>> Announcer: Live from Las Vegas, it's theCUBE. Covering IBM Think 2018. (upbeat music) Brought to you by IBM. >> Welcome back to IBM Think 2018 everybody. My name is Dave Vellante and I'm with my co-host Peter Burris. You're watching theCUBE, the leader in live tech coverage. This is day three of our wall to wall coverage of IBM Think. The inaugural Think conference. Good friend Eric Herzog is here. He runs marketing for IBM storage. They're kicking butt. You've been in three years, making a difference, looking great, new Hawaiian shirt. (laughter) Welcome back my friend. >> Thank you, thank you. >> Good to see you. >> Always love being on theCUBE. >> So this is crazy. I mean, I miss Edge, I loved that show, but you know, one stop shopping. >> Well, a couple things. One when you look at other shows in the tech industry, they tend to be for the whole company so we had a lot of small shows and that was great and it allowed focus, but the one thing it didn't do is every division, including storage, we have all kinds of IBM customers who are not IBM storage customers. So this allows us to do some cross pollination and go and talk to those IBM customers who are not IBM storage customers which we can always do at a third party show like a VM World or Oracle World, but you know those guys tend to have a show that's focused on every division they have. So it could be a real advantage for IBM to do it that way, give us more mass. And it also, you know, helps us spend more on third party shows to go after a whole bunch of new prospects and new clients in other venues. >> You, you've attracted some good storage DNA. Yourself and some others, Ed Walsh was on yesterday. He said Joe Tucci made a comment years ago Somebody asked him what's your biggest fear. If IBM wakes up and figures it out in storage. Looks like you guys are figuring it out. >> Whipping it up and figuring it out. >> Four quarters of consistent growth, you know redefining your portfolio towards software defined. One of the things we've talked about a lot, and I know you brought this was the discipline around you know communicating, getting products to market, faster cycles, because people buy products and solutions right? So you guys have really done a good job there, but what's your perspective on how you guys have been winning in the last year or so? >> Well I think there's a couple of things. One is pure accident, okay. Which is not just us, is one of the leaders in the industry, where I used to work and Ed used to work has clearly stubbed its toe and has lost its way and that has benefited not only IBM but actually even some of our other competitors have grown at the expense of, you know, EMC. And they're not doing as well as they used to do and they've been cutting head count and you know, there's a big difference in the engineering spend of what EMC does versus what Michael Dell likes to spend on engineering. We have been continuing to invest. Sales resources, marketing resources, tech support resources in the field, technical resources from a development perspective. The other thing we did as Ed came back was rationalize the portfolio. Make sure that you don't have 27 products that overlap, you have one. And maybe it has a slight overlap with the product next to it, but you don't have to have three things that do the same thing and quite honestly, IBM, before I showed up, we did have that. So that's benefited us and then I think the third thing is we've gone to a solution-oriented focus. So can we talk about, as nerdy as tracks per sector and TPI and BPI and, I mean all the way down to the hard drive or to the flash layer? Sure we can. You know what, have you ever... You guys have been doing this forever. Ever met a CIO who was a storage guy? >> No, no. CIOs don't care about storage. >> Exactly, so you've got to... >> We've had quite a couple of ex-CIOs who were storage guys. (laughter) >> So you've really got to talk about applications, workloads, and use cases. How you solve the business problems. We've created a whole set of sales tools that we call the conversations available to the IBM sales team and our business partners which is how to talk to a CIO, how to talk to a line of business owner, how to talk to the VP of software development in a global enterprise who doesn't care at all, and also to get people to understand that it's not... Storage is a critical foundation for cloud, for AI, for other workloads, but if you talk latency right off the top, especially with a CIO or the senior executive, it's like what are you talking about? What you have to say is we can make your cloud sing, we can make your cloud never go down. We can make sure that the response time on the web browser is in a second. Whereas you know Google did that test about if you click and it takes more than two and a half seconds, they go away. Well even if that's your own private cloud, guess what they do the same thing. So you've got to be able to show them how the storage enables cloud and AI and other workloads. >> Let's talk about that for a second. Because I was having a thought here. It's maybe my only interesting thought here at Think, being pretty much overwhelmed. But the thought that I had was if you think about all the things that IBM is talking about, block chain, analytics, cloud, go on down the list, none of them would have been possible if we were still working at 10, 20, 30 milliseconds of wait time on a disc head. The fundamental change that made all of this possible is the move from disc to flash. >> Eric: Right. >> Storage is the fundamental change in this industry that has made all of this possible. What do you think about that? >> So I would agree with that. There is no doubt and that's part of the reason I had said storage is a critical foundation for cloud or AI workloads. Whether