>> From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. (smooth music) >> Hi, I'm Stu Miniman, and welcome to this special exclusive presentation from theCUBE. We're digging into Pensando and their Future Proof Your Enterprise event. To help kick things off, welcoming in a friend of the program, Scott Raynovich. He is the principal analyst at Futuriom coming to us from Montana. I believe first time we've had a guest on the program in the state of Montana, so Scott, thanks so much for joining us. >> Thanks, Stu, happy to be here. >> All right, so we're going to dig a lot into Pensando. They've got their announcement with Hewlett Packard Enterprise. Might help if we give a little bit of background, and definitely I want Scott and I to talk a little bit about where things are in the industry, especially what's happening in networking, and how some of the startups are helping to impact what's happening on the market. So for those that aren't familiar with Pensando, if you followed networking I'm sure you are familiar with the team that started them, so they are known, for those of us that watch the industry, as MPLS, which are four people, not to be confused with the protocol MPLS, but they had very successfully done multiple spin-ins for Cisco, Andiamo, Nuova and Insieme, which created Fibre Channel switches, the Cisco UCS, and the ACI product line, so multiple generations to the Nexus, and Pensando is their company. They talk about Future Proof Your Enterprise is the proof point that they have today talking about the new edge. John Chambers, the former CEO of Cisco, is the chairman of Pensando. Hewlett Packard Enterprise is not only an investor, but also a customer in OEM piece of this solution, and so very interesting piece, and Scott, I want to pull you into the discussion. The waves of technology, I think, the last 10, 15 years in networking, a lot it has been can Cisco be disrupted? So software-defined networking was let's get away from hardware and drive towards more software. Lots of things happening. So I'd love your commentary. Just some of the macro trends you're seeing, Cisco's position in the marketplace, how the startups are impacting them. >> Sure, Stu. I think it's very exciting times right now in networking, because we're just at the point where we kind of have this long battle of software-defined networking, like you said, really pushed by the startups, and there's been a lot of skepticism along the way, but you're starting to see some success, and the way I describe it is we're really on the third generation of software-defined networking. You have the first generation, which was really one company, Nicira, which VMware bought and turned into their successful NSX product, which is a virtualized networking solution, if you will, and then you had another round of startups, people like Big Switch and Cumulus Networks, all of which were acquired in the last year. Big Switch went to Arista, and Cumulus just got purchased by... Who were they purchased by, Stu? >> Purchased by Nvidia, who interestingly enough, they just picked up Mellanox, so watching Nvidia build out their stack. >> Sorry, I was having a senior moment. It happens to us analysts. (chuckling) But yeah, so Nvidia's kind of rolling up these data center and networking plays, which is interesting because Nvidia is not a traditional networking hardware vendor. It's a chip company. So what you're seeing is kind of this vision of what they call in the industry disaggregation. Having the different components sold separately, and then of course Cisco announced the plan to roll out their own chip, and so that disaggregated from the network as well. When Cisco did that, they acknowledged that this is successful, basically. They acknowledged that disaggregation is happening. It was originally driven by the large public cloud providers like Microsoft Azure and Amazon, which started the whole disaggregation trend by acquiring different components and then melding it all together with software. So it's definitely the future, and so there's a lot of startups in this area to watch. I'm watching many of them. They include ArcOS, which is a exciting new routing vendor. DriveNets, which is another virtualized routing vendor. This company Alkira, which is going to do routing fully in the cloud, multi-cloud networking. Aviatrix, which is doing multi-cloud networking. All these are basically software companies. They're not pitching hardware as part of their value add, or their integrated package, if you will. So it's a different business model, and it's going to be super interesting to watch, because I think the third generation is the one that's really going to break this all apart. >> Yeah, you brought up a lot of really interesting points there, Scott. That disaggregation, and some of the changing landscape. Of course that more than $1 billion acquisition of Nicira by VMware caused a lot of tension between VMware and Cisco. Interesting. I think back when to Cisco created the UCS platform it created a ripple effect in the networking world also. HP was a huge partner of Cisco's before UCS launched, and not long after UCS launched HP stopped selling Cisco gear. They got heavier into the networking component, and then here many years later we see who does the MPLS team partner with when they're no longer part of Cisco, and Chambers is no longer the CEO? Well, it's HPE front and center there. You're going to see John Chambers at HPE Discover, so it was a long relationship and change. And from the chip companies, Intel, of