>> Live from Boston, Massachusetts, this is the CUBE, covering Spark Summit East 2017 brought to you by databricks. Now, here are your hosts Dave Valante and George Gilbert. >> Welcome back to Boston everyone. It's snowing like crazy outside, it's a cold mid-winter day here in Boston but we're here with the CUBE, the world-wide leader in tech coverage. We are live covering Spark Summit. This is wall to wall coverage, this is our second day here. John Landry with us, he's the distinguished technologist for HP's personal systems data science group within Hewlett Packard. John, welcome. >> Thank you very much for having me here. >> So I was saying, I was joking, we do a lot of shows with HPE, it's nice to have HP back on the CUBE, it's been awhile. But I want to start there. The company split up just over a year ago and it's seemingly been successful for both sides but you were describing to us that you've gone through an IT transformation of sorts within HP. Can you describe that? >> In the past, we were basically a data warehousing type of approach with reporting and what have you coming out of data warehouses, using Vertica, but recently, we made an investment into more of a programming platform for analytics and so where transformation to the cloud is about that where we're basically instead of investing into our own data centers because really, with the split, our data centers went with Hewlett Packard Enterprise, is that we're building our software platform in the cloud and that software platform includes analytics and in this case, we're building big data on top of Spark and so that transformation is huge for us, but it's also enabled us to move a lot faster, the velocity of our business and to be able to match up to that better. Like I said, it's mainly around the software development really more than anything else. >> Describe your role in a little bit more detail inside of HP. >> My role is I'm the leader in our big data investments and so I've been leading teams internally and also collaborating across HP with our print group and what we've done is we've managed to put together a strategy around our cloud-based solution to that. One of the things that was important was we had a common platform because when you put a program platform in place, if it's not common, then we can't collaborate. Our investment could be fractured, we could have a lot of side little efforts going on and what have you so my role is to pry the leadership in the direction for that and also one of the reasons I'm here today is to get involved in the Spark community because our investment is in Spark so that's another part of my role is to get involved with the industry and to be able to connect with the experts in the industry so we can leverage off of that because we don't have that expertise internally. >> What are the strategic and tactical objectives of your analytics initiatives? Is it to get better predictive maintenance on your devices? Is it to create new services for customers? Can you describe that? >> It's two-fold, internal and external so internally, we got millions of dollars of opportunity to better our products with cost, also to optimize our business models and the way we can do that is by using the data that comes back from our products, our services, our customers, combining that together and creating models around that that are then automated and can be turned into apps that can be used internally by our organizations. The second part is to take the same approach, same data, but apply that back towards our customers and so with the split, our enterprise services group also went with Hewlett Packard Enterprise and so now, we have a dedicated effort towards creating manage services for the commercial environment. And that's both on the print size and on the personal system side so to basically fuel that, analytics is a big part of the story. So we've had different things that you'll see out there like touch point manager is one of our services we're delivering in personal systems. >> Dave: What is that? >> Touch point manager is aimed at providing management services for SMB and for commercial environments. So for instance, in touch point manager, we can provide predictive type of capabilities for support. A number of different services that companies are looking for when they buy our products. Another thing we're going after too is device as a service. So there's another thing that we've announced recently that basically we're invested into there and so this is obviously if you're delivering devices as a service, you want to do that as optimal as possible. Well, being able to understand the devices, what's happening with them, been able to predictive support on them, been able to optimize the usage of those devices, that's all important. >> Dave: A lot of data. >> The data really helps us out, right? So the data that we can collect back from our devices and to be able to take that and turn that around into applications that are delivering information inside or outside is huge for us, a huge opportunity. >> It's interesting where you talk about internal initiatives and manage services, which sound like they're most external, but on the internal ones, you were talking about taking customer data and internal data and turning those into live models. Can you elaborate on that? >> Sure, I can give you a great example is on our mobile products, they all have batteries. All of our batteries are instrumented as smart batteries and that's an industry standard but HP actually goes a step further on that, it's the information that we put into our batteries. So by monitoring those batteries and the usage in the field is we can tell how optimally they're performing, but also how they're being used and how we can better design batteries going forward. So in addition, we can actually provide information back into our supply chain. For instance, there's a cell supplier for the battery, there's a pack supplier, there's our unit manufacturer for the product, and so a lot of things that we've been able to uncover is that we can go and improve process. And so improving process alone helps to improve the quality of what we deliver and the quality of the experience to our customers. So that's one example of just using the data, turning that around into a model. >> Is there an advantage to having such high volume, such market share in getting not just more data, but sort of more of the bell curve, so you get the edge conditions? >> Absolutely, it's really interesting because when we started out on this, everybody's used to doing reporting which is absolute numbers and how much did you shift and all that kind of stuff. But, we're doing big data, right? So in big data, you just need a good sample population. Turn the data scientist into that and they've got their statistical algorithms against that. They give you the confidence factor based upon the data that you have so it's absolutely a good factor for us because we don't have to see all the platforms out there. Then, the other thing is, when you look at populations, we see variances in different customers so we're looking at, like one of our populations that's very valuable to us is our own, so we take the 60 thousand units that we have internally at HP and that's one of our sample populations. What a better way to get information on your own products? But, you take that and you take it to one of our other customers and their population's going to look slight different. Why? Because they use the products differently. So one of the things is just usage of the products, the environment they're used in, how they use them. Our sample populations are great in that respect. Of course, the other thing is, very important to point out, we only collect data under the rules and regulations that are out there, so we absolutely follow that and we absolutely keep our data secure and we absolutely keep everything and that's important. Sometimes, today they get a little bit spooked sometimes around that, but the case is that our services are provided based on customers signing up for them. >> I'm guessing you don't collect more data than Google. >> No, we're nowhere near Google. >> So, if you're not spooked at Google - >> That's what I tell people. I say if you got a smartphone, you're giving up a lot more data than we're collecting. >> Buy something from Amazon. Spark, where does Spark fit into all of this? >> Spark is great because we needed a programming platform that could scale in our data centers and in our previous approaches, we didn't have a programming platform. We started with a Hadoop, the Hadoop was very complex though. It really gets down to the hardware and you're programming and trying to distribute that load and getting clusters and you pick up Spark and immediately abstraction. The other thing is it allows me to hire people that can actually program on top of it. I don't have to get someone that knows Map Reduce. I can sit there and it's like what do you know? You know R, Scala, you know Python, it doesn't matter. I can run all of that on top of it. So that's huge for us. The other thing is flat out the speed because as you start getting