(soothing music) >> Announcer: Live from the Javits Center in New York City, it's theCUBE. Covering Inforum 2017. Brought to you by Infor. (energetic music) >> Welcome back to theCUBE's coverage of Inforum 2017. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by Debbie Krupitzer, she is the vice president at Capgemini based in San Francisco. Thanks so much for joining us. >> Thank you for having me. >> It's your first time on theCUBE, so we're going to-- >> It is, I'm excited! >> It's going to be great. >> Great. >> It's going to be great. So, Capgemini has had a longstanding relationship with Infor but this year, things got a little more serious. So-- >> Debbie: It did! >> So tell us, give us a status update. >> I think we both saw the writing on the wall, which is around, my space is digital manufacturing, that's where I play, and they see it to. Right, so we see such a great opportunity around connected factory and enterprise asset management, and all these really good things that are happening in the space, and so it sort of naturally came together. So we've always worked with them, but we really saw an opportunity for this year to say, hey, this is an investment piece, we both have a lot of energy, a lot of passion around it, let's go make this happen. And so it's been super fun, lots of fun this week. >> AI has been a really big theme at this conference with the introduction of Coleman. Can you tell us a little bit about where Capgemini is putting its resources when it comes to artificial intelligence? >> Absolutely, I mean, we know it's the future. We know it's where it's at. And you know, I had a quote from Elon Musk, which was saying AI, they're taking over the world, robots are going to take over the world in less than about 45 years. I don't know if that's so much true, but what we are really focused on is the business value of AI, not in the sort of trend, or what's the hype of AI. Where can you practically use it? So for us, artificial intelligence could be consumer feedback, or it could be around machines, it could be where are we getting machines to talk to us, to tell us what's wrong? We see a ton of opportunity around this, and it's really exciting for us, but always with a pragmatic what's going to make us money, what's going to save us money, and our customers, that's what we're always focused on. >> So it's the business value. >> Always the business value. The technology hype is just the technology hype, and I think that's what we really love about this conference is that there's a practicality about it. So there's not this sort of, hey it's trendy, it's cool, let's just go do it. There's a lot of thought behind it, there's a lot of thought behind what we want to do, what we want to achieve, and what we want to invest in. And we see this as a big investment. >> So let's talk about people, process, and technology. On theCUBE, everybody always says technology's the easy part, and I think it's generally true. I think technology's generally well understood, there's a lot of open source stuff, pretty much everybody has access to generally the same technology, it's how they apply it, the processes they put behind it, and the people that really make the difference. Okay, so when you think about digital manufacturing, help us understand it, it's surely not my wheelhouse. You bring in the IT and the whole OT thing, you're bringing the IT and the operations technology worlds together, and those are worlds that have never really collided, so wonder if you could talk about that a little bit-- >> Debbie: I would love to. >> Some of the challenges that brings? >> Oh, and there's a lot! Right, so we call it the IT OT Convergence. So there's actually a name for it. So that's Operational Technology and Informational Technology, and you're right, the plant has always been its own kingdom. So whenever you think of manufacturing, these plants are like we are the kings, we do it the way we want, and they never really wanted IT involvement. But what we're finding is that the CFOs, the people who are spending the money, have already seen the value of IT in terms of Cloud, cost savings, enterprise, infrastructure. How do you apply those to the plant to get the savings, and how do you replicate it? So what we're finding is that there's always again, there's a cost factor, right? So they're going is there a way for us to leverage technologies across multiple plants where we can get those savings, versus plants just going and buying whatever they want. And that' what we're seeing as the big change. Now, you're always going to get a shift, 'cause our plant guys and girls, they're used to doing it the way they want. But the thing that we see is that we're not coming in and totally putting robots to replace these jobs. What we're coming in is making their jobs easier. We're making it more efficient. We're seeing ways to save them money. And so the plants get incented when they have outcomes where they save money, so they're really pretty interested in doing this too. >> So give us some examples of a robot working along side of someone on a factory floor. >> So, you know it's funny, but I'd say 80% of the companies we work with don't have robots. Robots are sort of a sexy cool thing that everybody thinks is out there, and they are out there and they're really cool, but normally with the robots its already highly processed, it's a highly structured environment, usually around high tech or the car companies. I'll tell you what's more fun for me, when they don't have anything, where it's still paper-based. That's more fun, because what you're doing is you're going in and showing them how you can add a sensor to a machine to give you information you've never had before. How can this tell us how to do something differently? Is there a process issue? And when you talked about technology always being the easy part, it really is. When we go into a factory, it's normally a people challenge, that's operator, whether the operator's not doing something correctly, or in the right sequence. It's process, is there a