you're talking not just pure performance but availability and reliability. So we have a public reference Medicat. They deliver healthcare services as a service, so it's a software as a service model. Well guess what? They provide patient records into hospitals and clinics that tend to be focused at the university level like the University of California Health Center for the students. Well guess what? If not only does it need to be fast, if it's not available then you can't get the healthcare records can you? So, and while it's a cloud model, you have to be able to have that availability characteristic, reliability. So storage is, again, that critical foundation. If you build a building in a major city and the foundation isn't very good, the building falls over. And storage is that critical foundation for any cloud, any AI, and even for the older workloads like an SAP Hana or a Oracle workload, right? If, again if the storage is not resilient, oh well you can't access the shipping database or the payroll database or the accounts receivable database cause the storage is down and then obviously if it's not fast, it takes forever to get Dave Vellante's bill, right. And that's a waste of time. >> So it's plumbing, but the plumbing's getting more intelligent isn't it? >> Well that's the other thing we've done is we are automating everything. We are imbuing our software, and we announced this, that our range are going to be having an intelligent infrastructure software plane if you will that is going to help do diagnostics. For example, in one of the coming releases, if a customer allows access, if a power supply is going bad, we will tell them it's going bad and it'll automatically send a PO to IBM with a serial number, the address, and say please send me a new power supply before the power supply actually fails. But it also means they don't have to stock a power supply on their shelf which means they have a higher cost of cap ex. And for a big shop there's a bunch of power supplies, a bunch of flash modules, maybe hard drives if they're still dinosauric in how they behave. And they have those things and they buy them from us and our competitors. So imbuing it with intelligence, automating everything we can automate. So automatically tiering data, moving data around from tier to tier, moving it out to the cloud, what we do with the reuse of backup sets. Instead of doing it the old way of back up. And I know you've got Sam Warner coming on later today and he'll talk about modern data protection, how that is revolutionizing what dev ops and other guys can do with their, essentially, what we would've called in the old days back up data. >> Let's talk about your spectrum launch. Spectrum NAS, give us some plugs for that. What's the update there? >> So we announced on the 20th of February a whole set of changes regarding the Spectrum family. We have things around Spectrum PROTECT, with GDPR, Spectrum PROTECT Plus as a service as well as some additional granularity features and I know Sam Warner's going to come on later today. Spectrum NAS software defined network attached storage. Okay, we're not going to sell any infrastructure with it. We have for big data analytics our Spectrum scale, but think of Spectrum NAS as traditional network attached storage workloads. Home directories. Things like that. Small file service where Spectrum scale has one of our public references, and they were here actually at Edge a couple of years ago, one of the largest banks in the world, their entire fraud detection system is based on Spectrum scale. That's not what you would use Spectrum NAS for. So, and it's often common as you know in the file world to have sort of a traditional file system and then a big one that does big data, analytics and AI and is very focused on that and so that's what we've done. Spectrum NAS is a software only, software defined, rounds out our block, now gives a traditional file. We had scale out file already and IBM cloud object storage is also software defined. >> Well how about the get put world. What's happening there? I mean we've been waiting for it to explode. >> Ah so the get put world is all about NVME. NVME, new storage protocol as you know it's been scuzzy forever. Scuzzy and/or SATA. And it's been that way for years and years and years and years, but now you've got flash. As Peter pointed out spinning disc is really slow. Flash is really fast and the protocol of Scuzzy was not keeping up with the performance so NVME is coming out. We announced an NVME over InfiniBand Fabric solution. We announced that we will be adding a fiber channel. NVME fabric based and also in ethernet. Those will come and one of the key things we're doing is our hardware, our infrastructure's all ready to go so all you have to do is a non-disruptive software upgrade and for anyone who's bought today, it'll be free. So you can start off with fiber channel or ethernet fabrics today or InfiniBand fabric now that we can ship, but on the ethernet and fiber channel side, they buy the array today and then later this year in the second half software upgrade and then they'll have NVME over fiber channel or NVME over ethernet. >> Explain why NVME and NVME over fabric is so important generally but in particular for this sort of new class of applications that's emerging. >> Well the key thing with the new class of applications is they're incredibly performance and latency sensitive. So we're trying to do real artificial intelligence and they're trying to, for example, I just did a presentation and one of our partners, Mark III has created a manufacturing system using AI and Watson. So you use cameras all over, which has been common, but it actually will learn. So it'll tell you whether cans are bad. Another one of our customers is in the healthcare space and they're working on