>>Hey everybody. Welcome back. Good afternoon. Lisa Martin here with John Feer live in Detroit, Michigan. We are at Coon Cloud Native Con 2020s North America. John Thank is who. This is nearing the end of our second day of coverage and one of the things that has been breaking all day on this show is news. News. We have more news to >>Break next. Yeah, this next segment is a company we've been following. They got some news we're gonna get into. Managing Kubernetes life cycle has been a huge challenge when you've got large organizations, whether you're spinning up and scaling scale is the big story. Kubernetes is the center of the conversation. This next segment's gonna be great. It >>Is. We've got two guests from Specter Cloud here. Please welcome. It's CEO Chenery Fu and co-founder and it's c g a co-founder Sta Mallek. Guys, great to have you on the program. Thank >>You for having us. My pleasure. >>So Timary, what's going on? What's the big news? >>Yeah, so we just announced our Palace three this morning. So we add a bunch, a new functionality. So first of all we have a Nest cluster. So enable enterprise to easily provide Kubernete service even on top of their existing clusters. And secondly, we also support seamlessly migration for their existing cluster. We enable them to be able to migrate their cluster into our CNC for upstream Kubernete distro called Pallet extended Kubernetes, GX K without any downtime. And lastly, we also add a lot of focus on developer experience. Those additional capability enable developer to easily onboard and and deploy the application for. They have test and troubleshooting without, they have to have a steep Kubernetes lending curve. >>So big breaking news this morning, pallet 3.0. So you got the, you got the product. This is a big theme here. Developer productivity, ease of use is the top story here. As developers are gonna increase their code velocity cuz they're under a lot of pressure. This infrastructure's getting smarter. This is a big part of managing it. So the toil is now moving to the ops. Steves are now dev teams. Security, you gotta enable faster deployment of apps and code. This is what you guys solve while you getting this right. Is that, take us through that specific value proposition. What's the, what are the key things on in this news release? Yeah, >>You're exactly right. Right. So we basically provide our solution to platform engineering ship so that they can use our platform to enable Kubernetes service to serve their developers and their application ship. And then in the meantime, the developers will be able to easily use Kubernetes or without, They have to learn a lot of what Kubernetes specific things like. So maybe you can get in some >>Detail. Yeah. And absolutely the detail about it is there's a big separation between what operations team does and the development teams that are using the actual capabilities. The development teams don't necessarily to know the internals of Kubernetes. There's so much complexity when it comes, comes into it. How do I do things like deployment pause manifests just too much. So what our platform does, it makes it really simple for them to say, I have a containerized application, I wanna be able to model it. It's a really simple profile and from there, being able to say, I have a database service. I wanna attach to it. I have a specific service. Go run it behind the scenes. Does it run inside of a Nest cluster? Which we'll talk into a little bit. Does it run into a host cluster? Those are happen transparently for >>The developer. You know what I love about this? What you guys are doing in the news, it really points out what I love about DevOps. Because cloud, let's face a cloud early adopters, we're all the hardcore cloud folks as it goes mainstream. With Kubernetes, you start to see like words like platform engineering. I mean I love that term. That means as a platform, it's been around for a while. For people who are building their own stuff, that means it's gonna scale and enable people to enable value, build on top of it, move faster. This platform engineering is becoming now standard in enterprises. It wasn't like that before. What's your eyes reactions that, How do you see that evolving faster? Or do you believe that or what's your take on >>It? Yeah, so I think it's starting from the DevOps op team, right? That every application team, they all try to deploy and manage their application under their own ING infrastructure. But very soon all these each application team, they start realize they have to repeatedly do the same thing. So these will need to have a platform engineering team to basically bring some of common practice to >>That. >>And some people call them SREs like and that's really platform >>Engineering. It is, it is. I mean, you think about like Esther ability to deploy your applications at scale and monitoring and observability. I think what platform engineering does is codify all those best practices. Everything when it comes about how you monitor the actual applications. How do you do c i CD your backups? Instead of not having every single individual development team figuring how to do it themselves. Platform engineer is saying, why don't we actually build policy that we can provide as a service to different development teams so that they can operate their own applications at scale. >>So launching Pellet 3.0 today, you also had a launch in September, so just a few weeks ago. Talk about what these two announcements mean from Specter Cloud's perspective in terms of proof points, what you're delivering to the end users and the value that they're getting from that. >>Yeah, so our goal is