going with this, we get this pull all of a sudden. It's like well I only need the data like once a month, it's like I need it once a week, I need it once a day, I need the output of this by the hour now. So, the scale and the speed of that is huge and then when you put that on the cloud platform, you know, Spark on a cloud platform like Amazon, now I've got access to all the compute instances. I can scale that, I can optimize it because I don't always need all the power. The flexibility of Spark and being able to deliver that is huge for our success. >> So, I've got to ask some columbo questions and George, maybe you can help me sort of frame it. So you mentioned you were using Hadoop. Like a lot of Hadoop practitioners, you found it very complex. Now, Hewlett Packard has resources. Many companies don't but so you mentioned people out doing Python and R and Scale and Map Reduce, are you basically saying okay, we're going to unify portions of our Hadoop complexity with Spark and that's going to simplify our efforts? >> No, what we actually did was we started on the Hadoop side of it. The first thing we did was try to move from a data warehouse to more of a data lake approach or repository and that was internal, right? >> Dave: And that was a cost reduction? >> That was a cost reduction but also, data accessibility. >> Dave: Yeah, okay. >> The other thing we did was ingesting the data. When you're starting to bring data in from millions of devices, we had a problem coming through the firewall type approach and you got to have something in front of that like a Kafka or something in front of it that can handle it. So when we moved to the cloud, we didn't even try to put up our own, we just used Kinesis and that we didn't have to spend any resources to go solve that problem. Well, the next thing was, when we got the data, you need to ingest the data in and our data's coming in, we want to split it out, we needed to clean it and what you, we actually started out running Java and then we ran Java on top of Hadoop, but then we came across Spark and we said that's it. For us to go to the next step of actually really get into Hadoop, we were going to have to get some more skills and to find the skills to actually program in Hadoop was going to be complex. And to train them organically was going to be complex. We got a lot of smart people, but- >> Dave: You got a lot of stuff to do, too. >> That's the thing, we wanted to spend more time getting information out of the data as opposed to the framework of getting it to run and everything. >> Dave: Okay, so there's a lot of questions coming out. You mentioned Kinesis, so you've replaced that? >> Yeah, when we went to the cloud, we used as many Amazon services as we can as opposed to growing something for ourselves so when we get onto Amazon, you know, getting data into an S3 bucket through Kineses was a no-brainer. When we transferred over to the cloud, it took us less than 30 days to point our devices at Kinesis and we had all our data flowing into S3. So that was like wow, let's go do something else. >> So I got to ask you something else. Again, I love when practitioners come on. So, one of the complaints that I hear sometimes from AWS users and I wonder if you see this is the data pipeline is getting more and more complex. I got an API for Kinesis, one for S3, one for DynamoDB, one for Elastic Plus. There must be 15 proprietary APIs that are primitive, and again, it gets complicated and sometimes it's hard to even figure out what's the right cost model to use. Is that increasingly becoming more complex or is it just so much simpler than what you had before and you're in nirvana right now? >> When you mentioned costs, just the cost of moving to the cloud was a major cost reduction for us. >> Reduction? >> So now it's - >> You had that HP corporate tax on you before - >> Yeah, now we're going from data centers and software licenses. >> So that was a big win for you? >> Yeah, huge, and that released us up to go spend dollars on resources to focus on the data science aspect. So when we start looking at it, we continually optimized, don't get me wrong. But, the point is, if we can bring it up real quickly, that's going to save us a lot of money even if you don't have to maintain it. So we want to focus on creating the code inside of Spark that's actually doing the real work as opposed to the infrastructure. So that cost savings was huge. Now, when you look at it over time, we could've over analyzed that and everything else, but what we did was we used a rapid prototyping approach and then from there, we continued to optimize. So what's really good about the cloud is you can predict the cost and with internal data centers and software licenses and everything else, you can't predict the cost because everybody's trying to figure out who's paying for what. But in the case of the cloud, it's all pretty much you get your bill and you understand what you're paying. So anyway - >> And then you can adjust accordingly? >> We continue to optimize so we use the services but if we have for some reason, it's going to deliver us an advantage, we'll go develop it. But right now, our advantage is we got umteen opportunities to create AI type code and applications to basically automate these services, we don't even have enough resources to do it right now. But, the common programming platform's going to help us. >> Can you drill into those umpteen examples? Just some of them because - >> I mentioned the battery one for instance. So take that across the whole system so now you've got your storage devices, you've got your software that's running on there, we've got built into our system security monitoring at the firmware level just basically connecting into that and adding AI around that is huge because now we can see a tax that may be happening upon your fleet and we can create services out of that. Anything that you can automate around that is money in our pocket or money in our customers' pocket so if we can save them money with these new services, they're going to be more willing to come to HP for products. >> It's actually more than just automation because it's the stuff you couldn't do with 1,000 monkeys trying to write Shakespeare. You have data that you could not get before. >> You're right, what we're doing, the automation is helping us uncover things that we would've never seen and you're right, the whole gorilla walking through the room, I could sit there and I could show you tons of examples of where we're missing the boat. Even when we brought up our first data sets, we started looking at them and some of the stuff we looked at, we thought this is just bad data and actually it wasn't, it was bad product. >> People talk about dark data - >> We had no data models, we had no data model to say is it good or bad? And now we have data models and we're continuing to create those data models around, you create the data model and then you can continue to teach it and that's where we create the apps around it. Our primitives are the data models that we're creating from the device data that we have. >> Are there some of these apps where some of the intelligence lives on the device and it can, like in a security attack, it's a big surface area, you want to lock it down right away. >> We do. The good example on the security is we built something into our products called Sure Start. What essentially it is is we have ability to monitor the firmware layer and so there's a local process that's running independent of everything else that's running that's monitoring what's happening at that firmware level. Well, if there's an attack, it's going to immediately prevent the attack or recover from the attack. Well, that's built into the product. >> But it has to have a model of what this anomalous behavior is. >> Well in our case, we're monitoring what the firmware should look like and if we see that the firmware, you know you take check sums from the firmware or the pattern - >> So the firmware does not change? >> Well basically we can take the characteristics of the firmware and monitor it. If we see that changing, then we know something's wrong. Now it can get corrupt through hardware failure maybe because glitches can happen maybe. I mean solar flares can cause problems sometimes. So, the point is we've found that customers had problems sometimes where basically their firmware would get corrupted and they couldn't start their system. So we're like are we getting attacked? Is this a hardware issue? Could it be bad Flash devices? There's always all kinds of things that could cause that. Well now we monitor it and we know what's going on. Now, the other cool thing is we create logs from that so when those events occur, we can collect those logs and we're monitoring those events so now we can have something monitor the logs that are monitoring all the units. So, if you've got millions of units out there, how are you going to do that manually? You can't and that's where the automation comes in. >> So the logs give you the ability up in the cloud or at HP to look at the ecosystem of devices, but there is intelligence down on the - >> There's intelligence to protect