VMs and moving data around and creating working sets and enriching metadata that they that's not the best use of their time so we've developed a portfolio of solutions specifically designed to optimize them as a resource as a very valuable resource so I would vehemently agree with your premise we talked about the rise of the infrastructure developer right so at the end of the day I'm glad you brought this topic up because it's not just customers it's personas Pete IBM talks to different personas within our client base or our prospect base about why is this infrastructure important to to them and one of the core components is skill if you have when we talk about this rise of the infrastructure developer what we mean is I need to be able to build composable intelligent programmatic infrastructure that I as IT can set up not have to worry about a lot of risk about it break have to do in a lot of troubleshooting but turn the keys over to the users now let them use the infrastructure in such a way that helps them get their job done better faster stronger but still keeps the business protected so don't make copies into production and screw stuff up there but if I want to make a copy of the data feel free go ahead and put it in a place that's safe and secure and it won't it won't get stolen and it also won't bring down the enterprise's is trying to do its business very key key components - we talked about I infused data protection and I infused storage at the end of the day it's what is an AI infused data center right it needs to be an intelligent data center and I don't have to spend a lot of time doing it the new IT person doesn't want to be troubleshooting all day long they want to be in looking at things like arm and vme what's that going to do for my business to make me more competitive that's where IT wants to be focused yeah and it's also we just to kind of again build on this this whole idea we haven't talked a lot about it but there's obviously a cost element to all this right I mean you know the enterprise's are still very cost-conscious and they're still trying to manage budgets and and they don't have an unlimited amount of capital resources so things like the ability to do fractional consumption so by you know pay paper drink right buy small bits of infrastructure and deploy them as you need and also to Steve's point and this is really Steve's kind of area of expertise and where he's a leader is kind of data efficiency you you also can't afford to have copy sprawl excessive data movement poor production schemes slow recovery times and recall times you've got a as especially as data volumes are ramping you know geometrically the efficiency piece and the cost piece is absolutely relevant and that's another one of the things that often gets lost in translation between kind of the maker level and the deployment level so I wanted to do a little thought exercise for those of you think that this is all you know bromide and des cloud 2.0 is also about we're moving from a world of cloud services to one where you have this mesh which is ubiquitous of of digital services you talked about intelligence Steve you know the intelligent data center so all these all these digital services what am I talking about AI blockchain 3d printing autonomous vehicles edge computing quantum RPA and all the other things on the Gartner hype cycle you'll be able to procure these as services they're part of this mesh so here's the thought exercise when do you think that owning and driving your own vehicle is no longer gonna be the norm right interesting thesis question like why do you ask the question well because these are some of the disruptions so the questions are designed to get you thinking about the potential disruptions you know is it possible that our children's children aren't gonna be driving their own car it's because it's a it's a cultural change when I was 16 year olds like I couldn't wait but you started to see a shifted quasi autonomous vehicles it's all sort of the rage personally I don't think they're quite ready yet but it's on the horizon okay I'll give you another one when will machines be able to make better diagnosis than doctors actually both of those are so so let's let's hit on autonomous and self-driving vehicles first I agree they're not there yet I will say that we have a pretty thriving business practice and competency around working with a das providers and and there's an interesting perception that a das autonomous driving projects are like there's okay there's ten of them around the world right maybe there's ten metal level hey das projects around the world what people often don't see is there is a gigantic ecosystem building around a das all the data sourcing all the telemetry all the hardware all the network support all the services I mean building around this is phenomenal it's growing at a had a ridiculous rate so we're very hooked into that we see tremendous growth opportunities there if I had to guess I would say within 10 to 12 years there will be functionally capable viable autonomous vehicles not everywhere but they will be you will be able as a consumer to purchase one yeah that's good okay and so that's good I agree that's a the time line is not you know within the next three to five years all right how about retail stores will well retail stores largely disappeared we're we're rainy I was just someplace the other day and I said there used to be a brick-and-mortar there and we were walking through the Cambridge Tseng Galleria and now the third floor there's no more stores right there's gonna be all offices they've shrunken down to just two floors of stores and I highly believe that it's because you know the brick you know the the retailers online are doing so well I mean think about it used to be tricky and how do you get in and and and I need the Walmart minute I go cuz I go get with Amazon and that became very difficult look at places like bombas or Casper or all the luggage plate all this little individual boutique selling online selling quickly never having to have to open up a store speed of deployment speed of product I mean it's it's it's phenomenal yeah and and frankly if if Amazon could and and they're investing billions of dollars and they're trying to solve the last mile problem if Amazon could figure out a way to deliver ninety five percent of their product catalog Prime within four to six hours brick-and-mortar stores would literally disappear within a month and I think that's a factual statement okay give me another one will banks lose control traditional banks lose control of the payment systems you can Moselle you see that banks are smart they're buying up you know fin tech companies but right these are entrenched yeah that's another one that's another one with an interesting philosophical element to it because people and some of its generational right like our parents generation would be horrified by the thought of taking a picture of a check or using blockchain or some kind of a FinTech coin or any kind of yeah exactly so Bitcoin might I do my dad ask you're not according I do I don't bit going to so we're gonna we're waiting it out though it's fine by the way I just wanted to mention that we don't hang out in the mall that's actually right across from our office I want to just add that to the previous comment so there is a philosophical piece of it they're like our generation we're fairly comfortable now because we've grown up in a sense with these technologies being adopted our children the concept of going to a bank for them will be foreign I mean it will make it all have no context for the content for the the the process of going to speak face to face to another human it just say it won't exist well will will automation whether its robotic process automation and other automation 3d printing will that begin to swing the pendulum back to onshore manufacturing maybe tariffs will help to but again the idea that machine intelligence increasingly will disrupt businesses there's no industry that's safe from disruption because of the data context that we talked about before Randy and I put together a you know IBM loves to use were big words of transformation agile and as a sales rep you're in the field and you're trying to think about okay what does that mean what does that mean for me to explain to my customer so he put together this whole thing about what his transformation mean to one of them was the taxi service right in the another one was retail so and not almost was fencers I mean you're hitting on on all the core things right but this transformation I mean it goes so deep and so wide when you think about exactly what Randy said before about uber just transforming just the taxi business retailers and taxis now and hotel chains and that's where the thing that know your customer they're getting all of that from data data that I'm putting it not that they're doing work to extract out of me that I'm putting in so that autonomous vehicle comes to pick up Steve Kenaston it knows that Steve likes iced coffee on his way to work gives me a coupon on a screen I hit the button it automatically stops at Starbucks for me