really to help enterprise to deploy and around Kubernetes anywhere, right? Whether it's in cloud data center or even at Edge locations. So in September we also announce our HV two capabilities, which enable very easy deployment of Edge Kubernetes, right at at at any any location, like a retail stores restaurant, so on and so forth. So as you know, at Edge location, there's no cloud endpoint there. It's not easy to directly deploy and manage Kubernetes. And also at Edge location there's not, it's not as secure as as cloud or data center environment. So how to make the end to end system more secure, right? That it's temper proof, that is also very, very important. >>Right. Great, great take there. Thanks for explaining that. I gotta ask cuz I'm curious, what's the secret sauce? Is it nested clusters? What's, what's the core under the hood here on 3.0 that people should know about it's news? It's what's, what's the, what's that post important >>To? To be honest, it's about enabling developer velocity. Now how do you enable developer velocity? It's gonna be able for them to think about deploying applications without worrying about Kubernetes being able to build this application profiles. This NEA cluster that we're talking about enables them, they get access to it in complete cluster within seconds. They're essentially having access to be able to add any operations, any capabilities without having the ability to provision a cluster on inside of infrastructure. Whether it's Amazon, Google, or OnPrem. >>So, and you get the dev engine too, right? That that, that's a self-service provisioning in for environments. Is that, Yeah, >>So the dev engine itself are the capabilities that we offer to developers so that they can build these application profiles. What the application profiles, again they define aspects about, my application is gonna be a container, it's gonna be a database service, it's gonna be a helm chart. They define that entire structure inside of it. From there they can choose to say, I wanna deploy this. The target environment, whether it becomes an actual host cluster or a cluster itself is irrelevant to them. For them it's complete transparent. >>So transparency, enabling developer velocity. What's been some of the feedback so far? >>Oh, all developer love that. And also same for all >>The ops team. If it's easy and goods faster and the steps >>Win-win team. Yeah, Ops team, they need a consistency. They need a governance, they need visibility, but in the meantime, developers, they need the flexibility then theys or without a steep learning curve. So this really, >>So So I hear a lot of people say, I got a lot of sprawl, cluster sprawl. Yeah, let's get outta hand does, let's solve that. How do you guys solve that problem? Yeah, >>So the Neste cluster is a profit answer for that. So before you nest cluster, for a lot of enterprise to serving developers, they have to either create a very large TED cluster and then isolated by namespace, which not ideal for a lot of situation because name stay namespace is not a hard isolation and also a lot of global resource like CID and operator does not work in space. But the other way is you give each developer a separate, a separate ADE cluster, but that very quickly become too costly. Cause not every developer is working for four, seven, and half of the time your, your cluster is is a sit there idol and that costs a lot of money. So you cluster, you'll be able to basically do all these inside the your wholesale cluster, bring the >>Efficiency there. That is huge. Yeah. Saves a lot of time. Reduces the steps it takes. So I take, take a minute, my last question to you to explain what's in it for the developer, if they work with Spec Cloud, what is your value? What's the pitch? Not the sales pitch, but like what's the value pitch that >>You give them? Yeah, yeah. And the value for us is again, develop their number of different services and teams people are using today are so many, there are so many different languages or so many different libraries there so many different capabilities. It's too hard for developers to have to understand not only the internal development tools, but also the Kubernetes, the containers of technologies. There's too much for it. Our value prop is making it really easy for them to get access to all these different integrations and tooling without having to learn it. Right? And then being able to very easily say, I wanna deploy this into a cluster. Again, whether it's a Nest cluster or a host cluster. But the next layer on top of that is how do we also share those abilities with other teams. If I build my application profile, I'm developing an application, I should be able to share it with my team members. But Henry saying, Hey Tanner, why don't you also take a look at my app profile and let's build and collaborate together on that. So it's about collaboration and be able to move >>Really fast. I mean, more develops gotta be more productive. That's number one. Number one hit here. Great job. >>Exactly. Last question before we run out Time. Is this ga now? Can folks get their hands on it where >>Yes. Yeah. It is GA and available both as a, as a SaaS and also the store. >>Awesome guys, thank you so much for joining us. Congratulations on the announcement and the momentum that Specter Cloud is empowering itself with. We appreciate your insights on your time. >>Thank you. Thank you so much. Right, pleasure. >>Thanks for having us. For our guest and John Furrier, Lisa Martin here live in Michigan at Co con Cloud native PON 22. Our next guests join us in just a minute. So stick around.