a genomic process for breast cancer along with radiology and they've collected over 20 million radiological samples of breast cancer analysis. So guess what, how are you going to sort through that? Are you or I could sort through 20 million images? Well guess what, AI can do that, narrow it down, and say whether it's this type of breast cancer or that type of breast cancer. And then the doctor can decide what to do about it. And that's all empowered by AI and that requires incredible performance which is what NVME delivers. Again, that underlying foundation of AI, in this case going from flash with Scuzzy, flash to NVME, increasing the power that AI can deliver because of its storage foundation. >> But even those are human time transactions. What about when we start taking the output of that AI and put it directly into operational transactions that have to run like a bat out of hell. >> Which is where NVME will come in as well. You cannot have the performance that we've had these last almost 30 years with Scuzzy and even slower when you talk about SATA. That's just not going to cut it with flash. And by the way, you know there's going to be things beyond flash that will be faster than flash. So flash two, flash three, it's just the way it was with the hard drive world, right? It was 2400 RPM then 36 then 54 then 72 then 10k then 15/5. >> More size, more speed, lower energy. >> Which is what NVME will help you do and you can do it as a fabric infrastructure or you can do it in the array itself. You dual in box and out of box connectivity with NVME increasing the performance within your array and increasing the performance outside of the array as you go out to your host and out into your switching infrastructure. >> So I'm loving Think. It's too many people to count, I've been joking all week. 30,000 40,000. We're still tallying up. I'm going to miss Edge for sure. I'm going to miss the updates in the you know, late spring. But so let's get 'em now. What can we expect? What are you trying to accomplish in the next six to nine months? What should we be looking for without giving any confidential information. >> Well we've already publicly announced that we'll be fleshing out NVME across the board. >> Dave: Right. >> So we already publicly announced that. That will be a big to-do. The other thing we're looking at is continuing to imbue what we do with additional solution sets. So that's something we have a wide set of software. For example, we publicly announced this week that the Versa stack, all flash array will be available with IBM cloud private with a CYSCO validated design in May. So again, in this case taking a very powerful system, the Versa Stack all flash, which already delivers ROI and TCO, but still is if you will a box. Now that box is a converge box with compute with switching with all flash array and with a virtual environment. But now we're putting, again as a bundle, IBM cloud private on there. So you'll see more and more of those types of solutions both with the rest of IBM but also from third parties. So if that offers the right solution set to cut capx/opx, automate processes, and again, for the cloud workloads, AI workloads and any workloads, storage is that foundation. The critical foundation. So we will make sure that we'll have solutions wrapped around that throughout the rest of this year. >> So it's great to see the performance in the storage division. Great people. We're under counting it. We're not even counting all the cloud storage that goes and counts somewhere else. You guys are doing a great job. You know, best of luck and really keep it up Eric, thanks very much for coming back on theCUBE. >> Great thank you very much. >> We really appreciate it. >> Thanks again Peter. >> Alright keep it right there everybody we'll be back with our next segment right after this short break. You're watching theCUBE live from Think 2018. (upbeat music)

>> Narrator: Live from Las Vegas, it's theCube. Covering Interconnect 2017. brought to you by IBM. >> Hey welcome back everyone. We're live here in Las Vegas for IBM Interconnect 2017. Siliconangle's theCube's Exclusive coverage of IBM Interconnect 2017, I'm John Furrier. My co-host Dave Vellante. Our next two guests, Eric Herzog, Vice President of Marketing for IBM Storage. Nice to see you again, you were on yesterday. And Mark Godard, Manager of Customer Success and Partnership at Sparkcognition, a customer. Guys, welcome to theCube, good to see you again. Welcome for the first time. >> Thank you. >> Thank you. >> Okay, so we're going to talk about some stories we did yesterday, but you've got the customer here. What's the relationship, why are you guys here? >> We provide the storage platform. They use our flash technology. Spark is a professional software company. It's not a custom house, they are a software company. >> And Spark, not related to Spark OpenSource. Just the name Spark, Sparkcognition. Make sure to get that out of the way. Go ahead, continue. >> So they're a hot startup. They have a number of different use case including cybersecurity, real-time IoT, predictive analytics and a whole bunch of other things that they do. When a customer goes on premise 'cause they deliver either through a service model or on premise, when it's in their service model they use our flash and our power servers. When it's on premise they recommend here's the hardware you should use to optimize the software if the customer buys a non-premised version. They offer it both ways, but part of the reason we thought it would be interesting is they're a professional software company. A lot of the people here as you know are regular developers, in-house developers. In this case these guys are a well-funded VC startup that delivers software to the end user base. >> Tell us more about Sparkcognition. Give