and it pre-ordered it for me you're talking about that whole ecosystem wrapped around just autonomous vehicles and data now it's it's unbeliev we're not far off from the Minority Report era of like Anthem fuck advertising targeted at an individual in real time I mean that's gonna happen it's almost there now I mean you just use point you will get if I walk into Starbucks my phone says hey why don't you use some points while you're here Randy you know so so that's happening at facial recognition I mean that's all it's all coming together so and again underneath all this is infrastructure so infrastructure clearly matters if you don't have the infrastructure to power these new workloads you're drugged yeah and I would just add and I think we're all in agreement on that and and from from my perspective from an IBM perspective through my eyes I would say we're increasingly being viewed as kind of an arms dealer and that's a probably a horrible analogy but we're being used we're being viewed as a supplier to the providers of those services right so we provide the raw materials and the machinery and the tooling that enables those innovators to create those new services and do it quickly securely reliably repeatably at a at a reasonable cost right so it's it's a step back from direct engagement with consumer with with customers and clients and and architects but that's where our whole industry is going right we are increasingly more abstracted from the end consumer we're dealing with the sort of assembly we're dealing with the assemblers you know they take the pieces and assemble them and deliver the services so we're not as often doing the assembly as we are providing the raw materials guys great conversation I think we set a record tends to be like that so thank you very much for no problem yeah this is great thank you so much for watching everybody we'll see you next time you're watching the cube

>> Live from Las Vegas, it's theCube. Covering VMworld 2017. Brought to you by vmware and it's ecosystem partner. (techno beat) >> I'm Stu Miniman and this is theCube. Happy to welcome back to the program two guests we've had on a few times. Randy Arseneau, who's the CMO of Inifindat, and Bryan Carmody who's the CTO at Inifinidat. Gentlemen, thanks so much for joining us. >> Hey, Stu, what's going on? >> Thanks, Stu, good to be here. >> Alright, so, it's Vmworld time again, a lot going on. We set, kind of a vibe of the show already. It's about the same attendance as last year, but the vibe feels good. Pat is actually hitting the stride on the keynote, talking about Amazon, talking about momentum that they have. You guys have had some announcements recently. Randy, why don't you start us off. Tell us about, you know, the update of Inifinidat, how many customers you've got, what can you share? >> So, thanks, Stu, thanks for having us again, it's great to, great to be back on theCube. So, yeah, we've, over the last few years, three and a half years that we've been shipping our product, we've been able to sustain a really good, consistent cadence of growth, and that's continued into this year. So, a few weeks ago, a couple weeks ago, we announced our most recent performance, financial performance. We're continuing to more than double each quarter year over year. We are profitable from a gap revenue perspective, which is kind of unheard of in our industry, so we think we're breaking the trend of a lot of the storage startups, and even some established storage players that are having a really difficult time making their financial and business model and go to market work. Ours is clearly working, we're generating revenue, we're growing our customer base. We now have over two exabytes of storage in service world wide. We figured out about 80-ish, 80 percent, plus or minus a couple of percentage points of that is running vmware. So, we think this is kind of an obvious place for us to be in terms of the affinities of our customer base. And about 50 percent of our systems are actually dedicated to vmware, so they're running huge vmware farms. So, the financial performance has continued to be really solid, we're bucking the trend in the industry in terms of being profitable, and continuing to grow the business at a really aggressive rate. Because the solution works, I mean it's not rocket science, right, we have a product. >> I hear you have trouble raising money if you're profitable, so that's challenging. Yeah, and congratulations on the momentum. The joke a few years ago have been Vmworld became storage world, and you know, we spent years talking about, oh it's, you know flash is there, and then the software to find data center. The only mention of like, storage that I really heard in the keynote this morning was Pat talking about "Oh, they've got about 10,000 customers running vsand." So, Bryan, a lot of waves going on, we've had a number of conversations about where you fit, bring us up to speed as to, you know, what are the conversations you're having with customers, you know, what are the important trends to them, and where your technology, how do you position yourselves there? >> Oh, sure, so I mean, I think from customers perspectives it's all just storage, they're all data stores, and the different architectures, and the different delivery models are, they don't really matter at the end of the day. What your CIO, which he cares about, is what is the acquisition cost, what's the operational cost? What's the performance that it delivers, the latency in through put? And what's the availability of the data store? And, you know, what we're seeing, especially with the software defined storage systems, and vsands, is they work, but they work for small capacities, when you try to scale them, what every customer, without exception, sees is that they add three dollars in server cost for every dollar in storage array cost avoidance. So, you know, these projects tend to not be very, they tend to be career limiting, you know, and that's why what we're hearing, especially at Wall Street, is that it's vscam, not vsand. >> Stu: Wow. >> Yeah, but for small workloads where, and environments where you're looking to get a, you have a single person who needs to do storage, and manage the hyper visors, it absolutely works. I think it's a, it's a killer robome and small business solution. >> Yeah, it's interesting, cos there's this growth of solutions that use storage, but they aren't in a position to storage, and vsands, and a lot of hyper conversions like that. >> I like the virtualization admin, I've got some app, I just want a management, I don't have to want a, you know, God, that storage stuff's hard. You know, so they'll kind of do that pieces, versus, you know, real storage, you know, like care about reliability. >> Go ask Pat what the average size of those customers are. Like, my grandmother is one of those customers, you know, she uses it. But, yeah, so I >> You come from a heavy technology family, though, so. >> Yes, exactly. She was the first vm certified person I probably know. So, clearly the part of the market that we're going after is very different from that. Our systems start at, well they get interesting at a petabyte of usable capacity. By far our most popular model is a petabyte and a half of effective capacity. Our largest system scales up to ten petabytes in a single system manage, in a single rack. So, these are big monster cloud scale vmware environments. That's where, you know, our customers are having awesome success. And you know, it's not just limited to vmware, though. You know, you can take the same system, the same skew, and you can use it to replace data domain systems for backup to disc. The largest splunk insulation in the world is running at one of the US, big US telecoms and is running the same skew that our customers are using for their big petabyte scale vmware environments. Analytics, which is probably the biggest growing thing, one area that Randy is working pretty heavily in, it's absolutely exploding. >> Yeah, it's, which is cool in a sense, because we've been, you know, and I kind of use the tongue in cheek term "accidental tourists." I mean, we sell this system into an incredibly wide range of workload environments, and enterprise environments. Which is why we have a really strong presence in every vertical, I mean we're strong in health care and life sciences, we're strong in financial services, we're strong in retail and manufacturing, we're strong in utilities, we're strong in cloud providers. And it's exactly because of the fact that the system is designed and architected expressly to be very flexible and very adaptable. So, we never shy away from the concept of general purpose storage, I mean that became very unfashionable about five or six years ago, when everything had to be hyper-specialized and fit for purpose. But, when we can walk into an environment, and as Bryan said, most of our customers tend to be fairly, you know, midsize and large enterprises, they don't have one particular type or class of workload, they've got 100. And they're running 100 different storage systems. So we can go in as a consolidation play and say "Look, let's