the device in an auto recover which is really cool. So in the past, you had to get your repair. Imagine if someone attacked your fleet of notebooks. Say you got 10 thousand of them and basically it brought every single one of them down one day. What would you do? >> Dave: Freak. >> And everything you got to replace. It was just an attack and it could happen so we basically protect against that with our products and at the same time, we can see that may be a current and then from the footprints of it, we can then do analysis on it and determine was that malicious, is this happening because of a hardware issue, is this happening because maybe we tried to update the firmware and something happened there? What caused that to happen? And so that's where collecting the data from the population then helps us do that and then mix that with other things like service events. Are we seeing service events being driven by this? Thermal, we can look at the thermal data. Maybe there's some kind of heat issue that's causing this to happen. So we starting mixing that. >> Did Samsung come calling to buy this? >> Well, actually what's funny is Samsung is actually a supplier of ours, is a battery supplier of ours. So, by monitoring the batteries, what's interesting is we're helping them out because we go back to them. One of the things I'm working on, is we want to create apps that can go back to them and they can see the performance of their product that they're delivering to us. So instead of us having to call a meeting and saying hey guys let's talk about this, we've got some problems here. Imagine how much time that takes. But if they can self-monitor, then they're going to want to keep supplying to us, then they're going to better their product. >> That's huge. What a productivity boost because you're like hey, we got a problem, let's meet and talk about it and then you take an action to go and figure out what it is. Now if you need a meeting, it's like let's look at the data. >> Yeah, you don't have enough people. >> But there's also potentially a shift in pricing power. I would imagine it shifts a little more in your favor if you have all the data that indicates the quality of their product. >> That's an interesting thing. I don't know that we've reached that point. I think that in the future, it would be something that could be included in the contracts. The fact that the world is the way it is today and data is a big part of that to where going forward, absolutely, the fact that you have that data helps you to better have a relationship with your suppliers. >> And your customers, I mean it used to be that the brand used to have all the information. The internet obviously changed all that, but this whole digital transformation and IOT and all those log data, that sort of levels the playing field back to the brand. >> John: It actually changes it. >> You can now add value for the consumer that you couldn't before. >> And that's what HP's trying to do. We're invested to exactly do that is to really improve or increase the value of our brand. We have a strong brand today but - >> What do you guys do with - we got to wrap - but what do you do with databricks? What's the relationship there? >> Databricks, again we decided that we didn't want to be the experts on managing the whole Spark thing. The other part was that we're going to be involved with Spark and help them drive the direction as far as our use cases and what have you. Databricks and Spark go hand in hand. They got the experts there and it's been huge, our relationship, being able to work with these guys. But I recognize the fact that, and going back to software development and everything else, we don't want to spare resources on that. We got too many other things to do and the less that I have to worry about my Spark code running and scaling and the cost of it and being able to put code in production, the better and so, having that layer there is saving us a ton of money and resources and a ton of time. Just imagine time to market, it's just huge. >> Alright, John, sorry we got to wrap. Awesome having you on, thanks for sharing your story. >> It's great to talk to you guys. >> Alright, keep it right there everybody. We'll be back with our next guest. This is the CUBE live from Spark Summit East, we'll be right back.

>>from around the globe. It's the Cube covering HP Discover Virtual experience Brought to you by HP >>Hi, welcome to the Cube's coverage of HP Discover 2020 Virtual experience. I'm Lisa Martin, and I'm pleased to welcome from scale any one of our long Time Cube alumni. We have, all specially the chief product officer at agility. Hey, Paul, welcome back to the Cube. >>Hi, Lisa. It's been a long time, and it's just wonderful to be back. Thank you. >>This is our new virtual cube that appear where everybody is very socially distant but socially connected. So since it's been a while since we've had you on and your peers from stability tell us a little bit about scale and then we'll dive into what you're doing with HP, >>Okay? Absolutely. Let me give you kind of a pop down recap of where we're at. So interestingly, we're now it 10 year old company. We actually celebrated our never anniversary last year. Um, we still have our flagship product, the Ring, which we launched originally in 2000 and 10 that is distributed file and object storage software. But about three years ago, we added a second product called Zenko, which is for multi cloud data management. We do continue to invest in the ring a lot, both on the file side and the object side. The current release now is Ring eight. The target market for this is pretty broad, but we really focus on financial services institutions. That's a big base for us. We have something like half of the world's banks, about 60% of the world service providers, a lot of government institutions. But what's been fastest growing for us now is healthcare. We have a lot of growth there in medical imaging and genomics research. And then I guess the last thing I'll add is that partners are just super important to us. We continue to certify and test with SDI Solutions. I think we have 80 of them now deployed and ready to go. But there's a real focus here now on partners like Said Era and with a Iot and Splunk VM HP East or one. So those partners are critical to our business and we just love to partner with them. >>Do you been partners with HP for quite a while? Tell me about the evolution of the partnership as you've evolved your technology. >>Yeah, absolutely. It's interesting, cause I just noted this Ah, a couple of weeks ago. The company is 10 years old. We've been partners with HP for over half of that. It's about 5.5 years. The way to think about this is that we have a worldwide OM relationship with HP for the Apollo 4000 server line. The official name for our product is HP Apollo 4000 systems with scale itty ring scalable storage. Also quite a mouthful, but very descriptive. Ah, and then we work very closely with the HP storage and big data teams. I'm very tied into the product side, talking to the product managers, but also the marketing side and very much so. On the sales side, we've had super success with them in Europe, also here in the US, and there's growing business, but also in a P J in Japan. Specifically, >>you mentioned that one of the doctors right now that's really urging a healthcare and given the fact that we are three months into a global pandemic, anything that's interesting that you want to share in terms of how skeleton is helping some of your health care customers rapidly pivot in this very unprecedented time. >>Yeah, I would say that there's a couple of very notable trends here. The 1st 1 started a few years ago. We really, honestly didn't focus so much on health care until about 2000 and 17 18. But since that time, we now have something like 40 hospital hospital systems globally using our product and notably on H P E servers. Uh, and that's to retain medical images for long term retention. These are things like digital diagnostic images. MRI's CAT scans CT scans. These hospitals are mandated to keep them for a long term right, sometimes for five years, 10 years or even page patient Lifetime. I would say the newer thing that we're seeing now just in the last year or so is genomics research. There's so much concentration now on pharmaceutical and biotechnology around genomics. That data tends to be very voluminous, you know, it can go from hundreds of terabytes and petabytes, and moreover, they need to run simulations on that to do you know, fast iteration on different drug research. We've now been applied to that problem, and a lot of times we do it with a partner or something like a fast tier one file system and then us as the archive here. But we're seeing that the popularity of that just wrote tremendously within hospitals, hospital groups and also just dedicated research for biotechnology. >>The vault. You talked about volumes there, and the volumes are growing and growing each year as his retention periods, depending on the type of data, the type of of ah, imagery, for example. But from a use case perspective, what is it that you're helping your health care