process challenge? The technology is normally the easy part. So for me, I'm that person who likes the really immature factory, 'cause that to me is where you make the most change. Somebody's already got robots, you're already doing cool stuff. I'm probably not going to show you too much. It's the ones where they have that ah-ha moment, where they go wow. >> And we've been hearing this, that a lot of this stuff is change management. So how, from Capgemini perspective, how do you approach these challenges? >> You want to get always executive buy-in, right? So it's when it's coming from the top, I think that always is really valuable. But for us, we're plant floor people. I mean, I say you got to go talk to these folks and make them understand why you're doing it and what you're doing. Because there's always fear, right? Fear of anything, fear it's going to take your job, or fear you're not going to have a job, and what we're saying is it's a reallocation. The fact is this, in our space we've got an aging workforce. And aging workforce's going away. And the Millennials don't want to work a factory floor. And the reason they don't want to work a factory floor, it's dirty or they don't think it's the kind of work they want to do. We're trying to modernize that. Use an iPad, get IoT, get technology. You're not working the plant floor, you're working a dashboard. You're looking at data, you're driving data decisions, and so we call it From Shop Floor to Top Floor. How can we drive that so our Millennials, the ones who really do want to be the guys to take, and girls, to be taking these jobs, how can we make it more exciting for them, and we think there's good opportunity for that. >> So it really is all about the data, and when you think about the factory floor, a lot of analog data. And when you talk about process, a lot of process that's changing as a result of that analog to digital. So could you talk about the data, the data architecture that you're seeing and what the discussion is around data, data value, and how to get the value, how to monetize data, not necessarily by selling data directly but how it contributes to revenue generation or cost cutting? >> Well, we say data is the new oil, but I always tell my clients it's new oil, but it's not refined oil, and you've got to refine it. And refining the oil or refining the data is finding the business value out of that data. And you're right, there's a lot of data out there. The questions we get from the manufacturers are, what data is valuable, what is not valuable, what do I need, what do I not need, what can I aggregate up? I think the most interesting thing, and I love stories, is that when you look at a line, you've got machine number one to machine number 10. And before they would never know that something that was happening on machine number one, even a small configuration or change in a widget was actually impacting machine number 10. They never had that before. Now with that data, we're taking the data off of those singular machines, we're putting it up into the Cloud, we're aggregating it, we're able to see these anomalies and go, wow, that's the reason why. We never had that before. So you'd have engineers that would go, it must be machine number 10 or it must be machine number nine, or we don't really know what's going on. Now we're able to trace that; that's great. >> So I wonder if you could share with us any insights you have around discussions going on around IP, and data ownership? Because imagine, hypothetically for example, you've got some kind of programmable logic controller, and the PLC manufacturer is collecting data because they're trying to predict the maintenance, or whatever it is, and then of course the factory is the whole system and they're collecting data. So who owns that data-- >> Debbie: Oh that's a good question. >> And what's that conversation? >> Well, I'm no lawyer and so I'm not going to get into it. So I think what you'd find is that it depends. And that's a consultant answer, but I'm going to say it depends. If you're talking about the machine data, you have bought machines that are from a manufacturer. The manufacturers would love to have that machine data, 'cause they want to know what's going on with their machines. You want to know what's going on with the machine on the floor, very specific use case, which is what's happening in my space. The manufacturers want to know what's going on in a general way, how do we make our product better, how our are customers using it? In my mind, a plant shouldn't mind about that. A manufacturer wants to get that data to make better product, faster to market, make it cheaper, easier to buy, great, take it. I think where you get challenges is when there's outcomes that are coming out of data that people are leveraging to resell as business models. I think that's where people go, but that's our proprietary customer information about how we do a specific process, or how we do something. I think that's where people get a little iffy. And I don't really see that happening so much. So much, right, and I get everybody is really scared about the Cloud. I think the interesting thing is they'll say, well we don't want all of our data, our proprietary data in the Cloud 'cause it's not secure, and what I want to tell 'em, it's more secure in the Cloud than it is at your plant. >> So that's, I'm less concerned about the security of the Cloud, maybe it's different and you got to do some extra work to figure it out. I'm more concerned with our clients around the other thing you were talking about. I'll ask you specifically. If I'm using some kind of AI and I'm developing a model using machine learning and I'm training that model, maybe it's my data, but the model, my data's informing that model. How do I know that that model is not, somehow that IP of mine is not going to end up at my competitors, and is that going into discussions and contracts and agreements? >> Absolutely it is, and I think what you'll find is a lot of vendors that are out there that are dealing with AI and data are having to set clauses up that say you will not use this data to feed into any of your algorithms, into your IP. Like do not take my data. 