take all of that vmware environment, all of that, you know, take your data domain and your backup protection environment, your analytic workload environments, and move them off of these disparate platforms onto this one, you know, very capable, very flexible system. They all peacefully coexist, they all perform phenomenally well. It's immensely easy. We have a customer presentation that's going to be talking about exactly how easy it is. We have another Cube session where another one of our customers is going to share the beauty of integration and orchestration automation using our API. So, we kind of have a large enterprise class, extremely flexible, fully composable storage system that you can really plugin anywhere. I mean, we've talked before about how in some environments, there might be one or two little fringe applications somewhere that require some weird configuration of flash, or you know, in memory database or something that's five terabytes, that's running on some strange system, and that's fine, like, we're happy to leave that there. We will go after the other 95 percent of the workloads in your environments and we'll take them all, and do so very happily. >> Yeah, it's interesting, we tracked kind of that wave of big data, and especially like hadoop, and I went to all of these shows and they'd be like "Oh, you know, hgfs, you know, don't put it on a storage array because it's too expensive!" And when you dug into it, it was, you know, a couple of servers sitting under somebody's desk. >> Right. >> So, it wasn't real storage, like you said, but it was cost, and it was there, but what I'm excited about is when I'm hearing about the new kind of analytics things. When you start talking about, you know, AI and machine learning, and everything like that, you've got to have, you've got real storage issues, and how are you attacking the price, and how are you architecting to be ready for those types of applications? >> Yeah, and to Bryan's point the telecoms we've got the one running the largest splunk environment of the world, we've got another that's running a huge elk environment, the architect presented at elasticon this year, that's all running on Inifinibox. So, again, we haven't specifically architected the solution necessarily for those, but our customers, you know, God bless 'em, bring it in, plug it in, try it, because it's so simple, there's really no downside to experimenting with it, and they discover "Wow, this actually works exceptionally well." >> Yeah, and I think if, if you kind of step back from specific workloads, analytics or vmware, or whatever, what customers for the next decade are asking for is pretty consistent. And it's pretty easy to understand. They want to be able to do sub-millisecond response times. They want to do very high multi-gigabyte per second, throughput. They want to do it over petabyte scale datacents, and they want to do it at a vastly lower cost per gigabyte than the kind of traditional enterprise storage products. And if you build that, they will come. And I think that's what we did, and I think it's a huge part of, you know, the success that our customers are having. And the momentum that kind of our company has right now, is just doing all of those things simultaneously. >> Alright, so, from a price standpoint, I mean, price and simplicity, kind of been the things that we've been beaten on for the storage industry. You know, what, how do you position that, you know, what is kind of the killer, you know, thing that makes the customers come to you and say, you know, "Wow, you guys are different and that's going to solve." >> You know, so, we unabashedly, in every business school, you know, whatever, they tell you "Don't sell on price, sell on value," and we have kind of been doing the opposite of that. Since day one, since day one. The first communication to a potential customer is we put a number out there. That number will be a tenth, on a cost per gig basis, of, you know, of what they're paying today. And it's a, it's something that nobody can say no to, it's a demonstration that we're really serious about what we're capable of doing. So, then that only works if you back it up, then, when the customer does an evaluation, and the bake offs, and the competitive stuff. You have to absolutely destroy everything else out there, or else you get pigeonholed as a tier two, a tier three. And I think a lot of the, a lot of the newer companies are kind of falling into that, where they're, they have traction, but they're really not getting into enterprise accounts, they're not getting life safety and mission critical workloads put on them. So, we unabashedly lead with price, and, you know, at the end of the day, every time you instantiate a cost function reduction, in storage, it makes new types of computing possible. You put that storage in the hands of developers, and they tell their management teams "Here's what we can do with this." We are trying to make storage less expensive. >> Yeah, and although, you know, you're not supposed to sell on price, you sell on value, the problem is nobody buys on value, so you still have to be price sensitive. And you have to have a solution that is economically feasible, and viable, and attractive. So, we've got a very, very attractive TCO structure and model that we've used in just about every of our major sales campaigns. And we have to monstroubly, significantly lower cost, not just of acquisition, but of ongoing operation. So, when you layer all those things together, you can sell to the pure technologists who love the kind of robustness and the feature richness of the capability, and where they can apply it, and how they can apply it, but it also has a very attractive financial story, so when you're selling it to the business owners and the kind of, you know, other constituencies, it's a story that everybody likes, so. >> Yeah, lot of people in the storage industry, it's always, you know, that next thing, flash was a wavy road for a while, you know, when I go talk to the storage geeks, it's the "Oh, nvme over fabric." It's going to dramatically change everything. >> Bryan: It is. >> What's your take? >> Oh, yeah, yeah, it's huge, it's huge. You know, it's always a catch up game between the network and the transport technologies, and then the storage media. So, you know, nvme over fabric's is huge, but you know, you have to use it the right way, and I think that it's not being used correctly by the marketers, you know, who are running you know. A lot of the storage companies, they're using it as a way to justify their pricing. They're using it as a way to make storage expensive. And it's kind of the, again, it's the opposite of our strategy. What every customer is demanding from their vendors, is "I need my storage next quarter to be less expensive than it is, this quarter next year needs to be cheaper than this year, how are you going to do that for me?" So, advanced technologies, like, nvme and nvme over fabrics, and optane and three d crosspoint, these things all have, they're incredibly strategic technologies. But, you have to use them the right way, you have to always keep an eye on the bottom line, and be very suspicious of technologists that are trying to make infrastructure more expensive, rather than less. >> Yeah, and I mean, it's always, it's not just the technology, it's the application, and I think a lot of vendors in our space have a tendency to focus exclusively on the technology, and how to build an architecture around it, or repurpose an existing architecture, more commonly, without really thinking about the application of that technology. Where is it going to be used, how is it going to be used, what's the cost structure have to look like, what's the use-case environment look like, what verticals am I going to sell it to, what's the channel ecosystem look like? They kind of tend to save that for the last, so they develop this whiz bang, you know, solution, which is again, typically, an aging architecture that maybe has some new foundational layers of technology or media built into it, without really thinking about the end game. So, that's one of the many things that I think Moshe Yanai does better than anybody, is he looks at the problem from the outside in. He meets with customers on a daily basis, I mean, he's kind of a maniac in terms of traveling around and meeting with customers. He has a phenomenal reputation, for obvious reasons, and he listens. He listens to their problems, he listens to what they confront and what they fight with every day, to kind of make a solution that works for them, and then he adapts that to his design ethos. Not, it's not the other way around. So, we don't develop something and then go try to force fit it into a market or into an environment. >> Yeah, last thing I wanted to ask you is; users coming to a show like this, they love to be able to hear from their peers, you've got a whole bunch of customers telling their stories, what are some of the key takeaways that, you know, peers talking to peers, that they're going to be hearing this week at the show? >> Yeah, so, there's, it's a lot of the same things we've