customers achieve? Is it is it backup targets? Is it disaster? Recovery is speed of access All the above. >>Yeah, so where we focus in health care is really on the unstructured data. This is all the file content that they deal with, you know, in a hospital. Think about all the different medical image studies that they have, things like digital files for CAT scans and MRI's. These are becoming huge files, you know. One multi slice X ray or digital scan, for example, can be gigabytes in size and profile, and that's per patient. Now think about the number of patients and the right attention of all of that. It's a perfect use case for what we do, which is capacity optimized storage for long term retention. But we can also be used for other things. For example, backups of the electronic patient records. Those are typically stored in databases, but they need to be backed up. What we found is that we're an ideal long term backup target. So the way hospitals look at us is that they can consolidate multiple use cases, undo our ring system on HP. They can grow it over time. They could just keep adding servers, and typically what they do is they start with a single use case, what they think of as a single modality, perhaps an imaging. And then they grow over time to encompass more and more and eventually think about a comprehensive image management system within a hospital. But those are popular today. Hospitals are also starting to look at other use cases. Obviously, we mentioned genomics, but hybrid cloud is coming at them as well. >>Talk to me about that as we see growing volumes of data, different types of modalities, lots of urgent need to you, said backup data, So data protection is critical. But as as healthcare organizations move to multi cloud, how considerate Ian HP help facilitate that migration? >>Yeah, So what we've noticed is, you know, there's both a feeling that they're fast and they're slow to embrace the public clouds. But one of the things that's obvious is that from a sass perspective, software as a service, they've really embraced it. Most of the big EMR systems, the electronic medical records, are already SAS based, so they are there, and in fact they're probably already multi cloud. But on the data management side, that's where we focus. And we hear a lot of use cases that would involve taking older data from on Prem and perhaps archiving it long term in a HIPAA compliant cloud in the US, for example, for long term retention. But there are other things. For example, they may want to push some data that they've generated on Prem to a public cloud like Amazon or azure, and do some kind of computing against it. Perhaps an analytic service, some kind of image recognition or, you know, image pattern detection. Um, the 3rd 1 that we see now in hybrid cloud is their interest in having second copies of the data so that they can continue operations. Right? I think we all know that hospitals have an absolute uptime need. They need to be running 24 by seven. One of the things that's starting to happen is rather than a second physical data center. They established a second site in a public cloud on and then they stage their applications and we can help with HP. Move the data from on Prem to the public cloud to have this sort of cloud disaster recovery solution. >>So cloud here interesting topic. Do you see there that in healthcare in particular, that hospitals and healthcare organizations are getting less concerned about cloud from a security perspective and more open to it as an enabler of scale? >>I think what they've seen is that the cloud vendors have really matured in terms of providing all of the hardening that you want in terms of data, privacy and data security. You know, 10 years ago, if you looked at the cloud, you would have been extremely nervous about putting your data up there. But now all of the right principles are there in terms of multi tenancy. Ah, secure authentication based on very strong keys. Encryption of the data. One of the first healthcare customers we worked with was completely ready to do this. But then, of course, they said, the images that we store in the cloud must be infected. So we were able to work in collaboration with them, to develop encryption and actually use their own management service for encrypting those images so that our system or the HP servers don't store the keys for encryption. So I would say yes, It's a combination of the cloud's becoming super mature. Some of them are now certified and compliant for this use case on, the customers are just sort of. They passed the first step of trying it on there really to sort of go into these use cases a little bit more broadly. >>And so with that maturity of the technologies and the more the willingness on the part of the customer to try and tell me how to HP and scale a go to market together. >>Yeah, so what we do is we've really focused on specific market verticals, healthcare being one of them, but there are others. Financial services is where we've had other success with them. The way we do it is that we first start by building very specific swim lanes. In HP parlance, that helps aimed the Salesforce on where we can provide a great solution not only with Ring but perhaps with complementary software. Like I mentioned H p e store once for data protection backup. They have other partner solutions that we just love to work with. Vendors like Wicca. Iot has a wonderful fast file system that is now useful in biotech. Um, and they use a system like the ring for storing the data from their file system and the snapshots in that. But the way it's been organized is really by vertical and to go and have specialized kind of teams that understand how to sell that message. We jointly sell with them, so their teams and our teams Goto calls together. It's obviously been very virtual, but we've usually collaborated very extensively in the field working kind of air cover at the marketing level, and now one of the newer things with obviously the new way of working is lots of virtual events were not only doing a discover virtual experience, but we started doing more and more webinars, especially with HP and these other joint part >>and carries in this new virtual era where everything like, he said, This is how we're communicating now. And thankfully, we have the technology. Couple questions on that related to sales and engagement. One. What are some of the things that the sales team but the joint sales teams are hearing now from customers that might be changing requirements given the Koven situation? First >>question. Yeah, I think what one of the things we've certainly seen is that almost nothing has slowed down in these industries. I mean, we're focused on industries that seem to kind of think long term, right? I obviously healthcare. They're dealing with the current crisis as much as they can. But what we've seen is that there still planning, right, so they want to build their I T infrastructure. They're certainly thinking about how to leverage hybrid cloud. I think that's it becomes very clear that they see that as not only a way to offer new services in the future, but also to save money today. They're very interested in that right. How can they save on capital expenses and human talent is an example. I think those have been the themes for us. You know, we do have some exposure to industries that might have a little bit more, you know, sensitivity to the current climate, things like travel related services. But honestly, it's been minor. And what we're finding is that even those companies are still investing in this kind of technology, really to think about the 2 to 3 and you're being horizon and beyond. >>Have you done any any messaging, your positioning changes? I know you also in product marketing or corporate marketing that relate to customers. You know, everybody prepares for different types of disruptions or natural disasters. But now we have this invisible disruptor. Any change in your messaging, your positioning either at stability or with the partnership with HP that will help customers understand if you're not on this journey yet, why they need to be >>so, Yeah, we have looked at how we message the technology and the solution, especially in the light of the pandemic. You know, we stayed true to kind of a top level hybrid cloud data management message, but underneath the covers, what do customers care about? They care about a solution that you provide, but they also care about what they pay for it. Let's let's be honest. One of the things we've done very historically is to have a very simplified pricing model. It's based on usable protected capacity. So the user says I have a petabytes of data. That's the license fee. It's not based on how much disk they have or how many copies they want to create or how many sites they want to spread it across. So one of the things we want to do is make that a little bit more clear. Eso that's come out a bit more in our messaging in recent months. The second is that what we feel is that customers really want to know us as a company. They want to feel assured that were here, that will support them in all cases and that were available at all times. And