course, has built a sizeable networking business. We talked a bit about Mellanox and the acquisitions they've done. One you didn't mention but caused a huge impact in the industry, and something that Pensando's responding to is Amazon, but Annapurna Labs, and Annapurna Labs, a small Israeli company, and really driving a lot of the innovation when it comes to compute and networking at Amazon. The Graviton, Compute, and Nitro is what powers their Outposts solutions, so if you look at Amazon, they buy lots of pieces. It's that mixture of hardware and software. In early days people thought that they just bought kind of off-the-shelf white boxes and did it cheap, but really we see Amazon really hyper optimizes what they're doing. So Scott, let's talk a little bit about Pensando if we can. Amazon with the Nitro solutions built to Outposts, which is their hybrid solution, so the same stack that they put in Amazon they can now put in customers' data center. What Pensando's positioning is well, other cloud providers and enterprise, rather than having to buy something from Amazon, we're going to enable that. So what do you think about what you've seen and heard from Pensando, and what's that need in the market for these type of solutions? >> Yes, okay. So I'm glad you brought up Outposts, because I should've mentioned this next trend. We have, if you will, the disaggregated open software-based networking which is going on. It started in the public cloud, but then you have another trend taking hold, which is the so-called edge of the network, which is going to be driven by the emergence of 5G, and the technology called CBRS, and different wireless technologies that are emerging at the so-called edge of the network, and the purpose of the edge, remember, is to get closer to the customer, get larger bandwidth, and compute, and storage closer to the customer, and there's a lot of people excited about this, including the public cloud providers, Amazon's building out their Outposts, Microsoft has an Edge stack, the Azure Edge Stack that they've built. They've acquired a couple companies for $1 billion. They acquired Metaswitch, they acquired Affirmed Networks, and so all these public cloud providers are pushing their cloud out to the edge with this infrastructure, a combination of software and hardware, and that's the opportunity that Pensando is going after with this Outposts theme, and it's very interesting, Stu, because the coopetition is very tenuous. A lot of players are trying to occupy this edge. If you think about what Amazon did with public cloud, they sucked up all of this IT compute power and services applications, and everything moved from these enterprise private clouds to the public cloud, and Amazon's market cap exploded, right, because they were basically sucking up all the money for IT spending. So now if this moves to the edge, we have this arms race of people that want to be on the edge. The way to visualize it is a mini cloud. Whether this mini cloud is at the edge of Costco, so that when Stu's shopping at Costco there's AI that follows you in the store, knows everything you're going to do, and predicts you're going to buy this cereal and "We're going to give you a deal today. "Here's a coupon." This kind of big brother-ish AI tracking thing, which is happening whether you like it or not. Or autonomous vehicles that need to connect to the edge, and have self-driving, and have very low latency services very close to them, whether that's on the edge of the highway or wherever you're going in the car. You might not have time to go back to the public cloud to get the data, so it's about pushing these compute and data services closer to the customers at the edge, and having very low latency, and having lots of resources there, compute, storage, and networking. And that's the opportunity that Pensando's going after, and of course HPE is going after that, too, and HPE, as we know, is competing with its other big mega competitors, primarily Dell, the Dell/VMware combo, and the Cisco... The Cisco machine. At the same time, the service providers are interested as well. By the way, they have infrastructure. They have central offices all over the world, so they are thinking that can be an edge. Then you have the data center people, the Equinixes of the world, who also own real estate and data centers that are closer to the customers in the metro areas, so you really have this very interesting dynamic of all these big players going after this opportunity, putting in money, resources, and trying to acquire the right technology. Pensando is right in the middle of this. They're going after this opportunity using the P4 networking language, and a specialized ASIC, and a NIC that they think is going to accelerate processing and networking of the edge. >> Yeah, you've laid out a lot of really good pieces there, Scott. As you said, the first incarnation of this, it's a NIC, and boy, I think back to years ago. It's like, well, we tried to make the NIC really simple, or do we build intelligence in it? How much? The hardware versus software discussion. What I found interesting is if you look at this team, they were really good, they made a chip. It's a switch, it's an ASIC, it became compute, and if you look at the technology available now, they're building a lot of your networking just in a really small form factor. You talked about P4. It's highly programmable, so the theme of Future Proof Your Enterprise. With anything you say, "Ah, what is it?" It's a piece of hardware. Well, it's highly programmable, so