been talking about here, you know. It's cost takeout, frankly, I mean, first and foremost, these are customers that are under tremendous cost pressure. They have used us as a consolidation platform to take costs out, but deliver a higher quality of service. We have, so we have a breakfast we organize, we've got a bunch of our customers. The other thing I love about our customers is they have a tendency to be kind of groupies, and I use that term you know, very favorably, because they're immensely loyal to the system, because it simply makes their life better and easier and allows them to focus on other tasks. So, they're talking about cost reduction and consolidation, they're talking about delivering higher performance. Very, very simply, they're talking about the ease of integration and orchestration and automation using our API. So, plugging our system in and just, it becomes a magnet for workloads. They bring it in for a particular project, and as other growth occurs, in insularly areas, it just gets moved on to the infinibox because it's incredibly easy, and it's a painless, seamless, frictionless process, so. >> Bryan, I'm going to give you a final word, takeaways for the show that you want people to have from Infinidat. >> Oh, I just, I really want everybody to have a great time, come by, check out the booth, we have an espresso machine, we'll talk a little bit about some of the computer science behind the system, and, but more than anything, I want everybody to have a really good time at the event. >> Well, great point, everybody, Vmworld, always a great community, lots of great conversations, everybody geeking out on the technology, and getting some caffeine to help them through what is a very long week. So, we're at the beginning of three days of live coverage here, double set. Thank you, Randy Arseneau, Bryan Carmody. >> Thanks, Stu, if you chroma key my shirt, just be gentle, that's all I ask, thank you. >> Alright, we'll be back with lots more coverage. Thanks for watching theCube. (techno music)

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE! Now, here's your host, Stu Miniman! (bubbly music) >> Hi, I'm Stu Miniman and you're watching theCUBE's Boston area studio. Happy to welcome back to the program Randy Arseneau and Steve Kenniston. Gentlemen. >> We're back! (Stu laughs) >> Absolutely. >> It sees like only minutes ago we were here. >> How can I miss you if you don't go away, oh wow. Gentlemen, thank you so much. We've been talking storage and SDI, which of course, is software defined infrastructure essentials. We're gonna dig inside and Steve, let's start with, you know, sometimes we argue over definitional things, and when you hear software defined, oh it's about software, and especially when you talk about the storage world, it's like wait, there's always been software when we talk about storage. So explain why it's a little bit different now, then what we were doing, you know, even five years ago. >> Sure thing Stu, I think one of the number one things that we run into a lot of that we hear, conversationally, it's storage and it's software. And now we're hearing a lot more about data services, right? The ability to connect data, so forget the physical storage for a second. Connect the data to the people, right? Because as we've been talking all along today, right, this evolutionary platform is being able to provide more people access to more data than they've ever had before in a very secure way, right? Such that, they can actually not only get their jobs done, get 'em done better, get 'em done stronger, get 'em done faster, without all the, as my boss likes to say bouja bouja, (laughs) dealing with having to get at that data. So, if I look at before and after, right? If I look at the before aspect that dealt with, you know, how do I get to my data, right? Or how do I get access to that data if I'm a developer of five years ago, right? It ends up being, and you can attest to this, and please do, right? A conversation between the developer and IT. And then it becomes a myriad of questions back and forth about why do you need it. What do you need it for? How much do you need? Where does it need to live? How fast does it need to be? All of the things that you can get today programmed, right? Into a programmable infrastructure, and just say click and then we provide that to you, right? So before it was all these conversations. Then I gotta buy it, then I gotta procure it, then I gotta secure it, then I gotta know how many LUNs you need, and, how fast it is, and then I gotta provision that out to you, right? And you go okay, and then you say well now you need that. And then the next conversation is well I can't get to it with this application which is on this server which is, now it's another whole, you know, before a developer can start working, it could be a month. Whereas afterwards, it should be, if you have a good programmable infrastructure, I have my application like a Chef or a Puppet or an Ansible, and I push a button and it says, okay I need this particular data set, and it needs to have this set of services. It needs to be this available, it needs to perform this fast, I need to be able to make these types of copies, click go, right? That's kinda where we're at today with what we're hoping the software can do for you. >> Yeah, I always worry, sometimes we try to oversimplify things and we miss, kinda the why, so. One of my favorite jokes we all had is, you know, there is no cloud, it's just a computer somewhere else, because, and it was like no, no, no, wait, there's still gear underneath it. But I missed the: well why does that matter? It's like, oh wait, I've got an order of magnitude more, you know, that I can access for short periods of time, and therefore I can do things that I couldn't do before. And when I think about data it's, you know, you know, big data, you know, massive data, things like no, no, no, no, no, it's not just storing bunch of bits somewhere in case I need them for a regulatory thing, because I've gotta do governance compliance, blah, blah, blah, and everything, but, it's like: Wow! You know, data is, you know, a driver for business, and therefore, you know, data services that allow me to protect and secure and access all those data, is so super important. >> Absolutely, and there's another analog I think is it's, if you think about data services, as we're talking about it right now, it's becoming more of, and the self service metaphor is a very important one I think because, to Steve's earlier point, in the old world, you know, you used to have to go through this complicated workflow and checkpoints and sign-offs and all these procedural, you know, loopholes that you'd have to jump through in order to provision something which, by the time it became available it was probably outdated, right? So, you're constantly behind, right? Now, at the pace of global business and digital business and the importance of data as a driver of global business, you just don't have that luxury anymore, you can't afford to be waiting on the availability of that piece of information so, not only does the immediacy of it improve the actual value of the data, because there's always a temporal element of value for that data, and every second you waste is potentially value that's diminished from data. So not only do we now reduce that kind of latency, we also provide much better reuse, so we talk about this idea of incremental value, right? So you can take the same data element or data construct and now instantaneously repurpose it in multiple ways to extract additional maybe unforeseen value from it, right? So we've gotten away from the concept of these kinda siloed systems that are, you know, this is my production system, this is my OLAP system, my reporting system, we now have this convergence, cross pollination of data, that flows between and among all these systems, which can now be made available via something like a data services platform or a, you know, fill in the blank as a service type platform in the self service mode, where it can be used by any number of different applications and users for any number of different purposes. >> Yeah and what I like about it, what I hear from customers, it used to be, you had to have the budget of a nation or a team of PhDs to try to figure this out. And as you say, with this cross pollination, when I get the data versus when I'm gonna need the data, yes there's the temporal piece, but sometimes it's, I might be doing it for a different purpose, or I'm not sure and things are changing all the time. So that, going back to our initial conversation, that agility and flexibility, being able to access and, you know, tie into that data, and have a range of services is so critically important. >> Well that's a good comment, right? You need to have the budget of a nation in order to do this, and then along came all the cloud providers and said: no you don't, swipe your credit card and start working, right? The tricky part for me, the consumer, of that, great, now I've got the processing power I need, but I need my data to build my particular application. I love, sorta to finish