us the highlights. >> Yeah, appreciate it. Sparkcognition, we're a cognitive algorithms company. We do data science, machine learning, natural language processing. Kind of the whole gambit there. Working, we have three products. SparkPredict is our predictive analytic, our predictive maintenance product. SparkSecure is our network log security product. And Deep Armor is a machine learning endpoint protection product. In that you kind of hear we're in the IoT, the industrial IoT, the IIoT of things. It also, in cybersecurity we've done use cases, other machine learning use cases as well. But the predictive maintenance and cybersecurity are two most, most advanced use cases, industrial areas. So we've been around about three years. We have around 100 people. Appreciate Eric talking about how well-financed we are and how our success really is budding this far. We're happy to be here. >> John: Where are you guys located? >> We're based out of Austin, Texas. >> John: Another Austin. >> Yeah Austin, Texas. >> Dominant with Austin. >> It's always good to have financing. You can't go out of business if you don't run out of money. Talk about the industrial aspect. One of the things that is hot, it's not a mainstream here, is blockchain is the big announcement. But IoT is the big one. But industrial IoT's interesting because now you have the digitization of business as a big factor. And that data is going to be throwing off massive analog digital data now. So analog to digital, what's going on there? What are you guys doing there to help and where does the storage fit in? >> Yeah, I appreciate that. So IIoT, industrial there's obviously there's big clients there. There's value in this information. For us it's predictive maintenance is the big play. A study I read the other day by a Boston consulting group talks about how its services and applications in the industrial internet of things. There's an $80 billion market in the next five years with predictive maintenance leading the way as the most mature application there. So we're happy to be kind of riding on the front of that wave, really pushing the state of the art there. Predictive maintenance is valuable to clients because the idea is to predict failures, do optimization of resources, so to get more energy out of your wind farm, get more gas out of the ground, you name it. Having the software that can provide those solutions efficiently to clients without a lot of start up, but each new iteration. So having a product that can deliver that intellectual property efficiently is important. The whole goal is to be able to reduce maintenance costs and extend the useful life of assets. So that's what SparkPredict is our product, SparkPredict our product, Sparkcognition has been laboring to do. We have a successful deployment of 1,100 turbines with Invenergy, which is the largest wind production company in the United States. We're doing work with Duke, Nexterra, several other large electrical production companies, oil and gas companies as well. In Austin we're near Houston, we have a lot of energy production opportunity there. So predictive maintenance for us is a big play. >> So you guys did a session this week. You hosted a panel, is that right? So I mean no offense, but what we're talking about now is really even more interesting than storage. But it's a storage panel you were hosting, right? So what was the conversation like? >> The conversation around that was we had three software companies, Sparkcognition and two other software companies. Then we had a federal integrator. All of them are doing cloud delivery. So for example, one of the other software companies Medicat, delivers medical record keeping as a service to hospitals. They're doing predictive analytics and predictive maintenance, and also some cybersecurity out. So there were three professional software companies, and integrator. And in each case the issues were A, we need to be up and going all the time and the user doesn't know what storage we're using. But we can never fail because we're real time. In fact, one of the customers is the IRS. So the federal integrator, the IRS cloud runs on IBM storage. The entire IRS runs under IBM cloud. On our storage, but it's their cloud. It's their private cloud that they put together, that the integrator put together. The idea was we've got a cloud deployment. There's two key things your storage has to do. A, it needs to be resilient as heck because these guys and the other two companies on the software side if they cannot serve it as a service then no one's going to buy the software, right? Because software is the service. So for them it's critical in their own infrastructure that it be resilient. Then the second thing, it needs to be fast. You've got to meet the SLAs, right? So when you're thinking the system's integrator at the IRS, what do you think the SLAs are and they've got like 14 petabytes of all flash. >> You forgot dirt cheap. You got resilient as heck, lightning fast, and it's got to be dirt cheap, too. >> Well of course. >> They want all three, right? >> You have this panelist, so what Jenny, what were Jenny's three? Industrial ready, cloud based, and cognitive to the core. So you guys are, I'm on your website. It's cognitive this, cognitive that. You're cognitive to the core. You're presumably you're using industrial ready infrastructure and it's all cloud based, right? Talk about that a little bit, then I've got a follow up. >> To tie into what Eric is saying about the premium hardware, the cloud opportunity, for us to be able to to AI software, to be able to do machine learning models, these are very intensive applications that require massive amounts of CPU, IO, fast storage. To be able to get the value from that data quickly so that it's useful and actionable takes that premium