>> from Santa Clara in the heart of Silicon Valley. It's the Q covering the Churchills 2019 brought to you by Silicon Angle Media. >> Hey, welcome back, everybody. Jefe Rick here with the Cube. We're in Santa Clara, California At the Churchills. It's the ninth annual kind of awards banquet at the Church O Club. It's on, and the theme this year is all about leadership. And we're excited to have not one of the winners, but one of the newest board members of the church, Oh, club. And someone is going to be interviewing some of the winners at a very many time. Cuba LEM Ray Wong, You know, from Constellation Research of founder, chief analyst >> and also >> a new board member for the Churchill Club Brigade, is >> also being back here. I love this event. There's one my favorite ones. You get to see all the cool interviews, >> right? So you're interviewing Grandstand from Pallet on for the life changer award. >> Yeah, so this is really incredible. I mean, this company has pretty much converge right. We're talking, It's media, It's sports, It's fitness. It's like social at the same time. And it's completely changed. So many people they've got more writers than soul cycle. Can you believe that? >> Yeah. I like to ride my bike outside, so I'm just not part of this whole thing. But I guess I guess on those bikes you can write anywhere >> you can write anywhere, anywhere with anyone. But it's not that. It's the classes, right? You basically hop on. You see the classes. People are actually pumping you up there. Okay, Go, go, go. You can see all the other riders are in the space. It's kind >> of >> addictive. Let's let's shift gears. Talk about leadership more generally, because things were a little rough right here in the Valley right now. And people are taking some hits and black eyes. You talk to a lot of leaders. She go to a tonic, shows you got more shows. A. We go to talk to a lot of CEOs when you kind of take a step back about what makes a good leader, what doesn't make a good leader? What are some of the things that jump into your head? >> You know, we really think about a dynamic leadership model. It's something conceit on my Twitter handle. It's basically the fact that you got a balance. All these different traits. Leaders have to perform in different ways in different situation. Something like Oh, wow, that's a general. They've done a great job commanding leadership. Other times we had individuals, a wonderful, empathetic leader, right? There's a balance between those types of traits that have to happen, and they curve like seven different dimensions and each of these dimensions. It's like sometimes you're gonna have to be more empathetic. Sometimes you got to be more realistic. Sometimes you're going to be harder. And I think right now we have this challenge because there's a certain style that's being imposed on all the leaders that might not be correct >> theater thing. The hypothesis for you to think about is, you know, when a lot of these people start the Silicon Valley companies the classic. It's not like they went to P and G and work their way up through the ranks. You know, they started a company, it was cool. And suddenly boom. You know, they get hundreds of millions of dollars, the I po and now you've got platforms that are impacting geopolitical things all over the world. They didn't necessarily sign up for that. That's not necessarily what they wanted to do, and they might not be qualified. So, you know, Is it? Is it fair to expect the leader of a tech company that just built some cool app that suddenly grew into, ah, ubiquitous platform over the world that many, many types of people are using for good and bad to suddenly be responsible? That's really interesting situation for these people. >> Well, that's what we talked about the need for responsive and responsible leadership. Those are two different types of traits. Look, the founding individual might not be the right person to do that, but they can surround themselves with team members that can do that. That could make sure that they're being responsive or responsible, depending on what's required for each of those traits. You know, great examples like that Black Mirror episode where you see the guru of, like, some slasher meet a guy. Some guys like Colin is like, you know, he wants to make sure that you know someone's paying attention to him. Well, the thing is like a lot of times, at least folks are surrounded by people that don't have that empathetic You might not have had what a founder is looking at, or it could be the flip side. The founder might not be empathetic. They're just gung ho, right, ready to build out the next set of features and capabilities that they wanted to d'oh! And they need that empathy that's around there. So I think we're going to start to see that mix and blend. But it's hard, right? I mean, going through a start up as a CEO and founder is very, very different than coming in through the corporate ranks. There's a >> very good running a company, you know. It's funny again. You go to a lot of shows. We get a lot of shows, a lot of key, knows a lot of CEO keynotes, and it's just interesting. Some people just seem to have that It factor one that jumps off the top is Dobie. You know, some people just seemed >> like the have it >> where they can get people to follow, and it's it's really weird. We just said John W. Thompson, on talking about Sathya changing the culture at Microsoft, with hundreds and hundreds of thousands of employees distributed all over the world. What a creative and amazing job to be able to turn that ship. >> Oh, it is. I mean, I can turn on the charm and just, like, get your view Lee excited about something just like