that outright. In order to build the right application, to make sure it works for what I'm gonna do because at some point, right, and I know Dave likes to talk about this a lot, I need to be able to integrate it into my existing systems, and if the performance isn't the same and that's what, I'm gonna probably run into a lot of buggy stuff over here. So I need to be careful with that. What I think is interesting is, and I love to use the analogy, think of the first bank that came up with the application where I could snap a picture of my check, right? I bet the CIO of the competitive company went to his development team the next morning after seeing that commercial and said I want one of those. Do you think, I mean, if you broke, and I don't like always like to use particular verticals, because I think this conversation extends across all applications. But do you, if you had to break down what does one banking customer cost the bank over time, right? And then you said for every day I'm late I lose five customers, and you had to go through that whole lengthy process just to get started for the development team to start working on something like that, with their data, right? I've gotta make sure that the data fits into how does a deposit work, how does it transact, how does it show, when does it show? All those stuff matter to my data, right? I need to put that underneath. But now I can do it, if I can do it programmatically, or provide the infrastructure as a service, hit a button, and I don't have to be a rocket scientist, right, I can just do it for my application, now I'm up and running. >> Well, and flipping it, and looking at it from the providers perspective. So if I'm the consumer of those data services, it's great for me. If I'm the provider of those services it's also very beneficial to me, because now, having that elasticity and that fractional consumption model where I can offer you exactly as much compute or storage or, you know, analytic horsepower you need for your particular use-case and environment for as long as you need it. That gives me a tremendous value proposition that I can then provide to you, so it's really, mutually beneficial on both the supply and demand side, if you think about it. >> Actually, so David Floyer from our team has done lots of research talking about just, real business value that can be driven back when I can like leverage that data. It's like, oh wait, now I have things like Flash that allow me, you know, very fast to make snaps, wait, I have real, this is the actual data, and then I can test on that and then how fast can I get that back into production if need be. There's a lot of things we can do now that just those enablers in the new technologies at scale. >> Well, and I also think it's pretty interesting, we talked earlier about our three patterns, right? Modernize, transform, and the next gen. If you think about the next gen, right? That's, now what I wanna do as a corporation is I wanna bring on new people and I wanna do some data analytics. That data analytics is gonna allow me to learn stuff about my business, and I'm gonna wanna start to do stuff in a new way. I'm gonna wanna start to do stuff in a new way, today. Right? I don't wanna wait and say okay now that I know what I think I wanna do is X, and wait six months for the infrastructure to be there to start programming against it. No, I wanna make real time decisions today, I wanna do real time things today. That's how that evolution starts to happen and it needs to be faster for those people. >> Yeah well one of the promises is, you know, we're at cloud computing, when I need it, it's there, I don't need to worry about what's available. Talk to me about what is scale, and you know, that speed mean to your customers? What kinda architectures do they need to go to to be able to, you know, have that kind of experience no matter where they are? >> Yeah I think you're starting to boil it down into products and while that's good, right new technologies, and new capabilities like Flash and NVME and that sorta thing, that's the raw performance, that's the engine of the car, right? But you start thinking about all the telemetry data that racers collect to then tweak the car, not just the engine, the car, the foils, and that sorta thing, in order to get the maximum amount of speed out of the car, that's really the performance stuff that we're talking about and that's all the instrumentation around the different products and that sorta thing that sit within the portfolio, that enable things like self service, it enables things like, which is speed in its own way, right? And it's data protection, and it's faster RPOs and RTOs, different types of protections sets of services. It's disaster recovery, faster replication, replication to the cloud, lower costs, right, replication into the cloud, maybe not necessarily in on the data center. All of those thins in the portfolio equal speed. Whether speed be raw performance, getting from A to B, or speed of business, which means I can be, I can be doing whatever that thing is that makes me more competitive, quicker than the other guy can do it. >> Yeah, and when you have these services which are much more encapsulated and kind of, you know, to use a very old term, kinda self documenting in a way. If you think about taking a data element or a data structure or some piece of knowledge or information that we're gonna do some kind of processing on, and you load that with as many definitional characteristics as you can, without A: slowing it down, or B: making it too expensive, then you inherently improve the value of that thing, whatever that is, right? So, you know, great, there's a million examples, the picture of the check is a good one, you know, the telemetry coming from delivery trucks, for FedEx or UPS, there's a million examples where the ability to gather data, which would be gathered anyway, and used for some other purpose, but now you layer on some of these additional service characteristics and dimensions to it, and it becomes a whole new entity that now has a whole other set of values that can be expanded upon. So it's really this multiplicative effect that we see, that allows you to take your data, which is your most valuable asset typically, and leverage it across multiple use-cases and in multiple dimensions. >> Yeah, so, Steve, when I think about data services, you know, if I think the old world was rather fixed, and the new world is, you know where are we today, and what's kinda the near future look like? Help us walk through that a little bit. >> Yeah, I think you painted it very well in the beginning, right, we always like to look out front and say this is utopia, this is where we're going. Where are people today? I think there're a lot of technologies out there that, if you're starting to modernize, and I think we're in that modernization trend right now, where a lot of the newer technologies, or even some of the older technologies that you might have installed in your environment, are building out a robust API set. Because the new stuff is all API driven, so if I'm an incumbent and I'm in a data center and I wanna maintain my hold, my footprint, I need to start working with other things. Newer versions of a lot of the incumbent technology is building in restful APIs. Now you're bringing in newer technologies, maybe a Chef or Puppet sitting on top of your infrastructure that has restful APIs. The trick for the infrastructure, for the IT team, is to slowly evolve into that infrastructure developer that we talked about. Now they're learning how to connect those two, right? And as I'm learning how to connect those two, I'm also learning about how to make that data available in other locations, or how to make those applications talk in other locations, that they'll impact my production, right? So where are we today? I think we're slowly starting to understand what these API connectivities are, and if there isn't that connectivity, I think folks are really starting to look at what do I replace that incumbent with to make sure that I'm getting that out of what I'm gonna need for the future, so, I'd say we're 20% down the road, right? But things are moving fast, I mean, as time goes forward it goes faster and faster and faster, and, you know, a year from today we might be at 50%, right? Or 60. >> Yeah, and I would just add to that that the level of integration that exists between the products in the spectrum portfolio is very foundational and very, you know, it's a very intricate structure, right? So, as we evolve products and solutions, you know, we just had an announcement this week of the new Flash platform on the hardware side, so there's, as these things become available, they start to then elevate the value and improve the capabilities of other parts of the portfolio as well. So there's this kind of platform story that you start to be able to tell, and that's really what these, this series has been about and what the follow on sessions will be about drilling into specific solutions at a lower level of detail, is how do we build, you know, the information platform of the future for our