hardware. So that's why we've done testing with flash system, with our cybersecurity product. One of the most innovative things that we did in the previous year was to move from a traditional architecture using X86, 64 where we had a cluster of eight servers there. Brought that down to one flash system array and we're able to get up to 20 times the performance doing things like analyzing, sorting, and ingesting data with our cybersecurity platform. So in that regard we were very much tied closely to the flash system product. That was a very successful use case. We offered a white paper on that. If anyone wants to read more that's available on the IBM website. >> Where do you find that, search it? >> Yeah, it's on IBM.com and it's basically how they used it to deliver software as a service. >> What do I search? >> If you search Sparkcognition IBM you'll find it on Google. >> My other question, my follow up is you talk about these IoT apps which are distributed by their very nature. Can we talk about the data flow? What are you seeing in terms of where the data flows? Everybody wants to instrument the windmill. You've got to connect it then you've got to instrument it. Where's the data going? You're doing analytics locally, you're sending data back. What are you seeing in the client base? >> Yeah, that's a great question. Those in the field use cases for the wind turbines for example, most of our clients they already have a data storage solution. We're not a data storage provider. The reason, and someone asked me this yesterday in a different conversation. They said why are wind turbines so ripe for the picking? It's because they're relatively modern assets. They were built with the sensors onboard. The data, they have been collecting the data since the invention of the modern wind turbine, they've been collecting this data. Generally it's sent in from the field at 10 minute intervals, usually stored in some sort of large data center. For our purposes though, we collect a feed off that data of the important information, run our models, store a small data set a few months, whatever we think we need to train that machine learning model and to retrain and balance that model. That's sort of an example where we're doing the analysis in a data center or in the cloud sort of out of the field. The other approach is sort of an edge analytics approach, you might have heard that term. That's usually for smaller devices where the value of the asset doesn't justify the infrastructure to relay the information and then deploy this large scale solution. So we actually are developing edge analytic solution, a version of our product as well working with a company called Flowserve, their the world's largest pump manufacturing company. To be able to say how can we add some intelligence the to these pumps that may operate near a pipeline or out in the oil field and be able to make those machines smarter even though they don't necessarily justify the robust IT infrastructure of a full wind turbine fleet. >> Is there a best practice that you guys see in terms of the storage? Because you bring out edge and the network. Great point, lot of diversity at the edge now, from industrial to people. But the data's got to be stored somewhere. I mean, is there a best practice? Is there a pattern to developing that you're seeing in terms of how people are approaching the data problem and applying algorithms to it? Just talk, do I move the data? Do I push to compute to the data? Thoughts on what you guys are seeing in terms of best practices. >> One of the other companies that was on the panel also is doing predictive modeling. They take 600 different feeds in real time then munge it for mostly for industrial markets, but mostly for the goods. So the raw goods that they need to make a machine or make a table or make the paper that is used behind us, or make the lights that are used here, they look at all that commodities and then they feed it out to all these consumers, not consumers but the companies that build these products. So for them, they need it real time so they need storage that's incredibly fast because what they're doing is they're putting out on super powerful CPUs loaded with D-ram, but you can only put so much D-ram in a server. They're building these giant clusters to analyze all this data and everything else is sitting on the flash. Then they push that out to their customers. Slightly different model from what Sparkcognition does, but a slightly similar except their taking it from 600 constant data sources in real time, 24 by seven, 365 and then feeding it back out to these manufacturing companies that are looking to buy all these commodities. >> You have "software defined" in your title. That was kind of the big buzzwords a few years ago. Everybody wanted to replicate the public cloud on prem. We think of it as programmable infrastructure, right? Set it and then you can start making API calls and set SLAs and thresholds, etc. Where are we at with software defined? Do you guys, does it resonate with you or is it just an industry buzzword? I'll start with Eric. >> For us we're the largest provider of software defined storage in the world. Hundreds and hundreds and hundreds of millions of dollars every year. We don't sell any infrastructure. We just sell the raw software and they use commodity infrastructure, whatever they want: hard drives, flash drives, CPUs, anything they buy from their local reseller and then create basically high-performance arrays using that software. So they create on their own. Everything is built around automation so we automatically can replicate data, snapshot data, migrate data around from box to box, move it from on-premise to a cloud through what we call transparent cloud tiering. All of that in the software defined storage is done based