'Cause everyone thinks, what we do is special, and some of it may be, do not take that and learn from us. That's very specific in clauses and contracts that we're seeing. >> Is it kind of like the honor system, or is there, is there a digital way to track that? >> Yeah, I think what's getting interesting is we get the data, like the companies aren't dumb. They're hiring their own data scientists, they're not letting us go to external parties. They're saying we're going to hire our own data scientists, and we'll start segmenting the data for you. They're very clever, you know, business people are in business because they know how to make money. They're not dumb. So what they're doing is getting a whole new set of roles. They're hiring data scientists. They're hiring data architects. They're hiring people in that understand the data structures so that they can keep track of what's valuable and what's not, don't worry about it. So, I think that's a smart thing to do. Because it used to be pretty rogue. I mean, five years ago, people would be like, well I don't care if you take the data off my machine. I think people have gotten a lot more clever, and also seeing that some of the vendors are repurposing some of this data for their own profit. Nobody wants that, don't take my stuff and use it to profit yourself. >> And you were talking about earlier, just the idea of what's valuable data and what'd not valuable data, and we find we are in this deluge of data. And we don't even really know, you can't say for certain, that data is not valuable, so don't worry about it. >> Exactly, and I think that's the challenge we get is that everybody thinks it's like a pile of money. Like, that's money, don't get rid of that money. >> Rebecca: It's oil! >> Oil, don't get rid of that, right? But what we find is you're getting so much data, some of the data is really not as valuable. And I'll give an example. An on-off switch telling me the motor is running on a machine is not valuable, it doesn't matter. It matters to that company because they need to know that the machine is working, so what we want to do is segment data, and we want to be able to give the business value, or have a hypothesis around what that data is bringing us. And sometimes, I'll tell you, a lot of times a hypothesis from my business users is wrong. So they'll say, what we think of A and B is super valuable, and then we'll go in and like, actually it's not A and B. It's E, E is actually the data stream that actually has the most value for you, and this is why. And so that to me is a really fun part, 'cause they have to have that moment where they go, oh, well we were wrong about that. It wasn't, I say, you're not wrong, it's just different. So I think having that data and then understanding what you're holding on the edge, what you're putting on Cloud, what you're putting on print, what you're able to share just makes people smarter about what they've got. >> So the accounting industry doesn't have standards as to how to value data on a balance sheet. We know that. But are there off-balance sheet discussions going on that you're having with your clients in terms of helping them understand the value of their data, quantifying that value? Everybody talks about the data is the new oil, you got to be a data-driven company and all this commentary, but how do you turn that into actionable, tangible results? >> That's the hard part, right? So that's the meat of the problem. And I think what we do is we really have to deep dive with our clients to understand what's the business model, or what do they think is going on? Because we've had lots of byproduct data that's come off of certain things that they had, and we were like, this is actually a more interesting tangent here, which is a byproduct of that data that you've got. Have you guys thought about selling that? So we'll come in and come up with business models, and so Capgemini has got, we've got Cap Consulting, we have these great acquisitions that we've just made where they'll come in and we've got people who do that. Who say, this is a new business model, have you thought of a resale, or this is something that's very valuable. And we'll go in and deep dive, a lot of times it's just discovery. We don't know either. So we'll go in and say, okay, this looks interesting, have you thought about this, and just new ways, it's just new business models. >> Do you see organizations and are you helping organizations actually apply maybe conventional financial measures, whether it's NPV or enterprise value, and are they beginning to track that, and what can you share with us? >> It's so funny you said that 'cause I just, when I just was coming here and I had a lead, I had a hot lead but I had to leave and come and do this interview, and he was asking me, and I said, the one thing we do is value map your processes and your data. And it was a thing that intrigued him. He was like, how do you do that? How are you doing that? I'm like, well, what we're doing is actually, we take all of your data from a historical standpoint, and we can see what's going on historically. Now the interesting part is how do you go forward with that? And so what we're finding is that you look at this data and you say what's the value mapping in terms of where you make money? And that's different for every company, and so we work with our customers. And so literally what I do is plot here's this process, there might be 15 processes that are going on. Here's the data outcome of that process. Now you talk to me about the value in terms of where you guys make the most money. >> You know, that's interesting, because data has unique value for different processes, obviously, so you have to understand it's not fungible like a dollar bill. And so that's what you can do is share this video with your hot prospect. (laughter) >> Debbie: Exactly! >> Maybe start a deeper conversation. >> I did, I told him, I have