clients? >> Yeah, great. And I know there's more coming in the future, but the last thing I wanted to ask you here is we had a while that we were saying well I'm just gonna simplify everything. Public cloud is cheap and easy, you know, hyper converge is gonna boil everything down, and it's just like this one box. Well, and if you look at both of those spaces, they've evolved and now the line I've used I think if I was going to, you know, build compute in Amazon, or go buy a server from pick your favorite OEM of choice, you know, the cloud probably has more options and is more complicated to buy. You know, we'll figure out how the pricing is depending on whether you buy the three years reserved instances or anything like that. But, you know, customers, the paradox in choices is really tough for people as they do so. How do you balance that flexibility, but still try to make it easier, because you know, staffing, you know, I can't have, you know engineers dedicated to, you know, helping trying to figure this out. Oh, the next release comes out in a month, and everything I learned is already old. >> I'd appreciate your input and feedback on that, because what I'm about to say is, is I think easy is relative, right? And it's relative to the person who needs to access the systems or the data or the equipment or that sorta thing right, so, if I may, if I'm someone who's graduated from college and looking to join the IT workforce today or in the next few years, right? To me, simple means, right, a myriad of things, right? And I might've been trained on and educated on, but I'm gonna stay in that world. Why do you continually buy an iPhone? You don't switch over to Android, why? >> They're from different ecosystems, yeah exactly. >> Right, or why does someone go the other way, right? It doesn't matter, you pick one, and that's what you know, and that becomes easy for you, right? And then, as I'm learning, so lets say I pick AWS, right? As I start to continue to learn, and they come out with new things, and that's what I pay attention to, I find these new things that plug in, right? And it's only when vendors come to you and talk about not just hey I've got this new fidget spinner, (chuckles) right, or wiz bang technology, it's, it's I know how to integrate with your platforms to make your life easier. Those are the conversations that actually pick peoples head up and go, oh okay that does make my life easier, right? >> And that's exactly what I was just gonna say, we talked earlier about this whole concept of integrate and automate verus rip and replace. You know, innovate as opposed to institutionalized, so this is exactly that. This is, we as trusted advisors and integrators at a factor are able to go to our clients and say look you have typically a complex environment that has multiple different platforms and stacks that you're working with. You may be able to standardize on a common model or a common model portfolio structure for everything. Not likely though, you're probably gonna continue to have, you know, different pieces of the puzzle. We, it's our job and our sellers job and our partners job to develop an integration strategy and an automation strategy that exploits each of those that are in place to the best of their current ability, and provides a path forward, so to your point, eventually will all things live in the cloud, and will the cloud become, you know, so self aware and so sophisticated that's it's able to provision itself and manage itself and write your apps for you, perhaps. You know, probably not in our lifetimes. So, in the mean time, large organizations and small organizations still have to get from point A to point B. They have to run their business, they can't afford to spend, you know, a king's ransom on IT. But they also have to be secure and reliable and perform, etc. So, we provide a portfolio of solutions that plugs into exiting infrastructure, augments it, maybe replaces it but not necessarily, and helps our clients get between here and there and provides them the headroom to grow into the future as well. >> What's your answer to the simple and easy? >> Yeah, well, first of all right. I think, just on the definitional piece, you know, we'll all be living in the cloud when we've just redefined that means it's, it's hooked up to the internet. (Steve and Randy laughing) It means that it's cloud because everything is. And, here's the challenge of the day, no one can keep up on everything, you know, I've had the pleasure to talk to some of the smartest dang people in this industry, and, >> Thank you. (laughs) even the ones, you know, absolutely, but the people that, you know, are creating new stuff, and you know, whatever it is, they're like, I can't keep up with my own firm. >> How do you have time to learn? >> You know, it's like, they say, you know, the doctor would need, you know, every week would need a thousand hours to read up on everything in their specialty. But, it doesn't mean that we're out of jobs, actually we've got lots of new jobs because we love, we've done some events with MIT, where it's, you know, racing with machines, it's people plus machines, you know, automation does not get rid of your job, what it hopefully gets rid of is the crap you didn't wanna deal with anyway. We saw for a while, we wanted to get rid of undifferentiated heavy lifting, well, let's hope that, you know, as, if you talk to the IT people, it's like the thing that you look at every month and you're like oh God I have to do that, or can't you automate that piece of it? >> And it actually raises a really interesting point, and we haven't touched on it, I don't think, up til this point, but, this is another one of the areas where IBM is uniquely positioned in this discussion, and in this space, to bring to bare a level of sophistication and advanced artificial intelligence, cognitive capability, machine learning, we are, today, delivering the worlds most sophisticated, powerful, capable solutions in that space, and not surprisingly that technology is imbuing everything that we do. So our entire portfolio is interspersed with very sophisticated AI capabilities, analytic capabilities for self adaptation, you know, self learning, self healing. So, it gives us again a competitive advantage I think, as we take these solutions to market that they are imbued with this very sophisticated level of advanced processing. >> Yeah and, the other thing I'd say, I know Steve you brought it up in one of our discussions there, IBM has a lot of partners. I never look at IBM saying we are the only one, we are the be all and end all, we'll have everything, no. The CIs and the MSPs and the CSPs and, you know, software partners and everything like that. You mentioned competitive environment, I think the first time I heard the word cooperative, it was almost always about IBM. Because, yes, IBM probably has a product that does something along those lines, but they know they're not the only ones and they'll continue to partner to make sure that customers get the solutions they need. Alright, any final words you wanna leave on this segment, gentleman? >> I wanna thank you very much for hosting us for this event. >> Indeed. Yeah, thank you for your insight, Stu, we appreciate it, you know it's always important for us to not read our own press clippings too much, it's important to get the external viewpoint and get the outside perspective, so we appreciate your input. >> Well hey, and thank you so much for bringin', both of you, we've worked with you for many years, always appreciate your viewpoints, and look forward to continuing the conversation. Alright, thank you so much as always. Give us any feedback if you have, check out theCUBE.net for all the websites, Randy Arseneau, Steve Kenniston, I'm Stu Miniman, thanks for watching theCUBE. (bubbly music)