what that's translated into is a more of a customer community focus. We are very much carrying about, you know, our customers. We see them invest in our systems today, but they also continue to expand. So we're doing things like new community portals where they can engage us in discourse. They can ask questions live. We're online. We have a lot of tips and knowledge available for them. So I would say that those are the two changes that we put in our messaging, both on pricing and on a community involved >>and where community involvement is concerned. It's even more critical now because we can't get together face to face and have conversations or meetings or conferences as chief product officer. Imagine that was a lot of what you were doing before. Tell me what it is from your perspective to engage with the community, to engage with sales and your partners during this TBD timeframe of we don't know when we're going to get back together. What do you find? It works really well for continuing continuing that engagement. >>Yeah, I think the keyword for me has just been transparency. You know, customers have always bonded to know, really, what's what's going on behind the scenes. How does the tech work? Right? What's the architecture? And I think now what we're seeing is there sort of a ramp up on that. For example, what's very important for community is for people to know what's coming right? They want to know the roadmaps. They want to be alerted to new things that are not only the next quarter, but in the next year. Right? So I think that's our focus here is to make this community a place where people can learn absolutely everything so that they can plan not only for the next year, but like we said there, they're thinking three years and beyond. So we're going to do our best to be totally transparent and be expressed as we can possibly be >>transparent entrusted. Paul, those are two great words to end on. We Thank you so much for joining us on the Cube, sharing what's new at stability and with the HP partnership. >>It's been a pleasure. Lisa. Thank you for your time. >>Likewise. For my guest, Paul Scott. Sally, I am Lisa Martin. You're watching the Cube's coverage of HP Discover 2020. The virtual experience. Yeah, yeah, yeah, yeah

>>from around the globe. It's the Cube covering HP. Discover Virtual experience Brought to you by HP. >>Welcome to the Cube's coverage of HP Discover 2020 Virtual Experience. I'm Lisa Martin, and I'm pleased to be joined by two guests from HP longtime partner Tech Data. We have calling Blair the vice president of sales and marketing of I. O. T. And Data Solutions and David Smith, H P E Pre Sales Field Solutions are common. And David, Welcome to the Cube. Thanks, Lisa. Great to see. So let's start with the calling. HP and Technical have been partners for over 40 years, but tell our audience a little bit about tech data before we get into the specifics of what you're doing and some of the cool I o. T. Stuff with HP. I >>think that the Tech data is a Fortune 100 distributor. We continued to evolved to be a solutions aggregator in these next generation technology businesses. As you've mentioned, we've been serving the I T distribution markets globally for for 40 plus years, and we're now moving into next generation technologies like Wild Analytics, I O. T and Security bubble Lifecycle Management services. But to be able todo position ourselves with our customer base and the needs of their clients have. So I'm excited to be here today to talk a little bit about what we're doing in I, O. T. And Analytics with David on the HPC side >>and in addition to the 40 plus years of partnership calling that you mentioned that Detected and HP have you've got over 200 plus hp. Resource is David, you're one of those guys in the field. Talk to us about some of the things that you're working on with Channel Partners Table David to enable them, especially during such crazy times of living and now >>absolutely, absolutely so. What we can do is we can provide strong sales and technical enablement if your team, for example, wants to better understand how to position HP portfolio if they require assistance and architect ing a secure performance i o t. Solution. We can help ensure that you're technical team is fully capable of having that conversation, and it's one that they're able to have of confidence, weaken validate the proposed HP solutions with the customers, technical requirements and proposed use case. We can even exist on a customer calls, if it would, would benefit our partner to kind of extend out to that. We also have a a a deep technical bench that Colin can speak to in the OT space toe lean on as well. For so solution is that kind of span into the space beyond where HP typically operates, which would be edge, compute computing and network. Sic security. >>Excellent call and tell me a little bit about Tech Data's investments in I o. T. When did this start? What are you guys doing today? >>Sure, we started in the cloud space. First tackle this opportunity in data center modernization and hybrid cloud. That was about seven years ago. Shortly thereafter we started investing very materially in the security cyber security space. And then we follow that with Data Analytics and then the Internet of things. Now we've been in those spaces with our long term partners for some time. But now that we're seeing this movement to the intelligent edge and a real focus on business outcomes and specialization, we've kind of tracked with the market, and we feel like we've invested a little bit ahead of where the channel is in terms of supporting our ecosystem of partners in this space. >>So the intelligent edge has been growing for quite some time. Poland in the very unique times that we're living in in 2020 how are you seeing that intelligent edge expand even more? And what are some of the pressing opportunities that tech data and HPC i O T solutions together can address? >>So a couple. So the first is a Xai mentioned earlier just data center modernization. And so, in the middle of code 19 and perhaps postcode 19 we're going to see a lot of clients that are really focused on monetizing the things that they've got. But doing so to drive business outcomes. We believe that increasingly, the predominance of use cases and compute and analytics is going to move to the edge. And HP has got a great portfolio for not just on premise high performance computing but also hybrid cloud computing. And then when we get into the edge with edge line and networking with Aruba and devices that need to be a digitized and sense arised, it's a really great partnership. And then what we're able to do also, Lisa, is we've been investing in vertical markets since 2000 and seven, and I've been a long the ride with that team, most all of that way. So we've got deep specialization and healthcare and industrial manufacturing, retail and then public sector. And then the last thing we've kind of turned on here recently just last month is a strategic partnership in the smarter cities space. So we're able to leverage a lot of those vertical market capabilities. Couple that with our HP organization and really drive specialized repeatable solutions in these vertical markets, where we believe increasingly, customers are going to be more interested in a repeatable solutions that can drive quick proof of value proof of concepts with minimal viable what kinds of products. And that's that's kind of the apartment today with RHB Organization and the HP Corporation >>David. Let's double click into some of those of vertical markets that Colin mentioned some of the things that pop into minor healthcare manufacturing. As we know, supply chains have been very challenged during covered. Give us an insight into what you're hearing from channel partners now virtually, but what are some of the things that are pressing importance? >>So from a pressing and important to Collins exact point, and your exact point as well is really it's all about the edge computing space now from a product perspective Azaz Colin had mentioned earlier. HP has their edge line converged systems, which is kind of taking the functionality of OT and edge T Excuse me of OT and I t and combine it into a single edge processing compute solution. You kind of couple that with the ability to configure components such as Tesla GP, use in specific excellent offerings to offer an aid and things like realtime, video processing and analytics. Uh, and a perfect example of this is, ah so for dissing and covert space. If if I need to be able to analyze a group of people to ensure they're staying as far apart as possible or, you know within self distant guidelines, that is where kind of the real time that's like an aspect of things can be taken advantage of same things with with the leveraging cameras where you could actually take temperature detection as as well, so it's really kind of best to think of Edge Lines Solutions is data center computing at the edge kind of transition into the Aruba space. Uh Rubio says offerings aid in the island Security is such a clear pass device inside, which allows for device discovery of network and monitoring of wired and wireless