to go but I'll be back, so hopefully he's still warm over there. But I think people don't realize that the value mapping that you do is really a standard value, like you staid, standard financial models, the net present value, all those things, ROI, all those things we've always traditionally done on every project we do the same exact thing with this. For around digital manufacturing, because what we want to do is optimize. We want to optimize on what's going to save you the most money or make you the most money. And it's really that simple. Does it save you money, does it make you money. >> So you're applying sort of conventional measures to data, mapping that to processes, and then driving business outcomes, and then quantifying that over a lifecycle. >> You got it, that's exactly it. So you gave away my secret, so now you're going to start a technology firm. >> So that's high level, sounds good, but it's not trivial to do that, you need expertise, you need the main expertise. >> You do, and every manufacturer is different, right? So I work in discrete and process manufacturing, very different, very different processes, very different ways. Process manufacturing has a little bit more complexity, not that discrete doesn't, but it's interesting because what we do is find different things for different industries too, right? Now, there's some comparables, like food and pharma. Food processing, pharma is very similar, and people don't realize that, but it's very similar. And so we're always making comparisons. Pharma's a little bit more regulated, I think that might scare people, right, 'cause they want their food to be really, it is regulated, but maybe not as regulated as your drugs. And so what we find is the hypothesis or use cases that we can leverage and repurpose across industries. And I can't tell you how many times I've been in an industry and I just had one, and it was automotive, and I gave them a consumer packaging use case where they looked at me like I was crazy. And they said, I don't get it. And I connected the dots for 'em. And I said, do you see where if you've got this in consumer packaging, what they're looking at the quality of the packaging from start to finish, and I gave them the, you know, I won't go into the details. But they had this, they just went, oh yeah. And so I think what we're finding is industries that used to be like, if you don't know automotive, if you don't know mining, you don't know consumer packaging-- >> Dave: So true. >> You don't know us, you don't know us. >> And that's changed. >> And that's changed. So what they're seeing is they're going, you know what, 'cause they're seeing like the Amazons, they're seeing these companies, you know Amazon just bought Whole Foods. What? And they didn't buy Whole Foods for the grocery, they bought them for the data. And so I say like, guys, think of this in a different way. You've got to look at other industries, and so we're getting that more and more. We'll bring them out to have discussions about innovation or what's new, cool technology, and I bring it from every sector. Now, most of the time they'll go, show me how that's applicable? And I'll show 'em, and they go, wow. We get it. >> That's a great observation. Because digital means data, and data means you can traverse industries in new ways, so I love that CPG example. You would think, what? But you're getting people to rethink. >> You really are, and they're seeing, they're like, you know, they've got to reinvent themselves. Companies are having to reinvent themselves to this digital age, and they're scared. And they're saying, we sell a commodity, what can we do differently? How are we going to survive? I don't want to be the Kodak, I don't want to be the Blockbuster, I don't want to be that company. And so we're constantly pushing our product, companies that go what are you doing different, how are you going to the next level, is it data, is it services? >> Dave: What business are you in? (laughter) Right, I mean. >> Exactly. >> Well everyone's a software company. >> It's causing people to rethink that, I mean it sort of, we're back to the what business are you really in question. Like we were twenty years ago. >> It really is, it just cycles, right? And I say everything cycles around, we're doing the same thing, we're just repackaging, call it something else. So we all do the same thing over and over. >> Well, but there are some differences. >> There are, of course, more technology, better technology, cheaper technology. I think is what I'm finding is that the price of sensors and the price of technology is going down, that it's becoming more affordable. So, what I used to hear from the manufacturers is like, well I can't afford that, we can't do that. 'Cause there're very lean margins in manufacturing, I mean there's a lot going on. And we're being able to show them, hey, it's not a ton of investment, this isn't like a 20 million dollar ERP. Small increments of money that show you how to get the save. >> Well, 20 years ago, you were purpose-building specific technology stacks for your customers, and today you're leveraging. Whether it's Cloud, a security layer, a data layer, you pick it and you're building on top of this digital matrix. And really focused on the business models, more so than the technology. >> It is, and that's what we're seeing. And I say that's why, to get back to the first question about OT IT Convergence, that's what my CFOs see. They go, we get it. We get it, now let's apply it to the plant, so let's go see how we can scale this. 'Cause you're talking anywhere from companies having 20 plants to 200 plants, that's a lot. And they want to see how they can repeat in scale, and so that's what we love about it. It's turning into a business conversation. It's not a technology conversation, which I love. >> Debbie, thank you so much for joining us. >> Thank you! >> You made it! >> I did it, yay! I got it, thank you so much. >> I'm Rebecca Knight for Dave Vellante, we will have more Inforum just after this. (rippling music) (rippling music)