on automation play. So the software defined storage allows them to if you will, build their own version of our flash system by just buying the raw software and buying flash from someone else, which is okay with us because the real value's in the software, obviously as you know. That allows them to then create infrastructure of their own, but they've got the right kind of software. They're not home brewing the software it's all built around automation. That's what we're seeing in the software defined space across a number of different industries, whether it be cloud providers, banks. We have all kinds of banks that used our software defined storage and don't buy the actual underlying storage from us, just the storage software. >> Do you, you may not have visibility in this, but getting kind of geeky on it. Do you guys adopt that sort of software defined mentality in your approach? >> Yeah, so for us software defined storage is something that we've deployed for our proof of concept evaluations. The nature of the work that we do is the solution is innovative to the point where everyone needs to have some sort of proof point for themselves before the company or the client will invest in a large scale. So software defined storage and embracing that perspective has allowed us to deploy a small scale implementation without having our own dedicated hardware, for example, at different clients. That's enabled us to spin up an instance quickly, to provision that small scale deployment, to be able to prove out results at a low cost to our client. That's where we really leverage that approach. We also have used a similar approach in the cloud where we've used multi-tenant environments to be able to support our cybersecurity product, SparkSecure in a multi-tenant cloud hosted environment which brings down delivery costs as well. It allows us to slice up that data and deliver it at a low cost. As far as our large scale physical deployments for the asset monitoring and such, we really, we generally end up with a piece of a flash system or flash storage, bare metal deployment because that speed is critical whether that's the client wants to have instant monitoring of a critical asset or they have a financial services use case where we're looking for anomalies or looking for threats in the cybersecurity landscape. Having that real-time model building and model result is very critical. So having that bare metal flash system type installation is kind of our preferred route. The only other thing I would say on that is you asked earlier about our approach. For us, the security data is very important. Most of our assets are what are called critical assets. So clients are very sensitive to the security of the data. Some are still uncomfortable with a cloud deployment. Another reason why we have an affinity for the hardware deployment with IBM. >> Why IBM? >> Our company has really deep roots with IBM. My founder Amir Hussein, was actually on the board of directors of the original IBM Watson Project as well as Manoj Saxena was the original GM of the IBM Watson program. We have just a long relationship with IBM. We have a lot of mutual interest and respect for the entity. We've also found that the products are superior in many ways. We are hardware agnostic and we're an independent advisor to our clients when it comes to how to deliver our solutions. But our professional opinion based on the testing that we've done is that IBM is a top-tier option. So we continue to prescribe that to our clients. When they feel that's appropriate they make that purchase through IBM. >> Great testimonial. Eric, excited to hear that nice testimonial for you guys? Congratulations. >> He's done several panels with us and again, part of the reason for here was A, all about IoT which they're all into. All about commo which they're all into. And to show that you can do a software as a service model even in-house. They happen to be a professional software company but if you're a giant global enterprise you may actually do software as a service to your remote branch offices which is very similar to what these guys to do other companies. This gives them an example, the other two software companies the same way, to show in-house developers if you're going to have a private cloud, not go public, you can deliver software as a service internally to your own company through the dev model and do it that way. Or you can use someone like Sparkcognition or Medicat or the other companies that we showed, Z-Power, all of which were using us to deliver their software as a service with IBM flash technology. >> Dave: And you're using Watson or Watson analytics? >> Yes, so we have done integrations with Watson for our cybersecurity product. We've also done integrations with Watson rank and retrieve using the NPL capabilities to advise the analysts both in the Predict space and in the Secure space. Sort of an advisor to say what a client user could see something happening on a turbine and say what does this mean? Using a Watson corpus. I was going to add one thing, we were talking about why IBM? IBM really has been a leader in the space of cognitive computing and they've invested in bringing and nurturing small companies and bringing up entrepreneurs in that space to build that out. So we appreciate that. I think it's important to mention that. >> All right Mark, thanks so much for joining in, the great testimonial, the great insight. Good luck with your business. Congratulations on the success startup taking names and kicking butt. Eric, great to see you again, thanks for the insight and congratulations on great, happy customers and see you again. Okay, we're watching theCube live here at Interconnect 2017. More great coverage, stay with us. There will be more after this short break. (upbeat instrumental music)