devices. There's also Aruba asset tracking and real time location of solutions, and that's particularly important in the healthcare space as well. If I have a lot of high value assets, things like wheelchairs, things like ventilation devices, where these things low located within my facilities and how can I keep keep track of them? They also, and by that I mean HP. They also kind of leveraging expanse ecosystem of partners. As an example, they leverage thing works allow their i o t solutions as well, when you kind of tying it all together with HP Point. Next to the end, customers provided with comprehensive loyalty solution. >>So, Colin, how ready? Our channel partners and the end user customers to rapidly pivot and start either deploying more technologies at the edge to be able to deliver some of the capabilities that David talked about in terms of analytics and sensors for social distancing. How ready are the channel partners and customers to be able to understand, adopt and execute this technology. >>So I think on the understanding side, I think the partners are there. We've been talking about digital transformation in the channel for a couple of years now, and I think what's happened through the 19 Pandemic is that it's been a real spotlight on the need for those business outcomes to to solve for very specific problems. And that's one of the values that we serve in the channel. So we've got a solution offering that we call our solution factory. And what we do really says is we leverage a process to look outside the industry. At Gartner, Magic Quadrant Solutions forced a Wave G two crowd. You know, top leaders, visionaries and understand What are those solutions that are in demand in these vertical markets that we talked about? And then we do a lot of work with David and his team internally in the HP organization to be able to do that and then build out that reference architectures so that we know that there's a solution that drives a bill of materials and a reference architecture that's going to work that clients are going to need and then we can do it quickly. You know, Tech data. Everything's about being bold, acting now getting scale. And we've got a large ecosystem partners that already have great relationships. So we pride ourselves on being able to identify what are those solutions that we can take to our partners that they can quickly take to their end users where you know we've We've kind of developed out what we think the 70 or 80% of that solution is going to look like. And then we drive point next and other services capabilities to be able to complete that last mile, if you will, of some of the customization. So we're helping them. For those who aren't ready, we're helping them. For those who already have very specific use cases and a practice that they drive with repeatable solutions were coming alongside them and understanding. What can we do? Using a practice builder approach, which is our consultative approach to understand where our partners are going in the market, who their clients are, what skill sets do they have? What supplier affinities do they want to drive? What brand marketing or demand generation support do they need? And that's where we can take some of these solutions, bring them to bear and engage in that consultative engagement to accelerate being ready as, as you rightly say, >>so tech. It has a lot of partners. You in general. You also have a lot of partners in the i o T space calling What? How do you from a marketing hat perspective? How do you describe the differentiation that Tech data and HP ease Iot solutions delivered to the channel to the end user? >>A couple of different things? I think that's that's differentiation. And that's one of the things that we strive for in the channel is to be specialized and to be competitively differentiated. And so the first part, I say to all of my team, Lisa, is you know, whether it's our solution consultants or our technical consultants, our solutions to the developers or the software development team that works my organization. Our goal is to be specialized in such a way that we're having relevant value added conversations not only our channel partners, but also end users of our partners want to bring us into those conversations, and many do. The next is really education and enablement as you would expect. And so there's a lot of things that are specialized in our technical. We drive education certification programs, roadshows, seminars, one of the things that we're seeing a lot of interest now. Lisa is for a digital marketing, and we're driving. Some really need offerings around digital marketing platforms that not only educate our partners but also allow our partners to bring their end users and tour some of this some of these technologies. So whether it's at our Clearwater office, where we've got an I. O T. Solution center, that we we take our partners and their clients through or we're using our facilities Teoh to do executive briefings and ideation as a service that, you know, kind of understanding the art of the possible. With both our resellers and their clients work, we're using our solution. Our solution catalogs that we've built an interactive pdf that allows our partners to understand over 50 solutions that we've got and then be able to identify. Where would they like to bring in David and his team and then my consultants to do that, that deep planning on business development, uh, that we talked about a little bit earlier. >>So the engagement right now is maybe even more important than it has been in a while because it's all hands off and virtual David. Talk to me about some of the engagement and the enablement piece that call and talked about. How are you able to really keep a channel partner and their end user customers engaged and interested in what you're able to deliver through this from New Virtual World? >>That's a great, great question. And we work in conjunction with our marketing teams to make sure that as new technologies and quite in I O. T space as well as within the HP East base as well that that our channel partners are educated and aware that these solutions exist. I know for a fact that for the majority of them you kind of get this consistent bombardment of new technology. But being able to actually have someone go out and explain it and then being able to correspondingly position it's use case and it's functionality and why it would provide value for your end customer is one of the benefits of tech data ads to kind of build upon that previous statement. The fact that We have such a huge portfolio of partners, so you kind of have HP and the edge compute space. But we have so many different partners in the OT space where it's really just a phone call, an email, a Skype message, a way to have that conversation around interoperability and then provide those responses back to our partners. >>Excellent. One more question before we go. Colin for you, A lot of partners. Why HP fry Mt. >>So a couple of reasons? One of the one of the biggest reasons as HP is just a great partner. And so when you look at evaluating I. O. T solutions that tend to be pretty comprehensive in many cases, Lisa it takes 10 or 12 partners to complete a really i o t solution and address that use case that that's in the field. And so when you have a partner like HP who's investing in these programs, investing in demand generation, investing in the spectrum of technology, whether it's hybrid Cloud Data Center, compute storage or your edge devices and Iot gateways, then to be able to contextualize those into what we call market ready solutions in each one of these vertical markets where there's references and there's use cases. And there were coupling education that specific rest of solutions. You know HP can do all of those things, and that's very important. Because in this new world, no one can go it alone anymore. It takes it takes partnerships, and we're all better together. And HP really does embrace that philosophy. And they've been a great partner for us in the Iot space. >>Excellent. Well, Colin and David, thank you so much for joining me today on the Cube Tech data. H p e i o t better together. Thank you so much. It's been a pleasure talking with you. >>Thank you. >>Thank you. Lisa. >>And four Collet and David. I am Lisa Martin. You're watching the Cube's virtual coverage of HP Discover 2020. Thanks for watching. Yeah, yeah, yeah, yeah.