that, right? And it's also about making sure you bring in the input and make people feel that they're inclusive. But you gotta make decisions at some point, too. Sometimes you have to make the tough choices. You cut out products, you cut out certain types of policies, or sometimes you gotta be much more responsive to customers. Right? Might look like you're eating crow. But you know what? At the inn today, cos they're really built around customers or state Kohler's stay close air bigger today than just shareholders. >> Right. Last question. Churchill Club. How'd you get involved? What makes you excited to jump on board? >> You know, this is like an institution for the valley, right? This is you know, if you think about like the top interviews, right? If you think about the top conversations, the interesting moments in the Valley, they've all happened here. And it's really about making sure that you know, the people that I know the people that you know there's an opportunity to re create that for the next set of generations. I remember coming here when it's like I go back, I think give Hey, just I don't hear anybody in 96 right? And just thinking like, Hey, what were the cool activities? What were the interesting conversations and the church? The club was definitely one of those, and it's time to give back. >> Very good. All right, well, congrats on that on that new assignment. And good luck with the interview tonight. Hey, thanks a lot. All right. He's Ray. I'm Jeff. You wanted the Cube with that? Churchill's in Santa Clara, California. Thanks for watching. We'll see you next time.

>> Live, from Times Square, in New York City, it's theCUBE covering IBM's Change the Game: Winning with AI. Brought to you by IBM. >> Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage, and we're here covering a special presentation of IBM's Change the Game: Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50-60 analysts here. They've got a partner summit going on, and then tonight, at Terminal 5 of the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreesha Rao who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. >> Thank you, Dave. >> Well, thanks Dave for having us. >> Yes, always a pleasure Seth. We've known each other for a while now. I think we met in the snowstorm in Boston, sparked something a couple years ago. >> Yep. When we were both trapped there. >> Yep, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bhandari, and you guys are doing inside of IBM. I want to talk a little bit more about your other half which is working with clients and the Data Science Elite Team, and we'll get into what you're doing with Niagara Bottling, but let's start there, in terms of that side of your role, give us the update. >> Yeah, like you said, we spent a lot of time talking about how IBM is implementing the CTO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM, and we found a consistent theme with all the clients, in that, they needed help learning how to implement data science, AI, machine learning, whatever you want to call it, in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise, so we felt really strongly that it was important for the future of IBM that all of our clients become successful at it because what we don't want to do is we don't want in two years for them to go "Oh my God, this whole data science thing was a scam. We haven't made any money from it." And it's not because the data science thing is a scam. It's because the way they're doing it is not conducive to business, and so we set up this team we call the Data Science Elite Team, and what this team does is we sit with clients around a specific use case for 30, 60, 90 days, it's really about 3 or 4 sprints, depending on the material, the client, and how long it takes, and we help them learn through this use case, how to use Python, R, Scala in our platform obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open-source, if they're not happy with what the product looks like, they can take their toys and go home afterwards. It's on us to prove the value as part of this, but there's a key point here. My team is not measured on sales. They're measured on adoption of AI in the enterprise, and so it creates a different behavior for them. So they're really about "Make the enterprise successful," right, not "Sell this software." >> Yeah, compensation drives behavior. >> Yeah, yeah. >> So, at this point, I ask, "Well, do you have any examples?" so Shreesha, let's turn to you. (laughing softly) Niagara Bottling -- >> As a matter of fact, Dave, we do. (laughing) >> Yeah, so you're not a bank with a trillion dollars in assets under management. Tell us about Niagara Bottling and your role. >> Well, Niagara Bottling is the biggest private label bottled water manufacturing company in the U.S. We make bottled water for Costcos, Walmarts, major national grocery retailers. These are our customers whom we service, and as with all large customers, they're demanding, and we provide bottled water at relatively low cost and high quality. >> Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. I always observed, really got into a lot of organizations. I was always observed that it was really the heads of Application that drove AI because they were the glue between the business and IT, and that's really where you sit in the organization, right? >> Yes. My role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. >> So take us the through the project if you will. What were the drivers? What were the outcomes you envisioned? And we can kind of go through the case study. >> So the current project that we leveraged IBM's help was with a stretch wrapper