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE! Now here's your host, Stu Miniman! (bubbly music) >> I'm Stu Miniman and this is theCUBE's Boston area studio, we're talking about storage and SDI solutions. But before we get into STI and all the industry buzz, we're gonna talk a little bit about some of the real business drivers. And joining me for this segment, happy to welcome back, Randy Arseneau and Steve Kenniston, gentleman, great to see you. >> Thanks for bein' here Stu. >> Thanks Stu great to be here. >> Thank you! Alright, so, talkin' about transformation, customers are going through transformations, IBM's going through transformation, everything's going in some kind of journey. But, let's talk about, you know, it used to be IT sat on the side, Randy we talked about in the intro, you know, IT in the business, you know, wait, they actually need to talk, communicate, work together. What are some of the key drivers that you're hearing from customers? >> So, it's a good question, and we talked a little bit about it on the previous segment. But, I think what's really happening now is that a lot of the terms that our industry has kind of overused and commoditized, have sorta become devalued, right? So, they no longer really mean anything significant. Terms like agility and flexibility and IT business alignment and transformation, which we hear a million times everyday, they've become just kind of background noise, but the reality is, especially now, in this era where, you know, information and data and analytics are driving businesses, and they're no longer, you know, nice things to have for the super advanced very sophisticated companies, they're table stakes, I mean, they're needed to survive in today's global economy. So they've taken on a whole different meaning, so when we talk about agility, for instance, agility means something very specific in the context of IT business alignment, and our solution stacks in particular. Generally speaking, the kinda the way I like to think of it is, I, I overuse sports analogies, but I think this one's relevant. So, a good quarterback is able to read and react. So, as the defense is shifting and making pre-snap adjustments, the quarterback views the field, sees what's happening, and is able to very quickly develop or institute a new offensive game, play, to take advantage of that situation. So that whole read and react idea is something that's very important for a business, especially now. So businesses are under constant pressure, competitive pressure, market pressure, compliance pressure, to be able to exploit their own IP, and their own data, and their own information, very very quickly. So that's number one. By using things like integration and automation within their IT organization as opposed to the old, you know, kind of vertical method of doing things. IT organizations are now able to respond to those rapid course corrections much more effectively. Same thing with flexibility, so when an organization needs to be flexible, or wants to be flexible, to adapt to a very rapidly changing environment. Things like, and this is really where Steve's product line is particularly relevant, things like data reuse, right? So we've got organizations that are running their business on this data, which is their most important asset. We're helping them develop new and creative ways to repurpose that data, efficiently, quickly, cost-effectively, so they can expand the value. So any given piece of data can now have a multiplicative value compared to its original form. >> Yeah, I think it's actually pretty important. When you think about, we're out there talking about products, right? And a lot of vendors are doing this, right? Buy my products you'll get agility or you'll get flexibility or that sorta thing, or maybe even more importantly, in a lot of the enablement we use to educate people on, we'll say, you know, this product enables data reuse. Well what does that really mean, right? What does that mean for business, right? And, when you say okay well it makes the business more agile well, how do you do that? Then it encompasses a whole breadth of solution sets around making that data available for the user, things like software defined storage, things like particular technologies, that can do data reuse. So, it kinda boils itself down in the stack, but to Randy's point, it's been so commoditized, the words, that we don't really understand what they mean, and I think part of what we're trying to do is, make sure when we talk agility, flexibility, or even our three patterns that we talked about, modernize and transform. What do they mean to us? What do they mean to you, the user? What do they mean--? Because that's very important to connect those two. >> Yeah, and I love, 'cause for a while we used to say, it used to be well, you know, do I get it faster, better, or cheaper? Or maybe I can give you some combination, and there're certain customers you talk to and it's like look, if you can just go faster, faster, faster, that's what I need. But, it's not speed alone, like the differentiation for things like agility is, number one: we are all horrible at predicting. It's like, okay, I'm gonna buy this, I'm gonna use this for the next three to five years, and six months into it, I either greatly over or underestimated, or everything changed, we made an acquisition, competition came in. I need to be able to adjust to that, so that was, I love the sports analogy, we love sports analogies on theCUBE. >> Well, you know. >> So that, you know, if I planned for, you know, this was the plan of attack, and what do ya know, they traded for a player the day before or their star quarterback went down, and the backup, who I didn't train against, all of a sudden their offense is different and we get torn apart, because we didn't plan, we couldn't react to it, you run back at halftime and try to adjust, but, you know, you need to be able to change. >> And again, I think another, from my perspective, from and IT business alignment, another, another metaphor that works well, is, you know, kinda what I call the DevOps-ification of business, right? So what's happening now, and it's interesting I think, is that you're seeing some of the practices around DevOps and agile development, which by the way, IBM uses for our own products. You're seeing that push upstream to the business, so the business is actually adopting DevOps-like methodologies for prototyping, you know, testing hypothesis, they're doing interesting things that kinda grew out of that world. So if you think about, even 10 years ago, that would've been kind of unimaginable, you would always have the business applying pressure, and projecting it's requirements onto IT, now you're seeing much more of a collaborative approach to attacking the market, gaining competitive advantage, and succeeding financially. >> Yeah, and if people aren't really familiar with DevOps, the thing that, you know, I really like about it is, number one it's no longer, you know, we used to be on these release trains. Okay, everybody on board the 18 to 24 month release train, we're gonna plan, oh wait, we didn't get this feature in, it'll be in the next one, we'll do a patch in six or eight months, no, no, no. There's the term CICD, continuous, you know, integration, and continuous deployment. It's, you know, push. Often. You know, daily, if not hourly, if not more, and, it's like wait what about security, what about all these things? No, no, no. If we actually plan and have a culture that buys in and understands and communicates, and you've got proper automation. You know, it's a game changer, all of those things that you used to be like: ugh, I couldn't do it. Now it's like no, we can do it, so. >> The only thing constant in business these days is change. >> Absolutely. >> So, if you know that, and you have to be able to plan and articulate and be ready for change, how do you make sure that the underlying infrastructure is ready to kind of adapt to whatever request you may have of it, right? It's now alive, right, it's like a person, I wanna ask it a question, and I need it to help respond quickly. >> And a lot of the focus of this series, as we talked about in the intro section, is our software defined infrastructure portfolio, which in many ways is kinda the fabric upon which a lot of these things are being woven now, right? So, we talk about DevOps, we talk about this rapid cycle, and this continuous pace of change and adaptability, adaptation. We're delivering solutions to market that really accelerate and enable that, right, so, one of the things we wanna make sure we communicate, you know, both internally and externally is the connective tissue that exists between solutions, products, technology capabilities on the software defined infrastructure side, and how that affects the business, and how that allows the business to be more agile, to be more flexible, to transform the way it thinks about taking solutions to market, competing, opening up new markets, you know, seizing opportunities in the marketplace. >> Yeah, it's, if you think about when you talk about strategy, smart companies, they've got feedback loops, and strategy is something you revisit often and come back leads to, when you talk about modernizing an environment, I always used to, you poke fun at marketing, oh we're going to make you future ready! Well when can I be in the future? Well, the future will be soon, well, then when I get to there am I now out of date because the future's not now? So, what is modernize, what does that really mean, and, you know, how does that fit in? >> Yeah, and it's a great point, and I think, we look at modernization as kind of the the constant retooling, right? So, IT is constantly looking for ways to be more responsive, to be more agile, be more closely aligned than a lockstep with the business, and align business. And again, we're trying to deliver solutions to market that enable them to do that effectively, cost effectively, quickly, you know, get up to speed rapidly. There's another, so we talked a little bit in the intro section about the C-level survey, the study that was done globally by IBM, it's done every year, the 2018 one was introduced recently, or published recently. Another one of the themes that was very important is that it's this concept of innovate don't institutionalize and the idea is that old companies, slow moving companies, more traditional companies, have a tendency to solve a problem or