>>from our studios in the heart of Silicon Valley. Palo ALTO, California It is a cute conversation. >>Hi, and welcome to the Cube studios for another cube conversation where we go in depth with thought leaders driving innovation across the technology industry. I'm your host, Peter Boris. Digital businesses affecting every enterprise of every size, small and large, and the types of solutions that required the types of outcomes that are being pursued are extremely complex and require an enormous amount of work from some of the best and brightest people on the business side as well as the technology side. And that means not just from a large company. It means from an entire ecosystem of potential sources of genius and insight and good hard work. So the consequence for every enterprises, how do they cobble together that collection of experts and capabilities that are gonna help them transform their business more successfully, Maur completely and more certainly than they would otherwise? And that's we're gonna talk about today. Today we're here with Kelly Ireland, who's the founder and C E o. C. B Technologies. Kelly. Welcome to the >>Cube. Thank you, Peter. Happy to be here, >>so let's start by finding a little bit about CV Technologies to also about what you do. >>Um, I have a IittIe background, so I have been in it for 40 years. In 2001 I decided I had a better idea of how to both support clients as well as my employees. So I opened CB Technologies were value added reseller, um, and then say about five years ago, I decided to do some transforming of the company itself. I saw what was going on in the industry, and I thought this was the time for us to get going. Turned out we were a little early, but we wanted to transform from what you would call it the value added reseller two systems integrator. Because that was the only words what they had for. You know what that end result would be? Now I've heard it's the, um, domain expert integrator, which we like a lot better. And what we've done is gone from this value add, which we've all seen over the last couple of decades, into actually engineering solutions, and mostly with consortiums, which will talk about of the O. T. I t. Convergence and what's going to be needed for that to make our customers successful. >>Well, you just described. In many respects, the vision that businesses have had and how it's changed over years were first. The asset was the hardware. Hence the var. Today, the asset really is the date of the application and how you're going to apply that to change the way your business operates the customer experiences, you provide the profitability that you're able to return back to shareholders. So let's dig into this because that notion of data that notion of digital transformation is especially important in a number of different names, perhaps no more important than in the whole industrial and end of things domain. That intersection of I t know Tia's, you said, Tell us a little bit about what you're experiencing with your customers as they try to think about new ways of applying technology technology rich data to their business challenges. >>We'll use the perfect word you said dig, because this is all about layers. It's all about it was technology and software. Now it's about technology, software and integration. In fact, the conversations were having with our clients. Right now we don't even talk about a no Yim's name. Where before you would. But we haven't our head. What? We know what would be best. What we look at now is the first thing you do is go in and sit down with the client. And not only with the client, the you know, the executives or the C I or the C T. O's et cetera, but the employees themselves. Because what we've seen with I I I o t o t i t Convergence, it's You have to take into account what the worker needs and the people that are addressing it that way. Um, this project that we started with Hewlett Packard Enterprise, they started up what we call the refinery of the future. It could be acts of the future. It doesn't really matter. But it was getting at least up to five use cases with a consortium of partner companies that could go address five different things within the refinery. And the reason that I think it's been so successful is that the owner, the CEO Doug Smith and the VP of ops Linda Salinas, immediately wrap their arms around bringing employees. They're a small company there, maybe 50. They brought half of them to HPD Lab to show them what a smart pump laws for their chemical plant text. More chemical in Galina Park in Texas. Starting from that, it was like they put him on a party bus, took them down, put them in the lab, told them, showed them what a smart pump was and all of a sudden the lights turned on for the workers. These are people that have been, you know, manual valves and turning knobs and, you know, looking at computer screens they'd never seen what a smart, censored pump waas all of it sudden on the drive back to the company, ideas started turning. And then HP took it from there, brought in partners, sat everybody in the room, and we started feathering out. Okay, what's needed. But let's start with what the client needs. What do those different business users within the chemical plant need, and then build use cases from that? So we ended up building five use cases. >>Well, so what? Get another five years cases in a second? But you just described something very interesting, and I think it's something that partners have historically been able to do somewhat uniquely on that is that the customer journey is not taken by just an individual within the business. What really happens is someone has an idea. They find someone, often a partner, that can help them develop that idea. And then they go off and they recruit others within their business and a local partner that has good domain expertise at the time. And energy and customer commitment could be an absolutely essential feature of building the consensus within the organization to really accelerate that customer journey. If I got that right? >>Absolutely, absolutely. And what we saw with Refinery of the Future was getting those partnerships HP East started. It created the project kind of through information out to many of their ecosystem partners trying to gain interest because the thing was is this was kind of our bet was a very educated bet, but it's our bet to say, Yeah, we think this makes sense. So, you know, like I said, I think there's about 14 partners that all joined in both on the I t om side the ot oh am side and then both Deloitte and CB Technologies for the S. I and like expert domain expert integration where you really get into How do you tie OT and I t together? >>All right, so we've got this situation where this is not As you said, It's not just in the refining process, manufacturing businesses. It's in a lot of business. But in this particular one, you guys have actually fashioned what you call the refinery of of the future has got five clear use cases. Just give us an example of what those look like and how you've been RCB technology has been participated in the process of putting those together. >>Um, the 1st 1 was pretty wrapped around Predictive Analytics, and that was led by Deloitte and has a whole host of OT and I t integration on it >>again, not limited to process manufacturing at all >>at all, but and a good group, you know, you have national instruments, Intel flow. Serve. Oh, it's ice off Snyder Electric, PTC riel, where they're such a host >>of the >>consortium and I I think what was most important to start this whole thing was H P E. Came in and said, Here's an MOU. Here's a contract. You all will be contract ID to the overall resorts results. Not just your use case. Not just one or two use cases you're in, but all five because they all can integrate in some sense so >>that all can help. Each of you can help the others think. Problems. Truce. That's the 1st 1 about the 2nd 1 >>The 2nd 1 is video is a sensor that was Intel CB Technologies. I think we have as you're in there as well, doing some of the analytics, some P T. C. And what that was all about was taking video. And, you know, taking a use case from Linda and saying, Where where do you need some sort of video analytics Taking that processing it and what we ended up doing with that one was being able to identify, you know, animals or aggressive animals within the train yard. A downed worker transients that shouldn't be there because we can't decipher between you know, someone that's in text marks p p ease versus somebody that's in street clothes. So taking all that analyzing the information, the pictures, training it to understand when it needs to throw and alert >>lot of data required for that. And that's one of the major major drivers of some of the new storage technologies out there. New fabrics that are out there. How did that play? A role? >>As you can imagine, H p E is the under underlying infrastructure across the entire refinery. The future from compute with the, uh, EJ data center into the Reuben network into nimble storage for storing on site. Um, what we're finding, no matter who we talked to in the industry, it is. Most of them still want to keep it on Prem. In some sense, security. They're still all extremely cautious. So they want to keep it on Prem. So having the nimble storage right in the date, having the edge data center having everything in the middle of this chemical plant was absolutely a necessity. And having all of that set up having my team, which was the C B Tech team that actually did all the integration of setting up the wireless network, because guess what? When you're in a different kind of environment, not inside a building, you're out where there's metal pumps. There's restrictions because ah, flash could cause an explosion so intrinsically safe we had to set up all that and determined how? How could we get the best coverage? Especially? We want that video signal to