project. Each pallet that we produce--- we produce obviously cases of bottled water. These are stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper, and this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around a pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. >> And over breakfast we were talking. You guys produce 2833 bottles of water per second. >> Wow. (everyone laughs) >> It's enormous. The manufacturing line is a high speed manufacturing line, and we have a lights-out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods, pallets of water, going out. It's called pellets to pallets. Pellets of plastic coming in through one end and pallets of water going out through the other end. >> Are you sitting on top of an aquifer? Or are you guys using sort of some other techniques? >> Yes, in fact, we do bore wells and extract water from the aquifer. >> Okay, so the goal was to minimize the amount of material that you used but maintain its stability? Is that right? >> Yes, during transportation, yes. So if we use too much plastic, we're not optimally, I mean, we're wasting material, and cost goes up. We produce almost 16 million pallets of water every single year, so that's a lot of shrink wrap that goes around those, so what we can save in terms of maybe 15-20% of shrink wrap costs will amount to quite a bit. >> So, how does machine learning fit into all of this? >> So, machine learning is way to understand what kind of profile, if we can measure what is happening as we wrap the pallets, whether we are wrapping it too tight or by stretching it, that results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. >> I.e. too much material, right? >> Too much material is conservative, and aggressive is too little material, and so we can achieve some savings if we were to alternate between the profiles. >> So, too little material means you lose product, right? >> Yes, and there's a risk of breakage, so essentially, while the pallet is being wrapped, if you are stretching it too much there's a breakage, and then it interrupts production, so we want to try and avoid that. We want a continuous production, at the same time, we want the pallet to be stable while saving material costs. >> Okay, so you're trying to find that ideal balance, and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share with that. >> Yes, so each pallet takes about 16-18 wraps of the stretch wrapper going around it, and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight which is the amount of plastic that goes around each of the pallet. >> So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data that was, "if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? >> Yes. >> So it's a truly predictive and iterative model that we've built with them. >> And, you're obviously injecting data in terms of the trip to the store as well, right? You're taking that into consideration in the model, right? >> Yeah that's mainly to make sure that the pallets are stable during transportation. >> Right. >> And that is already determined how much containment force is required when your stretch and wrap each pallet. So that's one of the variables that is measured, but the inputs and outputs are-- the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. >> And the data comes from where? Is it observation, maybe instrumented? >> Yeah, the instruments. Our stretch-wrapper machines have an ignition platform, which is a Scada platform that allows us to measure all of these variables. We would be able to get machine variable information from those machines and then be able to hopefully, one day, automate that process, so the feedback loop that says "On this profile, we've not had any breaks. We can continue," or if there have been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. >> Yeah, so think of it as, it's kind of a traditional manufacturing production line optimization and prediction problem right? It's minimizing waste, right, while maximizing the output and then throughput of the production line. When you optimize a production line, the first step is to predict what's going to go wrong, and then the next step would be to include precision optimization to say "How do we maximize? Using the constraints that the predictive models give us, how do we maximize the output of the production line?" This is not a unique situation. It's a unique material that we haven't really worked with, but they had some really good data on this material, how it behaves, and that's key, as you know, Dave, and probable most of the people watching this know, labeled data is the hardest part of doing machine learning, and building those features from that labeled data, and they had some great data for us to start with. >> Okay, so you're collecting data at the edge essentially, then you're using that to feed the models, which is running, I don't know, where's it running, your data center? Your cloud? >> Yeah, in our data center, there's an instance of DSX Local. >> Okay. >> That we stood up. Most of the data is running through that. We build the models there. And then our goal is to be able to deploy to the edge where we can complete the loop in terms of the feedback that happens. >> And iterate. (Shreesha nods) >> And DSX Local, is Data Science Experience Local? >> Yes. >> Slash Watson Studio, so they're the same thing. >> Okay now, what role did IBM and the Data Science Elite Team play? You could take us through that. >> So, as we discussed earlier, adopting data science is not that easy. It requires subject matter, expertise. It requires understanding of data science itself, the tools and techniques, and IBM brought that as a