introduce and implement a system of some sort and be wed to it, because they adapt all of the ancillary work flows and everything around it, to fit that model. Which may make sense the day that it implements and goes live, but it almost immediately becomes obsolete or gets phased out, so, you need to have the ability to integrate, automate, innovate, like constantly be changing and adapting. >> Yeah, I love that, actually, in the innovation communities they say you don't want best practices, you want next practices, because I always need to be able to look at how I can do, right, learn what works and share that information, but, you're right not, this is the way we're doing it, this is the way it must always be, so let it be written, so let it be done. You know, no, we need to move and adjust. >> And I think, if you think about these things as in, in the beginning of the year when IBM launched global refining was, when we launched kind of our educational context for our sellers in the beginning of the year, it was really three patterns, right? There was modernize and transform, next gen applications, and then application refactoring. And in the beginning when we started to talk about, which I think is where 90% of the clients fall into, it's this modernize and transform, right? Easy to say, but what does it really mean? So, if you break it down into that fact that we know what clients have today, right? We know, you know, VMware's big, KVM is big, you know, Sequel is big, Oracle is big, right? If that's foundationally who you're talking to on an everyday basis, how do you help them take that solution set, and, don't start refactoring today, right, but take them to a point where when they start to do the refactoring, they're well positioned to do that simply and easily, right? So it's a long journey, but to get there you really need to kind of free up and shake loose some of that, some of the bolts, so that it's a lot more flexible over here. >> Yeah, so, talking about things that are changing all the time, so tell me, transformation, it's not about an angle, it's, you know, it's about journeys and being ready, so, you know, help us close the loop on that. >> Yeah, so we talk a lot about that internally, and again, transformation is another one of those kinda buzzwords that we're, we're trying to sorta demystify it, because it can be applied in a million different ways, and they're all relevant and valid, right, so transformation is a very broadly applicable term. When we talk about transformation, we're specifically talking about kind of the structural transformation of the infrastructure itself, so how are we making the storage and the compute more cloud like, more flexible, more easily provisioned, more self-service. So there's kind of a foundational level piece at the infrastructure level. We talk about transformation at the workflow level, so things like DevOps, like continuous development and integration. How do we provide our clients with the material they need, the raw materials, whether it's software, technology, education, best practices, all of the above, to be able to implement these new ways of doing business? And then there's really transformation of the business itself, now, a couple of those, the first two, are kind of happening within IT, but they are being driven by the transformation that the business is undergoing, so, the business is constantly, again, if they're still around and they're prospering, they're constantly looking for new markets to reach into, looking for ways to compete more effectively, looking for ways to gain and sustain competitive advantage in this very very dynamic environment. So transformation touches all of those, they're all equally valid, from our perspective, specifically as IBM, we're trying to tackle the sorta foundational level, and then kinda, by using assets like this, you know, research that we do at the C-level, we're trying to kinda build the connective tissue between the ground level IT stuff, and how the business is changing. >> Think, think, I mean, really as importantly, right, we're trying to build the foundation such that as we're thinking about the business, think taxis transforming to I wanna be more Uber like, or think even automotive industries wanting to be more Uber like, right? I read an interesting article about, you know, auto manufacturers today are thinking about no more buying of cars, right? That's a transformation of my business, right? How do I do that? Now a lot of it is, you know, I gotta set up the infrastructure, I gotta set up, you know, people, and process to do some of this, but the infrastructure has to adapt as well, right? And we gotta cause, and that's not gonna happen tomorrow, to your point Stu, like I wanna design for tomorrow, then the next day, then the next day, then the future, when is the future, right? But I need to have an infrastructure that can evolve with me as my business evolves and I get to this goal. >> And the shifts are now happening, they're no longer kinda tectonic shifts, they're seismic, right, they're not gradual, incremental, I mean they are in some cases, but they're more often seismic changes, and that's a great example. Uber burst onto the scene and fundamentally changed the way humans transport themselves from one place to another. And there's a million examples of that right? There's been genomic research, and even in media and entertainment, there's lots of ways and lots of places in which this shift towards more seismic change in the industry, or in a particular use-case is happening everyday. >> Yeah, so I love your insight, when you talk about your partners, you know, the old days were great where you used to just say hey, you've got a problem, I've got a product that will solve what you need, transaction, box, done. Now, it's like, we've been saying, when are we gonna have that silver belt in security, it's like, never. It's like, security is, you know, it's a practice, or, you know, it's a general theme that you have to do, it's like DevOps isn't a product, it's something we need to do. I heard a great line it was like, you know, oh, this whole AI stuff, well can I have a box and a data scientist and I can solve this stuff? No, no, no, this is going to be an initiative, we're gonna go through lots of iterations, and there's lots of pieces, so. It's a different world today, how do you help people through this as to, you know, what the relationship is now? >> No, it's very interesting, and to your point, can I buy a box that does that, right? We were at Think this year, and our security team, or actually I think it was our blockchain team was up, and I'm very interested in blockchain and what is it gonna do for the community as we kinda grow and that sorta thing. And up on the, on a chart they put this slide that had a million different, I mean thousands of different partners that we partner with, and we also enable to kinda deliver stuff, and in some cases we're competitive, in some cases it solves security, in some cases it does this. Now all of a sudden, it's not one thing anymore, it's like how does it fit into our infrastructure, but, back to your point about partnerships. I think IBM is constantly looking to its partners because they have really that trusted value and trusted relationship with the client, and at the end of the day, as much as we can come in and say oh this box will solve your problem, we don't really know what their problems are, right? It's the people who have those relationships that know where they're going along that evolutionary scale, that we really need to work and tie in with closely, to make sure that the solutions that we deliver on the underlying side are meeting their needs, which then in tern meet our clients needs, I think that's where we're goin'. >> And actually the blockchain is a great example of kinda building these vibrant ecosystems, right? Which is something else that large companies like IBM sometimes struggle with kinda building these very dynamic, very vibrant ecosystems, but I think IBM's very good at it, and I think we've demonstrated that in a number of different places, blockchain being a recent example, but there are many others. And the STI portfolio is no different right, we've got strong partnerships across the board with other software providers, other go-to-market partners, you know, other content providers, there's a million different angles that we are able to, to introduce into the conversation. So we think all of those things taken together allow our sellers and our partners to bring a solution to their clients, regardless of their industry or their size or their particular use-case, that helps them optimize their performance in this new world of super agile, constantly changing, continuous transformation, and do so, we think, better than anyone else in the industry >> Constantly changing, distributed in nature, sounds just like the blockchain itself. (both laugh) Alright, Steve, Randy, thank you so much for helpin' us demystify some of these key business drivers that we're going to. Lots more we'll be covering, I'm Stu Miniman, thanks for watching theCUBE. (bubbly music)