today they position it for security, telemetry, observability, but if there's other services that I need to get to edge, so you laid out really well a couple of those edge use cases and if something comes up and I need that in the future, well, just like we've been talking about for years with software-defined networking, and network function virtualization, I don't want a dedicated appliance. It's going to be in software, and a form factor like Pensando does, I can put that in lots of places. They're positioning they have a cloud business, which they sell direct, and expect to have a couple of the cloud providers using this solution here in 2020, and then the enterprise business, and obviously a huge opportunity with HPE's position in the marketplace to take that to a broad customer base. So interesting opportunity, so many different pieces. Flexibility of software, as you relayed, Scott. It's a complicated coopetition out there, so I guess what would you want to see from the market, and what is success from Pensando and HPE, if they make this generally available this month, it's available on ProLiant, it's available on GreenLake. What would you want to be hearing from customers or from the market for you to say further down the road that this has been highly successful? >> Well, I want to see that it works, and I want to see that people are buying it. So it's not that complicated. I mean I'm being a little superficial there. It's hard sometimes to look in these technologies. They're very sophisticated, and sometimes it comes down to whether they perform, they deliver on the expectation, but I think there are also questions about the edge, the pace of investment. We're obviously in a recession, and we're in a very strange environment with the pandemic, which has accelerated spending in some areas, but also throttled back spending in other areas, and 5G is one of the areas that it appears to have been throttled back a little bit, this big explosion of technology at the edge. Nobody's quite sure how it's going to play out, when it's going to play out. Also who's going to buy this stuff? Personally, I think it's going to be big enterprises. It's going to start with the big box retailers, the Walmarts, the Costcos of the world. By the way, Walmart's in a big competition with Amazon, and I think one of the news items you've seen in the pandemic is all these online digital ecommerce sales have skyrocketed, obviously, because people are staying at home more. They need that intelligence at the edge. They need that infrastructure. And one of the things that I've heard is the thing that's held it back so far is the price. They don't know how much it's going to cost. We actually ran a survey recently targeting enterprises buying 5G, and that was one of the number one concerns. How much does this infrastructure cost? So I don't actually know how much Pensando costs, but they're going to have to deliver the right ROI. If it's a very expensive proprietary NIC, who pays for that, and does it deliver the ROI that they need? So we're going to have to see that in the marketplace, and by the way, Cisco's going to have the same challenge, and Dell's going to have the same challenge. They're all racing to supply this edge stack, if you will, packaged with hardware, but it's going to come down to how is it priced, what's the ROI, and are these customers going to justify the investment is the trick. >> Absolutely, Scott. Really good points there, too. Of course the HPE announcement, big move for Pensando. Doesn't mean that they can't work with the other server vendors. They absolutely are talking to all of them, and we will see if there are alternatives to Pensando that come up, or if they end up singing with them. All right, so what we have here is I've actually got quite a few interviews with the Pensando team, starting with I talked about MPLS. We have Prem, Jane, and Sony Giandoni, who are the P and the S in MPLS as part of it. Both co-founders, Prem is the CEO. We have Silvano Guy who, anybody that followed this group, you know writes the book on it. If you watched all the way this far and want to learn even more about it, I actually have a few copies of Silvano's book, so if you reach out to me, easiest way is on Twitter. Just hit me up at @Stu. I've got a few copies of the book about Pensando, which you can go through all those details about how it works, the programmability, what changes and everything like that. We've also, of course, got Hewlett Packard Enterprise, and while we don't have any customers for this segment, Scott mentioned many of the retail ones. Goldman Sachs is kind of the marquee early customer, so did talk with them. I have Randy Pond, who's the CFO, talking about they've actually seen an increase beyond what they expected at this point of being out of stealth, only a little over six months, even more, which is important considering that it's tough times for many startups coming out in the middle of a pandemic. So watch those interviews. Please hit us up with any other questions. Scott Raynovich, thank you so much for joining us to help talk about the industry, and this Pensando partnership extending with HPE. >> Thanks, Stu. Always a pleasure to join theCUBE team. >> All right, check out thecube.net for all the upcoming, as well as if you just search "Pensando" on there, you can see everything we had on there. I'm Stu Miniman, and thank you for watching theCUBE. 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>> 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)