part of the Data Science Elite Team. They brought both the tools and the expertise so that we could get on that journey towards AI. >> And it's not a "do the work for them." It's a "teach to fish," and so my team sat side by side with the Niagara Bottling team, and we walked them through the process, so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? So it's side by side with their team. Our team sat there and walked them through it. >> For how many weeks? >> We've had about two sprints already, and we're entering the third sprint. It's been about 30-45 days between sprints. >> And you have your own data science team. >> Yes. Our team is coming up to speed using this project. They've been trained but they needed help with people who have done this, been there, and have handled some of the challenges of modeling and data science. >> So it accelerates that time to --- >> Value. >> Outcome and value and is a knowledge transfer component -- >> Yes, absolutely. >> It's occurring now, and I guess it's ongoing, right? >> Yes. The engagement is unique in the sense that IBM's team came to our factory, understood what that process, the stretch-wrap process looks like so they had an understanding of the physical process and how it's modeled with the help of the variables and understand the data science modeling piece as well. Once they know both side of the equation, they can help put the physical problem and the digital equivalent together, and then be able to correlate why things are happening with the appropriate data that supports the behavior. >> Yeah and then the constraints of the one use case and up to 90 days, there's no charge for those two. Like I said, it's paramount that our clients like Niagara know how to do this successfully in their enterprise. >> It's a freebie? >> No, it's no charge. Free makes it sound too cheap. (everybody laughs) >> But it's part of obviously a broader arrangement with buying hardware and software, or whatever it is. >> Yeah, its a strategy for us to help make sure our clients are successful, and I want it to minimize the activation energy to do that, so there's no charge, and the only requirements from the client is it's a real use case, they at least match the resources I put on the ground, and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. >> So you've got to have skin in the game obviously, an IBM customer. There's got to be some commitment for some kind of business relationship. How big was the collective team for each, if you will? >> So IBM had 2-3 data scientists. (Dave takes notes) Niagara matched that, 2-3 analysts. There were some working with the machines who were familiar with the machines and others who were more familiar with the data acquisition and data modeling. >> So each of these engagements, they cost us about $250,000 all in, so they're quite an investment we're making in our clients. >> I bet. I mean, 2-3 weeks over many, many weeks of super geeks time. So you're bringing in hardcore data scientists, math wizzes, stat wiz, data hackers, developer--- >> Data viz people, yeah, the whole stack. >> And the level of skills that Niagara has? >> We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it, and that's the whole journey that we are in, in trying to quantify with the help of data, and be able to connect our systems with data, systems and models that help us analyze what happened and why it happened and what to do before it happens. >> Your team must love this because they're sort of elevating their skills. They're working with rock star data scientists. >> Yes. >> And we've talked about this before. A point that was made here is that it's really important in these projects to have people acting as product owners if you will, subject matter experts, that are on the front line, that do this everyday, not just for the subject matter expertise. I'm sure there's executives that understand it, but when you're done with the model, bringing it to the floor, and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some guy or lady sitting up in the ivory tower saying "thou shalt." >> Now you don't know the outcome yet. It's still early days, but you've got a model built that you've got confidence in, and then you can iterate that model. What's your expectation for the outcome? >> We're hoping that preliminary results help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process. And through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plants. And if the plants decide, hey they have a subjective preference for one profile versus another with the data we are capturing we can measure when they deviated from what we specified. We have a lot of learning coming from the approach that we're taking. You can't control things if you don't measure it first. >> Well, your objectives are to transcend this one project and to do the same thing across. >> And to do the same thing across, yes. >> Essentially pay for it, with a quick return. That's the way to do things these days, right? >> Yes. >> You've got more narrow, small projects that'll give you a quick hit, and then leverage that expertise across the organization to drive more value. >> Yes. >> Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing. >> Thank you. >> Congratulations. You must be really excited. >> No. It's a fun project. I appreciate it. >> Thanks for having us, Dave. I appreciate it. >> Pleasure, Seth. Always great talking to you, and keep it right there everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel. cubenyc #cubenyc Check out the ibm.com/winwithai Change the Game: Winning with AI Tonight. We'll be right back after a short break. (minimal upbeat music)