move quite fast over the WiFi. How do we get all that set up? So it takes the most advantage of, you know, the facility and the capabilities of the Aruban network. >>So that's 12345 quickly were >>three worker safety, which hasn't started yet. We're still waiting for one of the manufacturers to get the certification they need. Um, four we have is connected worker, which is on fire, having a work >>of connected worker on fire and worker >>safety. >>Yeah, they don't sound, but just think of all the data and having the worker have it right at his fingertips. And, oh, by the way, hands free. So they're being ableto to take in all this data and transmit data, whether it's by voice or on screen back >>from a worker central perspective, from one that sustains the context of where the worker is, what stress there under what else? They've got to do it said. >>And and what are they trying to complete and how quickly? And that's where right now we have r A y that's in the 90% which is off the chart. But it's and and what's great about being at Text Mark is we actually can prove this. I can have somebody walk with me, a client that wants to look at it. They can go walk the process with me, and they will immediately see that we reduce the time by 90%. >>So I've given your four. What's the 5th 1? >>Acid intelligence, which is all about three D Point Cloud three D visualization. Actually being able to pull up a smart pump. You know it really? Any pump, you scan the facility you converted into three D and then in the program that we're using, you can actually pull up a pump. You can rotate it 360 degrees. It's got a database behind it that has every single bit of asset information connected videos, cad cams, P and I. D s. For the oil and gas industry. Everything's in their e mails could be attached to it, and then you can also put compliance reports. So there you might need to look a corrosion. One of those tests that they do on a you know, annual or every five year basis. That's point and click. You pull it up and it tells you where it sits, and then it also shows you green, yellow, red. Anything in red is immediate, attest that tension yellow is you need to address it greens. Everything's 100% running. >>So the complexity that we're talking about, the kind of specificity of these solutions, even though they can be generalized. And you know, you talked about analytics all the way out to asset optimization Intel intelligence. There are We can generalize and structure, but there's always going to be, it seems to us there's going to be a degree of specificity that's required, and that means we're not gonna talk about package software that does this kind of stuff. We're talking about sitting down with a customer with a team of experts from a lot of different places and working together and applying that to achieve customer outcome. So I got that right >>absolutely, and what we did with the consortium looking at everything. How they first addressed it was right along that line, and if you look at software development, agile following agile process, it's exactly what we're doing in four I I o T o R O T I t Convergence, because if you don't include all of those people, it's never going to be successful. I heard it a conference the other day that said, POC is goto I ot to die, and it's because a lot of people aren't addressing it the right way. We do something called Innovation Delivery as a service, which is basically a four day, 3 to 4 day boot camp. You get all the right people in, in in the room. You pull in everything from them. You boot out the executive team partway through, and you really get in depth with workers and you have them say what they wouldn't say in front of their bosses that this happened with Doug and Linda and Linda said it was mind blowing. She goes. I didn't realize we had so many problems because she came back in the room and there was a 1,000,000 stickies. And then she said, the more she read it and the more you know, we refined it down, she said it was absolutely delivered, you know, the use case that she would have eventually ended up with, but loved having all the insights from, >>well, work. Too often, tech companies failed to recognize that there's a difference between inventing something and innovation. Inventing is that engineering act of taking what you know about physics or social circumstance Secreting hardware software innovation is a set of social acts that get the customer to adopt it, get a marketplace to adopt it, change their behaviors. And partners historically have been absolutely essential to driving that innovation, to getting customers to actually change the way to do things and embed solutions in their operations. And increasingly, because of that deep knowledge with customers are trying to doing, they're participating. Maurine, the actual invention process, especially on the softer side of you said, >>Yeah, yeah, I think what's really interesting in this, especially with Coyote. When I look back a few years, I look at cloud and you know everything was cloud and everybody ran to it and everybody jumped in with both feet, and then they got burned. And what we're seeing with this whole thing with I o t you would think we're showing these are lies, return on. Investments were showing all this greatness that can come out of it and and they're very slow at sticking their toe in. But what we've found is no one arrives should say the majority of corporations anymore don't want to jump in and say, Let's do it two or five or $10 million project. We see your power point. No, let's let's depart Owen with with what we're doing, it's, you know, a really small amount of money to go in and really direct our attention at exactly what their problem is. It's not off the shelf. It's but it's off the shelf with customization. It's like we've already delivered on connected worker for oil and gas. But now we're are so starting to deliver multiple other industries because they actually walk through text mark. We could do tours, that text mark. That was kind of the trade off. All these partners brought technology and, you know, brought their intelligence and spent. We were now on two years of proving all this out. Well, they said, Fine, open the kimono will let your customers walk through and see it >>makes text mark look like a better suppliers. >>Well, it's enhanced their business greatly. I can tell you they're just starting a new process in another week. And it was all based on people going through, you know, a client that went through and went. Wait >>a minute. I >>really like this. There are also being able to recruit technologists within the use in industry, which you would think text marks 50 employees. It's a small little plant. It's very specialized. It's very small. They pulled one of the top. Uh, sorry. Lost not. I'm trying to think of what the name >>they're. They're a small number of employees, but the process manufacturing typically has huge assets. And any way you look at it, we're talking about major investments, major monies that require deep expertise. And my guess is the text Mark is able to use that to bring an even smarter and better >>people smarter and better. People that are looking at it going they're ahead of the curve, for they're so far ahead of the curve that they want to be on board were that they're bringing in millennials on they're connected. Worker Carlos is there trainload lead. And he dropped an intrinsically safe camera and it broke and he tried to glue it together, tried to super glue it together. And then he ran back to Linda and he said I broke the case and this case is like £10. They call it the Brick. They gotta lug it up. They got to climb up the train car, leg it up, take a picture that they have sealed the valves on all the cars before they leave. Well, he had used the real where had, you know, device. And he went into Linda and he said, I know there's a camera in there. There's camera capabilities. Can I use that until we get another case? And she's like, Yeah, go ahead. Well, he went through, started using that toe like lean over, say, Take photo. We engineered that it could go directly back to the audit file so that everybody knew the minute that picture was taken, it went back into the audio file. This is where we found the process was reduced by 90% of time. But he turned around and trained his entire team. He wasn't asked to, but he thought, this is the greatest thing. He went in trainable. And now, about every two weeks, Carlos walks in to my team that sits a text mark and comes up with another use case for connected worker. It's amazing. It's amazing what you know were developed right out of the customer by using their workers and then, you know, proactively coming to us going. Hey, I got another idea. Let's add this where I think at version 7.0, for connected worker. Because of that feedback because of that live feed back in production. >>Great story, Kelly. So, once again, Callie Ireland is a co founder and CEO of CB Technologies. Thanks for being on the tube. >>Thank you for having me >>on once again. I wanna thank all of you for joining us for another cute conversation. I'm Peter burgers. See you next time.